1 00:00:01,040 --> 00:00:11,240 Speaker 1: Hi, Brian, Hi Katie. Today our podcast is Bicoastal. Brian 2 00:00:11,400 --> 00:00:14,520 Speaker 1: is in California. I am in New York. You probably 3 00:00:14,600 --> 00:00:18,279 Speaker 1: are very well aware that the two thousand and sixteen 4 00:00:18,280 --> 00:00:25,720 Speaker 1: presidential election is less than a week away. I'm sure 5 00:00:25,760 --> 00:00:29,040 Speaker 1: that's probably how you feel. Brian is the only person 6 00:00:29,160 --> 00:00:32,200 Speaker 1: I think on the planet who's going to be sad, well, 7 00:00:32,200 --> 00:00:35,480 Speaker 1: maybe a couple of cable networks as well to see 8 00:00:35,520 --> 00:00:40,440 Speaker 1: this election come to a close. Are you having withdrawals yet? Brian? No, 9 00:00:40,600 --> 00:00:42,960 Speaker 1: I'm just trying to enjoy it. Well at last, it's 10 00:00:43,000 --> 00:00:46,040 Speaker 1: like burning Man for me. I think, oh, yeah, you're 11 00:00:46,120 --> 00:00:48,760 Speaker 1: such a Bernie Man guy. By the way, I mean, 12 00:00:48,800 --> 00:00:51,960 Speaker 1: there's been plenty to keep us busy when it comes 13 00:00:52,000 --> 00:00:55,760 Speaker 1: to talking about all things political. So much has happened. 14 00:00:56,040 --> 00:00:58,640 Speaker 1: I think we could change the lyrics of the September 15 00:00:58,720 --> 00:01:03,160 Speaker 1: song to the November song. Brian, don't you I feel 16 00:01:03,200 --> 00:01:06,319 Speaker 1: like this is your queue. Oh it's a long, long 17 00:01:06,520 --> 00:01:12,200 Speaker 1: while from me to November. I was right about that. 18 00:01:12,319 --> 00:01:15,360 Speaker 1: I like that it was nice Bobrado, But so much 19 00:01:15,360 --> 00:01:18,080 Speaker 1: has happened. I mean, it's really hard to keep all 20 00:01:18,200 --> 00:01:21,640 Speaker 1: this straight, isn't it. Well, there's a rich history of 21 00:01:21,760 --> 00:01:26,280 Speaker 1: October surprises in American politics. You go back to eight 22 00:01:26,319 --> 00:01:29,160 Speaker 1: there was a bombing halt in North Vietnam, which was 23 00:01:29,160 --> 00:01:34,080 Speaker 1: supposed to help then Democratic candidate Hubert Humphrey. In seventy two, 24 00:01:34,440 --> 00:01:38,120 Speaker 1: Henry Kissinger said pieces at hand, also in Vietnam, and 25 00:01:38,160 --> 00:01:43,360 Speaker 1: that supposedly helped Nixon into The former Reagan Defense secretary 26 00:01:43,480 --> 00:01:46,679 Speaker 1: was indicted, which supposedly hurt George H. W. Bush. There 27 00:01:46,680 --> 00:01:49,280 Speaker 1: are a lot of examples of this. Shall answer man, 28 00:01:49,320 --> 00:01:53,240 Speaker 1: what happened in for crying out loud? What was a 29 00:01:53,280 --> 00:01:59,400 Speaker 1: internal action? Sorry, of course it was scratch that you 30 00:01:59,440 --> 00:02:04,560 Speaker 1: want to talk about at the McKinley ray. But but 31 00:02:04,720 --> 00:02:08,720 Speaker 1: I think what's interesting about all of these October surprises, 32 00:02:08,760 --> 00:02:11,400 Speaker 1: from Bush's d w I to the bin Laden tape 33 00:02:11,400 --> 00:02:15,079 Speaker 1: to Hurricane Sandy, just to deploy a few more, is 34 00:02:15,400 --> 00:02:19,200 Speaker 1: we're unsure about what the electoral impact is. It's very 35 00:02:19,280 --> 00:02:23,480 Speaker 1: hard to prove causation, and so we really don't know. 36 00:02:23,960 --> 00:02:26,799 Speaker 1: To use a phrase that's not often used by the media, 37 00:02:26,880 --> 00:02:29,520 Speaker 1: we really don't know what this means. And you know, 38 00:02:29,560 --> 00:02:31,399 Speaker 1: we're gonna have to wait and see what somebody who 39 00:02:31,440 --> 00:02:35,400 Speaker 1: probably has a good idea is Nate Silver he's become 40 00:02:35,520 --> 00:02:40,120 Speaker 1: almost a godlike figure for political junkies. Has any Brian? 41 00:02:40,480 --> 00:02:43,040 Speaker 1: He is, he is a god among geeks and I 42 00:02:43,080 --> 00:02:48,720 Speaker 1: include myself proudly in that category. Uh. He's he's invented 43 00:02:48,720 --> 00:02:52,119 Speaker 1: a whole new category really of journalism in which he 44 00:02:52,639 --> 00:02:56,640 Speaker 1: uses analytics and statistical models to put pulling into this 45 00:02:56,680 --> 00:03:00,400 Speaker 1: big machine that he's created and hopefully push wisdom out 46 00:03:00,440 --> 00:03:02,320 Speaker 1: that will give us a little bit of a better 47 00:03:02,360 --> 00:03:05,679 Speaker 1: sense about where the race actually stands. And you are 48 00:03:05,800 --> 00:03:09,680 Speaker 1: so excited that he's our guests on today's podcast, aren't you? Brian? 49 00:03:10,120 --> 00:03:14,760 Speaker 1: I am all a flutter, even from three thousand miles away. 50 00:03:16,960 --> 00:03:19,080 Speaker 1: I need silver welcome. It is so great to have 51 00:03:19,200 --> 00:03:21,480 Speaker 1: you cool, Thank you for having me, Katie. First, we 52 00:03:21,520 --> 00:03:24,160 Speaker 1: have to start out with comy Gate. Yeah, how big 53 00:03:24,160 --> 00:03:26,480 Speaker 1: a bombshell or how big an impact do you think 54 00:03:26,520 --> 00:03:28,680 Speaker 1: this is going to have on the election? So it's 55 00:03:28,720 --> 00:03:29,959 Speaker 1: a little hard to tell, which is none of a 56 00:03:30,040 --> 00:03:33,680 Speaker 1: very satisfying answer. Um, we began to detect another people 57 00:03:33,720 --> 00:03:37,360 Speaker 1: who average poles begin to detect some momentum back toward 58 00:03:37,440 --> 00:03:39,880 Speaker 1: Trump about a week ago, a week and a half 59 00:03:39,920 --> 00:03:42,680 Speaker 1: ago or so, um, where he had had a very 60 00:03:42,800 --> 00:03:46,560 Speaker 1: very bad string of events with three debates that he 61 00:03:46,640 --> 00:03:51,280 Speaker 1: was judged to have lost. UM, the UH, the access 62 00:03:51,320 --> 00:03:53,400 Speaker 1: Hollywood tape I'm never quite sure how to refer to 63 00:03:54,080 --> 00:03:56,600 Speaker 1: the P word tape. UM. And then all these accusations 64 00:03:56,600 --> 00:03:59,440 Speaker 1: were women that he had sexually assaulted them, and it 65 00:03:59,520 --> 00:04:02,520 Speaker 1: was about is a bad um a three weeks as 66 00:04:02,520 --> 00:04:05,400 Speaker 1: a candic can have. UM. I'm not saying any of 67 00:04:05,480 --> 00:04:08,560 Speaker 1: it isn't his fault, right, but you know, sometimes when 68 00:04:08,640 --> 00:04:11,200 Speaker 1: you go from a terrible news cycle to a better 69 00:04:11,200 --> 00:04:14,240 Speaker 1: new cycle, you begin to rebound just because your own 70 00:04:14,280 --> 00:04:16,400 Speaker 1: base begins to come home to you. So we began 71 00:04:16,440 --> 00:04:19,800 Speaker 1: to see Trump's numbers begin to go up, not by 72 00:04:19,839 --> 00:04:22,159 Speaker 1: leaps and bounds, but a little bit um as you 73 00:04:22,200 --> 00:04:25,880 Speaker 1: regain Republican votes. UM. And then you had Friday when 74 00:04:25,880 --> 00:04:27,800 Speaker 1: you have this FBI news another story. I'm not quite 75 00:04:27,839 --> 00:04:30,560 Speaker 1: sure how to characterize it exactly, but comy, we'll call 76 00:04:30,600 --> 00:04:34,479 Speaker 1: it UM. And we've seen some further tightening since then, 77 00:04:34,520 --> 00:04:36,760 Speaker 1: although it's not clear whether that was already baked dinner 78 00:04:36,839 --> 00:04:40,599 Speaker 1: because of of Comy. My impression is that UM. Trump 79 00:04:40,640 --> 00:04:42,880 Speaker 1: had gained one or two points on Clinton over the 80 00:04:42,920 --> 00:04:45,840 Speaker 1: past two weeks and maybe gained another point or so 81 00:04:46,520 --> 00:04:50,279 Speaker 1: UM over the weekend. And when you describe the tightening, Nate, 82 00:04:50,440 --> 00:04:53,560 Speaker 1: just to be clear, are you talking about Hillary going 83 00:04:53,640 --> 00:04:55,920 Speaker 1: down at all or is it just Trump coming up 84 00:04:55,920 --> 00:04:58,640 Speaker 1: with core Republican voter. That's it's the latter. We know 85 00:04:58,680 --> 00:05:01,080 Speaker 1: Trump's coming up. How much that is for publicans versus independence, 86 00:05:01,080 --> 00:05:04,120 Speaker 1: Probably a mix of both. But she's in national polls 87 00:05:04,160 --> 00:05:09,080 Speaker 1: she has stayed at about UM and Trump's gone from 88 00:05:10,680 --> 00:05:13,720 Speaker 1: now forty one or so, so he's regained some ground 89 00:05:13,880 --> 00:05:18,120 Speaker 1: on Clinton UM. But you know, forty one percent not 90 00:05:18,240 --> 00:05:20,520 Speaker 1: enough to win. For that matter, forty six percent is 91 00:05:20,560 --> 00:05:22,680 Speaker 1: not quite enough. So it's kind of like, you know, 92 00:05:23,200 --> 00:05:25,320 Speaker 1: if she gets another point or two out of her 93 00:05:25,320 --> 00:05:28,600 Speaker 1: base gets up to forty seven, she gets to be 94 00:05:28,640 --> 00:05:31,599 Speaker 1: in a pretty safe position. If she gets stuck, she's 95 00:05:31,600 --> 00:05:33,920 Speaker 1: probably a favorite, but it'll be more suspense on election day. 96 00:05:34,240 --> 00:05:37,839 Speaker 1: And if she declines, then things are really competitive. And 97 00:05:37,880 --> 00:05:41,000 Speaker 1: there's I guess some speculation, isn't there, that that there 98 00:05:41,080 --> 00:05:44,000 Speaker 1: may be something brewing at the FBI, some kind of 99 00:05:44,040 --> 00:05:47,520 Speaker 1: investigation involving Donald Trump, and the Democrats would like nothing 100 00:05:47,560 --> 00:05:51,719 Speaker 1: more than have that get leaked to say we'll wait 101 00:05:51,760 --> 00:05:54,560 Speaker 1: a second. They may be looking at these emails on 102 00:05:54,600 --> 00:05:59,000 Speaker 1: Anthony Wiener's device. That sounded dirty. I'm so sorry, but 103 00:05:59,080 --> 00:06:04,360 Speaker 1: they're also teen at potential bad behavior on the part 104 00:06:04,360 --> 00:06:08,040 Speaker 1: of Donald Trump, the Russian government and the hacking of emails. 