Speaker 1: Welcome back to Beyond the Polls.
Speaker 2: This week, I explore the world of modern polling and
Speaker 2: it's international dimensions with British pollster James Kennegasaurium.
Speaker 3: Let's dive in.
Speaker 2: Well, what a night that was. It was in one way,
Speaker 2: it is the passing of an era in Texas. And
Speaker 2: I don't just mean the end of John Cornan's long
Speaker 2: political career and tenure in the Senate. I mean the
Speaker 2: passing of an era in the Republican Party. We've been
Speaker 2: going through it for a while, We've been seeing it
Speaker 2: ever since Donald Trump's storming through the Republican primaries in
Speaker 2: twenty sixteen. But the utter stomping of John Cornan in
Speaker 2: Texas last night, I think in retrospect we'll be looked
Speaker 2: at as perhaps the moment when what's been clear for
Speaker 2: a long time has become crystal clear to all who
Speaker 2: will be willing to see it, and that is that
Speaker 2: even a competent, non scandal free, generally conservative but not
Speaker 2: maga conservative, meaning that he's got a disposition or she's
Speaker 2: got a disposition that is more in tune with the
Speaker 2: pre Trump Party than the current Trump Party simply cannot
Speaker 2: win a contested election. If there is a strong MAGA candidate,
Speaker 2: regardless of the personal weaknesses of that candidate, there's no
Speaker 2: other way to explain what happened. Ken Paxton has clearly
Speaker 2: been somebody who has been followed by accusations of scandal
Speaker 2: financial His wife is going through a divorce with him
Speaker 2: and has alleged says that she's doing the divorce on
Speaker 2: biblical grounds, which everyone interprets as significant adultery. And yet
Speaker 2: this guy plowed through an incumbent senator by nearly thirty points.
Speaker 2: He won every county but two. John Cornyan won Kennedy County,
Speaker 2: which cast only eight votes, and he won the state
Speaker 2: capitol Travis County, which includes Austin, by a narrow margin.
Speaker 2: Every other two hundred and fifty two counties voted for
Speaker 2: Ken Paxson. And you take a look at that, and
Speaker 2: you add it up with what happened in Louisiana, where
Speaker 2: you had a similar breakdown, which is that incumbent Senator
Speaker 2: Bill Cassidy only carried some upper income areas and the
Speaker 2: county where he got his start, and you take a
Speaker 2: look at it with other races over the last couple
Speaker 2: of years, or anyone who has the whiff of establishment
Speaker 2: about them. And I mean that not just in terms
Speaker 2: of what they say they'll do, but the way in
Speaker 2: which they carry themselves. The idea that they may say
Speaker 2: they're a fighter, but they don't have the demeanor of
Speaker 2: an MMF fighter. That person loses, and it's not usually
Speaker 2: not close, it's just not close. I think we will
Speaker 2: look back on last night as the end of the
Speaker 2: consolidation of Trumpism as the defining ethos of the modern
Speaker 2: Republican primary electorate. What that means going forward is in
Speaker 2: part dependent on what type of trump Ism emerges, and
Speaker 2: it's not part of this rant to go into that
Speaker 2: in any detail, because I'm not sure what I think
Speaker 2: about it yet. But I think it's pretty clear that
Speaker 2: if you want to have a future in today's Republican Party,
Speaker 2: you have to be some variation of Mega and that
Speaker 2: can be a religiously tinged variation, that can be an
Speaker 2: unhinged variation, that can be a nationalist variation. There's lots
Speaker 2: of variations, and certainly we see examples of all different
Speaker 2: types of people trying to latch onto Trump and the
Speaker 2: philosophical phenomena that he has unleashed, and you can see
Speaker 2: people taking different aspects, kind of like that old saying,
Speaker 2: you know the blind man. There's a bunch of blind
Speaker 2: men go around an elephant, and somebody feels a tale
Speaker 2: and says, oh, it's a snake, and somebody feels the
Speaker 2: foot and says, oh, it's this, when in fact it's
Speaker 2: all parts of a hole. Most people are still only
Speaker 2: doing parts of this. I think the Vice President and
Speaker 2: the Secretary of State are two people who are doing
Speaker 2: holes of it, which is one reason why they are
Speaker 2: there and one reason why they are viewed as potential
Speaker 2: national candidates. But how the old Guard reacts to this
Speaker 2: will be very telling. The old Guard here in Washington,
Speaker 2: d C. Continues to be in denial. They continue to
Speaker 2: as I like to say, I think that Trump is
Speaker 2: the dream season of Dallas. Where after Bobby leaves the show,
Speaker 2: and I'm dating myself with this, we go through a
Speaker 2: year where the Ewing family doesn't have Bobby. In the
Speaker 2: next morning, Bobby's back in the shower and they explain
Speaker 2: away and that's because the actor couldn't find the jobs
Speaker 2: that he wanted, so he came back to the hit
Speaker 2: show and it was all explained as his wife's dream.
Speaker 2: No the last decade has not been the dream season
Speaker 2: of the Republican Party. The old guard is not going
Speaker 2: to come back. This is a new party, and I
Speaker 2: think March, not March, May twenty six, two thousand and
Speaker 2: twenty six, is going to be the day when the
Speaker 2: old Republican Party is finally decisively buried as a dominant
Speaker 2: force within it and becomes has to deal with the
Speaker 2: fact that is now a significant minority force within that
Speaker 2: party coalition. The other thing I wanted to talk about
Speaker 2: is not looking back at the past, but looking at
Speaker 2: the future, and that is next week we have six
Speaker 2: count of six primaries, and I want to quickly go
Speaker 2: over the states and which races I'll be looking at
Speaker 2: in them. We have California, Iowa, New Jersey, Montana, South Dakota,
Speaker 2: and New Mexico. I don't really think there's a I'm
Speaker 2: not looking at any primaries in New Mexico, but I'm
Speaker 2: looking at ones in each of the other five states.
Speaker 2: The first few I want to talk about her in California,
Speaker 2: and California's unique system means you're looking for something different here,
Speaker 2: which is to say, it's an all party primary and
Speaker 2: the top two regardless of party go on. So part
Speaker 2: of what you're seeing is a gamesmanship between either two
Speaker 2: people in one party to see who will face get
Speaker 2: through the top two to face the person in the
Speaker 2: other party, or attempts to game the system so that
Speaker 2: you get the final to match up that you want
Speaker 2: with respect to finding somebody who is weaker or somebody
Speaker 2: who you fear more.
Speaker 1: In that race.
Speaker 2: California governor, it's pretty clear that the Republican Steve Hilton
Speaker 2: will go through. It looks like the other Republican, Chad Bianco,
Speaker 2: who had a shot to make one two, he's consistently
Speaker 2: pulling in fourth place. Now, I wouldn't put it past
Speaker 2: the possibility that there'll be a surprise eyes and the
Speaker 2: Democrats worst fear of two Republicans getting through in one
Speaker 2: of the most democratic states in the country will occur.
Speaker 2: But it's increasingly looking like the battle is going to
Speaker 2: between progressive billionaire Tom Steyer and a former h former
Speaker 2: cabinet secretary for Biden Chavy or Bishera. Stire, of course,
Speaker 2: is much likelier to be drawing from the upper income,
Speaker 2: progressive white community. Bishera likely to draw from the lower
Speaker 2: to middle income Hispanic community, which means we should expect
Speaker 2: Spire to do much better in the Bay Area and
Speaker 2: Bishera to do much better in his native Los Angeles.
Speaker 2: Central Valley has elements of both, depending on which county here.
Speaker 2: And that's the race that I'm looking at for governor
Speaker 2: who finishes second to Steve Hilton, because the odds are
Speaker 2: that that person becomes the next governor given how democratic
Speaker 2: the state is, then I'm taking a look at a
Speaker 2: few other things. California one is going to be a
Speaker 2: Democratic seat, but there's a strong Republican running. There's a
Speaker 2: lot of Republicans in the race, and it's highly likely
Speaker 2: that he will one of the top two who will
Speaker 2: be the Democrat who faces them. And here we have
Speaker 2: an ideological contest between I think it's Aubrey Denny and
Speaker 2: State Senator McGuire. Denny is a DSA Our Revolution endorsed candidate.
