1 00:00:00,800 --> 00:00:04,040 Speaker 1: Welcome to the Bloomberg Markets Podcast. I'm Paul Sweeney, alongside 2 00:00:04,040 --> 00:00:06,920 Speaker 1: my co host Matt Miller. Every business day, we bring 3 00:00:06,960 --> 00:00:11,520 Speaker 1: you interviews from CEOs, market pros, and Bloomberg experts, along 4 00:00:11,520 --> 00:00:15,600 Speaker 1: with essential market moving news. Find the Bloomberg Markets Podcast 5 00:00:15,600 --> 00:00:18,439 Speaker 1: on Apple Podcasts or wherever you listen to podcasts, and 6 00:00:18,480 --> 00:00:21,840 Speaker 1: at Bloomberg dot com slash podcast. Let's get to this 7 00:00:21,840 --> 00:00:24,520 Speaker 1: balloon business, because you know, how is at Colorado in 8 00:00:24,520 --> 00:00:27,280 Speaker 1: the Rocky Mountains ten eleven twelve looking did you see 9 00:00:27,320 --> 00:00:30,480 Speaker 1: any I did not see any balloons, and I can't imagine. 10 00:00:30,520 --> 00:00:32,840 Speaker 1: It's good to be in the balloon business these days. 11 00:00:33,320 --> 00:00:36,680 Speaker 1: Mc mulroy, co founder of the Lobo Institute, joins us. 12 00:00:36,680 --> 00:00:39,800 Speaker 1: He's a former Deputy Assistant Secretary of Defense for the 13 00:00:39,880 --> 00:00:42,680 Speaker 1: Middle East at the US Department of Defense, former Power 14 00:00:42,720 --> 00:00:45,360 Speaker 1: Military Operations officer at this thing called the c I A. 15 00:00:45,440 --> 00:00:47,319 Speaker 1: I'm not sure what those guys are up to. Uh. 16 00:00:47,360 --> 00:00:49,519 Speaker 1: And he was a marine for a very very long time, 17 00:00:49,560 --> 00:00:52,640 Speaker 1: so we thank him for service. Hey, Mick, what is 18 00:00:52,880 --> 00:00:55,200 Speaker 1: up with these balloons? Is this always been a thing, 19 00:00:55,920 --> 00:00:59,680 Speaker 1: but now we're just kind of hearing about it. It's 20 00:00:59,720 --> 00:01:02,680 Speaker 1: a it to be with you guys, balloons have you know, 21 00:01:02,720 --> 00:01:04,440 Speaker 1: ever since we had them have been used in some 22 00:01:04,520 --> 00:01:07,640 Speaker 1: capacity to collect intelligence. Those goes back World War One 23 00:01:08,480 --> 00:01:11,800 Speaker 1: all the way until today obviously, but you know, now 24 00:01:11,840 --> 00:01:16,760 Speaker 1: we're shooting down these haven't been identified yet, these objects. 25 00:01:16,800 --> 00:01:20,720 Speaker 1: Some of those might simply be weather balloons. But um, 26 00:01:20,760 --> 00:01:22,319 Speaker 1: you know, we won't know until we get to these 27 00:01:22,319 --> 00:01:28,280 Speaker 1: remote sites and be able to collect and study the debris. Um. So, 28 00:01:28,800 --> 00:01:31,880 Speaker 1: after such a long stint at the C, I a mick, 29 00:01:31,959 --> 00:01:39,199 Speaker 1: when did you start there? Um? Are there aliens? Well? 30 00:01:39,280 --> 00:01:42,160 Speaker 1: I came right after nine eleven, so I was kind 31 00:01:42,160 --> 00:01:45,280 Speaker 1: of focused on the kind of terrorism issue, not the 32 00:01:45,600 --> 00:01:47,800 Speaker 1: extra dress real issue. So I don't know. I know 33 00:01:47,880 --> 00:01:51,000 Speaker 1: that there are people. Obviously we're taking it more serious. 34 00:01:51,240 --> 00:01:55,080 Speaker 1: We are studying as uh. Pentagon has an office that's 35 00:01:55,120 --> 00:02:00,640 Speaker 1: now looking at what these um anomalies are. You know, 36 00:02:00,720 --> 00:02:04,400 Speaker 1: so I don't want to make a decision. Was only kidding, 37 00:02:04,800 --> 00:02:08,600 Speaker 1: but I assume that these unidentified flying objects are like 38 00:02:08,720 --> 00:02:11,720 Speaker 1: college experiments that have just floated away. It seems so 39 00:02:11,800 --> 00:02:15,880 Speaker 1: much more likely, right And um, now that you think 40 00:02:15,880 --> 00:02:19,240 Speaker 1: about it, if we haven't been looking you know, above 41 00:02:19,800 --> 00:02:25,360 Speaker 1: sixties seventy uh feet, then maybe there is just a 42 00:02:25,400 --> 00:02:28,160 Speaker 1: bunch of debris floating around there, just like there's a 43 00:02:28,160 --> 00:02:33,800 Speaker 1: lot of space debris. Yes, absolutely, Yeah. Talking specifically about 44 00:02:33,800 --> 00:02:35,799 Speaker 1: these objects that we've been shooting down in the last 45 00:02:35,800 --> 00:02:39,840 Speaker 1: few days, I don't think there's any extraterrestrial thing about them. 46 00:02:39,880 --> 00:02:42,919 Speaker 1: I think this is likely either it is a Chinese 47 00:02:42,960 --> 00:02:45,520 Speaker 1: surveillance balloon it's a smaller size that they're using to 48 00:02:45,560 --> 00:02:48,639 Speaker 1: try to probe our defenses, or these things may being 49 00:02:48,720 --> 00:02:50,920 Speaker 1: up there and we just haven't seen them. Now that 50 00:02:50,960 --> 00:02:55,239 Speaker 1: we're looking closer, now that we have a signature based 51 00:02:55,240 --> 00:02:58,040 Speaker 1: on the on the on following the balloon that went 52 00:02:58,320 --> 00:03:00,480 Speaker 1: all the way across the United States, which might have 53 00:03:00,560 --> 00:03:02,560 Speaker 1: been able to find tune our radar so that we 54 00:03:02,600 --> 00:03:04,880 Speaker 1: can see these type of things. It doesn't mean that 55 00:03:04,960 --> 00:03:08,360 Speaker 1: these these are all new. It could be these these 56 00:03:08,520 --> 00:03:11,520 Speaker 1: uh weather balloons. We just don't know. Until we get 57 00:03:11,520 --> 00:03:15,720 Speaker 1: to the debris and do an analysis, we should withhold judgment. 58 00:03:16,440 --> 00:03:18,320 Speaker 1: But if it is the Chinese and it looks like 59 00:03:18,360 --> 00:03:22,280 Speaker 1: to me a deliberate provocation to the United States, So, Mike, 60 00:03:22,360 --> 00:03:26,520 Speaker 1: I mean, we have satellites, lots of satellites. I'm assuming 61 00:03:26,639 --> 00:03:31,240 Speaker 1: China has lots of satellites, aren't satellites better than balloons? 62 00:03:31,280 --> 00:03:34,440 Speaker 1: I mean, isn't balloons so last century? Last on an 63 00:03:34,440 --> 00:03:38,880 Speaker 1: early part of the last century. So that is what 64 00:03:39,320 --> 00:03:43,720 Speaker 1: the common belief is that they Chinese have our satellites 65 00:03:43,840 --> 00:03:46,000 Speaker 1: and as you might imagine, most of them are focused 66 00:03:46,000 --> 00:03:48,560 Speaker 1: on the United States, so they can collect a lot 67 00:03:48,560 --> 00:03:53,680 Speaker 1: of this information via their satellite program. But these balloons, 68 00:03:53,960 --> 00:03:56,280 Speaker 1: they are steerable, they seem to be able to log 69 00:03:56,360 --> 00:03:58,960 Speaker 1: or over areas. They're closer to the ground, so they 70 00:03:58,960 --> 00:04:03,040 Speaker 1: can collect signals intelligence. So there is an advantage to 71 00:04:03,320 --> 00:04:07,400 Speaker 1: having both platforms because you can't do that with satellites. 72 00:04:07,440 --> 00:04:11,160 Speaker 1: They just simply can't just sit right over, say Maelstrom 73 00:04:11,160 --> 00:04:13,920 Speaker 1: Airbase where I am in Montana and stare at our 74 00:04:14,840 --> 00:04:18,440 Speaker 1: I C B M silos. This balloon could do that 75 00:04:18,560 --> 00:04:21,239 Speaker 1: and did do that. So that's uh, that's a concern 76 00:04:21,279 --> 00:04:23,640 Speaker 1: to us. We have to be able to defend ourselves 77 00:04:24,520 --> 00:04:27,640 Speaker 1: from collection of this type of really sensitive information. It 78 00:04:27,640 --> 00:04:30,960 Speaker 1: would be great, um if we were ready to defend ourselves, 79 00:04:31,000 --> 00:04:32,560 Speaker 1: but it seems like what we'd like to do is 80 00:04:32,560 --> 00:04:34,880 Speaker 1: take a week to think about it first. You know, 81 00:04:35,279 --> 00:04:39,200 Speaker 1: we spotted this uh alleged spy balloon when it was 82 00:04:39,279 --> 00:04:41,920 Speaker 1: over Alaska and then waited until it got to the 83 00:04:41,960 --> 00:04:45,800 Speaker 1: Carolinas to shoot it down. Doesn't that seem to you 84 00:04:46,680 --> 00:04:52,720 Speaker 1: a little bit stupid? I mean, if they're collecting intelligence, um, 85 00:04:52,800 --> 00:04:54,960 Speaker 1: we let them have everything they could get and then 86 00:04:54,960 --> 00:04:58,919 Speaker 1: stop them when they got back to the ocean. Yeah, 87 00:04:58,960 --> 00:05:01,360 Speaker 1: that's a really good point. I mean, if you're if 88 00:05:01,360 --> 00:05:03,400 Speaker 1: you're asking me, I want to advise the president to 89 00:05:03,400 --> 00:05:05,159 Speaker 1: shoot it down as soon as we saw it. I 90 00:05:05,200 --> 00:05:06,960 Speaker 1: don't know that we did see it until it was 91 00:05:07,000 --> 00:05:09,960 Speaker 1: actually this was just my speculation until it was down 92 00:05:10,080 --> 00:05:13,520 