Along with a great deal of shock and dismay, one thing that struck me about the results of the EU referendum was how well the Remain vote held up in Welsh speaking Wales. In England, the remain vote was concentrated in big cities and prosperous regions – Remain voting Gwynedd and Ceredigion don’t really fit that pattern. We all saw those graphics showing demographic factors that predicted voting in the referendum, can we add Welsh-speaking to the list?
Firstly, I got the Leave vote share for each Authority region and census figures on percentage of Welsh speakers and ran a correlation.The two variables were highly negatively correlated: -.40 (Spearman’s correlation). So yes, at first glance the data seem to bear this out.
It seems a bit premature to call it for Yr Iaith Gymraeg yet though, economic disadvantage appears to be a predictor of voting leave too and while Y Fro Gymraeg is not a prosperous area, there are probably fewer pockets of really marked poverty than other areas of Wales. The figure below shows percentage of Welsh speakers on the X-axis and Leave vote share on the Y-axis. The redder the name of a region, the more areas it has in the most deprived 10% of areas according to the Welsh Index of Multiple Deprivation.
As you can see, the regions with high numbers of Welsh speakers tend towards lower Leave votes, but also tend not to have lots of areas in the most deprived 10% of regions.
To try and get at this in a more statistical way, I fitted a regression model to the data predicting Leave vote share using number of areas in the lowest 10% WIMD and saw whether adding in the percentage of Welsh-speakers as a second predictor improved the model – it did. When I did the opposite, seeing if a model that used percentage of Welsh-speakers could be improved by adding in the deprivation data, this didn’t improve the model.
Maybe using the most deprived 10% of area is the wrong measure – it could be that pockets of severe deprivation are less important for explaining voting behaviour than a more diffuse pattern of poverty. I reran the analysis, this time using the proportion of areas that fell into the bottom 50% of areas in the WIMD. This was indeed a better predictor of Leave vote share – .49 as opposed to .35. When I reran the regression models, the model that included both the percentage of Welsh speakers and percentage of areas in the most deprived 50% performed best. Both deprivation and percentage of Welsh speakers are helpful in explaining vote share. Here’s a graph of how vote share (Y-axis) was related to the proportion of areas in the bottom 50% according to the WIMD (X-axis), this time with the percentage of Welsh speakers reflected by how green the name is. Note that many of the more Welsh-speaking regions are below the line of best fit, suggesting lower levels of support for leave than you would expect based on deprivation data alone.
There remain a few plausible alternative explanations – both Gwynedd and Ceredigion have university towns (although so do Wrexham and Swansea who voted out) and West Wales gets lots of EU funding (although so do the Valleys who voted out). But yes, to conclude, it seems plausible that having a high number of Welsh speakers was associated with lower levels of Leave support, and this wasn’t purely to do with the underlying pattern of poverty in Wales.
P.S. Sorry for the long wait between posts by the way, we had a baby recently!