Testing for localization: A new approach

Yasusada Murata, Ryo Nakajima and Ryuichi Tamura
Additional contact information
Ryo Nakajima: Faculty of Economics, Keio University
Ryuichi Tamura: Faculty of Economics, Keio University

No 2017-017, Keio-IES Discussion Paper Series from Institute for Economics Studies, Keio 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: 47 pages
Date: 2017-05-03
New Economics Papers: this item is included in nep-geo, nep-tid and nep-ure
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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Related works:
Working Paper: Testing for localization: A new approach (2016)
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Persistent link: https://EconPapers.repec.org/RePEc:keo:dpaper:2017-017

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