Master Thesis
Study on entry barriers and profit distribution
In Global Notebook PC Value Chain
Faculty of Economics and Business
Rijksuniversiteit Groningen
Name: Kun Pang Student Number: 1702769 Supervisor: Dr. D.H.M Akkermans
Table of Contents:
Acknowledgements
1. Introduction and Problem Statement……….……….4
1.1 Introduction……….………....4
1.2 Problem Statement……….………….….5
1.3 Research Questions……….……….6
1.4 Importance of this study………..…9
2. Theory………..9
2.1 Globalization………..….9
2.2 Global Value Chain (GVC)………...10
2.3 Governance in GVC………...…...………13
2.4 Rents and Barriers to entry………....14
2.5 Definition of Notebook Personal Computer………...………...16
2.6 Structure of Global Notebook PC Value Chain………..…...17
3. Methodology………..22
3.1 Introduction………...……….22
3.2 Data Collection………...………...22
3.3 Research methodology and Hypothesis……….23
3.3.1 Research Model……….23
3.3.2 Dependent variable………24
3.3.3 Independent Variables and Hypothesis………...……..24
4. Analyzing and Findings………...………26
4.1 Description of Variables………...…….26
4.2 Diagnostic Checks……….27
4.3 Regression Results……….29
5. Conclusions and Limitations………...…31
5.1 Conclusions………...…31
5.2 Limitations………34
Acknowledgement
Writing an English master thesis is not an easy task for me indeed. During the past half year, I learned a lot from self-study which is quite different from bachelor study in my university life. This is the first time for me realizing that reading, analysis and discussion are such good ways to learn new knowledge besides lectures. This method put forward me to be motivated and creative thinking, from which I obtained more empirical research experience. Studying in the Rijksuniversiteit Groningen will be a pleasant and deep impressed memory to me.
Good idea is significant to start a new research, but I met many problems from the very beginning. Fortunately, Dr. Akkermans, my thesis supervisor, has abundant knowledge in this research field and gave me lots of helps and advices. My questions were always replied promptly and answered patiently. Here, I would like to thanks him a lot for his kindness and his comments for my proposal, thesis and every question. Without his guidance, I would never succeed in finishing my thesis.
I also appreciated encouragements from my family and friends very much. Their support and trust accompanied me to go through the most disappointing August.
Key Words:
1.
Introduction and Problem Statement 1.1 IntroductionGlobalization and outsourcing are the main trends of global economy in the past two decades. With the help of fast improvements of technology and communication, worldwide cooperation become much more common (Lukianov and Kisliak, 2008). Producing processes cross national boundaries transferring to regions have competitive advantages (Gereffi and Memedovic, 2003; Sturgeon, Van Biesebroeck and Gereffi, 2008). A large number of developing countries join in Global Value Chains, because of their rich raw materials, large pool of labor force and, the most important, low labor cost (Ernst and Guerrieri, 1997;
Sanjaya and Manuel, 2004). Meanwhile, multinational enterprises in developed countries decrease their producing costs, test the goods they purchase using their own quality-control requirements and give producing support when necessary.
In recent years there has been a growing body of work analyzing globalization processes from the perspective of “value chain” (Gereffi and Kaplinsky, 2001). It shifts the focus from production alone to the whole range of activities from design to marketing and it helps us ask questions about the winners and losers in the globalization process, how and why the gains from globalization are spread. The distribution of profit is often used as the primary indicator of global income shares in value chain analysis (Gereffi and Kapalinsky, 2001). To the participators in global value chain (GVC), their market power and positioning in chain segments (in which they can create and appropriated high returns) are two sources of power. These attributes are derived from a multiplicity of barriers to entry (Kaplinsky, 1998). Furthermore, entry barriers are expected to have significant influences on profit distribution along the value chain.
four presents econometric tests and regression results. Last section contains conclusions of the thesis and limitations which should be improved in future researches.
1.2 Problem Statement
China started entering the world market around the end of 1970s and participated in many global value chains of manufacturing industry. In last decade, China kept an amazing GDP growth rate, almost 10% each year, which is much higher than world average level as well as many other countries’ GDP growth rate. See Table1. China is a large exporting country and also the supplier of many kinds of product for the whole world. However, up to now, China is still one of the countries which have huge amount of low payment labor force. This situation is not the result of globalization per se which has had an adverse poverty impact, but the particular way in which countries have inserted themselves in the global economy (Kaplinsky, 2005). In order to earn in global value chain, nationality is not the key effect, but also competitive capabilities each country owns (Ernst and Guerrieri, 1997).
Increasing Rate of GDP (%) 2002 2003 2004 2005 2006 Korea 7.0 3.1 4.7 4.2 5.0 Japan 0.3 1.4 2.7 1.9 2.2 U.S.A. 1.6 2.5 3.9 3.2 3.3 China 9.1 10.0 10.1 10.4 11.1 World 1.8 2.6 4.0 3.3 3.9
Table 1: GDP Increasing Rate, 2002-2006
Source: National Bureau Statistics of China and IMF
comparison with multinational companies and the global retailers, China gets least profit in this value adding chain.
Then, how does profit distribute among industries scattered in different countries? What are the factors influence competitiveness capability and further affect profit distribution among industries in global value chain?
