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 --> 00:00:19,959 Speaker 1: twenty seven hundred journalists and analysts and more than a 7 00:00:20,040 --> 00:00:22,959 Speaker 1: hundred and twenty countries and Jason. You can download Bloomberg 8 00:00:22,960 --> 00:00:26,079 Speaker 1: Business Week on iTunes, SoundCloud, al Bloomberg dot com. You 9 00:00:26,079 --> 00:00:28,240 Speaker 1: can also listen to our radio show at two pm 10 00:00:28,320 --> 00:00:31,159 Speaker 1: Eastern on Bloomberg Radio every weekday, or watch us on 11 00:00:31,200 --> 00:00:36,040 Speaker 1: YouTube by searching Bloomberg Global News. A lot of folks, 12 00:00:36,400 --> 00:00:38,880 Speaker 1: us included, are thinking about what it's going to take 13 00:00:38,880 --> 00:00:41,559 Speaker 1: to get back to the office. We're hearing that a 14 00:00:41,600 --> 00:00:44,000 Speaker 1: lot here in the Tri state area. I mentioned a 15 00:00:44,000 --> 00:00:46,280 Speaker 1: few minutes ago that New York City is starting to 16 00:00:46,600 --> 00:00:50,800 Speaker 1: reopen as of next Monday. Neighboring Connecticut is also thinking 17 00:00:50,800 --> 00:00:53,040 Speaker 1: about this a lot. Let's get into that with Dr 18 00:00:53,159 --> 00:00:56,520 Speaker 1: Eric Shott. He is the founder and CEO of Semaphore. 19 00:00:56,640 --> 00:01:00,240 Speaker 1: He joins us on the phone from Connecticut and Dr shot. 20 00:01:00,320 --> 00:01:02,880 Speaker 1: It's really nice to to talk to you. I'm fascinated 21 00:01:02,920 --> 00:01:07,000 Speaker 1: by your background and your experience in both the clinical 22 00:01:07,080 --> 00:01:10,920 Speaker 1: and researches also and also the commercial help us understand 23 00:01:10,959 --> 00:01:14,520 Speaker 1: what back to work testing looks like, especially when you're 24 00:01:14,520 --> 00:01:18,440 Speaker 1: looking at Connecticut. Yeah, well great, too great to be on. 25 00:01:19,080 --> 00:01:22,600 Speaker 1: So what it looks like is we need a more 26 00:01:22,680 --> 00:01:28,120 Speaker 1: holistic solution, not just focused on testing, but really seeking 27 00:01:28,120 --> 00:01:31,680 Speaker 1: to understand the context in which each employee is existing, 28 00:01:32,319 --> 00:01:35,119 Speaker 1: like whether they have symptoms or not, who they're being 29 00:01:35,200 --> 00:01:38,640 Speaker 1: exposed to, and so on, in addition to the testing, 30 00:01:38,840 --> 00:01:42,680 Speaker 1: and we kind of wrap that altogether to provide guidance 31 00:01:42,720 --> 00:01:47,800 Speaker 1: to employers on whether a given employee is uh, you know, 32 00:01:47,960 --> 00:01:50,960 Speaker 1: low enough risk and not testing as positive and should 33 00:01:51,000 --> 00:01:54,600 Speaker 1: be allowed no problem back into work, versus somebody at 34 00:01:54,680 --> 00:01:57,600 Speaker 1: high risk UH and maybe should not be coming in, 35 00:01:57,640 --> 00:02:01,240 Speaker 1: should stay remote. So this is what you guys are doing, 36 00:02:01,320 --> 00:02:03,480 Speaker 1: and I'm reading something from the Stanford Advocate. I mean, 37 00:02:03,520 --> 00:02:07,160 Speaker 1: you guys are increasing the capacity for your new coronavirus 38 00:02:07,160 --> 00:02:09,880 Speaker 1: testing program to at least ten thousand tests daily up 39 00:02:09,880 --> 00:02:13,840 Speaker 1: to twenty and we're talking about the test of whether 40 00:02:13,919 --> 00:02:18,280 Speaker 1: or not you've got the virus. But also antibody testing. Correct. Yeah, 41 00:02:18,360 --> 00:02:21,160 Speaker 1: that's correct. And a lot of that volume driven by 42 00:02:21,400 --> 00:02:24,400 Speaker 1: you know, uh, you know, working with the State of 43 00:02:24,400 --> 00:02:27,880 Speaker 1: Connecticut Governor Lamont and the progressive strategy they have to 44 00:02:27,960 --> 00:02:30,880 Speaker 1: really uh, you know, get people back to work. So 45 00:02:30,919 --> 00:02:36,280 Speaker 1: proud to be helping those guys along. And so, I mean, 46 00:02:36,320 --> 00:02:38,480 Speaker 1: I guess and I love the fact that you're looking 47 00:02:38,480 --> 00:02:43,160 Speaker 1: at it very holistically, uh, Eric, I mean, just as 48 00:02:43,320 --> 00:02:45,920 Speaker 1: a sort of rank and follow employees and you know, 49 00:02:46,000 --> 00:02:49,639 Speaker 1: folks who are commuting and things like that, as as 50 00:02:49,720 --> 00:02:55,519 Speaker 1: human beings, and obviously folks who have pre existing conditions 51 00:02:55,560 --> 00:02:58,160 Speaker 1: and are more at risk. I think there's a different, 52 00:02:58,240 --> 00:03:02,000 Speaker 1: different set of circumstances. But what should the everyday person 53 00:03:02,040 --> 00:03:05,160 Speaker 1: who's thinking about their office be Is there sort of 54 00:03:05,160 --> 00:03:08,000 Speaker 1: a checklist that we should be thinking about in terms 55 00:03:08,000 --> 00:03:13,600 Speaker 1: of our own uh thresholds, in our own sense of safety. Yeah. 56 00:03:13,639 --> 00:03:16,400 Speaker 1: In fact, it's interesting and the dozens of employers we've 57 00:03:16,480 --> 00:03:19,920 Speaker 1: talked to about these go back to work strategies and 58 00:03:20,080 --> 00:03:23,440 Speaker 1: use of testing, A lot of this is employee driven, 59 00:03:23,880 --> 00:03:27,200 Speaker 1: like the employee wanting to understand how safe are they 60 00:03:27,200 --> 00:03:29,280 Speaker 1: going to be at the workplace? Are they going to 61 00:03:29,360 --> 00:03:31,360 Speaker 1: be exposed? Are they going to carry something back to 62 00:03:31,400 --> 00:03:35,240 Speaker 1: the households which may have vulnerable others. So we're seeing 63 00:03:35,240 --> 00:03:37,800 Speaker 1: a lot of this driven by the employees. Some of 64 00:03:37,840 --> 00:03:41,080 Speaker 1: the employers like they need individuals back to be productive, 65 00:03:41,080 --> 00:03:43,320 Speaker 1: they want come back in the workplace. And so what 66 00:03:43,400 --> 00:03:47,240 Speaker 1: we're seeing is the employees really saying, well, we'll make 67 00:03:47,320 --> 00:03:48,840 Speaker 1: us safe if we do that. So there are a 68 00:03:48,880 --> 00:03:52,920 Speaker 1: bunch of guidelines to the employers about how to you know, 69 00:03:52,960 --> 00:03:56,520 Speaker 1: maintain social distancing within the workplace and so on. Um, 70 00:03:56,600 --> 00:04:00,000 Speaker 1: there's the kind of symptom tracking that you want individual 71 00:04:00,040 --> 00:04:03,800 Speaker 1: rules coming into to be taking. So increase awareness in 72 00:04:03,880 --> 00:04:07,280 Speaker 1: the employee on whether they're you know, they're not feeling 73 00:04:07,320 --> 00:04:10,200 Speaker 1: well or they've been exposed outside of the work environment. 74 00:04:10,200 --> 00:04:13,400 Speaker 1: All of that, through the kind of digital engagement we have, 75 00:04:13,640 --> 00:04:17,920 Speaker 1: can be reported and cataloged and used to assess risk 76 00:04:18,360 --> 00:04:21,400 Speaker 1: in addition to the testing. So again it's it's trying 77 00:04:21,440 --> 00:04:25,800 Speaker 1: to cover as many variables as possible. Well, once you're 78 00:04:25,839 --> 00:04:28,960 Speaker 1: in the work environment again, you know, just smart maintaining 79 00:04:29,000 --> 00:04:32,000 Speaker 1: social distancing, wearing a mask if you're in public areas, 80 00:04:32,040 --> 00:04:35,120 Speaker 1: are exposed to other individuals and so on. And I 81 00:04:35,120 --> 00:04:36,720 Speaker 1: want to press you on one point because you brought 82 00:04:36,800 --> 00:04:38,880 Speaker 1: up and I think it's so important, which is this 83 00:04:38,960 --> 00:04:42,840 Speaker 1: idea that if someone and I certainly trust my employer. 84 00:04:43,000 --> 00:04:44,800 Speaker 1: I've worked for a great company and I'm not just 85 00:04:44,839 --> 00:04:47,520 Speaker 1: saying that because I'm on its air. But you know, 86 00:04:47,720 --> 00:04:49,720 Speaker 1: the thing that I think many people worry about is 87 00:04:49,760 --> 00:04:53,200 Speaker 1: exactly what you said, the sort of inadvertently bringing bringing 88 00:04:53,240 --> 00:04:55,719 Speaker 1: something home and is that just something? Is that a 89 00:04:55,839 --> 00:04:57,960 Speaker 1: risk we're just going to have to to live with 90 00:04:58,040 --> 00:05:00,599 Speaker 1: And how much of this goes down to kind of 91 00:05:00,640 --> 00:05:04,359 Speaker 1: that individual responsibility and maybe the in between things that 92 00:05:04,400 --> 00:05:09,760 Speaker 1: are happening between work and and the workplace and vice versa. Yeah, 93 00:05:09,760 --> 00:05:12,880 Speaker 1: it's it's a bit complicated because it has to take 94 00:05:12,880 --> 00:05:15,640 Speaker 1: into account, you know, to to what extent does an 95 00:05:15,680 --> 00:05:19,880 Speaker 1: employer actually need you working in the office. So one 96 00:05:19,880 --> 00:05:22,160 Speaker 1: of the things we do with the employer groups is 97 00:05:22,200 --> 00:05:26,080 Speaker 1: assessing helping them assess is an employee really needed to 98 00:05:26,200 --> 00:05:29,040 Speaker 1: come back in or can they be remote? And if 99 00:05:29,040 --> 00:05:32,200 Speaker 1: they fall