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台灣經濟形貌: 增進公共政策效益之動態群聚模式 - 政大學術集成

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(1)國立政治大學亞太研究 英語博士學位學程 International Doctoral Program of Asia Pacific Studies College of Social Sciences National Chengchi University, Taipei 博士論文 Doctoral Dissertation. 台灣經濟形貌: 增進公共政策效益之動態群聚模式 Economic Landscape of Taiwan: Dynamic Cluster Models for Public Policy Effectiveness. Student: Janet Tan Co-Advisor: Dr. Wu, Linjun and Lin, Yehyun 中華民國 105 年 5 月 June 2016.

(2) i. 台灣經濟形貌: 增進公共政策效益之動態群聚模式 Economic Landscape of Taiwan: Dynamic Cluster Models for Public Policy Effectiveness. 研究生: 談仲光 Student: Janet Tan 指導教授: 吳玲君 與 林月雲 Advisor: Wu, Linjun & Lin, Yehyun 國立政治大學 亞太研究博士學位學程 博士論文. A Dissertation Submitted to International Doctoral Program in Asia Pacific Studies National Chengchi University, Taipei. 中華民國 105 年 5 月 June 2016. i.

(3) ii. Acknowledgement I would like to thank my advisors Dr. Linjun Wu and Dr. Yehyun Lin for their patience and advices. I have also been blessed by my committee professors. Professor Paul Shihjun Hsu you have given me the vision of cross-discipline topics and scales, Professor David Blundell for your writing guidance and social science perspective, Professor Pingyin Kuan endless discussions on statistics, and Andy Jihnfa Jan for your full support on mapping and web development. I would also like to thank Professor Michael Porter for your kindness to support me on the framework of cluster mapping and making Taiwan cluster mapping possible. Thanks to Porter’s Information Director, Richard Bryden, for your guidance on Taiwan’s cluster code and analytical computing skills. Many thanks to Professor Christine Ketels for your support on my learning in your Microeconomics of Competitiveness class at Harvard Business School allowing me to fully participate with classmates and the group project. I own so much to many people for the completion of my dissertation. Most of all, I would like to thank my husband, Chi Liu for his full support. Lastly, I would like to thank my beautiful daughter for her endless support and love.. ii.

(4) iii. Abstract Economic dynamics have been driven in a high pace, and understanding the trajectory of economics is important. Technology advancement and globalization have triggered a paradigm shift in recent economic growths from a supply-driven to demand-driven market. The change greatly impacted the policy makers’ decision flow to make effective policies in a timely base. Three economic policy decision models: top-down, bottom-up, and interactive, are researched and compared in this paper to reflect the differences. This research utilizes cluster mapping framework and growth dynamics to derive Taiwan’s economic landscape and agglomeration models. Through the analyses of cluster dynamism, challenges and potentials are pointed out, and a graphical tool is designed to unite policy makers, practitioners, and researchers for effective economic development initiatives. Finally, a conclusion is made with recommendations on cluster initiatives in Taiwan. Keywords: Economic Development, Cluster Mapping, Economic Landscape, Specializations, Agglomeration, Spillovers, Supply-Chain Migration, Dynamism, Growth Analyses. iii.

(5) iv. Table of Contents 1. Introduction ......................................................................................................................1  2. Research Flow ..................................................................................................................3  3. Contributions....................................................................................................................5  4. Literature Review...........................................................................................................12  4.1. Economic Growth................................................................................................14  4.2. Economic Landscape ...........................................................................................14  4.3. Agglomeration .....................................................................................................15  4.4. Economic Clusters ...............................................................................................16  4.5. Cluster Mapping ..................................................................................................17  4.6. Spillovers .............................................................................................................18  4.7. Location ...............................................................................................................19  4.8. Productivity – wage importance ..........................................................................20  4.9. Cluster Initiatives ................................................................................................21  4.10. Defining Regions ...............................................................................................22  4.11. Diamond Analysis .............................................................................................23  4.12. The Demand for Uniting Policy Makers and Practitioners in Asia ...................24  4.13. A Versatile Tool for Effective Cluster Initiatives .............................................25  5. Methodology and Framework ........................................................................................27  5.1. Qualitative Analysis ............................................................................................27  5.2. Quantitative Analyses..........................................................................................27  5.2.1. Data ........................................................................................................28  5.2.2. Taiwan’s Districts ..................................................................................28  5.2.3. Analytical Framework ...........................................................................29  5.2.4. Clusters’ Dynamics with the Desired Presentations ..............................30  iv.

(6) v 5.2.5. Details of Growth Analysis....................................................................31  5.2.6. Location Quotient – Specialization Calculation ....................................32  5.3. Statistic Correlations ...........................................................................................34  5.4. Types of Clusters – Analyzing the Growth: ........................................................37  5.4.1. Dominated Clusters................................................................................38  5.4.2. Highly Specialized Clusters ...................................................................38  5.4.3. Strong Clusters .......................................................................................39  5.5. Setting up a Web Tool for Users .........................................................................43  5.6. Taiwan’s Cluster Definitions...............................................................................45  6. Policy Decision Models – Taiwan, Europe, US ............................................................46  7. Taiwan’s Economic Landscape .....................................................................................53  7.1. About Taiwan ......................................................................................................53  7.1.1. Taiwan’s Demographics ........................................................................57  7.1.2. Taiwan’s Geography ..............................................................................58  7.1.3. Taiwan’ Economics ...............................................................................60  7.2. Taiwan’s Specializations .....................................................................................65  7.3. Evaluation Factors on Dynamics .........................................................................67  7.3.1. Employment Factor................................................................................67  7.3.2. Revenue Factor ......................................................................................77  7.3.3. Wage Factor ...........................................................................................81  8. Taiwan’s Economic Dynamics ......................................................................................86  8.1. Taiwan’s Cluster Shares ......................................................................................86  8.1.1. The Traded Clusters ...............................................................................89  8.1.2. Local Clusters ........................................................................................99  8.2. Clusters Compared to the US ............................................................................102  v.

