A STUDY OF THE SHIFTING PATTERNS OF GLOBAL SOURCING IN … · May 2011 . Chair: Ting Chi . The U.S....

A STUDY OF THE SHIFTING PATTERNS OF GLOBAL SOURCING IN THE U.S.

TEXTILE AND APPAREL INDUSTRY

By

LIN HE

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

MASTER OF ARTS IN APPAREL, MERCHANDISING, DESIGN, AND TEXTILES

WASHINGTON STATE UNIVERSITY Department of Apparel, Merchandising, Design, and Textiles

May 2011

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To the Faculty of Washington State University:

The members of the Committee appointed to examine the thesis of Lin He find it

satisfactory and recommend that it be accepted.

___________________________________

Chi Ting, Ph.D., Chair

___________________________________ Joan Ellis, Ph.D.

___________________________________ Charles Munson, Ph.D.

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ACKNOWLEGEMENTS

I would like to express my gratitude to Dr. Chi Ting for being an outstanding advisor. His

constant encouragement, support, and invaluable suggestions made this work successful. I am

deeply indebted to my committee members Dr. Ellis Joan, Dr. Munson Charles, and Dr. Salusso

Carol for their time and effort in reviewing this work, and for their valuable feedback that helped

me improve my thesis.

I also would like to thank the department of Apparel, Merchandising, Design & Textiles

(AMDT), College of Agricultural, Human, and Natural Resource Sciences (CAHNRS),

Washington State University, for supporting my work on this thesis by a graduate student

scholarship and an assistantship for the academic year 2009-2010.

Without my family and friends, my thesis could not been completed. Thanks to my

mother, Yan Fen Yao, who has always supported me in any endeavor. She is the major reason I

am striving for a master’s degree now. I love you mom. In addition, my two and only two best

friends, Shito Wu and Shico Zhong, who have always been there for me regardless of my

situations. My dear friend Todd Felton who helped me edit this thesis multiple times and gave

me an abundant amount of mental encouragement.

Finally, I have to thank God for being my greatest support. The process of conducing this

study along with other ongoing issues was very stressful for me until I met God. Now with his

blessing, I feel I have the strength to push forward and complete my duties. Special thanks to my

significant other: Han Zhou. Thank you for his love which strengthen me and helps me overcame

many difficulties.

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A STUDY OF THE SHIFTING PATTERNS OF GLOBAL SOURCING IN THE U.S.

TEXTILE AND APPAREL INDUSTRY

Abstract

by Lin He, M.A.

Washington State University May 2011

Chair: Ting Chi

The U.S. textile and apparel industry is a long chained business that involves billions of

dollars throughout the world. Although this industry is overly competitive and complicated, the

profitability is enormous. The highly saturated market depends on the global sourcing to obtain a

competitive advantage. Due to the radical changes in the industry, global sourcing has been

shifting drastically as well. To understand these shifts is important. However, previous studies of

this issue are limited. Therefore, this thesis is designed to fill this gap.

The purpose of this study is to identify the shifting patterns of global sourcing in the U.S.

textile and apparel industry by analyzing four selected data set points in 1996, 2002, 2006, and

2009. These shifting patterns are yielded from cluster analysis with five perspectives. Each

country’s trade regulations, costs, logistics, cross-culture and technology levels are examined.

This study identified 28 leading supplying countries, and determined China, Vietnam,

India, Mexico, Canada, and Hong Kong1

1 Hong Kong is an area but rather than a country. However, in this study Hong Kong is discussed as an individual country.

as the most important supplying countries. Shifting

patterns with different perspectives are various. This reflects the U.S. textile and apparel

industry’s changefulness and complications. In the 1990s, Mexico, Canada, Hong Kong, and

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more countries were top suppliers of the U.S. market. However, only China lasted and became

the ultimate dominator in this field over time. The reasons behind China’s achievement are

included: a large amount of cheap labor, favored trade conditions, ongoing upgraded technology,

and infrastructure supports. Combining all these competitive advantages together, China has

been accelerating towards to succeed powerfully. “Made in China” will continue its growth in

the U.S. textile and apparel market.

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TABLE OF CONTENTS

Page

ACKNOWLEGEMENTS .......................................................................................................... III

ABSTRACT ................................................................................................................................. IV

LIST OF TABLES ........................................................................................................................ X

LIST OF FIGURES .................................................................................................................. XII

CHAPTER ONE ........................................................................................................................... 1

INTRODUCTION ............................................................................................................................ 1

The U.S. Textile and Apparel Industry .................................................................................... 1

Evolution of Global Sourcing in the Textile and Apparel Industry ........................................ 4

PURPOSE OF THE STUDY ............................................................................................................... 8

STRUCTURE OF THE THESIS .......................................................................................................... 9

CHAPTER TWO ........................................................................................................................ 10

LITERATURE REVIEW ................................................................................................................. 10

Global Sourcing .................................................................................................................... 10

Challenges and Benefits of Global Sourcing ........................................................................ 11

Complexity ........................................................................................................................ 11

Competitive Advantage .................................................................................................... 13

Drivers of Global Sourcing ................................................................................................... 14

Trade Regulations ............................................................................................................. 15

Overview of the U.S. Textile and Apparel Trade Policies ........................................... 19

Caribbean Basin Initiative (CBI) .................................................................................. 21

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North American Free Trade Area (NAFTA) ................................................................ 21

China’s accession in World Trade Organization (WTO) ............................................. 22

Central American Free Trade Agreement (CAFTA) .................................................... 22

Costs .................................................................................................................................. 23

Logistic ............................................................................................................................. 24

Cross-Culture .................................................................................................................... 25

Technology ....................................................................................................................... 28

Summary ............................................................................................................................... 29

Theoretical Framework ........................................................................................................ 29

Global Commodity Chain ................................................................................................. 30

Heckscher-Ohlin Theory ................................................................................................... 32

The Pattern of Global Sourcing from Previous Studies........................................................ 33

RESEARCH QUESTIONS .............................................................................................................. 38

CHAPTER THREE .................................................................................................................... 39

METHODOLOGY ......................................................................................................................... 39

Data Selection ....................................................................................................................... 39

Data Sources ......................................................................................................................... 41

Cluster Analysis .................................................................................................................... 43

CHAPTER FOUR ....................................................................................................................... 45

RESULTS AND ANALYSIS............................................................................................................ 45

Leading Exporting Countries ................................................................................................ 45

Cluster Analysis Results ........................................................................................................ 56

Descriptions of Shifting Patterns by Country ....................................................................... 74

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Descriptions of Shifting Patterns by Type ............................................................................ 80

FINDING AND DISCUSSIONS ........................................................................................................ 96

Lasting Patterns by Type ...................................................................................................... 99

Discussions of Shifting Patterns by Type ............................................................................ 100

Roles of Variables ............................................................................................................... 106

SUMMARY ................................................................................................................................ 108

CHAPTER FIVE ...................................................................................................................... 113

CONCLUSION ............................................................................................................................ 113

LIMITATION, AND RECOMMENDATION ..................................................................................... 114

REFERENCES .......................................................................................................................... 116

APPENDIX ................................................................................................................................ 126

CLUSTER ANALYSIS AND ANOVA STATISTICAL RESULTS ...................................................... 126

1996 Textile Value .............................................................................................................. 126

1996 Textile Volume ........................................................................................................... 129

1996 Apparel Value ............................................................................................................ 132

1996 Apparel Volume ......................................................................................................... 135

2002 Textile Value .............................................................................................................. 138

2002 Textile Volume ........................................................................................................... 141

2002 Apparel Value ............................................................................................................ 144

2002 Apparel Volume ......................................................................................................... 147

2006 Textile Value .............................................................................................................. 150

2006 Textile Volume ........................................................................................................... 153

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2006 Apparel Value ............................................................................................................ 156

2006 Apparel Volume ......................................................................................................... 159

2009 Textile Value .............................................................................................................. 162

2009 Textile Volume ........................................................................................................... 165

2009 Apparel Value ............................................................................................................ 168

2009 Apparel Volume ......................................................................................................... 171

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LIST OF TABLES

Table 1 Summary of Variables ..................................................................................................... 40

Table 2 U.S. Total Textile Import Value in U.S. Dollars (in Millions) ........................................ 46

Table 3 U.S. Total Textile Import Value in U.S. Dollars (in Millions) ........................................ 47

Table 4 U.S. Total Apparel Import Value in U.S. Dollars (in Millions) ...................................... 48

Table 5 U.S. Total Apparel Import Value in U.S. Dollars (in Millions) ...................................... 49

Table 6 U.S. Total Apparel Import Volume in SME unit (in Millions) ....................................... 51

Table 7 U.S. Total Apparel Import Volume in SME unit (in Millions) ....................................... 52

Table 8 U.S. Total Textile Import Volume in SME unit (in Millions) ......................................... 53

Table 9 U.S. Total Textile Import Volume in SME unit (in Millions) ......................................... 53

Table 10 1996's Textile Clusters ................................................................................................... 57




Table 14 1996's Apparel Clusters ................................................................................................. 61




Table 18 1996's Textile Clusters with Variables .......................................................................... 66




Table 22 1996's Apparel Clusters with Variables ......................................................................... 70

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Table 26 Types of Textile Import by Value .................................................................................. 97

Table 27 Types of Textile Import by Volume .............................................................................. 97

Table 28 Types of Apparel Import by Value ................................................................................ 98

Table 29 Types of Apparel Import by Volume ............................................................................. 98

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LIST OF FIGURES

Figure 1 U.S. Textile and Apparel Goods Values .......................................................................... 3

Figure 3 Market Share in 2007 ....................................................................................................... 3

Figure 2 Market Share in 1989 ....................................................................................................... 3

Figure 4 U.S. Trade Regulations................................................................................................... 23

Figure 5 1996's Five Types of Supplying Countries ..................................................................... 82





Figure 10 2002's Five Types of Supplying Countries ................................................................... 86











1

Chapter One

Introduction

Global Sourcing is the most logical approach for gaining competitive edge in the U.S

textile and apparel industry (Monczka & Trent, 1991). However, the practice of global sourcing

in this complex industry is very challenging. For example, crossing border business activities are

often influenced by many unavoidable and uncontrollable factors. Therefore, to understand how

global sourcing has changed and why it has changed is very crucial and valuable. This empirical

study investigated the shifting patterns of global sourcing based on the U.S. textile and apparel

import data in 1996, 2002, 2006, and 2009. The findings derived from this study provided a

comprehensive view of the changes in the industry throughout the last two decades.

The U.S. Textile and Apparel Industry

From the very first textile production machinery in the 19th Century to the latest

technologies in 21st Century, the U.S. textile and apparel industry has experienced enormous

transformations. From home production to large-scale industrialized factories, and then the later

globalization, the economic scale of the U.S. textile and apparel industry has been increasing

rapidly as time goes by. Nowadays, the size of the U.S. textile and apparel market is remarkable.

Sales values from apparel sector alone were estimated at U.S. $286 billion in 2008, and are

expected to reach $327 billion in 2013 (Euromonitor International, 2009). This multibillion

dollar business heavily relies on its supply chain management which is comprised of

innumerous loops working together closely (Singhal, 2003).

From the inspiration to the finished goods that are selling-floor-ready, large amounts of

tangible and intangible resources have to go through the upper stream all the way to the

2

downstream industries. Therefore, industries such as the chemical industry and the retail industry

are also considered parts of the textile and apparel industry and should not be overlooked. Due to

the fact that the supply chain in textile and apparel industry is long and complicated, this industry

always is one of the most dynamic sectors in global trade (Saheed, 2006). Despite the complexity

of this industry, the enormous size and the profitability of the textile and apparel industry is very

desirable for business. According to a report of Bureau of Industry and Security (2003), the gross

margin of U.S. apparel industry is as high as 44.12%. Compared with other industries within the

report, apparel industry has the highest figure. Although parts of the U.S. textile and apparel

industry are dwindling, the industry as a whole remained in a good shape, in terms of financial

performance and international competiveness (Bureau of Industry and Security, 2003).

In addition, to take part in the U.S. textile and apparel industry is relatively undemanding.

The requirement of starting funds is low and the necessity of knowledge is not incompetent

(Rudell, 2006). With the rapid growing technology, businesses don’t even require a brick and

mortar site to begin with. These comparative low entry conditions encourage more and more

companies to participate in the industry. Along with the rapid development in the textile and

apparel industry, the market has become highly saturated. The competition in the field is

incredibly intense and furious (Su, Gargeya, & Richter, 2005). It not only comes from domestic

and international pressures, but also within the whole industry network (G. A. Gereffi, 1999). As

a result, for the purpose of gaining competitive advantages, companies started to look for new

opportunities that are outside the traditional approaches. Globalization, a new complexion was

raising high in the global economy. To proactively respond to the increasing competition, one of

the outcomes of globalization was adopted by the textile and apparel industry: the strategy of

global sourcing (Monczka & Trent, 1991). By 2009, the U.S. textile and apparel imports were

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valued approximately at U.S. $81 billion. Compare with the value in 1989 at U.S. $26 billion, the

figure had increased 212% (see Figure 1). Figure 2 shows there was about only 16% textile and

apparel goods were imported from the world in 1989, while in Figure 3 the figure grew to 50%

in 2007 (Bureau of Economic Analysis, 2009; Office of Textile and Apparel, 2007). According

to American Apparel & Footwear Association, in 2007, about 95% apparel sold in the U.S.

market were imported from other counties (2008). These remarkable figures put global sourcing

of the U.S. textile and apparel in the spotlight.

Figure 1 U.S. Textile and Apparel Goods Values

Figure 2 Market Share in 1989 Figure 3 Market Share in 2007

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Along with the maturity of globalization, more and more companies began to adopt

global sourcing as a competitiveness-gaining strategy in this industry (Su, Dyer, & Gargeya,

2009). Global sourcing is defined as “an advanced approach to sourcing and supply management

that involves integrating and coordinating common materials, processes, designs, technologies

and suppliers across worldwide buying, design and operating locations” (Trent & Monczka, 2005,

p. 24). This integrated strategy allows companies to do business in a more cost-effective way.

This strategic approach also enables companies to manage their supply chain more efficiently

and wisely. Global sourcing is often referred as the key to obtain competitive advantage (Cho-

Che, 1998). As a result of the complexity of the textile and apparel industry, the passage of

global sourcing is not ordinary (Gargeya, Birdwell, & Marlill, 2001; Su et al., 2005). In fact, it

changes so frequently and dramatically and has drawn many scholars’ attention to study this

subject (Carr & Pearson, 2001; Ellram & Carr, 1994; G. Gereffi & Korzeniewicz, 1994; G. A.

Gereffi, 1999; Leung, 2000; Monczka & Trent, 1991; Petersen, Frayer, & Scannell, 2000; Trent

& Monczka, 1994). However, the published empirical based studies on global sourcing shifting

patterns of the U.S. textile and apparel industry are very little. Therefore, updated studies that are

dedicated to empirically understanding global sourcing patterns in the U.S. textile and apparel

industry are always in high demand. In such cases, the findings of this research will meet that

demand and fill the gap in the literature.

Evolution of Global Sourcing in the Textile and Apparel Industry

Tracing back to the early age of human history, silk and fur were the very early textile

and apparel goods that people exchanged from one region to another throughout the world. The

Silk Road, one of the earliest international trade paths, was named by Feidinand von Richthofen,

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a German geographer in the 19th-century. However, silk was not a trade commodity, but rather a

luxury tribute paid to the important monarchs when the Silk Road occurred. Silk was a highly

valuable textile among the common people, who did not have any access to the silk products

until the 5th or 4th century B.C. (Channel Four, 2005). As far as textile and apparel international

trade goes, it was very limited until the 20th century.

The modern history of the U.S. textile and apparel global sourcing was not so significant

until the end of World War II (WWII). At that time, the political sensitiveness between the U.S.

and communist countries were very high. To limit the diffusions of communism in the Asian

region, the U.S. domestic textile and apparel industry was sacrificed for gaining alliance from

Japan who was also a capitaism nation. Since the textile and apparel industry was very labor-

intensive and less likely to turn into military uses, it became the safest and the most rational

option for the U.S. government to choose then. The U.S. government decided to export raw

cotton to Japan and then import back the finished goods (such as cotton fabrics). This allowed

the U.S. to take advantage of cheaper labor costs overseas, which also officially kicked off the

game of global sourcing in U.S. history (Dickerson, 1999). In 1960, The U.S. apparel companies

started to contract more and more textile and apparel works to other Asian countries for the

demand of low-cost production. This is when global sourcing of The U.S. apparel industry

became a crucial topic, and East Asian countries were the most engaging partners. After a decade

of development, in the 1970s, U.S. apparel industry started to suffer their first significant conflict

between the domestic suppliers and retailers, due to the high pressure from trade deficit. To

soften this divergence, Japan was willing to pay a higher export tax to the U.S. However, this

strategy stimulated the growth of other two strong competitors, Hong Kong and Taiwan2

2 Taiwan is an area but rather than a country. However, in this study Taiwan is discussed as an individual country.

. These

two locations are also capitaist countries like Japan, but with more competitiveness due to the

6

lower wages (Rosen, 2002). Ever since then, complicated trade restrictions were introduced to

constrain the growth of importing textiles and apparel productions to the U.S.. The purpose of

trade constrains were to protect the U.S. textile and apparel industry’s domestic products

originally, but in later decades, trade constraints also became used as a foreign affair strategy.

The nature of apparel business included intensive labor work, simple technology, small

starting funds, and liberal economic environment. All of the uniqueness pushes the industry to

favor low labor cost factors the most, in terms of sourcing (Brenton & Hoppe, 2007; Mehta,

1996). Although textile sector is not considered as a labor intensive (Doeringer & Crean, 2006),

the large amount of work forces that apparel sector requires make the U.S. textile and apparel

industry demand abundant of workers as a whole. Cost as the major motivation or driver has led

the trend of global sourcing conventionally for a long time. The “Race of bottom” means the

companies keep changing different suppliers from different countries as long as the price is

cheaper than the last one. Finding the lowest-cost production is the goal of the “Race to the

bottom” (Appelbaum, Bonacich, & Quan, 2005). Therefore, production is always on the move

towards the lower-cost destination.

In the late 1970s, China began to capture the increasing export demand from the U.S. as a

result of its economic reform. The reform opened up Chinese exporting market significantly and

stimulated business to grow competitively. However, it’s not until the middle of 1980s, China

and the U.S. normalized their affiliation and established a new trade partnership. China finally

became one of the most important trade partners with the U.S. and reached its first apparel export

peak (Dyer & Dyer, 2001; Zhang, 2002). Even since then, China has begun to grow bigger and

bigger. By 1995, China became the world’s largest textile and apparel manufacturing country

and maintained its status to present (Appelbaum et al., 2005; Ji, 2006; Yeung & Mok, 2004).

7

Along with the rapid growth of textile and apparel foreign imports, global sourcing

started to face heavier and heavier import restraints, such as high tariff and limited quota, when

compared in other categories’ import goods (Mehta, 1996). Although China’s exporting goods

have faced even harsher trade restraints, the expansions of Chinese production remains strong

(Dyer & Dyer, 2001; Mehta, 1996; Song, 2006; Zhang, 2002). Along with the “Asian

Renaissance” from the 1980s to 1990s, there are other competitive suppliers such as Mexico,

Caribbean basin countries, Canada, India, Vietnam, Thailand, Cambodia, Malaysia, and other

countries (Appelbaum et al., 2005; Dyer & Dyer, 2001; Haisley, 2002; Ji, 2006; Mehta, 1996).

Competition in this field reached a level that was the highest than in any given period before

(Monczka & Trent, 1991). Due to the economic crisis in the late 1990s, the demand of the U.S.

textile and apparel goods have declined slightly. Also, foreign suppliers were facing more and

more complicated trade restraints from the U.S. government. The U.S. textile and apparel global

sourcing experienced extremely intricate development (Ellram & Carr, 1994; Zhang, 2002).

Moreover since Quick Responses (QR) arrangement was adopted by increasing number

of U.S. textile and apparel companies, global sourcing of textile and apparel has turned into a

new stage (Cho & Kang, 2001). QR is a strategy that a supply chain communicates with its

members closely and electronically, so that production cycle time can be reduced and

efficiencies can be increased (Dickerson, 1999). Findings of many global sourcing development

related researches are not coincident with each other. Based on different perspectives of on-going

changes in trade policies, varying consumer behaviors, new economic factors, and other point of

views are likely responsible for this issue (Borneman, 2002; Dickerson, 1999; Nowell, 2005).

Transition in the U.S. textile and apparel industry has been rapid and dramatic for a long period

of time (Su et al., 2009). In some time periods, there were no obvious regional import patterns

8

that can be observed easily. Also, shifting patterns yield from subjective method which rely on

researchers’ personal judgments are less reliable (Brenton & Hoppe, 2007). Therefore, to use an

objective methodology is more encouraged. It can help researchers to identify groups with

certain similarities and observe the possible patterns with less bias. It is very imperative and

meaningful to be aware of the changes of U.S. textile and apparel global sourcing patterns that

happened in the most recent two decades.

Purpose of the Study

The purpose of the study is to identify the shifting patterns of global sourcing in the U.S.

textile and apparel industry by analyzing four selected data set points in 1996, 2002, 2006, and

2009. This study is conducted with the theoretical frameworks of Global Commodity Chain

Theory (that analyzes global industry shifts through position changing) and Heckscher-Ohlin

Theory (that the lowest cost labor in manufacturing would yield the best comparative advantage).

Trade data of selected years are analyzed using cluster analysis so that countries with similar

characteristics are identified and classified into the same group. After the determination of

groups, the characteristics of each group are further studied. From the results, the country that

shifted from one group to another and country with no changes are revealed. With the research

findings, the U.S. textile and apparel industry will be able to see the position changes of

supplying countries and the overall shifting patterns of its global sourcing. In addition,

companies can identify where they stand among those leading suppliers and evaluate their

current position. This approach not only provides valuable sourcing strategy information for U.S.

companies, but also helps foreign suppliers understand their challenges and opportunities. As a

9

result, the global textile and apparel industry have the advanced ability to prepare a forward

moving plan with more comprehensive information.

Structure of the Thesis

This thesis study includes five chapters: introduction, literature review, methodology,

result and analysis, and conclusion. The literature review is comprised of five sub-topics: what

global sourcing is, its challenges and benefits, an explanation of what influential factors are, and

finally the discussion of the theoretical frameworks. The presentation of Global Commodity

Chain Theory and Heckscher-Ohlin Theory is followed by the discussion of previous studies of

global sourcing shifts specifically. The literature review section ends with the research questions

pertinent to this thesis.

The research methodology section introduces the selected data and its usage followed by

the explanation of cluster analysis.

Result and analysis section presents the finding of this study and discussions of research

questions’ answers. Also, a summary of the learning is provided.

Finally, this thesis ends with the overall conclusion of this study’s, and discussions of its

limitations and recommendations. The last section is bibliography list and appendix of statistic

outputs.

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Chapter Two

Literature Review

Global Sourcing

There are several terms that need to be clarified before reviewing the literatures. “Supply

chain management is defined as the coordination of production, inventory, location, and

transportation among the participants in a supply chain to achieve the best mix of responsiveness

and efficiency for the market being served” (Hugos & Thomas, 2006). According to Trend and

Monczka (2005, p.24), global sourcing is defined as the activities and processes that “involve

proactively integrating and coordinating common items and materials, processes, designs,

technologies, and suppliers across worldwide purchasing, engineering, and operating locations”

(Birou & Fawcett, 1993; Monczka & Trent, 1991; Trent & Monczka, 2003). Meanwhile,

international sourcing or international purchasing is defined as buying products from foreign

suppliers (Monczka & Trent, 1991). Traditional logistics is defined as one independent

organization runs procurement, distribution, maintenance, and inventory mainly within a country

(Hugos & Thomas, 2006). In brief, global sourcing is a component of supply chain management,

whereas international sourcing or purchasing is a scaled down version of global sourcing and

without any integration. However, all terms are subject to change or may be interoperated

differently overtime due to the lack of constancy in strategic objective.

The goal of global sourcing is to have the product right, which means that the company

should have the right products with the right price, for the right consumers, at the right place and

in the right time (Hugos & Thomas, 2006; Su et al., 2005). How to balance the economics and

logistics is the key to meet those five “right” criteria. “Economics are worker productivity,

manufacturer overhead, quota availability and cost, textile and trim access, and transportation

11

costs. Meanwhile, logistics issues are all about speed” (Borneman, 2002, p. 52). However, the

constant changes of this long chain also need to be considered by business decision makers.

Those uncertainties always add more difficulties for the process of global sourcing. These

changes included currency exchange, change in resources, trade legislation, and consumer

demands (Borneman, 2002).

The existence of global sourcing is not only because of the opportunities for low-cost

production, but also for other reasons. “The opportunity to dramatically decrease labor costs

drove much of apparel manufacturing offshore during the last decades. Due to the need for

specialized machinery and labor skills, quota and customs considerations, and political factors,

the supply chain for apparel went from a somewhat vertically integrated domestic process to a

multi-tier, distributed global process that spans time zones, languages, cultures and geography”

(Seagrave, 2002, para.7). Although the approach of acquiring textile and apparel goods is getting

more and more complex and difficult, the advantage of being more competitive is extremely

attractive for most companies. As global sourcing became the mainstream in the U.S. textile and

apparel industry, the complexity of being competitive has increased dramatically. Although

global sourcing provides a lot of benefits, it is still a formidable challenge for any domestic and

international firms that compete with each other intensively for maintaining the market share of

textile and apparel products.

Challenges and Benefits of Global Sourcing

Complexity

The coordination of global sourcing among the textile and apparel supply chain is very

challenging. The complexity is the principal and unsolvable issue. The textile and apparel supply

12

chain is a very long and complicated chain, which is common for most of the textile and apparel

firms involved in the businesses (Gargeya et al., 2001). Complexity of textile and apparel supply

chain is the result from this industry’s typical characteristics and high involvement of a large and

diversified range of activities, which engage in a variety mix of workforce and resources (Audet,

Safadi, & Organisation for Economic Co-operation and Development, 2004). The textile and

apparel industry has high and indirect involvement and interaction with other industries. As a

result, the more people involve in the chain, the more complicated situation would occur.

In addition to the challenging network coordination, diversified global sourcing strategies

in the textile and apparel industry increase the complexity as well (Bruce, Daly, & Towers, 2004;

Cesca, 2005). Companies with different market positions often adopt different global sourcing

strategies to achieve their goals.

There are a few more challenges identified from prior studies, such as communication,

culture relationships, technology, production processes, supplier arrangements, and

transportation infrastructure (Gargeya et al., 2001).For the U.S. textile and apparel industry,

every region owns different level of challenges. However, all those challenges contain certain

degree of advantages and disadvantages. Although challenges can be headache for some people,

Meixell and Gargeya (2005) found they are also blessing:

“Substantial geographical distances in these global situations not only increase

transportation costs, but complicate decisions because of inventory cost tradeoffs due to

increased lead-time in the supply chain. Different local cultures, languages, and practices

diminish the effectiveness of business processes such as demand forecasting and material

planning. Similarly, infrastructural deficiencies in developing countries in transportation

and telecommunications, as well as inadequate worker skills, supplier availability,

13

supplier quality, equipment and technology provide challenges normally not experienced

in developed countries. These difficulties inhibit the degree to which a global supply

chain provides a competitive advantage” (Meixell & Gargeya, 2005, p. 533).

