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The location quotient as an estimator of industrial concentration

  • Billings, Stephen B.
  • Johnson, Erik B.
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    We construct the location quotient (LQ) from a discrete data generating process and formally test its statistical properties. First, we show that the LQ is typically unbiased, but exhibits finite sample bias when assuming a Poisson distribution. Second, we determine the accuracy of statistical tests, which depends of both sample size as well as desired confidence levels. After constructing LQs using County Business Patterns (2000) data, we find improved accuracy in statistical tests when one increases spatial as well as industrial aggregation. Results show a clear tradeoff between precise statistical inference and power in detecting industrial concentration.

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    File URL: http://www.sciencedirect.com/science/article/pii/S0166046212000269
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    Article provided by Elsevier in its journal Regional Science and Urban Economics.

    Volume (Year): 42 (2012)
    Issue (Month): 4 ()
    Pages: 642-647

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    Handle: RePEc:eee:regeco:v:42:y:2012:i:4:p:642-647
    Contact details of provider: Web page: http://www.elsevier.com/locate/regec

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    1. Thomas J. Holmes & John J. Stevens, 2002. "Geographic Concentration and Establishment Scale," The Review of Economics and Statistics, MIT Press, vol. 84(4), pages 682-690, November.
    2. Joseph P. Romano & Michael Wolf, 2003. "Stepwise multiple testing as formalized data snooping," Economics Working Papers 712, Department of Economics and Business, Universitat Pompeu Fabra.
    3. Briant, A. & Combes, P.-P. & Lafourcade, M., 2010. "Dots to boxes: Do the size and shape of spatial units jeopardize economic geography estimations?," Journal of Urban Economics, Elsevier, vol. 67(3), pages 287-302, May.
    4. Paulo Guimarães & Octávio Figueiredo & Douglas Woodward, 2008. "Dartboard Tests for the Location Quotient," FEP Working Papers 273, Universidade do Porto, Faculdade de Economia do Porto.
    5. Gemechis D. Djira & Frank Schaarschmidt & Bichaka Fayissa, 2008. "Inferences for Selected Location Quotients with Applications to Health Outcomes," Working Papers 200809, Middle Tennessee State University, Department of Economics and Finance.
    6. George Deltas, 2003. "The Small-Sample Bias of the Gini Coefficient: Results and Implications for Empirical Research," The Review of Economics and Statistics, MIT Press, vol. 85(1), pages 226-234, February.
    7. Gilles Duranton & Henry G. Overman, 2005. "Testing for Localization Using Micro-Geographic Data," Review of Economic Studies, Oxford University Press, vol. 72(4), pages 1077-1106.
    8. Dan O'Donoghue & Bill Gleave, 2004. "A Note on Methods for Measuring Industrial Agglomeration," Regional Studies, Taylor & Francis Journals, vol. 38(4), pages 419-427.
    9. Paulo Guimaraes & Octavio Figueiredo & Douglas Woodward, 2000. "A tractable approach to the firm location decision problem," NIMA Working Papers 2, Núcleo de Investigação em Microeconomia Aplicada (NIMA), Universidade do Minho.
    10. Kurt Schmidheiny & Marius Brülhart, 2009. "On the Equivalence of Location Choice Models: Conditional Logit, Nested Logit and Poisson," CESifo Working Paper Series 2726, CESifo Group Munich.
    11. JH Ll DEWHURST, . "Regional Size, Industrial Location And Input-Output Expenditure Coefficients," Dundee Discussion Papers in Economics 074, Economic Studies, University of Dundee.
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