(7) vi 8.3. Can Taiwan Brand? ...........................................................................................104  8.4. Learning from Successful Cluster Initiatives ....................................................107  8.5. Can Hsinchu Become Another Silicon Valley? ................................................110  9. Regional Analyses .......................................................................................................111  9.1. North District –Pei-Pei-Kee ..............................................................................112  9.2. Northwest District Region – Tao-Chu-Miao .....................................................114  9.3. Middle District Region – Chung-Chang-Tou ....................................................115  9.4. Southwest District – Yun-Chia-Nan..................................................................117  9.5. South District – Kao-Ping .................................................................................119  9.6. East District - Yilan, Hualien, Taitung .............................................................122  9.7. Outer Island District – Penghu, Kinmen, Lienchiang .......................................123  10. Cluster Analyses and Case Studies ............................................................................125  10.1. Two Taiwan’s Agglomeration Models............................................................126  10.1.1. Spillovers Model - IT Cluster ............................................................126  10.1.2. Supply-Chain Migration Model - Production Technology and Heavy Equipment Cluster .........................................................................................134  10.2. Taiwan’s Biotech Initiative – a case study ......................................................139  10.3. Business Service Cluster Development Potential ...........................................146  11. Research Summary ....................................................................................................148  12. Conclusion .................................................................................................................151  References ........................................................................................................................162  Appendix 1. - Research Background ...............................................................................166  Appendix 2. - Data Preparation .......................................................................................168  Appendix 3. - The US Clusters Code Definitions............................................................172  Appendix 4. - Taiwan Clusters Code Definitions ............................................................180 . vi.

(8) vii Appendix 5. - Taiwan’s Population Density ....................................................................180  Appendix 6. - Propositions and Variable Relations .........................................................181  Appendix 7. - District Cluster ID Scores .........................................................................183  Appendix 8. - Regional Level Economic Analysis ..........................................................184  The North District – Pei-Pei-Kee .............................................................................184  Northwest – Tao-Chu-Miao District ........................................................................192  Middle – Chung-Chang-Tou District .......................................................................200  Southwest –Yun-Chia-Nan District..........................................................................208  South – Kao-Ping District ........................................................................................217  East District – Yilan, Hualien, Taitung ....................................................................223  Outer Islands District................................................................................................230  Appendix 9. - Other Cluster Analyses .............................................................................237  Distribution and Electronic Commerce ....................................................................237  Construction Products and Services .........................................................................241  Hospitality and Tourism ...........................................................................................243  Downstream Metal Products ....................................................................................246  Transportation and Logistics ....................................................................................247  Taiwan’s Dynamic Analysis ....................................................................................248 . vii.

(9) List of Figures Figure 1 Research Flow: Economic structure on dynamism. ............................................. 4  Figure 2 Cluster Shares and Growth Analysis Guideline. ................................................ 32  Figure 3 Location Quotient Equation. .............................................................................. 33  Figure 4 Revenue LQ Equation. ....................................................................................... 33  Figure 5 Cluster Shares and LQ conditions. ..................................................................... 33  Figure 6 Cluster ID Scoring Digit-Sliced Evaluation System DSE. ................................. 36  Figure 7 Cities’ Cluster ID Scores. ................................................................................... 37  Figure 8 Digit Sliced Evaluation System. ......................................................................... 39  Figure 9 Strong Cluster Definition with Specialization and Growth Rate Qualification. 40  Figure 10 Taiwan’s Industrial Agglomeration – Strong Clusters in all Cities. ................ 40  Figure 11 Active Strong Clusters in All Cities. ................................................................ 41  Figure 12 Taiwan’s Strong Clusters. ................................................................................ 43  Figure 13 US Cluster Mapping. ........................................................................................ 44  Figure 14 Taiwan’s Policy-driven Top-down Cluster Decision Model............................ 48  Figure 15 European Initiative Driven Bottom-up Cluster Decision Model...................... 49  Figure 16 US Cluster Initiative Cluster Initiative Decision Flow..................................... 51  Figure 17 Comparison on Taiwan-EU-US cluster initiative decision models. ................. 52  Figure 18 Gross World Product Growth Rate by Region21F21F. ............................................ 55  Figure 19 GDP/capita and PPP/capita for Taiwan and Singapore. ................................... 56  Figure 20 Map of Taiwan. ................................................................................................ 57  Figure 21 Taiwan in East Asia. ......................................................................................... 58  Figure 22 Taiwan’s Geography. ....................................................................................... 59  Figure 23 GINI’s Concentration Coefficient. ................................................................... 60  Figure 24 Taiwan’s Income gaps. ..................................................................................... 60  Figure 25 Taiwan’s Unemployment Rate. ........................................................................ 61  Figure 26 Taiwan’s Current Account. .............................................................................. 62  Figure 27 US Patents by Selected Countries. ................................................................... 63  Figure 28 Taiwan’s Regional Cluster ID Scores. ............................................................. 64  Figure 29 Taiwan Leading Specializations. ...................................................................... 65  Figure 30 Taiwan’s Leading Specialization in Districts. .................................................. 66 .

(10) i Figure 31 Taiwan’s Industrial Cluster Shares................................................................... 68  Figure 32 Traded Clusters’ Job Creation. ......................................................................... 69  Figure 33 City Leading Specialization Landscape. .......................................................... 70  Figure 34 Taiwan’s Specializations in Cities.................................................................... 71  Figure 35 Taiwan’s Employment Growth Composition................................................... 74  Figure 36 Employment Shares and Specialization Impacts. ............................................ 76  Figure 37 employment growth rate calculation. ............................................................... 76  Figure 38 Employment Growth and Specialization Relations. ......................................... 77  Figure 39 Taiwan’s Revenue Growth Composition by Cities. ......................................... 78  Figure 40 Revenue Growths and Specializations Relations. ............................................ 79  Figure 41 Taiwan’s Traded Clusters’ Revenue Composition. .......................................... 80  Figure 42 Taiwan’s Local Clusters’ Revenue Composition. ............................................ 81  Figure 43 Annual Wage Ranking of Taiwan’s Traded Clusters. ...................................... 82  Figure 44 Average Annual Wage of Taiwan’s Local Clusters. ........................................ 83  Figure 45 Taiwan’s Wage Growth by the Cities. ............................................................. 85  Figure 46 Taiwan’s Clusters’ Shares. ............................................................................... 87  Figure 47 Taiwan’s Cluster Composition. ........................................................................ 88  Figure 48 Taiwan’s Traded Clusters Employment Composition. .................................... 89  Figure 49 Taiwan Traded Cluster Dynamics. ................................................................... 93  Figure 50 Taiwan’s Local Cluster Employment Composition. ...................................... 100  Figure 51 Taiwan Local Cluster Dynamics. ................................................................... 101  Figure 52 Taiwan and US Cluster Ratio Comparison. ................................................... 102  Figure 53 US and Taiwan Productivity Ratios in Traded and Local Clusters. .............. 103  Figure 54 North District Specialties. .............................................................................. 112  Figure 55 North District Specialties and Growths. ......................................................... 113  Figure 56 Northwest District Specialties and Growths................................................... 114  Figure 57 Northwest District Specialties and Growths................................................... 115  Figure 58 Middle District Specialties. ............................................................................ 116  Figure 59 Middle District Specialties and Growths. ....................................................... 117  Figure 60 Southwest District Specialties. ....................................................................... 118  Figure 61 Southwest District Specialties and Growths................................................... 119  i.

