1 00:00:02,440 --> 00:00:07,520 Speaker 1: Bloomberg Audio Studios, Podcasts, radio news. 2 00:00:11,960 --> 00:00:16,360 Speaker 2: This is the Bloomberg Surveillance Podcast. Catch us live weekdays 3 00:00:16,400 --> 00:00:19,880 Speaker 2: at seven am Eastern on applecar Player, Android Auto with 4 00:00:19,920 --> 00:00:23,360 Speaker 2: the Bloomberg Business App. Listen on demand wherever you get 5 00:00:23,360 --> 00:00:26,239 Speaker 2: your podcasts, or watch us live on YouTube. 6 00:00:26,720 --> 00:00:29,720 Speaker 3: We now turn to Claudia sam without question my Economists 7 00:00:29,760 --> 00:00:32,839 Speaker 3: of the Year for two twenty five or impact her 8 00:00:33,680 --> 00:00:37,879 Speaker 3: informed debate off the Simmer role. Of course, all of it, 9 00:00:38,000 --> 00:00:40,959 Speaker 3: you know, really focused on Jackson Hole, and she had 10 00:00:40,960 --> 00:00:43,400 Speaker 3: a dinner at Jackson Hole, folks. I couldn't go to 11 00:00:43,440 --> 00:00:47,400 Speaker 3: it because a golf stream was leaving Dodge. But Claudia 12 00:00:47,479 --> 00:00:50,640 Speaker 3: Sim with huge influence here in twenty twenty four, so 13 00:00:50,640 --> 00:00:52,320 Speaker 3: I got to go just to the ratios and I 14 00:00:52,320 --> 00:00:55,400 Speaker 3: look at the unemployment rate four point one percent, the 15 00:00:55,520 --> 00:01:02,000 Speaker 3: quiescence there. The revisions are a little more less enthusiastic 16 00:01:02,080 --> 00:01:05,479 Speaker 3: than maybe what we saw twenty nine days ago. I'm 17 00:01:05,520 --> 00:01:08,120 Speaker 3: just looking at you know, there's eight nine six statistics here, 18 00:01:08,200 --> 00:01:13,360 Speaker 3: manufacturing payrolls negative six thousand, and the revision features up 19 00:01:13,400 --> 00:01:15,560 Speaker 3: twenty three, now up twenty there's a little bit of 20 00:01:15,560 --> 00:01:19,240 Speaker 3: a pullback, and the equity MARKETSIVIX twenty two point zero eight. 21 00:01:19,520 --> 00:01:22,280 Speaker 3: Paul Sweeney always going to the two year yeo Apolic 22 00:01:22,360 --> 00:01:23,319 Speaker 3: comes in with a vengeance. 23 00:01:23,360 --> 00:01:25,080 Speaker 4: Here, that's the point one two percent. 24 00:01:25,200 --> 00:01:26,560 Speaker 5: Yeah, four point one two percent. 25 00:01:26,640 --> 00:01:28,720 Speaker 6: So again the revision's time just from the last month, 26 00:01:29,480 --> 00:01:31,960 Speaker 6: changing non farm payrolls, we were at two fifty four 27 00:01:32,080 --> 00:01:33,839 Speaker 6: now revised down to two twenty three, so a little 28 00:01:33,840 --> 00:01:37,080 Speaker 6: bit weaker. But again the headline today twelve thousand changing 29 00:01:37,120 --> 00:01:39,640 Speaker 6: non farm payrolls. Consensus was one hundred thousand. But again 30 00:01:39,680 --> 00:01:42,440 Speaker 6: the economists have been warning us that, you know, we 31 00:01:42,560 --> 00:01:44,840 Speaker 6: had the hurricanes, we've got some various strikes, and that's 32 00:01:44,880 --> 00:01:46,520 Speaker 6: going to make this number, you know, a little bit tough. 33 00:01:46,680 --> 00:01:50,120 Speaker 3: This Yeah, and again the net revision is a negative 34 00:01:50,280 --> 00:01:51,200 Speaker 3: one twelve. 35 00:01:52,000 --> 00:01:53,000 Speaker 4: So it's easy math. 36 00:01:53,080 --> 00:01:57,040 Speaker 3: I take twelve thousand positive minus one twelve is a 37 00:01:57,080 --> 00:02:00,240 Speaker 3: negative one hundred thousand. Claudius sim is going, and why 38 00:02:00,280 --> 00:02:03,880 Speaker 3: am I dealing with these financial media tools, she joins 39 00:02:03,960 --> 00:02:06,960 Speaker 3: us right now, I mean the value here, Claudie, is 40 00:02:07,000 --> 00:02:11,160 Speaker 3: going to be am I correct massive revisions thirty sixty 41 00:02:11,280 --> 00:02:15,160 Speaker 3: even ninety days out. 42 00:02:14,600 --> 00:02:17,920 Speaker 7: When not even necessarily revisions, we could just see next 43 00:02:17,960 --> 00:02:21,760 Speaker 7: month's number be a big, strong positive, right, kind of unwinded. 44 00:02:22,080 --> 00:02:24,720 Speaker 7: They're really we had two major hurricanes and we had 45 00:02:24,760 --> 00:02:27,240 Speaker 7: a strike happening in the period that we're trying to 46 00:02:27,280 --> 00:02:30,840 Speaker 7: measure jobs. It probably really was a hit to the jobs. Now, 47 00:02:30,919 --> 00:02:33,520 Speaker 7: I will say I feel pre vindicated. My substack piece 48 00:02:33,560 --> 00:02:36,000 Speaker 7: this week that was the preview for this was talking 49 00:02:36,000 --> 00:02:39,480 Speaker 7: about this could be our negative payroll print, and it's 50 00:02:39,760 --> 00:02:41,480 Speaker 7: it's not negative. So I was wrong on that one, 51 00:02:41,520 --> 00:02:43,240 Speaker 7: but it is this was a low print. And the 52 00:02:43,360 --> 00:02:46,320 Speaker 7: argument there is even if we had even if like 53 00:02:46,400 --> 00:02:49,360 Speaker 7: the reality is we're kind of moving along at trend growth, 54 00:02:49,400 --> 00:02:51,480 Speaker 7: which is a little under two hundred thousand, like that 55 00:02:51,639 --> 00:02:54,720 Speaker 7: was the pace before the pandemic. Well, you get one 56 00:02:54,800 --> 00:02:57,799 Speaker 7: hundred thousand knocked off because of hurricanes and the strike, 57 00:02:57,840 --> 00:03:00,960 Speaker 7: which was Governor Chris Waller's as sestimate of what that 58 00:03:01,000 --> 00:03:03,800 Speaker 7: effect would be. And then you take into account that 59 00:03:03,800 --> 00:03:08,680 Speaker 7: these are survey estimates, they are imprecise. The BLS tells 60 00:03:08,760 --> 00:03:12,880 Speaker 7: us that always the confidence interval around an estimate is 61 00:03:12,960 --> 00:03:17,000 Speaker 7: over one hundred thousand jobs. So we really that number, 62 00:03:17,040 --> 00:03:20,280 Speaker 7: that twelve thousand we got today that could be entirely 63 00:03:20,360 --> 00:03:23,040 Speaker 7: consistent with the labor market that's growing at Trent and frankly, 64 00:03:23,080 --> 00:03:26,000 Speaker 7: the un employment rate staying low. That really goes in 65 00:03:26,040 --> 00:03:26,480 Speaker 7: that court. 66 00:03:26,760 --> 00:03:27,400 Speaker 4: Let me get out that. 67 00:03:27,480 --> 00:03:28,720 Speaker 7: This was more disruptions. 68 00:03:28,840 --> 00:03:29,600 Speaker 4: Let me get out front. 69 00:03:29,600 --> 00:03:31,840 Speaker 3: It's November one, so it's not tacky. If I was 70 00:03:31,880 --> 00:03:34,639 Speaker 3: saying she was our economist of the Year in September, 71 00:03:34,639 --> 00:03:35,520 Speaker 3: that would be tacky. 72 00:03:35,560 --> 00:03:37,120 Speaker 4: But come on November one. 73 00:03:37,280 --> 00:03:40,680 Speaker 3: Neil Dutta last year killed it with his optimism on 74 00:03:40,840 --> 00:03:45,160 Speaker 3: three percentage GDP. Claudius, I'm killed it this year, charging 75 00:03:45,200 --> 00:03:48,320 Speaker 3: the debate here on our labor economy. She's a New 76 00:03:48,320 --> 00:03:51,880 Speaker 3: Century Advisors, Claudius, I'm SIA next month joining us right 77 00:03:51,920 --> 00:03:55,280 Speaker 3: now in studio Ellen Zenner, she runs an Apple iPhone 78 00:03:55,360 --> 00:03:57,840 Speaker 3: Like no one do you need an iPhone sixteen to 79 00:03:57,880 --> 00:03:59,080 Speaker 3: go through the data faster? 80 00:03:59,240 --> 00:04:00,880 Speaker 4: Now normally going this. 81 00:04:01,120 --> 00:04:02,000 Speaker 8: Is my work phone. 82 00:04:02,200 --> 00:04:05,360 Speaker 9: I am a Android Samsummer. 83 00:04:05,480 --> 00:04:07,000 Speaker 8: Yeah, I'm that age guys. 84 00:04:07,160 --> 00:04:10,480 Speaker 9: Yeah, as I'm forced to use an iPhone and I 85 00:04:10,480 --> 00:04:12,280 Speaker 9: only know how to check email slow motion. 86 00:04:12,400 --> 00:04:15,880 Speaker 3: Lisa Shllett has the iPhone sixteen pro Ellen Center with 87 00:04:16,000 --> 00:04:19,440 Speaker 3: us now, of course, with Morgan Stanley's Global Investment Office. 88 00:04:19,560 --> 00:04:24,680 Speaker 3: You linked consumption into labor like nobody I know. We 89 00:04:24,760 --> 00:04:27,960 Speaker 3: had a buoyant consumption number are we taking it all 90 00:04:28,000 --> 00:04:31,240 Speaker 3: from savings and is it a fake labor economy? 