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An Economic Topology of the Brexit vote

Pawel Dlotko, Simon Rudkin Orcid Logo, Wanling Qiu

Swansea University Author: Simon Rudkin Orcid Logo

Abstract

A quest to understand the decision of the UK to leave the European Union, Brexit, in the referendum of June 2016 has occupied academics, the media and politicians alike. As the debate about what the future relationship will look like rages, the referendum is given renewed importance as an indicator...

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Published: 2019
URI: https://cronfa.swan.ac.uk/Record/cronfa51886
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first_indexed 2019-09-12T20:48:45Z
last_indexed 2019-09-23T14:18:16Z
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spelling 2019-09-23T13:41:58.9533421 v2 51886 2019-09-12 An Economic Topology of the Brexit vote 93f12293ea6ed07ae8162cf25659c5f2 0000-0001-8622-7318 Simon Rudkin Simon Rudkin true false 2019-09-12 ECON A quest to understand the decision of the UK to leave the European Union, Brexit, in the referendum of June 2016 has occupied academics, the media and politicians alike. As the debate about what the future relationship will look like rages, the referendum is given renewed importance as an indicator of the likely success, or otherwise, of any forward plans. Topological data analysis offers an ability to faithfully extract maximal information from complex multi-dimensional datasets of the type that have been gathered on Brexit voting. Within the complexity it is shown that support for Leave drew from a far more similar demographic than Remain. Obtaining votes from this concise set was more straightforward for Leave campaigners than was Remain's task of mobilising a diverse group to oppose Brexit. Broad patterns are consistent with extant empirical work, but the strength of TDA Ball Mapper means that evidence is offered to enrich the narrative on immobility, and being ``left-behind'' by EU membership, that could not be found before. A detailed understanding emerges which comments robustly on why Britain voted as it did. A start point for the policy development that must follow is given. Working paper Brexit, Data Topology, TDA Ball Mapper 10 9 2019 2019-09-10 COLLEGE NANME Economics COLLEGE CODE ECON Swansea University 2019-09-23T13:41:58.9533421 2019-09-12T16:23:29.1735414 Faculty of Humanities and Social Sciences School of Management - Economics Pawel Dlotko 1 Simon Rudkin 0000-0001-8622-7318 2 Wanling Qiu 3 0051886-12092019162521.pdf AnEconomicTopologyoftheBrexitvote.pdf 2019-09-12T16:25:21.8030000 Output 2118785 application/pdf Author's Original true 2019-09-12T00:00:00.0000000 true eng
title An Economic Topology of the Brexit vote
spellingShingle An Economic Topology of the Brexit vote
Simon Rudkin
title_short An Economic Topology of the Brexit vote
title_full An Economic Topology of the Brexit vote
title_fullStr An Economic Topology of the Brexit vote
title_full_unstemmed An Economic Topology of the Brexit vote
title_sort An Economic Topology of the Brexit vote
author_id_str_mv 93f12293ea6ed07ae8162cf25659c5f2
author_id_fullname_str_mv 93f12293ea6ed07ae8162cf25659c5f2_***_Simon Rudkin
author Simon Rudkin
author2 Pawel Dlotko
Simon Rudkin
Wanling Qiu
format Working paper
publishDate 2019
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Economics{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Economics
document_store_str 1
active_str 0
description A quest to understand the decision of the UK to leave the European Union, Brexit, in the referendum of June 2016 has occupied academics, the media and politicians alike. As the debate about what the future relationship will look like rages, the referendum is given renewed importance as an indicator of the likely success, or otherwise, of any forward plans. Topological data analysis offers an ability to faithfully extract maximal information from complex multi-dimensional datasets of the type that have been gathered on Brexit voting. Within the complexity it is shown that support for Leave drew from a far more similar demographic than Remain. Obtaining votes from this concise set was more straightforward for Leave campaigners than was Remain's task of mobilising a diverse group to oppose Brexit. Broad patterns are consistent with extant empirical work, but the strength of TDA Ball Mapper means that evidence is offered to enrich the narrative on immobility, and being ``left-behind'' by EU membership, that could not be found before. A detailed understanding emerges which comments robustly on why Britain voted as it did. A start point for the policy development that must follow is given.
published_date 2019-09-10T04:03:56Z
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score 11.036706

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