Speaker 1: Pushkin from Pushkin Industries. This is Deep Background, the show
Speaker 1: where we explore the stories behind the stories in the news.
Speaker 1: I'm Noah Feldman. On Saturday, we'll be continuing our special
Speaker 1: series Deep Bench, about the Right Word turn of the
Speaker 1: Supreme Court. But today, in the midst of an election season,
Speaker 1: we thought we should still deliver a regular Deep Background
Speaker 1: episode to you. We're about two weeks out from the
Speaker 1: presidential election now, and in most polls it looks like
Speaker 1: Biden has a serious lead. I don't know about you,
Speaker 1: but I don't pay much attention to the polls until
Speaker 1: I do, and I've entered that phase of the election
Speaker 1: where I can't quite help myself, like a guilty pleasure
Speaker 1: or maybe a non pleasure. I keep stealing back to
Speaker 1: the websites to see what the polls say. Can we
Speaker 1: trust the polls that are out there? What have posters
Speaker 1: learned from recent experiences, including the twenty sixteen election, and
Speaker 1: what methods do they use to get the results that
Speaker 1: we see when we look online. Here to discuss these
Speaker 1: questions with us is Anthony Salvanto. He is the Elections
Speaker 1: and Surveys Director at CBS News, which puts him in
Speaker 1: charge of one of the most significant operations doing polling
Speaker 1: and calling elections in the country. He's also the author
Speaker 1: of the book Where did You get This Number? A
Speaker 1: polsters Guide to making sense of the world. Anthony, thank
Speaker 1: you for being here. I sometimes feel like the rest
Speaker 1: of us are being cruel to posters because three and
Speaker 1: three quarters years we're interested in what you're saying, we're
Speaker 1: intrigued with what you're saying, and then for a tiny
Speaker 1: run up to a election, we suddenly blitz you, demand
Speaker 1: that you perfectly predict the future and predictively complain if
Speaker 1: you get it even a little bit wrong. So a
Speaker 1: collective apology in advance of my doing exactly those things
Speaker 1: in our conversation. You know what, It's all good. The
Speaker 1: history of polling is one such that elections were used
Speaker 1: as a benchmark. It was never really intended to be
Speaker 1: a tool to just quote unquote predict elections. They were
Speaker 1: used as a benchmark by posters in the earlier part
Speaker 1: of the twentieth century to basically show people, yes, this works,
Speaker 1: Yes we can do a scientific example of the population
Speaker 1: and then get something external to validate that. People tend
Speaker 1: to conflate the ideas of predicting with understanding. It kind
Speaker 1: of went from there, and that just somewhat comes with
Speaker 1: a territory. But it's not at all cruel if it
Speaker 1: brings attention to what we do, because I think the
Speaker 1: large or point of what we do is to try
Speaker 1: to understand the public mind and to try to understand
Speaker 1: people through the lens of looking at aggregate data to
Speaker 1: what they do as mass behavior. And if that brings
Speaker 1: us attention, and then if people say, hey, you know what,
Speaker 1: these polls are kind of interesting, maybe I will pay
Speaker 1: attention to them in twenty twenty one or twenty twenty
Speaker 1: two and see what people think of public policy and
Speaker 1: other important issues, then it's all good. Anthony, you said
Speaker 1: something kind of profound right out of the box, which
Speaker 1: is impressive at eight in the morning. You were just
Speaker 1: saying that we tend to conflate our ability to predict
Speaker 1: something convincingly with our ability to understand that same thing.
Speaker 1: And when you put it that way, you're making the
Speaker 1: point that actually prediction isn't the same thing as understanding.
Speaker 1: You know, understanding. We actually want to know why people
Speaker 1: do things, whereas prediction in theory could be blind to
Speaker 1: the question of why as long as we know what
Speaker 1: people are going to do. And I guess I want
Speaker 1: to ask you more about this idea that you're seeking
Speaker 1: to under stand people. Is it do you see when
Speaker 1: you wake up every morning your goal to be I
Speaker 1: want to make the best predictions that I can, or
Speaker 1: do you see it as I want to understand why
Speaker 1: people are going to do the things that you're going
Speaker 1: to do, including voting the way they're going to vote.
