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Finally, in Section 4. This allows us to answer questions such as whether fiscal cuts affected the referendum result more in regions with weaker fundamentals.
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All of the four tables pertaining to results for the four groups of predictor variables Tables 1—4 follow the same logic: the first column shows the one variable that has the best predictive power among all variables in the variable group. The subsequent columns show the different best subsets for regressions with two regressors Column 2 , three regressors Column 3 , etc.
The last column reports the full set of regressors. Table 1. Predictors of Brexit vote: EU exposure immigration, trade and structural funds. Notes : Table reports results from OLS regressions. The dependent variable is the share of the Leave vote in a local authority area in England, Scotland and Wales. Robust standard errors are presented in parentheses, asterisks indicate. It is important to remember that the best subset of k — 1 predictors is not necessarily nested in the best subset of k predictors.
Table 2 is a case in point where the regressor in Column 1 does not appear in Column 2.
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Note that we standardize all right-hand side variables to mean 0 and a standard deviation of 1 to ease comparability of coefficient estimates. The left-hand side variable is the percentage of the Leave vote, i. The variation from the initial EU 15 migrant resident share in Column 1 alone generates an R 2 of Adding the measure of EU trade dependence in Column 2 increases the R 2 further. These two regressors together have the largest explanatory power of any two variables in this first group of predictors, jointly explaining The subsequent columns add only marginally to the R 2. Overall, the full set of regressors explains Using the AIC as our degree-of-freedom-adjusted measure of goodness of fit, Column 6 turns out to provide the best trade-off between parsimony and overall explanatory power.
All subsequent tables follow the same logic. We use migrant resident shares in levels for the year and their growth between and for three subgroups: migrants from the 12 EU accession countries that joined in and , from the initial EU 15 countries and from non-EU countries. It turns out that migrant shares in levels are negatively correlated with the Brexit vote as those immigrants predominantly moved to urban areas that subsequently voted for Remain in The striking observation is that in terms of migrant share growth , only migration from the mainly Eastern European EU accession countries positively correlates with the Vote Leave share.
The well-established literature studying the economic implications of migration on labour market outcomes supports the notion that there are distributional consequences of low-skilled migration putting pressure on wages for low-skilled natives see e. Migration from Eastern Europe, predominantly of low-skilled workers, affected areas with a lower-skilled resident population.
In terms of the point estimates, their interpretation is simplified by the fact that all regressors are standardized to have mean 0 and a standard deviation of 1. For instance, in the best subset specification displayed in Column 6, a one-standard deviation higher initial EU 15 migrant share is associated with a 3. In Online Appendix B we explore, in a speculative way, what may have happened to the EU referendum vote under alternative scenarios in which migration to the United Kingdom would have been different.
We find that since the vote shares do not appear very sensitive to migration, only a large reversal of the EU accession immigration experience would have swayed the vote.
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We stress, however, that such speculative scenarios must be taken with a large grain of salt, not least since various regressors on the right-hand side are correlated and a causal interpretation is generally not possible. The EU trade dependence of local authority areas is also positively correlated with the Vote Leave share.
The reason is that areas with a heavy concentration of manufacturing such as the North East of England tend to disproportionately import from and export to EU countries, and those areas were likely to vote Leave. This finding has been highlighted in the public discussion before: those areas most dependent on trade integration with the EU were more likely to vote Leave see Los et al. As a consequence, regions receiving more money may loathe the EU more. Interestingly, Cornwall, the area receiving the largest amount of EU Structural Funds per capita, voted Leave but immediately after the referendum on 24 June pleaded with the UK government to continue payments after EU money runs out.
Finally, we include matched vote shares from the EU referendum as an additional regressor. There is a strong negative association between voting Leave in and , suggesting different underlying attitudes and considerations across voting areas see Section 2. In Table 2 , we observe that the share of residents in a local authority area who commute to London is a strong predictor for voting Remain.
On the other hand, house ownership is strongly correlated with the Vote Leave share.
This correlation may not be surprising as house ownership is highest among the older section of the population. The share of the population in rented council housing, a measure of those potentially under increased pressure from migration of largely low-skilled Eastern European migrants, also has a strong positive correlation with the Vote Leave share.
Another important predictor in this group of variables is the extent of total fiscal cuts. Local authorities experiencing more fiscal cuts are more likely to vote in favour of leaving the EU. Importantly, fiscal cuts were implemented as de-facto proportionate reductions in grants across all local authorities Innes and Tetlow, This setup implies that reliance on central government grants is a proxy variable for deprivation, with the poorest local authorities being more likely to be hit by the cuts.
This makes it impossible in the cross-section and challenging in a panel to distinguish the effects of poor fundamentals from the effects of fiscal cuts. With this caveat on the interpretation in mind, our results suggest that local authorities experiencing more fiscal cuts were more likely to vote in favour of leaving the EU. Given the nexus between fiscal cuts and local deprivation, we think that this pattern largely reflects pre-existing deprivation. In Online Appendix B , we provide speculative scenarios for fiscal cuts.