into higher risk categories, i e. They have 100 00:05:32,720 --> 00:05:39,000 Speaker 1: individuals at home who are more vulnerable, elderly elderly parents 101 00:05:39,040 --> 00:05:42,320 Speaker 1: living with them, immune compromise individuals, and so on. So 102 00:05:42,400 --> 00:05:45,640 Speaker 1: that's number one. Number two is age, age plays a 103 00:05:45,720 --> 00:05:49,520 Speaker 1: significant role in in the severity of response you may 104 00:05:49,640 --> 00:05:52,800 Speaker 1: undergo if you get infected, so all of those parameters 105 00:05:52,839 --> 00:05:56,200 Speaker 1: are taken into account. Education is a key part of this, 106 00:05:56,800 --> 00:05:59,880 Speaker 1: and it's helping everybody understand the risk to what is 107 00:06:00,040 --> 00:06:02,200 Speaker 1: center they needed in the office, to what extent can 108 00:06:02,200 --> 00:06:06,080 Speaker 1: they remain remote? And based on those, various thresholds are 109 00:06:06,080 --> 00:06:08,919 Speaker 1: set for the different risk groups. Our guest at this 110 00:06:09,000 --> 00:06:11,880 Speaker 1: hour Dr Eric Shot, founder and CE at Semaphore, a 111 00:06:12,000 --> 00:06:16,240 Speaker 1: patient centric predictive health companies, also Dean for Precision Medicine 112 00:06:16,520 --> 00:06:20,240 Speaker 1: and amount Sinai Professor and Predictive Health and Computation Biology 113 00:06:20,520 --> 00:06:23,000 Speaker 1: at the Icon School of Medicine at Mount sina As 114 00:06:23,040 --> 00:06:27,479 Speaker 1: we said, he's had lots of education and lots of schooling. Um, 115 00:06:27,600 --> 00:06:29,440 Speaker 1: doctor Shot, it is nice to have you here with us, 116 00:06:29,440 --> 00:06:31,680 Speaker 1: and you're talking about, you know, the work that you 117 00:06:31,680 --> 00:06:33,799 Speaker 1: guys are doing at Semaphore with the State of Connecticut 118 00:06:33,800 --> 00:06:37,279 Speaker 1: in terms of providing more access and more testing. Given 119 00:06:37,320 --> 00:06:40,560 Speaker 1: your druthers, would you, as a member of the medical community, 120 00:06:40,560 --> 00:06:45,640 Speaker 1: pervert that people ultimately just stay home a little longer. Yeah, 121 00:06:45,720 --> 00:06:48,520 Speaker 1: I think we're seeing, uh are already seeing that some 122 00:06:48,600 --> 00:06:52,400 Speaker 1: of the timelines on when when governments wanted to see 123 00:06:52,440 --> 00:06:54,719 Speaker 1: people back to work and people wanting to be back 124 00:06:54,760 --> 00:06:57,760 Speaker 1: to work, that kind of getting delayed. Let things right 125 00:06:57,839 --> 00:07:00,200 Speaker 1: out a little longer. The you know, the number are 126 00:07:00,240 --> 00:07:03,960 Speaker 1: definitely going down. But I do ultimately think that testing 127 00:07:04,200 --> 00:07:07,080 Speaker 1: is sort of a core part of a key part 128 00:07:07,080 --> 00:07:10,040 Speaker 1: of evidence, along with other parameters that sort of assess 129 00:07:10,160 --> 00:07:13,680 Speaker 1: whether somebody does have active infection or whether they've been 130 00:07:13,680 --> 00:07:17,800 Speaker 1: infected and may be immune and dr shot. You know, 131 00:07:18,080 --> 00:07:21,040 Speaker 1: I know that this is top of mind for you 132 00:07:21,200 --> 00:07:24,160 Speaker 1: because you have been involved in the collection of a 133 00:07:24,160 --> 00:07:28,080 Speaker 1: lot of very sensitive data. How do we protect privacy 134 00:07:28,440 --> 00:07:32,200 Speaker 1: amid all of this, Yeah, that's a great question. You know. 135 00:07:32,240 --> 00:07:35,800 Speaker 1: The digital solution we sort of provide to an employer 136 00:07:35,880 --> 00:07:38,840 Speaker 1: that includes both an employer portal where they can upload 137 00:07:38,920 --> 00:07:41,520 Speaker 1: who what employees do they want to have back in 138 00:07:41,560 --> 00:07:44,920 Speaker 1: the workplace, so who's eligible, and then for the employee 139 00:07:44,920 --> 00:07:48,559 Speaker 1: employee portals that we engage in clinical testing all the time. 140 00:07:48,640 --> 00:07:53,280 Speaker 1: So this technology that keeps you know, we're dealing with HIPPA, uh, 141 00:07:53,400 --> 00:07:57,240 Speaker 1: personal personally identifiable health information like all of that, we 142 00:07:57,480 --> 00:08:00,440 Speaker 1: stay of the art um you know, encryption techn oologies 143 00:08:00,480 --> 00:08:03,080 Speaker 1: and so on, and many of the employers, like once 144 00:08:03,120 --> 00:08:07,120 Speaker 1: these tests are run unemployees, whether they're positive or negative, UM, 145 00:08:07,160 --> 00:08:09,560 Speaker 1: you know, the employers often don't want to see that information. 146 00:08:09,600 --> 00:08:12,000 Speaker 1: They don't want to take in, um, you know, what's 147 00:08:12,080 --> 00:08:14,920 Speaker 1: medically happening to the individuals. So we have the ability 148 00:08:14,960 --> 00:08:19,000 Speaker 1: to shield that information to guide UH employees who test 149 00:08:19,080 --> 00:08:21,440 Speaker 1: is positive into the right channels of care. So it's 150 00:08:21,440 --> 00:08:24,560 Speaker 1: employing the same kinds of techniques and technologies that we 151 00:08:24,640 --> 00:08:28,640 Speaker 1: imply in clinical medicine to to to perform this kind 152 00:08:28,640 --> 00:08:31,600 Speaker 1: of testing. I do feel like, you know, doctor Shot, 153 00:08:31,640 --> 00:08:35,920 Speaker 1: that we have been talking about disrupting the medical community 154 00:08:35,960 --> 00:08:37,440 Speaker 1: for a long time, and I feel like it's been 155 00:08:37,480 --> 00:08:40,480 Speaker 1: resistant for many different reasons. But I do feel like 156 00:08:40,520 --> 00:08:43,920 Speaker 1: what's happened with COVID nineteen is is revealing that we 157 00:08:44,000 --> 00:08:46,720 Speaker 1: really need to change how we do things. And unfortunately, 158 00:08:46,760 --> 00:08:48,680 Speaker 1: as a result of that, we are going to be 159 00:08:48,720 --> 00:08:50,400 Speaker 1: giving up more data, and there's going to be more 160 00:08:50,440 --> 00:08:52,200 Speaker 1: sharing of data, and there's going to have to be 161 00:08:52,320 --> 00:08:57,160 Speaker 1: data pools. Is that just kind of our new reality? Yeah, Carol, 162 00:08:57,200 --> 00:08:59,760 Speaker 1: and I think you've hit it exactly on the head. 163 00:09:00,160 --> 00:09:03,559 Speaker 1: This is going to be a transformational event that way, 164 00:09:03,679 --> 00:09:06,760 Speaker 1: like there's going to be much more remote monitoring of 165 00:09:06,880 --> 00:09:11,080 Speaker 1: patients and engagements remotely, a lot more information being transmitted 166 00:09:11,800 --> 00:09:16,640 Speaker 1: digitally that way. I think increasingly patients and consumers more 167 00:09:16,679 --> 00:09:21,439 Speaker 1: generally becoming more comfortable that UM sharing that kind of information, 168 00:09:21,559 --> 00:09:24,880 Speaker 1: having more information collected on them will have lots of benefits. 169 00:09:24,880 --> 00:09:29,479 Speaker 1: So there's kind of that risk benefit ratio that employees, 170 00:09:29,600 --> 00:09:32,360 Speaker 1: you know, employees consumers always have in mind, and so 171 00:09:32,440 --> 00:09:35,240 Speaker 1: I think, yeah, we'll gravitate towards a new equilibrium that 172 00:09:35,360 --> 00:09:38,839 Speaker 1: I think will have us UM you know, tolerating higher 173 00:09:38,960 --> 00:09:43,440 Speaker 1: risk and maybe greater privacy risk violations in trade for 174 00:09:44,400 --> 00:09:47,200 Speaker 1: ensuring we stay healthier and don't bring bad stuff into 175 00:09:47,200 --> 00:09:50,120 Speaker 1: the home. Last question for you before you let you go, 176 00:09:50,160 --> 00:09:52,400 Speaker 1: what do you worry about the most interns of back 177 00:09:52,440 --> 00:09:58,640 Speaker 1: to work? Yeah, I think it's having a holistic enough 178 00:09:58,679 --> 00:10:03,600 Speaker 1: solution that well enough characterizes what's happening in an individual 179 00:10:03,800 --> 00:10:07,360 Speaker 1: and what is their risk profile. Really, as we start 180 00:10:07,679 --> 00:10:10,240 Speaker 1: getting into different types of sample collection and make it 181 00:10:10,280 --> 00:10:14,080 Speaker 1: more convenient, whether it's saliva and at home collections, you know, 182 00:10:14,120 --> 00:10:16,800 Speaker 1: are those sensitivity and bessicity of the tests? Are they 183 00:10:16,800 --> 00:10:19,000 Speaker 1: going to be as accurate? So we're like a lot 184 00:10:19,040 --> 00:10:22,079 Speaker 1: of energy being spent both with the state of Connecticut 185 00:10:22,080 --> 00:10:24,440 Speaker 1: and all the testing we're doing to assess all of 186 00:10:24,480 --> 00:10:27,440 Speaker 1: those different parameters to you know, we're all learning together. 