In fact, challenges are not absolute troubles, but favorable in some cases. These problematic

areas certainly deserve companies to devote resources to handle, for the purpose of competitive

advantage gaining.

Competitive Advantage

In Chapter One, there is a discussion about the urgency of being competitive and the

significance of global sourcing as a successful approach for gaining competitiveness both are

obvious. In this section, the discussion is focused on how global sourcing obtains

competitiveness and what generate the advantages of global sourcing.

For the U.S. textile and apparel industry, one of the most essential determinants of

companies’ competitiveness is the accessibility of high-quality products. To ensure the quality,

equipment and technology are the keys that cannot be overlooked (Audet et al., 2004). In other

opinions, competitive advantage can be acquired by controlling manufacturing and sourcing

costs (Singhal, 2003). In addition, the determination of successful global sourcing is a set of

closely collaborated business practices. “The opportunity to improve competitiveness, when

using global sourcing, depends on the location, products purchased, competitive nature of the

supply markets for those products, and strategies employed to reduce total costs” (Cho-Che,

1998; Garg, 2002, p. 1). All the criteria are considered equally critical in terms of performing

global sourcing (Su et al., 2005). This idea is also supported by Audet et al. (2004) but in slightly

different angle. They consider trade policies, labor adjustment, technology and innovation and

14

other regulations are important indicators of competitiveness in the global sourcing. In the

perspective of trade policy, some important determinants are transportation efficiency,

communication technology, and customs service. The transportation options, logistical cost, port

services, efficient customs service and reliability of the system complement each other in

increasing one country’s international competitiveness.

Besides discussed topics above that contribute to the competitive advantage of global

sourcing, other factors will be discussed in next section such as important determinants of global

sourcing.

Drivers of Global Sourcing

A numbers of factors have driven the shifts of global sourcing in the U.S. textile and

apparel industry essentially. Due to the different objectives found in different studies as well as

the dependence on the researchers’ background, the findings on this topic can be analogical or

distinctive. Therefore, reviewing only a few broad categories rather than all small points is more

efficient and appropriate for this research.

There are five influential drivers that were summarized from the previous section, which

will be discussed in this section in detail. They are different issues from the point of view in

trade regulations, cost, logistic, cross-culture, and technology. These sub-topics were identified

and analyzed from many prior studies, which are focused on the issues of international trade,

supply chain management, and global sourcing in behalf of the textile and apparel industry

directly (e.g., Cho-Che, 1998; Cho & Kang, 2001; Garg, 2002; Glenn, 2006; Kurt Salmon, 2007;

Shelton & Wachter, 2005; Su et al., 2005)

15

After identification of shifting patterns, causation of the shifts will be examined by

analyzing the driving forces. The study of these forces can build a more solid foundation for this

research. In addition, the answer of what determined the shifts of global sourcing can be found

and the objective of this study will be fulfilled.

Trade Regulations

Since global sourcing is a cross-country activity, it is obvious that legal overseeing is

required by the government. Trade policy has been shaped by the patterns of global trade

effectively (Brenton & Hoppe, 2007; Ji, 2006; Su et al., 2005). Regulations not only control trade

by restriction, but also by facilitation. In some case, government administration is not always the

activist in terms of constituting new trade policies but rather the industry. Wemer Birti who is the

president and CEO of a textile manufacturing company stated that the industry needs

government’s help. Also, to stimulate the growth of business in closed market policy makers

were asked to enforce regulations more strictly (Borneman, 2002). This claim indicates how the

government can use its power to stimulate businesses and illustrates the close relationship that

the U.S. textile and apparel industry has with the U.S. government. Actually, this phenomenon is

not observed in many other industries but only in the textile and apparel industry (Francois,

Glismann, & Spinanger, 2000; Rivoli, 2005). In fact, the U.S. textile and apparel industry has

always been treated differently by the U.S. government in a good and bad way. The U.S. textile

and apparel suppliers always face more restrictions in trade.

Trade quota limits the amount of imports from different countries to enter the U.S.

market. The purpose of quota is to protect domestic production and to deal with the issue of trade

deficit (Cho-Che, 1998). Unfortunately, trade quota restriction benefits most of those low-wage

16

countries by fragmentizing the global supply chain. Low-wage countries only focus on

specializing certain production stages rather than full services. In addition, quota restriction not

only increases the complexity of trading in the textile and apparel world, but also discourages the

growth of entire global textile and apparel supply chain dramatically (Audet et al., 2004). This

means that the U.S. textile and apparel market share has been cut and given out to foreign

suppliers, regardless their competitiveness, by the quota policy which is against the goal of

protecting the domestic industry.

Besides the trade quota, tariffs can also be a serious trade barriers/ protection in textile

and apparel trade (Audet et al., 2004). For example, the average U.S. apparel tariff rate is

significantly higher than those of other commodities (W. Martin & Anderson, 2005). The

average tariff applied on apparel products is about 16% compared to 6% on general manufacture

goods (Audet et al., 2004; Mehta, 1996). This has restricted the number of apparel imports. This

policy is in favor of apparel manufacturers but not retailers. Removing trade restrictions on

textiles and apparel will damage one country’s domestic industry if it is not well prepared for the

international competition. However, the customer can gain benefits by paying less but still

getting quality products. The non-distorted market structure encourages businesses to become

more cost-efficient, which spurs the market to grow rapidly (Audet et al., 2004). Overall, the

implementation of the quota system and tariffs both are arguable decisions.

Other than quota and tariffs, there are other trade barriers such as currency exchange rate

and governmental subsidies. Governmental subsidies are additional benefits that governments

provide to exporters or specific groups of people to stimulate export of certain types of goods.

Governmental subsidies could distort textile and apparel trade flow between countries through

influencing product export competitiveness. Actually, in the global textile and apparel industries,

17

not only the governments in developing countries provide subsidies but also developed countries

do so. As a matter of fact, the European Union (E.U.) and U.S. are the superpowers of subsidy

due to their abundant state aid dedicated for export stimulation (Green, 2005). For instant, the

U.S. government provide $775 million to domestic cotton industry and $134 million to wool for

protecting competition from the world market in 1991(Dickerson, 1999). In 2002, the U.S.

government spent $2.3 billion, Chinese government spent $1.2 billion, and the E.U. spent about

$700 million subsidies respectively on cotton sector only (Gillson, Poulton, Balcombe, & Page,

2004). In the meantime, combining both textile and apparel sectors, China spent about 17%

values of textile and apparel exports as exporting subsidies in 2008, which is estimated at $29

billion (Ray, 2008). Also, India had provided about $12 billion for its textile and apparel industry

as subsidies in 2006 (Cororaton et al., 2008). When comparing with other merchandises, textile

and apparel export performance has showed relatively strong positive correlation with

governmental exporting subsidies. Foreign governments’ exporting subsidies enabled their

suppliers to offer products in a much lower price than average prices of textile and apparel in the

global trading market, so that to encourage the U.S. companies to import more products from

overseas. Due to the significant cost saving, global sourcing is advocated by most retailers. As a

result, global sourcing was often impacted by supply countries’ subsidies heavily (Cororaton et

al., 2008; W. Martin & Anderson, 2005). To support exporters to compete more effectively in the

world market, exporting subsidies are more often used by countries which economies largely rely

on exporting performance. However, subsidies always were considered as unfair trade practices.

It distorts the normal market environment and contributes to an inefficient use of resources such

as dumpling (Dickerson, 1999; Gillson et al., 2004; Green, 2005). There are high demands of

removal on exporting subsidies to increase the efficiencies and fairness in the world textile and

18

apparel market. Fair trade environment is more beneficial for the development of global

economy as a whole (W. Martin & Anderson, 2005).

Besides exporting subsidies, currency exchange rate as a global sourcing factor is often

overlooked (Glenn, 2006).The health of a country’s currency exchange rate ties to the country’s

trade condition closely. In the past, the strong surges in the value of the U.S. dollar deteriorated

the trade position of the U.S. textile and apparel industry dramatically (Kilduff, 2005). On the

other hand, the current exchange rate devaluation of foreign supply countries ensured and

stimulated low-priced products to enter the U.S. market (Amponsah & Boadu, 2002). These two

currency phenomena show a relationship between the value of one country’s money and its

imports or exports. Countries with weak currencies that are worth less value attract their foreign

buyers to import goods, especially those buyers who have stronger currencies that are worth

more value in exchanging goods. In fact, the fluctuations of bilateral currency exchange rates

influence the U.S. textile and apparel global sourcing significantly. Cheaper imports, one of the

goals of global sourcing, can be achieved by choosing a supply country that has a weaker

currency than the U.S. currency (Glenn, 2006). It impacts the overall acquisition cost which

eventually influences the economic performance of the supply chain. Currency was highly

recognized in the study of globalization issues (Meixell & Gargeya, 2005).

In 2001, a crisis in the U.S. textile industry was formed by a combination of the Asian

currency devaluations and the U.S. strong dollar policies (American Textile Manufacturers

Institute, 2001; Dyer & Dyer, 2001). That crisis encouraged more companies to adopt global

sourcing strategy to maintain their competitive edge in the industry. In most current trade issues,

the U.S. President Barack Obama demanded the Chinese government to adopt a strong Yuan

(Chinese currency) policy. One of the purposes behind this act is to increase the U.S. exports by

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selecting the markets having stronger currency exchange rates to U.S. dollar. However, this

approach would not affect the U.S. textile and apparel industry since the shrinkage of

manufacturing cannot be reversed easily. Despite the arguments, sufficient previous studies have

indicated how important role the currency exchange rate has in terms of trade performance and

global sourcing. Currency exchange rate is a major factor in the global sourcing decision (Glenn,

2006).

Debates on trade policies are always furious. With different perspectives and interests

from different groups of business, judgment will never be unified. Not only developed countries’

companies have the pressure from high competition, but also those companies in developing

countries. The competition in the field of textile and apparel is furious. Therefore, a lot of

participants in the field always ask for help from government to formulate trade policies

according to their own interests (Audet et al., 2004).

Overview of the U.S. Textile and Apparel Trade Policies

The Japanese five-year “Voluntary” restraint is the first U.S. textile and apparel import

restraints. It was applied to 20 Japanese cotton textile articles exports in 1957. After that, General

Agreements on Tariffs and Trade (GATT) formed a U.S. based textile trade conference and

achieved a set of new trade policies under the Short Term Arrangement Regarding International

Trade in Cotton Textiles (STA). This arrangement took cotton textile trade out of international

trade’s Most Favored Nation (MFN) system with a one year timeline. In 1962, Long Term

Arrangement Regarding International Trade in Cotton Textiles (LTA) came into force and

replaced STA. LTA extended the STA policy’s coverage from cotton only to wool and man-

made fibers. The ramification of LTA is Multi-fiber Arrangement (MFA) in 1974, which was

20

developed based on previous policies but start to cover up more and more types of product that

are made in cotton, wool and man-made fibers, such as textile and apparel. MFA is a general and

multilateral framework of managing textile and apparel trade of members who belong to GATT.

Since its first introduction, MFA was modified, revised, and extended a few times. MFA first

renew is MFAII in 1977; second renew is MFAIII in 1981; and its last renew is MFAIV in 1986.

MFAIV again extended coverage to even vegetable fibers and silk blends (Zhang, 2002). MFA

was lasted for more than two decades. As the world’s economy developed rapidly, GATT was no

longer capable of mentoring the new dynamic international trade in the 1990s. After the Uruguay

Round of GATT negotiations, World Trade Organization (WTO) was found and would begin to

replace GATT by 1995. When MFA under GATT is finally ended in 1995, Agreement on

Textile and Clothing (ATC) under WTO was introduced (Hertel, Bach, Dimaranan, & Martin,

1996). Originally, the ATC restriction would be phased out in three stages by the end of 2005.

Under great pressures from the U.S. domestic textile and apparel industry towards Chinese

products, safeguard was used as the final protection until the end of 2008 (U. S. Government

Accountability Office, 2005). After 2008, the U.S. textile and apparel industry is stand in a more

liberal and free position compare with pervious eras.

Besides those major agreements discussed above, there are many bilateral or multilateral

preferential or free trade agreements. Preferential Trade Agreements or Area (PTA) and Free

Trade Agreements or Area (FTA) are agreements on establishing special trade benefits by

reducing barriers such as eliminating quota and/or lower tariff rates among a group of its

member countries (Womach & The Library of Congress 2005). Generally speaking, PTA and

FTA increase the comforts of the U.S. textile and apparel businesses about sourcing products

with “free trade partner” countries (Borneman, 2002). They encourage global sourcing by

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lowering trade berries so that to spur the shifts of global souring initiatively. Some significant

trade establishments, PTA, and FTA were created in last two decades will be discussed below.

Caribbean Basin Initiative (CBI)

CBI officially known as the Caribbean Basin Economic Recovery Act was enacted in

1983. Its goal is to use special trade privileges to increase 22 Caribbean countries. By 1987, this

agreement extended its coverage to 27 nations permanently (Dickerson, 1999). Ever since the

implementation of CBI, a significant amount of textile and apparel production had shifted from

Asian countries to Caribbean countries. CBI not only has competitive low-cost labor, but also the

proximity to the U.S. market (G. Gereffi, 1997). The close distances between Caribbean

countries and the U.S. enable U.S. companies to gain more control and confidence in production

lead time. Also, the geography condition reduces transportation cost dramatically compare with

sourcing in Asian countries. However, only companies that use fabrics are both made and cut in

the U.S. to manufacture can enjoy the maximum degree of liberal trade system (Dickerson,

1999). Overall, this free trade agreement triggered outsourcing in the U.S. textile and apparel

industry.

North American Free Trade Area (NAFTA)

NAFTA is a comprehensive trading arrangement between the U.S., Canada, and Mexico

implemented in 1995. The goal of NAFTA is to facilitate intra-regional business activities. It’s a

mega bloc, with an emphasis on textile and apparel trade, emerged from last decade. In the 1990s,

Mexico was facing increasing competitive pressure from Asian countries. Until NAFTA was

established, the declining U.S. market share of Mexican textile and apparel products rebounded

22

(Mehta, 1996). The short geography distance and free trade promises stimulated the U.S.

companies to source more from products its nearest neighbor. The U.S. textile and apparel

industry gained benefits from NAFTA, such as low cost products, quick turnaround time, and

large demand of U.S. textile exports; as well as threats, such as job loss and accelerating the

decline of apparel manufacture (Amponsah & Boadu, 2002).

China’s accession in World Trade Organization (WTO)

After 15 years of on-and-off negotiations, China finally became a member of WTO in

2001. This is considered as one of the most important events in the history of 21st Century (Li,

2005). There are abundant studies on this topic, and most of them concluded that the rest of the

world can hardly compete with the Chinese textile and apparel exports with fewer and fewer

trade restrictions (Comino, 2007; Fang & Babcock, 2003; Liu & Sun, 2004; MacDonald, Pan,

Somwaru, & Tuan, 2004; M. F. Martin, 2007; Song, 2006; Whalley & National Bureau of

Economic, 2006; Yeung & Mok, 2004). This important change has reshaped the patterns of U.S.

global sourcing significantly. With less trade barriers, the passion from U.S. industry towards

Chinese imports will only rise without a doubt. China is truly the dominating power in the textile

and apparel trade.

Central American Free Trade Agreement (CAFTA)

CAFTA is a trade agreement developed under Title II of the Trade Act of 2000 and Trade

Act of 202 of The United States-Caribbean Basin Trade Partnership Act (CBTPA) in 2005

(Kathleen & Haesun, 2005).. This agreement was also known as the Dominican Republic-

Central America-United States Free Trade Agreement (US-CAFTA-DR) or (CAFTA-DR). It

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transforms CBI from a unilateral trade agreement to a true liberal status This means that

members of CAFTA can trade with each other reciprocal duty free (Pan, Welch, Mohanty,

Fadiga, & Ethridge, 2008). This agreement allows this region’s textile and apparel industry to

obtain excess towards the U.S. market. CAFTA trade bloc has a significant role in terms of

textile and apparel exporting. The most competitiveness of this trade bloc is the combination of

the short geographical distance and relatively low wage (Kathleen & Haesun, 2005). Major

beneficiaries in this region are Costa Rica, the Dominican Republic, El Salvador, Honduras,

Guatemala, and Nicaragua (Kathleen & Haesun, 2005; Pan et al., 2008). Figure 4 illustrates the

major changes of U.S. trade regulations over last six decades.

Figure 4 U.S. Trade Regulations

Costs

Cost saving is the primary benefit gained from global sourcing. A well developed global

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Sourcing strategy is able to reduce cost from 15% to even more than 30% for a U.S.

apparel firm (Trent & Monczka, 2002). This strong performance of global sourcing persuades

companies to consider cost reduction as their priority concern. In fact, the saving of global

sourcing correlates with companies’ financial performance positively (Carr & Pearson,

2001).Textile and apparel production is very labor-intensive. A significant amount of overall

costs is constituted by labor costs. This cost input is larger than any other efforts

disproportionately (Bureau of Industry and Security, 2003). When saving is very important in

some opinions, other studies suggest that only if the ability to maintain world class quality levels

is secured and yet the cost is still remain low, then the cost is saved indeed (Monczka & Trent,

1991).

Some studies argued that a low-wage strategy is no longer the best way to gain

competitiveness. Instead, higher value-added services, which includes design, sourcing or retail

distribution of the supply chain, became more and more popular (Audet et al., 2004; Cesca,

2005). Besides production related costs, another major financial input is logistic costs which are

essential to the global sourcing (Singhal, 2003). In some cases, logistic costs can be ranged from

4% to as high as 30% of sales revenue, which would impact business performance dramatically

(Zeng & Rossetti, 2003). How logistic arrangement impacts global sourcing will be discussed

below.

Logistic

While some studies suggest costs are the main driver of global sourcing, Walsh (2008)

argued that location comes first. Geographical distance is always an essential barrier of global

sourcing in the textile and apparel industry (Trent & Monczka, 2003). The farther the distance is,

25

the longer turnaround time, higher transportation costs, and more other uncertainties involve in

global sourcing. On the other hand, shorter physical distance can reduce the lead time and lower

uncertainty risks significantly. The ability to move from ideas to the market in reduced time is

essential (Monczka & Trent, 1991).

Lead time is very important in textile and apparel industry since products are highly

sensitive with seasons and fashion trend. Besides the savings of on-time delivery such as

inventory carrying cost and warehouse space, getting re-order products within a timely matter is

also a big concern in the industry. The cost of running out of inventory could be much higher

than that of overstock. Therefore, a less challenging country in terms of logistic matters can

encourage global souring.

Besides physical issues, the intangibility problems of logistics also impact the firm’s

decision on global sourcing. The management of supply chain takes big part of that, and directly

responds for the issues. The availability and reliability of transportation system and

intermediaries in the global environment can impact the performance of global sourcing (Cho-

Che, 1998). To maximize the outcome of “QR” (shorten lead time strategy) and “Just-In-Time”

(computerized automatic replenishment strategy) system effectively, logistic must be managed

carefully. Otherwise, poor logistic condition can easily discourage the implementation of global

sourcing. Overall, logistic is one of major forces that affect the patterns of global sourcing in the

U.S. textile and apparel industry.

Cross-Culture

“Culture is a learned set of ideals, values, and standards that is shared by members of a

society” (Jeannet & Hennessey, 2004, p. 63). Cultures have different and similar characteristics.

26

Both of them have impacted the global sourcing strongly. The most influential cultural

component is language. Since language is embedded in people’s daily life, language is the most

distinguish symbol from culture to culture. The very first step of business activities is based on

communication, which always involves the usage of language. “Without the capability to

communicate, it is very difficult to develop insights into cross-culture business practices; risk

and uncertainty; core information, experience, and competencies; and negotiation” (Petersen et

al., 2000). Some countries share the same language or at least similar, such as United States,

United Kingdom, Ireland, Canada, and New Zealand all have very high English-speaking

population. In this case, communication between these countries would be easier and smoother

than communicating with non-English-speaking countries like China or Honduras. Besides

conversational function of language, there are other disciplines that should be handled

accordingly, like legal documents and contracts. This convenience of sharing the same language

decreases some difficulties and risks that are involved in global sourcing; meanwhile, language

barrier increases uncertainties and decreases efficiencies of global sourcing. Close collaboration

between companies in different geography locations or even in different time zones are required.

Communication has become essentially critical, when face-to-face is not always an option.

International language capabilities impact the development of global sourcing significantly

(Trent & Monczka, 2002). Communication effectively triggers a better performance for the

whole textile and apparel supply chain; meanwhile, ineffective communication caused by the

language barrier would discourage global sourcing development.

Beside language, religion is another issue that people are aware of typically. Different

customs that are based on different religion practices are common. Some religions are more

aggressive than the others, which can increase conflicts and even end up with war. For example,

27

9-11 attract was the result of the rising tension between many westerners and Muslims, which is

a religion related issue. The religion differences between countries directly affect the decision

and performance of global sourcing. The increased uncertainties in a country’s security would

discourage global sourcing activities heavily.

Beyond the above issues, there are more culture related issues in global sourcing

practices, like lifestyle. In Mexico, whether a company would provide a family lunch or not can

be a significant issue in terms of job hunting; or in China, before establishing a business, people

should spend more time on networking instead of on market researching. Besides the previously

mentioned unique customs, people’s lives in Austria and Brazil have more public holidays than

most other countries. Also, Indian workers care about earning from overtime working less when

comparing with Chinese workers. Even under the same business conditions, Italian businesses

have a more relaxed business environment than Japanese companies. Understanding all of these

differences help companies to gain capabilities for global sourcing, which is very important to

pursue success effectively (Trent & Monczka, 2002). Decision makers have to plan their global

sourcing strategies with serious considerations of all the cross-cultural dimensions.

Overall, there is a variety of potential cultural and social influences in global sourcing. To

understand and to recognize the role of culture in global sourcing is very essential (Petersen et

al., 2000). Without this knowledge, companies would fail to establish a long-term and reliable

relationship with other members of the supply chain, which is an important indicator of success

in global sourcing (Birou & Fawcett, 1993). In addition, conflicts occurring from different

countries’ misunderstandings happen very often. To avoid unnecessary troubles, the

consideration of other cultures in the process of global sourcing is essential.

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Technology

Technology has been one of the most important founding bases of the textile and apparel

industry. The industrial revolution triggered the development of the textile and apparel industry

and eventually became the groundwork for the technology innovation movement. Technology

enabled large scale production, which is the prerequisite of trading in the textile and apparel

industry (G. A. Gereffi, 1999). Without the participation of technology in the industry, the scale

of textile and apparel production was very small and the growth was restricted. However, along

with the birth of communication technology and modern transportation, the textile and apparel

industry is one of the earliest globalized industries (Dickerson, 1999; G. Gereffi & Korzeniewicz,

1994; Hugos & Thomas, 2006). Other than the significant contribution technology had in the

early development of textile and apparel industry, technology is still one of the important factors

that impact global sourcing considerably.

Global sourcing activities heavily depend on computers and internet in many perspectives,

such as information collecting, pattern grading, order and replenishment, and many other

services. Extensive electronic linkages and information exchanges replace face-to-face

interaction to enhance efficiency and accuracy. When seeking improvements in lead time,

inventory positioning, cost, delivery, customer service, and other key areas, information

technology (IT) is a must have tool (Petersen et al., 2000). The participation of technology not

only increases the speed of processing information, but also reduces operational expenses.

Technology certainly helps the industry overcome the inherent complexities of global sourcing

and makes comparative advantages available (Trent & Monczka, 2002).

In addition to basic communicating function, technology contributes to innovative

production and management in the textile and apparel industry significantly (Abend, 2001).

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Technology access attracts companies who are looking for applied scientific or specialized

knowledge and skills that are applicable to manufacturing. Technology assists companies to

enter niche markets by providing product customization and differentiation as an approach of

competitiveness (Birou & Fawcett, 1993; Bureau of Industry and Security, 2003). In the

management perspective, access to advanced technology in supply chain management, such as

radio frequency identification system (RFI) for easy inventory management is another

competitive edge. Global industrialization and informationization influences the shifting patterns

of global sourcing in the U.S. textile and apparel industry.

Summary

Overall, explanations of the global sourcing patterns in the U.S. textile and apparel

industry are plenty. Excluded factors, such as country risks, political stability, currency exchange

rate, history, law and regulation, and many other issues can also be concerns that alter global

sourcing in this industry. Nevertheless, there is no single driver that can ever impact global

sourcing individually. There must be multiple elements involved in every decision and practice

of global sourcing. In fact, they all influence each other and become a unique composite force to

change the pattern of global sourcing.

Theoretical Framework

In this section, theory of Global Commodity Chain with its further developments and

Heckscher-Ohlin will be reviewed in an effort to place the role of the global sourcing in the U.S.

textile and apparel industry in a theoretical context.

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Global Commodity Chain

Global Commodity Chain analysis is a theory derived from Wallerstein’s (1974) World

Systems Theory. It was originally proposed and developed by Gray Gereffi and others in a

perspective of political economy of development and underdevelopment. Global Commodity

Chain is defined as “ a network of labor and production processes whose end result is a finished

commodity” (Hopkins & Wallerstein., 1986, p. 159). The purpose of Global Commodity Chain

is to analyze and evaluate global industry. In addition, particular patterns of coordinated trade

can also be illustrated by this theory. Commodity industries, either buyer-driven or producer-

driven commodity, have different arguments in this theory. Industrial and commercial capita

promoted globalization through these two types of internationalized economic networks (G. A.

Gereffi, 1999).

Producer-driven commodities are production networks that are coordinated and

determined by the large manufacturers mainly. It mainly refers to automobiles, aircraft, industrial

machinery, and computer industry that involves intensive capita and technology. The barrier of

entering this type of commodity industries is relatively high. On the other hand, buyer-driven

commodities illustrates a model which is associated with commodity chains that buyer is the

dominating power over its international and domestic producers. In this approach, buyers

determine the design or market but not producing (G. Gereffi, 1994). It mainly refers to retail,

textile and apparel, consumer electronic, and furniture industry that are labor-intensive but

technology extensive. The barrier of entering this type of commodity industries is relatively low

(G. A. Gereffi, 1999).

In this study, trade countries are connected by obtaining different roles in supplying

textile and apparel products to the U.S. market. Different arrangements in the textile and apparel

31

industry are categorized by role: mere-assembly, original equipment manufacturing (OEM), and

original brand name manufacturing (OBM) (G. A. Gereffi, 1999). The determination of each role

is based on the accumulated available resources and capabilities over time (Porter, 1998). Each

special position contributes to parts of the textile and apparel imports to the U.S. market.

Mere-assembly based exporters carry out assembly function only. It requires the least

cooperation from a supply chain and competes on low cost labor. Mere-assembly is considered a

least profit and least sustainable role. OEM based exporter provides full-package service which

includes performing design, cutting, assembly and marketing task. Finished goods will be finally

sold via another company’s brand name. It requires technical skill in manufacturing field and

competes on value-added (Lee, Lee, & Moore, 2004). OEM is a role having moderate profit and

some stability. OBM based exporters design, manufacture, conduct marketing, and retail goods

in domestic and international markets under their own brand names (G. A. Gereffi, 1999). It

requires effective integration in various services and skills. OBM is a role with higher profit and

long-term sustainability. In buyer-driven commodity chains profits are concentrated in the

activities of research, design, sales, marketing and financial services.

