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Spatial Distance and Representation in Closed PR List: Revisiting the U-Curve Argument Cover

Spatial Distance and Representation in Closed PR List: Revisiting the U-Curve Argument

By: Dejan Bursać and  Dušan Radujko  
Open Access
|Sep 2023

Abstract

The authors are testing the patterns of geographical representation in single nationwide district closed-list PR systems, in the framework of an earlier study made by Latner and McGann (2005), who suggested that MPs mostly reside in central metropolitan areas, as well as in distant regions, to the detriment of descriptive representation of areas adjacent to the capital. In this way, spatial distance serves as an incentive for parties to nominate lists comprised mostly of candidates from metropolitan urban centres who can easily reach the mid-distance municipalities for campaigning and constituency service, but also of those candidates residing in peripheral regions in which there is some sort of political or ethno-cultural saliency, prompting the voters to prefer their local candidates over capital city politicians. Authors are offering a novel approach of measuring and comparing spatial distance to the data on representation of local administrative units and regional subdivisions of four countries (Montenegro, Netherlands, Serbia and Slovakia). While the findings indeed indicate overrepresentation of capital cities and underrepresentation of neighbouring areas, the representation of peripheral areas is not significantly pronounced and seemingly depends more on a contextual case-to-case basis than on a general pattern related to spatial distance producing political or ethno-cultural saliency.

DOI: https://doi.org/10.2478/pce-2023-0023 | Journal eISSN: 2787-9038 | Journal ISSN: 1801-3422
Language: English
Page range: 471 - 487
Published on: Sep 28, 2023
Published by: Metropolitan University Prague
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2023 Dejan Bursać, Dušan Radujko, published by Metropolitan University Prague
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.