Speaker 1: It's the latter, It's entirely the latter. And I've told
Speaker 1: people you should judge me on whether I help you
Speaker 1: understand what is going on in the world around you.
Speaker 1: Here's an example. You always see around this time of year,
Speaker 1: people saying, well, I can't talk to friends and family
Speaker 1: because they're for the other side. You know, one person's
Speaker 1: a Democrat, one person is a Republican. Let's provide for Trump.
Speaker 1: We don't get wrong anymore. We don't know what they're thinking. Well,
Speaker 1: suppose you had a tool that could help you understand
Speaker 1: a little bit about what the other side is thinking,
Speaker 1: then maybe that could facilitate a conversation. Maybe basic level.
Speaker 1: You just have a better time at dinner. You have
Speaker 1: that ability if you read and understand a good pull.
Speaker 1: We posters often try to avoid the name posters, and
Speaker 1: we prefer to be called survey researchers because we talk
Speaker 1: to people and that idea can be carried through to
Speaker 1: the user as well. So I like to think all
Speaker 1: the way back to the start of your question, if
Speaker 1: we can deliver that understanding, then that's really the goal,
Speaker 1: that's really the job. Let me ask you a few
Speaker 1: questions about key concepts that you and all other survey
Speaker 1: researchers use but that at least for me, aren't as
Speaker 1: clear as I would like them to be, and I
Speaker 1: might be not alone in this. Let's start with likely voter,
Speaker 1: which is obviously hugely important concept. It's great to get
Speaker 1: a sense of how the average person would vote, but
Speaker 1: the average person isn't the person necessarily who was going
Speaker 1: to vote, because we have turnout levels in the United
Speaker 1: States that are much lower than they are in some
Speaker 1: other countries, and so a lot rests on your prediction
Speaker 1: of whether someone is likely to vote, and then you
Speaker 1: have to weigh that person's opinion in your overall analysis
Speaker 1: of your research data, balanced by the probability that you
Speaker 1: assigned the likely of their voting. So how do you
Speaker 1: know what are the measures that you consider most reliable
Speaker 1: when you're asking if someone is a likely voter. So
Speaker 1: the likely voter model, which we used in a survey,
Speaker 1: basically says, how can we combine things you the respondent,
Speaker 1: tell us you're going to do, and whether or not
Speaker 1: we believe, for lack of a better word, that you're
Speaker 1: going to do what you say you're going to do.
Speaker 1: This is the behavioral component of a poll. We know
Speaker 1: the attitudinal component. I think the economy is good. I
Speaker 1: like the president and only the president. This is the
Speaker 1: behavioral stuff, which frankly is a lot harder. Well. On
Speaker 1: one hand, we can take everyone who in a previous
Speaker 1: poll told us they were going to vote, and we
Speaker 1: can go call them back after the election and see
Speaker 1: how many of them tell us that they actually did. Now,
Speaker 1: there may be some overreporting in that, but for the
Speaker 1: most part, this is a pretty good measure, meaning that
Speaker 1: people don't necessarily tell you the truth. If they did vote,
Speaker 1: they're happy to tell you, But if they didn't vote,
Speaker 1: they might be ashamed to say, yeah, I didn't actually
Speaker 1: turn out. That's right, And we know that there is
Speaker 1: some overreporting of that. It is not large by the way,
Speaker 1: Just as a quick aside, I don't mix that with
Speaker 1: people lying. This is usually what happens. They say they're
Speaker 1: going to vote, they intend to vote, and then things
Speaker 1: get in the way. Absolutely, we do that, and we
Speaker 1: also know from lots of other economic data that we
Speaker 1: do all kinds of predictions about ourselves which overstate our
Speaker 1: ability to stick to our word exactly. So we've got
Speaker 1: some rough measure, and we can go back and look
Speaker 1: at that from past polls, and let's say it is
Speaker 1: the case about ninety percent of people tell us they
Speaker 1: vote actually vote. Well, then we've got other measures, like
Speaker 1: from the social science literature, from political science literature. If
Speaker 1: people feel like they're part of a community, they are
Speaker 1: increasingly likely to vote. So someone who is a homeowner
Speaker 1: who's lived in a place for a long time is
Speaker 1: more likely to vote. Now, how do you put that
Speaker 1: into a pull In our case, you build an aggression
Speaker 1: model that would have worked in the past, or you
Speaker 1: can back test your data on and you assign everybody
Speaker 1: in the poll a likelihood of voting based on their
Speaker 1: characteristics such as they're known in the aggregate. If we
Speaker 1: know that ninety five percent of people who have voted
Speaker 1: in every single election, vote in the next one. So
Speaker 1: and then so we have a respondent who's voted in
Speaker 1: every single election, give them a point nine five probability
Speaker 1: score of voting in this one. And then the final
Speaker 1: technical side of that poll is that you take everybody
Speaker 1: with a weight assigned to them, a likely voter score,
Speaker 1: and you sum those up to get the overall likely
Speaker 1: voter estimate. As one way of doing a likely voter model.