Speaker 2: McGuire is the establishment straight through. This includes some establishment
Speaker 2: friendly areas. Of course, as a state senator, McGuire represents
Speaker 2: a large part of this district. State senate districts in
Speaker 2: California are larger. They're in congressional district. But we know
Speaker 2: that ideological candidates can break through, and there are strong
Speaker 2: ideological components of the district, you know, for example around
Speaker 2: Mendocino or in Humboldt County County, around Humboldt State. It'll
Speaker 2: be very interesting to see from the Democratic partisan standpoint,
Speaker 2: who wins the ideologue or the lawmaker. In California seven,
Speaker 2: we have long time incumbent Doris Matsui who's being challenged
Speaker 2: from the left by a woman named Vang and a Republican.
Speaker 2: Now this is a seat where it's not entirely pop
Speaker 2: sure that a Republican is going to get through. So
Speaker 2: what Matt Suey wants to do and is try and
Speaker 2: boost the Republican a little bit so that Vang finishes
Speaker 2: third and then it's her versus a Republican. Of course,
Speaker 2: what Vang wants to do is make sure she finishes second,
Speaker 2: so then the Republicans don't have a choice and perhaps
Speaker 2: she can just take out the incumbent and a Democrat
Speaker 2: On Democrat five, I'll be very interested to see the
Speaker 2: strength of a generation change left wing challenge to a
Speaker 2: long term establishment Democrat. California eleven is a Speaker Pelosi's
Speaker 2: old seat, and this is again a seat where no
Speaker 2: Republican is going to make the runoff here. I think
Speaker 2: Donald Trump got under ten percent of the vote or
Speaker 2: just around ten percent of the vote here. So this
Speaker 2: is which two Democrats are going to face one another.
Speaker 2: State Senator Scott Wiener is a person who leads in
Speaker 2: the polls, and then you have the question is is
Speaker 2: it going to be a more establishment friendly person person
Speaker 2: polosees endorsed, or is it going to be AOC's former
Speaker 2: chief of staff chach Rabarti. That's the race I'm going
Speaker 2: to see, because again, it'll be very interested to see
Speaker 2: when you have a clear person who is going to
Speaker 2: make it through. Is the progressive ideologue strength in San
Speaker 2: Francisco sufficiently strong enough and sufficiently coalesced around Chakra Party
Speaker 2: that he can get twenty five to thirty percent, because
Speaker 2: that's probably what he needs to guarantee or have a
Speaker 2: good shot at getting through. And it's the polls that
Speaker 2: do exist, which are very few, suggests it's a toss
Speaker 2: up there. California twenty two is two for Democrats to
Speaker 2: see who's going to take on one of the most
Speaker 2: vulnerable Republicans in the House. David Valdeo has won the
Speaker 2: various versions of the seat despite heavy odds for many years.
Speaker 2: There's a lot of popularity. And here we have another
Speaker 2: establishment versus progressive. Viegis who is endorsed by AOC and
Speaker 2: Bernie and our Revolution versus Baines. Be very interested in
Speaker 2: this highly Hispanic district, and Baines is a state assembly person.
Speaker 2: It'll be very interested in this district to see who
Speaker 2: gets through against Valaday. Obviously Republicans would probably prefer the
Speaker 2: more extreme person. But it's again yet another test in
Speaker 2: a very progressive state as to the strength that the
Speaker 2: progressive left within the Democratic Party primary. It is reversed
Speaker 2: in California forty. This is a vote sinc for Republicans,
Speaker 2: and we have two Republican incumbents, Ken Calvert and Young
Speaker 2: Kim who are facing off to see who will have
Speaker 2: the right to get the Democrat because there is enough
Speaker 2: Democratic strength that a Democrat will probably get a third
Speaker 2: of the voter. So there and then the question is
Speaker 2: which a Republican is going to get through. They've spent
Speaker 2: millions of dollars attacking each other. Listeners of this podcast
Speaker 2: heard some of those ads a few weeks ago as
Speaker 2: they both were trying to compete for basically the same constituency.
Speaker 2: Kim represents more of this area has represented more of
Speaker 2: this area in the past. That's often a good sign
Speaker 2: as to who wins member on member fights. But we
Speaker 2: shall see. That's the other one I'm looking at. And
Speaker 2: then the last one I'm looking at is California four.
Speaker 2: This is a seat that Democrats are trying to pick up.
Speaker 2: Darryl Isa the Republican was drawn into the seat that
Speaker 2: leans Democrat but is not a safe Democratic seat in
Speaker 2: a normal year. There's a strong Republican candidate super San
Speaker 2: Diego Supervisor Jim Desmond, and there is a four way
Speaker 2: clown car race to take him on, and it's totally
Speaker 2: unclear who is going to do it, but one of
Speaker 2: the leading candidates is a person camp Nihar, who is
Speaker 2: a perennial candidate, has failed in races before, but has
Speaker 2: the advantage of being the boyfriend of nearby Congresswoman Sarah Jacobs,
Speaker 2: and she has the advantage of being the heiress of
Speaker 2: a billionaire fortune. We shall see whether or not the fortune,
Speaker 2: the connections, and so forth gets him the fifteen or
Speaker 2: twenty percent. He probably needs to face Desmond and hence
Speaker 2: become perhaps the favorite in this Democratic leaning seat. Now
Speaker 2: let's jump to Iowa. The only race I'm looking at
Speaker 2: there is who's going to be the Democratic senatorial nominee.
Speaker 2: And this is another one Walls versus Turek, where you
Speaker 2: have a little bit of a progressive versus establishment matchup,
Speaker 2: Walls being more progressive, Torek being a little bit more establishment.
Speaker 2: New Jersey seven, we have a four way Democratic race
Speaker 2: to space Representative Thomas Kane. This is another seat that
Speaker 2: Democrats have in their targets. It is one that, given
Speaker 2: the demographics and the closeness of it, is a prime
Speaker 2: likelihood to flip. And that is compounded by the fact
Speaker 2: that kin has been absent from Congress for the last
Speaker 2: couple of months for an unspecified and undisclosed health issue.
Speaker 2: I have no feel who is coming through. There's no
Speaker 2: obvious ideological break between the four candidates, all of whom
Speaker 2: have raised and spent over a million dollars. But there's
Speaker 2: no runoff in New Jersey and this is going to
Speaker 2: be an interesting race. Not so in New Jersey twelve.
Speaker 2: By not so, what I mean is there is a
Speaker 2: clear issue here, and that is whether or not Adam Homaway,
Speaker 2: a progressive squad endorsed Bernie Sanders endorse person is going
Speaker 2: to win the thirteen way Yes, thirteen way Democratic primary
Speaker 2: for a safe Democratic seat made open by the retirement
Speaker 2: of Bonnie Watson Coleman. Obviously, this is one particularly given
Speaker 2: the candidate's views on Israel, where the Jewish community in
Speaker 2: the Democratic Party would like to see anything but a
Speaker 2: Hamaway victory. I'll be very interested to see whether or
Speaker 2: not Hamaway can negotiate a thirteen way primary to get
Speaker 2: a plurality triumph. Then we go to Montana. Both parties
Speaker 2: have open multi candidate races to succeed retiring Representative Ryan
Speaker 2: Zinke in Montana one. This is one of those seats
Speaker 2: that should be won by a Republican, but it is
Speaker 2: the more democratic of the two seats. It includes mining
Speaker 2: communities and universities in the state capital places, so it
Speaker 2: has a much stronger Democratic base. And this is one
Speaker 2: that in a wave or quasi wave here which this
Speaker 2: may be shaping up to be, Democrats would be looking at.
Speaker 2: It'll be very interested to see what the matchup is there.
Speaker 2: And then finally, in South Dakota, we have a four
Speaker 2: way Republican race to succeed Christino. The appointment the lieutenant
Speaker 2: governor who stepped up is not dominating the race, but
Speaker 2: neither is state Representative Dusty Johnson, who is leaving his
Speaker 2: seat in order to run for governor. The winner needs
Speaker 2: thirty five percent in order to avoid a runoff. The
Speaker 2: most recent public poll has none of the candidates breaking that.
Speaker 2: This may very well be the first time that South
Speaker 2: Dakota's runoff provision is triggered in a statewide race. So
Speaker 2: those are twelve races that I'm looking at. But let's
Speaker 2: return to Texas, and let's return to the historic moment
Speaker 2: that befell us. When a high ranking Republican who has
Speaker 2: had a successful career, winning statewide, who has been set
Speaker 2: by no scandal, loses by nearly thirty points. That's not
Speaker 2: an accident, and that's not unimportant. This remarks a sea
Speaker 2: change in the Republican Party, and we will continue to
Speaker 2: see that sea change work its way through the party system,
Speaker 2: both the remainder of this year and going into twenty
Speaker 2: twenty eight. Well, in a podcast called Beyond the Polls,
Speaker 2: I do spend a lot of time on polling, because
Speaker 2: while we want to go beyond that, it's often the
Speaker 2: most accessible and sometimes the deepest information that is readily
Speaker 2: accessible to everybody.
