the impact of free trade agreements on macroeconomic and...

The Impact of Free Trade Agreements on Macroeconomic and Firm Level Financial Factors

Ch. Mazhar Hussain, Ph.D (Finance) Scholar 1 | P a g e

The Impact of Free Trade Agreements on

Macroeconomic and Firm Level Financial Factors (A doctoral dissertation PhD-Finance)

Researcher: Supervisor:

Ch. Mazhar Hussain Dr. Syed Zulfiqar Ali Shah

Reg. # 28-FMS/PhDFIN/S-11

Faculty of Management Sciences

INTERNATIONAL ISLAMIC UNIVERSITY,

ISLAMABAD



The Impact of Free Trade Agreements on

Macroeconomic and Firm Level Financial Factors (A doctoral dissertation PhD-Finance)

Ch. Mazhar Hussain

Reg. # 28-FMS/PHDFIN/S-11

Submitted in partial fulfillment of the requirements for the

Ph.D degree with the specialization in Finance

at the Faculty of Management Sciences

International Islamic University

Islamabad

Dr. Syed Zulfiqar Ali Shah May 2017



(Acceptance by the viva voice committee)

Title of Thesis: “The Impact of Free Trade Agreements on Macroeconomic and Firm Level

Financial Factors”

Name of Student: Ch. Mazhar Hussain

Registration No: 28-FMS/PHDFIN/S-11

Acceptance by the Faculty of Management Sciences, INTERNATIONAL ISLAMIC

UNIVERSITY ISLAMABAD, in partial fulfillment of the requirements for the Doctor of

Philosophy Degree in Management Sciences with specialization in Finance.

viva voice committee

__________________

Dr. Syed Zulfiqar Ali Shah

(Supervisor)

____________________

(External Examiner)

____________________

(External Examiner)

____________________

(External Examiner)

____________________

(Chairman HS & R)

____________________

(Dean)

Date:



FORWARDING SHEET

The thesis title “THE IMPACT OF FREE TRADE AGREEMENTS ON MACROECONOMIC

AND FIRM LEVEL FINANCIAL FACTORS” Submitted by Ch. Mazhar Hussain ( 28-

FMS/PHDFIN/S-11) in partial fulfillment of doctor of Philosophy degree in Management

Sciences with specialization in Finance has been completed under my guidance and supervision.

After receiving two reports from foreign evaluators, required changes have been incorporated.

The suggestions advised by internal and external examiners have been incorporated. I am

satisfied with the quality of student’s research work and allow him to submit this thesis for

further process as per IIU rules & regulations.

Date: Signature____________________

Name : Dr. Syed Zulfiqar Ali Shah



DECLARATION

I hereby declare that this thesis, neither as a whole nor as a part thereof, has been copied out

from any source. It is further declared that I have prepared this thesis entirely on the basis of my

personal efforts made under the sincere guidance of my supervisor. No portion of the work,

presented in this thesis, has been submitted in support of any application for any degree or

qualification of this or any other university or institute of learning.

Ch. Mazhar Hussain

PhD Scholar

Faculty of Management Sciences



To

All helping hands especially my teachers, parents and family

© Ch. Mazhar Hussain (2017). All right reserved. No Part of this Publication may be reproduced

without the written permission of the copyright holder



Acknowledgements

I owe a great deal to people for providing help, information and guidance for this thesis. This

thesis would not be possible without contributions and support from them. I will try to thank all

of them in the lines below. Please forgive me if I forgot someone.

I owe a debt of gratitude to my supervisor Dr. Syed Zulfiqar Ali Shah for his exemplary

guidance, patience and constant untiring advice and encouragement throughout my research

project. I also extend my thanks to the administrative staff of Higher Studies and Research

department for their assistance during my PhD program.

On a more personal note I wish to thank my parents, late Mr & Mrs Ch. Asghar Hussain, who

provided me with the spiritual and intellectual inspiration to persevere under any circumstances,

for educating me, for financing me, for unconditional support and encouragement to pursue my

interests. I can do no more than reaffirm my eternal devotion.

Moreover, my studies would not have been possible without the loving support of my wife,

kids and other family members. They deserve special thanks for their enormous love, support,

patience, and granting me their time which was spent in finishing my studies.

I begin and end this thesis by acknowledge the help, protection and guidance of the Almighty

Allah.



ABSTRACT

The globalization of the world economy has developed at a rapid pace due to which no country

can survive in isolation economically. Free Trade Agreements are one of the outcomes of global

interactions between member countries to develop their economies. The prime objective of this

study is to analyze the pre and post impact of FTAs on overall economy, and on non-financial

firms. At macro level pre and post-economic analysis, the study aim is to investigate the impact

of FTAs on real Gross Domestic Product (GDP), trade balance, output and trade in different

sectors, and welfare. Moreover, the micro firm level financial objective of the study is to

examine the pre and post impact of FTAs on firm’s leverage, dividend payouts and profitability.

This study investigates BTAs impact on macroeconomic factors for the period from 2000 to

2014. The sample of the study is Pakistan’s BTAs with China, Malaysia, and Sri Lanka. For this

purpose the pre and post economic analysis is conducted through Global Trade Analysis Project

(GTAP) model which is used for Computable General Equilibrium (CGE) model. Furthermore,

the pre and post FTA effect on Pakistani non-financial firms’ leverage, profitability, and

dividend payouts is scrutinized by regression for the same time period.

The results of the analysis are clearly indicated that Pakistan is in trade deficit which is in favor

of China. This is all happen because of China’s size of the economy, its production base, and the

differences in overall competitiveness. However, it is recommended that Pakistan should

improve trade with China by increasing production or diverting exports to China in terms of high

potential exports such as textile, wearing apparel, leather products, plant-based fibers, chemical

products, vegetable oil and fats, and metal products. Similarly, the results of Pakistan-Malaysia

FTA showed that Pakistan does not get the benefit from this FTA, however, Pakistan identify

potential exports sectors such as process rice, textiles, wearing apparel, chemical products,

plastic, rubber, metal products, cement and machinery and equipment. Moreover, it is observed

that Pakistan is getting benefit from Pakistan- Sri Lanka FTA in terms of real GDP, trade and

welfare as compare to Sri Lanka. Furthermore, at firm level financial factors, the finding of this

research reveals that due to change in import tariffs, those companies which are under import

competition tend to reduce profits, increase leverage and decline dividend payouts. Nevertheless,

those companies which improve efficiency by adopting new technology lead to increase profits,

reduce leverage and increase in dividend disbursement. The decline in export tariffs are related

with increase in profits, decrease in leverage and increase in dividend payments subject to export

orientation of the companies. However, the export competitions reduce the profits, increase

leverage and decline in dividend payouts of companies.

It is concluded that although Pakistan is in trade deficit with China and Malaysia, but overall

trade volume is increased with these countries and potential exports sectors are identified to

exploit the export opportunity in China and Malaysia. Therefore, it is recommended that Pakistan

may be developed the long term strategy to focused on these industries and allocate the resources

efficiently on these sectors. In this way, Pakistan improves its exports to enhance its GDP

growth, trade balance and welfare. Moreover, the Pakistan -Sri Lanka FTA is favorable for

Pakistan and it is recommended that both countries establish the linkage between their business

communities and policy makers for achieving better results. At micro level, it is noteworthy to

mention that general perception is that trade deficit is not good of any economy, although

Pakistan is in trade deficit with China and Malaysia, but the companies which are importing new

technology from China and Malaysia improve the efficiency lead to improvement in profits and

dividend payments and reduction in leverage. Therefore, these companies achieve net gain from



these FTAs. The contemporary area for future research is China Pakistan Economic Corridor

(CPEC) and other proposed FTAs like Pakistan-Turkey FTA and Pakistan-Thailand FTA may be

investigated by the researchers.



Table of Contents

CHAPTER 1 1

1. Introduction 1

1.1 Back Ground 1

1.2 Theoretical Framework 5

1.3 Gap Identification 10

1.4 Problem Statement 12

1.5 Significance of the Study 12

1.5.1 Significance of the Study in Pakistani Context 13

1.5.2 Contribution to Knowledge 13

1.5.2.1 Contextual Contribution 13

1.5.2.2 Applied Contribution 14

1.5.2.3 Theoretical Contribution 14

1.6 Research Questions 14

1.7 Objectives of the Study 15

CHAPTER 2 16

2. Overview of Trade Performance and Trade Agreements of Pakistan 16

2.1. The Contemporary Circumstances of Pakistan’s Performance 16

2.2. Trade Trend of Pakistan (2013-14) 17

2.3. Concise Review of Pakistan Trade Balance 20

2.4. Review of the Trade Policies of Pakistan 22

2.5. Trade Agreements of Pakistan 24

2.6. Potential Free Trade Agreements of Pakistan 25

2.6.1 Pakistan-China FTA 25

2.6.1.1 Relative Significance of Bilateral Trade 27

2.6.2. Pakistan-Malaysia FTA 28

2.6.3. Pakistan-Sri Lanka FTA 29

CHAPTER 3 31

3. Literature Review 31

3.1 Some Empirical Evidence 33

3.2 Effect of FTA on Real GDP 39

3.3 Effect of FTA on Trade Balance 40



3.4 FTA effect on Trade and Output 41

3.5 FTA effect on Country’s Welfare 43

3.6 Impact of FTA on leverage of the Firms 44

3.7 Impact of FTA on profitability of the Firms 45

3.8 Conceptual Framework 47

CHAPTER 4 48

4. Methodology 48

4.1. Population 48

4.2. Sample 48

4.3. Data Analysis- Macroeconomic Factors 51

4.4. Data Analysis- Firm Level Financial Factors 52

4.5. Descriptive Statistics 55

4.6.The Data -GTAP database 55

4.6.1.Model Parameters and Benchmark Parameters Requirements 56

4.6.2.Behavioral parameters 57

4.6.3.Macroeconomic closures—three macroeconomic closures are incorporated

in version.9.

57

4.7. Methodology for Macroeconomic Factors 58

4.7.1. Computable General Equilibrium (CGE) Model 58

4.7.2 CGE Model: Circular Flow explanation 60

4.7.3 Review of GTAP Model 62

4.7.3.1 Closed Economy without Taxes 63

4.7.3.2 Open Economy without Taxes 65

4.8. Global Trade Analysis Project (GTAP) 66

4.9. Methodology for Firm Level Financial Factors 70

CHAPTER 5 77

5. Results Discussion 77

5.1. Descriptive Statistics 77

5.2. Results of GTAP Simulation 83

5.2.1. Pakistan China Free Trade Agreement 83

5.2.2. Pakistan Malaysia Free Trade Agreement 90

5.2.3. Pakistan Sri Lanka Free Trade Agreement 96

5.3. Result Discussion of Firm Level Financial Factors 103

5.3.1. Pakistan and China FTA 103

5.3.2. Pakistan and Malaysia FTA 118



5.3.3. Pakistan and Sri Lanka FTA 132

CHAPTER 6 146

6. Conclusion 146

6.1. Future Research 152



LIST OF ABBREVIATIONS

Applied General Equilibrium model AGE

ASEAN-India FTA AIFTA

Asia Free Trade Agreement AFTA

Association of South East Asian Nations ASEAN

Average Annual Growth Rate AAGR

Bilateral Trade Agreements BTAs

Centre d’Etudes Prospectives et d’Informations Internationales CEPII

China Pakistan Economic Corridor CPEC

Coalition Support Fund CSF

Computable General Equilibrium CGE

Cost of Insurance and Freight Cif

Current Account Balance CAB

Early Harvest Program EHP

Economic Community Of West African States ECOWAS

Economic Cooperation Organization Trade Agreement ECOTA

European Union EU

Export Bonus Scheme EBS

Export Oriented Industry EOI

Food and Agriculture Organization of the United Nations FAO

Foreign Direct Investment FDI

Free on Board Fob

Free Trade Agreements FTAs

Global Trade Analysis Project GTAP

Gross Domestic Product GDP

Harmonized System HS

House Hold HH

Import License Scheme ILS

Industrial Incentive Reform Cell IIRC

International Energy Agency IEA

International Food Policy Research Institute IFPRI

International Monetary Fund IMF

Karachi Stock Exchange KSE



Khyber Pakhtunkhwa KPK

Least Developed Countries LDCs

Malaysia Pakistan Closer Economic Partnership Agreement MPCEPA

Most Favored Nation MFN

Non-tariff barriers NTB

Non-Tariff Measures NTMs

North American Free Trade Agreement NAFTA

Open General Licenses OGL

Ordinary Least Square OLS

Organization of Economic Co-operation and Development OECD

People Republic of China PRC

Preferential Trade Agreement PTA

Price Index PS

Product Life Cycle PLC

Prospective Technological Studies IPTS

Regional Comprehensive Economic Partnership RCEP

Regional household’s demand for saving REGINN

Regional Trade Agreement RTA

Rest Of the World ROW

SAARC Preferential Trade Agreement SAPTA

Sectoral Analysis of Liberalization of Trade in the East Asian Region

model SALTER

South Asian Association of Regional Corporation SAARC

South Asian Free Trade Agreement SAFTA

Statutory Regulatory Orders S.R.Os

Strategic Trade Policy Framework STPF

Tariff Rate Quota TRQ

Terms of Trade TOT

Trans-Pacific Partnership TPP

Value of Domestic Purchases by Firms at Agents’ prices VDFA

Value of Domestic purchases by Government households at Agents’

prices VDGA

Value of Domestic Purchases by Private households at Agents’ prices VDPA

Value of Exports at Market prices by Destination VXMD

Value of Import by Firms at Agent' price VIFA

Value of Import by government households at Agent' price VIGA



Value of Import by private household at Agent' price VIPA

Value of Output at Agents VOA

Value of Output at the Agent’s price VOA

Vector Auto-Regressive VAR

World Bank WB

World Trade Organization WTO



LIST OF TABLES

Table: 2.1 Trade Trend of Pakistan 18

Table: 2.2 Trade Balance of Pakistan (Million Dollar) 20

Table: 4.1 Data Available of the Companies for Pakistan- China FTA 49

Table: 4.2 Data Available of the Companies for Pakistan- Malaysia FTA 50

Table: 4.3 Data Available of the Companies for Pakistan- Sri Lanka FTA 50

Table: 5.1 Descriptive Statistics Pakistan-China FTA 78

Table: 5.2 Descriptive Statistics of Control Variables Pakistan-China FTA 79

Table: 5.3 Descriptive Statistics Pakistan-Malaysia FTA 80

Table: 5.4 Descriptive Statistics of Control Variables Pakistan-Malaysia

FTA 81

Table: 5.5 Descriptive Statistics Pakistan-Sri Lanka FTA 82

Table: 5.6 Descriptive Statistics of Control Variables Pakistan-Sri Lanka

FTA 82

Table: 5.7 Simulated aggregate Real GDP effects of Pakistan-China FTA 84

Table: 5.8 Simulated aggregate trade effects of Pakistan-China FTA 85

Table: 5.9 Simulated Sectoral Effects of the Pakistan China FTA on Pakistan

(% change) 86

Table: 5.10 Simulated Sectoral Effects of the Pakistan China FTA on China

(% change) 88

Table: 5.11 Simulated Welfare Effects of Pakistan China FTA and

Decomposition ($ millions) 90

Table: 5.12 Simulated aggregate Real GDP effects of Pakistan-Malaysia

FTA 90

Table: 5.13 Simulated aggregate trade effects of Pakistan-Malaysia FTA 91

Table: 5.14 Simulated Sectoral Effects of the Pakistan Malaysia FTA on

Pakistan (% change) 93

Table: 5.15 Simulated Sectoral Effects of the Pakistan Malaysia FTA on

Malaysia (% change) 94

Table: 5.16 Simulated Welfare Effects of Pakistan Malaysia FTA and


Table: 5.17 Simulated aggregate Real GDP effects of Pakistan-Sri Lanka

FTA 97

Table: 5.18 Simulated aggregate trade effects of Pakistan-Sri Lanka FTA 97



Table: 5.19 Simulated Sectoral Effects of the Pakistan Sri Lanka FTA on

Pakistan (% change) 99

Table: 5.20 Simulated Sectoral Effects of the Pakistan Sri Lanka FTA on Sri

Lanka (% change) 100

Table: 5.21 Simulated Welfare Effects of Pakistan Sri Lanka FTA and


Table: 5.22 Pakistan China FTA-Effect of tariff changes on Profits-Tobit 1

All Firms 104

Table: 5.23 Pakistan China FTA-Effect of tariff changes on Profits-Tobit 2 106

Table: 5.24 Pakistan China FTA-Effect of tariffs changes on Profits- OLS

with Random Effects 107

Table: 5.25 Pakistan China FTA-Effect of tariff changes on Profits- OLS

with First Difference 108

Table: 5.26 Pakistan China FTA-Effect of tariff changes on Leverage- Tobit

1 110

Table: 5.27 Pakistan China FTA-Effect of tariff changes on Leverage- Tobit

2 111

Table: 5.28 Pakistan China FTA-Effect of tariff changes on Leverage- OLS

with Fixed Effects 112

Table: 5.29 Pakistan China FTA-Effect of tariff changes on Leverage- OLS


Table: 5.30 Pakistan China FTA-Effect of tariff changes on Dividend

Payout- OLS Fixed Effects 116

Table: 5.31 Pakistan China FTA-Effect of tariff changes on Dividend

Payout-OLS First Difference 118

Table: 5.32 Pakistan Malaysia FTA-Effect of tariff changes on Profits- Tobit

1 120

Table: 5.33 Pakistan Malaysia FTA-Effect of tariff changes on Profits- Tobit

2 121

Table: 5.34 Pakistan Malaysia FTA-Effect of tariff changes on Profits- OLS


Table: 5.35 Pakistan Malaysia FTA-Effect of tariff changes on Profits- OLS


Table: 5.36 Pakistan Malaysia FTA-Effect of tariff changes on Leverage-

Tobit 1 124


Tobit 2 125




OLS with Random Effects 127


OLS First Difference 128

Table: 5.40 Pakistan Malaysia FTA-Effect of tariff changes on Dividend

Payout-OLS with Random Effects 130

Table: 5.41 Pakistan Malaysia FTA-Effect of tariff changes on Dividend

Payout-OLS with First Difference 131

Table: 5.42 Pakistan Sri Lanka FTA-Effect of tariff changes on Profits-

Tobit 1 132

Table: 5.43 Pakistan Sri Lanka FTA-Effect of tariff changes on Profits-Tobit

2 134

Table: 5.44 Pakistan Sri Lanka FTA-Effect of tariff changes on Profits-OLS


Table: 5.45 Pakistan Sri Lanka FTA-Effect of tariff changes on Profits- OLS

with first Difference 136

Table: 5.46 Pakistan Sri Lanka FTA-Effect of tariff changes on Leverage-

Tobit 1 137


Tobit 2 138


OLS with Fixed Effects 140


OLS First Difference 141

Table: 5.50 Pakistan Sri Lanka FTA-Effect of tariff changes on Dividend

Payout-OLS with Random Effects 143

Table: 5.51 Pakistan Sri Lanka FTA-Effect of tariff changes on Dividend

Payout- OLS with First Difference 144



LIST OF FIGURES

Figure 3.1 Conceptual Framework 47



CHAPTER 1

1. Introduction

This chapter elaborates the back ground, theoretical framework, gap identification, problem

statement, significance of the study, contribution to knowledge, objectives and research

questions of the study in the following sections.

1.1 Back Ground

The world is like a global village and the globalization of the world economy has developed at a

rapid pace due to which no country can survive in isolation economically. Free Trade

Agreements are one of the outcomes of global interactions between member countries to develop

their economies. Nevertheless, Free Trade Agreements (FTAs), which are preferential trading

agreements, have both favorable and non-favorable impact on the partner economies.

Theoretically, an FTA is an agreement between member economies to eliminate import and

export tariffs and maintain separate tariff rates for non-member countries. Furthermore, the

member countries try to move towards customs union, European Union is one of the successful

examples among regional trade agreements in the world.

The micro level analysis of firms in the trade literature is relatively new. This is all because of

development in new theories and availability of data (Filatotchev et al., 1999, 2001). This



literature change focus from trade analysis of economies to firms, which actually involve in trade

activities. In addition to that, the literature also highlights the investigation of latest trade

hypotheses and endogenous growth theories. Therefore, the performance of firms can be

measured instead of countries. Furthermore, the firm level comparative scrutiny can be

conducted with help of firm level data within a country while it is not possible with country level

data (Shevtsova, 2010). The firm level data also assist in analysis of sectors, which is one of the

main focuses of this study.

Under the discussion of international financial management, one of the arguments is that the

firms are facing systematic risk and try to reduce it by investing internationally in different

countries. The FTA is one of the option through which firms can invest and trade with their

member countries’ firms and enjoy the economies of scales, reduce the cost of production,

increase in exports and ultimately improve their returns. In this way, a firm can achieve its

objective to maximize the shareholder wealth at micro level.

According to Panagariya and Krishna (2002), in fact an FTA is based on certain rules, the prices

of the commodities are different among member countries and each partner of an FTA is liberal

in context of its tariff regime against non-partner countries. Therefore, the main concern of

financial economists and financial managers is to analyze what is FTAs’ impact on trade and

welfare effect on the economy of the member countries at macro level. Moreover, they also can

scrutinize the effect of FTAs on firms of the member countries at micro level because changes in

tariff affect the firms’ profitability, leverage and dividend payouts.



Furthermore, one of the basic concerns of financial economist is to analyze the significance of

financial leverage as compare to equity in financing decision of the firm. The subject matter has

greatest importance in the international market because how firms react in making financial

leverage decision when there is a change in trade policy. In this context, MacKay et al. in 2001

said that due to trade liberalization it might happened that the firms increase the leverage as

compare equity to which likely to increase the chances of insolvency. Similarly, the firm’s

profitability and dividend payouts are also important aspect to investigate because these factors

also effect due to change in trade policy.

The countries which are specialized in production of certain product, the trade liberalization

permit them to export as they use their resources efficiently and import from cheapest suppliers

was argued by Plummer (2007). The FTA similarly allows the member countries to trade with

each other and tries to get the benefit from it for economic prosperity of their respective country.

Currently Pakistan economy is facing enormous confronts like the problem of energy crisis along

with law and order challenges. Moreover, the war against terrorism is also making a big hurdle in

the economic development of Pakistan. Karachi is the financial and economic hub of Pakistan

but it still facing law and order problems. Likewise, other provinces like Punjab, Khyber

Pakhtunkhwa (KPK) and Balochistan also facing the same problems. The international economic

crisis of 2008 is continuing weaken the global growth and that is also affecting the Pakistani

trade.



In this critical scenario, the importance of BTAs plays a vital role of any economy. Therefore,

the different Bilateral Trade Agreements (BTAs) are significantly contributing in the exports of

Pakistan like Pakistan-China volume of trade increased from US$ 4.1 billion to US$ 8.9 billion

during the time period of 2006 to 2012. The Pakistani exports increased by 279 percent in 2011-

12 as compared to 2006-07. Similarly, Pakistan’s imports from China were increased by 90

percent during the same time period1. The significance of Pakistan China relationships are

growing with the passage of time. Pakistan’s vision 2025 is to achieve the per capita GDP of US

$4200 which can be attained with the support of China Pakistan Economic Corridor (CPEC).

China has made a Plan-I to invest US $46 billion in multiple projects into Pakistan which

ultimately generate billions of dollars along with creating various job opportunities for the

people of Pakistan (Fact Book on CPEC 2016). These facts motivate the researcher to investigate

the impact of Pakistan-China FTA on macroeconomic and firm level financial factors of

Pakistan.

Moreover, the trade volume of Pakistan-Malaysia had increased from US$ 1017.80 million to

US$ 2.66 billion during the time period of 2006 to 2012. Pakistan’s exports to Malaysia were

increased by 211% in 2011-12 as compared to 2006-07. In the same manner, Pakistan’s imports

increased by 157% during this period. The bilateral volume of trade between the Pakistan and Sri

Lanka increased from US$ 200 million to US$ 374 million during 2004 to 2012. Pakistan’s

exports increased by approximately 100 percent in 2011-2012 as compare to 2004 to 2005 that is

1(Pakistan Economic Survey 2011-2012)



US$ 155 million in 2004-05 exports and US$ 305 million in 2011-12. (Pakistan Economic

Survey 2011-2012)

The above said facts and figures highlighted the importance of FTAs for Pakistan that is why this

study analyzes the FTAs of Pakistan from 2000 to 2014. In this critical scenario, Pakistan needs

some concrete policies to handle these challenges and put the economy on right track. That is

why; the study investigates the impact of FTA on economy, industry and firms. So the outcome

of this study assists the policy makers to formulate the foreign trade policies of Pakistan.

Furthermore, this study also helpful for creditors and financial managers for making their

investment decision.

1.2Theoretical Framework

The international business theory explains the benefits of free trade on the economy. The idea of

the Comparative Advantage theory which was introduced by Adam Smith and David Ricardo is

on the same ground, which is the overarching theory of this study. The Comparative Advantage

theory explains free exports and imports of goods among countries because a country specializes

in few products and it may not produce other products. As the comparative advantage of firms

permit them to enter into international market which leads to the changes in firms’ profitability,

leverage and dividend payouts. Therefore, the impact of change in tariffs on the macroeconomic

level as well as on firm level can be analyzed which is core idea of this study.

Viner (1950) argued that free trade agreements could have a negative impact on welfare. The

main concepts of his model are trade creation and trade diversion. Concisely, trade creation



decreased the local production that is fulfilled by importing from more efficient source. While,

trade diversion is increased in imports from more efficient non-member country as compare to

less efficient member country. Therefore, the country tariff revenue on the imports will be

reduced. In the FTA, the imports from non-partner country are shifted to member country

because the imports from member country are cheaper as compare to non-partner country.

Modern researchers have studied FTAs in the context of large number of commodities, while the

Viners’ model focused on only one commodity. He discussed about only one imported

commodity market and did not consider other products’ market due to which there is change in

terms of trade. However, the other researchers like Meade (1955), Lipsey (1970), and Wonnacott

and Wonnacott (1981) found that the member countries get benefit from an FTA rather than

unilateral trade liberalization, because if non-member countries impose trade restrictions against

them or the member countries bear high transport costs in exporting to non-member countries.

This argument supports the creation of FTAs among countries which are geographically near

with each other as compare to non-member countries. Hence, their analytical results found the

welfare consequences of free trading agreements and welfare is one of the variables in this study.

The models expressed by Meade-Lipsey and Wonnacott-Wonnacott are useful when it

necessitates for a country to forecast the direction of welfare following an FTA. However, these

models do not practically estimates the magnitude of change in a country’s welfare. Moreover,

there models do not consider the variety of traded commodities and trade policies. The

assumptions are rigid, and they not have certain formulations. Due to this, contemporary

quantitative analyses of the welfare impact of FTAs rely majorly on the theoretical assumptions



of models that have dimensions with respect to commodities and trading partners, and

emphasizes on a general equilibrium model developed by Lloyd and Schweinberger 1988,

Grinols and Wong 1991, Baldwin and Venables 1995, Lloyd and Maclaren 2004. These models

majorly highlight details in terms of structure of production, consumption, and trade in a country

for analyses of its trade policy.

These models quantify the welfare impact of an FTA and identifies the amount of expenditure

would be needed following an FTA to maintain the welfare of households to the pre-existing

level. If this value is positive, it affirms that the FTA has reduced welfare to that extent.

However, if this extent turns out in negative, it means that appropriate incentives needs to be

developed so that this money injects in the economy from the households, thus the impact of

FTA must have raised welfare by that amount. According to these assumptions, the models have

capability to measure the changes in the indictors like trade volumes, trade balance, production

and consumption as result of an FTA. The availability of data on these key indicators makes

these models feasible and ready to use. Moreover, Kemp and Wan theory (1976) explains that

the regional agreement enhance the welfare of individual member country as well as improve the

economy of the collective group , and does not have any negative implication on the rest of the

world.

It is important to highlight the dynamic of practical implications of FTAs. These dynamic are by

establishing a bigger market for companies in partner countries. The FTA will permit the firms to

take benefit of large market share. Therefore, achieve the low cost of production. The companies

will able to offer lower selling prices to the customers, which explains the phenomena of “cost-



reduction effect” (Corden, 1972). In the consequence, these companies will turn out to be highly

competitive in domestic market but as well as in international markets. In each partner country

consumers will get variety of commodities due to establishment of bigger market. Nevertheless,

before the FTA the cost of production was high and unprofitable business for firms. These above

mentioned theoretical models are related with pre and post impact of FTAs on the

macroeconomic factors which is the main focus of this study.

Hawley explains the profit as compensation price paid by community against the risk taken by

the businessman. In fact a businessman expects profit in excess of actuarial value of the risk

which is premium for taking risk (Carver, 1901). The firm takes risk after entering into the

regional agreements by penetrating new markets and earns profit by enhancing its sales and

lowering its cost of production.

Myron (1963) and John (1962) introduced the notion of the theory of dividend relevance, this

theory express that there is relationship between firms’ market value and the firm’s dividend

policy. They argue that, if the firm pay dividend than the uncertainty of shareholders will be

reduced. So the shareholders discount the future earnings of the company at lower discounted

rate which create high value for the firm. On the other hand, if dividends are decreased or are not

distributed among shareholders than uncertainty of shareholders will increase and they will

discount the earnings at high rate of return which cause low value for the firm.

The concept of relevance of dividend policy and its market value can be extended and one can

make the relationship of above said concept with trade liberalization. It is significant to highlight



that trade liberalization has two general impact on market scenario for a local company. Firstly,

reduction in import tariffs creates the high competition in the local market for local companies as

foreign companies enter into the market as rivalry. Secondly, reduction in export tariffs creates

an opportunity for local companies to increase access in international market to attract large

number of customers by selling the products at lower price.

In the result of high competition due to the reduction in domestic tariffs the local firms’

profitability will be reduced. So the change in tariff may adversely affect the performance of the

firm. Consequently, the firm will not able to pay the dividend which reflects in the share price of

the firm and the prices will go down. Conversely, the reduction in export tariffs increases the

competitive position of local company in order to receive high profits. Thus, the FTA gives an

opportunity to the financial managers to increase the firms’ profitability and then firm will be

able to pay the higher dividend payout ratio to the shareholders which enhance value of the firm.

Therefore, the change in tariff affects the ability of the firm to pay the dividend or not and how

the shareholders discount the firm’s earnings under these circumstances as the above theory

suggests. This argument is tested in the study with the help of regression analysis.

In 1984 Myers developed the theory of pecking order model of capital structure which is

considered in this study. The pecking order model proposes the financing choices of the firm.

The first choice of the firm financing its projects through retained earnings, then debt and the last

choice is equity. This model determines the level of leverage needed by the firm on the basis of

costs and benefits analysis of debt and the amount of investment and profits. The capital



structure relevance and pecking order models are related with this study because it is pertinent to

investigate how firm react to make financing decision when there is any change in trade policy.

Kraus and Litzenberger (1973) explained the trade-off model which is base on capital structure

relevance. They argued that there is a tradeoff between the tax benefit of debt and chances of

bankruptcy if the firm increases certain level of debt in its capital structure. This model,

theoretically explained that reduction in import tariffs increases chances of bankruptcy without

affecting the tax benefit of debt because of high competition faced by local firm. In the result

firm reduces the optimal level of leverage in its capital structure. On the other hand, the

reduction in export tariffs increased the access to the international market will reduces the

chances of insolvency without affecting the tax benefit. Therefore, firm increases the optimal

level of leverage in its capital structure. Nevertheless, the pecking order model proposes exactly

the contradictory result. Due to the reduction in import tariffs the firms’ profit will be reduced

and in the result the firm will borrow more through debt in order to finance its projects.

Conversely, reduction in export tariffs increase the competitive position of local firm in the

international market and in the result the firms’ profit increases which permit the firm to finance

its projects with internal source of financing lead to reduction in leverage.

As discussed above, both the trade-off and pecking order theories propose the results in

contradictory direction. Thus, it is significant to investigate both theories in case of pre and post

impact of FTA on financing decision of the firms. This investigation is done with the help of

regression analysis.

1.3Gap Identification



Despite the growing interest in the subject matter of Free Trade Agreements (FTAs),

most of the studies on FTAs are limited to specific FTAs like impact of Association of

South East Asian Nations (ASEAN) on economies of South East Asian countries

(Pradumna, 2006; Kawai & Wignaraja, 2007; Sasatra & Prasopchoke, 2007; Chandrima

& Biswajit, 2011; David, 2010; Ken & Hiro, 2012). The Indian-ASEAN economic

relations and future prospects had been examined by Karmakar (2005), Veeramani and

Saini (2010). Moreover, the impact of South Asian Free Trade Agreement (SAFTA) on

the economic factors of South Asian Association of Regional Corporation (SAARC)

member countries had been examined by Dushni (2009), Kemal (2005), Alam et al.

(2011), Coulibaly (2007), Hassan (2001), Akhter and Ghani (2010), Rahman et al.

(2006). Nevertheless, the literature is silent regarding pre and post Bilateral Trade

Agreements’ (BTAs) impact (BTAs of Pakistan with China, Malaysia and Sri Lanka) on

macroeconomic factors in a single study.

Above mentioned studies also lacking in establishing the linkage of international trade

theories like comparative advantage theory, Viner’s Model, Meade-Lipsey and

Wonnacott-Wonnacott Models, Lolyd- Maclaren Model, Kemp- Wan theory with the

finance theories like capital structure theory, theory of Dividend and risk theory of profit.

Furthermore, Baggs and Brander (2006) investigated only the effect of North American

Free Trade Agreement (NAFTA) on non-financial firm’s leverage and profitability. The

impact of any FTA on micro level financial factor like dividend payouts is totally ignored

in the literature. So the pre and post impact of Pakistan BTAs on micro level financial

factors is also not available in the literature.



This research highlights empirically the role of comparative advantage affect on

macroeconomic and micro firm level financial aspect, which is lacking in the literature.

Therefore, this study focuses on these issues and tries to fill the gap in the existing literature.

1.4 Problem Statement

In fact Pakistan is developing economy and facing enormous economic challenges. Nevertheless,

it has significant strategic geographical location in the world. Pakistan can improve its economic

condition through Free Trade Agreements (FTAs) because an FTA enhances the welfare,

production and trade balances. However, the problem arises that whether Pakistan is getting

macroeconomic level and micro firm level financial benefit or not from existing Bilateral Trade

Agreements (BTAs).

1.5 Significance of the Study

The significance of FTAs is growing among emerging countries due to their role in economic

prosperity and growth. This issue has immense importance from both theoretical and practical

context. It has been extensively highlighted from1500 to 1800’s, after the studies of Smith

(1776) and Ricardo (1817). A lot of researchers contributed in this discussion and emphasize the

role of FTA in the economic development like Rose (2002), Plummer (2006), Karmakar (2005),

Kawai and Wignaraj (2007) and Mai et al. (2010). However, the discussion on current FTAs’

implication is an ongoing process.

This research is an attempt to examine the pre and post impact of FTA on macroeconomic

factors of Pakistan. Furthermore, it will focus on both the pre and post effect of FTA on non-

../Theoratical%20Frame%20work-Ref/Term%20of%20trade-Chinese-Economy-FTA.pdf



financial firms at micro level is especially lacking in the existing literature. That is why; a

comprehensive study is required to uncover the effect of FTA on non-financial firms of Pakistan.

Therefore, the study significantly contributes to the body of knowledge related to the impact of

FTAs on macroeconomic factors as well as micro level financial factors.

1.5.1 Significance of the Study in Pakistani Context

The study aim is to investigate the pre impact of BTAs on macroeconomic factors like real Gross

Domestic Product (GDP), trade balance, output and trade in different sectors and welfare context

of Pakistan. Additionally, this research also focus on post impact of BTAs on macroeconomic

factors which includes real Gross Domestic Product (GDP), trade balance, output and trade in

different sectors, and welfare in context of Pakistan. Moreover, the author tries to analyze the pre

and post impact of BTAs on Pakistani non-financial firm’s leverage, dividend payouts, and

profitability to cover the micro aspects. So this research helpful to examine the effect of BTAs

on overall betterment of the economy and as well as non-financial firms of Pakistan.