Speaker 1: There are others, but it is taking what you see
Speaker 1: in the aggregate and behavior in the aggregate and trying
Speaker 1: to apply it to an individual, which for anybody, whether
Speaker 1: you're trying to gauge voting or whether somebody will buy
Speaker 1: your product, can be a tricky business. There are factors
Speaker 1: that are particular to any given election which are much
Speaker 1: harder to incorporate. So in a given election, somebody might
Speaker 1: be really motivated to vote against or for an incumbent,
Speaker 1: or there might be closed polling places and it might
Speaker 1: be harder to vote, or it might be that you've
Speaker 1: recently moved and don't know where you're going to vote.
Speaker 1: All of those things are variables that are much harder
Speaker 1: to gauge. I want to ask you and drill down
Speaker 1: on those elections specific features because they seem to, at
Speaker 1: least in my generalist perception. So I've had a big
Speaker 1: impact on the twenty sixteen election, and one imagines they
Speaker 1: might on the twenty twenty election. So are you and
Speaker 1: our other posters trying now to make strong predictive answers
Speaker 1: to the following question, our Biden voters going to be
Speaker 1: more likely to really want to turn out to vote
Speaker 1: against Trump, or our Trump voters are going to really
Speaker 1: be likely to turn out in high percentages to defend
Speaker 1: the president as opposed to what people do and let's
Speaker 1: say the immedian election, or as opposed to what they
Speaker 1: did in twenty sixteen. I mean, that seems to be
Speaker 1: certainly from the standpoint of the way the campaigns talk
Speaker 1: maybe the whole ball of wax here right that if
Speaker 1: you know, if one side can really motivate its base,
Speaker 1: it's going to have a huge advantage in winning. So
Speaker 1: I guess what I'm wondering is, are you and our
Speaker 1: other polls actually trying to call that feature in? So
Speaker 1: tell us how you bake it in. Yeah, what we're
Speaker 1: doing is, first we ask attitudinal questions, are you motivated
Speaker 1: to vote? Are you motivated more than you were? I
Speaker 1: have questions like how long would you stand in line?
Speaker 1: And the majority of people tell us they'll stand in
Speaker 1: line quote as long as it takes. Is that the case?