(11) ii Figure 62 South District Specialties and Growths. ......................................................... 120  Figure 63 South District Specialties and Growths. ......................................................... 121  Figure 64 East District Specialties. ................................................................................. 122  Figure 65 East District Specialties and Growths. ........................................................... 123  Figure 66 Outer Island District Specialties. .................................................................... 124  Figure 67 Outer Islands District Specialties and Growths. ............................................. 125  Figure 68 Dynamism by the Growth Rates Interpretations. ........................................... 126  Figure 69 IT Cluster Growth Map. ................................................................................. 127  Figure 70 IT Cluster Dynamics....................................................................................... 128  Figure 71 Revenue-Compensation Ratio of IT cluster. .................................................. 129  Figure 72 Spillovers Potential Factors for IT Cluster. .................................................... 129  Figure 73 Production Technology and Heavy Equipment Revenue and Employment Growths. .......................................................................................................................... 135  Figure 74 Production Technology and Heavy Machinery Cluster Dynamics. ............... 136  Figure 75 Production Technology and Heavy Machinery Labor Productivity............... 137  Figure 76 Production Technology Cluster Growth Composition. .................................. 137  Figure 77 Biopharmaceutical’s Concentration in Cities. ................................................ 140  Figure 78 Biopharmaceutical Growth Composition. ...................................................... 141  Figure 79 Biopharmaceuticals Revenue-Compensation Ratio. ...................................... 142  Figure 80 Medical Devices Concentration in Cities. ...................................................... 143  Figure 81 Medical Devices Growth Composition. ......................................................... 143  Figure 82 Medical Devices Revenue-Compensation Ratio. ........................................... 144  Figure 83 Business Services Revenue-Compensation Ratio. ......................................... 146  Figure 84 Agglomeration Factors for Business Services Cluster. .................................. 147  Figure 85 Cluster Agglomeration Growth Variable Relations ....................................... 182  Figure 86 North District Specialties & Growths............................................................. 184  Figure 87: New Taipei Specialties. ................................................................................. 185  Figure 88 New Taipei Specialty and Growths. ............................................................... 186  Figure 89 Taipei Specialties............................................................................................ 188  Figure 90 Taipei Specialties and Growths. ..................................................................... 189  Figure 91 Keelung Specialties. ....................................................................................... 190  ii.

(12) iii Figure 92 Keelung City Specialty & Growths. ............................................................... 191  Figure 93 Northwest District Specialties. ....................................................................... 192  Figure 94 Northwest District Specialties & Growths. .................................................... 193  Figure 95 Taoyuan Specialties. ....................................................................................... 194  Figure 96 Taoyuan Specialties and Growths. ................................................................. 195  Figure 97 Hsinchu City Specialties................................................................................. 196  Figure 98 Hsinchu City Specialties and Growths. .......................................................... 197  Figure 99 Hsinchu County Specialties............................................................................ 198  Figure 100 Hsinchu County Specialties and Growths. ................................................... 198  Figure 101 Miaoli Specialties. ........................................................................................ 199  Figure 102 Miaoli Specialty and Growths ...................................................................... 200  Figure 103 Middle District Specialties. .......................................................................... 201  Figure 104 Middle District Specialties and Growth. ...................................................... 202  Figure 105 Taichung Specialties. .................................................................................... 203  Figure 106 Taichung Specialty and Growth ................................................................... 204  Figure 107 Changhua Specialties.................................................................................... 205  Figure 108 Changhua Specialties and Growths. ............................................................. 206  Figure 109 Nantou specialties......................................................................................... 207  Figure 110 Nantou Specialties and Growths. ................................................................. 208  Figure 111 Southwest District Specialties. ..................................................................... 209  Figure 112 Southwest District Specialties and Growths................................................. 210  Figure 113 Yunlin Specialties......................................................................................... 211  Figure 114 Yunlin Specialties and Growth..................................................................... 212  Figure 115 Chiayi City Specialties. ................................................................................ 213  Figure 116 Chiayi Specialties and Growths.................................................................... 213  Figure 117 Chiayi County Specialties. ........................................................................... 214  Figure 118 Chiayi County Specialties and Growths. ...................................................... 215  Figure 119 Tainan Specialties......................................................................................... 216  Figure 120 Taiwan Specialties and Growths. ................................................................. 217  Figure 121 South District Specialties. ............................................................................ 218  Figure 122 South District Specialties and Growths ........................................................ 219  iii.

(13) iv Figure 123 Kaohsiung Specialties. ................................................................................. 219  Figure 124 Kaohsiung Specialties and Growths. ............................................................ 220  Figure 125 Pingtung Specialties. .................................................................................... 221  Figure 126 Pingtung Specialties and Growths. ............................................................... 222  Figure 127 East District Specialties. ............................................................................... 223  Figure 128 East District Specialties and Growths. ......................................................... 224  Figure 129 Yilan Specialties. .......................................................................................... 225  Figure 130 Yilan Specialties and Growths. .................................................................... 226  Figure 131 Hualien Specialties. ...................................................................................... 227  Figure 132 Hualien Specialties and Growths.................................................................. 228  Figure 133 Taitung Specialties. ...................................................................................... 229  Figure 134 Taitung Specialties and Growths. ................................................................. 230  Figure 135 Outer Islands District Specialties. ................................................................ 231  Figure 136 Outer Islands District Specialties and Growths. ........................................... 232  Figure 137 Penghu Specialties. ....................................................................................... 233  Figure 138 Penghu Specialties and Growths. ................................................................. 233  Figure 139 Kinmen Specialties. ...................................................................................... 234  Figure 140 Kinmen Specialties and Growths. ................................................................ 235  Figure 141 Lienchiang Specialties. ................................................................................. 236  Figure 142 Lienchiang Specialties and Growths. ........................................................... 236  Figure 143 Distribution and Ecommerce Revenue and Employment Growths.............. 237  Figure 144 Distribution and Electronic Commerce Cluster Dynamics. ......................... 238  Figure 145 Distribution and Electronic Commerce Labor Productivity......................... 239  Figure 146 Agglomeration Potential for Distribution Electronic Commerce Industry. . 239  Figure 147 Construction Products and Services Cluster Revenue and Employment Growths. .......................................................................................................................... 241  Figure 148 Construction Products and Services Cluster Dynamics. .............................. 242  Figure 149 Construction Products and Services. ............................................................ 242  Figure 150 Hospitality and Tourism Revenue and Employment Growths. .................... 244  Figure 151 Hospitality and Tourism Labor Productivity. ............................................... 244  Figure 152 Hospitality and Tourism Agglomeration Factors. ........................................ 245  iv.