Buyers and brand-name merchandisers strategically establish links between factories and

exporters in foreign countries with domestic consumer markets. Early commodity chain activities

mainly occur in underdeveloped countries. This system essentially secures the vast majority of

profits in the core countries (G. Gereffi & Korzeniewicz, 1994).

Overall, the changes of positions explain the restructuring of the international textile and

apparel industry, which yields the shifting patterns of global sourcing in the U.S. textile and

apparel industry.

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Heckscher-Ohlin Theory

“The Heckscher-Ohlin (HO) model is a general equilibrium mathematical model of

international trade, emphasizes the nature of demand, the marginal costs and benefits of

production within the framework of comparative advantage” (Walsh, 2008, p. 20). This model

was developed by two Swedish economists, Eli Heckscher and Bertil Ohlin in the 1920s. This

theory argues sources where resources are plentiful with the lowest cost should be utilized for

gaining comparative advantage (Walsh, 2008). It mainly considers labor and capita between two

countries as the two critical factors in terms of producing two commodities. A country, who has

a combination of high low-cost labor and less capita, is suggested to specialize in and export

labor-intensive goods; on the other hand, the other countries, who have a combination of more

capita and less low-cost labor, should specialize in exporting capita-intensive goods in exchange

for labor-intensive goods (Czinkota, Ronkainen, & Moffett, 1999). The differences between

these two types of countries are the purposes of trade. Only in these ways, countries can

sufficiently obtain comparative advantage with their maximized productivity through goods

exchanges (Glenn, 2006).

The HO theory assumes constant return and homogeneity. This means inputs and outputs

are positively correlated; also, goods are considered replaceable since branding or quality is not

considered as differentiation factors (Markusen, Melvin, Kaempfer, & Maskus, 1995). In

addition, HO theory assumes countries are in favor of each other’s strength and technology level

is similar (productions require similar technology inputs) between countries as well. In the case

of textile and apparel industry, production is labor-intensive and capita-extensive. Comparing

the industry’s characteristics with pervious assumptions, textile and apparel trade fits in this

theory appropriately(Glenn, 2006). As a result, developed countries should focus on the use of

33

capita to gain comparative advantage; whereas, less developed countries should focus on the use

of workforce to gain comparative advantage. In others words, developed countries can

consolidate and enhance their competitive advantage by conducting global sourcing with less

developed countries.

HO theory has been applied in various theoretical and empirical studies on the textile and

apparel trade issues. Zhang and Hathcote (2008) used this theory as a guideline for their study of

factors that influence apparel exports from China to the U.S. Walsh (2008) compared the

Weberian Location Theory (1929) (emphasis transportation costs determine location selection

the most) with HO theory in her study. The results show that HO theory is more relevant in

global sourcing practices, particularly in terms of production location selection. In addition,

Tengstam (2009) used HO theory to identify variables to study international clothing industry

location issues. Above studies all confirm that the validity and dependability of HO theory are

suitable for research on location changes of apparel productions. In this thesis study, HO theory

will serve as the research guideline due to its appropriate capacity in global sourcing.

The Pattern of Global Sourcing from Previous Studies

Studies that have been done on global sourcing issue in the U.S. textile and apparel

industry are not plentiful by any means due to its ever-evolving feature. Also, researches that are

focused on the longitudinal shifting patterns are even less. Findings of these kinds of research are

mostly either limited to textile segment’s or apparel segment’s shifts in a relatively short time

period. On the other hand, there is only one study that covers both the textile and apparel

industry. Su, Gargeya, and Richter (2005) have conducted a relatively in-depth study on global

sourcing shifts on both textile and apparel sectors. They used a data analysis technique called

34

cluster analysis to group supplying countries by certain similarities. The findings of this study

suggest sourcing from Mexico, Canada, and Caribbean Basin Initiative countries was expanded,

which resulted in South Korea and Taiwan to become less important when compared to the

1990s (Dyer & Dyer, 2001; Su et al., 2005). Mexico has shifted its position incredibly. Another

important finding of this research talks about cheap labor is no longer the only leading factor in

global sourcing, but also lead time and other factors. Global sourcing requires a fully developed

supply chain management, which means sourcing is not a solo work but a set of closely

connected works (Su et al., 2005).Their findings are significantly valuable for companies and

industry practitioners to adjust and develop their sourcing strategies. However, this study only

used two sets of data points, which were years of 1993 and 2002. The time gap between 2002

until nowadays creates a blind spot. As a result, a new research was demanded to fill the hold of

global sourcing knowledge in the U.S. textile and apparel industry.

Gereffi (1997) discovered that there is a dual trend in global sourcing of U.S. apparel

industry. The apparel production location has shifted from Asia to the Americas. Also the North

American supply shifted from assembly to full-package manufacturing services. The rising of

Mexico after the implementation of NAFTA and the Caribbean Basin countries with the

continued enforcement of CBI has gradually replaced Hong Kong, Taiwan, and South Korea;

meanwhile, China has declined little by little.

Kilduff (2005) studied the changing pattern of textile and apparel industry in the

perspective of firms’ strategies and structures throughout last four decades. He argued that

environmental changes, such as demand, technology and competition together had shaped and

re-shaped the industry significantly throughout three eras: the pre-industrial and early industrial

period, the mass production period, and the information period. Textile and apparel industry

35

transferred from simple to turbulent then chaotic environment. The market became less

predictable but more competitive. Responding to the environmental changes, firms’ structures

went from simple to integrated supply chain or merged large group, then specialized and

diversified. Also, strategies changed along with those two elements. The goal of strategies

changed from simple to supply chain coordination, then core competencies focuses .

Gereffi (2001)investigated the shifting patterns in terms of the U.S. apparel import shares

by country. He demonstrated the shifts of the U.S. apparel trade supply countries by creating a

position map. He draws the conclusion that China and Mexico are the two primary apparel

exporting countries to the U.S. since last decade and the rest of the world only has changed

modestly. This study identifies the significant shifts of the U.S. apparel. However, the data

analyzed only covers the time frame of 1990 until 1998, which leaves a time gap on this subject.

Also, the textile segment was not examined in this study.

Ji (2006) has done a study on strategic shifts in textile production from 1994 to 2006. The

author argues that the changes of the textile segment reflect the shifts of the apparel segment.

Also, the apparel segment pattern follows the path of textiles closely. Apparel production has

shifted from developed countries or high-cost countries to low-cost developing countries. The

author uses Textile Product Complex as the theoretical framework for this study. Textile Product

Complex is a model that illustrates linkages and activities between production, consumption,

trade, and material flows along the supply chain. It covers production of fiber, yarns, fabrics, and

end-use products. Suggestions were provided by the author, such as the use of other statistical

analysis methods might generate results that would yields benefits. The study mainly covers

textile segment as well as the apparel segment.

36

Martin (2007) have studied the trends of the U.S. textile and apparel trade impacted by

trade policies. His findings rejected the general prediction that most of the textile and apparel

sourcing would shift to China, which makes China the biggest winner in Free Trade era. There’s

no doubt that China has gained more exports to the U.S. market, but the rate is not as drastic as

projected. As a matter of a fact, India and Mexico, who are more covert, are the unassailable

importers that should not be overlooked. In fact, India captured the most significant growth in

the U.S. imports, and Mexico became a major beneficiary who has been gaining more and more

benefits from U.S. liberal trade policies, such as NAFTA (Dyer & Dyer, 2001).

Mutuc, Mohanty, Malaga, and Rejesus (2008) looked into the changing export patterns of

U.S. cotton industry after MFA elimination. The findings of this study suggested that China

would replace other countries that exported cotton apparel to the U.S., and become the largest

cotton apparel exporter using the U.S. cotton. Also, U.S. cotton that is exported to China will

increase (Mutuc et al., 2008). Although this research uses the most current data sets, they only

focus on the U.S. cotton market but do not cover the aggregate textile and apparel segments. This

study only brings out the changes from a niche perspective.

In summary, there are few studies that cover both textile and apparel segments on the

issue of the global sourcing shifting patterns. Furthermore, studies in this field tend to only focus

on single perspective rather than a comprehensive view. At last, most of those studies are not

updated enough. Since the U.S. textile and apparel industry is a highly responsive industry, the

need of timely updated information is always expected. There is a demand of new research that

will cover both textile and apparel segments with the latest available data sets but exclude bias.

This thesis aims to meet that request satisfactorily.

38

Research Questions

Based on the previous studies and the summary of literature review, there are four

questions are developed to be addressed in this study. They are

1. What were the shifting patterns of global sourcing in the U.S. textile and apparel

industry in the last two decades?

2. Who were the leading exporting countries to the U.S. textile and apparel markets?

3. What positions did those leading exporting countries hold?

4. Why the global sourcing patterns of U.S. textile and apparel industry had shifted

over the time period?

39

Chapter Three

Methodology

This research focuses on identifying the shifting patterns of global sourcing in the U.S.

textile and apparel industry for the past two decades. A series of trade data and important global

sourcing indicators as possible driving forces were gathered from various databases from

multiple highly reliable domestic and international organizations. These variables were

suggested by pervious literature reviews. This ensures the data is the most authoritative and

appropriate for this research. The research results will give an intergraded outlook of the track

and the direction of the changes in the global sourcing of the U.S. textile and apparel industry. In

addition, the study’s finding illustrates the position changing patterns in the perspective of

foreign exporters and contributes knowledge for future researches.

Data Selection

To meet the goal of this study, there are three main categories of variables that need to be

identified from the data of the U.S. textile and apparel imports. They are significant years, major

supplying countries, and global sourcing drivers’ indicators. From the literature review, the years

1996, 2002, 2006, and 2009 were identified as the critical years in terms of examining the import

shifts. The end of MFA and the founding of NAFTA became a strong effect on textile and

apparel trade in 1996. Entry of China into the WTO in 2001 started to stimulate international

trade drastically in 2002. A year after the end of the ATC stabilized the liberal trade environment,

which influenced textile and apparel trade radically in 2006. The most current serious economic

crisis has been striking business since the end of 2008, therefore 2009 trade data which can

represent the changes more accurately is selected for this study.

40

For the purpose of identifying major supplying countries in each assigned year, trade data

were analyzed. According to the particular situation of each assigned year, only certain numbers

of top countries that together accounted for more than 80% of U.S. textile and apparel imports

from the world are qualified as the major supplying countries. After the first initial country

screening processed for each assigned year, significant supplying countries that appeared in all

four investigated years were made to the final list of the country selection.

Based on the literature review, five important drivers were identified. They are trade

regulations, cost, logistic, cross-culture, and technology. Based on each of these drivers,

variables for statistical analysis were identified. Trade partnerships between countries or

international trade organizational membership status represents the level of tariffs, which reflect

the U.S. trade regulations assigned to the individual supplying country. Labor cost represents the

level of cost. Since the labor cost is too diverse to quantify, population, and GDP per capita are

used as representatives of labor properties, which directly impact the labor cost. Geographic

distance between countries and infrastructure level represent the logistic level. Language

represents cross-culture. Technology level represents technology. The U.S. textile and apparel

total import value and import volume are used separately in two cluster analyses to answer the

Research Questions. All the variables used in the statistical analysis are summarized in the Table

1.

Table 1 Summary of Variables Drivers

Trade Regulations Cost Logistic Cross-culture Technology

Variable 1 Tariff level

Population Distance Language Technology

Variable 2 GDP per capita Infrastructure

41

All variables for selected countries in each investigated year were analyzed by the cluster

analysis method respectively.

Data Sources

Office of Textile and Apparel (OTEXA) is a division of International Trade

Administration, which is a commerce bureau under the U.S Department of Commerce. The

mission statement of OTEXA has two main points. One is to improve the competitiveness of the

U.S. textile and apparel industry in the domestic scale as well as the international scale. The

other one is to help importers and retailers by smoothing the progress of fair trade that involves

textile and apparel. From the website of OTEXA, the most information that is related to the U.S.

textile and apparel trade can be found. For example, the major shipper reports, import data

reports, trade balance reports, free trade agreements, trade preference programs, and other

references or resources. For this thesis, trade statistic record that presents the annual value and

volume of the U.S. textile and apparel imports by category for all countries will be obtain from

this governmental maintain database respectively (Office of Textile and Apparel, 2007).

The World Economic Forum (WEF) is a non-profit and independent organization. Its

goal is through partnering industry leaders from around the world improve the quality of the

world (World Economic Forum, 2011). One of this organization’s significant contributions is its

annual published Global Competitiveness Report. Numerous governments, academia, and

independent researchers use its reports as references. In this study, a series of rating data, such as

Infrastructure Level and Technology Readiness, were collected from the Global Competitiveness

Reports.

Besides the above two authoritative organizations, International Monetary Fund (IMF),

Central Intelligence Agency (CIA), U.S. Census Bureau, and WTO are also used for this study’s

42

data collection. Tariff level data is yielded from WTO. Population data is from U.S. Census.

GDP per capita data is based on IMF records. Language reference is found from CIA’s The

World Factbook.

43

Cluster Analysis

To answer Research Questions 1 and 3 (listed previously), cluster analysis was used

because the multivariate technique is appropriate for recognizing groups of supplying countries

that share similar and dissimilar criteria. Cluster Analysis is not a new invented quantitative

research method. However, it is a relatively new innovative approach that was introduced by Su,

Gargeya, & Richter in a study of global sourcing shifting patterns in 2005. The result of analysis

generates different groups that are greatly similar or dissimilar from each other in terms of

characteristics (Kaufman & Rousseeuw, 1990). In the case of this study, findings will show the

changes of countries that shifted from one supply type to another over time, which is what would

be considered as shifting patterns.

In the past, how to group objects in certain ways is done in a subjective way. This means

that conducting clustering study highly depends on the researcher’s perception and opinion. In

this case, research results are less likely to be duplicated. Since the reliability of studies will not

be high, studies’ results are less valuable. Furthermore, in some more complicated or advanced

studies, which involve multivariate criteria, human eye-brain system cannot perform

classification function accordingly and efficaciously. As a result, a computable method is more

competent for carrying out clustering function in data that are complicated or just beyond

human’s observation abilities. In this study, cluster analysis was used. In detail, for the purpose

of getting the interpretable results, hierarchical technique with Between-groups Linkage method

was used (Kaufman & Rousseeuw, 1990; Wuensch, 2007).

The purpose of cluster analysis is to discover groups in data. How to group objects that

share similarities into classification is always an important subject. In many research disciplines,

one of the early learning processes of an object is to begin with observing the similarities and

44

dissimilarities. After that, the next process is to classify objects in various categories on the basis

of similarities and dissimilarities. “Basically, one wants to form groups in such a way that

objects in the same group are similar to each other, whereas objects in different groups are as

dissimilar as possible” (Kaufman & Rousseeuw, 1990, p. 15). This is the goal of cluster analysis

and yet the goal of this study. The objective of this research is to form groups of supplying

countries into classifications which are highly homogeneous and classifications, which are highly

heterogeneous from each other, in terms of exporting values. Classifying different supplying

countries of the U.S. textile and apparel products into certain categories identifies and

demonstrates the changing pattern that has occurred in global sourcing field for the U.S textile

and apparel industry throughout years.

After conducting cluster analysis, One-Way ANOVA is used to examine each group’s

characteristics in terms of homogeneous and heterogeneous. Each group’s position is defended

as one individual type that composed of different characteristics. “Significant differences

between clusters can be tested with ANOVA” (Burns & Burns, 2008, p. 567). In addition, based

on the variables’ ANOVA descriptive information, each variable are summarized into different

levels. Each level is suggested by individual variable’s mean value (Wuensch, 2007).

45

Chapter Four

Results and Analysis

Leading Exporting Countries

A simple longitudinal trade data analysis of leading exporting countries is provided for

the purpose of reviewing the overall picture of the U.S. textile and apparel imports. Textile

imports and apparel imports are examined separately. Trade data in Table 2, 3 show U.S. import

values of textile increased substantially from 1989 to 2009. U.S. total textile imports from the

world grew from U.S.$ 5.7 billion in 1989 to U.S.$ 17.9 billion in 2009, increasing by more than

214% in the last two decade. For the significant years, the U.S. total textile imports from the

world increased from U.S.$ 9.5 billion in 1996 to U.S.$ 15.2 billion in 2002, grew about 60%

over six years. From U.S. $ 15.2 billion in 2002 to U.S. $ 21.6 billion in 2006, imports grew

roughly 42% over four years. From U.S. $ 21.6 billion in 2006 to U.S. $ 17.9 billion in 2009,

imports decreased approximately 17% over three years.

Trade data in Table 4, 5 show U.S. import values of apparel also increased substantially

from 1989 to 2009. U.S. total apparel imports from the world grew from U.S.$ 21 billion in 1989

to U.S.$ 63.1 billion in 2009, increasing by more than 200% in the last two decade. For the

significant years, the U.S. total apparel imports from the world increased from U.S.$ 36.3 billion

in 1996 to U.S.$ 56.9 billion in 2002, grew about 57% over six years. From U.S. $ 56.9 billion in

2002 to U.S. $ 71.6 billion in 2006, imports grew roughly 26% over four years. From U.S. $ 71.6

billion in 2006 to U.S. $ 63.1 billion in 2009, imports decreased approximately 12% over three

years.

46

Table 2 U.S. Total Textile Import Value in U.S. Dollars (in Millions) Country 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

World 5702 5999 6445 7398 7863 8594 9304 9527 11175 12222 12948 China 698 816 908 1174 1317 1342 1282 1120 1536 1589 1759 Vietnam 0 0 0 0 0 0 1 0 0 1 2 India 229 236 280 356 396 415 516 549 662 771 859 Indonesia 68 67 84 109 133 146 153 167 276 314 273 Mexico 147 170 206 217 245 300 470 670 878 959 1083 Bangladesh 4 10 10 20 25 32 47 53 51 68 78 Pakistan 191 223 257 299 295 331 415 450 580 754 742 Honduras 4 5 7 5 2 3 3 3 4 6 6 Cambodia 0 0 0 0 0 0 0 0 0 2 2 El Salvador 15 16 17 17 17 23 25 27 27 33 35 Thailand 152 157 181 273 308 337 380 351 404 513 565 Italy 359 340 343 376 365 458 497 554 620 627 646 Canada 274 313 369 469 566 728 881 1047 1197 1335 1455 Sri Lanka 7 13 19 20 35 61 97 132 158 180 201 Guatemala 14 15 20 23 19 21 16 10 9 9 10 Philippines 63 75 60 63 75 106 162 202 249 327 364 Nicaragua 0 0 0 0 0 0 0 0 0 0 0 Egypt 30 30 51 42 47 64 85 57 82 109 92 Taiwan 633 645 676 693 664 675 706 758 741 717 735 South Korea 514 561 587 559 593 607 645 666 770 747 784 Jordan 0 0 0 1 2 2 1 2 1 1 1 Turkey 0 0 0 1 2 2 1 167 214 286 350 Peru 28 22 23 20 25 25 27 24 28 23 18 Dominican Republic 24 29 48 53 48 44 56 49 57 53 48 Malaysia 31 36 50 64 69 67 70 59 65 65 73 Israel 35 48 60 77 93 100 112 104 122 144 157 Japan 520 480 504 525 504 520 426 398 417 419 384 Hong Kong 183 190 213 223 181 200 202 170 166 194 209 Total of 28 Countries 4223 4496 4972 5678 6026 6608 7276 7791 9312 10245 10931 % Share of World 74% 75% 77% 77% 77% 77% 78% 82% 83% 84% 84% Source: Compiled from Official statistics of U.S. Department of Commerce, Office of Textiles and Apparel, http://otexa.ita.doc.gov.

47

Table 3 U.S. Total Textile Import Value in U.S. Dollars (in Millions) Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

World 14460 13779 15220 16272 18543 20492 21649 22487 21619 17901 China 2029 1934 3150 4351 5630 7262 8550 9578 9756 8257 Vietnam 3 1 57 110 157 156 174 199 202 263 India 955 916 1091 1210 1416 1640 1844 1934 2005 1754 Indonesia 325 338 287 218 217 206 231 225 213 160 Mexico 1280 1134 1195 1037 1108 1168 1079 1102 943 751 Bangladesh 89 104 107 91 88 85 84 88 96 113 Pakistan 914 992 1104 1200 1408 1646 1838 1672 1589 1445 Honduras 5 4 4 5 4 7 5 7 8 8 Cambodia 7 18 19 12 13 14 15 11 10 17 El Salvador 33 34 35 38 37 27 25 21 38 33 Thailand 627 624 485 360 399 317 284 293 311 232 Italy 729 670 674 761 834 790 759 796 705 430 Canada 1603 1577 1589 1549 1581 1571 1420 1241 953 831 Sri Lanka 206 193 114 57 36 27 20 17 9 5 Guatemala 11 9 11 12 13 15 12 13 11 8 Philippines 394 357 227 187 153 91 83 72 64 46 Nicaragua 1 0 0 0 0 0 0 0 0 1 Egypt 112 121 125 153 142 170 181 173 172 148 Taiwan 692 664 631 574 555 504 492 504 466 342 South Korea 808 749 819 761 771 754 753 698 614 525 Jordan 9 16 1 1 0 0 1 1 1 0 Turkey 415 406 488 486 595 665 586 587 529 386 Peru 23 13 12 12 15 21 21 19 23 20 Dominican Republic 26 23 11 4 7 6 3 4 9 11 Malaysia 71 53 55 52 52 48 53 37 27 16 Israel 174 192 204 225 254 256 241 221 247 234 Japan 404 313 289 301 352 346 355 391 373 276 Hong Kong 221 192 155 116 110 96 82 89 63 40 Total of 28 Countries 12167 11649 12939 13881 15948 17887 19192 19992 19436 16349 % Share of World 84% 85% 85% 85% 86% 87% 89% 89% 90% 91% Source: Compiled from Official statistics of U.S. Department of Commerce, Office of Textiles and Apparel, http://otexa.ita.doc.gov.

48

Table 4 U.S. Total Apparel Import Value in U.S. Dollars (in Millions) Country 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999

World 21047 21937 22595 26713 28216 31386 34649 36389 42827 48176 50795 China 2429 2739 2842 3409 3450 3589 3518 3769 4488 4312 4370 Vietnam 0 0 0 0 0 2 17 24 26 28 36 India 513 557 553 760 890 1105 1098 1187 1347 1517 1525 Indonesia 572 629 565 826 978 1025 1183 1326 1596 1659 1686 Mexico 500 508 673 901 1127 1594 2566 3560 5050 6494 7538 Bangladesh 324 429 440 705 741 896 1067 1125 1448 1627 1676 Pakistan 188 206 208 334 357 437 550 561 618 674 733 Honduras 87 113 196 365 506 645 918 1219 1659 1873 2158 Cambodia 0 0 0 0 0 0 0 2 99 359 585 El Salvador 42 54 90 166 251 398 582 721 1052 1171 1329 Thailand 378 437 515 717 824 897 1037 1049 1257 1452 1509 Italy 709 699 675 691 684 813 967 1149 1226 1348 1355 Canada 153 170 231 354 454 589 770 948 1204 1421 1598 Sri Lanka 354 425 486 635 805 832 928 1007 1204 1308 1269 Guatemala 129 191 330 451 546 591 682 796 962 1136 1233 Philippines 835 1022 999 1177 1262 1351 1540 1503 1597 1745 1792 Nicaragua 0 0 1 3 11 29 74 142 182 232 277 Egypt 41 62 75 112 149 191 234 255 307 356 333 Taiwan 2609 2334 2521 2339 2197 2154 2049 1974 2071 2115 1974 South Korea 2424 2158 1861 1867 1884 1841 1622 1381 1518 1891 2103 Jordan 1 6 1 11 12 19 15 10 3 3 2 Turkey 302 297 236 329 368 517 630 579 672 781 834 Peru 42 56 66 61 75 104 125 147 193 223 306 Dominican Republic 642 694 910 1203 1410 1572 1731 1753 2216 2342 2337 Malaysia 432 478 530 607 609 637 675 648 650 717 737 Israel 107 137 144 190 211 269 306 298 286 361 418 Japan 167 116 91 87 79 64 55 52 56 53 51 Hong Kong 3503 3609 3728 4094 3776 4205 4189 3861 3935 4428 4256 Total of 28 Countries 17484 18126 18968 22394 23654 26367 29131 31045 36923 41625 44019 % Share of World 83% 83% 84% 84% 84% 84% 84% 85% 86% 86% 87%

Source: Compiled from Official statistics of U.S. Department of Commerce, Office of Textiles and Apparel, http://otexa.ita.doc.gov.

49

Table 5 U.S. Total Apparel Import Value in U.S. Dollars (in Millions) Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

World 57232 56460 56963 61162 64768 68713 71630 73923 71568 63105 China 4499 4602 5594 7258 8928 15143 18518 22745 22923 23503 Vietnam 47 48 895 2375 2562 2725 3222 4359 5223 5068 India 1786 1717 1902 2002 2217 2976 3187 3170 3073 2846 Indonesia 2055 2215 2042 2158 2403 2875 3670 3981 4028 3861 Mexico 8413 7811 7424 6904 6685 6078 5297 4523 4015 3391 Bangladesh 2116 2101 1883 1848 1978 2372 2914 3103 3442 3410 Pakistan 920 932 878 1015 1138 1259 1412 1499 1490 1306 Honduras 2323 2344 2440 2503 2673 2622 2440 2511 2604 2032 Cambodia 808 935 1042 1240 1429 1713 2136 2425 2376 1871 El Salvador 1583 1612 1675 1720 1720 1619 1408 1486 1534 1298 Thailand 1820 1817 1719 1712 1799 1808 1840 1766 1668 1219 Italy 1400 1393 1357 1422 1427 1354 1309 1437 1333 885 Canada 1747 1585 1610 1569 1504 1273 1167 960 699 480 Sri Lanka 1472 1505 1413 1436 1549 1650 1682 1573 1467 1210 Guatemala 1487 1604 1658 1762 1947 1816 1666 1451 1388 1103 Philippines 1895 1891 1815 1853 1786 1830 2002 1722 1362 1016 Nicaragua 336 374 433 484 595 716 879 968 934 892 Egypt 406 387 348 382 422 444 625 697 742 742 Taiwan 2064 1811 1576 1611 1549 1134 1005 861 721 468 South Korea 2264 2182 2062 1806 1809 1155 913 627 505 281 Jordan 43 184 384 582 956 1083 1253 1145 971 765 Turkey 1048 1045 1190 1257 1169 944 726 559 402 257 Peru 383 371 384 504 677 800 844 814 794 600 Dominican Republic 2425 2252 2162 2124 2059 1849 1548 1057 841 613 Malaysia 781 761 720 686 712 678 686 683 639 457 Israel 476 447 416 396 336 289 243 191 173 118 Japan 66 132 171 221 290 87 66 69 47 31 Hong Kong 4486 4211 3877 3702 3849 3511 2811 2035 1553 277 Total of 28 Countries 49147 48268 49070 52530 56167 61802 65469 68418 66946 60003 % Share of World 86% 85% 86% 86% 87% 90% 91% 93% 94% 95%


50

While trade values have been drastically changing over time, the U.S. textile and apparel

import volumes are changing as well (Table 6, 7, 8, 9). The U.S. import volume of textiles

increased substantially from 1989 to 2009. U.S. total textile imports from the world grew from 6

billion units (in SEM) in 1989 to 25.2 billion units (in SEM) in 2009, increasing by more than

315% in the last two decades. For the significant years, U.S. total textile imports from the world

increased from 9.4 billion units (in SEM) in 1996 to 21 billion units (in SEM) in 2002, grew

approximately 124% over six years. From 21 billion units (in SEM) in 2002 to 29.6 billion units

(in SEM) in 2006, imports grew about 41% over four years. From 29.6 billion units (in SEM) in

2006 to 25.2 billion units (in SEM) in 2009, imports decreased roughly 15% over three years.

