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Simulating confidence for the Ellison–Glaeser index

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  • Cassey, Andrew J.
  • Smith, Ben O.

Abstract

The Ellison and Glaeser (1997) index is an unbiased statistic of industrial localization. Though the expected value of the index is known, ad hoc thresholds are used to interpret the extent of localization. We improve the interpretation of the index by simulating confidence intervals that a practitioner may use for a statistical test. In the data, we find cases whose index value is above the ad hoc threshold that are not statistically significant. We find many cases below the ad hoc threshold that are statistically significant. Our simulation program is freely available and is customizable for specific applications.

Suggested Citation

  • Cassey, Andrew J. & Smith, Ben O., 2014. "Simulating confidence for the Ellison–Glaeser index," Journal of Urban Economics, Elsevier, vol. 81(C), pages 85-103.
    • Handle: RePEc:eee:juecon:v:81:y:2014:i:c:p:85-103
    DOI: 10.1016/j.jue.2014.02.005
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    Cited by:

    1. 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.
    2. 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.
    3. Tanaka, Kiyoyasu & Hashiguchi, Yoshihiro, 2015. "Agglomeration effects of informal sector: evidence from Cambodia," IDE Discussion Papers 495, Institute of Developing Economies, Japan External Trade Organization(JETRO).
    4. Craig Wesley Carpenter & Anders Van Sandt & Scott Loveridge, 2022. "Measurement error in US regional economic data," Journal of Regional Science, Wiley Blackwell, vol. 62(1), pages 57-80, January.
    5. 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.
    6. Billings, Stephen B. & Johnson, Erik B., 2016. "Agglomeration within an urban area," Journal of Urban Economics, Elsevier, vol. 91(C), pages 13-25.
    7. Roy Cerqueti & Eleonora Cutrini, 2024. "Testing for Localization with Entropy-Based Measures," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 173(1), pages 227-247, May.
    8. repec:bof:bofrdp:urn:nbn:fi:bof-201512111472 is not listed on IDEAS
    9. Cayson Chong & Franz Fuerst, 2026. "Powered by Proximity? The Micro-Locational Behaviour of Knowledge-Intensive Businesses," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 17(1), pages 119-137, February.

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    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • L11 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance - - - Production, Pricing, and Market Structure; Size Distribution of Firms
    • R14 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Land Use Patterns

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