1 00:00:00,720 --> 00:00:03,720 Speaker 1: This is Bloomberg Business Week. I'm Carol Masser and I'm 2 00:00:03,800 --> 00:00:06,320 Speaker 1: Jason Kelly. We're right here every day bringing you the 3 00:00:06,400 --> 00:00:11,160 Speaker 1: latest news from the world's of business and finance, plus technology, politics, economics, 4 00:00:11,240 --> 00:00:14,280 Speaker 1: all harnessing the power of Business Week reporters and editors, 5 00:00:14,440 --> 00:00:16,439 Speaker 1: and of course Carol that's part of a team of 6 00:00:16,520 --> 00:00:20,439 Speaker 1: twenty seven hundred journalists and analysts more than a hundred 7 00:00:20,480 --> 00:00:23,280 Speaker 1: and twenty countries and Jason. You can download Bloomberg Business 8 00:00:23,280 --> 00:00:26,239 Speaker 1: Week on iTunes, SoundCloud, bl Bloomberg dot com. You can 9 00:00:26,280 --> 00:00:28,640 Speaker 1: also listen to our radio show at two pm Eastern 10 00:00:28,680 --> 00:00:31,560 Speaker 1: on Bloomberg Radio every weekday, or watch us on YouTube 11 00:00:31,600 --> 00:00:37,200 Speaker 1: by searching Bloomberg Global News. Well, another week of so 12 00:00:37,479 --> 00:00:41,879 Speaker 1: many headlines related to the virus. It is such a 13 00:00:41,880 --> 00:00:45,200 Speaker 1: pleasure as always to catch up with Dr Ian les Bader, 14 00:00:45,640 --> 00:00:49,320 Speaker 1: our soothsayer, our truth teller, our guy who really helps 15 00:00:49,360 --> 00:00:52,440 Speaker 1: us understand where we're going and where we are, Clinical 16 00:00:52,479 --> 00:00:55,480 Speaker 1: Associate Professor of Medicine at n y us Lango Medical Center. 17 00:00:55,560 --> 00:00:58,040 Speaker 1: He joins us on the phone from New York City. Ian, 18 00:00:58,080 --> 00:01:01,760 Speaker 1: how are you hi? H Happy Friday, hugsum. I hope 19 00:01:01,800 --> 00:01:05,400 Speaker 1: everyone is doing well. We are so far thriving, but 20 00:01:05,520 --> 00:01:09,040 Speaker 1: seeing lots of patients and UH and seeing some longhaulers 21 00:01:09,120 --> 00:01:12,319 Speaker 1: and some COVID patients as well, So UH, we are 22 00:01:12,440 --> 00:01:16,040 Speaker 1: not out of the woods. Just so let's talk about that. 23 00:01:16,080 --> 00:01:20,160 Speaker 1: I mean, what is the reality check for this area 24 00:01:20,360 --> 00:01:23,880 Speaker 1: that we all live in, specifically the New York City 25 00:01:23,880 --> 00:01:26,520 Speaker 1: area I'm talking about, Like, where are we right now? 26 00:01:26,560 --> 00:01:30,720 Speaker 1: We see hot spots reported, but you you are quite 27 00:01:30,760 --> 00:01:33,640 Speaker 1: literally in on the front lines. Tell me more about 28 00:01:33,640 --> 00:01:38,360 Speaker 1: what you're seeing. So you know, we're definitely seeing UH 29 00:01:38,480 --> 00:01:41,440 Speaker 1: in increase in cases. And I think whenever you see 30 00:01:41,760 --> 00:01:46,600 Speaker 1: hot spots, you know, the danger is that extending into 31 00:01:46,640 --> 00:01:50,760 Speaker 1: other areas of the community. We're certainly seeing in the 32 00:01:50,800 --> 00:01:53,920 Speaker 1: Midwest as well, not just in New York City some 33 00:01:54,000 --> 00:01:57,240 Speaker 1: hot spots, but throughout the country. So I think until 34 00:01:57,320 --> 00:01:59,960 Speaker 1: we are all able to get a prevented amount of 35 00:02:00,040 --> 00:02:05,520 Speaker 1: US and UH, or until monoclonal antibody and fusions are 36 00:02:05,560 --> 00:02:09,040 Speaker 1: widely available, we really have to resort to the old 37 00:02:09,080 --> 00:02:13,640 Speaker 1: traditional UH infectious disease controls of you know, mass scoring, 38 00:02:13,680 --> 00:02:18,280 Speaker 1: even though we know that's not acent effective and social distancing. 39 00:02:18,960 --> 00:02:22,720 Speaker 1: And last week we talked about some in the in 40 00:02:22,760 --> 00:02:27,760 Speaker 1: the more religious community, and it is important that everyone follow, 41 00:02:28,080 --> 00:02:32,119 Speaker 1: you know, follow the guidelines. There is no uh problem, 42 00:02:32,160 --> 00:02:38,760 Speaker 1: there's no um conflict between religion and spirituality and good 43 00:02:38,880 --> 00:02:43,680 Speaker 1: good health and science or religions really emphasize healthy behavior 44 00:02:43,840 --> 00:02:46,720 Speaker 1: and and uh this is just part of that for 45 00:02:46,760 --> 00:02:49,919 Speaker 1: the time being. So there should be no conflict with 46 00:02:50,080 --> 00:02:53,680 Speaker 1: being observant in in the way you feel as appropriate 47 00:02:53,800 --> 00:02:58,560 Speaker 1: and also doing um good behavior for the community. Right. 48 00:02:58,600 --> 00:03:00,560 Speaker 1: I mean, I feel like that's been one and theme 49 00:03:01,160 --> 00:03:04,200 Speaker 1: um in that we've talked about. You know, it's it's 50 00:03:04,240 --> 00:03:06,160 Speaker 1: the whole virus is all about a sense of community. 51 00:03:06,320 --> 00:03:08,760 Speaker 1: It's protecting others. I mean, you want to protect yourself, 52 00:03:08,760 --> 00:03:10,520 Speaker 1: of course, but you really have to think about that's 53 00:03:10,520 --> 00:03:15,440 Speaker 1: what mask wearing is ultimately about. When you look at 54 00:03:15,720 --> 00:03:18,040 Speaker 1: what's going on around the country. I think I was 55 00:03:18,120 --> 00:03:21,680 Speaker 1: kind of thrown when I was watching some some programs, 56 00:03:21,720 --> 00:03:23,320 Speaker 1: some news programs, and they were showing a map of 57 00:03:23,320 --> 00:03:25,680 Speaker 1: the country and showing where we're seeing increases in cases, 58 00:03:25,720 --> 00:03:29,600 Speaker 1: and it was almost blanketed across the country. So is 59 00:03:29,680 --> 00:03:31,839 Speaker 1: this kind of the second wave that we've all been 60 00:03:31,880 --> 00:03:35,560 Speaker 1: talking about. Is this the beginning of it? Yes? I 61 00:03:35,560 --> 00:03:39,200 Speaker 1: think that is the case. You know, we certainly saw 62 00:03:39,280 --> 00:03:41,600 Speaker 1: a drop in the number of cases. And really around 63 00:03:41,600 --> 00:03:45,600 Speaker 1: the world we're seeing, you know, the second waves. And unfortunately, 64 00:03:45,640 --> 00:03:48,800 Speaker 1: if you look at even back to nine eighteen the 65 00:03:48,840 --> 00:03:52,520 Speaker 1: Spanish flu, you know, these viral infections do come in waves, 66 00:03:52,560 --> 00:03:56,160 Speaker 1: they do mutate a bit. UH. People do develop some 67 00:03:56,720 --> 00:04:00,440 Speaker 1: slowly but surely her herd immunity. UH. And again, the 68 00:04:00,480 --> 00:04:03,200 Speaker 1: idea is to kind of flatten the curve. You know, 69 00:04:03,440 --> 00:04:06,840 Speaker 1: no one ever said we're going to completely stop cases. 70 00:04:07,840 --> 00:04:12,080 Speaker 1: But if you're able to decrease hospital surging by wearing 71 00:04:12,160 --> 00:04:16,960 Speaker 1: mouks and um, you know, using prudent behavior, that will 72 00:04:17,040 --> 00:04:19,640 Speaker 1: kind of protect the health care systems so that people 73 00:04:19,640 --> 00:04:22,800 Speaker 1: who need ventilators or people who are sicker can be 74 00:04:22,920 --> 00:04:26,800 Speaker 1: admitted and there's you know, more reserve in the system. 75 00:04:26,880 --> 00:04:28,800 Speaker 1: So I think we are seeing the second wave. We're 76 00:04:28,800 --> 00:04:32,200 Speaker 1: seeing it really around the world. It's this has happened 77 00:04:32,200 --> 00:04:36,599 Speaker 1: in prior pandemics UM, and I think we have to continue, 78 00:04:37,040 --> 00:04:41,800 Speaker 1: certainly to be prudent. So Ian, I have a feeling 79 00:04:41,800 --> 00:04:43,360 Speaker 1: that you've been getting a lot of phone calls and 80 00:04:43,400 --> 00:04:48,840 Speaker 1: text and emails from friends, family, patients about everything we've 81 00:04:48,839 --> 00:04:50,919 Speaker 1: seen over the last week or so, especially with the 82 00:04:50,920 --> 00:04:55,000 Speaker 1: treatment of the president around various drugs. We count on 83 00:04:55,040 --> 00:04:56,839 Speaker 1: you to separate the signal from the noise. What do 84 00:04:56,880 --> 00:04:58,800 Speaker 1: we need to know about what we've learned, especially in 85 00:04:58,880 --> 00:05:03,000 Speaker 1: terms of therapeutics over the last few days. So not 86 00:05:03,120 --> 00:05:06,680 Speaker 1: everyone gets the same treatment as as the president, but 87 00:05:06,800 --> 00:05:09,800 Speaker 1: I think he had a very aggressive regiment and I 88 00:05:09,839 --> 00:05:13,840 Speaker 1: think actually a very smart regiment. Um it's certainly something 89 00:05:13,839 --> 00:05:17,840 Speaker 1: that it would be great to offer many patients. And 90 00:05:17,920 --> 00:05:20,880 Speaker 1: really all of these medications work in different ways, just 91 00:05:21,000 --> 00:05:24,840 Speaker 1: like we do HIV cocktails are hepathetis sea cocktails, where 92 00:05:24,839 --> 00:05:29,720 Speaker 1: we have several medications that work slightly differently for the viruses. 