105 00:06:08,120 --> 00:06:10,800 Speaker 1: Right well, Trump creates problem by Thursday, we should know, 106 00:06:10,960 --> 00:06:13,040 Speaker 1: I mean, incidentally, we should point out to the listeners 107 00:06:13,080 --> 00:06:15,880 Speaker 1: that were recording this on Monday, were in the time machine, 108 00:06:15,960 --> 00:06:18,599 Speaker 1: and when people hear this on Thursday, you know, we 109 00:06:18,600 --> 00:06:21,039 Speaker 1: could have like three or four front page New York 110 00:06:21,040 --> 00:06:24,080 Speaker 1: Times scandals between now and that's. I mean, it had 111 00:06:24,120 --> 00:06:27,839 Speaker 1: been this kind of oddly anticlimactic finish before the comy 112 00:06:27,920 --> 00:06:30,640 Speaker 1: thing struck. You know, the debates kind of inted early 113 00:06:30,720 --> 00:06:32,480 Speaker 1: this year, and it's been a long campaign, so I 114 00:06:32,520 --> 00:06:35,240 Speaker 1: think that's part of why people jumped on it. They 115 00:06:35,279 --> 00:06:37,000 Speaker 1: kind of proclaimed it to be a huge game changer, 116 00:06:37,360 --> 00:06:40,120 Speaker 1: um before people have seen any evidence for really what 117 00:06:40,120 --> 00:06:42,440 Speaker 1: it was or how it would move the polls. How 118 00:06:42,480 --> 00:06:45,240 Speaker 1: responsible do you think the media is for jenning this up? 119 00:06:45,320 --> 00:06:48,120 Speaker 1: I know you tweeted quote. The FBI story also broke 120 00:06:48,120 --> 00:06:50,440 Speaker 1: at the exact time when the media was eager for 121 00:06:50,520 --> 00:06:55,040 Speaker 1: a dramatic twist slash complication in the Clinton Coast narrative. 122 00:06:55,640 --> 00:06:59,279 Speaker 1: I mean, so to give a counterexample, think about Wiki leaks, 123 00:06:59,279 --> 00:07:02,560 Speaker 1: and that's never really broken through and been the main story, 124 00:07:02,680 --> 00:07:05,600 Speaker 1: in part because that was, um, We're all coming out 125 00:07:05,640 --> 00:07:07,760 Speaker 1: at the time that Trump was making a lot of news, 126 00:07:08,240 --> 00:07:11,920 Speaker 1: and by the time that Trump quieted down, Wiki leaks 127 00:07:11,960 --> 00:07:13,880 Speaker 1: was old news. So I do think the timing matters 128 00:07:13,920 --> 00:07:18,320 Speaker 1: a lot. Julian was probably so pistol and if he 129 00:07:18,400 --> 00:07:21,680 Speaker 1: releases in the primaries where you'd have more um, inter 130 00:07:21,840 --> 00:07:25,600 Speaker 1: party warfare, and that might have been more devastating for Clinton. UM. 131 00:07:25,640 --> 00:07:28,920 Speaker 1: But you know, I do have the sense that, um, look, 132 00:07:29,000 --> 00:07:32,960 Speaker 1: the media has incentives to to play up how competitive 133 00:07:33,000 --> 00:07:35,920 Speaker 1: the races. Trumpet had a very difficult for your weeks. 134 00:07:35,920 --> 00:07:37,520 Speaker 1: They were working the rests a lot, and I do 135 00:07:37,640 --> 00:07:40,120 Speaker 1: think that, um. And by the way, is also though 136 00:07:40,160 --> 00:07:46,320 Speaker 1: that you had this very exciting, devastating, impactful bulletin right 137 00:07:46,680 --> 00:07:48,360 Speaker 1: and then it didn't really live up to the billing, 138 00:07:48,360 --> 00:07:51,560 Speaker 1: and so people are are hesitant to adjust for that sometimes. 139 00:07:51,560 --> 00:07:53,680 Speaker 1: But it is interesting Clinton decided I'm not gonna go 140 00:07:53,760 --> 00:07:56,040 Speaker 1: after the media. I'm gonna go after Comey himself. If 141 00:07:56,040 --> 00:07:58,160 Speaker 1: it were a Republican, they would say, oh my god, 142 00:07:58,200 --> 00:07:59,720 Speaker 1: you made his guys made something out of nothing, and 143 00:07:59,720 --> 00:08:01,960 Speaker 1: you've got headline wrong on this and that. But Democrats 144 00:08:02,000 --> 00:08:03,920 Speaker 1: don't attack the media in the way that Republicans often 145 00:08:04,000 --> 00:08:06,600 Speaker 1: very effectively due to rile up their base. But Brian 146 00:08:06,640 --> 00:08:08,920 Speaker 1: and Nate, I mean, don't you think it's so strange 147 00:08:09,080 --> 00:08:12,280 Speaker 1: that there will not be any kind of resolution to 148 00:08:12,400 --> 00:08:16,080 Speaker 1: this whole quote unquote crisis before the election. I mean, 149 00:08:16,120 --> 00:08:19,160 Speaker 1: it's it's what they're There are hundreds of thousands of 150 00:08:19,240 --> 00:08:23,400 Speaker 1: emails or thousands anyway, I guess sid and all, but 151 00:08:23,440 --> 00:08:26,400 Speaker 1: a lot of those are Anthony Weiner's emails, right, Brian, 152 00:08:26,520 --> 00:08:30,160 Speaker 1: So what kind of impact I'm interested from you? Brian 153 00:08:30,360 --> 00:08:34,200 Speaker 1: n Nate? Does this kind of dark cloud have hanging 154 00:08:34,240 --> 00:08:37,240 Speaker 1: over Hillary Clinton's head? Are people going to dismiss it 155 00:08:37,679 --> 00:08:42,080 Speaker 1: and say it's a much ado about nothing? Well, we'll see. 156 00:08:42,080 --> 00:08:44,200 Speaker 1: I mean, there's been some reporting that the FBI would 157 00:08:44,200 --> 00:08:47,920 Speaker 1: try to make some characterization like, oh, there's something new 158 00:08:47,960 --> 00:08:51,280 Speaker 1: here or there's not right. What's too late for that? Though? 159 00:08:51,440 --> 00:08:52,960 Speaker 1: Maybe if there's day Friday, they can If they said 160 00:08:52,960 --> 00:08:55,360 Speaker 1: that Monday morning or Tuesday morning, right while people are 161 00:08:55,720 --> 00:08:58,000 Speaker 1: are voting, how does that affect things potentially? And by 162 00:08:58,000 --> 00:09:00,400 Speaker 1: the way, um, you know, a lot of people in 163 00:09:00,400 --> 00:09:02,600 Speaker 1: the country have voted. About a quarter of the vote 164 00:09:02,679 --> 00:09:05,800 Speaker 1: roughly is in in swing states with early voting, their 165 00:09:05,800 --> 00:09:10,040 Speaker 1: states like Nevada, where substantial numbers, maybe more than half 166 00:09:10,080 --> 00:09:12,400 Speaker 1: the vote will be done before election day, and so 167 00:09:12,760 --> 00:09:15,160 Speaker 1: you know the election is already underway. Now I've heard 168 00:09:15,160 --> 00:09:17,880 Speaker 1: that that is good news for Hillary Clinton in terms 169 00:09:17,920 --> 00:09:20,520 Speaker 1: of the way early voting is shaping up in states 170 00:09:20,559 --> 00:09:23,880 Speaker 1: like Florida. What are you sort of postulating from those 171 00:09:23,920 --> 00:09:27,920 Speaker 1: early voting returns. So it's a little tricky because Democrats 172 00:09:28,000 --> 00:09:30,240 Speaker 1: usually put a bigger emphasis on early voting But the 173 00:09:30,240 --> 00:09:33,439 Speaker 1: two states where her numbers look good are North Carolina 174 00:09:33,559 --> 00:09:38,040 Speaker 1: and Nevada. Um. North Carolina registration is by party Idiana 175 00:09:38,120 --> 00:09:40,800 Speaker 1: Nevada for that matter. UM. And so you see not 176 00:09:40,840 --> 00:09:43,160 Speaker 1: necessarily the African American turnout that Obama had, but you 177 00:09:43,200 --> 00:09:47,240 Speaker 1: see big support from highly educated white people, which in 178 00:09:47,280 --> 00:09:48,839 Speaker 1: North Carolina you actually have quite a few of in 179 00:09:48,880 --> 00:09:53,079 Speaker 1: the research triangle and Charlotte and whatnot, and they're turning out. Um, 180 00:09:53,200 --> 00:09:56,760 Speaker 1: we presume for Clinton in big numbers. UM. And Nevada 181 00:09:56,800 --> 00:09:59,760 Speaker 1: is a case where it's a low turnout state. If 182 00:09:59,760 --> 00:10:01,760 Speaker 1: you have a ground game the other can it does not, 183 00:10:02,040 --> 00:10:05,480 Speaker 1: then that's a big advantage when we should say Republicans 184 00:10:05,520 --> 00:10:08,360 Speaker 1: have a history of showing up in much larger numbers 185 00:10:08,360 --> 00:10:12,400 Speaker 1: on election day and making up deficits with early voting. 186 00:10:12,640 --> 00:10:14,840 Speaker 1: So it's not clear yet that there isn't gonna be 187 00:10:14,840 --> 00:10:18,320 Speaker 1: a huge Trump turnout on November eight, right, Um, But 188 00:10:18,480 --> 00:10:21,800 Speaker 1: it does mean that if you hear about a low turnout, 189 00:10:21,880 --> 00:10:23,760 Speaker 1: usually think of Democrats want to hire turn out. Right? 190 00:10:23,760 --> 00:10:26,000 Speaker 1: It could be that if you have a lower turnout 191 00:10:26,440 --> 00:10:28,720 Speaker 1: in some states on November eighth, then you know that 192 00:10:28,760 --> 00:10:30,360 Speaker 1: Democrats have already banked a lot of votes there for 193 00:10:30,440 --> 00:10:32,880 Speaker 1: the higher proportion of the vote, And so you know 194 00:10:33,360 --> 00:10:35,520 Speaker 1: which can you wants to hear reports of higher turnouts? 195 00:10:35,559 --> 00:10:37,680 Speaker 1: A little bit unclear this time around? Can I ask 196 00:10:37,720 --> 00:10:40,720 Speaker 1: a stupid question? When it comes to early voting. They 197 00:10:40,720 --> 00:10:44,319 Speaker 1: don't get counted until after election day, do they? So 198 00:10:44,960 --> 00:10:48,000 Speaker 1: what many states do is they'll report statistics by party 199 00:10:48,040 --> 00:10:51,520 Speaker 1: I D. So we know, for example that um, something 200 00:10:51,559 --> 00:10:55,240 Speaker 1: like forty thou more Democrats and Republicans have voted as 201 00:10:55,240 --> 00:10:57,560 Speaker 1: of Sunday in Clark County, Nevada. That Nember might be wrong, 202 00:10:57,640 --> 00:11:00,800 Speaker 1: but somewhere in that ballpark right. Um. Either way, people 203 00:11:00,800 --> 00:11:03,439 Speaker 1: in Nevada get very pissed when you call it Nevada. 204 00:11:03,520 --> 00:11:06,280 Speaker 1: He said, well, well, Nate said Nevada first, and then 205 00:11:06,320 --> 00:11:11,400 Speaker 1: I was like, you go, Nate, and Donald Trump says Nevada. 206 00:11:13,040 --> 00:11:15,720 Speaker 1: But the only possible thing I could teach you today 207 00:11:15,800 --> 00:11:18,560 Speaker 1: that you don't already know, but half my families from there, 208 00:11:18,840 --> 00:11:21,480 Speaker 1: and they hate Nevada. I don't know why. Let's call 209 00:11:21,520 --> 00:11:26,080 Speaker 1: the whole thing off. Brian, you know Savada and I 210 00:11:26,200 --> 00:11:30,199 Speaker 1: say Nevada. The word for a person from Utah is 211 00:11:30,240 --> 00:11:33,679 Speaker 1: a Utah, not a Utah. And oh I didn't know people. 