Speaker 1: in the comminentally United States, and then the military decided 93 00:05:13,839 --> 00:05:17,080 Speaker 1: either it was too dangerous to shoot down over populated 94 00:05:17,120 --> 00:05:21,280 Speaker 1: area or they saw and this has been reported that 95 00:05:21,360 --> 00:05:25,239 Speaker 1: they stopped transmitting information back to China. So the US 96 00:05:25,480 --> 00:05:28,920 Speaker 1: military and intelligence community may have wanted to let it 97 00:05:28,960 --> 00:05:33,080 Speaker 1: go since it wasn't um sending information back to be 98 00:05:33,160 --> 00:05:35,000 Speaker 1: able to collect on what it was doing and how 99 00:05:35,000 --> 00:05:37,960 Speaker 1: it did interesting, and then we shot it down right 100 00:05:37,960 --> 00:05:41,080 Speaker 1: after it got finished. But the Chinese, if it's true 101 00:05:41,080 --> 00:05:44,800 Speaker 1: that they didn't transmit anything, never got the intelligence they 102 00:05:44,880 --> 00:05:47,080 Speaker 1: collected because that went to the bottom of the ocean, 103 00:05:47,160 --> 00:05:50,679 Speaker 1: and we are currently trying to get that up and 104 00:05:51,200 --> 00:05:54,640 Speaker 1: analyze it. Mico, what does this mean about just or 105 00:05:55,040 --> 00:05:56,960 Speaker 1: that doesn't mean anything, or what does it mean about 106 00:05:57,080 --> 00:06:00,160 Speaker 1: US China? You know relations here over the last to 107 00:06:00,240 --> 00:06:05,400 Speaker 1: ten days here, So we're we're hitting a pretty uh 108 00:06:05,520 --> 00:06:09,320 Speaker 1: substantial law. Quite frankly, I don't think it's been my 109 00:06:09,480 --> 00:06:14,239 Speaker 1: knowledge this way until before we normalize relations in nineteen 110 00:06:14,279 --> 00:06:18,480 Speaker 1: seventy nine. So, but these things do happen. We collect 111 00:06:18,520 --> 00:06:22,320 Speaker 1: intelligence on all our adversaries, they collect intelligence on us. 112 00:06:22,600 --> 00:06:25,840 Speaker 1: We try to prevent them from collect intelligence on us 113 00:06:25,880 --> 00:06:29,680 Speaker 1: just like they do. It's it is what nations do. Um. 114 00:06:29,720 --> 00:06:33,840 Speaker 1: It has caused a diplomatic riff. Secretary blinking is canceled, 115 00:06:33,920 --> 00:06:37,000 Speaker 1: is or postponed at least is tripped there. But I 116 00:06:37,040 --> 00:06:39,599 Speaker 1: do think it's important for both countries to realize that 117 00:06:39,680 --> 00:06:43,400 Speaker 1: we're completely intertwined on so many things. Were both superpowers, 118 00:06:43,440 --> 00:06:48,960 Speaker 1: if you will, and we have to maintain diplomatic direct communications. 119 00:06:48,960 --> 00:06:52,960 Speaker 1: Like the idea that they didn't answer Secretary Austin's call 120 00:06:53,080 --> 00:06:57,400 Speaker 1: our Secretary defense is really irresponsible. We need to make 121 00:06:57,440 --> 00:07:01,880 Speaker 1: sure that nothing leads to a broader conflict that neither country, 122 00:07:02,000 --> 00:07:05,360 Speaker 1: uh would one? Right? All right, man, great stuff, good 123 00:07:05,360 --> 00:07:08,599 Speaker 1: to check in with you on this evolving story. Mc mulroy. 124 00:07:08,600 --> 00:07:10,880 Speaker 1: He's a co founder of the Lobo Institute. A lot 125 00:07:10,960 --> 00:07:13,360 Speaker 1: of experience and all the power military stuff. Former power 126 00:07:13,400 --> 00:07:16,120 Speaker 1: Military Operations officer at c I, A former U. S. 127 00:07:16,160 --> 00:07:20,400 Speaker 1: Marine Infantry officer, and former Deputy Assistant Secretary of Defense 128 00:07:20,440 --> 00:07:22,280 Speaker 1: for the Middle East at the U. S. Department of Defense. 129 00:07:22,320 --> 00:07:26,080 Speaker 1: So broad range of experienced. Sara trying to the bottom 130 00:07:26,120 --> 00:07:28,720 Speaker 1: of kind of this balloon issue which has kind of 131 00:07:28,720 --> 00:07:31,000 Speaker 1: comeing become a top story over the last week or so. 132 00:07:31,040 --> 00:07:33,920 Speaker 1: How important is it? Uh, you know, how concerned should 133 00:07:33,960 --> 00:07:36,760 Speaker 1: we be? What does it mean for the future or 134 00:07:36,800 --> 00:07:39,880 Speaker 1: even the immediate future international relationship between the U. S. 135 00:07:39,920 --> 00:07:42,840 Speaker 1: And China. So we'll continue to monitor the reporting. They're 136 00:07:42,840 --> 00:07:45,840 Speaker 1: looking at the markets here SMP up one half of 137 00:07:45,920 --> 00:07:49,080 Speaker 1: one per cent. We'll love more coming up. This is Bloomberg. 138 00:07:53,360 --> 00:07:55,760 Speaker 1: But it was a big night for the Kansas City Chiefs. 139 00:07:55,760 --> 00:07:57,840 Speaker 1: A big night. I think we're gonna find out for America, 140 00:07:58,040 --> 00:08:00,760 Speaker 1: for America, big night for Fox who the broadcast, right, 141 00:08:00,800 --> 00:08:02,480 Speaker 1: So we'll get the ratings later today. I'm sure they're 142 00:08:02,480 --> 00:08:05,280 Speaker 1: gonna be pretty pretty darn good. Question is how big 143 00:08:05,280 --> 00:08:08,559 Speaker 1: a night was it for advertisers who put some big, 144 00:08:08,600 --> 00:08:11,240 Speaker 1: big money to work. Seven million dollars for a thirty 145 00:08:11,240 --> 00:08:13,720 Speaker 1: second spot. Mark Douglas, President and CEO of Mountain Joints, 146 00:08:13,800 --> 00:08:17,480 Speaker 1: is here Mark again for advertisers, this is the biggest 147 00:08:17,560 --> 00:08:19,480 Speaker 1: day of the year for a lot of these folks. 148 00:08:19,520 --> 00:08:22,720 Speaker 1: They put up huge money to reach what is a 149 00:08:22,800 --> 00:08:26,360 Speaker 1: huge audience. What are there you takeaways? Um, well, there 150 00:08:26,360 --> 00:08:28,280 Speaker 1: were a lot of good ads. I mean, I think 151 00:08:28,280 --> 00:08:30,760 Speaker 1: there are a few people going, Um, why did we 152 00:08:30,880 --> 00:08:34,160 Speaker 1: do that? Because you watch a few of the ads 153 00:08:34,160 --> 00:08:38,559 Speaker 1: and you're like, um, who approved that exactly? And then 154 00:08:38,600 --> 00:08:40,920 Speaker 1: spend seven million dollars on top of it to area? 155 00:08:41,040 --> 00:08:43,160 Speaker 1: So how much is seven million dollars for like a 156 00:08:43,200 --> 00:08:47,760 Speaker 1: minute long spot or um? And just you know, five 157 00:08:47,840 --> 00:08:49,839 Speaker 1: years ago was two million dollars, you know. So it's 158 00:08:49,840 --> 00:08:52,840 Speaker 1: just amazing the inflation. And again the second was such 159 00:08:52,880 --> 00:08:56,320 Speaker 1: a spectacle for me, having just come back from living 160 00:08:56,320 --> 00:08:58,760 Speaker 1: in Berlin for the past six years. I'm not used 161 00:08:58,800 --> 00:09:01,120 Speaker 1: to this kind of patriotism him. But then it's patriotism 162 00:09:01,200 --> 00:09:04,120 Speaker 1: on steroids. And then it's almost like a parody of patriotism. 163 00:09:04,200 --> 00:09:07,000 Speaker 1: It's like a circus making fun of how patriotic they are. 164 00:09:07,040 --> 00:09:09,280 Speaker 1: It was pretty crazy, but I guess that's just where 165 00:09:09,280 --> 00:09:11,200 Speaker 1: we are. Well, the NFL knows how to put on 166 00:09:11,200 --> 00:09:13,439 Speaker 1: a good show. I was at last year's Super Bowl, 167 00:09:13,679 --> 00:09:17,920 Speaker 1: and you know, I got Doctor Dre, I got Snoop Dog. 168 00:09:18,520 --> 00:09:22,839 Speaker 1: The halftime show was the game basically last year. So well, 169 00:09:22,880 --> 00:09:25,640 Speaker 1: we Re was apparently very good this year. Yeah, she 170 00:09:25,720 --> 00:09:27,720 Speaker 1: put on a good show. She played all the hits 171 00:09:27,800 --> 00:09:31,240 Speaker 1: and no one was complaining at all. And she she 172 00:09:31,240 --> 00:09:35,120 Speaker 1: she's not afraid of heights. It was like practically the stadium, 173 00:09:35,280 --> 00:09:38,480 Speaker 1: even with child. All right, so let me first get 174 00:09:38,520 --> 00:09:42,040 Speaker 1: your take on how the game went, because I've heard 175 00:09:42,240 --> 00:09:46,080 Speaker 1: a lot of complaints this morning that officials are deciding, 176 00:09:46,720 --> 00:09:49,719 Speaker 1: you know, the outcome with calls that are kind of 177 00:09:49,760 --> 00:09:53,560 Speaker 1: penny any Yeah, that that the call, and right before 178 00:09:53,679 --> 00:09:56,520 Speaker 1: the last score, you know, they scored a field goal, 179 00:09:56,800 --> 00:09:59,240 Speaker 1: and you know, the official I don't think there was 180 00:09:59,280 --> 00:10:01,480 Speaker 1: a hole there, you know, when that's not a sports 181 00:10:01,520 --> 00:10:04,840 Speaker 1: talk show, But I don't think that the defensive corner 182 00:10:05,000 --> 00:10:09,400 Speaker 1: actually held the running back and that basically and created 183 00:10:09,440 --> 00:10:12,520 Speaker 1: the game that basically, I mean Kansas City was already 184 00:10:12,559 --> 00:10:14,880 Speaker 1: pretty close to the goal. Wasn't like they weren't going 185 00:10:14,920 --> 00:10:16,480 Speaker 1: to be able to kick a field goal, but they 186 00:10:16,520 --> 00:10:18,080 Speaker 1: brought got him a lot too. By the way, I 187 00:10:18,160 --> 00:10:21,839 Speaker 1: was rooting for Kansas City only because I think, well, 188 00:10:21,880 --> 00:10:24,800 Speaker 1: Philadelphia fans are the worst fans in the league. I mean, 189 00:10:24,800 --> 00:10:29,040 Speaker 1: they're worse than Ohio State fans. And then, um, my favorite, 190 00:10:29,200 --> 00:10:31,960 Speaker 1: one of my favorite commercials of all time is the 191 00:10:32,000 --> 00:10:37,160 Speaker 1: Snickers commercial when the ground staff is doing the end 192 00:10:37,280 --> 00:10:40,400 Speaker 1: zone in Kansas City and they accidentally right Kansas City chefs. 