93 00:05:30,200 --> 00:05:32,920 Speaker 1: In combination, they can be highly effective. And I think, 94 00:05:33,120 --> 00:05:36,080 Speaker 1: you know, the President is a good example. You know, 95 00:05:36,120 --> 00:05:42,080 Speaker 1: he was in a high risk group being male, older, overweight, 96 00:05:42,320 --> 00:05:44,839 Speaker 1: and there are other risk factors that other people have 97 00:05:44,920 --> 00:05:49,400 Speaker 1: high blood pressure, diabetes, lung disease, UM, kidney disease, you 98 00:05:49,440 --> 00:05:51,800 Speaker 1: know that we all know about. And I think that's 99 00:05:51,839 --> 00:05:54,479 Speaker 1: also why our death rate or or a case fatality 100 00:05:54,560 --> 00:05:56,360 Speaker 1: rate is a little higher here. You know, we say 101 00:05:56,520 --> 00:05:59,040 Speaker 1: like why is an America not doing so well? Part 102 00:05:59,080 --> 00:06:01,800 Speaker 1: of it is our full you know, our population tends 103 00:06:01,839 --> 00:06:05,280 Speaker 1: to be overweight, more sedentary, couch potatoes. I don't want 104 00:06:05,279 --> 00:06:08,159 Speaker 1: to generalize, but we we definitely have You're not wrong, 105 00:06:08,279 --> 00:06:10,000 Speaker 1: and I think that's a fair I think that's a 106 00:06:10,000 --> 00:06:12,240 Speaker 1: fair assessment. We talk about data. I think the data 107 00:06:12,240 --> 00:06:16,760 Speaker 1: would bear you right. Exactly. We have almost a thirty 108 00:06:16,880 --> 00:06:20,800 Speaker 1: percent obesity rate, and quite frankly, that's a high risk 109 00:06:21,000 --> 00:06:24,160 Speaker 1: with COVID patients, and I think that is why we 110 00:06:24,320 --> 00:06:27,240 Speaker 1: have a higher case fatality rate. I don't think it 111 00:06:27,279 --> 00:06:30,280 Speaker 1: has anything to do with management, which is kind of standardized. 112 00:06:30,520 --> 00:06:33,640 Speaker 1: But in any case, he got this cocktail of monoclonal 113 00:06:33,720 --> 00:06:38,159 Speaker 1: Lanta Buddies by Regeneron and Lily is working on this regeneration, 114 00:06:38,200 --> 00:06:42,080 Speaker 1: by the way, is headed by a colleague classmate, George 115 00:06:42,080 --> 00:06:46,240 Speaker 1: Ancopolis from Columbia Medical School. I'm glad he made the 116 00:06:46,279 --> 00:06:49,279 Speaker 1: distinction that, you know, we can't all just like copped 117 00:06:49,320 --> 00:06:51,800 Speaker 1: her over to Walter Reed and be like, give me everything, 118 00:06:51,880 --> 00:06:54,159 Speaker 1: give me what you got, um, But how do we 119 00:06:54,240 --> 00:06:58,720 Speaker 1: think about it? So, for one, it does show you 120 00:06:58,760 --> 00:07:01,120 Speaker 1: that in the White House, even though people are tested 121 00:07:01,680 --> 00:07:06,200 Speaker 1: every day, because there's an incubation period, you can be 122 00:07:06,279 --> 00:07:10,400 Speaker 1: infected and not recover virus from the naves of pharynx 123 00:07:10,440 --> 00:07:13,800 Speaker 1: or from saliva. That this is not a hundred percent 124 00:07:13,920 --> 00:07:17,960 Speaker 1: full proof approach testing people every day, whether it's back 125 00:07:18,000 --> 00:07:20,320 Speaker 1: to school, and you know, we do need to accept 126 00:07:20,360 --> 00:07:24,000 Speaker 1: that that it is imperfect, no matter how careful you 127 00:07:24,040 --> 00:07:27,160 Speaker 1: can be. And obviously at the White House they're very 128 00:07:27,200 --> 00:07:31,800 Speaker 1: meticulous and in a close proximity people working together. Really 129 00:07:31,840 --> 00:07:35,760 Speaker 1: all you need is one person, uh where that test 130 00:07:35,800 --> 00:07:40,160 Speaker 1: doesn't catch it until they are more more vi remaic, 131 00:07:40,200 --> 00:07:43,600 Speaker 1: where there's more virus there. So, um, you know, he 132 00:07:43,680 --> 00:07:48,040 Speaker 1: became symptomatic and really got a cocktail and unproven, but 133 00:07:48,120 --> 00:07:52,240 Speaker 1: I think a very intelligent, smart cocktail um with not 134 00:07:52,320 --> 00:07:55,160 Speaker 1: all of those drugs yet FDA approved. You know, one 135 00:07:55,160 --> 00:07:58,800 Speaker 1: was room desiviere, which is approved for as an anti viral, 136 00:07:58,840 --> 00:08:00,960 Speaker 1: and we do give those to pay in the hospital. 137 00:08:01,760 --> 00:08:06,320 Speaker 1: Decks in methasone, which typically is given. You don't want 138 00:08:06,320 --> 00:08:08,440 Speaker 1: to give that too early because it is a mildest 139 00:08:08,560 --> 00:08:11,760 Speaker 1: as a steroid in immuno suppressant um, but we do 140 00:08:11,840 --> 00:08:14,760 Speaker 1: give it to people who are getting more inflammation. So 141 00:08:15,080 --> 00:08:18,160 Speaker 1: I suspect they were a little concerned about him with 142 00:08:18,200 --> 00:08:21,240 Speaker 1: blood oxygen or perhaps his X ray was abnormal. I 143 00:08:21,280 --> 00:08:26,640 Speaker 1: don't know. That, but that definitely decreases inflammation and probably 144 00:08:26,680 --> 00:08:29,760 Speaker 1: one of the key drugs which is yet to be 145 00:08:29,800 --> 00:08:35,120 Speaker 1: approved UH monoclonal antibodies and regeneration is is Um, one 146 00:08:35,160 --> 00:08:37,600 Speaker 1: of a couple of companies working on this and at 147 00:08:37,640 --> 00:08:41,199 Speaker 1: least with the regenera on Um system headed by one 148 00:08:41,200 --> 00:08:44,280 Speaker 1: of our Columbia P and S alumni. Just to put 149 00:08:44,320 --> 00:08:48,679 Speaker 1: in a plug um, these are mice that have a 150 00:08:48,800 --> 00:08:52,640 Speaker 1: humanized immune system, and monoclonal antibodies are developed to the 151 00:08:52,679 --> 00:08:56,480 Speaker 1: spike protein, which is the common protein. So even though 152 00:08:56,520 --> 00:08:59,840 Speaker 1: the virus may mutate, that spike that's typical on the 153 00:09:00,000 --> 00:09:02,360 Speaker 1: out or shell of the of the virus has has 154 00:09:02,480 --> 00:09:06,400 Speaker 1: monoclonal antibodies and that can dramatically reduce, uh, the amount 155 00:09:06,440 --> 00:09:08,880 Speaker 1: of virus in the bloodstream and really help the body's 156 00:09:08,920 --> 00:09:12,200 Speaker 1: own you know, by time for the virus to be 157 00:09:12,280 --> 00:09:16,120 Speaker 1: cleared in in your own UH immune system clear it. 158 00:09:16,520 --> 00:09:19,240 Speaker 1: So that I think has great potential. You know. The 159 00:09:19,280 --> 00:09:22,600 Speaker 1: problem of course is it's given intravenously, so you can't 160 00:09:22,640 --> 00:09:27,360 Speaker 1: just pop up right right and and it is yet 161 00:09:27,400 --> 00:09:30,120 Speaker 1: to be approved, but I think that certainly will be 162 00:09:30,160 --> 00:09:34,160 Speaker 1: a big addition to the armamentarium. And like many other 163 00:09:34,280 --> 00:09:37,640 Speaker 1: viruses and drugs. Having that combination therapy and anti viral 164 00:09:37,720 --> 00:09:41,800 Speaker 1: to reduce viral replication antibodies immediately, so you don't have 165 00:09:41,840 --> 00:09:46,040 Speaker 1: to wait for your body to develop antibuddies. You're given 166 00:09:46,080 --> 00:09:49,840 Speaker 1: these pasid antibodies which soak up the virus and then 167 00:09:50,240 --> 00:09:53,240 Speaker 1: perhaps decks and methodsone and some patients, So you know, 168 00:09:53,320 --> 00:09:56,960 Speaker 1: this makes sense. I think we're making progress on on 169 00:09:56,960 --> 00:10:00,160 Speaker 1: on treatments and we need to do that until al 170 00:10:00,200 --> 00:10:03,600 Speaker 1: of vaccine arrived. Switch again, we'll probably be very helpful. 