A stochastic independence approach for measuring regional specialization and concentration | Zendy

Haedo Christian | Zendy; Mouchart Michel | Zendy

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A stochastic independence approach for measuring regional specialization and concentration

Author(s) -

Haedo Christian,

Mouchart Michel

Publication year - 2018

Publication title -

papers in regional science

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.937

H-Index - 64

eISSN - 1435-5957

pISSN - 1056-8190

DOI - 10.1111/pirs.12294

Subject(s) - contingency table , independence (probability theory) , joint probability distribution , marginal distribution , econometrics , distribution (mathematics) , table (database) , contingency , economics , mathematics , statistics , computer science , random variable , data mining , mathematical analysis , philosophy , linguistics

This paper proposes an integrated framework for discussing issues related to regional concentration, sectorial specialization and overall localization by considering these concepts as a row‐column association– or non‐independence– in a two‐way contingency table ‘regions × sectors’. This is the approach of stochastic independence, in which the degree of concentration, or of specialization, is measured by discrepancies among distributions: between profiles and a uniform distribution for absolute concepts; between profiles and the corresponding marginal distribution for relative concepts; or between the joint distribution and the product of the marginal distributions for overall localization. This paper discusses the benefits of this integrating approach, particularly for the practitioner facing a multifaceted literature.

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