187 00:10:27,520 --> 00:10:31,000 Speaker 1: We have to adapt and as we learn, and and uh, 188 00:10:31,240 --> 00:10:33,400 Speaker 1: you know, but yeah, it's that we'd be accurate enough, 189 00:10:33,600 --> 00:10:35,720 Speaker 1: I guests would be mine. Now. I think that's I 190 00:10:35,760 --> 00:10:37,400 Speaker 1: think that's exactly right. I mean, we had a great 191 00:10:37,440 --> 00:10:40,920 Speaker 1: story a few weeks ago colleague of ours in London 192 00:10:41,559 --> 00:10:45,120 Speaker 1: did about getting four tests and essentially two of them 193 00:10:45,120 --> 00:10:47,240 Speaker 1: are negative, to them were positive, and so you do 194 00:10:47,360 --> 00:10:50,199 Speaker 1: wonder about the accuracy and that ultimately will make a 195 00:10:50,280 --> 00:10:53,720 Speaker 1: huge difference our Thanks to Dr Eric Shott, founder CEO 196 00:10:53,840 --> 00:10:57,200 Speaker 1: of Semaphore Jinus on the phone from Stanford, Connecticut. They 197 00:10:57,200 --> 00:10:59,560 Speaker 1: are working with the state Carol to get that state 198 00:11:00,160 --> 00:11:03,640 Speaker 1: back to work. And I take his point at the 199 00:11:03,720 --> 00:11:07,480 Speaker 1: end there. It's a really important one because that the 200 00:11:07,600 --> 00:11:12,280 Speaker 1: data are only good if they're real and true, right correct, 201 00:11:12,440 --> 00:11:14,440 Speaker 1: and it needs to be consistent. We've talked about this 202 00:11:14,520 --> 00:11:16,120 Speaker 1: that if everybody is doing kind of their own thing 203 00:11:16,120 --> 00:11:18,720 Speaker 1: and there's no consistency in terms of collection, and then 204 00:11:19,240 --> 00:11:20,920 Speaker 1: you know how you look at it, it's not going 205 00:11:20,960 --> 00:11:24,440 Speaker 1: to be ultimately useful in the end, so really really important. 206 00:11:24,800 --> 00:11:28,400 Speaker 1: This is Bloomberg Business Week with Carol Masser and Jason 207 00:11:28,480 --> 00:11:32,080 Speaker 1: Kelly on Bloomberg Radio. Well. Among our most read stories 208 00:11:32,120 --> 00:11:34,880 Speaker 1: on the Bloomberg today is one in the magazine this week. 209 00:11:34,920 --> 00:11:37,719 Speaker 1: It's about how a decade of progress for women has 210 00:11:37,760 --> 00:11:40,720 Speaker 1: evaporated overnight because of the pandemic. It was one of 211 00:11:40,720 --> 00:11:43,199 Speaker 1: those stories we were talking about on our planning call 212 00:11:43,320 --> 00:11:46,320 Speaker 1: this morning. The story by Shelley Banjo Bloomberg News is 213 00:11:46,760 --> 00:11:50,319 Speaker 1: uh technology reporters. She joins us in a second lost 214 00:11:50,400 --> 00:11:53,240 Speaker 1: nice place. She is our senior writer. Forgive me Shelley. 215 00:11:53,360 --> 00:11:55,600 Speaker 1: Shelley Banjo is our senior writer at Bloomberg News. Wanted 216 00:11:55,600 --> 00:11:58,720 Speaker 1: to get her title correctly. On the phone from Dallas, Texas, 217 00:11:58,720 --> 00:12:01,079 Speaker 1: she joins us along with Bloomber Business Week editor Joe 218 00:12:01,200 --> 00:12:04,400 Speaker 1: Weber on the phone in Brooklyn and Joel, I mean 219 00:12:04,480 --> 00:12:06,920 Speaker 1: it's a special magazine this week in terms of what 220 00:12:07,000 --> 00:12:09,840 Speaker 1: you guys are doing. What I love about this story 221 00:12:10,120 --> 00:12:13,000 Speaker 1: is that you really dig into what's going on telling 222 00:12:13,000 --> 00:12:16,360 Speaker 1: the story, uh, the numbers, the stories that really give 223 00:12:16,440 --> 00:12:22,080 Speaker 1: us an idea of how the pandemic specifically is impacting women. Yeah, 224 00:12:22,120 --> 00:12:25,560 Speaker 1: I mean every issue is special, Carol. You know, this 225 00:12:25,640 --> 00:12:28,680 Speaker 1: is one of those stories that the momentum we started 226 00:12:28,679 --> 00:12:31,760 Speaker 1: talking about it. I just said, this is supremely important, 227 00:12:32,320 --> 00:12:35,160 Speaker 1: and you know, it's been sort of bubbling for a second, 228 00:12:35,200 --> 00:12:37,120 Speaker 1: like we've seen some of these numbers come out, but 229 00:12:37,160 --> 00:12:40,080 Speaker 1: this was the first time that we actually really managed 230 00:12:40,120 --> 00:12:42,440 Speaker 1: to put them all in one place to get a 231 00:12:42,520 --> 00:12:47,040 Speaker 1: sense of just how dramatic this moment is for women 232 00:12:47,120 --> 00:12:51,000 Speaker 1: specifically who have basically shouldered the burden of the pandemic 233 00:12:51,040 --> 00:12:54,160 Speaker 1: in a way that um that men haven't. Um, Shelley, 234 00:12:54,200 --> 00:12:55,719 Speaker 1: as you did. You're reporting. What were the numbers that 235 00:12:55,760 --> 00:13:00,880 Speaker 1: really jumped out to you? Oh, good question. I mean 236 00:13:00,920 --> 00:13:04,000 Speaker 1: the first thing is definitely the job losses. Uh. You know, 237 00:13:04,280 --> 00:13:06,480 Speaker 1: a lot of terms. We compared this to what happened 238 00:13:06,480 --> 00:13:08,440 Speaker 1: in two thousand and eight, and a lot of those 239 00:13:08,480 --> 00:13:11,600 Speaker 1: sectors that were most hard hit were things like housing, 240 00:13:12,080 --> 00:13:14,480 Speaker 1: UM construction areas where a lot of men were and 241 00:13:14,520 --> 00:13:17,559 Speaker 1: this time around it's a complete flip, but it's um 242 00:13:17,760 --> 00:13:22,000 Speaker 1: areas where where women are. For that of the job losses, UM, 243 00:13:22,320 --> 00:13:24,240 Speaker 1: you know, was a big one for me. And the 244 00:13:24,280 --> 00:13:26,880 Speaker 1: other number that really shoot out to me was just 245 00:13:27,040 --> 00:13:31,040 Speaker 1: the sheer number of women that are breadwinners in their family. 246 00:13:31,200 --> 00:13:34,720 Speaker 1: I guess I just didn't really quite realize, um, you know, 247 00:13:34,800 --> 00:13:41,120 Speaker 1: how much how much that was, and how much of this, Shelley. 248 00:13:41,200 --> 00:13:44,480 Speaker 1: I mean, this was a stunning story in many ways, 249 00:13:44,760 --> 00:13:47,520 Speaker 1: although as both Carol and Joelhood said, it was one 250 00:13:47,520 --> 00:13:50,079 Speaker 1: of these things that probably if you think about it, 251 00:13:50,200 --> 00:13:53,320 Speaker 1: was was hiding in plain sight. I mean, to me, 252 00:13:53,400 --> 00:13:55,840 Speaker 1: one of the most troubling aspects of it is that 253 00:13:56,200 --> 00:13:58,680 Speaker 1: it's going to be really it's gonna be a slow 254 00:13:59,240 --> 00:14:05,160 Speaker 1: slog back to even where we were much less real progress. 255 00:14:05,280 --> 00:14:08,440 Speaker 1: Talked to talk to us about that and and sort 256 00:14:08,440 --> 00:14:10,960 Speaker 1: of how this may how hard it may be to 257 00:14:10,960 --> 00:14:14,200 Speaker 1: to really reverse this, and and Jason, one thing to 258 00:14:14,240 --> 00:14:16,839 Speaker 1: add just there is that last year or women made 259 00:14:16,920 --> 00:14:20,080 Speaker 1: up the majority of the US workforce for almost the 260 00:14:20,120 --> 00:14:23,160 Speaker 1: first time in a decade, right, sull you like, just 261 00:14:23,480 --> 00:14:25,480 Speaker 1: think about that in context as you as you sort 262 00:14:25,480 --> 00:14:28,680 Speaker 1: of respond to that. That's exactly right. I mean, I 263 00:14:28,720 --> 00:14:30,800 Speaker 1: think your point on hiding in a plane site is 264 00:14:30,840 --> 00:14:32,800 Speaker 1: a good one because a lot of these things you 265 00:14:32,840 --> 00:14:35,920 Speaker 1: don't see, I mean, you don't see the hours, um 266 00:14:35,960 --> 00:14:39,320 Speaker 1: that women are necessarily staying up later at night to 267 00:14:39,760 --> 00:14:43,200 Speaker 1: finish and this kind of burnout potential of you know, 268 00:14:43,320 --> 00:14:45,760 Speaker 1: I finish my work for the day, and then I'm 269 00:14:45,760 --> 00:14:47,600 Speaker 1: with my kids, and then I'm planning on and again 270 00:14:47,680 --> 00:14:50,200 Speaker 1: to work until two o'clock in the morning. And those 271 00:14:50,240 --> 00:14:52,280 Speaker 1: are the type of kind of burnout that that starts 272 00:14:52,280 --> 00:14:55,760 Speaker 1: to um, that starts to really weigh on people. UM. 273 00:14:56,560 --> 00:14:59,240 Speaker 1: And I think that could you know, have some bigger, 274 00:14:59,720 --> 00:15:02,720 Speaker 1: long good term implications. UM. You look at all the 275 00:15:02,760 --> 00:15:05,200 Speaker 1: women that are just kind of bowing out of the workforce, 276 00:15:05,240 --> 00:15:09,040 Speaker 1: taking leave, taking parental leave. Now, UM, that's going to 277 00:15:09,120 --> 00:15:13,840 Speaker 1: impact longer term, longer term earnings and income for women. UM. 278 00:15:14,040 --> 00:15:15,920 Speaker 1: And then you look at a lot of women that 279 00:15:15,960 --> 00:15:19,880 Speaker 1: have no choice like that. They they're single women who um, 280 00:15:19,920 --> 00:15:22,920 Speaker 1: you know, provide everything for their family and so they 281 00:15:22,960 --> 00:15:25,120 Speaker 1: have no choice but to work. And so for them 282 00:15:25,160 --> 00:15:28,400 Speaker 1: that means putting their kids in a lot of precarious 283 00:15:28,400 --> 00:15:31,480 Speaker 1: and unsafe situations. You know what's interesting, Shelley and I 284 00:15:31,560 --> 00:15:33,840 Speaker 1: think Jason, back to some of the early conversations we 285 00:15:33,920 --> 00:15:36,200 Speaker 1: had once we went into you know, working from home, 286 00:15:36,280 --> 00:15:39,360 Speaker 1: is that folks thought, you know, this might be helpful 287 00:15:39,400 --> 00:15:41,960 Speaker 1: to women and giving them more flexibility in terms of 288 00:15:42,000 --> 00:15:45,280 Speaker 1: working operations because everybody was at home. We were realizing 289 00:15:45,320 --> 00:15:48,840 Speaker 1: it was working more effectively than I think anybody imagined 290 00:15:49,240 --> 00:15:51,680 Speaker 1: and thought that this might be a boon for women, 291 00:15:52,040 --> 00:15:56,680 Speaker 1: and it's interesting to see how it's playing out ultimately. Yeah, 292 00:15:56,720 --> 00:15:58,480 Speaker 1: I think that's right. I mean, working from home does 293 00:15:58,520 --> 00:16:02,480 Speaker 1: save you sometime in terms of commute and things like that. Um, 294 00:16:02,520 --> 00:16:04,920 Speaker 1: but you can't do two things that wants. You can't 295 00:16:05,000 --> 00:16:08,400 Speaker 1: both you know, breastfeed the child and uh, I'll lead 296 00:16:08,400 --> 00:16:12,000 Speaker 1: a conference call, which so I have seen that before. Um, 297 00:16:12,040 --> 00:16:14,720 Speaker 1: but you know, it's it's you can't do two things 298 00:16:14,720 --> 00:16:16,920 Speaker 1: at the same time. You can't be two places at once, 299 00:16:17,040 --> 00:16:20,480 Speaker 1: and so kids some um, you know, I think it 300 00:16:20,560 --> 00:16:22,560 Speaker 1: sums up in my fur in the very first line 301 00:16:22,560 --> 00:16:24,840 Speaker 1: of the story, which is the reality of the pandemic. 302 00:16:24,880 --> 00:16:27,120 Speaker 1: If someone has to stay with the kids, and so 303 00:16:27,160 --> 00:16:29,520 Speaker 1: as long as you have kids, someone has to watch them, 304 00:16:29,840 --> 00:16:32,040 Speaker 1: and in most cases that's ending up on the women. 305 00:16:32,200 --> 00:16:35,080 Speaker 1: And when that means is then they can't do their jobs. 306 00:16:35,120 --> 00:16:37,800 Speaker 1: And so you know, to me, in a frustrating part 307 00:16:37,800 --> 00:16:41,120 Speaker 1: of this, companies are saying, yeah, we're trying to be um, 308 00:16:41,200 --> 00:16:45,040 Speaker 1: we're trying to be understanding of this, but but really, 309 00:16:45,400 --> 00:16:47,520 Speaker 1: you know, I think it takes some greater policies, and 310 00:16:47,560 --> 00:16:51,000 Speaker 1: so that's something I thought was very interesting, UM from 311 00:16:51,040 --> 00:16:54,040 Speaker 1: some of the think tanks that I spoke to, which said, 312 00:16:54,040 --> 00:16:55,800 Speaker 1: you know, this is really a moment where we can 313 00:16:55,840 --> 00:16:58,760 Speaker 1: either take what's happened and make things worse, or we 314 00:16:58,760 --> 00:17:02,080 Speaker 1: can take what's happened and make things better and institutionalize 315 00:17:02,120 --> 00:17:04,880 Speaker 1: some of these things that we've started to do around 316 00:17:05,240 --> 00:17:08,040 Speaker 1: working from home and paid sickly even UM, some of 317 00:17:08,119 --> 00:17:11,600 Speaker 1: these temporary provisions that have been written in for workers 318 00:17:11,720 --> 00:17:13,919 Speaker 1: right now, and think, you know, how can we actually 319 00:17:13,960 --> 00:17:17,240 Speaker 1: turn these into long term solutions because what the pandemic 320 00:17:17,400 --> 00:17:19,840 Speaker 1: unveiled was, you know, we don't have this kind of 321 00:17:19,880 --> 00:17:24,560 Speaker 1: safety net written into um, the government, into laws, into 322 00:17:25,240 --> 00:17:28,560 Speaker 1: the way companies work and across the board. And so 323 00:17:28,840 --> 00:17:30,600 Speaker 1: you know, where can we what can we learn from this, 324 00:17:30,720 --> 00:17:32,960 Speaker 1: what can we take away, and how can we emerge 325 00:17:33,000 --> 00:17:38,200 Speaker 1: from this better. It also revealed, speaking from personal experience here, 326 00:17:38,440 --> 00:17:41,800 Speaker 1: the importance of school where you can put kids and 327 00:17:41,800 --> 00:17:45,119 Speaker 1: and then actually, you know, go do your job. And 328 00:17:45,520 --> 00:17:48,240 Speaker 1: you you know, just continuing that thought that you had 329 00:17:48,240 --> 00:17:52,639 Speaker 1: there Shelly about what employers are are doing and can doing, 330 00:17:52,680 --> 00:17:54,800 Speaker 1: Like what did you what else did you learn about 331 00:17:55,080 --> 00:17:58,639 Speaker 1: um um you know, most effective policies that employers are 332 00:17:58,720 --> 00:18:03,240 Speaker 1: using during this time. I think having a policy is effective. 333 00:18:03,440 --> 00:18:05,119 Speaker 1: So a lot of companies have said, you know what, 334 00:18:05,160 --> 00:18:09,480 Speaker 1: we're behind our employees, Well, we're gonna make um We're 335 00:18:09,480 --> 00:18:11,760 Speaker 1: gonna make adjustments. But what I found from talking to 336 00:18:11,760 --> 00:18:13,879 Speaker 1: a lot of these women are I don't want to 337 00:18:13,920 --> 00:18:16,200 Speaker 1: ask for an adjustment. I don't want to ask for 338 00:18:16,440 --> 00:18:19,400 Speaker 1: a special treatment just because I have to take care 339 00:18:19,440 --> 00:18:22,399 Speaker 1: of my kids. It's a bad economic time. People are 340 00:18:22,440 --> 00:18:25,080 Speaker 1: worried that their job prospects are shaky or that their 341 00:18:25,440 --> 00:18:29,159 Speaker 1: next um, you know, promotion might not be there. If 342 00:18:29,160 --> 00:18:30,800 Speaker 1: they're the ones who had to say, oh, you know what, 343 00:18:30,920 --> 00:18:33,320 Speaker 1: during this time, it actually need you to give me 344 00:18:33,359 --> 00:18:36,080 Speaker 1: a break a little bit, because for every person who 345 00:18:36,160 --> 00:18:38,040 Speaker 1: might need a break right now, there are people who 346 00:18:38,080 --> 00:18:41,960 Speaker 1: don't necessarily have the same kind of caregiving responsibilities that 347 00:18:42,080 --> 00:18:46,160 Speaker 1: others have. And so UM, I think having a policy 348 00:18:46,240 --> 00:18:50,119 Speaker 1: in place is something that makes it, um you know, 349 00:18:50,160 --> 00:18:53,800 Speaker 1: streamlines the situation a little bit easier for people to take. 350 00:18:54,240 --> 00:18:55,960 Speaker 1: And I think you know, I'm hearing too from a 351 00:18:56,000 --> 00:18:58,000 Speaker 1: lot of folks that those policies need to be very 352 00:18:58,040 --> 00:19:01,760 Speaker 1: clear concise, understood and really written out so that people 353 00:19:01,840 --> 00:19:04,959 Speaker 1: understand truly, you know, what a company is, okay with 354 00:19:05,000 --> 00:19:08,080 Speaker 1: it followed without any repercussion, exactly, followed up on and 355 00:19:08,240 --> 00:19:11,240 Speaker 1: enforced and enforced fairly. Yeah, I think it's it's a 356 00:19:11,280 --> 00:19:13,919 Speaker 1: really important point. This is a great story. Yeah, I 357 00:19:13,920 --> 00:19:16,200 Speaker 1: think it's really important and I love I mean, I'm 358 00:19:16,200 --> 00:19:18,320 Speaker 1: going to put it out on Twitter because the numbers 359 00:19:18,320 --> 00:19:20,760 Speaker 1: in it to really tell you what's going on right 360 00:19:20,760 --> 00:19:24,679 Speaker 1: now on and in particular, um the disproportionate impact that 361 00:19:24,880 --> 00:19:27,679 Speaker 1: women are seeing. Shelley Banjo. She is senior writer at 362 00:19:27,680 --> 00:19:30,200 Speaker 1: Bloomberg News. She joined us on the phone from Dallas, Texas, 363 00:19:30,280 --> 00:19:33,320 Speaker 1: along with Joe Weber, editor a Bloomberg business Week on 364 00:19:33,680 --> 00:19:37,520 Speaker 1: the remote line from Brooklyn. Check out that story in 365 00:19:37,560 --> 00:19:41,720 Speaker 1: the issue. You're listening to Bloomberg Business Week with Carol 366 00:19:41,800 --> 00:19:46,280 Speaker 1: Masser and Jason Kelly on Bloomberg Radio. All right, let's 367 00:19:46,280 --> 00:19:48,560 Speaker 1: talk a little Business Week economics. We talked about this 368 00:19:48,600 --> 00:19:50,840 Speaker 1: story a little bit. We teed it up, I think 369 00:19:51,480 --> 00:19:55,520 Speaker 1: rather nicely. For our next guest, Elena Shats, She's quoted 370 00:19:55,560 --> 00:19:57,680 Speaker 1: in the story that is one of the most read 371 00:19:57,720 --> 00:20:00,399 Speaker 1: on the Bloomberg and it's all about what may happen 372 00:20:00,560 --> 00:20:03,640 Speaker 1: next when it comes to who may lose their jobs 373 00:20:03,800 --> 00:20:07,240 Speaker 1: in the aftermath of this pandemic. She, of course, is 374 00:20:07,280 --> 00:20:10,560 Speaker 1: senior US economists for Boomberg Economics, joining us on the 375 00:20:10,560 --> 00:20:14,480 Speaker 1: phone from Long Island and Yelena, I have to say, 376 00:20:14,720 --> 00:20:18,359 Speaker 1: you know, reading this research and understanding the data that 377 00:20:18,520 --> 00:20:22,240 Speaker 1: you and your team collected, it is troubling for a 378 00:20:22,359 --> 00:20:25,040 Speaker 1: lot of our audience in many ways, who I think 379 00:20:25,280 --> 00:20:28,320 Speaker 1: was probably able to say, Okay, well you know, I'm 380 00:20:28,320 --> 00:20:32,200 Speaker 1: not an hourly restaurant worker. I don't work in retail. 