171 00:10:03,600 --> 00:10:06,120 Speaker 1: It may not work in everyone, but again, as long 172 00:10:06,160 --> 00:10:09,520 Speaker 1: as we have a sixty effectiveness and as long as 173 00:10:09,559 --> 00:10:12,800 Speaker 1: we're able to get to more of a herd immunity um, 174 00:10:12,920 --> 00:10:15,520 Speaker 1: that should make a big difference. But that's not until spring, 175 00:10:15,840 --> 00:10:20,640 Speaker 1: right exactly. And as our reporter are Bob Lingreth, who's 176 00:10:20,679 --> 00:10:22,959 Speaker 1: got the cover story of the magazine and he's done 177 00:10:22,960 --> 00:10:25,240 Speaker 1: a lot of reporting on Giliad and Rambesca. Here he said, 178 00:10:25,679 --> 00:10:29,680 Speaker 1: you know, Generation only has about fifty doses available, and 179 00:10:29,679 --> 00:10:32,400 Speaker 1: he says, we've got fifty cases of the virus popping 180 00:10:32,480 --> 00:10:35,040 Speaker 1: up every day, so the numbers are not in our 181 00:10:35,080 --> 00:10:38,720 Speaker 1: favor for even some of the treatments. Like that. So 182 00:10:38,800 --> 00:10:42,959 Speaker 1: it's it's it's tricky and just got about thirty seconds left. Yep, 183 00:10:43,120 --> 00:10:45,640 Speaker 1: you know we're making progress. I think they're going to 184 00:10:45,760 --> 00:10:49,000 Speaker 1: ramp up. That's why we have to do what we 185 00:10:49,080 --> 00:10:53,000 Speaker 1: can do while further therapeutics and vaccines are being developed, 186 00:10:53,120 --> 00:10:56,280 Speaker 1: which is um acting smart and we know what to do, 187 00:10:56,520 --> 00:11:02,480 Speaker 1: and social distancing absolutely got it. This is Bloomberg Business 188 00:11:02,559 --> 00:11:06,559 Speaker 1: Week with Carol Masser and Jason Kelly on Bloomberg Radio. 189 00:11:06,840 --> 00:11:08,520 Speaker 1: Can I just say, it's a story, it's a map, 190 00:11:08,559 --> 00:11:11,680 Speaker 1: it's a podcast. That's how important this story is. Yeah, 191 00:11:11,720 --> 00:11:15,040 Speaker 1: it is such incredible work that this guy has been 192 00:11:15,040 --> 00:11:17,760 Speaker 1: doing throughout the course of the pandemic. Although even before 193 00:11:17,880 --> 00:11:21,360 Speaker 1: I feel like he shifted effortlessly from telling us everything 194 00:11:21,400 --> 00:11:23,640 Speaker 1: we needed to know about the trade war to everything 195 00:11:23,640 --> 00:11:27,079 Speaker 1: we needed to know about the economic impact of the pandemic, 196 00:11:27,280 --> 00:11:31,440 Speaker 1: especially when it comes to the embodiment of the K 197 00:11:31,559 --> 00:11:33,640 Speaker 1: shaped recovery. In a lot of ways, Carol, you know 198 00:11:33,720 --> 00:11:36,880 Speaker 1: like he is helping us understand this. I think through 199 00:11:36,960 --> 00:11:39,560 Speaker 1: the stories of real people, and this story is no 200 00:11:39,600 --> 00:11:41,480 Speaker 1: exception to talk about Sean Donn and he has senior 201 00:11:41,480 --> 00:11:44,480 Speaker 1: Trade and globalization reporter for Bloomberg he joins us on 202 00:11:44,520 --> 00:11:46,880 Speaker 1: the phone from Maryland. But a lot of this is 203 00:11:46,920 --> 00:11:51,000 Speaker 1: based on some incredible reporting Sean you did in Cleveland. 204 00:11:51,400 --> 00:11:53,840 Speaker 1: Tell us about what you've found, because this is part 205 00:11:53,840 --> 00:11:57,360 Speaker 1: of a series of stories that really capture what's going 206 00:11:57,400 --> 00:12:01,240 Speaker 1: on in America. Thanks Jason, You guys are much too kind. 207 00:12:01,320 --> 00:12:04,040 Speaker 1: I should just let you guys talk about this story. No, no, no, no, 208 00:12:04,400 --> 00:12:06,320 Speaker 1: we're just gonna talk about how great you are. You 209 00:12:06,480 --> 00:12:10,880 Speaker 1: have to tell us about the actual don't go on. Look. 210 00:12:11,000 --> 00:12:13,800 Speaker 1: I think one of the things we've we've been struggling 211 00:12:13,800 --> 00:12:16,160 Speaker 1: to get our head heads around as as people in 212 00:12:16,200 --> 00:12:20,040 Speaker 1: America right now, is this idea of different letter shaped recoveries. 213 00:12:20,080 --> 00:12:21,920 Speaker 1: What does a V shape recovery mean? What does a 214 00:12:22,000 --> 00:12:25,679 Speaker 1: K shaped recovery mean? We're gonna see a W Nike swish? 215 00:12:25,760 --> 00:12:28,360 Speaker 1: What what are we gonna say? And what's clear is 216 00:12:28,400 --> 00:12:33,040 Speaker 1: that we're seeing a recovery that is being felt differently 217 00:12:33,679 --> 00:12:36,800 Speaker 1: depending on where you are on the income ladder, where 218 00:12:36,840 --> 00:12:39,360 Speaker 1: you are in terms of home ownership, where you are 219 00:12:39,400 --> 00:12:42,960 Speaker 1: in terms of your ability to work from home, whether 220 00:12:43,000 --> 00:12:46,080 Speaker 1: you're part of what I've started calling bread baking America. 221 00:12:46,360 --> 00:12:48,360 Speaker 1: You know, that's those of us who are who are 222 00:12:48,400 --> 00:12:50,920 Speaker 1: out there learning how to how to bake sour dough 223 00:12:51,080 --> 00:12:53,600 Speaker 1: and and and have really bought a lot of flour 224 00:12:53,640 --> 00:12:56,599 Speaker 1: in the last six months. Or Breadline America, which is 225 00:12:56,640 --> 00:12:58,960 Speaker 1: a much sadder place, which is those people who are 226 00:12:58,960 --> 00:13:02,800 Speaker 1: out there who are struggling with housing, security and food 227 00:13:02,840 --> 00:13:06,080 Speaker 1: and security UH as a result of having lost their 228 00:13:06,120 --> 00:13:08,440 Speaker 1: their jobs as a result of the pandemic and the 229 00:13:08,480 --> 00:13:12,120 Speaker 1: economic collapse we've we've seen since then. One of the 230 00:13:12,160 --> 00:13:14,640 Speaker 1: things I set out to look at was the question 231 00:13:14,679 --> 00:13:17,640 Speaker 1: of housing, and I really was was my head was 232 00:13:17,720 --> 00:13:20,840 Speaker 1: spinning on this one, and finally I settled on the 233 00:13:20,880 --> 00:13:24,760 Speaker 1: idea of telling it through the story of two different houses. UM. 234 00:13:24,840 --> 00:13:28,000 Speaker 1: And that really gets at the divide in the recovery 235 00:13:28,120 --> 00:13:31,480 Speaker 1: right now. If you are a homeowner in a suburb 236 00:13:31,559 --> 00:13:36,320 Speaker 1: of Cleveland called Lakewood, you are seeing demand for property 237 00:13:36,400 --> 00:13:39,319 Speaker 1: sore right now. You're seeing property prices sore right now. 238 00:13:39,800 --> 00:13:43,640 Speaker 1: And we talked to a newlywed couple who bought a 239 00:13:43,679 --> 00:13:47,240 Speaker 1: house on del View Drive there UH and they ended 240 00:13:47,280 --> 00:13:49,760 Speaker 1: up being one of half a dozen people who bid 241 00:13:49,880 --> 00:13:53,199 Speaker 1: for this this this house within hours of it going online. 242 00:13:53,240 --> 00:13:55,520 Speaker 1: And they have a very happy story to tell. Their 243 00:13:55,559 --> 00:13:58,760 Speaker 1: two tech workers and they are are doing well in 244 00:13:58,840 --> 00:14:01,240 Speaker 1: life right now there from home, and they've got this 245 00:14:01,320 --> 00:14:03,679 Speaker 1: brand new home that they're that they're moving into on 246 00:14:03,720 --> 00:14:06,600 Speaker 1: the other side of town. We went to the Mount 247 00:14:06,600 --> 00:14:09,880 Speaker 1: Pleasant neighborhood and then I've been spending some time listening 248 00:14:09,920 --> 00:14:14,240 Speaker 1: to proceedings in the Cleveland Housing Court, and in doing that, 249 00:14:14,679 --> 00:14:17,720 Speaker 1: I ran across an eviction case involving a woman called 250 00:14:17,800 --> 00:14:21,920 Speaker 1: Calais Gavings. Calais Gavings is a nursing assistant. She has 251 00:14:22,000 --> 00:14:26,160 Speaker 1: rented a house UH in the Mount Pleasant neighborhood UH 252 00:14:26,400 --> 00:14:28,840 Speaker 1: for nine hundred dollars a month, and at one point 253 00:14:28,840 --> 00:14:32,280 Speaker 1: earlier this year she was laid off, she fell behind 254 00:14:32,280 --> 00:14:35,520 Speaker 1: on the rent. When she was back at work, she 255 00:14:35,880 --> 00:14:38,400 Speaker 1: just wasn't making as much as she used to, and 256 00:14:38,480 --> 00:14:41,000 Speaker 1: so she ended up in the Cleveland Housing Court, caught 257 00:14:41,080 --> 00:14:44,800 Speaker 1: up in an eviction crisis. And so these are today 258 00:14:44,880 --> 00:14:48,080 Speaker 1: these two very different stories that you see at play 259 00:14:48,080 --> 00:14:50,200 Speaker 1: in the housing market. But then you start scratching the 260 00:14:50,240 --> 00:14:54,320 Speaker 1: surface of these things and you discovered that actually you 261 00:14:54,360 --> 00:14:57,600 Speaker 1: can find really the legacy of the last crisis way 262 00:14:57,640 --> 00:14:59,720 Speaker 1: back in two thousand and eight in the subprime and 263 00:14:59,760 --> 00:15:03,680 Speaker 1: four closure crisis in the property records that involved Kellai 264 00:15:03,760 --> 00:15:07,600 Speaker 1: Gaving's house, and and and also just in the diverging 265 00:15:07,720 --> 00:15:12,000 Speaker 1: paths that you've had between homes and predominantly black neighborhoods 266 00:15:12,000 --> 00:15:14,440 Speaker 1: in Cleveland and their values and those that you've seen 267 00:15:14,840 --> 00:15:17,960 Speaker 1: and predominantly white neighborhoods. So it's a recovery that's unequal, 268 00:15:18,040 --> 00:15:20,280 Speaker 1: but it's also building on the legacy of the last crisis. 