Speaker 1: Probably the case for most of them, but things get
Speaker 1: in the way. But I also see people who say
Speaker 1: they'll only stand in line for half an hour. That's
Speaker 1: useful information we have and turnout model in every poll
Speaker 1: number that you see from US. Joe Biden's fortunes right now, frankly,
Speaker 1: including in all the polls that I put out, is
Speaker 1: that he is heavily dependent on people who tell us
Speaker 1: that they're going to vote for the first time, so
Speaker 1: they're included in the poll. But does past behavior predict
Speaker 1: future behavior? That's harder to know. And if they don't
Speaker 1: show up, this election will be much tighter. How much
Speaker 1: of a discount factor are you applying? I mean, just
Speaker 1: to make it as practical as possible. If the first
Speaker 1: time TI voter on average did not turn up, how
Speaker 1: bad is that for Biden? Whereas if the first time
Speaker 1: voter who says, yes, I'm voting, but I've never done
Speaker 1: it before does turn out and vote, how good is
Speaker 1: that for Biden. Oh, it varies state by state, but
Speaker 1: if the first time voter turns out, then what you
Speaker 1: see in the polling will will manifest itself, meaning Biden's
Speaker 1: big lead will be carried out. He'll win by a lot. Yeah,
Speaker 1: I mean I shy away from saying anybody will win,
Speaker 1: but it would greatly advantage him. However, if those folks
Speaker 1: don't show up, that's a much much tighter race. And look,
Speaker 1: I will add this because it really applies here. The
Speaker 1: uncertainty in this election, in my mind, is something that
Speaker 1: I have a great deal of difficulty quantifying, and that
Speaker 1: is the mechanism of voting. It is the balloting process.
Speaker 1: We talk a lot about turnout as though it is
Speaker 1: all based on motivation, because in much money part parts
Speaker 1: it is, there's a motivation, etc. But in this case,
Speaker 1: you have people transitioning from old habits to new ones.
Speaker 1: They stood in line at their local polling place, their
Speaker 1: local school, which is two blocks away for twenty five years,
Speaker 1: and now that polling place has been closed because counties
Speaker 1: are consolidating due to COVID, and now they have to
Speaker 1: drive across the county to a massive voting center where
Speaker 1: they need to find parking and where they need to
Speaker 1: stand in longer line, and where they need to know
Speaker 1: where it is in the first place. Or they're requesting
Speaker 1: a mail ballot, Well, okay, this big envelope comes in
Speaker 1: the mail, and it's not that difficult. But now you've
Speaker 1: opened up these new forms and you have to sign
Speaker 1: it in the right place, and in some places you
Speaker 1: have to stick it inside another envelope. All of those
Speaker 1: changes and how they affect people are really unknown to us,
Speaker 1: and I think that's a great deal of uncertainty in
Speaker 1: this election. And I suspect if turnout patterns vary a lot,
Speaker 1: or by more than we think that they are, that
Speaker 1: that's going to be one explanation why that person in
Speaker 1: the example I just used, Well, they decided they couldn't
Speaker 1: find parking at Dodgers Stadium, which they turned into a big,
Speaker 1: giant polling place. Maybe no surprise there, Or they didn't
Speaker 1: quite navigate the mail ballot process correctly and their ballot
Speaker 1: didn't get there or was rejected or what have you.
Speaker 1: So all of that is incredibly difficult to quantify. I
Speaker 1: suspect we'll get some help in that in the aggregate
Speaker 1: as we get closer to the election, because we we'll
Speaker 1: see is the return rate on a lot of mail votes,
Speaker 1: and we'll also have the total early vote from all
Speaker 1: these early voting locations, so we'll start to have a
Speaker 1: sense of who's done that, how much of it has
Speaker 1: sort of been cannibalized from what would have been election
Speaker 1: day vote, and then we'll narrow down to what remains
Speaker 1: the people who haven't voted yet when we wake up
Speaker 1: on the morning of November third. That'll be a help,
Speaker 1: but from a polling standpoint, that is all very, very
Speaker 1: hard to quantify. We'll be right back. You mentioned aggregation,
Speaker 1: and that's also something that I'm totally fascinated by, and
Speaker 1: it's something that in recent years has become more and
Speaker 1: more salient to anybody who reads polls in the newspapers
Speaker 1: or watches them on television. I'm specifically thinking of the
Speaker 1: aggregation of lots of polls. There are lots of different polls.
Speaker 1: They have different methodologies. Some are local, some are national.
Speaker 1: But there are sites and experts, and you're one of
Speaker 1: them who say, look, here's what my polling data shows,
Speaker 1: but here's what my aggregation of all everybody's polling data shows.
Speaker 1: And there's some implication there, a kind of wisdom of
Speaker 1: crowds idea that it's better to have as many different polls,
Speaker 1: even if they different methodology, some of which might not
Speaker 1: be your favorite methodology, than just to rely on one poll.