212 00:11:34,520 --> 00:11:39,920 Speaker 1: They both sound strange syllables. See I learned something today 213 00:11:39,920 --> 00:11:42,880 Speaker 1: from Nate. Did you know that I did not? I 214 00:11:42,920 --> 00:11:47,520 Speaker 1: got us off. Let's talk about undecided voters because Brian 215 00:11:47,520 --> 00:11:49,240 Speaker 1: and I talked about this and we're like, who the 216 00:11:49,280 --> 00:11:52,600 Speaker 1: hell is undecided at this point in this crazy election? 217 00:11:52,920 --> 00:11:56,120 Speaker 1: Could there really be people who are saying Hillary Clinton 218 00:11:56,160 --> 00:12:01,280 Speaker 1: Donald Trump, which one? I love them both, right? Or 219 00:12:02,440 --> 00:12:06,640 Speaker 1: are the undecided really choosing between Trump or a third party, 220 00:12:06,760 --> 00:12:09,920 Speaker 1: or Hillary Clinton or staying at home? I mean, what 221 00:12:09,960 --> 00:12:13,120 Speaker 1: do we know about the pool of undecided? So we 222 00:12:13,200 --> 00:12:17,240 Speaker 1: know that there are people who mostly like President Obama, 223 00:12:17,880 --> 00:12:19,640 Speaker 1: but I think the country is on the wrong track, 224 00:12:19,720 --> 00:12:22,599 Speaker 1: which is a weird set of circumstances. We know that 225 00:12:22,720 --> 00:12:25,000 Speaker 1: people who mostly dislike both candidates, there aren't a lot 226 00:12:25,040 --> 00:12:28,600 Speaker 1: of people who love their choices. Um. To me, these 227 00:12:28,600 --> 00:12:31,200 Speaker 1: feel like people who might wind up staying at home 228 00:12:31,520 --> 00:12:35,000 Speaker 1: and and sitting out. UM. The only silver lining to 229 00:12:35,000 --> 00:12:38,319 Speaker 1: the Clinton campaign from comy gate is that I'm sure 230 00:12:38,320 --> 00:12:40,880 Speaker 1: they're pretty happy that they people feel the election is 231 00:12:41,559 --> 00:12:44,439 Speaker 1: compative enough that their vote matters, because you always worry 232 00:12:44,440 --> 00:12:47,840 Speaker 1: about complacency and election where there's a lot of uncertainty 233 00:12:48,000 --> 00:12:50,600 Speaker 1: and there are a lot of undecided That's how Trump wins. Right. 234 00:12:50,960 --> 00:12:55,600 Speaker 1: If Trump's undecideds come out and Clinton's don't, um, then 235 00:12:55,600 --> 00:13:00,120 Speaker 1: he has a path. It's so much about turnout, isn't it. Um. 236 00:13:00,280 --> 00:13:02,760 Speaker 1: Some states it matters more than others. I mean, if 237 00:13:02,800 --> 00:13:04,680 Speaker 1: you have early voting in a state, in some ways 238 00:13:04,679 --> 00:13:08,200 Speaker 1: there's more opportunity to take advantage of it. The ground 239 00:13:08,240 --> 00:13:11,600 Speaker 1: game probably matters more in a state like Pennsylvania where 240 00:13:11,600 --> 00:13:13,880 Speaker 1: you know who your voters are and it's just smarrogating 241 00:13:13,920 --> 00:13:15,679 Speaker 1: them to turn out. Then in a state like New 242 00:13:15,679 --> 00:13:17,280 Speaker 1: Hampshire where there are a lot of swing voters. That's 243 00:13:17,280 --> 00:13:21,880 Speaker 1: a persuasion state. Pennsylvania, North Carolina, these are states where Ohio, 244 00:13:22,120 --> 00:13:25,360 Speaker 1: these are states where it's a turnout state. Well, when 245 00:13:25,360 --> 00:13:27,960 Speaker 1: we come back, we're going to talk about what makes 246 00:13:28,320 --> 00:13:32,559 Speaker 1: Nate silver tick or analyze or I don't know what 247 00:13:32,600 --> 00:13:35,480 Speaker 1: it is you do exactly, what would you say curate? 248 00:13:36,120 --> 00:13:39,440 Speaker 1: I mean, yeah, I don't know, report, I guess in 249 00:13:39,440 --> 00:13:41,960 Speaker 1: a weird way. And and also I want to talk 250 00:13:42,000 --> 00:13:45,280 Speaker 1: to you more about polling and how reliable it is 251 00:13:45,400 --> 00:13:47,840 Speaker 1: and when it's been wrong and if it could be 252 00:13:47,880 --> 00:13:51,240 Speaker 1: wrong this go round. So we'll do that right after this. 253 00:14:00,960 --> 00:14:04,920 Speaker 1: So we asked, and you all very nicely answered. We 254 00:14:05,000 --> 00:14:08,000 Speaker 1: asked how you're coping in these final days of this 255 00:14:08,240 --> 00:14:13,040 Speaker 1: insane election cycle. Hi, Katie, this is Shelley Davy. How 256 00:14:13,200 --> 00:14:17,920 Speaker 1: I've been managing to cope through this election season is 257 00:14:18,040 --> 00:14:21,520 Speaker 1: to use them as teachable moments for my thirteen year 258 00:14:21,560 --> 00:14:24,480 Speaker 1: old and my ten year old. There's plenty of good 259 00:14:24,480 --> 00:14:29,080 Speaker 1: examples and bad examples to choose from, and with my 260 00:14:29,200 --> 00:14:33,640 Speaker 1: thirteen year old in particular, we now have a new statement. 261 00:14:34,600 --> 00:14:39,240 Speaker 1: If behavior is going badly, we say, don't be a 262 00:14:39,320 --> 00:14:43,080 Speaker 1: Donald Trump. And it usually stops him in his tracks. 263 00:14:43,120 --> 00:14:47,240 Speaker 1: Thanks for your show. Bye Wow. Oh gosh, that's a 264 00:14:47,280 --> 00:14:51,160 Speaker 1: sad commentary, isn't it, Brian? It is it is, and 265 00:14:51,200 --> 00:14:53,920 Speaker 1: we hope that some Trump supporters will call in and 266 00:14:54,320 --> 00:14:58,080 Speaker 1: let us know how they're teaching their children as well. Meanwhile, 267 00:14:58,120 --> 00:15:02,680 Speaker 1: we have another caller. Hello. My name is Madeline ben Kane, 268 00:15:02,720 --> 00:15:07,400 Speaker 1: and you asked about coping mechanisms for surviving the election. 269 00:15:08,040 --> 00:15:11,440 Speaker 1: What I do is write political limericks. Here's a sample. 270 00:15:11,920 --> 00:15:15,360 Speaker 1: It's cool going to town about the election. This election 271 00:15:15,480 --> 00:15:19,080 Speaker 1: is nearing its end. None to shoot. I've gone plunged 272 00:15:19,160 --> 00:15:22,120 Speaker 1: round the bend. I can't handle more. Gie. If my 273 00:15:22,200 --> 00:15:26,120 Speaker 1: mind's taken a dive, it needs Hillary's village to mend. 274 00:15:27,920 --> 00:15:30,840 Speaker 1: Thanks very much. I've been enjoying your podcast. Take care 275 00:15:30,960 --> 00:15:35,480 Speaker 1: bye bye. Quite a poet. I like limericks. That's very funny, 276 00:15:35,480 --> 00:15:39,040 Speaker 1: and that's actually a nice way to let off steam. Brian, 277 00:15:39,080 --> 00:15:41,240 Speaker 1: what have you been doing? Have you been doing anything 278 00:15:41,320 --> 00:15:45,360 Speaker 1: to kind of disconnect from all of this stuff? I 279 00:15:45,440 --> 00:15:48,680 Speaker 1: listened to podcasts about the election to disconnect from the 280 00:15:48,680 --> 00:15:53,800 Speaker 1: election generally. Nate Silver, it's always great to talk to you. 281 00:15:53,880 --> 00:15:56,120 Speaker 1: Do you feel like a rock star because all my 282 00:15:56,160 --> 00:16:00,440 Speaker 1: friends say, Nate Silver says, well, I've had Nate Silver. 283 00:16:01,000 --> 00:16:03,720 Speaker 1: I mean, they drop your name like it's going out 284 00:16:03,720 --> 00:16:06,520 Speaker 1: of style, just having funny to you. It's weird. I mean, 285 00:16:06,960 --> 00:16:08,800 Speaker 1: you know, it happens once every four years, then a 286 00:16:08,880 --> 00:16:11,480 Speaker 1: very small spike around the mid term before we talk 287 00:16:11,560 --> 00:16:15,320 Speaker 1: more about this crazy campaign. By the way, do you 288 00:16:15,320 --> 00:16:17,280 Speaker 1: feel like that's all you can talk about? And do 289 00:16:17,320 --> 00:16:19,040 Speaker 1: you wonder what you're going to start talking about on 290 00:16:19,080 --> 00:16:23,160 Speaker 1: November nine? I mean, I found some Frequent Flower Award 291 00:16:23,160 --> 00:16:26,560 Speaker 1: tickets to Italy And are you going to go away? Yeah, 292 00:16:26,800 --> 00:16:29,200 Speaker 1: I mean not right away, because there's a lot of 293 00:16:29,280 --> 00:16:32,040 Speaker 1: risk of their being after math. I mean, just think 294 00:16:32,080 --> 00:16:34,760 Speaker 1: of all the scenarios between recounts. And by the way, 295 00:16:34,760 --> 00:16:36,840 Speaker 1: the senate's really close now too, and so you could 296 00:16:36,840 --> 00:16:39,320 Speaker 1: have a recount in a Senate race that matters between 297 00:16:39,360 --> 00:16:42,000 Speaker 1: I hope not, but civil unrest between an electoral college 298 00:16:42,000 --> 00:16:45,040 Speaker 1: popular route split, which is a likelier outcome than people 299 00:16:45,120 --> 00:16:49,680 Speaker 1: might think. Um, so you know, and just if Trump loses, 300 00:16:50,280 --> 00:16:53,320 Speaker 1: does he concede? If Trump wins, how is the country react? 301 00:16:53,440 --> 00:16:57,120 Speaker 1: I mean, yeah, but no, I mean it makes me 302 00:16:57,200 --> 00:16:59,480 Speaker 1: exhausted just thinking about it. Now. That's to dinner, and 303 00:16:59,520 --> 00:17:01,920 Speaker 1: the other table near me was talking about how Maine 304 00:17:01,920 --> 00:17:05,600 Speaker 1: in Nebraska they're not my friends, right, split their votes 305 00:17:05,640 --> 00:17:08,440 Speaker 1: by congressional district, and so people are it's very hard 306 00:17:08,440 --> 00:17:11,800 Speaker 1: to get away from the election. Um let's talk. Let's 307 00:17:11,800 --> 00:17:14,840 Speaker 1: talk about you, Nate, shall we. You grew up in Michigan. 308 00:17:15,359 --> 00:17:17,960 Speaker 1: What's a nice boy like you doing in a crazy 309 00:17:18,000 --> 00:17:20,720 Speaker 1: business like this? And it's kind of unintentional, you know, 310 00:17:20,800 --> 00:17:23,800 Speaker 1: I covered? Um So, I went to college, got agree 311 00:17:23,800 --> 00:17:26,959 Speaker 1: in economics from the University of Chicago and Chicago, got 312 00:17:27,040 --> 00:17:30,760 Speaker 1: a consult go Cubs. I was, got a consulting job, 313 00:17:30,800 --> 00:17:33,040 Speaker 1: got bored, started working on the side to develop a 314 00:17:33,080 --> 00:17:35,919 Speaker 1: midle of the forecast how baseball players we do and 315 00:17:35,960 --> 00:17:39,400 Speaker 1: write about baseball for several years, um and then figure 316 00:17:39,400 --> 00:17:41,960 Speaker 1: out if people are spending all this time doing analytics 317 00:17:41,960 --> 00:17:44,720 Speaker 1: on Baseball's was the time when shortly after Moneyball came out, 318 00:17:45,320 --> 00:17:48,040 Speaker 1: I want to take the same approach towards politics. And 319 00:17:48,080 --> 00:17:50,960 Speaker 1: this was in early or late two thousand and seven. 