Brexit Vote: Social and Political Science Insight for Policymakers - Essay Sample

Introduction

Social science, as well as political science, has a key role to play in helping policymakers, politicians, and the public, among others, understand the referendum by providing evidence, analysis, and theory to the debate. For instance, in 2016, the United Kingdom voted to leave the European Union were 17.4 million people voted to leave, and 16.1 million voted to remain, marking a turning point in the history of the United Kingdom (Goodwin & Heath, 2016). The reasons behind the Brexit vote remains a pressing question, which has attracted significant comments and analysis. The answer to this question ultimately helps interpret policy guidelines moving forward to the post Brexit era. Additionally, it will help inform the type of relationship with the European Union that would be acceptable to the public debate as part of the eventual Brexit deal (Goodwin & Heath, 2016). The purpose of the research is to provide an overview of the main drivers that can explain the Brexit outcome. Additionally, the study hopes to stimulate debate, discussions, and further research in understanding the fundamental determinants underpinning Brexit outcome.

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The study focuses on socio-economic indicators that we grouped into five categories: Demography and education, ethnic diversity; the economic structure of the areas; European Union immigrant status; and previous support for United Kingdom Independent Party. The paper seeks to adopt a multivariate linear regression analysis model that captures the subset of variables from each category, which best explains the actual referendum outcome.

Literature Review

Previous research has linked poverty, demography, education, and support for Brexit. This body of research links supports for Brexit to regions with the older population and low levels of educational qualifications. Additionally, the areas that re predominantly associated with supporting for Brexit are also likely to experience deprivation and have demographic changes due to the inward migration of European Union citizens. For example, Goodwin and Health analyzed data from 380 regions across the United Kingdom and linked the information to the 2011 census (Goodwin & Heath, 2016). Their study indicated that the highest support for Brexit was from areas where the population had no educational qualifications and were not well equipped to survive in a global economy brought about by the European Union.

Support for Brexit finding is also consistent with past research on support for the United Kingdom independent party that pushes for the importance of marginalized communities. For instance, Robert Ford and Mathew Goodwin analyzed areas with the highest support for the United Kingdom's independent party. Their research indicates that UKIP support was strongest in regions where there were older, poor, and white voters (Johnston, Manley, Pattie & Jones, 2018). On the other hand, support was weaker among the younger, ethnically diverse, and financially stable voters.

Early studies have also established a relationship and support for Brexit. However, there have been mixed findings in this relationship, existing evidence indicates a weak relationship between support for Brexit and immigrants' growth (Johnston, Jones & Manley, 2018). Overall, past research in this area is quite mixed, indicating the need to examine deeply to both the area and important to the individual level as well.

Hypothesis

In this section, the research discusses the main hypothesis proposed to explain the Brexit results and how they are determined in the analysis. The study looks at a vote to leave as the dependent variable; however, we also look at the previous support for the United Kingdom Independent Party as an alternative dependent variable (Becker, Fetzer & Novy, 2017). The study builds on the existing literature to examine the results of the 2016 Brexit vote in more depth, exploring how different variables influenced the likelihood to vote remain or leave the European Union. Furthermore, the study examines the influence of the UKIP vote share in understanding the Brexit outcome.

The study seeks to answer the hypothesis that there is a high association between previous support for the United Kingdom Independent Party and the Vote to leave outcome of the 2016 EU referendum. In the years prior to the EU referendum vote, UKIP has been pushing the agenda of Britain leaving the European Union (Heath & Goodwin, 2017). UKIP popularity has also been growing as witnessed in the 2014 EU parliament elections, where it won the highest votes, ahead of the Labor Party and Conservative Party (Clarke, Goodwin & Whiteley, 2017). United Kingdom Independent party there demonstrated the capacity to mobilize voters. It is, therefore, important to understand UKIP support concerning Brexit's outcome.