Elements of the Value Chain Costs in Value Chain
Assembly in China $3
Parts from suppliers for China $14 Corporate costs + profits $8 Global distributors and retailers $15
Total costs $40
Table 2: Costs in Computer Mouse Value Chain
Source: Paul A. Strassmann, 2004 1.3 Research Questions
Lukianov and Kisliak (2008) argue that barriers to entry are seen as a set of economic, technological, and institutional conditions that enable incumbent firms to both hold prices above minimum average costs over the long term and to prevent potential entrants from making profits at the same level as the incumbent firms prior to entry. Moreover, entry barriers are needed both to impede the entry of new comers and protect profits, and an element of a competitive strategy (Lukianov and Kisliak, 2008). As many reputed articles tell us (Orr, 1974; Gorecki, 1991; Baldwin et al., 1995; Guo, 1998; Gereffi, 1999; Kaplinsky, 2005), barriers to entry is one of the significant factors in a product chain, which influences profit distribution among firms, regions and countries involved. Profit is greatest in the concentrated parts of global value chains that have high entry barriers for new firms (Gereffi and Memedovic, 2003).
Barriers to entry are expected to have a positive relationship with average profit rates (Bain, 1956; Mann, 1966; Kaplinsky, 2005). Most previous empirical works (Bain, 1956;
Mann, 1966; Orr, 1974) found that industries with high entry barriers enjoyed distinctly higher long-run average profit rates than other industries with substantial barriers, and
Nevertheless, in China, Guo (1998) found out the result from his study on 40 industries that an appropriate degree of barriers to entry, not free or high barriers to entry, is beneficial to Chinese industrial performance. These literatures are almost focus on national industries, for example, Orr’s two publications in 1974 studying on the bank industry in Canada and 71 Canadian industries respectively, and Guo (1998)’s research of Chinese industries.
How does profit distribu te among industries in global value chain ?
The smiling curve first coined by Stain Shih (the founder of the Acer Group) may answer this question. He suggests that in many industries profit at various stages of value chain has come to follow a U-shaped curve high at the upstream and downstream processes (like design and marketing) and low at the midstream processes (usually production). This statement is proved by the studies of Chen (2004) and Ye (2005) that China is at the bottom of the “U” shaped value chain and attracts the labor-intensive producing- or assembling-process. See Figure 1.
Research Question:
Figure 1: The cross-border network and profit distribution of the Notebook PC industry
Source: Chen, 2004
Barriers to entry to industries in Notebook PC Chain
↓←Notebook GVC
Profitability of industries in Notebook PC Chain
Figure 2: Research ModelThe contribution of this paper is that it estimates the influences of barriers to entry to profitability base on the global value chain theory. Normally, entry barriers are used to calculate effects on different industrial performances in one country, such as Orr’s two articles in 1974 and Guo (1998)’s research of Chinese industries. Other researches of many different industries’ value chain were focus on national market, for instance, Kaplinsky did research in Furniture industry value chain in South Africa (2001), Jeans manufacturing value chain in Dominican Republic (1993), Footwear value chain, and Schuurhuizen (2006) studied the fish value chain in Kenya, etc. Moreover, the research related to China are almost all about the value chains in traditional labor intensive industry, like Textile industry, Clothing industry and Footwear industry.
I found that seldom people studied on the value chain of IT related industries, especially the Notebook Personal Computer Industry (Notebook PC Industry), additionally, in the global market. As Notebook PC market is prosperous with customers flooding in recent years, I suppose that research on Notebook PC GVC will be interesting and meaningful. Moreover, the global value chain of Notebook industry has not be concentrated on in previous
econometric research, so what I will do is a start of this global value chain.
2. Theory
2.1 Globalization
Nevertheless, greater levels of outputs encourage greater degrees of fragmentation. Allowing countries to specialize accordingly increases productivity across all countries, since they are focus on sectors which they own the lowest opportunity cost of production. This
specialization translates into cheaper goods, and a greater variety of them for all the
consumers (Drezner, 2004). Jones and Kierzkowski (1990) argue that the increasing return is the crucial push of the outsourcing phenomenon, because the input prices and the quality of raw material suppliers could offer are different all over the world. To most of the developing countries, if they could not supply competitively a whole product, they could as a minimum capture production of certain segments and components (Jones and Kierzkowski, 1990; 2004) or assembling.
Globalization has been defined as “not merely the geographical extension of economic activity across national boundaries but also—and more importantly— the functional integration of such internationally dispersed activities” (Dicken, 1998). It tends to diminish the influence of national origins on business systems, and to highlight the role of design, production, and marketing core competencies and strategic capabilities within and between economic sectors. Kaplinsky (2000; 2005) points out that the process of globalization is characterized by a reduction of barriers to the cross-border flow of factors, products,
information, technology and values. Additionally, this process is the outcome of technological advances. The way firms do business in the global economy thus is determined to an
increasing extent by their position in Global Value Chains.