91 00:04:31,600 --> 00:04:32,520 Speaker 8: No, not at all. 92 00:04:32,880 --> 00:04:35,040 Speaker 9: I mean, look, new data that we got some weeks 93 00:04:35,040 --> 00:04:38,719 Speaker 9: ago showed that it turns out we've spent a lot 94 00:04:38,760 --> 00:04:42,640 Speaker 9: more than we thought, and savings was still higher because 95 00:04:42,640 --> 00:04:45,159 Speaker 9: income numbers are just been revised up that much. 96 00:04:45,760 --> 00:04:48,000 Speaker 8: So we still got income that's really robust. 97 00:04:48,640 --> 00:04:50,920 Speaker 9: And I agree with Claudia that there is still a 98 00:04:51,000 --> 00:04:53,279 Speaker 9: lot of disruption in these numbers, and this probably is 99 00:04:53,360 --> 00:04:56,600 Speaker 9: consistent with a labor market growing at trend, and so 100 00:04:56,640 --> 00:05:00,800 Speaker 9: we're still producing enough income to support consumption. I mean, 101 00:05:00,880 --> 00:05:04,440 Speaker 9: look at consumption of lower income consumers. They are really 102 00:05:04,480 --> 00:05:07,240 Speaker 9: going to love these lower gas prices that frees up 103 00:05:07,240 --> 00:05:11,080 Speaker 9: a lot of buying power as well. So still yeah, yeah, 104 00:05:11,120 --> 00:05:16,520 Speaker 9: and wage growth is still healthy while inflation's coming down. 105 00:05:16,600 --> 00:05:18,600 Speaker 9: So you got real wages that are positive, You've got 106 00:05:18,600 --> 00:05:21,560 Speaker 9: gas prices that are lower, and so you're getting some 107 00:05:21,600 --> 00:05:23,920 Speaker 9: support coming back for lower income households as well. 108 00:05:24,760 --> 00:05:27,080 Speaker 6: So you mentioned the average hourly earnings on a year 109 00:05:27,120 --> 00:05:31,320 Speaker 6: of a year basis four percent. That's pretty solid, right, 110 00:05:31,360 --> 00:05:34,160 Speaker 6: I mean, give us a sense of historically where that 111 00:05:34,240 --> 00:05:35,839 Speaker 6: number typically comes in. 112 00:05:36,200 --> 00:05:41,520 Speaker 9: Well, that number, so look average hourly earnings in this report. 113 00:05:41,760 --> 00:05:45,240 Speaker 9: It's a tiny sample and it can be distorted from 114 00:05:45,279 --> 00:05:48,000 Speaker 9: month to month depending on what hours worked do so 115 00:05:48,200 --> 00:05:52,000 Speaker 9: it's a calculation thing. And so four percent average dollity 116 00:05:52,040 --> 00:05:54,200 Speaker 9: earnings on you over your basis, you would say, gosh, 117 00:05:54,279 --> 00:05:56,880 Speaker 9: that's a little bit higher than what would be consistent 118 00:05:56,920 --> 00:06:00,120 Speaker 9: with the fed's two percent goal. But you look, I'll 119 00:06:00,120 --> 00:06:03,159 Speaker 9: get the Employment Cost Index, which came out this week. 120 00:06:03,600 --> 00:06:05,760 Speaker 9: That was Yellen's favorite measure. It continues to be the 121 00:06:05,760 --> 00:06:09,760 Speaker 9: Fed's favorite measures moment. It's a much more comprehensive measure 122 00:06:10,400 --> 00:06:14,240 Speaker 9: of wages and labor costs. And that is absolutely right 123 00:06:14,240 --> 00:06:17,320 Speaker 9: now consistent with the fed's two percentle which means that 124 00:06:17,400 --> 00:06:18,039 Speaker 9: yes it is. 125 00:06:18,120 --> 00:06:20,280 Speaker 8: It is healthy. So you're right, it's healthy, but it's 126 00:06:20,320 --> 00:06:20,680 Speaker 8: not too. 127 00:06:21,160 --> 00:06:23,640 Speaker 4: Amazing, not too fast. I'm bringing it up while you're talking. 128 00:06:24,279 --> 00:06:27,760 Speaker 3: ECI from five percent five percent into three point nine 129 00:06:27,800 --> 00:06:31,360 Speaker 3: percent year of year nowhere near back to two point 130 00:06:32,040 --> 00:06:36,279 Speaker 3: seventy five percent, sort of pre pandemic as well. You know, 131 00:06:36,279 --> 00:06:38,240 Speaker 3: we should talk to her about hourly earnings. In the 132 00:06:38,240 --> 00:06:40,200 Speaker 3: effect of the Yankees defense, I. 133 00:06:40,160 --> 00:06:42,520 Speaker 9: Mean, and I want to talk about the Yankees, I can't. 134 00:06:42,600 --> 00:06:44,960 Speaker 9: I turned it off at the eighth inning on Monday. 135 00:06:45,680 --> 00:06:47,800 Speaker 9: I turned it off in that last game. 136 00:06:48,600 --> 00:06:50,080 Speaker 8: I was just I was disgusted. 137 00:06:50,240 --> 00:06:53,400 Speaker 4: I choose a third row with Ellen. 138 00:06:53,440 --> 00:06:55,000 Speaker 5: What do you think this Federal Reserve should do with 139 00:06:55,120 --> 00:06:56,000 Speaker 5: data like this? 140 00:06:56,520 --> 00:06:58,360 Speaker 6: Here to any other data we've seen over the last 141 00:06:58,480 --> 00:07:01,960 Speaker 6: several weeks as a get together, next week cut cut? 142 00:07:02,240 --> 00:07:02,640 Speaker 10: Yeah? 143 00:07:02,680 --> 00:07:03,159 Speaker 5: How much? 144 00:07:03,279 --> 00:07:04,400 Speaker 8: Yeah? Twenty five? 145 00:07:04,480 --> 00:07:09,400 Speaker 9: Okay, Yeah, the the the economy's fine. And every time 146 00:07:09,440 --> 00:07:12,520 Speaker 9: we get great data, the market wants to pressure and say, well, 147 00:07:12,520 --> 00:07:14,760 Speaker 9: the Fed doesn't need to do more here, but what 148 00:07:14,920 --> 00:07:17,880 Speaker 9: is cheripalganas stress next week? Yeah, the economy is fine, 149 00:07:17,880 --> 00:07:20,520 Speaker 9: the economy is great, so let's keep it there. Let's 150 00:07:20,600 --> 00:07:23,120 Speaker 9: keep it there. And look, we've got ample room to cut. 151 00:07:23,160 --> 00:07:24,680 Speaker 9: We don't know where neutral is, so we're going to 152 00:07:24,760 --> 00:07:27,679 Speaker 9: baby step it there. I mean, they're in a great position, 153 00:07:27,760 --> 00:07:30,880 Speaker 9: having started the cutting cycle before the election, so they're 154 00:07:30,880 --> 00:07:34,160 Speaker 9: in a great position. If there's some something that impacts 155 00:07:34,160 --> 00:07:36,480 Speaker 9: the outlook for the economy, you can speed up rate 156 00:07:36,520 --> 00:07:39,560 Speaker 9: cuts easily, you can pull rate cuts forward. I'm more 157 00:07:39,560 --> 00:07:42,840 Speaker 9: in the camp of the risk of more than less 158 00:07:43,200 --> 00:07:43,840 Speaker 9: from the Fed. 159 00:07:44,360 --> 00:07:46,840 Speaker 4: We are so thrilled, folks, So this job stay covers. 160 00:07:46,840 --> 00:07:49,200 Speaker 3: You have Claudia sim with wis constant hundred before with 161 00:07:49,320 --> 00:07:52,080 Speaker 3: Eiu and to have doctor Sam with us, and then 162 00:07:52,160 --> 00:07:55,200 Speaker 3: Ellen Zendner Lizaye Saunders is on deck looking at the 163 00:07:55,200 --> 00:08:00,400 Speaker 3: equity market response, particularly out beyond the election, and then 164 00:08:00,480 --> 00:08:04,240 Speaker 3: Ira Jersey will uh show up with us? We don't 165 00:08:04,280 --> 00:08:06,000 Speaker 3: care at all what Irah thinks all we want to 166 00:08:06,000 --> 00:08:07,240 Speaker 3: do is talk about Astonville. 167 00:08:07,640 --> 00:08:08,360 Speaker 8: I thought it was all you. 168 00:08:08,280 --> 00:08:09,760 Speaker 9: Want to do is have a man on the show, 169 00:08:09,800 --> 00:08:12,360 Speaker 9: because it's an all woman This is the all woman warning. 170 00:08:12,640 --> 00:08:14,640 Speaker 4: You know, I don't think there's any you know, we 171 00:08:14,680 --> 00:08:15,800 Speaker 4: didn't do that diversity. 172 00:08:16,760 --> 00:08:18,800 Speaker 8: It's about diversity. 173 00:08:19,280 --> 00:08:21,520 Speaker 4: We it just worked out that way. 174 00:08:22,440 --> 00:08:24,080 Speaker 9: How I would it just so happens all the women 175 00:08:24,120 --> 00:08:26,000 Speaker 9: are the experts. It just worked out that way. 176 00:08:27,480 --> 00:08:28,240 Speaker 4: As tough as. 177 00:08:28,200 --> 00:08:31,600 Speaker 6: Nails, Ellen, How about the consumer here? I mean the 178 00:08:31,600 --> 00:08:35,560 Speaker 6: consumers employed, the consumers getting wage gains, inflation's coming down. 179 00:08:36,120 --> 00:08:37,880 Speaker 5: How do you feel about the US consumer these days? 180 00:08:38,000 --> 00:08:41,559 Speaker 9: I think the used consumers is it's I would say great, 181 00:08:41,760 --> 00:08:43,400 Speaker 9: except that we still have a lot of our eggs 182 00:08:43,400 --> 00:08:46,200 Speaker 9: in one basket. The wealthy consumer is still driving the 183 00:08:46,200 --> 00:08:50,360 Speaker 9: majority of spending. But but like I said, I'm encouraged 184 00:08:51,440 --> 00:08:55,600 Speaker 9: at some of the developments for lower income groups that 185 00:08:55,640 --> 00:08:59,000 Speaker 9: we're continuing to create jobs, wages are growing. 