Speaker 1: And tonight I have.
Speaker 2: One of the more interesting posters in the entire world here,
Speaker 2: and that is James Kannagasurium, the chief research officer of
Speaker 2: Britain's Focal Data. But he's not just a British poster.
Speaker 2: We'll get into that. James, welcome to be on the Polls.
Speaker 3: Thank you, Henry, delight to be here.
Speaker 2: Well, you know, one of the things that's attracted me
Speaker 2: to your work is the wide depth of questions, why
Speaker 2: breadth of questions that you do, the sort of deep stuff,
Speaker 2: and I want to get in that, But I want
Speaker 2: to start a little bit with what's it like to
Speaker 2: be upholster these days in the world where random samples
Speaker 2: are impossible to come by.
Speaker 3: Let's start easy, shall I?
Speaker 1: So oh yeah, well let's be on the pole.
Speaker 2: So we're going to get into the high level calculus
Speaker 2: behind those things soon. So prepare your regression analysis and
Speaker 2: Greek letter.
Speaker 3: If I take a kind of if I go up
Speaker 3: kind of a thousand feet in the air and I
Speaker 3: think about why, how has polling evolved? I think we've
Speaker 3: basically moved in a direction where the underlying samples that
Speaker 3: we obtain from people have become harder to obtain, which
Speaker 3: I think is the premise of your question exactly, And
Speaker 3: therefore what you do with the data we all have
Speaker 3: to work harder in order to get a result. So
Speaker 3: the modeling that is undertaken has become more intensive and
Speaker 3: more technical in order to basically compensate for difficulties around samples.
Speaker 3: But I'll take a couple of couple of steps back.
Speaker 3: So obviously polling as a US construct, it is, it
Speaker 3: is downstream of It is an American invention, very very
Speaker 3: clearly in terms of its history, whether that's from straw
Speaker 3: polls or the history of gallop and you know, even
Speaker 3: from the failure of readers digest and how that was taken,
Speaker 3: and the whole concept of what is a representative sample.
Speaker 3: We've obviously shifted as an industry away from you know,
Speaker 3: taking people's views from magazines and going onto landlines and
Speaker 3: sells cell phones and obtaining it that way. And then
Speaker 3: we had the advent obviously of internet polling in and
Speaker 3: around kind of the early kind of two thousands as
Speaker 3: it were, really where that kind of took off. But
Speaker 3: the theme that kind of unites everything. The common thread
Speaker 3: through it is that basically, as our attention and our
Speaker 3: time has contracted because of the number of distractions that
Speaker 3: exist now in the world, people's propensity and people's type
Speaker 3: to take poles has collapsed. Pham poles have moved.
Speaker 4: From a environment where maybe one in three adults would say,
Speaker 4: of course, I'll take a poll, I'll you know, I'll
Speaker 4: give you my views, to a world where it's far
Speaker 4: below zero point five percent.
Speaker 3: In terms of so one in every two hundred adults
Speaker 3: if we rang them, we're sent to taking a survey.
Speaker 2: And the word, of course, one of the things is
Speaker 2: that when it's down to one or two hundred, it's
Speaker 2: no longer a random assessment of who will take that.
Speaker 3: So I think that's right, and the hope I think
Speaker 3: with internet polling, which is the idea that people can
Speaker 3: sent to taking surveys for polsters on online platforms, online communities,
Speaker 3: was the idea that you could then in some way
Speaker 3: compensate for that. But the reality is that today to
Speaker 3: achieve a representative sample, and so what posters obviously mean
Speaker 3: by that is it is the group of people I'm
Speaker 3: looking at by state, by nation, by subregion, representative on
Speaker 3: things like age and gender, race, ethnicity, education, but also
Speaker 3: more complicated demographics, and then the interaction between all of
Speaker 3: those that becomes difficult. And I think what you're getting
Speaker 3: at is that even in the advent of international and
Speaker 3: internet polling from firms like the Yugov and Morning consult
Speaker 3: and all these other firms that now operate on an
Speaker 3: Internet basis, is that even then it's very challenging. And why,
Speaker 3: And it's not necessarily about the demographics, because the act
Speaker 3: of being willing to take a poll has embedded and
Speaker 3: nested within it behavioral and political and belief kind of
Speaker 3: differences basically to do with that correlate very sharply to
Speaker 3: things like social trusts and the idea that you trust
Speaker 3: the pollster, and that correlates to things like trusting government
Speaker 3: and trusting institutions. And that becomes a problem because those
Speaker 3: precise things are in fact then related to if you're
Speaker 3: a political polster, voting intention. So the very method by
Speaker 3: which you are seeking to reach people then becomes problematic
Speaker 3: because you're putting through a filter that is not demographic,
Speaker 3: but is non demographic, but fundamentally changes beyond just individual
Speaker 3: level markers of someone how they might vote. And I
Speaker 3: think that's why as a polster, particularly in the US context,
Speaker 3: we live in what's called a mixed method mode to
Speaker 3: your very geeky listeners, where polsters then will say, okay,
Speaker 3: I need to take multiple approaches. I might need to
Speaker 3: find sample from internet panels, I might need to use
Speaker 3: cell phones. I might need to use what's called texter Web,
Speaker 3: so you know, you'll send a survey on your phone
Speaker 3: and then it follows a link and it gets more
Speaker 3: and more complicated, and the art and because it is
Speaker 3: a bit of both drawing together representative samples on a
Speaker 3: demographic basis, but also non demographic features, so making sure
Speaker 3: that people aren't too interested in politics? Who are booking
Speaker 3: your survey? Right? Who think about? Who are the types
Speaker 3: of people to respond to the invitation? It's usually it's
Speaker 3: usually the opinionated, it's usually the very interested in politics.
Speaker 3: It's normally the higher trust. And I think, particularly in
Speaker 3: a US context, this is really the architecture in which
Speaker 3: sits repeated polling failures and misses the US cycle, usually
Speaker 3: in one direction, which is to fail to capture enough
Speaker 3: Republicans in voting intention, particularly for federal elections, where turnout
Speaker 3: is higher and we have occasional voters, So it's a
Speaker 3: challenge everywhere. Your question is what is it like as opolster. Well,
Speaker 3: because people are honest, and I hope mostly my colleagues
Speaker 3: across different firms and internationally are quite transparent about those
Speaker 3: problems where people just don't want to take surveys. There
Speaker 3: is then a need to adjust those surveys with quite
Speaker 3: intensive data computation. I'm chief research officer. My job is
Speaker 3: to interpret pulse. I'm not the person that goes on
Speaker 3: and creates these complicated models. I may have built some
Speaker 3: very basic stuff ten to fifteen years ago, but what
Speaker 3: happens now is of a totally different order of magnitude.
Speaker 3: Henry and we can kind of get into it, But
Speaker 3: now polsters will They might impute, for example, someone's propensity
Speaker 3: to turn out based off a regression model, which means
Speaker 3: that we have to work out whether someone really is
Speaker 3: likely to turn out based on validated information and then
Speaker 3: create models. Or it might be that it is so
Speaker 3: hard to get the interaction of all these different variables,
Speaker 3: so it's not enough to have the right number of
Speaker 3: people by ethnicity that's correct and matches. For example, the
Speaker 3: voter file, it might not be enough. Then it matches
Speaker 3: by having the correct composition in a state and having
Speaker 3: the right numbers of people by congressional districts or county
Speaker 3: or precinct. It then gets into what is the interaction
Speaker 3: between the two. And that's critical because what we've seen
Speaker 3: across the developed world is very complex voter flows, with
Speaker 3: people swapping who they're going to vote for between elections
Speaker 3: in a way that is very complex that it becomes
Speaker 3: doubly hard if you can't get those interactions correct. And
Speaker 3: once you've done all of that, Henry, you still have
Speaker 3: to ask the right question. And the way I think
Speaker 3: about it is that when you're polling a race or
Speaker 3: a federal, national, or sub regional election, you'd have thought
Speaker 3: that asking what we call the horse race polling, which
Speaker 3: is candidate X versus candidate Y for party X and
Speaker 3: Party Y. You'd have thought simply asking the exact question
Speaker 3: is the right way to go about it. But what
Speaker 3: I have found, both from our work at Focal Data
Speaker 3: but also other colleagues, is that really polling operates on
Speaker 3: much more of a traffic light system, which is about signals,
Speaker 3: which is saying my horse race polling might say that
Speaker 3: this candidate is four points ahead of this candidate, But
Speaker 3: when I look into the issue set, in other words,
Speaker 3: which candidate is doing better on issues like healthcare or
Speaker 3: immigration or the cost of living and how salient are
Speaker 3: those issues, I might get a slightly different signal, and
Speaker 3: that's as important all I have. Might, for example, ask
Speaker 3: my survey respondents, who do you think is going to
Speaker 3: win in your seat? Because sometimes if I haven't captured
Speaker 3: exactly white people, I still might be able to get
Speaker 3: a better community based prediction, and I would necessarily for
Speaker 3: voting intention, or I might ask whole heap of other questions.