In a similar manner, pressure on the public health system matters. In regions where the share of suspected cancer patients waiting for treatment for less than 62 days is larger, the Vote Leave share is lower. By symmetry, where waiting times are longer, Vote Leave gains. Finally, areas with a larger share of the workforce in public employment, a measure of a availability of public services and b public jobs, the Vote Leave share is lower.
In summary, results indicate that provision of public services and the severity of fiscal cuts mattered for the referendum result. In Table 3 , we explore whether demography and education variables predict the referendum result. As predictors, we use both the baseline levels in and the growth between and of the share of the population that has no qualifications or a high qualification, respectively. The middle qualification range is the reference group.
The results indicate that a larger baseline share of the population with no qualifications is associated with a larger Vote Leave share.
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A stronger increase in that share between and is further associated with a higher Vote Leave share. In contrast, the share of the population that has a high qualification is associated with a lower Vote Leave share. But somewhat surprisingly, faster growth of the share with a high qualification is associated with a larger Vote Leave share. We cannot exclude that this partially captures a generally faster increase in the population, which in turn might be associated with pressure on housing and public services.
This is consistent with polls in the run-up to the referendum indicating a clear age gradient in the Vote Leave share, with younger voters intending to vote Remain and older voters intending to vote Leave. We also add life satisfaction scores from the well-being questions in the APS. The mean score is insignificant. However, the coefficient of variation is positively related to the Vote Leave share. This finding suggests that a higher relative dispersion of well-being across voting areas, which can be interpreted as a measure of life satisfaction inequality, has positive predictive power for the Vote Leave share.
In Table 4 , we concentrate on variables characterizing the sectoral structure of voting areas, both in terms of levels in the baseline year and in terms of their changes from to We single out employment in retail, manufacturing, construction and finance, and subsume all other sectors in the residual reference category. This reference category is of course quite heterogeneous, containing sectors such as agriculture, the public sector and various service sectors.
This being said, a higher share of employment in the baseline year in any of the four sectors highlighted in Table 4 is associated with a larger Vote Leave share compared with the reference category. As for the change in employment between and , a stronger increase in manufacturing, construction and finance employment is associated with a higher Vote Leave share. The growth of retail employment is not significantly associated with the Vote Leave share. We also include median hourly pay as well as the interquartile pay range as a measure of inequality, again both in terms of levels and their changes with and as the relevant years.
A higher median hourly pay in the year is not significantly related to the vote. We mostly do not find a significant relationship for the interquartile pay range, if anything a negative relationship in levels. Finally, we add the unemployment rate, the self-employment rate and the general labour participation rate in the year prior to the referendum. A larger unemployment rate is associated with a larger Vote Leave share, but the self-employment and participation rates have no predictive power for the Vote Leave share.
The strongest explanatory power lies with demography and education variables in Table 3. Figure 3 gives a visual overview of the goodness of fit across Tables 1—4 , while as a comparison the first bar represents the explanatory power of the regression underlying Column 2 in Table 5. Table 5. Predictors of Brexit vote: blocked variable selection approach.
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Column 1 shows the best subset across all four groups of variables analysed in Tables 1—4. Column 2 is the full specification based on the best subsets determined in Tables 1—4. For comparison, Columns 3—6 re-display the optimal specifications from Tables 1 to 4. Goodness of fit measured as R 2 in separate regressions explaining the Leave vote shares at the local authority area level using only regressors from the respective group of variables.
The analysis of variables by group mainly served the purpose of considering different aspects of the referendum result in more detail and to see how well different groups of variables perform relative to each other. This is what we turn to in Section 4. In Table 5 , we use the BSS procedure for variables across all groups.
Column 2 displays a full specification including all variables without performing another round of BSS that yields essentially the same R 2 , despite the fact that the model of Column 1 is a restricted version of the model in Column 2. As a comparison, Columns 3—6 re-display estimates using only the best subsets uncovered in each of the four variable groups from the previous tables.
We stress that as in previous tables, Table 5 just reports conditional correlations with no causal identification. We need to point out one caveat when it comes to the interpretation of Column 2 of Table 5.
While the point estimates, coefficient signs and statistical significance of variables within variable groups are internally consistent when we add successive regressors using the same procedure underlying Tables 1—4 , some coefficient signs and statistical significance patterns are different in the combined model of Column 2 compared with Columns 3—6. This is not surprising per se. The differences are attributable to the tight correlation between regressors across variable groups.
For example, in Column 2 the coefficient on total fiscal cuts is negative in contrast to the positive coefficient in Column 4. In particular, the demographic variables are tightly correlated with other key variables of interest. Hence, it is not surprising that when we remove the qualification measures from the analysis, the coefficient patterns across fiscal cuts and EU accession migration growth remain stable see Table A2 in the Online Appendix in contrast to Table 5.
For completeness and as a robustness check, we also perform a BSS exercise focusing on variables in levels see Table A3 in the Online Appendix and variables in changes see Table A4 in the Online Appendix. In our baseline Table 5 we have two sets of regressors. First, we have a common core of variables that are in levels only. Second, we have a set of variables for which both changes as well as baseline levels are available mostly qualification variables and employment shares.
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