186 00:08:59,520 --> 00:09:01,200 Speaker 4: This is import We're going to stop here, gas price. 187 00:09:01,200 --> 00:09:02,040 Speaker 4: This is really. 188 00:09:01,800 --> 00:09:04,920 Speaker 3: Important, folks. We're going to take the Morgan Stanley continuum 189 00:09:04,960 --> 00:09:07,560 Speaker 3: me here right now, let's take it into death siles 190 00:09:07,600 --> 00:09:12,320 Speaker 3: tenth of economy. The pandemic stereotype is the rich did well, 191 00:09:12,440 --> 00:09:15,439 Speaker 3: no surprise there, and the lower one and two death 192 00:09:15,480 --> 00:09:18,080 Speaker 3: siles were benefited because they were taking care of the rich. 193 00:09:18,520 --> 00:09:21,920 Speaker 3: What does that death sile construction look like into Q 194 00:09:22,120 --> 00:09:23,679 Speaker 3: one twenty twenty. 195 00:09:23,360 --> 00:09:26,280 Speaker 9: Five, so well, the hope is that that the spending 196 00:09:26,360 --> 00:09:31,840 Speaker 9: is diffused more across those death siles. So before COVID, 197 00:09:32,000 --> 00:09:35,320 Speaker 9: the top the top quintiles, so the top two death 198 00:09:35,320 --> 00:09:39,199 Speaker 9: stiles represented about forty percent of consumer spending. That bumped 199 00:09:39,280 --> 00:09:42,000 Speaker 9: up to forty five percent of consumer spending. And it's 200 00:09:42,040 --> 00:09:42,760 Speaker 9: stayed there. 201 00:09:43,280 --> 00:09:44,079 Speaker 4: It's still there. 202 00:09:44,160 --> 00:09:44,880 Speaker 8: It's still there. 203 00:09:44,960 --> 00:09:47,840 Speaker 9: The top two quintiles are more than sixty percent of 204 00:09:47,880 --> 00:09:50,560 Speaker 9: consumer spending. And so what you want to see is 205 00:09:50,640 --> 00:09:54,680 Speaker 9: we start to have some share come back to lower 206 00:09:54,720 --> 00:09:58,320 Speaker 9: income households because they're contributing more broadly to consumers. 207 00:09:58,320 --> 00:10:00,960 Speaker 4: But you haven't seen this yet. Into the election. 208 00:10:01,520 --> 00:10:04,400 Speaker 9: Haven't seen it yet, right, and that's important right into 209 00:10:04,400 --> 00:10:07,520 Speaker 9: the election. Yes, we haven't seen any to interpolate. 210 00:10:06,880 --> 00:10:10,880 Speaker 3: Those death sile analysis to say that the haves have 211 00:10:11,000 --> 00:10:14,240 Speaker 3: a GDP of four or five six percent real GDP 212 00:10:14,720 --> 00:10:16,439 Speaker 3: and the have nots are near recession. 213 00:10:17,320 --> 00:10:19,240 Speaker 9: Well, the have nots have been in recession for a 214 00:10:19,280 --> 00:10:21,920 Speaker 9: couple of years. I would say that they're emerging from that. 215 00:10:22,640 --> 00:10:29,439 Speaker 9: What delayed them moving into recession was that incredible savings 216 00:10:29,480 --> 00:10:32,680 Speaker 9: cushion that they received when we put in all the 217 00:10:32,679 --> 00:10:35,960 Speaker 9: stimulus during COVID. Once they spent through that savings, but 218 00:10:36,040 --> 00:10:40,040 Speaker 9: still we're dealing with extraordinarily high rent inflation, food inflation, 219 00:10:41,120 --> 00:10:46,520 Speaker 9: energy prices inflation, you know that, and wage growth that 220 00:10:46,679 --> 00:10:50,160 Speaker 9: was not outpacing inflation. That's when they were in recession, 221 00:10:50,480 --> 00:10:53,720 Speaker 9: and you could follow companies that cater to them and 222 00:10:53,800 --> 00:10:56,520 Speaker 9: the troubles that they were having. You could see it, 223 00:10:56,559 --> 00:10:59,280 Speaker 9: and the products consumers were buying. It was all being 224 00:10:59,360 --> 00:11:01,679 Speaker 9: supported by wealthy. We don't want to have our eggs 225 00:11:01,760 --> 00:11:06,320 Speaker 9: in one basket, right. We want consumer spending to diffuse more. 226 00:11:06,120 --> 00:11:07,400 Speaker 8: Broadly across households. 227 00:11:07,400 --> 00:11:11,080 Speaker 9: We want all households to participate in the expansion, and 228 00:11:11,160 --> 00:11:14,520 Speaker 9: so we do have some positive developments here. Wages are 229 00:11:14,559 --> 00:11:17,840 Speaker 9: growing on an inflation adjusted basis, the gas prices to 230 00:11:17,920 --> 00:11:21,160 Speaker 9: all of that, right, they've still got very high cost 231 00:11:21,200 --> 00:11:25,319 Speaker 9: of living just in terms of housing costs. As the 232 00:11:25,360 --> 00:11:28,240 Speaker 9: FED cuts rates, you know that the market prices in 233 00:11:28,320 --> 00:11:32,280 Speaker 9: the whole path of Fed cuts immediately. Fed even hints 234 00:11:32,320 --> 00:11:36,000 Speaker 9: at doing something. What doesn't move yet until they actually 235 00:11:36,040 --> 00:11:38,800 Speaker 9: cut rates. Are the rates say that you pay on 236 00:11:38,880 --> 00:11:41,920 Speaker 9: your credit card debt, and it is younger and lower 237 00:11:41,920 --> 00:11:44,760 Speaker 9: income households that revolve a lot of that credit card debt. 238 00:11:44,960 --> 00:11:47,440 Speaker 9: And so as the Fed continues to cut rates, that's 239 00:11:47,480 --> 00:11:49,920 Speaker 9: going to take some pressure off of them as well. 240 00:11:50,559 --> 00:11:53,800 Speaker 3: We're talking Pennsylvania swing steak Gurz out there trying every 241 00:11:53,840 --> 00:11:59,280 Speaker 3: beer today. Have you ever brown trout fished in Pennsylvania? 242 00:11:59,440 --> 00:12:02,560 Speaker 3: Like real, it's like nineteenth century trout fishing. 243 00:12:02,559 --> 00:12:03,400 Speaker 4: Have you ever done that? 244 00:12:03,920 --> 00:12:06,560 Speaker 9: Yeah, that's that's where we go for small stream fishing. 245 00:12:06,679 --> 00:12:09,599 Speaker 9: Small stream and a half foot rod is perfect, a 246 00:12:09,679 --> 00:12:11,320 Speaker 9: flick of the wrist, It's very easy. 247 00:12:11,360 --> 00:12:12,160 Speaker 8: Small streams. 248 00:12:12,480 --> 00:12:15,200 Speaker 9: The brown trouts are fierce there. 249 00:12:15,880 --> 00:12:16,160 Speaker 4: Really. 250 00:12:16,400 --> 00:12:19,640 Speaker 8: Yeah, you're going after out west. 251 00:12:19,800 --> 00:12:25,480 Speaker 9: Yeah, compared to ten inch twelve inch little brown trout. 252 00:12:25,600 --> 00:12:29,160 Speaker 8: Fierce they're fears, fierce fighters. 253 00:12:29,000 --> 00:12:32,480 Speaker 4: Catching release or like we cook in thechen release. You're 254 00:12:32,520 --> 00:12:34,160 Speaker 4: not cooking a cot release. 255 00:12:34,280 --> 00:12:36,880 Speaker 3: You're not cooking up three of them, you know, surf Elzabone. 256 00:12:36,880 --> 00:12:40,560 Speaker 3: Saunders would be charged. She'd have the grill going, yeah, out. 257 00:12:40,440 --> 00:12:43,199 Speaker 9: There doing hey, maybe maybe a brook trout here? 258 00:12:43,240 --> 00:12:45,800 Speaker 4: And where's your secret stream in Pennsylvania? Don't give this 259 00:12:45,960 --> 00:12:48,640 Speaker 4: the name? But what which beer is it? 260 00:12:48,640 --> 00:12:49,360 Speaker 9: It makes no sense. 261 00:12:49,440 --> 00:12:49,680 Speaker 8: Tom. 262 00:12:49,720 --> 00:12:52,079 Speaker 9: You said the word secret. What's your secret stream? And 263 00:12:52,120 --> 00:12:53,720 Speaker 9: it's no longer a secret anymore? 264 00:12:54,559 --> 00:12:57,840 Speaker 4: See Ellen Saner, thank you so much for jobs today. 265 00:12:57,920 --> 00:13:01,760 Speaker 4: Let's move on to someone constructive. Okay, Alen Xander with Burgocilli. 