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Industry-specific firm growth and aggolmeration

Author

Listed:
  • Matthias Duschl

    (Department of Geography, University Marburg)

  • Tobias Scholl

    (House of Logistics and Mobility GmbH, University Marburg)

  • Thomas Brenner

    (Department of Geography, Philipps University Marburg)

  • Dennis Luxen

    (Karlsruhe Institute of Technology)

  • Falk Raschke

    (House of Logistics and Mobility GmbH, University Frankfurt)

Abstract

This paper studies the industry-specific relationship between industrial clustering and firm growth. Micro-geographically defined agglomeration measures, free of the modifiable areal unit problem (MAUP), are used to study 23 industries. The spatial impacts of agglomeration of related economic and knowledge generating activities are examined by using travel time distances, a flexible log-logistic decay function framework and quantile regression techniques. We find that firms’ growth prospects tend to be hampered by the agglomeration of own-industry employment, but improved by proximate scientific activity. Results depend on the kind and age of industry. Furthermore, the optimal decay functions that measure agglomeration effects considerably vary both between the industries and variables. Three illustrative cases of industries are discussed in more details.

Suggested Citation

  • Matthias Duschl & Tobias Scholl & Thomas Brenner & Dennis Luxen & Falk Raschke, 2012. "Industry-specific firm growth and aggolmeration," Working Papers on Innovation and Space 2012-06, Philipps University Marburg, Department of Geography.
    • Handle: RePEc:pum:wpaper:2012-06
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    14. Pietro Moncada-Paterno-Castello & Sara Amoroso & Dimitris Pontikakis & Emanuele Pugliese & Alexander Tuebke, 2019. "Industrial Innovation for Transformation: 7th European Conference on Corporate R&D and Innovation CONCORDi 2019. Background Note," JRC Research Reports JRC117787, Joint Research Centre.
    15. Liang, Jiaochen & Goetz, Stephan J., 2018. "Technology intensity and agglomeration economies," Research Policy, Elsevier, vol. 47(10), pages 1990-1995.
    16. Wenwen Wang, 2021. "The heterogeneity of agglomeration effect: Evidence from Chinese cities," Growth and Change, Wiley Blackwell, vol. 52(1), pages 392-424, March.
    17. Augusto Cerqua & Guido Pellegrini, 2014. "Beyond the SUTVA: how policy evaluations change when we allow for interactions among firms," Working Papers 2/14, Sapienza University of Rome, DISS.
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    More about this item

    Keywords

    Firm growth; industrial clusters; agglomeration; MAUP; distance decay function; quantile regression;
    All these keywords.

    JEL classification:

    • C31 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models; Quantile Regressions; Social Interaction Models
    • D92 - Microeconomics - - Micro-Based Behavioral Economics - - - Intertemporal Firm Choice, Investment, Capacity, and Financing
    • L25 - Industrial Organization - - Firm Objectives, Organization, and Behavior - - - Firm Performance
    • R11 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Regional Economic Activity: Growth, Development, Environmental Issues, and Changes

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