193 00:10:41,120 --> 00:10:44,440 Speaker 1: They forget the eye. Well, I think I think all 194 00:10:44,520 --> 00:10:48,040 Speaker 1: New Yorkers agree they don't like the Phillies, but you 195 00:10:48,080 --> 00:10:50,720 Speaker 1: know they lit the Empire State Building and their colors 196 00:10:50,840 --> 00:10:54,720 Speaker 1: just a few weeks that it's kind of crazy. So Mark, 197 00:10:54,760 --> 00:10:57,040 Speaker 1: I mean, you buying a thirty second spot for seven 198 00:10:57,040 --> 00:10:59,240 Speaker 1: million dollars, it's just a monster's premium to what you 199 00:10:59,240 --> 00:11:03,400 Speaker 1: would ordinarily pay. Is there when you I mean you're 200 00:11:03,440 --> 00:11:05,439 Speaker 1: in this business, there a payoff? Is there a payoff? 201 00:11:05,480 --> 00:11:07,560 Speaker 1: Is there a payback? I mean, if you're trying, if 202 00:11:07,600 --> 00:11:11,000 Speaker 1: you're in I think there's this fine line between your 203 00:11:11,160 --> 00:11:14,200 Speaker 1: brand that people are starting to know but don't fully 204 00:11:14,320 --> 00:11:17,640 Speaker 1: know yet, and this is your moment. And when I 205 00:11:17,679 --> 00:11:19,679 Speaker 1: look at a lot of ads, I thought that there 206 00:11:19,720 --> 00:11:22,000 Speaker 1: was an ad for pop Corners which was a take 207 00:11:22,080 --> 00:11:26,160 Speaker 1: on Breaking Bad, So there there to give a real 208 00:11:26,200 --> 00:11:28,600 Speaker 1: quick description on the ad. They're basically in the trailer 209 00:11:29,200 --> 00:11:32,679 Speaker 1: and they have devised this new chip to eat and 210 00:11:32,760 --> 00:11:37,760 Speaker 1: it's better than drugs. And the ad ends they're out 211 00:11:37,760 --> 00:11:40,640 Speaker 1: in the desert doing a deal for these pop these 212 00:11:40,679 --> 00:11:44,240 Speaker 1: pop chips. I beg popcorners. I wouldn't have known about 213 00:11:44,240 --> 00:11:46,679 Speaker 1: that project otherwise. And I'm probably gonna try it when 214 00:11:46,679 --> 00:11:48,480 Speaker 1: I go to the supermarket. I'm gonna buy it back. 215 00:11:48,559 --> 00:11:50,679 Speaker 1: And I think I'm gonna try prime. I never heard 216 00:11:50,679 --> 00:11:53,040 Speaker 1: about that, but apparently the kids, it's all the rage. 217 00:11:53,080 --> 00:11:54,640 Speaker 1: It's like a sports drink. I don't know if it's 218 00:11:54,679 --> 00:11:57,640 Speaker 1: any different from Gatorade or uh whatever any of the 219 00:11:57,679 --> 00:11:59,840 Speaker 1: new ones are. But they got a lot of advertising 220 00:12:00,040 --> 00:12:02,720 Speaker 1: terday and apparently they're already huge on social Yeah for sure. 221 00:12:02,840 --> 00:12:05,400 Speaker 1: And then I think the whole night. Um, A lot 222 00:12:05,440 --> 00:12:09,480 Speaker 1: of the best ads were remakes of like shows from 223 00:12:09,480 --> 00:12:11,960 Speaker 1: the two thousands and nineties, so there was, you know, 224 00:12:12,080 --> 00:12:15,200 Speaker 1: basically we just talked about Breaking Bad. There was like 225 00:12:15,320 --> 00:12:19,480 Speaker 1: John Travolta to the musical ads, which was pretty funny. Um, 226 00:12:19,600 --> 00:12:24,120 Speaker 1: the clueless like Alicia Silverstone basically and looking exactly the 227 00:12:24,200 --> 00:12:28,600 Speaker 1: same and shout out to that film. I don't know 228 00:12:28,720 --> 00:12:31,280 Speaker 1: if either of you guys have read Jane Austen's novel, 229 00:12:31,480 --> 00:12:35,199 Speaker 1: but that's basically a redo of Emma. Ok yeah, so 230 00:12:35,400 --> 00:12:38,920 Speaker 1: what a what a good film that it was? Okay 231 00:12:40,400 --> 00:12:43,800 Speaker 1: that clueless And I lived in l A. So you know, 232 00:12:43,840 --> 00:12:45,880 Speaker 1: I kind of as a matter of fact, while John 233 00:12:45,880 --> 00:12:49,280 Speaker 1: Travolta was singing and dancing, I actually think I recognized 234 00:12:49,320 --> 00:12:52,160 Speaker 1: the street in Los Angeles was on filming that. So 235 00:12:52,200 --> 00:12:54,880 Speaker 1: what about this, I mean, as more and more programming 236 00:12:54,920 --> 00:12:56,800 Speaker 1: goes to streaming services, what do you think the NFL 237 00:12:56,880 --> 00:12:59,439 Speaker 1: is going to do with the rights to the big 238 00:12:59,520 --> 00:13:03,360 Speaker 1: rights for the NFL, like during the season, the you know, 239 00:13:03,720 --> 00:13:06,719 Speaker 1: obviously the playoffs, then of course the Super Bowl. Well, 240 00:13:06,760 --> 00:13:09,760 Speaker 1: you know, that's interesting. There's been people who moved all 241 00:13:09,800 --> 00:13:13,120 Speaker 1: over America and if I think basically streaming fits right 242 00:13:13,120 --> 00:13:15,520 Speaker 1: into that if you want, I remember when I moved 243 00:13:15,520 --> 00:13:17,800 Speaker 1: from New York City where I grew up, to California. 244 00:13:18,240 --> 00:13:20,720 Speaker 1: At the time, I was a huge Giants fan, New 245 00:13:20,800 --> 00:13:23,800 Speaker 1: York Giants fans, and like you couldn't really watch them anymore. 246 00:13:24,160 --> 00:13:26,480 Speaker 1: You can do that now on direct TV, but it's 247 00:13:26,480 --> 00:13:28,960 Speaker 1: so much better on streaming and you can watch it 248 00:13:29,000 --> 00:13:31,600 Speaker 1: at any time. Of course you want to watch it live. 249 00:13:31,960 --> 00:13:35,240 Speaker 1: So I think the NFL is really adepted just adapting 250 00:13:35,360 --> 00:13:37,720 Speaker 1: and streaming just fits right in. It actually brings me 251 00:13:37,760 --> 00:13:40,320 Speaker 1: back to Disney. You know, last couple weeks you've been 252 00:13:40,320 --> 00:13:47,120 Speaker 1: talking about, um, the House of Mouse because uh, the 253 00:13:47,160 --> 00:13:52,240 Speaker 1: CEO came back and they had earnings. And with ESPN, 254 00:13:52,280 --> 00:13:53,880 Speaker 1: I feel like they're leaving so much money on the 255 00:13:53,920 --> 00:13:57,199 Speaker 1: table because I'm not gonna get cable just to get ESPN. 256 00:13:57,280 --> 00:13:59,920 Speaker 1: But I also ESPN Plus should be called ESPN My 257 00:14:00,080 --> 00:14:03,640 Speaker 1: this like, it doesn't have anything, So why can't I 258 00:14:03,760 --> 00:14:05,920 Speaker 1: stream that? I would pay them twenty five dollars a 259 00:14:05,960 --> 00:14:07,839 Speaker 1: month if I had all the content. Yeah, I mean 260 00:14:08,400 --> 00:14:12,120 Speaker 1: ESPN is actually a really good example of how basically 261 00:14:12,160 --> 00:14:15,360 Speaker 1: just using the Internet you can basically reproduce a TV network. 262 00:14:15,840 --> 00:14:18,320 Speaker 1: And so there's so much good sports content not on 263 00:14:18,440 --> 00:14:21,280 Speaker 1: the ESPN that I think they have a lot of 264 00:14:21,280 --> 00:14:23,760 Speaker 1: people like why do I need to watch ESPN? And 265 00:14:23,800 --> 00:14:26,080 Speaker 1: they have to adapt, and I agree with you, they 266 00:14:26,120 --> 00:14:29,080 Speaker 1: haven't done a great job, and it's it's a huge opportunity. 267 00:14:29,080 --> 00:14:31,800 Speaker 1: They have the brand they have the following and and 268 00:14:31,800 --> 00:14:34,960 Speaker 1: it needs to happen. So there's so much change on 269 00:14:35,680 --> 00:14:39,520 Speaker 1: the Super Bowl streaming, you know, advertising, it's all changing, 270 00:14:39,520 --> 00:14:42,440 Speaker 1: and I think it's huge opportunities for everyone. Thirty seconds Mark, 271 00:14:42,480 --> 00:14:45,480 Speaker 1: anything to slow this NFL jugging out down in terms 272 00:14:45,560 --> 00:14:47,520 Speaker 1: of just ratings. I mean, they had some issues a 273 00:14:47,520 --> 00:14:51,160 Speaker 1: few years ago, but man, they seem to be just dominant. Well, Ryan, 274 00:14:51,240 --> 00:14:53,800 Speaker 1: and you know, Ryan Reynolds is trying with Wrexham. Maybe 275 00:14:53,840 --> 00:14:57,360 Speaker 1: we can get a few more soccer fans. So I 276 00:14:57,360 --> 00:15:00,520 Speaker 1: think that works way better in Scotland. And here I 277 00:15:00,520 --> 00:15:05,120 Speaker 1: was gonna say, I'll watch Ryan's content, but not if 278 00:15:05,200 --> 00:15:08,320 Speaker 1: it's if I have to watch soccer exactly. That's right. 