320 00:17:51,280 --> 00:17:54,919 Speaker 1: Um So, you had a spectacular Democratic primary, very interesting 321 00:17:54,960 --> 00:17:58,440 Speaker 1: for that matter, Republican primary, and then a historic general election. 322 00:17:58,880 --> 00:18:01,080 Speaker 1: Um So, it was kind of being in the right 323 00:18:01,080 --> 00:18:03,199 Speaker 1: place at the right time. It's so interesting to me 324 00:18:03,200 --> 00:18:06,800 Speaker 1: because you grew up with your two passions math and sports, right, 325 00:18:07,000 --> 00:18:10,440 Speaker 1: So so how did that morph into politics? I mean 326 00:18:11,080 --> 00:18:13,560 Speaker 1: just because my frustration, because you've seen progress in the 327 00:18:13,560 --> 00:18:16,639 Speaker 1: way sports was covered became more analytical, a little smarter. 328 00:18:17,000 --> 00:18:21,280 Speaker 1: Um And you know, UM, I'm not sure that's true 329 00:18:21,280 --> 00:18:23,720 Speaker 1: of political coverage. I mean, very much value good reporting, 330 00:18:23,840 --> 00:18:28,359 Speaker 1: but um, but the average pundit is not particularly data driven, 331 00:18:28,359 --> 00:18:30,159 Speaker 1: doesn't know that much about polling, makes a lot of 332 00:18:30,160 --> 00:18:33,480 Speaker 1: assertions without evidence. UM than two thousand eight, for example, 333 00:18:33,720 --> 00:18:36,399 Speaker 1: people were like, well, Hillary is gonna win for sure 334 00:18:36,520 --> 00:18:39,040 Speaker 1: in Obama, you know, too far behind. Of course that 335 00:18:39,080 --> 00:18:42,280 Speaker 1: was before Iowa and Iowa UM is often more predictive 336 00:18:42,280 --> 00:18:45,520 Speaker 1: the results than than national polls early in the primary race. 337 00:18:45,560 --> 00:18:48,040 Speaker 1: So it's more out of frustration where it's like, I 338 00:18:48,080 --> 00:18:50,439 Speaker 1: have to build this myself. I guess, Nate, where do 339 00:18:50,480 --> 00:18:53,080 Speaker 1: you go from here? Are you going to continue to 340 00:18:53,119 --> 00:18:55,080 Speaker 1: be with five thirty eight? I know you're working with 341 00:18:55,359 --> 00:19:00,040 Speaker 1: ESPN and ABC now you deep kind of through the 342 00:19:00,080 --> 00:19:03,920 Speaker 1: New York Times over there. You dumped the New York Times, 343 00:19:04,040 --> 00:19:07,040 Speaker 1: which is, well, who does that need? We're only we're 344 00:19:07,040 --> 00:19:09,040 Speaker 1: only two blocks in a building they might be listening 345 00:19:09,040 --> 00:19:11,800 Speaker 1: in on this, but um no, look five three D 346 00:19:11,880 --> 00:19:14,199 Speaker 1: is a whole wonderful biased website and then we have 347 00:19:14,240 --> 00:19:18,120 Speaker 1: thirty or thirty five journalists working for us. We cover economics, sports, 348 00:19:18,200 --> 00:19:20,359 Speaker 1: politics that aren't just elections, and so we have a 349 00:19:20,440 --> 00:19:24,280 Speaker 1: robust site. I know people love our election coverage, um, 350 00:19:24,320 --> 00:19:27,320 Speaker 1: but we're looking forward to getting a little bit of rest, um, 351 00:19:27,560 --> 00:19:32,320 Speaker 1: to letting our non politics coverage shine. But also there 352 00:19:32,359 --> 00:19:35,840 Speaker 1: are going to be consequences to this election, win or lose. 353 00:19:35,880 --> 00:19:39,639 Speaker 1: They're going to persist for for many years. If you 354 00:19:39,680 --> 00:19:41,760 Speaker 1: cover policial living. It's good in that sense that you're 355 00:19:41,800 --> 00:19:45,280 Speaker 1: not going to be bored anytime soon. Um, but it's 356 00:19:45,320 --> 00:19:47,840 Speaker 1: going to take a lot of time again regardless of 357 00:19:47,840 --> 00:19:52,800 Speaker 1: who wins, too to unwrap everything that's that's happened. You 358 00:19:52,880 --> 00:19:56,760 Speaker 1: wrote a piece actually about why you got Trump so wrong. Yeah, 359 00:19:57,040 --> 00:20:01,399 Speaker 1: until November December, until about a year ago, Um, we 360 00:20:01,400 --> 00:20:04,400 Speaker 1: were quite skeptical. And why was that? I mean, did 361 00:20:04,400 --> 00:20:06,679 Speaker 1: you just not see it coming? Or well, you know, 362 00:20:07,080 --> 00:20:10,600 Speaker 1: there aren't a lot of historical examples of primaries or 363 00:20:10,600 --> 00:20:16,000 Speaker 1: seven team way primaries, but look, Trump did violate um, 364 00:20:16,040 --> 00:20:18,919 Speaker 1: a lot of history where there's never been anything remotely 365 00:20:18,960 --> 00:20:21,640 Speaker 1: like this, at least in my lifetime. I'm thirty eight. 366 00:20:21,680 --> 00:20:23,919 Speaker 1: And if you're making forecasts, you're kind of trying to 367 00:20:23,960 --> 00:20:29,800 Speaker 1: make extrapolations from history. So when something unusual happens, Um, 368 00:20:29,840 --> 00:20:32,400 Speaker 1: you're liable to get that wrong. The question is still, 369 00:20:32,440 --> 00:20:34,600 Speaker 1: if you kind of replayed that race a hundred times, 370 00:20:35,080 --> 00:20:36,879 Speaker 1: how often would Trump when I mean, I don't know, 371 00:20:36,920 --> 00:20:39,440 Speaker 1: I mean, he proved that there's a very different Republican 372 00:20:39,480 --> 00:20:44,800 Speaker 1: electorate than a lot of us thought. On the other hand, Um, 373 00:20:44,840 --> 00:20:47,320 Speaker 1: he had the element of surprise where there hadn't been 374 00:20:47,320 --> 00:20:50,159 Speaker 1: a candidate like him, Um, where he committed so much 375 00:20:50,160 --> 00:20:52,880 Speaker 1: attention to kind of rode the momentum from the polls 376 00:20:53,000 --> 00:20:54,760 Speaker 1: and made sure that he took up so much oxygen 377 00:20:54,800 --> 00:20:57,520 Speaker 1: that you could never have remember the day when Um 378 00:20:58,080 --> 00:21:00,879 Speaker 1: kind of brilliant, tactically right. Rubio had had some debate, 379 00:21:00,880 --> 00:21:02,760 Speaker 1: I forget which one. There were so many some debate 380 00:21:02,800 --> 00:21:06,120 Speaker 1: whe Rubio done really well, was getting very good buzz. Um. 381 00:21:06,480 --> 00:21:07,800 Speaker 1: It was kind of catching up to Trump. And then 382 00:21:07,800 --> 00:21:11,000 Speaker 1: that's when Trump unveiled Chris Christie's endorsementyone dropped what they 383 00:21:11,040 --> 00:21:13,160 Speaker 1: were doing and went and covered Trump and of course 384 00:21:13,160 --> 00:21:15,800 Speaker 1: it was very theatrical. They had like flown Chris Christie 385 00:21:15,800 --> 00:21:19,960 Speaker 1: out somewhere surreptitiously. Um so his sense of theater and 386 00:21:20,000 --> 00:21:22,280 Speaker 1: a sense of kind of how do you cater to 387 00:21:22,320 --> 00:21:25,640 Speaker 1: the twenty of people who are really interested in your product? 388 00:21:26,040 --> 00:21:27,879 Speaker 1: I mean that was a very trying out to be 389 00:21:28,000 --> 00:21:30,760 Speaker 1: very helpful instinct for him. But you know, it does 390 00:21:30,840 --> 00:21:33,120 Speaker 1: go against a lot of history. You guess what I'm 391 00:21:33,119 --> 00:21:36,679 Speaker 1: saying is that at some point, um, the polls override 392 00:21:37,320 --> 00:21:39,439 Speaker 1: the history. At some point, when you get into November 393 00:21:39,560 --> 00:21:43,040 Speaker 1: December and Trump has a big lead nationally and a 394 00:21:43,119 --> 00:21:46,480 Speaker 1: lade in most wing states, um or most early primary states, 395 00:21:46,480 --> 00:21:48,600 Speaker 1: I should say then I think we're a little slow 396 00:21:48,600 --> 00:21:51,240 Speaker 1: on the draw there, certainly. Well, and it's interesting you 397 00:21:51,240 --> 00:21:57,640 Speaker 1: you assigned very specific odds or percentages to Trump's chances. 398 00:21:57,840 --> 00:22:02,440 Speaker 1: You put his chances at two percent in August and December. 399 00:22:04,720 --> 00:22:07,760 Speaker 1: I think people would be really interested in kind of 400 00:22:08,000 --> 00:22:11,080 Speaker 1: peeling back the curtain and learning a little bit more 401 00:22:11,080 --> 00:22:15,560 Speaker 1: about how the eight machine works and why the process 402 00:22:15,600 --> 00:22:18,440 Speaker 1: for assigning odds to Trump was so different from what 403 00:22:18,480 --> 00:22:22,159 Speaker 1: you're doing right now. Well, I think people lose the context, 404 00:22:22,520 --> 00:22:26,520 Speaker 1: and you know, I kind of think in percentages, right, 405 00:22:26,600 --> 00:22:28,360 Speaker 1: you know, kind of if someone's like, what's the chance 406 00:22:28,400 --> 00:22:31,120 Speaker 1: you want to get Italian food tonight or whatever? Right? 407 00:22:32,240 --> 00:22:36,480 Speaker 1: When people putting often probably maybe I don't know. Um. 408 00:22:36,600 --> 00:22:39,480 Speaker 1: When um, when we put numbers some things, people assume 409 00:22:39,520 --> 00:22:41,480 Speaker 1: that means it's a model, and usually it is. Right 410 00:22:41,480 --> 00:22:44,200 Speaker 1: when we say that Clinton has a seventy four chance 411 00:22:44,240 --> 00:22:47,400 Speaker 1: of winning, that comes from an algorithm. A program will 412 00:22:47,440 --> 00:22:49,760 Speaker 1: just kind of feed data in. Obviously there's a lot 413 00:22:49,800 --> 00:22:51,480 Speaker 1: of thought and how the programs put together. It's not 414 00:22:51,560 --> 00:22:54,040 Speaker 1: like it has a mind of its own. Um, But 415 00:22:54,119 --> 00:22:56,800 Speaker 1: you know that is the output from a computer program. 