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Why did Brexit happen? Using causal mapping to analyse secondary, longitudinal data

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  • Shaw, Duncan
  • Smith, Chris M.
  • Scully, Judy

Abstract

The outcome of the UK's referendum on whether the UK should leave or remain in the European Union (so-called Brexit) came as a jolt to many across Europe. In this paper, we use causal mapping from soft OR to analyse longitudinal data from nine televised Brexit debates spread across the 4 weeks leading up to the referendum. We analyse these causal maps to build one view on why Brexit happened. The maps are analysed for the breadth, depth and consistency of arguments in the debate and, broadly, finds that the Leave campaign focused more consistently on a smaller set of campaign themes, contributed more detail to those themes, and focused on their own core issues rather than being diverted onto Remain strongholds. In contrast, Remain shared more information but across a broader range of themes (meaning they were less consistent), and followed Leave into themes that were clearly not their core battleground. The novelties for soft OR in this paper include: the difficulties of building and validating causal maps from secondary data; new techniques for analysing a group of causal maps to uncover the properties of arguments that spread longitudinally through a campaign; a methodology for a teaching case using publicly availability data; linking the paper, philosophically, to critical realism given the unique dataset. Finally, we identify differences in the Leave and Remain debate campaigns to offer one answer to the question ‘Why did Brexit happen?’

Suggested Citation

  • Shaw, Duncan & Smith, Chris M. & Scully, Judy, 2017. "Why did Brexit happen? Using causal mapping to analyse secondary, longitudinal data," European Journal of Operational Research, Elsevier, vol. 263(3), pages 1019-1032.
  • Handle: RePEc:eee:ejores:v:263:y:2017:i:3:p:1019-1032
    DOI: 10.1016/j.ejor.2017.05.051
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    4. Shaw, Duncan & Smith, Chris M. & Scully, Judy, 2019. "From Brexit to Article 50: Applying Critical Realism to the design and analysis of a longitudinal causal mapping study," European Journal of Operational Research, Elsevier, vol. 276(2), pages 723-735.
    5. Cunico, Giovanni & Aivazidou, Eirini & Mollona, Edoardo, 2021. "Building a dynamic theory of citizens’ awareness of European Cohesion Policy interventions," European Journal of Operational Research, Elsevier, vol. 289(2), pages 758-773.
    6. Liberini, Federica & Oswald, Andrew J & Proto, Eugenio & Redoano, Michela, 2017. "Was Brexit Caused by the Unhappy and the Old?," CAGE Online Working Paper Series 342, Competitive Advantage in the Global Economy (CAGE).
    7. Igor Pyrko & Colin Eden & Susan Howick, 2019. "Knowledge Acquisition Using Group Support Systems," Group Decision and Negotiation, Springer, vol. 28(2), pages 233-253, April.
    8. Tsionas, Mike G., 2022. "Convex non-parametric least squares, causal structures and productivity," European Journal of Operational Research, Elsevier, vol. 303(1), pages 370-387.
    9. Spanellis, Agnessa & MacBryde, Jillian & Dӧrfler, Viktor, 2021. "A dynamic model of knowledge management in innovative technology companies: A case from the energy sector," European Journal of Operational Research, Elsevier, vol. 292(2), pages 784-797.

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