Speaker 1: What's your sense of that. Are you one of those
Speaker 1: people who believes that will have better information by aggregating
Speaker 1: lots of polls even if some of them have a
Speaker 1: methodology that you wouldn't be crazy about, or are you
Speaker 1: somebody who thinks, no, we're better off relying on carefully done,
Speaker 1: well done polls and only those polls. The second, I'm
Speaker 1: not a fan of aggregation. I understand why it's done,
Speaker 1: and in general it's not unreasonable. The comparison I make
Speaker 1: is do you want a beat you can dance to
Speaker 1: or do you want to hear the song? If you
Speaker 1: go to a nightclub and the DJ is combining a
Speaker 1: lot of different songs together to give you a really
Speaker 1: good beat, or blending one after another, that's really fun
Speaker 1: and that's really useful because you just want to dance
Speaker 1: and have a good time. If you really want to
Speaker 1: appreciate music and you want to dive into what an
Speaker 1: artist put out as their song, and you listen to
Speaker 1: a song, and that to me is a little closer
Speaker 1: to what the polster is trying to deliver. Any good
Speaker 1: poll is trying to tell you what people think and
Speaker 1: explain why people think it. And they've offered you a
Speaker 1: model of the electorate. They've implicitly or directly offered you
Speaker 1: a model of behavior because of the questions that they asked,
Speaker 1: because of the things they tried to test or examine
Speaker 1: by asking those questions. Is it about the candidates personalities,
Speaker 1: is it about public policies, etc. So they're offering that
Speaker 1: and you can take it for what it's worth. But
Speaker 1: if it's done well, it should offer you a good
Speaker 1: study of the electorate, and you can compare it. If
Speaker 1: you have the time or the inclination, you can compare
Speaker 1: that study to another one that's a really comprehensive and
Speaker 1: I would dare say a little more sophisticated view of
Speaker 1: how to approach getting information. But again it depends on
Speaker 1: what you want, how deep you want to go. So
Speaker 1: the aggregate is for perhaps a shorthand who's winning? I
Speaker 1: can see it from this, Okay, God, move on with
Speaker 1: my life. Do I want to understand what's going on.
Speaker 1: I don't think you get that with aggregation at all.
Speaker 1: I love the analogy that what you do is you're
Speaker 1: a musician. You know, you're a soloist. You're singing your
Speaker 1: song or you're playing your piece of music, and we
Speaker 1: get a certain kind of depth and appreciation out of that.
Speaker 1: And what the aggregators are doing, you know, on five
Speaker 1: thirty eight or you know what the New York Times
Speaker 1: aggregation is doing is they're DJs. You know, they're they're
Speaker 1: playing it all. They're mixing and matching and there's a
Speaker 1: beat that emerges, and you know that can be as
Speaker 1: you say, that can be great too. It sounds like
Speaker 1: it depends on what you want to listen to in
Speaker 1: a given moment or circumstance. I think that's a brilliant metaphor. So,
Speaker 1: but let me ask you, what song are you playing
Speaker 1: right now? What's your feeling at a musical interpretive level
Speaker 1: of the information you're gathering. That's a great question for
Speaker 1: many many people, it is a referendum on the president. Now.