279 00:15:08,320 --> 00:15:10,680 Speaker 1: I forgot about that. He's big over there. Well all right, yeah, Mark, 280 00:15:10,720 --> 00:15:12,680 Speaker 1: we're going to let you go. Mark Douglas, President CEO 281 00:15:12,720 --> 00:15:14,960 Speaker 1: of of Mountain, joining us live here on our Bloomberg 282 00:15:15,360 --> 00:15:17,560 Speaker 1: Interact a broker studio, just kind of getting a breakdown 283 00:15:17,600 --> 00:15:20,200 Speaker 1: a little bit of the the day after of the Super Bowl. Uh, 284 00:15:20,720 --> 00:15:23,760 Speaker 1: we're gonna get some ratings later today from Nielsen for 285 00:15:23,880 --> 00:15:26,200 Speaker 1: the Fox Network. Presumably those numbers are gonna be pretty 286 00:15:26,240 --> 00:15:29,800 Speaker 1: darn impressive, and that's what advertisers are paying up for again. 287 00:15:29,960 --> 00:15:33,280 Speaker 1: Seven million dollars for thirty second spot is a huge 288 00:15:33,360 --> 00:15:36,000 Speaker 1: premium um, but a lot of those advertisers they think 289 00:15:36,000 --> 00:15:43,080 Speaker 1: it's worth it more. Coming up this is Bloomberg, all right, 290 00:15:43,120 --> 00:15:46,080 Speaker 1: I'm looking at my Bloomberg terminal. I got my two year, 291 00:15:46,160 --> 00:15:50,800 Speaker 1: ten year thing on the bond stuff. I mean it's inverted. 292 00:15:50,880 --> 00:15:55,200 Speaker 1: It's been inverted for a long time. And there's somebody 293 00:15:55,200 --> 00:15:56,680 Speaker 1: out there who thinks that this is kind of a 294 00:15:56,680 --> 00:15:59,920 Speaker 1: big deal. His name is Cam Harvey, uh, Professor Finance 295 00:16:00,000 --> 00:16:02,960 Speaker 1: at the Fucal School of Business at Duke University, and 296 00:16:03,000 --> 00:16:05,960 Speaker 1: he's kind of the go to person on this from 297 00:16:05,640 --> 00:16:08,520 Speaker 1: a research perspective, being one of the leaders on this 298 00:16:08,560 --> 00:16:11,320 Speaker 1: whole topic of looking at the yield curve and Camp I, 299 00:16:11,320 --> 00:16:14,280 Speaker 1: I you know, full disclosure, I may have been on 300 00:16:14,320 --> 00:16:16,760 Speaker 1: the Duke golf course when you talked about this way 301 00:16:16,760 --> 00:16:18,280 Speaker 1: back in the day, so I might have missed it 302 00:16:18,280 --> 00:16:20,760 Speaker 1: a little bit. I was known to do that back 303 00:16:20,760 --> 00:16:23,360 Speaker 1: in my FUCAL days. UM, talk to us about kind 304 00:16:23,360 --> 00:16:25,400 Speaker 1: of how you're looking at this yield curve. What is 305 00:16:25,400 --> 00:16:27,520 Speaker 1: it telling us? What do you think the FETE is 306 00:16:27,520 --> 00:16:33,040 Speaker 1: taking away from this? So it's complicated. Uh So the 307 00:16:33,120 --> 00:16:37,640 Speaker 1: yield curve predicts economic growth, and indeed that was my 308 00:16:37,920 --> 00:16:42,880 Speaker 1: dissertation in six at the University of Chicago. UM. It's 309 00:16:42,880 --> 00:16:47,960 Speaker 1: got a great track record for forecasting recessions. So eight 310 00:16:48,480 --> 00:16:51,600 Speaker 1: of the last eight and then since I published my dissertation, 311 00:16:52,040 --> 00:16:55,560 Speaker 1: it's four be four with no false signals, and now 312 00:16:55,720 --> 00:17:00,600 Speaker 1: it's inverted. And essentially when I look at this, UM, 313 00:17:00,640 --> 00:17:03,440 Speaker 1: I see the model that I proposed, a very simple 314 00:17:03,480 --> 00:17:06,960 Speaker 1: model and with only one thing that you look at, 315 00:17:07,320 --> 00:17:09,720 Speaker 1: and that's the difference between the tenure yield and the 316 00:17:09,760 --> 00:17:13,520 Speaker 1: three months a treature Bill yielled And I think that 317 00:17:13,680 --> 00:17:17,720 Speaker 1: this time around it's giving a false signal. The three 318 00:17:17,720 --> 00:17:21,920 Speaker 1: months ten years spread is uh way inverted, right, I 319 00:17:21,960 --> 00:17:26,840 Speaker 1: mean negative one and one basis points. I can understand, UM, 320 00:17:26,840 --> 00:17:29,480 Speaker 1: and we've talked about this before that now that we 321 00:17:29,520 --> 00:17:33,439 Speaker 1: all know about it, since since you published UM, you know, 322 00:17:33,480 --> 00:17:35,919 Speaker 1: that changes the equation a little bit because all market 323 00:17:36,000 --> 00:17:39,600 Speaker 1: participants under you know, are expecting something or they know 324 00:17:39,640 --> 00:17:42,960 Speaker 1: what they should or could expect when they see this inversion. UM. 325 00:17:43,200 --> 00:17:45,639 Speaker 1: I can understand that if it was only a slight inversion, 326 00:17:45,680 --> 00:17:49,040 Speaker 1: but since it's such a big inversion, doesn't that raise 327 00:17:49,119 --> 00:17:55,080 Speaker 1: red flags? So it is a pretty substantial inversion. So 328 00:17:55,119 --> 00:17:58,159 Speaker 1: I totally agree with you, But again, you need to 329 00:17:58,240 --> 00:18:02,040 Speaker 1: look at the bigger picture here, and every single business 330 00:18:02,080 --> 00:18:06,320 Speaker 1: cycle is different, and this one has a number of 331 00:18:06,359 --> 00:18:11,840 Speaker 1: things that I think, uh, contrasted with other sort of 332 00:18:11,880 --> 00:18:16,280 Speaker 1: business cycles. UH. And one thing that is really unusual 333 00:18:16,920 --> 00:18:20,399 Speaker 1: is the excess demand for labor, where we've got like 334 00:18:20,440 --> 00:18:24,800 Speaker 1: ten million job openings and one point eight job openings 335 00:18:24,840 --> 00:18:29,000 Speaker 1: for every person that's unemployed. Like that is just really unusual. 336 00:18:29,520 --> 00:18:31,840 Speaker 1: And that means that even if we do slow down, 337 00:18:31,880 --> 00:18:35,480 Speaker 1: and I actually believe that we will slow down, but 338 00:18:35,720 --> 00:18:39,480 Speaker 1: dodge a hard landing. So when we slow down, uh, 339 00:18:39,520 --> 00:18:43,560 Speaker 1: there is enough capacity to absorb those that are laid off, 340 00:18:44,400 --> 00:18:49,959 Speaker 1: and that kind of dulls the blow of the slow economy. 341 00:18:50,320 --> 00:18:54,160 Speaker 1: And what do you say about people kind of expecting 342 00:18:54,760 --> 00:18:59,120 Speaker 1: looking at the indicator and the changes behavior. That's what 343 00:18:59,160 --> 00:19:02,639 Speaker 1: we've been seeing. So we see this deeply inverted yield curve. 344 00:19:03,000 --> 00:19:09,159 Speaker 1: We see companies proactively taking measures to trim uh some 345 00:19:09,240 --> 00:19:12,560 Speaker 1: of their workforce to put them in a stronger position 346 00:19:13,080 --> 00:19:16,080 Speaker 1: when we do go into slower growth. They don't need 347 00:19:16,160 --> 00:19:20,000 Speaker 1: to do anything drastic. And I think that that's overall 348 00:19:20,080 --> 00:19:22,800 Speaker 1: good for the economy. So people are looking at it, 349 00:19:22,840 --> 00:19:27,520 Speaker 1: they take it seriously. That's interesting. So so basically, you know, 350 00:19:27,720 --> 00:19:31,000 Speaker 1: a few decades back you said, look, here's a signal, 351 00:19:31,440 --> 00:19:35,879 Speaker 1: UM that shows us, uh that this outcome is is approaching. 352 00:19:35,920 --> 00:19:39,720 Speaker 1: And now that everyone knows about it, companies say, hey, Cam, 353 00:19:39,760 --> 00:19:43,119 Speaker 1: Harvey publishes paper and he's been right the last few times. 354 00:19:43,160 --> 00:19:45,080 Speaker 1: So now that we see this signal, let's make changes 355 00:19:45,280 --> 00:19:51,040 Speaker 1: to avoid this outcome. Yeah, exactly. So it's called risk management. 