2.2 Global Value Chain (GVC)
The concept of value chain was first used in the 1960s and the 1970s to analyze the intra-enterprise activities. During the 1990s, value chain analysis has become widely used because of the works of Porter (1985, 1986), who employed the notion of the value chain which was named as commodity chain at the level of the individual firm (Dicken and Kelly et al., 2001).
value chain describes the full range of activities which is required to bring a product or service from conception, through the different phases of production, delivery to final
consumers, and final disposal after use. See Figure 3. Lead firms in GVC have narrowed their focus to product development and marketing while outsourcing production and production-related functions to suppliers. This is the result of rising costs of brand development that stem from increasing product diversity, shorter product life-cycles, and intensified international competition, meanwhile, the capabilities in the supply base have improved (Gereffi and Kaplinsky, 2001). Production per se is only one of a number of a value added links (Kaplinsky and Morris, 2003). Being the most powerful enterprises or the owner of core technology, lead firms control the fate of value chain and contribute more to the value added of the final products.
Figure 3: Basic Value Chain
Source: Kaplinsky and Morris, 2003
After Gereffi building the definition of value chain against the background of
chain as “world is being transformed from a ‘space of places’ into a ‘space of flows’”.
Gereffi (1994) classifies GVC creatively into two types. One is buyer-driven global value chain, in which the key buyer performs the governing responsibility. The other GVC is producer-driven, meaning the core manufacturers who own important technological
knowledge or reputations control the organization of the whole chain. Gereffi and
Korzeniewicz (1994) states that the most characteristic of producer-driven value chain is that large corporations (usually transnationals) play the central role in coordinating production networks including backward and forward linkages. Lead firms in both these two kinds of chain are almost located in developed countries (Wang and Hou, 2007) and include not only multinational manufacturers, but also large retailers and brand-name firms (Gereffi and Kaplinsky, 2001). In producer-driven chains, manufacturers making advanced products like aircraft, automobiles, and computers are the key economic agents not only in terms of their earnings, but also in their ability to exert control over backward linkages with raw material and component suppliers, and forward linkages into distribution and retailing. The lead firms in producer-driven chains usually belong to global oligopolies (Gereffi, 1999). Research carried out on particular sectors, such as garments, electronics and agricultural commodities, have provided valuable insights into the role of lead firms in constructing these chains. The lead firms play a significant role in specifying what is to be produced, how and by whom (Gereffi and Kaplinsky, 2001). In the Notebook PC Global Value Chain, coordination of the activities appears to be based on the combination of arm’s-length market relations and a network-style of governance based upon a division of competences between firms (Gereffi and Kaplinsky, 2001; Sturgeon, 2003; Kraemer and Dedrick, 2003).
Theory of value chain helps to explain the distribution of benefit to those participators in the global economy. The primary returns accrue to the parties who are able to protect
production, such as design, branding and marketing (Gereffi and Kaplinsky, 2001). However, this is too simple a conclusion, since even within production some activities involve greater barriers to entry (Kaplinsky and Morris, 2003).
Kaplinsky (2005) states that there are three key elements could be used to analyze value chain, Barriers to Entry, Governance and System Efficiency, which are closely linked. In order to be the winner in the global business competitions, participators in global value chain want to keep rents in hand or search for the new rent and further try to govern the chain. Therefore, they act over an increasingly large geographical and institutional terrain to search for systemic efficiency (Kaplinsky, 2000).
2.3 Governance in GVC
The concept of global value chain provides a pragmatic and useful framework as we look into the dynamic economic geography of industries and value adding activities (Sturgeon, Van Biesebroeck and Gereffi, 2008). It emphasizes on the internal governance structure of the chains and on the role of diverse lead firms in setting up global production and sourcing networks (Gereffi and Kaplinsky, 2001).
Governance, which is important to understand Global Value Chain (Gereffi, 1994), ensures the industries and firms in particular value chain are interrelated but not organized randomly, because activities and task arrangements are all under the control of governance.
Humphrey and Schmitz (2004) call it nonmarket coordinating. Differences in understanding of market demand and non-price competitions require firms in the chain to be managed, in order to avoid risk of supplier failures (Humphrey and Schmitz, 2004). Lead firms always have more advantages than their suppliers. What distinguishes lead firms from non-lead firms is that they control access to major resources, such as product design, new technologies, brand names or consumer demand, which generate the most profitable returns in the industry
their domestic markets (Keesing and Lall, 1992).
Understanding the governance of a chain helps us to look into the structure of this value chain. Kaplinsky (2000) suggests that the ability to govern often rests in intangible
competences, such as R&D, design, branding, marketing, which are characterized by high barriers of entry and command high returns usually reaped by developed country firms. Developing country firms tend to be locked in to tangible activities, producing to the parameters set by the governors, suffering from low barriers of entry and reaping low returns. Governance related distribution issues are critical to the debate on whether there is a spreading of the gains from globalization (Gereffi and Kaplinsky, 2001).
2.4 Rents and Barriers to entry
Focus on barriers to entry in value chain analysis enables us to understand the dynamics of the distributional outcomes, identifying activities which are subject to growing competition within the steps of production, design and branding (Kaplinsky and Morris, 2003).