416 00:22:57,040 --> 00:22:59,879 Speaker 1: The Trump forecast were we're not and I think in 417 00:23:00,000 --> 00:23:03,639 Speaker 1: the future, to be honest, we're gonna avoid putting numbers 418 00:23:03,640 --> 00:23:06,560 Speaker 1: on things unless it actually is the output of a 419 00:23:06,600 --> 00:23:09,560 Speaker 1: model or a forecast. And when you talk about feeding 420 00:23:09,560 --> 00:23:13,840 Speaker 1: in the data, you're talking about aggregating a whole bunch 421 00:23:13,880 --> 00:23:19,280 Speaker 1: of polls. Basically every poll is incorporated in the model 422 00:23:19,480 --> 00:23:23,080 Speaker 1: in in some way, right, Um, And it's all automated. 423 00:23:23,080 --> 00:23:25,000 Speaker 1: I mean, someone has to manually enter in the polls. 424 00:23:25,119 --> 00:23:27,880 Speaker 1: But whenever a polls entered into a database, that triggers 425 00:23:27,880 --> 00:23:30,320 Speaker 1: a run of the forecast that occurs on a server 426 00:23:30,520 --> 00:23:36,200 Speaker 1: and so we're not intervening in the program once it's built. Um, 427 00:23:36,280 --> 00:23:38,119 Speaker 1: there can be bugs, and we fix the bugs to 428 00:23:38,160 --> 00:23:40,760 Speaker 1: be honest. But um, that's why I like all they say, Oh, 429 00:23:40,840 --> 00:23:44,760 Speaker 1: Nate's giving Clinton chance now in Pennsylvania. It's like, not really. 430 00:23:44,800 --> 00:23:47,399 Speaker 1: I mean, it's like a computer program that my colleagues 431 00:23:47,400 --> 00:23:50,840 Speaker 1: and I designed has given her that chance based on 432 00:23:50,840 --> 00:23:53,320 Speaker 1: on polls that are kind of automatically fit into the model. 433 00:23:53,480 --> 00:23:55,639 Speaker 1: And then I guess that's a perfect segue though, Brian 434 00:23:55,720 --> 00:23:58,720 Speaker 1: to the question about how how accurate are these polls? 435 00:23:58,840 --> 00:24:02,399 Speaker 1: You know, there's been so speculation about polling today and 436 00:24:02,480 --> 00:24:07,760 Speaker 1: about are they really legitimate reflections of the electorate. Uh, 437 00:24:08,359 --> 00:24:11,160 Speaker 1: you know, I know the response rate for posters has 438 00:24:11,200 --> 00:24:15,919 Speaker 1: gone down. Now there's online polling, there's telephone polling. Um, 439 00:24:16,640 --> 00:24:19,399 Speaker 1: there there's a lot of talk about a secret Trump 440 00:24:19,480 --> 00:24:22,800 Speaker 1: vote that's not being reflected honestly in the polls because 441 00:24:22,840 --> 00:24:25,960 Speaker 1: people are embarrassed to say they're supporting Donald Trump. So 442 00:24:26,200 --> 00:24:27,879 Speaker 1: where do you stand on all of this? And does 443 00:24:27,920 --> 00:24:30,280 Speaker 1: it worry you that the data you're using to come 444 00:24:30,359 --> 00:24:34,320 Speaker 1: up with these percentages just isn't right? So the percentages 445 00:24:34,520 --> 00:24:37,480 Speaker 1: account for the possible of the polls could be systematically wrong. 446 00:24:37,600 --> 00:24:41,960 Speaker 1: So again, we're recording this on on Monday. As of Monday, 447 00:24:42,280 --> 00:24:46,200 Speaker 1: Clinton is ahead in the polls in more than enough states. Right. 448 00:24:46,240 --> 00:24:50,000 Speaker 1: She's ahead in the polls in Pennsylvania and North Carolina, Nevada, 449 00:24:50,160 --> 00:24:54,040 Speaker 1: New Hampshire, more than enough states. Right. However, Um, there's 450 00:24:54,040 --> 00:24:55,480 Speaker 1: a chance of polls could be wrong, and we say 451 00:24:55,520 --> 00:24:58,439 Speaker 1: there's a chance that Trump could win. Some of that 452 00:24:58,520 --> 00:25:01,280 Speaker 1: is the possibility of another October surprise or November surprise. 453 00:25:01,320 --> 00:25:03,840 Speaker 1: It would be at this point, um. But it's also 454 00:25:03,880 --> 00:25:07,520 Speaker 1: that the polls could turn out to be relatively far off. 455 00:25:07,560 --> 00:25:09,760 Speaker 1: I mean to have a four or five point polling 456 00:25:09,800 --> 00:25:12,040 Speaker 1: nous is on the high side, but not unheard of. 457 00:25:12,320 --> 00:25:14,640 Speaker 1: In the Brexit vote, you had about a four point 458 00:25:14,720 --> 00:25:18,399 Speaker 1: miss in two thousand people. Forget this one. Bush was 459 00:25:18,440 --> 00:25:21,639 Speaker 1: favored by three or four points in the popular vote 460 00:25:21,920 --> 00:25:24,880 Speaker 1: and lost the popular vote. But you know, we actually 461 00:25:24,920 --> 00:25:27,960 Speaker 1: have the model that gives the best chance to Trump. 462 00:25:27,960 --> 00:25:29,760 Speaker 1: There are other people who have models that process the 463 00:25:29,840 --> 00:25:33,159 Speaker 1: data in a different way that literally give him a 464 00:25:33,280 --> 00:25:35,919 Speaker 1: one percent chance. When we say it's a chance, and 465 00:25:35,960 --> 00:25:39,960 Speaker 1: we think those models do not account sufficiently for the 466 00:25:40,000 --> 00:25:42,919 Speaker 1: possibility of a of a systematic error in the polling. 467 00:25:43,600 --> 00:25:45,679 Speaker 1: And just to break this down a little bit further, 468 00:25:46,240 --> 00:25:49,800 Speaker 1: when you build your model, do you assign greater weight 469 00:25:49,920 --> 00:25:52,520 Speaker 1: or influence to certain polls that you think are better 470 00:25:52,560 --> 00:25:55,520 Speaker 1: than others? Because I know a lot of campaign professionals 471 00:25:55,560 --> 00:25:58,160 Speaker 1: look at some public polls and say, you know, those 472 00:25:58,200 --> 00:26:04,280 Speaker 1: are well done and others that they think are just crap. Yeah. UM, 473 00:26:04,320 --> 00:26:07,880 Speaker 1: you know, we do weight polls based on a formula 474 00:26:08,000 --> 00:26:11,600 Speaker 1: that looks at how accurate they've been historically. Plus UM 475 00:26:11,640 --> 00:26:14,919 Speaker 1: a couple of methodological benchmarks. So one is are you 476 00:26:15,080 --> 00:26:18,280 Speaker 1: a live collar poll that calls cell phones as well 477 00:26:18,320 --> 00:26:20,560 Speaker 1: as landlines. It's still kind of the gold industry standard 478 00:26:20,560 --> 00:26:24,920 Speaker 1: practice UM. And number two is are you part of 479 00:26:25,400 --> 00:26:30,639 Speaker 1: UM disclosure and transparency initiatives where the pulster is revealing 480 00:26:31,160 --> 00:26:34,840 Speaker 1: substantial amounts of information about how the poll was conducted, 481 00:26:35,280 --> 00:26:38,960 Speaker 1: who was called, everything else? Right. On the other hand, 482 00:26:39,560 --> 00:26:41,720 Speaker 1: you know, we want to try and use all the 483 00:26:41,800 --> 00:26:47,280 Speaker 1: data that we can. UM. As response rates to telephone 484 00:26:47,280 --> 00:26:52,600 Speaker 1: poles decrease, online polls, we actually respond to more UM. 485 00:26:52,640 --> 00:26:54,359 Speaker 1: The problem is that it's hard to get a random 486 00:26:54,400 --> 00:26:57,960 Speaker 1: sample online, So Frankly, we're happy. I think that we 487 00:26:58,080 --> 00:27:01,760 Speaker 1: have the mix we have right now of telephone polls 488 00:27:01,800 --> 00:27:04,119 Speaker 1: and online polls. They don't always tell the same story. 489 00:27:04,119 --> 00:27:08,000 Speaker 1: The online polls have the race a little closer. Um, 490 00:27:08,080 --> 00:27:10,639 Speaker 1: they also show a steadier race, though they tend not 491 00:27:10,720 --> 00:27:13,840 Speaker 1: to show the swings that telephone poles do. And then 492 00:27:13,840 --> 00:27:15,400 Speaker 1: why is that and why is it harder to get 493 00:27:15,400 --> 00:27:18,320 Speaker 1: a random sampling online? I don't understand that because I 494 00:27:18,320 --> 00:27:21,680 Speaker 1: can call you, so it used to be probably not anymore, right, Um, 495 00:27:21,760 --> 00:27:23,520 Speaker 1: But I used to could call you or your family 496 00:27:23,800 --> 00:27:26,960 Speaker 1: by randomly dialing a number on the phone, right, And 497 00:27:27,000 --> 00:27:29,520 Speaker 1: I know that let's say seven seven, two four or 498 00:27:29,520 --> 00:27:32,040 Speaker 1: three six and then dotill last four. We know that 499 00:27:32,200 --> 00:27:34,840 Speaker 1: those prefixes or suffixes work, right, right, That's how I 500 00:27:34,880 --> 00:27:37,160 Speaker 1: used to make crank calls in junior high. Yeah, right, 501 00:27:37,280 --> 00:27:42,200 Speaker 1: it's the same principle. But now, um, that's become much harder. 502 00:27:42,240 --> 00:27:44,720 Speaker 1: People number one don't us their phone number two. People 503 00:27:44,720 --> 00:27:46,679 Speaker 1: don't necessarily have land lines to begin with. They have 504 00:27:46,720 --> 00:27:49,439 Speaker 1: cell phones. The phone number, you know, I still have 505 00:27:49,520 --> 00:27:52,000 Speaker 1: a a Chicago area cell phone number. I've lived in 506 00:27:52,000 --> 00:27:55,800 Speaker 1: New York for for seven years. Interesting, it's just find 507 00:27:55,800 --> 00:27:58,320 Speaker 1: ways around that. But you know, but so it's becoming 508 00:27:58,359 --> 00:28:01,280 Speaker 1: hard to get some randomly, it's still it's easier, at 509 00:28:01,359 --> 00:28:05,000 Speaker 1: least in theory. UM. Then, I mean, how kind of 510 00:28:05,080 --> 00:28:08,399 Speaker 1: randomly ping someone online? Do I have their email? I 511 00:28:08,400 --> 00:28:10,520 Speaker 1: mean maybe if I'm the N s A or something 512 00:28:10,560 --> 00:28:13,879 Speaker 1: I do. Right, You know, some services trying to interrupt you. 513 00:28:13,920 --> 00:28:16,440 Speaker 1: Instead of showing you an AD, they'll show you a poll. 514 00:28:16,840 --> 00:28:20,399 Speaker 1: But if you're perturbed by that interruption, you might just 515 00:28:20,440 --> 00:28:23,840 Speaker 1: click on whatever candidate's name you see first to get through. 