Will turnout weighting prove to be the pollsters’ Achilles heel in #GE2017?


By Patrick Sturgis and Will Jennings, University of Southampton.

The 2017 election campaign has confounded expectations in many ways, none more so than Labour’s continuing surge in the opinion polls. From an average vote share of around 26% at the start of the campaign, they now stand at an average of 36% in the polls conducted over the past week. It is fair to say that few, if any, commentators expected Labour’s support to be at this level as we head into the final week of the campaign.

One of the theories advanced to explain Labour’s unexpectedly lofty position is that the opinion polls are, once again, wrong; their historical tendency to over-state Labour support has not been adequately addressed by the pollsters since the debacle of 2015. Key to this line of thinking is that Labour’s support appears to be ‘soft’, in the sense that those who say they will vote Labour in the polls are more likely to also report that they may change their mind before election day, compared to Conservative ‘intenders’. Labour’s core support is also concentrated within demographic groups that are, historically at least, less likely to cast a ballot, particularly younger voters.

Patterns of turnout across demographic groups will, of course, be key to determining the outcome of the election. But might turnout – and how pollsters deal with it – also be the cause of another polling miss on June the 8th?

Who will turnout and who won’t?

Adjusting for turnout is one of the most difficult tasks a pollster must confront. Polls work by collecting samples of individuals and weighting them to match the general public on characteristics such as age, gender, region, and education for which the population distribution is known. But around a third of  any representative sample of eligible voters will not vote, so an additional adjustment has to be made to filter out likely non-voters from the vote share estimate. The problem here is that there is no entirely satisfactory way of doing this.

The most obvious approach to determining whether poll respondents will vote or not is to ask them. This is indeed the way that the vast majority of polls in the UK have approached turnout weighting in previous elections. In order to allow respondents to express some level of uncertainty, pollsters usually ask them to rate their probability of voting on a 1 to 10 scale (where 1 = certain not to vote and 10 = certain to vote). The problem with this approach is that, for a variety of reasons, people are not very good at estimating their probability of voting. So turnout weights based on self-report questions tend to have high error rates, mainly of the ‘false-positive’ variety. Some pollsters use additional questions on turnout at previous elections to produce a turnout probability but these also suffer from problems of recall and socially desirable responding.

A second approach is to use historical survey data containing a measure of actual turnout (either self-reported after the election or via validation of actual votes using the electoral register). Such data is used to build a statistical model which predicts turnout on the basis of demographic characteristics of respondents. This ‘historical’ model can then be applied to current polling data in order to produce turnout probabilities based on actual turnout patterns from the previous election. While this gets round the problems with faulty reporting by respondents, with this approach we must believe that patterns of turnout haven’t changed very much since the previous election, an assumption which cannot be tested at the time the estimates are required. And, as the EU referendum showed, sharp changes in patterns of turnout from one election to another can and do arise.

In sum, turnout weighting is an essential component of accurate polling but there is no failsafe way of doing it.

The inquiry into the 2015 election polling concluded that, although the turnout probabilities used by the pollsters in that election were not very accurate, there was little evidence to suggest these were the cause of the polling errors. Might inaccuracies in the turnout weights be more consequential in 2017?

Effect of turnout weighting on vote intention estimates

We can get some handle on this by comparing the poll estimates of the Conservative-Labour margin before and after turnout weights have been applied. The table below shows estimated Conservative and Labour vote shares before and after turnout weighting for eleven recently published polls. It is clear that the turnout weights have a substantial effect on the size of the Conservative lead. Without the turnout weight (but including demographic and past-vote weights), the average Conservative lead over Labour is 5 percentage points. This doubles to 10 points after turnout weights have been applied.


Vote estimates with turnout weight Vote estimates without turnout weight
Pollster Fieldwork End Date CON LAB CON CON LAB CON
(%) (%) lead (%) (%) lead
ORB/Sunday Telegraph 4th June 46 37 9 44 38 6
IpsosMORI/Standard 1st June 45 40 5 40 43 -3
Panelbase 1st June 44 36 8 40 39 1
YouGov/Times 31st May 42 39 3 41 39 2
Kantar 30th May 43 33 10 40 34 6
ICM/Guardian 29th May 45 33 12 41 38 3
Survation (phone) 27th May 43 37 6 43 37 6
ComRes/Independent 26th May 46 34 12 43 38 5
Opinium 24th May 45 35 10 42 36 6
Survation (internet) 20th May 46 34 12 43 33 10
GfK 14th May 48 28 20 45 29 16
Mean  = 10   Mean  = 5
      S.D.  = 4.5  S.D. = 4.9


Particularly notable are the Ipsos-MORI estimates, which change a 3-point Labour lead into a 5-point lead for the Conservatives. Similarly, ICM’s turnout adjustment turns a 3-point Conservative lead into a 12-point one. It is also evident that pollsters using some form of demographic modeling to produce turnout probabilities tend to have somewhat higher estimates of the Conservative lead. For this group (Kantar, ICM, ORB, Opinium, ComRes), the turnout weight increases the Conservative lead by an average 5.4 points compared to 3.7 points for those relying on self-report questions only.

It is also worth noting that the standard deviation of the Conservative lead is actually slightly lower with the turnout weights (4.5) than without (4.9). So, the turnout weighting would not appear to be the main cause of the volatility between the polls that has been evident in this campaign.

This pattern represents a substantial change in the effect of the turnout weights compared to polls during the 2015 campaign, where the increase in the Conservative lead due to turnout weighting was less than one percentage point (for the nine penultimate published polls conducted by members of the British Polling Council).

Why is turnout weighting having a bigger effect now than it did in 2015? One reason is that many pollsters are applying more aggressive procedures than they did in 2015, with the aim of producing implied turnout in their samples that is closer to what it will actually be on election day. While there is a logic to this approach it seems, in effect, to rely on getting the turnout probabilities wrong in order to correct for over-representation of likely voters in the weighted samples.

A second reason turnout weighting matters more in this election is that the age gap in party support has increased since 2015, with younger voters even more likely to support Labour and older voters to support the Conservatives.  Thus, any adjustment that down-weights younger voters will have a bigger effect on the Conservative lead now than it did in 2015.

Corbyn-mania among younger voters?

Another idea that has been advanced in some quarters is that young voters are over-stating their likelihood to vote in this election even more than they did in 2015. Come election day, these younger voters will end up voting at their recent historical levels and Labour will underperform their polling as a result.

We can obtain some leverage on this by comparing the distributions of self-reported likelihood to vote for young voters, aged 18-24, in 2015 and 2017 (the 2017 figures are from the polls in the table above, the 2015 estimates are taken from the penultimate published polls in the campaign). We also present these estimates for the oldest age category (65+). There is no evidence here that younger voters are especially enthused in 2017 compared to 2015. And, while the implied level of turnout is substantially too high for both age groups, the 20 point gap between them is broadly reflective of actual turnout in recent elections.


The inquiry into the 2015 polling miss found that representative sampling was the primary cause of the under-statement of the Conservative lead. The fact that implied turnout is still so high in the current polls suggests that the representativeness of samples remains a problem in 2017, on this measure at least. Turnout weighting is having a much bigger effect on poll estimates now than it did in 2015. This may be because the pollsters have improved their methods of dealing with the tricky problem of turnout weighting. However, it also suggests that getting turnout weighting right in 2017 is likely to be both more difficult and more consequential than it was in 2015.

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