Barriers to entry are used to create and protect profits (rents), which are the results of scarcity. Scarcity here means having something others do not possess, such as a resource, a capability and knowledge. Rents are achieved by taking advantage of or by creating barriers to the entry of competition. The greater the barriers to entry lead to the higher level of
profitability (Kaplinsky and Morris, 2003). Kaplinsky in his book “Globalization, poverty and inequality (2005)” states that the relationship between rents and barriers to entry is positive that the lower the barriers to entry and the easier it is to copy a particular activity, the lower the associated rents and incomes. He also separates rents in the production chain to two categories, endogenous rents and exogenous rents, base on the causes of the rents. The endogenous rents are constructed by producers themselves, acting either independently or in concert with other producers. Exogenous rents are rents accruing from factors outside the value chain. Similarly, Gereffi (2001) indicates that leading firms use barriers to entry to generate different kinds of rents in global industries. These assets may be tangible
(2001) suppose that entry barriers in intangible activities are growing faster than those in tangible activities.
Entry rate is normally influenced by variables that represent incentives encouraging entry and barriers discouraging entry (Orr, 1974; Gorecki, 1991; Baldwin et al., 1995; Guo, 1998). The entry of new firms has an important influence on the evolution of market structure and performance, and it may lead to the erosion of high profits among incumbent firms (Yang, 2007). Net entry ratio could be the variable to measure industrial profitability and expected to have negative effects on profitability. Moreover, a larger industry size provides scope for larger number of players. The expected growth of the market, growth of industry’s sales revenue, is tested as an important factor to entry, and shows positive impact (Orr, 1974;
Baldwin et al., 1995; Guo, 1998). In the previous industrial researches, barriers to entry are indicated by capital requirement, economies of scale, product differentiation and the degree of firm concentration (Bain, 1956; Mann, 1966; Guo, 1998). Average enterprise size among the largest enterprises (which account for 50% of the industry output value) is popular used to estimate economies of scale (Bain, 1956; Orr, 1974; Guo, 1998). They argue that entry barriers raise accompany with higher level of this variable, which means economies of scale has positive effects on profits. When analyzing entry barriers in Indian manufacturing industries, Basant and Saha (2005) adopt the average scale of operation in the industry as the variable reflecting barriers created by economies of scale, because of data lack of largest enterprises. I will adopt their method to identify economies of scale in my model. Kaplinsky
(2005) emphasizes that the effects of technological investment (R&D investment) should be considered in the estimation of influences to profits. In order to measure the technology level of an industry, R&D spending is a good proxy for overall efforts towards advances in
technology (Slaughter, 2004). Previous empirical studies on barriers to entry (Bain, 1956;
protect their market and further to earn profits from competition (Baldwin and Gorecki, 1987;
Smiley, 1988; Singh et al., 1998). Product differentiation and innovation have become increasingly important sources of competitive advantage (Schmitz and Humphrey, 2002), and the measurement of product differentiation could use R&D intensity (Basant and Saha, 2005). Besides, R&D activities are a major determinant of innovation (Cohen and Levinthal, 1989). Profit requires support from technology improvement and R&D investment (Gereffi and Memedovic, 2003). However, Kraemer and Dedrick (2003) point out that there is generally little formal input from PC manufacturers in their R&D efforts, because the PC is a modular product with standard components developed by upstream suppliers (Intel is an exception because Intel sets hardware standards for the whole industry). Lastly, labor cost (workers’ hourly income) always influences activities spreading over the world. Empirical study of
Egger and Egger (2003) shows that reduction of trade barriers and low wages in East Europe attracted countries segment their labor intensive stages of production there. Hourly wage is more reasonable than monthly or annual wage to measure labor cost, because it considers the working hour differences in different industries and regions. Hill (2002) uses hour wage measurement in his paper to report the effects of labor cost in textile manufacturing. Additionally, labor cost is correlated with labor forces’ skill level. Technological change is with respect to labor, favoring the demand for skilled labor and increasing relative wages of skilled labor force (Barbosa and Faria, 2008). In other words, workers own higher technology and skills are expected to have higher payments. Normally, skillful workers bring more profits to enterprises than non-skill workers. With regard to these two opposite aspects of the
influences of labor cost to profitability, I am not sure which aspect of the influences is bigger in Notebook PC GVC. Therefore, the results of regression model will tell us the answer.
2.5 Definition of Notebook Personal Computer
According to the interpretation in Wikipedia, Notebook PC is defined as follows
“A laptop computer or laptop (also notebook computer, notebook and notepad) is a small mobile computer, typically weighing 3 to 12 pounds (1.4 to 5.4 kg), although older laptops may weigh more. Laptops contain components that are similar to their desktop counterparts and perform the same functions, but are
miniaturized and optimized for mobile use and efficient power consumption, although typically less powerful for the same price.”
2.6 Structure of Global Notebook PC Value Chain
In my thesis, I utilize HS 2002 (Harmonized Commodity Description and Coding System), NAICS codes (North American Industry Classification System), and ISIC codes (International Standard Industry Classification) to define different products, components and industries in the global Notebook PC value chain.
I concentrate on the Global Notebook PC Value Chain due to the following two reasons. Firstly, China has the highest market share of Notebook PC assembling and components supply, 70% of the portable computers sold in the world market are shipped out from China in 2007. Secondly, Notebook PC has an increasing market share as the positive result of the improvement of technology and shorter product life cycle (Cai, 2005; Tian, 2007). Take U.S. market in 2007 for example, which is the biggest market of Notebook PC, sales volume of notebook is always double of the number of desktop computer, and quantity difference enlarges to third times in hot sales seasons. Additionally, annual sales growth ratio of Notebook PC is around 40% computed by quantity (Report published by Displaysearch, 2007, Figure a&b in appendix).
