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Testing for localization: A new approach

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  • MURATA, Yasusada
  • NAKAJIMA, Ryo
  • TAMURA, Ryuichi
  • 田村, 龍一

Abstract

Recent empirical studies document that knowledge spillovers attenuate and industry localization decays with distance. It is thus imperative to detect localization accurately especially at short distances. We propose a new approach to testing for localization that corrects the first-order bias at and near the boundary in existing methods while retaining all desirable properties at interior points. Employing the NBER U.S. Patent Citations Data File, we illustrate the performance of our localization measure based on local linear density estimators. Our results suggest that the existing kernel density methods and regression approaches can be substantially biased at short distances.

Suggested Citation

  • MURATA, Yasusada & NAKAJIMA, Ryo & TAMURA, Ryuichi & 田村, 龍一, 2016. "Testing for localization: A new approach," IIR Working Paper 16-11, Institute of Innovation Research, Hitotsubashi University.
    • Handle: RePEc:hit:iirwps:16-11
    Note: August 19, 2016
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    References listed on IDEAS

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    1. William R. Kerr & Scott Duke Kominers, 2015. "Agglomerative Forces and Cluster Shapes," The Review of Economics and Statistics, MIT Press, vol. 97(4), pages 877-899, October.
    2. Yasusada Murata & Ryo Nakajima & Ryosuke Okamoto & Ryuichi Tamura, 2014. "Localized Knowledge Spillovers and Patent Citations: A Distance-Based Approach," The Review of Economics and Statistics, MIT Press, vol. 96(5), pages 967-985, December.
    3. Stuart S. Rosenthal & William C. Strange, 2003. "Geography, Industrial Organization, and Agglomeration," The Review of Economics and Statistics, MIT Press, vol. 85(2), pages 377-393, May.
    4. Adam B. Jaffe & Manuel Trajtenberg & Rebecca Henderson, 1993. "Geographic Localization of Knowledge Spillovers as Evidenced by Patent Citations," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 108(3), pages 577-598.
    5. Gilles Duranton & Henry G. Overman, 2005. "Testing for Localization Using Micro-Geographic Data," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(4), pages 1077-1106.
    6. Kristy Buzard & Gerald A. Carlino & Jake Carr & Robert M. Hunt & Tony E. Smith, 2015. "Localized Knowledge Spillovers: Evidence from the Agglomeration of American R&D Labs and Patent Data," Working Papers 15-3, Federal Reserve Bank of Philadelphia.
    7. Gilles Duranton & Henry G. Overman, 2008. "Exploring The Detailed Location Patterns Of U.K. Manufacturing Industries Using Microgeographic Data," Journal of Regional Science, Wiley Blackwell, vol. 48(1), pages 213-243, February.
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    11. Carlino, Gerald & Kerr, William R., 2015. "Agglomeration and Innovation," Handbook of Regional and Urban Economics, in: Gilles Duranton & J. V. Henderson & William C. Strange (ed.), Handbook of Regional and Urban Economics, edition 1, volume 5, chapter 0, pages 349-404, Elsevier.
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    Cited by:

    1. Buzard, Kristy & Carlino, Gerald A. & Hunt, Robert M. & Carr, Jake K. & Smith, Tony E., 2020. "Localized knowledge spillovers: Evidence from the spatial clustering of R&D labs and patent citations," Regional Science and Urban Economics, Elsevier, vol. 81(C).

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    More about this item

    Keywords

    localization; knowledge spillovers; local linear density; boundary bias; micro-geographic data;
    All these keywords.

    JEL classification:

    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • O31 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Innovation and Invention: Processes and Incentives

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