Testing for localization: A new approach

Yasusada Murata, Ryo Nakajima, Ryuichi Tamura and 龍一 田村

No 16-11, IIR Working Paper from Institute of Innovation Research, Hitotsubashi University

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.

Keywords: localization; knowledge spillovers; local linear density; boundary bias; micro-geographic data (search for similar items in EconPapers)
JEL-codes: O31 R12 (search for similar items in EconPapers)
Pages: 44 pages
Date: 2016-08
New Economics Papers: this item is included in nep-geo, nep-sbm and nep-ure
Note: August 19, 2016
References: View references in EconPapers View complete reference list from CitEc
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https://hermes-ir.lib.hit-u.ac.jp/hermes/ir/re/28106/070iirWP16-11.pdf

Related works:
Working Paper: Testing for localization: A new approach (2017)
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Persistent link: https://EconPapers.repec.org/RePEc:hit:iirwps:16-11

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