269 00:15:20,400 --> 00:15:22,840 Speaker 1: That's what's so troubling, Sean. And that's what you know, 270 00:15:22,920 --> 00:15:25,360 Speaker 1: Like you say, whether it was the east side of 271 00:15:25,400 --> 00:15:27,200 Speaker 1: town or the west side of town, west side being 272 00:15:27,240 --> 00:15:29,880 Speaker 1: wealthy or east side not so much, where it's mostly 273 00:15:29,960 --> 00:15:33,080 Speaker 1: black neighborhoods and a high percentage of rental properties, it 274 00:15:33,160 --> 00:15:37,080 Speaker 1: was already a struggling area, right, and then you layer 275 00:15:37,160 --> 00:15:41,160 Speaker 1: on the pandemic and it's just gotten you know, worse again. 276 00:15:41,200 --> 00:15:43,360 Speaker 1: But it's also, as you said, it was already an 277 00:15:43,440 --> 00:15:46,800 Speaker 1: area that wasn't able to completely catch its breath from 278 00:15:46,840 --> 00:15:49,040 Speaker 1: the last crisis. And you know, when we talk about 279 00:15:49,080 --> 00:15:51,200 Speaker 1: wealth creation, I think this is a part of it. 280 00:15:51,280 --> 00:15:54,600 Speaker 1: Like there these groups of people are never given a 281 00:15:54,720 --> 00:15:58,720 Speaker 1: chance to create any kind of real wealth in their lives. 282 00:15:58,880 --> 00:16:00,520 Speaker 1: It is such a huge part of it. And it 283 00:16:00,600 --> 00:16:03,360 Speaker 1: has to do with with with neighborhood stability in a 284 00:16:03,400 --> 00:16:05,720 Speaker 1: lot of ways. I mean the house that kill a 285 00:16:05,800 --> 00:16:09,640 Speaker 1: gatherings and she moved out at the end of September 286 00:16:09,800 --> 00:16:13,080 Speaker 1: out of this house, but it's owned by UH, an 287 00:16:13,120 --> 00:16:17,920 Speaker 1: anonymous LLCO limited liability company that's actually, if you kind 288 00:16:17,960 --> 00:16:22,120 Speaker 1: of track it down, is a shell company owned by 289 00:16:22,560 --> 00:16:27,080 Speaker 1: French investors who have flocked into Cleveland UH to buy 290 00:16:27,160 --> 00:16:30,200 Speaker 1: up these cheap properties because they're searching for yield. UH. 291 00:16:30,480 --> 00:16:33,760 Speaker 1: You know, the investor who owns that property bought it 292 00:16:33,800 --> 00:16:37,320 Speaker 1: for sixty five thousand dollars a year ago. UH. They're 293 00:16:37,320 --> 00:16:40,520 Speaker 1: collecting nine dollars a month in rent on it. That's 294 00:16:40,560 --> 00:16:44,960 Speaker 1: a gross annual turn of like sixt It almost doesn't 295 00:16:44,960 --> 00:16:48,720 Speaker 1: matter if the value of the house goes up, right. 296 00:16:48,840 --> 00:16:52,040 Speaker 1: This is a cash flow play for those investors. And 297 00:16:52,920 --> 00:16:55,720 Speaker 1: on the flip side, you have these these predominantly white 298 00:16:55,760 --> 00:16:58,960 Speaker 1: suburbs where you have had pretty steady capital appreciation. This 299 00:16:59,040 --> 00:17:01,760 Speaker 1: house that we looked at in the neighborhood of Lakewood 300 00:17:01,760 --> 00:17:04,560 Speaker 1: had actually more than doubled in value in the last 301 00:17:04,640 --> 00:17:08,840 Speaker 1: eight or nine years. And Seawan, I feel like, you know, 302 00:17:09,320 --> 00:17:12,720 Speaker 1: one of the most powerful pieces of your reporting is 303 00:17:12,800 --> 00:17:17,240 Speaker 1: that you're telling stories that I am sure people in 304 00:17:17,720 --> 00:17:21,159 Speaker 1: a number of different cities, countless cities across America, and 305 00:17:21,200 --> 00:17:23,280 Speaker 1: you've been to some of them. You certainly talked to people, 306 00:17:23,280 --> 00:17:26,120 Speaker 1: and a lot of them this could be their city too. 307 00:17:26,240 --> 00:17:29,679 Speaker 1: This is not a Cleveland problem. Oh look, absolutely, it's 308 00:17:29,680 --> 00:17:32,120 Speaker 1: a problem right here in Washington, d C. I'm out 309 00:17:32,160 --> 00:17:35,679 Speaker 1: in the predominantly white suburbs of Washington, d C. I 310 00:17:35,760 --> 00:17:39,399 Speaker 1: have a very different economic reality from people who are 311 00:17:39,560 --> 00:17:43,159 Speaker 1: in predominantly black neighborhoods on the other side of the 312 00:17:43,200 --> 00:17:48,000 Speaker 1: Anacostia River. It's the same thing in New York. It's 313 00:17:48,080 --> 00:17:51,280 Speaker 1: the same thing in you know out west there is 314 00:17:51,359 --> 00:17:54,840 Speaker 1: There isn't almost no American city where this kind of 315 00:17:54,920 --> 00:17:59,000 Speaker 1: unequal recovery isn't the story? Yeah, well, can I just 316 00:17:59,200 --> 00:18:00,960 Speaker 1: I know we've got were at that time. We could 317 00:18:00,960 --> 00:18:02,840 Speaker 1: talk a lot more about this, and I highly recommend 318 00:18:02,880 --> 00:18:05,080 Speaker 1: I'll put it out on Twitter. I may have already, 319 00:18:05,119 --> 00:18:06,440 Speaker 1: but I'll do it again because I think it's a 320 00:18:06,480 --> 00:18:10,160 Speaker 1: must read. But is there any hope that things change? 321 00:18:10,200 --> 00:18:13,560 Speaker 1: Sean and your reporting? We just have about seconds here. Yeah. No, 322 00:18:13,680 --> 00:18:15,520 Speaker 1: I think one of the really interesting things is what 323 00:18:15,560 --> 00:18:17,840 Speaker 1: do you do about something like this. And part of 324 00:18:17,840 --> 00:18:21,639 Speaker 1: the problem here is that the policy response that's aimed 325 00:18:21,680 --> 00:18:25,600 Speaker 1: at the broader economy is actually helping accelerate this, right. 326 00:18:25,640 --> 00:18:28,679 Speaker 1: I mean, we know that the said pouring money into 327 00:18:28,800 --> 00:18:31,720 Speaker 1: into into markets, lowering interest rates, that lowers mortgage rates, 328 00:18:31,760 --> 00:18:34,680 Speaker 1: that helps homeowners and so on. There needs to be 329 00:18:34,800 --> 00:18:38,479 Speaker 1: some kind of targeting for someone like to Lake Athings 330 00:18:38,520 --> 00:18:40,560 Speaker 1: that say that nine hundred dollars a month needs to 331 00:18:40,600 --> 00:18:45,399 Speaker 1: go into building equity rather than simply going to rent. 332 00:18:45,480 --> 00:18:49,560 Speaker 1: That is helping an offshore investor who has no interest 333 00:18:49,600 --> 00:18:54,040 Speaker 1: in that neighborhood beyond cash flow and return. Yeah, yeah, 334 00:18:54,080 --> 00:18:56,399 Speaker 1: it's really I mean, the whole the wealth gap is 335 00:18:56,480 --> 00:18:58,840 Speaker 1: so apparent. Uh, and that's a big part of it. 336 00:18:58,840 --> 00:19:02,119 Speaker 1: All right, Sean Donn And thank you really really appreciate it. Uh, 337 00:19:02,200 --> 00:19:05,680 Speaker 1: you're reporting it is must read as always, So check 338 00:19:05,720 --> 00:19:08,120 Speaker 1: out this story. It's in the new double issue Bloomberg 339 00:19:08,119 --> 00:19:10,720 Speaker 1: Business Week magazine, available in newsstands and Bloomberg dot com. 340 00:19:10,800 --> 00:19:15,960 Speaker 1: It's also featured in the Stephonomics podcast with our colleague 341 00:19:16,040 --> 00:19:18,680 Speaker 1: Stephanie Flanders, So get that where you get your podcast. 342 00:19:18,920 --> 00:19:22,520 Speaker 1: This is Bloomberg Business Week with Carol Masser and Jason 343 00:19:22,600 --> 00:19:25,920 Speaker 1: Kelly on Bloomberg Radio. All right, so we talked about 344 00:19:25,920 --> 00:19:27,960 Speaker 1: her yesterday. She is the subject of a story in 345 00:19:27,960 --> 00:19:31,360 Speaker 1: the magazine which reveals how she is the economist that's 346 00:19:31,400 --> 00:19:35,080 Speaker 1: become a pandemic sensation by helping stressed out parents. Uh, 347 00:19:35,119 --> 00:19:37,440 Speaker 1: and we've talked with her before in Today's Business Week 348 00:19:37,440 --> 00:19:40,520 Speaker 1: Economics were so delighted excited to have back with us 349 00:19:40,680 --> 00:19:43,679 Speaker 1: Emily Austar, author and professor of economics at the Watson 350 00:19:43,680 --> 00:19:47,040 Speaker 1: Institute for International and Public Affairs at Brown University. She's 351 00:19:47,040 --> 00:19:49,600 Speaker 1: a Bloomberg opinion columnist, and she joins us on the 352 00:19:49,640 --> 00:19:53,439 Speaker 1: phone from Providence, Rhode Island. Um, Emily, we really are delighted. 