Speaker 1: In some sense that's not unusual because there's an incumbent
Speaker 1: on the ballot, but it is very particularly the case
Speaker 1: now and for Democrats They say that they are voting
Speaker 1: as much, if not more, to vote against the president
Speaker 1: than for Joe Biden. They say that they are motivated
Speaker 1: to vote. At the same time, you have the president
Speaker 1: who has as solid and a core base of supporters
Speaker 1: as we've ever seen, and that kind of allegiance is
Speaker 1: something unlike we've seen. We did a study right before
Speaker 1: the Republican Convention where we ask does Donald Trump deserve
Speaker 1: your loyalty? And many Republicans said yes. In fact, I
Speaker 1: even tried this. We said what's more important to you
Speaker 1: being a Republican or being a Trump supporter? And Moore
Speaker 1: said it was being a Trump supporter. That kind of
Speaker 1: connection is strong. So you have deeply held, very emotional
Speaker 1: connections to their positions on each side. And it's one
Speaker 1: of the reasons that you can explain in the aggregate
Speaker 1: why you've seen the president's approval ratings hold so historically
Speaker 1: incredibly steady throughout his presidency, where the economy has gone
Speaker 1: up or whether it's gone down, no matter what he said,
Speaker 1: no matter what he's done. And it's also one reason
Speaker 1: why you find remarkable stability in the polls this year
Speaker 1: that Joe Biden edge has been about what it's been
Speaker 1: and it's not like twenty sixteen at all, where we
Speaker 1: saw a large fluctuations in the polling. That is on
Speaker 1: the personal level. On the individual level, part of the
Speaker 1: explanation for what you see in the aggregate that is
Speaker 1: the song of twenty twenty. It is those deeply held
Speaker 1: convictions about what people are seeing. And the final maybe
Speaker 1: a coda if you will, on this is that there
Speaker 1: are very different views of what kind of shape the
Speaker 1: country is in, not just differences on how to solve
Speaker 1: the problems. The Republicans feel that the coronavirus outbreak is
Speaker 1: not as bad as the medical folks say that it is.
Speaker 1: They believe that the death toll is overreported. They are
Speaker 1: less concerned about the virus themselves. Democrats, by contrasts, believe
Speaker 1: that the death toll is underreported. They are themselves much
Speaker 1: more deeply concerned about the virus, and there's some personal
Speaker 1: connection and experience in that, and that they tell us
Speaker 1: it's affecting their communities more. The Democrats live in cities, etc.
Speaker 1: That may be changing as they outbreak unfortunately spreads to
Speaker 1: other places. So there's a very different view. And that's
Speaker 1: just one example of what is happening in the country
Speaker 1: that we don't even agree on what the facts are,
Speaker 1: let alone the more traditional or conventional arguments around politics
Speaker 1: of what do we do to solve this? And that
Speaker 1: is something supposed We're still kind of wrestling with what
Speaker 1: happens in that environment. That remains a question for me.
Speaker 1: Exit pulling. How do you do exit pulling in this
Speaker 1: weird COVID year where many people have mailed in ballots?
Speaker 1: How does that game change and how will you do
Speaker 1: the exit pulling this time around? Is it easier because
Speaker 1: of the mail in votes or is it harder because
Speaker 1: of the disparities in the different ways that votes are
Speaker 1: being cast in different places. Well, the mail in votes
Speaker 1: will have to be interviewed by phone, so that'll either
Speaker 1: be a random digit dial phone looking for somebody voted
Speaker 1: by mail, or where possible, you take the voter rolls,
Speaker 1: you know the folks who returned mail ballots, and you
Speaker 1: attach a phone number to that you call them. So
Speaker 1: that's a change. We've always been able to reach out
Speaker 1: and we have reached out to people who voted by
Speaker 1: mail by telephone. That's always been a component of the
Speaker 1: exit polls. However, this year obviously is going to be
Speaker 1: a much larger one in the early vote. What we're
Speaker 1: doing this year, and this is changed or it's rather
Speaker 1: enlarged or expanded, is positioning exit poll interviewers at the
Speaker 1: early voting sites, so they're out there now and they'll
Speaker 1: be interviewing people who are lined up for early voting
Speaker 1: the same way they would have been on election day
Speaker 1: as people are leaving the polling place. And then the
Speaker 1: final part of that, the actual election day exit poll
Speaker 1: is going to have to deal with the fact that
Speaker 1: polling places are consolidated, there may be more people at
Speaker 1: those polling places, and there will be COVID protections in place,
Speaker 1: so the interviewers will be wearing a mask, they'll be
Speaker 1: hand sanitizer at the table. All of those are sort
Speaker 1: of added this year. As far as the actual sampling
Speaker 1: is concerned, people sometimes think I sort of take every
Speaker 1: chance I can to address this. People sometimes think that
Speaker 1: the exit poll goes to a set of Bellweather precincts.