Notebook PC value chain is scattered worldwide. According to previous researchers' argument, computer global value chain is a good example of producer-driven value chain, both capital- and technology-intensive (Gereffi and Korzeniewicz, 1994; Gereffi, 1999).
(Sanjaya and Manuel, 2004), which is mainly practiced in developing countries. Notebook PC global value chain connects developed and developing countries through task arrangements, although lead firms in western countries hold the earnings and control over backward linkages with raw material and component suppliers, and forward linkages into distribution and
retailing (Gereffi, 1999).
Lead firms or industries can be located upstream or downstream from manufacturing, or can be involved in the supply of critical components, for instance, microprocessor companies like Intel and software firms like Microsoft Co. (Gereffi, 1999). In tangible production process, developing countries join in, mainly those located in Asia. East Asia has played a critical role as an increasingly important supply base both for final assembly and component manufacturing (Encarnation, 1992; Hobday, 1993; Ernst, 1996; Ernst and Guerrieri, 1997), but based on the foreign technology (Tian, 2007). Marketing is always controlled by brand marketers who outsourcing production and sale Notebook PC with their own brands and sales system, such as HP, Dell, Apple, etc. Moreover, these brand companies occupy the design stage (they do market investigations and make decisions what is the direction of market consumption). Those big brand marketers are almost owned by developed countries, especially U.S. As long as U.S., Europe and Japan remain the major Notebook PC markets, and common designs can be used across those markets, U.S. and Japanese vendors will continue to lead the industry (Kraemer and Dedrick, 2003).
plastic casing, but not a part of Notebook PC. Thus, I did not contain this product in my model as well.
Table 3: Industries in Notebook PC GVC
*: OEM (Original Equipment Manufactures)
**: ODM (Original Design Manufactures)Source:Cai, 2005; Chen, 2004; HS Classification; ISIC Classification; NAICS Classification
(1)
Brand marketers in U.S. create new product concepts and control marketing stages. Iconcentrate on U.S. on these stages, because U.S. is the largest destination of Note-book exporting from China in 2007, (almost 30%, followed by HongKong, Germany and Netherlands, 13.83%, 10.60%, 9.13% respectively; Source: UN Comtrade Data-base). I assume that Hong Kong, Germany and Netherlands appear on top importer list is the result of big ports they have (Hamburg in Germany and Rotterdam in the Netherlands). These seaports are core distribution places for goods entering the whole markets of Asia and Europe.
(2)
Taiwan is an important region in Notebook GVC doing technological design and de-velopment. Large OEM/ODM companies in Taiwan have historical good cooperation with brands in U.S. and common cultural/language advantages with Mainland China (Ernst and Guerrieri, 1997; Sturgeon, 2003). They control large percentage ofNote-Industry No.
HS Code (Product Code)
Product Stage Country ISIC/NAICS Code
(Industry Code)
(1) 84.71.30 Brand marketers Design and Marketing US NAICS code 334111 (2) 84.71.30 OEM*/ODM** Development Taiwan ISIC code 3000 (3) 39.26.10 Plastic Raw material Supply China Chinese Industry
Classification 3070 (4) 84.71.60 84.71.70 84.73.30 84.71.30 Standard components and Assembling Production and Assembling China Chinese Industry Classification code 4041 (5) 84.73.30 Core Component (CPU) Design and Production US NAICS code 334413 (6) 90.13.80 LCD Panel Design and
Production
Korea ISIC code 3320 (7) 85.23.40
Software
book market share, as high as 90% in the first quarter of 2008. See Table4 and Figure4. Therefore, they improve R&D capability to make improvements or do de-sign for the concepts from brand marketers and outsource labor-intensive producing or assembling processes to mainland China.
Company Name World Market Share 2008Q1
Major Customers
1 Quanta 31%
HP, Dell, Apple, Acer, Lenovo 2 Compal 24% HP, Dell, Acer, Toshiba,
Lenovo
3 Wistron 16%
Acer, Dell, HP, Lenovo, FSC
4 Inventec 11%
HP, Toshiba, FSC, Acer
5 Pegatron 8%
ASUS, Toshiba, Dell
Total 90%
Table 4: World market share of Top5 Notebook OEM/ODM in Taiwan, 2008Q1
Source: http://www.displaysearch.com/cps/rde/xchg/displaysearch/hs.xsl/062608_QNB_PR.asp, November 20, 2008
Figure 4: High percentage of Notebook OEM/ODM in Taiwan, 2001-2002
Source: Zhong, 2004
OBM (Original Brand Manufactures)
(3)
Raw material supplier China occupies largest market share of plastic (the main mate-rial to make the Notebook casing).in Asia is now being established in Mainland China (Sturgeon, 2003). More, assem-bling of Notebook is mainly done in China.
Figure 5: Trade partners of China in Notebook Global Value Chain, 2007
Source: Data Calculated base on UN Comtrade Database
The arrows illustrate trade flows between China and other countries, or domestic trade within China.