1.5.2 Contribution to Knowledge

1.5.2.1 Contextual Contribution

A lot of literature is available on effect of North American Free Trade Agreement

(NAFTA), EU and ASEAN on macroeconomic factors. However, little literature is

available on micro level firm financial factors. Nevertheless, it is difficult to find a study

in which impact of BTAs is studied with both aspect of macro and micro. According to

Contingency Theory the context always matter, so the macroeconomic factors like real

Gross Domestic Product (GDP), trade balance, output and trade in different sectors, and



welfare and as well as non-financial firm’s leverage, dividend payouts, and profitability

in one economy may be unlike from that of other economy. This study considers the

above revealed macroeconomic factors for Pakistan. Moreover, this research also

uncovers the FTAs’ effect on micro level financial factors of Pakistan.

1.5.2.2 Applied Contribution

The positive effect of FTAs on economy alleviates poverty and therefore elevates

welfare of the society. The stable and improved economic conditions are necessary for

any country. The outcome of the research is pertinent for the corporate managers in

formulating their corporate strategies as well as at macro level for foreign trade policy.

1.5.2.3 Theoretical Contribution

Even though, this research is not suggested to establish a new theory. However, this study

add to a number of theories like comparative advantage theory, Viner’s Model, Meade-

Lipsey and Wonnacott-Wonnacott Models, Lolyd- Maclaren Model, Kemp- Wan theory,

capital structure theory, theory of Dividend and risk theory of profit. This research in fact

incorporates the above said theories into a single model. In the light of these theories it is

more plausible to comprehend the pre and post impact of FTAs on macroeconomic

factors of Pakistan and as well as the pre and post impact of FTAs on non-financial firms

of Pakistan. It is pertinent to mention that the role of comparative advantage affect on

macroeconomic and micro firm level financial aspect is empirically investigated.

1.6 Research Questions



This study addresses the following research questions:

1. What is the pre and post effect of FTAs on macroeconomic factors?

2. What is the pre and post effect of FTAs on non-financial firms?

1.7 Objectives of the Study

The main objective of this study is to analyze the pre and post impact of FTAs on overall

economy, and on non-financial firms. In this way, this research covers both macroeconomic and

micro firm level financial aspects. At macro level pre-economic analysis, the study aim is to:

Investigate the impact of FTAs on real Gross Domestic Product (GDP), trade balance,

output and trade in different sectors, and welfare;

At the macro level post-economic evaluation, the aim of the study is to:

Investigate the impact of FTAs on real Gross Domestic Product (GDP), trade balance,

output and trade in different sectors, and welfare.

The micro firm level financial objective of the study is to:

Examine the pre and post impact of FTAs on firm’s leverage, dividend payouts, and

profitability of firm.

The rest of the manuscript is in following sequence. The chapter 2 is related with the overview of

trade performance and trade agreements of Pakistan. Moreover, in chapter 3 literature review,

hypothesis and conceptual framework is discussed followed by methodology and overview of

Computable General Equilibrium (CGE) Model and Global Trade Analysis Project (GTAP) in

chapter 4. Chapter 5 explains the results discussion. The last chapter highlights the conclusion.



CHAPTER 2

2. Overview of Trade Performance and Trade Agreements of Pakistan

This chapter explains the recent scenario of Pakistan’s trade performance, the trade direction and

the balance of payments position. Furthermore, it also highlights the trade policy initiatives of

Pakistan since 1947 with a concise explanation of the Pakistan’s free trade agreements (FTAs)

and their significance for Pakistani economic prosperity.

2.1. The Contemporary Circumstances of Pakistan’s Performance

In the last fiscal year, the overall world trade performance has been unsatisfactory and grew by

only 2.8 percent during 2014. According to WTO report, the growth rate of the world trade will

probably to be sluggish during coming next two years and will attain to only 4 percent by 2016.

There are numerous reasons which are liable for this slow growth rate as identified by WTO.

Such as, international geographical tensions, natural phenomena and appreciation of the US

dollar against other currencies during last fiscal year. In spite of the slow growth rate of

international trade, energy deficit, security intimidation and depreciation of the regional

currencies, Pakistan’s external sector performance stayed satisfactory with consistency in its

exchange rate, enhancement in the balance of payments and a noticeable enhancement in the

current accounts with a decline in the current account deficit (53%) from 2.9 billion US $ to 1.4

billion US$ and significant inflow of foreign exchange reserves because of the large inflow of

Coalition Support Fund (CSF). Nevertheless, the surplus in capital account declined to 3.2 billion

US$ from 5.3 billion US $ in the last year. In April 2014, the foreign exchange reserves soar to



17.8 billion US dollar against 14.2 billion dollar. During July-April (2014-15), growth rate of

exports of Pakistan has been observed negative which amounted 19.9 US billion dollars against

21 billion US dollars in the same period last year. The contribution of Pakistan in world exports

stayed stationary with only 0.15 percent as compare to India’s contribution of 1.7 percent. The

major exports items presented negative growth rates, like ‘food group’ decreased by 2 percent

beside with declined in export of rice by 5.4 percent. The rest of the food group demonstrated

positive growth rate except fish and fish related product that showed a slight decrease. In spite

getting the GSP plus status from European Union, the overall textile sector’s exports decreased

by 1.2 percent during 2014-15 as contrasted to the respective same last year with a major

declined had been observed in the exports of raw cotton (26 percent), cotton yarn by 8 percent

and cotton garments by 23 percent. On the other side, because of reduction in world oil price and

decline in world imports price, Pakistan’s import did not enhance to the targeted level. However,

a slight increase of 1.8 percent had been observed during July-April (2014-15) amounted 37.8

billion US dollar as contrasted to 37.1 billion US dollar with respect to the same time period last

year. The import of petroleum related product had been declined by 2366 million US dollar

(19.4 %). In the result, the import of machinery group, food, transport items and other agriculture

related imports had been increased.

2.2. Trade Trend of Pakistan (2013-14)

The table-2.1 shows, the trade trend of Pakistan with different regions and major trading partner

during 2013-14. This representation of the trade trend is significant for preparing aggregation

scheme of the GTAP model. In spite being a member of the South Asian Free Trade Agreement

(SAFTA), SAARC accounts for only 5 % of exports and less than 5 % of imports in total exports



and imports of Pakistan respectively. This specifies the unsatisfactory performance of SAFTA

and low attention in the region by Pakistan. One of the major factors is Pakistan- India political

issues in insignificance progress of SAFTA. The Table-2.1 also reports the importance of Arab

league and the Organization of Economic Co-operation and Development (OECD) in Pakistani

trade. The major source of imports is Arab League that accounts for approximately 37 % of total

imports while OECD is the major export destination that shows about 35 % of the total exports

and accounts for about 17 % of the total imports which shows significant trade relationship with

these regions. The table also highlights China as major exports destination that shows

approximately 10% of the total exports and major individual economy source of imports of

Pakistan as it contributes approximately 15% in total imports of Pakistan. Nevertheless, USA is

at the top among exports destination as it accounts for approximately 15 % in total exports,

however, contributes only 4 % in total imports of Pakistan. Among ASEAN countries, Malaysia

is the main exports destination for Pakistani products and also shares about 5% imports in total

imports of Pakistan. However, Sri Lanka is a better exports destination that shows about 1.34%

of the total export as compare to Malaysia’s contributes only 0.96% of total exports of Pakistan.

In view of above scrutiny of trade direction it is clearly understand that Pakistan has well

diversified its trade direction both in terms of exports destination and imports direction.

Moreover, Pakistan has poorly focused in regional trade as shown by the low share of the

regional and neighboring economies in total exports and imports.

Table: 2.1 Trade Trend of Pakistan

Regions Exports (Rs % share in Imports (Rs % share in



Million) total Exports Million) Total Imports.

Arab League 329485 0.13923 1622563 0.373013

UAE 205413 0.0868011 690150 0.15866

S Arabia 47279 0.0199786 474685 0.109126

Kuwait 8218 0.0034727 376777 0.086618

ECO 46875 0.019808 3050 0.000701

Iran 9412 0.0039772 14600 0.003356

Turkey 37463 0.0158307 15900 0.003655

OECD 848776 0.358666 768565 0.176687

USA 341283 0.1442156 180433 0.04148

CANADA 20846 0.0088089 62100 0.014276

Germany 93550 0.0395313 105600 0.024277

France 93680 0.0395863 46100 0.010598

UK 121172 0.0512035 51652 0.011874

Japan 16083 0.0067962 199500 0.045863

SAARC 132847 0.056137 208480 0.047928

Bangladesh 68673 0.0290191 5999 0.001379

India 31700 0.0133954 195500 0.044944

Sri Lanka 31718 0.013403 6785 0.00156

ASEAN 66105 0.027934 509065 0.11703

Malaysia 22887 0.0096713 245890 0.056528

Indonesia 18940 0.0080035 106500 0.024483



Source: Pakistan Economic Survey (2013-14), various issues.

Note: Columns 3 and 5 shows the % share of exports and imports by regions and some of the major trading partners

of Pakistan in the respective regions in total exports and imports of Pakistan respectively. For example China’s share

is 10 % and 15 % in total exports and imports of Pakistan respectively

2.3. Concise Review of Pakistan Trade Balance

Since 1947, Pakistan had surplus trade balance during 1950’s and then in subsequent years

Pakistan had consistently facing deficit in its trade balance as shown in Table 2.2. This was all

happened because of high level of imports and the poor performance of exports sectors.

Table: 2.2 Trade Balance of Pakistan (Million Dollar)

Years Exports Imports Balance

1950 406 353 53

1955 156 203 -47

Singapore 7180 0.003034 75356 0.017324

Thailand 11102 0.0046914 77794 0.017884

Pillpine 5996 0.0025337 3526 0.000811

Other Asian 364409 0.153988 797491 0.183336

China 252522 0.1067079 656615 0.15095

Hong Kong 38870 0.0164253 9945 0.002286

N. Korea 7 2.958E-06 3785 0.00087

S. Korea 27997 0.0118307 77545 0.017827

Pakistan 2366478 1 4349879 1



1960 114 457 -343

1965 253 605 -352

1970 420 757 -337

1975 1137 2067 -930

1980 2958 5409 -2451

1985 3070 5634 -2564

1990 6131 7619 -1488

1995 8707 11805 -3098

2000 9202 10729 -1527

2005 984,841 1,711,158 -726317

2010 2,120,847 3455287 -1334440

Source: Pakistan Economic Survey 2012-13

The significance of external sector is very high in economic development of any country that is

why the core concern of trade deficit has been addressed by Pakistan. Pakistan always tries to

improve its trade policy in terms of trade liberalization and signing bilateral and multi-lateral

trade agreements to reduce the trade deficit and to enhance the performance of the external

sector. Unfortunately, it has not been thriving to do so. As highlighted in the introduction of this

study that this research is mainly focus on the examination of impact of bilateral trade

agreements of Pakistan on macroeconomic and firm level financial factors to understand what

are the weakness and strengths of Pakistan in this regard.



2.4. Review of the Trade Policies of Pakistan

Pakistan has adopted numerous kinds of economic policies during the first and second decades,

after the independence. In 1950’s it was realized that progress of the economy could be possible

only because of industrial growth. Therefore, the government comprehended the growth of

industrial sector at any cost. For this purpose, to safeguard the local industry from foreign

competition the government adopted restrictive trade policies. The government supported tariff

protection by imposing low tariffs on imported raw materials and capital goods as compare to

high tariff rates on luxury and consumer commodities. Moreover, the government had also

shifted resources from agriculture sectors to industrial sectors to encourage the newly born firms.

Due to this strategy, even though industrial sector achieved 10.3 % an average growth rate

during 1950-55, however, the agriculture sector did not perform well and during 1950-58, its

growth rate was negatively reported. So, this strategy did not generate significant results for the

economy. In the second decade (1958-68), the economic and trade policies had been changed

from direct control to the indirect control methods of trade barriers. For the encouragement of

exports, the government introduced the Export Bonus Scheme (EBS) to promote exports2.

Nevertheless, this policy additionally increased the inequality in the economy as it was became

the core matter with the capitalistic development during 1960’s and was suspended later on in

1970’s to decrease disparity in the economy. In 1972, there was a major change in trade policies

because government suspended the EBS scheme and multiple exchange rate regimes is one of

the core reasons in enhancing inequality in 1960’s. The government became more liberal by

2The main purpose of the EBS was subsidizing exports and to allow import of goods, while maintaining import

control and maintaining exchange rate.



abolishing the import licensing system and enhanced the number of items in the free list. In fact,

the government replaced the ‘Import License Scheme’ (ILS) with the recent import license

system which was the ‘Open General Licenses (OGL)’ that created more opportunities for new

entrants into the trade sector. In 1971-72, Pakistan became a pioneer in the South Asian region

by starting numerous economic reforms. The outward-oriented export-led development policy

had been adopted by replacing with the inward-oriented strategy. At that time, the same policy

was implemented by East Asian economies.

On the recommendation of International Monetary Fund (IMF) and World Bank (WB) during

1980’s the economy had adopted a number of structural changes. According to these reforms, it

was noticed that generally the economy had shifted towards liberalization. The new liberalization

policies had various key attributes that included reduction in both negative and restricted lists,

increase of goods in the free list and reduction in import tariff. The government also encouraged

exports by adopted a number of export promotion measures such as rebate on the exports of

manufactured goods and concessions in corporate income tax. Furthermore, exporters were also

encouraged by granted import facilities and they can borrow finances at lower rates. The

Industrial Incentive Reform Cell (IIRC) was replaced with Tariff commission during 1989,

which caused a dramatic decline in import tariff rates of industrial raw materials and capital

goods. In 1982, the flexible exchange rate regime was adopted and fixed exchange rate system

was eliminated. The IMF and World Bank suggested a comprehensive polices regarding trade, in

1988. Therefore, at that time Pakistan experienced an amazing change in its trade policies. These

policies change included replacement of non-tariff barriers (NTB) by tariff, decline of the



maximum tariff by 125 % and decrease of goods in the banned list from 400 goods to only 80

goods.

2.5. Trade Agreements of Pakistan

The role of regional trade is considered to be a significant in economic prosperity. Pakistan has

realized that and aggressively engaged in many regional trading agreements like South Asian

Preferential Trade Agreement (SAPTA). The SAPTA was upgraded to SAFTA in 2006 as

Preferential Trade Agreement (PTA). Pakistan has signed FTA with India. However, it is not a

successful FTA because of Pakistan India political conflicts. The Economic Cooperation

Organization Trade Agreement (ECOTA) of 2003 and SAFTA are not fully operative in Pakistan

as these agreements have permitted very few items in its positive list of trading items with the

SAFTA members’ countries. The other member countries have liberalized trade with each

members of SAFTA under the SAFTA rules and regulations and enjoying the benefits of the

same. Up till now, Pakistan adopted prevented trade policies as contrast to its regional

counterpart economies such as India, Sri Lanka and Bangladesh. Due to the political tension

between Pakistan and India, both countries have failed to improve their trade relations. Even

though, Pakistan has granted Most Favored Nation (MFN) status to India. On the other hand,

India signed free trade agreement (FTA) with Sri Lanka and Bangladesh and avoided the

SAARC cooperation (Pohit. S, 2013). In 2010, after analyzing East Asian policy, India has

joined free trade agreements with individual economies of ASEAN as well as with ASEAN.

Similarly, Pakistan has also signed free trade agreements with Sari Lanka and China. In Pakistan

China FTA, Pakistan deeply reduces the tariff rates along with increase in tariff lines (Pohit. S,



2013)3. In view of above, the importance of free trade agreements and trade liberalization in the

progress of Pakistan has been observed, that is why the government has also started negotiations

with the member economies of ASEAN (Malaysia, Singapore and Indonesia). In this context,

Pakistan has already signed FTA with Malaysia in 2008.

2.6. Potential Free Trade Agreements of Pakistan

Following is the concise discussion on selective potential Free Trade Agreements (FTAs) such as

Pakistan China FTA, Pakistan Malaysia FTA and Pakistan Sri Lanka FTA. In this study, these

FTAs are examined in detail on both macroeconomic and micro firm level financial factors

aspect.

2.6.1 Pakistan-China FTA

In the global economy, China has become a major player and views FTAs as a significant

part of its international trading strategy. The Chinese industrial sectors rely on Foreign

Direct Investment (FDI) inflows and export of raw material and intermediate

commodities. In fact, its export industries are embedded in current regional and global

production networks (Zhang 2010). Moreover, according to Qi and Muhammad (2014),

China has become the world’s largest trading economy in commodities, ending the

United States’ post-war international trade dominance.

During last three decades, it has been observed that China’s GDP is soaring at a swift

rate. The GDP was $9,240,270 million in 2013 and the same has been enhancing by over

3Pohit. S (2013) indicated that by 2013, India is leading in FTA status with total 34 FTA’s out of which 13 FTA’s

are signed and in effect. In contrast, Pakistan’s total FTA’s are 27 with only 6 are singed and in effect. The rest are

just proposed or signed but not in effect yet.



one trillion dollar from 2012 i.e. $8,229,490 million. China’s trade share a huge portion

of the world economy, and it is the second largest economy in the world. China’s surplus

trade balance was $260,587 million (Pakistan Business Council 2015).

According to Muhammad and Qi (2014), China should enhance the collaboration with

the different nations and regions according to their resource characteristics, economic

structure and technological intensity. Keeping in view of above global importance of

China, Pakistan signed a FTA with China on 24th November 2006, which came into

effect in 2007. The agreement was consist of two phases. In phase-I, China abolished

tariffs on 6418 product lines and similarly Pakistan also eliminate/decrease tariffs on

6711 product lines within 5 years. The phase-I ended in December 2012. Pakistan

provide market entry to China mostly on machinery, organic, and inorganic chemicals,

fruits & vegetables, medicaments and other raw materials for various industries including

engineering sector, intermediary goods for engineering sectors, etc. While China

abolished tariff on industrial alcohol, cotton fabrics, bed linen and other home textiles,

marble and other tiles, leather articles leather articles, sports goods, mangoes, citrus fruit

and other fruits and vegetables, iron and steel products and engineering goods. China also

eliminated 50% tariff on products such as fish, dairy sectors; frozen orange juice, plastic

products, rubber products, leather products, knitwear, woven garments etc. (Ministry of

Commerce, Pakistan).

Since July 2013, phase-II negotiations are ongoing between both countries. The initial

objective of the FTA is to abolish tariff on at least 90% of all products. Currently, it’s



important for Pakistan to exploit this opportunity for enhancing Pakistan’s market entry

to the Chinese’s markets. The objective of phase-II agreement is that to improve the

bilateral trade up to $15 billion beside with to enhance economic cooperation among the

two economies through trade. The bilateral trade attained to almost $9,278 million at the

end of 2013 as compared to $3,421.96 million in 2006 earlier to the FTA being carried

out (Pakistan Business Council 2015).

2.6.1.1 Relative Significance of Bilateral Trade

Pakistan and China are closed friends and they have excellent strategic link for

many decades with each other. China always takes keen interest in development

of Pakistan’s industrialization, defense, technology and infrastructure. Pakistan

China – FTA is golden opportunity for Pakistan towards enhancing its overall

trade development.

In Pakistan’s imports, China had been sharing importantly, even before the FTA

was signed and after the FTA execution in 2007, it has significantly enhanced its

ranking. However, Pakistan’s contribution to China’s imports from world did not

observe any significant increase during 2006 to 2016, it was enhanced from

0.06% to just 0.18%. China’s contribution to Pakistan’s imports was even more,

like the contribution increases from 13% to 24% in the time period of 2006 to

2012. (United Nations Commodity Trade Statistics Database).



2.6.2. Pakistan-Malaysia FTA

Malaysia came into existence in 1960s and after that its economic development is very

impressive, and shows extraordinary economic prosperity among the East Asian

countries. In the early years, Malaysia economy was relying on its important natural

resources, mineral and agriculture produces. However, later on government of Malaysia

took steps for the development of industrial sector. The government transformed its

economic structure in 1968, and adopted multiple trade reforms that included Export

Oriented Industry (EOI) policy. This strategy changes the Malaysian economy from

reliance on primary goods to depending on state of the art technology and knowledge

based industrial commodities. Along with EOI strategy, the government also took inward

looking policy like import-alternative. Moreover, the government also established capital

intensive industries during the same time period. Due to EOI strategies, Malaysia ranked

it as 3rd largest economy of the East-Asian region. Malaysia GDP per capita in 2014 was

$10,803 (world Economic Outlook). During 2014, the Foreign Direct Investment (FDI)

attracted approximately $10 billion (Central Bank of Malaysia). Since 2004, it has had a

continuous trade surplus which was reached to $25 billion in 2014 (International Trade

Center- Trade Map).

In view of above, highlighted discussion it is clear that Malaysia has significant role in

East Asian region and for the seek of economic cooperation, the desire to progress and

economic prosperity, Pakistan and Malaysia initially engaged in Early Harvest Program

(EHP). The objectives of this arrangement were to protect market for their export

products and expand the economic and trade relationship being significant members of



the region. After the successful completion of the EHP program, the first FTA was signed

between Pakistan and Malaysia on November 8th 2007. This agreement was first

Pakistan’s extensive FTA about combining trade in goods, trade in services, investment

and economic cooperation and for Malaysia it was first FTA with any South Asian

country. The objective of this FTA is to eliminate the export and import tariffs for the

improvement of bilateral trade between the two countries. This research investigates the

Pakistan- Malaysia FTA which was come into effect in 2008. (Ministry of Commerce,

Pakistan)

2.6.3. Pakistan-Sri Lanka FTA

The economic profile of Sri Lanka is not better than Pakistan. The GDP Average Annual

Growth Rate (AAGR) in Sri Lanka averaged 6.59% from 2003 until 2014, while

Pakistan’s GDP AAGR for the same period was 11.11%. The total trade of Sri Lanka

with world raised by 110% during 2004-2013 while Pakistan’s trade raised by 120%

during same time period. The both countries have had deficit trade balances during 2004-

2013, the Pakistani trade balance deficit rising by 308% whereas Sri Lanka trade

imbalance rising by 232% during same time period. (Pakistan Business Council, April

2015)

The FTA was signed in July 2002 between Pakistan and Sri Lanka and became effective

from 12th June 2005. The objective of the FTA is to eliminate tariffs on a number of

items during a specific time-frame. Instantly, Pakistan had given 100% concession on

206 items at the 6-digit Harmonized System (HS) level while similarly Sri Lanka had

given 100% concession on 102 items at the same HS level. In no concession list Pakistan



included 540 items at the 6 digit HS level whereas the no concession list of Sri Lanka

included 697 items at the same level, which were reduced to 607 items in April 2013.

Moreover, duty free tariff rate quota (TRQ) on tea products had been given by Pakistan to

Sri Lanka. In addition to that TRQ at 35% margin of preference on the MFN rate on

several clothing had been given by Pakistan to Sri Lanka. Reciprocally, Sri Lanka had

been given duty free TRQ on long grain basmati rice and potatoes to Pakistan. The terms

and conditions required for Pakistan was to phase out tariffs on commodities not on it no-

concession list by 2008, whereas the same do by Sri Lanka till 2010. The major exports

of Pakistan, which got full concession from Sri Lanka, were cotton, apparel, knitted

fabrics, and cement. Similarly, the major exports of Sri Lanka, which got full concession

from Pakistan, were rubber, coconut products and vegetable products. (Ministry of

Commerce, Pakistan)

In view of above discussion, it has been concluded that these highlighted FTAs are significant

for Pakistani economic development, prosperity and progress. The extensive scrutiny is required

to identify the potential trade commodities and sectors which include the financial aspects at firm

level to increase the Pakistani exports, which will help the Pakistan to become the economic

leader in the Asia. This scrutiny is the core part of this study. The next chapter explains the

literature review.



CHAPTER 3

The objective of this chapter is to discuss regarding literature review, hypotheses and conceptual

framework of the study.

3. Literature Review

The concept of mercantilism was formed by the trade theory during the time period of 1500 to

1800. Mercantilism apprehended that the wealth of a country was measured by its holding of

gold. The theory said that if countries’ exports are more than they imports then those countries

receive gold from those countries that are in deficits (Magnusson, 1994).

Adam Smith criticized the theory of mercantilism and said that the actual wealth of a country

composed of the goods and services accessible to its citizens. Adam introduced the theory of

absolute advantage and argued that some countries are efficiently producing certain commodities

than other countries. Therefore, international efficiency can enhance through free trade. He also

raised the question that why a consumer of any country should have to purchase locally produced

commodities when they could purchase those commodities at lower price from another country.

David Ricardo extended the concept of absolute advantage to the concept of comparative

advantage and he talked about the specialization of a country’s production in context of

international trade.

The theories of Adam Smith and David Ricardo do not explain in how much and what

commodities will be traded through specialization on country to country basis. However, the size

of the country helps explain these differences. Thus, the theory of country size tells that larger



countries differ in numerous ways from smaller countries. They likely to export little and import

a smaller amount of commodities for consumption, and they bear large amount of transport costs

for international trade. Moreover, they can manage huge scale of production (Krugman, 1980).

There is another criticism on Smith’s and Ricardo’s theories that these theories did not find the

kinds of commodities that would probably provide an advantage to a country. Later on

Heckscher and Ohlin in 1991 had introduced the factor-proportions theory based on factor of

production of a country. This theory assumed that there are differences in countries’ endowments

of factor of production which create the differences in the cost of production. These differences

provide the opportunity to certain country to increase its production and exports to other

countries due to the cheaper factor of production. Another international trade theory is the

product life cycle (PLC) which was introduced by Vernon (1966). He categorized the cycle into

four phases such as introduction, growth, maturity, and decline. So a multinational company

faces this product cycle, start with local production then exports its products and if there is

demand in foreign market than open a subsidiary in any host country. The MNC can survive only

in case, if subsidiary introduce a new product line otherwise it will wind up the business.

Up till now, the above mentioned theories discuss about the trade among countries are happened

due to the differences among themselves. These dissimilarities of the trade among countries are

trade between an industrial country and an emerging economy or trade between a temperate

country and a tropical one. On the basis of these theories, one can argue that if these differences

are large among countries then the chances of international trade will be high.



Another aspect due to which the trade takes place internationally is that some countries have

good industrial base and other countries are good in agricultural products. The country-

similarity theory states that after observing the market situations in the home market a firm

introduced a new product in local market and it will market the same product in international

market. Moreover, the markets of industrial countries can encourage products and their

alternative products. Therefore, companies produce different product and belongs to different

countries enjoy the profits from foreign markets (Linder, 1961).

The theory of dependency was developed by Raul and the theory helps to explain the

independence, interdependence and dependence of international trade models and trade policies

of different economies. This theory developed three different ranges such as independence at one

extreme dependence on the other, and interdependence anywhere in the center. No countries are

located at either extreme of these ranges but some tend to be closer to one extreme than the

other.

3.1 Some Empirical Evidence

There are various studies using Computable General Equilibrium (CGE) modeling, such as

Faruqui, Ara and Qamruzzaman (2015), Cheong (2013), Rahman and Cheong (2014) Oduncu

et.al (2014), Xin (2014), Badri and Sharma (2014) and Petri et.al (2011) to measure the effect of

Trans-Pacific Partnership (TPP) on different economies. One of the research conducted by Hiro

and Itakura (2014) in which they used Global Trade Analysis Project (GTAP) model to examine

the welfare effect of Regional Comprehensive Economic Partnership (RCEP) and TPP on

numerous economies.



Computable General Equilibrium (CGE) by Johansen (1960) had used models to estimate

empirically the pre and post economic effects of an FTA, which are explained as mention below.

The theoretical models of FTAs are limited to have number of countries and products in the

analysis. These models also ignored attributes such as economies of scale or imperfect markets

scenario. The researchers now use computer-based modeling to control these restrictions and to

give more realistic policy recommendation. This kind of computer-based modeling has the

capacity to incorporate any number of goods and member countries or economic attributes.

These models are also capable enough to perform general equilibrium scrutiny with various

dimensions.

The analysis of FTAs is done with the help of CGE models because of numerous reasons. Firstly,

CGE modeling assumptions are coherent with the microeconomic theory. Secondly, CGE

models provide quantitative results which make the ease for policy makers to examine who gains

and who loses from an FTA. Thirdly, the scrutiny may be too difficult using algebraic methods

due to an FTA includes alterations in trade policy in various economies. Finally, CGE scrutiny

may produce imminent regarding the role of specific economic assumptions in shaping the

results of an FTA.

Naseem and Zaheer (2005), argued that South Asian countries increased intra regional trade due

to economic cooperation with each member country and effective collective negotiations of

better items for common interest with the WTO to take advantage of market access in the

globalised trading system. They agreed that the fruits of coexistence are many and peace is a

worthy option. However a different perspective needs to be considered with regard to regional

../SAFTA/Eco%20growth%20and%20development%20with%20and%20without%20Regional%20Cooperation.pdf



trade. The World is moving towards regional trade. There are today about 300 FTA's and RTA's

now in operation in various parts of the World. If both are absent, the success will be limited.

NAFTA is the most successful example of RTA. It is estimated that between NAFTA, EU and

ASEAN they will control 80 percent of world trade by the time SAFTA comes into being.

Shaikh (2009) argued that Pakistan would get the largest welfare benefit under the mutual policy

reform of the SAFTA whereas the SAFTA on its own provides the second largest welfare

benefit. Due to the SAFTA the member countries can attain bigger economies of scale in

production, specialization, increase competitiveness and diversify their export basket, hence

helping in local economic reform.

Conversely, in 2010 Khan investigated that presently very small intra-industry trade exists

among Pakistan, India, Bangladesh and Sri Lanka. He also establishes that greater intra-industry

trade existed in textile sector, because Pakistan has good quality of cotton and yarn production.

Moreover, his Study shown that the amount of trade diversion in majority cases was bigger than

the amount of trade creation for majority of South Asian member countries. The regional

integrations normally have to bear the cost of trade diversion.

According to the World Bank report (2005, pp. 66), the average tariff of NAFTA member

countries comes to about 3% and that of AFTA, slightly less than 5%. Clearly, the impacts of

these FTAs, for improvement or worse, will eventually not be determined by the standard net

efficiency calculations. The economics of FTAs have become complex and it is difficult for

economic analysis and negotiators have regularly failed to keep pace.

../SAFTA/055-2009.pdf

../Theoratical%20Frame%20work-Ref/Global%20Trade%20prospect%202005-world%20bank.pdf



In 2005 Karmakar examined the impact of Indian-ASEAN economic cooperation on trade

services sector. He proposed that, through this FTA much could be gained by India and ASEAN

in services sector. In fact ASEAN get more benefit from this BTA because that region remains

relatively closed to foreign service providers as compare to India. Nevertheless, his research was

also not supported by any theoretical model. Even though few research work examined the

impact of the India-ASEAN FTA on the basis of theoretical model. The most current research

works are discussed below.

The CGE model was used by Kawai and Wignaraja in 2007 to analyze the economic impact of

numerous FTAs in East Asia between certain groups such as ASEAN+1 (ASEAN+China,

ASEAN+Japan, ASEAN+Republic of Korea, ASEAN+India and ASEAN+CER) and also

ASEAN+3 (ASEAN, China, Japan and the Republic of Korea), ASEAN+6 (ASEAN+3,

Australia, New Zealand and India). Their results suggested that the ASEAN+6 produced the

maximum gains in East Asia among all the above mentioned FTAs. Moreover, they also

highlighted that the plus-three countries (China, Japan and the Republic of Korea) required

cooperating with each other more closely, and India requires working more on structural

reforms.

Taneja et al. (2013) argued that in South Asia India has the lead role in Economic integration

with other economies of the region. Moreover, they also said that allowances provided by India

under SAFTA for LDCs have significantly benefitted Bangladesh while allowances given to

non-LDC partners have applied to its imports from Pakistan. The special allowances have also

../Theoratical%20Frame%20work-Ref/India-Asean%20Corporation%20in%20service-Karmakar%202005.pdf

../Theoratical%20Frame%20work-Ref/asean%203.asean.6-Kawai%20Wignaraja%202007.pdf

../../SAFTA/India%20Role%20in%20SAFTA.pdf



given by India to Sri Lanka and Nepal under SAFTA. In 2011 Pakistan has granted the Most

Favored Nation status to India which is one of the main breakthroughs in the history.

According to Sachs et al. (1995) the countries which are the strong trade reformers had attained

positive economic growth by 1994. However, very few strong reformers countries experienced a

smaller cumulative loss of GDP between 1989 and 1994. The strong trade reformers countries

are central and Eastern Europe and the Baltic states, whereas none of the states of the former

Soviet Union is included in this category. Moreover, they did not consider the other factors like

geography, politics and resource endowments which might helpful in growth performance.

Venables (2001) had an argument that the FTAs among low income countries lead to divergence

of member country incomes, whereas FTAs among high income countries are source of

convergence. Hence, the results proposed that LDCs are probably to be better served by ‘north-

south’ than by ‘south-south’ BTAs. Likewise, Mutti, Sampson and Yeung (2000), had discussed

that those sectors which have comparative advantage are able to get benefit of allowances

offered by foreign countries in the Uruguay Round. However, those sectors which have

comparative disadvantage will lose as a result of reduction in import tariff.

Alam et al. (2011) argued that if we compare the intra-SAARC trade with other regional blocks

of the rest of the world, the results showed the intra-SAARC trade is at very small scale. The

causes of small trade scale are socio-economic barriers in trade. However, they have identified

../../Hypothesis-Ref/Impact%20of%20GDP-Hypothesis/GDP-FTA/Sachs_Warner_1995.pdf

../Theoratical%20Frame%20work-Ref/Winners%20and%20losers%20from%20regional%20integ%20agreements.pdf

../Hypothesis-Ref/Signalling%20Theory/uruguay-US%20industry.pdf

../SAFTA/Trade%20barriers%20and%20Facilitation%20in%20SAACR.pdf



that WTO regulations and respective trade policies of the countries are also trying to enhance

trade between SAARC nations.

In 2003 Rodriguez investigated the investors’ expectations of NAFTA’s impact on the

profitability of firms in the USA, Canada, and Mexico. He proposed that investors understood

that due to the NAFTA the companies which are rich in factors intensity maximize their profits.

However, those companies which have scarce factors reduce their profitability. Therefore, the

factor intensity was a foundation of comparative advantage.

David (2010) shared the simulation results regarding terms of trade (TOT) and he disclosed that

the TOT was improved for six out of the nine ASEAN countries (particularly for Cambodia).

Nevertheless, non-ASEAN countries faced decline in TOT. In addition, Mai et al. (2010) had

investigated the impact of terms of trade (TOT) and growth of the People’s Republic of China’s

(PRC) neighboring countries. Their results revealed that the impact on the growth and TOT of

the PRC’s neighboring countries are comparatively small.

Likewise, Chandrima and Biswajit (2011) also had explored the FTA and said that India reduces

the tariff on commodities listed under the normal and sensitive tracks. Consequently, the import

prices of those products in India reduce due to this tariff. Therefore, the exporters of ASEAN get

the benefit by supplying their commodities at competitive prices to India.


../Theoratical%20Frame%20work-Ref/Impact%20of%20Indian-ASEAN%20FTA.pdf



3.2 Effect of FTA on Real GDP

The impact of FTA on real GDP had been examined by Kawasaki in 2003. He found that the

real GDP of Japan had been improved from the Japan-China FTA as well as from the Japan-

Philippines FTA. Furthermore, Boumellassa, Decreux and Fontagné (2006) had a point of view

after conducted the simulations, the ASEAN countries will improve their GDP up to more than

2% up till 2020.