381 00:20:32,600 --> 00:20:36,240 Speaker 1: This is going to be much more widespread and as 382 00:20:36,320 --> 00:20:39,359 Speaker 1: this continues, we're talking about a lot of higher paying 383 00:20:39,720 --> 00:20:44,680 Speaker 1: white color jobs, right UH. And I think the key 384 00:20:44,840 --> 00:20:49,639 Speaker 1: point here is that a lot of white college jobs 385 00:20:49,640 --> 00:20:53,760 Speaker 1: and a lot of jobs in adjustent industry to those 386 00:20:53,840 --> 00:20:58,600 Speaker 1: that work heat very hard in the first wave of leoffs. 387 00:20:58,720 --> 00:21:03,480 Speaker 1: I still at RIPA. So even though we saw better 388 00:21:03,520 --> 00:21:08,560 Speaker 1: than expected number in terms of a DP payrolls this morning, UH, 389 00:21:08,600 --> 00:21:14,480 Speaker 1: another report from UH non Manufacturing sim Services suggested that 390 00:21:15,160 --> 00:21:20,560 Speaker 1: things that really not improving that well, and the employment 391 00:21:20,640 --> 00:21:25,840 Speaker 1: component of the survey was still at a very low level. 392 00:21:26,320 --> 00:21:32,160 Speaker 1: So that tells me that maybe this optimism about how 393 00:21:32,280 --> 00:21:35,520 Speaker 1: quickly the job market is going to improve is a 394 00:21:35,560 --> 00:21:38,560 Speaker 1: little bit permecure. You know what can I just say, Lena, 395 00:21:38,680 --> 00:21:42,960 Speaker 1: It reminds me of like using the um SPLC function 396 00:21:42,960 --> 00:21:45,760 Speaker 1: on the Bloomberg the supply chain analysis, Right, Like you 397 00:21:45,760 --> 00:21:47,320 Speaker 1: you look at a company, but you've got to look 398 00:21:47,359 --> 00:21:49,320 Speaker 1: who do they buy from? Who do they sell from? Right, 399 00:21:49,359 --> 00:21:52,399 Speaker 1: whenever there's a story, it's so much more, you know, 400 00:21:52,520 --> 00:21:56,400 Speaker 1: wide reaching than just one entity. And that's what this is, right. 401 00:21:56,760 --> 00:21:59,720 Speaker 1: You know that if you know restaurants are hit, well, 402 00:21:59,760 --> 00:22:04,120 Speaker 1: there the whole supply chain and it includes technology, you know, workers, 403 00:22:04,160 --> 00:22:07,159 Speaker 1: that includes health care, educate. I mean, there's just so 404 00:22:07,240 --> 00:22:10,760 Speaker 1: much that goes into it. So what we did, what 405 00:22:10,880 --> 00:22:14,040 Speaker 1: our team did in terms of the analysis, we looked 406 00:22:14,080 --> 00:22:18,440 Speaker 1: at input output tables from the Bureau of Economic Analysis 407 00:22:18,480 --> 00:22:24,080 Speaker 1: to kind of trace the linkages between different industries. So 408 00:22:24,560 --> 00:22:27,960 Speaker 1: let me give you another example. So, uh, a really 409 00:22:28,080 --> 00:22:32,760 Speaker 1: state less or somebody who rents out those um big 410 00:22:32,800 --> 00:22:38,119 Speaker 1: retail spaces. They are in danger basically because a lot 411 00:22:38,200 --> 00:22:42,240 Speaker 1: of stores are closing and maybe they're closing permanently. So 412 00:22:42,720 --> 00:22:47,200 Speaker 1: these companies are at risk of losing jobs. So what 413 00:22:47,240 --> 00:22:50,440 Speaker 1: we did we tried to estimate across the whole economy 414 00:22:51,200 --> 00:22:56,360 Speaker 1: how many in more industries in danger because of that 415 00:22:56,560 --> 00:23:01,080 Speaker 1: first wave of layoffs that we saw back March in April, 416 00:23:01,520 --> 00:23:06,119 Speaker 1: and our estimates suggests that as many as six million 417 00:23:06,800 --> 00:23:10,959 Speaker 1: jobs in danger because of um this potentially of an 418 00:23:10,960 --> 00:23:15,720 Speaker 1: adjustent industries, and also because white college jobs are now 419 00:23:15,920 --> 00:23:20,399 Speaker 1: a risk given that a lot of frontline workers lost 420 00:23:20,400 --> 00:23:24,000 Speaker 1: their jobs and they might not be coming back. Well, 421 00:23:24,040 --> 00:23:26,520 Speaker 1: that whole notion of coming back, I guess you'll and 422 00:23:26,600 --> 00:23:30,399 Speaker 1: I want to just emphasize that point because one of 423 00:23:30,480 --> 00:23:32,119 Speaker 1: the things that's given I think a lot of people 424 00:23:32,280 --> 00:23:36,399 Speaker 1: comfort so far. And I'm sorry to dwell on this maybe, 425 00:23:36,480 --> 00:23:38,239 Speaker 1: but I think it's so important is this idea of like, oh, 426 00:23:38,280 --> 00:23:40,359 Speaker 1: it's gonna come back, It's gonna come back, and and 427 00:23:40,400 --> 00:23:43,040 Speaker 1: I do feel like we're starting to get a sense 428 00:23:43,200 --> 00:23:46,320 Speaker 1: that some of these jobs, especially the ones you're talking about, 429 00:23:46,600 --> 00:23:50,680 Speaker 1: don't so easily come back. It is there a delineation 430 00:23:50,800 --> 00:23:53,640 Speaker 1: that that you make between sort of that first wave 431 00:23:53,680 --> 00:23:57,919 Speaker 1: in second wave? Can you quantify how likely it is 432 00:23:58,520 --> 00:24:02,280 Speaker 1: in the snap back? It's very difficult, and of course 433 00:24:02,320 --> 00:24:06,400 Speaker 1: there are a lot of different factors around it. So yes, 434 00:24:06,480 --> 00:24:10,760 Speaker 1: things may turned really great and all of a sudden 435 00:24:10,840 --> 00:24:17,240 Speaker 1: everybody rebounds in terms of growth and in terms of jobs. 436 00:24:17,280 --> 00:24:21,920 Speaker 1: But I I think it's very unlikely. And uh, even 437 00:24:21,920 --> 00:24:25,320 Speaker 1: though we we have seen a lot of optimistic signs, 438 00:24:25,520 --> 00:24:29,280 Speaker 1: say coming from auto sales report. We so yesterday it 439 00:24:29,359 --> 00:24:33,080 Speaker 1: was better than expected. Another report this morning, VP was 440 00:24:33,119 --> 00:24:37,760 Speaker 1: better than expected. There's a lot of uh this um, 441 00:24:38,119 --> 00:24:41,639 Speaker 1: you know, setbacks in terms of how much demand we 442 00:24:41,720 --> 00:24:46,480 Speaker 1: will have in the industry, in the economy, on the 443 00:24:46,520 --> 00:24:50,760 Speaker 1: part of consumers, on the part of other industries that 444 00:24:50,960 --> 00:24:54,200 Speaker 1: might not be coming back in a V shaped form. Well, 445 00:24:54,240 --> 00:24:56,159 Speaker 1: and you do wonder I think, Jason, I harken back 446 00:24:56,200 --> 00:24:58,960 Speaker 1: to our conversation with Margret Keen and Synchrony Financial, which 447 00:24:59,040 --> 00:25:01,440 Speaker 1: is a bit of your talks in the magazine this week. 448 00:25:01,800 --> 00:25:04,679 Speaker 1: But who talked about, you know, people who had asked 449 00:25:04,680 --> 00:25:06,920 Speaker 1: for more time to pay off some of those store 450 00:25:06,960 --> 00:25:10,160 Speaker 1: credit cards, about them said, you know, we don't need 451 00:25:10,200 --> 00:25:14,400 Speaker 1: these deferrals anymore, but they are paying down debt. And 452 00:25:14,520 --> 00:25:16,280 Speaker 1: you know that also goes to if you're a little 453 00:25:16,280 --> 00:25:18,439 Speaker 1: bit nervous about what the outlook is, you may not 454 00:25:18,480 --> 00:25:20,640 Speaker 1: do any new spending. You're gonna you know, try and 455 00:25:20,720 --> 00:25:23,520 Speaker 1: kind of shore up your financials. Uh. And what's interesting 456 00:25:23,600 --> 00:25:25,760 Speaker 1: is one of the data points, more than one third 457 00:25:25,800 --> 00:25:27,879 Speaker 1: of households making a hundred thousand dollars per year have 458 00:25:27,920 --> 00:25:31,400 Speaker 1: lost some employment income since mid March, and and some 459 00:25:31,480 --> 00:25:34,359 Speaker 1: of them, the statistics show they're worried about, you know, 460 00:25:34,520 --> 00:25:37,879 Speaker 1: making next month's rent or paying the mortgage. So you know, 461 00:25:38,119 --> 00:25:41,560 Speaker 1: it's it's really hitting all, you know, aspects of the 462 00:25:41,640 --> 00:25:44,760 Speaker 1: income spectrum. All right, Elena selective it. Thank you so 463 00:25:44,840 --> 00:25:49,280 Speaker 1: much in your economist for Bloomberg Economics, A really nice one. 464 00:25:49,320 --> 00:25:52,800 Speaker 1: Two punches that were between our Bloomberg Economics team and 465 00:25:52,800 --> 00:25:56,280 Speaker 1: our Bloomberg News team really synthesizing some great data that 466 00:25:56,359 --> 00:25:59,040 Speaker 1: Elena and her group put together. And it's all about 467 00:25:59,359 --> 00:26:05,480 Speaker 1: white colored jobs not being safe from this pandemic that 468 00:26:05,600 --> 00:26:08,680 Speaker 1: continues to really wreak havoc, not just from health perspective, 469 00:26:08,720 --> 00:26:12,320 Speaker 1: but from an economic perspective as well. Carol, and you 470 00:26:12,400 --> 00:26:15,920 Speaker 1: also start to think about it in the context of 471 00:26:16,000 --> 00:26:19,520 Speaker 1: the civil unrest that we're going through and everything going 472 00:26:19,560 --> 00:26:23,200 Speaker 1: on there that's also laying there, Uh, some really really 473 00:26:23,240 --> 00:26:26,120 Speaker 1: tough economic truths. We're going to talk about that next hour. 474 00:26:26,359 --> 00:26:30,199 Speaker 1: Nothing makes people feel more desperate than financial troubles, right, 475 00:26:30,359 --> 00:26:32,679 Speaker 1: and either not having enough money to support your family, 476 00:26:32,840 --> 00:26:34,920 Speaker 1: take care of your family, feed your family, keep a 477 00:26:34,960 --> 00:26:37,520 Speaker 1: roof over your head. And you know, it's things that 478 00:26:37,640 --> 00:26:39,639 Speaker 1: certain aspects of our society have dealt with for a 479 00:26:39,640 --> 00:26:43,280 Speaker 1: long time, but it's certainly hitting a lot more today. 