356 00:19:51,680 --> 00:19:55,320 Speaker 1: And it's not just businesses, but I think consumers too. 357 00:19:55,680 --> 00:19:58,720 Speaker 1: This indicators in their face and with the eight out 358 00:19:58,760 --> 00:20:02,400 Speaker 1: of eate record, um, you take it seriously. And that 359 00:20:02,480 --> 00:20:06,000 Speaker 1: means you don't uh borrow on your credit card to 360 00:20:06,080 --> 00:20:09,119 Speaker 1: take a vacation at this point because you want to 361 00:20:09,160 --> 00:20:12,240 Speaker 1: be careful. So all of this feeds into behavior, this 362 00:20:12,359 --> 00:20:16,040 Speaker 1: risk management, and again the indicator is giving a correct 363 00:20:16,119 --> 00:20:20,119 Speaker 1: signal in my opinion, in terms of slower growth. But 364 00:20:20,240 --> 00:20:24,440 Speaker 1: I do think that we can dodge a recession, assuming 365 00:20:24,920 --> 00:20:28,760 Speaker 1: the Federal Reserve doesn't mess it up. So that kind 366 00:20:28,760 --> 00:20:30,919 Speaker 1: of goes to my next question, Cam. I mean, you know, 367 00:20:31,119 --> 00:20:33,440 Speaker 1: we have a lot of ECO data this week we've 368 00:20:34,400 --> 00:20:37,680 Speaker 1: what do you think the Federal Reserve should do over 369 00:20:37,680 --> 00:20:41,919 Speaker 1: its next couple of meetings, Well, they should pause, and 370 00:20:42,080 --> 00:20:45,640 Speaker 1: indeed they should have paused last time. So inflation over 371 00:20:45,680 --> 00:20:49,200 Speaker 1: the last six months is running at less than two 372 00:20:49,200 --> 00:20:54,679 Speaker 1: percent annualized. Right. So uh, if you look at the 373 00:20:54,760 --> 00:20:59,040 Speaker 1: key drivers of inflation, a third of the c p 374 00:20:59,200 --> 00:21:02,959 Speaker 1: I and four percent of the personal consumption and expenditure, 375 00:21:03,080 --> 00:21:06,600 Speaker 1: a deflator is linked to shelter. And we've already seen 376 00:21:06,960 --> 00:21:10,720 Speaker 1: what's happened to shelter. We've got rents going down, housing 377 00:21:10,760 --> 00:21:14,879 Speaker 1: prices going down, housing starts permits down, so that is 378 00:21:14,920 --> 00:21:18,440 Speaker 1: definitely cooled. It will take a while to work its 379 00:21:18,480 --> 00:21:23,480 Speaker 1: way through into the inflation rate, but I think that 380 00:21:24,320 --> 00:21:27,640 Speaker 1: the time to pause is now, and that they continue 381 00:21:27,720 --> 00:21:31,639 Speaker 1: to raise. Every single time they do this, they heighten 382 00:21:31,760 --> 00:21:36,520 Speaker 1: the probability pushing us into a hard landing recession, and 383 00:21:36,560 --> 00:21:41,159 Speaker 1: that's completely unnecessary. Hey, can let's switch gears a little bit. 384 00:21:41,200 --> 00:21:44,760 Speaker 1: I know in addition to all your someone will work 385 00:21:44,840 --> 00:21:46,800 Speaker 1: on the yield curve and the FED, you've also done 386 00:21:46,840 --> 00:21:51,359 Speaker 1: a lot of work and research on crypto. Um i'd 387 00:21:51,359 --> 00:21:53,200 Speaker 1: love to get I'm just looking a bitcoin here, that's 388 00:21:53,200 --> 00:21:54,720 Speaker 1: just kind of my go to on my screen here 389 00:21:54,720 --> 00:21:59,159 Speaker 1: it's back up above pushing tys per token UM. So 390 00:21:59,200 --> 00:22:02,280 Speaker 1: that's recovered. When people come up to you in a 391 00:22:02,280 --> 00:22:05,520 Speaker 1: cocktail party and ask you your opinion on crypto, give 392 00:22:05,600 --> 00:22:08,280 Speaker 1: us that level of discussion about how you think about it. 393 00:22:10,080 --> 00:22:12,960 Speaker 1: So the way I look at it is the crypto 394 00:22:13,320 --> 00:22:18,080 Speaker 1: is not just bitcoin, uh, it is a whole ecosystem, 395 00:22:18,160 --> 00:22:22,600 Speaker 1: and it is a way to potentially rebuild our financial 396 00:22:22,600 --> 00:22:29,400 Speaker 1: system whereby we can greatly reduce transactions costs, we can 397 00:22:29,440 --> 00:22:33,600 Speaker 1: do many things that we couldn't do before. UH. And 398 00:22:34,000 --> 00:22:38,120 Speaker 1: this idea of tokenizing all stocks, all bonds, all assets 399 00:22:38,240 --> 00:22:44,639 Speaker 1: is very very attractive. So this builds potentially new companies, 400 00:22:45,240 --> 00:22:48,440 Speaker 1: the so called web three revolution that's happening right now, 401 00:22:49,119 --> 00:22:53,200 Speaker 1: and it shouldn't be just focused on bitcoin or doge coin. 402 00:22:53,880 --> 00:22:57,840 Speaker 1: That this is very diverse, and much of what's happening 403 00:22:58,280 --> 00:23:02,840 Speaker 1: is below the radar screen. The financial system that we've 404 00:23:02,880 --> 00:23:07,040 Speaker 1: got essentially hasn't changed that much in a hundred years. 405 00:23:07,480 --> 00:23:10,960 Speaker 1: There's no reason that it should take two days to 406 00:23:11,000 --> 00:23:15,679 Speaker 1: settle a stock transaction. That should be something that happens instantly. 407 00:23:16,240 --> 00:23:19,920 Speaker 1: But there's a thick layer of middle people that makes 408 00:23:19,960 --> 00:23:24,040 Speaker 1: some money from all of these delays well also transfers, 409 00:23:24,080 --> 00:23:26,560 Speaker 1: I should point out can that people uh you know, 410 00:23:27,040 --> 00:23:30,560 Speaker 1: modern consumers still like reverse ability. I don't know what 411 00:23:30,640 --> 00:23:32,879 Speaker 1: the official word is that in business school talk, but 412 00:23:33,440 --> 00:23:39,120 Speaker 1: you know, if you uh send someone money with um, 413 00:23:39,160 --> 00:23:45,600 Speaker 1: you know uh venmo or what's the one ze zell um, 414 00:23:45,680 --> 00:23:49,120 Speaker 1: it happens instantly. That's great, but you can never get 415 00:23:49,119 --> 00:23:52,360 Speaker 1: it back. So if you're scammed or if you decide 416 00:23:52,400 --> 00:23:54,199 Speaker 1: you want to reverse it, you can do that with 417 00:23:54,280 --> 00:23:56,040 Speaker 1: a normal bank. You can do that with like three 418 00:23:56,119 --> 00:24:00,560 Speaker 1: day settlement, but you can't do it with an instant transfer. Yeah, 419 00:24:00,640 --> 00:24:03,520 Speaker 1: so it's definitely true. So they're always trade offs here. 420 00:24:04,000 --> 00:24:08,879 Speaker 1: So there's costs, there's benefits, and within the crypto space, 421 00:24:09,119 --> 00:24:12,200 Speaker 1: everything is final, so you need to be very careful 422 00:24:12,640 --> 00:24:17,160 Speaker 1: if you're sending something to somebody, um that you're sending 423 00:24:17,200 --> 00:24:21,399 Speaker 1: into the right person. And uh, if you're receiving something 424 00:24:21,640 --> 00:24:23,960 Speaker 1: like some goods, then you need to make sure there's 425 00:24:24,000 --> 00:24:28,320 Speaker 1: some sort of escrow system whereby your money is transferred 426 00:24:28,920 --> 00:24:33,119 Speaker 1: into escrow until you're satisfied with the good So so 427 00:24:33,160 --> 00:24:39,440 Speaker 1: again this is again a technology that essentially gets rid 428 00:24:39,720 --> 00:24:45,439 Speaker 1: of the commercial banking infrastructure makes things much easier to 429 00:24:45,480 --> 00:24:50,240 Speaker 1: deal with much faster. It's a technology of financial inclusion. Also, 430 00:24:50,840 --> 00:24:54,359 Speaker 1: you just need a mobile phone and you're banked. So 431 00:24:54,359 --> 00:24:56,159 Speaker 1: so I do think it's a big deal, and I 432 00:24:56,200 --> 00:25:01,320 Speaker 1: do think that it's largely below the radar, and and 433 00:25:01,320 --> 00:25:04,480 Speaker 1: and what it does is it changes the way that 434 00:25:04,520 --> 00:25:07,879 Speaker 1: we interact on the Internet. Also, we're super easy to 435 00:25:07,920 --> 00:25:10,840 Speaker 1: be paid or to pay. You don't need to worry 436 00:25:10,840 --> 00:25:15,200 Speaker 1: about the banks um because of the time. But we'll 437 00:25:15,200 --> 00:25:18,080 Speaker 1: get back on that for sure. Cam Harvey, Professor Finance 438 00:25:18,160 --> 00:25:25,760 Speaker 1: at Duke University. This is Bloomberg. What happens when Florida 439 00:25:25,800 --> 00:25:29,720 Speaker 1: and Texas blacklist Wall Street's largest municipal bond underwriters because 440 00:25:29,760 --> 00:25:33,680 Speaker 1: of their support for environmental, social and governance practice. Our 441 00:25:33,680 --> 00:25:36,240 Speaker 1: next guest has the answer to that, Matt Winkler. He 442 00:25:36,359 --> 00:25:39,200 Speaker 1: is the editor in chief emeritus for Bloomberg News. Hecky 443 00:25:39,320 --> 00:25:43,080 Speaker 1: is the founder of Bloomberg News way back in the day. Matt, 444 00:25:43,080 --> 00:25:45,520 Speaker 1: thanks for joining us here in our Bloomberg Interactive Broker studio. 445 00:25:45,960 --> 00:25:48,280 Speaker 1: So again, what happens to states like Florida and Texas 446 00:25:48,400 --> 00:25:51,400 Speaker 1: that bands And he's really big underwriters because of their 447 00:25:51,440 --> 00:25:54,199 Speaker 1: stance on e s G and I guess other political stuff. 448 00:25:54,720 --> 00:25:57,280 Speaker 1: Great to be with you. Florida and Texas right now, 449 00:25:57,880 --> 00:26:04,320 Speaker 1: which you have perfect triple A ratings, are paying uh. 450 00:26:05,400 --> 00:26:13,199 Speaker 1: In Florida's case, record spreads over inferior California in the market. 