Mai et al. (2010) highlighted that during 1997–2005, the People Republic of China’s (PRC) real

GDP was 8.9%. They forecasted that during the period of 2005–2010, the growth trend of 8.9%

per year will remain consistent. Moreover, they also predicted that without technological

convergence the PRC’s real GDP would grow at a rate of only 4.8% per year which is much less

than the convergence rate of 8.9%.

Similarly Chandrima and Biswajit (2011) also picked the GDP as macroeconomic indicators to

evaluate the impact of FTA. It was observed by them that India get nothing whether there is full

reduction of tariff or tariff changes as per current scenario. Malaysia, Singapore, Thailand and

the rest of ASEAN get gains in case of full reduction of tariff. While Cambodia majorly

negatively affected. Furthermore, Myanmar, Viet Nam and Indonesia receive the positive impact

of the FTA. In the contemporary situation, the same three ASEAN countries get the significant

gains. In the final situation, between all the ASEAN countries, Singapore’s gains are the highest.

The rest of the smaller countries show the same results as in the case of full liberalization.

H1: FTA has significant impact on real GDP of a country that joins an FTA.

../Theoratical%20Frame%20work-Ref/Impact%20of%20FTA%20on%20Asia.pdf

../Theoratical%20Frame%20work-Ref/FTA%20Between%20EU%20and%20ASEAN.pdf


../Theoratical%20Frame%20work-Ref/Impact%20of%20Indian-ASEAN%20FTA.pdf



3.3 Effect of FTA on Trade Balance

The significant impact of FTA on trade balance of a country had been analyzed by Kawasaki

(2003). He found that the Japanese import volumes were more than its export volumes. The trade

balances of Japanese were worsening due to the Japan- Malaysia FTA and the Japan-Philippines

FTA. However, Japan-China FTA showed the different results because Japan had an improved

trade balance. The Japan-Korea FTA worsening the trade balance due to reduction in production

of transport equipment industries was proposed in both countries. However, contrary results had

been found in case of Japan and ASEAN countries FTA because the Japanese trade balance

improved.

Furthermore, David (2010) had found that the trade balances of all ASEAN countries had been

deteriorating due to increase in imports than in exports. Nevertheless, it is pertinent to mentioned

that before simulation results showed that trade balances in 2004 of all ASEAN countries was in

excess excluding Lao PDR, Myanmar, and Viet Nam. Moreover, he identified for non-ASEAN

countries that exports from the People’s Republic China (PRC) and the rest of East Asia reduce,

whereas exports of Japan increase which gave the favorable trade balance for Japan.

H2: FTA has significant impact on country’s trade balance.



../Theoratical%20Frame%20work-Ref/WP52_Ex_Ante_Economic_Evaluation.pdf



3.4 FTA effect on Trade and Output

According to Kawasaki (2003), six Asian countries significantly high rates of change in trade,

production and capital formation as compare to Japan. As Japan is good in technology, thus it is

expected it would get gain in technology and capital intensive trade and production. Whereas,

ASEAN countries have cheap labor so these countries would get gain in labor-intensive

industries. It is noted that the results are different from general expectation that Japan might lose

in machinery and equipment sector, whereas ASEAN countries get gain in machinery and

equipment sector excluding Thailand.

Furthermore, Brooks et al. (2005) studied the trade related effect. They observed firstly that the

efficiency of trade has more growth capability than change in tariff. If a country consistently

maintains the efficiency of trade than it will enjoy higher real income and trade. Secondly,

Southeast Asian countries like Malaysia, Singapore, Thailand, and Viet Nam, have enjoyed the

benefits from the FTA. Even though, the PRC and Japan get the gains in terms of the highest real

income. The importance of export and increase in real income had been realized by the Southeast

Asian nations, thus these countries expand regional trade and liberalized their economies. It is

very motivated to observe that the continuous decrease in trade costs contributing increase in per

annum growth of bilateral trade flows approximately 5 to 8 percent. Hence, the significant

benefits can be achieved if trade is assisted by tariff reform and structural reforms. Similarly,

David (2010) argued that after the simulation of ASEAN FTA it had been observed that

Vietnamese’s sectoral output and trade changed. The output of all primary and secondary sectors

reduced excluding the Grains and Crops sector. The export price and volume of Grains and


../Theoratical%20Frame%20work-Ref/Asia%20Long%20term%20growth%20and%20integration.pdf




Crops sector increased as compare to other sectors. Nevertheless, Viet Nam‘s real GDP had

negative impact due to the reduction in the Processed Food and Other Services sectors. Other

sectors like Utilities and Construction contributed positive impact on growth. The highest

decrease in Processed Food sector was –4.02% as compare to other sectors due to the increased

in import volume of approximately 16%. The Processed Food sector’s import price decreased as

compare to other sectors. While the import volumes of all primary and secondary sectors

increased.

Moreover similar investigation had been done by Coulibaly (2007), and found that except for

South Asia Pacific Trade Agreement (SAPTA), during 1960 to 1999 all these RTAs generated

positive impact on their members' countries. Asia Free Trade Agreement AFTA showed positive

impact on imports of partner countries from the rest of the world (ROW), however, its effect on

their exports of member countries to the ROW is not substantial. Due to the tension between

India and Pakistan SAPTA did not generate positive impact on its partner countries.

In 2001 Hassan proposed that SAARC partner countries must put effort to attain trade-creating

gains. There is need to develop the suitable policies to achieve more regional integration because

regional integration among SAARC countries create substantial benefits for all the economies in

the region. He recommended that through elimination of tariff and nontariff restrictions the

border must be liberalize for free flow of trade which strengthens bilateral trade relations among

the member countries under the South Asian Preferential Trading Arrangement.

../Hypothesis-Ref/Impact%20on%20Trade/Evaluating%20Trade%20effect%20of%20developing%20regional%20trade%20effect.pdf



Akram (2013) used the random effects model and his results showed that industry-specific

variables are not as such relevant in measuring intra-industry trade than country-specific

variables. He identified that market size is positively correlated with intra-industry trade which

is consistent with the results of Turkcan (2005). The distance among trading partners’ decreases

bilateral trade, this result is consistent with result of Shahbaz and Leitao (2010). The differences

in human capital have positive relationship with Intra-industry trade among member countries.

Akhter and Ghani (2010) analyzed the SAFTA and their results showed that due to regional

trade agreement the trade is diverted among the member countries as well as for the non-member

countries. Nevertheless, if Pakistan, India, and Sri Lanka sign regional trade agreements than

trade volume will be improved.

H3: FTA effect on trade and output in different sectors within the country.

3.5 FTA effect on Country’s Welfare

Boumellassa, Decreux and Fontagné (2006), had been examined that the welfare gains have

been achieved by the ASEAN countries through the liberalization in services. Similarly, Disdier

and Marette (2009) also talked about welfare. They estimate the welfare with the help of gravity

equation and their results showed an increase in both domestic and international welfare.

../SAFTA/Pak%20SAARC%20Intra%20Tarde.pdf

../SAFTA/Regional%20Integration%20in%20SAARC-Gravity.pdf

../Theoratical%20Frame%20work-Ref/FTA%20Between%20EU%20and%20ASEAN.pdf

../Hypothesis-Ref/Welfare/Gravity%20and%20welfare.pdf

../Hypothesis-Ref/Welfare/Gravity%20and%20welfare.pdf



Likewise in 2010 Veeramani and Saini also measured impact of India-ASEAN FTA on the

welfare of the member countries. The simulated shows that according to this FTA after reduction

in tariff the import of plantation commodities is probably to increase in India which is due to

mainly by trade creation rather than trade diversion. Moreover, the India’s government

significantly losses the tariff revenue because of elimination of import tariff under this

agreement. Their study is limited to only India- ASEAN FTA for India for few specific

plantation goods and evaluated the impact on welfare which is a partial equilibrium model.

Moreover, Ken and Hiro (2012) measured the impact on welfare changes and sectoral output

adjustments. They investigated an ASEAN+3 FTA and Trans-Pacific Partnership (TPP) 9

agreement and revealed that major partner countries get high welfare gains.

H4: FTA has significant effect on country’s welfare.

Moreover, following are the hypotheses through which Micro level firms’ financial factors are

tested:

3.6 Impact of FTA on leverage of the Firms

A lot of literature has been accessible regarding relationship between financial leverage and

product market, like Allen (1990), Brander et al. (1986), Titman (1984) and Chevalier (1995)

discussed this theoretical model in their research work. These studies mainly consider on the

causal link from financial structure to product market strategy.

../Theoratical%20Frame%20work-Ref/IGIDR_Veeramani_2010.pdf

../../Hypothesis-Ref/Welfare/Lee_h-2012.pdf



In 1991 Maksimovic et al. revealed that if a company picks high level of debt than it will face

high level of risk. Whereas Kovenock et al. (1995) propose that the capital structure decision is

totally depend on long term changes in industry demand and supply situations. Moreover,

MacKay et al. (2001) pointed out that the financial structure decision depends on grouping of

firms within industries and the attributes of companies themselves. Baggs and Brander (2006), in

fact investigated the impact of trade liberalization on leverage of firms.

H5: FTA has significant impact on leverage of the firms.

3.7 Impact of FTA on profitability of the Firms

The notion of changes in tariffs have an impact on profits is discussed by the international trade

researchers. The prominent researchers who had worked on this issue are Brander and Spencer

(1984) and Buffie and Spiller (1986). However, the empirical literature regarding impact of trade

liberalization on profit is astonishingly little. In 2001 Hay investigated the impact of the post-

1990 Brazilian trade liberalization on manufacturing companies.

H6: FTA has significant impact on profitability of the firms.

H7: FTA has significant impact on dividend payouts of the firms.

In view of above said theories and empirical evidence it can be argued that some of the

researchers explained that the FTAs and Regional Trade Agreements (RTAs) are beneficial for

the economic prosperity (Pradumna, 2006; Kawai & Wignaraja, 2007; Sasatra & Prasopchoke,

2007; Chandrima & Biswajit, 2011; David, 2010; Ken & Hiro, 2012; Karmakar, 2005;



Veeramani and Saini, 2010). Nevertheless, few of them have the point of view that FTAs and

RTAs are not practically contributing in the development of the economy and alleviation of

poverty of member countries (Dushni, 2009; Kemal, 2005; Alam et al., 2011; Coulibaly, 2007;

Hassan, 2001; Akhter and Ghani, 2010; Rahman et al.,2006) . Therefore, the impact of FTA on

the economy may be positive or negative. Moreover, Allen (1990), Brander et al. (1986), Titman

(1984) and Chevalier (1995) discussed the relationship between financial leverage and product

market and not included the effect of FTA on leverage. The empirical literature studying the

effect of trade liberalization on profit is surprisingly small which include Brander and Spencer

(1984), Buffie and Spiller (1986) and Hay (2001). Nevertheless, Baggs and Brander (2006)

examined the effect of NAFTA on non-financial firm’s leverage and profitability.

The subject matter is very critical and has great significant for analysis because the implication

of FTAs’ impact on the economy at macro level and on financial factors of firm at micro level

is still a matter of discussion among researchers, academicians, policy makers and investors.



3.8 Conceptual Framework Figure # 3.1

FTA

Leverage of the Firms

Profitability of the Firms

Dividend Payouts of the

Firms

Pakistan

GDP

Trade Balance

Trade and Output in

different Sector

Country’s welfare

Post-FTA Impact

Pakistan

Pre- FTA Impact

Leverage of the Firms

Profitability of the Firms

Dividend Payouts of the

Firms

GDP

Trade Balance

Trade and Output in

different Sector

Country’s welfare



CHAPTER 4

4. Methodology

This chapter discusses about the research design, population, sample, data analysis, descriptive

statistics, GTAP database and research methodology used in this study.

4.1. Population

The population for macroeconomic variables consists of 140 regions and 59 sectors in the GTAP

database. The total countries are 244 which are aggregated into 140 regions. The total population

of Free Trade Agreements of Pakistan is 8 which include Pakistan-China, Pakistan-Malaysia,

Pakistan-Sri Lanka, South Asian Free Trade Agreement (SAFTA), Pakistan-Afghanistan,

Pakistan-Iran, Pakistan-Mauritius, Pakistan-Indonesia. Moreover, the population for the firm

level financial analysis consists of all the listed non-financial firms at Karachi Stock Exchange.

4.2. Sample

For the purpose of macroeconomic analysis, the sample of 4 main countries such as Pakistan,

China, Sri Lanka and Malaysia are aggregated separately from the population of 244 countries.

These FTAs are selected due the failure of SAFTA as mentioned in the literature review and later

on Pakistan involved in two prominent bilateral agreements of China and Malaysia. Moreover,

due to China Pakistan Economic Corridor (CPEC) the economic ties are very strong with China

and that is also the main concern of selection of China. Malaysia is a member of ASEAN,

however, Pakistan is not a member of ASEAN and that is why Malaysia is part of the sample.

Among SAARC countries the Sri Lanka has good political and trade relationship with Pakistan.

Therefore, Sri Lanka is chosen for the analysis. The exports which are US $ 1 million are equal



to and above from 2003 to 2014 are considered as major Pakistani exports sectors to Malaysia.

The imports which are US $ 2 million are equal to and above from 2003 to 2014 are considered

as major Pakistani imports sectors from Malaysia. The exports which are US $ 10 million are

equal to and above from 2002 to 2014 are considered as major Pakistani exports sectors to

China. The imports which are US $ 10 million are equal to and above from 2002 to 2014 are

considered as major Pakistani imports sectors from China. The exports which are US $ 1 million

are equal to and above from 2000 to 2014 are considered as major Pakistani exports sectors to

Sri Lanka. The imports which are US $ 500,000 are equal to and above from 2000 to 2014 are

considered as major Pakistani imports sectors from Sri Lanka. The above mentioned major

sectors are selected for exports and imports in terms of monetary value for China, Malaysia and

Sri Lanka on the basis of their major monetary contribution and included in top ten export and

import sectors of Pakistan. Furthermore, the selected sample for the firm level financial factors is

presented in the following tables:

Table # 4.1 shows the major 8 sectors and 179 companies used in the Pakistan-China FTA

analysis.

Table

# 4.1

Data Available of the

Companies for Pakistan-

China FTA

Sr. NO Sector

No. of

Firms

1 Textile 111

2 Leather 5

3 Auto 15

4 Chemical 20

5 paper-board 9

6 Engineering/Metal 8

7 Vanaspati and Allied 4



8 Electrical 7

Total 179

Table # 4.2 shows the major 6 sectors and 168 companies used in the Pakistan-Malaysia FTA

analysis.

Table

# 4.2

Data Available of the

Companies for

Pakistan-Malaysia

FTA

Sr. No. Sector

No. of

Firms

1 Textile 111

2 Auto 15

3 Chemical 20

4 Paper-board 9


6 Beverage & Tobacco 9

Total 168

Table # 4.3 shows the major 6 sectors and 157 companies used in the Pakistan-Sri Lanka FTA

analysis.

Table # 4.3 Data Available of the Companies for Pakistan- Sri Lanka FTA

Sr.No. Sector

No. of

Firms

1 Textile 111

2 Paper-board 9

3 Engineering-metal 8


5 Cement 16

6 Beverages & Tobacco 9

Total 157



4.3. Data Analysis- Macroeconomic Factors

Mapping of GTAP codes with Harmonized System (HS) 6 codes used in the study

For the purpose of data analysis the mapping of Harmonized System (HS) codes has been done

with GTAP codes. The Pakistan Malaysia FTA’s mapping of major exports commodities of

GTAP codes with HS 6 codes. The exports which are US $ 1 million are equal to and above from

2003 to 2014 are considered as major Pakistani exports to Malaysia. The mapping of major

imports commodities of GTAP codes with HS 6 codes. The imports which are US $ 2 million are

equal to and above from 2003 to 2014 are considered as major Pakistani imports from Malaysia.

The Pakistan China FTA’s mapping of major exports commodities of GTAP codes with HS 6

codes. The exports which are US $ 10 million are equal to and above from 2002 to 2014 are

considered as major Pakistani exports to China. Moreover, the mapping of major imports

commodities of GTAP codes with HS 6 codes. The imports which are US $ 10 million are equal

to and above from 2002 to 2014 are considered as major Pakistani imports from China.

In case of Pakistan Sri Lanka FTA, the mapping of major exports goods of GTAP codes with HS

6 codes also has been done. The exports which are US $ 1 million are equal to and above from

2000 to 2014 are considered as major Pakistani exports to Sri Lanka. Furthermore, the mapping

of major imports commodities of GTAP codes with HS 6 codes has been done. The imports

which are US $ 500,000 are equal to and above from 2000 to 2014 are considered as major

Pakistani imports from Sri Lanka.



4.4. Data Analysis- Firm Level Financial Factors

One of the primary objectives of this study is to relate the change in tariff with financial

variables. As this is possible only for non-financial firms, therefore, this research is forced to

restrict attention to the non-financial firms’ sector. The data set consist of listed non-financial

firms at Karachi Stock Exchange (KSE). The pre and post FTA effect on Pakistani non-financial

firms’ leverage, profitability, and dividend payouts is scrutinized by regression. The data of

above mentioned variables are collected from the annual reports of the companies. So the book

values of total debt, equity, total assets, profitability and dividend are used. All the data is on

annual basis. For the Pakistan-China FTA and Pakistan-Malaysia FTA analysis the data is

gathered from 2002 to 2014 and from 2003 to 2014 respectively. While for Pakistan-Sri Lanka

FTA analysis the data is collected from 2000 to 2014. The time period of the data collection is in

accordance with the date of FTAs’ signature between the nations. The software E-Views is used

for the analysis purpose. This empirical analysis has great significant contribution in the existing

literature.

The major sectors have been selected for the purpose of data scrutiny. The exports and imports

data is collected from Trade Map. In Pakistan Malaysia FTA’s major export sectors are selected

on the basis of the exports which are US $ 1 million are equal to and above from 2003 to 2014

are considered as major Pakistani export sectors to Malaysia. The major import sectors are

selected on the basis of the imports which are US $ 2 million are equal to and above from 2003

to 2014 are considered as major Pakistani import sectors from Malaysia.

The Pakistan-China FTA’s major export sectors selection criteria is that the exports which are

US $ 10 million are equal to and above from 2002 to 2014 are considered as major Pakistani



export sectors to China. Whereas, the major import sectors are those which are US $ 10 million

are equal to and above from 2002 to 2014 are considered as major Pakistani import sector from

China. Nevertheless, in case of Pakistan-Sri Lanka FTA, the major export sectors are those

which are US $ 1 million are equal to and above from 2000 to 2014. Furthermore, the import

sectors are those which are US $ 500,000 are equal to and above from 2000 to 2014.

In order to assess the impact of changes in tariff on profit, leverage, dividend and value of firm,

the link is established between these variables and tariff data. The pre import tariff rates of

Pakistan are obtained from Pakistan Custom Tariff (The Schedule- Clause 4(15)). However, the

pre import tariff rates of China, Malaysia and Sri Lanka are gathered from World Trade

Organization- MFN applied tariff. Moreover, the Pakistan’s post import tariff rates are collected

from S.R.Os issued by Revenue Division, Ministry of Finance & Revenue, Government of

Pakistan for China, Malaysia and Sri Lanka. Whereas, the post import tariff rates of China,

Malaysia and Sri Lanka are collected from Free Trade Agreements of each country, which are

available on web site of Ministry of Commerce, Government of Pakistan. The pre and post tariff

rates of each sector are calculated by taking average of each sub-sector on annual basis. Every

company is connected for each year with the changes in tariffs for HS 6 digit code of every good.

The profit which is use for analysis is the profit before tax. That is basically the profit before

corporate tax is paid. In those years, the firms do not earn profit and these are in loss with

negative accounting profit. So their negative accounting profit is coded as zero.



The leverage is measured by total debt to total asset ratio, where debt is consist of both short-

term and long-term debt. The decisions for working capital management are done through the

changes in short term liabilities. However, in this study the analysis of leverage is conducted

with both current and long-term financial management.

Companies often change their bank debt and other liabilities. However, new equity issue and

new bond offering are relatively infrequent happening. In fact leverage changes almost every

year for almost every company. Hence, changes in leverage are mostly occurred due to the

change in short-term liabilities rather than change in long-term capital structure decisions.

The import intensity presents the share of China/Malaysia/Sri Lanka imports in total sales for a

given sector of Pakistan. The export intensity presents the share of the sales in a given sector of

Pakistan that was exported to China/Malaysia/Sri Lanka. Baggs and Brander (2006) used these

trade intensity variables as an estimate of exposure to import competition and of export

orientation respectively.

The control variable of age of the firms is calculated by subtracting the first year of analysis from

year of incorporation and it continues to the last year which is 20144. Some of the explanatory

variables are not included in the regression and these are viewed as entering the error term.

Furthermore, there are certain macroeconomic variables that affect profits, leverage and

dividend. For that purpose, exchange rate and interest rate are used as control variables. The data

4 Goel and Sharma (2015)

../../Theory%20of%20profit/Trade%20lib%20profit%20and%20leverage.pdf



of exchange rate and interest rate is collected from State Bank of Pakistan which is the economy-

wide observation for each year.

4.5. Descriptive Statistics

The basic attributes of the data of firm level financial factors are described through descriptive

statistics. For that purpose, in this study the measures of central tendency include mean, and

median, while measures of variability include the maximum and minimum values of the

variables, skewness and kurtosis.

4.6.The Data -GTAP database

In fact, the GTAP database is based on two kinds of data resources. The first resource of data is

the regional input-output table collected from the GTAP member countries. The second resource

of the GTAP database is the data which is gathered from international organizations. The data

gathered from these global organizations and their resources are mentioned below:

The commodities data for the GTAP member countries is received from the UN comtrade

database.

The services data for the GTAP member countries is extracted from the UN trade website

and EUROSTAT.

Macroeconomic data (GDP, data on private consumption, gross fixed capital formation

government consumption, capital stock and depreciation data) is gathered from the

World Bank Development Indicators.

Tariff data for the GTAP member countries is collected from International Tariff

Commission and International Trade Center.



The GTAP database also uses the income and taxes data which is gathered from the IMF’

government finance statistics and the energy data is received from International Energy

Agency (IEA).

4.6.1.Model Parameters and Benchmark Parameters Requirements

The CGE analysis required benchmark parameters. The GTAP database gives these parameters

and the modeling framework for the CGE model. According to Clarete & Roumasset (1986), the

selection of the benchmark parameters is significant as the validity of the model is entirely

dependent on the credibility of these parameters.

Data and the aggregation scheme in GTAP version-9

Currently, the new version of the GTAP model was released in May 2015. In this study, this

latest version is used for the analysis of FTAs. This database is different from the previous

versions of the database, has more than one reference years: 2004, 2007 and 2011 with 140

regions and 57 sectors5. The number of countries in the standard GTAP has been increased from

226 to 244 countries aggregated into 140 regions. The new database ‘version-9’ has been

improved with the newest input-output tables of only 19 countries in the GTAP database.

Pakistan is one of the 19 countries for which the GTAP database has the newest input-output

table. The input-output tables of the rest of the countries are pre-adjusted to match the reference

year 2011. The macroeconomic data (data on GDP, private consumption, government

consumption and investment) for the member countries were extracted from the World Bank and

was used for updating the input-output tables. Data on the physical stock and depreciation was

extracted from Penn World Tables version 8.0.3. Trade data for the GTAP member countries

5 The lists of regions and sectors are given appendix-1 and 2 to this document.



was collected from COMTRADE and was combined with same data gathered from IMF to

improve the data quality. Protection Data (output subsidies, input subsidies, land-based

payments, labor-based payments and capital-based payments) has been collected from Institute

for Prospective Technological Studies (IPTS). Data sources of the agriculture export subsidies in

GTAP. 9 are World Trade Organization (WTO), Food and Agriculture Organization of the

United Nations (FAO) and International Food Policy Research Institute (IFPRI). Energy data for

GTAP.9 has been collected from International Energy Agency (IEA).

4.6.2.Behavioral parameters

These include the Armington and Trade elasticities that have been obtained from GTAP.8. The

CDE elasticities have been standardized from expenditure elasticities.

4.6.3.Macroeconomic closures—three macroeconomic closures are incorporated in

version.9.

1. Saving-investment equality

2. Balance of trade and

3. Balance of budget

Regional and Sectoral Aggregation used in this study

For the analysis purpose of certain countries, the data for a CGE analysis is usually aggregated

by regions, sectors and factors. In this study, the data on the 140 countries given in the GTAP

database version-9 are aggregated into 10 regions: 4 main countries such as Pakistan, China, Sri

Lanka and Malaysia are aggregated separately because the main focus of bilateral trade analysis

is on these countries. The remaining countries are aggregated into six regions name as ASEAN,



Rest of SAARC, Rest of America, European Union, Rest of West Asia and the last region is Rest

of World. The GTAP database has data on 57 sectors, which have been aggregated into 43

sectors according to the nature of outputs (Appendix- 3).

In the GTAP database, the five factors are included such as land, natural resources, unskilled

labor, skilled labor and capital. These are left disaggregated in this analysis. Land and natural

resources are presumed to be perfectly immobile between sectors. Nevertheless, unskilled labor,

skilled labor, and capital are perfectly mobile. The benchmark year for this CGE scrutiny is 2011

as the data from the GTAP database is from version-9 which is from the same year.

4.7. Methodology for Macroeconomic Factors

4.7.1. Computable General Equilibrium (CGE) Model

The CGE model could not be defined correctly, therefore different definitions are analyzed. This

model is also called Applied General Equilibrium (AGE) model. It is a multi-sector economic

model that utilizes real economic data of one or various economies to predict as to how an

economy might respond to the alternations in policy, and technology or other external factors.

Moreover, the model also clearly identifies the behavior of several economic agents in the

economy. Like the households want to maximize their utility and companies wants to maximize

their profits or minimize their costs. These assumptions emphasize the role of commodity and

factor prices. The decision of households and firms regarding consumption and production are

depend on prices. Furthermore, these assumptions assist to clarify the way demand and supply

decisions made by different economic agents and decide the prices of at least few commodities

and factors. The core benefit of CGE model is that they provide numerical results. The



coefficients and parameters of the equations are predicted on the bases of numerical data base.

The model is usually a set of inputs and outputs which is the important database for the model.

This database is showing the accounts for the specific year the flow of commodities and factors

between sectors.

The mainly concise definition was introduced by Shoven and Whalley (1984) which is

mentioned below:

‘CGE model is one in which all market clear simultaneously’.

The definition of Shoven and Whalley (1984) has certain flaw; however, it still communicates

the core notion about CGE model. The CGE model can incorporate unemployment, thus, it does

not essentially propose that all markets clear. In 1989, Robinson argued that, if the model

exactly has following four fundamentals then it is called a CGE model:

I. The economic agents are clearly explained, whose scrutiny of behavior is requisite.

II. The agents perform functions under certain rules and Conditions of their behavior, which

must be clearly known. For instance, the households maximize utility and firms.

III. The factors which impact on decision making of economic agents must be known like

prices.

IV. Identification of current economic structure like perfect competition.

Basically, the CGE model is a contemporary version of Walras’ model of competitive economy.

The exclusive attribute of general equilibrium modeling is derived from Walrasian general

economic equilibrium theory, which believes economy as a set of agents. These agents act

together in numerous markets for the same number of goods under a specified set of preliminary



endowments and income distribution. Each agent explains its supply or demand behavior by

optimizing its particular goals. The decision of agents generates a set of surplus supply functions

which obey the conditions of Walras law and that are the global identity of income and

expenditures. The same was verified by Arrow and Debreu (1954), under some general

circumstances, there are set of prices that carry supply and demand into equilibrium.

Nevertheless, few modelers expand CGE model ahead of the actual Walrasian model to capture

the effect of market imperfections. Few of the modelers used the term general equilibrium

programming (Zalai, 1982a) or generalized equilibrium modeling (Nesbitt, 1984) and explain the

flexibility of the CGE model.

4.7.2 CGE Model: Circular Flow explanation

The circular flow describes the basis of a CGE model. The main circular flowchart of income

demonstrates the market transactions between households and firms in the economy (see Figure

4.1, Ghadimi, 2007). The households being vendors of labor and capital supply their services to

firms and receive payments from firms in the shape of wages and capital income. On the other

hand, households purchase goods and services from firms and pay for that to them. The

households and firms are motivated due to utility maximization and profit maximization

respectively. The households maximize its utility by acquiring set of commodities and services

within limited budget, which is an evaluation of welfare of households. Conversely, firms use

the latest technology for their production and sale these commodities and services for

maximization of their profits. The utility and profit maximization behavioral functions are

incorporated severely and entirely in accordance with the microeconomic literature and built-into

the CGE model.



The main circular flowchart also comprise of interaction among firms. The firms purchase inputs

for their production processes from other firms, which is the example of inter-firm transactions.

These connections are explained by input-output tables. Because of these connections, a shift in

consumption of household that straightly impact production in one set of industries consequently

to an indirect shift in production in another set of industries.

In 2007, Ghadimi adds three more agents of the economy in the core circular flowchart (see

Figure 4.2). These agents included the government, rest of world (ROW) and a capital account.

The government gathers direct and indirect taxes from other economic agents. Initially, the

government acquires commodities and services from firms and the remaining revenues are

shifted to households and firms. The imports are purchased by the country from ROW and ROW

purchases exports from the country. The lending and borrowing of funds is described by the

capital account. The borrowing side is the investment, which is demand for funds are driven by

saving, the lending side, which is supply of funds. The total savings includes savings of

households, firms, government surplus/deficit and net capital inflow of the ROW. The

investment consists of changes in acquisition of new capital stocks (fixed investment) and

inventory by industry and government. The acquisition of capital goods and construction

services creates new capital stock.

Some of closure rules are vital part of CGE model. These rules put aggregate limitations on the

economic activity simulated in the CGE model and connect how core macroeconomic accounts

modify to recover equilibrium in reply to adjusts in economic activity. The core macroeconomic

accounts consist of government, trade, and capital accounts. Their identities are mentioned

below:



a) Government account identity is:

Surplus or deficit (if negative) = Revenue - Expenditure

b) Trade account identity is:

Net Value of Capital Income from ROW= Imports - Exports

c) Capital account identity is:

Savings = Investments

In 1989, Robinson endorsed the idea that accounting identities exist in every scenario for any

economy or macroeconomic model. If there is change in economic activity than closure rules

establish a system, which retain the three key macro accounts in balance. These rules participate,

significant part in a way, a change of policy works all over the economy. For instance, if closure

rules fix both government deficit and the real government expenditures, then a change of policy

that increases government revenue will essentially result in lower taxes.

The CGE model has the capacity to measure the results of shocks to the economy or the probable

economy-wide impacts of a hypothetical adjustment in economic policy. The pre and post

economic analysis can be possible in CGE model after simulating a policy change.

4.7.3 Review of GTAP Model

The world has become global village and in the result all the economies are more integrated than

ever before. Therefore, the researchers are now more interested in quantitative examination of

policies issues on a global basis. These issues include the impact of agreements on international



countries, international trade, worldwide welfare, and economic implications of climate change,

economic growth or any other issues impacting the world as a whole. The GTAP goal is to assist

such multi-country, economy wide scrutiny.

The Global Trade Analysis Project (GTAP) was created in 1992 for researchers who are

interested in conducting quantitative analysis of international economic issues in an economy

wide framework. The project has following numerous attributes:

i. It is a global database, completely documented, and publicly accessible

ii. It is a standard modeling framework

iii. The RUNGTAP is a software for analyzing the data and executing the standard model

iv. The international researchers linked with each other through internet and they are

interested in multi-regional and multi-sector analysis of trade and resource subject

matters

v. The software, data and other project related items are distributing through a World

Wide Web site

vi. The leadership and base level support is provided by an association of national and

international agencies

4.7.3.1 Closed Economy without Taxes

The overview of economic activity is explained in the figure 4.3, which is the simplest

version of the GTAP model (see Brockmeier 1996). This figure shows only one region,



therefore, there is no trade, no depreciation, and no taxes or subsidies in the model.

Regional household is at the top of this diagram. The household do expenditures, which

are administered by an aggregate utility function that distributes expenditures into three

extensive types: private, government, and saving expenditures. According to Cobb-

Douglas (1928) utility functions the regional household allocates constant budget shares

to each type of expenditures in the standard closure.

In fact the regional expenditures have some different pros and cons. The main important

flaw is the failure to relate government expenditures to tax revenues. In the GTAP model,

the reduction in taxes means that there is decrease in government expenditures. The main

benefit of this regional expenditure showed in figure 4.3 is that it clearly indicates

welfare suggested by the regional utility function.

As assumed above that there are no taxes, the only basis of income is ‘sale’ of

endowment commodities to firms by regional households VOA (endw) shows “value of

output at Agents” prices of endowment commodities which represent the income flow.

These endowment goods are combined by the firms with intermediate commodities

(VDFA = value of domestic purchases by firms at Agents’ prices) for the purpose to

produce final commodities on demand. This includes sales to private households (VDPA

= value of domestic purchases by private households at agents’ prices), government

households (VDGA = value of domestic purchases by government households at agents’

prices) and the sale of investment commodities to convince the regional household’s

demand for saving (REGINN). In this way, a closed economy without taxes explained the

circular flow of income, expenditure and production.



4.7.3.2 Open Economy without Taxes

Figure 4.4 is depicted from Brockmeier (1996), which bring in international trade by

including Rest of the World (ROW), shown at the bottom of the figure. This new region

has some structure as of domestic economy. However, details are mentioned in figure

4.4. The imports are purchased by the regional economy from ROW, and it also exports

at the destination (VXMD = value of exports at market prices by destination). It is worth

noting that the private household imports (VIPA) the goods through agents by made

payment to ROW. Similarly, government households (VIGA) and firms (VIFA) do the

same. This model is adopted from (Sectoral Analysis of Liberalization of Trade in the

East Asian Region) SALTER model (Jomini et.al. 1991) and create an innovative idea for

the scrutiny of trade policy in regions. In figure 4.4, there are two global sectors are

introduced. The first one is the global bank/savings presented in the middle of this

diagram, and act as intermediary between global savings and regional investment. The

regional investment in the form of portfolio investment in goods, and in shares, in order

to fulfill the demand of regional households for their savings.

The second sector is the accounts for international trade and transport activity. It brings

together regional exports of trade, transport and insurance services, which make the

goods movable from one region to another region. The global exports are evaluated on

the basis of free on board (fob) and global imports are evaluated on the basis of cost of

insurance and freight (cif).



In fact, the GTAP model support the CGE model in its global version by providing modeling

framework and database for CGE model. The GTAP is the main source of data for global CGE

model, which is discussed in the following sections.

4.8. Global Trade Analysis Project (GTAP)6

The researchers and policy makers are linked with each other through the GTAP for analysis

purpose. The GTAP database is the main source which has the record of annual flows of

commodities and services with a given base year. The database is internally consistent and has

the ability to simulate the effect of changes on individual countries wise as well as region wise

polices at the international level. According to Smith (1998), basically the GTAP model is a

multi-region CGE model and it has structure to handle with comparative static examination of

the trade policy reforms. Moreover, the nature of GTAP model is multi-sectoral and multi-

regional. Therefore, it has capability to manage the general equilibrium analysis of changes in

trade policy reforms. The data is provided by individuals who give the data in the form of input

output table of their relevant economies. Furthermore, the experts give the data of exports, macro

level data, trade and protection data.

There are various equations, which maintain the GTAP model. These equations are of two kinds,

the first one handle the accounting relationship and it balance the receipts and expenditures of

each agents of the economy. The second kind of the equations is the behavioral equations that

indicate the behavior of agent’s optimization. The behavioral and accounting equations of GTAP

structure are discussed in detail in appendex-4.

6 Hertel (1997)



In this study the following proxies and equations (Hertel, 1997) are used for the variables on the

basis of which the pre and post macroeconomic analysis is conducted:

Real GDP in million $ is measured through qgdp

The Quantity Index

Here “qgdp(r)” depicts for quantity index that accounts for changes in the regional

quantities because of policy shock.