480 00:26:43,480 --> 00:26:47,080 Speaker 1: This is Bloomberg Business Week with Carol Masser and Jason 481 00:26:47,160 --> 00:26:52,760 Speaker 1: Kelly on Bloomberg Radio. In our weekly Bloomberg Green segment, Adjason, Today, 482 00:26:52,800 --> 00:26:55,720 Speaker 1: investors are focusing on an interesting idea that we need 483 00:26:55,800 --> 00:26:59,040 Speaker 1: to think of cars as more than just an automobile 484 00:26:59,080 --> 00:27:03,480 Speaker 1: to keep selling that in a post COVID nineteen future. Provocative. 485 00:27:03,600 --> 00:27:06,280 Speaker 1: So let's get more on this from Emily Chason. She 486 00:27:06,359 --> 00:27:09,560 Speaker 1: is Sustainability editor a Bloomberg News and she joins us 487 00:27:09,560 --> 00:27:11,240 Speaker 1: on the phone in New York City. I'm only good 488 00:27:11,280 --> 00:27:14,119 Speaker 1: to have you back with us, so tell us about 489 00:27:14,280 --> 00:27:19,320 Speaker 1: this this story. Yeah, So, what we're seeing, thanks for 490 00:27:19,359 --> 00:27:21,080 Speaker 1: having me back, by the way, is what we're seeing 491 00:27:21,160 --> 00:27:24,160 Speaker 1: is that people are coming out of COVID all over 492 00:27:24,200 --> 00:27:26,720 Speaker 1: the world and they're a little bit nervous about using 493 00:27:26,760 --> 00:27:29,280 Speaker 1: public transportation. So they're what they're doing is they're looking 494 00:27:29,280 --> 00:27:32,399 Speaker 1: at cars again and then, but you haven't using your 495 00:27:32,440 --> 00:27:34,120 Speaker 1: car for a while, so you might not buy anyone 496 00:27:34,440 --> 00:27:37,280 Speaker 1: right And before this, everybody wanted people to switch to 497 00:27:37,359 --> 00:27:40,200 Speaker 1: electric vehicles. Um, automakers have a lot of bottles that 498 00:27:40,240 --> 00:27:44,800 Speaker 1: are coming out. Um there's now inventory. Um, so people 499 00:27:44,840 --> 00:27:47,679 Speaker 1: are still buying electric vehicles all over the world. Um, 500 00:27:47,720 --> 00:27:49,159 Speaker 1: probably because they were still on a wait list. It 501 00:27:49,240 --> 00:27:52,680 Speaker 1: took months to get all but um, yeah, I guess 502 00:27:52,760 --> 00:27:55,320 Speaker 1: electric vehicles have this optionality to them where they could 503 00:27:55,320 --> 00:27:57,000 Speaker 1: be more than just a cars for people. They can 504 00:27:57,040 --> 00:27:59,240 Speaker 1: hook up to the electric grid. There's a software they 505 00:27:59,359 --> 00:28:02,400 Speaker 1: use there see if autonomous vehicle stuff. So you could 506 00:28:02,400 --> 00:28:04,520 Speaker 1: actually think of your car as having an additional value 507 00:28:04,560 --> 00:28:07,560 Speaker 1: composition in the future. Um, if it's an orcer car 508 00:28:07,680 --> 00:28:09,879 Speaker 1: and people are sort of trying to unpack them with 509 00:28:10,000 --> 00:28:12,880 Speaker 1: that looks like in what kind of future that could 510 00:28:12,880 --> 00:28:16,000 Speaker 1: make for automakers. Well, and it's interesting to think about 511 00:28:16,040 --> 00:28:19,320 Speaker 1: to Emily, and and this is an area very familiar 512 00:28:19,359 --> 00:28:21,840 Speaker 1: to you. It's there's also an element, at least there 513 00:28:21,880 --> 00:28:24,879 Speaker 1: has been so far that you know, buying an electric 514 00:28:24,880 --> 00:28:27,280 Speaker 1: car sort of says something about you, you know, it 515 00:28:27,359 --> 00:28:29,240 Speaker 1: sort of says something about who you want to be. 516 00:28:29,560 --> 00:28:33,600 Speaker 1: And I do wonder from a lifestyle perspective, how that 517 00:28:33,680 --> 00:28:37,359 Speaker 1: has changed in your estimation, not just in terms of 518 00:28:37,400 --> 00:28:40,120 Speaker 1: the dollars and cents of buying a car and some 519 00:28:40,200 --> 00:28:44,920 Speaker 1: of the infrastructure, but also about the maybe the softer 520 00:28:45,080 --> 00:28:49,240 Speaker 1: side or the harder to quantify side of this. Yeah, well, 521 00:28:49,280 --> 00:28:53,640 Speaker 1: people were looking to buy electric cars. UM probably still were. UM, 522 00:28:53,880 --> 00:28:56,160 Speaker 1: so it was sort of mutually did right. But today 523 00:28:56,200 --> 00:28:58,400 Speaker 1: I think Mary Nickels out in California, who has in 524 00:28:58,480 --> 00:29:01,160 Speaker 1: California everypoard, said they want to make the whole state 525 00:29:01,160 --> 00:29:04,840 Speaker 1: in California electric vehicles only by so electro people are 526 00:29:04,880 --> 00:29:06,600 Speaker 1: not going to be so rare anymore as they were 527 00:29:06,880 --> 00:29:09,280 Speaker 1: in the past, Which means there's this whole infrastructure about 528 00:29:09,280 --> 00:29:12,360 Speaker 1: elect of cars that really needs to be created right now. UM, 529 00:29:12,400 --> 00:29:14,400 Speaker 1: are charging infrastructure is not there yet, it's the rest 530 00:29:14,400 --> 00:29:17,640 Speaker 1: of the US. UM. The battery replacement structure is not there, 531 00:29:17,640 --> 00:29:21,240 Speaker 1: battery recycling. UM, there's a whole systimity created. But there's 532 00:29:21,240 --> 00:29:23,000 Speaker 1: a lot of cool opportunities. I talked to this investor. 533 00:29:23,120 --> 00:29:26,040 Speaker 1: He said, like you could think about your electric cars 534 00:29:26,040 --> 00:29:28,400 Speaker 1: serving another pupots. It could be a generator for your house. Like, 535 00:29:28,440 --> 00:29:30,960 Speaker 1: there's whole whole potential to have five days of power 536 00:29:31,400 --> 00:29:34,040 Speaker 1: from your house, um, from your cars. You could plug 537 00:29:34,080 --> 00:29:36,080 Speaker 1: your car into your house in the future. Um, And 538 00:29:36,160 --> 00:29:41,000 Speaker 1: that's not really available yet, but automakers are looking at it. Yeah. 539 00:29:41,040 --> 00:29:44,240 Speaker 1: I also do wonder how public policy or government policy 540 00:29:44,280 --> 00:29:46,239 Speaker 1: can play into this. Again, we know that when you 541 00:29:46,280 --> 00:29:49,880 Speaker 1: get you know, incentives, I mean we replaced some windows 542 00:29:49,920 --> 00:29:51,920 Speaker 1: in our house years ago because we got a break 543 00:29:51,960 --> 00:29:54,680 Speaker 1: because they were going to be you know, better insulated, 544 00:29:54,720 --> 00:29:56,479 Speaker 1: and so on and so forth. Like, I just do 545 00:29:56,600 --> 00:29:59,400 Speaker 1: think about how government policy, whether it's a tax break 546 00:29:59,480 --> 00:30:01,920 Speaker 1: or something, can really induce people to do things. And 547 00:30:01,960 --> 00:30:04,440 Speaker 1: I wonder if what you're hearing is we're going to 548 00:30:04,520 --> 00:30:06,360 Speaker 1: need a little bit more of this going forward. And 549 00:30:06,440 --> 00:30:09,760 Speaker 1: actually though, can government afford it after some of the 550 00:30:09,800 --> 00:30:13,040 Speaker 1: spending that they've had to do because of the shutdowns 551 00:30:13,720 --> 00:30:18,400 Speaker 1: caused by the virus and and other well, electric car 552 00:30:18,440 --> 00:30:21,040 Speaker 1: incentives are going to be really powerful going forward. We 553 00:30:21,120 --> 00:30:25,080 Speaker 1: saw that like where electric car sales collapsed in covid Um, 554 00:30:25,080 --> 00:30:27,360 Speaker 1: there weren't as many strong subsidies as there were in 555 00:30:27,400 --> 00:30:29,800 Speaker 1: other places, and where cars electric cars was continued in 556 00:30:29,840 --> 00:30:33,280 Speaker 1: covid Um, there were strong exensities. Um. So we're probably 557 00:30:33,320 --> 00:30:35,600 Speaker 1: gonna need more that, especially because also automatic to have 558 00:30:35,600 --> 00:30:37,320 Speaker 1: a lot of extra supply. They have been selling that 559 00:30:37,360 --> 00:30:39,560 Speaker 1: many cars lately. They're going to lower their prices. Cell 560 00:30:39,560 --> 00:30:42,000 Speaker 1: electric cars are usually more expensive, and they're going to 561 00:30:42,040 --> 00:30:44,040 Speaker 1: have to try extra hard to compete, So we're gonna 562 00:30:44,080 --> 00:30:47,040 Speaker 1: be those incentives. Studism might actually be incentivized to do 563 00:30:47,160 --> 00:30:49,600 Speaker 1: this because they don't want all these people coming in 564 00:30:49,640 --> 00:30:51,720 Speaker 1: their cars and creating extra air pollution, which we know 565 00:30:51,760 --> 00:30:54,520 Speaker 1: as the problems for covid um and lots of other things. 