(14) v Figure 153 Downstream Metal Products Growth Composition. .................................... 246  Figure 154 Transportation and Logistics Growth Composition. .................................... 247  Figure 155 Taiwan’s Growth Factors. ............................................................................ 248  Figure 156 Taiwan Traded Clusters’ Growth Dynamics. ............................................... 250  Figure 157 Taiwan’s Traded Clusters Dynamics............................................................ 251  Figure 158 Taiwan Local Clusters Dynamics................................................................. 254 . v.

(15) 1. What I've come to see as probably my greatest gift is the ability to take an extraordinarily complex, integrated, multidimensional problem and get arms around it conceptually in a way that helps, that informs and empowers practitioners to actually do things. -In Porter’s 2010 interview (Kiechel, 2010). 1. Introduction Economic development has been the most important policy for most countries since the Second World War ended because successful policies have brought prosperity and wealth. Especially, the ending of the Cold War has merged the world population into resource sharing. With over a quarter of the world population joining the competition and the latest technology explosion, competitiveness paradigm has shifted, and the game rules are changed from profitability to competitiveness. Globalization has sped up communications among people and has changed the way people live. The pace of changes is so fast that before a new technology is fully adapted by the new lifestyle, it is already obsolete. The 17th century’s industrial revolution of mass production factories that guaranteed retirement to workers also has become obsolete. What is left is constant changing innovations and challenges that new technologies dominate the lifestyle change on a daily basis; therefore, changes over time, the study of dynamics, has been much in demand, especially to policy makers who decide on resource allocations for their people. In fact, the globalization is bringing everyone in an equal virtual distance; one small change may affect others far away. The longitude studies in the old time may take ten years of observations to study human behavior has evolved, and now, the timeframe is shortened and the changes are driving the dynamics so fast that the fluctuation is causing instability and shocks. Without an exception, Taiwan’s policy makers are facing the same issue in needing a dynamic assessment system for policy planning and initiative responses (Lee, Tu, Sheng, Mao, & Jen, 2010, p. 12). The policy report points out the needs for policy makers to move from a static review and information dissemination system into a dynamic response system, and moving from a top-down policy-driven model into a bottle-up demand-.

(16) 2 driven interactive mechanism. In 2011 a recommendation report by a group of industrial researchers stated “The industrial planning policies covers many industries but lacking integrations. The current planning is based on traditional divisionary, and it is difficult to see the future expansion… There [Taiwan] is lack of bridges between the policies and industries…it is difficult for people to understand the policies(Yu et al., 2011, p. 14).” Although Taiwan’s researchers and policy makers have acknowledged the needs for a dynamic assessment system, there is no evidence that the bridges are built between the policy makers and industries in the recent years. In an interview with a machine tooling business owner 1, “The government is too slow for 0F0F. our trend, and they don’t really understand our needs. Even if they do, they can’t move fast enough to accommodate our needs.” This voice has not only been heard from one field survey, but many to confirm the point. Having a dynamic response system to bridge the policy makers and business practitioners is crucially important for global competition. Taiwan is not alone on this phenomenon; likewise, other advanced countries have already built systems to compensate the dynamic challenges, such as the EU Cluster Observatory (Observatory, 2009) and the US Cluster Mapping (EDA, 2014). This dissertation provides part of the solution to a framework and tools to make industrial assessment dynamic and interactive. This research also includes a comparative study on the structural differences among Taiwan, the US and EU’s economic initiatives’ policy decision flows. Furthermore, Taiwan’s economic landscape is presented by using cluster-mapping framework to analyze the economic dynamics. The following chapters are organized in: 1) Research flow, 2) Contributions, 3) Literature Review, 4) Methodology and Framework, 5) Policy Decision Models, 7) Taiwan’s Economic Landscape, 8) Taiwan’s Economic Dynamics, 9) Regional Analyses, 10) Cluster Analyses and Case Studies, 11) Research Summary, and finally 12) Conclusion.. 1. The operating personnel of a machine tooling company in Yunlin was interviewed in Taichung on July 15, 2015. The non-profit organization of the Precision Machinery Research Development Center officer also confirms the difficulty communicating financing issues with policy makers: “the policies are set in favor of larger corporations…, but majority of companies are small and medium businesses.” 2.

(17) 3. 2. Research Flow The research question for this dissertation is: What are Taiwan’s economic dynamics which can be analyzed for the economic development? This is a data-driven inductive research by converting Taiwan’s five years’ census data utilizing a cluster mapping framework to derive the dynamics of all business clusters from growth rates. Dynamic factors and relations are discussed for the framing the agglomeration models from Taiwan’s industry development. Two agglomeration models are identified by the observations, and they are spillovers and supply-chain migration models. In the meantime, to answer policy effectiveness issue, Taiwan’s economic policy decision model is examined against the European and the US models. The research has been structured to answer a comprehensive question of economic development, and it involves multiple theorems. Figure 1 Research Flow shows that economic development drives the whole research, to include theories of demand-driven market, growth theory, agglomeration, geo-economics on spillovers, dynamism, cluster mapping, policy decision models, and microeconomics of competitiveness. The economic landscapes are built upon the relations of growth rates to display the economic dynamics of clusters in regions. At the end, a 3D Dynamics Analysis web-tool has been developed for the ease of use. The research flow chart is as follows:.

(18) 4 Figure 1 Research Flow: Economic structure on dynamism. Economic Development Demand Driven Big Data on Market B h i. Population for Business Agglomeration. Business Strategies and Competitivenes s. Cluster Mapping Tool. Public Policies on Shared Vision and R. Cluster I iti ti. Cluster Organization. Revenue GrowthMarket Demand. Employme nt GrowthLabor S l. Stimulations by Industrial Innovations and Location Specializations. Labor PoolContextua. Wages Growth- Social Reverse Relations when labor supply is low.. 4. 3-D Dynami cs.

(19) 5. 3. Contributions Ineffective policies can stall economic progress and create social instability. While I started the research in 2008, I intended to answer the research question: what makes Taiwan economic sustainable and competitive? I researched national and international data which were either inconsistent or hard to understand. The difficulty of managing the international comparative research gave me the motivation to set a research on Taiwan’s economic landscape that is to really understand what Taiwan has in the first place. Along the research, I found evidence that there is a big gap between Taiwan’s economic policies and the market demands. Then, I come to the realization that there is a need to create tools to bridge the gap between policy makers and practitioners. To answer the research questions, the research designs a tool to unite policy makers, practitioners and researchers in delivering effective industrial initiatives. By using the tool, one can clearly see Taiwan’s economic landscape and its microeconomic growing paths, and make assessments for future planning and collaborations whether it is for macroeconomic policies or firm level industrial strategies. There are three parts to this research. First is to qualitatively analyze Taiwan’s economic policy decision model and compares to the United States (US) and European Union (EU) models. Second is to derive Taiwan’s economic landscape to present Taiwan’s agglomeration models by quantitative analysis on growth dynamics. The final part is to set a model for the complex data to be presented in graphics and easily understandable charts. The majority part of this dissertation is on quantitative analyses; the research utilizes a cluster mapping framework to assess Taiwan's economic landscape. The assessment is based on the economic dynamics from 2006 to 2011 by studying the growth factors of revenue, employment and wages to derive Taiwan's agglomeration models. The three growth factors combine to determine the two agglomeration models – spillovers and supply-chain migration. From the findings, the wage rates make the noticeable differences between the two models. The spillover model demonstrates the rising wage effect, whereas the supply-chain migration model does not. The wage is affected by the labor pool supply contextually, which the spillover model requires more skilled/knowledgeable manpower, while the supply-chain model requires lower wage 5.