The reporter is China
The percentages indicate how important are these trade partners to China in Notebook GVC
(a), (b), (c) and (d): how many percentages of the components China received for assembling are exported from trade partners respectively
(e): 70% of the Notebook traded worldwide are shipped out from China Percentages in the most above box: the concentrations of these destinations in Chi-na’s Notebook exporting shipment
Countries listed in the boxes are largest three trade partners with China in Notebook GVC(5)
Core component CPU in Notebook industry is monopoly by Intel and AMD Co. in U.S. (Lan, 2005), because they hold the key technologies (Tian, 2007). The global market share of these two companies together is as high as 99.72% in 2007.Rank CPU Market share 2007
U.S.--29.22% Hongkong--13.83% Germany---10.60% Netherlands---9.13% (e) Assembling [HS 84.71.30] China--70%
1 Intel 77.46%
2 AMD 22.26%
Total 99.72%
Table 5: Market share of CPU industry in Notebook PC GVC, 2007
Source: IDC (http://product.ccidnet.com/art/3369/20080123/1352067_1.html, November 20, 2008)
(6)
LCD Panel is also a significant component for Notebook PC whose largest supplycountry is Korea. During 2007, Korea’s exporting ratio of LCD panel to China for as-sembling is 31.98% (Source: Figure 5), surpasses any other country.
(7)
Software industry (Operating System) is oligopoly by Microsoft Co. (Windows OS) and Apple Inc. (Mac OS) in U.S. Their joint market share is approximately 99% which shows high degree of oligopoly, so I focus on this industry in U.S.Rank Operating System Market share 1 Microsoft Co. 90.89%
2 Apple INC. 7.94%
Total 98.83%
Table 6: Market share of Software industry in Notebook PC GVC, June 2008
Source: http://www.freerepublic.com/focus/f-chat/2039581/posts, November 20, 2008 3. Methodology
3.1 Introduction
In this chapter I will explain the design of the research. Section 3.2 gives data sources, in section 3.3, model used in the research is illustrated and the variables are explained.
3.2 Data Collection
industry level. United Nations Statistics Division website provides the coordination list between HS- and ISIC- Classifications.
The database of UN Comtrade is the primary sources of international trading data at product level. UNIDO database and EU KLEMS Project Database supply countries’ industrial data according to industry codes. I collect the industrial data base on industrial classifications (ISIC or NASIC in U.S.). National Bureau Statistics and the published Statistic Yearbooks in each country provide information both at national industry-level and country-level, such as Bureau of Economic Analysis (U.S. department of Commerce), Korea National Statistical Office, National Bureau Statistics of China, etc.
For those countries do not use US dollars as the reported currency, I will compute the numbers to US dollars base on the average exchange rate each year.
3.3 Research methodology and Hypothesis
3.3.1 Research Model
P
it=α
o+β
1(NE
it) +β
2(SG
it) +β
3(RD
it) +β
4(AS
it) + β
5(LC
it) + ε
it• i means industry i ( i=1,…, n; n=7) ; t means year t (t=2001,…, 2006) • P: Profitability of industry i in year t
• αo indicates the general intercept
• NE: Net Entry Ratio of industry i in year t
• SG: Growth of product Sales revenue of industry i in year t • RD: R&D intensity of industry i in year t
• AS: Average Scale of Operation of industry i in year t • LC: Labor Cost of industry i in year t
• εit is the error term
3.3.2 Dependent variable
Profitabilityit indicates the return of investment of industry i in particular country in year t, which is calculated by value added of industry i in year t divided by all assets of industry i in year t (Martin, 1979).
3.3.3 Independent Variables and Hypothesis
1) NE it (%) = (number of new entrants of industry i in year t)/ (Total number of existing firms
of industry i in year t)
The first independent variable is Net Entry Ratio which is defined by the number of net entrants over the number of existing firms of industry i in year t.
Hypothesis 1: The higher net entry rate indicates lower entry barriers. Therefore, according to
the positive relationship between entry barriers and profitability, this variable is expected to have negative effect on profitability, i.e., β1 < 0.
2) SG it (%) = Annual Growth of product Sales revenue of industry i in year t
Hypothesis 2: Sales Growth Ratio is expected to have negative effect on profitability, i.e., β2
< 0.
3) RD it (%)=(R&D investment of industry i in year t)/ (Total investment of industry i in year
t)
R&D intensity: Since patent number is not an exhaustive measure of innovating ability (Rasiah, 2005), R&D investment is a useful instrument to examine cross industry differences in technology capability. The third independent variable R&D intensity represents the percentage of expenses on research and technological development of industry i in year t divide total investment this industry attracted.
Hypothesis 3: R&D intensity is expected to have a positive effect on profitability, i.e., β3 > 0.
4) AS it = (Industry Sales of industry i in year t)/ (Number of firms in industry i in year t)
Average Scale of operation: The fourth independent variable measures the economies of scale level in industry i in year t. The higher economies of scale the higher entry barriers are. Thus,
Hypothesis 4: this variable is expected to have positive effect on profitability, i.e., β4 > 0.
5) LC it =Average wage per hour of industry i in year t
the characteristics of Notebook PC global value chain, standard components and modularized production (Sanjaya and Manuel, 2004) require ordinary workers more than skillful or technical workers. The fact of adopting outsourcing strategy by enterprises in developed countries (outsource production processes to developing regions where have a large number of low skill and low payment labors) could also prove that low labor cost is an important factor to increase profitability in Notebook GVC. Hence, I expect that the negative impact of labor cost surpasses its positive impact in Notebook PC GVC.