451 00:26:13,280 --> 00:26:17,760 Speaker 1: That's what's happening since two Uh. Well, there are a 452 00:26:17,840 --> 00:26:22,640 Speaker 1: number of reasons. But if you exclude, for example, say 453 00:26:22,760 --> 00:26:28,439 Speaker 1: four banks that you've heard of, City Group, Goldman, Sachs, JP, Morgan, 454 00:26:28,560 --> 00:26:34,280 Speaker 1: and Bank America, which account for of all municipal underwriting 455 00:26:34,359 --> 00:26:37,360 Speaker 1: over the past four years, if you exclude them from 456 00:26:37,359 --> 00:26:40,240 Speaker 1: your routine dead underwritings, it stands to reason that you 457 00:26:40,280 --> 00:26:43,520 Speaker 1: may suffer at least a little liquidity and maybe worse 458 00:26:43,560 --> 00:26:47,960 Speaker 1: than that. So that's uh, that's a start. Um. But 459 00:26:48,720 --> 00:26:54,120 Speaker 1: when you exclude underwriters in general, you're taking away an 460 00:26:54,119 --> 00:26:59,520 Speaker 1: opportunity to get the lowest rates the highest prices. It's 461 00:26:59,560 --> 00:27:05,119 Speaker 1: called petitive bidding, and it's uncompetitive. It's interesting because to 462 00:27:05,160 --> 00:27:07,040 Speaker 1: me it seems like it's part of a broader trend, 463 00:27:07,080 --> 00:27:10,560 Speaker 1: maybe funded by all the fiscal spending that we've gotten. 464 00:27:10,840 --> 00:27:14,480 Speaker 1: We saw a study in the UK that shows each 465 00:27:14,480 --> 00:27:18,680 Speaker 1: household paid an extra thousand pounds because of Brexit now 466 00:27:18,760 --> 00:27:21,200 Speaker 1: we're seeing a study with there's a FED economist that 467 00:27:21,240 --> 00:27:23,719 Speaker 1: you site and a u PEN professor saying that Texas, 468 00:27:23,800 --> 00:27:28,320 Speaker 1: for example, paid an extra five million and change because 469 00:27:28,440 --> 00:27:32,720 Speaker 1: of because they shunned banks that they see as unfriendly 470 00:27:32,800 --> 00:27:35,879 Speaker 1: to gunmakers. Yeah, well, it's not a bad analogy. I 471 00:27:35,880 --> 00:27:37,639 Speaker 1: hadn't thought of it that way. But in the case 472 00:27:37,680 --> 00:27:41,679 Speaker 1: of Brexit, as you bring up, Uh, so much of 473 00:27:41,960 --> 00:27:46,080 Speaker 1: EU trade is with the UK, or was before Brexit, 474 00:27:46,680 --> 00:27:49,439 Speaker 1: and now that the UK doesn't have access to that 475 00:27:49,520 --> 00:27:52,320 Speaker 1: trade at least the way it did, it's paying a 476 00:27:52,400 --> 00:27:57,360 Speaker 1: much higher cost as a result. So similarly, with respect 477 00:27:57,400 --> 00:28:02,159 Speaker 1: to Florida and Texas, they're excluding, you know, banks that 478 00:28:02,320 --> 00:28:08,280 Speaker 1: willingly want to compete to give Texas and Florida rate payers, 479 00:28:08,640 --> 00:28:12,760 Speaker 1: if you like citizens, the lowest possible rate, and their 480 00:28:12,840 --> 00:28:17,200 Speaker 1: governors and legislatures are saying, no, uh, we don't want 481 00:28:17,320 --> 00:28:21,640 Speaker 1: you to participate because these banks advocate for gun safety. 482 00:28:21,680 --> 00:28:26,000 Speaker 1: Because these banks acknowledge the importance of gun safety. I 483 00:28:26,040 --> 00:28:29,200 Speaker 1: wouldn't say their lobbyists so much, it's just that their 484 00:28:29,240 --> 00:28:34,440 Speaker 1: policies are we don't believe that people with an unlimited 485 00:28:34,480 --> 00:28:38,360 Speaker 1: supply of weapons should go into elementary schools and cavalierly 486 00:28:38,800 --> 00:28:42,400 Speaker 1: murder children. The banks are saying, you know, we should 487 00:28:42,720 --> 00:28:47,160 Speaker 1: have gun safety laws that protect against that. Texas specifically says, 488 00:28:47,200 --> 00:28:50,280 Speaker 1: if you take that view, we consider that hostile to 489 00:28:50,360 --> 00:28:54,400 Speaker 1: the firearms industry. We're going to exclude you from underwriting 490 00:28:54,400 --> 00:28:57,920 Speaker 1: our bonds. The real question should be among all of 491 00:28:58,000 --> 00:29:01,800 Speaker 1: us is why are Florida and Texas citizens paying so 492 00:29:01,880 --> 00:29:05,520 Speaker 1: much more than California. I mean, if we're all capitalists here, 493 00:29:05,760 --> 00:29:08,960 Speaker 1: how come nobody's asking that question? So I guess, just 494 00:29:09,000 --> 00:29:11,880 Speaker 1: from the perspective of I guess the governor's feel and 495 00:29:11,920 --> 00:29:14,800 Speaker 1: the other elected officials in these states feel like it's 496 00:29:14,840 --> 00:29:18,240 Speaker 1: popular from a political perspective that, if you know, it's 497 00:29:18,240 --> 00:29:21,200 Speaker 1: hard to explain them paying more slightly higher yield on 498 00:29:21,320 --> 00:29:23,880 Speaker 1: the municipal bond across the state. It's hard to explain 499 00:29:23,920 --> 00:29:27,160 Speaker 1: that concept visa v Maybe the political benefits of saying, hey, 500 00:29:27,160 --> 00:29:29,920 Speaker 1: we're standing up to some of these quote unquote woke 501 00:29:30,000 --> 00:29:33,440 Speaker 1: establishments or entities, Well, they're actually doing the very thing 502 00:29:33,520 --> 00:29:37,520 Speaker 1: they're accusing the banks of. They're actually saying, because of 503 00:29:37,560 --> 00:29:41,160 Speaker 1: our ideology, we're gonna get in way of the free market. 504 00:29:41,400 --> 00:29:44,400 Speaker 1: We're going to deprive our voters from getting the best 505 00:29:44,440 --> 00:29:49,560 Speaker 1: possible rates because we think our ideology is superior to 506 00:29:49,640 --> 00:29:54,200 Speaker 1: everybody else's. That's essentially it. Uh. And what is their ideology? 507 00:29:54,240 --> 00:29:58,480 Speaker 1: They don't believe in diversity and exclusion and inclusion. Okay. Uh. 508 00:29:58,520 --> 00:30:01,440 Speaker 1: And we're talking about Florida State where fifty years ago 509 00:30:02,120 --> 00:30:06,960 Speaker 1: very racist policies prevailed. In Texas pretty much the same thing. Okay, 510 00:30:07,040 --> 00:30:11,720 Speaker 1: So these are these are states that formally enslaved people 511 00:30:12,280 --> 00:30:16,000 Speaker 1: and barely got past it just fifty years ago. Has 512 00:30:16,000 --> 00:30:19,200 Speaker 1: there been any change in the political climate since you've 513 00:30:19,240 --> 00:30:23,880 Speaker 1: alda since the murder of so many children in that school? Um, 514 00:30:24,120 --> 00:30:27,680 Speaker 1: has there been any shift? And we just passed, you know, 515 00:30:27,840 --> 00:30:32,160 Speaker 1: a gun control law, so that gives you some indication 516 00:30:32,280 --> 00:30:35,840 Speaker 1: that public sentiment shifted. And if you look at polls, 517 00:30:35,920 --> 00:30:38,320 Speaker 1: which is always a risky proposition, but if you do 518 00:30:38,360 --> 00:30:42,320 Speaker 1: look at polls, most Americans favor gun safety laws. That's 519 00:30:42,320 --> 00:30:46,120 Speaker 1: a popular position. Okay, that's not controversial. What are the 520 00:30:46,160 --> 00:30:49,640 Speaker 1: big investment banks you mentioned Bank of America, you know, 521 00:30:49,720 --> 00:30:54,200 Speaker 1: City and in a couple other golden sacks. What are they? 522 00:30:54,800 --> 00:30:57,280 Speaker 1: I'm sure their bankers want to make be in these 523 00:30:57,320 --> 00:30:59,560 Speaker 1: deals and get paid and do all that kind of stuff. 524 00:30:59,560 --> 00:31:02,160 Speaker 1: But what's been the banks response? Are they're trying to 525 00:31:02,160 --> 00:31:04,880 Speaker 1: work with these states or are they just acknowledging that 526 00:31:05,040 --> 00:31:08,360 Speaker 1: we have a difference. These banks, by the way, like Bloomberg, 527 00:31:08,760 --> 00:31:13,600 Speaker 1: have employees and have customers, and like Bloomberg, their customers 528 00:31:13,600 --> 00:31:18,920 Speaker 1: and their employees believe in diversity and inclusion, believe in justice, 529 00:31:19,080 --> 00:31:22,880 Speaker 1: believe in equality, believe in all kinds of things that 530 00:31:23,000 --> 00:31:29,240 Speaker 1: come under this uh three letter thing called E s G, 531 00:31:30,080 --> 00:31:34,280 Speaker 1: where sustainability is probably the central pillar of it, which 532 00:31:34,360 --> 00:31:37,600 Speaker 1: is climate change is the biggest threat to all of us, 533 00:31:37,880 --> 00:31:40,720 Speaker 1: and these banks are very mindful of that, and they're 534 00:31:40,720 --> 00:31:45,080 Speaker 1: doing everything they can to try try. Is the right 535 00:31:45,120 --> 00:31:50,440 Speaker 1: word be accommodative of sustainability. Um, So there's a lot 536 00:31:50,480 --> 00:31:54,720 Speaker 1: of company here. Remember E s G is the convergence 537 00:31:55,000 --> 00:31:59,360 Speaker 1: of people's preferences in public policy. It's what around the world, 538 00:31:59,400 --> 00:32:03,280 Speaker 1: what recreasingly people want because they want to figure out 539 00:32:03,320 --> 00:32:07,480 Speaker 1: how do we not harm ourselves going forward? Has that 540 00:32:07,520 --> 00:32:10,800 Speaker 1: been borne out also by the investment data I mean 541 00:32:11,440 --> 00:32:14,000 Speaker 1: um for example, inflos into E t f s or 542 00:32:14,120 --> 00:32:18,440 Speaker 1: performance of E s G assets. Okay, so we're talking 543 00:32:18,440 --> 00:32:22,640 Speaker 1: about assets of thirty five trillion dollars. Okay, that's the 544 00:32:22,720 --> 00:32:25,240 Speaker 1: so called E s G pool of money that we're 545 00:32:25,240 --> 00:32:30,479 Speaker 1: talking about as um you know, and that has happened, 546 00:32:30,520 --> 00:32:34,520 Speaker 1: as I said, because around the world, this is where 547 00:32:34,640 --> 00:32:39,120 Speaker 1: investment is increasingly going. UM. It's something that people want 548 00:32:39,200 --> 00:32:42,040 Speaker 1: to do. It's not imposed upon them. They want to 549 00:32:42,080 --> 00:32:45,120 Speaker 1: do it. And this is where both Texas and Florida 550 00:32:45,280 --> 00:32:51,320 Speaker 1: cynically uh try to uh mislead because what they're arguing 551 00:32:51,360 --> 00:32:54,760 Speaker 1: against is something that people want, not something that's being 552 00:32:54,800 --> 00:32:58,400 Speaker 1: imposed upon them. It bears repeating that California only has 553 00:32:58,400 --> 00:33:03,320 Speaker 1: a double A credit rating, UM, and yet pays less 554 00:33:03,440 --> 00:33:07,640 Speaker 1: in interest than triple A credit rated Texas. I mean, 555 00:33:07,720 --> 00:33:10,920 Speaker 1: it's just mind blowing to me, like twenty basis points correct. 556 00:33:11,520 --> 00:33:13,840 Speaker 1: So we talked about Florida and Texas, and those are 557 00:33:13,880 --> 00:33:16,240 Speaker 1: two huge states. Of course, are there other states that 558 00:33:16,280 --> 00:33:22,800 Speaker 1: are looking to follow Texas and Florida. Interns. Look pretty much, 559 00:33:23,560 --> 00:33:29,160 Speaker 1: if you like, every red state is got something going 560 00:33:29,560 --> 00:33:36,680 Speaker 1: that is specifically opposed to uh E s G. Whether 561 00:33:36,960 --> 00:33:40,760 Speaker 1: it's actually been enacted or it's in progress. Uh. We're 562 00:33:40,760 --> 00:33:44,200 Speaker 1: talking about at least half a dozen states already that 563 00:33:44,320 --> 00:33:47,440 Speaker 1: are following Florida and so we see data like this, 564 00:33:47,960 --> 00:33:51,320 Speaker 1: is there a pushback within Florida, within within Texas when 565 00:33:51,360 --> 00:33:53,840 Speaker 1: they see data like you've brought up in your column 566 00:33:53,840 --> 00:33:57,560 Speaker 1: today about the incremental cost. Well, it's probably too early 567 00:33:57,600 --> 00:34:02,880 Speaker 1: to say, because our profession isn't writing the headline. Why 568 00:34:02,920 --> 00:34:07,120 Speaker 1: are the citizens of Florida and Texas paying more on 569 00:34:07,200 --> 00:34:12,680 Speaker 1: their borrowing when lower rated California is paying a lot less? Yeah, 570 00:34:12,920 --> 00:34:16,120 Speaker 1: that's not a recurring headline. All right, interesting, all right, 571 00:34:16,719 --> 00:34:19,880 Speaker 1: great stuff. Really appreciate it. Matt Winkler, uh with his 572 00:34:20,000 --> 00:34:25,839 Speaker 1: he's a emeritus editor guy at Bloomberg's a founder. Have 573 00:34:25,880 --> 00:34:28,719 Speaker 1: you founded anything there? You founded anything? I mean, but 574 00:34:28,760 --> 00:34:35,040 Speaker 1: he's a founder. This Saturday, to take New Jersey Transit 575 00:34:35,120 --> 00:34:37,040 Speaker 1: into the city to go see a Portland Nets game, 576 00:34:37,239 --> 00:34:42,520 Speaker 1: train was packed standing room only Todayturday on Saturday, people 577 00:34:42,560 --> 00:34:47,239 Speaker 1: coming in to see shows, games, restaurants, party, whatever's today. 578 00:34:47,360 --> 00:34:49,319 Speaker 1: I get on the train for my Monday commute. It 579 00:34:49,400 --> 00:34:52,440 Speaker 1: was basically meaning the conductor that was it. Nobody coming 580 00:34:52,480 --> 00:34:55,359 Speaker 1: into work on Monday's, probably Fridays, and who knows what else. 581 00:34:55,600 --> 00:34:58,480 Speaker 1: That's called hybrid working. The question is what's it costing 582 00:34:58,800 --> 00:35:02,040 Speaker 1: the city of New York. Unfortunately, that is the subject 583 00:35:02,440 --> 00:35:04,719 Speaker 1: of the Bloomberg Big Take Story of the day. You 584 00:35:04,760 --> 00:35:08,879 Speaker 1: know these big Take stories really deeply researched and extraordinarily 585 00:35:08,920 --> 00:35:12,160 Speaker 1: well written, real deep dives into these things. Uh, today 586 00:35:12,400 --> 00:35:14,239 Speaker 1: we have Donna Bore Actually he's a senior reporter for 587 00:35:14,320 --> 00:35:17,879 Speaker 1: Bloomberg Industry Group co wrote this article. Here, Donna, what's 588 00:35:17,920 --> 00:35:21,759 Speaker 1: this remote work this hybrid work model? What's it costing Manhattan? 589 00:35:23,400 --> 00:35:27,120 Speaker 1: A whopping twelve point four billion dollars um? It's sort 590 00:35:27,120 --> 00:35:32,839 Speaker 1: of amazing, right, yeah, a year per um, so per worker. Uh. 591 00:35:33,280 --> 00:35:36,920 Speaker 1: We we ended up working with UM researchers out of 592 00:35:36,960 --> 00:35:42,200 Speaker 1: the Work from Home UM Research Group and Jose Maria Berrero, 593 00:35:42,800 --> 00:35:48,600 Speaker 1: who is a finance professor at Mexico's Institute Technological Autonomo 594 00:35:49,120 --> 00:35:52,680 Speaker 1: and has been part of this team. He basically helped 595 00:35:52,800 --> 00:35:55,720 Speaker 1: us to to figure out what this this giant whopping 596 00:35:55,800 --> 00:36:00,200 Speaker 1: number was. So there's a Work from Home Research group. 597 00:36:00,400 --> 00:36:05,600 Speaker 1: Did they ever come into the office. I suspect that 598 00:36:05,640 --> 00:36:08,840 Speaker 1: they do. So this is what how do you calculate 599 00:36:08,880 --> 00:36:12,359 Speaker 1: the costs? Hears this just me not buying, uh, you know, 600 00:36:12,960 --> 00:36:16,160 Speaker 1: a lunch sandwich on Monday's and Fridays and kind of 601 00:36:16,160 --> 00:36:19,880 Speaker 1: add all that up and everything. Yeah, so so what 602 00:36:19,960 --> 00:36:23,520 Speaker 1: happened and and and what Jose's uh that this was 603 00:36:23,600 --> 00:36:28,000 Speaker 1: exclusive data that was provided to Bloomberg News. Essentially, what 604 00:36:28,040 --> 00:36:31,319 Speaker 1: they did is they've been working UM and tracking work 605 00:36:31,360 --> 00:36:34,160 Speaker 1: from home habits uh and and trend lines for quite 606 00:36:34,280 --> 00:36:38,080 Speaker 1: quite some time. And so in they had a survey 607 00:36:38,080 --> 00:36:40,840 Speaker 1: that they asked participants, Hey, so what did you actually 608 00:36:40,880 --> 00:36:46,360 Speaker 1: spend in on meals, shopping, entertainment UM near the workplace? 609 00:36:46,880 --> 00:36:49,439 Speaker 1: And you know, they've been tracking this research long enough 610 00:36:49,480 --> 00:36:52,239 Speaker 1: that they know that this trend line for work from 611 00:36:52,320 --> 00:36:55,200 Speaker 1: home has really become permanent right there. At a moment, 612 00:36:55,600 --> 00:36:58,080 Speaker 1: just even in the last year, there was a sense 613 00:36:58,120 --> 00:37:01,200 Speaker 1: of like, oh, this is stickiness, but actually work from 614 00:37:01,239 --> 00:37:04,480 Speaker 1: home is sort of a permanent phenomenon UM. And in 615 00:37:04,560 --> 00:37:07,719 Speaker 1: Manhattan that's about thirty less. So people are coming in 616 00:37:07,760 --> 00:37:10,520 Speaker 1: about three and a half days a week according to 617 00:37:10,560 --> 00:37:13,480 Speaker 1: their research, and so they were able to scale what 618 00:37:13,560 --> 00:37:17,200 Speaker 1: people were spending back in twenty nineteen UM to what 619 00:37:17,239 --> 00:37:19,360 Speaker 1: it is, you know, based on a three and a 620 00:37:19,400 --> 00:37:22,400 Speaker 1: half day work week. And so what they found was 621 00:37:22,800 --> 00:37:27,800 Speaker 1: Manhattan was leading across twelve major cities, and per worker 622 00:37:28,160 --> 00:37:31,040 Speaker 1: they were spending four thousand, six D sixty one dollars 623 00:37:31,400 --> 00:37:35,759 Speaker 1: less per year, which is added all together with you 624 00:37:35,760 --> 00:37:38,680 Speaker 1: know inward commuters and those that are residents in Manhattan 625 00:37:38,680 --> 00:37:42,600 Speaker 1: that are working there, um a really giant number. So 626 00:37:43,480 --> 00:37:45,560 Speaker 1: my first thought is where's that money going? I mean, 627 00:37:45,600 --> 00:37:50,360 Speaker 1: are consumers banking it? Are they spending you know money 628 00:37:50,400 --> 00:37:53,840 Speaker 1: at a launch place in Summit, New Jersey or you 629 00:37:53,840 --> 00:37:58,799 Speaker 1: know Scarsdale, New York? Um? Are are they looking at 630 00:37:58,920 --> 00:38:02,880 Speaker 1: entertainment options outside of the city. What's who are the 631 00:38:02,880 --> 00:38:06,319 Speaker 1: winners if New York is the loser? Yeah, I mean 632 00:38:06,360 --> 00:38:08,759 Speaker 1: there there are a couple of things going on here, right. 