(20) 6 manpower. Both models face labor pool supply issues, but they agglomerate differently. Each model differs in its geographical agglomerations. The spillover model agglomerates by spreading the influences to the adjacent locations for a higher productivity, whereas the supply-chain migration model duplicates the operation model to another location outside of the region for a lower operation cost. These two models cover over majority of traded clusters’ workforce in Taiwan. The research followed its own development to reach the conclusion for this report. The research contributes to five ways, and the following paragraphs explain them in details. First, in the qualitative analysis, the research compares Taiwan, the US and EU’s economic policy decision models and displays the differences. Through the research, international economic progresses from other economies have been compared, and the result concludes that the policy decision models are different from different continents, namely Asia, Europe, and the Americas. Due to their differences on political systems, the policy decision models are structured differently. Most Asian countries are operating in authoritarian systems exercising a top-down policy decision model despite their democracy status. EU, a full democratic community, has a confederated system that tends to value citizens’ demand, thus, has a bottom-up model. The US, a contemporary democracy, tends to be innovative in policy effectiveness, and has recently designed an interactive model. Taiwan is a young democratic society still progressively upgrading its political system from the authoritarian system toward an open market system (Blundell, 2012). The transition is not yet complete, and the policy makers and people are keenly aware that Taiwan is no longer suited for a total authoritarian policy making system. The changes will take time. People have been making demands on policy-making transparency, and policy makers have been reviewing and try to improve the policy decision system. Many incidents have been calling for changes, and Taiwan is on its way for better revisions while social conflicts trigger the demands for change 2. The 2016 presidential election and 1F1F. 2. The Sunflower Movement is a demand for system changes. Young student leaders voiced for an open policy decision system, and they fought against government “closedoor policy making” which may affect people’s preferences. 6.

(21) 7 legislative election results reflect the demand for changes on policy decision model from law to practice. It is clear that the authoritarian policy decision model is ineffective and being questioned in a democratic system. Therefore, the European and the US models are reviewed to see how do full democratic systems operate their policy decision flows to promote their economic initiatives. Taiwan, thus, can use the result of comparative studies to formulate their democratic progress in delivering a full democratic society. Of course, democracy might not be the only answer to an effective economic policy delivery system, Singapore for example has an authoritarian system and is successfully forming cluster organizations to manage cluster initiatives which can also deliver promising results. Either way, cluster mapping provides a powerful foundation for economic initiatives. Furthermore, Taiwan has been missing out on information dissemination on important economic facts to and from international organizations, which delays Taiwan from participating the international activities or negotiations at the first-hand. This is a real big disadvantage for Taiwan. Taiwan’s earlier economic rise has been replaced by the later China’s economic rise. Taiwan was earlier known for its “Made in Taiwan” economic identity: cheap labor selling clothing in K-Mart which is equivalently to China sells everything to Walmart today. This makes this a joke of Walmart’s name may have been started by marketing products from the Great Wall. Taiwan needs to identify its next leap strategy and rises to the next level of developed nation in order to compete globally. First-hand information is crucially important to plan for the next leap. The planning will depend on a more thorough and complete information sharing system for the general public and policy makers to work on economic initiatives together, and the clustermapping framework has been identified as a useful tool to be utilized. Taiwan’s economic policy decision flow and policy makers’ interactions with people can seriously sway the success of the economic initiatives. Second, in the quantitative analysis, the research provides a data alignment interface in an international format that regroups existing industrial data labeling into the US and European’s cluster standards for future comparative studies. The data interface is framed to derive Taiwan’s economic landscape and to further analyze the competitiveness. 7.

(22) 8 Recently, there are calls for big data analytical tools in all fields. Data alignments on big data from everywhere has been a part of the globalization process that one either conforms or be marginalized. There is an international awareness issue in which one can be marginalized by lack of participation. During the research, it is very difficult to find Taiwan’s data internationally or data to be normalized into comparative studies. This is especially true when it comes to comparative research work that Taiwan is always out of the world’s comparative maps. Labeling Taiwan is one issue; some databanks may have partial data but uses different economic labels such as Chinese Taipei, Taipei China, Taiwan China, and Chinese Taiwan despite that Taiwan is an independent economy with its own currency, but lack of data is the real problem. Of course, there are some exceptions. For example, Asia Pacific Economic Corporation (APEC) and International Trade Center (ITC) of the World Trade include complete sets of data collected for their trade documentation since Taiwan plays an important role in the international trade that the data is important not only for Taiwanese but for other trading partners. Additionally, Taiwan is somehow hidden in the big data jungle, which fades out from time to time depending who is managing the data, and thus Taiwan’s data is used for references with limited ways. This makes an economic researcher very difficult to identify Taiwan’s international relations on economic topics. Hence, Taiwan is isolated not being able to participate many international trade activities due to lack of most updated information provided. Moreover, Taiwan does not appear on many important international economic comparative studies for the same reason, and this forbids Taiwan from international comparative research to be complete. Internationalization should not only be the communication in international literacy and currency conversions 3, but also 2F2F. community participations and project collaborations among international researchers and people regardless of the political status. Taiwan’s scholars and policy makers may be from time to time participating in many international conferences and events, and they may take some leaderships in hosting international conferences 4, but they rarely 3F3F. 3. Field interviews to the National Statistics Bureau, National Development Administration, and other research institutes. They recognize the importance of internationalization but are not their priority. 4 Such as SBAOEA taking initiative in disaster resilience projects. 8.