Speaker 3: And generally what pulsters will do and it's not a
Speaker 3: defensive or kind of an obtuse way of answering the question,
Speaker 3: but many of them will operate, whether it's conscious or not.
Speaker 3: A traffic light system, which is on balance, the traffic
Speaker 3: lights suggests in full green that this candidate is going
Speaker 3: to win with some caveats and amber you know, amber
Speaker 3: lights on these types of questions and these types of analyses.
Speaker 3: And that is the job of the polster, which is
Speaker 3: the constant trade off between signal and conviction, and that
Speaker 3: can be very hard, but it is very enjoyable. And
Speaker 3: I guess the other aspect of it is that you
Speaker 3: then might turn your head to the other half of
Speaker 3: the field of market research, which is qualitative work, which
Speaker 3: is then listening to people's conversations. Whether that's why are
Speaker 3: focus groups or as is now happening across the world
Speaker 3: actually AI moderated interviews with real people, which means that
Speaker 3: you can do kind of focus groups in scale, and
Speaker 3: that again might give you a different signal. And we
Speaker 3: have seen in many elections where the qualitative data and
Speaker 3: the conversations. You know, not to quote Jeff Bezos, but
Speaker 3: you know when he says, when I'm got the anecdotes
Speaker 3: and the data, I go with the anecdotes. That's not
Speaker 3: quite how we operate. But it is a totally different
Speaker 3: and fairly independent signal of polling data. So in the round, Henry,
Speaker 3: it's exciting, it's interesting. It's far more interesting understanding the
Speaker 3: why than what, because any given poster will call elections incorrectly.
Speaker 3: The most important thing is that that there's transparency and
Speaker 3: clarity over why you were wrong or right. Actually, you
Speaker 3: can be right for the wrong reasons, so that you
Speaker 3: can then go and adjust, but it's also about providing
Speaker 3: confidence bands and caveats so that people understand exactly what's
Speaker 3: being articulated. But that's had in an Internet age, in
Speaker 3: an age of headlines, in an age of the need
Speaker 3: to be one hundred percent convinced of a particular thing.
Speaker 3: That's a hard thing to articulate today. So it is
Speaker 3: a real mixture of listening, of probing, of asking none
Speaker 3: obvious things, of data extraction, data modeling, and then storytelling
Speaker 3: because the why is not that important leading up to
Speaker 3: the election, but the why is absolutely critical for the
Speaker 3: years after, because there are many circumstances in which election
Speaker 3: outcomes are not particularly kind of described or interpreted correctly.
Speaker 2: Basically, that happens here. I know it's happened in Britain.
Speaker 2: I do want to ask a couple of questions. Polling
Speaker 2: failure isn't just an American thing?
Speaker 3: You know?
Speaker 2: Is that you had the Brexit large polling fail You
Speaker 2: had virtually everybody predicted the Labor victory correctly but massively
Speaker 2: inflated vote shares for Labor. Both of those instances created,
Speaker 2: of course, significant introspection in the British polling industry, and
Speaker 2: you have a much more collective British polling council that
Speaker 2: encourages that. You know, us being in the wild West,
Speaker 2: we don't have an American polling council that kind of
Speaker 2: gets all the leading bolsters together and says, hey, how
Speaker 2: did we collectively all screw up? Everyone says they have
Speaker 2: the secret sauce and tries to sell it. What do
Speaker 2: you think British pollsters learned from twenty sixteen and why
Speaker 2: did the things they learned from twenty sixteen not lead
Speaker 2: to They led to the correct directional outcome, but not
Speaker 2: the magnitude outcome in twenty twenty four because that of
Speaker 2: course relates to method of how one's sample or questioning
Speaker 2: or taking signals in some way messed up in pretty
Speaker 2: big ways.
Speaker 3: So yeah, I mean UK polling absolutely has is very
Speaker 3: transparent endeavor, but there have been a number of misses
Speaker 3: and a number of successes, and I kind of i'll
Speaker 3: talk through talk through that that kind of history. The
Speaker 3: twenty sixteen referendum was actually preceded by the twenty fifteen
Speaker 3: polling miss There was we had a general election, right
Speaker 3: so where David Cameron, the Conservative leader, want a clear
Speaker 3: majority against the Labor Party and the polls had a
Speaker 3: very small conservative leader or actually a tie, and I
Speaker 3: think he ended up winning by seven points, which in
Speaker 3: the first part the post system is definitely and there
Speaker 3: was actually a polling inquiry then in twenty fifteen, which
Speaker 3: was that it was a it was a data issue
Speaker 3: as opposed to a modeling issue. People. You know, it
Speaker 3: sounds very tautological, which is that people's underlying samples because
Speaker 3: we had to send them in and there was a convening,
Speaker 3: and people's underlying samples did not have enough what we
Speaker 3: call time poor center right voters as opposed to populace
Speaker 3: right voters, which then is a problem that comes is
Speaker 3: a problem that appears the next year in twenty sixteen.
Speaker 3: Is actually it was less of appollingness because there were
Speaker 3: posters who got it fundamentally correct. You go was the
Speaker 3: UK firm that actually very quietly unveiled what's called MRP.
Speaker 1: Which I do want to get to m RPS.
Speaker 3: Right, which we're going to get to MRPs later and
Speaker 3: we don't need to talk for it now, but you know,
Speaker 3: it's a technique that fundamentally allows it's called multi level
Speaker 3: reggression and post gratification. As modeling technique where you can
Speaker 3: bine national sample to get to local level estimates. Basically,
Speaker 3: it's an American technique. It was created by Andrew Gelman,
Speaker 3: who's a professor of political science at Columbia. Again another
Speaker 3: US innovation, but that very quietly got the election and
Speaker 3: the referendum in twenty sixteen correct and even the range correct.
Speaker 3: But of course you have also released a traditional poll
Speaker 3: at the referendum which was not correct, and there that
Speaker 3: was very much a case of a little bit of
Speaker 3: a case of data input, but actually there was also
Speaker 3: a case of maybe there's a future here of modeling
Speaker 3: technique that needs to be looked at, and that was
Speaker 3: very much verified at the then twenty seventeen general election. Man,
Speaker 3: your listeners at this stage may have forgotten that we
Speaker 3: basically held general elections and referendums as annual events for
Speaker 3: a period of British political history. And again the polls
Speaker 3: kind of failed and that was again an issue of
Speaker 3: time because but you got it.
Speaker 1: MRP did not. That's where the MRP made it did
Speaker 1: not fail.
Speaker 3: It captured very accurately a resurgent populace left Labor Party
Speaker 3: led by Jeremy Corbyn, and they very specifically didn't just
Speaker 3: call a national picture, but they forecast the six hundred
Speaker 3: and thirty two seats across Great Britain in our Parliament,
Speaker 3: including some very surprising changes, including my home seat of Canterbury,
Speaker 3: which had been a kind of traditional Conservative vote with
Speaker 3: you know, has a cathedral city flipped labor and.
Speaker 1: Field one. Sorry that one was exactly that's right.
