Social Capital and Business Development in High-Technology Clusters: An Analysis of Contemporary U.S. Agglomerations (International Studies in Entrepreneurship, 18)

✍ Scribed by Neslihan Aydogan, Yiu Por Chen

• Publisher: Springer
• Year: 2008
• Tongue: English
• Leaves: 120
• Category: Library

✦ Synopsis

Neslihan Aydogan It is now widely accepted that geographical proximity matters to economic and - cial life. Not only does it provide externalities that reduce transaction costs, but it also helps transacting parties form networks from which they can bene t greatly. More than ever, economists have been trying to bridge the development gap between poor and rich regions. Agglomerations are investigated toward that target because economists think that if certain regions can mix up that special formula to conti- ously innovate and produce, then understanding and forming such agglomerations could be one way to go about that growth path. We have designed the chapters of this book to work out the mechanics of geographical agglomerations in the United States with the focus of identifying the characteristics of such special formula Chapters 1–3 are designed to investigate the high-tech clusters that have sprung up in the United States due to their innovative capacity to engage in high-val- added activities. The rst question we ask is, What promotes the productivity of high-tech rms? We ask this question by taking into account the region in which a rm is located and the spillover effects of the region on the rm. In particular, we ask if the presence of a variety of industries or of similar industries promotes the productivity of high-tech rms. In this regard, we are interested in distingui- ing the high- and low-tech rms in terms of their driving factors.

✦ Table of Contents

Acknowledgments
Contents
Introduction
to 1 How High-Tech Industries Benefit from the Economies of Agglomeration
1.1 Introduction
1.2 Static Externalities and Industry Productivity
1.3 Industry Productivity Equations
1.4 Empirical Observations
1.4.1 Data
1.4.2 Preliminary Facts
1.5 Conclusions
to 2 Tacit Knowledge Transfer, Geographical Proximity, and Inter-Firm Contracts: The Silicon Valley Case
2.1 Introduction
2.2 Silicon Valley Firms and Firm Organization
2.3 Ranking the Inter-Firm Contracts
2.4 Hypotheses and Data
2.4.1 Hypotheses
2.4.2 Data and Methodology
2.4.3 Variable Definitions
2.4.3.1 Dependent Variable
2.4.3.2 Independent Variables
2.4.3.3 Control Variables
2.5 Results
2.5.1 The Ordered Probit Specification
2.5.2 Regression Results
2.6 Discussion and Concluding Remarks
to 3 Reciprocity, Proximity and Performance of Research Consortia
3.1 Introduction
3.2 Model
3.3 Conclusion
to 4 Citizenship, Social Capital, and Spatial Assimilation of Highly Skilled Labor and Location Choice
4.1 Introduction
4.2 Literature Review: Highly Skilled Labor, Immigration, Assimilation, and Social Capital
4.3 The Spatial Distribution of Science and Engineering PhD's
4.4 Hypotheses and Data
4.4.1 Hypotheses
4.4.2 The Data
4.4.3 Variables and Definitions
4.4.3.1 The Dependent Variable
4.4.3.2 Independent Variables
4.4.3.3 Control Variables
4.5 Methodology and Results
4.6 Conclusions and Discussion
4.6 Appendix: Variable Descriptions
to 5 Ethnic and Technical Clustering: Native-Born Americans Versus Foreign S0E Graduates
5.1 Introduction
5.2 H-1B Petitions: A Brief Review by Citizenship
5.3 Literature Review: Technology Clustering, Ethnic Clustering, and Social Capital
5.4 Data, Key Variable Distribution, Hypotheses, and Method
5.4.1 Data
5.4.2 Spatial Distribution of S&E PhDs, Including Citizenship, Ethnicity, and Occupation in the United States, Using the SDR 2001 Sample Data
5.4.3 Hypotheses
5.4.4 Method: Estimation Strategy and Variables
5.4.4.1 Estimation Strategy
5.4.4.2 The Dependent Variable
5.4.4.3 Independent Variables
5.4.4.4 Other Control Variables
5.5 Results
5.6 Conclusion and Policy Implications: United States
Index