(23) 9 collaborate in international projects and release collaborative reports 5 because the 4F 4F. opportunities are mostly occupied by China for its One China Principle. Taiwan has its own isolated economy without a full relationship with Chinese policy makers; it is very difficult to make any international agreements or negotiations without running into a stop on One China Principle. Therefore, some network mechanisms are needed to break out the isolation to be connected directly in international economic activities for Taiwan. This research solves a small part of the solution by providing a networking mechanism with a set of international data alignment definitions (see Appendix - Taiwan Clusters Code Definitions), which enables Taiwan’s data to be compared internationally. This compensates Taiwan’s questionable national status and solves part of the problem for researchers to put Taiwan’s economic progress on international maps. Further evidence supports that the biggest economic challenge Taiwan faces today is negotiating Free Trade Agreements (FTAs). Every country in the world may negotiate FTA freely except for Taiwan due to its political status. Taiwan has signed FTAs with limited friendly countries, and these countries are mostly small and not as impactful in trades with Taiwan. When Professor Michael Porter visited Taiwan on October 24, 2014, and made a presentation on “Taiwan’s Competitiveness”, he said “There are many ways to compete besides using FTAs, and Taiwan should find other ways to deal with this.” From the research field surveys, many business owners including socks 6, hand tools, and 5F5F. auto components industries told the vivid stories about how they lost their orders over night while their competing neighboring countries signed FTAs with the competing nations. Therefore, Taiwan needs practical tools to help policy makers and business owners to create initiatives to work on alternatives to overcome the setback from the lack of FTAs including getting in touch through international scholars in comparative research on economic trade topics.. 5. An ADB official stated in a field interview, “Taiwan’s government normally avoids to apply for projects in ADB because of its official name [ROC] issue. Therefore, there are no projects for Taiwan. We have no problem granting Taiwan projects because they are a full member of ADB.” 6 The business owner of Black Dog Brother in Shetou with a site interview in 2012. 9.

(24) 10 Third, the research derives Taiwan’s economic landscape by utilizing a cluster-mapping framework to analyze industrial dynamics. The research defines Taiwan’s cluster code. Two sets of Taiwan’s census data were extracted for the study of agglomeration and growths. Fortunately, the data contains a period of a global financial shock, and the clusters’ analyses captured Taiwan’s economic landscape closely on the dynamic shift after the shock. Taiwan, a small economy with limited endowments, survived the 2008 global financial crisis and its ripples. The wealth of data is worth for the resilience analysis. The external shocks changed Taiwan’s economic landscape and created opportunities for those who were well prepared. An assessment is due to describe the changes with an accurate framework. The survival is as important as what have been failed, and how have been succeeded. The lessons here can be set as a model for other economies to be researched. Fourth, this research analyzes Taiwan’s agglomeration models and potentials. This research includes the recent census data from 2006 to 2011 with a real external shock the 2008 global financial crisis. With the shock, the data is rich to measure Taiwan’s economic sustaining power. This gives an excellent backdrop for the research to present the results from the two extremes, prosperities and challenges. Taiwan, after the global financial crisis has a new economic landscape. The changes reflect the strengths and weaknesses in each cluster and city (here, city is defined as Taiwan’s geographical divisions of cities and counties). Taiwan’s cluster mapping data could accurately present Taiwan’s industries’ dynamics based on three sets of growth rates (employment, revenue, and wages) and their relations. The agglomeration research contributes to the theoretical constructs in differentiating two types of industrial agglomeration models, and by using the inductive research method to define the relations among these growth rates to analyzed the agglomeration potentials. The two agglomeration models are supply-chain migration and spillovers models. For the supply-chain migration model, Taiwan is famous for making Apple products, especially after the world financial crisis of 2008, companies such as Foxconn profited from the hot selling market of iPad and iPhone. Foxconn stands for its branding in OEM (Original Equipment Manufacturing) making electronic products for brand marketing companies. 10.

(25) 11 The company is a Taiwan’s success story built its business from a precision machinery company from ground-up to an OEM. The company is famous for its low cost and high quality productivity that helped Apple to be richer than its own country. In 2011, Foxconn got even more famous by the employees’ suicide incidents that people started to notice the company with over one million employees in China actually was a Taiwanese company. Today, Foxconn acquired Sharp, a Japanese Corporate, who made the first LCD television display was failing, and the merger and acquisition finally came through after a long negotiation 7. The successful growth path of Foxconn is evolving from a 6F6F. production company into a production management company; its winning strategy is to involve most of its vendors to migrate with as a group (supply-chain cluster migration) and utilizing modulized production line to rebuild and reshape its production workforce with precision and produce quality products and high efficiency. Although, its net profit margin is only around 2%, Foxconn is the world largest manufacture producing the highest quality products. The second model is the high-tech knowledge-based spillovers model. When talk about OEM, one cannot ignore Taiwan Semiconductor Manufacturing Company (TSMC), the company is making to the top ranking in the semiconductor industry with a net profit margin of 35%. This is comparatively much higher than the supply-chain migration model of 2%. The semiconductor industry is a fast growing cluster with very high average wages. The dynamic effects of spillover will be shown in the cluster maps in the Spillovers Model - IT Cluster. Taiwan is not without private brandings. One industry worth mentioning is Taiwan’s pride in bicycles, the Giant and Merida. Giant has earned its international brand recognition for its quality and creativity. The bicycle branding suits for the spillovers from the marching tooling industry. Both Foxconn and TSMC are all production companies that they all started their businesses by contract services. All these companies have one thing in common; they started from the Production Machine cluster. Therefore, machine tooling industry is the important backbone for Taiwan’s prosperity today.. 7. http://fortune.com/2016/04/02/its-official-foxconn-and-sharp-inked-their-deal/, retrieved on 4/28/2016. 11.

(26) 12 Thus, Taiwan is an excellent place to study OEM businesses agglomeration. Foxconn utilizes the strategy of enlarging the labor pool by migration and relocating the supplychain clusters, and TSMC is part of a dominated and dynamic cluster based on knowledge creations and spillovers. Their common bases are both related to the supporting clusters of machine tooling industry on precision machineries and automations. Besides the agglomeration models, Taiwan sets the entrepreneurship model from bottom-up rising to the top. Taiwan has 97.6% Small and Medium Enterprises (SMEs), and the most successful companies started from ground-up. In this research, Taiwan’s economic landscape has been built upon from its local specializations into the agglomerations of the world OEM manufacturers by the people and their entrepreneurship. Thus, Taiwan’s clusters provide good development paths for entrepreneurial agglomeration. Fifth, for the ease of use component, this dissertation is written for readers across the board, for practitioners, policy makers, and researchers and wishing to make some influences to the globalization changes. A graphical web display tool is provided for an easy access to the cluster maps 8. This is a comprehensive data-drive research utilizing 7F7F. data from various Taiwan’s national data banks and data from other nations such as national accounts, labor data, and trade data. In the meanwhile, the research records the descriptive surveys on the research process because the process itself is a living document for the research development, and this report may be used as teaching materials.. 4. Literature Review A nation or region is competitive to the extent that firms operating there are able to compete successfully in the regional and global economy while maintaining or improving wages and living standards for the average citizen. – Michael Porter 9 8F8F. Industry matters when it comes to labor productivity. Prior research indicates that the impact of human resource system on productivity is influenced by capital intensity,. 8 9. http://tisc.nccu.edu.tw/bubble In his speech on Oct. 24, 2014 in Taipei on “Taiwan’s Competitiveness.” 12.