353 00:19:53,440 --> 00:19:55,919 Speaker 1: We had a great conversation with Esme Dupress, who wrote 354 00:19:56,240 --> 00:19:59,320 Speaker 1: this great story about you that really gets into your 355 00:19:59,320 --> 00:20:02,080 Speaker 1: background and and also the impact you're having on our 356 00:20:02,119 --> 00:20:04,560 Speaker 1: world today, especially parents, which we'll talk about in a moment. 357 00:20:05,040 --> 00:20:08,800 Speaker 1: But talk to us about kind of how you grew 358 00:20:08,920 --> 00:20:11,200 Speaker 1: up and how you got to where you are today, 359 00:20:11,200 --> 00:20:14,160 Speaker 1: because I feel like economics is in your bones, your blood, 360 00:20:14,200 --> 00:20:17,480 Speaker 1: your genetic makeup. No, it's it's totally right. So thank 361 00:20:17,520 --> 00:20:19,600 Speaker 1: you guys for having me. And I'm like incredibly grateful 362 00:20:19,640 --> 00:20:22,320 Speaker 1: to me for having written this article. It's sort of 363 00:20:22,320 --> 00:20:24,959 Speaker 1: always a little bit nerve wracking to read about yourself 364 00:20:25,000 --> 00:20:28,080 Speaker 1: and but um, I think she just I was so 365 00:20:28,160 --> 00:20:30,639 Speaker 1: happy with it. Um. So yeah, I grew up my 366 00:20:30,680 --> 00:20:32,680 Speaker 1: both of my parents are economists. That's probably the most 367 00:20:32,760 --> 00:20:35,359 Speaker 1: valient piece of information. So I have two economist parents. 368 00:20:35,359 --> 00:20:38,040 Speaker 1: I'm actually married to an economist. So I'm sort of 369 00:20:38,080 --> 00:20:41,320 Speaker 1: like steeped from from birth more or less in the 370 00:20:41,440 --> 00:20:45,000 Speaker 1: idea of using economics for decision making in your household. 371 00:20:47,240 --> 00:20:52,600 Speaker 1: And so how does that help you maybe think of 372 00:20:52,640 --> 00:20:56,360 Speaker 1: it differently? You know, in this sense that if you're 373 00:20:56,400 --> 00:20:58,359 Speaker 1: kind of living and breathing it all the time, if 374 00:20:58,400 --> 00:21:02,199 Speaker 1: it really has sort of baked into your very uh being, 375 00:21:02,400 --> 00:21:06,479 Speaker 1: do you think that maybe you naturally think a different 376 00:21:06,520 --> 00:21:10,159 Speaker 1: way than other people do? Make sure it's I'm not 377 00:21:10,400 --> 00:21:12,280 Speaker 1: totally sure, but I do think that there's a sense 378 00:21:12,280 --> 00:21:13,760 Speaker 1: in which you sort of grow up with a way 379 00:21:13,760 --> 00:21:17,199 Speaker 1: of approaching problem and um, you know, and with like 380 00:21:17,320 --> 00:21:19,040 Speaker 1: some as they tell the story, like my my mom 381 00:21:19,080 --> 00:21:22,000 Speaker 1: didn't want to grocery shop when I was a kid. 382 00:21:22,000 --> 00:21:23,560 Speaker 1: I said, you know, why don't we go grocery shopping? 383 00:21:23,600 --> 00:21:26,120 Speaker 1: Like everyone else, why are you ordering groceres? Did you say, well, 384 00:21:26,160 --> 00:21:28,200 Speaker 1: I have a very high opportunity cost of my time 385 00:21:28,880 --> 00:21:30,639 Speaker 1: and you know, And I feel like for me, I 386 00:21:30,680 --> 00:21:32,520 Speaker 1: was like, oh, we were like okay, that makes sense, 387 00:21:32,560 --> 00:21:34,080 Speaker 1: you know what I think. But I think for like, 388 00:21:34,280 --> 00:21:35,919 Speaker 1: for a lot of people, that would be like what 389 00:21:36,200 --> 00:21:40,399 Speaker 1: what are you talking? Yeah, how do you like that 390 00:21:40,520 --> 00:21:42,600 Speaker 1: pick up like you know, pack and gum if you're 391 00:21:42,600 --> 00:21:45,879 Speaker 1: not at the grocery store having those opportunities? I mean no, 392 00:21:46,200 --> 00:21:48,480 Speaker 1: I mean I think as a kid, it's like, where, 393 00:21:48,520 --> 00:21:50,840 Speaker 1: like how was I going to get to negotiate over 394 00:21:50,840 --> 00:21:53,600 Speaker 1: the sugary peanut butter if we were just ordering on online? 395 00:21:55,200 --> 00:21:58,480 Speaker 1: But but but at the same time, like that, that 396 00:21:58,600 --> 00:22:02,199 Speaker 1: notion of learning opportunity any costs though and thinking about 397 00:22:02,240 --> 00:22:06,760 Speaker 1: it is a really um kind of intriguing one because 398 00:22:06,840 --> 00:22:10,360 Speaker 1: if you it's funny you say that because I've actually, um, 399 00:22:10,520 --> 00:22:13,280 Speaker 1: I guess, sort of showing my predilections as well. I've 400 00:22:13,320 --> 00:22:16,600 Speaker 1: actually had that conversation with I've talked about opportunity costs 401 00:22:16,680 --> 00:22:20,200 Speaker 1: with with my teenagers, and I do think that once 402 00:22:20,280 --> 00:22:23,080 Speaker 1: you start to look at that at the world through 403 00:22:23,240 --> 00:22:26,560 Speaker 1: a lens like that, it's hard to then not look 404 00:22:26,600 --> 00:22:29,160 Speaker 1: at it that way, right, Yeah, I think it's very 405 00:22:29,160 --> 00:22:31,199 Speaker 1: hard to turn it off. And I think sometimes, you know, 406 00:22:31,240 --> 00:22:33,560 Speaker 1: it's good that I'm married to another economist, because I'm 407 00:22:33,600 --> 00:22:35,080 Speaker 1: not sure that I could turn it off. And I 408 00:22:35,119 --> 00:22:37,959 Speaker 1: think that for some people of this approach feels like 409 00:22:38,080 --> 00:22:41,719 Speaker 1: really sort of foreign and odd, and it's like, what, like, 410 00:22:41,760 --> 00:22:43,720 Speaker 1: what kind of argument is that for? You know, well, 411 00:22:43,760 --> 00:22:45,480 Speaker 1: I don't you know, I don't feel like I should 412 00:22:45,520 --> 00:22:48,080 Speaker 1: have to load the dishwasher because it's not my comparative advantage. 413 00:22:48,440 --> 00:22:51,000 Speaker 1: That isn't an argument that like works as well if 414 00:22:51,000 --> 00:22:56,520 Speaker 1: your spouse is not also also an economist. Oh my god, 415 00:22:56,560 --> 00:22:58,560 Speaker 1: I studied economics, and I don't know if I if 416 00:22:58,560 --> 00:23:00,080 Speaker 1: I tried that at home, I think my health and 417 00:23:00,160 --> 00:23:02,120 Speaker 1: we would just be like, yeah, you were done. We're done. 418 00:23:02,400 --> 00:23:06,600 Speaker 1: I've heard enough stories about your family. It's not gonna 419 00:23:06,640 --> 00:23:09,560 Speaker 1: work now. But it is interesting, especially, you know, you know, 420 00:23:09,680 --> 00:23:11,640 Speaker 1: fast forward a little bit, like we are in such 421 00:23:11,680 --> 00:23:15,360 Speaker 1: a data centric world, right, and I think about there 422 00:23:15,480 --> 00:23:17,720 Speaker 1: is so much data out there for us to assess 423 00:23:17,760 --> 00:23:23,240 Speaker 1: situations and kind of a rational economic analytic way, and 424 00:23:23,240 --> 00:23:25,880 Speaker 1: and that might help us get through some of the noise. 425 00:23:25,960 --> 00:23:28,280 Speaker 1: That's really hard to get through, especially if you think 426 00:23:28,320 --> 00:23:31,800 Speaker 1: about something like a pandemic. Yeah, I mean I think 427 00:23:31,920 --> 00:23:34,479 Speaker 1: you know, as as I have been trying to do 428 00:23:34,520 --> 00:23:36,920 Speaker 1: more sort of talking to to a lay audience, both 429 00:23:36,960 --> 00:23:39,280 Speaker 1: of the pandemic and even and even before I sort 430 00:23:39,320 --> 00:23:42,120 Speaker 1: of I really rely on data, and for me, sort 431 00:23:42,119 --> 00:23:45,639 Speaker 1: of seeing things in the data is really useful in 432 00:23:45,720 --> 00:23:47,720 Speaker 1: it to be like a key input tough decision making. 