Speaker 1: That is not the case. It is a randomly sampled
Speaker 1: set of precincts, and so that sampling will have to
Speaker 1: be based on where they open up polling places, and
Speaker 1: then people will be deployed out to a sample of them.
Speaker 1: In the more conventional way, decision desk. You sit on
Speaker 1: the CBS decision desk, And these decision desks are fascinating
Speaker 1: institutions to me because I would say, and my day
Speaker 1: job as constitutional law professor, that the decision desks, though
Speaker 1: unmentioned in our written constitution, have come to be crucial
Speaker 1: elements in how we in fact do the constitutional practice
Speaker 1: of deciding who won elections. We don't have in the
Speaker 1: United States, but a lot of countries have a central
Speaker 1: electoral Commission for the whole country that says, here are
Speaker 1: the results. We have fifty states which each have to
Speaker 1: do their processing on their own, and we never wait
Speaker 1: for all of those to count all of the votes
Speaker 1: before we quote unquote call the election. You call the election.
Speaker 1: And I don't just mean UCBS, I mean you, the
Speaker 1: person sitting in charge of the decision desk with your team.
Speaker 1: So how does it work and how different do you
Speaker 1: think it's going to be in this strange year. It's
Speaker 1: going to be different. People may need to be patient. However,
Speaker 1: I don't think if folks pay attention and they listen
Speaker 1: to us and they watch, I'll do a plug here
Speaker 1: if they watch CBS News, but really, if they watch
Speaker 1: a good network broadcast, they won't be confused because and
Speaker 1: this goes to a question on how does it work.
Speaker 1: What we do is we report what we see unfolding
Speaker 1: from an event, which frankly has already happened. Pretty unusual
Speaker 1: from a pulling perspective, right for once in your lives,
Speaker 1: you're not predicting a future event. You're sort of predicting
Speaker 1: a past event. That's exactly right, that's exactly right. We
Speaker 1: are like a puzzle being revealed piece by piece. There's
Speaker 1: votes in a bank somewhere, there's votes at a voting
Speaker 1: center that are being reported, and the puzzle pieces you
Speaker 1: could think of them as sort of county by county.
Speaker 1: As that's revealed, we start to see a picture, and
Speaker 1: what we're doing our best to do is report what
Speaker 1: that picture looks like, perhaps a little ahead of anybody
Speaker 1: else that could see it that doesn't necessarily have the
Speaker 1: tools that we have at our disposal, and then telling
Speaker 1: you this is what we believe has been revealed. So
Speaker 1: that's the big picture of how it works. What we do,
Speaker 1: specifically is we combine a bunch of different kinds of
Speaker 1: data that we're collecting. One of them is the exit polls.
Speaker 1: So we've got a sense from talking to voters who've
Speaker 1: left their polling place. Now, sometimes in a state that's
Speaker 1: completely lopsided, that's enough to make a projection about what's happened. Well,
Speaker 1: we've heard from voters and so and so has a
Speaker 1: fifty point lead. Okay, that person has one after the
Speaker 1: polls have closed. But in most cases, and in certainly
Speaker 1: in battleground tight states, that's not enough. So now you
Speaker 1: start to get county vote coming in, and what we
Speaker 1: do with that is we look for patterns in it.
Speaker 1: You can look for patterns and you can model data
Speaker 1: in a bunch of different ways. So one would be
Speaker 1: to compare it to pass vote. Let's suppose in county
Speaker 1: after county you see Joe Biden running five points better
Speaker 1: than any Democrat has than the last time, typical Democrats
Speaker 1: or whatever. Well, if you get twenty counties and in
Speaker 1: every single one he's in sagurated example, every single one
Speaker 1: he's five points better than past Democrats, well you can
Speaker 1: make a pretty good inference about what he'll do in
Speaker 1: the remaining counties, maybe five points better than what if
Speaker 1: the Democrat did there, and you can extrapolate that out
Speaker 1: to what he might get statewide, and off you go
Speaker 1: to get a statewide estimate and maybe a projection. But
Speaker 1: where it gets more difficult is, let's suppose you get
Speaker 1: county by county and there's a lot of variants in that.