266 00:13:01,960 --> 00:13:04,640 Speaker 3: This is great, Claudius, I'm Ellen Zetner here with your 267 00:13:04,720 --> 00:13:08,719 Speaker 3: job market inequities improve a perfect security into Liz and 268 00:13:09,000 --> 00:13:10,960 Speaker 3: Saunder wouldn't know a brown trout if it hit her 269 00:13:11,000 --> 00:13:14,439 Speaker 3: over the head, Liz Anne, I look at futures up thirty, 270 00:13:14,880 --> 00:13:18,800 Speaker 3: I look at the mag seven hysteria. Liz Anne, I'm 271 00:13:18,840 --> 00:13:22,079 Speaker 3: begging get us into Q one twenty twenty five. 272 00:13:22,480 --> 00:13:24,480 Speaker 4: What is our commitment to equities? 273 00:13:26,240 --> 00:13:28,960 Speaker 10: Well, I do worry a little bit about some some 274 00:13:29,120 --> 00:13:32,000 Speaker 10: froth in the market. Although you've had some weakness that 275 00:13:32,080 --> 00:13:34,560 Speaker 10: might ease a little bit of that even outside of 276 00:13:34,600 --> 00:13:38,600 Speaker 10: traditional investor sentiment measures. You know, the consumer competence report 277 00:13:38,640 --> 00:13:41,120 Speaker 10: that came out, which was better in terms of headline confidence, 278 00:13:41,160 --> 00:13:44,160 Speaker 10: also had a record high percentage of respondent saying they 279 00:13:44,160 --> 00:13:47,600 Speaker 10: thought stock prices would be higher. So you see another 280 00:13:47,720 --> 00:13:50,800 Speaker 10: behavioral data. So as I think between now and the 281 00:13:50,840 --> 00:13:54,360 Speaker 10: first part of twenty twenty five, especially now with twenty 282 00:13:54,360 --> 00:13:59,200 Speaker 10: five estimates coming down for earnings, my biggest concern is 283 00:13:59,280 --> 00:14:02,920 Speaker 10: just that back of a little bit of frauthy sentiment 284 00:14:02,960 --> 00:14:05,199 Speaker 10: all l sequel. I think that means if you get 285 00:14:05,200 --> 00:14:08,640 Speaker 10: any kind of negative catalyst, I don't know that today's 286 00:14:08,679 --> 00:14:11,000 Speaker 10: weaker jobs report would be that because of what in 287 00:14:11,120 --> 00:14:15,160 Speaker 10: first for bed policy. That to me is the background 288 00:14:15,240 --> 00:14:16,600 Speaker 10: risk heatting into twenty twenty five. 289 00:14:17,240 --> 00:14:19,920 Speaker 3: To my essay of the year two years ago, which 290 00:14:20,000 --> 00:14:23,560 Speaker 3: was a brilliant effort by Lawrence MacDonald on the wall 291 00:14:23,720 --> 00:14:26,880 Speaker 3: of money that was out there in twenty twenty two, 292 00:14:27,360 --> 00:14:30,040 Speaker 3: liz Ane Saunders, is there a wall of money still 293 00:14:30,080 --> 00:14:31,920 Speaker 3: out there trying to find a warm place. 294 00:14:32,840 --> 00:14:35,680 Speaker 10: Well, A lot of people point to the six trillion 295 00:14:35,720 --> 00:14:39,840 Speaker 10: plus in my market funds as some wall that could 296 00:14:39,840 --> 00:14:42,280 Speaker 10: come into the equity market. I'm not sure I agree 297 00:14:42,320 --> 00:14:44,760 Speaker 10: with that assessment. I think that a lot of that 298 00:14:44,840 --> 00:14:47,520 Speaker 10: money is pretty sticky. A lot of that money came 299 00:14:47,520 --> 00:14:51,160 Speaker 10: in because of higher yields and out of riskier areas, 300 00:14:51,200 --> 00:14:53,840 Speaker 10: whether it's on the fixed income side of things or 301 00:14:53,880 --> 00:14:55,800 Speaker 10: in the equity side of things. And actually, if you 302 00:14:55,800 --> 00:15:00,920 Speaker 10: look historically at the early part of cutting cycles, money 303 00:15:00,960 --> 00:15:04,320 Speaker 10: actually continues to flow into money market funds, not in 304 00:15:04,360 --> 00:15:08,560 Speaker 10: the opposite direction. So I don't view that as some 305 00:15:08,840 --> 00:15:11,920 Speaker 10: imminent wall of money that could find its way into equities. 306 00:15:11,960 --> 00:15:14,360 Speaker 10: I think that's probably a good chunk of that is 307 00:15:14,400 --> 00:15:15,080 Speaker 10: pretty sticky. 308 00:15:16,440 --> 00:15:19,360 Speaker 6: Luzanne, what are you seeing from earnings so far this quarter? 309 00:15:19,360 --> 00:15:21,600 Speaker 6: We had started off strong with the big banks, We've 310 00:15:21,640 --> 00:15:24,840 Speaker 6: had a bunch of tech earnings. What are you seeing 311 00:15:24,840 --> 00:15:26,160 Speaker 6: and what do you think the market needs to see 312 00:15:26,160 --> 00:15:26,720 Speaker 6: from earnings? 313 00:15:27,320 --> 00:15:29,640 Speaker 10: Well, this is obviously a big week with five of 314 00:15:29,760 --> 00:15:33,560 Speaker 10: Magnificent seven reporting, and in terms of Meta and Microsoft, 315 00:15:33,880 --> 00:15:36,720 Speaker 10: at least a quick glance, they didn't look bad. But 316 00:15:37,240 --> 00:15:39,520 Speaker 10: at the expense side of things, I think it's the 317 00:15:39,560 --> 00:15:43,280 Speaker 10: monetization story that really kicked in in the second quarter. 318 00:15:43,600 --> 00:15:46,640 Speaker 10: I think it's continued in the third quarter. Reporting season, 319 00:15:46,720 --> 00:15:51,360 Speaker 10: which is the vast amount of expenses associated with AI, 320 00:15:51,560 --> 00:15:55,280 Speaker 10: not just among the director indirect AI players, but across 321 00:15:55,280 --> 00:15:59,480 Speaker 10: the spectrum of industries and sectors, and maybe the time 322 00:15:59,560 --> 00:16:04,200 Speaker 10: gap between those investments and the monetization thereof, either from 323 00:16:04,240 --> 00:16:07,720 Speaker 10: a revenue perspective or a productivity perspective. So I think 324 00:16:07,760 --> 00:16:10,360 Speaker 10: we're continuing that theme and it helps to explain some 325 00:16:10,440 --> 00:16:12,120 Speaker 10: of the weakness like yesterday and tech. 326 00:16:12,400 --> 00:16:15,080 Speaker 3: We're going to go CEFA inside baseball. Right now, Lizzie 327 00:16:15,120 --> 00:16:21,200 Speaker 3: and I am apoplectic over this simplistic income statement analysis 328 00:16:21,320 --> 00:16:22,720 Speaker 3: of the financial media. 329 00:16:23,320 --> 00:16:24,479 Speaker 4: You got to pull. 330 00:16:24,280 --> 00:16:29,440 Speaker 3: In profit, distribution of profit, use of cash is the buzzword, 331 00:16:29,840 --> 00:16:34,320 Speaker 3: and even into improved balance sheet dynamics. Are we slaves 332 00:16:34,400 --> 00:16:38,000 Speaker 3: to a simple income statement analysis or do you see 333 00:16:38,000 --> 00:16:40,640 Speaker 3: more out there when you look at free cash flow, 334 00:16:40,720 --> 00:16:43,160 Speaker 3: free cash flow growth and what it means to a 335 00:16:43,240 --> 00:16:44,440 Speaker 3: dominant balance sheet. 336 00:16:45,400 --> 00:16:47,920 Speaker 10: I do see more of that out there, especially as 337 00:16:47,960 --> 00:16:50,800 Speaker 10: you move away from the dominant cap weighted indexes like 338 00:16:50,840 --> 00:16:52,640 Speaker 10: the S and P and the NASDAC and you go 339 00:16:52,760 --> 00:16:56,040 Speaker 10: into some of the smaller cap indexes within say the 340 00:16:56,120 --> 00:16:58,440 Speaker 10: Rustle two thousand. I think that's where you do see 341 00:16:58,440 --> 00:17:02,440 Speaker 10: that factor differentiation that is more acute, whether it's high 342 00:17:02,480 --> 00:17:05,919 Speaker 10: interest coverage versus low interest coverage, or zombie companies versus 343 00:17:05,960 --> 00:17:09,960 Speaker 10: non zombie companies, profitability type factors to your point, Tom, 344 00:17:10,800 --> 00:17:13,760 Speaker 10: high return on equity, strong free cash flow, And as 345 00:17:13,800 --> 00:17:15,719 Speaker 10: you know because we've talked about it a ton on 346 00:17:15,720 --> 00:17:20,600 Speaker 10: this program, is I think that factor orientation, maybe not 347 00:17:20,800 --> 00:17:24,080 Speaker 10: instead of a sector orientation, but as an overlay to 348 00:17:24,240 --> 00:17:27,320 Speaker 10: that sector orientation, I think has been crucial and I 349 00:17:27,359 --> 00:17:30,920 Speaker 10: think will continue to be crucial. There's much more consistency 350 00:17:31,520 --> 00:17:34,359 Speaker 10: in performance at the factor level than there is at 351 00:17:34,400 --> 00:17:35,160 Speaker 10: the sector level. 352 00:17:35,240 --> 00:17:38,200 Speaker 3: Well, you just heard their folks as gospel, liz Ane Saunders. 