Speaker 3: Who now sits? I think is an independent MP? But
Speaker 3: she she she won that. And again the different problem
Speaker 3: that came about was of late swing, where the twenty
Speaker 3: seventeen election genuinely had huge vote shift changes during the
Speaker 3: election cycle and the campaign, and I was polling it
Speaker 3: very very closely, and Jeremy Corbyn put on double digits
Speaker 3: in vote share. If you think you think about the
Speaker 3: last time that might have happened in a US context,
Speaker 3: it's quite rare for that to happen. But Yugov's model
Speaker 3: was able to kind of adjust for that and from
Speaker 3: a time period perspective, so we really all as an
Speaker 3: industry there was a sigh of relief of the twenty
Speaker 3: nineteen get Brexit done. Boris Johnson's not landslide but very comfortable,
Speaker 3: large majority victory where the Conservative Party I think got
Speaker 3: three hundred and sixty five seats, having declined previously in
Speaker 3: twenty seventeen, and everyone was directionally correct. Everyone forecast that
Speaker 3: there was going to be a Conservative minority, but underneath
Speaker 3: the surface, actually the scale of the Conservative victory was
Speaker 3: slightly underpredicted and wasn't wasn't that fully understood or known.
Speaker 3: So and so we get to the twenty twenty four election,
Speaker 3: which is I think what your question was at, which
Speaker 3: was by far and away the kind of biggest failure
Speaker 3: of the UK polling industry, hidden by the fact that
Speaker 3: everyone was directionally correct. There was such a large difference
Speaker 3: between the fortunes of the Conservative Party, which was back
Speaker 3: then hemorrhaging votes to reform UK, which went from kind
Speaker 3: of low double digits to fourteen percent. You know, the
Speaker 3: gap at a seat level was very very clear. So
Speaker 3: it was very clear from about a year a year
Speaker 3: and a half that the Labor Party were going to
Speaker 3: win a majority. But again in terms of vote shares,
Speaker 3: the industry was five or six points out, which was
Speaker 3: much bigger polling failure than any of the previous elections
Speaker 3: referendums that I've just described, and it was specifically the
Speaker 3: Labor Party vote where which was overscored by literally everyone
Speaker 3: bar a few. And we can get into the very
Speaker 3: specifics because there were a few holding companies that have
Speaker 3: slightly different approaches that got it right. But interestingly, Henry,
Speaker 3: the models were broadly okay. So the models told the
Speaker 3: story that Labour were going to win between four hundred
Speaker 3: four point fifty seats they won towards the lower end
Speaker 3: and around four point fifteen that somehow the vote shares
Speaker 3: were totally out And that's because in many, many safe
Speaker 3: labor areas, the vote share shifted dramatically down and they
Speaker 3: won many many seats on a hugely marginal vote share,
Speaker 3: which is the story of how British politics has continued
Speaker 3: to be volatile, which is the ability to win quite
Speaker 3: big mandates on very small slices of public opinion. You know,
Speaker 3: we talk about we might get into the president's low
Speaker 3: approval ratings, but I was looking at it. He's still
Speaker 3: liked by thirty seven percent of the American public. Most
Speaker 3: European leaders would be very happy with thirty seven percent
Speaker 3: of the public. Now, some of that as a function
Speaker 3: of a two party system, but it is it is structurally,
Speaker 3: very very different. So that's the story of UK polling.
Speaker 3: But again there's been some fantastic work that's gone on
Speaker 3: in the industry around the modeling, which I think more
Speaker 3: people are feeling comfortable with. There are challenges around our
Speaker 3: rapidly diversifying society, so much of the pollingness was concentrated
Speaker 3: in ethnically diverse areas and non white voters. We now
Speaker 3: have a multi party system in the UK, which is
Speaker 3: a very obvious point and not I think the feature
Speaker 3: of this discussion. But we have a Green Party, we
Speaker 3: have a populist right party called Reform, We have independents
Speaker 3: who vote very much in certain areas along ethnic blocks
Speaker 3: based on foreign policy. We then have a Liberal Democrat party,
Speaker 3: and then we have two nationalist party parties. Now this
Speaker 3: creates a massively complex picture in which to get it right.
Speaker 3: The bar is very very high. Most countries with a
Speaker 3: system that is that complex with that many parties five
Speaker 3: effected parties, but in many areas seven means most countries
Speaker 3: that have that many number of parties have a proportional system,
Speaker 3: like Germany, which you know doesn't have overhang seats anymore,
Speaker 3: but it's still proportional. Beyond the threshold. So that's a
Speaker 3: unique challenge, which means we all have to work very
Speaker 3: very hard. But each country presents very very different challenges.
Speaker 3: They're not the same in terms of the difficulties in
Speaker 3: getting data, and the modeling is never the same either,
Speaker 3: even if the kind of toolkit can have some resonance
Speaker 3: between different countries.
Speaker 2: Well, that actually got to is going to get to
Speaker 2: my next question, which is you do polling in another country,
Speaker 2: but let's focus on the United States because you have
Speaker 2: started to do polled. I featured your mrp of America's
Speaker 2: poll and my Washington Post column and some people said,
Speaker 2: I'll bet you money that that's not going to be
Speaker 2: right because it's too good for Republicans. We can come
Speaker 2: back to that in a minute. But what are the
Speaker 2: methodological challenges that you that are different here than you face,
Speaker 2: say in your native country of Great Britain or are
Speaker 2: they pretty much very similar? Is that you have similar
Speaker 2: non response rates and similar technological barriers with different types
Speaker 2: of voters getting through them. How do you approach an
Speaker 2: American pole that might be a little bit different than
Speaker 2: approaching a British pole.
Speaker 3: It's a great question, and so the demand for polling
Speaker 3: in the US, and the structure of the system is
Speaker 3: very different. It depends on what you're pulling. So let's
Speaker 3: say you did a congressional districts poll or for example,
Speaker 3: a county level poll or a state pole. The expectation
Speaker 3: is that you don't model those out comes. You actually
Speaker 3: do a survey of people who literally live in those areas,
Speaker 3: and that means that you approach it very differently. It
Speaker 3: means that you have to find sample from wherever you can,
Speaker 3: whether that's online, whether it's by cell, whether that's texta web.
Speaker 3: You know, the demand for modeled answers by state is
Speaker 3: much less because people just say, well, you can just
Speaker 3: do a pole of Texas of a thousand people, which
Speaker 3: is what we did last week, instead of modeling what
Speaker 3: the state would hypothetically do. If the demographics of Texas
Speaker 3: and the demographics of the country look like X, then
Speaker 3: you kind of impute what Texas should look like, which
Speaker 3: is what a model does. People just say, we'll just
Speaker 3: run a pole, just ask people what it is. So
Speaker 3: there is that challenge in the US where there is
Speaker 3: just a broad expectation and if you're going to make
Speaker 3: a horse race call or do a pole of an area.
Speaker 3: It is a pole and not a model. Now that's
Speaker 3: interesting because what we found at the twenty twenty four
Speaker 3: cycle is that our modeling of how states would vote
Speaker 3: was in many ways more stable and more accurate than
Speaker 3: literally doing a pole of the state, which is you know,
Speaker 3: I think we talked about it at the time a
Speaker 3: couple of years ago, and as we did the MRP's
Speaker 3: multi level regression and post ratification. You know, these big
Speaker 3: computationally intensive models that split the public into millions of groups,
Speaker 3: and you take a national survey that then basically works
Speaker 3: out what demographics were, which way in which area all
Speaker 3: interacted with each other, and then you basically work out
Speaker 3: what's happening at a national level, and you impute what's
Speaker 3: happening at a state level. Those seem to be more
Speaker 3: stable and more accurate than going in and doing a
Speaker 3: state pole. That's one challenge in the US. The second
Speaker 3: is obviously the resources are different. You guys have a
Speaker 3: voter file right where you can literally see who's on there.
Speaker 3: We have very different laws in the UK regarding GDPR.
Speaker 3: We don't have things like a voter file that polsters
Speaker 3: and can use, and the laws were even stricter in
Speaker 3: Europe as well, so the challenges are very different in
Speaker 3: terms of who doesn't want to take polls. There are similarities, Henry,
Speaker 3: there are. We do see groups of people who are
Speaker 3: low trust. By that I mean less trusting of institutions,
Speaker 3: less trusting upholsters, less willing to take surveys. They do
Speaker 3: appear to be groups that are harder to survey, particularly
Speaker 3: across the English speaking world. I wouldn't necessarily say Europe,
Speaker 3: for example, French polls don't underestimate Bardella all A Penn.
Speaker 3: We saw no evidence of that at the Assembly elections,
Speaker 3: but that does appear to be a particularly acute problem
Speaker 3: in the English speaking world, I think where and again,
Speaker 3: I would also say that some of the failures at
Speaker 3: a twenty to twenty four election, which were captured correctly
Speaker 3: by another British friend called Jail Partners, run by James Johnson,
Speaker 3: a colleague of mine who's actually America based for British.
