Interesting election results in the UK over the weekend, where the Conservatives romped to victory. This was despite a widespread consensus that neither the Conservative or Labour party would get a majority. This was a triumph for uncertainty and random error over the deterministic, as none of the statistical forecasts appeared to deem such a decisive victory probable. The UK election is a lot harder to model, for numerous reasons, when compared to the US.

This means that a lot of pollsters and political forecasters will have to go back to the drawing board and re-evaluate their methods. Obviously, the models used to forecast the 2015 election could not handle the dynamics of the British electorate. However, there is a high degree of persistence within electuary constituencies. Let’s explore this persistence by looking at the relationship between coal and % Conservative (Tory) votes.

Following a tweet by Vaughan Roderick and using the methodology of Fernihough and O’Rourke (2014), I matched each of the constituencies to Britain’s coalfields creating a “proximity to coal” measure. What the plot below shows is striking. Being located on or in close proximity to a coal field reduces the tory vote share by about 20%. When we control (linearly) for latitude and longitude coordinates, this association decreases in strength, but not by much. For me, this plot highlights a long-standing relationship between Britain’s industrial revolution, the urban working class, and labour/union movement. What I find interesting is that this relationship has persisted despite de-industrialization and the movement away from large-scale manufacturing industry.

> summary(lm(tory~coal,city)) Call: lm(formula = tory ~ coal, data = city) Residuals: Min 1Q Median 3Q Max -42.507 -10.494 2.242 10.781 29.074 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 42.9492 0.7459 57.58 <2e-16 *** coal -24.9704 1.8887 -13.22 <2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 14.36 on 630 degrees of freedom Multiple R-squared: 0.2172, Adjusted R-squared: 0.216 F-statistic: 174.8 on 1 and 630 DF, p-value: < 2.2e-16 # robust to lat-long? > summary(lm(tory~coal+longitude+latitude,city)) Call: lm(formula = tory ~ coal + longitude + latitude, data = city) Residuals: Min 1Q Median 3Q Max -44.495 -8.269 1.485 9.316 28.911 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 246.4355 18.9430 13.009 < 2e-16 *** coal -15.1616 1.8697 -8.109 2.68e-15 *** longitude 1.4023 0.4015 3.493 0.000512 *** latitude -3.8621 0.3651 -10.578 < 2e-16 *** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 12.76 on 628 degrees of freedom Multiple R-squared: 0.3838, Adjusted R-squared: 0.3809 F-statistic: 130.4 on 3 and 628 DF, p-value: < 2.2e-16