433 00:23:47,800 --> 00:23:49,480 Speaker 1: Although one of the things I've realized is that it 434 00:23:49,520 --> 00:23:52,480 Speaker 1: can be very hard for people, say who do not 435 00:23:52,560 --> 00:23:56,399 Speaker 1: depend their whole childhood discussing data, uh, to kind of 436 00:23:56,840 --> 00:24:00,120 Speaker 1: like put data in there in a way that it's 437 00:24:00,160 --> 00:24:02,800 Speaker 1: helpful for making decisions as opposed to just being sort 438 00:24:02,800 --> 00:24:05,520 Speaker 1: of numbers that come out at you. And I think 439 00:24:05,560 --> 00:24:08,119 Speaker 1: that some of the challenge is really like how do 440 00:24:08,200 --> 00:24:11,600 Speaker 1: we put these numbers into context where people can actually 441 00:24:11,680 --> 00:24:14,800 Speaker 1: use them, use them as opposed to just see them 442 00:24:15,280 --> 00:24:18,040 Speaker 1: and kind of not quite understand how to fit them 443 00:24:18,040 --> 00:24:20,000 Speaker 1: in their decision making. I think that's that's kind of 444 00:24:20,000 --> 00:24:23,199 Speaker 1: the tea of economics. So talk to us about some 445 00:24:23,280 --> 00:24:26,040 Speaker 1: of the examples, because because you've really connected with a 446 00:24:26,200 --> 00:24:30,200 Speaker 1: very very broad audience and an audience that normally would 447 00:24:30,200 --> 00:24:35,040 Speaker 1: really shy away from you know, thinking about things through 448 00:24:35,080 --> 00:24:37,719 Speaker 1: this framework. So give us an exist, give us one 449 00:24:37,720 --> 00:24:41,879 Speaker 1: of the good examples where people who aren't as schooled 450 00:24:41,880 --> 00:24:45,680 Speaker 1: in this go oh okay, I got it. I mean 451 00:24:45,760 --> 00:24:48,719 Speaker 1: I think that the you know, the biggest, the biggest 452 00:24:48,720 --> 00:24:50,480 Speaker 1: thing that people sort of is at a hob moment 453 00:24:50,560 --> 00:24:52,560 Speaker 1: for me when I try to explain risk to people 454 00:24:52,640 --> 00:24:55,159 Speaker 1: is I talk about cars. So people are sort of 455 00:24:55,160 --> 00:24:56,720 Speaker 1: have a lot of like, I don't want to take 456 00:24:56,760 --> 00:24:58,320 Speaker 1: that risk. I don't want to take that riskus times 457 00:24:58,320 --> 00:24:59,800 Speaker 1: I'll be like, well do you ever get in a car? 458 00:25:00,359 --> 00:25:03,119 Speaker 1: And then they're like, oh, I see, you know what. 459 00:25:03,280 --> 00:25:06,600 Speaker 1: I think that angle of sort of saying like, oh, actually, 460 00:25:06,720 --> 00:25:08,760 Speaker 1: I am taking some of these risks. It must be 461 00:25:09,000 --> 00:25:11,720 Speaker 1: that I am willing to adopt some risk. Let me 462 00:25:11,760 --> 00:25:14,720 Speaker 1: now flame other things in that. I think that is 463 00:25:14,760 --> 00:25:19,240 Speaker 1: often a moment of like, huh, I see what you're saying. So, Emily, 464 00:25:19,840 --> 00:25:22,640 Speaker 1: let's get down to the brass tacks of parenting during 465 00:25:22,680 --> 00:25:27,160 Speaker 1: this pandemic. It is brutal for a lot of people. 466 00:25:27,200 --> 00:25:29,080 Speaker 1: I mean, I have teenagers and a two and a 467 00:25:29,119 --> 00:25:31,879 Speaker 1: half year old, and I feel like I'm actually in 468 00:25:31,880 --> 00:25:34,399 Speaker 1: pretty good shape because the older ones are self sufficient, 469 00:25:34,480 --> 00:25:36,000 Speaker 1: and the two and a half year old is just 470 00:25:36,040 --> 00:25:38,480 Speaker 1: adorable and you know, kind of living your best life 471 00:25:38,480 --> 00:25:42,399 Speaker 1: amid all of this. But I do feel like for people, 472 00:25:42,640 --> 00:25:47,000 Speaker 1: especially with school age kids, sort of elementary school kids 473 00:25:47,160 --> 00:25:52,200 Speaker 1: who are also trying to work, this is really really difficult. Yeah, 474 00:25:52,280 --> 00:25:54,199 Speaker 1: I mean I think the spring was really hard and 475 00:25:54,200 --> 00:25:56,919 Speaker 1: then sort of going into the fall. Um. Yeah, I 476 00:25:56,960 --> 00:25:58,520 Speaker 1: was talking to somebody the other day was saying that 477 00:25:58,560 --> 00:26:00,560 Speaker 1: her six year old is expected to be on zoom 478 00:26:00,560 --> 00:26:02,840 Speaker 1: from eight thirty three thirty with a thirty minute break. 479 00:26:03,119 --> 00:26:06,600 Speaker 1: I'm doing, you know, really tough. That's crazy. It's that 480 00:26:06,880 --> 00:26:10,560 Speaker 1: hard for the kids, really hard for the parents. So 481 00:26:10,560 --> 00:26:12,919 Speaker 1: so how do you do? Yeah, because I mean this 482 00:26:13,040 --> 00:26:14,919 Speaker 1: is like parents have been looking to you to like, 483 00:26:14,960 --> 00:26:19,880 Speaker 1: for for kind of guidance on this whole school thing. Yeah, 484 00:26:19,920 --> 00:26:21,399 Speaker 1: and I think that, you know, it's sort of at 485 00:26:21,440 --> 00:26:23,199 Speaker 1: some point I realized that it was very hard to 486 00:26:23,200 --> 00:26:25,280 Speaker 1: get that guy into that data and we didn't have 487 00:26:25,520 --> 00:26:27,520 Speaker 1: a good data and so, you know, one of the 488 00:26:27,560 --> 00:26:30,679 Speaker 1: big pandemic projects I've been trying to do is to 489 00:26:30,720 --> 00:26:33,479 Speaker 1: try to actually collect some data on um on tools. 490 00:26:33,520 --> 00:26:34,960 Speaker 1: And as they talked a little bit about that in 491 00:26:35,000 --> 00:26:37,240 Speaker 1: the in the piece, but we've you know, got some 492 00:26:37,520 --> 00:26:39,639 Speaker 1: We've now got in some data. We're starting to you know, 493 00:26:39,680 --> 00:26:42,840 Speaker 1: work with schools, try to collect COVID tracking data over 494 00:26:42,840 --> 00:26:44,440 Speaker 1: time to really look at, you know, what are the 495 00:26:44,520 --> 00:26:47,119 Speaker 1: risks and then I think even the next few weeks 496 00:26:47,359 --> 00:26:48,679 Speaker 1: being able to look at you know, what are the 497 00:26:48,800 --> 00:26:51,679 Speaker 1: what are the kind of mitigation practices of schools are 498 00:26:51,720 --> 00:26:54,520 Speaker 1: taking that are helping them keep cases down so we 499 00:26:54,560 --> 00:26:56,880 Speaker 1: can help places reopen, because I think that is really 500 00:26:56,880 --> 00:26:59,440 Speaker 1: got to be our goal is to get especially younger 501 00:26:59,520 --> 00:27:01,960 Speaker 1: kids back in school. Well, what is the data showing 502 00:27:01,960 --> 00:27:04,920 Speaker 1: when it comes to schools? Um? And you know, it's interesting. 503 00:27:04,920 --> 00:27:07,040 Speaker 1: I was We're talking about real estate earlier, and it's 504 00:27:07,040 --> 00:27:09,399 Speaker 1: always location, location, location, And I feel like even with 505 00:27:09,440 --> 00:27:13,040 Speaker 1: a virus to some extent, it's location, location location. UM 506 00:27:13,119 --> 00:27:15,879 Speaker 1: that you know, every school system, every city, every town 507 00:27:16,080 --> 00:27:17,840 Speaker 1: is not going to be every state is not going 508 00:27:17,880 --> 00:27:19,640 Speaker 1: to be the same. But I'm curious what your data 509 00:27:19,720 --> 00:27:22,440 Speaker 1: showing when it comes to all of this. Yeah, so 510 00:27:22,480 --> 00:27:24,159 Speaker 1: I think our data in general is showing you know, 511 00:27:24,200 --> 00:27:26,760 Speaker 1: fairly low rates. UM. So we actually have you know, 512 00:27:26,760 --> 00:27:29,080 Speaker 1: a bunch of geographics coverage and for you know, in 513 00:27:29,119 --> 00:27:32,600 Speaker 1: person school we're seeing over the last two weeks of September. 514 00:27:33,080 --> 00:27:34,840 Speaker 1: You in the average school of a thousand kids, we're 515 00:27:34,840 --> 00:27:37,800 Speaker 1: seeing about one point five cases. Um, so you know, 516 00:27:37,880 --> 00:27:40,920 Speaker 1: not no cases, but but you know, relatively low, um 517 00:27:41,720 --> 00:27:44,439 Speaker 1: low rates and uh and you know, the rates are 518 00:27:44,520 --> 00:27:46,800 Speaker 1: much lower in elementary than high school in the data 519 00:27:46,840 --> 00:27:49,080 Speaker 1: that we're seeing so far. So it certainly seems like 520 00:27:49,240 --> 00:27:51,720 Speaker 1: there's kind of a stronger argument maybe for bringing back 521 00:27:51,800 --> 00:27:55,800 Speaker 1: younger kids and older kids. Yeah. So, Emily, when you 522 00:27:55,840 --> 00:27:58,719 Speaker 1: think about this from a broader perspective, you know, a 523 00:27:58,720 --> 00:28:00,680 Speaker 1: lot of it. We talked about this on the program 524 00:28:00,760 --> 00:28:03,560 Speaker 1: all the time, this notion that kids being back at 525 00:28:03,560 --> 00:28:07,320 Speaker 1: school is a critical part of the broader economic equation 526 00:28:07,440 --> 00:28:10,600 Speaker 1: in terms of really getting the economy back to some 527 00:28:10,680 --> 00:28:14,640 Speaker 1: sense of normalcy for you know, blue collar workers, white 528 00:28:14,680 --> 00:28:17,959 Speaker 1: color words for the whole workforce. How how do you 529 00:28:18,200 --> 00:28:20,600 Speaker 1: attack that problem or how do you get your arms 530 00:28:20,640 --> 00:28:24,240 Speaker 1: around this notion of measuring the either the loss of 531 00:28:24,280 --> 00:28:28,000 Speaker 1: productivity or whatever it is with kids being at home 532 00:28:28,160 --> 00:28:32,159 Speaker 1: as it plays through the workforce and the and the 533 00:28:32,200 --> 00:28:35,520 Speaker 1: broader employment picture. Yeah, so I think there's sort of 534 00:28:35,520 --> 00:28:38,200 Speaker 1: two things to say about that. So probably the most 535 00:28:38,200 --> 00:28:40,240 Speaker 1: directly to see those kind of impacts. Is to look 536 00:28:40,240 --> 00:28:42,440 Speaker 1: at the labor force numbers, which I think came out 537 00:28:42,520 --> 