Speaker 1: At the whole, was on this podcast two years ago
Speaker 1: to talk about.
Speaker 3: A great guy, and I think, you know, they very
Speaker 3: accurately captured how what was going on amongst the low
Speaker 3: trust non white vote across the seven Swing states which
Speaker 3: was absolutely critical if you at least that's the thesis
Speaker 3: of David Shaw, the polster who runs Blue Rose, a
Speaker 3: Democrat who's a bit different, you know. He was very
Speaker 3: much in the view that the seven swing states were
Speaker 3: very much delivered by this kind of low trust, ethnically diverse,
Speaker 3: slightly younger group of people who basically came out voted
Speaker 3: for Trump and then didn't bother voting down ballot, which
Speaker 3: slightly explains the number of split states at the election. Again,
Speaker 3: I don't think that's a million miles away from what
Speaker 3: happened with the overestimation of the labor vote in the
Speaker 3: UK election, So there are resonances. What I have to
Speaker 3: say is the reason why things have converged is partly
Speaker 3: because the US is no longer a low turnout democracy,
Speaker 3: and turnout in the UK has actually declined quite sharply
Speaker 3: over the last ten years. So we've gone from around
Speaker 3: seventy two percent turnout at the twenty seventeen election. I
Speaker 3: think it was about fifty nine to fifty eight percent
Speaker 3: at the last election, which I believe is actually lower
Speaker 3: than the last president the US presidential.
Speaker 2: It is, we we at about sixty two percent, and
Speaker 2: by that for my listeners. We're not talking about registered voters.
Speaker 2: We're talking about what we were called the voter eligible population,
Speaker 2: the citizens who are adults of voting age and not
Speaker 2: disqualified by things like legal inhibitions or insanity.
Speaker 3: Yeah, so there are there are, There are big residences,
Speaker 3: but the challenges are always slightly different. So I mean,
Speaker 3: that's why you need domain experts. That's why most of
Speaker 3: the uk as that operate in America and do forecasting America,
Speaker 3: we have teams of people here, some of whom are American. Right.
Speaker 3: You need that domain knowledge, that expertise, You need that
Speaker 3: kind of deep, deep level knowledge. Really.
Speaker 2: But you know, one of the things I do want
Speaker 2: to talk about is, you know, when I'm listening to this,
Speaker 2: I'm thinking, gee, this sounds a lot like wine making.
Speaker 2: Is that the grapes are the same, but the terr
Speaker 2: are and then the skill of the wine maker produces
Speaker 2: you know, the difference between a ten dollars bottle of
Speaker 2: cable wine and a five hundred dollars bottle of petrus.
Speaker 2: But I got the race largely right because I dove
Speaker 2: into what I do is I look at when I
Speaker 2: make my biennial predictions, which you know, I owe it
Speaker 2: to my readers or followers to you know, make the
Speaker 2: hard call. Instead of saying it's too hard, it's too
Speaker 2: close to call, I said, now, this is what I think.
Speaker 1: I'm usually directionally right. Once I was very very wrong.
Speaker 2: But I looked at all the average and what struck
Speaker 2: me the reason why they thought Kamala Harris would narrowly
Speaker 2: win and I thought Trump would win and might win
Speaker 2: the popular vote was because they systematically underestimated the share
Speaker 2: of Republicans in the elector, which is a way you
Speaker 2: could have teased out of the aggregate cross tab data.
Speaker 2: Exactly the methodological problem. But there was also, I think
Speaker 2: for some people a modeling question, which was there was
Speaker 2: one firm, which we can discuss offline. I don't want
Speaker 2: to name it here. They produced a very detailed Friday
Speaker 2: before the election, Here's how every state is going to vote,
Speaker 2: and they got every swing state wrong, because every swing
Speaker 2: with nationally and through every swing state, they over projected
Speaker 2: the Democratic vote. And that's clearly a modeling question, is
Speaker 2: that their model of how they were interpreting the data
Speaker 2: was simply wrong. Do we run that risk if we
Speaker 2: look at individual polsters that the Winemaker maybe getting too
Speaker 2: much yeast in the must too early, and that if
Speaker 2: we're not looking at averages or doing multi polling analysis,
Speaker 2: we're just looking at one person that garbage in, garbage out.
Speaker 2: If your model is wrong, even if it's been right before,
Speaker 2: you will be horribly off in an important race.
Speaker 3: I know the difficulty there is. Of course, you have
Speaker 3: off cycle midterms as well, which have a totally different,
Speaker 3: totally different elector. You know a lot of the story.
Speaker 2: And one of the things we're seeing is people saying, well,
Speaker 2: Democrats are more educated, therefore they'll have a larger overturnout
Speaker 2: share of the election. And presumably that assumption, which has
Speaker 2: been born out historically, may be correct.
Speaker 1: But it may not be.
Speaker 2: And we have the Virginia redistricting referendum where that should
Speaker 2: have been the case, and in fact Republicans out voted Democrats,
Speaker 2: and it was the lower educated Republican areas that had
Speaker 2: a higher share of the twenty twenty four presidential vote
Speaker 2: turnout for this April off cycle only theoretical redistrict thing initiative,
Speaker 2: which if I were Polster, I would say, warning, warning,
Speaker 2: Will Robinson, danger, You know that my assumption of education
Speaker 2: driving turnout and low motivated Trump only voters may not
Speaker 2: be right.
Speaker 3: And I think the wine maker analogy is a short one,
Speaker 3: maybe also because of the stretched the metaphor, Henry, all
Speaker 3: the way through. Vintages are different, like firms are not
Speaker 3: static things like you can have different teams working at
Speaker 3: different times. You might be missing that one analyst who
Speaker 3: looks at things from a completely different perspective. You might
Speaker 3: have got a model that was carefully calibrated to one
Speaker 3: election that the electric fundamentally changes at the next. You
Speaker 3: might not feel the need to reinvent from first principles
Speaker 3: what you've done because you've been successful. It's often my failure.
Speaker 3: I'm very interested in firms, you know, and sometimes including
Speaker 3: when I and other firms I've been involved with have
Speaker 3: got it wrong. Often I'm very interested in their first
Speaker 3: call after the event, because very often it could be right,
Speaker 3: because they basically have to go back to the drawing
Speaker 3: board and redo it. And for a pole watchers yourself,
Speaker 3: I don't know of anyone who knows as much as
Speaker 3: international polling as you, Henry Enough spoken to a few
Speaker 3: people about this, but you know that that creates challenges
Speaker 3: for observer.
Speaker 2: I mean not everyone runs German language crosstabs through a
Speaker 2: Google translation I understand.
Speaker 3: Which I know you do. But that creates problems because
Speaker 3: people who were good before can suddenly get it wrong,
Speaker 3: and people who got it totally wrong before, who no
Speaker 3: one's paying attention to get it fundamentally completely correct and
Speaker 3: have completely and that creates a kind of a kind
Speaker 3: of problem of continuity of trust. And that's very important
Speaker 3: action to take a step back and to use I
Speaker 3: guess the traffic light analogy, even if you're pole watching,
Speaker 3: to be like, here are the signals, and I'm going
Speaker 3: to get them from everywhere and everything. And that's why
Speaker 3: really delving into the corners of an industry yields a
Speaker 3: lot of games, because there might be a new model
Speaker 3: or a new approach that is very very interesting that
Speaker 3: might be part of the future.
Speaker 1: You know.
Speaker 3: MRP was announced with no fanfare, got a huge referendum correct,
Speaker 3: and basically no one paid attention for about a year
Speaker 3: until they had been validated again. So from my perspective,
Speaker 3: it is very much.
Speaker 2: Validated against the grain because I was following twenty seventeen
Speaker 2: and pretty much you had Yugov saying, hey, there's a
Speaker 2: really good chance. May it's not going to win her majority,
Speaker 2: even if she wins the popular vote by X number
Speaker 2: of points, because of these demographics that are moving, that
Speaker 2: have effect in your six hundred and thirty two non
Speaker 2: northern Irish seats, And kind of like the reaction up
Speaker 2: until election day, as far as I could tell from
Speaker 2: the British public, not necessarily the private calls on the
Speaker 2: pollsters was yeah, yeah, yeah, okay, thanks, and then of
Speaker 2: course you proved to be right inward. Yeah yeah yeah,
Speaker 2: isn't this great? And then now everybody does MRPs it
Speaker 2: seems before your election, some of them better than others.