516 00:28:23,960 --> 00:28:26,439 Speaker 1: And it's interesting. I get those sometimes, Brian, do you 517 00:28:26,480 --> 00:28:28,760 Speaker 1: when you're reading an article or you're online. I get 518 00:28:28,800 --> 00:28:32,040 Speaker 1: these polls, but I always think they're crazy spam and 519 00:28:32,080 --> 00:28:35,240 Speaker 1: if I open it, someone will infect my whole Gmail 520 00:28:35,240 --> 00:28:38,720 Speaker 1: account or yeah, who can actually the opposite. I don't. 521 00:28:39,160 --> 00:28:42,760 Speaker 1: I don't respond online polls, but I do sometimes respond 522 00:28:42,760 --> 00:28:45,080 Speaker 1: to telephone posters, but I don't know. They scare me. 523 00:28:45,120 --> 00:28:46,960 Speaker 1: The online poll scare me because you never know if 524 00:28:46,960 --> 00:28:48,840 Speaker 1: they're legitimate or not, and what they're going to do 525 00:28:48,960 --> 00:28:52,000 Speaker 1: to your and our legitimate you know. And the key 526 00:28:52,000 --> 00:28:56,120 Speaker 1: differentiator is is a poll trying to get a random 527 00:28:56,120 --> 00:28:59,760 Speaker 1: and representative sample of the electorate. Um You can try 528 00:28:59,760 --> 00:29:02,440 Speaker 1: and do that and still screw it up. But are 529 00:29:02,480 --> 00:29:04,240 Speaker 1: you trying to do it versus a poll which is 530 00:29:04,480 --> 00:29:09,400 Speaker 1: purely voluntary, right that the Drudge Report quote unquote poll 531 00:29:09,600 --> 00:29:11,600 Speaker 1: or the local news station that has a quote unquote 532 00:29:11,640 --> 00:29:13,680 Speaker 1: poll that people can just click from anywhere as many 533 00:29:13,680 --> 00:29:16,640 Speaker 1: times as they want. Those don't tell you anything. It 534 00:29:16,760 --> 00:29:19,840 Speaker 1: So the polls that Trump sites that showed him winning 535 00:29:19,840 --> 00:29:23,920 Speaker 1: all three debate we're not scientific polls. And I think 536 00:29:23,920 --> 00:29:26,600 Speaker 1: a lot of people misunderstood that well. I think he's 537 00:29:26,640 --> 00:29:30,160 Speaker 1: deliberately obscuring the boundary. Now the resist tightened a little bit. 538 00:29:30,160 --> 00:29:32,440 Speaker 1: All of a sudden, he cites these polls that he likes, right, 539 00:29:32,440 --> 00:29:36,080 Speaker 1: He's very opportunistic, although, to be honest, lots of people 540 00:29:36,080 --> 00:29:39,880 Speaker 1: are opportunistic about Oh, all of a sudden, you know. Um, 541 00:29:39,920 --> 00:29:42,040 Speaker 1: I mean, we're probably with ABC News some little biased here, 542 00:29:42,040 --> 00:29:45,000 Speaker 1: but ABC News Watchington Post Bowl is a very good poll, 543 00:29:45,360 --> 00:29:47,680 Speaker 1: um good track record. When it went from showing a 544 00:29:47,720 --> 00:29:50,280 Speaker 1: twelve point lead for Clinton to a one point lead, 545 00:29:50,680 --> 00:29:53,160 Speaker 1: um Democrats all of a sudden found a lot of 546 00:29:53,160 --> 00:29:55,920 Speaker 1: methodological flaws they hadn't thought about before. But that does 547 00:29:55,920 --> 00:29:59,960 Speaker 1: seem kind of like a huge, huge drop in her support. 548 00:30:00,400 --> 00:30:03,280 Speaker 1: That's why you take an average, right. I mean, the 549 00:30:03,360 --> 00:30:06,760 Speaker 1: key is you never take anyone poll to be the 550 00:30:06,800 --> 00:30:11,600 Speaker 1: gospel truth. Right. You treat every individual polling result skeptically. 551 00:30:11,720 --> 00:30:15,360 Speaker 1: And yet lo and behold, if you're able to aggregate, 552 00:30:15,960 --> 00:30:18,320 Speaker 1: um several dozen polls together, you can get a pretty 553 00:30:18,320 --> 00:30:21,360 Speaker 1: accurate reading, usually on the race. And what about is 554 00:30:21,400 --> 00:30:24,600 Speaker 1: Katie mentioned this theory that has a lot of sway 555 00:30:24,640 --> 00:30:27,760 Speaker 1: among Trump supporters that there are missing millions of white 556 00:30:27,840 --> 00:30:31,480 Speaker 1: voters whom Trump is gonna energize and bring out to 557 00:30:31,560 --> 00:30:33,880 Speaker 1: the polls. I mean, look, on the one hand, we 558 00:30:33,920 --> 00:30:37,840 Speaker 1: say there as a possibility of an error in either direction. Um. 559 00:30:37,880 --> 00:30:41,200 Speaker 1: It could also be that UM polls are under sampling 560 00:30:41,320 --> 00:30:44,920 Speaker 1: Latino voters, for example, especially those that don't speak English. 561 00:30:45,120 --> 00:30:47,000 Speaker 1: But I would say a couple of things about Trump supporters. 562 00:30:47,040 --> 00:30:50,280 Speaker 1: Number one, Um, you didn't see this happen in the 563 00:30:50,360 --> 00:30:53,360 Speaker 1: primaries where Trump was underestimated by people like me, but 564 00:30:53,360 --> 00:30:54,920 Speaker 1: not based on the polls. The polls actually did a 565 00:30:55,000 --> 00:30:57,440 Speaker 1: very good job of pegging Trump's vote. In fact, there 566 00:30:57,440 --> 00:31:00,200 Speaker 1: were some states like Iowa where he was overestimated by 567 00:31:00,200 --> 00:31:03,800 Speaker 1: the polls. UM. And number two, you know the notion 568 00:31:03,840 --> 00:31:05,640 Speaker 1: of a shy Trump voter. I'm not sure how many 569 00:31:05,680 --> 00:31:07,600 Speaker 1: Trump voters you've met, but shy is not the first 570 00:31:07,600 --> 00:31:11,000 Speaker 1: word that would come to mind. Um, not so much shy. 571 00:31:11,240 --> 00:31:15,120 Speaker 1: But I think there are there there, at least people 572 00:31:15,200 --> 00:31:19,800 Speaker 1: that I have met who I think are not outwardly 573 00:31:20,120 --> 00:31:24,320 Speaker 1: supporting him, but would tell you privately, or tell some 574 00:31:24,360 --> 00:31:28,480 Speaker 1: people privately that they were supporting him. I don't know 575 00:31:28,600 --> 00:31:32,080 Speaker 1: any of these secret Trump supporters. The Trump supporters I 576 00:31:32,120 --> 00:31:34,000 Speaker 1: know are pretty loud and proud about it, but I 577 00:31:34,080 --> 00:31:37,040 Speaker 1: keep hearing these stories that you know, there are Trump 578 00:31:37,080 --> 00:31:39,320 Speaker 1: supporters who don't want to tell their liberal friends at 579 00:31:39,320 --> 00:31:41,640 Speaker 1: dinner parties and don't want to incite a big argument, 580 00:31:41,680 --> 00:31:44,160 Speaker 1: and so they don't mention it. But we'll see on 581 00:31:44,200 --> 00:31:47,720 Speaker 1: election day. And you know, why is Donald Trump doing 582 00:31:47,800 --> 00:31:51,600 Speaker 1: so well among non college educated white voters? Is there 583 00:31:51,640 --> 00:31:55,040 Speaker 1: anything in your sort of data other than the fact 584 00:31:55,080 --> 00:31:57,920 Speaker 1: that his message is resonating and that there are a 585 00:31:57,960 --> 00:32:02,040 Speaker 1: lot of people in this country who feel marginalized and disenfranchised. 586 00:32:02,560 --> 00:32:04,400 Speaker 1: I mean, so to put a little perspective on this, 587 00:32:04,560 --> 00:32:09,160 Speaker 1: you know, Trump isn't doing that well except relatively speaking. Right, 588 00:32:09,240 --> 00:32:11,760 Speaker 1: He's only has even with his search lightly only has 589 00:32:11,760 --> 00:32:15,760 Speaker 1: about forty one the vote. That's pretty low. Mitt Romney 590 00:32:15,760 --> 00:32:19,600 Speaker 1: got forty seven per cent. UM, but you know, bad 591 00:32:19,720 --> 00:32:24,120 Speaker 1: number from bad number from Romney. UM. To be perfectly honest, 592 00:32:24,680 --> 00:32:30,239 Speaker 1: I think the data suggests that UM, racial anxiety is 593 00:32:30,280 --> 00:32:34,040 Speaker 1: strongly correlated with some types of Trump supporters. That's part 594 00:32:34,080 --> 00:32:38,520 Speaker 1: of it. UM. Part of it also is that UM 595 00:32:38,600 --> 00:32:40,520 Speaker 1: that he gets a lot of loyal Republicans, not all 596 00:32:40,560 --> 00:32:44,160 Speaker 1: of them, but some of them to turn out for him. UM. 597 00:32:44,320 --> 00:32:46,120 Speaker 1: I think part of it is that there's a certain 598 00:32:46,720 --> 00:32:51,959 Speaker 1: anti elitism, and Trump has become a champion kind of 599 00:32:51,960 --> 00:32:53,800 Speaker 1: culturally of the working class. I think it has more 600 00:32:53,840 --> 00:32:57,880 Speaker 1: to do with culture than economics per se. And this 601 00:32:57,960 --> 00:33:01,920 Speaker 1: is a big debate in the literature. UM and culture 602 00:33:01,960 --> 00:33:05,640 Speaker 1: and race and economics education. These are all blurry boundaries 603 00:33:05,680 --> 00:33:09,680 Speaker 1: between those categories. UM. But certainly in the primaries of 604 00:33:09,760 --> 00:33:12,560 Speaker 1: states where Trump did well are places where you historically 605 00:33:12,600 --> 00:33:16,640 Speaker 1: had higher rates of anti black racism. One in the 606 00:33:16,680 --> 00:33:22,720 Speaker 1: general election, isn't hyperpartisanship a huge factor? You know, Ronald 607 00:33:22,760 --> 00:33:25,600 Speaker 1: Reagan got about a quarter of the Democratic vote. It's 608 00:33:25,640 --> 00:33:29,400 Speaker 1: hard to imagine anything like that happening today because we 609 00:33:29,480 --> 00:33:33,040 Speaker 1: all live in our little bubbles and we get information 610 00:33:33,080 --> 00:33:35,680 Speaker 1: that reinforces what we already believe. And so if you're 611 00:33:35,680 --> 00:33:38,960 Speaker 1: a Republican, I think you kind of think Hillary is 612 00:33:39,000 --> 00:33:42,320 Speaker 1: worse as bad as Trump is on some stuff. You 613 00:33:42,400 --> 00:33:45,719 Speaker 1: probably think, you know, based on everything you're hearing. I mean, 614 00:33:45,720 --> 00:33:48,520 Speaker 1: we all get so used talking about Trump, but um, 615 00:33:48,760 --> 00:33:52,640 Speaker 1: most Hillary voters are voting for Clinton because of Clinton, 616 00:33:53,200 --> 00:33:56,360 Speaker 1: and most Trump voters voting for Trump because of Clinton. 617 00:33:56,480 --> 00:33:59,840 Speaker 1: So Clinton is actually the kind of key um figure 618 00:34:00,440 --> 00:34:03,440 Speaker 1: in the race in in some ways, I hate to 619 00:34:03,480 --> 00:34:07,160 Speaker 1: say it, but in some ways, the kind of very 620 00:34:07,240 --> 00:34:11,759 Speaker 1: ill put comment Clinton made about deplorables was kind of 621 00:34:11,800 --> 00:34:14,920 Speaker 1: getting in the direction of how you might go about 622 00:34:15,239 --> 00:34:18,239 Speaker 1: dividing his support up if you have you know, maybe 623 00:34:19,640 --> 00:34:23,000 Speaker 1: that is from people who, um, who don't like people 624 00:34:23,000 --> 00:34:24,919 Speaker 1: who don't look like them. But there is a lot 625 00:34:24,960 --> 00:34:28,200 Speaker 1: too that is for other reasons, and for um, for 626 00:34:28,239 --> 00:34:31,359 Speaker 1: anti Clinton, for partisan reasons. There are people who think 627 00:34:31,360 --> 00:34:33,959 Speaker 1: he is a breath of fresh area. And as you said, 628 00:34:34,120 --> 00:34:36,120 Speaker 1: I mean, all these things are very hard to quantify, 629 00:34:36,200 --> 00:34:38,840 Speaker 1: and I imagine, Nate, I'm sure there's a lot of overlap. 