00:28:44,960 Speaker 1: last week, where you see basically huge drops in labor 538 00:28:45,000 --> 00:28:48,600 Speaker 1: force participation. Make labor force participation overall was down about 539 00:28:48,600 --> 00:28:51,520 Speaker 1: a million UM in primary adults, and it's almost all 540 00:28:51,520 --> 00:28:53,520 Speaker 1: of that is moong women. UM. So I think we're 541 00:28:53,560 --> 00:28:56,360 Speaker 1: really seeing a lot of women labor labor force, presumably 542 00:28:56,440 --> 00:29:00,160 Speaker 1: because there are these additional caregiving obligations at home. But 543 00:29:00,200 --> 00:29:02,960 Speaker 1: I also think we're framing this as somehow like it's 544 00:29:02,960 --> 00:29:05,320 Speaker 1: the economy versus health and we need to open schools 545 00:29:05,360 --> 00:29:07,440 Speaker 1: to the economy and like, let you know, the help 546 00:29:07,680 --> 00:29:09,840 Speaker 1: throw caution to the win on health. But I actually 547 00:29:09,920 --> 00:29:14,120 Speaker 1: think it's there's also real significant health issues depression, anxiety, 548 00:29:14,240 --> 00:29:16,840 Speaker 1: mental health, food and security. There are reasons that we 549 00:29:16,880 --> 00:29:19,520 Speaker 1: want kids in school that have nothing to do with 550 00:29:19,640 --> 00:29:22,160 Speaker 1: you know, we've got to get our economy going right. 551 00:29:22,680 --> 00:29:25,960 Speaker 1: I mean there's also you do wonder what happens to 552 00:29:26,080 --> 00:29:30,160 Speaker 1: kids right developmentally UM and education wise for some of 553 00:29:30,200 --> 00:29:34,760 Speaker 1: those critical years if they're not in school with peers no, 554 00:29:34,920 --> 00:29:36,880 Speaker 1: and I think that you know, there's there's learning losses, 555 00:29:36,880 --> 00:29:40,400 Speaker 1: there's socio emotional development losses. Um, there's you rockouts and 556 00:29:40,520 --> 00:29:42,640 Speaker 1: I think we're going to see the impacts on this, 557 00:29:42,800 --> 00:29:46,360 Speaker 1: on this on kids, you know, basically forever. Yeah. And 558 00:29:46,400 --> 00:29:48,040 Speaker 1: I know, like I knew, some of the positions you've 559 00:29:48,040 --> 00:29:51,800 Speaker 1: taken haven't always been well received by lots of people. 560 00:29:51,840 --> 00:29:53,760 Speaker 1: I know, as it gets into you know, what kind 561 00:29:53,760 --> 00:29:56,720 Speaker 1: of pushback have you gotten? So I think on the 562 00:29:56,880 --> 00:29:59,040 Speaker 1: you know, on the school stuff, UM, you know, the 563 00:29:59,480 --> 00:30:01,720 Speaker 1: position we need more data is something I think many 564 00:30:01,720 --> 00:30:04,960 Speaker 1: people agree with. UM. But you know, I also, UM, 565 00:30:05,080 --> 00:30:07,600 Speaker 1: I have taken the position that we should be reopening 566 00:30:07,600 --> 00:30:09,600 Speaker 1: at least more than we than we have been. And 567 00:30:09,600 --> 00:30:12,520 Speaker 1: I think there, you know, there is pushback there. UM. 568 00:30:12,560 --> 00:30:15,720 Speaker 1: And I think, you know, sometimes you know, you have 569 00:30:15,800 --> 00:30:20,040 Speaker 1: to think about like this argument is grounded in in data, um, 570 00:30:20,320 --> 00:30:24,120 Speaker 1: and we can have an argument on the merits. Um. 571 00:30:24,160 --> 00:30:27,360 Speaker 1: I think it's really important. And reason Witherspoon says she 572 00:30:27,480 --> 00:30:29,680 Speaker 1: likes what you're doing. I'm convinced she's going to play 573 00:30:29,720 --> 00:30:31,720 Speaker 1: you in a miniseries on Netflix. Can I just put 574 00:30:31,720 --> 00:30:35,719 Speaker 1: that out there? That would be amazing, UM, But I 575 00:30:35,760 --> 00:30:40,440 Speaker 1: doubt it. But I I've gotten there's been the life 576 00:30:40,520 --> 00:30:43,400 Speaker 1: is a little bit different in some of those dimensions 577 00:30:43,400 --> 00:30:45,560 Speaker 1: than than it was maybe five years ago. It is 578 00:30:45,600 --> 00:30:47,760 Speaker 1: time for the drive for the clothes. Let's check in 579 00:30:47,800 --> 00:30:50,280 Speaker 1: with Brad McMillan. He is the Chief Investment Officer, Managing 580 00:30:50,280 --> 00:30:56,600 Speaker 1: Principle for Commonwealth Financial Network. Johnny's on the phone from Waltham, Massachusetts. Brad, 581 00:30:56,840 --> 00:31:00,440 Speaker 1: how is life in lovely New England. It's a nice 582 00:31:00,440 --> 00:31:03,960 Speaker 1: time of year, right, this is the time to live 583 00:31:04,000 --> 00:31:06,760 Speaker 1: in New England. The sun is out, the trees are changing. 584 00:31:07,440 --> 00:31:10,400 Speaker 1: From a from a weather perspective, life is good, yeah, 585 00:31:10,440 --> 00:31:13,840 Speaker 1: But otherwise things are a little topsy turvy. They remain 586 00:31:14,000 --> 00:31:15,680 Speaker 1: so I feel like, you know, we talked to you 587 00:31:16,040 --> 00:31:19,560 Speaker 1: a month or two ago, and what's different now. I mean, 588 00:31:19,560 --> 00:31:22,440 Speaker 1: we're much closer to an election which seems to be 589 00:31:22,760 --> 00:31:26,160 Speaker 1: very much friend of mine for investors. Um we have 590 00:31:26,240 --> 00:31:28,200 Speaker 1: a back and forth and back and forth and back 591 00:31:28,240 --> 00:31:30,880 Speaker 1: and forth on stimulus, which certainly is top of mind 592 00:31:30,920 --> 00:31:34,760 Speaker 1: as well. What's top of mind for you. I'm watching 593 00:31:34,760 --> 00:31:37,520 Speaker 1: a couple of things. I'm watching the pandemic. I mean, 594 00:31:37,640 --> 00:31:41,320 Speaker 1: we've seen, we saw things get better, get worse, get better, 595 00:31:41,800 --> 00:31:44,240 Speaker 1: and now they're getting worse again and the risks are rising. 596 00:31:44,960 --> 00:31:48,280 Speaker 1: And obviously we've got the risk coming up with the election. 597 00:31:48,320 --> 00:31:53,000 Speaker 1: There's some real concerns there, and we're looking at the 598 00:31:53,040 --> 00:31:57,760 Speaker 1: economic recovery start to slow down. And what's perverse about 599 00:31:57,760 --> 00:32:00,680 Speaker 1: all this is the market is pretty much kept moving up. 600 00:32:01,200 --> 00:32:03,360 Speaker 1: And what I've come to the conclusion is the worst 601 00:32:03,360 --> 00:32:06,280 Speaker 1: things get, the better they are for the market. Because 602 00:32:06,520 --> 00:32:09,840 Speaker 1: investors are now thinking about the stimulus. It's kind of 603 00:32:09,880 --> 00:32:11,960 Speaker 1: like when bad news was good news because it meant 604 00:32:11,960 --> 00:32:14,440 Speaker 1: that that was going to cut rates. I think we 605 00:32:14,480 --> 00:32:18,520 Speaker 1: have the same kind of anticipated policy rally now that 606 00:32:18,600 --> 00:32:22,320 Speaker 1: we saw then. Hm. So what do you do right 607 00:32:22,320 --> 00:32:26,640 Speaker 1: now as an investor? What's your advice to investors? Well, 608 00:32:26,640 --> 00:32:29,160 Speaker 1: it depends. I mean, if you're an older person or 609 00:32:29,160 --> 00:32:30,800 Speaker 1: somebody who's going to need the money in the next 610 00:32:30,800 --> 00:32:33,240 Speaker 1: couple of years, maybe now is the time to start 611 00:32:33,280 --> 00:32:36,959 Speaker 1: thinking a little bit more cautiously. What's what's older nowadays? 612 00:32:37,200 --> 00:32:39,720 Speaker 1: I am curious about that too, because I think, no, seriously, 613 00:32:39,760 --> 00:32:43,080 Speaker 1: I mean, people are working a lot longer, um, some 614 00:32:43,160 --> 00:32:45,160 Speaker 1: because I have to, some because they want to, and 615 00:32:45,280 --> 00:32:47,880 Speaker 1: can um. And I just do wonder what you know, 616 00:32:48,040 --> 00:32:51,920 Speaker 1: the old formulas about asset allocation and age. I do 617 00:32:52,000 --> 00:32:56,480 Speaker 1: wonder what's different nowadays? Well, I'm fifty five, So from 618 00:32:56,520 --> 00:32:58,720 Speaker 1: my perspective, I'm going to be working at least another 619 00:32:58,760 --> 00:33:01,280 Speaker 1: ten years. I don't really consider myself to be an 620 00:33:01,360 --> 00:33:04,440 Speaker 1: older investor, But if I was really looking to retire 621 00:33:04,440 --> 00:33:06,880 Speaker 1: in the next five years or so, that's when I 622 00:33:06,960 --> 00:33:09,479 Speaker 1: might start to think of myself in the quote older 623 00:33:09,560 --> 00:33:13,720 Speaker 1: unquote context as an investor, I'd start paying more attention 624 00:33:13,760 --> 00:33:18,080 Speaker 1: to avoiding losses than maximizing gains. I think that's probably 625 00:33:18,080 --> 00:33:20,320 Speaker 1: the best way to think