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Testing for localization: A new approach

Author

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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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    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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