The U.S. import volume of apparel also increased substantially from 1989 to 2009. U.S.

total textile imports from the world grew from 6 billion units (in SEM) in 1989 to 21.3 billion

units (in SEM) in 2009, increasing by more than 252% in the last two decades. For the

significant years, U.S. total textile imports in the world increased from 9.6 billion units (in SEM)

in 1996 to 17.2 billion units (in SEM) in 2002, grew approximately 79% over six years. From

17.2 billion units (in SEM) in 2002 to 22.5 billion units (in SEM) in 2006, imports grew about

31% over four years. From 22.5 billion units (in SEM) in 2006 to 21.3 billion units (in SEM) in

2009, imports decreased roughly 5% over three years.

51

Table 6 U.S. Total Apparel Import Volume in SME unit (in Millions) Country 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999



52

Table 7 U.S. Total Apparel Import Volume in SME unit (in Millions) Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009



53

Table 8 U.S. Total Textile Import Volume in SME unit (in Millions) Country 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999



9

54

Table 9 U.S. Total Textile Import Volume in SME unit (in Millions) Country 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009



55

The main reasons for the dramatic changes of U.S. apparel imports were the competitive

advantages that the global sourcing offered, trade agreements influences, and economic crisis

impacts. The U.S. textile and apparel importers constantly changed their global sourcing

practices to meet the market’s varying environment and consumers’ shifting demands

accordingly. Tables above show the changing import values and volumes of the U.S. textile and

apparel imports from overseas respectively.

After a brief investigation of the trade data of the U.S. textile and apparel imports from

the world, 28 countries were yielded as the leading supplying countries. They are China,

Vietnam, India, Indonesia, Mexico, Bangladesh, Pakistan, Honduras, Cambodia, El Salvador,

Thailand, Italy, Canada, Sri Lanka, Guatemala, Philippines, Nicaragua, Egypt, Taiwan, South

Korea, Jordan, Turkey, Peru, Dominican Republic, Malaysia, Japan, Israel, and Hong Kong. This

piece of information addressed Research Question 2 for this study. In 1996, 2002, 2006, and

2009, U.S. imports of textiles from the 28 countries accounted for more than 80% of U.S. total

imports from the world. U.S. textile imports from these 28 countries aggregated 82%, 85%, 89%,

and 91% of the total U.S. textile import values from the world in 1996, 2002, 2006, and 2009

respectively. U.S. apparel imports from these 28 countries aggregated 85%, 86%, 91%, and 95%

of the total U.S. apparel import values from the world in 1996, 2002, 2006, and 2009

respectively. In addition, they accounted for 86%, 88%, 92%, and 93% of U.S. textile import

volumes and about 85%, 86%, 92%, and 96% of U.S. apparel import volumes from the world for

the same time period.

These 28 countries are the leading exporters to the U.S. market during the last two

decades. However, these selected countries contain some similar and some different

characteristics in terms of exporting textiles and apparel to the U.S. in 1996, 2002, 2006, and

56

2009. The further analysis of the U.S. global sourcing of textiles and apparel from these 28

countries is provided in the next section.

Cluster Analysis Results

After conducting cluster analysis, five types of countries from which the U.S. imported

textile in 1996, 2002, 2006, and 2009 are identified correspondingly (Tables 10, 11, 12, and 13).

The numbers of types is suggested based on statistic results. Types of countries from which the

U.S. imported apparel in 1996, 2002, 2006, and 2009 are also identified correspondingly (Tables

14, 15, 16, and 17). Import values and import volumes were both examined in these analyses for

a more comprehensive result. Thereafter, Research Question 1 was addressed accordingly by

analyzing the changes between these four years’ clustered types.

57

Table 10 1996's Textile Clusters 1996 Textile By Import Value By Import Volume Type 1 China

India China India

Type 2 Indonesia Mexico Thailand Italy Egypt South Korea Taiwan Jordan Peru Turkey Malaysia Israel

Indonesia Thailand Italy Philippines Egypt South Korea Taiwan Jordan Peru Turkey Malaysia Israel

Type 3 Canada Hong Kong

Mexico

Type 4 Philippines

Canada Hong Kong

Type 5 Japan Japan Ungroup countries (data not available)

Vietnam Bangladesh Pakistan Honduras Cambodia El Salvador Sri Lanka Guatemala Nicaragua Dominican Republic


Significant Variables Population (.000) GDP per capita (.004) Infrastructure (.000) Technology (.044)

Trade Volume (.000) Population (.000) GDP per capita (.003) Infrastructure (.004) Language (.011) Technology (.040)

Insignificant Variables Trade Value (.532) Tariff level (.757) Distance (.759) Language (-)

Tariff level (.717) Distance (.229)

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Table 11 2002's Textile Clusters 2002 Textile By Import Value By Import Volume Type 1 China

India China India

Type 2 Vietnam Vietnam Type 3 Indonesia

Mexico Bangladesh Honduras El Salvador Thailand Italy Sri Lanka Guatemala South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan

Indonesia Mexico Bangladesh Honduras El Salvador Thailand Italy Sri Lanka Guatemala South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan


Canada Hong Kong

Type 5 Philippines Philippines Ungroup countries (data not available)

Pakistan Cambodia Egypt Nicaragua


Significant Variables Trade Value (.061) Population (.000) GDP per capita (.060) Infrastructure (.030)

Population (.000) GDP per capita (.060) Infrastructure (.030)

Insignificant Variables Tariff level (-) Distance (.670) Technology (.565) Language (-)

Trade Volume (.169) Tariff level (-) Distance (.670) Technology (.565) Language (-)

59

Table 12 2006's Textile Clusters 2006 Textile By Import Value By Import Volume Type 1 China China Type 2 Vietnam Vietnam Type 3 India India Type 4 Indonesia

Mexico Bangladesh Pakistan Honduras Cambodia El Salvador Thailand Italy Sri Lanka Guatemala Philippines Egypt Nicaragua South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan

Indonesia Mexico Bangladesh Pakistan Honduras Cambodia El Salvador Thailand Italy Sri Lanka Guatemala Philippines Egypt Nicaragua South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan


Canada Hong Kong

Significant Variables Trade Value (.000) Population (.000) GDP per capita (.021) Infrastructure (.077) Language (.000) Technology (.041)

Trade Value (.000) Population (.000) GDP per capita (.021) Infrastructure (.077) Language (.000) Technology (.041)

Insignificant Variables Tariff level (-) Distance (.815)

Tariff level (-) Distance (.815)

60

Table 13 2009's Textile Clusters 2009 Textile By Import Value By Import Volume Type 1 China China Type 2 Vietnam

Indonesia Mexico Bangladesh Honduras El Salvador Thailand Italy Sri Lanka Guatemala Egypt South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan

Vietnam Indonesia Mexico Bangladesh Honduras El Salvador Thailand Italy Sri Lanka Guatemala Egypt South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan

Type 3 India India Type 4 Canada

Hong Kong Canada Hong Kong

Type 5 Philippines Philippines Significant Variables Trade Value (.000)

Population (.000) GDP per capita (.040) Infrastructure (.031) Technology (.031)

Trade Value (.000) Population (.000) GDP per capita (.040) Infrastructure (.031) Technology (.031)

Insignificant Variables Tariff level (-) Distance (.787) Language (-)

Tariff level (-) Distance (.787) Language (-)

61

Table 14 1996's Apparel Clusters 1996 Apparel By Import Value By Import Volume Type 1 China

India China India

Type 2 Indonesia Thailand Italy Philippines Egypt South Korea Taiwan Jordan Peru Turkey Malaysia Israel

Indonesia Thailand Italy Philippines Egypt South Korea Taiwan Jordan Peru Turkey Malaysia Israel

Type 3 Mexico Mexico Type 4 Canada


Type 5 Japan Japan Ungroup countries (data not available)





Insignificant Variables Tariff level (.757) Distance (.290)

Tariff level (.757) Distance (.290)

62

Table 15 2002's Apparel Clusters 2002 Apparel By Import Value By Import Volume Group 1 China

India China India

Group 2 Vietnam Vietnam Group 3 Indonesia

Bangladesh Honduras El Salvador Thailand Italy Sri Lanka Guatemala Philippines South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan

Indonesia Bangladesh Honduras El Salvador Thailand Italy Sri Lanka Guatemala Philippines South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan

Group 4 Mexico Mexico Group 5 Canada


Ungroup countries (data not available)



Significant Variables Trade Value (.000) Population (.000) GDP per capita (.075) Infrastructure (.062) Language (.000)


Insignificant Variables Tariff level (-) Distance (.518) Technology (.572)

Tariff level (-) Distance (.518) Technology (.572)

63

Table 16 2006's Apparel Clusters 2006 Apparel By Import Value By Import Volume Type 1 China China Type 2 Vietnam Vietnam Type 3 India India Type 4




Canada Hong Kong


Trade Value (.000) Population (.000) GDP per capita (.021) Infrastructure (.077) Language (.000) Technology (.041)

Insignificant Variables Tariff level (-) Distance (.815)

Tariff level (-) Distance (.815)

64

Table 17 2009's Apparel Clusters 2009 Apparel By Import Value By Import Volume Type 1 China China Type 2 Vietnam

Indonesia Mexico Bangladesh Honduras El Salvador Thailand Italy Sri Lanka Guatemala Egypt South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan

Vietnam Indonesia Mexico Bangladesh Honduras El Salvador Thailand Italy Sri Lanka Guatemala Egypt South Korea Taiwan Jordan Turkey Peru Dominican Republic Malaysia Israel Japan

Type 3 India India Type 4 Canada


Type 5 Philippines Philippines Significant Variables Trade Value (.000)

Population (.000) GDP per capita (.040) Infrastructure (.031) Technology (.031)

Trade Value (.000) Population (.000) GDP per capita (.040) Infrastructure (.031) Technology (.031)

Insignificant Variables Tariff level (-) Distance (.787) Language (-)

Tariff level (-) Distance (.787) Language (-)

65

Above Tables (10, 11, 12, 13, 14, 15, 16, and 17) show the results of cluster analysis for

each of the selected year’s textile and apparel import values and volume categories. After cluster

analysis, each year’s types are studied further. One-Way ANOVA is conducted to examine each

type’s different positions. For the purpose of this study, variables are placed into three levels

based on mean values. This is a very common classifications used in Cluster Analysis for the

purpose of data interpretations (Burns & Burns, 2008; Wuensch, 2007). Each level’s information

is shown in the code section of Tables 18, 19, 20, 21, 22, 23, 24, and 25.

66

Table 18 1996's Textile Clusters with Variables

Variable 1996 Textile Type

codes

1 2 3 4 5

Country

China India

Indonesia, Mexico, Thailand, Italy, Egypt, South Korea, Taiwan, Jordan , Peru, Turkey, Malaysia, Israel

Canada Hong Kong

Philippines

Japan

Import Value

H M M L M H: High M:Medium L: Low

Tariff level M L L L L H: High M:Medium L: Low

Population L S S S S L: Large S:small GDP (per capita)

L L M L H H: High M:Medium L: Low

Distance F F M F F F: Far M:Medium C:Close Infrastructure

M H H M H H: High M:Medium L: Low

Language N N E E N E:English N:Non-English Technology M H H M H H: High M:Medium L:

Low


codes

1 2 3 4 5

Country

China India

Indonesia, Thailand, Italy, Philippines, Egypt, South Korea, Taiwan, Jordan, Peru, Turkey, Malaysia, Israel

Mexico Canada Hong Kong

Japan

Import Volume

M L H L L H: High M:Medium L: Low



L L M M H H: High M:Medium L: Low

Distance F F M M F F: Far M:Medium C:Close Infrastructure

M M M H H H: High M:Medium L: Low

Language N N N E N E:English N:Non-English Technology M H M H H H: High M:Medium L:

Low

67



codes

1 2 3 4 5

Country

China India

Vietnam Indonesia, Mexico, Bangladesh, Honduras, El Salvador, Thailand, Italy, Sri Lanka, Guatemala, South Korea, Taiwan, Jordan, Turkey, Peru, Japan, Dominican Republic, Malaysia, Israel

Canada Hong Kong

Philippines

Import Value and Import Volume

H L L L L H: High M:Medium L: Low

Tariff level L H L L L H: High M:Medium L: Low

Population L S S S S L: Large S:small GDP (per capita) L L L H L H: High M:Medium L:

Low Distance F F M M F F: Far M:Medium

C:Close Infrastructure H M H H H H: High M:Medium L:

Low Language N N N E E E:English N:Non-English Technology M M M H M H: High M:Medium L:

Low

68


Variable 2006 Textile Type 1 2 3 4 5

Country

China Vietnam India Indonesia, Mexico, Bangladesh, Pakistan, Honduras, Cambodia, El Salvador, Thailand, Italy, Sri Lanka, Guatemala, Philippines, Egypt, Nicaragua, South Korea, Taiwan, Jordan, Turkey, Peru, Dominican Republic, Malaysia, Israel, Japan

Canada Hong Kong




Population L S L S S L: Large S:small GDP (per capita) L L L L H H: High M:Medium L:

Low Distance F F F F M F: Far M:Medium

C:Close Infrastructure M M M M H H: High M:Medium L:

Low Language N N N N E E:English N:Non-English Technology H H H H H H: High M:Medium L:

Low

69


Variable 2009 Textile Type 1 2 3 4 5

Country China Vietnam, Indonesia, Mexico, Bangladesh, Pakistan, Honduras, Cambodia, El Salvador, Thailand, Italy, Sri Lanka, Guatemala, Egypt, Nicaragua, South Korea, Taiwan, Jordan, Turkey, Peru, Dominican Republic, Malaysia, Israel, Japan

India Canada Hong Kong

Philippines



Tariff level L L L L L H: High M:Medium L: Low

Population L S L S S L: Large S:small GDP (per capita) L L L H L H: High M:Medium L:

Low Distance F F F M F F: Far M:Medium

C:Close Infrastructure H M M H M H: High M:Medium L:

Low Language N N N E E E:English N:Non-English Technology H M M H M H: High M:Medium L:

Low

70

Table 22 1996's Apparel Clusters with Variables Variable 1996 Apparel Type

codes

1 2 3 4 5

Country

China India

Indonesia, Thailand, Italy, Philippines, Egypt, South Korea, Taiwan, Jordan, Peru, Turkey, Malaysia, Israel

Mexico

Canada Hong Kong

Japan

Import Value H L H H L H: High M:Medium L: Low



L L L M H H: High M:Medium L: Low

Distance F F C M F F: Far M:Medium C:Close

Infrastructure M M M H H H: High M:Medium L: Low


Low Import Volume

M L H M L H: High M:Medium L: Low



L L L M H H: High M:Medium L: Low

Distance F F C M F F: Far M:Medium C:Close

Infrastructure M M M H H H: High M:Medium L: Low


Low

71


codes

1 2 3 4 5

Country

China India

Vietnam

Indonesia, Bangladesh, Honduras, El Salvador, Thailand, Italy, Sri Lanka, Guatemala, Philippines, South Korea, Taiwan, Jordan, Turkey, Peru, Japan, Dominican Republic, Malaysia, Israel

Mexico

Canada Hong Kong

Import Value M L L H M H: High M:Medium L: Low


Population L S S S S L: Large S:small GDP (per capita) L L L L H H: High M:Medium L:

Low Distance F F F C M F: Far M:Medium


Low Language N N N N E E:English N:Non-

English Technology H M H H H H: High M:Medium L:

Low Import Volume

M L L H L H: High M:Medium L: Low



L L L L H H: High M:Medium L: Low

Distance F F F C M F: Far M:Medium C:Close

Infrastructure H M H H H H: High M:Medium L: Low

Language N N N N E E:English N:Non-English

Technology M M M M H H: High M:Medium L: Low

72


codes 1 2 3 4 5

Country

China Vietnam

India Indonesia, Mexico, Bangladesh, Pakistan, Honduras, Cambodia, El Salvador, Thailand, Italy, Sri Lanka, Guatemala, Philippines, Egypt, Nicaragua, South Korea, Taiwan, Jordan, Turkey, Peru, Dominican Republic, Malaysia, Israel, Japan

Canada Hong Kong




Population L S L S S L: Large S:small GDP (per capita) L L L L H H: High M:Medium L:

Low Distance F F F F M F: Far M:Medium


Low Language N N N N E E:English N:Non-

English Technology H H H H H H: High M:Medium L:

Low

73


1 2 3 4 5 Country China Vietnam, Indonesia,

Mexico, Bangladesh, Pakistan, Honduras, Cambodia, El Salvador, Thailand, Italy, Sri Lanka, Guatemala, Egypt, Nicaragua, South Korea, Taiwan, Jordan, Turkey, Peru, Dominican Republic, Malaysia, Israel, Japan

India Canada Hong Kong

Philippines



Tariff level L L L L L H: High M:Medium L: Low

Population L S L S S L: Large S:small GDP (per capita)

L L L H L H: High M:Medium L: Low

Distance F F F M F F: Far M:Medium C:Close

Infrastructure H M M H M H: High M:Medium L: Low

Language N N N E E E:English N:Non-English

Technology H M M H M H: High M:Medium L: Low

74

Descriptions of Shifting Patterns by Country

Tables 10, 11, 12, and 13 demonstrate the different types of leading countries of U.S.

textile imports. China, India, Mexico, Canada and Hong Kong, Vietnam, and Philippines all

dominated more than one time of an individual type. Denominating means there is only one

country in a cluster and this country’s characteristics become one typical type of supplies. Also,

when a type is dominated, there is no country is similar to the dominated country in terms of

characteristics. Based on the unique characteristics of cluster analysis, each type is significantly

different from each other. Also, members within one type are significantly similar with one

another. Interpreting these in this study, China, India, Mexico, Canada and Hong Kong, Vietnam,

and Philippines are all very different in characteristics between each other. These countries are

clustered as a single type without other members. This uniqueness suggests that this study is to

invest each of these countries further. In addition, due to Canada and Hong Kong always appear

together and dominate a single type, it is worthy to study these two countries further as well. The

discussion below is based on the combined results of textile import value and volume since they

are highly similar.

In 1996 and 2002, China and India are grouped together. However, in 2006 these two

countries split into two individual types and remained in 2009. In 1996, 2002, 2006, and 2009,

Canada and Hong Kong remained as an individual type for four selected years. In 2002 and 2006,

Vietnam appeared as an individual type. In 2002 and 2009, Philippines appeared as an individual

type. In addition, Philippines appeared in 1996 as an individual type in terms of textile import

value only. In 1996, Mexico also appeared as an individual type in terms of textile import

volume only.

75

Tables 14, 15, 16, and 17 demonstrate the different types of leading countries of U.S.

apparel imports. China, India, Mexico, Canada and Hong Kong, Vietnam, and Philippines all

dominated one or more times of an individual type. This list is identical in the textile sector. The

discussion below is based on the combined result of apparel import value and volume since they

are the same.

In 1996 and 2002, China and India are grouped together. However, in 2006 these two

countries split into two individual types and remained in 2009. In 1996, 2002, 2006, and 2009,

Canada and Hong Kong remained as an individual type for four selected years. In 2002 and 2006,

Vietnam appeared as an individual type. In 1996 and 2002, Mexico appeared as an individual

type. In 2009, Philippines appeared as an individual type.

Overall, in both of textile and apparel sectors, only Canada and Hong Kong remained as

an individual type without changes over past two decades. The majority supplying countries have

been change over time. This finding reveals the dynamic characteristic in the U.S. textile and

apparel outsourcing. In addition, the difference between import value and volume is remarkably

small. One different member was found in 1996’s textile sector only. Furthermore, the

differences between the textile sector and the apparel sector are not significant. In 1996, one

different member was found between textile’s value and volume. In 2002, one different member

was found again in 2002 between textile sector and appeal sector. In 2006, the cluster lists

remain the same in both of textile sector and apparel sector, as well as import values and

volumes. The result of 2009 is the same situation. This finding is somewhat expected since many

of previous studies (Kilduff, 2005; M. F. Martin, 2007; Su et al., 2005) considered textile and

apparel as one combined category; meanwhile, few of the studies focused on only textile or

apparel sector.

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The following part includes further analysis of the supplying countries whose individual

characteristics are counted as one individual supplying type. They are China, India, Mexico,

Canada and Hong Kong, Vietnam, and Philippines. The patterns of each of the countries range

from 1996 to 2009 were examined and identified. Research Question 3 is addressed by the

following discussions. Textile sector’s value and volume results are different so that they are

discussed separately. On the other hand, apparel sector’s value and volume result are completely

identical so that are discussed as apparel imports in general. These statistic results’

interpretations3

China’s textile volumes changed from medium to high and remained as high. Tariff rates

changed from medium tariff to low and remained as low. Its infrastructure rankings improved

from the medium to high, then lowered to medium, and then improved to high again. China’s

trade values were high, populations were large, GDPs per capita were low, distances were far,

non-English speaking, and technologies were medium all the way.

are focused on variables’ changes within one country.

China’s apparel values and volumes changed from high to medium, and then increased to

high again. Its tariff rates changed from medium tariff to low and remained as low. Infrastructure

and technology rankings improved from the medium to high. China’s populations were large,

GDPs per capita were low, distances were far, and it is non-English speaking all the way.

India’s textile values changed from high to low. Its textile volumes changed from

medium to high, then reduced to low. Tariff rates change from medium to low. Infrastructure

rankings and technology levels changed from medium to high, and then lowered to medium.

3 Interpretations are purely based on statistic results. Since changes are measured by mean values which can be affected by other members within the group, descriptions may or may not correctly reflect one country’s realistic conditions when the country is clustered with others. For example, distance between countries will change if there is membership change within the cluster.

77

India’s populations were large, GDPs per capita were low, distances were far, and it is non-

English speaking all the way.

India’s apparel values and volumes changed from high to medium, and then decreased to

low. Tariff rates change from medium to low. Technology levels changed from medium to high,

and then lowered to medium. India’s populations were large, GDPs per capita were low,

distances were far, and it is non-English speaking all the way.

Mexico’s textile values changed from medium to low. Distances were changed from far

to medium, and then to far. Infrastructure rankings decreased from high to medium. Technology

levels changed between medium and high. Mexico’s tariff rates were low, populations were

small, GDPs per capita were low, and it is a non-English speaking country all the way.

Mexico’s textile volumes dramatically dropped from high to low. GDPs per capita

changed from medium to low. Distances were changed from medium to far. Infrastructure

rankings changed from medium to high, and then dropped to medium. Technology levels were

improved from medium to high, and then decreased to medium. Its tariffs were low, populations

were small, and it is a non-English speaking country all the way.

Mexico’s apparel values and volumes dramatically dropped from high to low. Distances

were changed from close to far. Technology levels improved from medium to high, and then

lowered to medium. Its tariffs were low, populations were small, GDPs per capita were low,

infrastructures were medium, and it is a non-English speaking country all the way.

Canada and Hong Kong have very moderate changes over the time. Their textile values

changed from medium to low. GDPs per capita were increased from medium to high. These

two’s textile volumes were low, tariff rates were low, populations were small, distances were

78

medium , infrastructure and technology rankings were high, and it is English-speaking all the

time.

Canada and Hong Kong’s apparels value and volumes dropped dramatically from high to

low. GDPs per capita were increased from medium to high. Their tariff rates were low,

populations were small, distances were medium, infrastructure and technology rankings were

high, and it is English-speaking all the time.

Vietnam’s textile values were changed from medium to low. Its tariff rates changed from

low to high, and then low. Infrastructure and technology levels were changed from high to

medium. Vietnam’s textile volumes were low, populations were small, GDPs per capita were

low, distances were far, and it is non-English speaking all the time.

Vietnam’s tariff rates changed from low to high, and then low. Technology levels were

changed from high to medium, and improved to high but decreased to medium again. Vietnam’s

apparel values and volumes were low, populations were small, GDPs per capita were low,

distances were far, infrastructure levels are medium, and it is non-English speaking all the time.

Philippines’s changes are moderate. Its infrastructure and technology levels went from

the middle to high, and then decreased to medium. It shifted between English-speaking and non-

English-speaking. Philippines’s textile values were always low, tariff rates were low, populations

were small, GDPs per capita were low, and distances were far.

Philippines’ infrastructure levels changed from medium to high, and then to medium.

Technology levels shifted between high and medium. It shifted between English-speaking and

non-English-speaking. Philippines’ textile volumes were low, tariff rates were low, populations

were small, GDPs per capita were low, and distances were far.

79

Philippines shifted between English-speaking and non-English-speaking. Technology

levels changed from high to medium. Philippines’s apparel values and volumes were low, tariff

rates were low, populations were small, GDPs per capita were low, distances were far, and

infrastructure levels were medium.

Other countries, such as Indonesia, Bangladesh, Honduras, El Salvador, Thailand, Italy,

Sri Lanka, Guatemala, Egypt, South Korea, Jordan, Turkey, Peru, Dominican Republic,

Malaysia, Israel, Japan, and Taiwan, also have changed over time. These countries were grouped

together as one supplying type. Their textile values changed from medium to low. Their

distances changed from far to medium, and then to far. Infrastructure levels decreased from high

to medium. Technology rankings shifted between high and medium. These countries’ tariff rates

were low, populations were small, GDPs per capita were low, and they are non-English-speaking

for all the time.

These countries’ distances changed from far to medium, and then to far. Infrastructure

levels changed from medium to high, and then to medium. Technology rankings shifted between

high and medium. These countries’ textile volumes were low, tariff rates were low, populations

were small, GDPs per capita were low, and they are non-English-speaking for all the time.

These counties’ technology rankings changed from high to medium. These countries’

apparel values and volumes were low, tariff rates were low, populations were small, GDPs per

capita were low, distances were far, infrastructure levels were medium, and they are non-

English-speaking for all the time.

Besides the individual country’s scope of shifting, the overviews of the U.S. global

sourcing patterns have changed from 1996 to 2009. The overall shifting patterns of supplying

types are summarized in charts and discussed in the following section.

80

Descriptions of Shifting Patterns by Type

Figures 5, 6, 7, and 8 show the shifting patterns of U.S. textile imports by value. Figure 5

shows, in 1996, Type 1 countries are high level of import value, medium tariff rate, large

population, low GDP per capita, far distance, medium infrastructure level, non-English speaking,

and medium level of technology. Type 2 countries are low level of import value, low tariff rate,

small population, low GDP per capita, far distance, medium infrastructure level, non-English

speaking, and high level of technology. Type 3 country is high level of import value, low tariff

rate, small population, low GDP per capita, close distance, medium infrastructure level, non-

English speaking, and medium level of technology. Type 4 countries are high level of import

value, low tariff rate, small population, medium GDP per capita, medium distance, high

infrastructure level, English-speaking, and high level of technology. Type 5 country is low level

of import value, low tariff rate, small population, high GDP per capita, far distance, high

infrastructure level, non-English speaking, and high level of technology.