Speaker 3: Some of them better than others. I mean, what I
Speaker 3: would say, Henry, I think in defense of the polling
Speaker 3: industry is that a lot of the experts like yourself
Speaker 3: who are making calls are doing so from an advantaged position,
Speaker 3: because a polster can only project what their own projection is.
Speaker 3: But you get to sit around taking ten firms worth
Speaker 3: of polling data and then look at the qualitative information,
Speaker 3: and then get on the ground and then have conversations
Speaker 3: and then in the round be able to take a
Speaker 3: very detailed look. So we've just had a series of
Speaker 3: local elections here in the UK, and it was very
Speaker 3: clear that the expert projections were better than the MLP.
Speaker 3: But my question goes, what do we think the experts
Speaker 3: were looking at, which was everyone's models inaggregate and working
Speaker 3: out where they convert to where they differed, and then
Speaker 3: using multiple methods and filling in the gaps and therefore
Speaker 3: be able to give a bird's eye view. So my
Speaker 3: view as ever is that this kind of analysis layer,
Speaker 3: this whole ecosystem that sits on top of poling, is
Speaker 3: only able to be as good as it is precisely
Speaker 3: because it sits on the bed of all these people
Speaker 3: making singular specific calls about elections, and so it kind
Speaker 3: of works both ways in my opinion. That's why we
Speaker 3: have people like yourself, because you get the privileged position
Speaker 3: very often. You know the posters will be briefing you
Speaker 3: in detail. You're the only person. No one else in
Speaker 3: the US or the UK would be able to extract
Speaker 3: all about information and have a bird's eye view of
Speaker 3: everything because I don't know what another firm is doing
Speaker 3: and what that firm is doing. But you're the lucky
Speaker 3: person at least, and so your listeners to be able
Speaker 3: to say, actually, well, Henry's seen this signal here, and
Speaker 3: he's seen this signal here, and he's read this bit
Speaker 3: of information here, and then he's visited here and now
Speaker 3: here's my kalates. So there is this. You know, a
Speaker 3: lot of the costs are born by the industry for
Speaker 3: others projections, and I think you know, you're very good
Speaker 3: at kind of acknowledging that. But I think that's really
Speaker 3: really important going forward.
Speaker 2: Well, let me ask you a penultimate question then, and
Speaker 2: that is what work are you going to be doing
Speaker 2: in the United States? I know you've just come over here.
Speaker 2: You mentioned the Texas Paul. Obviously I was privileged enough
Speaker 2: to release your mrp of the congressional races. What other
Speaker 2: work are you doing here or are looking at doing
Speaker 2: in the United States during the interim.
Speaker 3: Second, so, we have a media partnership, the Financial Times,
Speaker 3: and we're basically partnering with them to provide not just
Speaker 3: horse race polling, because that is not super interesting. You
Speaker 3: can go on polling aggregators and look at it, but
Speaker 3: slightly going in underneath the surface, you might say, beyond
Speaker 3: the polls, providing information on what are the top issues?
Speaker 3: Where have we moved, what they're basically getting in the
Speaker 3: why before the what you know. And in a US context,
Speaker 3: it's very clear to me that the current low presidential
Speaker 3: approval ratings have very much driven off pole ratings to
Speaker 3: do with the economy, because prior to the election, the
Speaker 3: statistical modeling showed that that was one of the greatest
Speaker 3: singular factors behind the president's victory, particularly in the swing states,
Speaker 3: and that is also one of the issues on which
Speaker 3: has moved the most away from the Republicans to the Democrats.
Speaker 3: And it's looking into those kind of issues and trying
Speaker 3: to really unpick the why before the what happens. So
Speaker 3: that's why we're interested in looking at the Senate races.
Speaker 3: And it's so there are so many, you know, was
Speaker 3: it thirty five races, thirty three plus the two specials
Speaker 3: that my count, we've got four Democrat defends that you
Speaker 3: know in New Hampshire, Minnesota, Georgia, Michigan. Two of those
Speaker 3: look hypermarginal, and then we've got six potential flips, all
Speaker 3: of but one look hyper margin. You know, North Carolina
Speaker 3: to me feels a bit more kind of Roy Cooper asked,
Speaker 3: But at least Senate Poland we've been looking at in it. Maine.
Speaker 3: Maine is a very hard state to poll. Whenever anyone
Speaker 3: asked me what are the hardest states to poll? I
Speaker 3: will always say, other than all of the swing states,
Speaker 3: Maine and Alaska. And you know, it's unfortunate that those
Speaker 3: two now are to the sixth.
Speaker 1: May I just jump in here.
Speaker 2: That is something that has been observed in the United
Speaker 2: States by American pulsters that in twenty twenty, literally every
Speaker 2: single public poll said that Susan Collins who's going to lose,
Speaker 2: and it wasn't going to be close, and she.
Speaker 3: Won seventeen points.
Speaker 1: They were seventeen points.
Speaker 2: I happen to be talking with the senator friend of
Speaker 2: mine beforehand, and he told me, Susan says, it's fine.
Speaker 1: So Susan's polster was getting it right. She was lined
Speaker 1: to her colleagues.
Speaker 2: Is another one that doesn't have quite that ignomious example
Speaker 2: of the twenty twenty error, but it too is known
Speaker 2: as a very difficult place to pull. And if I
Speaker 2: look at say, I think, what do the two states
Speaker 2: have in common. They have a significant white and non
Speaker 2: Alaska has a significant non white, non college but it
Speaker 2: has a very significant rural rocky terrain, people living in
Speaker 2: different places. Does that help contribute to a lack of
Speaker 2: trust that if you're in a fifty person town and
Speaker 2: you're connected to the world by you know, by a
Speaker 2: road and intermittent internet service, that that or is that
Speaker 2: just a spurious factor.
Speaker 3: I also think the partisanship is a bit weaker in
Speaker 3: those states like May and main In particular, the demographic
Speaker 3: trends of who votes Republican and who votes Democrat is
Speaker 3: actually very complex and very very different to the rest
Speaker 3: of the US, and that's slightly hidden by the county well.
Speaker 3: Actually the small level maps, you know, including the ones
Speaker 3: where you're showing counties with nobody living in there, they
Speaker 3: make it look like there's a clear geographic division, which
Speaker 3: might make you think, actually the coalitions are quite similar
Speaker 3: to the rest of the US. They're not at all,
Speaker 3: which makes it really hard. If you're opposed, you like,
Speaker 3: I'm going to get three hundred Democrats and three hundred Publicans,
Speaker 3: and it's like, well, which Democrats and which Republicans. But
Speaker 3: if they're very very complex, My instinct on Alaska is
Speaker 3: that's also the same as well. I do think if
Speaker 3: you think about it, one of the things I've noticed
Speaker 3: as an outsider, because of course there are Americans with
Speaker 3: deep expertise who actually do really get it right all
Speaker 3: the time, is that many of the states that aren't
Speaker 3: quite right on ones that where there is still some
Speaker 3: degree of split ticket voting, and also where the ideological
Speaker 3: part of zian Ship preferences are incredibly complicated, where the
Speaker 3: singular definition is not quite there. So I was very interested,
Speaker 3: for example, that polsters didn't really get Pennsylvania that wrong
Speaker 3: at the election versus you know, there was still a
Speaker 3: systemic bias towards Harris that shouldn't have been there. But
Speaker 3: for me that as a polster, you're looking through that
Speaker 3: data and you're thinking, well, you know, Pennsylvania feels like
Speaker 3: a state where as you said, I think you said
Speaker 3: it three regions melded into one, right, where you can
Speaker 3: have a stronger handle on Well, that's a Republican group
Speaker 3: of people, and they are very much Republican. And that's
Speaker 3: a Democrat people and they are very much Democrat. And
Speaker 3: that's a group of swing voters, and they are very
Speaker 3: much a swing And that maybe makes it slightly easier
Speaker 3: to poll than some of these other states which are
Speaker 3: a bit more nonconformist, a bit more complex and a
Speaker 3: bit hard. And I think maybe Iowa is one of
Speaker 3: those states that also falls into that. I guess if
Speaker 3: you look at the polling.
Speaker 2: Is another one that had a significant twenty twenty polling
Speaker 2: directional or margin error, you know, which is that there
Speaker 2: was a lot of polls suggesting Joany Ernst might be
Speaker 2: in trouble. In the twenty eighteen there were poles suggesting
Speaker 2: Democrats might win, and of course Republicans won and both
Speaker 2: those races a very comfortable.