Speaker 1: So Joe Biden in some places is doing five points better,
Speaker 1: someplaces fifteen points better, and someplaces ten points worse and
Speaker 1: some places twenty points worse. Well, there's no clear pattern
Speaker 1: in that. So what do you do with it? Well,
Speaker 1: not much. You wait before you make a projection. What
Speaker 1: I would add after all that is if you see
Speaker 1: a network say oh we're waiting for vote someplace or
Speaker 1: it's too close to call, that's not always the case.
Speaker 1: What I will tell you, when I try to tell
Speaker 1: you is the patterns here are uncertain. We can't get
Speaker 1: a read on this just yet. It's not that we
Speaker 1: don't have any information. It's that that information looks like
Speaker 1: a big cloud instead of a straight line. And that
Speaker 1: I think, I hope is helpful to people as we
Speaker 1: are transparently trying to describe for you what we see
Speaker 1: being revealed as it's revealed. I don't think of myself
Speaker 1: as filling a constitutional duty, and I remind people we
Speaker 1: do not as networks seat anyone in office. We don't
Speaker 1: certify any votes. We are reporting, I would dare say,
Speaker 1: merely reporting what it is that the voters have done.
Speaker 1: And that's not a sort of false humility. It is,
Speaker 1: in fact the case, as much as the bright lights
Speaker 1: and TV cameras make it seem like it's very, very
Speaker 1: important well, and the fact is that the candidates also
Speaker 1: act on that basis of information. At least back in
Speaker 1: the day, when there were concessions and declarations of victory
Speaker 1: that were credible, they were often based on precisely the
Speaker 1: data that you were aggregating. Thank you for taking the time, Anthony,
Speaker 1: really thank you. I really appreciate it. No, thank you,
Speaker 1: and I hope we get to talk more soon. Talking
Speaker 1: to Anthony Salvanto led to a few powerful takeaways for me.
Speaker 1: Most significantly, Anthony made the point that a lot of
Speaker 1: the reliability of the existing polls depends on people who
Speaker 1: say they are first time voters and that they will
Speaker 1: vote for Joe Biden. The lead that Biden has in
Speaker 1: so many polls is, he says, in important ways, a
Speaker 1: product of believing those voters what they do will have
Speaker 1: a large impact on the outcome of the election. Another
Speaker 1: their takeaway is just how different this time around may
Speaker 1: be from previous times. People will be voting in different
Speaker 1: places than usual, and that means that exit polling also
Speaker 1: has to follow a different approach. What's more, there will
Speaker 1: be mail in votes, potentially in very large numbers, and
Speaker 1: those two need to be incorporated into changed models. Last,
Speaker 1: but by no means least, there's Anthony's idea that individual polls,
Speaker 1: like the kind he conducts, give you the music of
Speaker 1: the election, as opposed to the aggregated beat that comes
Speaker 1: from adding lots of polls together, and for him, the
Speaker 1: music of the election. The song of this election is
Speaker 1: Democratic voters very eager to get rid of Donald Trump
Speaker 1: on the one side, and deeply loyal Trump voters, for
Speaker 1: whom being a Trump supporter is even more important than
Speaker 1: being a Republican on the other. In other words, the
Speaker 1: future of our republic is genuinely at stake. Remember to
Speaker 1: tune into our special series Deep Bench this Saturday. Until
Speaker 1: the next time I speak to you, be careful, be safe,
Speaker 1: and be well. Deep Background is brought to you by
Speaker 1: Pushkin Industries. Our producer is Lydia Gencott, our engineer is
Speaker 1: Martin Gonzalez, and our showrunner is Sophie Crane mckibbon. Theme
Speaker 1: music by Luis Guerra at Pushkin. Thanks to Mia Lobell,
Speaker 1: Julia Barton, Heather Faine, and Carlie mcliori, Mackie Taylor, Eric Sandler,
Speaker 1: and Jacob Weisberg. You can find me on Twitter at
Speaker 1: Noah R. Feldman. I also write a column for Bloomberg Opinion,
Speaker 1: which you can find at Bloomberg dot com slash Feldman.
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