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Disproportionality measures of concentration, specialization, and polarization

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  • Bickenbach, Frank
  • Bode, Eckhardt

Abstract

The paper extends the methodological toolbox of measures of industrial concentration and regional specialization. First, a taxonomy is proposed which gives rise to a modular construction system for disproportionality measures based on three characteristic features: the projection function, the reference distribution, and the weighting scheme. The taxonomy helps reduce the mismatch between research purpose, data and statistical measure which has been one of the major obstacles to reliable inferences in the literature. Second, the taxonomy is extended (i) to measures of polarization which evaluate specialization and concentration simultaneously and allow for a nested analysis at different spatial and industrial scales, and (ii) to spatial concentration measures which deal with the checkerboard problem and MAUP by taking into account information from neighboring regions.

Suggested Citation

  • Bickenbach, Frank & Bode, Eckhardt, 2006. "Disproportionality measures of concentration, specialization, and polarization," Kiel Working Papers 1276, Kiel Institute for the World Economy (IfW Kiel).
    • Handle: RePEc:zbw:ifwkwp:1276
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    References listed on IDEAS

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    Cited by:

    1. Haedo, Christian & Mouchart, Michel, 2018. "Automatic biclustering of regions and sectors," LIDAM Discussion Papers ISBA 2018026, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    2. Haedo, Christian & Mouchart, Michel, 2019. "Two-mode clustering through profiles of regions and sectors," LIDAM Discussion Papers ISBA 2019014, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    3. Eleonora CUTRINI, 2006. "The Balassa Index Meets the Dissimilarity Theil Index: a Decomposition Methodology for Location Studies," Working Papers 274, Universita' Politecnica delle Marche (I), Dipartimento di Scienze Economiche e Sociali.
    4. Haedo, Christian & Mouchart, Michel, 2016. "Automatic biclustering of regions and sectors," LIDAM Discussion Papers ISBA 2016042, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
    5. Cutrini, Eleonora, 2009. "Using entropy measures to disentangle regional from national localization patterns," Regional Science and Urban Economics, Elsevier, vol. 39(2), pages 243-250, March.
    6. Christian Haedo & Michel Mouchart, 2022. "Two-mode clustering through profiles of regions and sectors," Empirical Economics, Springer, vol. 63(4), pages 1971-1996, October.
    7. HAEDO, Christian & MOUCHART, Michel, 2012. "A stochastic independence approach for different measures of concentration and specialization," LIDAM Discussion Papers CORE 2012025, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    8. Olga Alonso-Villar, 2008. "What are we assuming when using inequality measures to quantify geographic concentration? An axiomatic approach," Working Papers 0801, Universidade de Vigo, Departamento de Economía Aplicada.

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    More about this item

    Keywords

    Regional specialization; Industrial concentration; Economic polarization; Geographical weighting; statistical measures;
    All these keywords.

    JEL classification:

    • F15 - International Economics - - Trade - - - Economic Integration
    • R12 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - General Regional Economics - - - Size and Spatial Distributions of Regional Economic Activity; Interregional Trade (economic geography)
    • C43 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Index Numbers and Aggregation

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