630 00:34:38,880 --> 00:34:42,200 Speaker 1: There's some you know, probably then diagrams all over the 631 00:34:42,200 --> 00:34:46,960 Speaker 1: place that have people who fall into maybe both categories, right, absolutely, 632 00:34:47,000 --> 00:34:48,719 Speaker 1: I mean, and you and you know, I mean, I 633 00:34:48,760 --> 00:34:51,160 Speaker 1: think we're learning. One of the big mistakes I think 634 00:34:51,160 --> 00:34:55,239 Speaker 1: people made this year was underestimating the kind of complexity 635 00:34:55,800 --> 00:34:59,640 Speaker 1: other Republican electorate, where people said, well, um, you have 636 00:34:59,719 --> 00:35:03,560 Speaker 1: the aablishment lane and the evangelical lane and the libertarian 637 00:35:03,680 --> 00:35:07,200 Speaker 1: lane and whatever else, right, And in fact, those voters 638 00:35:07,200 --> 00:35:09,920 Speaker 1: are all kind of all over the place, right, uh, 639 00:35:09,960 --> 00:35:12,120 Speaker 1: and they all have a degree of being fed up 640 00:35:12,160 --> 00:35:15,640 Speaker 1: with Washington and frustrated. So you know, I mean, you know, 641 00:35:15,840 --> 00:35:18,480 Speaker 1: certainly the mood that Trump invokes. And we are getting 642 00:35:18,440 --> 00:35:21,520 Speaker 1: a little subjective here, but you know, the convention speech 643 00:35:21,600 --> 00:35:27,560 Speaker 1: that he delivered, um was a dark, even angry speech. 644 00:35:28,719 --> 00:35:30,600 Speaker 1: I'd like to go to go out on a limb 645 00:35:30,600 --> 00:35:34,600 Speaker 1: if you could nate. Um, if the election were held 646 00:35:34,680 --> 00:35:37,640 Speaker 1: tomorrow instead of a week from tomorrow, what do you 647 00:35:37,640 --> 00:35:39,799 Speaker 1: think is going to happen? I mean, this is why 648 00:35:39,800 --> 00:35:42,920 Speaker 1: we put things in probabilities, right, because on the one hand, 649 00:35:43,480 --> 00:35:46,040 Speaker 1: it's not so close, I don't think, and again we're 650 00:35:46,040 --> 00:35:47,719 Speaker 1: taking a couple days in advance. It's not so close 651 00:35:47,760 --> 00:35:50,120 Speaker 1: that you can say we're not sure what the polls say. 652 00:35:50,400 --> 00:35:53,480 Speaker 1: The polls say Clinton. Now it's my job trying to 653 00:35:53,520 --> 00:35:56,000 Speaker 1: figure out how luckily the polls are to be accurate 654 00:35:56,440 --> 00:35:59,320 Speaker 1: or not. And we think because the number of undecided 655 00:35:59,400 --> 00:36:02,319 Speaker 1: voters and number of wing states in play, the disagreement 656 00:36:02,360 --> 00:36:05,160 Speaker 1: in the polls, the risk of an error is higher 657 00:36:05,160 --> 00:36:08,200 Speaker 1: than usual in either direction. Frankly, you know, but look 658 00:36:08,200 --> 00:36:12,080 Speaker 1: Clinton's and even money, you'd be very happy to take Clinton. 659 00:36:12,719 --> 00:36:15,080 Speaker 1: We can start to debate what odds you would need 660 00:36:15,840 --> 00:36:18,879 Speaker 1: to take Trump. And you're saying, this is somebody who 661 00:36:18,920 --> 00:36:22,120 Speaker 1: made his money as an online poker player for a 662 00:36:22,160 --> 00:36:24,680 Speaker 1: couple of years. Yes, so you're always trying to figure 663 00:36:24,680 --> 00:36:26,920 Speaker 1: out wins an advantageous situation. I mean, the difference be 664 00:36:26,920 --> 00:36:29,320 Speaker 1: caen this In two thousand twelves and two twelve, we 665 00:36:29,400 --> 00:36:32,439 Speaker 1: spent a lot of time with the same model um 666 00:36:32,600 --> 00:36:34,560 Speaker 1: saying that people are calling us race a toss up, 667 00:36:34,600 --> 00:36:37,319 Speaker 1: and no, it's not true. Obama has been favored all 668 00:36:37,400 --> 00:36:40,520 Speaker 1: year long in polls of key swing states. Right this 669 00:36:40,560 --> 00:36:44,440 Speaker 1: time around, people are like, are in in disbelief that 670 00:36:44,480 --> 00:36:48,960 Speaker 1: Trump has a shot. But you know, there are maybe 671 00:36:49,080 --> 00:36:51,440 Speaker 1: enough people in the country for Trump to win narrowly. 672 00:36:51,480 --> 00:36:54,000 Speaker 1: And that's what we're talking about, right, We're talking about 673 00:36:54,080 --> 00:36:57,400 Speaker 1: a narrow Clinton win, a big Clinton win, or just 674 00:36:57,520 --> 00:37:03,040 Speaker 1: maybe a narrow, narrow Trump win UM involving probably him 675 00:37:03,040 --> 00:37:09,560 Speaker 1: overperforming in the Midwest, maybe even losing the popular vote nationwide. UM. 676 00:37:09,680 --> 00:37:11,879 Speaker 1: But you know, Clinton is doing really well in California 677 00:37:11,960 --> 00:37:15,360 Speaker 1: and Texas, for example, relative to Obama, not two states 678 00:37:15,360 --> 00:37:17,759 Speaker 1: that are helpful with the electoral vote very much. If 679 00:37:17,800 --> 00:37:19,560 Speaker 1: you gain a bunch of votes in California went by 680 00:37:19,640 --> 00:37:22,600 Speaker 1: thirty instead of twenty, well that boosts your popular vote 681 00:37:22,600 --> 00:37:25,640 Speaker 1: marchin by about a point nationally, but doesn't help you 682 00:37:25,680 --> 00:37:28,200 Speaker 1: in the electoral college at all. And any other states 683 00:37:28,200 --> 00:37:31,239 Speaker 1: that we should be paying particularly close attention to that 684 00:37:31,320 --> 00:37:34,879 Speaker 1: you think will be meaningful on election night, I mean, 685 00:37:34,880 --> 00:37:37,200 Speaker 1: there are some fun ones. So Utah is a state 686 00:37:37,200 --> 00:37:40,759 Speaker 1: where you have a third party candidate, Evan McMullan, who's 687 00:37:40,760 --> 00:37:43,040 Speaker 1: popular with the Mormon community there, which is about two 688 00:37:43,080 --> 00:37:45,360 Speaker 1: thirds of the electorate UM, and so he's giving Trump 689 00:37:45,840 --> 00:37:50,280 Speaker 1: a race. UM. You know, Arizona is a state where 690 00:37:50,920 --> 00:37:54,080 Speaker 1: if Clinton is having a pretty good night, UM, that 691 00:37:54,080 --> 00:37:55,920 Speaker 1: could be a benchmark for that becomes a really good 692 00:37:56,040 --> 00:37:57,799 Speaker 1: night for her and becomes something people will think of 693 00:37:57,800 --> 00:38:01,959 Speaker 1: as a landslide mandate hype outcome that, along with North 694 00:38:01,960 --> 00:38:04,280 Speaker 1: Carolina are the two states Obama lost in two thousand 695 00:38:04,320 --> 00:38:08,040 Speaker 1: twelve which she is most likely to flip. Um. Conversely, 696 00:38:08,120 --> 00:38:10,480 Speaker 1: I mean you can have states go in either directions. 697 00:38:10,480 --> 00:38:15,040 Speaker 1: So Trump is probably a favorite in Iowa, for example. Um. 698 00:38:15,120 --> 00:38:18,960 Speaker 1: And so you could have Iowa go red, Arizona go blue, 699 00:38:19,560 --> 00:38:22,920 Speaker 1: Texas not go blue, but come closer, right, and Utah 700 00:38:23,040 --> 00:38:25,520 Speaker 1: probably not go blue, but maybe go whatever color. And 701 00:38:25,680 --> 00:38:28,440 Speaker 1: McMillan is purple in our chart. Um. And so that 702 00:38:28,480 --> 00:38:31,200 Speaker 1: would be an indication of of how the electorate has 703 00:38:31,239 --> 00:38:34,840 Speaker 1: begun to change. We had a very similar map two thousand, 704 00:38:34,840 --> 00:38:38,520 Speaker 1: two thousand, eight twelve. This year you begin to see 705 00:38:39,000 --> 00:38:41,600 Speaker 1: some new threads come into play. It won't all resolve 706 00:38:41,600 --> 00:38:45,000 Speaker 1: itself this year. Um. But maybe we are talking about 707 00:38:45,040 --> 00:38:47,799 Speaker 1: the four Corners states in the future, where Arizona and 708 00:38:47,920 --> 00:38:52,279 Speaker 1: Utah are more competitive. Um. And maybe we're talking about 709 00:38:52,280 --> 00:38:55,359 Speaker 1: how Iowa is, like Missouri, a formerly swing state that's 710 00:38:55,360 --> 00:38:57,600 Speaker 1: now our red state, for example. And aren't we really 711 00:38:57,600 --> 00:39:01,800 Speaker 1: talking about the decline of democratics part among the white 712 00:39:01,800 --> 00:39:04,840 Speaker 1: working class. It already happened in West Virginia, it's happening 713 00:39:04,880 --> 00:39:08,080 Speaker 1: now in Iowa and Ohio. But the rise of Democratic 714 00:39:08,160 --> 00:39:12,239 Speaker 1: support among the professional, college educated class in places like 715 00:39:12,360 --> 00:39:15,480 Speaker 1: Arizona and North Carolina, and you see migration patterns. I mean, 716 00:39:15,600 --> 00:39:18,520 Speaker 1: I think Clinton is not going to get UM to 717 00:39:18,600 --> 00:39:23,200 Speaker 1: a winning margin in Georgia this year. It could be close, um, 718 00:39:23,239 --> 00:39:26,000 Speaker 1: But with four more years of tech sector growth in 719 00:39:26,000 --> 00:39:28,960 Speaker 1: the Atlanta area and growth of the African American population 720 00:39:29,000 --> 00:39:31,719 Speaker 1: there and college students, then Georgia could go the way 721 00:39:31,840 --> 00:39:35,520 Speaker 1: of of North Carolina, for example, and the immigration to 722 00:39:35,640 --> 00:39:40,240 Speaker 1: cities right and um. But again this doesn't play purely 723 00:39:40,320 --> 00:39:44,120 Speaker 1: into Democrats hands that it does tend to even out 724 00:39:44,360 --> 00:39:48,799 Speaker 1: and yeah, you're gonna see um maybe the access of 725 00:39:48,800 --> 00:39:51,799 Speaker 1: competition shift from the Midwest, where the Midwest will go 726 00:39:51,840 --> 00:39:56,360 Speaker 1: increasingly read um, to the south, both the Southwest and 727 00:39:56,400 --> 00:39:59,839 Speaker 1: the Southeast. Well. I think the fascinating thing to cover 728 00:40:00,120 --> 00:40:03,400 Speaker 1: and for you obviously need to pay attention to, is 729 00:40:03,520 --> 00:40:06,880 Speaker 1: the future of the Republican Party. I can't help but 730 00:40:07,040 --> 00:40:10,120 Speaker 1: think that it's going to turn into kind of a 731 00:40:10,160 --> 00:40:15,040 Speaker 1: two class, two party system, with more educated elites trending 732 00:40:15,120 --> 00:40:19,439 Speaker 1: towards the Democratic Party and less educated people becoming Republicans. 