Data and Methodology

The study is based on a synthesis of evidence from local authority data drawn from 380 0ut of 382 counting regions in the United Kingdom. The two counting regions (Gibraltar and Northern Ireland), lacked comparable data on certain variables and were excluded from this analysis. Additionally, one counting region was dropped during the data cleaning process because it contained missing information on some variables. The study, therefore, used a final of 379 counting regions that are used in the final analysis. Notably, analysis relied on aggregated data, therefore drawing inferences about attitudes and voting behaviors of voters at an individual level should be done with caution. However, the data still provides a useful insight into the factors that might have contributed to the results, and let to Brexit. Table 1 below describes the variables used in this study.

The study takes a comprehensive approach to understand the influencers behind the Brexit referendum outcome. The analysis in this study cannot possibly claim causality. However, the study tries to capture the predictive power of various groups of independent variables to see which variables explain in a bigger share of the dependent variables (vote to leave, UKIP). The approach is appropriate in this setting, where the study seeks to analyze cross-sectional variation only. It is expected that the influence of some variables would change when more influential variables added to obtain the full model. The study builds a full model of the correlation structures between the independent variables at the local authority level (379 regional units) and a dependent variable, which is either the proportion of votes to leave or UKIP (Wheatley, 2016).

Results

Correlation Between Variables

Figure 1 below shows the correlations between different variables used in this study ranging from 1 through to -1, indicating a high positive correlation to a high negative correlation, respectively. The figure indicates a positive correlation between UKIP, age 65 plus, being white, no academic qualifications and working-class to be positively correlated with support to leave while voter turnout in 2014, being a migrant, younger age(18 to 30 years), high education qualifications, professionals and median income are negatively correlated with the support for vote to leave. On the other hand, turnout in 2016, old age, being white, low education qualification, and working-class are positively related with UKIP while being emigrant, young age, non- white and high education qualifications are negatively correlated with UKIP.

Figure1: Correlation matrix showing correlation between the variables used in the study

UKIP and Support Vote for Leave

The results in this study indicate that the UKIP vote share is important in explaining Brexit outcome. The analysis indicates that understanding the UKIP vote pattern seems crucial in understanding the leave vote. Over the years, UKIP has been pushing the agenda of Britain exiting the European Union. The result in favor of its agenda also demonstrates its ability to mobilize a larger number of voters. The figure below shares the relationship between UKIP and the Leave vote share

Figure 2: UKIP Vote share (in %) in 2014 plotted against the leave vote shares in 2016 Brexit referendum

Demography Education and Voter Decisions

The exploratory analyze indicates that older voters (65 years and above) were more likely to vote to leave; however, younger voters (18 to 30 years) supported remain. Additionally, voters with low levels of education are more likely to vote leave compared to those with high levels of education who voted in favor of remain. The decision may be because younger voters might find it harder may not have a better chance of grasping opportunities from globalization brought about by European Union membership.

Figure 3: Scatter plot showing the relationship between Education level of voters (in %) and the Vote Leave share (%)

The results show that the less educated are skeptical about Britain remaining at the European Union. The figure above shows the association between the proportion of people who have low educational qualifications and the proportion who voted in favor of leave. It is also worth noting that there was high variation in voting among the population of less-educated with some areas receiving votes lower than expected.

The study also finds a clear association between the demographic profiles of the voters and the leave vote. Figure 4, below shows the association between the percentage of younger people (age 18 t0 30) and the percentage of people that voted in favor of leave. Additionally, the study shows the association between older people (age 65 years and above) and the leave vote. As shown in the figure, the leave vote tends to be lower among the young.

Figure 4: Scatter plot showing age distribution and support for leave vote

Multivariate Analysis

In this section, the study carries an in-depth multivariate analysis in order get a clearer idea of the combined impact of the different variables discussed above. To get the first indication of different groups of variables are related to 2016 referendum results, the study first regress the vote share separately on the variables of different groups. This allows us to concentrate on the importance of variables within a thematic group as an influencer of the dependent variable. The study then looks at adjusted R squared for groups of variables to inform about the influence power of thematic groups relative to each other. The study finally highlights the role played by key independent variables. Table 2 presents results from multiple linear regression models, and the study developed two models with dependent variables as the leave of support for leaving European Union and UKIP respectively.