Export Value change in million $ is measured by VXWD

Value index for exports by good ‘i’ for region ‘r’

The below mentioned equation depicts the change in the value of exports of ‘i’ from ‘r’

aggregated over regions.

VXW(i,r) * vxwfob(i,r) = VXWD(i,r,s) * [qxs(i,r,s) + pfob(i,r,s)] sREG + VST(i,r) * [qst(i,r) + pm(i,r)]……………………………….(55)

The left hand side presents the percentage in the value of exports measured at ‘fob’ price

weighted by the total value exports of ‘i’ from region ‘r’ to region‘s’. The first term on

the right hand side is the total of the percentage change in the quantity of exports ‘i’ from

‘r’ and the percentage change in the ‘fob’ price of ‘i’ from ‘r’ to ‘s’ weighted by the value

of exports of ‘i’ from ‘r’ to ‘s’ measured at the ‘fob’ price. The last term on the right hand

side is the total of the percentage change in the quantity of transport services of ‘i’

supplied from ‘r’ and the percentage change in the local price of exports in region ‘r’

weighted by the total value of exports of ‘i’ from ‘r’ calculated at the fob price.

Import Value change in million $ is measured by VIWS

Value Index for Import of commodity ‘i’ and region ‘s’



Similarly, the value index for the percentage change in the value of total imports of ‘i’ by

region‘s’ is depicted as follows:

VIW(i,s) * viwcif(i,s) = VIWS(i,r,s) * [pcif(i,r,s) + qxs(i,r, s)]---(56) rREG

The term on the right hand side is the total of the percentage change in ‘cif’ price of

export of ‘i’ from ‘r’ to ‘s’ and the percentage change in the quantity of exports of ‘i’

from ‘r’ to ‘s’ at the exporter’s local price weighted by the total value of imports of ‘i’

from ‘r’ to ‘s’ measured at the ‘cif’ price. The left hand side of the equation presents the

percentage change in the value of total imports of ‘i’ in ‘s’ based on ‘cif’ price, weighted

by the total value of imports of ‘i’ measured at the importer (s) local market.

Change in percentage Term of Trade is measured by tot

The terms of trade, is measured as the difference between the aggregate price index for

the prices received for commodities sold and the price index for price actually paid for

commodities acquired by each region in the GTAP model. The difference between the

two price indices of a region ‘r’ yields the percentage change in terms of trade of that

region. The two indices are formulated as follows.

Equation (46) is the aggregate price index for the price received from the sale of exports.

Similarly, the aggregated price index for the price paid against the acquisition of tradable

commodities is expressed in the below mentioned equation.



The Terms of Trade equation for region ‘r’ is measured by the difference between the

two indices depicted above.

Percentage change in domestic output is measured by qo. The equation 4.19 is used to

calculate qo

qds represent the percentage change in quantity domestically sold, qst is the percentage

change in quantity of transport services of ‘i’ supplied from ‘r’ and qxs is the percentage

change in quantity of exports ‘i’ from ‘r’.

Percentage in exports volume is measured by qxw

Volume index for exports of ‘i’ by region ‘r’

Percentage in imports volume is measured by qiw

Volume index for imports of ‘i’ by region ‘s’

Percentage change in export prices is measured by pxw

Price index for exports of by good and region

)

Percentage change in import prices is measured by pim. The equation 21 is used to

calculate pim



Where MSHRS (i,k,s) is the contribution of import of ‘i’ from region ‘r’ to region ‘s’ in

total composite import in region ‘s’. pms (i,k,s) is market price of any specific good ‘i’

imported from ‘r’ to ‘s’ accumulated over ‘k’ regions.

Welfare is measured by as follows:

Welfare = Allocative efficiency + Terms of Trade effects

The allocative efficiency is the efficiency of resources utilization and terms of trade is the

change in the relative price of exports to imports both weighted by benchmark year

quantities.

The rest of the equations related to international transportation and summary indices are

mentioned in appendix-4.

4.9. Methodology for Firm Level Financial Factors

The Regression Analysis

The pre and post effect of tariff changes of Pakistan-China FTA, Pakistan- Malaysia FTA and

Pakistan-Sri Lanka FTA on non-financial firms’ profitability, leverage, and dividend payouts is

scrutinized by regression methodology.

The profits of the firms are used as dependent variable and the natural logarithm of profits is

taken to normalize the data. The basic explanatory variables are the changes in export tariffs and

import tariffs. The below mentioned regression equation (1) has been tested by Baggs and

Brander (2006), however, few changes are made for the requirement of the analysis:

../Theory%20of%20profit/Trade%20lib%20profit%20and%20leverage.pdf

../Theory%20of%20profit/Trade%20lib%20profit%20and%20leverage.pdf



ln (πit) = α0 + α1 τitm + α2 τit

x + α3Dimportit+ α4Dexportit+ α5……… α12Industry Dummy

Variables+ αc Cit + εit ………………………………………………………………….. (1)

In the equation (1) π denotes profit, τm denotes the import tariff, τx denotes the tariff on exports,

C denotes a control variables and ε is a random error. The subscripts i and t refer to the ith firm

at time t. The change in tariffs is the change between the last period and the current period. Thus,

a tariff reduction appears as a positive τ. As for control variables, it can be controlled for firm

size by using the natural logarithm of assets (a measure of capital) and other control variables are

age of firm, import intensity, export intensity, import interaction, export interaction, exchange

rate as explanatory variables.

Dimport represents the interaction dummy7 of import tariff and Dexport represents the

interaction dummy of export tariff. The slope coefficient α3 express the effect of percentage

change in import tariff on profit and the slope coefficient α4 shows the impact of percentage

change in export tariff on profit. Moreover, to analyze the pre and post effect of percentage

change in tariffs on dependent variable, in this study the dummy variable is created. For instance,

in Pakistan and China FTA, “0” for years from 2002 to 2006 and “1” for years from 2007 to

2014. Therefore, Dimport capture the pre and post effect of percentage change in import tariff on

profit and Dexport capture the pre and post impact of percentage change in export tariff on

profit. Similarly, for Pakistan-Malaysia FTA “0” for years from 2003 to 2007 and “1” for years

from 2008 to 2014 and for Pakistan- Sri Lanka FTA “0” for years from 2000 to 2004 and “1” for

7 Asteriou (2006)



years from 2005 to 2014. Dimport and Dexport are also created to measure the pre and post

impact of tariffs on profit of Pakistan-Malaysia FTA and Pakistan- Sri Lanka FTA.

In case of Pakistan-China FTA, for a sample of 179 firms in 8 industries, the dummy variables

are constructed to find out any systematic differences which are attributable to industry classes as

mentioned by Brown (1968). In fact, the dummy variables are created for explaining the industry

differences which cannot be explained by the control variables (firm size, age of firm, import

intensity, export intensity, import interaction, export interaction, exchange rate). The coefficients

“α5……… α12Industry Dummy Variables” represents the 8 industries which are major in imports

and exports between Pakistan and China FTA. To avoid the dummy variable trap, one of the

industry dummy variables is eliminated at time of running the regression in E-views. In the same

manner, for Pakistan-Malaysia FTA and Pakistan-Sri Lanka FTA 6 industry dummy variables

are created.

The leverage is calculated by the debt to asset ratio and it is regressed with the changes in tariffs.

Other appropriate control variables are shown in the following regression specification run by

Baggs and Brander (2006), nevertheless, few changes are made:

Levit = β0 + β1 τitm + β2 τit

x + β3Dimportit+ β4Dexportit+ α5……… α12Industry Dummy

Variables+ βc Cit + εit ………………………………………………………………………………………………………………….(2)

C is control variables (profit, age of firm, import intensity, export intensity, import interaction,

export interaction, exchange rate). In addition to other control variables, interest rate is



introduced in this regression because firm might adopt debt as method of finance which reflects

the cost of debt as mentioned in the market timing’ approach to financial structure.

To test the effect of change in tariff on firms’ dividend payouts the following equation (3) is

used:

Dit = δ0 + δ1 τitm + δ2 τit

x + δ3Dimportit+ δ4Dexportit+ δ5……… δ12Industry Dummy

Variables+ δc Cit + εit………………………………………………………………………………………………………………. (3)

Where D is the dividend payout ratio of the firm which can be calculated as follows:

Dividend payout ratio = Dividends per Share / Earnings per share.

C represents a vector of control variables such as firm size, age of firm, import intensity, export

intensity, import interaction, export interaction, exchange rate.

The data is a balanced panel covering the period 2002 to 2014 for Pakistan-China FTA, 2003 to

2014 for Pakistan-Malaysia FTA and 2000 to 2014 for Pakistan-Sri Lanka FTA. Companies

might be in the panel for any length of time period from first year to the last year for a specific

FTA. There are various methods to analyze the panel structure of the data. In this study, three

most standard methods are used such as first differences, firm-specific fixed effects and firm-

specific random effects. Moreover, these methods are appropriate for correctness for profit

differences arise from company-specific effects. The data consist of large N firms and T time

period.



It is significant to ascertain which companies are probably to be affected by changes in import

and export tariffs. The trade intensity variables and related interaction terms effects are used to

achieve above mentioned objective. The import intensity is measured by total import of the

sector from member country divided by the total sales of that particular sector. The export

intensity is measured by total export of the sector to member country divided by the total sales of

that particular sector. The first interaction term is the product of import tariff changes and

import intensity and the second interaction term is the product of export tariff changes and export

intensity. The interaction terms’ coefficient reveals whether tariff impacts increase in import

competition or export orientation ascents.

Another significant aspect is the data truncation of the profit variable. Therefore, all the

observations which are non-positive profits are considered as 0. Despite the issues of data

accuracy, this type of dependent variable can be managed by using Tobit estimation procedure,

as explained by Wooldridge (2002). Nevertheless, one of the solutions is to drop the non-positive

profit observations from the data and use the Ordinary Least Squares (OLS). In this study, two

Tobit regressions are used for profit and leverage for all observations. Moreover, three

regressions are used only for positive profit observations. In case of equation 2 where leverage is

regress with change in tariffs, the profits of companies is used as regressor and only positive

profits observations are used. For equation 3, OLS with fixed effects, OLS with random effects

and first difference are used which are used to estimate effect of change in tariffs on dividend

payout. The profitability and dividend both are used to measure the effect of change in tariffs

because if the firm only making profit and not paying dividend than the firm is not able to create

value for firm, therefore both variables are used in this study.



Multi-collinearity8

One of the significant issues is collinearity between Pakistan-China FTA, Pakistan- Malaysia

FTA and Pakistan-Sri Lanka FTA tariff changes. This investigation needs that there is

differentiation between reductions in these three specific FTAs’ tariffs. For instance, if there is

reduction in Pakistani tariff on a commodity than there is an equal reduction in Chinese tariff

reduction on a commodity. So in this scenario, the correlation would be 1.0 and it would be

complex to separately find the impact of Pakistan and China tariff changes. Nevertheless, the

tariff reductions were not collinear because the primary tariff structures were significantly

different. The correlation between export and import tariff reductions of Pakistan-China FTA is -

0.11, Pakistan- Malaysia FTA is 0.067 and Pakistan-Sri Lanka FTA is 0.46.

Panel Unit Root Test9

The panel unit root test is applied on the data to check the stationary of panel. For that purpose,

Levin, Lin and Chu, (2002) LL test is applied on the panel data. It is proved in the literature that

the LL test is one of the very common test used for checking of the stationary of panel data . The

validity of this test and its use in common practice would assist the researcher to achieve the

accuracy of the results. Therefore, to check the stationary of variables, Levin, Lin and Chu test is

applied. Stationary of the series is checked by considering both individual intercept and

individual intercept and trend. All the variables in the test are found significant in Pakistan-China

8 Asteriou (2006)

9 Asteriou (2006)



FTA, Pakistan- Malaysia FTA and Pakistan-Sri Lanka FTA and it is concluded that the data is

stationary at level.

In view of above discussion, according to Robinson (1989) and Walrasian model, the CGE

model is based on perfect competition with zero abnormal profit earn by firms, however, later on

few economists like Zalai (1982) and Nesbitt (1984) explained the flexibility of the CGE model

and incorporate the effect of imperfect market competition. Moreover, Ghadimi (2007)

explained the circular flowchart income which is based on CGE model. The circular flowchart

demonstrates the behavior of firms which use the latest technology for their production and sale

these goods and services for maximization of their profits. This profit maximization behavioral

function is incorporated severely and entirely in accordance with the microeconomic literature

and built-into the CGE model. Similarly, the regression analysis allows firms to earn profits

according to the imperfect competition which we observe in practical life. Therefore, aggregate

level CGE analysis is consistent with the firm-level regression analysis which is rational for

using these methodologies in this study. After applying the above said methodologies, the results

are discussed in the next chapter.



CHAPTER 5

5. Results Discussion

The objective of this chapter is to discuss the features of firm level financial factors’ data set and

the results of pre and post effects of Pakistan-China FTA, Pakistan-Malaysia FTA and Pakistan-

Sri Lanka FTA on macroeconomic factors. Moreover, it also explains the results of regression

analysis which is done to investigate the pre and post impact of Pakistan-China FTA, Pakistan-

Malaysia FTA and Pakistan-Sri Lanka FTA on firm level financial factors.

5.1. Descriptive Statistics

i. Pakistan- China FTA

The mean value of leverage in case of Pakistan-China FTA is about 0.64, as shown in

table # 5.1. This implying that the average firm has about 64% of its assets finance

through debt and about 36% is finance by equity. Median leverage is 0.67, which is

greater than mean. The leverage is negatively skewed because mean is less than median

and negative sign also indicate the same. The value of kurtosis of leverage is less than 3,

it’s implying that the values of leverage are not closely bunched round the mode and the

curve has a flatter top and comparatively narrower tails than the normal curve and is

called to be platy-kurtic. Few companies (approximately 4% in the data) show total debt

that exceed total assets, indicate that equity is negative for these companies. This is

occurred because the book value of assets does not depict their full economic value.

Nevertheless, companies might have sometimes real negative equity, means that creditors



are doubtfully to be paid off in full. This can be happen when the companies is operating

under insolvency protection.

The main interest is to observe the data of export and import tariffs, which are the core

explanatory variables in this research. For Pakistan, the largest tariff is 36.76% and the

median is 7.8%. For China the largest tariff is 17.45% and the median is 11.46%. The

smallest tariff for Pakistan is zero while for China it is 4.19% under this FTA.

The average value of dividend payout ratio is approximately 0.28, this depict that the

average company is paying about 28% dividend among shareholders and retains 72% of

the earnings in the business. The median is 0.20 which is less than mean, which indicate

that the dividend payout is positively skewed. The kurtosis value of dividend payout is

greater than 3, its’ implying that the curve is more sharply peaked and has wider tails

than the normal curve and is called to be lepto-kurtic.

The value of profit is positively skewed because average value of profit is greater than

median. Furthermore, kurtosis value of profit is greater than 3, implying that the curve is

lepto-kurtic.

Table #5.1 Descriptive Statistics

Pakistan-

China FTA

Profit Rs.

million

Leverage

(debt/asset)

Export

Tariff

%

Import

Tariff

%

Dividend

pay out

Mean 414.82 0.64 8.63 10.81 0.28

Median 34.58 0.67 7.8 11.46 0.20

Maximum 33166.05 0.99 36.76 17.45 2.00

Minimum 0.00 0.00 0 4.19 0.02



Skewness 12.20 -0.59 1.54 -0.08 1.52

Kurtosis 210.67 2.80 6.37 1.69 7.80

The descriptive statistics of control variables are presented in table # 5.2.Total assets are

used as proxy of the firm size which is one of the control variables. The mean value of

assets is greater than median that is why the value of assets is positively skewed. The

median company has assets of approximately Rs.1,277.07 million, while the average

value of assets is an order of significantly larger at over Rs.4,034.71 million. On the other

hand one of the control variables is age. The average age of the firm is about 31.53,

however, the median age value is 28.00.

Table # 5.2 Descriptive Statistics of Control Variables

Pakistan-

China FTA

Assets Rs.

million

Age Import

Intensity

Export

Intensity

Mean 4034.71 31.53 0.10 0.05

Median 1277.07 28.00 0.03 0.04

Maximum 97048.58 72.00 3.28 1.90

Minimum 0.01 3.00 0.001 0.00

Skewness 4.48 0.50 7.53 11.24

Kurtosis 32.88 2.30 71.84 175.10

ii. Pakistan- Malaysia FTA

Table #5.3 shows the average value of leverage is approximately 0.64, whereas median

value of leverage is greater than average value of the same. This clearly indicates that the



leverage is negatively skewed with negative sign. The kurtosis is platy-kurtic because the

value of kurtosis of leverage is less than 3.

The import and export tariffs are very significant to explain in any FTA. In Pakistan-

Malaysia FTA, for Pakistan the maximum tariff is 16.00% and median is 4.76%. While

for Malaysia the greatest tariff is 18.75%. The lowest tariff for Pakistan and Malaysia is

zero as both countries are willing to eliminate the tariff on few commodities.

The mean value of dividend payout is greater than median which are 0.30 and 0.20

respectively. So it is depicted that the dividend payout is positively skewed. The value of

kurtosis of dividend payout is less than 3, which show that the kurtosis is platy-kurtic.

The mean value of profit is bigger than median value. Therefore, the value of profit is

positively skewed. The kurtosis of profit is called as lepto-kurtic because kurtosis value is

greater than 3.


Pakistan-

Malaysia

FTA

Profit Rs.

million

Leverage

(debt/asset)

Export

Tariff %

Import

Tariff %

Dividend

pay out

Mean 432.24 0.64 6.42 11.64 0.300

Median 31.08 0.67 4.76 13.33 0.200

Maximum 33166.05 1.00 16.00 18.75 0.950

Minimum 0.00 0.00 0.00 0.00 0.020

Skewness 11.71 -0.61 0.77 -0.79 0.925

Kurtosis 190.92 2.87 2.54 2.58 2.867



Table # 5.4 depicts the descriptive statistics of control variables. The average value of

assets is Rs. 3,867.42 million and median is Rs. 1,244.33 million. So the value assets are

positively skewed. The mean value of age of firm is approximately 31.64 and median age

of firm is 28.00. The kurtosis of age of firm is platy-kurtic as the value of age of firm is

less than 3.

Table #5.4 Descriptive Statistics of Control Variables

Pakistan-

Malaysia

FTA

Assets Rs.

millions

Age Import

Intensity

Export

Intensity

Mean 3867.42 31.64 0.35 0.003

Median 1244.33 28.00 3.04E-05 0.003

Maximum 97048.58 81.00 29.89 0.008

Minimum 0.01 4.00 0.00 0.00

Skewness 4.80 0.62 8.13 0.305

Kurtosis 36.59 2.71 73.74 1.956

iii. Pakistan- Sri Lanka FTA

Table # 5.5 presents the values of leverage of mean and median of Pakistan-Sri Lanka

FTA are same as mentioned in Pakistan-China FTA and Pakistan-Malaysia FTA. That is

the reason the leverage is negatively skewed. Moreover, the leverage value of kurtosis is

lower than 3, it’s depict that the kurtosis is platy-kurtosis. The maximum value of tariffs

for Pakistan is 37.50% and for Sri Lanka is 34.09%. Whereas, the minimum value of

tariffs of both countries are zero. The median value of dividend payout is 0.00, which is

less than average value of the same. That is why the dividend payout is positively



skewed. The mean value of profit of firms in case of Pakistan-Sri Lanka is larger than

median value, implying that the profit value is positively skewed.


Pakistan-

Sri

Lanka

FTA

Profit Rs.

million

Leverage

(debt/asset)

Export

Tariff %

Import

Tariff %

Dividend

pay out

Mean 256.37 0.64 10.77 12.85 0.16

Median 23.48 0.67 5.42 11.67 0.00

Maximum 14456.37 1.00 37.50 34.09 12.50

Minimum 0.00 0.00 0.00 0.00 0.00

Skewness 7.69 -0.58 1.26 0.62 13.45

Kurtosis 82.63 2.92 3.08 2.19 250.28

Table #5.6 depicted the descriptive statistics of control variables of Pakistan- Sri Lanka

FTA. The average value of assets is positively skewed and mean value of age of firm is

approximately 30.77 but the median value is 27.00.

Table #5.6 Descriptive Statistics of Control Variables

Pakistan-Sri

Lanka FTA

Assets Rs.

million

Age Import

Intensity

Export

Intensity

Mean 3695.20 30.77 0.001 0.015

Median 1165.55 27.00 7.82E-05 0.018

Maximum 97048.58 81.00 0.086 0.068

Minimum 0.01 1.00 0.000 0.000

Skewness 4.64 0.63 11.049 0.651

Kurtosis 31.98 2.66 164.931 3.515



5.2. Results of GTAP Simulation Effects of Pakistan China Free Trade Agreement,

Pakistan Malaysia Free Trade Agreement and Pakistan Sri Lanka Free Trade Agreement

The GTAP simulation has been performed on Pakistan China FTA, Pakistan Malaysia FTA and

Pakistan Sri Lanka FTA. In this simulation, the ad valorem tariffs on imports from Pakistan into

China and imports from China into Pakistan are all reduced to zero. Similarly, the ad valorem

tariffs on imports from Pakistan into Malaysia and imports from Malaysia into Pakistan are all

reduced to zero. Moreover, in the same manner the ad valorem tariffs on imports from Pakistan

into Sri Lanka and imports from Sri Lanka into Pakistan are all reduced to zero. For the purpose

of this simulation, the closure (i.e., the treatment of equilibrium in the model) used is the

standard GTAP multiregional general equilibrium closure. The solution algorithm used is the

Gragg 4 8 12 method with automatic correctness to obtain a high level of accuracy in the results.

In the following sections each of the bilateral trade agreements are analyzed in detail by applying

above mentioned simulation.

5.2.1. Pakistan China Free Trade Agreement

i. Simulated Aggregate Effects

For the purpose of simulation the equation of ‘qgdp’ is used. First the business as usual is

determined which reflect the pre-FTA effect on the bases of bench mark year. Than

tariffs are considered as zero and simulation is done in the RunGTAP software. The

simulated aggregate impact of the Pakistan China FTA in terms of real GDP is depicted

in table # 5.7. The post FTA impact result shows that there is negative change of $-

95.86million for Pakistan and positive change of $217million for China. These figures



clearly indicate that the Chinese’s real GDP is expanded while Pakistan’s real GDP is

contracted.

Table #5.7

Real GDP Business as

Usual $

million

Post-FTA $

million

Change $

million

Pakistan 213,686.2 213,590.34 -95.86

China 7,321,874.5 7,322,091.5 217

Notes: The GTAP variables used are: (i) qgdp for Real GDP

Source: Author’s results from a GTAP simulation.

The export value change in million $ is measured by ‘VXWD’ equation, the import value

change in million $ is determined by ‘VIWS’ equation and change in percentage term of

trade is measured by ‘tot’ equation. Table # 5.8 shows the simulated aggregate trade

effects of Pakistan China FTA, which presents that Pakistan’s trade growth is more than

China and both countries experience, enhance in export values. Moreover, Pakistan faces

trade deficit because its imports are more than exports. In the base year pre-simulation,

Pakistan was in trade deficit with China and remains in deficit after running the

simulation. Whereas, China was in trade surplus in the base year before simulation and

maintains its trade surplus after simulation. Nevertheless, the exports of China are more

than Pakistan as shown in table # 5.8 and China attains the trade surplus. In context of

terms of trade, the result depicts that there is enhancement for China, however, a

worsening for Pakistan.



Table # 5.8

Aggregate

Effects

Change in

Export Value

($ Million)

Change in

Import Value ($

Million)

Change in

Trade Bal.

value($

Million)

Change in

Terms of

Trade (%)

Pakistan 546.0855 6053.6967 -5507.6112 -1.0060

China 5638.494 588.7774 5049.7166 0.029

Notes: The GTAP variables used are: (i) VXWD for export value, (ii) VIWS for import value, (iii) VXWD for the initial level of

exports and VIWS for the initial level of imports and (iv) tot for the terms of trade.


ii. Simulated Sectoral Effects

The percentage change in domestic output is measured by ‘qo’ equation, the percentage

change in exports volume is measured by ‘qxw’ equation, the percentage change in

imports volume is measured by ‘qiw’ equation, the percentage change in export prices is

measured by ‘pxw’ and the percentage change in import prices is calculated by ‘pim’

equation. Moreover, DQXS for the volume change in exports and imports in terms of

$millions. The Table # 5.9 depicts the Pakistan China FTA effects on Pakistani sectors.

Plant-based fiber has the largest relative output expansion 2.91% because of increase in

export volume at $25.79million from base year. Textile sector has output expansion of

2.82% due to largest increase in export volume at $359.27million from base year.

Wearing apparel has the output expansion of 1.6% because of enhancement in export

volume at $8.95million from base year. Vegetable oil and fats has relative output

expansion of 2.02% due to increase in export volume at $1.60million from base year. The

other important export sectors are Metals products whose export volume percentage

change is increased due to increase in export volume at $20.50million from base year.

Leather product sector, whose export volume percentage change is enhanced because of



the second largest relative increase in export volume at $72.65million from base year.

Furthermore, chemical products export volume percentage change is increased due to

increase in export volume at $32.79million from base year. The results present in table #

5.9 shows that the sectors have absolute percentage changes of less than 1.96% for export

prices and less than 20.61% for export volume. The largest drop in output is observed in

auto parts sector and decrease in import price of -4.8% due to an increase in import

volume of 12.46% and at $887.64million from base year. Moreover, there is largest drop

in import price and largest increase import volume percentage change in leather products

with an increase in import volume at $311.57million from base year, which substitute for

and reduce the domestic supply of leather products and auto parts products in Pakistan’s

local market.

Since increase in import volumes of textile at $1096.12million, machinery and

equipment at $1043.05million, auto parts at $887.64million, metal products at

$1148.12million, chemical products at $729.77 million, leather products at

$311.57million, wood products at $285.09million, manufactures nec at $171.50million

and wearing apparel at $144.49million from base year are the prominent sectors in

reduction in Pakistan’s real GDP. The general increase in import volumes can be

characteristics to tariff elimination and decrease in import prices in all of these above

mentioned sectors.

Simulated Sectoral Effects of the Pakistan China FTA on Pakistan (% change)

Table # 5.9

GTAP

Code

Pakistan –Sectors Domestic

Output (qo)

Export

Prices

(pxw)

Exports

(qxw)

Import

prices

(pim)

Imports

(qiw)

Pdr Paddy rice 0.13 -0.96 7.67 -4.93 17.48



Gro Cereal grains nec 0.58 -1.02 2.62 -0.01 -0.54

v_f Vegetables, fruit, nuts 0.25 -0.85 2.75 -0.07 -1.55

Osd Oil seeds 0.15 -0.85 9.08 -0.01 -0.65

Pfb Palnt-based fibers 2.91 -0.27 8.71 -0.01 2.24

Ocr Other crops -0.11 -0.64 3.99 -1.34 0.49

Frs Forestry -0.5 -1.68 7.75 -0.13 -3.07

Fsh Fishing -0.16 -1.7 14.06 0 -2.29

Pcr Processed rice 0.08 -1.36 5.64 -0.03 -3.74

Ofd Food Products nec, Process food -0.36 -1.27 5.51 -4.93 8.56

Tex Textiles 2.82 -1.1 10.6 -8.86 23.91

Wap Wearing apparel 1.6 -1.85 14.12 -15.29 67.46

Lea Leather products -1.18 -1.26 20.61 -15.78 77.17

Wood Wood product,Paper product,publishing -1.59 -1.42 8.85 -4.81 10.74

Vol Vegetable oil & fats 2.02 -1.96 10.98 -0.05 -4.31

mineral Minerals nec -0.08 -1.44 2.07 -0.08 -1.3

Crp Chemical,rubber,plastis prods 0.62 -1.47 12.91 -1.66 0.88

Ome Machinary & Equip nec -1.9 -1.35 11.56 -3.45 6.57

Omf Manufactures nec 0.8 -1.4 10.69 -6.35 15.33

Auto Autoparts-otn-mvh -3.22 -1.38 9.08 -4.8 12.46

p_c Petroleum,coal products -0.01 -0.2 1.28 -0.22 -0.08

Metal Metals -2.65 -1.27 8.26 -5.63 10.2 Notes: The GTAP variables used to calculate percentage changes are (i) qo for domestic output, (ii) pxw for export price (equal to pm, i.e., output price, in this simulation)(iii) pim for import price; and (iv) qxw for aggregate exports of i from region r, FOB

weights, qiw for aggregate imports of i into region s, CIF weights and DQXS for the volume change in exports and imports in

terms of $millions. HS 6 Codes and product description is mentioned in Appendex-5


The change in China’s sectoral output and trade are mentioned in table # 5.10 because of

the simulated Pakistan China FTA. There is increased in output of textile, wood products,

petroleum and coal products, chemical products, metal products, auto parts, leather

products, paddy rice, and other crops and remaining sectors face reduction in output. The

export prices of all sectors are increased. Furthermore, the sectors which contribute in

improvement in China’s real GDP are textile, wood products, petroleum and coal



products, chemical products, metal products, auto parts, machinery and equipment,

paddy rice and other crops due to increase in export volume percentage change. The

largest relative increase in output is 0.34% of other crops sector and second largest

relative increase in output is 0.09% of auto parts sector, which is due to increased in

export volume of about 0.63% and 0.80% respectively. The largest relative decrease in

import price of textile sector because of largest relative increased in import volume of the

same sector among other sectors.

Simulated Sectoral Effects of the Pakistan China FTA on China (% change)

Table # 5.10

GTAP

Code

China- Sectors Domestic

Output (qo)

Export

Prices

(pxw)

Exports

(qxw)

Import

prices

(pim)

Imports

(qiw)

Pdr Paddy rice 0.02 0.06 5.18 -0.03 0.46

Gro Cereal grains nec 0 0.05 -0.14 -0.01 0.07

v_f Vegetables, fruit, nuts 0 0.05 -0.18 -0.03 0.13

Osd Oil seeds -0.07 0.04 -0.21 -0.01 0.03

Pfb Palnt-based fibers -0.01 0.05 -0.36 -0.07 0.21

Ocr Other crops 0.34 0.12 0.63 -0.01 0.11

Frs Forestry -0.01 0.05 -0.11 0 0.11

Fsh Fishing 0 0.05 -0.09 -0.15 0.26

Pcr Processed rice -0.01 0.05 -0.47 -0.06 0.28

Ofd Food Products nec, Process food 0 0.05 0.02 -0.03 0.15

Tex Textile 0.08 0.03 0.44 -0.21 0.96

Wap Wearing apparel -0.06 0.03 -0.17 -0.02 0.19

Lea Leather products 0.05 0.04 0.16 -0.17 0.9

Wood Wood product,Paper product,publishing 0.01 0.04 0.13 0 0.13

Vol Vegetable oil & fats -0.03 0.04 -0.28 -0.03 0.2

mineral Minerals nec 0 0.04 -0.05 0 0.03

Crp Chemical,rubber,plastis prods 0.01 0.04 0.17 0 0.13

Ome Machinary & Equip nec -0.01 0.04 0 0 0.14

Omf Manufactures nec -0.02 0.05 -0.11 0 0.19



Auto Autoparts-otn-mvh 0.09 0.04 0.8 0 0.16

p_c Petroleum,coal products 0.02 0.01 0.18 0 0.03

Metal Metals 0.02 0.04 0.35 0 0.14

Notes: The GTAP variables used to calculate percentage changes are (i) qo for domestic output, (ii) pxw for export price (equal

to pm, i.e., output price, in this simulation)(iii) pim for import price; and (iv) qxw for aggregate exports of i from region r, FOB weights, qiw for aggregate imports of i into region s, CIF weights and DQXS for the volume change in exports and imports in



iii. Simulated Welfare Effects of the Pakistan China FTA

The welfare is measured with the help of allocative efficiency and terms of trade effects.

Table # 5.11 presents the simulated welfare effects of Pakistan China FTA. China

experience a positive total welfare change from this FTA, while Pakistan experience

negative total welfare change. The import prices of 9 sectors of China show no change

but remaining sectors has low import prices. Due to the elimination of tariffs with

Pakistan, China’s export prices of all sectors are increased. Consequently, China’s terms

of trade enhance because it receives higher price for its exports in contrast of Pakistani

export prices which are reduced after the simulation. Pakistan is the loser in net welfare

with negative change in allocative efficiency. However, China achieves the net welfare

gain with positive change in allocative efficiency. The Chinese positive allocative

efficiency proves the shift of resources from inefficient sectors to more efficient sectors.

Nonetheless, Pakistan face negative change in allocative efficiency because it does not

shift its resources from inefficient sectors to efficient sectors. In context of Pakistan’s

allocative efficiency, the worst performing sectors are auto parts, petroleum and coal

products, vegetable oil and fats, machinery and equipment, other crops and chemical

products. The net simulated welfare effect on China is net welfare gain of



$719.7296million, but Pakistan has net welfare loss of $-417.9981million. Pakistan faces

net welfare loss mainly due to the negative terms of trade effects.

Table # 5.11

Note: The Global Trade Analysis Project (GTAP) variable containing the decomposed numbers above is welfare.


5.2.2. Pakistan Malaysia Free Trade Agreement


Table # 5.12 presents the simulated aggregate effects of the Pakistan Malaysia FTA in

terms of real GDP. This FTA causes a negative change of $ -1.90 million for Pakistan.

While, there is positive change $58.60 million for Malaysia before and after FTA. In

other words, there is contraction of real GDP in the Pakistan, however, an expansion of

real GDP in the Malaysia.

Welfare Allocative

Efficiency

Terms of

Trade Effects

Total

Pakistan -91.1583 -326.8398 -417.998

China 156.4868 563.2428 719.7296

Table

#5.12


Usual $ million

Post-FTA $

million

Change $

million

Pakistan 213,686.2 213,684.3 -1.9

Malaysia 289,259.56 289,318.16 58.6

Notes: The GTAP variable used is: (i) qgdp for Real GDP




As for trade, both Pakistan and Malaysia experience an increase in export values, with

Pakistan’s trade expansion being more than Malaysia as depicted in table # 5.13. Pakistan

has a larger increase in imports than in exports, worsening its trade balance. There was

trade deficit of Pakistan with Malaysia in base year pre-simulation and it remains in

deficit post-simulation, while there was trade surplus of Malaysia in base year before

simulation and it remains in surplus after simulation. However, the results show in table #

5.13 that exports of Malaysia are more than Pakistan that is why Malaysia achieve trade

surplus. As for the terms of trade, the simulation results in an improvement for Malaysia,

but a deterioration for Pakistan.

Table#5.13

Aggregate

Effects

Change in

Export Value

($ Million)

Change in

Import Value

($ Million)

Change in

Trade Bal.

value($

Million)

Change in

Terms of

Trade (%)

Pakistan 404.3424 1648.407 -1244.0646 -0.2486

Malaysia 1515.1586 455.1514 1060.0072 0.076

Notes: The GTAP variables used are: (i) VXWD for export value, (ii) VIWS for import value, (iii) VXWD for the initial level of exports and VIWS for the initial level of imports and(iv) tot for the terms of trade.



The Pakistan Malaysia FTA produces mixed effects on different sectors in Pakistan

(Table # 5.14). The Processed rice has the second largest relative output expansion



0.96% driven by a largest increase in export volumes at $260.139 million from base year.

The Beverages & Tobacco has the largest relative output expansion 1.55%, however, the

increase in export volume at $50.75 million from base year which is less than processed

rice. In the results, the sectors have absolute percentage changes of less than 0.49% for

export prices and less than 14.76% for export volume. The percentage change in export

price turns out to be equal to the percentage change in the local price in each sector, in

this simulation. There is largest drop in output and import prices in vegetable oil and fats

products sector. This is due to an increase in import volume in terms of percentage is

26.97% and increase in import volume in terms of amount is $1102.15 million from base

year, which substitute for and reduce the local supply of vegetable oil and fats products in

Pakistan’s domestic market.