733 00:40:19,520 --> 00:40:23,960 Speaker 1: Is that a fair assessment, you guys? I mean clearly 734 00:40:24,040 --> 00:40:28,560 Speaker 1: these things have the opportunity to be self reinforcing. Um, 735 00:40:29,120 --> 00:40:31,080 Speaker 1: where the people who were fed up with Reblican Party 736 00:40:31,120 --> 00:40:33,480 Speaker 1: this year might not come back anytime soon. Especially it 737 00:40:33,520 --> 00:40:36,960 Speaker 1: seems like more of a Trumpian party, and if there's 738 00:40:36,960 --> 00:40:40,719 Speaker 1: a Trumpian plurality or majority than even without Trump, then 739 00:40:40,719 --> 00:40:44,359 Speaker 1: it can still control many candidates destiny. I mean, one 740 00:40:44,360 --> 00:40:47,120 Speaker 1: thing we definitely learned is that the reason that much 741 00:40:47,120 --> 00:40:52,200 Speaker 1: of a market for Marco Rubio type of conservatism where 742 00:40:52,200 --> 00:40:55,560 Speaker 1: it's kind of um well presented but ultimately pretty conservative 743 00:40:55,560 --> 00:40:58,600 Speaker 1: and kind of Reagan like Reagan reinvented, right, because the 744 00:40:58,640 --> 00:41:02,040 Speaker 1: market for that you to be among voters in the suburbs, 745 00:41:02,360 --> 00:41:05,919 Speaker 1: upper middle class usually, and that group has gone more 746 00:41:05,960 --> 00:41:09,400 Speaker 1: and more democratic. So that's been draining away the potential 747 00:41:09,480 --> 00:41:12,440 Speaker 1: number of votes that Republicans might might win. So what 748 00:41:12,560 --> 00:41:17,400 Speaker 1: brand of Republicanism works, well, we don't know. I mean, Trump, 749 00:41:17,400 --> 00:41:19,600 Speaker 1: who I think wasn't really a Republican that kind of 750 00:41:19,640 --> 00:41:23,000 Speaker 1: reinvented the party in his image, beat out sixteen other 751 00:41:23,160 --> 00:41:27,840 Speaker 1: versions of Republicanism. The one complicating thing. Um, So we 752 00:41:27,880 --> 00:41:30,839 Speaker 1: don't don't get too fixated on this neat narrative about 753 00:41:30,880 --> 00:41:34,320 Speaker 1: Trump projecting the party is that you did have UM, 754 00:41:34,640 --> 00:41:39,400 Speaker 1: very very few upsets in Republican Senate and gubinatorial primaries. 755 00:41:39,440 --> 00:41:43,239 Speaker 1: The establishment one plenty of those, and the GOP has 756 00:41:43,280 --> 00:41:46,520 Speaker 1: a fairly strong set of candidates, which may mitigate their 757 00:41:46,560 --> 00:41:51,439 Speaker 1: losses UM in the Senate for example. UM, So maybe 758 00:41:51,440 --> 00:41:53,920 Speaker 1: there's some hope that Trump was truly one of a 759 00:41:54,040 --> 00:41:58,560 Speaker 1: kind and that he'll go away quietly. UM, because in 760 00:41:58,640 --> 00:42:03,720 Speaker 1: some ways it's if in some ways if Trump wins UM, 761 00:42:03,920 --> 00:42:05,879 Speaker 1: unless he has a great first term, and some ways 762 00:42:05,880 --> 00:42:10,759 Speaker 1: of Trump wins, it's more problematic for the GOP. And 763 00:42:10,800 --> 00:42:12,440 Speaker 1: I know we have to go soon ate, but but 764 00:42:12,560 --> 00:42:17,040 Speaker 1: briefly before we do, what's your assessment on House and 765 00:42:17,120 --> 00:42:20,960 Speaker 1: Senate control at this point? So the Senate is incredible, 766 00:42:20,960 --> 00:42:24,680 Speaker 1: and that we have UM six races that are really 767 00:42:24,680 --> 00:42:28,000 Speaker 1: as close to toss ups as you can get. UM. 768 00:42:28,040 --> 00:42:31,680 Speaker 1: It looks like Democrats might have a slight edge in Pennsylvania, 769 00:42:31,840 --> 00:42:38,320 Speaker 1: but New Hampshire, Nevada, Missouri, Indiana, North Carolina, UM are 770 00:42:38,440 --> 00:42:40,799 Speaker 1: very close. And we don't say things are too close 771 00:42:40,840 --> 00:42:43,439 Speaker 1: to call just to stay out of trouble. I mean, 772 00:42:43,960 --> 00:42:46,200 Speaker 1: you know, you can really find lots of polls lining 773 00:42:46,280 --> 00:42:49,440 Speaker 1: up on either side of those races. But the history 774 00:42:49,440 --> 00:42:52,480 Speaker 1: of this is that the key Senate races tend to 775 00:42:52,480 --> 00:42:55,200 Speaker 1: break one way or the other depending on the presidential 776 00:42:55,200 --> 00:42:57,880 Speaker 1: as well, and it looked like they were breaking toward Democrats. 777 00:42:57,880 --> 00:42:59,840 Speaker 1: And now one week later, I mean, we have the 778 00:43:00,040 --> 00:43:03,319 Speaker 1: creds as a Monday as about a six chance to 779 00:43:03,320 --> 00:43:05,839 Speaker 1: take the centup. But that was declining a little bit 780 00:43:06,120 --> 00:43:08,479 Speaker 1: um as we began to see some Republicans come home, 781 00:43:08,520 --> 00:43:12,040 Speaker 1: I think. And finally, what about Gary Johnson and Jill Stein? 782 00:43:12,120 --> 00:43:15,359 Speaker 1: Are they just non factors? I mean they do even 783 00:43:15,360 --> 00:43:19,560 Speaker 1: though their percentages have declined, Gary Johnson's in particular since 784 00:43:19,960 --> 00:43:23,800 Speaker 1: the all time high at one p m, that's still 785 00:43:24,760 --> 00:43:27,160 Speaker 1: I mean, do you think it's siphoning votes away from 786 00:43:27,239 --> 00:43:29,839 Speaker 1: Hillary Clinton or from Donald Trump? And is it enough 787 00:43:29,880 --> 00:43:32,160 Speaker 1: to make a difference in the outcome? I mean, what 788 00:43:32,239 --> 00:43:34,560 Speaker 1: you've seen is that when the candidates have surged and 789 00:43:34,600 --> 00:43:37,480 Speaker 1: have good moments, right Clinton after the debates or her convention, 790 00:43:38,000 --> 00:43:42,320 Speaker 1: Um Trump more recently, Um, they begun to pull voters 791 00:43:42,400 --> 00:43:45,640 Speaker 1: away from the third party candidate, and once those voters 792 00:43:45,800 --> 00:43:48,359 Speaker 1: go to a major party candidate, they don't go back 793 00:43:48,400 --> 00:43:51,080 Speaker 1: because they don't want to wind up wasting their vote UM, 794 00:43:51,120 --> 00:43:53,440 Speaker 1: and so that vote for Johnson's decline from about ten 795 00:43:53,480 --> 00:43:56,960 Speaker 1: percent to five percent, stying from about four percent to 796 00:43:57,000 --> 00:43:59,799 Speaker 1: one or two percent UM. There's not much that much 797 00:43:59,800 --> 00:44:02,279 Speaker 1: low they would go. But but sure, I mean, if 798 00:44:02,320 --> 00:44:05,480 Speaker 1: you have Johnson with five percent of the vote UM 799 00:44:05,680 --> 00:44:10,040 Speaker 1: and the margin in Pennsylvania's five points, then those are 800 00:44:10,120 --> 00:44:13,040 Speaker 1: voters who potentially could make a difference at the end. 801 00:44:14,000 --> 00:44:17,000 Speaker 1: Is there any statistical analysis that would tell us if 802 00:44:17,040 --> 00:44:21,239 Speaker 1: you're most likely scenario happens that Clinton wynn but a 803 00:44:21,280 --> 00:44:24,960 Speaker 1: Republican House, maybe a Democratic Senate, maybe a Republican Senate, 804 00:44:25,239 --> 00:44:29,120 Speaker 1: whether we'll just get more gridlock or that something different 805 00:44:29,120 --> 00:44:32,759 Speaker 1: will actually occur. I mean, gridlock seems like a very 806 00:44:32,800 --> 00:44:36,560 Speaker 1: safe bet um in part because I don't think you're 807 00:44:36,560 --> 00:44:41,960 Speaker 1: going to have um a Democratic house. UM. I think 808 00:44:41,960 --> 00:44:44,239 Speaker 1: Clinton would have had need to have had a win 809 00:44:44,320 --> 00:44:45,880 Speaker 1: at her back in the last week of the campaign 810 00:44:45,880 --> 00:44:48,640 Speaker 1: instead of burning into the wind a little bit. If 811 00:44:48,680 --> 00:44:52,000 Speaker 1: you have Trump, you're gonna have UM, not the traditional 812 00:44:53,160 --> 00:44:57,320 Speaker 1: uh necessarily well trained bureaucrats and cabinet and everything else. 813 00:44:57,360 --> 00:44:59,920 Speaker 1: I mean, so you know, this is why I say 814 00:45:00,040 --> 00:45:03,319 Speaker 1: eight um. And again, you would much rather win the 815 00:45:03,320 --> 00:45:06,279 Speaker 1: presidency than lose it. But whoever wins the presidency is 816 00:45:06,280 --> 00:45:09,400 Speaker 1: going to probably face a difficult first two years and 817 00:45:09,480 --> 00:45:15,000 Speaker 1: difficult mid term in Nate Silver, Nate, thank you, Thank you, Katie, 818 00:45:15,000 --> 00:45:20,680 Speaker 1: Thank you, Brian too, thank you. So we want to 819 00:45:20,719 --> 00:45:23,759 Speaker 1: thank as always johnnah Palmer for producing the show and 820 00:45:23,840 --> 00:45:27,439 Speaker 1: Jared O'Connell for engineering and mixing it. Thanks to Mark 821 00:45:27,600 --> 00:45:31,160 Speaker 1: Phillips for our fantastic theme music. We'll be back next 822 00:45:31,200 --> 00:45:34,840 Speaker 1: week with a very special election episode, really a post 823 00:45:34,880 --> 00:45:38,040 Speaker 1: election episode, and we want to hear from you once 824 00:45:38,040 --> 00:45:40,359 Speaker 1: it's over. What will you be doing to Phillip all 825 00:45:40,360 --> 00:45:44,440 Speaker 1: the time you've spent obsessively following this election. Leave us 826 00:45:44,440 --> 00:45:47,560 Speaker 1: a message. Brian will be manning the phones. I'm kidding 827 00:45:47,600 --> 00:45:52,360 Speaker 1: about that to nine to two four four, six, three seven, 828 00:45:52,400 --> 00:45:54,319 Speaker 1: and we really look forward to hearing what you have 829 00:45:54,440 --> 00:45:57,920 Speaker 1: to say. And remember, beside our voicemail line, you can 830 00:45:57,960 --> 00:46:02,080 Speaker 1: also email us at comments at current podcast dot com 831 00:46:02,160 --> 00:46:05,839 Speaker 1: or even better, rate and review us on iTunes. 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