566 00:30:54,680 --> 00:30:57,880 Speaker 1: All right, so electric cars they may find reason to 567 00:30:57,920 --> 00:30:59,640 Speaker 1: do it, or they may even find sort of a 568 00:31:00,080 --> 00:31:05,280 Speaker 1: Harbon tax incentive. All right, Emily Jason, thank you so much, 569 00:31:05,360 --> 00:31:08,840 Speaker 1: our sustainability editor for Bloomberg, joining us on the phone. 570 00:31:09,600 --> 00:31:12,000 Speaker 1: How to make your electric car more than a car. 571 00:31:12,160 --> 00:31:14,320 Speaker 1: I think people think about this a lot. I mean, 572 00:31:14,360 --> 00:31:17,720 Speaker 1: even just looking at my neighborhood and sort of seeing 573 00:31:18,000 --> 00:31:20,080 Speaker 1: you know, when people buy new cars, they're they're definitely 574 00:31:20,080 --> 00:31:22,080 Speaker 1: thinking more about it. We've been talking about it. I mean, 575 00:31:22,120 --> 00:31:25,080 Speaker 1: I know you're waiting for the electric station wagon because 576 00:31:25,120 --> 00:31:27,280 Speaker 1: you're a wagony stuff, but we do. But we talk 577 00:31:27,360 --> 00:31:29,680 Speaker 1: about it a lot too. And you know who you 578 00:31:29,720 --> 00:31:31,960 Speaker 1: know who we're you know, kind of waiting to do 579 00:31:32,080 --> 00:31:36,080 Speaker 1: something UM so that it makes sense. I don't know 580 00:31:36,120 --> 00:31:39,200 Speaker 1: if I would. I don't know. We've definitely talked about it, 581 00:31:39,480 --> 00:31:42,880 Speaker 1: UM and I know your dad has one, UM and 582 00:31:42,920 --> 00:31:44,680 Speaker 1: I know loves it, and I know everybody who has 583 00:31:44,720 --> 00:31:47,560 Speaker 1: one loves it. I think, you know, we need to 584 00:31:47,600 --> 00:31:51,120 Speaker 1: make sure that there are the charging stations that are around. UM. 585 00:31:51,280 --> 00:31:53,160 Speaker 1: You really have to have the infrastructure to make it work. 586 00:31:53,160 --> 00:31:55,160 Speaker 1: But I really do think, and I do wonder, is 587 00:31:55,160 --> 00:31:58,280 Speaker 1: electric vehicles the ultimate answer because you still have those 588 00:31:58,280 --> 00:32:01,000 Speaker 1: batteries you've got to deal with, right, Yeah, true? And 589 00:32:01,040 --> 00:32:04,120 Speaker 1: I just true. And you've got power facilities that have 590 00:32:04,200 --> 00:32:06,240 Speaker 1: to charge the batteries. And I don't know if that's 591 00:32:06,320 --> 00:32:08,880 Speaker 1: ultimately the answer. So you just want cars that run 592 00:32:08,920 --> 00:32:13,720 Speaker 1: on magic? What you're going for. I've been talking to 593 00:32:13,720 --> 00:32:16,480 Speaker 1: Walt Disney and they've got some fairy dust that can 594 00:32:16,680 --> 00:32:19,360 Speaker 1: Bob Iger in his role as executive chairman, is going 595 00:32:19,400 --> 00:32:23,120 Speaker 1: to we can solve everything magic cars that don't run 596 00:32:23,160 --> 00:32:26,520 Speaker 1: on anything. They've just run on pixie dust, hydrocarbon water, 597 00:32:26,720 --> 00:32:29,360 Speaker 1: Like wouldn't that be great solar using the stuff. Like, 598 00:32:29,360 --> 00:32:31,920 Speaker 1: there's just I just think we haven't pushed it, and 599 00:32:31,960 --> 00:32:34,440 Speaker 1: maybe we need a global collaboration of people really trying 600 00:32:34,440 --> 00:32:36,760 Speaker 1: to kind of push the envelope. Stop laughing at me, 601 00:32:36,840 --> 00:32:43,000 Speaker 1: Jason Kelly, I'm laughing at you. Full confessions. Carol Masster 602 00:32:43,040 --> 00:32:51,640 Speaker 1: pulls up in her Pixie desk. Car anyway, bro journal, Yeah, 603 00:32:51,680 --> 00:32:59,520 Speaker 1: but you let me drive? Oh no, no, no, I 604 00:32:59,600 --> 00:33:14,880 Speaker 1: want to Bible. Just drying baby, good questions, trying this 605 00:33:15,760 --> 00:33:19,920 Speaker 1: drive to the globe. Thanks, we'll drying us down on 606 00:33:20,160 --> 00:33:24,200 Speaker 1: Bloomberg Radio. All right, Well, as we head to another 607 00:33:24,360 --> 00:33:27,280 Speaker 1: green Clothes it's become a bit of a habit lately. 608 00:33:27,320 --> 00:33:31,040 Speaker 1: Let's check in with Michael Sheldon, executive director, chief investment 609 00:33:31,080 --> 00:33:34,160 Speaker 1: officer for High Tower r d M Financial Group. Johnnys 610 00:33:34,200 --> 00:33:37,240 Speaker 1: on the phone from Westport. Michael, nice to have you 611 00:33:37,560 --> 00:33:40,040 Speaker 1: back with us. I trust all as well over there 612 00:33:40,040 --> 00:33:43,880 Speaker 1: in Connecticut. Yeah, so far, so good, Thank you. Yeah. 613 00:33:44,040 --> 00:33:46,440 Speaker 1: I mean it's interesting, you know, hearing a little bit 614 00:33:46,440 --> 00:33:50,240 Speaker 1: more about reopening plans. We had a doctor on earlier 615 00:33:50,240 --> 00:33:53,920 Speaker 1: who based out near you, a little down down down 616 00:33:53,920 --> 00:33:56,280 Speaker 1: the coast from you and uh in Stanford, you know, 617 00:33:56,360 --> 00:34:00,200 Speaker 1: talking about some pretty aggressive testing that's going to go on. 618 00:34:00,440 --> 00:34:03,960 Speaker 1: And we've heard a lot from your governor about what 619 00:34:04,120 --> 00:34:07,240 Speaker 1: reopening looks like from where you are. What does reopening 620 00:34:07,640 --> 00:34:10,960 Speaker 1: look like for Connecticut? Well, I think all the states 621 00:34:10,960 --> 00:34:13,080 Speaker 1: are sort of in the same boat. I think the 622 00:34:13,120 --> 00:34:15,640 Speaker 1: governors are looking at they want to see less cases 623 00:34:15,640 --> 00:34:18,160 Speaker 1: going on, They want to see less people in hospitals, 624 00:34:18,200 --> 00:34:21,320 Speaker 1: they want to see less mortality, less people dying. Obviously 625 00:34:21,360 --> 00:34:24,719 Speaker 1: from this, they want to see some improvement. And I 626 00:34:24,760 --> 00:34:27,120 Speaker 1: think generally in Connecticut was one of the first states 627 00:34:27,160 --> 00:34:30,840 Speaker 1: to get hard hit, and especially Westport and Fairfield County 628 00:34:30,840 --> 00:34:34,120 Speaker 1: where I am, and we've definitely seen some improvement, which 629 00:34:34,719 --> 00:34:38,439 Speaker 1: is a positive sign looking ahead and sort of sort 630 00:34:38,440 --> 00:34:41,719 Speaker 1: of bringing that towards the markets. I wanted to start 631 00:34:41,760 --> 00:34:44,600 Speaker 1: off by saying that there's an old saying in the markets, 632 00:34:44,800 --> 00:34:47,160 Speaker 1: which used to be don't fight the FED, and I 633 00:34:47,200 --> 00:34:49,239 Speaker 1: think the new saying now is don't fight the Fed 634 00:34:49,280 --> 00:34:53,960 Speaker 1: in Congress together. Fed in Congress have basically spent about 635 00:34:54,000 --> 00:34:57,280 Speaker 1: six to seven trillion dollars over the past couple of months, 636 00:34:58,080 --> 00:35:01,360 Speaker 1: and they're what they spent that need to temporarily replace 637 00:35:01,480 --> 00:35:04,400 Speaker 1: the lost income from slowing down and shutting down the 638 00:35:04,440 --> 00:35:07,600 Speaker 1: economy due to COVID nineteen. And now we're starting to 639 00:35:07,640 --> 00:35:10,240 Speaker 1: see as the economy starts to open up, we're starting 640 00:35:10,280 --> 00:35:13,600 Speaker 1: to see some improvement, which is now reflected in the 641 00:35:13,680 --> 00:35:16,640 Speaker 1: equity markets. I feel like it's also Michael, you know, 642 00:35:16,719 --> 00:35:20,360 Speaker 1: don't Fed, don't fight UM Germany, don't fight the e 643 00:35:20,400 --> 00:35:22,600 Speaker 1: c B. You know, there's a headline that just crossed 644 00:35:23,040 --> 00:35:26,360 Speaker 1: Anglo Merkel's coalition reaching a deal on a German stimulus package. 645 00:35:26,400 --> 00:35:29,000 Speaker 1: If one thing investors can kind of count on right 646 00:35:29,000 --> 00:35:33,719 Speaker 1: now is that leaders central banks they're going to do 647 00:35:33,760 --> 00:35:36,319 Speaker 1: what they need to do to make sure everything financially 648 00:35:36,840 --> 00:35:40,239 Speaker 1: continues to work and move smoothly, and that usually means 649 00:35:40,239 --> 00:35:42,520 Speaker 1: pumping a lot of money into the system to make 650 00:35:42,560 --> 00:35:46,879 Speaker 1: sure that there's no credit crunches or liquidity squeezes. Yeah, 651 00:35:46,920 --> 00:35:48,840 Speaker 1: I think you're right. Central banks around the world have 652 00:35:48,920 --> 00:35:52,360 Speaker 1: definitely coordinated and they pump money into the economy. In 653 00:35:52,400 --> 00:35:55,560 Speaker 1: the United States, talking about liquidity, for example, when the 654 00:35:55,560 --> 00:35:59,400 Speaker 1: Federal Reserve rolled out, it's planned to actually start purchasing 655 00:35:59,440 --> 00:36:03,239 Speaker 1: corporate both in the primary and secondary market. A few 656 00:36:03,239 --> 00:36:05,279 Speaker 1: weeks ago, I thought to me that was a bit 657 00:36:05,320 --> 00:36:07,720 Speaker 1: of a game changer, because the credit markets were really 658 00:36:08,360 --> 00:36:11,839 Speaker 1: becoming on glued and just before the Fed even bought 659 00:36:11,840 --> 00:36:14,400 Speaker 1: any corporate bonds, which they started just