On the Shift in UK Immigration Sentiment (preliminary observations)
Inspired by Rob Ford’s Medium blog on this (back in January) and contemporary twitter discussions I’ve been poking at the most recent British Election Study data (notes on the dataset/how to read variables referenced in graphs at the bottom).
This post is an attempt to put down some preliminary observations in a semi-coherent lump rather than spraying them randomly over twitter threads. I say preliminary observations because none of this fully digested and the graphs are all exploratory constructs (rather than the things you build *after* you’ve decided what the narrative is you are trying to convey).
Is there such a thing as “UK Immigration Sentiment”? It’s perfectly conceivable that the variation in immigration sentiment variables could break into two dimensions (same way variation in political sentiments does) or more.
Turns out, that doesn’t happen — there is just one significant(!) axis of “UK Immigration Sentiment”. Below is the output from a specific version of Principal Components Analysis (Factor Analysis) — it’s the laziest/dumbest form of data analysis there is (bar staring at raw correlations — see later)[1].
If a person thinks immigration is *bad for the economy*, they probably also think *it’s bad for culture* and *places a burden on the welfare state* and *think Brexit negotiations should prioritise controlling immigration over preserving the Single Market*.
For the rest of the article, I’ll stick with individual variables (because doing this on a per-wave basis would be a hassle — problem for Part II) — but just bear in mind that the immigration-sentiment specific variables do appear to be mostly all sampling the same thing — hence a somewhat cavalier attitude in which ones I do/don’t include in any graph.
Has it changed over time? Yes, quite significantly. You can see significant shifts before/after UKIP’s 2014 vote (around their EU election victory; later W3->W4) and during/after the 2016 EU referendum (later W8->W9). You can also see what *might* be a falling back in the last wave.
Can we really just take these shifts in the mean at face value? Being paranoid, I assumed not and built a huge series of extremely over-engineered Sankey diagrams to visualise the flow of people who were in every wave for a specific variable, coloured by their initial answer.
Take home point — yes, you more or less can take the means at face value — the shifts in the means don’t rely on people of just one opinion shifting — a similarish (eyeball precision!) chunk of people in every group shifted whenever there was a shift.[2]
Okay — but *why* did those people change their opinions after the 2016 EU referendum result? Good question. I thought I’d keep things simple by looking at *just the people with the most immigration-negative position during the 2016 EU referendum campaign* and create a variable called ChangedMind (=1 if their answer changed after the 2016 EU referendum result, =0 if it stayed the same).
I then looked at every other variable in the British Election Study dataset[3] and then looked at the top (20 — not 30 as table states!) each most negatively and positively correlating variables. I did this for each of the immigration variables with decent coverage for wave 8 and 9/10 (1000+ in each category of ChangedMind/didn’t ChangeMind).
The upshot was … well, more interesting for what it *didn’t* show. One of the first proposed explanations of the thawing of anti-immigration sentiment is that people changed their mind *because they believed immigration would be more controlled/levels would be lower*. Actually, immigration-most-negative people were *less likely* to change their mind the *more completely* they believed post-Brexit immigration would be controlled.
The overall pattern is that the positive correlates are almost entirely “being more positive about immigration”, negative correlates are almost entirely “being more authoritarian/anti-immigration/pro-Brexit”.
When I say “authoritarian” I don’t just mean immigration/Brexit related variables — or standard authoritarian questions like stance on death penalty — I mean questions like whether you think “Attempts to give equal opportunities to ethnic minorities” have “Gone much too far”.
Conclusion: At this coarse level of “analysis”, what distinguishes people who changed their mind from those who didn’t … is that they were just a bit less generally socially conservative/just a bit less committed to their anti-immigration sentiments.
That doesn’t answer the question of *why* they shifted — but it does underline that it’s hard to separate possible causal factors from the Hard/Softcore Anti-Immigrant position (hardcore position: the level of immigration is getting a lot higher, but Brexit will give us complete control, softcore position: level of immigration is getting a bit higher/staying the same, but Brexit will give us a lot of/some control)
Notes on dataset/how to read the variables:
The British Election Study dataset I’m looking at is high quality, freely available, online panels ~30,000 people polled per wave with reasonable levels of retention between waves. Each of the thirteen waves (W1…W13) was carried out at a specific time over the period Feb 2014 — June 2017 (not evenly spread — they focus on … elections — and not every question is asked in each wave).
The variables I refer to relate to questions in this questionnaire. Where variables are ordered (“How do you feel about immigration — would you like it: lower/same/higher”) I’ve added the topmost (non “Don’t Know”) answer category after the variable with two underscores (“feelImmigration__higher”). Where the variables are not ordered (“What’s your favour animal? Cat/Dog/Pigeon”) I just create 0/1 variables (“favouriteAnimal_Cat, favouriteAnimal_Dog, favouriteAnimal_Pigeon”). That makes analysis easier and in *most* cases makes variables human readable without having to look up the full question wording (but you can in that questionnaire links above).
Footnotes:
[1] There’s a lot of interesting structure — but it’s all second-order party-political stuff, next three components are basically BlameLabour Yes/No, BlameConservatives Yes/No, UKIP Yay/Boo
[2] Ipsos Mori saw the same thing in their own dataset, but find people who are pro-immigration more stable — not clear if that’s a confound with nowhere to go from “most favourable” when there’s a shift in favour of immigration, but I would hazard they accounted for that: https://www.ipsos.com/ipsos-mori/en-uk/shifting-ground-attitudes-towards-immigration-and-brexit
[3] Some winnowing on coverage, dropping/mean-replacing DKs, linear-ish variables turned into numbers, categorical variables dummied). I left directly related variables in there as a sanity check (/reasonable baseline /laziness).