The textiles and wearing apparel sector’s output and export volumes percentage change

are increased due to increase in export volumes at $65.09million and $21.10 million from

base year respectively. Similarly, due to increase in export volumes at $3.54 million from

base year, the chemical products sector’s output and export volume percentage change

are also increased.

The contraction in Pakistan’s real GDP is due, in order of importance, to vegetable oil

and fats, auto parts, chemical products, Machinery and Equipment, wood products and

textile and metals products, these sectors’ import volumes increase such as

$1102.15million, $154.71million, $140.83million, $83.65million, $72.25million,

$41.48million and $38.18million from base year respectively. The general increase in

import volumes can be attributed to tariff reductions and drops in import prices in all of

these above highlighted sectors.



GTAP

Code

Pakistan- Sectors

Domestic

Output (qo)

Export

Prices

(pxw)

Exports

Volume

(qxw)

Import

prices

(pim)

Imports

Volume

(qiw)

Wht Wheat 0.01 -0.16 1.3 0 -0.83

Gro Cereal grains nec 0.8 -0.15 0.29 -0.01 -0.71

v_f Vegetables, fruit, nuts 0.05 -0.2 0.6 -0.01 -0.37

Sugar Sugar -0.12 -0.31 1.52 -0.75 1.31

Fsh Fishing -0.08 -0.48 1.09 -0.38 -0.19

Pcr Processed rice 0.96 -0.3 14.14 -0.07 -0.68

Ofd Food Products nec -0.11 -0.31 1.15 -0.53 0.53

b_t Beverages & Tabacco 1.55 -0.31 14.76 -0.22 -0.12

Tex Textiles 0.82 -0.27 2.45 -0.18 -1.93

Wap Wearing apparel 0.37 -0.29 2.63 -0.03 -0.94

Wood Wood products -0.44 -0.35 2.58 -1.05 2.09

Vol Vegetable oil & fats -14.16 -0.31 2.52 -11.34 26.97

Mineral Minerals 0.12 -0.3 0.42 -0.03 -0.22

Crp

Chemical,rubber,plastis

products 0.31 -0.49 3.46 -0.25 -0.58

Ome Machinary & Equip nec 0.21 -0.33 2.77 -0.2 -0.4

Omf Manufactures nec 0.88 -0.33 2.36 -0.03 -0.93

Auto Autoparts -0.5 -0.34 2.26 -0.78 1.33

p_c Petroleum,coal products -0.01 -0.04 0.2 0 -0.11

Metal Metals 0.33 -0.27 1.74 -0.13 -0.33

Notes: The GTAP variables used to calculate percentage changes are (i) qo for domestic output, (ii) pxw for export price (equal to pm, i.e., output price, in this simulation)(iii) pim for import price; and (iv) qxw for aggregate exports of i from region r, FOB




Table # 5.15 shows how Malaysia’s sectoral output and trade change due to the simulated

Pakistan Malaysia FTA. Except for vegetable oil and fats, textiles, wood products, auto

parts, fishing and food products all other sectors experience a contraction in output. The

export prices of all the sectors are increased except beverages and tobacco, processed

rice, food products and wearing apparel. Moreover, the export volumes percentage

Simulated Sectoral Effects of the Pakistan Malaysia FTA on Pakistan (% change)

Table # 5.14



change are increased in vegetable oil and fats, textiles, wood products, paper

products, process food, beverages and tobacco, and auto parts; these sectors explain

the positive movement in Malaysia’s real GDP. Vegetable oil and fats displays the largest

relative increase in output by 1.36%, which can be traced to an increased export volume

of about 1.94%. The import price of processed rice drops the most in percentage terms

relative to other sectors and due to which there is largest relative increased in import

volume of the same sector among other sectors.

Simulated Sectoral Effects of the Pakistan Malaysia FTA on Malaysia (%change)

Table # 5.15 GTAP

Code

Malaysia- Sectors

Domestic

Output (qo)

Export

Prices

(pxw)

Exports

Volume

(qxw)

Impor

t

prices

(pim)

Imports

Volume

(qiw)

Wht Wheat -1.59 0.21 -1.81 -0.01 0.13

Gro Cereal grains nec -0.75 0.4 -0.97 -0.01 -0.2

v_f Vegetables, fruit, nuts -0.83 0.46 -1.25 -0.01 0.18

Sugar Sugar -0.9 0.6 -1.11 0 0.17

Fsh Fishing 0.01 0.1 -0.21 -0.01 0.2

Pcr Processed rice -5.44 -1.77 9.47 -8.44 14.74

Ofd Food Products nec 0.19 -0.06 0.41 -0.01 0.01

b_t Beverages & Tabacco -0.88 -0.4 0.88 -4.49 1.81

Tex Textiles 0.58 0.01 1.45 -0.34 0.86

Wap Wearing apparel -0.01 -0.02 0.14 -0.2 0.34

Wood Wood products 0.13 0.07 0.4 0 0.16

Vol Vegetable oil & fats 1.36 0.59 1.94 0.01 2.38

Mineral Minerals -0.13 0.02 -0.02 0 -0.14

Crp Chemical,rubber,plastis products -0.09 0.08 -0.05 0 0.16

Ome Machinary & Equip nec -0.12 0.06 -0.12 0 0.1

Omf Manufactures nec -0.32 0.08 -0.52 0 0.13

Auto Autoparts 0.68 0.05 2.92 0 0.29

p_c Petroleum,coal products -0.02 0.01 -0.04 0 -0.04

Metal Metals -0.15 0.05 -0.11 0 0.03

Notes: The GTAP variables used to calculate percentage changes are (i) qo for domestic output, (ii) pxw for export price (equal to pm, i.e., output price, in this simulation)(iii) pim for import price; and (iv) qxw for aggregate exports

of i from region r, FOB weights, qiw for aggregate imports of i into region s, CIF weights and DQXS for the volume change in exports and imports in terms of $millions. HS 6 Codes and product description is mentioned in

Appendex-5 Source: Author’s results from a GTAP simulation.



iii. Simulated Welfare Effects of the Pakistan Malaysia FTA

As this simulation of the Pakistan Malaysia FTA does not include any changes in

endowment or technical and productivity parameters, no welfare effects can be

characteristics to these two sources. Moreover, this simulation’s welfare results

considered only to changes in allocative efficiency (the efficiency of resource utilization)

and terms of trade (the change in the relative price of exports to imports both weighted by

benchmark-year quantities).

The right most column of Table # 5.16 shows the total welfare change for Pakistan and

Malaysia. Malaysia is with positive total welfare change from the Pakistan Malaysia

FTA. However, Pakistan is with negative total welfare change. Since Malaysia’s import

prices of 9 sectors has no change and other sectors has lower import prices. The export

prices of Malaysia are increased in all sectors except beverages and tobacco, processed

rice, food products and wearing apparel due to tariff reductions with Pakistan. Therefore,

Malaysia’s terms of trade improve because it receives a higher price for its exports as

compare to Pakistani export prices. The net welfare gainer with the positive change in

allocative efficiency is Malaysia while Pakistan is the loser in net welfare with negative

change in allocative efficiency. The Malaysia positive allocative efficiency reflects the

fact that it had some level of tariff protection before the simulation. The removal of tariffs

shifted resources from protected but inefficient sectors to more efficient sectors.

Nevertheless, Pakistan is not successful to shift its resources from inefficient sectors to

efficient sectors, therefore, it experience drop in allocative efficiency. If the change in



Pakistan’s allocative efficiency is broken down by sector, the seven worst performing

sectors are auto parts, chemical products, machinery and equipment, petroleum and coal

products, metal products, textile and wood products. Pakistan has net welfare loss of

$84.8079 million while Malaysia has net welfare gain of $225.4793 million from this

FTA. Table # 5.16 shows that Pakistan suffers mainly due to negative terms of trade

effects.

Simulated Welfare Effects of Pakistan Malaysia FTA and Decomposition ($

millions)

Table # 5.16 Welfare Allocative Efficiency

Terms of Trade

Effects

Total

Pakistan

-1.3045 -83.5034 -84.8079

Malaysia

39.064 186.4153 225.4793



5.2.3. Pakistan Sri Lanka Free Trade Agreement


In context of real GDP, the simulated aggregate effect of Pakistan Sri Lanka FTA is

presented in table #5.17. Both countries experience positive change in their real GDP,

Pakistan’s change is $4.22million and Sri Lanka’s change is $5.91million. This indicates

that both countries’ real GDP is expanding.



Table # 5.17


Usual $ million

Post-FTA $

million

Change $

million

Pakistan 213,686.2 213,690.42 4.22

Sri Lanka 59,178.04 59,183.96 5.91

Notes: The GTAP variables used are: (i) qgdp for Real GDP


The trade impact of Pakistan Sri Lanka FTA is depicted in table #5.18. Both countries

face increase in export values, however, Sri Lanka’s trade growth is more than Pakistan.

Furthermore, Sri Lanka’s imports are more than its exports due to that experiences trade

deficit. Nevertheless, Pakistan’s exports are more than its imports and achieve trade

surplus. Before simulation, Pakistan’s trade was in surplus with Sri Lanka and maintain it

surplus trade position after running the simulation, whereas, Sri Lanka was in trade

deficit with Pakistan before simulation and remain in trade deficit after the simulation has

been executed. Moreover, the result shows that Pakistan terms of trade are improved

while Sri Lanka terms of trade are worsted.

Table #5.18

Aggregate Effects Change in

Export Value ($

Million)

Change in

Import

Value ($

Million)

Change in

Trade Bal.

value($

Million)

Change in Terms

of Trade (%)

Pakistan 143.0056 21.1414 121.8642 0.0718

Sri Lanka 19.2916 167.4087 -148.1171 -0.1209 Notes: The GTAP variables used are: (i) VXWD for export value, (ii) VIWS for import value, (iii) VXWD for the initial level of exports and VIWS for the initial level of imports and (iv) tot for the terms of trade.





The Pakistan Sri Lanka FTA simulation impact on sectors of Pakistan is depicted in table

# 5.19. Beverages and Tobacco has the largest relative output expansion 0.53% due to

increase in export volume at $18.99million from base year. Textile sector has output

increase of 0.21% because of second largest increase in export volume at $6.01million

from base year. Wearing apparel has output enhancement of 0.23% as a result of

expansion in export volume at $2.68million from base year. Ferrous metals have the

second largest output increase of 0.39% with an expansion in export volume at

$4.53million from base year. The other significant export sectors are processed rice,

vegetable, fruits and nuts and mineral products nec whose output and export volume

percentage change are expanded as a result of increase in export volumes at

$16.25million, $5.96million and $2.74million from base year. Since these above

highlighted sectors contribute in expansion in Pakistan’s real GDP. The general increase

in export volumes can be attributed to tariff reduction and increase in export prices in all

of these above mentioned sectors. Table #5.19 shows that the sectors have absolute

percentage changes of less than 0.12% for export prices and less than 6.93% for export

volume. The decrease in output in wood products sector has been observed with a largest

drop in import price of -0.09% because a largest increase in import volume of 0.53% and

at $1.07million from base year. Furthermore, the output of vegetable oil and fats

decreases with a decrease in import price because an expansion in import volume of

0.22% and at $2.84million from base year which clearly indicate the substitute for and

reduce the local supply of wood products and vegetable oil and fats products in local

Pakistani market.



Simulated Sectoral Effects of the Pakistan Sri Lanka FTA on Pakistan (% change)

Table # 5.19

GTAP

Code

Pakistan –Sectors Domestic

Output

(qo)

Export

Prices

(pxw)

Exports

Volume

(qxw)

Import

prices

(pim)

Imports

Volume

(qiw)

Wht Wheat 0.05 0.11 -0.28 0 0.34

V_f Vegetables, fruit, nuts 0.07 0.12 2.42 -0.01 0.24

Osd Oil seeds -0.13 0.07 1.33 0 -0.04

OCR Crops nec 0.04 0.12 3.15 0 0.39

Sgr Sugar 0.01 0.09 -0.35 0 0.25

Pfb Palnt-based fibers -0.22 0.05 -0.21 0 -0.13

Oap Animal Product nec 0 0.11 -0.25 -0.01 0.16

Fsh Fishing 0.01 0.09 -0.16 0 0.12

PCR Processed rice 0.5 0.12 1.21 0 0.53

OFD Food Products nec 0.17 0.1 0.74 -0.01 0.16

Vol Vegetable oil & fats -0.1 0.06 -0.35 -0.03 0.22

b_t Beverages & Tobacco 0.53 0.08 6.93 0 0.1

Tex Textiles 0.21 0.06 0.33 -0.01 0.1

Wap Wearing apparel 0.23 0.06 0.33 -0.02 0.26

Lea Leather products 0.18 0.08 0.32 0 0.23

Wood Wood products -0.07 0.07 0.23 -0.09 0.53

PPP Paper product,publishing -0.06 0.06 0.05 -0.01 0.16

Crp

Chemical,rubber,plastic

products -0.08 0.06 0.73 -0.01 0.07

omn Minerals nec -0.04 0.06 -0.08 0 0.03

Nmm Mineral products nec 0.04 0.07 0.63 -0.01 0.22

fmp Metal products 0.07 0.06 0.19 0 0.04

Nfm Metals nec -0.18 0.06 -0.26 0 0.1

i_s Ferrous metals 0.39 0.06 4.17 0 0.07

Autoparts Autoparts -0.03 0.04 0.79 0 0.08

Ele Electronic equipment -0.07 0.06 0.86 0 0.2

Ome Machinary & Equip nec -0.3 0.05 -0.32 0 0.03

Omf Manufactures nec -0.19 0.06 -0.41 -0.01 0.22

Notes: The GTAP variables used to calculate percentage changes are (i) qo for domestic output, (ii) pxw for export price (equal

to pm, i.e., output price, in this simulation)(iii) pim for import price; and (iv) qxw for aggregate exports of i from region r, FOB


terms of $millions. HS 6 Codes and product description is mentioned in Appendex-5 Source: Author’s results from a GTAP simulation.



Table # 5.20 depicts the simulation effects of Pakistan Sri Lanka FTA on Sri Lanka’s

sectoral output and trade. The output of wheat, vegetable, fruit and nuts, fishing,

processed rice, beverages and tobacco, and ferrous metals are decreased and rest of the

sectors show increase in output. All the sectors show the decrease in export prices.

Moreover, the sectors which are prominent in reduction in Sri Lanka’s trade balance are

vegetable, fruit and nuts, processed rice, beverages and tobacco, ferrous metals, wearing

apparel, textile and mineral products nec because of increase in import volume

percentage change. Vegetable oil & fats has the largest relative expansion in output is

1.34% and wood products have the second largest relative enhance in output is 0.69%,

which is because of increase in export volume of approximately 1.84% and 1.78%

respectively. Processed rice has decline in import price of -10.01% due to largest relative

expansion in import volume of 31.93%.

Simulated Sectoral Effects of the Pakistan Sri Lanka FTA on Sri Lanka (% change)

Table # 5.20

GTAP

Code

Sri Lanka –Sectors

Domestic

Output (qo)

Export

Prices

(pxw)

Exports

Volume

(qxw)

Import

prices

(pim)

Imports

Volume

(qiw)

Wht Wheat -0.11 -0.04 0.34 -0.04 0.1

V_f Vegetables, fruit, nuts -0.06 -0.25 0.99 -0.84 1.17

Osd Oil seeds 0.43 -0.1 0.43 -2.4 8.26

OCR Crops nec 0.23 -0.15 0.9 -0.22 0.56

Sgr Sugar 0.26 -0.14 0.7 0 0

Pfb Plant-based fibers 0.15 -0.04 0.15 0 0.29

Oap Animal Product nec 0.02 -0.21 0.76 -0.01 -0.23

Fsh Fishing -0.01 -0.08 0.15 -0.03 -0.07

PCR Processed rice -1.19 -0.52 2.58 -10.01 31.93

OFD Food Products nec 0.07 -0.11 0.42 -0.47 0.53

Vol Vegetable oil & fats 1.34 -0.09 1.84 0 -0.09

b_t Beverages & Tabacco -1 -0.09 0.19 -13.56 24.51

Tex Textiles 0.33 -0.05 0.44 -0.06 0.26



Wap Wearing apparel 0.27 -0.06 0.41 -0.39 1.03

Lea Leather products 0 -0.05 0.43 -0.49 0.97

Wood Wood products 0.69 -0.04 1.78 -0.04 0.45

PPP Paper product,publishing 0.02 -0.03 0.75 0 0.04

Crp Chemical,rubber,plastic products 0.23 -0.05 0.61 -0.05 0.08

Omn Minerals nec 0.04 -0.03 0.05 0 0.02

Nmm Mineral products nec 0 -0.04 0.35 -0.26 0.47

Fmp Metal products 0.15 -0.14 1.04 -0.06 0.16

Nfm Metals nec 0.38 -0.05 0.49 -0.06 0.31

i_s Ferrous metals -0.04 -0.05 0.45 -0.27 0.3

Autoparts Autoparts 0.19 -0.07 0.41 -0.01 0

Ele Electronic equipment 0.15 -0.06 0.52 -0.01 0

Ome Machinary & Equip nec 0.43 -0.08 0.75 0 0.01

Omf Manufactures nec 0.45 -0.07 0.54 -0.02 0.08 Notes: The GTAP variables used to calculate percentage changes are (i) qo for domestic output, (ii) pxw for export price (equal to pm, i.e., output

price, in this simulation)(iii) pim for import price; and (iv) qxw for aggregate exports of i from region r, FOB weights, qiw for aggregate imports

of i into region s, CIF weights and DQXS for the volume change in exports and imports in terms of $millions. HS 6 Codes and product

description is mentioned in Appendex-5 Source: Author’s results from a GTAP simulation.

iii. Simulated Welfare Effects of the Pakistan Sri Lanka FTA

The simulated welfare impact of Pakistan Sri Lanka FTA is presented in table # 5.21.

This simulation impact show Pakistan faces a positive total welfare change whereas Sri

Lanka faces negative total welfare change. The import prices of 16 sectors of Pakistan

depicts no change, however, rest of the sectors has low import prices. Since the reduction

of tariffs with Sri Lanka, the export prices of all sectors of Pakistan are increased. In the

result, Pakistan’s terms of trade increase due to it receives higher price for its exports in

comparison of Sri Lanka’s export prices, which are declined after the simulation.

Pakistan achieves the net welfare gain with positive change in allocative efficiency while

Sri Lanka is the loser in net welfare but with positive change in allocative efficiency.

Pakistan positive allocative efficiency shows the shift of resources from inefficient



sectors to more efficient sectors but achieves less allocative efficiency than Sri Lanka.

The best performing sectors of Pakistan which participate in achieving positive allocative

efficiency are vegetable oil and fats, metal products, chemical products and auto parts.

The net simulated welfare impact on Pakistan is net welfare gain of $26.4297million,

however, Sri Lanka has net welfare loss of $-7.7351million. Pakistan experiences net

welfare gain mainly because of positive terms of trade effects.

Simulated Welfare Effects of Pakistan Sri Lanka FTA and Decomposition ($

millions)

Table # 5.21



The above mentioned results are consistent with the literature, like the results of Pakistan-China

FTA and Pakistan-Malaysia FTA show that China and Malaysia real GDP improved while

Pakistan real GDP reduced. In case of Pakistan-Sri Lanka FTA the real GDP of Pakistan and Sri

Lanka are increased. So these results are similar with the finding of Kawasaki (2003) and

Chandrima and Biswajit (2011) and satisfied the H1. The trade balances of China, Malaysia are

increased while Pakistan faces reduction in trade balances as result of Pakistan-China FTA and

Pakistan-Malaysia FTA. Pakistan trade balances improved in Pakistan-Sri Lanka FTA and Sri

Lanka faces decrease in exports and increase in imports which clearly show that there is

significant impact of FTA on trade balances. According to David (2010) and Kawasaki (2003)

and H2 the trade balance results are supportive. The trade and output of sectors results of the

analysis are consistent with the study of Brooks et.al (2005) and Akram (2013) and H3. The

Welfare Allocative

Efficiency

Terms of

Trade

Effects

Total

Pakistan 4.0279 22.4018 26.4297

Sri Lanka 7.7145 -15.4496 -7.7351



results of welfare are similar with the investigation of Disdier and Marette (2009), Ken and Hiro

(2012) and Veeramani and Saini (2010) and satisfy the H4.

5.3. Result Discussion of Firm Level Financial Factors

5.3.1. Pakistan and China FTA

i. Impact of Pakistan-China FTA tariff Changes on Profit

The impact of Pakistan and China FTA tariff changes on profit of the companies is

depicted in Table #5.22. The specification 1 which is the tobit regression, shows the

results of tariff changes and relevant control variables as explanatory variables,

however, it does not include import and export intensity impact. The import tariff

change variable and the control variables such as age, assets, and exchange rate are

significant with expected signs. The import tariff changes impact on profit of the

companies is significant with negative sign because the companies face import

competition and consequently the profits are reduced10. However, the export tariff

changes impact on profit of the companies is insignificant with positive sign because

few Pakistani companies are export oriented tends to enhance profits.

The control variable that is use for size is assets, which independently significant

proposes that this is the appropriate way to control size and better than merely

dividing profits by assets. The exchange rate has a negative sign coefficient and is

highly significant showing that Pakistani rupee is depreciating against dollar which is

good for the profits of the companies involves in exporting to China and an

10 Kambhampati & Parikh (2003)



appreciation is bad for the Pakistani companies. Nevertheless, Pakistani companies

are not exporting significantly to China that is why the profits are not increased. The

age coefficient has significantly positive sign which depict that the companies are

getting profits as they become older11.

Due to the existence of multi-collinarity between Dimport (interaction term) and

change in import tariff, the Dimport is dropped. The Dexport (Dummy * Change in

Export Tariffs) is insignificant with negative sign ,which show the post -2006

period, the change in export tariffs after signing of FTA do not increase profitability

of the companies due to export competition in China for Pakistani products. The

Auto Industry dummy variable is positively significant because Auto industry

increase its imports during sample period and in the result the efficiency of the

company’s improved which leads to increase in the profitability of the industry12.

Table #5.22 Pakistan China FTA-Effect of tariff changes on Profits

Tobit 1- All Firms

Variable Coefficient Std. Error z-Statistic Prob.

Intercept -5.10 0.62 -8.22 0.00

Age 0.02 0.00 3.27 0.00

Asset 1.13 0.04 29.02 0.00

Dexport -10.24 13.27 -0.77 0.44

Δ Export Tariff 10.02 13.23 0.76 0.45

Exchange Rate -0.01 0.00 -2.19 0.03

Δ Import Tariff -35.66 9.72 -3.67 0.00

DTextile -0.42 0.46 -0.91 0.36

DAuto 1.52 0.51 3.00 0.00

DChemical 0.92 0.49 1.88 0.06

DElectrical -0.68 0.55 -1.23 0.22

13 Baggs and Brander (2006)





DEngineering/Metal 1.02 0.54 1.90 0.06

Dleather 0.43 0.60 0.73 0.47

DPaperboard 0.61 0.53 1.14 0.26

Log likelihood -4178.43 Dependent Variable = ln(profits+1)

The most important specification is specification 2 presented in Table #5.23,

which incorporates import and export intensity and related interaction variables.

The import intensity variable is significant with negative sign reveal that,

companies in the industry are facing competition with the imports from the China

impacting the profitability negatively13. On the other hand, the export intensity is

insignificant with positive sign, indicating that Pakistani companies which are

exporting to China are increasing profitability than other companies.

The import interaction is the product of the change in import tariff and import

intensity, whereas the export interaction is the product of the change in export

tariff and export intensity. The import interaction term is significant with positive

sign indicating that few industries like auto, chemical and engineering are

importing equipment from China for the betterment of their efficiency by using

new technology and attain high profits. Nevertheless, the export interaction term

is significant with negative sign as exports lead to lowering profits14 of the

companies due to export competition faced by Pakistani companies in China.

13 Patibandla, M. (1998)

14 Krishna and Mitra (1998) and Levinsohn (1993)



Table # 5.23 Pakistan China FTA-Effect of tariff changes on Profits

Tobit 2

Variable Coefficient

Std.

Error z-Statistic Prob.

Intercept -5.46 0.65 -8.45 0.00

Age 0.02 0.00 3.35 0.00

Asset 1.13 0.04 29.29 0.00

Dexport -14.30 14.39 -0.99 0.32

Δ Export Tariff 15.13 14.45 1.05 0.30

Exchange Rate -0.01 0.00 -1.50 0.13

Δ Import Tariff -66.42 15.57 -4.27 0.00

DTextile -0.21 0.48 -0.43 0.67

DAuto 1.85 0.54 3.41 0.00

DChemical 1.19 0.53 2.27 0.02

DElectrical -0.25 0.62 -0.41 0.68


DLeather 0.67 0.61 1.10 0.27

DPaperboard 1.03 0.57 1.81 0.07

Export Intensity 0.59 0.72 0.82 0.41

Export Interaction -53.26 24.45 -2.18 0.03

Import Intensity -0.97 0.28 -3.41 0.00

Import Interaction 205.98 75.47 2.73 0.01

Log likelihood -4164.82

Dependent Variable = ln(profits+1)

Specification 3 and 4 are related with fixed effects and random effects

respectively using only the positive profit observations. Nevertheless, the random

effects results are better than fixed effects as Hausman Test suggests. Therefore,

Table #5.24 shows the results of random effects. As there are large number of

cross sectional units (companies) and a comparatively modest number of time

series observation for every company, so it is expected that the use of random and

fixed effects might be measured some of the impact due to change in tariffs. The



result of random effects shows the almost same results as mentioned in

specification 2, however, the import intensity and import interaction are

insignificant with negative sign. Moreover, export intensity is significant with

positive sign indicating that the exports are increasing the profits of those

companies which involve in exporting to China.

Table # 5.24 Pakistan China FTA-Effect of tariffs changes on Profits

OLS with Random Effects


Std.

Error t-Statistic Prob.

Intercept -3.57 0.41 -8.68 0.00

Age 0.01 0.00 3.03 0.00

Asset 0.92 0.02 42.36 0.00

Dexport 7.02 5.99 1.17 0.24

Δ Export Tariff -6.28 6.02 -1.04 0.30

Exchange Rate -0.01 0.01 -1.99 0.047

Δ Import Tariff 0.81 0.81 1.00 0.32

DTextile 0.58 0.38 1.52 0.13

DAuto 1.86 0.42 4.37 0.00

DChemical 1.76 0.42 4.22 0.00

DElectrical 0.35 0.46 0.74 0.46


DLeather 0.47 0.48 0.97 0.33

DPaperboard 1.48 0.43 3.46 0.00




Import Interaction -9.18 9.85 -0.93 0.35

R-squared 0.61

Adjusted R-squared 0.60 Dependent Variable = ln(profits+1)



Specification 5 uses first difference with only positive profits observations and this is

depicted in Table #5.25. The results show that the change in exchange rate is positively

significant which indicating that the change in profit of the companies is increasing

because with the increase in devaluation of Pakistani rupee, the export oriented

companies increase their exports. The export interaction and change in assets show

almost the similar results as mentioned in specification 2 & 4. Nevertheless, other

variable is not effecting the change in profitability of the companies.

Table #5.25 Pakistan China FTA-Effect of tariff changes on Profits

OLS with First Difference


Std.


Intercept 0.020 0.430 0.047 0.962

Age -0.003 0.003 -1.055 0.292

Dexport 12.527 9.417 1.330 0.184

Δ Export Tariff -11.794 9.471 -1.245 0.213

Δ Import Tariff -1.089 1.307 -0.833 0.405





Δ Exchange Rate 0.021 0.011 1.938 0.053

Δ Asset 0.531 0.114 4.674 0.000

DTextile 0.455 0.384 1.186 0.236

DAuto 0.364 0.443 0.823 0.411

DChemical 0.598 0.437 1.369 0.171

DElectrical 0.385 0.486 0.792 0.428




DLeather 0.330 0.479 0.688 0.491

DPaperboard 0.539 0.463 1.164 0.245

R-squared 0.027

Adjusted R-squared 0.016 Dependent Variable = Δln(profits+1)

The random effects specification dominates over fixed effect since companies are

probable to be more homogeneous in a well-explained industry group and individual

company impacts will not be correlated with the explanatory variables. This proposes

that, disaggregated industry level scrutiny may eliminate heterogeneity and give

improved results15.

ii. Impact of Pakistan-China FTA tariff Changes on Leverage

Table #5.26 provides the result of tobit regression and showing the relationship

between leverage and tariff changes. In this analysis and also in other regression,

only positive profits observations are considered. The primary investigation is to

see the impact on leverage due to changes in tariffs. It is appeared that the

changes in import and export tariffs are not significantly impact on leverage. The

post -2006 period (see coefficient of Dummy * change in export tariff) also show

insignificant impact on leverage with negative sign, the negative sign indicating

that the reductions in export tariff tend to reduce leverage16. The industries like






textile, auto, chemical, engineering, leather and paper & board are significant

with negative sign, reveal that these industries lead to lower the leverage due to

export orientation.

Table # 5.26 Pakistan China FTA-Effect of tariff changes on Leverage

Tobit-1


Std.


Intercept 0.88 0.05 16.46 0.00

Dexport -0.78 1.50 -0.52 0.60

Δ Import Tariff 0.00 0.02 -0.11 0.91

Δ Export Tariff 0.76 1.50 0.51 0.61

Exchange Rate 0.00 0.00 -4.32 0.00

Interest Rate 0.32 0.31 1.05 0.29

Age 0.00 0.00 1.21 0.23

DTextile -0.08 0.04 -2.02 0.04

DAuto -0.19 0.04 -4.38 0.00

DChemical -0.22 0.04 -5.14 0.00

Delectrical -0.02 0.05 -0.31 0.76

DEngineering/Metal -0.21 0.05 -4.54 0.00

DLeather -0.16 0.06 -2.63 0.01

DPaperboard -0.31 0.05 -6.52 0.00

Log likelihood 301.4143

Dependent Variable = Leverage (total debt/total assets)

The trade intensity variables and related interaction terms are considered in

specification 2 which are the central interest of the study, shown in table #5.27. The

change in import and export tariffs and Dexport depict the similar result as

mentioned in specification 1. Moreover, the import intensity and import interaction



term appear to be insignificant with positive sign, depicting that companies in import

competing industries lead to have more response of leverage to import tariff changes

than other companies. The export intensity is insignificant with positive sign, means

that due to export competition the companies increase leverage than other companies.

While, the export interaction term is insignificant, however with negative sign,

implying that companies with high export orientation likely to decrease leverage

larger in response to export tariff declines than other companies. The auto, chemical,

engineering, leather and Paper & board are significantly negative, expressing that

these industries decline leverage because of export orientation. The profits is use as

regressor in specification 2, showing negative impact and highly significant,

indicating that higher profit leads to decrease leverage, that support the pecking order

theory.


Tobit-2


Std.


Intercept 0.89 0.06 14.85 0.00

Dexport -0.90 1.51 -0.60 0.55

Δ Import Tariff -0.17 0.18 -0.93 0.35

Δ Export Tariff 0.92 1.52 0.61 0.54

Exchange Rate 0.00 0.00 -3.71 0.00

Interest Rate 0.40 0.31 1.29 0.20

Age 0.00 0.00 1.61 0.11

Profit -0.01 0.00 -2.37 0.02





Import Intensity 0.00 0.03 0.05 0.96


DTextile -0.08 0.05 -1.52 0.13

DAuto -0.17 0.06 -2.90 0.00

DChemical -0.21 0.06 -3.59 0.00

DElectrical -0.01 0.06 -0.15 0.88


DLeather -0.13 0.06 -2.07 0.04

DPaperboard -0.30 0.06 -4.86 0.00



The specifications 3 and 4 are related with fixed effects and random effects of panel

data regression respectively. Nevertheless, the Hausman Test suggests that the result

of fixed effects is better than random effects presented in Table #5.28 and depicted

the similar qualitative properties to specification 2. However, the export intensity is

insignificant with negative sign indicating that due to export orientation the

companies decrease leverage. Furthermore, the import intensity is negatively

insignificant means that due to improve efficiency, companies decline leverage.

Although, the age is positively significant, implying as the company become older

the leverage increases.


OLS with Fixed Effects

Variable Coefficient Std. Error

t-

Statistic Prob.

Intercept 1.00 0.26 3.83 0.00



Dexport -0.61 1.26 -0.49 0.63

Δ Import Tariff -0.17 0.15 -1.08 0.28

Δ Export Tariff 0.61 1.26 0.48 0.63

Exchange Rate 0.00 0.00 -4.12 0.00

Interest Rate 0.31 0.26 1.20 0.23

Age 0.00 0.00 5.16 0.00

Profit -0.01 0.00 -3.90 0.00

Export Intensity -0.07 0.12 -0.58 0.56


Import Intensity 0.00 0.02 -0.21 0.83


DTextile -0.02 0.32 -0.06 0.95

DAuto -0.67 0.31 -2.17 0.03

DChemical -0.75 0.26 -2.90 0.00

DElectrical -0.32 0.24 -1.34 0.18


DLeather -0.40 0.15 -2.75 0.01

DPaperboard -0.61 0.11 -5.57 0.00

R-squared 0.50

Adjusted R-squared 0.45 Dependent Variable = Leverage (total

debt/total assets)

Table #5.29 shows the specification 5 and depicts a regression expressing first

differences in leverage. The results of this regression show that all the variables are

insignificant. Except change in interest rate is positively significant, indicating that

interest rate increases with increase in leverage.






Std.


Intercept -0.007 0.039 -0.185 0.854

Dexport -0.941 0.907 -1.037 0.300

Δ Import Tariff 0.101 0.128 0.784 0.433

Δ Export Tariff 0.936 0.914 1.024 0.306

Δ Exchange Rate -0.001 0.001 -0.771 0.441

Age 0.000 0.000 0.132 0.895

Profit -0.003 0.002 -1.355 0.176


Export Interaction 0.079 0.202 0.392 0.695



DTextile -0.003 0.035 -0.078 0.938

DAuto 0.009 0.040 0.225 0.822

DChemical 0.012 0.040 0.296 0.768

DElectrical 0.009 0.044 0.204 0.838


DLeather -0.036 0.044 -0.809 0.419

DPaperboard 0.022 0.042 0.519 0.604

Δ Interest Rate 0.415 0.171 2.423 0.016

R-squared 0.011

Adjusted R-squared -0.003

Dependent Variable = Δ Leverage



The profits have a very significant negative impact on leverage as mentioned in

specification 2 and 3. These results are coherent with theoretical expectations17. This

clearly indicates that if profits decrease the company is forced to increase its short term

debt, whereas, if profits increase the company may decrease short-term borrowing.

Hence, the impacts on leverage that is depicted here contain and might be dominated by

working capital management rather than by long-run debt decisions.

iii. Impact of Pakistan-China FTA tariff Changes on Dividend Payout

The impact of Pakistan and China FTA tariff changes on dividend payout is shown in

Table #5.30. The specification 1 and 2 are related with fixed effects and random effects.

However, the results of fixed effects are better than random effects as Hausman Test

recommends. The Import tariff change variable is positively significant, indicating that

the companies which are involve in imports due to reduction in import tariff are

improving their efficiency and earn profits and tend to distribute dividend among

shareholders which create value for firm. This result supports the theory of dividend

relevance. However, the change in export tariff is insignificant with negative sign, means

that Pakistani companies which reduce dividend due to export competition lead not to

increase profit as compare to other companies.

It is observed that the export intensity and export interaction are insignificant but with

positive sign, this indicate that the companies which are export oriented are distributing

profit in the form of dividend than other companies. The import intensity is insignificant





with negative sign, but import interaction is significant with negative sign, means that the

companies which are facing import competition, and in the result the profits are reduced

tend not to distribute dividend.

