Innovation and Knowledge Spillover with Geographical and Technological Distance

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Innovation and Knowledge Spillover with
Geographical and Technological Distance

• Klaus Wersching
• BIELEFELD UNIVERSITY
• kwersching_at_wiwi.uni-bielefeld.de

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Contents

• Introduction
• The Agent Based Simulation Model
• Simulation Setup
• Results of the Simulation Studies
• Conclusions

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I. Introduction

• Innovation crucial for understanding economic
  growth
• Knowledge is basis for innovation
• Learning (knowledge spillover) is influenced by
• Geographical distance (e.g. Asheim and Gertler,
  2005)
• Technological distance (e.g. Nooteboom, 2000)

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Aims and Features

• Aim better understanding of
• Technological and geographical location decision
  of firms
• Evolution of specific knowledge in an industry
• Features of the model
• Dynamic perspective
• Industry with differentiated products
• Heterogeneous agents interact on product markets
  and exchange knowledge
• Process and product innovations

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II. The Agent Based Simulation Model

• Model is based on Dawid and Reimann (2004, 2005)
• Extension heterogeneous knowledge spillover

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• Technological distance
• Path on circular Technology Space
• Difference in level of knowledge

• Geographical distance
• Core / Cluster
• Learning from competitors
• Cost of scarce resources (e.g. rents, congestion)
• Periphery
• No involuntary loss of knowledge

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Decision Making of Firms

• Geographical location according to location
  evaluation
• Additional cost of the scarce resources
• Potential knowledge spillover in the core
• Technological lead in certain technologies
• Technological location based on market
  evaluation
• Profits
• Technological distance to own technological focus

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Knowledge and Innovations

• Knowledge stocks for process innovations and
  product innovations
• Knowledge increases by own RD and by knowledge
  spillover
• Firms differ in their RD capabilities
• Process innovations are deterministic and reduce
  per unit costs
• Product innovations are stochastic and generate
  new submarkets

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Knowledge Spillovers

• Knowledge spillover
• Geographical distance
• Technological distance

• Absorptive capacity
• Technological gap

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III. Simulation Setup

• 100 runs over 100 periods
• Starting with 5 sub-markets, uniformly
  distributed over the technology space
• 10 firms with random knowledge, firm strategies
  and geographical location
• Regarding the impact of different spatial firm
  distributions on the technological development

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IV. Results of the Simulation Studies

• Relevance of geographical proximity

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Comparison of four scenarios

• 0 core
• all firms are always in the periphery, no
  learning between firms
• 50 core
• Half of the firms are always located in the core
• Variable
• Decision rule for changing location
• 100 core
• All firms are always in the core

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Number of successful Product innovations

• Number of successful product innovations raises
  with more firms exchanging knowledge

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Maximum Level of Process Innovations

• Process innovations are highest in the case with
  variable location

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Average Level of Savings after 100 Periods

• Results for the average profitability are similar
  in scenarios with fixed location

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V. Conclusions

• Micro-funded simulation model describes industry
  evolution and learning processes
• Focus on strategic decision of geographical and
  technological location
• Results
• Geographical proximity matters for the
  technological development of an industry
• Firms are indifferent whether all located in the
  core or all in the periphery ? room for economic
  policy

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• Next Technological Specialisation of Firms and
  Cluster

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Appendix References
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