Figure 6 shows, in 2002, Type 1 countries are medium level of import value, low tariff

rate, large population, low GDP per capita, far distance, medium infrastructure level, non-

English speaking, and high level of technology. Type 2 country is low level of import value, high

tariff rate, small population, low GDP per capita, far distance, medium infrastructure level, non-

English speaking, and medium level of technology. Type 3 countries are low level of import

value, low tariff rate, small population, low GDP per capita, far distance, medium infrastructure

level, non-English speaking, and high level of technology. Type 4 country is high level of import

value, low tariff rate, small population, low GDP per capita, close distance, medium

infrastructure level, English-speaking, and high level of technology. Type 5 countries are

81

medium level of import value, low tariff rate, small population, high GDP per capita, medium

distance, high infrastructure level, English speaking, and high level of technology.

Figure 7 shows, in 2006, Type 1 country is high level of import value, low tariff rate,

large population, low GDP per capita, far distance, medium infrastructure level, non-English

speaking, and high level of technology. Type 2 country is low level of import value, high tariff

rate, small population, low GDP per capita, far distance, medium infrastructure level, non-

English speaking, and high level of technology. Type 3 country is low level of import value, low

tariff rate, large population, low GDP per capita, far distance, medium infrastructure level, non-

English speaking, and high level of technology. Type 4 countries are low level of import value,

low tariff rate, small population, low GDP per capita, far distance, medium infrastructure level,

English-speaking, and high level of technology. Type 5 countries are low level of import value,

low tariff rate, small population, high GDP per capita, medium distance, high infrastructure level,

English speaking, and high level of technology.


large population, low GDP per capita, far distance, high infrastructure level, non-English

speaking, and high level of technology. Type 2 country is low level of import value, low tariff


English speaking, and medium level of technology. Type 3 country is low level of import value,

low tariff rate, large population, low GDP per capita, far distance, medium infrastructure level,

non-English speaking, and medium level of technology. Type 4 country is low level of import

value, low tariff rate, small population, high GDP per capita, medium distance, high

infrastructure level, English-speaking, and high level of technology. Type 5 countries are low

82

level of import value, low tariff rate, small population, low GDP per capita, far distance, medium

infrastructure level, English speaking, and medium level of technology.

Figure 5 1996's Five Types of Supplying Countries

0

2

4

6

8

10Import value

Tariff level

Population

GDP per capital

In-do Distance

Infrastructure

Language

Technology

1996 Textile Value

Type 1

Type 2

Type 3

Type 4

Type 5


83



84

Figures 9, 10, 11, and 12 show the shifting patterns of U.S. apparel import by volumes.

Figure 9 shows, in 1996, Type 1 countries are medium level of import volume, medium tariff



volume, low tariff rate, small population, low GDP per capita, far distance, medium

infrastructure level, non-English speaking, and high level of technology. Type 3 country is high

level of import volume, low tariff rate, small population, low GDP per capita, close distance,

medium infrastructure level, non-English speaking, and medium level of technology. Type 4

countries are medium level of import volume, low tariff rate, small population, medium GDP per

capita, medium distance, high infrastructure level, English-speaking, and high level of

technology. Type 5 country is low level of import volume, low tariff rate, small population, high

GDP per capita, far distance, high infrastructure level, non-English speaking, and high level of

technology.

Figure 10 shows, in 2002, Type 1 countries are medium level of import volume, low

tariff rate, large population, low GDP per capita, far distance, high infrastructure level, non-

English speaking, and medium level of technology. Type 2 country is low level of import

volume, high tariff rate, small population, low GDP per capita, far distance, medium

infrastructure level, non-English speaking, and medium level of technology. Type 3 countries are

low level of import volume, low tariff rate, small population, low GDP per capita, far distance,

high infrastructure level, non-English speaking, and medium level of technology. Type 4 country

is high level of import volume, low tariff rate, small population, low GDP per capita, close

distance, high infrastructure level, English-speaking, and medium level of technology. Type 5

85

countries are low level of import volume, low tariff rate, small population, high GDP per capita,

medium distance, high infrastructure level, English speaking, and high level of technology.

Figure 11 shows, in 2006, Type 1 country is high level of import volume, low tariff rate,


speaking, and high level of technology. Type 2 country is low level of import volume, high tariff


English speaking, and high level of technology. Type 3 country is low level of import volume,


non-English speaking, and high level of technology. Type 4 countries are low level of import



level of import volume, low tariff rate, small population, high GDP per capita, medium distance,

high infrastructure level, English speaking, and high level of technology.



speaking, and high level of technology. Type 2 country is low level of import volume, low tariff



volume, low tariff rate, large population, low GDP per capita, far distance, medium infrastructure

level, non-English speaking, and medium level of technology. Type 4 country is low level of

import volume, low tariff rate, small population, high GDP per capita, medium distance, high


86

level of import volume, low tariff rate, small population, low GDP per capita, far distance,

medium infrastructure level, English speaking, and medium level of technology.



87



88

Figures 13, 14, 15, and 16 show the shifting patterns of U.S. textile import by values.

Figure 13 shows, in 1996, Type 1 countries are high level of import value, medium tariff rate,


speaking, and medium level of technology. Type 2 countries are medium level of import value,

low tariff rate, small population, low GDP per capita, far distance, high infrastructure level, non-

English speaking, and high level of technology. Type 3 countries are medium level of import

value, low tariff rate, small population, medium GDP per capita, medium distance, high

infrastructure level, English-speaking, and high level of technology. Type 4 country is low level

of import value, low tariff rate, small population, low GDP per capita, far distance, medium

infrastructure level, English-speaking, and medium level of technology. Type 5 country is

medium level of import value, low tariff rate, small population, high GDP per capita, far distance,

high infrastructure level, non-English speaking, and high level of technology.

Figure 14 shows, in 2002, Type 1 countries are high level of import value, low tariff rate,


speaking, and medium level of technology. Type 2 country is low level of import value, high



low tariff rate, small population, low GDP per capita, medium distance, high infrastructure level,




level of import value, low tariff rate, small population, low GDP per capita, far distance, high


89



speaking, and high level of technology. Type 2 country is low level of import value, high tariff


English speaking, and high level of technology. Type 3 country is low level of import value, low

tariff rate, large population, low GDP per capita, far distance, medium infrastructure level, non-

English speaking, and high level of technology. Type 4 countries are low level of import value,

low tariff rate, small population, low GDP per capita, far distance, medium infrastructure level,

English-speaking, and high level of technology. Type 5 countries are low level of import value,

low tariff rate, small population, high GDP per capita, medium distance, high infrastructure level,

English speaking, and high level of technology.



speaking, and high level of technology. Type 2 country is low level of import value, low tariff







level of import value, low tariff rate, small population, low GDP per capita, far distance, medium


90



91



92

Figures 17, 18, 19, and 20 show the shifting patterns of U.S. textile import by volume.

Figure 17 shows, in 1996, Type 1 countries are medium level of import volume, medium tariff




infrastructure level, non-English speaking, and high level of technology. Type 3 country is high

level of import volume, low tariff rate, small population, medium GDP per capita, medium

distance, medium infrastructure level, non-English speaking, and medium level of technology.

Type 4 countries are low level of import volume, low tariff rate, small population, medium GDP

per capita, medium distance, high infrastructure level, English-speaking, and high level of

technology. Type 5 country is low level of import volume, low tariff rate, small population, high

GDP per capita, far distance, high infrastructure level, non-English speaking, and high level of

technology.

Figure 18 shows, in 2002, Type 1 countries are high level of import volume, low tariff

rate, large population, low GDP per capita, far distance, high infrastructure level, non-English

speaking, and medium level of technology. Type 2 country is low level of import volume, high



volume, low tariff rate, small population, low GDP per capita, medium distance, high

infrastructure level, non-English speaking, and medium level of technology. Type 4 country is

low level of import volume, low tariff rate, small population, high GDP per capita, medium

distance, high infrastructure level, English-speaking, and high level of technology. Type 5

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countries are low level of import volume, low tariff rate, small population, low GDP per capita,

far distance, high infrastructure level, English speaking, and medium level of technology.



speaking, and high level of technology. Type 2 country is low level of import volume, high tariff


English speaking, and high level of technology. Type 3 country is low level of import volume,


non-English speaking, and high level of technology. Type 4 countries are low level of import



level of import volume, low tariff rate, small population, high GDP per capita, medium distance,

high infrastructure level, English speaking, and high level of technology.



speaking, and high level of technology. Type 2 country is low level of import volume, low tariff



volume, low tariff rate, large population, low GDP per capita, far distance, medium infrastructure

level, non-English speaking, and medium level of technology. Type 4 country is low level of

import volume, low tariff rate, small population, high GDP per capita, medium distance, high


94

level of import volume, low tariff rate, small population, low GDP per capita, far distance,

medium infrastructure level, English speaking, and medium level of technology.



95



96

Finding and Discussions

The U.S. global sourcing patterns of textiles and apparel have been shifting during the

last two decades. A discussion addressing Research Question 4 is provided in this section.

Observations yield from Tables (26, 27, 28, and 29) are summarized below.

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Table 26 Types of Textile Import by Value Year 1996 2002 2006 2009 Type 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Import Value H M M L M H L L L L H L L L L H L L L L Tariff level M L L L L L H L L L L H L L L L L L L L Population L S S S S L S S S S L S L S S L S L S S GDP per capita (per capita) L L M L H L L L H L L L L L H L L L H L Distance F F M F F F F M M F F F F F M F F F M F Infrastructure M H H M H H M H H H M M M M H H M M H M Language N N E E N N N N E E N N N N E N N N E E Technology M H H M H M M M H M H H H H H H M M H M Market Share %

18

38

13 2 4

21

<1

36

11 1

39 1 9

33 7

46

30

10 5

<1

Table 27 Types of Textile Import by Volume Year 1996 2002 2006 2009 Type 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Import Volume

M L H L L H L L L L H L L L L H L L L L

Tariff level M L L L L L H L L L L H L L L L L L L L Population L S S S S L S S S S L S L S S L S L S S GDP (per capita)

L L M M H L L L H L L L L L H L L L H L

Distance F F M M F F F M M F F F F F M F F F M F Infrastructure

M M M H H H M H H H M M M M H H M M H M

Language N N N E N N N N E E N N N N E N N N E E Technology

M H M H H M M M H M H H H H H H M M H M

Market Share %

14

27

19

2 3 31

<1

37

16 1 4

1 1 6 36 8 4

8 34 7 4 <

1

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Table 28 Types of Apparel Import by Value Year 1996 2002 2006 2009 Type 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Import Value

H L H H L M L L H M H L L L L H L L L L


L L L M H L L L L H L L L L H L L L H L

Distance F F C M F F F F C M F F F F M F F F M F Infrastructure

M M M H H M M M M H M M M M H H M M H M

Language N N N E N N N N N E N N N N E N N N E E Technology

M H M H H H M H H H H H H H H H M M H M

Market Share %

13

26

10

14

<1

13 2 4

4 13 10 2

6 4 4 51 6 37 5

0 5 1 2

Table 29 Types of Apparel Import by Volume Year 1996 2002 2006 2009 Type 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 1 2 3 4 5 Import Volume

M L H M L M L L H L H L L L L H L L L L


L L L M H L L L L H L L L L H L L L H L

Distance F F C M F F F F C M F F F F M F F F M F Infrastructure

M M M H H H M H H H M M M M H H M M H M

Language N N N E N N N N N E N N N N E N N N E E Technology

M H M H H M M M M H H H H H H H M M H M

Market Share%

9 23 11 9 <1

12 2 47 13 7 29 4 4 51 3 40 49 4 <1 2

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Lasting Patterns by Type

Patterns that lasted the same are discussed in this sector. All lasting patterns are

supplying countries’ types that did not change over time. In terms of the U.S. textile imports by

value, between 2002 and 2006, there is one type of countries remains the same which is 2002’s

Type 4 and 2006’s Type 5. Between 2006 and 2009, there is one type of countries remains the

same which is 2006’s Type 5 and 2009’s Type 4. These repeatedly occurring types are the same

one. In the case of textile imports by volume, a same pattern showed.

In terms of the U.S. apparel imports by value, between 1996 and 2002, there is only one

type of countries remains the same which is 1996’s Type 2 and 2002’ Type 3. Between 2002 and

2006, there is one type of countries remains the same which is 2002’s Type 3 and 2006’s Type 4.

Between 2006 and 2009, there is only one type of countries remains the same which is 2006’s

Type 5 and 2009’s Type 4. Over all, there is no type of countries remains the same throughout

1996 to 2009.

In terms of the U.S. apparel imports by volume, between 2002 and 2006, there is only

one type of countries remains the same which is 2002’s Type 5 and 2006’s Type 5. Between

2006 and 2009, there is one type of countries remains the same which is 2006’s Type 5 and

2009’s Type 4. There is no type of countries remains the same throughout two decades as well.

There are slightly more repetitions occurred in the apparel section than the textile section.

However, within the textile sector, both import value and volume have extremely similar patterns.

On the other hand, within the apparel sector, import value has a slightly different pattern than

volume. Besides few patterns that remained the same, more patterns had shifted over time. The

following discussions are focused on the patterns that had shifted over time.

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Discussions of Shifting Patterns by Type

In the textile sector, based on the observation of the import pattern by value, under

relatively small changes, 1996’s Type 1 became 2002’s Type 1, 1996’s Type 2 became 2002’

Type 3, 1996’s Type 3 became 2002’s Type 4, and 1996’s Type 4 became 2002’s Type 5. In

addition, 1996’s Type 5 was totally replaced by 2002’s Type 2 due to the relatively large

changes. The ramifications from 1996 to 2002 have a main theme: decreasing import value,

lowing tariff level, increasing GDP per capita, improving infrastructure level, and decreasing

technology level. The negative changes in import value and positive changes in infrastructure are

especially significant. Most of shifting is logical and reasonable based on reality situations. For

example, China’s entry of WTO in 2001 brought down the tariff rate for China to export

merchandise to the U.S. The lower tariff along with improved infrastructure of China stimulated

more imports from China to the U.S. which eventually result in decreases imports in other

countries, such as Indonesia, Mexico, Thailand, Italy, Egypt, South Korea, Taiwan, Jordan, Peru,

Turkey, Malaysia, Israel, and etc. Canada and Hong Kong’s GDP per capita, which variable

represents cost in this study, is increased so that their exports are also decreased. Japan was

replaced by Vietnam due the Japan’s high GDP per capita is less significant than Vietnam’s high

tariff level.

Besides the import value shifting patterns, shifting patterns of textile import volume are

also identified. Under relatively small modifications, 1996’s Type 1 became 2002’s Type 1,

1996’s Type 2 became 2002’s Type 3, 1996’s Type 4became 2002’s Type 4, and 1996’s Type 5

became 2002’s Type 5. In addition, 1996’s Type 3 was totally replaced by 2002’s Type 2 due to

the relatively large changes. The ramifications from 1996 to 2002 have a main theme: increasing

import volume, decreasing tariff level, decreasing or increasing GDP per capita, shortening

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distances, improving infrastructure level, more English-speaking, and decreasing technology

level. The positive changes in infrastructure and the negative changes in technology are

especially significant. Most of shifting is logical and reasonable. For example, China with less

tariff rate and improvements in infrastructure gained more market and defeated Mexico, who

once used to be the dominator of the U.S. textile import market. Japan was also defeated by

Vietnam’s high tariff level and lower GDP per capita; even though Japan had higher technology

level than Vietnam. However, Philippines which are similar to Vietnam’s qualities with only

language and tariff differences were listed as an individual type. This shows language and tariff

are important considerations. Canada and Hong Kong remained in the same position although

their GDP per capita has gone up.

Since the textile import value and volume’s shifting patterns are the same in 2002, 2006,

and 2009, shifting patterns between these two periods are discussed together in the following two

paragraphs. Supplying Countries’ positions had changed from 2002 to 2006. With small changes,

2002’s Type 1 became 2006’s Type 1 and Type 3, 2002’s Type 2 became 2006’s Type 2, and

2002’s Type 3 became 2006’s Type 4. In addition, 2002’ Type 4 remained as 2006’s Type 5, and

2002’s Type 5 was eliminated. Infrastructure level decreased significantly and technology

increased dramatically are the majority trends of changes. Also, import is decreased in one type,

and distance was extended from medium to far. In 2000s, technology such as computer and

internet were more available and affordable which positively impact the technology level across

the board. Along with the last stage of ATC phased out in the end of 2005, China’s market share

surged like no others. To protect the U.S. domestic industry, government could not help but use

safeguard to restrict imports from China. Although China was restricted, the import gap between

China and India, who was clustered in the same type previously, reached a newer level that these

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two countries need to be split as two types even their other characteristics were the same. This is

a very interesting finding that two countries which showed similar abilities and capacities have

essentially different performance in U.S. textile market. In contrast to the changes, Canada and

Hong Kong remained in the same position although their GDP per capita has gone up.

Philippines, unlike Canada and Hong Kong who are also English-speaking countries, was

clustered to the type of countries like Indonesia, Mexico, Bangladesh, Pakistan, Honduras,

Cambodia, El Salvador, Thailand, Italy, Sri Lanka, Guatemala, Philippines, Egypt, Nicaragua,

South Korea, Taiwan, Jordan, Turkey, Peru, Dominican Republic, Malaysia, Israel, and Japan.

The major difference between Philippines and India is their population size and language. Since

Philippines was replaced by India which generated as an individual new type, language

preferences is less important than population size. On the other hand, Vietnam remained possibly

due to its higher tariff rate imposed by the U.S.. This means that language difference is less

important than tariff level in global sourcing. These shifting patterns between 2002 and 2006 are

significant, shifting patters between 2006 and 2009 are also meaningful.

Supplying countries’ positions had changed from 2006 to 2009. With moderate changes,

2006’s Type 1 became 2009’s Type 1, 2006’s Type 4 became 2009’s Type 2, and 2006’s Type 3

became 2009’s Type 3. In addition, 2006’s Type 5 remained as 2009’s Type 4, and 2006’s Type

3 is total replaced by 2009’s Type 5. Compare with previous years’ changes, the scale of this

period’s changes were much smaller. China remained its dominator position with the update of

infrastructure level. Except India whose technology level was decreased, other countries were

mostly in high level. Canada and Hong Kong remained again in the same position. Along with

Vietnam’s entry of WTO in 2007, its tariff level was lower so that this type of country is no

longer listed separately. Due to this change, Philippines which is similar to Vietnam, Indonesia,

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Mexico, Bangladesh, Pakistan, Honduras, Cambodia, El Salvador, Thailand, Italy, Sri Lanka,

Guatemala, Egypt, Nicaragua, South Korea, Taiwan, Jordan, Turkey, Peru, Dominican Republic,

Malaysia, Israel, and Japan, was raised up due to its language advantage. This confirmed that

language is inferior to tariff level in terms of U.S. textile global sourcing.

Paralleling with textile sector which had experienced various shifts over last two decades,

apparel sector had moderate amount of shifts over the same time period. Supplying countries of

U.S. apparel imports by value had changed their positions from 1996 to 2002. With moderate

changes, 1996’s Type 1 became 2002’s Type 1, 1996’s Type 3 became 2002’s Type 4, and

1996’s Type 4 became 2002’s Type 5. In addition, 1996’s Type 2 remained as 2002’s Type 3,

and 1995’s Type 5 is totally replaced by 2002’s Type 2. The ramifications from 1996 to 2002

have a main theme: decreasing import value, lowing tariff level, increasing GDP per capita,

decreasing infrastructure level, and improving technology level. The negative changes in import

value and positive changes in technology are especially significant. In next section, causes of

shifts are discussed.

Most of shifts are expected, and they are logical and reasonable. For example, China’s

entry of WTO in 2001 brought down the tariff rate for China to export merchandise to the U.S.

Unlike in the textile sector, the lower tariff rates and improved technological level of China did

not stimulate a higher import value. Instead, it decreased from high to medium level. In the case

of import volume, it remained the same from 1996 to 2002. As a result, U.S. importers paid less

for the same amount of apparel imports which means that prices were lowered. This situation is

resulted from the cost saving yielded from the reduction of tariff rates and China’s technological

improvement. Costs tend to have some negative relationships with imports. In the case of Canada

and Hong Kong, when their GDP per capita, which variable represents product cost in this study,

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increased, their exports to the U.S. decreased. Japan was replaced by Vietnam, due to the

significant difference cause by level of tariff. This means that tariff level is more important than

GDP per capita differences4

Between 2002 and 2006, supplying countries of U.S. apparel imports by value had

changed their positions again. With moderate changes, 2002’s Type 1 became 2006’s Type1 and


addition, 2002’s Type 3 remained as 2006’s Type4, and 2002’s Type 4 is eliminated. Changes

mainly reflected on import value. China took more market share so that other countries, such as

India, Canada, and Hong Kong, experienced import decreases. Indonesia, Mexico, Bangladesh,

Pakistan, Honduras, Cambodia, El Salvador, Thailand, Italy, Sri Lanka, Guatemala, Philippines,

Egypt, Nicaragua, South Korea, Taiwan, Jordan, Turkey, Peru, Dominican Republic, Malaysia,

Israel, and Japan all remained in the same position. In addition, Vietnam remained its status due

to a significant difference: high tariff level. Mexico lost its dominating position and replaced by

India’s large population. Even Mexico’s distance is significantly different than others, it was

placed into a type with different distance level; one the other hand, India become an individual

new type. This shows population is more important than distance. Although shifting patterns are

various, they are very similar to textile sector’s changes in the same period.

. Mexico improved its technology level and managed to keep its

position. Also, the type of Indonesia, Thailand, Italy, Philippines, Egypt, South Korea, Taiwan,

Jordan, Peru, Turkey, Malaysia, and Israel remained the same.

4 Even Japan had high level of GDP; the type of countries that Japan shifted to only had low level of GDP. Compare to Vietnam, the only differences is tariff level but it became an individual type instead of grouping into another similar type. In addition, Vietnam’s infrastructure was ranked as medium and Japan was high. However, Japan was clustered in the type has medium infrastructure instead of Vietnam who is actually belongs to this type. Infrastructure factor’s difference was ignored in fort of tariff factor. As a result, tariff level is more significant than GDP.

105

Besides apparel import value patterns had changed, the shifting patterns of apparel import

volume were different as well. Based on observations of the shifting patterns of U.S. apparel

import volume, between 1996 and 2002, positions had changed. With moderate changes, 1996’s

Type 1 became 2002’s Type 1, 1996’s Type 2 became 2002’s Type 2, 1996’s Type 3 became

2002’s Type 4, and 1996’s Type 4 became 2002’s Type 5. In addition, 1996’s Type 5 is totally

replaced by 2002’s Type 2. Changes are moderate compare with apparel imports by value. They

are: decreasing import volume, lowering tariff level, improving infrastructure, and decreasing

technology level. Positive changes on infrastructure happened to most types, such as China,

Mexico, Indonesia, Thailand, Italy, Philippines, Egypt, South Korea, Taiwan, Jordan, Peru,

Turkey, Malaysia, and Israel. Beside China experienced tariff reduction result from its entry of

WTO in 2001, Canada and Hong Kong experienced import volume decrease. Japan is replaced

by Vietnam. This reflected that Vietnam’s high tariff level is significantly important in global

sourcing. Japan was replaced by Vietnam’s high level of tariff. This means that tariff level is

more important than GDP per capita differences. It seems like tariff level has high priority to

GDP per capita.

After analyzing the patterns between 1996 and 2002, shifting positions between 2002 and

2006 are discussed as well. With some small changes, 2002’s Type 1 became 2006’s Type 1 and


addition, 2002’s Type 5 remained as 2006’s Type 5, and 2002’s Type 4 is eliminated. Shift

occurred in terms of increasing or decreasing import volume. Also, decreasing infrastructure

level and increasing technology level happened to most of the types respectively. China’s import

volume increased along with ACT restriction’s further phased out which impacted other

countries more or less. This caused similar effect on India in the textile sector. India became an

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individual type even its qualities is as similar to China. In the case of Mexico, it lost its

dominator position in 2002 by reductions of import volume. Although Mexico has very

competitive geographical condition over other countries, its type is no longer significant in 2006.

Mexico lost its dominating position to India who had large population. Even Mexico’s distance

is significantly different than others, it was placed into a type with different distance level; one

the other hand, India become an individual new type. This shows population is more important

than distance.

Since the apparel import value and volume’s shifting patterns are as same as textile sector in

2006 and 2009, shifting patterns is not discoed repeatedly.

Overall, the textile sector has more fluctuations in pattern shifting when comparing with

apparel sector. Most of the shifting patterns can be explained by the information in the literature

review. Based on the overall shifting patterns, there is no one single type of countries remains the

same regardless textile or apparel sectors by either import value or import volume. The global

sourcing in the U.S. textile and apparel industry has been changing constantly. This finding is

consistent with previous research findings that global sourcing of U.S. textile and apparel is

dynamic and variable.

The findings from analyzing shifting patterns over the years in different sectors are

valuable and meaningful. The next section summarizes the variables’ roles in generating five

different types of supplying modes to the U.S. based on the previous discussions.

Roles of Variables

There are eight variables included in this study. They are import level (by either value or

volume), tariff level, population, GDP per capita, geographic distance, infrastructure level,

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language commonality, and technology level. There are some interrelationships between these

variables revealed by the previous discussion. Without considering import and technology level,

language is the least significant in global sourcing of U.S. textile and apparel; on the other hands,

tariff level and population are both more important variables. This is because tariff rate is more

influential than GDP per capita level, infrastructure, and language, population is more important

than language and distance, and distance is more important than language. These two major

driving forces of global sourcing are indeed essential. Although textile industry is less considered

as labor intensive industry, the large amount of work forces that apparel industry requires make

the U.S. textile and apparel industry as a whole demands abundant workers from the labor force.

This is because apparel sector occupied a very large proportion of the combined demand. This

unique industrial characteristic foreordains the U.S. global sourcing favors those large population

supplying countries. Not only nature irresistible incident, such as large labor demand, drives the

shifting direction of global sourcing; but also the acquired irresistible force, such as

governmental interferes and trade regulations, should not be overlooked. Different supplying

countries have different tariff rates and quota limitations which directly impact the availabilities

of U.S. textile and apparel imports from overseas. In this case, tariff level which represents the

degree of trade regulation, is suggested as a more important variable. These two findings are

meaningful. They not only contribute to the body of knowledge for academia, but also provide

important insights for the industrial practitioners. People can use this study as supplementary

information when they try to understand the complicated global sourcing patterns and strategies

adopted by the U.S. textile and apparel industry.

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Summary

Global sourcing shifts are the results of continued trade liberalization, such as China’s

and Vietnam’s WTO entry in 2001 and 2007 respectively, ATC quota restriction’s phased out,

and an increasing number of free/preferential trade agreements. These changes in the U.S. textile

and apparel trade environment forced the U.S. importers to constantly adjust their sourcing

strategies. Over the years, China has become the most dominating suppler, while other nearby

Asian countries or areas, such as Hong Kong who was important trade partner with the U.S. in

the 1990s, became less important in the last decade. Imports from this region have shifted to

China and possibly India where they have large and cheap workforce. Although there is other

Asian countries, such as Vietnam, have been increasing their market share, China still hold the

most advanced position. Vietnam was a relatively new country in the global textile and apparel

export market, which rose in the last decade. Due to the lack of distinguished advantages, it

could not challenge China. Thanks to significant improvements on infrastructure and

technological levels, China’s strengthened and consolidated its domination. Due to the increasing

advantages that China holds, many other countries lost their shares in the U.S. import market.