633 00:38:09,040 --> 00:38:11,880 Speaker 1: So you know, throughout doing this reporting, I mean we 634 00:38:12,000 --> 00:38:14,960 Speaker 1: definitely tried to look at was there a displacement of 635 00:38:15,000 --> 00:38:17,719 Speaker 1: the economic shift of spending, right if you weren't coming 636 00:38:17,719 --> 00:38:21,440 Speaker 1: into Manhattan where you then spending greater amounts you know, 637 00:38:21,520 --> 00:38:24,399 Speaker 1: in your neighborhood or in the outer boroughs. I think 638 00:38:24,440 --> 00:38:28,319 Speaker 1: we all saw that once the pandemic hit, people moved outwards, right, 639 00:38:28,360 --> 00:38:32,440 Speaker 1: they left the they left, um, the island of Manhattan. Um. 640 00:38:32,520 --> 00:38:35,440 Speaker 1: What our reporting has shown, and we used a wide 641 00:38:35,560 --> 00:38:38,799 Speaker 1: range of data sources trying to quantify this, is that 642 00:38:38,880 --> 00:38:43,239 Speaker 1: you really do see a significant uptick um in you know, 643 00:38:43,360 --> 00:38:49,040 Speaker 1: bookings for you know, dining out, retail spending, um, you know, 644 00:38:49,080 --> 00:38:52,680 Speaker 1: and so forth, really in different parts of Brooklyn and 645 00:38:52,680 --> 00:38:56,840 Speaker 1: the Bronx outside of Manhattan. Manhattan is really lagged um 646 00:38:56,880 --> 00:39:00,440 Speaker 1: when it comes to consumer spending. The thing that we 647 00:39:00,480 --> 00:39:04,120 Speaker 1: couldn't answer because this is also fluid, right, We were 648 00:39:04,200 --> 00:39:07,279 Speaker 1: just now getting a sense of like what the real 649 00:39:07,360 --> 00:39:11,040 Speaker 1: impact is is that you can see how much Manhattan 650 00:39:11,120 --> 00:39:14,840 Speaker 1: is losing, but it's much harder to quantify what the 651 00:39:14,880 --> 00:39:17,319 Speaker 1: outer boroughs are actually gaining as a result of this. 652 00:39:17,640 --> 00:39:20,360 Speaker 1: You can look at um sales tax revenue and you 653 00:39:20,360 --> 00:39:22,960 Speaker 1: can see an uptick there, but it's gonna take some 654 00:39:23,040 --> 00:39:25,360 Speaker 1: time for us to really get a sense of the 655 00:39:25,480 --> 00:39:30,400 Speaker 1: full economic impact and how much has shifted. So, Donna, 656 00:39:30,440 --> 00:39:33,120 Speaker 1: you have some great data in this uh, in this story, 657 00:39:33,200 --> 00:39:35,520 Speaker 1: as usual in these big take stories, and one I 658 00:39:35,560 --> 00:39:38,880 Speaker 1: liked was just kind of Manhattan office workers are returning, 659 00:39:38,960 --> 00:39:40,960 Speaker 1: just not every day, and you have some foot traffic 660 00:39:41,040 --> 00:39:44,200 Speaker 1: data percentage of you know, kind of normal traffic through 661 00:39:44,239 --> 00:39:46,399 Speaker 1: a lot of famous buildings in New York City. Thirty 662 00:39:46,480 --> 00:39:49,040 Speaker 1: rockeffell Or Plaza, two VC Street where I used to 663 00:39:49,040 --> 00:39:51,600 Speaker 1: work back in the day, two Park Avenue, and the 664 00:39:51,600 --> 00:39:56,879 Speaker 1: bottom line is it's like on a good day. I mean, 665 00:39:57,000 --> 00:39:59,680 Speaker 1: when you talk to these companies, are I mean, are 666 00:39:59,680 --> 00:40:02,080 Speaker 1: we ever gonna go back? Or is this really the 667 00:40:02,080 --> 00:40:05,960 Speaker 1: new normal? Yeah? I think this really is the new normal. 668 00:40:06,080 --> 00:40:09,080 Speaker 1: And you can see that, you know, even though companies 669 00:40:09,120 --> 00:40:12,759 Speaker 1: have tried to impose work from home um sorry, you know, 670 00:40:12,800 --> 00:40:16,560 Speaker 1: in person work policies, is that it's it's really not working. 671 00:40:16,920 --> 00:40:19,759 Speaker 1: I think that because of the lasting effect of the pandemic, 672 00:40:20,040 --> 00:40:23,200 Speaker 1: people's behavior, both when it comes to spending and how 673 00:40:23,200 --> 00:40:28,120 Speaker 1: they live has really become UM entrenched. And so you know, 674 00:40:28,560 --> 00:40:31,680 Speaker 1: we talked to a bunch of people UM doing field work, 675 00:40:32,120 --> 00:40:35,200 Speaker 1: you know, getting a sense of what their weekly routines 676 00:40:35,239 --> 00:40:38,080 Speaker 1: are these days, and everyone really wants to work from 677 00:40:38,080 --> 00:40:40,600 Speaker 1: home on a on a Friday. It's you know, it's 678 00:40:40,600 --> 00:40:42,880 Speaker 1: part of like an extension of of a long weekend. 679 00:40:42,920 --> 00:40:45,560 Speaker 1: They can log off a little bit earlier, and so 680 00:40:45,760 --> 00:40:48,160 Speaker 1: you can really see it in the data that you know, 681 00:40:48,200 --> 00:40:53,360 Speaker 1: Friday's especially UM office attendance like tanks. What's interesting is 682 00:40:53,640 --> 00:40:56,440 Speaker 1: um Linley Lynn, who helped us analyze a lot of 683 00:40:56,440 --> 00:40:59,560 Speaker 1: this data. You know, really a lot of these trends 684 00:40:59,560 --> 00:41:02,799 Speaker 1: are an exceleration of what was already happening. Um, you know, 685 00:41:02,920 --> 00:41:06,200 Speaker 1: consumer spending was already you know a little bit down 686 00:41:06,239 --> 00:41:08,920 Speaker 1: on Mondays and Fridays to begin with. Um, people came 687 00:41:08,960 --> 00:41:11,200 Speaker 1: in a little bit less you know on those days, 688 00:41:11,239 --> 00:41:14,960 Speaker 1: trying to have longer weekends. But really the pandemic has 689 00:41:15,040 --> 00:41:18,680 Speaker 1: just accelerated all of that, and I think that business 690 00:41:18,680 --> 00:41:21,440 Speaker 1: districts are now having to wrestle with what do we 691 00:41:21,480 --> 00:41:24,640 Speaker 1: do next? What does it mean for the infrastructure, for 692 00:41:24,680 --> 00:41:30,719 Speaker 1: the rail and roads. Yeah, I mean the fewer, fewer commuters, 693 00:41:30,800 --> 00:41:34,560 Speaker 1: fewer people like foot traffic, um coming into Manhattan. Obviously 694 00:41:34,600 --> 00:41:37,920 Speaker 1: it's going to translate into you know, less tax revenue, 695 00:41:38,040 --> 00:41:41,520 Speaker 1: less fair revenue. Um. You end up having you know, 696 00:41:41,719 --> 00:41:44,400 Speaker 1: fewer dollars in the coffer that you know eventually end 697 00:41:44,440 --> 00:41:48,560 Speaker 1: up impacting just the vitality and the economic recovery of 698 00:41:48,600 --> 00:41:51,920 Speaker 1: the city. Right if you have less tax revenue coming in, 699 00:41:52,000 --> 00:41:55,040 Speaker 1: whether it's like personal income tax because people have decided 700 00:41:55,080 --> 00:41:57,160 Speaker 1: that they don't want to they don't need to work 701 00:41:57,160 --> 00:42:00,920 Speaker 1: in Manhattan anymore, they can work elsewhere, or less US revenue, 702 00:42:01,120 --> 00:42:05,840 Speaker 1: you know, there's uh, it hurts, big, big, big story, 703 00:42:05,920 --> 00:42:09,279 Speaker 1: big big numbers as well. Remote work is costing in 704 00:42:09,320 --> 00:42:12,080 Speaker 1: Manhattan more than twelve billion dollars a year. That's a 705 00:42:12,080 --> 00:42:14,319 Speaker 1: big Take story. You can find out a Bloomberg dot 706 00:42:14,360 --> 00:42:17,160 Speaker 1: Com slash a Big Take or ni Space Big Take 707 00:42:17,239 --> 00:42:20,000 Speaker 1: go on the Bloomberg terminal. Donna Barak one of the 708 00:42:20,000 --> 00:42:22,879 Speaker 1: reporters joining us here to bring us that story. She's 709 00:42:22,920 --> 00:42:27,000 Speaker 1: a senior reporter. Not here from home today. Why not? 710 00:42:27,239 --> 00:42:29,440 Speaker 1: You can do it. It's a Monday. The kids are 711 00:42:29,440 --> 00:42:38,440 Speaker 1: doing it. This is Bloomberg. Thanks for listening to the 712 00:42:38,480 --> 00:42:42,400 Speaker 1: Bloomberg Markets podcast. You can subscribe and listen to interviews 713 00:42:42,400 --> 00:42:46,719 Speaker 1: with Apple Podcasts or whatever podcast platform you prefer. I'm 714 00:42:46,719 --> 00:42:51,160 Speaker 1: Matt Miller. I'm on Twitter at Matt Miller, three pt 715 00:42:51,160 --> 00:42:53,760 Speaker 1: on Fall Sweey, I'm on Twitter at pt Sweeney. Before 716 00:42:53,760 --> 00:42:56,600 Speaker 1: the podcast. You can always catch us worldwide at Bloomberg 717 00:42:56,640 --> 00:42:56,879 Speaker 1: Radio