Hypothesis 5: The influences of labor cost (β5) to profitability are expected to be negative, i.e.,
β5 < 0.
4. Analyzing and Findings
4.1 Description of Variables
Descriptive statistics provide the basic characteristics of variables, such as mean value, median value, max and minimum values of data, the standard deviation (dispersion of the series). Table 7-1 represents the results. The indexes of profitability show great discrimination among industries, high profitability in core industry and low invest return ratio in developing country. There is also big difference in Notebook PC GVC of labor cost. Chinese industry has lowest labor payment USD0.47 per hour, in comparison to high salary in U.S., USD26.39 per hour. Net entry ratio interprets the overall impacts of entry barriers on industry. In China, this rate is positive and higher than it in any other countries. By contrast, net entry ratio is low in industries in U.S., even negative growth in some years.
The econometric tests used in my thesis are all estimated using Eviews 5.0.
Observa-tions 42 42 42 42 42 42
Table 7-1: Output of Descriptive Statistics
4.2 Diagnostic Checks
Diagnostic checks which test the structure of data series, is important to distinguish whether the regression model is feasible or should be improved. Multicollinearity, heteroskedasticity, autocorrelation and nonstationarity of the regression model will be checked in my model.
4.2.1 Multicollinearity
Multicollinearity means that two or more independent variables in the regression model are highly linear correlated. A sign of multicollinearity is that the value of correlation
4.2.2 Heteroskedasticity
Heteroskedasticity exists if the variances for all the observations are not the same. White Test is the formal method to check the existence of heteroskedasticity. If the p-value of White test is higher than 0.05, there is no heteroskedasticity. For both of the equations, p-values are 0.0534 and 0.0188 respectively. I will adopt White estimator in equation 2 to solve
heteroskedasticity problem.
White Heteroskedasticity
Test: Equation 1
F-statistic 2.198522 Probability 0.053491
Obs*R-squared 14.60229 Probability 0.067356
Table 7-3a: Output of White Test, Equation 1
White Heteroskedasticity
Test: Equation 2
F-statistic 2.758193 Probability 0.018873
Obs*R-squared 16.83 Probability 0.031929
Table 7-3b: Output of White Test, Equation 2 4.2.3 Nonstationarity
Since nonstationary series possible lead to trouble with estimation and interpretation of the results, Unit Root Test (ADF Test) is adopted here to estimate whether time series is stationarity or nonstationarity. As Table 7-4 shows, t-statistic value of ADF test for each variable is lower than the value at 1% significant level, which indicates that null-hypothesis is rejected (nonstationarity does not exist). Therefore, this model passes the ADF test.
Augmented Dickey-Fuller
test statistic Profitability Net Entry Ratio GrowthSales R&D in-tensity
Average Scale of
operation Labor Cost
t-Statistic -6.61421 -10.8453 4 -12.9275 7 -5.86837 8 -6.469709 -6.195265 1% level -3.605593 -3.605593 -3.605593 -3.605593 -3.605593 -3.605593 5% level -2.936942 -2.936942 -2.936942 -2.936942 -2.936942 -2.936942 10% level -2.606857 -2.606857 -2.606857 -2.606857 -2.606857 -2.606857
Table 7-4: Output of ADF Test 4.2.4 Autocorrelation
regression model are not autocorrelated, if D.W. statistic value is close to 2. However, if autocorrelation exists, I will overcome this problem by adding AR (1) or AR (2) in my model equation.
4.3 Regression Results
The R-square statistic is introduced as a measure of the proportion of variation in the dependent variable explained by all the explanatory variables in the linear model. The value varies between 0 and 1. The model is better, if adjusted R-square is closer to 1. These two equations both show quite good fitness of the model, with adjusted R2 equal to 0.8864 and 0.8989 respectively. The p-value of F-statistic is 0.000 for both equations, meaning that the overall significance of the regression model is high. See Table 7-5. Besides, D.W. values are both very close to 2 that imply autocorrelation problem is solved, after adding AR (1) and AR (2) in the equations. P-values for all the dependent variables are low, almost significant at 1% or 5% level, except variable AS in equation 2.
Equation (1) without RD (2) without LC
Variables Coefficient Coefficient
Constant (c) 0.217836*** 0.255683***
Net Entry Ratio -0.057681*** -0.155734***
Sales Growth -0.01092** -0.02395*** Average Scale of operation -0.001144** -0.000488 R&D intensity --- -0.178171*** Labor Cost -0.00571*** ---Adjusted R-Squared 0.886483 0.898962 F-Statistic(Prob) 129.1633(0.0000) 147.0191(0.0000) D.W. value 1.973759 1.910777
The results of both equation one and equation two show that net entry ratio and sales growth have negative effects on industrial profitability, which are the same as my hypothesis 1 and 2. These two variables represent the results of overall effects of entry barriers, for instance, high entry barriers lead to low entry rate and bring high profits. The results prove that entry barriers have positive correlation with profitability.