Mexico is one of them. It has the unbeatable geographical adjacency advantages and relatively

cheaper labor cost. However, Mexico lacks other improvements which made it lose its position

in recent years. Besides Mexico, India also lost its exaltation of the market. India has been

competing with China over years. From the shifting patterns, they were grouped together in 1996

and 2002, and then separated in 2006 and 2009. The major forces that pull these two apart are

differences of infrastructure and technology levels. Like China, India also had larger labor force

and low price, and even better it has WTO membership for a long time while China was not.

However, India failed to keep up their trade performance with China due to lack of infrastructure

109

and technological supports. From the shifting patterns, China has been in the most important

exporting position for the last decades. Through exerting its connatural characteristics along with

its endless acquired efforts, China managed to stay on top of the U.S. textile and apparel import

marker over time. This finding is consistent with Mutuc et al. (2008) and Gereffi (2001) that

China will be the largest supplying country of the U.S. textile and apparel market. Furthermore,

this study disagree with other studies (G. Gereffi, 1997; G. Gereffi, 2001; M. F. Martin, 2007; Su

et al., 2005) which did not recognize China’s exclusively advanced position in U.S. market.

The U.S. global sourcing drivers including trade regulation, cost, logistic, culture, and

technology, always have significant impacts on the U.S. textile and apparel imports from the

world. However, trade regulations and cross-cultural issues mostly grew toward the stable and

positive direction. For example, more and more countries became normalized trade partners, who

enjoy lower tariff rate, with the U.S.. New trade agreements were signed to promote a more

liberal trade environment. In terms of cross-culture, the usages of English in non-English-

speaking countries are increasing. Although cultural differences add risks and uncertainties to

global sourcing, the overall impact is relatively small within the countries that were studies in

this work. Thanks to foraging supplying countries’ governmental supports, both trade regulation

and culture drivers have been facilitating textile and apparel trade positively. Nevertheless, cost,

logistic, and technology drivers require different levels of input and much more effort to

maintain a competitive position. The improvement of logistics and technology is especially rapid

across the board. Limited investments on logistics and technology could result in failing

country’s failure in obtaining competitive advantages in textile and apparel trade. Sometimes,

when the progress made by a country is not big enough compared to its competitors, the gap

between these countries will become bigger and bigger. Therefore, logistics and technology are

110

very critical drivers for supplying countries to achieve competitiveness. At last, cost driver is a

very complicated issue. Costs of textile and apparel imports include labor cost, material cost,

logistic cost, tariff cost, and any other tangible or intangible costs. Although costs have multiple

perspectives, there is only one expectation of it: low. This is essentially important when the

enormous global economic systems are in a serious recession. The U.S. consumer’s textile and

apparel demands have been dropping since 2007 when the economic crisis started. Since then,

the U.S. textile import values have declined about 3.7% but volume has grown 0.6% in 2008. In

2009, both the U.S. textile import value and volume declined about 17.2% and 8.6% respectively.

Similarly, both U.S. apparel import values and import volume declined about 3.2% and 2.7% in

2008 respectively. This decreasing trend continued in 2009, both the U.S. apparel import value

and import volume declined about 11.8% and 6% respectively. The further decline shows the

recovery of the U.S. economic crisis cannot be foresee easily at this moment. Over all, these five

drivers have been driving the shifts of U.S. global sourcing in textiles and apparel. Based on this

study’s findings, trade regulations and cost are the more important factors.

The discussion of analysis results has underscored Gereffi’s Global Commodity Chain

theory and Heckscher and Ohlin’s OH theory. Global Commodity Chain theory argued that

within a supply chain each sector accounts for certain degrees of economic surplus. However, in

a buyer-driven supply chain, the surplus is allocated less towards upstream of the chain and more

to the downstream. The upstream are manufacturers who are mostly based in underdeveloped

countries and make the relatively small amount of profit. On the other hands, the downstream are

retailers who are based in developed countries and make the relatively large portion of profit. In

the case, most of the important supplying countries of U.S. textile and apparel are

underdeveloped countries. Although few are developed countries, the shifting patterns show that

111

their position became less and less important and their market share had dramatically shrunken

over time. The finding of this study supports this theory’s argument thoroughly. OH theory

emphasized that underdeveloped countries have comparative advantages on labor intensive

exports; meanwhile, developed countries should focus on capita-intensive exports. In this case,

U.S. textile and apparel supplying countries tend to fall in these two categories appropriately.

This study find that underdeveloped countries export large volume of labor-intensive but capita-

extensive textile and apparel to the U.S., while developed countries export small volume of high

value products. In addition, OH theory argued that production cost is the essential condition of

creating comparative advantage for supplying countries. In this study, cost is also considered

more important than other drivers. OH theory is supported by this study’s findings.

This study successfully identified meaningful shifting patterns by adopting cluster

analysis method. Since cluster analysis was used in few studies of identifying shifting patterns,

this study added recognitions to this method. This is important to apply relatively new testing

concept in analysis. This study provided some new information for literature, as well as

methodology references for future researches. Compare to those studies only observed single

perspective of global sourcing, this study provided a more comprehensive investigation. Five

shifting drivers were examined simultaneously, and the results are meaningful. The

comprehensive analysis extended the literature. Besides the contributions to academia, industry

can also be benefited by this study’s findings. The shifting patterns illustrate the trend of

supplying countries to the U.S. market. If supplying country is continually rising, it means the

U.S. market responses to this country positively and perceives it as an attractive trade partner,

otherwise, vise verse. This can be valuable information to the industry for better understanding

the movements of the U.S. textile and apparel market globally. Also, industry can adjust their

112

sourcing strategy based on evaluating their current supplying countries and identify their position.

The more the industry understands the complicated and challenging practices of global sourcing,

the better decisions can be made for future success. Global sourcing involves a high level of

complexity but the outcomes are valuable and desirable for the U.S. textile and apparel importers.

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Chapter Five

Conclusion

Overall, this study made the following six main contributions. First, global sourcing of

the U.S. textile and apparel industry has shifted between 1996 and 2009. The shifting patterns

clearly revealed the trends in both textile and apparel sectors. Second, all investigated drivers of

global sourcing were developed based on what U.S. buyers demand, such as lower costs, less

governmental restrictions, shorten lead time, minimizing risks, simplifying business interactions,

and other individual conditions. Third, this study identified the 28 leading supplying countries

and areas: China, Vietnam, India, Indonesia, Mexico, Bangladesh, Pakistan, Honduras,

Cambodia, El Salvador, Thailand, Italy, Canada, Sri Lanka, Guatemala, Philippines, Nicaragua,

Egypt, Taiwan, South Korea, Jordan, Turkey, Peru, Dominican Republic, Malaysia, Japan, Israel,

and Hong Kong. Fourth, though there are 28 competitors, China is the one and only ultimate

dominator of U.S. textile and apparel imports throughout the last decade. Other competing

countries could not challenge China’s position. Fifth, the competition of the textile and apparel

trade is furious. Also, the changes of this industry are dramatic. As a result, the shifting patterns

from 1996 to 2009 are mostly various and dissimilar. These constant changes increased the

complexity and difficulty of global sourcing in U.S. textile and apparel. Finally, global sourcing

shifts can be caused by many factors, such as modifications of trade regulations, cost, logistic

developments, cultural differences, and technology improvements. Although the scope of trade

regulation and cost are more significant than other drivers, they are affecting the global sourcing

activities done by the U.S. textile and apparel industry simultaneously. This study answered the

proposed questions appropriately and made some new contributions to the knowledge on this

subject.

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Limitation, and Recommendation

Although this study covered multiple perspective of global sourcing, there are some

limitations which should be mentioned. First, cluster analysis is a relatively new statistical

method used for this kind of analysis. The applicability of this method still needs further

verification from more empirical studies. Second, although variables are suggested by previous

studies, their statistic significances in this study are not all high. This can be caused by the lack

of previous experience since this set of variables was not empirically tested before as a whole.

Also, there is no an absolutely appropriate clustering method for analyzing the data. There are

many different types of cluster analyses with a large freedom of modifications of setting. Each of

these changes may yield a different result. Third, the variables’ measurements may or may not be

the most reliable. This is resulted from the diversity of variables, such as measurements of costs

(labor, material, transportation, etc.). In addition, evaluation of logistic, technology, and cross-

culture can be dramatically vary. In fact, there is no way to quantify some of these drivers

perfectly. Fourth, countries’ positions are determined by the ANOVE result which is based on

the mean value of each variable. This can be problematic since the mean value can be affected by

outlier from other members within type. Therefore, some countries’ interpretation is less reliable.

Although the limitations of the research are basically unavoidable, more studies should

be conducted to further understand the U.S. sourcing across the world. Here are some

recommendations for future studies.

First, besides the quantitative analysis, qualitative research can be carried out with a

different perspective for understanding this topic. Surveys about each of the leading countries’

performances can be conducted with the current U.S.’s textile and apparel importing companies.

The format and the content of these surveys can be various. Researchers can ask the importers to

115

rate the competiveness of each of the supplying countries, or satisfactions of each of the

suppliers from different countries. The point is to gain firsthand information from the importers,

such as how they perceive different supplying countries in terms of textile or apparel trade.

Second, combining both quantitative analysis and qualitative research together will

produce a more comprehensive understanding of this subject. Research can collect secondary

data and survey data, then analysis them both for greater understanding.

Third, applying this study’s analytical method to the specific category of trade goods

within the scope of textile and apparel, to test if the result would still hold, could provide

advantageous results. This can examine how applicable this method can be. In addition, this

method also can be used to test other types of trade goods that are not textile or apparel.

Fourth, for more advanced studies, Structural Equation Modeling (SEM) can be used to

test the relationships between each variable. With this technique, researchers can enhance the

study to another level by further analyzing the structural issues of this proposed analysis method.

In sum, this study only examined a very niche subject of the U.S. textile and apparel trade.

There are many other researches that can be done in this field. Researchers just need to be

creative and explore new areas in the field.

116

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APPENDIX Cluster Analysis and ANOVA Statistical Results

1996 Textile Value Cluster

Agglomeration Schedule Stage Cluster Combined

Coefficients Stage Cluster First Appears

Next Stage Cluster 1 Cluster 2 Cluster 1 Cluster 2

1 5 14 2.010 0 0 2 2 5 9 2.612 1 0 3 3 3 5 3.743 0 2 6 4 10 15 3.970 0 0 6 5 4 13 4.163 0 0 7 6 3 10 5.690 3 4 7 7 3 4 9.413 6 5 11 8 1 2 9.874 0 0 16 9 11 12 9.881 0 0 12 10 6 17 10.575 0 0 13 11 3 7 10.715 7 0 12 12 3 11 12.445 11 9 14 13 6 16 14.578 10 0 15 14 3 8 14.582 12 0 15 15 3 6 19.710 14 13 16 16 1 3 25.059 8 15 0

Cluster Membership

Case 5 Clusters 1:China 1 2:India 1 3:Indonesia 2 4:Mexico 2 5:Thailand 2 6:Canada 3 7:Italy 2 8:Philippines 4 9:Egypt 2 10:Korea, South 2 11:Taiwan 2 12:Jordan 2 13:Peru 2 14:Malaysia 2 15:Israel 2 16:Japan 5 17:Hong Kong 3

Oneway

Descriptives

N Mean

Std. Deviation Std. Error

95% Confidence Interval for Mean Minimu

m Maximu

m Lower Bound Upper Bound Trade 96 1 2 .50 .707 .500 -5.85 6.85 0 1

2 11 .82 .405 .122 .55 1.09 0 1 3 2 1.00 .000 .000 1.00 1.00 1 1 4 1 1.00 . . . . 1 1

5 1 1.00 . . . . 1 1 Total 17 .82 .393 .095 .62 1.03 0 1

Pop 96 1 2 1082803.50 205171.982 145078.500 -760593.62 2926200.62 937725 1227882 2 11 54027.18 55929.800 16863.469 16453.03 91601.33 4282 201118 3 2 18209.00 16711.762 11817.000 -131940.22 168358.22 6392 30026 4 1 74336.00 . . . . 74336 74336 5 1 125645.00 . . . . 125645 125645 Total 17 176253.24 348618.576 84552.424 -2989.90 355496.37 4282 1227882

GDP.96 1 2 544.85 218.679 154.629 -1419.91 2509.60 390 699 2 11 7754.02 7671.474 2313.036 2600.26 12907.79 1162 22164 3 2 22669.72 2705.277 1912.920 -1636.23 46975.67 20757 24583 4 1 1206.14 . . . . 1206 1206 5 1 36930.26 . . . . 36930 36930 Total 17 9991.75 11050.405 2680.117 4310.16 15673.35 390 36930

Distance 1 2 7210.50 381.131 269.500 3786.18 10634.82 6941 7480 2 11 6443.82 2556.673 770.866 4726.22 8161.41 1882 10163 3 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 4 1 8570.00 . . . . 8570 8570 5 1 6790.00 . . . . 6790 6790 Total 17 6429.65 2624.701 636.583 5080.15 7779.14 455 10163

inf 96 1 2 6.7950 .64347 .45500 1.0137 12.5763 6.34 7.25 2 11 9.7982 1.39504 .42062 8.8610 10.7354 7.57 12.35 3 2 14.6400 .90510 .64000 6.5080 22.7720 14.00 15.28 4 1 5.9900 . . . . 5.99 5.99 5 1 13.5800 . . . . 13.58 13.58 Total 17 10.0129 2.66505 .64637 8.6427 11.3832 5.99 15.28

English 1 2 1.00 .000 .000 1.00 1.00 1 1 2 11 1.00 .000 .000 1.00 1.00 1 1 3 2 .00 .000 .000 .00 .00 0 0 4 1 .00 . . . . 0 0 5 1 1.00 . . . . 1 1 Total 17 .82 .393 .095 .62 1.03 0 1

Tech 96 1 2 2.6850 .07778 .05500 1.9862 3.3838 2.63 2.74 2 11 3.5236 .63192 .19053 3.0991 3.9482 2.63 4.29 3 2 3.9450 .33234 .23500 .9590 6.9310 3.71 4.18 4 1 3.0000 . . . . 3.00 3.00 5 1 5.1500 . . . . 5.15 5.15 Total 17 3.5394 .74105 .17973 3.1584 3.9204 2.63 5.15

NonVa96

1 2 834.50 403.758 285.500 -2793.12 4462.12 549 1120 2 11 349.82 371.900 112.132 99.97 599.66 0 1047 3 2 362.00 271.529 192.000 -2077.59 2801.59 170 554 4 1 202.00 . . . . 202 202 5 1 398.00 . . . . 398 398 Total 17 402.41 359.437 87.176 217.61 587.22 0 1120

ANOVA

Sum of Squares df Mean Square F Sig. Trade 96 Between Groups .334 4 .084 .469 .757

Within Groups 2.136 12 .178 Total 2.471 16

Pop 96 Between Groups 1.871E12 4 4.677E11 76.201 .000 Within Groups 7.366E10 12 6.138E9 Total 1.945E12 16

GDP.96 Between Groups 1.358E9 4 3.395E8 6.836 .004

Within Groups 5.959E8 12 4.966E7 Total 1.954E9 16

Distance Between Groups 1.483E7 4 3707409.811 .466 .759 Within Groups 9.540E7 12 7949601.553 Total 1.102E8 16

inf 96 Between Groups 92.945 4 23.236 13.474 .000 Within Groups 20.695 12 1.725 Total 113.640 16

English Between Groups 2.471 4 .618 . . Within Groups .000 12 .000 Total 2.471 16

Tech 96 Between Groups 4.677 4 1.169 3.414 .044 Within Groups 4.110 12 .342 Total 8.786 16

NonVa96 Between Groups 447277.981 4 111819.495 .828 .532 Within Groups 1619844.136 12 134987.011 Total 2067122.118 16

1996 Textile Volume Cluster




1 5 14 1.800 0 0 3 2 10 15 1.905 0 0 5 3 5 9 2.529 1 0 4 4 3 5 3.762 0 3 5 5 3 10 4.707 4 2 6 6 3 13 6.801 5 0 11 7 11 12 7.447 0 0 11 8 1 2 7.584 0 0 15 9 4 7 8.367 0 0 14 10 6 17 9.460 0 0 13 11 3 11 10.535 6 7 12 12 3 8 13.307 11 0 14 13 6 16 14.317 10 0 16 14 3 4 16.309 12 9 15 15 1 3 19.193 8 14 16 16 1 6 22.903 15 13 0

Cluster Membership


Oneway

Descriptives

N Mean



m Maximu


2 10 .80 .422 .133 .50 1.10 0 1 3 2 1.00 .000 .000 1.00 1.00 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 17 .82 .393 .095 .62 1.03 0 1

Pop 96 1 2 1082803.50 205171.982 145078.500 -760593.62 2926200.62 937725 1227882

2 10 51686.90 57733.166 18256.830 10387.08 92986.72 4282 201118 3 2 75883.00 26185.578 18516.000 -159385.09 311151.09 57367 94399 4 2 18209.00 16711.762 11817.000 -131940.22 168358.22 6392 30026 5 1 125645.00 . . . . 125645 125645 Total 17 176253.24 348618.576 84552.424 -2989.90 355496.37 4282 1227882

GDP.96 1 2 544.85 218.679 154.629 -1419.91 2509.60 390 699 2 10 6044.67 6494.330 2053.687 1398.91 10690.43 1162 19041 3 2 13026.84 12922.037 9137.260 -103073.06 129126.74 3890 22164 4 2 22669.72 2705.277 1912.920 -1636.23 46975.67 20757 24583 5 1 36930.26 . . . . 36930 36930 Total 17 9991.75 11050.405 2680.117 4310.16 15673.35 390 36930

Distance 1 2 7210.50 381.131 269.500 3786.18 10634.82 6941 7480 2 10 7306.90 2050.815 648.525 5839.83 8773.97 3508 10163 3 2 3191.50 1851.913 1309.500 -13447.28 19830.28 1882 4501 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 6790.00 . . . . 6790 6790 Total 17 6429.65 2624.701 636.583 5080.15 7779.14 455 10163

inf 96 1 2 6.7950 .64347 .45500 1.0137 12.5763 6.34 7.25 2 10 9.5120 1.71713 .54300 8.2836 10.7404 5.99 12.35 3 2 9.3250 2.48194 1.75500 -12.9744 31.6244 7.57 11.08 4 2 14.6400 .90510 .64000 6.5080 22.7720 14.00 15.28 5 1 13.5800 . . . . 13.58 13.58 Total 17 10.0129 2.66505 .64637 8.6427 11.3832 5.99 15.28

English 1 2 1.00 .000 .000 1.00 1.00 1 1 2 10 .90 .316 .100 .67 1.13 0 1 3 2 1.00 .000 .000 1.00 1.00 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 1.00 . . . . 1 1 Total 17 .82 .393 .095 .62 1.03 0 1

Tech 96 1 2 2.6850 .07778 .05500 1.9862 3.3838 2.63 2.74 2 10 3.5540 .62772 .19850 3.1050 4.0030 2.63 4.29 3 2 3.1100 .60811 .43000 -2.3537 8.5737 2.68 3.54 4 2 3.9450 .33234 .23500 .9590 6.9310 3.71 4.18 5 1 5.1500 . . . . 5.15 5.15 Total 17 3.5394 .74105 .17973 3.1584 3.9204 2.63 5.15

NonVo96

1 2 675.00 151.321 107.000 -684.56 2034.56 568 782 2 10 216.30 213.175 67.412 63.80 368.80 0 629 3 2 1382.00 388.909 275.000 -2112.21 4876.21 1107 1657 4 2 168.50 51.619 36.500 -295.28 632.28 132 205 5 1 242.00 . . . . 242 242 Total 17 403.29 442.657 107.360 175.70 630.89 0 1657

ANOVA




GDP.96 Between Groups 1.400E9 4 3.500E8 7.581 .003 Within Groups 5.539E8 12 4.616E7 Total 1.954E9 16

Distance Between Groups 3.891E7 4 9728309.871 1.637 .229

Within Groups 7.131E7 12 5942634.867 Total 1.102E8 16


English Between Groups 1.571 4 .393 5.235 .011 Within Groups .900 12 .075 Total 2.471 16


NonVo96 Between Groups 2549318.929 4 637329.732 13.055 .000 Within Groups 585806.600 12 48817.217 Total 3135125.529 16

1996 Apparel Value Cluster




1 5 14 1.450 0 0 3 2 7 15 1.597 0 0 4 3 5 9 2.456 1 0 5 4 7 10 3.262 2 0 6 5 3 5 3.955 0 3 6 6 3 7 5.424 5 4 7 7 3 13 6.655 6 0 9 8 11 12 7.635 0 0 9 9 3 11 10.680 7 8 11 10 1 2 11.491 0 0 15 11 3 8 13.979 9 0 13 12 6 17 14.001 0 0 14 13 3 4 14.578 11 0 15 14 6 16 19.162 12 0 16 15 1 3 20.390 10 13 16 16 1 6 23.956 15 14 0

Cluster Membership


Oneway

Descriptives

N Mean



m Maximu

m Lower Bound Upper Bound AppVa96

1 2 2478.00 1825.750 1291.000 -13925.71 18881.71 1187 3769 2 11 856.91 655.254 197.567 416.70 1297.11 10 1974 3 1 3560.00 . . . . 3560 3560 4 2 2505.00 1917.674 1356.000 -14724.61 19734.61 1149 3861 5 1 52.00 . . . . 52 52 Total 17 1353.18 1268.870 307.746 700.78 2005.57 10 3861

Trade 96 1 2 .50 .707 .500 -5.85 6.85 0 1

2 11 .82 .405 .122 .55 1.09 0 1 3 1 1.00 . . . . 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 17 .82 .393 .095 .62 1.03 0 1

Pop 96 1 2 1082803.50 205171.982 145078.500 -760593.62 2926200.62 937725 1227882 2 11 52203.27 54797.260 16521.995 15389.97 89016.57 4282 201118 3 1 94399.00 . . . . 94399 94399 4 2 18209.00 16711.762 11817.000 -131940.22 168358.22 6392 30026 5 1 125645.00 . . . . 125645 125645 Total 17 176253.24 348618.576 84552.424 -2989.90 355496.37 4282 1227882

GDP.96 1 2 544.85 218.679 154.629 -1419.91 2509.60 390 699 2 11 7510.07 7847.301 2366.050 2238.18 12781.96 1162 22164 3 1 3889.58 . . . . 3890 3890 4 2 22669.72 2705.277 1912.920 -1636.23 46975.67 20757 24583 5 1 36930.26 . . . . 36930 36930 Total 17 9991.75 11050.405 2680.117 4310.16 15673.35 390 36930

Distance 1 2 7210.50 381.131 269.500 3786.18 10634.82 6941 7480 2 11 7051.82 2121.555 639.673 5626.54 8477.10 3508 10163 3 1 1882.00 . . . . 1882 1882 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 6790.00 . . . . 6790 6790 Total 17 6429.65 2624.701 636.583 5080.15 7779.14 455 10163

inf 96 1 2 6.7950 .64347 .45500 1.0137 12.5763 6.34 7.25 2 11 9.6545 1.69623 .51143 8.5150 10.7941 5.99 12.35 3 1 7.5700 . . . . 7.57 7.57 4 2 14.6400 .90510 .64000 6.5080 22.7720 14.00 15.28 5 1 13.5800 . . . . 13.58 13.58 Total 17 10.0129 2.66505 .64637 8.6427 11.3832 5.99 15.28

English 1 2 1.00 .000 .000 1.00 1.00 1 1 2 11 .91 .302 .091 .71 1.11 0 1 3 1 1.00 . . . . 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 1.00 . . . . 1 1 Total 17 .82 .393 .095 .62 1.03 0 1

Tech 96 1 2 2.6850 .07778 .05500 1.9862 3.3838 2.63 2.74 2 11 3.5527 .59552 .17956 3.1527 3.9528 2.63 4.29 3 1 2.6800 . . . . 2.68 2.68 4 2 3.9450 .33234 .23500 .9590 6.9310 3.71 4.18 5 1 5.1500 . . . . 5.15 5.15 Total 17 3.5394 .74105 .17973 3.1584 3.9204 2.63 5.15

ANOVA

Sum of Squares df Mean Square F Sig. AppVa96 Between Groups 1.446E7 4 3614018.890 3.836 .031


Trade 96 Between Groups .334 4 .084 .469 .757 Within Groups 2.136 12 .178 Total 2.471 16



Distance Between Groups 3.519E7 4 8796369.811 1.407 .290 Within Groups 7.504E7 12 6253281.553 Total 1.102E8 16




1996 Apparel Volume Cluster




1 7 15 1.401 0 0 5 2 5 14 1.444 0 0 3 3 5 9 2.353 2 0 4 4 3 5 3.953 0 3 6 5 7 10 4.150 1 0 6 6 3 7 5.582 4 5 7 7 3 13 6.721 6 0 10 8 11 12 7.197 0 0 10 9 1 2 10.215 0 0 15 10 3 11 10.638 7 8 12 11 6 17 13.919 0 0 13 12 3 8 14.303 10 0 14 13 6 16 16.889 11 0 16 14 3 4 17.357 12 0 15 15 1 3 19.942 9 14 16 16 1 6 23.485 15 13 0

Cluster Membership


Oneway

Descriptives

N Mean



m Maximu


2 11 .82 .405 .122 .55 1.09 0 1 3 1 1.00 . . . . 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 17 .82 .393 .095 .62 1.03 0 1

Pop 96 1 2 1082803.50 205171.982 145078.500 -760593.62 2926200.62 937725 1227882

2 11 52203.27 54797.260 16521.995 15389.97 89016.57 4282 201118 3 1 94399.00 . . . . 94399 94399 4 2 18209.00 16711.762 11817.000 -131940.22 168358.22 6392 30026 5 1 125645.00 . . . . 125645 125645 Total 17 176253.24 348618.576 84552.424 -2989.90 355496.37 4282 1227882

GDP.96 1 2 544.85 218.679 154.629 -1419.91 2509.60 390 699 2 11 7510.07 7847.301 2366.050 2238.18 12781.96 1162 22164 3 1 3889.58 . . . . 3890 3890 4 2 22669.72 2705.277 1912.920 -1636.23 46975.67 20757 24583 5 1 36930.26 . . . . 36930 36930 Total 17 9991.75 11050.405 2680.117 4310.16 15673.35 390 36930

Distance 1 2 7210.50 381.131 269.500 3786.18 10634.82 6941 7480 2 11 7051.82 2121.555 639.673 5626.54 8477.10 3508 10163 3 1 1882.00 . . . . 1882 1882 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 6790.00 . . . . 6790 6790 Total 17 6429.65 2624.701 636.583 5080.15 7779.14 455 10163

inf 96 1 2 6.7950 .64347 .45500 1.0137 12.5763 6.34 7.25 2 11 9.6545 1.69623 .51143 8.5150 10.7941 5.99 12.35 3 1 7.5700 . . . . 7.57 7.57 4 2 14.6400 .90510 .64000 6.5080 22.7720 14.00 15.28 5 1 13.5800 . . . . 13.58 13.58 Total 17 10.0129 2.66505 .64637 8.6427 11.3832 5.99 15.28