The control variable such as assets is significant with negative sign, implying that the

companies which are not distributing dividend, in-fact they retain their profits and invest

in assets for value creation. The age is not significant, but with negative sign means that

those companies which are not profitable with the passage of time are not giving dividend

to their shareholders. Nevertheless, the exchange rate is positively significant, indicating

that the companies which are export oriented give more dividends since devaluation of

Pakistani rupee against dollar.

The Dexport (Dummy * Change in export tariff) is insignificant with positive sign, show

the post- 2006 period, depicting that the companies which are export oriented are giving

dividend as compare to other companies. However, most of the industries such as textile,

auto, chemical, electrical, engineering and leather are negatively insignificant, implying

that these industries are not able to generate sufficient profits to distribute dividend. The

only exceptional industry is paper & board which is positively significant, implying that

in this industry companies are exporting to china and able to generate profits which lead

to distribution of profit.

Table #5.30 Pakistan China FTA-Effect of tariff changes on Dividend Payout

OLS-Fixed Effects


Std.


Intercept 1.28 1.79 0.72 0.47

Dexport 0.21 1.81 0.11 0.91



Δ Import Tariff 1.65 0.29 5.65 0.00

Δ Export Tariff -0.23 1.80 -0.13 0.90

Exchange Rate 0.00 0.00 4.26 0.00

Age 0.00 0.00 -1.00 0.32

Asset -0.01 0.01 -1.99 0.05





DTextile -1.18 1.95 -0.61 0.54

DAuto -1.17 1.94 -0.61 0.55

DChemical -1.35 1.94 -0.69 0.49

DElectrical -1.32 1.95 -0.68 0.50


DLeather -1.16 1.96 -0.59 0.55

DPaperboard 0.36 0.14 2.61 0.01

R-squared 0.39

Adjusted R-squared 0.32

Dependent Variable = Dividend Payout = Dividend per share/ Earnings per share

Table #5.31 depicts the results of specification 3 of OLS-first difference. The results are

almost same as mentioned in specification 1. However, the change in asset coefficient is

positive and insignificant that means the change in assets is not affecting the change in

dividend payout. The paper & board industry is not significant with negative sign

indicating that due to some of the companies are in import competition, consequently not

increasing dividend payout. Furthermore, export interaction is negatively insignificant,

due to export rivalry for companies in China lead to decline dividend disbursements.



Table #5.31 Pakistan China FTA-Effect of tariff changes on Dividend Payout

OLS-First Difference

Variable Coefficient Std. Error t-Statistic Prob.

Intercept 0.07 0.08 0.89 0.37

Dexport 1.03 1.75 0.59 0.56

Δ Import Tariff 0.92 0.24 3.86 0.00

Δ Export Tariff -0.92 1.76 -0.52 0.60

Δ Exchange Rate 0.00 0.00 1.75 0.08

Age 0.00 0.00 -0.10 0.92

Δ Asset 0.00 0.02 0.18 0.86





DTextile -0.05 0.07 -0.72 0.47

DAuto -0.01 0.08 -0.15 0.88

DChemical -0.05 0.08 -0.60 0.55

DElectrical -0.01 0.09 -0.15 0.88


DLeather -0.07 0.08 -0.81 0.42

DPaperboard -0.07 0.09 -0.85 0.39

R-squared 0.06


Dependent Variable =Δ Dividend Payout = Dividend per share/ Earnings per share

5.3.2. Pakistan and Malaysia FTA

i. Impact of Pakistan-Malaysia FTA tariff Changes on Profit



The effect of Pakistan and Malaysia FTA tariff changes on profit is presented in Table

#5.32. The specification 1 incorporates the tobit regression. The import tariff change

variable is insignificant with positive sign, implying that, in general, the companies are

importing new technology and improve efficiency lead to enhance profit as compare to

other companies. The Dexport and Dimport are creating multi-collinarity with change in

export tariff and change in import tariff respectively, that is why change in export tariff

and Dimport are dropped while running the regression. The post FTA- 2008 period,

Dexport (Dummy * Change in export tariff) is showing the negative insignificant impact

on profits of the companies, indicating that, after reduction in export tariff, the

companies are not able to export to Malaysia due to export competition lead not to

increase in profits. Moreover, the pre FTA period starting from 2003 to 2007 showing

the similar results as mentioned above when Dexport is dropped and change in export

tariff is included in the regression.

The control variables such as assets and age are significant with expected sign. The

coefficient of assets is positive, means that assets are generating profits. The coefficient

of age is also positive, indicating that the mature companies are improving profits. The

exchange rate is not significant and with negative sign showing that the rupee is reducing

against dollar and the companies which are export oriented are generating profits than

other companies.

All the selected industries like textile, auto, chemical, paper & board and vanaspati and

allied are negatively significant, means that all the industries are in high levels of import

competition lead to have lower profits.



Table #5.32 Pakistan Malaysia FTA-Effect of tariff changes on Profits

Tobit 1


Intercept -3.46 0.52 -6.72 0.00

Age 0.01 0.01 2.09 0.04

Asset 1.12 0.04 27.65 0.00

Dexport -0.09 0.25 -0.35 0.73

Exchange Rate 0.00 0.00 -0.68 0.50

Δ Import Tariff 2.90 3.44 0.84 0.40

DTextile -2.47 0.29 -8.52 0.00

DAuto -0.75 0.35 -2.12 0.03

DChemical -0.96 0.34 -2.78 0.01

DPaperboard -1.33 0.39 -3.37 0.00

Dvanaspati -1.79 0.54 -3.31 0.00


The specification of highest interest is specification 2 depicted in Table #5.33, which

includes import intensity and export intensity and associated interaction terms. The

import intensity variable is negatively insignificant, indicating that the companies are not

profitable because of import competition. While, the export intensity is significant with

negative sign, implying that the companies are not able to increase profits due to decline

in Pakistani export to Malaysia. The import interaction is insignificant with positive sign

means that those companies which are importing equipments from Malaysia are using

new technology and attain efficiency lead to increase profits. However, the export

interaction is insignificant with positive sign which depicts that exports oriented

companies are enhancing profits. Furthermore, the exchange rate is positively

significant, reveals that the export oriented companies increase profits.




Tobit 2


Intercept -4.47 0.58 -7.70 0.00

Age 0.01 0.01 2.12 0.03

Asset 1.11 0.04 27.70 0.00

Dexport -0.84 0.66 -1.28 0.20

Exchange Rate 0.01 0.01 2.08 0.04

Δ Import Tariff 0.68 3.67 0.19 0.85

DTextile -1.55 0.36 -4.29 0.00

DAuto -0.87 0.35 -2.48 0.01

DChemical -0.91 0.36 -2.54 0.01

DPaperboard -1.48 0.39 -3.77 0.00

Dvanaspati -2.01 1.20 -1.68 0.09







The positive profit observations are considered in specification 3 and 4 which are linked

with fixed effects and random effects respectively. The Hausman Tests recommends that

the results of random effects are better than fixed effects. Therefore, Table #5.34 depicts

the results of random effects. The random effects results are nearly similar to

specification 2. Nevertheless, the change in import tariff is insignificant with negative

sign, indicating that the profit is reducing. Furthermore, the export intensity coefficient

sign is same as shown in specification 2, but with insignificance impact on profitability



which clearly indicate that the few of the companies’ could not successful in achieving

the Malaysian market share.




Intercept -2.52 0.30 -8.48 0.00

Age 0.00 0.00 0.20 0.84

Asset 0.92 0.02 41.55 0.00

Dexport -0.26 0.26 -1.01 0.31

Exchange Rate 0.01 0.00 5.80 0.00

Δ Import Tariff -0.27 1.48 -0.18 0.86

DTextile -1.13 0.22 -5.06 0.00

DAuto -0.08 0.25 -0.33 0.74

DChemical -0.30 0.25 -1.20 0.23

DPaperboard -0.62 0.29 -2.18 0.03

Dvanaspati -1.33 0.60 -2.22 0.03





R-squared 0.63


The results of specification 5 includes first difference presented in Table #5.35. For this

specification only positive profits observations are used. There are few differences from

specification 2 and 4, firstly the Dexport is insignificant but with positive sign, implying

that there is positive change in profits of the companies due to reduction in export tariff

after 2007 period. Secondly, the textile and chemical sectors have positive significant



impact on change in profitability which means that these two industries are contributing

in exports of Pakistan to Malaysia.




Intercept 0.03 0.22 0.13 0.90

Age 0.00 0.00 0.22 0.83

Dexport 0.01 0.42 0.02 0.99

Δ Import Tariff 1.62 2.50 0.65 0.52





Δ Exchange Rate 0.05 0.01 3.77 0.00

Δ Assets 0.37 0.13 2.95 0.00

DTextile 0.88 0.22 4.05 0.00

DAuto 0.02 0.21 0.08 0.94

DChemical 0.46 0.22 2.09 0.04

DPaperboard 0.25 0.25 1.02 0.31

Dvanaspati 0.50 0.85 0.59 0.55

R-squared 0.03

Adjusted R-squared 0.02 Dependent Variable =Δ ln(profits+1)



ii. Impact of Pakistan-Malaysia FTA tariff Changes on Leverage

In Specification 1, the impact of Pakistan- Malaysia FTA tariff changes on leverage is

analyze through tobit regression, the results of the same is shown in Table #5.36. It is

observed that the changes in import tariffs are insignificant with negative sign, implying

that due to improvement in efficiency some of the companies reduce leverage. The

Dexport (Dummy * change in export tariff) showing the post-2007 period, is also

insignificant with positive sign, means that due to export competition for the Pakistani

companies in Malaysia tend to increase in leverage. The chemical and paper & board

industries are negatively significant, indicating that the companies of these industries are

export oriented tend to reduce the leverage. Nevertheless, the textile industry is

positively significant implying that the companies belong to this industry is under import

competition lead to increase the leverage.

Table #5.36 Pakistan Malaysia FTA-Effect of tariff changes on Leverage

Tobit-1


Std.


Intercept 0.69 0.04 18.78 0.00

Dexport 0.01 0.02 0.30 0.76

Δ Import Tariff -0.14 0.33 -0.42 0.68

Exchange Rate 0.00 0.00 -0.23 0.81

Interest Rate -0.63 0.27 -2.35 0.02

Age 0.00 0.00 -0.66 0.51

DTextile 0.08 0.02 3.39 0.00

DAuto -0.03 0.03 -1.14 0.26

DChemical -0.07 0.03 -2.41 0.02

DPaperboard -0.14 0.03 -4.39 0.00

Dvanaspati 0.17 0.05 3.40 0.00





Specification 2 incorporates the import and export intensity and associated interaction

terms mentioned in Table #5.37. The results related with change in import tariff and

Dimport is similar with specification 1. The results of change in export and Dexport are

insignificant but with negative sign, indicating that there is reduction in leverage for

those companies which are export oriented. Furthermore, the import intensity is

insignificant with negative sign, showing that few companies reduce borrowing through

debt due to improvement in efficiency of these companies lead to increase in profits.

Nevertheless, when the change in import tariff is multiplied with import intensity the

results show the positive insignificant impact on leverage, implying that some of the

companies are increasing borrowing through leverage as they are facing import

competition. Whereas, the export intensity and export interaction are with negative sign,

but export intensity is highly significant, means that due to export orientation, companies

are not borrowing more via debt as compare to other companies. For instance, the paper

& board industry is more export oriented than textile industry that is why the paper &

board industry is significantly reduced leverage as compare to textile industry which is

more under import competition lead to significantly increase the leverage. In this

regression, the profits is use as independent variable, depicting the negative insignificant

impact on leverage means that as profit increases the leverage is decreasing.


Tobit-2




Intercept 0.67 0.04 17.19 0.00

Dexport 0.00 0.06 -0.01 1.00

Δ Import Tariff -0.20 0.34 -0.60 0.55

Exchange Rate 0.00 0.00 0.98 0.33

Interest Rate -0.41 0.28 -1.47 0.14

Age 0.00 0.00 -0.26 0.80

Profit 0.00 0.00 -0.97 0.33





DTextile 0.13 0.03 4.36 0.00

DAuto -0.03 0.03 -1.25 0.21

DChemical -0.04 0.03 -1.55 0.12

DPaperboard -0.15 0.03 -4.63 0.00

Dvanaspati 0.20 0.11 1.74 0.08

Log likelihood 229.69 Dependent Variable = Leverage (total debt/total assets)

For specification 3 and 4, the Hausman Test suggests that the results of random effects

are better than fixed effects which is presented in Table #5.38. The results are similar to

specification 2, but the age is significant with positive sign, depicting that the mature

companies are getting more leverage. The profit is negatively significant, implying that

as company earn more profit the leverage decreases. The vanaspati and allied industry is

positively significant, means that the companies of these industry is facing import

competition lead to increase in leverage. Furthermore, the exchange rate is significant

with positive sign, indicating that those companies which are not export oriented

enhance leverage than other companies.






Std.

Error

t-

Statistic Prob.

Intercept 0.65 0.05 12.57 0.00

Dexport -0.01 0.05 -0.26 0.80

Δ Import Tariff -0.39 0.28 -1.42 0.16

Exchange Rate 0.00 0.00 1.97 0.05

Interest Rate -0.35 0.23 -1.55 0.12

Age 0.00 0.00 2.38 0.02

Profit -0.01 0.00 -2.42 0.02





DTextile 0.13 0.05 2.83 0.00

DAuto -0.09 0.05 -1.74 0.08

DChemical -0.06 0.05 -1.16 0.25

DPaperboard -0.19 0.06 -3.15 0.00

Dvanaspati 0.28 0.11 2.48 0.01

R-squared 0.09

Adjusted R-squared 0.08 Dependent Variable = Leverage (total debt/total

assets)

Specification 5 includes first difference regression, presented in Table #5.39. The only

significant variable is change in interest rate with positive sign, implying that interest

rate is increased with the increase in leverage. However, rests of the explanatory

variables are insignificant.






Std.


Intercept 0.00 0.02 -0.05 0.96

Dexport 0.00 0.04 0.12 0.91

Δ Import Tariff -0.40 0.22 -1.79 0.07

Δ Exchange Rate 0.00 0.00 -1.05 0.29

Age 0.00 0.00 -0.06 0.95

Profit 0.00 0.00 -1.25 0.21





DTextile -0.02 0.02 -0.87 0.38

DAuto 0.01 0.02 0.57 0.57

DChemical 0.01 0.02 0.50 0.62

DPaperboard 0.01 0.02 0.58 0.56

Dvanaspati 0.02 0.07 0.24 0.81

ΔInterest Rate 0.54 0.25 2.16 0.03

R-squared 0.017

Adjusted R-squared 0.004 Dependent Variable = Δ Leverage

iii. Impact of Pakistan-Malaysia FTA tariff Changes on Dividend payout

Table #5.40 shows the effect of Pakistan- Malaysia FTA tariff changes on dividend

payout of the companies. The fixed effects and random effects are incorporated in

specification 1 and 2 respectively. Moreover, the Hausman Test recommends the random

effects. The changes in import tariff on dividend is insignificant with positive sign

because after reduction of import tariff companies use new technology which cause



increase in profit tend to increase in dividend payment. Moreover, the Dexport (Dummy

* change in export tariff) post FTA-2007 period shows positive insignificant impact on

dividend, indicating that those companies which are export oriented lead to increase in

dividend distribution among shareholders.

The assets and exchange rate are positively significant, indicating that with the increase

in assets, companies’ dividend payout ratio is increased. Furthermore, as the Pakistani

rupee devalued against dollar than the companies which are export oriented generate

profits lead to increase in dividend payments as compare to other companies.

The export intensity and export interaction having negative sign and the export intensity

is significant whereas export interaction is insignificant. This scenario shows that the

companies’ exports are not sufficient to increase profitability due to export competition

lead not to enhance dividend payments. In addition to that, the import intensity and

associated interaction term are insignificant but the import intensity is with negative sign

which clearly implying that the companies are under import competition tend to reduce

profits, so the dividend payout is declined than other companies. The textile, chemical,

paper & board and vanaspati and allied having significantly negative impact on dividend

payouts means that these industries are in export competition and not able to increase the

dividend payouts.



Table #5.40 Pakistan Malaysia FTA-Effect of tariff changes on Dividend Payout

OLS-With Random Effects


Std.


Intercept 0.31 0.20 1.58 0.12

Dexport 0.01 0.10 0.09 0.93

Δ Import Tariff 0.13 0.59 0.22 0.82

Exchange Rate 0.01 0.00 7.50 0.00

Age -0.01 0.00 -4.00 0.00

Asset 0.05 0.01 3.75 0.00


Export

Interaction -31.85 61.77 -0.52 0.61


Import

Interaction 0.08 0.85 0.09 0.93

DTextile -0.77 0.17 -4.59 0.00

DAuto -0.36 0.20 -1.81 0.07

DChemical -0.65 0.19 -3.34 0.00

DPaperboard -0.76 0.21 -3.55 0.00

Dvanaspati -0.62 0.28 -2.24 0.03

R-squared 0.07

Adjusted R-

squared 0.07


The results of specification 3 of OLS-first difference is shown in Table #5.41. The

results are nearly similar as reported in specification 2. Nevertheless, the change in

import tariff is negatively insignificant indicating that due to import competition the

change in dividend payout is not increasing. Furthermore, the change in asset coefficient



is negative and insignificant, implying that the change in assets is declining the change in

dividend payout.

Table #5.41 Pakistan Malaysia FTA-Effect of tariff changes on Dividend Payout

OLS-With First Difference


Std.


Intercept 0.12 0.04 2.75 0.01

Dexport 0.01 0.08 0.16 0.87

Δ Import Tariff -0.24 0.48 -0.49 0.63

Δ Exchange Rate 0.01 0.00 2.62 0.01

Age 0.00 0.00 -1.41 0.16

Δ Asset 0.00 0.02 -0.02 0.99


Export

Interaction -22.27 48.16 -0.46 0.64


Import

Interaction -0.20 0.67 -0.30 0.76

DTextile -0.10 0.04 -2.22 0.03

DAuto -0.07 0.04 -1.57 0.12

DChemical -0.08 0.04 -1.80 0.07

DPaperboard -0.13 0.05 -2.76 0.01

Dvanaspati -0.09 0.13 -0.68 0.50

R-squared 0.012

Adjusted R-

squared 0.004

Dependent Variable = Δ Dividend Payout = Dividend per share/ Earnings per share



5.3.3. Pakistan and Sri Lanka FTA

i. Impact of Pakistan-Sri Lanka FTA tariff Changes on Profit

Specification 1 includes the tobit regression to investigate the impact of Pakistan- Sri

Lanka FTA tariff changes on profit is depicted in Table #5.42. There is an issue of multi-

collinarity between Dexport and changes in export tariff and Dimport and changes in

import tariff. Therefore, change in export tariff and Dimport are dropped and when

change in import tariff and Dexport tariff are dropped the regression provides the same

results. The changes in import tariff is positively insignificant, indicating that, the

companies are improving their efficiency by importing new technology tend to increase

profits as compare to other companies. The post FTA- 2005 period captured through

Dexport (Dummy * change in export), depicting the insignificant with negative sign,

implying that, the companies are not export oriented lead to lowering the profitability

than other companies. The pre FTA is considered from 2000 to 2004 when the change in

export tariff is used instead of Dexport in the regression gives the same results. The

beverages & tobacco industry is highly positively significant, means that due to export

orientation, companies of this industry is increasing profits. Nevertheless, remaining

industries are in import competition, tend to decrease the profits.

Table #5.42 Pakistan Sri Lanka FTA-Effect of tariff changes on Profits

Tobit 1




Intercept -3.40 0.48 -7.03 0.00

Age 0.00 0.00 -0.88 0.38

Asset 1.05 0.04 25.43 0.00

Dexport -13.31 16.66 -0.80 0.42

Exchange Rate 0.00 0.00 -0.75 0.45

Δ Import Tariff 5.60 4.01 1.40 0.16

DTextile -1.40 0.28 -4.91 0.00

DBeverages

&Tobacco 1.15 0.37 3.11 0.00

DCement -2.22 0.34 -6.57 0.00

DPaperboard -0.16 0.37 -0.44 0.66

Dvanaspati -0.97 0.52 -1.87 0.06


Table #5.43 presents the specification 2, incorporates the import and export intensity and

related interaction terms. The import intensity is insignificant with negative sign, means

that the companies are in import competition tend to decline profits than other

companies, for instance, industries such as textile and cement, which are highly

significant with negative sign. Nevertheless, import interaction is positively significant,

showing that the companies are increasing profits due to better technological usage as

compare to other companies. Whereas, the export intensity is significant with negative

sign, implying that the companies are in export competition lead to lowering profits than

other companies. However, when change in export tariff is multiplied with export

intensity, the interaction term gives the significant with positive sign result, indicating



that the companies are export oriented lead to increase profits than other companies, for

example, the companies belong to beverages and tobacco.


Tobit 2


Intercept -3.57 0.49 -7.22 0.00

Age 0.00 0.00 -0.65 0.51

Asset 1.07 0.04 25.87 0.00

Dexport -97.29 27.31 -3.56 0.00

Exchange Rate 0.00 0.01 0.22 0.83

Δ Import Tariff 5.22 4.28 1.22 0.22

DTextile -1.34 0.31 -4.33 0.00

DBeverages

&Tobacco 0.90 0.41 2.20 0.03

DCement -2.16 0.39 -5.53 0.00

DPaperboard -0.22 0.42 -0.52 0.61

Dvanaspati -0.90 0.59 -1.53 0.13







In specification 3 and 4 which incorporates fixed effects and random effects respectively,

only positive profit observations are included. After running the Hausman Test, the

result proposes that the random effects is better than fixed effects. Table #5.44 shows the

results of random effects which are almost same to specification 2. However, there is

negative significant (at 0.07 levels) impact on changes in import tariff on profit means

that companies are import competition lead to decline in profits.






Intercept -2.02 0.26 -7.87 0.00

Age 0.00 0.00 0.62 0.54

Asset 0.84 0.02 38.59 0.00

Dexport -6.18 11.06 -0.56 0.58

Exchange Rate 0.01 0.00 7.03 0.00

Δ Import Tariff -2.96 1.68 -1.76 0.08

DTextile -0.85 0.21 -4.11 0.00

DBeverages

&Tobacco -0.02 0.27 -0.06 0.95

DCement -0.17 0.25 -0.68 0.49

DPaperboard -0.21 0.27 -0.77 0.44

Dvanaspati -1.20 0.39 -3.04 0.00





R-squared 0.60


Specification 5 encompasses first difference regression and the results are presented in

Table #5.45. The results are different from specification 2 and 4 in a way like after post -

2005 period, the Dexport is insignificant, however, with positive sign showing that

companies are export oriented with Sri Lanka and increasing their profits. The export

intensity is also with positive sign with significant impact on increase in profits, which



clearly indicate that Pakistani companies are able to capture the market share of Sri

Lankan markets and attain profits. The industries such as textile, cement and paper &

board are positively significant, implying that theses industries are exporting their

products to Sri Lanka and obtain profits.




Intercept -0.27 0.22 -1.25 0.21

Age 0.00 0.00 0.74 0.46

Dexport 4.02 18.15 0.22 0.82

Δ Import Tariff -8.48 2.64 -3.22 0.00





Δ Exchange Rate 0.02 0.01 1.89 0.06

Δ Assets 0.39 0.11 3.48 0.00

DTextile 0.43 0.19 2.26 0.02

DBeverages

&Tobacco 0.23 0.24 0.96 0.34

DCement 1.49 0.24 6.20 0.00

DPaperboard 0.57 0.26 2.22 0.03

Dvanaspati 0.47 0.38 1.24 0.22

R-squared 0.06

Adjusted R-squared 0.05 Dependent Variable =Δ ln(profits+1)



ii. Impact of Pakistan-Sri Lanka FTA tariff Changes on Leverage

Table #5.46 shows the results of specification 1 of tobit regression regarding the scrutiny

of impact of Pakistan- Sri Lanka FTA tariff changes on leverage. The changes in import

tariff and Dexport (Dummy * change in export tariff) is insignificant with positive sign,

means that the companies which are under import and export competition are increasing

leverage than other companies. The industries like textile, beverages & tobacco and

vanaspati & allied are positively significant, indicating that these industries are

increasing leverage as these industries are facing import competition. Nevertheless,

paper & board industry is negatively significant, implying that the companies related to

this industry are export oriented tend to reduced leverage. The exchange rate is

negatively insignificant, explaining the fact that the depreciation of Pakistani rupee is

good for export oriented companies as they are reducing leverage than other companies.

Table #5.46 Pakistan Sri Lanka FTA-Effect of tariff changes on Leverage

Tobit-1


Intercept 0.62 0.04 17.31 0.00

Dexport 1.44 1.41 1.02 0.31

Δ Import Tariff 0.35 0.33 1.05 0.29

Exchange Rate 0.00 0.00 -2.57 0.01

Interest Rate 0.01 0.14 0.05 0.96

Age 0.00 0.00 0.17 0.86

DTextile 0.12 0.02 5.72 0.00

DBeverages

&Tobacco 0.06 0.03 2.30 0.02

DCement -0.03 0.03 -1.19 0.24

DPaperboard -0.10 0.03 -3.29 0.00



Dvanaspati 0.18 0.05 3.71 0.00



The import intensity and export intensity and their related interaction terms are included

in specification 2, the results of the same is depicted in Table #5.47. The changes in

import tariff and Dexport are showing the similar results as explaining in specification 1.

The import intensity is insignificant with positive sign, expressing the phenomena that

the companies are increasing leverage because of import competitions than other

companies. While, import interaction is insignificant with negative sign, implying that

the companies are reducing leverage because those companies which are successfully

improve the efficiency by using new technology tend to increase profit as compare to

other companies. The export intensity and export interaction are with positive sign, but

export intensity is significant which clearly explains that few of the companies are

increasing leverage because these companies are facing export competition as compare

to other companies. For instance, companies of textile, beverage & tobacco, cement and

vanaspati & allied positively significant, while companies which are related to Paper &

Board are reducing leverage because these companies are export oriented. It is also

observed that profits as explanatory variable is highly significant with negative sign,

indicating that as the company’s profits increases tend to decline the leverage.


Tobit-2


Intercept 0.69 0.04 19.40 0.00

Dexport 1.56 2.30 0.68 0.50



Δ Import Tariff 0.07 0.33 0.23 0.82

Exchange Rate 0.00 0.00 0.03 0.98

Interest Rate 0.06 0.14 0.42 0.67

Age 0.00 0.00 1.86 0.06

Profit -0.04 0.00 -13.04 0.00





DTextile 0.11 0.02 5.10 0.00

DBeverages

&Tobacco 0.10 0.03 3.55 0.00

DCement 0.06 0.03 2.01 0.04

DPaperboard -0.10 0.03 -3.09 0.00

Dvanaspati 0.12 0.05 2.11 0.04



Table #5.48 depicts the results of specification 3 and 4 which are related to fixed effects

and random effects. The Hausman Test recommends the results of fixed effects since its

results are better than random effects. All the explanatory variables behave in the similar

manner to specification 2. However, the change in import tariff is negatively

insignificant, implying that the companies have increased their profitability by using

latest technology lead to reduce the leverage. Moreover, paper & board industry is

positively insignificant, means that few companies in this industry are under import

competition which show increase in leverage.




OLS-With Fixed Effects


Intercept 0.50 0.12 4.09 0.00

Dexport 2.52 1.88 1.34 0.18

Δ Import Tariff -0.22 0.27 -0.82 0.41

Exchange Rate 0.00 0.00 -0.91 0.36

Interest Rate 0.02 0.11 0.21 0.84

Age 0.00 0.00 3.32 0.00

Profit -0.04 0.00 -12.48 0.00





DTextile 0.32 0.16 1.99 0.05

DBeverages &Tobacco 0.29 0.12 2.40 0.02

DCement 0.20 0.08 2.51 0.01

DPaperboard 0.06 0.11 0.52 0.60

Dvanaspati 0.80 0.15 5.33 0.00

R-squared 0.56


Dependent Variable = Leverage (total

debt/total assets)

The results of Specification 5 incorporate first difference regression which is shown in

Table #5.49. The results are different from specification 2 and 3 in a way that Dexport

(Dummy * Change in export tariff), the post FTA-2005 period, is insignificant with

negative sign. Similarly, export intensity is with negative sign but having significant

impact on leverage. This indicates that companies are export oriented and generating



profits tend to reduction in leverage. For example the industries such as beverages &

tobacco, cement, paper & board and vanaspati and allied are negatively significant.

Moreover, the change in interest rate is highly significant with positive sign, implying

that as leverage increases the interest rate is also increased.




Std.


Intercept 0.14 0.03 4.74 0.00

Dexport -2.27 2.52 -0.90 0.37

Δ Import Tariff -0.91 0.35 -2.57 0.01

Δ Exchange Rate 0.00 0.00 2.20 0.03

Age 0.00 0.00 -1.12 0.26

Profit -0.01 0.00 -3.23 0.00





DTextile 0.01 0.02 0.61 0.54

DBeverages &Tobacco -0.08 0.03 -2.70 0.01

DCement -0.06 0.03 -1.85 0.06

DPaperboard -0.07 0.03 -1.92 0.06

Dvanaspati -0.15 0.06 -2.51 0.01

ΔInterest Rate 0.82 0.11 7.31 0.00

R-squared 0.14

Adjusted R-squared 0.13 Dependent Variable = Δ Leverage



iii. Impact of Pakistan-Sri Lanka FTA tariff Changes on Dividend Payout

The impact of Pakistan- Sri Lanka FTA tariff changes on dividend payout of the

companies is presented in Table #5.50. Specification 1 and 2 includes fixed effects and

random effects respectively and Hausman Test suggests the results of random effects are

better than fixed effects. The changes in import tariff is insignificant with positive sign,

indicating that companies improve their efficiency lead to increase in dividend payout

than other companies. Whereas, the post FTA-2005 period, Dexport (Dummy * change

in export tariff) shows the negative insignificant impact on dividend payout, means that

even after the reduction in export tariff companies are not able to increase dividend

payout due to export competition.

The export intensity is significantly negative, implying that most of the companies

reduces dividend payments and may reinvest in the business. Whereas, export interaction

is positively insignificant, indicating that some of the companies are export oriented

tends to increase dividend payments, for example companies related with Paper & board

and beverages & tobacco industries. The import intensity is significant with negative

sign, means that those companies which are facing import competition lead to decline in

dividend payout than other companies, for instance companies belong to textile, cement

and vanaspati and allied. However, the import interaction is significant with positive

sign, explains the phenomena that the companies which have achieved the efficiency by

adopting latest technology tend to increase dividend payouts.



Table #5.50 Pakistan Sri Lanka FTA-Effect of tariff changes on Dividend Payout



Intercept 0.05 0.05 1.07 0.28

Dexport -4.13 3.03 -1.36 0.17

Δ Import Tariff 0.01 0.42 0.04 0.97

Exchange Rate 0.00 0.00 6.92 0.00

Age 0.00 0.00 0.92 0.36

Asset 0.00 0.00 0.29 0.77


Export

Interaction 178.35 118.65 1.50 0.13


Import

Interaction 533.16 244.75 2.18 0.03

DTextile -0.06 0.04 -1.75 0.08

DBeverages

&Tobacco 0.08 0.05 1.66 0.10

DCement -0.07 0.05 -1.44 0.15

DPaperboard 0.16 0.05 3.24 0.00

Dvanaspati -0.11 0.08 -1.38 0.17

R-squared 0.14

Adjusted R-

squared 0.13




Specification 3 includes first difference, the results of the same is depicted in Table

#5.51. The results are different from specification 2. The major difference is like, the

changes in import tariff is insignificant with negative sign means that due to import

competition, companies are not able to distribute profit. Moreover, export intensity and

export interaction is showing positive sign, but export intensity is significant, explaining

that companies are export oriented leads to distribute dividend among shareholders. The

cement industry is highly significant with positive sign, explaining the same phenomena

as mentioned above. The import intensity and import interaction are insignificant with

positive sign, indicating that the companies which have improved the efficiency are able

to increase dividend payouts.

Table #5.51 Pakistan Sri Lanka FTA-Effect of tariff changes on Dividend Payout

OLS-With First Difference


Intercept -0.03 0.03 -0.94 0.35

Dexport -5.80 3.43 -1.69 0.09

Δ Import Tariff -0.08 0.45 -0.18 0.85

Δ Exchange Rate 0.00 0.00 2.09 0.04

Age 0.00 0.00 1.14 0.26

Δ Asset 0.00 0.02 -0.02 0.98





DTextile 0.01 0.03 0.32 0.75



DBeverages

&Tobacco -0.01 0.04 -0.29 0.77

DCement 0.08 0.04 2.20 0.03

DPaperboard 0.01 0.04 0.33 0.75

Dvanaspati 0.08 0.07 1.10 0.27

R-squared 0.03

Adjusted R-

squared 0.02

Dependent Variable = Δ Dividend Payout = Dividend per share/ Earnings per share

The pre and post CGE analysis simulations results are significant for Pakistan because from

these FTAs policy makers can get the idea which sectors has potential to contribute in increase

Pakistan’s GDP, sectors output and trade balances. Therefore, the policy makers when negotiate

with China, Malaysia and Sri Lanka for future agreements may be considered and eliminate the

tariffs of these major sectors and sub-sectors. The next chapter highlights the concluding

remarks, recommendations and future research.



CHAPTER 6

6. Conclusion

This study provides a scrutiny of and insight into the impact of Pakistan China FTA. The analysis

is focused on real GDP, trade and sector wise output and trade variables and welfare condition of

China and Pakistan. It is assumed that the ad valorem tariffs imports from Pakistan into China and

imports from China into Pakistan are all reduced to zero. The effect of the FTA as précis from the

simulation results is mentioned as follows:

China’s real GDP is positive due to improvement in exports of textile, wood product,

petroleum and coal products, chemical products, metal products, auto parts, machinery and

equipment, other crops and paddy rice. Nonetheless, the real GDP of Pakistan is reduced

because of increase in imports of textile, machinery and equipment, auto parts, metal

products, chemical products, leather products, wood products, manufactures nec and

wearing apparel.

The change in exports value of Pakistan and China are increased significantly. However,

Pakistan has a large increase in imports than in exports due to which it has trade deficit. In

contrast, China has large increase in exports than in imports and has trade surplus.

Pakistan terms of trade are reduced due to export prices are decreased in all sectors.

Furthermore, Pakistan’s net welfare is in loss with negative change in allocative efficiency.

Nevertheless, China is the gainer in net welfare because of its positive terms of trade and

allocative efficiency. This welfare scenario of China enhances because of its export prices

are increased in all sectors and import prices are lower in main sectors in connection of

terms of trade gain.



The results of the analysis are clearly indicated that Pakistan is in trade deficit which is in favor

of China. This is all happen because of China’s size of the economy, its production base, and the

differences in overall competitiveness. In future it is foreseeable that China will remain have a

positive trade balance with Pakistan. However, it is recommended that Pakistan should improve

trade with China by increasing production or diverting exports to China in terms of high

potential exports such as textile, wearing apparel, leather products, plant-based fibers, chemical

products, vegetable oil and fats, and metal products. By doing so, Pakistan will be in a position

to improve its trade balance, welfare and GDP growth. It is also recommended that imports

which have swelled sharply after the FTA should also be watchfully monitored and items may be

added to Pakistan’s protection list if local industries are suffering as a result. These above

highlighted recommendations may be considered by Pakistan Government as Phase –II

negotiations of the Pakistan China FTA are currently underway.

The second FTA on which this study presents the extensive analysis is the Pakistan-Malaysia

FTA on the GDP, trade and sector wise output and trade variables and welfare position of

Pakistan and Malaysia. The simulation in this study is assumed that the ad valorem tariffs

imports from Pakistan into Malaysia and imports from Malaysia into Pakistan are all reduced to

zero. The impact of the FTA as summarized from the simulation results is mentioned below:

The real GDP of Pakistan is negative due to increase in imports of vegetable oil and fats,

auto parts, chemical products, Machinery and Equipment, wood products, textile and

metal products. Nevertheless, Malaysia’s real GDP is positive because of increase in

exports of vegetable oil and fats, textiles, wood products, process food, beverages and

tobacco, paper products, processed rice and auto parts.