353 00:17:38,200 --> 00:17:41,879 Speaker 3: What are the factor constructive tone of MAG seven? 354 00:17:42,119 --> 00:17:44,800 Speaker 4: Which factors benefit meg seven? 355 00:17:45,680 --> 00:17:49,919 Speaker 10: Well, they are the most part very strong cash generation companies, 356 00:17:51,160 --> 00:17:55,520 Speaker 10: and not just the MAG seven but the megacap tech 357 00:17:55,640 --> 00:17:59,119 Speaker 10: tech related kind of names. They really took advantage of 358 00:17:59,240 --> 00:18:03,480 Speaker 10: the rate backdrop that preceded the hiking cycle. And in 359 00:18:03,560 --> 00:18:06,879 Speaker 10: most cases, many of these megacap companies are earning more 360 00:18:06,920 --> 00:18:10,120 Speaker 10: interest on their cash and they're paying interest on that. 361 00:18:10,119 --> 00:18:13,280 Speaker 10: They're not at the mercy of where we sit in 362 00:18:13,320 --> 00:18:15,880 Speaker 10: this cycle. The one thing I want to say though 363 00:18:15,920 --> 00:18:19,080 Speaker 10: about the mag seven is the moniker was created when 364 00:18:19,119 --> 00:18:22,040 Speaker 10: those were the seven largest stocks in the S and 365 00:18:22,040 --> 00:18:24,920 Speaker 10: P five hundred. Last year, they were not the seven 366 00:18:25,000 --> 00:18:28,480 Speaker 10: best performing stocks, but they were all very strong performers. 367 00:18:28,720 --> 00:18:30,960 Speaker 10: That's not the case this year. They're not even the 368 00:18:31,000 --> 00:18:33,879 Speaker 10: seven largest stocks anymore. Tessel's not even in the top 369 00:18:33,920 --> 00:18:36,879 Speaker 10: ten anymore. You have to go down to ranking number 370 00:18:36,880 --> 00:18:38,760 Speaker 10: three hundred and fifty three within the S and P 371 00:18:38,880 --> 00:18:42,800 Speaker 10: five hundred to capture all seven of those names. If 372 00:18:42,800 --> 00:18:45,000 Speaker 10: you look at the top ten best performers in the SMP, 373 00:18:45,320 --> 00:18:47,879 Speaker 10: only one of them is in the Magnificent seven, and 374 00:18:47,880 --> 00:18:50,240 Speaker 10: it's not even the best performer. The best performer is 375 00:18:50,280 --> 00:18:53,480 Speaker 10: the utility. So I think we have to change our mindset. 376 00:18:53,520 --> 00:18:57,280 Speaker 10: We get wrapped into these monikers in these groupings, even 377 00:18:57,320 --> 00:19:00,359 Speaker 10: if they fail to represent what they did when the 378 00:19:00,400 --> 00:19:04,679 Speaker 10: idea was first created. Berkshire Hathaway is now number seven. 379 00:19:04,800 --> 00:19:07,520 Speaker 10: I haven't heard anybody suggesting that gets put into the 380 00:19:07,560 --> 00:19:12,240 Speaker 10: MAGS seven. So a broader thinking around market construction. 381 00:19:13,200 --> 00:19:15,600 Speaker 3: Lizen gotta leave it there. We look forward to getting 382 00:19:15,640 --> 00:19:17,280 Speaker 3: you on again soon. Lizzie Saunder. 383 00:19:17,400 --> 00:19:20,440 Speaker 4: Of course at Connick with Charles Schwab. 384 00:19:25,840 --> 00:19:30,120 Speaker 2: This is the Bloomberg Surveillance Podcast. Listen live each weekday 385 00:19:30,200 --> 00:19:33,720 Speaker 2: starting at seven am Eastern on Applecarplay and Android Auto 386 00:19:33,800 --> 00:19:36,680 Speaker 2: with the Bloomberg Business app. You can also listen live 387 00:19:36,760 --> 00:19:39,960 Speaker 2: on Amazon Alexa from our flagship New York station Just 388 00:19:40,040 --> 00:19:43,480 Speaker 2: Say Alexa playing Bloomberg eleven thirty Jessica Taylor. 389 00:19:43,560 --> 00:19:48,240 Speaker 3: She is exquisite on Senate Dynamics and she publishes this morning. 390 00:19:48,560 --> 00:19:50,359 Speaker 4: Jessica, thank you so much for joining us. 391 00:19:50,400 --> 00:19:53,359 Speaker 3: Love the cook political report telling people to shut up 392 00:19:53,359 --> 00:19:56,840 Speaker 3: and subscribe. At the bottom of your note, you've got 393 00:19:56,840 --> 00:20:00,800 Speaker 3: a bar chart of money made, money earned, money coming 394 00:20:00,840 --> 00:20:05,560 Speaker 3: in versus vote. What's a correlation of money coming in 395 00:20:05,680 --> 00:20:10,280 Speaker 3: Nebraska Osborne and Fisher? What's a correlation of money coming 396 00:20:10,320 --> 00:20:12,320 Speaker 3: in to electoral outcome? 397 00:20:13,359 --> 00:20:13,479 Speaker 6: Well? 398 00:20:13,520 --> 00:20:15,520 Speaker 11: I think that when you have a state like Nebraska, 399 00:20:15,560 --> 00:20:19,159 Speaker 11: this very Republican, and you have Dan Osborne, who's an independent, 400 00:20:19,240 --> 00:20:21,520 Speaker 11: trying to stress that he's not a Democrat, even though 401 00:20:21,520 --> 00:20:23,400 Speaker 11: a lot of the outside money coming in to help 402 00:20:23,480 --> 00:20:27,320 Speaker 11: him is from outside democratic groups. He's been able to 403 00:20:27,320 --> 00:20:29,640 Speaker 11: control the airwaves and set the narrative. We even see 404 00:20:29,640 --> 00:20:31,879 Speaker 11: his latest ads running against deb Fisher there in a 405 00:20:31,920 --> 00:20:35,080 Speaker 11: state that Trump won by twenty points. Making an out 406 00:20:35,119 --> 00:20:38,000 Speaker 11: overt appeal to Trump voters. You know, he's the most 407 00:20:38,119 --> 00:20:40,479 Speaker 11: unique candidate of this cycle. And the Nebraska I did 408 00:20:40,480 --> 00:20:43,560 Speaker 11: not expect on November first to be talking about Nebraska 409 00:20:43,600 --> 00:20:46,320 Speaker 11: at all. You know, he's underscoring his blue collar roots. 410 00:20:46,320 --> 00:20:48,440 Speaker 11: You know, he says that actually, for him, the one hundred 411 00:20:48,440 --> 00:20:50,800 Speaker 11: and seventy four thousand dollars the salary of a senator 412 00:20:50,800 --> 00:20:52,600 Speaker 11: would be a pay race. So I think that's breaking 413 00:20:52,600 --> 00:20:54,399 Speaker 11: through with a lot of people when we look at 414 00:20:54,440 --> 00:20:57,159 Speaker 11: where the economy everything is, so no one would know 415 00:20:57,200 --> 00:20:59,040 Speaker 11: who he is if he was not able to get 416 00:20:59,040 --> 00:21:00,840 Speaker 11: this money and to get his name out there. 417 00:21:00,880 --> 00:21:02,680 Speaker 3: With your Senate focus, I want to get you into 418 00:21:02,720 --> 00:21:06,520 Speaker 3: the Amy Walter timeout chare. With your Senate focus, can 419 00:21:06,560 --> 00:21:10,600 Speaker 3: you frame out probability of a Republican Trump sweep. 420 00:21:12,600 --> 00:21:14,600 Speaker 11: I mean, I think that if there is going to 421 00:21:14,600 --> 00:21:16,600 Speaker 11: be a trifecta, it will be a Republican one. And 422 00:21:16,640 --> 00:21:20,280 Speaker 11: that's simply because the Senate is overwhelmingly favored to go 423 00:21:20,320 --> 00:21:25,000 Speaker 11: to Republicans. My final Senate analysis is publishing maybe momentarily 424 00:21:25,040 --> 00:21:25,840 Speaker 11: on our website. 425 00:21:25,920 --> 00:21:28,000 Speaker 4: I see it. It's out, Oh, it is up. 426 00:21:28,040 --> 00:21:31,119 Speaker 11: Okay, we predict that Republicans will pick up two to 427 00:21:31,160 --> 00:21:33,480 Speaker 11: five seats, which gives them a majority of anywhere between 428 00:21:33,520 --> 00:21:35,920 Speaker 11: fifty one and fifty four seats. You know, we came 429 00:21:35,960 --> 00:21:40,040 Speaker 11: into this cycle with Democrats having to depend twenty three 430 00:21:40,080 --> 00:21:43,440 Speaker 11: seats to just eleven for Republicans. Republicans are going to 431 00:21:43,440 --> 00:21:46,159 Speaker 11: pick up West Virginia. We know that they're favorite in 432 00:21:46,280 --> 00:21:49,359 Speaker 11: Montana over Democratic Senator Shared Brown, and that gives them 433 00:21:49,400 --> 00:21:51,479 Speaker 11: to fifty one. So it just means that, you know, 434 00:21:51,560 --> 00:21:53,800 Speaker 11: do they add Ohio where the race is neck and 435 00:21:53,880 --> 00:21:57,000 Speaker 11: neck Shared Brown could defy the odds. And when they're 436 00:21:57,040 --> 00:21:59,480 Speaker 11: even in a state that Trump carried twice by eight points, 437 00:21:59,560 --> 00:22:04,080 Speaker 11: those other Blue Wall states Michigan, Wisconsin, and Pennsylvania all 438 00:22:04,119 --> 00:22:05,879 Speaker 11: remain very close in toss ups. 439 00:22:06,160 --> 00:22:07,360 Speaker 5: So I just have a. 440 00:22:07,280 --> 00:22:11,080 Speaker 11: Hard time seeing, you know, they Democrats need to float Nebraska. 