Abstract
Heavy geographic patterning to the 2016 Brexit vote in UK and Trump vote in US has resulted in numerous ecological analyses of variations in area-level voting behaviours. We extend this work by employing modelling approaches that permit regionally-specific associations between outcome and explanatory variables. We do so by generating a large number of regional models using penalised regression for variable selection and coefficient evaluation. The results reinforce those already published in that we find associations in support of a ‘left-behind’ reading. Multivariate models are dominated by a single variable—levels of degree-education. Net of this effect, ‘secondary’ variables help explain the vote, but do so differently for different regions. For Brexit, variables relating to material disadvantage, and to a lesser extent structural-economic circumstances, are more important for regions with a strong industrial history than for regions that do not share such a history. For Trump, increased material disadvantage reduces the vote both in global models and models built mostly for Southern states, thereby undermining the ‘left-behind’ reading. The reverse is nevertheless true for many other states, particularly those in New England and the Mid-Atlantic, where comparatively high levels of disadvantage assist the Trump vote and where model outputs are more consistent with the UK, especially so for regions with closer economic histories. This pattern of associations is exposed via our regional modelling approach, application of penalised regression and use of carefully designed visualization to reason over 100+ model outputs located within their spatial context. Our analysis, documented in an accompanying github repository, is in response to recent calls in empirical Social and Political Science for fuller exploration of subnational contexts that are often controlled out of analyses, for use of modelling techniques more robust to replication and for greater transparency in research design and methodology.
Highlights
This work extends the analysis presented in Beecham et al [8] through a more explicit and detailed examination of regional structure via penalised regression and, importantly, introducing a comparison with the 2016 Trump vote—an event occurring in a country much larger and more diverse than United Kingdom (UK)
The scale at which we explore regionally-varying effects is UK Government Office Region (GOR) and United States (US) state—the 380 Local Authority Districts of GB are grouped into 11 Government Office Regions (GOR) and the 3,108 counties of mainland US are grouped into 48 states and District of Columbia
Studying the Brexit vote in UK and Trump vote in US, we find associations and model outputs that would be expected given this interpretation
Summary
Recent ecological analyses provide empirical support to this ‘leftbehind’ places reading, with models presented that explain area-level variation in both the Brexit and Trump votes through variables describing employment structure, education levels and migration profiles [4], or variables closely related to these themes [7]. Through a multilevel modelling framework, and local modelling approaches, Beecham et al [8] found that did the size of associations between area-level socio-economic variables and the Brexit vote diverge between regions, but that in some cases their direction shifted between positive and negative associations. Deploying modelling techniques that estimate different effects between variables for different parts of the US and UK, we Regionally-structured explanations behind area-level populism expect to observe some regionally-specific explanation—and characterising this variation and its geography will be an important analysis task. Certain US counties and GB LADs will have experienced greater demographic change over this period than others and, especially for US counties with smaller populations, this may introduce additional uncertainty into our modelling that cannot be accounted for
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