The exports of both countries increase substantially; however, Pakistan has a large

increase in imports than in exports, worsening its trade balance. There is trade deficit of

Pakistan while Malaysia has trade surplus.

Malaysia’s welfare gain is positive due to its positive terms of trade gain and positive

allocative efficiency. This welfare position of Malaysia improves because its export

prices are increased in all the main sectors and import prices are lower in main sectors in

context of terms of trade gain. However, Pakistan terms of trade are negative because

export prices are decreased in all sectors. Moreover, Pakistan is the loser in net welfare

with negative change in allocative efficiency.

Historically, Pakistan’s world exports are agriculture based commodities. It has been observed in

this study that in Pakistan- Malaysia FTA, Pakistan top exports to Malaysia is rice and cotton.

Conversely when looking at Malaysia’s exports to Pakistan, exports are based more on goods

manufactured within the country, along with a focus on palm oil. This shows a more diversified

economy with a divided focus on both agro based and manufactured goods for export.

Thus, to conclude, it might be said that on the whole, the Pakistan-Malaysia FTA is likely to

fetch much of the desired results for Malaysia: increased trade, better market access for

Malaysian products to Pakistan, GDP growth and improved welfare for Malaysia. While

Pakistan does not get the benefit from this FTA, however, Pakistan identify potential exports

sectors such as process rice, textiles, wearing apparel, chemical products, plastic, rubber, metal

products, cement and machinery and equipment. Therefore, Pakistan may be develop the long

term strategy to focused on these industries and allocate the resources efficiently on these



sectors. In this way, Pakistan improves its exports to enhance its GDP growth, trade balance and

welfare.

The third FTA is Pakistan Sri Lanka on which extensive investigation is conducted in this study.

The scrutiny mainly has been executed on real GDP, trade and sector wise output and trade

variables and welfare situation of Sri Lanka and Pakistan. It is presumed that the ad valorem

tariffs imports from Pakistan into Sri Lanka and imports from Sri Lanka into Pakistan are all

eliminated. The impact of the FTA is briefly explained as follows:

Beverages and Tobacco, metal products , processed rice and vegetable, fruits and nuts

,these sectors contribute in expansion in Pakistan’s real GDP due to increase in their

exports to Sri Lanka. Nevertheless, the sectors which are prominent in reduction in Sri

Lanka’s trade balance are vegetable, fruit and nuts, processed rice, beverages and

tobacco, metal products, wheat, plant-based fibers, textile and mineral products nec

because of increase in imports from Pakistan.

Pakistan has large expansion in exports than in imports and get trade surplus.

Nevertheless, Sri Lanka has a large increase in imports than in exports because of which

it has trade deficit.

Pakistan is the gainer in net welfare due to its positive terms of trade and allocative

efficiency. This welfare situation of Pakistan increases due to its exports prices are

enhanced in all sectors whereas import prices are lower in main sectors in context of

terms of trade gain. However, Sri Lanka’s terms of trade are dropped because of export

prices are reduced in almost all sectors. Moreover, Sri Lanka’s net welfare is in loss

mainly because of its negative change in terms of trade.



In view of above results, it is observed that Pakistan is getting benefit from this FTA in terms of

real GDP, trade and welfare as compare to Sri Lanka. However, for achieving better results for

both sides, it is recommended that both countries establish the linkage between their business

communities and policy makers. For that purpose, the trade delegations and single country

exhibitions must be held regularly, industrialist and exporters should have involvement in the

drafting of future FTAs and forum should be created for the swift arbitration of trade disputes.

The results mentioned above at macroeconomic level are supported by the international trade

theories.

This study also scrutinizes the pre and post impact of the Pakistan-China FTA, Pakistan-

Malaysia FTA and Pakistan-Sri Lanka FTA of firm level financial factors. The firm level factors

such as profit, leverage and dividend payouts and product market competition are closely

interrelated with each other. It is therefore argued that the changing in competition structure of

the output market might change the profitability, leverage and dividend payouts patterns of the

companies. This study specifically focuses on the changes in output market scenarios of Pakistan

due to these three selective FTAs. That is why this study emphasize the importance of these

FTAs and analyze the impact of profits, leverage and dividend payouts.

This is an excellent opportunity to analyze the policy events. These policy events were the

Pakistan-China FTA of 2007, Pakistan-Malaysia FTA of 2008 and Pakistan- Sri Lanka FTA

2005, which ushered in a reductions of tariffs and on few products the tariffs become zero within

time period of 5 years of each FTA in the non-financial sectors for trade between Pakistan and

China, Pakistan and Malaysia and Pakistan and Sri Lanka. In Pakistan, practically manufacturing

companies are either export oriented or face import competition, export competition, improve



efficiency through import of new technology and many falls into each categories. This situation

gives the opportunity to researchers to investigate the idea that these FTAs might impact profits,

leverage and dividend payouts and to estimate the nature of these impacts.

The finding of this research reveals that due to change in import tariffs, those companies which

are under import competition tend to reduce profits, increase leverage and decline dividend

payouts. Nevertheless, those companies which improve efficiency by adopting new technology

lead to increase profits, reduce leverage and increase in dividend disbursement. The decline in

export tariffs are related with increase in profits, decrease in leverage and increase in dividend

payments subject to export orientation of the companies. However, the export competitions

reduce the profits, increase leverage and decline in dividend payouts of companies. These results

are supported by the risk theory of profit, pecking order theory and theory of dividend.

The results of this study assist the financial managers to take decisions. The findings are in align

with general perception that export oriented companies and usage of imported technology by the

companies are getting benefits from reduction in export tariffs and decrease in import tariffs

respectively. Moreover, import and export competition face by the companies are harmed by

reduction in import tariffs and decline in export tariffs respectively.

The most significant aspect of these findings is the response of profits changes in tariffs,

specifically export tariffs. Therefore, results highlight the significance of export markets and the

importance of getting benefits from reforms in trade policy. It is also very noteworthy to mention

that general perception is that trade deficit is not good of any economy, although Pakistan is in

trade deficit with China and Malaysia, but the companies which are importing new technology

from China and Malaysia improve the efficiency lead to improvement in profits and dividend



payments and reduction in leverage. Therefore, these companies achieve net gain from these

FTAs.

Furthermore, it is pertinent to highlight that changes in trade policy might induce a change in the

rational financial structure of the firm. Nevertheless, it is probable that numerous financial

managers do not react efficiently to such trade shifts. Even those who proactive and react,

various financial managers are changing leverage by default as profits decline or increase rather

than by predicting the impacts and acting accordingly. It is recommended that financial managers

may predict at early stage and do better decision regarding financial structure.

From the results financial managers also predict appropriate dividend policy accordingly with the

changes in trade policy. The companies which are export oriented and improve efficiency by

importing latest equipments can pay dividend among shareholders and be consistent in this

policy ultimately create value of firms.

One of the aspects of results is that through these FTAs companies enhance profits, reduce

leverage and increase in dividend payouts. These companies can offset losses from increasing

export competition by getting export opportunities. Furthermore, the reduction of leverage also

reduces chances of insolvency for the companies. These results provide interesting information

for financial managers in the private sector as well as for policy makers to make better trade

policies.

6.1. Future Research

The contemporary area for future research is China Pakistan Economic Corridor (CPEC) since

the impact of CPEC on Pakistan economy will be marvelous. The CPEC create new job



opportunities, reduce poverty, development of transportation, development in energy sector,

information technology, tourism, education, agricultural development, and public health and

ultimately open new horizon for researchers to explore CPEC research projects. Moreover, some

of the proposed FTAs like Pakistan-Turkey FTA and Pakistan-Thailand FTA. Therefore, the

researchers can investigates these research areas at macroeconomic as well as at micro level

financial factors.



Figure 4.1 Core Flow Chart of Income



Figure # 4.2 Complete Circular Flow Chart of Income

Source: Hodjat Ghadimi (2007)



Figure 4.3 One Region Closed Economy without Government Intervention Brockmeier (1996)



Figure 4.4 Multi Region Open Economy without Government Intervention



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Appendix. 1. Commodity aggregation in GTAP.9

N0. Codes Description N0 Codes Description N0. Codes Description

1 pdr Paddy rice 20 omt Meat products 39 otn Transport equipment nec

2 wht Wheat 21 vol Vege.. oils % fats 40 ele Electronic equipment

3 gro Cereal grains nec 22 mil Dairy products 41 ome Machinery and equipment

4 v_f

Vegetables, fruit,

nuts 23 pcr Processed rice 42 omf Manufactures nec

5 osd Oil seeds 24 sgr Sugar 43 ely Electricity

6 c_b

Sugar cane, sugar

beet 25 ofd Food products 44 gdt

Gas manufacture,

distribution

7 pfb Plant-based fibers 26 b_t

Beverages and

tobacco products 45 wtr Water

8 ocr Crops nec 27 tex Textiles 46 cns Construction

9 ctl

Cattle,sheep,goats

,horses 28 wap Wearing apparel 47 trd Trade

10 oap

Animal products

nec 29 lea Leather products 48 otp Transport nec

11 rmk Raw milk 30 lum Wood products 49 wtp Sea transport

12 wol

Wool, silk-worm

cocoons 31 ppp

Paper products,

publishing 50 atp Air transport

13 for Forestry 32 p_c

Petroleum, coal

products 51 cmn Communication

14 fsh Fishing 33 crp

Chemical, rubber

plastic prods 52 ofi Financial services nec

15 col Coal 34 nmm Mineral products 53 isr Insurance

16 oil Oil 35 i_s Ferrous metals 54 obs Business services nec

17 gas Gas 36 nfm Metals nec 55 ros

Recreation and other

services

18 omn Minerals 37 fmp Metal products 56 osg

PubAdmin/Defiance/Healt

h/Educat

19 cmt Meat: 38 mvh Motor. V and parts 57 dwe Dwellings

Source: GTAP version.9

Appendix. 2. Regional Aggregation in GTAP. 9

No. Codes Description No. Codes Description No. Codes Description

1 AUS Australia 25 LKA Sri Lanka 48 XCA R.O C. America

2 NZL New Zealand 26 XSA R.O. South Asia 49 DOM

Dominican

Republic

3 XOC Rest of Ocean 27 CAN Canada 50 JAM Jamaica

4 CHN China 28 USA USA 51 PRI Puerto Rico

5 HKG Hong Kong 29 MEX Mexico 52 TTO

Trinidad

&Tobago



6 JPN Japan 30 XNA Rest of N. America 53 XCB Caribbean

7 KOR Korea Rep. of 31 ARG Argentina 54 AUT Austria

8 MNG Mongolia 32 BOL Bolivia 55 BEL Belgium

9 TWN Taiwan 33 BRA Brazil 56 CYP Cyprus

10 XEA Rest E. Asia 34 CHL Chile 57 CZE Czech Republic

11 BRN B. Darussalam 35 COL Colombia 58 DNK Denmark

12 KHM Cambodia 36 ECU Ecuador 59 EST Estonia

13 IDN Indonesia 37 PRY Paraguay 60 FIN Finland

14 LAO Lao People 38 PER Peru 61 FRA France

15 MYS Malaysia 39 URY Uruguay 62 DEU Germany

16 PHL Philippines 40 VEN Venezuela 63 GRC Greece

17 SGP Singapore 41 XSM Res.O S. America 64 HUN Hungary

18 THA Thailand 42 CRI Costa Rica 65 IRL Ireland

19 VNM Viet Nam 43 GTM Guatemala 66 ITA Italy

20 XSE R.O.S.E. Asia 44 HND Honduras 67 LVA Latvia

21 BGD Bangladesh 45 NIC Nicaragua 68 LTU Lithuania

22 IND India 46 PAN Panama 69 LUX Luxembourg

23 NPL Nepal 47 SLV El Salvador 70 MLT Malta

24 PAK Pakistan 48 LKA Sri Lanka 71 NLD Netherlands

Appendix 2. Regional aggregation in GTAP version.9

No. Codes Description No. Codes Description No. Codes Description

72 POL Poland 95 AZE Azerbaijan 118 NGA Nigeria

73 PRT Portugal 96 GEO Georgia 119 SEN Senegal

74 SVK Slovakia 97 BHR Bahrain

120

TGO Togo

75 SVN Slovenia 98 IRN Iran 121 XWF Rest of W. Africa



76 ESP Spain 99 ISR Israel 122 XCF Central Africa

77 SWE Sweden 100 JOR Jordan 123 XAC S. Central Africa

78 GBR U. Kingdom 101 KWT Kuwait 124 ETH Ethiopia

79 CHE Switzerland 102 OMN Oman 125 KEN Kenya

80 NOR Norway 103 QAT Qatar 126 MDG Madagascar

81 XEF Rest of EFTA 104 SAU Saudi Arabia 127 MWI Malawi

82 ALB Albania 105 TUR Turkey 128 MUS Mauritius

83 BGR Bulgaria 106 ARE UAE 129 MOZ Mozambique

84 BLR Belarus 107 XWS Rest of W. Asia 130 RWA Rwanda

85 HRV Croatia 108 EGY Egypt 131 TZA Tanzania

86 ROU Romania 109 MAR Morocco 132 UGA Uganda

87 RUS Russian. Fed 110 TUN Tunisia 133 ZMB Zambia

88 UKR Ukraine 111 XNF Rest of North Africa 134 ZWE Zimbabwe

89 XEE R.O E. Europe 112 BEN Benin 135 XEC Rest of E. Africa

90 XER Rest of Europe 113 BFA Burkina Faso 136 BWA Botswana

91 KAZ Kazakhstan 114 CMR Cameroon 137 NAM Namibia

92 KGZ Kyrgyzstan 115 CIV Cote d'Ivoire 138 ZAF South Africa

93 XSU R.O. F. Sov. U 116 GHA Ghana 139 XSC Rest of S. Afric..

94 ARM Armenia 117 GIN Guinea 140 XTW Rest of World

Source: GTAP version.9

Appendix-3 Sectoral Aggregation used in the study

No. Old Code Sector Description No. New Code Sector Description

1 pdr Paddy rice 1 Pdr Paddy rice

2 wht Wheat 2 Wht Wheat

3 gro Cereal grains nec 3 Gro Cereal grains nec

4 v_f Vegetables, fruit, nuts 4 V_f Vegetables, fruit, nuts

5 osd Oil seeds 5 Osd Oil seeds

6 c_b Sugar cane, sugar beet 7 Sugar Sugar cane, sugar beet

7 pfb Plant-based fibers 9 Pfb Palnt-based fibers

8 ocr Crops nec 6 OCR Crops nec

9 ctl Cattle,sheep,goats,horses 10 Ctl Cattle,sheep,goats,horses



10 oap Animal products nec 12 Oap Animal Product nec

11 rmk Raw milk 11 Animalprod rawmilk,wool,silkwo

12 wol Wool, silk-worm cocoons 11 Animalprod rawmilk,wool,silkwo

13 frs Forestry 13 Frs Forestry

14 fsh Fishing 14 Fsh Fishing

15 coa Coal 15 minerals Coal

16 oil Oil 16 Oil Oil

17 gas Gas 17 Gas Gas

18 omn Minerals nec 32 omn Minerals nec

19 cmt Meat: cattle,sheep,goats,horse 23 Meatfood Meat:Cattl,sheep,goat,horse

20 omt Meat products nec 20 ProcessFood Procerice,meatpro,foodpro

21 vol Vegetable oils and fats 21 Vol Vegetable oil & fats

22 mil Dairy products 22 Mil Dairy products

23 pcr Processed rice 18 PCR Processed rice

24 sgr Sugar 8 sgr Sugar

25 ofd Food products nec 19 OFD Food Products nec

26 b_t Beverages and tobacco products 24 b_t Beverages & Tabacco

27 tex Textiles 25 Tex Textiles

28 wap Wearing apparel 26 Wap Wearing apparel

29 lea Leather products 27 Lea Leather products

30 lum Wood products 28 Wood Wood products

31 ppp Paper products, publishing 29 PPP Paper product,publishing

32 p_c Petroleum, coal products 30 p_c Petroleum,coal products

33 crp Chemical,rubber,plastic prods 31 Crp Chemical,rubber,plastis prods

34 nmm Mineral products nec 33 Nmm Mineral products nec

35 i_s Ferrous metals 36 i_s Ferrous metals

36 nfm Metals nec 35 nfm Metals nec

37 fmp Metal products 34 fmp Metal products

38 mvh Motor vehicles and parts 37 Autoparts Motorvehiclespart,Transporequi

39 otn Transport equipment nec 37 Autoparts Motorvehiclespart,Transporequi

40 ele Electronic equipment 38 Ele Electronic equipment

41 ome Machinery and equipment nec 39 Ome Machinary & Equip nec

42 omf Manufactures nec 40 Omf Manufactures nec

43 ely Electricity 41 Util_Cons Utilities and Construction

44 gdt Gas manufacture, distribution 41 Util_Cons Utilities and Construction

45 wtr Water 41 Util_Cons Utilities and Construction

46 cns Construction 41 Util_Cons Utilities and Construction

47 trd Trade 42 TransComm Transport and Communication

48 otp Transport nec 42 TransComm Transport and Communication

49 wtp Sea transport 42 TransComm Transport and Communication

50 atp Air transport 42 TransComm Transport and Communication

51 cmn Communication 42 TransComm Transport and Communication

52 ofi Financial services nec 43 OthServices Other Services

53 isr Insurance 43 OthServices Other Services

54 obs Business services nec 43 OthServices Other Services

55 ros Recreation and other services 43 OthServices Other Services

56 osg PubAdmin/Defence/Health/Educat 43 OthServices Other Services

57 dwe Dwellings 43 OthServices Other Services



Appendix- 4

Accounting Relationships Equations

In this section, the basic notation and equations of the GTAP model are explained. Moreover, it

also discusses about the insight behind the GTAP model and gives the extensive description of

the accounting relationships. The distribution of the firm’s sale to the regional market is the first

part of accounting relationships in the GTAP model. As discussed in chapter 3, in the open

economy version of the GTAP model the firms mix the primary factors (endowments goods)

with intermediate inputs to produce final commodities. These final commodities are than sell to

the domestic market as well as to the international market. The below mentioned section provide

details of the allocation of firm’s sale between local and regional markets.

I. Distribution of Sale to the Regional Markets

There is one to one relationship between sectors and commodities in the GTAP model. It is

assumed that each sector in the model is producing only single output. The final product is sold

to domestic as well as to the regional market by the firms. The value of output of firm at the

agent’s price is mentioned below in the equation.

Where “VOA(i, r)” is the value of output at the agent’s price. In other words, this equation also

denotes 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 value of



output is changed into the value of output at the market prices by adding the producer’s tax

“PTAX (i, r)”. That is;

Where, “VOM(i, r)” is the value of firm’s output 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)”.

To articulate exports on fob-based value, exports tax (XTAX) is added in the following equation.

Where, “VXWD (i, r, s)” represents for the value of exports at the fob price of ‘i’ exported from

region ‘r’ to ‘s’ and “VXMD(i, r, s)” is the of exports at the domestic market price.

International Transportation Margin

The international transportation margin “VTWR(i, r, s) is calculated by subtracting the

value of exports at the fob price from the cif-based value of the world imports

“VIWS(i,r,s)” which is mentioned below.

In order to determine the value of this sale at the internal domestic price in ‘s’ “VIMS (i,

r, s)”, it is required to add import tax “MTAX (i, r, s):



The imports to region ‘s’ from different sources are combined into a single import good.

The value of composite imports is divided among three agents who demand for imports.

That is value of imports of ‘i’ in region ‘s’ imported by ‘private household’ measured at

the market price “VIPM(i, s)”, the value of imports at the market price by the government

“VIGM(i, s)” and the value of imports by firms in ‘s’ “VIFM((i, s)”. The below

mentioned equation show this relationship.

Where, “VIM (i, s)” presents the value of imports of industry ‘i’ from region ‘r’ to region

‘S’.

The link between industrial output and household acquisitions is also established by the

accounting relationships in the GTAP model. The below mentioned section identifies the

sources of household (HH) acquisitions in the GTAP model.

Sources of household acquisitions

In the GTAP model, it is essential to distribute total household expenditure on

commodity (i) in region (s) among different components. So the link is established

between the industrial output and household acquisition.

Where “VPA (i, s)” shows the value of the HH purchases at the agent’s price of ‘i’ in ‘s’,

which is the sum of HH expenditure on domestically produced goods (VDP) and



expenditure on the composite imports (VIPA) determined at the agent’ price. These

compositions of the value of private purchase, “VPA (i, s)”, can be easily changed into

the value of private purchase measured at the market price. Similarly, the government

acquisition can also be modeled in which the total value of government acquisition is

distributed between expenditure on domestically produced commodities and composite

imports.

Sources of Firm’s purchase

As discussed in previous section, the firm’s sale is distributed between firm’s sale to the

domestic market and also to the regional market. In the same way, the firm’s sale of ‘i’ of

region ‘r’ to region ‘s’ can also be expressed as expenditure on imports in ‘s’ which is

allocated between private household, government and firm’s expenditure. Firms in‘s’

acquire primary factors of production and also intermediate commodities from local

market. Therefore, total acquisition of firm in ‘s’ can be divided into firm’s expenditure

on domestic inputs and firm’s expenditure on imported inputs as highlighted in the below

section.

Value of firm’s Acquisition of ‘i’ in ‘s’ measured at agents’ price

“VFA(i,j,s)” in (4.8) shows for the value of firm’s acquisition in sector ‘j’ of input ‘i’ in

region ‘s’ measured at agent’s price. The two terms on the right hand side of the equation

are the two components of firm’s acquisition from local market and import from regional

market measured at the agent’s price. This can be expressed in terms of market price by



subtracting 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 form region ‘r’). The firm’s also acquire non-tradable

commodities such as land, labour and capital which are mentioned as endowment

commodities in the GTAP model. These can also be measured both at agent as well as

market price like mentioned above.

Relationship between firm’s receipts and acquisitions and the zero profit

In order to fulfill the zero profit requirement in the GTAP model, all receipts of the firm

should be equal to firm’s expenditure. That is;

On the right hand side of the equation, the first term is the firm’s expenditure on tradable

goods summed over ‘i’ tradable inputs and the second term is total expenditure on its

acquisition of the endowment goods measured at the agent’s price. “VOA ( j, s)” is the

firm’s total receipts by ‘sector ‘j’ in ‘s’ measured at the agent’s price from its sale of

output as mentioned in equation (4.1). The right hand side is the value of firm’s total

expenditures as showed in equation (4.8). By combining these two equations, the zero

profit condition in the GTAP model can be measured.

The households supply the factors to the firms and firms acquire those factors against payment

made to households. So the households received factor income. This relationship is explained in

the following section in detail.



II. Sources of HH factors service income

The endowment commodities can be divided into two groups in the GTAP model, the first is

mobile endowment (ENDWM_COMM) that earns same market returns and second is immobile

endowment (ENDWS_COMM) that earn differential returns. The associated income accounts of

both types of endowments are mentioned below.

Mobile Endowment –

VOA (i,s) is equal to the amount actually received by the private HH supplying the

factor to firms measured at the agent’s price. VOM (i,s) is equal to value of endowment

output measured at market price and HTAX ( i, s) is equal to HH income tax.

Immobile factor

The sluggish factors get differential returns across sectors. To manage this accounting

issue, these different returns are joined together into a single composite price/return of the

immobile factors measured at the market price, which can easily be presented in terms of

agent’s price as mentioned above. The below mentioned equation shows the value of

returns to households from the immobile factors measured at the market price.

Disposition and source of Regional Income



In the GTAP model, the regional household’s income should be spending on total

household expenditure. This income is generated in the region and provides to the

households in the same region and then distributed among different resources of

expenditure. That is;

The above equation fulfills the requirement that total regional income is spending on total

regional expenditure among different resources.

Border Intervention and Regional Income

The border intervention has two components which are border intervention by exports and

border intervention by imports in the GTAP. In this context, how the regional income is

determined is explained below.

Border intervention by exports and its impact on regional income

If subsidy is provided 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 reduces which is

presented below.

Conversely, when tax is imposed on exports of ‘i’ from region ‘r’ to region ‘s’, the

international price of exports is higher than the domestic price of exports. The



government collects revenue in the form of tax on exportable goods and therefore

regional income soars.

Border intervention by imports and its impact on regional income

If the market price of import commodities “PMS(i,r,s)” in the market (s) is higher than the

world price of import commodities PCIF(i,r,s), it shows the existence of import tax on

imports supplied from ‘r’ to ‘s’. In this scenario, the import border intervention positively

impact on the regional income. This scenario is presented in the below mentioned

equation.

Where ‘PMS(i,r,s)’ is the market price of imports of ‘i’ in region ‘s’ supplied from region

“r” and “PCIF (i,r,s)”(price based on the costs of insurance and freight) is the world

price of imports. Conversely, if the market price of import commodities “PMS(i,r,s)” in

the market (s) is less than the world price of import commodities PCIF(i,r,s), it shows the

reduction of import tax on imports supplied from ‘r’ to ‘s’. In this scenario, the import

border intervention negatively impact on to the regional income.

Global Sectors

The Global Transport Sector

In the GTAP model the accounting relationship of the global transport sector is the

difference between the values of exports of commodity ‘i’ supplied from region ‘r’ to



region ‘s’ measured at the world price (fob-based value) and the value of imports of the

same commodity 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

as shown in the below mentioned equation.

Where “VIWS(i,r,s)” is the value of imports of ‘i’ from ‘r’ to ‘s’ measured at the world

price and “VXWD(i,r,s)” is the value of exports from ‘i’ to ‘s’ at the world domestic price.

The global transport sector adds the regional exports of transport equipment and

insurance services to create combined transport commodity used to bring merchandise

among the regions. The individual regional economies exports the transport services to

the global transport sector and the suitable summation of these transport particular

commodities and all the routs, yields the total demand for world transport services which

is mentioned below.

Where,

VTWR ( i,r, s) is the total demand for transport services of ‘i’ shipped from ‘r’ summed

over the ith transported goods, over ‘r’ regions as well as summed over the supply by ‘s’

regions. “VST(i,r)”is the total supply of transport services supplied from region ‘r’ to

region ‘s’.

The Global Bank



In the GTAP model the ‘global banking sector’ plays the intermediary role between

global savings and global investment. The global bank generates a combined investment

commodity which is based on net portfolio investments of the respective regions. The

global bank proposes combined investment commodities to the regional households at

common price to fulfill their saving demands. According to Walras’ Law the global

savings must equal to global investment which fulfill the accounting relationship in the

GTAP model. This accounting relationship is presented in the following equation.

Where “GLOBINV” is global investment accumulated over ‘r’ regions. “REGINV(r)” is

investment of a single region and “VDEP(r)”is the value of depreciation of capital stock

of that region. Moreover, “SAVE(r)’is global savings accumulated over ‘r’ regions. In the

same way, in the GTAP model the value of ending capital stock is provided by the level

of capital stock in the region less depreciation. That relationship is presented as follows;

“VKE (r)” shows the value of ending capital stock, “VKB(r)” is the beginning period

level of capital stock, and “REGINV(r)” presents regional investment. The updated value

of ending capital stock can be measured by subtracting “VDEP(r)” depreciation from

regional investment and beginning period level of capital stock.



General Equilibrium Condition

In order to change the model into model of general equilibrium; the most of the economists have

favored to depict the equilibrium requirements in GTAP model in terms of quantities instead of

values. The accounting relationships in terms of values which have been discussed above

represent the equilibrium requirements that create the model a general equilibrium model in

nature. Therefore, these accounting relationships can easily be converted into quantities by

considering a common domestic price. For instance, the equilibrium requirement for traded

commodities is;

“VOM(i, r)” shows the total value of tradable goods in region ‘r’ measured at market price.

“VDM(i,r)” depicts value of tradable commodities sold in the domestic market and “VXMD( i,r)”

is the value of tradable commodities sold in the regional market added over ‘r’ regions. “VST (‘i’,

r) represents for the possible sales to the international transport sector. This accounting

relationship can also be depicted in terms of quantities by presenting a common price index as

mentioned below.

Dividing both sides by ‘PM ( i, r)’, the above equation can be shown in terms of quantities.



This concept can also be applied for non-tradable goods to confirm that all the accounting

relationship requirements in the GTAP model represent the essential general equilibrium

conditions and they are in-fact comprehensive relationships in nature.

Linearized depiction of accounting equations

In the GTAP model, the accounting relationships that have been discussed in above sections are

nonlinear in nature. Nevertheless, it is necessary to linearize the accounting equations for the

execution of the GTAP model. In 1991, Pearson had presented that the linearization of the non-

linearized model requires total differentiation of the equations. The linearized equations are

basically combination of the weighted price and quantity changes. If these transformed equations

are converted into value terms than the equations are multiplied by the common price. In the

GTAP model, the first accounting equation in this manner is the equation for the tradable market

clearing condition equation (1), by adding up the trade slack variable which is mentioned below.

Domestic market clearing condition for tradable commodities

In the GTAP model, the domestic market clearing requirements for tradable commodities

can be presented as follows.



The lower case variables (‘qo’, qds, qst, and qsx) present for the percentage changes in

the respective quantities multiplied by value of the respective variables measured at the

market price. “trade slack ( i, r)” is the trade slake variable which is introduced in the

accounting relationships to account for the additional supply over demand via endogen-

zing few of its selected components, when few of the market clearing requirements are

abolished. The variable is indexed over all tradable goods and all regions. The domestic

market for tradable goods can be divided into ‘domestic market for imports from region

‘r’ and ‘domestic market for domestically produced commodities’ in region‘s’, in the

GTAP model.

Market equilibrium in the local market for imports from region ‘r’

The below mentioned equation shows the local market clearing requirement for imports

from region ‘r’ in the linearized version with three components (value of imports by firms

in the ‘j’ sectors, value of imports by private households and value of imports by

government).

Market equilibrium in the local market for locally produced commodities

The market equilibrium requirement for locally produced commodities in the linearized

form is mentioned in the following equation.



The lower case letters presents the percentages changes in the respective variables

weighted by the values of respective quantities measured at the market price. The right

hand side variable presents the percentage in the quantity of the locally produced

commodities weighted by the value of the local commodities valued at the market price.

Equations (1) and (2) describe the percentage changes in imports of ‘i’ from ‘r’ and

locally produced commodities ‘i’ in‘s’.

Market clearing requirements for non-tradable endowment goods

The GTAP model divided primary factors (non-tradable endowments) into mobile and

immobile factors. The below mentioned two sub-sections explain the accounting

relationships and market clearing requirement in the GTAP model for the two kinds of

endowment goods.

Market clearing requirement for mobile endowment goods

“VOM ( i, r)” shows the total value of endowment goods measured at the common local

market price weighted by the percentage change. On the right hand side the first term is



the percentage change in total quantity of firm’s acquisition of the primary endowments

for sector ‘j’ in region ‘r’ weighted by the total value of firm’s acquisition of endowment

aggregated over ‘j’ sectors measured at the common local market price. The last term on

the right hand side is slack variable which shows fix rental price for the respective

endowment goods.

The market clearing requirement for immobile endowment goods

The below mentioned equation determines the percentage change in the immobile

endowment goods ‘i’ in sectors ‘j’ in region ‘r’.

There is no common price that exists for sluggish factors as like mobile endowments.

Therefore, no aggregate value can be identified. The accounting equation can be made by

equalizing the sectoral demand with the supply.

Zero profit requirement of the production sector

In the GTAP model the zero profit requirement can be presented in percentage form as

mentioned below.



‘ps’ shows for the percentage change in the local price of output weighted by the value of output

measured by agent’s price. On the right hand side “pf” is the percentage change in the price of

composite intermediate inputs aggregated over ‘i’ goods and “pfe” is the percentage change in

the price of endowment goods aggregated over ‘i’ endowments. The last term with profit slack in

this equation is used to abolish the zero profit requirement and fix output of the j sector in region

‘r’.

Similarly like above relationship, the zero profit requirement in the transport sectors can also be

presented in the percentage change form as mentioned below.

‘VT’ depicts the total value of transport services. The right hand side of the equation presents the

percentage change in the market price of transport services weighted by the value of transport of

‘i’ by region ‘r’ (VST) accumulated over ‘i’ and all regions.

Disposition of the regional income

In the GTAP model, the regional household is segregated into ‘private regional household, ‘firm’

and ‘government’. The regional income is distributed among these economic agents like first of

all deducting regional savings (firm’s expenditure) and government expenditure which are

exogenous in income accounts in the GTAP model and the remaining of the regional income is

allocated for private expenditure.



Price linkage equations

Differences between VOM and VOA:

The role of Income/output taxes is to make differences between the market value of

output and the value of output measured at the agent’s price, in the GTAP model. The

difference between the two values is called the power of the ad valorem tax which is

shown as follows;

‘TO(i,r)’ shows the tax rate, also termed as the power of the ad valorem tax which is

declared as the ratio of agent’s price to the market price18. In case of border taxes, it can

also be measured as the ratio of the market price to the world price. The percentage

change in the power of the ad valorem tax is calculated as mentioned below;

Where “to (i,r)” presents the percentage change in the power of the ad valorem tax and

this term is used in the GTAP model to create a relationship between the agent’s price

and the market price.

18If ‘TO’ > 1 then it implies subsidy and so the market price is greater than agent’s price and negatively contribute to

the government revenue. If less than 1, it is tax on output and the agent’s price is higher than the market price and so

it positively contributes to government revenue.



Price linking equation for VOA and VOM in the percentage change form

The following equation presents the percentage change in the power of the ad valorem

tax and creates relationship between the agent’s price and market price of outputs. In

other words it can be said that how VOM and VOA differ because of the income/output

tax.

‘ps ( i, r)’ and ‘pm( i, r)’ depict for the percentage changes in the agent and market price

of commodities.

Price linking equations for endowment commodities

Similarly, the percentage change in tax on the acquisition of endowment goods by firm in

sector ‘j’ of region ‘r’ is used to relate the agent and market prices. The below mentioned

equations present the relationship between the two prices of mobile and the immobile

endowments through the percentage change in the related tax rates.



Where ‘pfe ( i, j, r)’ depicts the percentage in the agent’s price of endowments and ‘pm( i,

r)’ and ‘pmes ( i, j, r)’ present for the percentage changes in the market price of mobile

and sluggish endowment goods.

Price linking equations for locally produced and tradable goods

In the GTAP model, the price linking equations for locally produced tradable and

importable goods can also be presented in the same way individually for each agent

“private HH, government and firms’ with their related tax rates as follows.

The price linking equations for imports of commodity ‘i’ from ‘r’ and agent’s price in

region ‘s are mentioned below:

Price linking equation for imported commodities



The below mentioned equation presents the percentage change in the local price of

tradable commodity ‘i’ in region ‘s’ (commodity imported from region ‘r’ to region ‘s’)

because of change in the border price of that commodity ‘pcif (i,r,s) and because of the

percentage change in the two kinds of border involvement (bilateral import tariff on

imports from ‘r’ and unilateral tariff by ‘s’ on the import of ‘i’ from ‘r’ to ‘s’). That is

why, the below mentioned equation divides the total percentage change in the local

market price of ‘i’ from ‘r’ to ‘s’ into three components, the percentage change in the

border price of these commodities, changes in bilateral tariff (tms ( i, r, s)) and change in

unilateral tariff (tm( i, s)).

In the GTAP model, the price ratio between the local price of ‘i’ and the price of the

composite imports is presumed fixed. In this context, Hertel and Tsigas (1997) presents

that in this kind of model closure, ‘tm (i, s)’ is permitted to vary so to fix the associated

price “pr (i,s)” to protect the local economy from variations in world prices. The below

mentioned equation depicts this phenomena.

