{'fraction_non_alphanumeric': 0.026862645717181957, 'fraction_numerical': 0.015205271160669031, 'mean_word_length': 4.910179640718563, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Who Voted for Brexit

The UK referendum on European Union membership on 23 June 2016 was a key moment for European (dis) integration. Although the outcome had been expected to be tight, in the days running up to the referendum bookmakers and pollsters predicted a win for the Remain side. Many observers were left puzzled and keen to understand who voted for Leave. Various newspapers and blogs were quick to link the referendum vote to key characteristics like the age profile of the population (Burn-Murdoch 2016). It was also pointed out that the Brexit vote relates to class identification and social attitudes more generally (Kaufmann 2016a). In our paper (Becker et al. 2016) we follow these early contributions and analyse the Brexit referendum vote in greater detail. We study the EU referendum result in England, Wales and Scotland in a disaggregated way across 380 local authorities (and across 107 wards in four English cities). We relate the vote to the fundamental socio-economic features of these areas. Figure 1 plots the Vote Leave shares across the local authority areas (excluding Northern Ireland and Gibraltar). We capture different subsets of socio-economic variables that best ‘predict’ the actual referendum result. We cannot possibly give a causal explanation of the referendum result, because the election outcome is obviously multi-causal and multi-faceted. In other words, our results reflect a broad range of correlation patterns. Figure 2 reports the goodness of fit in regressions that use different sets of explanatory variables. This helps to shed light on the relative explanatory power of different salient “issues”. For example, we find that demography and education (i.e., the age and qualification profile of the population across voting areas) explain just under 80% of the Vote Leave share. The economic structure explains just under 70%. Variables in this group include the employment share of manufacturing, unemployment, and wages.