However, the negative coefficient of average scales indicates that profitability decreases with this value increasing. Although p-value of AS in equation two illustrates that it is not significant, we can still find out their negative relationship from equation one. This result rejects my hypothesis 4 in which I expect them to be positive related. Explaining this inconsistency, I assume that using average operation scale of all enterprise as the measurement to calculate economic of scale probable bias the consequence, because it accounts small size enterprises. In the previous researches, average enterprise size among the largest enterprises is popular used which only considers about the enterprises account for 50% of the industry output value. I have to point out that this negative relation is very weak, since the coefficient equals to -0.0011 (1% positive change in average scale causes 0.0011% decrease in profitability).
Moreover, from equation 2, we can see that the coefficient of RD is negative, which rejects hypothesis 3. According to my interpretation, the influences of R&D investment to profitability should be positive, because higher R&D investment leads to greater product differentiation theoretically. Two reasons presumably explain this opposite result. One reason is that I collect data at 2- or 4-digit industry level for those industries do not publish
of advertisement expenses to profitability is ambiguous, big brands and advertisements are thought to be factors indicate product differentiation (Greuner and Kamerschen et al., 2000). The other reason is that R&D investment is not that positive to profitability on production stage of Notebook PC GVC. According to the interview to managers in Quanta Co. (the biggest OEM/ODM Company in Notebook PC GVC located in Taiwan), the reason why almost all of the Notebook PCs they produced are for big market brands is that profitability is higher to do so than producing their own brand Notebook PCs (Cai, 2005). High R&D investment comparing to insufficient commercial benefits is one of the factors they worry about. Therefore, it is possible that R&D spending is higher than profit it brings to company. If these assumptions really occur for some companies or industries, it is not weird that R&D intensity has negative impact on profitability.
Labor cost shows negative influence on industrial profitability, as the same as I explained in section 3.3.3. The consequence of this model proves my expectation, although I am not quite sure how are the effects of labor cost on profitability base on theories due to two opposite considerations. One is that low labor cost reduces total expenses, which leads to profit increasing. The other is that skillful workers are always who get higher payment and bring more benefit to enterprises. That is why labor cost is also thought to be positive to profitability. Concluded from the outcome of regression model, labor cost shows higher negative effects on profitability than its positive effects on Notebook PC GVC. I think that the consequence is the result of the features of Notebook PC GVC as I interpreted in hypothesis5.
5 Conclusions and Limitations
5.1 Conclusions
Taiwan and Republic of Korea) from 2001 to 2006. These 7 industries in different locations are connected by product trade flows, outsourcing, GVC governance, and international cooperation.
I draw a “smiling curve” belongs to Notebook PC GVC in Figure 6. The arrows on the above box indicate the value chain and profitability increasing direction. Midstream stages of GVC gain low profitability, while upstream and downstream industries share high profits. From the table below, we can find out that industries China involved in are middle stages in this chain, and the most up- and down-stages are occupied by U.S. industries. Taiwan and Korea are in-between them.
Figure 6: Smiling curve of Notebook PC GVC
(R&D intensity indicates the product differentiation, AS illustrates economic scales). Net entry rate reflects the consequences of entry barriers. In addition, labor cost and sales growth rate are also the influencing factors on profitability. This model shows good fitness (high R-square value) and significances of variables. According to theories, net entry ratio and sales growth rate decrease symbolize that entry barriers and profits are rising. Profitability has negative relationship with these two variables, as the same as my expectations, which indicates that entry barriers basically have positive effects on profitability. Labor cost as part of cost account shows negative effects on profitability, meaning cheap labor force with proper skills brings more benefits in Notebook GVC than those skillful labors with high payment. Whereas, my hypothesis 3 and 4 are rejected, R&D intensity and Average Scale do not have positive impacts on profitability. I believe that small number of observations in my model might one of the causes lead to this unexpected result. Since Notebook Personal Computer industry has not fast developed for a long period and concentrated on several countries (lead firms in U.S. and producers in Asia), abundant data collection is actually difficult when doing the research. Besides, some other possibilities are discussed in last section.
Guo (1998) concludes in his empirical research that high entry barrier industries do not have higher capability to obtain profits in industries of China. Orr (1974) states that barriers of entry have positive effects on profits in Canadian Bank industry. We can see that results are different depend on the research field differences-countries or industries.
5.2 Limitations
Data at 6-digit industry level are hard to obtain and the only choice for me is using 4-digit industry data for some variables. Entry barriers are complicated to estimate and there is no universal formula that could be used to calculate their “height”. Additionally, some barriers are not susceptible to quantitative measurement of any kind (Lukianov and Kisliak, 2008). Therefore, I do not have very good examples from previous empirical researches. The variables I used in my model are found out according to theories and some empirical studies.
Inflation of each country might be one of the impacts to the results, since there is time series and the industries are spreading in different countries.
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APPENDIX
Figure a: Weekly Sale of Desktop Computer and Notebook in U.S market, Dec.2006 to Sep.2007
Source: DisplaySearch Report, 2007
http://www.displaysearch.com/cps/rde/xchg/SID-0A424DE8-596AC88B/displaysearch/hs.xsl/recent_re search.asp
Figure b: Monthly Notebook Sell in U.S. Market, Aug, 2006 to Aug, 2007
Source: Displaysearch Report, 2007