Price linkage equation between ‘pcif (i,r,s)’ and ‘pfob (i,r,s)’



The below mentioned equation presents how the change in the ‘pcif (i,r,s)’ differ from

change in the ‘fob’ price.

Where ‘pcif ( i, r, s)’ shows for the percentage change in ‘cif’-based price (costs of

insurance and freight) of ‘i’ shipped from ‘r’ to ‘s’. ‘pfob ( i, r, s)’shows the percentage

change in ‘fob’ (free on board)-based price of ‘i’ transported from ‘r’ to ‘s’ and ‘pt’

shows the percentage change in the price of transportation services. ‘FOBSHARE’ and

‘TRNSHARE’ depict the shares of ‘fob’ costs and transport costs in total ‘cif’ costs

respectively.

Price linkage between ‘pfob(i,r,s)’ and ‘pm(i,r)’

Similarly, the relationship between the ‘fob’ price of ‘i’ and the local market price of ‘i’

in ‘r’ can be created which is depicted in the following equation.

‘pm( i, r)’ is the local price of ‘i’ in ‘r’, while, ‘tx (i, r)’(destination generic) and ‘txs(ir,s)

(destination-specific) are the two kinds of export taxes as mentioned in the case of

imports taxes above.



Behavioral Equations

Firms’ Behavior

In this section, the goal is to discuss about the firm’s behavior of acquiring and using

intermediate inputs. Since, firm acquires the intermediate inputs, few of which are locally

produced and few are imported commodities and then join them with the primary factors

of production to produce finished product. In the GTAP model, firm’s import of

intermediate inputs should be sourced from exporters to create the related accounting

equation coherent. Furthermore, firm’s employ combination of primary and intermediate

goods is based on the supposition of segregation in production both in terms of primary

factors and intermediate inputs as well as segregation between local and imported

intermediate inputs, in the GTAP model. The below mentioned equation discusses the

behavioral equation for imports and sourcing of imports.

Behavioral equation for imports and sourcing for imports

Where ‘pim(i, s)’ presents the percentage change in the composite price of imported

commodities in region ‘s’.“MSHRS ( i, k, s)” is the contribution of import of ‘i’ from

region ‘r’ to region ‘s’ in total composite imports in region ‘s’. Moreover, “pms ( i, k, s)”

is the market price of any specific good ‘i’ imported from ‘r’ to ‘s’ accumulated over ‘k’

regions. This equation depicts that how the composite price of imports fluctuates with the

fluctuations in the price of any specific imported commodity of ‘i’ from region ‘r’ to ‘s’.



The below mentioned equation presents the sourcing of imports as mentioned above that

imports from ‘r’ to ‘s’ should be sourced from exporters, to create the accounting

relationships consistent.

Where ‘qxs ( i, r, s)’ shows the sourcing of imports or, it is the percentage change in the

quantity of exports of ‘i’ from ‘r’ to ‘s’. ‘qim(i, s)’ depicts the percentage change in the

quantity of imports of ‘i’ in region ‘s’. The idea behind equation (22) is that this equation

divides the change in ‘qxs ( i, r, s)’ into two components, the ‘substitution impact’ which

is the product of the constant elasticity of substitution ‘δ’ and the percentage change in

the associated price of importable commodity. That is; the ratio of combine price of

imports in region “s” to the price of specific importable commodities from ‘k’ regions to

region ‘s’. The second part is the level impact presented by ‘qim(i, s)’.

Behavioral equation for firm’s demand for composite intermediate inputs

This section explains the requirement of the firm’s Constant Elasticity of Substitution

(CES) demand equation for combination of intermediate inputs in the region‘s’. This

demand function can be derived in a way that it is essential to create price index for

combination of intermediate inputs that gives the basis for deriving the input demand

function. The below highlighted equation presents the percentage change in the

composite price of intermediate inputs divided into change in the price of importable

intermediate inputs and change in the price of locally produced intermediate inputs



weighted by their respective contributions in the firm’s total demand for intermediate

inputs.

Where ‘pf ( i, j, r)’ shows the percentage change in the price of tradable inputs, ‘FMSHR(

i, j, r)’ is the contribution of imports of intermediate inputs in firm’s total demand for

intermediate inputs ‘i’ in sector ‘j’ of region ‘r’. The lower case notations of prices depict

the percentage change in the price of importable intermediate inputs and locally produced

intermediate inputs.

Firm’s derived demand for imported and locally intermediate inputs

As created the price index in equation (23), the firm’s conditional import demand for

intermediate inputs is depicted below. This equation has the similar interpretation as equation

(22)

The equation for firm’s demand for locally produced intermediate inputs can also be developed

in similar way with the similar interpretation and employing the similar price index as mentioned

in ‘equation (23)’, shown below.



Firm’s demand for endowment commodities and value-added nest of producers

The following equation depicts the percentage changes in the composite price of the value added

‘pva(j,r), that creates the price index to derive the related firm’s input demand function.

Where ‘SVA(i, j, r)”shows the contribution of primary endowments in total costs of the value-

added of producers. While “afe (k, j, r)” presents the term that captures the impact of

augmenting technical change on the primary factors. When ‘afe (k,j,r) > 0’, then it leads to

decrease in the effective price of primary factor ‘i’ leading to a decline in the costs of the value

added as mentioned in the equation (26). This decline in the effective price of primary

commodities leads to substitution of ‘i’ for other primary endowment goods as mentioned in the

following equation. The price index, in equation (26) is used to derive firm’s demand for

endowment commodities in the value-added nests.

The left hand side of the above equation presents the percentage change in firm’s demand for

intermediate inputs in the value-added nests. The right hand side of the equation has similar

interpretation like firm’s derived demand form endowment goods that mentioned above.

Total output nest and demand for composite value-added and intermediate inputs



These equations present firm’s demand for composite value-added and intermediate

inputs in the total output nests with the presumption of no substitutability between

intermediate inputs and the value added. Therefore, there is no relative price part in the

firm’s conditional input demand. Furthermore, three technical alterations are established

in firms’ input demand equation to account for the effect of technological enhancement in

firms’ input demand. The first one is the input augmented technical change in the value

added of sector ‘j’ in region ‘r’ ava ( j, r), which enhances firm’s demand for the value

added. The Second is “af ( i, j, r)”presents the input augmented technical change in

intermediates of ‘i’ in region ‘r’ of sector ‘j’. The third technical change is the Hicks-

neutral technical change (1932) “ao( j, r)of sector ‘j’ in region ‘r’ which decreases input

condition to produce a certain level of output.

Zero profit nested

The below mentioned equation reveals the zero profit requirement and depicts the effect of

technological change on the composite price of output. Therefore, the equation is intended to

measure the output price.



The firm’s behavior and Trade policy shock

This section explains the interacting firm’s behavior regarding the trade policy reform (declining

in tariff rate) using the framework of linearized behavioral equations which are interlinked with

each other. If the bilateral tariff rate on import of ‘i’ from ‘r’ to ‘s’is declined, the local market

price of imports in region ‘s’ reduced through equation (17). As the relative price of imports

reduces, local consumers will substitute the cheaper imports by competing imports. This reflects

in equation (22). The composite price of imposts faced by sector ‘j’ in region ‘r’ also reduces

because of decline in the bilateral tariff shown in equation (16) and (21) leading to rise in the

aggregate demand for imports as presented in equation (24). Low price import will yield decline

in the price of intermediate inputs through equation (23). The low price of intermediate inputs

will yield surplus profit for firms through equation (6). This surplus profit increases total output

through equation (28) which will lead to an increase effect presented in equation (29). Since the

increase effect, firm’s demand for primary factors enhances through equation (27). This will lead

to increase in the demand for mobile factor through equation (4) and therefore, the price of

primary endowment will rise.

Conversely, decline in import tariff rate on the commodities supplied to region ‘s’, produced in

region ‘r’ yields a rise in firm’s sale of ‘i’ from ‘r’ to ‘s’. This phenomenon is presented in



equation (22). Consequently, there will be increased in total output of ‘i’ in region ‘r’, which is

depicted in equation (1).

Household Behavioral equations

The regional households’ behavior is modeled in the GTAP as the aggregate utility function

which included private consumption, government consumption and saving. In fact according to

the Cobb-Douglas per-capita utility function the regional household disposes of the regional

income which are divided into three final demand such as private consumption, government

expenditure and savings as depicted in the below mentioned equation19.

The lower case letters such as, (u(r)), up(r) and ug(r) etc) presents percentage changes in the

relevant utilities. The change in the real expenditure on savings and government behavior in

region ‘r’ is determined by regional income and prices of the related variables in the GTAP

model. The below mentioned equations explains this accounting phenomena.

19 The three forms of final demand are private expenditure, government expenditure and savings with a constant

share in total regional income.



These equations consist of slack variables which are presented in turn to handle market clearing,

if the qsave and ug are considered as exogenously. Under this closure, when the regional income

exhausted, private household expenditure changes accordingly. To determine the percentage

change in the real government expenditures, the next step is to create aggregate price index for

the government purchase depicted in equation (34) mentioned below. This index gives the

foundation for obtaining the government’s required demand for combined tradable commodities

as mentioned in equation (35) below.

In order to distribute the combined demand mentioned in equation (35) between imports and

locally produced commodities, it is required to create a price index which will be base for such

distribution. This index presents the percentage change in the composite price of government

acquisition is a linear combination of the percentage change in the price of imports weighted by

the share of imports in total government acquisitions and the percentage change in the price of

locally produced commodities weighted by the share of locally produced commodities in total

government of tradable commodities.



Using the above combined price index, the government combined demand for tradable

commodities can be decomposed into demand for imports and demand for locally produced

commodities as depicted in the below mentioned equations.

Private Household Demand

The demand of private household can be derived from the utility function. Nevertheless, the

private household utility function takes into account the population growth. Therefore, the

percentage change in utility of private households is defined as per capita basis in the GTAP

model. In 1975, Hanoch argued that this kind of percentage change in utility is measured from

the constant difference elasticity functional form of utility. This specific functional form lies in

the midway between CES and fully flexible functional forms. Furthermore, the Constant

difference of elasticities (CDF) expenditure function in the implicit form is depicted by the

below mentioned identity.

Where “(E()” presents the minimum expenditure essential to obtain a certain level of private

household utility “UP(r)” given prices faced to the private household PP(r ). “” and “ᵧ” are

chosen to replicate the compensated own price elasticity of demand and the targeted income



elasticity of demand which is also called the calibration problem in the CGE model.

Differentiation of the above equation and applying Shepherd’s lemma provides expression for

private household demand as presented below.

The below mentioned equation determines the private household demand for composite tradable

commodities.

Macroeconomic Closure

The operationalizing of GATP database and GTAP model needs the fixation of a number of

macroeconomic closures. The GTAP model is not inter-temporal and not sequenced series

through time that obtain many equilibria. Therefore, investment cannot influence any industry or

region in the model as it does not come next period in the GTAP model. According to Sen

(1963), this indeterminacy of investment is called the problem of macroeconomic closure. To

resolve this issue, Dewatripont and Michael (1987) proposed to allow investment to alter

changes in savings, thus to retain savings-investment equality. Hertel (1997) pointed out the fact

that few of the applied general equilibrium models have no global closure for investment and

savings. These models fixes the current account balance (CAB) instead as macroeconomic

closure to force domestic savings to move in tandem with changes in investment. For this

purpose Dornbush (1980) had proposed the below mentioned macroeconomic equation.



S - I X + R - M

This equation explains that the current account balance is equal to the difference between

regional savings and investment. Since in the GTAP model there is no account for ‘R

(international transfer receipts)’, therefore, it is considered as equal to zero. In this scenario,

fixing the trade balance is equivalent to fixing the saving-investment equality as depicted in the

below mentioned equation.

By fixing the right hand side of the above equation on the regional basis, equality between

savings and investment is created even though formally there is no international bank to

intermediate between savings and investment at the international level. By doing so, investment

is forced to alter with changes in regional savings. Thus, as pointed out by Dewatripont and

Michel (1987) this approach of defining the macroeconomic closure (exogeneity of current

account balance) is neoclassical in nature. Conversely, the international bank in GTAP model is

presumed to acquire shares in the regional portfolio investment through its sale receipts from sale

of the homogenous savings to the regional households. The size of this portfolio investment of

the international bank is altered with changes in savings and therefore the international closure in

the model is of neoclassical in nature.

International Transportation



The regional transport services are combined to form a single composite transport commodities

related with its combined price index in the GTAP model. This price index is used to determine

the conditional input demand in the shipping servicing sector in the framework of the Cobb-

Douglas technology. The combined price index for transport services is mentioned as follows;

The conditional input demand function in the shipping service sector based on the above

combine price index is mentioned below;

43)

The two terms on right hand side of (43) are the expansion effect and substitution effect.

Use of transport services

It is presumed that the composite transport service are engaged in fixed proportion for shipping

good ‘i’ from ‘r’ to ‘s’ in the GTAP

‘qt’ is the percentage change in ‘QT’ where, ‘QT’‘ is the amount of homogenous commodities

used in shipping of commodities from ‘r’ to ‘s’ gathered over ‘i’ tradable commodities, ‘r’

regions and ‘s’ regions. That is;



By differentiating both sides of the above equation and then multiplying by the common

combined price index of transport services “equation (43)” yields equation (44) and substituting

[qxs ( i, r, s) - atr ( i, r ,s)] = qts (i, r, s), where ‘qts’ is the percentage change in the amount of

homogenous transport commodity used in shipping services. ‘atr’ presents for technical change

in transport services. The intuition for the presence of this term in the above formulation is that it

permits to capture the impact of technical change in the commodity route-specific transport

service as this change lower ‘cif’’ price of commodities shipped for given ‘fob’ price.

Introducing technical change in the transport services, equation (19) is modified as mentioned

below.

Summary indices

Equivalent Variation

The regional EV(r) is measured through below mentioned equation in the GTAP model.

EV(r) = u(r) * INC(r)/100

Where, “u(r)” presents for per capita welfare and “INC(r)” depicts the value of regional

expenditure in the initial equilibrium which must be equal to the regional income. Based on the

above equation, the regional equivalent variation is provided by the below mentioned equation.

This equation includes the change in population and so depicts the total regional welfare.



The worldwide ‘EV’ can be measured by the summation of the regional ‘EV’s as mentioned

below.

As mentioned above, the change in welfare for each region is the “equivalent variation,” i.e., the

change in money income that would produce the same effect on the region’s utility as the policy

shock. The GTAP model also easily decomposes the welfare change into five sources: (i)

allocative efficiency, (ii) endowment effects, (iii) technical changes, (iv) terms of trade effects,

and (v) investment-savings effects.

Regional Consumer Price Index

In the GTAP model the regional particular consumer price index is mentioned below;

Here “ppriv(r)” presents for change in consumer price index weighted by the regional private

expenditure. “pp(i,r)” depicts the percentage change in the price of ‘i’ weighted by the total

demand for commodities ‘i’ accumulated over ‘i’ in region (‘r’ VDA (i, r)’)

Quantity Index for Local Product

In the GTAP model, in turn to measure the quantity index for local product, the difference

between the value index and a price index is measured. The value index accounts for change in

both quantity and prices and is presented by the below mentioned equation.

The Value Index



Double summation means accumulated over commodities and regions. The price index accounts

for changes in prices and is measured as mentioned below.

Price index for local product

Thus the quantity index is measured as mentioned below.

Quantity indices for changes in the aggregate trade values

The below mentioned equations measure indices that detain the percentage changes in the values

of exports and imports by good and region, by region for all traded goods and by good for all

region in the world.

Value Index for region ‘r’ for all ‘i’



The below mentioned equation presents the value index for exports of ‘i’ from ‘r’ to ‘s’

aggregated over goods ‘i’.

The right hand side of above equation is the percentage change in value of exports of ‘i’ from ‘r’

measured at “fob” price weighted by the value of exports of ‘i’ from ‘r’ measured at the

exporters’ local market price. The remaining of the interpretation is similar as mentioned above.

Value of index of imports for region‘s’ aggregated over ‘i’ commodities

Similarly as mentioned in equation (57), the import value index for sth region aggregated over all

importable commodities is mentioned in following equation.

The right hand side of the above equation is the percentage change in total value of imports of ‘i’

by ‘s’ measured at “cif” price weighted by the value of imports of ‘i’ to ‘s’ measured at the

importers’ local market price. This equation measures the value index for imports to region ‘s’

which reflects the percentage change in the value of imports as result of import liberalization.

Value index of export of good ‘i’ accumulated over ‘r’ regions

The right hand side of the above value index is the percentage change in the value of exports of

‘i’ from ‘r’ measured at the ‘fob’ price weighted by the value of exports accumulated over ‘r’

regions. This equation depicts the percentage change in the total exports of ith good measured at

the local market price of exporters accumulated over ‘r’ regions.

Value index of import of good ‘i’ accumulated over‘s’ regions



Similarly as mentioned in equation (59), the above mentioned equation measures the percentage

change in the total value of imports of the ‘ith’ good measured at the local market price of

importer country accumulated over the ‘s’ regions.

Value index for world trade

Value index for world output

Price indices for changes in the aggregate trade values

In a same way, like the quantity indices that are mentioned above, the price indices for the

accumulate trade values can also be expressed in the below mentioned equations.

Price index for exports of by good and region

)

Price index for imports by good and by region

)

Price index for exports by region for all goods

Price Index for imports by region for all goods

Price Index for exports for all goods and all regions



Price Index for imports for all goods and by all regions

Price index for total world Trade

Price Index for world output

The above equation presents the percentage changes in the world output measured at the world

prices.

Indices of percentage changes in the aggregate trade and output

The below mentioned equations measured indices that account for the percentage changes in the

volume of aggregate trade and output by good and regions.

Volume index for all exports by region ‘r’

Volume index for all imports by region ‘s’



Volume Index for world exports of good ‘i’

Volume Index for world imports of good ‘i’

Volume Index for Total world Trade

Volume Index for total World Output

Value Index for Trade Balance by ‘i’ and ‘r’

The below mentioned equation measures the percentage change in the trade balance for good (i)

and region (r).

Value Index for the current account for each region



Appendix- 5- Harmonized System (HS) 6 codes used in the Simulation

Simulated Sectoral Effects of the Pakistan China FTA on Pakistan (% change)-Table # 5.9

Pakistani major exports products to China are Plant-based fiber- cotton, not carded or

combed (HS-520100). Textile sector HS 6 digit includes Cotton yarn (HS-520512), Cotton yarn

(HS-520511), Cotton yarn (HS-520532), Cotton yarn (HS-520513), Cotton yarn (HS-520522),

Twill weave cotton fabric, unbleached (HS-520912), Cotton yarn (HS-520531), Bed linen, of

cotton, nes (HS-630231), Cotton yarn (HS-520524), Cotton yarn (HS-520523), Plain weave

cotton fabric, unbleached (HS-520812), Plain weave cotton fabric, unbleached (HS-520911).

Wearing apparel comprise of Womens/girls trousers and shorts, of synthetic fibers, not knitted

(HS-620463), Mens/boys swimwear, of textile materials not knitted (HS-621111), Babies

garments and clothing accessories of cotton, not knitted (HS-620920). Vegetable oil and fats-

Flours and meals of oil seeds or oleaginous fruits, except mustard, nes (HS-120890), Oil-cake

and other solid residues, whether or not ground (HS-230641). Metals products- Waste and

scrap, copper or copper alloy (HS-740400), Slag, dross, (granulated slag) scaling & other waste

etc (HS-261900). Leather product sector- Leather further prepared after tanning or crusting

"incl. parchment-dr (HS-411310). Chemical products consist of Polyethylene waste and scrap

(HS-391510) and Plastics waste and scrap nes (HS-391590).

Simulated Sectoral Effects of the Pakistan China FTA on China (% change)-Table # 5.10

China’s major exports products to Pakistan are Textile- Staple fibers of polyesters, not

carded or combed (HS-550320), Textured yarn nes, of polyester filaments, not put up for retail

sale (HS-540233), Carpets of other textile material, of woven pile construct (HS-570239),

Blankets (o/t electric) and travelling rugs, of synthetic fibers (HS-630140), Yarn of viscose rayon

filaments, single, untwisted, nes (HS-540331), Filament yarn of polyester, incl. monofilament

(HS-540247), Yarn of viscose rayon filaments (HS-540332), Woven fabrics of artificial staple

fibers, printed, nes (HS-551694), Pile knitted or crocheted fabrics, of man-made fibers, nes (HS-

600192), Woven fabrics of cotton (HS-521119), Plain weave cotton fabric, bleached (HS-

520921), Woven fabrics of cotton, printed, nes (HS-521059), Textile fabric impregnated or

laminated with polyvinyl chloride ,nes (HS-590310), Sewing thread of artificial filaments (HS-

540120), Woven fabrics, containing artificial staple fibers, printed (HS-551614), Staple fibers of

viscose, not carded or combed (HS-550410), Metalized yarn ,beg textile yarn combined with

metal thread ,strip/powder (HS-560500), Nonwovens nes (HS-560394), Woven fabrics of silk,

nes (HS-500790), Synthetic filament elastomeric yarn, single, untwisted or with a twist (HS-

540244), Textile fabrics impregnated or laminated with plastics, nes (HS-590390), Woven

fabrics,>/=85% of textured polyester filaments, dyed, nes (HS-540752), Woven fabrics,

containing>/=85% of artificial staple fibers, dyed (HS-551612), Textile fabrics impregnated or

laminated with polyurethane ,nes (HS-590320), Dyed fabrics, knitted or crocheted, of synthetic

fibers (HS-600632), Tire cord fabric made of nylon or other polyamides high tenacity yarns (HS-

590210), Plain weave cotton fabrics (HS-520851), wood products- Multi-ply paper and

paperboard, coated on one or both sides with kaoli (HS-481092), Paper and paperboard, surface-

coloured, surface-decorated or printed (HS-481159), Paper and paperboard used for writing,

printing or other graphic (HS-481013) petroleum and coal products- Propane, liquefied (HS-



271112), Other petroleum oils and preparations (HS-271019), chemical products-

Urea/ammonium nitrate mix in aqueous/ammoniac (HS-310280), Polyether’s nes (HS-390720),

Pneumatic tires new of rubber for buses or Lorries (HS-401120), Urea, in aqueous solution in

packages (HS-310210), Diammonium phosphate, in packages (HS-310530), Insecticides (HS-

380891), Lysine and its esters; salts thereof (HS-292241), Amino-acids nes, and their esters; salts

thereof (HS-292249), Nucleic acids and their salts, whether or not chemically defined (HS-

293499), Methionine (HS-293040), Acrylic polymers nes, in primary forms (HS-390690),

Titanium pigments and preps, >80% titanium oxide (HS-320611), Heterocyclic compounds

containing a phenol thiamine ring-system and further fused (HS-293430), Vat dyes and

preparations based thereon (HS-320415), Mixed alkyl benzenes and mixed alkyl naphthalene’s

produced by the alkyl (HS-381700), Herbicides, anti-sprouting products and plant-growth

regulators (HS-380893), Heterocyclic compounds containing pyramiding ring/piperazine ring,

nes; nucleic acid (HS-293359), Citric acid (HS-291814), Chemical/allied industry

preparations/prods nes (HS-382490), Synthetic organic pigments and preparations based thereon

(HS-320417), Articles of plastics or of other materials nes (HS-392690), Algonac acid, its salts

and esters (HS-391310), Hair shampoos (HS-330510), Boxes, cases, crates & similar articles of

plastic (HS-392310), Organo-sulphur compounds, nes (HS-293090), Gramophone records and

other media for the recording of sound (HS-852380), Phosphoric acid and polyphosphoric acids

(HS-280920), Calcium carbide (HS-284910), Film and sheet etc, cellular of polyurethane (HS-

392113), Polyvinyl alcohol (incl. w. unhydrolysed acetate g) (HS-390530), Polyvinyl chloride,

not mixed with any other substances (HS-390410), metal products- Water tube boilers with a

steam production (HS-840211), Structures & parts, alum, eg plate, rods (HS-761090), Electric

conductors (HS-854460), Auxiliary plant for use with steam or vapor generating boilers nes (HS-

840410), Rock drilling/earth boring tools, working part cermets (HS-820713), Structures &parts

of structures (HS-730890), Vapour generating boilers nes, including hybrid boilers (HS-840219),

Cans, iron or steel (HS-731029), Flat rolled stainless ,in coils (HS-722530), Cold rolled

iron/steel, coils (HS-720917), Flat rolled plated or coated with zinc, nes (HS-721049), Flat rolled

stainless steel (HS-721931), Flat rolled product/nas, plated (HS-721012), Flat rolled not further

worked than hot rolled, nes (HS-720890), Hot rolled bar/rod, irregular coils (HS-721391),

Casings, tubing, drill pipe, for oil drilling use (HS-730429), Semi-fin cross-sect (HS-720711),

Flat rolled prod, stainless steel, 600mm or more wide, nes (HS-721990), Drill pipe, seamless, of

a kind used in drilling for oil or gas (HS-730423), Semi-finished products of alloy steel o/t

stainless (HS-722490), Hot roll iron/steel nes (HS-720838), Tubes ,pipe &hollow profiles, cross

section ,nes (HS-730431), Hot roll iron/steel, not coil (HS-720851), Flat rolled stainless steel

(HS-721935), Cold rolled iron/steel, coils (HS-720918), Tubes, pipe& hollow profiles ,stainless

steel of circ cross sect ,nes (HS-730449), Bars &rods ,alloy steel stainless , irregularly wound

coils ,nes (HS-722790), Hot rolled iron/steel, coils (HS-720810), auto parts- Parts for spark-

ignition type engines nes (HS-840991), Wheels including parts and accessories for motor

vehicles (HS-870870), Road tractors for semi-trailers (truck tractors) (HS-870120), Vessels and

other floating structures for breaking up (HS-890800), Rail locomotives, diesel-electric (HS-

860210), Locomotive parts nes (HS-860791), Motorcycles with reciprocate piston engine (HS-

871120), Motorcycles with other than a reciprocating piston engine (HS-871190), Aircraft nes of

an unlade (HS-880240), Railway cars nes (HS-860699), machinery and equipment- Electric

generating sets (HS-850239), AC generators (HS-850164), Machinery, plant/laboratory equip f

treat of mat by change of temp nes (HS-841989), Transformers electric have a power (HS-



850434), Static converters, nes (HS-850440), Electro-thermic hair dryers (HS-851631), Boards,

panels, include numerical control panels (HS-853710), Transformers electric power handling

capacity (HS-850431), Parts of electric motors, generators, generate sets & rotary converters

(HS-850300), Parts of gas turbines nes (HS-841199), Electric conductors (HS-854449), Liquid

dialect transfer (HS-850422), Electric lamps and lighting fittings, nes (HS-940540), Hydraulic

turbines and water wheels (HS-841013), Compressors of a kind used in refrigerating equipment

(HS-841430), Parts of machinery, plant and equipment (HS-841990), Instruments and appliances

used in medical or veterinary sciences, nes (HS-901890), Survey, hydrographic, oceanographic,

meteorological/geophysical inst nes (HS-901580), Centrifuges nes (HS-842119), Electric

conductors for a voltage insulated, fitted (HS-854442), Machines & mechanical appliances nes

having individual functions (HS-847989), Gas supply, production and calibrating meters (HS-

902810), Bearings, ball (HS-848210), Parts of electro-thermic apparatus (HS-851690), Mach f

mixing/kneading/crushing/grind etc nes have individual function (HS-847982), Valves, pressure

reducing (HS-848110), Needles, catheters, canola and the like, nes (HS-901839), Clutches and

shaft couplings (including universal joints) (HS-848360), Electric table, desk, bedside or floor

standing lamps (HS-940520), Medical, surgical, dental or veterinary furniture and parts nes (HS-

940290), Electrical machines and apparatus, having individual functions (HS-854370), Electrical

switches for a voltage not exceeding 1,000 volts, nes (HS-853650), Syringes, with or without

needles (HS-901831), Valves for oleo hydraulic or pneumatic transmissions (HS-848120),

Insulating fittings for electrical mach appliances or equipment, nes (HS-854790), Machinery for

cleaning or drying bottles or containers nes (HS-842220), Engines and motors nes (HS-841280),

Sewing machines, other than book-sewing machines, nes (HS-845229), Fans: table, roof etc w a

self-control (HS-841451),Taps, cocks, valves and similar appliances, nes (HS-848180), Electric

space heating apparatus & electric soil heating apparatus ,nes (HS-851629), Parts of electrical

ignition or starting equipment (HS-851190), Extruders for working rubber or plastics nes (HS-

847720), paddy rice- Rice in the husk (paddy or rough) (HS-100610).

Simulated Sectoral Effects of the Pakistan Malaysia FTA on Pakistan (% change)-Table#

5.14

Pakistani major exports to Malaysia are Processed rice are Rice, semi-milled or wholly milled,

whether or not polished or glazed (HS-100630) and Rice broken (HS-100640) , Beverages &

Tobacco includes Tobacco, unmanufactured, partly or wholly stemmed or stripped (HS-

240120), Tobacco extracts and essences (HS- 240399), Cigars, cheroots and cigarillos,

containing tobacco (HS-240210), Tobacco refuses (HS-240130), Non-alcoholic beverages nes,

exclude fruit/vegetables juices (HS-220290). The textiles HS-6 digits items are Cotton yarn (HS-

520511), Bed linen, of cotton, nes (HS-630231), Cotton yarn (HS-520512), Bed linen, of cotton,

printed, not knitted (HS-630221), Toilet &kitchen linen, of terry towel or similar terry fabric, of

cotton (HS-630260), Bed linen, of other textile materials, nes (HS-630239), Cotton yarn (HS-

520513), Cotton yarn, single, combed, not put up (HS-520522), Cotton, not carded or combed

(HS-520100), Cotton yarn ,single, combed, not put up (HS-520523), Plain weave cotton fabrics

printed (HS-520851), T-shirts, singlet and other vests, of other textile materials, knitted (HS-

610990), Bed linen, of textile knitted or crocheted materials (HS-630210), Full-length or knee-

length stockings, socks and other hosiery (HS-611595), Cotton yarn (HS-520521), T-shirts,

single and other vests, of cotton, knitted (HS-610910), Woven fabrics, containing of acrylic



staple fabric (HS-551229), Made up articles, of textile materials, nes, including dress patterns

(HS-630790), Ornamental trimmings in the piece knit, tassels, pompons & similar art (HS-

580890), Carpets of other textile materials, knotted (HS-570190), Sacks & bags, for packing of

goods, of other man-made textile materials (HS-630539) and wearing apparel HS-6 digits

include articles of apparel of leather or of composition leather (HS-420310), Shawls ,scarves,

veils & the like, of other textile materials, not knitted (HS-621490), Men/boys trousers and

shorts, of cotton, not knitted (HS-620342), Women/girls suits, of synthetic fabric, not knitted

(HS-620413), Garments nes, of other textile materials, knitted (HS-611490), Track suits, of

synthetic fabric, knitted (HS-611212), Gloves, mittens and mitts, nes, of cotton, knitted (HS-

611692). chemical products include Gelatin and gelatin derives; is in glass; glues of animal

origin, nes (HS-350300), Hydrogen chloride (hydrochloric acid) (HS-280610), Vinyl chloride

(chloroethylene) (HS-290321), Beauty or make-up preparations nes; sunscreen or sun tan

preparations (HS-330499), Insecticides (HS-380891), Phosphates of metals nes (HS-283529).

Simulated Sectoral Effects of the Pakistan Malaysia FTA on Malaysia (%change)-Table#

5.15

Malaysia’s major exports to Pakistan are vegetable oil and fats- Palm oil and its fractions

refined but not chemically modified (HS-151190), Palm oil, crude (HS-151110), Palm nut/kernel

oil-cake &other solid residues, whether/not ground/pellet (HS-230660), Vegetable fats &oils

&fractions hydrogenated (HS- 151620), Edible mix/prep of animal/vegetable fats& oils/of

fractions (HS-151790), Coconut (copra) oil& its fractions refined but not chemically modified

(HS-151319), textiles- Filament yarn of polyester, incl. monofilament (HS-540247), Textured

yarn nes, of polyester filaments, not put up for retail sale (HS-540233), Yarn of polyester staple

fibers mixed (HS-550951), Textured yarn nes, of nylon/other polyamides (HS-540232), wood

products- Medium density fiberboard MDF of wood, of a thickness (HS-441112), Medium

density fiberboard MDF of wood, of a thickness (HS-441114), Lumber, Meranti nes, Lauan,

Seraya, alan sawn (HS-440726), Fiberboard of wood or other ligneous materials (HS-441193),

paper products- Self-adhesive paper and paperboard, surface-coloured, surface-decorate (HS-

481141), process food - Fowl (gallus domesticus) meat, prepared/preserved (HS-160232), Food

preparations nes (HS- 210690) , Malt extract &food cocoa (HS-190190), processed rice- Rice,

husked (brown) (HS-100620), Rice, semi-milled or wholly milled, whether or not polished or

glazed (HS-100630), beverages and tobacco- Non-alcoholic beverages nes, exclude

fruit/vegetables juices of (HS-220290), Tobacco, unmanufactured, not stemmed or stripped (HS-

240110), and auto parts- Vessels and other floating structures for breaking up (HS-890800),

Parts and accessories of bodies nes for motor vehicles (HS-870829).

Simulated Sectoral Effects of the Pakistan Sri Lanka FTA on Pakistan (% change) Table#

5.19

Pakistani major exports to Sri Lanka are Beverages and Tobacco includes the item of HS-6

digit is Undenaturd ethyl alcohol of an alcohol (HS-220710). Textile sector items of HS-6 digit

are Plain weave cotton fabric, unbleached (HS-520911), Denim fabrics of cotton (HS-520942),

Dyed cotton fabrics, knitted or crocheted (HS-600622), Twill weave cotton fabrics (HS-520932),

Twill weave cotton fabrics printed (HS-520952), Denim fabrics of cotton (HS-521142), Cotton



sewg thread cotton, not put up for retail sale (HS-520411), Plain weave cotton fabric (HS-

521031), Woven fabrics of cotton nes (HS-520839), Twill weave cotton fabric unbleached (HS-

520912), Woven fabric of cotton (HS-521019), Plain weave cotton fabric unbleached (HS-

520811), Bed linen, of textile knitted or crocheted materials (HS-630210), Woven fabrics of

cotton (HS-521211), Cotton yarn (HS-520533), Plain weave cotton fabrics (HS-520931), Woven

fabrics of cotton, printed, nes (HS-520959), Twill weave cotton fabrics, bleached (HS-

520922),Twill weave cotton fabric, unbleached (HS-520813), Plain weave polyester staple fib

fabric, printed (HS-551341), Plain weave cotton fabric, unbleached (HS-520812), Woven fabrics

of cotton, dyed, nes (HS-521223). Wearing apparel includes Men/boys garments nes, of cotton,

not knitted (HS-621132). Ferrous metals of HS-6 digit such as Tubes, pipe & hollow profiles,

iron or steel, welded, nes (HS-730690).processed rice consist of Rice, broken (HS- 100640),

vegetable, fruits and nuts comprise of Mandarins (tang& sats) clementines &wilkgs &sim

citrus hybrids, fresh/drid (HS-080520) and mineral products nec comprise of Portland cement

nes (HS-252329), Float glass etc in sheets, non-wired nes (HS-700529), Hydraulic cement nes

(HS-252390).

Simulated Sectoral Effects of the Pakistan Sri Lanka FTA on Sri Lanka (% change) -

Table # 5.20

Sri Lanka’s major exports to Pakistan are Vegetable oil & fats includes Coconut (copra) oil &its

fractions refined but not chemically modified (HS-151319) and wood products comprise of

Medium density fiber board MDF of wood, of a thickness (HS-441112), Medium density fiber

board MDF of wood, of a thickness (HS-441114), Medium density fiber board MDF of wood, of

a thickness (HS- 441113).

the impact of free trade agreements on macroeconomic and...

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