about it. Are you looking 626 00:33:20,360 --> 00:33:22,800 Speaker 1: to maximize your gains? Are you at the point where 627 00:33:22,840 --> 00:33:25,520 Speaker 1: you're really looking to make sure you don't lose too much? 628 00:33:25,640 --> 00:33:30,000 Speaker 1: That's where you get older from an investment standpoint, Brad, 629 00:33:30,040 --> 00:33:32,080 Speaker 1: what do you think about the election as it plays 630 00:33:32,080 --> 00:33:35,520 Speaker 1: into an investment thesis at this point? Because I feel 631 00:33:35,560 --> 00:33:40,520 Speaker 1: like in past uh times and the before times as 632 00:33:40,520 --> 00:33:42,800 Speaker 1: it were, you know, we're looking at it. Actually, Hey, okay, 633 00:33:42,880 --> 00:33:45,280 Speaker 1: election is gonna happen. We're gonna know who the president is, 634 00:33:45,600 --> 00:33:49,360 Speaker 1: you know, within maybe a few hours, certainly a day 635 00:33:49,520 --> 00:33:51,920 Speaker 1: or in the case of two thousand, a couple of weeks, 636 00:33:52,200 --> 00:33:55,840 Speaker 1: a few weeks. But it feels like there isn't that 637 00:33:55,960 --> 00:33:59,480 Speaker 1: certainty for people right now. Does it change the way 638 00:33:59,520 --> 00:34:01,920 Speaker 1: you invest at all, or do you just kind of 639 00:34:02,200 --> 00:34:05,800 Speaker 1: hang on tight. It shouldn't it shouldn't change it the 640 00:34:05,880 --> 00:34:08,520 Speaker 1: doll And the reason I say that is, you're right, 641 00:34:08,840 --> 00:34:11,080 Speaker 1: we're going to see a lot of volatility. Markets are 642 00:34:11,120 --> 00:34:14,000 Speaker 1: expecting November to be pretty toughed, and you know, I 643 00:34:14,040 --> 00:34:16,239 Speaker 1: agree with that, and it might well extend beyond that. 644 00:34:16,880 --> 00:34:19,920 Speaker 1: But are we going to be thinking about that next 645 00:34:20,000 --> 00:34:24,359 Speaker 1: year at this time? I don't think so. Yeah, exactly. 646 00:34:24,960 --> 00:34:27,400 Speaker 1: So you know, it's it's like, even if it's a 647 00:34:27,480 --> 00:34:30,840 Speaker 1: monumental disaster, and I'm not saying it will be, it 648 00:34:30,920 --> 00:34:36,080 Speaker 1: will pass. You know, as of inauguration day, the existing 649 00:34:36,160 --> 00:34:40,160 Speaker 1: president will leave office and the new president will be inaugurated. 650 00:34:40,520 --> 00:34:42,920 Speaker 1: We don't know who that is. Their procedures in place, 651 00:34:43,000 --> 00:34:46,560 Speaker 1: and we'll get through it. It's gonna be ugly, quite possibly, 652 00:34:46,680 --> 00:34:50,640 Speaker 1: but this tool will pass. Yeah. You know, listen, you know, 653 00:34:50,719 --> 00:34:53,520 Speaker 1: we know good perspective. We will get through these things. 654 00:34:53,520 --> 00:34:55,920 Speaker 1: We've gotten through tough things before. We certainly will get 655 00:34:55,960 --> 00:34:57,480 Speaker 1: on the other side of this. The one thing I 656 00:34:57,520 --> 00:35:00,120 Speaker 1: do wonder that when we do get on the other 657 00:35:00,200 --> 00:35:04,560 Speaker 1: side of it, these systemic problems, these gaps within our society, 658 00:35:04,600 --> 00:35:06,480 Speaker 1: you know, we talk a lot about wealth creation, and 659 00:35:06,920 --> 00:35:08,960 Speaker 1: we had a great story, you know, by our shandon 660 00:35:09,120 --> 00:35:13,560 Speaker 1: and troubling story you know in Cleveland, you know Ohio 661 00:35:13,719 --> 00:35:16,919 Speaker 1: about you know, depending on where you are, you're either 662 00:35:16,960 --> 00:35:19,319 Speaker 1: doing really well and and and or you're not. And 663 00:35:19,360 --> 00:35:22,160 Speaker 1: it tends to be minorities who are not. They can't 664 00:35:22,200 --> 00:35:25,480 Speaker 1: own property, they just don't have the wealth to do it. Uh. 665 00:35:25,480 --> 00:35:27,480 Speaker 1: And it really tells kind of that bigger, broader story 666 00:35:27,480 --> 00:35:30,000 Speaker 1: in the country. I bring this up because, Brad, I mean, 667 00:35:30,160 --> 00:35:36,120 Speaker 1: ultimately the economy, society as a whole benefits when everybody benefits. 668 00:35:36,160 --> 00:35:38,360 Speaker 1: I mean, I think about what you do right, the 669 00:35:38,400 --> 00:35:40,239 Speaker 1: more people who are in the markets or who need 670 00:35:40,280 --> 00:35:43,520 Speaker 1: financial advice, that's good for you guys longer terms. So 671 00:35:43,600 --> 00:35:46,239 Speaker 1: I do wonder how you think about that, how you 672 00:35:46,280 --> 00:35:48,320 Speaker 1: think about how what we need to do to improve 673 00:35:48,320 --> 00:35:50,600 Speaker 1: it so that there are more people that are able 674 00:35:50,600 --> 00:35:52,600 Speaker 1: to actually create wealth in this country and that it's 675 00:35:52,600 --> 00:35:57,160 Speaker 1: not just concentrated in a few hands. Totally agree, Cal 676 00:35:57,360 --> 00:35:59,480 Speaker 1: And if you look at it as a citizen, I 677 00:35:59,480 --> 00:36:02,440 Speaker 1: could make with you more is an economist, I also 678 00:36:02,520 --> 00:36:05,239 Speaker 1: agree with you because the truth of the matter is 679 00:36:05,360 --> 00:36:09,680 Speaker 1: the most economically healthy society is we're purchasing power, the 680 00:36:09,719 --> 00:36:13,440 Speaker 1: ability to spend and invest is spread the most widely, 681 00:36:14,239 --> 00:36:16,799 Speaker 1: so it's in white and self interest to say this 682 00:36:16,840 --> 00:36:19,880 Speaker 1: is a problem we have to solve. And the political 683 00:36:19,960 --> 00:36:24,080 Speaker 1: system you can see, you know, we get these generational shifts, 684 00:36:24,080 --> 00:36:28,000 Speaker 1: and my guests would be this election or the next one, 685 00:36:28,080 --> 00:36:30,040 Speaker 1: we will start to do exactly that. We're going to 686 00:36:30,120 --> 00:36:34,680 Speaker 1: see a focus on younger people, less affluent people. You know, 687 00:36:34,719 --> 00:36:37,359 Speaker 1: I think the tax structure will probably is eventually look 688 00:36:37,440 --> 00:36:40,080 Speaker 1: more like it did in the nineteen fifties. You know, 689 00:36:40,160 --> 00:36:42,080 Speaker 1: the labor laws were looking more like they did in 690 00:36:42,160 --> 00:36:45,920 Speaker 1: the nineteen fifties. We've we've had more equal society. We 691 00:36:45,920 --> 00:36:49,040 Speaker 1: can do it again. That's a really interesting point. That's 692 00:36:49,040 --> 00:36:51,600 Speaker 1: a really interesting point. All right, Brad McMillan, thank you 693 00:36:51,640 --> 00:36:53,520 Speaker 1: so much. Nice to catch up with you, always so thoughtful, 694 00:36:53,600 --> 00:36:58,000 Speaker 1: Chief Investment Officer, Managing Principle at Commonwealth Financial Network. Journeys 695 00:36:58,040 --> 00:37:00,040 Speaker 1: on the phone from Waltham and a programming net for 696 00:37:00,080 --> 00:37:02,279 Speaker 1: all of our listeners. Jason Kelly, my partner and crime 697 00:37:02,320 --> 00:37:05,359 Speaker 1: my co host here at Bloomberg Business Week. He is 698 00:37:05,880 --> 00:37:08,920 Speaker 1: leaving our family, but not really leaving the Bloomberg family. 699 00:37:09,239 --> 00:37:11,560 Speaker 1: That's exactly right, Carol. I am headed over to be 700 00:37:11,600 --> 00:37:14,920 Speaker 1: the chief correspondent for Quick Take. It is our over 701 00:37:14,960 --> 00:37:18,360 Speaker 1: the top network, launching on November nine, so I'll still 702 00:37:18,400 --> 00:37:21,799 Speaker 1: be around and I won't go too far. But it 703 00:37:21,840 --> 00:37:24,120 Speaker 1: has been a massive pleasure working on the show and 704 00:37:24,160 --> 00:37:27,440 Speaker 1: working on this podcast. We're so humbled. I think it's 705 00:37:27,440 --> 00:37:30,160 Speaker 1: fair to say, Carol, by all the listeners support that 706 00:37:30,200 --> 00:37:32,960 Speaker 1: we've gotten the fun conversations we've gotten to have, and 707 00:37:33,040 --> 00:37:36,000 Speaker 1: I know you will continue to do that better and 708 00:37:36,000 --> 00:37:38,560 Speaker 1: better and better going forward. We love our audience and 709 00:37:38,600 --> 00:37:40,440 Speaker 1: they have really shaped who we want to talk to 710 00:37:40,520 --> 00:37:42,239 Speaker 1: and who we bring in, so we thank you so 711 00:37:42,320 --> 00:37:44,560 Speaker 1: much for that. And Jason, I can't even tell you 712 00:37:44,600 --> 00:37:47,160 Speaker 1: how much I will miss you. Um. I think you 713 00:37:47,280 --> 00:37:51,200 Speaker 1: know though, UM. For everybody else our podcast audience, we'll 714 00:37:51,200 --> 00:37:52,239 Speaker 1: see you here next week.