441 00:22:11,400 --> 00:22:13,520 Speaker 11: But even though you know, Osborne says you Wood and 442 00:22:13,560 --> 00:22:16,240 Speaker 11: Caucus with Democrats, Texas is the one. So there's just 443 00:22:16,359 --> 00:22:18,919 Speaker 11: far more paths for Republicans. And then I think that 444 00:22:19,000 --> 00:22:22,800 Speaker 11: if we're seeing Republicans win in Wisconsin, Pennsylvania, and Michigan 445 00:22:22,800 --> 00:22:25,640 Speaker 11: Senate races, that means that Trump has won those probably, 446 00:22:25,960 --> 00:22:28,439 Speaker 11: so that means that he's won the presidency, and I 447 00:22:28,440 --> 00:22:31,200 Speaker 11: think also, you know, the House is very narrow. It's 448 00:22:31,240 --> 00:22:33,400 Speaker 11: a true toss up, but I think that probably as 449 00:22:33,400 --> 00:22:36,320 Speaker 11: the presidency goes, as the House goes. So just because 450 00:22:36,320 --> 00:22:38,600 Speaker 11: of the way the Senate math is, I have a 451 00:22:38,600 --> 00:22:42,919 Speaker 11: hard time seeing Democrats winning the majority, and so that 452 00:22:42,920 --> 00:22:44,400 Speaker 11: would preclude them from a TRIFACTA. 453 00:22:44,480 --> 00:22:47,840 Speaker 6: I think, how about the House, Jessica, what racist should 454 00:22:47,880 --> 00:22:49,880 Speaker 6: we be looking at for the House of Representatives? 455 00:22:51,040 --> 00:22:53,800 Speaker 11: Yeah, so New York and California feature some of the 456 00:22:53,840 --> 00:22:56,639 Speaker 11: top races, and you know those are not presidential battlegrounds, 457 00:22:56,640 --> 00:22:59,240 Speaker 11: but Democrats suffered from lower turnout in the midterms. They're 458 00:22:59,280 --> 00:23:01,719 Speaker 11: hoping that higher residential turnout helps them. And there are 459 00:23:01,760 --> 00:23:05,640 Speaker 11: some races that we've you know, moved toward Democrats in California, 460 00:23:06,160 --> 00:23:08,560 Speaker 11: but some Republicans are still hanging on there, sort of 461 00:23:08,640 --> 00:23:11,600 Speaker 11: running ahead of where Trump is in those states. There's 462 00:23:11,640 --> 00:23:14,959 Speaker 11: a couple of open seats in Michigan seventh District, for instance, 463 00:23:15,040 --> 00:23:17,880 Speaker 11: the open seat that Alissa Slockin is leaving to run 464 00:23:17,920 --> 00:23:20,480 Speaker 11: for Senate, and Tom Barrett has been has been raising 465 00:23:20,480 --> 00:23:23,239 Speaker 11: a lot of money, you know, even though Democrats are 466 00:23:23,240 --> 00:23:26,160 Speaker 11: spending heavily there. We see that race as slightly favoring him. 467 00:23:26,160 --> 00:23:28,240 Speaker 11: And then you know, if you're looking for early seats 468 00:23:28,240 --> 00:23:30,160 Speaker 11: on election, I see how they go. There's a couple 469 00:23:30,160 --> 00:23:33,919 Speaker 11: in Virginia, Virginia second and Virginia seventh district that are 470 00:23:33,960 --> 00:23:36,440 Speaker 11: also very tight that could give us an early indication 471 00:23:36,520 --> 00:23:37,560 Speaker 11: of how the House is going. 472 00:23:38,040 --> 00:23:41,920 Speaker 3: Charlie Cook has an absolutely brilliant essay here folks out 473 00:23:41,920 --> 00:23:44,679 Speaker 3: in the last couple of days on the lack of 474 00:23:44,800 --> 00:23:47,159 Speaker 3: land slides like we used to have a dearth of 475 00:23:47,240 --> 00:23:50,800 Speaker 3: land slides. He goes into, you know, item by item, 476 00:23:50,840 --> 00:23:52,520 Speaker 3: the closeness. 477 00:23:51,760 --> 00:23:53,200 Speaker 4: Of these races over. 478 00:23:53,000 --> 00:23:56,320 Speaker 3: The years, and as you know, Jessica, Charlie goes to 479 00:23:56,359 --> 00:24:00,960 Speaker 3: the surprises. What's your biggest potential set the surprise. 480 00:24:02,119 --> 00:24:07,159 Speaker 11: I think it's Texas CRUs Yeah, I mean Democrats have 481 00:24:07,280 --> 00:24:10,800 Speaker 11: pulling that shows this race hide other public polling does not. 482 00:24:10,920 --> 00:24:12,560 Speaker 11: I still think those like you know, Cruse had a 483 00:24:12,640 --> 00:24:15,320 Speaker 11: very close race in twenty eighteen. He actually very much 484 00:24:15,359 --> 00:24:18,560 Speaker 11: benefited from it being a midterm year where the Republican 485 00:24:18,600 --> 00:24:22,040 Speaker 11: governor Greg Abbott had a far better turnout machine. I 486 00:24:22,040 --> 00:24:25,359 Speaker 11: think that Cruz is very vulnerable on abortion. You know, 487 00:24:25,480 --> 00:24:28,320 Speaker 11: we saw Harris go there last week to sort of 488 00:24:28,359 --> 00:24:31,840 Speaker 11: emphasize the state's very stringent abortion laws. In fact, you know, 489 00:24:31,920 --> 00:24:34,280 Speaker 11: while other states are able to have referendums to overturn 490 00:24:34,359 --> 00:24:36,919 Speaker 11: the Texas does not allow that. And you know, just 491 00:24:36,960 --> 00:24:39,000 Speaker 11: this week there was the story of a woman who 492 00:24:39,119 --> 00:24:42,000 Speaker 11: was denied care after a miscarriage and she died. So 493 00:24:42,359 --> 00:24:45,200 Speaker 11: I think those are very much in focus right now. 494 00:24:45,600 --> 00:24:47,639 Speaker 11: And this is what I you know, if Democrats have 495 00:24:47,680 --> 00:24:50,080 Speaker 11: a better night than expected, I think it probably has 496 00:24:50,080 --> 00:24:51,840 Speaker 11: to do with it. You know, I'm cognizant of the 497 00:24:51,840 --> 00:24:55,119 Speaker 11: fact that in twenty twenty two the polling actually underestimated Democrats. 498 00:24:55,600 --> 00:24:59,120 Speaker 11: And if Democrats have a better night overall, if Harris does, 499 00:24:59,280 --> 00:25:01,919 Speaker 11: if some one like Colin Allread in Texas does, I 500 00:25:01,920 --> 00:25:05,000 Speaker 11: think it's because the potency of abortion and the Dobbs 501 00:25:05,000 --> 00:25:09,520 Speaker 11: issue after Roefel is still very powerful and is driving 502 00:25:09,560 --> 00:25:11,639 Speaker 11: a lot of voters, particularly female voters. 503 00:25:11,760 --> 00:25:14,159 Speaker 3: Jessica Taylor, thank you so much for quok political report 504 00:25:14,400 --> 00:25:16,719 Speaker 3: her focus on the US Senate. 505 00:25:22,800 --> 00:25:27,080 Speaker 2: This is the Bloomberg Surveillance Podcast. Listen live each weekday 506 00:25:27,160 --> 00:25:30,680 Speaker 2: starting at seven am Eastern on applecar Play and Android Auto. 507 00:25:30,760 --> 00:25:32,240 Speaker 4: With the Bloomberg Business app. 508 00:25:32,320 --> 00:25:35,679 Speaker 2: You can also watch US live every weekday on YouTube 509 00:25:35,800 --> 00:25:38,119 Speaker 2: and always on the Bloomberg Terminal tier. 510 00:25:38,040 --> 00:25:41,760 Speaker 3: And our I call be on the Sikorski out west 511 00:25:41,760 --> 00:25:46,120 Speaker 3: of Pittsburgh, I might say towards Indiana. University of Pennsylvania. 512 00:25:46,200 --> 00:25:49,560 Speaker 3: David Gurra joins US now off of Wisconsin. He's the 513 00:25:49,600 --> 00:25:51,840 Speaker 3: one who said to me times shut up. It's Arizona 514 00:25:51,880 --> 00:25:55,159 Speaker 3: that matters. But now there'll be the trek east for 515 00:25:55,320 --> 00:26:00,000 Speaker 3: David Gurra. He will end up Paul Election night, sixteenth Street, Philadelphia. 516 00:26:00,600 --> 00:26:04,159 Speaker 4: Monk's Cafe is this is They. 517 00:26:04,000 --> 00:26:07,520 Speaker 3: Serve four hundred beers including Iron City Beer, and David 518 00:26:07,520 --> 00:26:08,720 Speaker 3: Girl will be there. 519 00:26:08,800 --> 00:26:10,320 Speaker 4: I want to go to Connor Lamb. 520 00:26:10,520 --> 00:26:16,719 Speaker 3: Okay, conservative Democrat ran against Fetterman, lost out in the 521 00:26:16,760 --> 00:26:21,200 Speaker 3: wilderness now of democratic politics. What is Connor Lamb's best 522 00:26:21,240 --> 00:26:23,400 Speaker 3: outcome in Pennsylvania Tuesday night. 