English 1 2 1.00 .000 .000 1.00 1.00 1 1 2 11 .91 .302 .091 .71 1.11 0 1 3 1 1.00 . . . . 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 1.00 . . . . 1 1 Total 17 .82 .393 .095 .62 1.03 0 1

Tech 96 1 2 2.6850 .07778 .05500 1.9862 3.3838 2.63 2.74 2 11 3.5527 .59552 .17956 3.1527 3.9528 2.63 4.29 3 1 2.6800 . . . . 2.68 2.68 4 2 3.9450 .33234 .23500 .9590 6.9310 3.71 4.18 5 1 5.1500 . . . . 5.15 5.15 Total 17 3.5394 .74105 .17973 3.1584 3.9204 2.63 5.15

AppVo96

1 2 581.50 396.687 280.500 -2982.59 4145.59 301 862 2 11 205.36 187.445 56.517 79.44 331.29 4 574 3 1 1099.00 . . . . 1099 1099 4 2 409.00 496.389 351.000 -4050.88 4868.88 58 760 5 1 5.00 . . . . 5 5 Total 17 314.35 331.456 80.390 143.93 484.77 4 1099

ANOVA





Distance Between Groups 3.519E7 4 8796369.811 1.407 .290





AppVo96 Between Groups 1002686.837 4 250671.709 3.984 .028 Within Groups 755119.045 12 62926.587 Total 1757805.882 16





1 7 13 .056 0 0 2 2 7 8 .289 1 0 3 3 7 19 .884 2 0 7 4 17 18 1.276 0 0 10 5 15 16 1.447 0 0 9 6 4 6 1.494 0 0 15 7 7 20 1.581 3 0 16 8 9 12 1.782 0 0 10 9 15 22 1.997 5 0 12 10 9 17 2.341 8 4 11 11 9 21 3.852 10 0 15 12 15 23 4.040 9 0 18 13 1 3 4.655 0 0 22 14 5 11 5.003 0 0 18 15 4 9 5.901 6 11 16 16 4 7 7.570 15 7 19 17 10 24 8.487 0 0 21 18 5 15 10.401 14 12 19 19 4 5 12.808 16 18 20 20 4 14 16.233 19 0 21 21 4 10 22.313 20 17 22 22 1 4 25.155 13 21 23 23 1 2 35.454 22 0 0

Cluster Membership

Case 5 Clusters 1:China 1 2:Vietnam 2 3:India 1 4:Indonesia 3 5:Mexico 3 6:Bangladesh 3 7:Honduras 3 8:El Salvador 3 9:Thailand 3 10:Canada 4 11:Italy 3 12:Sri Lanka 3 13:Guatemala 3 14:Philippines 5 15:Korea, South 3 16:Taiwan 3 17:Jordan 3 18:Turkey 3 19:Peru 3 20:Dominican Republic 3 21:Malaysia 3 22:Israel 3 23:Japan 3 24:Hong Kong 4

Oneway Descriptives

N Mean



m Maximu

m Lower Bound Upper Bound Trade 02 1 2 1.00 .000 .000 1.00 1.00 1 1

2 1 .00 . . . . 0 0 3 18 1.00 .000 .000 1.00 1.00 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 24 .96 .204 .042 .87 1.04 0 1

Pop 02 1 2 1158940.00 167803.510 118655.000 -348714.72 2666594.72 1040285 1277595 2 1 81258.00 . . . . 81258 81258 3 18 53457.22 59099.037 13929.777 24067.96 82846.48 4902 220019 4 2 19201.50 17592.110 12439.500 -138857.33 177260.33 6762 31641 5 1 84936.00 . . . . 84936 84936 Total 24 145196.13 318567.019 65027.221 10677.07 279715.18 4902 1277595

GDP.02 1 2 804.40 463.017 327.403 -3355.65 4964.44 477 1132 2 1 440.21 . . . . 440 440 3 18 7037.22 8621.366 2032.075 2749.92 11324.53 340 30756 4 2 23908.91 625.365 442.200 18290.23 29527.59 23467 24351 5 1 957.57 . . . . 958 958 Total 24 7395.60 9298.398 1898.028 3469.23 11321.97 340 30756

Distance 1 2 7210.50 381.131 269.500 3786.18 10634.82 6941 7480 2 1 8303.00 . . . . 8303 8303 3 18 5628.44 2977.707 701.852 4147.67 7109.22 1477 10163 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 8570.00 . . . . 8570 8570 Total 24 5885.29 2973.901 607.045 4629.52 7141.06 455 10163

Tech 02 1 2 3.5000 .07071 .05000 2.8647 4.1353 3.45 3.55 2 1 3.0400 . . . . 3.04 3.04 3 18 3.8822 .95886 .22601 3.4054 4.3591 2.60 5.87 4 2 4.7500 .53740 .38000 -.0784 9.5784 4.37 5.13 5 1 3.6900 . . . . 3.69 3.69 Total 24 3.8796 .89609 .18291 3.5012 4.2580 2.60 5.87

English 1 2 1.00 .000 .000 1.00 1.00 1 1 2 1 1.00 . . . . 1 1 3 18 1.00 .000 .000 1.00 1.00 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 .00 . . . . 0 0 Total 24 .88 .338 .069 .73 1.02 0 1

inf 02 1 2 3.1000 .42426 .30000 -.7119 6.9119 2.80 3.40 2 1 2.5000 . . . . 2.50 2.50 3 18 3.9944 1.14248 .26929 3.4263 4.5626 2.30 5.80 4 2 6.1500 .35355 .25000 2.9734 9.3266 5.90 6.40 5 1 2.3000 . . . . 2.30 2.30 Total 24 3.9667 1.29335 .26400 3.4205 4.5128 2.30 6.40

NonVa02

1 2 1425.00 719.835 509.000 -5042.46 7892.46 916 1934 2 1 1.00 . . . . 1 1 3 18 358.11 442.105 104.205 138.26 577.96 4 1577 4 2 431.00 337.997 239.000 -2605.78 3467.78 192 670 5 1 357.00 . . . . 357 357 Total 24 438.17 519.867 106.117 218.65 657.69 1 1934

ANOVA

Sum of Squares df Mean Square F Sig. Trade 02 Between Groups .958 4 .240 . .

Within Groups .000 19 .000 Total .958 23




Tech 02 Between Groups 2.544 4 .636 .759 .565 Within Groups 15.924 19 .838 Total 18.468 23



NonVa02 Between Groups 2260845.556 4 565211.389 2.715 .061 Within Groups 3955171.778 19 208166.936 Total 6216017.333 23





1 7 13 .057 0 0 2 2 7 8 .287 1 0 3 3 7 19 .884 2 0 8 4 15 22 .899 0 0 9 5 17 18 1.284 0 0 10 6 4 6 1.468 0 0 14 7 9 12 1.580 0 0 10 8 7 20 1.581 3 0 16 9 15 16 2.057 4 0 12 10 9 17 2.168 7 5 11 11 9 21 3.650 10 0 14 12 15 23 4.246 9 0 18 13 5 11 5.210 0 0 18 14 4 9 5.824 6 11 16 15 1 3 7.012 0 0 22 16 4 7 7.468 14 8 19 17 10 24 7.730 0 0 21 18 5 15 11.726 13 12 19 19 4 5 13.045 16 18 20 20 4 14 16.429 19 0 21 21 4 10 22.280 20 17 22 22 1 4 24.688 15 21 23 23 1 2 35.181 22 0 0

Cluster Membership


Oneway Descriptives

N Mean



m Maximu


2 1 .00 . . . . 0 0 3 18 1.00 .000 .000 1.00 1.00 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 24 .96 .204 .042 .87 1.04 0 1

Pop 02 1 2 1158940.00 167803.510 118655.000 -348714.72 2666594.72 1040285 1277595 2 1 81258.00 . . . . 81258 81258 3 18 53457.22 59099.037 13929.777 24067.96 82846.48 4902 220019 4 2 19201.50 17592.110 12439.500 -138857.33 177260.33 6762 31641 5 1 84936.00 . . . . 84936 84936 Total 24 145196.13 318567.019 65027.221 10677.07 279715.18 4902 1277595

GDP.02 1 2 804.40 463.017 327.403 -3355.65 4964.44 477 1132 2 1 440.21 . . . . 440 440 3 18 7037.22 8621.366 2032.075 2749.92 11324.53 340 30756 4 2 23908.91 625.365 442.200 18290.23 29527.59 23467 24351 5 1 957.57 . . . . 958 958 Total 24 7395.60 9298.398 1898.028 3469.23 11321.97 340 30756

Distance 1 2 7210.50 381.131 269.500 3786.18 10634.82 6941 7480 2 1 8303.00 . . . . 8303 8303 3 18 5628.44 2977.707 701.852 4147.67 7109.22 1477 10163 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 8570.00 . . . . 8570 8570 Total 24 5885.29 2973.901 607.045 4629.52 7141.06 455 10163

Tech 02 1 2 3.5000 .07071 .05000 2.8647 4.1353 3.45 3.55 2 1 3.0400 . . . . 3.04 3.04 3 18 3.8822 .95886 .22601 3.4054 4.3591 2.60 5.87 4 2 4.7500 .53740 .38000 -.0784 9.5784 4.37 5.13 5 1 3.6900 . . . . 3.69 3.69 Total 24 3.8796 .89609 .18291 3.5012 4.2580 2.60 5.87

English 1 2 1.00 .000 .000 1.00 1.00 1 1 2 1 1.00 . . . . 1 1 3 18 1.00 .000 .000 1.00 1.00 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 .00 . . . . 0 0 Total 24 .88 .338 .069 .73 1.02 0 1

inf 02 1 2 3.1000 .42426 .30000 -.7119 6.9119 2.80 3.40 2 1 2.5000 . . . . 2.50 2.50 3 18 3.9944 1.14248 .26929 3.4263 4.5626 2.30 5.80 4 2 6.1500 .35355 .25000 2.9734 9.3266 5.90 6.40 5 1 2.3000 . . . . 2.30 2.30 Total 24 3.9667 1.29335 .26400 3.4205 4.5128 2.30 6.40

NonVo02

1 2 2217.00 1670.186 1181.000 -12789.03 17223.03 1036 3398 2 1 43.00 . . . . 43 43 3 18 607.11 846.480 199.517 186.17 1028.06 4 3095 4 2 281.00 199.404 141.000 -1510.57 2072.57 140 422 5 1 267.00 . . . . 267 267 Total 24 676.42 949.257 193.766 275.58 1077.25 4 3398

ANOVA









NonVo02 Between Groups 5714800.056 4 1428700.014 1.808 .169 Within Groups 1.501E7 19 790013.883 Total 2.073E7 23





1 8 13 .164 0 0 2 2 7 8 .454 0 1 7 3 17 18 .955 0 0 10 4 9 12 1.130 0 0 10 5 15 22 1.220 0 0 8 6 4 6 1.301 0 0 15 7 7 20 1.753 2 0 9 8 15 16 1.906 5 0 12 9 7 19 1.971 7 0 16 10 9 17 2.179 4 3 11 11 9 21 3.584 10 0 15 12 11 15 3.918 0 8 13 13 11 23 4.500 12 0 18 14 1 3 5.876 0 0 21 15 4 9 6.025 6 11 16 16 4 7 7.563 15 9 18 17 10 24 9.997 0 0 22 18 4 11 11.961 16 13 19 19 4 14 15.849 18 0 20 20 4 5 20.114 19 0 21 21 1 4 21.061 14 20 22 22 1 10 24.815 21 17 23 23 1 2 35.133 22 0 0

Cluster Membership


Oneway

Descriptives

N Mean



m Maximu


1 2 3748.00 2610.638 1846.000 -19707.65 27203.65 1902 5594 2 1 895.00 . . . . 895 895 3 18 1406.67 698.160 164.558 1059.48 1753.85 171 2440 4 1 7424.00 . . . . 7424 7424 5 2 2617.00 1781.909 1260.000 -13392.82 18626.82 1357 3877 Total 24 1932.04 1642.057 335.183 1238.66 2625.42 171 7424

Trade 02 1 2 1.00 .000 .000 1.00 1.00 1 1 2 1 .00 . . . . 0 0 3 18 1.00 .000 .000 1.00 1.00 1 1 4 1 1.00 . . . . 1 1 5 2 1.00 .000 .000 1.00 1.00 1 1 Total 24 .96 .204 .042 .87 1.04 0 1

Pop 02 1 2 1158940.00 167803.510 118655.000 -348714.72 2666594.72 1040285 1277595 2 1 81258.00 . . . . 81258 81258 3 18 52482.56 58383.326 13761.082 23449.21 81515.90 4902 220019 4 1 102480.00 . . . . 102480 102480 5 2 19201.50 17592.110 12439.500 -138857.33 177260.33 6762 31641 Total 24 145196.13 318567.019 65027.221 10677.07 279715.18 4902 1277595

GDP.02 1 2 804.40 463.017 327.403 -3355.65 4964.44 477 1132 2 1 440.21 . . . . 440 440 3 18 6706.41 8739.874 2060.008 2360.17 11052.65 340 30756 4 1 6912.25 . . . . 6912 6912 5 2 23908.91 625.365 442.200 18290.23 29527.59 23467 24351 Total 24 7395.60 9298.398 1898.028 3469.23 11321.97 340 30756

Distance 1 2 7210.50 381.131 269.500 3786.18 10634.82 6941 7480 2 1 8303.00 . . . . 8303 8303 3 18 6000.00 2898.950 683.289 4558.39 7441.61 1477 10163 4 1 1882.00 . . . . 1882 1882 5 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 Total 24 5885.29 2973.901 607.045 4629.52 7141.06 455 10163

inf 02 1 2 3.1000 .42426 .30000 -.7119 6.9119 2.80 3.40 2 1 2.5000 . . . . 2.50 2.50 3 18 3.9500 1.19373 .28137 3.3564 4.5436 2.30 5.80 4 1 3.1000 . . . . 3.10 3.10 5 2 6.1500 .35355 .25000 2.9734 9.3266 5.90 6.40 Total 24 3.9667 1.29335 .26400 3.4205 4.5128 2.30 6.40

English 1 2 1.00 .000 .000 1.00 1.00 1 1 2 1 1.00 . . . . 1 1 3 18 .94 .236 .056 .83 1.06 0 1 4 1 1.00 . . . . 1 1 5 2 .00 .000 .000 .00 .00 0 0 Total 24 .88 .338 .069 .73 1.02 0 1

Tech 02 1 2 3.5000 .07071 .05000 2.8647 4.1353 3.45 3.55 2 1 3.0400 . . . . 3.04 3.04 3 18 3.8739 .95990 .22625 3.3965 4.3512 2.60 5.87 4 1 3.8400 . . . . 3.84 3.84 5 2 4.7500 .53740 .38000 -.0784 9.5784 4.37 5.13 Total 24 3.8796 .89609 .18291 3.5012 4.2580 2.60 5.87

ANOVA Sum of Squares df Mean Square F Sig.

AppVa02 Between Groups 4.374E7 4 1.093E7 11.367 .000 Within Groups 1.828E7 19 961942.211 Total 6.202E7 23

Trade 02 Between Groups .958 4 .240 . . Within Groups .000 19 .000 Total .958 23











1 8 13 .696 0 0 4 2 17 18 .986 0 0 9 3 9 12 1.132 0 0 9 4 7 8 1.356 0 1 7 5 15 16 1.442 0 0 8 6 4 6 1.742 0 0 15 7 7 20 1.996 4 0 10 8 15 22 2.007 5 0 12 9 9 17 2.167 3 2 11 10 7 19 3.181 7 0 16 11 9 21 3.582 9 0 15 12 11 15 4.040 0 8 13 13 11 23 4.568 12 0 18 14 1 3 5.349 0 0 21 15 4 9 6.705 6 11 16 16 4 7 8.119 15 10 18 17 10 24 9.777 0 0 22 18 4 11 12.334 16 13 19 19 4 14 16.017 18 0 20 20 4 5 18.539 19 0 21 21 1 4 20.457 14 20 22 22 1 10 24.677 21 17 23 23 1 2 35.001 22 0 0

Cluster Membership


Descriptives

N Mean



m Maximu


2 1 .00 . . . . 0 0 3 18 1.00 .000 .000 1.00 1.00 1 1 4 1 1.00 . . . . 1 1 5 2 1.00 .000 .000 1.00 1.00 1 1 Total 24 .96 .204 .042 .87 1.04 0 1

Pop 02 1 2 1158940.00 167803.510 118655.000 -348714.72 2666594.72 1040285 1277595 2 1 81258.00 . . . . 81258 81258 3 18 52482.56 58383.326 13761.082 23449.21 81515.90 4902 220019 4 1 102480.00 . . . . 102480 102480 5 2 19201.50 17592.110 12439.500 -138857.33 177260.33 6762 31641 Total 24 145196.13 318567.019 65027.221 10677.07 279715.18 4902 1277595

GDP.02 1 2 804.40 463.017 327.403 -3355.65 4964.44 477 1132 2 1 440.21 . . . . 440 440 3 18 6706.41 8739.874 2060.008 2360.17 11052.65 340 30756 4 1 6912.25 . . . . 6912 6912 5 2 23908.91 625.365 442.200 18290.23 29527.59 23467 24351 Total 24 7395.60 9298.398 1898.028 3469.23 11321.97 340 30756

Distance 1 2 7210.50 381.131 269.500 3786.18 10634.82 6941 7480 2 1 8303.00 . . . . 8303 8303 3 18 6000.00 2898.950 683.289 4558.39 7441.61 1477 10163 4 1 1882.00 . . . . 1882 1882 5 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 Total 24 5885.29 2973.901 607.045 4629.52 7141.06 455 10163

Tech 02 1 2 3.5000 .07071 .05000 2.8647 4.1353 3.45 3.55 2 1 3.0400 . . . . 3.04 3.04 3 18 3.8739 .95990 .22625 3.3965 4.3512 2.60 5.87 4 1 3.8400 . . . . 3.84 3.84 5 2 4.7500 .53740 .38000 -.0784 9.5784 4.37 5.13 Total 24 3.8796 .89609 .18291 3.5012 4.2580 2.60 5.87

English 1 2 1.00 .000 .000 1.00 1.00 1 1 2 1 1.00 . . . . 1 1 3 18 .94 .236 .056 .83 1.06 0 1 4 1 1.00 . . . . 1 1 5 2 .00 .000 .000 .00 .00 0 0 Total 24 .88 .338 .069 .73 1.02 0 1

inf 02 1 2 3.1000 .42426 .30000 -.7119 6.9119 2.80 3.40 2 1 2.5000 . . . . 2.50 2.50 3 18 3.9500 1.19373 .28137 3.3564 4.5436 2.30 5.80 4 1 3.1000 . . . . 3.10 3.10 5 2 6.1500 .35355 .25000 2.9734 9.3266 5.90 6.40 Total 24 3.9667 1.29335 .26400 3.4205 4.5128 2.30 6.40

AppVo02

1 2 1037.00 746.705 528.000 -5671.88 7745.88 509 1565 2 1 315.00 . . . . 315 315 3 18 462.17 305.770 72.071 310.11 614.22 27 1090 4 1 2157.00 . . . . 2157 2157 5 2 458.50 512.652 362.500 -4147.50 5064.50 96 821 Total 24 574.25 496.216 101.290 364.72 783.78 27 2157

ANOVA









AppVo02 Between Groups 3253507.500 4 813376.875 6.413 .002 Within Groups 2409797.000 19 126831.421 Total 5663304.500 23





1 17 21 .112 0 0 5 2 15 24 .134 0 0 4 3 8 18 .225 0 0 8 4 15 23 .564 2 0 8 5 17 22 .569 1 0 12 6 6 9 .705 0 0 10 7 5 10 .852 0 0 13 8 8 15 .885 3 4 13 9 20 26 .945 0 0 14 10 6 14 1.013 6 0 15 11 11 25 1.233 0 0 17 12 7 17 1.665 0 5 15 13 5 8 1.831 7 8 18 14 19 20 1.909 0 9 16 15 6 7 2.815 10 12 18 16 19 27 3.070 14 0 19 17 4 11 3.603 0 11 20 18 5 6 4.566 13 15 22 19 13 19 6.841 0 16 20 20 4 13 8.142 17 19 22 21 12 28 8.944 0 0 25 22 4 5 10.659 20 18 23 23 4 16 15.524 22 0 24 24 3 4 18.623 0 23 25 25 3 12 28.774 24 21 26 26 2 3 36.222 0 25 27 27 1 2 47.705 0 26 0

Cluster Membership

Case 5 Clusters 1:China 1 2:Vietnam 2 3:India 3 4:Indonesia 4 5:Mexico 4 6:Bangladesh 4 7:Pakistan 4 8:Honduras 4 9:Cambodia 4 10:El Salvador 4 11:Thailand 4 12:Canada 5 13:Italy 4 14:Sri Lanka 4 15:Guatemala 4 16:Philippines 4 17:Egypt 4 18:Nicaragua 4 19:Korea, South 4 20:Taiwan 4

21:Jordan 4 22:Turkey 4 23:Peru 4 24:Dominican Republic 4 25:Malaysia 4 26:Israel 4 27:Japan 4 28:Hong Kong 5

Oneway

Descriptives

N Mean



m Maximu

m Lower Bound Upper Bound Trade 06 1 1 1.00 . . . . 1 1

2 1 .00 . . . . 0 0 3 1 1.00 . . . . 1 1 4 23 1.00 .000 .000 1.00 1.00 1 1 5 2 1.00 .000 .000 1.00 1.00 1 1 Total 28 .96 .189 .036 .89 1.04 0 1

Pop 06 1 1 1304262.00 . . . . 1304262 1304262 2 1 85471.00 . . . . 85471 85471 3 1 1107624.00 . . . . 1107624 1107624 4 23 59229.13 61979.542 12923.628 32427.17 86031.09 5575 231820 5 2 19798.50 18184.665 12858.500 -143584.23 183181.23 6940 32657 Total 28 139258.00 307832.949 58174.959 19892.84 258623.16 5575 1304262

GDP.06 1 1 2063.87 . . . . 2064 2064 2 1 724.05 . . . . 724 724 3 1 791.15 . . . . 791 791 4 23 7626.93 10037.048 2092.869 3286.59 11967.28 419 34150 5 2 33395.00 8353.045 5906.495 -41654.13 108444.14 27489 39302 Total 28 8778.16 11709.933 2212.969 4237.52 13318.80 419 39302

Distance 1 1 6941.00 . . . . 6941 6941 2 1 8303.00 . . . . 8303 8303 3 1 7480.00 . . . . 7480 7480 4 23 5812.04 2904.210 605.570 4556.17 7067.92 1477 10163 5 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 Total 28 5894.36 2918.415 551.529 4762.71 7026.00 455 10163

inf 06 1 1 3.5400 . . . . 3.54 3.54 2 1 2.7900 . . . . 2.79 2.79 3 1 3.5000 . . . . 3.50 3.50 4 23 3.6570 1.12449 .23447 3.1707 4.1432 2.03 6.11 5 2 6.0500 .33941 .24000 3.0005 9.0995 5.81 6.29 Total 28 3.7871 1.21263 .22917 3.3169 4.2574 2.03 6.29

English 1 1 1.00 . . . . 1 1 2 1 1.00 . . . . 1 1 3 1 1.00 . . . . 1 1 4 23 .96 .209 .043 .87 1.05 0 1 5 2 .00 .000 .000 .00 .00 0 0 Total 28 .89 .315 .060 .77 1.01 0 1

Tech 06 1 1 4.2200 . . . . 4.22 4.22 2 1 4.1000 . . . . 4.10 4.10 3 1 5.0700 . . . . 5.07 5.07 4 23 4.3317 .50634 .10558 4.1128 4.5507 3.56 5.24 5 2 5.4750 .30406 .21500 2.7432 8.2068 5.26 5.69 Total 28 4.4275 .56769 .10728 4.2074 4.6476 3.56 5.69

NonVa06 1 1 8550.00 . . . . 8550 8550 2 1 174.00 . . . . 174 174 3 1 1844.00 . . . . 1844 1844 4 23 338.35 497.623 103.762 123.16 553.54 0 1838 5 2 420.50 478.711 338.500 -3880.55 4721.55 82 759 Total 28 685.39 1633.485 308.700 51.99 1318.79 0 8550

ANOVA








Tech 06 Between Groups 2.969 4 .742 2.977 .041 Within Groups 5.733 23 .249 Total 8.701 27

NonVa06 Between Groups 6.637E7 4 1.659E7 67.220 .000 Within Groups 5677003.717 23 246826.249 Total 7.204E7 27





1 17 21 .112 0 0 4 2 15 24 .134 0 0 5 3 8 18 .215 0 0 7 4 17 22 .495 1 0 14 5 15 23 .564 2 0 7 6 6 9 .708 0 0 8 7 8 15 .880 3 5 12 8 6 14 1.014 6 0 15 9 5 10 1.120 0 0 12 10 20 26 1.204 0 0 13 11 11 25 1.233 0 0 17 12 5 8 1.967 9 7 18 13 19 20 1.993 0 10 16 14 7 17 1.999 0 4 15 15 6 7 2.858 8 14 18 16 19 27 3.208 13 0 19 17 4 11 3.617 0 11 20 18 5 6 4.625 12 15 22 19 13 19 6.929 0 16 20 20 4 13 8.215 17 19 22 21 12 28 8.778 0 0 25 22 4 5 10.745 20 18 23 23 4 16 15.575 22 0 24 24 3 4 18.102 0 23 25 25 3 12 28.779 24 21 26 26 2 3 36.239 0 25 27 27 1 2 47.432 0 26 0

Cluster Membership



Oneway

Descriptives

N Mean



m Maximu


2 1 .00 . . . . 0 0 3 1 1.00 . . . . 1 1 4 23 1.00 .000 .000 1.00 1.00 1 1 5 2 1.00 .000 .000 1.00 1.00 1 1 Total 28 .96 .189 .036 .89 1.04 0 1

Pop 06 1 1 1304262.00 . . . . 1304262 1304262 2 1 85471.00 . . . . 85471 85471 3 1 1107624.00 . . . . 1107624 1107624 4 23 59229.13 61979.542 12923.628 32427.17 86031.09 5575 231820 5 2 19798.50 18184.665 12858.500 -143584.23 183181.23 6940 32657 Total 28 139258.00 307832.949 58174.959 19892.84 258623.16 5575 1304262

GDP.06 1 1 2063.87 . . . . 2064 2064 2 1 724.05 . . . . 724 724 3 1 791.15 . . . . 791 791 4 23 7626.93 10037.048 2092.869 3286.59 11967.28 419 34150 5 2 33395.00 8353.045 5906.495 -41654.13 108444.14 27489 39302 Total 28 8778.16 11709.933 2212.969 4237.52 13318.80 419 39302