Speaker 3: Very important. It's a state that Republicans win, can win
Speaker 3: by double digits, sometimes by single digits, but then it
Speaker 3: does have a predilection to various times at the gubernatorial
Speaker 3: level go the other way. If you look at the
Speaker 3: kind of senate Senate polling, which I know it's still
Speaker 3: Republicans are still favored to take Iowa. We look at
Speaker 3: the kind of governorship polling, I think that's rob Sam
Speaker 3: so standing there. Again, it's complex, you know. It's many
Speaker 3: of these states that we think of as not complex
Speaker 3: that are in fact much more complex than everyone realizes.
Speaker 3: And I think also the degree to which Utah went
Speaker 3: from megaskeptic to not It was also an underrated phenomena
Speaker 3: because how much polling expertise is there of the Mormon
Speaker 3: community and the shifting sands around there. So, I, you know, polsters,
Speaker 3: I guess to round it off, our people who love
Speaker 3: typically they're quite a patriotic lot. You don't want to
Speaker 3: stare at your own country and analyze it to death
Speaker 3: for multiple decades if you really care and you're interested
Speaker 3: in it. But also you're you're basically a deployed anthropologist,
Speaker 3: right You're looking for the stories, the data, the modeling
Speaker 3: to line up. Very often it doesn't, and you have
Speaker 3: to make a judgment call. But for me, you know,
Speaker 3: that's kind of what we'll be looking at, Henry. We're
Speaker 3: interested in the why, but we're also interested in the
Speaker 3: what because of how wide the landscape is in terms
Speaker 3: of both the Senate. You know, I would say the
Speaker 3: House of Representative has far fewer marginal seats.
Speaker 1: Thank you, Jerry, Yeah, thank you.
Speaker 3: But but I think you know, we were speaking about
Speaker 3: hungry and you very carefully handled our poll that we
Speaker 3: handed to suggesting the kind of strong victory for Magyar.
Speaker 3: The funny thing about redistricting and jerry mandering is it
Speaker 3: works until it doesn't, and it works within certain bounds,
Speaker 3: and the bounds that it works in are ones of
Speaker 3: basically temperate swing where maybe Republicans R plus seven, D
Speaker 3: plus seven, whatever it is, whatever the electric looks like
Speaker 3: in any given year. But where it really breaks down
Speaker 3: is when you have an election outcome that where one
Speaker 3: party is leading the other by double digits, because then
Speaker 3: you've created a cardret of districts or seats that would
Speaker 3: never have been marginal normally, but now suddenly they all are.
Speaker 3: So it can actually exacerbate a landslide the wrong way,
Speaker 3: depending on who's doing it in a special environment.
Speaker 2: One of the reasons why Tissa Magyor's party has a
Speaker 2: constitutional super majority is because they won the native Hungarian
Speaker 2: vote by fifteen to seventeen points, and that hand and
Speaker 2: effect across a wide variety of seats that had they
Speaker 2: won by only twelve points, so they would not have picked.
Speaker 3: Up's And that's the thing. So I'm interested to see
Speaker 3: that there's a story that's going to emerge at some
Speaker 3: point in the next two decades where that kind of
Speaker 3: pack of cards all comes down. But again, that a
Speaker 3: big difference. You know, the UK, we have the Boundary Commission.
Speaker 3: This is all done in an almost census like fashion,
Speaker 3: in an incredibly equal way basically, so again another difference
Speaker 3: to get a hold of. But every country is different.
Speaker 3: You know, we are a UK based firm, but some
Speaker 3: of these techniques are very applicable, whether that's France, whether
Speaker 3: that's Germany. But the key thing is to be very
Speaker 3: clear about what your projection is and why, and explain
Speaker 3: what you've done so that if it's if it's wrong,
Speaker 3: you have some sense of whites, and if it's right,
Speaker 3: instead of saying we're right, you're able to point to
Speaker 3: why you might have been right so that others can learn.
Speaker 2: One last question, looking at America in twenty twenty four,
Speaker 2: you talked about the why you know, which is you know,
Speaker 2: issue salience, issue viewpoints and other things, and the what,
Speaker 2: the partisan outcome or the job approval. Do you think
Speaker 2: you will see movement in the why before movement in
Speaker 2: the what? In other words, that let's say you take
Speaker 2: a poll in June and you start to see movements
Speaker 2: in saliens among issues, or you start to see, hey,
Speaker 2: I think the economy is getting better, and do you
Speaker 2: think that will predict a subsequent movement in like the
Speaker 2: job approval or the congressional generic ballot or is just
Speaker 2: another signal that you wait with all the others. In
Speaker 2: this qualitative art form that is modern polling slash political analysis.
Speaker 3: The why is very often it's correct a lot more
Speaker 3: times than the what. So the explanation of an event
Speaker 3: can often the polling that feeds into that can is
Speaker 3: often much more evergreen and robust than these specifics around
Speaker 3: the what, because of what can only it only has
Speaker 3: to be wrong by three points to be out, whereas
Speaker 3: the why is directional, and very often the why comes
Speaker 3: screaming out from the data, like the cost of living
Speaker 3: is hugely important in the Southern States, immigration control is
Speaker 3: incredibly important. Right, These are truths that have a massive
Speaker 3: order of magnitude that won't be wrong even with the
Speaker 3: polling error. But the shift in the why can be
Speaker 3: very predictive of outcomes. So, you know, should those numbers
Speaker 3: improve on the perception of the economy, you know, it's
Speaker 3: not just perception. Gas prices have a very strong corarel
Speaker 3: relationship to swing against the Republicans, So that's real data showing,
Speaker 3: like having real political consequences.
Speaker 2: So they did with Biden in the summer of twenty
Speaker 2: twenty two. Hence why he started getting licenses in Venezuela
Speaker 2: to ship and changing his stands on Saudi human rights
Speaker 2: to get more production and releasing this because he could
Speaker 2: see the same thing happening to him as guests broke
Speaker 2: five dollars a gallon in most of the country.
Speaker 3: Yeah, and I think so that's what we're tracking with
Speaker 3: the ft. That's what we're tracking here at Focal Data
Speaker 3: is are the explanations of what's happening makes sense? You know,
Speaker 3: We've seen the salience, for example, of the handling over
Speaker 3: the Iran situation become important out of nowhere. That's now
Speaker 3: a thing. We're now seeing that in fact, independence sit
Speaker 3: much closer to Democrat voters on that issue than versus
Speaker 3: a kind of the base of the Republican Party. To me,
Speaker 3: that's really important because I'm going to track the salience
Speaker 3: of that issue as much as we are. Who's doing
Speaker 3: well badly on it?
Speaker 2: Well, James, it's always fascinating chatting with you. I whish
Speaker 2: I could jump across the pond and continue this conversation,
Speaker 2: but all good things must come to an end, and
Speaker 2: so mucht are on air discussion.
Speaker 1: Where can my listeners follow your work?
Speaker 3: Three places. The first is focal Data dot com focl
Speaker 3: data dot com, where we have a blog with all
Speaker 3: of our work from not just myself but a team
Speaker 3: of twenty five researchers, some of which is very technical,
Speaker 3: some of which is not. The second is my substack,
Speaker 3: which is the Political Whiteboard, which is where the super
Speaker 3: super early stuff goes on big three four thousand word
Speaker 3: essays with modeling. And then I write occasionally four times
Speaker 3: a year for The London Times, where I set out
Speaker 3: at a much more high level some of my ideas
Speaker 3: that may or may not be interesting to your readers.
Speaker 3: The latest Times column London Times column is on the
Speaker 3: Democratic and its potential shift to the left of its base.
Speaker 2: I can recommend all of those personally, but for anyone
Speaker 2: who wants to get scared about the future of AI,
Speaker 2: I suggest you jump on Political Whiteboard and read what
Speaker 2: James had to say about that a few months ago.
Speaker 2: It will open your eyes. I'm not talking about dystopian
Speaker 2: terminator stype circumstances, but the political impacts that may be
Speaker 2: happening to the supposedly secure managerial class.
Speaker 1: Well read it and maybe weep.
Speaker 2: James, thank you and I look forward to having you
Speaker 2: back on Beyond the Polls.
Speaker 1: Thank you, Henry, pleasure to be here. That's it for
Speaker 1: this week.
Speaker 2: Next week I'll talk about redistricting, past, present, and future
Speaker 2: with Sean Trendy. Until then, let's reach for the stars
Speaker 2: together as we journey Beyond the Polls.