523 00:26:23,960 --> 00:26:25,439 Speaker 1: That's a great question. I mean, I think this is 524 00:26:25,440 --> 00:26:28,080 Speaker 1: going to be another tight race. And you know, I, 525 00:26:28,680 --> 00:26:30,840 Speaker 1: as you say, just came back from Wisconsin where the 526 00:26:30,840 --> 00:26:33,000 Speaker 1: candidate whom I was following there, Tammy Baldwin, was talking 527 00:26:33,000 --> 00:26:34,760 Speaker 1: about how she's in a fifty to fifty race. It's 528 00:26:34,800 --> 00:26:36,320 Speaker 1: on a razor's edge. We've been talking about this as 529 00:26:36,359 --> 00:26:38,800 Speaker 1: it's manifested itself in the polls over these last many months. 530 00:26:38,840 --> 00:26:41,639 Speaker 1: But these candidates are living it and this has been 531 00:26:41,680 --> 00:26:44,560 Speaker 1: a long slog for them. But nobody's taking anything for 532 00:26:44,600 --> 00:26:46,600 Speaker 1: granted here, I think in these final few days, and 533 00:26:46,680 --> 00:26:48,080 Speaker 1: something bastood out to me as I was there on 534 00:26:48,119 --> 00:26:51,520 Speaker 1: the campaign trailers. The attitude is every vote matters. And 535 00:26:51,520 --> 00:26:54,200 Speaker 1: we've talked a lot about these kind of mythic undecided voters. 536 00:26:54,480 --> 00:26:56,840 Speaker 1: That's not who these folks are focusing on now. It's 537 00:26:56,880 --> 00:26:59,040 Speaker 1: getting people who have voted in the past but might 538 00:26:59,080 --> 00:27:01,159 Speaker 1: not have last time or the time before that, to 539 00:27:01,240 --> 00:27:03,560 Speaker 1: recognize that it's important to get out there, mail that 540 00:27:03,600 --> 00:27:05,439 Speaker 1: ballot in or go to the polls in person. 541 00:27:05,520 --> 00:27:07,880 Speaker 3: I mean, I put boxes of Amazon outside my door 542 00:27:07,960 --> 00:27:10,120 Speaker 3: last night so the kids wouldn't trick or treat there. 543 00:27:10,400 --> 00:27:13,720 Speaker 4: I mean, Paul, And that's how antisocial I am. David 544 00:27:13,800 --> 00:27:16,840 Speaker 4: Gerd is knocking on doors matter anymore? 545 00:27:17,200 --> 00:27:17,399 Speaker 11: You know? 546 00:27:17,440 --> 00:27:20,600 Speaker 1: I talked to a number of Tammy Baldwin supporters, some 547 00:27:20,640 --> 00:27:22,359 Speaker 1: of whom are in unions. They're being organized by their 548 00:27:22,440 --> 00:27:24,120 Speaker 1: unions to go out. Maybe not even live in Wisconsin, 549 00:27:24,160 --> 00:27:26,800 Speaker 1: they live in Minnesota, or they live in Illinois and 550 00:27:27,440 --> 00:27:29,800 Speaker 1: the strategy has changed. It's not just kind of randomly 551 00:27:29,880 --> 00:27:32,600 Speaker 1: knocking on doors. They're being targeted, you know, go to 552 00:27:32,640 --> 00:27:35,600 Speaker 1: this Democrats house, go to this person who has voted 553 00:27:35,600 --> 00:27:36,960 Speaker 1: for us in the past and might do it again. 554 00:27:37,080 --> 00:27:39,080 Speaker 1: They're not doing the kind of cold knocking where it's 555 00:27:39,080 --> 00:27:40,600 Speaker 1: going to be a Trump supporter who's going to show 556 00:27:40,600 --> 00:27:43,360 Speaker 1: them off their front law. And they're really again trying 557 00:27:43,359 --> 00:27:45,160 Speaker 1: to get those who are been committed in the past 558 00:27:45,160 --> 00:27:48,560 Speaker 1: and might be but yes, and what the refrain was 559 00:27:48,640 --> 00:27:51,440 Speaker 1: is explain why you're doing it, why this election is important, 560 00:27:51,640 --> 00:27:53,760 Speaker 1: and seal the deal at the end. Try to get 561 00:27:53,800 --> 00:27:55,680 Speaker 1: that person to say, you know what, great to talk 562 00:27:55,720 --> 00:27:57,359 Speaker 1: to you. I'm going to show up at the polling 563 00:27:57,359 --> 00:27:58,080 Speaker 1: place on Tuesday. 564 00:27:58,119 --> 00:28:00,080 Speaker 4: How touch you feeling, Paula. They knocked on you. 565 00:28:00,520 --> 00:28:02,960 Speaker 6: I havn't knocked on my door, thankfully. They're welcome to 566 00:28:03,000 --> 00:28:06,800 Speaker 6: the Jersey Shore anytime. David, if it is a real 567 00:28:06,880 --> 00:28:09,080 Speaker 6: story to get the vote out, is there a feeling 568 00:28:09,119 --> 00:28:11,880 Speaker 6: when you were in Wisconsin or that maybe one side 569 00:28:11,920 --> 00:28:13,880 Speaker 6: is doing a better job than the other, has got 570 00:28:13,880 --> 00:28:15,840 Speaker 6: a better on the ground apparatus. 571 00:28:15,880 --> 00:28:18,879 Speaker 1: We know more detail about how the Democrats, how the 572 00:28:18,880 --> 00:28:21,679 Speaker 1: Harris campaign is approaching this. They're very forthright about how 573 00:28:21,720 --> 00:28:23,600 Speaker 1: much money they have and how they're deploying that across 574 00:28:23,680 --> 00:28:26,919 Speaker 1: the country. But the Trump campaign, in its memo to 575 00:28:26,960 --> 00:28:29,119 Speaker 1: interested parties, as they put it, say that they have 576 00:28:29,160 --> 00:28:31,400 Speaker 1: a very strong ground game. They're confident in how that's 577 00:28:31,400 --> 00:28:33,480 Speaker 1: going to play out here over these these next few days. 578 00:28:33,480 --> 00:28:36,040 Speaker 1: But when you look at the sheer number of people 579 00:28:36,040 --> 00:28:38,160 Speaker 1: who are out there on the pay role, it is 580 00:28:38,200 --> 00:28:40,160 Speaker 1: the Harris campaign that has the edge. And you see 581 00:28:40,160 --> 00:28:41,960 Speaker 1: that just in the way that the candidates are approaching 582 00:28:41,960 --> 00:28:44,800 Speaker 1: this race. Yes, Kamala Harris is doing big events. Donald 583 00:28:44,840 --> 00:28:46,360 Speaker 1: Trump is doing big events, but I think it's safe 584 00:28:46,400 --> 00:28:48,480 Speaker 1: to say he is relying more on the spectacle and 585 00:28:48,560 --> 00:28:51,560 Speaker 1: celebrity of these big, big arena events than she is. 586 00:28:51,760 --> 00:28:53,840 Speaker 1: Thinking that's going to trickle down to voters and get 587 00:28:53,880 --> 00:28:54,440 Speaker 1: them animated. 588 00:28:54,640 --> 00:28:57,440 Speaker 6: You know, David, as you know, all the experts are 589 00:28:57,480 --> 00:28:59,719 Speaker 6: telling us this is a toss up here. Just when 590 00:28:59,720 --> 00:29:02,280 Speaker 6: you're on the streets in Wisconsin, does. 591 00:29:02,120 --> 00:29:03,960 Speaker 5: It feel like a toss up? Does it feel that tight? 592 00:29:04,120 --> 00:29:06,840 Speaker 1: Yes, it feels extremely tight. You hear that from the candidates, 593 00:29:06,840 --> 00:29:08,280 Speaker 1: you hear that from those who are organizing for them. 594 00:29:08,320 --> 00:29:10,360 Speaker 1: You hear they're from the voters themselves, who are fatigued, 595 00:29:10,600 --> 00:29:12,640 Speaker 1: tired of all the ads that are everywhere, the ads 596 00:29:12,680 --> 00:29:14,760 Speaker 1: that are on TV, the ads that they're seeing on 597 00:29:14,800 --> 00:29:15,920 Speaker 1: the side of the road as well. I mean every 598 00:29:15,960 --> 00:29:18,280 Speaker 1: other billboard is a political ad, either from the candidates 599 00:29:18,360 --> 00:29:21,160 Speaker 1: or one of those political groups. So they're inundated by it. 600 00:29:21,600 --> 00:29:24,240 Speaker 1: But there is this broad recognition that in that state, 601 00:29:24,240 --> 00:29:27,360 Speaker 1: in particularly Wisconsin, but also Pennsylvania, things have changed, the 602 00:29:27,440 --> 00:29:29,840 Speaker 1: terrain has shifted. It's a closer race than it has. 603 00:29:29,720 --> 00:29:30,080 Speaker 4: Been the day. 604 00:29:30,120 --> 00:29:33,440 Speaker 3: David Charlie Cook right in a gorgeous essay here like 605 00:29:33,520 --> 00:29:35,880 Speaker 3: two days ago as well. He talks about the lack 606 00:29:35,920 --> 00:29:39,400 Speaker 3: of landslides, and then he's got a fifteen line paragraph 607 00:29:39,960 --> 00:29:44,120 Speaker 3: going from Bush Gore all the way through to Trump 608 00:29:44,200 --> 00:29:48,160 Speaker 3: twenty sixteen, then on the jee Biden, and he's talking 609 00:29:48,200 --> 00:29:53,480 Speaker 3: about the narrowness, the reality of the narrowness. So, Pennsylvania, 610 00:29:53,640 --> 00:29:55,360 Speaker 3: is it going to be ten thousand votes? 