Distance 1 1 6941.00 . . . . 6941 6941 2 1 8303.00 . . . . 8303 8303 3 1 7480.00 . . . . 7480 7480 4 23 5812.04 2904.210 605.570 4556.17 7067.92 1477 10163 5 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 Total 28 5894.36 2918.415 551.529 4762.71 7026.00 455 10163

inf 06 1 1 3.5400 . . . . 3.54 3.54 2 1 2.7900 . . . . 2.79 2.79 3 1 3.5000 . . . . 3.50 3.50 4 23 3.6570 1.12449 .23447 3.1707 4.1432 2.03 6.11 5 2 6.0500 .33941 .24000 3.0005 9.0995 5.81 6.29 Total 28 3.7871 1.21263 .22917 3.3169 4.2574 2.03 6.29

English 1 1 1.00 . . . . 1 1 2 1 1.00 . . . . 1 1 3 1 1.00 . . . . 1 1 4 23 .96 .209 .043 .87 1.05 0 1 5 2 .00 .000 .000 .00 .00 0 0 Total 28 .89 .315 .060 .77 1.01 0 1

Tech 06 1 1 4.2200 . . . . 4.22 4.22 2 1 4.1000 . . . . 4.10 4.10 3 1 5.0700 . . . . 5.07 5.07 4 23 4.3317 .50634 .10558 4.1128 4.5507 3.56 5.24 5 2 5.4750 .30406 .21500 2.7432 8.2068 5.26 5.69 Total 28 4.4275 .56769 .10728 4.2074 4.6476 3.56 5.69

NonVo06

1 1 12107.00 . . . . 12107 12107 2 1 200.00 . . . . 200 200 3 1 1814.00 . . . . 1814 1814 4 23 555.65 838.950 174.933 192.86 918.44 0 2894 5 2 179.00 127.279 90.000 -964.56 1322.56 89 269 Total 28 973

.54 2325.689 439.514 71.73 1875.34 0 12107

ANOVA









NonVo06 Between Groups 1.305E8 4 3.263E7 48.423 .000 Within Groups 1.550E7 23 673940.488 Total 1.460E8 27





1 17 21 .124 0 0 3 2 15 24 .135 0 0 5 3 17 22 .454 1 0 6 4 8 18 .502 0 0 12 5 15 23 .616 2 0 9 6 7 17 .648 0 3 14 7 6 9 .756 0 0 10 8 20 26 .886 0 0 13 9 10 15 .899 0 5 12 10 6 14 1.088 7 0 14 11 11 25 1.330 0 0 17 12 8 10 1.385 4 9 15 13 19 20 1.910 0 8 16 14 6 7 2.634 10 6 18 15 5 8 2.835 0 12 18 16 19 27 3.094 13 0 19 17 4 11 4.134 0 11 20 18 5 6 4.599 15 14 22 19 13 19 6.530 0 16 20 20 4 13 8.310 17 19 22 21 12 28 8.971 0 0 25 22 4 5 10.727 20 18 23 23 4 16 15.541 22 0 24 24 3 4 18.025 0 23 25 25 3 12 28.795 24 21 26 26 2 3 36.412 0 25 27 27 1 2 47.495 0 26 0

Cluster Membership



Oneway

Descriptives

N Mean



m Maximu


1 1 18518.00 . . . . 18518 18518 2 1 3222.00 . . . . 3222 3222 3 1 3187.00 . . . . 3187 3187 4 23 1583.57 1166.599 243.253 1079.09 2088.04 66 5297 5 2 2060.00 1062.074 751.000 -7482.36 11602.36 1309 2811 Total 28 2338.18 3375.342 637.880 1029.36 3647.00 66 18518

Trade 06 1 1 1.00 . . . . 1 1 2 1 .00 . . . . 0 0 3 1 1.00 . . . . 1 1 4 23 1.00 .000 .000 1.00 1.00 1 1 5 2 1.00 .000 .000 1.00 1.00 1 1 Total 28 .96 .189 .036 .89 1.04 0 1

Pop 06 1 1 1304262.00 . . . . 1304262 1304262 2 1 85471.00 . . . . 85471 85471 3 1 1107624.00 . . . . 1107624 1107624 4 23 59229.13 61979.542 12923.628 32427.17 86031.09 5575 231820 5 2 19798.50 18184.665 12858.500 -143584.23 183181.23 6940 32657 Total 28 139258.00 307832.949 58174.959 19892.84 258623.16 5575 1304262

GDP.06 1 1 2063.87 . . . . 2064 2064 2 1 724.05 . . . . 724 724 3 1 791.15 . . . . 791 791 4 23 7626.93 10037.048 2092.869 3286.59 11967.28 419 34150 5 2 33395.00 8353.045 5906.495 -41654.13 108444.14 27489 39302 Total 28 8778.16 11709.933 2212.969 4237.52 13318.80 419 39302

Distance 1 1 6941.00 . . . . 6941 6941 2 1 8303.00 . . . . 8303 8303 3 1 7480.00 . . . . 7480 7480 4 23 5812.04 2904.210 605.570 4556.17 7067.92 1477 10163 5 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 Total 28 5894.36 2918.415 551.529 4762.71 7026.00 455 10163

inf 06 1 1 3.5400 . . . . 3.54 3.54 2 1 2.7900 . . . . 2.79 2.79 3 1 3.5000 . . . . 3.50 3.50 4 23 3.6570 1.12449 .23447 3.1707 4.1432 2.03 6.11 5 2 6.0500 .33941 .24000 3.0005 9.0995 5.81 6.29 Total 28 3.7871 1.21263 .22917 3.3169 4.2574 2.03 6.29

English 1 1 1.00 . . . . 1 1 2 1 1.00 . . . . 1 1 3 1 1.00 . . . . 1 1 4 23 .96 .209 .043 .87 1.05 0 1 5 2 .00 .000 .000 .00 .00 0 0 Total 28 .89 .315 .060 .77 1.01 0 1

Tech 06 1 1 4.2200 . . . . 4.22 4.22 2 1 4.1000 . . . . 4.10 4.10 3 1 5.0700 . . . . 5.07 5.07 4 23 4.3317 .50634 .10558 4.1128 4.5507 3.56 5.24 5 2 5.4750 .30406 .21500 2.7432 8.2068 5.26 5.69 Total 28 4.4275 .56769 .10728 4.2074 4.6476 3.56 5.69

ANOVA

Sum of Squares df Mean Square F Sig. AppVa06 Between Groups 2.765E8 4 6.914E7 51.180 .000













1 17 21 .113 0 0 3 2 15 24 .152 0 0 4 3 17 22 .446 1 0 5 4 15 23 .686 2 0 10 5 7 17 .757 0 3 15 6 8 18 .824 0 0 10 7 5 10 .834 0 0 14 8 6 9 .854 0 0 12 9 20 26 .888 0 0 13 10 8 15 1.124 6 4 14 11 11 25 1.286 0 0 17 12 6 14 1.323 8 0 15 13 19 20 1.915 0 9 16 14 5 8 2.084 7 10 18 15 6 7 2.781 12 5 18 16 19 27 3.098 13 0 19 17 4 11 3.874 0 11 20 18 5 6 4.606 14 15 22 19 13 19 6.499 0 16 20 20 4 13 8.203 17 19 22 21 12 28 8.925 0 0 25 22 4 5 10.722 20 18 23 23 4 16 15.525 22 0 24 24 3 4 17.867 0 23 25 25 3 12 28.800 24 21 26 26 2 3 36.327 0 25 27 27 1 2 47.785 0 26 0

Cluster Membership



Oneway

Descriptives

N Mean



m Maximu


2 1 .00 . . . . 0 0 3 1 1.00 . . . . 1 1 4 23 1.00 .000 .000 1.00 1.00 1 1 5 2 1.00 .000 .000 1.00 1.00 1 1 Total 28 .96 .189 .036 .89 1.04 0 1

Pop 06 1 1 1304262.00 . . . . 1304262 1304262 2 1 85471.00 . . . . 85471 85471 3 1 1107624.00 . . . . 1107624 1107624 4 23 59229.13 61979.542 12923.628 32427.17 86031.09 5575 231820 5 2 19798.50 18184.665 12858.500 -143584.23 183181.23 6940 32657 Total 28 139258.00 307832.949 58174.959 19892.84 258623.16 5575 1304262

GDP.06 1 1 2063.87 . . . . 2064 2064 2 1 724.05 . . . . 724 724 3 1 791.15 . . . . 791 791 4 23 7626.93 10037.048 2092.869 3286.59 11967.28 419 34150 5 2 33395.00 8353.045 5906.495 -41654.13 108444.14 27489 39302 Total 28 8778.16 11709.933 2212.969 4237.52 13318.80 419 39302

Distance 1 1 6941.00 . . . . 6941 6941 2 1 8303.00 . . . . 8303 8303 3 1 7480.00 . . . . 7480 7480 4 23 5812.04 2904.210 605.570 4556.17 7067.92 1477 10163 5 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 Total 28 5894.36 2918.415 551.529 4762.71 7026.00 455 10163

inf 06 1 1 3.5400 . . . . 3.54 3.54 2 1 2.7900 . . . . 2.79 2.79 3 1 3.5000 . . . . 3.50 3.50 4 23 3.6570 1.12449 .23447 3.1707 4.1432 2.03 6.11 5 2 6.0500 .33941 .24000 3.0005 9.0995 5.81 6.29 Total 28 3.7871 1.21263 .22917 3.3169 4.2574 2.03 6.29

English 1 1 1.00 . . . . 1 1 2 1 1.00 . . . . 1 1 3 1 1.00 . . . . 1 1 4 23 .96 .209 .043 .87 1.05 0 1 5 2 .00 .000 .000 .00 .00 0 0 Total 28 .89 .315 .060 .77 1.01 0 1

Tech 06 1 1 4.2200 . . . . 4.22 4.22 2 1 4.1000 . . . . 4.10 4.10 3 1 5.0700 . . . . 5.07 5.07 4 23 4.3317 .50634 .10558 4.1128 4.5507 3.56 5.24 5 2 5.4750 .30406 .21500 2.7432 8.2068 5.26 5.69 Total 28 4.4275 .56769 .10728 4.2074 4.6476 3.56 5.69

AppVo06

1 1 6506.00 . . . . 6506 6506 2 1 947.00 . . . . 947 947 3 1 840.00 . . . . 840 840 4 23 519.74 405.451 84.542 344.41 695.07 6 1477 5 2 289.50 330.219 233.500 -2677.40 3256.40 56 523 Total 28 743.79 1194.844 225.804 280.47 1207.10 6 6506

ANOVA









AppVo06 Between Groups 3.482E7 4 8705236.445 53.741 .000 Within Groups 3725634.935 23 161984.128 Total 3.855E7 27





1 19 20 .123 0 0 18 2 2 9 .137 0 0 10 3 8 24 .154 0 0 11 4 10 15 .296 0 0 9 5 17 21 .311 0 0 7 6 11 25 .534 0 0 17 7 17 22 .547 5 0 15 8 18 23 .627 0 0 11 9 5 10 .700 0 4 16 10 2 6 .733 2 0 12 11 8 18 .849 3 8 16 12 2 4 1.078 10 0 14 13 13 26 1.174 0 0 20 14 2 7 1.456 12 0 19 15 14 17 1.563 0 7 17 16 5 8 1.824 9 11 21 17 11 14 2.211 6 15 19 18 19 27 3.614 1 0 20 19 2 11 4.681 14 17 21 20 13 19 5.443 13 18 23 21 2 5 6.245 19 16 23 22 12 28 8.313 0 0 26 23 2 13 11.226 21 20 24 24 2 16 16.181 23 0 25 25 2 3 17.445 24 0 26 26 2 12 29.765 25 22 27 27 1 2 47.003 0 26 0

Cluster Membership



Oneway

Descriptives

N Mean



m Maximu


2 23 1.00 .000 .000 1.00 1.00 1 1 3 1 1.00 . . . . 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 28 1.00 .000 .000 1.00 1.00 1 1

Pop 09 1 1 1323592.00 . . . . 1323592 1323592 2 23 60784.57 64402.183 13428.783 32934.97 88634.16 589 240272 3 1 1156898.00 . . . . 1156898 1156898 4 2 20271.00 18690.246 13216.000 -147654.20 188196.20 7055 33487 5 1 97977.00 . . . . 97977 97977 Total 28 143466.21 316286.060 59772.447 20823.28 266109.15 589 1323592

GDP.09 1 1 3734.61 . . . . 3735 3735 2 23 8529.14 11186.962 2332.643 3691.53 13366.74 583 39740 3 1 1031.59 . . . . 1032 1032 4 2 34730.32 6968.686 4927.605 -27880.84 97341.47 29803 39658 5 1 1747.82 . . . . 1748 1748 Total 28 9719.46 12561.463 2373.893 4848.63 14590.28 583 39740

Distance 1 1 6941.00 . . . . 6941 6941 2 23 5800.43 2893.198 603.273 4549.32 7051.55 1477 10163 3 1 7480.00 . . . . 7480 7480 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 8570.00 . . . . 8570 8570 Total 28 5894.36 2918.415 551.529 4762.71 7026.00 455 10163

inf 09 1 1 4.3100 . . . . 4.31 4.31 2 23 3.8965 .97549 .20340 3.4747 4.3184 2.39 5.83 3 1 3.4700 . . . . 3.47 3.47 4 2 6.2350 .43134 .30500 2.3596 10.1104 5.93 6.54 5 1 2.9100 . . . . 2.91 2.91 Total 28 4.0279 1.10388 .20861 3.5998 4.4559 2.39 6.54

English 1 1 1.00 . . . . 1 1 2 23 1.00 .000 .000 1.00 1.00 1 1 3 1 1.00 . . . . 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 .00 . . . . 0 0 Total 28 .89 .315 .060 .77 1.01 0 1

Tech 09 1 1 4.3100 . . . . 4.31 4.31 2 23 3.8965 .97549 .20340 3.4747 4.3184 2.39 5.83 3 1 3.4700 . . . . 3.47 3.47 4 2 6.2350 .43134 .30500 2.3596 10.1104 5.93 6.54 5 1 2.9100 . . . . 2.91 2.91 Total 28 4.0279 1.10388 .20861 3.5998 4.4559 2.39 6.54

NonVa09 1 1 8257.00 . . . . 8257 8257 2 23 253.26 351.971 73.391 101.06 405.46 0 1445 3 1 1754.00 . . . . 1754 1754 4 2 235.00 275.772 195.000 -2242.71 2712.71 40 430 5 1 46.00 . . . . 46 46 Total 28 584.00 1564.588 295.679 -22.68 1190.68 0 8257

ANOVA






inf 09 Between Groups 11.780 4 2.945 3.207 .031 Within Groups 21.121 23 .918 Total 32.901 27



NonVa09 Between Groups 6.329E7 4 1.582E7 129.907 .000 Within Groups 2801484.435 23 121803.671 Total 6.609E7 27





1 8 24 .154 0 0 11 2 2 9 .168 0 0 9 3 19 20 .227 0 0 18 4 10 15 .296 0 0 10 5 17 21 .311 0 0 6 6 17 22 .512 5 0 15 7 11 25 .535 0 0 17 8 18 23 .621 0 0 11 9 2 6 .742 2 0 12 10 5 10 .820 0 4 16 11 8 18 .846 1 8 16 12 2 4 1.087 9 0 14 13 13 26 1.114 0 0 20 14 2 7 1.334 12 0 19 15 14 17 1.551 0 6 17 16 5 8 1.867 10 11 21 17 11 14 2.207 7 15 19 18 19 27 3.730 3 0 20 19 2 11 4.680 14 17 21 20 13 19 5.457 13 18 23 21 2 5 6.256 19 16 23 22 12 28 8.253 0 0 26 23 2 13 11.257 21 20 24 24 2 16 16.196 23 0 25 25 2 3 16.918 24 0 26 26 2 12 29.760 25 22 27 27 1 2 47.315 0 26 0

Cluster Membership



Oneway

Descriptives

N Mean



m Maximu


2 23 1.00 .000 .000 1.00 1.00 1 1 3 1 1.00 . . . . 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 28 1.00 .000 .000 1.00 1.00 1 1

Pop 09 1 1 1323592.00 . . . . 1323592 1323592 2 23 60784.57 64402.183 13428.783 32934.97 88634.16 589 240272 3 1 1156898.00 . . . . 1156898 1156898 4 2 20271.00 18690.246 13216.000 -147654.20 188196.20 7055 33487 5 1 97977.00 . . . . 97977 97977 Total 28 143466.21 316286.060 59772.447 20823.28 266109.15 589 1323592

GDP.09 1 1 3734.61 . . . . 3735 3735 2 23 8529.14 11186.962 2332.643 3691.53 13366.74 583 39740 3 1 1031.59 . . . . 1032 1032 4 2 34730.32 6968.686 4927.605 -27880.84 97341.47 29803 39658 5 1 1747.82 . . . . 1748 1748 Total 28 9719.46 12561.463 2373.893 4848.63 14590.28 583 39740

Distance 1 1 6941.00 . . . . 6941 6941 2 23 5800.43 2893.198 603.273 4549.32 7051.55 1477 10163 3 1 7480.00 . . . . 7480 7480 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 8570.00 . . . . 8570 8570 Total 28 5894.36 2918.415 551.529 4762.71 7026.00 455 10163

inf 09 1 1 4.3100 . . . . 4.31 4.31 2 23 3.8965 .97549 .20340 3.4747 4.3184 2.39 5.83 3 1 3.4700 . . . . 3.47 3.47 4 2 6.2350 .43134 .30500 2.3596 10.1104 5.93 6.54 5 1 2.9100 . . . . 2.91 2.91 Total 28 4.0279 1.10388 .20861 3.5998 4.4559 2.39 6.54

English 1 1 1.00 . . . . 1 1 2 23 1.00 .000 .000 1.00 1.00 1 1 3 1 1.00 . . . . 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 .00 . . . . 0 0 Total 28 .89 .315 .060 .77 1.01 0 1

Tech 09 1 1 4.3100 . . . . 4.31 4.31 2 23 3.8965 .97549 .20340 3.4747 4.3184 2.39 5.83 3 1 3.4700 . . . . 3.47 3.47 4 2 6.2350 .43134 .30500 2.3596 10.1104 5.93 6.54 5 1 2.9100 . . . . 2.91 2.91 Total 28 4.0279 1.10388 .20861 3.5998 4.4559 2.39 6.54

NonVo09

1 1 12097.00 . . . . 12097 12097 2 23 408.52 553.744 115.464 169.06 647.98 0 2046 3 1 1830.00 . . . . 1830 1830 4 2 93.50 72.832 51.500 -560.87 747.87 42 145 5 1 53.00 . . . . 53 53 Total 28 841.54 2280.896 431.049 -42.90 1725.98 0 12097

ANOVA









NonVo09 Between Groups 1.337E8 4 3.343E7 113.886 .000 Within Groups 6751218.239 23 293531.228 Total 1.405E8 27





1 19 20 .111 0 0 18 2 8 24 .259 0 0 11 3 10 15 .298 0 0 9 4 17 21 .311 0 0 5 5 17 22 .504 4 0 15 6 11 25 .545 0 0 17 7 18 23 .621 0 0 11 8 2 9 .647 0 0 10 9 5 10 .733 0 3 16 10 2 6 .860 8 0 12 11 8 18 .894 2 7 16 12 2 7 .940 10 0 14 13 13 26 1.035 0 0 20 14 2 4 1.261 12 0 19 15 14 17 1.564 0 5 17 16 5 8 1.876 9 11 21 17 11 14 2.209 6 15 19 18 19 27 3.607 1 0 20 19 2 11 4.904 14 17 21 20 13 19 5.396 13 18 23 21 2 5 6.322 19 16 23 22 12 28 8.270 0 0 26 23 2 13 11.326 21 20 24 24 2 16 16.211 23 0 25 25 2 3 16.669 24 0 26 26 2 12 29.795 25 22 27 27 1 2 46.810 0 26 0

Cluster Membership



Oneway

Descriptives

N Mean



m Maximu


1 1 23503.00 . . . . 23503 23503 2 23 1368.39 1336.952 278.774 790.25 1946.53 31 5068 3 1 2846.00 . . . . 2846 2846 4 2 581.00 429.921 304.000 -3281.69 4443.69 277 885 5 1 1016.00 . . . . 1016 1016 Total 28 2142.86 4372.484 826.322 447.38 3838.33 31 23503

Trade 09 1 1 1.00 . . . . 1 1 2 23 1.00 .000 .000 1.00 1.00 1 1 3 1 1.00 . . . . 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 28 1.00 .000 .000 1.00 1.00 1 1

Pop 09 1 1 1323592.00 . . . . 1323592 1323592 2 23 60784.57 64402.183 13428.783 32934.97 88634.16 589 240272 3 1 1156898.00 . . . . 1156898 1156898 4 2 20271.00 18690.246 13216.000 -147654.20 188196.20 7055 33487 5 1 97977.00 . . . . 97977 97977 Total 28 143466.21 316286.060 59772.447 20823.28 266109.15 589 1323592

GDP.09 1 1 3734.61 . . . . 3735 3735 2 23 8529.14 11186.962 2332.643 3691.53 13366.74 583 39740 3 1 1031.59 . . . . 1032 1032 4 2 34730.32 6968.686 4927.605 -27880.84 97341.47 29803 39658 5 1 1747.82 . . . . 1748 1748 Total 28 9719.46 12561.463 2373.893 4848.63 14590.28 583 39740

Distance 1 1 6941.00 . . . . 6941 6941 2 23 5800.43 2893.198 603.273 4549.32 7051.55 1477 10163 3 1 7480.00 . . . . 7480 7480 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 8570.00 . . . . 8570 8570 Total 28 5894.36 2918.415 551.529 4762.71 7026.00 455 10163

inf 09 1 1 4.3100 . . . . 4.31 4.31 2 23 3.8965 .97549 .20340 3.4747 4.3184 2.39 5.83 3 1 3.4700 . . . . 3.47 3.47 4 2 6.2350 .43134 .30500 2.3596 10.1104 5.93 6.54 5 1 2.9100 . . . . 2.91 2.91 Total 28 4.0279 1.10388 .20861 3.5998 4.4559 2.39 6.54

English 1 1 1.00 . . . . 1 1 2 23 1.00 .000 .000 1.00 1.00 1 1 3 1 1.00 . . . . 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 .00 . . . . 0 0 Total 28 .89 .315 .060 .77 1.01 0 1

Tech 09 1 1 4.3100 . . . . 4.31 4.31 2 23 3.8965 .97549 .20340 3.4747 4.3184 2.39 5.83 3 1 3.4700 . . . . 3.47 3.47 4 2 6.2350 .43134 .30500 2.3596 10.1104 5.93 6.54 5 1 2.9100 . . . . 2.91 2.91 Total 28 4.0279 1.10388 .20861 3.5998 4.4559 2.39 6.54

ANOVA

Sum of Squares df Mean Square F Sig. AppVa09 Between Groups 4.767E8 4 1.192E8 69.377 .000













1 19 20 .111 0 0 18 2 17 21 .318 0 0 6 3 10 15 .345 0 0 8 4 8 24 .381 0 0 12 5 2 9 .399 0 0 10 6 17 22 .502 2 0 15 7 11 25 .542 0 0 17 8 5 10 .560 0 3 12 9 18 23 .629 0 0 16 10 2 7 .676 5 0 11 11 2 6 .898 10 0 14 12 5 8 .911 8 4 16 13 13 26 1.028 0 0 20 14 2 4 1.168 11 0 19 15 14 17 1.555 0 6 17 16 5 18 2.071 12 9 21 17 11 14 2.208 7 15 19 18 19 27 3.607 1 0 20 19 2 11 4.867 14 17 21 20 13 19 5.395 13 18 23 21 2 5 6.299 19 16 23 22 12 28 8.251 0 0 26 23 2 13 11.306 21 20 24 24 2 16 16.200 23 0 25 25 2 3 16.622 24 0 26 26 2 12 29.814 25 22 27 27 1 2 47.047 0 26 0

Cluster Membership



Oneway

Descriptives

N Mean



m Maximu


2 23 1.00 .000 .000 1.00 1.00 1 1 3 1 1.00 . . . . 1 1 4 2 1.00 .000 .000 1.00 1.00 1 1 5 1 1.00 . . . . 1 1 Total 28 1.00 .000 .000 1.00 1.00 1 1

Pop 09 1 1 1323592.00 . . . . 1323592 1323592 2 23 60784.57 64402.183 13428.783 32934.97 88634.16 589 240272 3 1 1156898.00 . . . . 1156898 1156898 4 2 20271.00 18690.246 13216.000 -147654.20 188196.20 7055 33487 5 1 97977.00 . . . . 97977 97977 Total 28 143466.21 316286.060 59772.447 20823.28 266109.15 589 1323592

GDP.09 1 1 3734.61 . . . . 3735 3735 2 23 8529.14 11186.962 2332.643 3691.53 13366.74 583 39740 3 1 1031.59 . . . . 1032 1032 4 2 34730.32 6968.686 4927.605 -27880.84 97341.47 29803 39658 5 1 1747.82 . . . . 1748 1748 Total 28 9719.46 12561.463 2373.893 4848.63 14590.28 583 39740

Distance 1 1 6941.00 . . . . 6941 6941 2 23 5800.43 2893.198 603.273 4549.32 7051.55 1477 10163 3 1 7480.00 . . . . 7480 7480 4 2 4320.50 5466.643 3865.500 -44795.33 53436.33 455 8186 5 1 8570.00 . . . . 8570 8570 Total 28 5894.36 2918.415 551.529 4762.71 7026.00 455 10163

inf 09 1 1 4.3100 . . . . 4.31 4.31 2 23 3.8965 .97549 .20340 3.4747 4.3184 2.39 5.83 3 1 3.4700 . . . . 3.47 3.47 4 2 6.2350 .43134 .30500 2.3596 10.1104 5.93 6.54 5 1 2.9100 . . . . 2.91 2.91 Total 28 4.0279 1.10388 .20861 3.5998 4.4559 2.39 6.54

English 1 1 1.00 . . . . 1 1 2 23 1.00 .000 .000 1.00 1.00 1 1 3 1 1.00 . . . . 1 1 4 2 .00 .000 .000 .00 .00 0 0 5 1 .00 . . . . 0 0 Total 28 .89 .315 .060 .77 1.01 0 1

Tech 09 1 1 4.3100 . . . . 4.31 4.31 2 23 3.8965 .97549 .20340 3.4747 4.3184 2.39 5.83 3 1 3.4700 . . . . 3.47 3.47 4 2 6.2350 .43134 .30500 2.3596 10.1104 5.93 6.54 5 1 2.9100 . . . . 2.91 2.91 Total 28 4.0279 1.10388 .20861 3.5998 4.4559 2.39 6.54

AppVo09

1 1 8623.00 . . . . 8623 8623 2 23 458.22 459.874 95.890 259.35 657.08 2 1612 3 1 907.00 . . . . 907 907 4 2 40.50 12.021 8.500 -67.50 148.50 32 49 5 1 335.00 . . . . 335 335 Total 28 731.61 1607.727 303.832 108.20 1355.02 2 8623

ANOVA









AppVo09 Between Groups 6.514E7 4 1.628E7 80.497 .000 Within Groups 4652792.413 23 202295.322 Total 6.979E7 27

A STUDY OF THE SHIFTING PATTERNS OF GLOBAL SOURCING IN … · May 2011 . Chair: Ting Chi . The U.S....

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Transcript of A STUDY OF THE SHIFTING PATTERNS OF GLOBAL SOURCING IN … · May 2011 . Chair: Ting Chi . The U.S....