(27) 13 growth, and differentiation (Datta, Guthrie, & Wright, 2005). For this research, the industry differentiation and the capital intensity are characterized by grouped industrial clusters. The research uses a multi-factor approach utilizing aggregated firm level data to derive economic landscapes by analyzing industrial clusters growth among revenue, employment, and compensation data sets. For a business, these three factors are most important for day-to-day operations. Revenue is the most important input factor for a business. Compensation is used to gauge how well a business is operating. Compensation-to-revenue ratio is what company used to assess how profitable a company is 10, also a measure to the labor productivity for a business operation (The 9F 9F. Goldman Sachs Group, 2011). Employment is a measure to the workforce participation for a region. Average wages are calculated based on compensation and employment and serve as a factor to evaluate clusters’ employment demand. The growth rates of these factors are used to measure industries’ performance and derive regional economic landscapes. The value delivered in this research is embedded in the mapping of the various clusters from each of the regions. There are five major components to the analyses. First is the comparative studies of economic policy making decision models; second is to define Taiwan’s cluster codes. Third is to derive Taiwan’s economic landscape by drawing data from Taiwan’s census data bank. Fourth is to identify Taiwan’s agglomeration models by analyzing economic dynamics data. The research utilizes the US Cluster Mapping framework to derive the maps of Taiwan's economic landscape. Finally, a programed tool is designed to show the maps for easy reading. The graphical assessments are based on the economic dynamics from 2006 to 2011 by studying the growth factors of revenue, employment and wage rates to derive Taiwan's agglomeration models. For the comprehensiveness, the research involves several theoretical backdrops to support the methodology. The literature review on each theory is listed as follows.. 10. http://www.hvacrbusiness.com/how-much-of-earnings-spent-on-payroll-andtaxes.html, retrieved on 4/28/2016. 13.

(28) 14. 4.1. Economic Growth Economic growth in general is measured by the growth of Gross Domestic Product (GDP) or GDP per capita, but it is not enough to tell the dynamics of an economy. The latest literature has been focusing on comparative economic growth in the globalized world (Acemoglu, Akcigit, Bloom, & Kerr, 2013; Fagerberg, 2013). There are numerous economic growth theories from Schumpeter’s innovative destruction to labor participation empirical studies, which confirms the spillover effect on economic growth (Acemoglu et al., 2013; Aghion & Howitt, 1998; Lentz & Mortensen, 2005, 2008). This research enriches the spillover research with empirical data of Taiwan’s dynamism to show the relationship among the growth rates. Theory also confirms that labor moves from lower to higher pay jobs (Lentz & Mortensen, 2005, p. 3) Borrowing from Schumpeter’s economic development theory and business cycle (Schumpeter, 1934, 1939), the user demand is the foremost important factor to start in his theory, followed by population which is the agglomeration criteria, and the following are the products, finance and organization. Innovation is stimulus to trigger the destruction to the business cycle which adds the momentum for market expansions and job creations in a cyclical way. The first two factors, user demand and population are the representation of revenue and employment. These two are also the most important factors for businesses gains and jobs. Jobs are affected by the labor supplies, and the wages reflect the labor pool conditions, which sets the inductive reasoning base for this research.. 4.2. Economic Landscape The economic landscape once built describes the shares of employment in each industry and in each region. From the US Cluster Definitions, each industry is clustered in simulated groups by utilizing the cluster-mapping framework (Delgado, Porter, & Stern, 2014), and political borders are regrouped into industrial districts by correlated growths. The economic dynamics provide the factors to assess how industries are doing from the perspectives of regional growths and cluster growths. Since the US Cluster Definitions is used to build the foundation of the economic landscape, Porter and related researchers’ work are cited thoroughly in later details. The multifactor dynamism analysis is new to the field of economic modeling: it is assessed by the cluster maps, which is designed for 14.

(29) 15 the non-statistical readers to see the graphical landscape and an added value to economic quantitative analysis.. 4.3. Agglomeration The dynamics of industrial agglomeration are important for business sustainability, especially in the mass production industries. (Cortright, 2006; Delgado, Porter, & Stern, 2010; Delgado et al., 2014; Feldman & Audretsch, 1999; Feser, Renski, & Goldstein, 2008; Glaeser & Kerr, 2009; Neffke, Henning, Boschma, Lundquist, & Olander, 2011; Porter, 2003; Rosenthal & Strange, 2004) Agglomeration is also important for the spillovers of knowledge and value chain sourcing. But where can people find the information and resources? In the old days, imbalance of information exchange gives hidden opportunities to make higher profit and owning the private market channels. With the recent data opening and information sharing, the world is demanding on the ease access of presentable formats. Romer (1993) stated “For many years, the use of formal mathematical models kept them from being able to talk about the economics of ideas in aggregate level discussions of growth and development. Cluster theory advocates building on emerging concentrations of companies and encourages the development of these fields with the strongest linkages to or spillovers within each other. (Porter, 2008, p. 223) Innovation is another important factor in measuring potentials for agglomeration. Clay Christensen’s disruptive innovation may be great for competition, but it also causes instability for some (Christensen, Horn, & Johnson, 2008; Christensen, 1997); hence, innovations can be a stimulus versus the most important factor. US patents are a good gauge for global innovations; Taiwan ranks 4th globally in terms of innovation from the US patent filings, which is an added advantage to Taiwan. 11 This gives Taiwan a fairly 10F10F. good background for the foundation being competitive and its growth.. 11. US IP reference web-link. 15.

(30) 16. 4.4. Economic Clusters Economic cluster research started as early as when Alfred Marshall use “industrial district” in his Principle of Economics (Marshall, 1920). Cluster research has evolved from the earlier Marshall’s industrial district (Marshall, 1892, 1920) into today’s multiindustrial networked analysis namely Clusters Mapping (Delgado, Bryden, & Zyontz, 2015; Porter, 2003). Industrial agglomeration has been an important study of economic geography especially in cluster research (Delgado, Bryden, et al., 2015; Delgado et al., 2014; Ellison & Glaeser, 1997; Krugman, 1990; Marshall, 1920; Porter, 1990a; Porter, 2011). There are three important drivers of agglomeration, input-output linkages, labor market pooling, and knowledge spillovers, which are associated with cost or productivity advantages to firms (Marshall, 1920). Porter has hence refined the cluster’s definitions and tested the definitions with rigor, and this is reason why this dissertation is adapting to the framework of cluster mapping. Below are more details on Cluster Mapping development. On a cluster formation, local market and specialties support the cluster’s vitality is important. Earlier studies have contributed to the understanding of clusters on competition, and agglomeration economies stresses input cost minimization, input specialization made possible because of the advantage of close to the front of markets learning firsthand in market demands and acceptances. (Porter, 2008, p. 223) Yet, diminishing returns to specialization in a location can cause convergence effect, which means the growth rates can be declining in a cluster. On the other hand, positive spillovers can provide the impetus for agglomeration (Delgado et al., 2014). Therefore, analyzing specialization within a region, growth rates, and their interactions are important to see the potentials for cluster’s spillovers or in convergence. Thus, measuring the rate of changes is an important part of this research. This research focuses primarily on dynamics, and the dynamics of a cluster are understood by the relations of the growth processes. In other words, by understanding Taiwan’s economic growth patterns, one can derive the cluster initiatives to plan for the next trajectory. Successful companies combine internal excellence with a presence in strong locations and clusters that leverage their strengths (Ketels, 2007, p. 7). In order to compete more 16.