a week or 660 00:36:14,400 --> 00:36:17,040 Speaker 1: two ago. They basically told the markets that they're going 661 00:36:17,080 --> 00:36:19,719 Speaker 1: to do sort of their Mario Draggy moment where they 662 00:36:19,760 --> 00:36:22,600 Speaker 1: said we're gonna do whatever it takes, and that's that 663 00:36:22,640 --> 00:36:25,840 Speaker 1: announcement by the Federal Reserve a few weeks ago, basically 664 00:36:25,880 --> 00:36:28,239 Speaker 1: started to free up the credit markets. And you need 665 00:36:28,360 --> 00:36:32,319 Speaker 1: a functioning credit market for the economy to function. So 666 00:36:32,360 --> 00:36:35,480 Speaker 1: what do you worry the most about here, Michael, I 667 00:36:35,480 --> 00:36:37,279 Speaker 1: think there are a number of concerns we have right now. 668 00:36:37,280 --> 00:36:39,840 Speaker 1: One is China US relations seem to be getting worse 669 00:36:39,920 --> 00:36:43,319 Speaker 1: after starting to get better last year. We have rising 670 00:36:43,360 --> 00:36:46,520 Speaker 1: debt levels, that levels arising very quickly as a result 671 00:36:46,560 --> 00:36:48,839 Speaker 1: of all the spending going on. We don't think that's 672 00:36:48,840 --> 00:36:51,719 Speaker 1: a problem for right now because it's so inexpensive to 673 00:36:51,760 --> 00:36:54,279 Speaker 1: finance all this debt, but but down the road we 674 00:36:54,360 --> 00:36:58,200 Speaker 1: may certainly face higher taxes. We have the upcoming election 675 00:36:58,239 --> 00:37:01,719 Speaker 1: this fall, which will bring some uncertain and then there's 676 00:37:01,880 --> 00:37:05,040 Speaker 1: obviously concerns about the strength the recovery, how long will 677 00:37:05,040 --> 00:37:07,759 Speaker 1: we replet, how long it will take to replace all 678 00:37:07,800 --> 00:37:10,160 Speaker 1: the jobs that have been lost. And then there's also 679 00:37:10,200 --> 00:37:12,040 Speaker 1: the fear as we go into the fall, that COVID 680 00:37:12,120 --> 00:37:14,920 Speaker 1: nineteen could make a return later this year. That's what 681 00:37:15,000 --> 00:37:18,480 Speaker 1: I worry about though, um certainly that the virus coming back, 682 00:37:18,520 --> 00:37:22,160 Speaker 1: but I do wonder about labor dislocations of you know, 683 00:37:22,239 --> 00:37:27,600 Speaker 1: temporary workers losing jobs and those temporary job cuts become permanent. 684 00:37:27,640 --> 00:37:29,960 Speaker 1: And we just did a story. We talked with our 685 00:37:30,239 --> 00:37:34,239 Speaker 1: Bloomberg Economics team. You'll initially of our senior US economist, 686 00:37:34,280 --> 00:37:36,719 Speaker 1: you know that it's not just blue collar now we're 687 00:37:36,719 --> 00:37:40,560 Speaker 1: looking at another way that will start to impact blue 688 00:37:40,719 --> 00:37:42,960 Speaker 1: white collar jobs as well. Right Like, It's just a 689 00:37:42,960 --> 00:37:45,400 Speaker 1: reminder that when they're just slow down in the economy, 690 00:37:45,480 --> 00:37:48,200 Speaker 1: it's not just one entity, one industry, one company that 691 00:37:48,239 --> 00:37:51,440 Speaker 1: gets impacted. It's their whole whole supply chain, who they 692 00:37:51,640 --> 00:37:54,920 Speaker 1: sell to, who they buy too. It'll be it'll be 693 00:37:55,000 --> 00:37:57,440 Speaker 1: very interesting. We get the monthly jobs before coming out 694 00:37:57,480 --> 00:38:00,120 Speaker 1: this Friday, and the numbers are expecting right now or 695 00:38:00,200 --> 00:38:02,799 Speaker 1: once again going to be sky high. They're expecting a 696 00:38:02,840 --> 00:38:06,040 Speaker 1: decline of about another eight million workers and the unemployment 697 00:38:06,080 --> 00:38:08,799 Speaker 1: rate of nine But I think one of the key 698 00:38:08,800 --> 00:38:11,719 Speaker 1: things going forward is when we had the last job's number, 699 00:38:11,760 --> 00:38:14,319 Speaker 1: the last jobs report, I think the number was something 700 00:38:14,360 --> 00:38:18,759 Speaker 1: like seventy eight five of all the workers said that 701 00:38:18,800 --> 00:38:23,359 Speaker 1: their jobs had just been temporarily downsized. So if those 702 00:38:23,440 --> 00:38:26,240 Speaker 1: jobs do ins fact, if those furtherard workers do eventually 703 00:38:26,239 --> 00:38:28,640 Speaker 1: come back in the not too distant future, I think 704 00:38:28,680 --> 00:38:31,400 Speaker 1: that would certainly set the stage for a more robust 705 00:38:31,480 --> 00:38:35,200 Speaker 1: pickup in the economy and consumer sentiment, consumer spending, So 706 00:38:35,280 --> 00:38:37,839 Speaker 1: that that the job market is definitely something that needs 707 00:38:37,880 --> 00:38:42,440 Speaker 1: to be watched over time. And and so just to 708 00:38:42,440 --> 00:38:45,800 Speaker 1: to press that point a little bit, Michael, the job picture, 709 00:38:46,320 --> 00:38:48,239 Speaker 1: you know, we saw a DP. Never say we see 710 00:38:48,320 --> 00:38:50,240 Speaker 1: jobless comes tomorrow, as you say, we see the monthly 711 00:38:50,320 --> 00:38:54,440 Speaker 1: jobs report on Friday, Like what's the what's the number 712 00:38:54,560 --> 00:38:57,600 Speaker 1: amid all of that, you know, or even a specific 713 00:38:57,680 --> 00:39:00,640 Speaker 1: data point within one of those reports that your most 714 00:39:01,440 --> 00:39:04,600 Speaker 1: uh focused on seeing. Well, I think it's important that 715 00:39:04,640 --> 00:39:08,319 Speaker 1: in general, we economic data has been trending higher. So 716 00:39:08,400 --> 00:39:10,960 Speaker 1: it's not the absolute level that investors are watching, it's 717 00:39:11,000 --> 00:39:13,160 Speaker 1: the rate of change, and that rate of change is 718 00:39:13,200 --> 00:39:16,400 Speaker 1: starting to get better. Weekly jobless claims have now declined 719 00:39:16,440 --> 00:39:19,319 Speaker 1: for eight consecutive weeks, and they were still over two 720 00:39:19,360 --> 00:39:22,719 Speaker 1: million last week, which is incredibly high number. Historically, but 721 00:39:22,880 --> 00:39:26,000 Speaker 1: the trend is definitely positive, and you're seeing that in 722 00:39:26,040 --> 00:39:28,560 Speaker 1: other areas like auto sales, for example, which rose from 723 00:39:28,560 --> 00:39:31,720 Speaker 1: about eight and a half to twelve million units yesterday. 724 00:39:31,920 --> 00:39:35,839 Speaker 1: The I S M Services Index rose from about five. 725 00:39:35,920 --> 00:39:39,360 Speaker 1: So you're seeing a number of economic statistics either getting 726 00:39:39,400 --> 00:39:42,480 Speaker 1: less worse or starting to get better. But jobs will 727 00:39:42,560 --> 00:39:45,640 Speaker 1: definitely be important. It could very well take several quarters 728 00:39:45,719 --> 00:39:48,239 Speaker 1: or two or three years until we make back the 729 00:39:48,280 --> 00:39:50,800 Speaker 1: majority of the jobs that were lost over the past 730 00:39:51,040 --> 00:39:55,120 Speaker 1: several months. Michael, the jump back that we've seen in stocks, 731 00:39:55,440 --> 00:39:58,000 Speaker 1: um the bounce back we're up about the S and 732 00:39:58,040 --> 00:40:01,440 Speaker 1: P five was still off are high, yes, but we're 733 00:40:02,040 --> 00:40:05,200 Speaker 1: still down about three percent so far to day. Makes 734 00:40:05,200 --> 00:40:07,239 Speaker 1: sense to you so far? Or is? Do you think 735 00:40:07,239 --> 00:40:08,840 Speaker 1: the equity markets are getting ahead of them and just 736 00:40:08,880 --> 00:40:12,440 Speaker 1: got about thirty seconds here? Sure? Well, looking at evaluation 737 00:40:12,520 --> 00:40:14,320 Speaker 1: right now, the S and P s trading at about 738 00:40:14,320 --> 00:40:17,040 Speaker 1: twenty four times this year's numbers and twenty times next 739 00:40:17,120 --> 00:40:19,640 Speaker 1: year's numbers, so a lot of good news has certainly 740 00:40:19,760 --> 00:40:21,880 Speaker 1: been been priced in. I think as long as the 741 00:40:21,920 --> 00:40:25,240 Speaker 1: direction of the economy continues to improve in the months ahead, 742 00:40:25,640 --> 00:40:27,600 Speaker 1: I think the benefit of the doubt will be towards 743 00:40:27,719 --> 00:40:30,560 Speaker 1: higher equity markets, but it won't be in a straight line, 744 00:40:31,719 --> 00:40:33,880 Speaker 1: all right, Michael Sheldon, thank you so much. Good to 745 00:40:33,920 --> 00:40:36,040 Speaker 1: catch up with you as always. Michael Sheldon is the 746 00:40:36,080 --> 00:40:41,080 Speaker 1: executive director, chief Investment Officer of High Tower RDM Financial Group, 747 00:40:41,160 --> 00:40:43,920 Speaker 1: joining us on the phone from Westport. Thanks so much 748 00:40:43,920 --> 00:40:46,840 Speaker 1: for listening to Bloomberg Business Week. Download the podcast on Itune, 749 00:40:46,840 --> 00:40:50,000 Speaker 1: South Cloud, Blomberg dot com or wherever you get your podcasts. 750 00:40:50,040 --> 00:40:51,839 Speaker 1: And of course you can always listen to our radio 751 00:40:51,880 --> 00:40:54,680 Speaker 1: show at two pm Eastern on Bloomberg Radio, or watch 752 00:40:54,719 --> 00:40:57,160 Speaker 1: us on YouTube by searching Bloomberg Global News