It was all so different in the spring, when the
Conservatives, Labour, the Lib Dems and the Brexit Party were all capturing a
similar share of the vote, around 20% (chart). Whatever else you might say
about Boris Johnson, he has the ability to tap into what a lot of people want
to hear, and by promising to “get Brexit done” he has consigned Nigel Farage to
the political sidelines by taking votes away from the limited company masquerading as a political party.
Jeremy Corbyn is unable to generate any form of feelgood factor. As I have
long suspected he will not be in any position to repeat his decent performance
in 2017 because he has dithered on Brexit, and in the eyes of many voters he is
simply untrustworthy.
Meanwhile, the Liberal Democrats’ new leader Jo Swinson does not come across well with voters and it increasingly looks as though her party’s commitment to revoke Article
50 was a major tactical blunder.
Whilst the opinion polls can be wrong, it certainly looks as
though the opposition parties will have their work cut out to limit the extent
of the Tories’ majority. Since the headline polls have proven to be a poor
guide to electoral outcomes in the recent past, the commentariat paid a lot of attention to the release this week of
YouGov’s Multilevel Regression and Post-stratification (MRP) model results.
This model, which correctly called a hung parliament in 2017 when predictions
based on aggregate survey results indicated a large Conservative majority,
suggests that on the basis of current data the Tories could win a 68 seat
majority on 12 December. I briefly touched on MRP models in the wake of the
2017 election (here)
but it is worth reminding ourselves of what political commentators – who would
not normally care about regression models – are getting excited about.
The MRP model proceeds in two steps. First, YouGov builds a
detailed description of UK local populations to determine the characteristics
of each parliamentary constituency. The modellers then use YouGov survey data
to determine how voting intentions are associated with individual population
characteristics (e.g. how likely a person is to vote Conservative or Labour
based on their education levels or their age). Combining these two pieces of
information, using survey data from the preceding seven days, allows pollsters
to predict voting intentions at the constituency level. It all sounds very
scientific but a few points are worth noting. For one thing, a track record
based on one set of observations is not very useful. As YouGov themselves note,
“despite the strong performance of the
method in the 2017 election, it is not magic and there are important
limitations to keep in mind.” Second, it does not offer a prediction for
what will happen at the election since data may change in the interim. Third,
the model is only as reliable as the data input and we can never be sure
whether respondents are telling the truth about their voting intentions. Finally,
the sample sizes used in each constituency are very small and thus subject to
significant sampling error.
Moreover, despite all the work which has gone into
constructing the model, it does not generate significantly different results
from a simple method which applies a uniform swing to each constituency. Whilst
it would be nice to have access to all the data in order to generate an MRP
model of my own, it is impossible for individuals to recreate a sample of
100,000 interviews in a short space of time. This got me thinking about whether
there are other ways to generate constituency models and I report the results
here, albeit subject to huge caveats.
Using Logit models to
predict the election outcome
The starting point is to try and find readily available
information on a constituency basis that might help us. I start from the
premise that MPs who have strong local support and who recorded a solid
majority last time out are more likely to be re-elected. Even if the MP is no longer
standing for re-election, I assume they enjoy the benefit bequeathed by the
previous incumbent. This is proxied by the size of the sitting MP’s majority
relative to the total number of votes cast (or alternatively, the share of the
vote achieved by the winning candidate). Since Brexit is such an important
factor in this election we can also assess whether the constituency’s pro- or
anti-Brexit bias is important in determining the outcome (see here for the results collated by Chris Hanretty). A final variable is the regional
polling data which, although not available at the local constituency level, is
assumed to be representative for each constituency in the region (e.g there are
73 constituencies in London and I assume that support for each party is broadly
the same as the London average).
My model is designed to predict whether the incumbent party
retains the seat at the 2019 election. To do this, I ran a series of
qualitative choice models (technically akin to a Logit model with fixed effects) for each of the five main parties (Tories, Labour, Lib
Dems, SNP and Plaid Cymru) across all constituencies in GB (Northern Ireland
was excluded). Comparing the results for the five models across constituencies,
I looked for the party with the highest probability of winning the seat. The
central case forecasts gave the Conservatives between 333 and 351 seats
(corresponding Labour figures: between 243 and 220). The SNP took anywhere
between 37 and 44 seats in Scotland (though I reckon it could go as high as 50)
whilst Plaid Cymru took between 3 and 5 Welsh seats. The model struggled to
give the Lib Dems many seats at all. Even making some manual adjustments, it is
difficult to see the Lib Dems picking up more than 15 seats.
How do the results compare with YouGov? The answer is pretty
well. Their central scenario gives the Tories 359 seats; Labour 211; Lib Dems
13; the SNP 43 and Plaid Cymru 4. For a lot less effort (basically, some
playing around with the data in a spreadsheet and a few lines of code in
EViews) I can broadly replicate the results. Crucially, the evidence from both
models suggests that the Tories can win an outright majority in the December
election. As noted above, this is not a done deal by any means – there is a
large margin of error associated with any such model. Electoral Calculus,
which runs a similar model to YouGov, also looks for the Tories to win 331
seats (Labour 235) but with a range between 252 and 429 (Labour 141 to 304). You
might think that is a sufficiently wide margin as to be meaningless, but they
do ascribe a 63% probability to the chance of a Tory majority.
Reality does, of course, make fools of us all. But I am
satisfied that my low budget modelling exercise replicates the work of the
highly-paid pollsters. I can thus either get it right at a much lower cost – or can
save someone a lot of money by getting it wrong for a lot less
