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Functional Regions for Policy: a Statistical ‘Toolbox’ Providing Evidence for Decisions between Alternative Geographies

Lookup NU author(s): Emeritus Professor Mike CoombesORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Labour market areas and other functional regions (FRs) are increasingly used withinresearch and policy, but how FRs are best defined is an unresolved issue. This isimportant because the policy impacts, or the research results, will differ depending onthe specific FR boundaries used. As a result of this sensitivity (termed the ModifiableAreal Unit Problem), quantitative metrics are needed so that differing sets of FRboundaries can be evaluated. To meet this need the paper firstly reviews the conceptand use of labour market areas – the form of FRs most widely used in policy – toidentify relevant criteria for evaluating any regionalisation comprising a set of FRs. Nexta range of potential measurable indicators for each of the criteria is defined. Thesecandidate indicators are then exemplified by applying them to a huge number ofalternative sets of FRs. From this empirical evidence a short-list of preferred indicatorsis identified, creating a statistical ‘toolbox’ for evaluating sets of FRs. The paper endsby first sketching possible processes within which applying the indicators can helppolicy-makers with a decision over the appropriate set of FRs for a specific policy,before finally outlining some potential future research developments.


Publication metadata

Author(s): Martínez-Bernabeu L, Coombes M, Casado-Díaz J-M

Publication type: Article

Publication status: Published

Journal: Applied Spatial Analysis and Policy

Year: 2020

Volume: 13

Issue: 3

Pages: 739–758

Print publication date: 01/09/2020

Online publication date: 08/11/2019

Acceptance date: 09/09/2019

Date deposited: 06/09/2019

ISSN (print): 1874-463X

ISSN (electronic): 1874-4621

Publisher: Springer Netherlands

URL: https://doi.org/10.1007/s12061-019-09326-2

DOI: 10.1007/s12061-019-09326-2


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Funding

Funder referenceFunder name
CSO2017-86474-R
Eurostat (50405.2010.004-2011.325)

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