(31) 17 effectively, regions need to understand their cluster strengths when compared with those of other regions (Porter, Ketels, & Delgado, 2007, p. 33). Initially, Porter uses the standard group of the NAICS-3 (The North American Industry Classification System) code do as the cluster definitions, but the cluster groups correlate badly since it is mostly defined to group products and services not based on the inter-industry complementarities (Delgado, 2007, 30). Limitations on using the NAICS in other economies that are weighted toward economic activities that are less prevalent in the US (e.g., ship building) or that is not well captured by US data (e.g., farming). They may also be less useful in countries with a lower level of technological development. (Delgado, 2007, 34) The core dataset is the city-level enterprises’ basic measure of its economic performance, covering employment and data associated with its standard of living. The NAICS codes may not contain the linkage relations among related industries, therefore, Porter and his team regrouped the code into a new set of Cluster Definitions (See Appendix - The US Clusters Code). Taiwan’s industries are very similar to the US NAICS codes, and therefore, by adopting to the US definitions is a natural transformation of Taiwan’s cluster codes (See Appendix - Taiwan Clusters Code Definitions).. 4.5. Cluster Mapping In this research, closely related industrial groupings are mapped on 3-Dimentional graphical charts. Porter defines clusters as geographic concentrations of interconnected companies, specialized suppliers, service producers, firms in related industries, and associated institutions (for example, universities, standard agencies, and trade associations) in particular fields that compete but also cooperate. Critical masses of unusual competitive success in particular business areas, clusters are a striking feature of virtually every national, state, and even metropolitan economy, especially those of more economically advanced nations (Porter, 2011, p. 197). In the earlier criticism when cluster concept was proposed by Porter in the 1980s, Porter received numerous criticisms. Porter offered an industry relational definitions of clusters including many functional divisions, categories, descriptions which is complex without explaining in the algorithms in his earlier writing which added confusion to other scholars. On the other hand, Porter’s cluster research was overwhelming well received by 17.

(32) 18 policy makers (Asheim, Cooke, & Martin, 2006, p. 3; Martin & Sunley, 2003). Researchers complained and contributed to a book of criticism collectively (Bathelt, 2008). Criticism ranged from “vague theoretical framing,” to “enormous confusing”, “too broad,” “one-size fits all” (Asheim et al., 2006, pp. 12,14,21). A similar term used in the Italian economic, “industrial districts,” has also stimulated a wide international debate about the competitive performance of these specific geographical concentrations of “localized industries” (Belussi, 2006, p. 69). Belussi continued to criticize Porter’s cluster concept indicating that “Most of the literature lacks sufficient power of theoretical generalization, and testable regularities are not precisely identified.” Furthermore, “Quantitative analyses have used spurious data bases, and research conducted on individual case studies confuses the two levels of analysis: the idiosyncratic component, related to the obvious specificity of territory examined, and the studied ‘clustering effect’ (Signorini, 1994) deriving exactly from the features of the phenomenon treated.” (Belussi, 2006, p. 71) Ivana Paniccia criticized that Porter “put forward a concept which lacked precise conceptual boundaries, so that the term, according to the critical appraisal by Marin and Sunley (Martin & Sunley, 2003), has acquired such a variety of uses, connotations and meanings that is has, in many respects, becomes a ‘chaotic concept’, in the sense of conflating and equating quite different types, processes and spatial scales of economic localization under a single, all-embracing universalistic notion.” Time has come to the proof that Porter is way ahead of his time in the earlier years trying to describe a structure to close the gap between the policy makers and practitioners. That is why policy makers welcome his presence and ask his help to reach out to public. Now that he has much more to offer after years of analytical framework being drawn in a much precise and versatile way (Huggins & Izushi, 2011; Ketels, 2011). Through the framework, the research is able to induce Taiwan’s economic models by spillovers effect and clustering of supply-chain effect.. 4.6. Spillovers Numerous research has confirmed spillovers are an important agglomeration effect (Audretsch & Aldridge, 2008; Audretsch & Feldman, 1996; Canina, Enz, & Harrison, 2005; Chyi, Lai, & Liu, 2012; Fallah & Ibrahim, 2004; Jun & Guanghua, 2006; 18.

(33) 19 Nooteboom, 2006; Rugman & Verbeke, 2003), and most studies are either empirical case or technology spillovers on innovations. This research contributes to the cluster spillovers from the adjacent clusters by the overflowing business and employment demands. The spillover is defined, in general, to as an industry that shares resources and knowledge with another industry such as common labor skills that may be used by both industries, or an over flow of knowledge from one industry to another industry. Adding to the general definition here, spillover is defined to be the adjacency employment overflow by sharing the same knowledge and specializations. Region in this case is defined as a related area which industries correlate and having spillover effects. Porter (2003) suggests that, regional economic performance is strongly affected by companies and institutions that generate innovations by the strengths of clusters and the vitality and plurality output, and regional performance differences are dominated by relative wage levels in the array of clusters that are present in a region, rather than the particular mix of clusters itself. Therefore, wage in the observations is an important factor to be measured by comparing to different locations, especially to the adjacencies to derive the spillovers. This research displays spillover effects from dominate clusters to supportive or correlated clusters on the properties of employment, revenue or wage growth rates. The spillover adjacency locations form the higher-end knowledge based clusters which has the effects to the wage growth.. 4.7. Location Location matters especially when it comes to spillover of knowledge, manpower pools, and even markets (Porter & Stern, 2001). In proposing a new theory of economic geography, Paul Krugman (Krugman, 1990, p. 55), asks, “What is the most striking feature of the geography of economic activity? The short answer is surely concentration...production is remarkably concentrated in space.” provided evidence that what Krugman observed to be true for production was even more pronounced for innovative activity (Audretsch & Feldman, 2004; Feldman & Florida, 1994). By the nature of industry such as Information and Technology which is high in knowledge innovation displays the concentration and agglomeration properties in Taiwan. The economic cluster agglomeration research has evolved from Cluster Definitions, 19.

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