611 00:29:55,800 --> 00:29:57,520 Speaker 1: I think it's going to be extremely close. And I 612 00:29:57,560 --> 00:29:59,520 Speaker 1: know that there are some skeptics who say maybe the 613 00:29:59,520 --> 00:30:01,920 Speaker 1: polling is wrong or there's something about it that's not 614 00:30:02,000 --> 00:30:03,960 Speaker 1: quite right. It's going to be a wider margin. Yet 615 00:30:04,400 --> 00:30:06,240 Speaker 1: what we do have in Pennsylvania. Which is interesting is 616 00:30:06,280 --> 00:30:11,120 Speaker 1: some visibility into these early votes. You know who the 617 00:30:11,160 --> 00:30:13,600 Speaker 1: state sent them out to and what parties they're affiliated with, 618 00:30:13,640 --> 00:30:15,680 Speaker 1: and which have come back, and so you see Democrats 619 00:30:15,720 --> 00:30:18,400 Speaker 1: having an edge there. Fascinating the story this morning, We've 620 00:30:18,440 --> 00:30:20,200 Speaker 1: covered it, The Washington Post is covering at New York 621 00:30:20,240 --> 00:30:22,200 Speaker 1: Times covering it this morning as well, is the role 622 00:30:22,280 --> 00:30:25,480 Speaker 1: of white women in the country broadly, and in Pennsylvania 623 00:30:25,520 --> 00:30:30,120 Speaker 1: in particular. It's a real difficulty for Democrats to claim 624 00:30:30,160 --> 00:30:32,120 Speaker 1: them as supporters. And we look at the last election, 625 00:30:32,640 --> 00:30:34,760 Speaker 1: Trump had them by a margin there. You look at 626 00:30:34,760 --> 00:30:36,760 Speaker 1: the one before that, Hillary Clinton was the candidate and 627 00:30:36,800 --> 00:30:39,960 Speaker 1: still wasn't able to secure them to the extent that 628 00:30:39,960 --> 00:30:42,920 Speaker 1: Democrats wanted them and needed them to do. So that 629 00:30:43,040 --> 00:30:45,400 Speaker 1: is a big push for the Harris campaign for Democrats 630 00:30:45,400 --> 00:30:47,720 Speaker 1: now is to go after that thirty percent of the electorate, 631 00:30:47,760 --> 00:30:51,040 Speaker 1: which is frankly a party electorate who does show up 632 00:30:51,080 --> 00:30:52,760 Speaker 1: on election day, does get to the polls. 633 00:30:53,320 --> 00:30:55,000 Speaker 5: Do we know who the undecideds are? 634 00:30:55,000 --> 00:30:57,600 Speaker 6: Like when you're on the streets in Wisconsin, does somebody 635 00:30:57,600 --> 00:30:58,640 Speaker 6: have a sign around their next. 636 00:31:01,320 --> 00:31:03,920 Speaker 1: They're ordering the spotted cow at the bar and Yeah, No, 637 00:31:03,960 --> 00:31:06,000 Speaker 1: I mean, I think that we have some vague sense 638 00:31:06,040 --> 00:31:07,280 Speaker 1: of who they are, but I think that they're a 639 00:31:07,320 --> 00:31:09,160 Speaker 1: rarer bird, and we've been led to believe, you know, 640 00:31:09,160 --> 00:31:11,200 Speaker 1: I think that again for many months, that's who we 641 00:31:11,240 --> 00:31:14,480 Speaker 1: thought these campaigns were targeting. At this point in the game, 642 00:31:14,600 --> 00:31:16,080 Speaker 1: I don't think that. I don't think they've given up 643 00:31:16,080 --> 00:31:17,720 Speaker 1: on them, of course, but I think that there is 644 00:31:18,600 --> 00:31:21,360 Speaker 1: just such strange mystique around what may be motivating somebody 645 00:31:21,400 --> 00:31:22,760 Speaker 1: like that to sit on the sidelines. As long as 646 00:31:22,760 --> 00:31:24,120 Speaker 1: they have that now, it's like that's not where they're 647 00:31:24,120 --> 00:31:24,640 Speaker 1: placing their rest. 648 00:31:24,680 --> 00:31:28,200 Speaker 3: So do you anticipate at eleven o'clock at night you're 649 00:31:28,240 --> 00:31:32,640 Speaker 3: gonna be on either your dogfish Head Rodenbach Crimson Crew, 650 00:31:33,800 --> 00:31:36,680 Speaker 3: or you're gonna be looking at your Crooked Stave member 651 00:31:36,800 --> 00:31:39,480 Speaker 3: Barry Sierra twenty sixteen beer. 652 00:31:39,320 --> 00:31:40,760 Speaker 5: I need Thomas my Ama. 653 00:31:41,800 --> 00:31:44,200 Speaker 4: You're gonna be on with AMH at eleven pm. And 654 00:31:44,240 --> 00:31:45,240 Speaker 4: they don't have a clue. 655 00:31:45,320 --> 00:31:47,440 Speaker 1: Right, Well, Look, I've been in touch with all of 656 00:31:47,440 --> 00:31:50,040 Speaker 1: these election offices across Pennsylvania, say what's the plan gonna be? 657 00:31:50,600 --> 00:31:52,920 Speaker 1: Most of them have these online dashboards and they say 658 00:31:52,920 --> 00:31:55,720 Speaker 1: come nine o'clock Eastern time, Wall Street Time. For those 659 00:31:55,720 --> 00:31:59,000 Speaker 1: in the audience, they're gonna begin putting those unofficial results up, 660 00:31:59,280 --> 00:32:01,320 Speaker 1: we might have a good sense of where things are 661 00:32:01,360 --> 00:32:05,240 Speaker 1: headed directionally. That evening they're not going to call my 662 00:32:05,280 --> 00:32:06,840 Speaker 1: bed is, they're not going to call that race for 663 00:32:06,960 --> 00:32:10,040 Speaker 1: several days. Officially, there's going to be a lot of uh, 664 00:32:10,120 --> 00:32:12,719 Speaker 1: you know, very very specific counting of votes, and then 665 00:32:12,720 --> 00:32:14,520 Speaker 1: I think there is the spectrum sort of legal action 666 00:32:14,600 --> 00:32:16,320 Speaker 1: that might come in in the days that follow them. 667 00:32:16,400 --> 00:32:21,400 Speaker 3: Twelve dollars chicken, liver, moose with pickled mustard seeds, house 668 00:32:21,480 --> 00:32:22,480 Speaker 3: made jam, and a. 669 00:32:22,440 --> 00:32:23,800 Speaker 4: Beer of your choice. 670 00:32:23,520 --> 00:32:23,760 Speaker 3: Nice. 671 00:32:24,160 --> 00:32:27,760 Speaker 1: I mean that's I did have a butterburger in Wisconsin 672 00:32:27,800 --> 00:32:30,280 Speaker 1: and some cheese curds at Culvers. What is a butter 673 00:32:30,520 --> 00:32:34,240 Speaker 1: butterburger is a Culver's tradition. It is a buttered white 674 00:32:34,280 --> 00:32:35,200 Speaker 1: bread bun. 675 00:32:36,000 --> 00:32:36,680 Speaker 5: With the burger. 676 00:32:37,240 --> 00:32:38,800 Speaker 1: I was told to get it. It was it was 677 00:32:40,120 --> 00:32:45,960 Speaker 1: you survived, slaked my hunger. I survived. 678 00:32:45,600 --> 00:32:52,880 Speaker 6: He wasn't meats and exactly in Rome, cheese steaks in Philly. 679 00:32:52,960 --> 00:32:57,680 Speaker 1: Travel seriously, yeah, down to Philadelphia and we're going to 680 00:32:57,720 --> 00:32:59,960 Speaker 1: spend Monday and Tuesday in those you know, fabled suburbs 681 00:33:00,200 --> 00:33:00,600 Speaker 1: County in. 682 00:33:00,520 --> 00:33:03,680 Speaker 4: The lot fifteen seconds. Six weeks ago you said Arizona matters, 683 00:33:03,720 --> 00:33:04,720 Speaker 4: still matters. 684 00:33:04,560 --> 00:33:06,480 Speaker 1: Still matters. But now I you know, I have to 685 00:33:06,480 --> 00:33:08,960 Speaker 1: say selfishly, Pennsylvania's the state to watch. 686 00:33:09,760 --> 00:33:10,440 Speaker 5: I'll be watching it. 687 00:33:10,720 --> 00:33:15,400 Speaker 3: Look for David Monk's cafe Philadelphia election. Now probably you know, 688 00:33:15,480 --> 00:33:16,600 Speaker 3: I assume Borro. 689 00:33:16,560 --> 00:33:17,080 Speaker 4: Will go down. 690 00:33:17,120 --> 00:33:18,680 Speaker 1: I can put it on your card right tw of course, 691 00:33:18,720 --> 00:33:20,120 Speaker 1: now it's on HTTO rheto keep for. 692 00:33:20,240 --> 00:33:23,840 Speaker 3: DMX is in charge of all amongst cafe that receipts. 693 00:33:24,080 --> 00:33:28,520 Speaker 2: This is the Bloomberg Surveillance podcast, available on Apple, Spotify, 694 00:33:28,680 --> 00:33:32,320 Speaker 2: and anywhere else you get your podcasts. Listen live each 695 00:33:32,360 --> 00:33:35,800 Speaker 2: weekday seven to ten am Eastern on Bloomberg dot com, 696 00:33:35,920 --> 00:33:39,480 Speaker 2: the iHeartRadio app, tune In, and the Bloomberg Business app. 697 00:33:39,760 --> 00:33:42,840 Speaker 2: You can also watch us live every weekday on YouTube 698 00:33:43,120 --> 00:33:44,920 Speaker 2: and always on the Bloomberg terminal.