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A nonparametric entropy-based measure of mass political polarization

Published online by Cambridge University Press:  15 August 2024

Le Bao Open the ORCID record for Le Bao [Opens in a new window]  and
Jeff Gill
Le Bao*
Affiliation:
Massive Data Institute, McCourt School of Public Policy, Georgetown University, Washington, D.C., USA
Jeff Gill
Affiliation:
Department of Government, Department of Mathematics & Statistics, American University, Washington, D.C., USA
*
Corresponding author: Le Bao; Email: le.bao@georgetown.edu
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Abstract

Political polarization has become an increasingly salient issue worldwide, but a systematic examination of the variation and sources of mass polarization across countries is limited by current measurement methods. This work proposes a nonparametric, entropy-based measure of mass political polarization. It exploits the specific structure of ordinal distributions in public opinion data, makes no prior assumptions about the form and spacing of the data, and can still draw reliable measures of issue-based polarization. We demonstrate the theoretical and practical superiority of the measure with analytical comparisons and simulations. We then apply the proposed measure to questions about mass polarization in the USA, the relationship between radical parties and polarization in Europe, and cross-country trends in affective and ideological polarization.

Keywords

comparative polarizationentropyideologyissue opinionmass polarizationmeasurementnonparametric
Type
Original Article
Information
Political Science Research and Methods , First View , pp. 1 - 20
Copyright
Copyright © The Author(s), 2024. Published by Cambridge University Press on behalf of EPS Academic Ltd

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