1 00:00:00,560 --> 00:00:02,920 Speaker 1: I tell you what's a surprise for me that day 2 00:00:03,240 --> 00:00:09,680 Speaker 1: I went to work. After two hours, they let um 3 00:00:09,720 --> 00:00:13,000 Speaker 1: know that we have to close the restaurant and going 4 00:00:13,080 --> 00:00:22,120 Speaker 1: to Human Reserve to receive a letter of later. Hello 5 00:00:22,200 --> 00:00:25,079 Speaker 1: and welcome to Stephanomics, the podcast that brings the global 6 00:00:25,079 --> 00:00:28,640 Speaker 1: economy to you. And that was Roy James talking about 7 00:00:28,680 --> 00:00:32,440 Speaker 1: his last shift on March nine at a restaurant inside 8 00:00:32,440 --> 00:00:35,720 Speaker 1: Miami International Airport, where he tended bar for the past 9 00:00:35,760 --> 00:00:39,200 Speaker 1: ten years after coming to the US from Cuba fifteen 10 00:00:39,280 --> 00:00:43,320 Speaker 1: years ago. More than thirty million Americans have far for 11 00:00:43,479 --> 00:00:47,120 Speaker 1: unemployment benefits in the past two months. Now. If you're 12 00:00:47,120 --> 00:00:49,360 Speaker 1: having trouble thinking about that very big number, try this. 13 00:00:49,680 --> 00:00:52,440 Speaker 1: It's the equivalent of wiping out all of the jobs 14 00:00:52,440 --> 00:00:56,360 Speaker 1: created in the US economy, since it's more than twenty 15 00:00:56,440 --> 00:00:59,320 Speaker 1: years of job gains gone in a matter of weeks. 16 00:01:00,080 --> 00:01:02,600 Speaker 1: As we record this, we're waiting for the official US 17 00:01:02,680 --> 00:01:05,000 Speaker 1: jobs report for April, but we already know it's going 18 00:01:05,040 --> 00:01:07,600 Speaker 1: to show a record breaking fall and employment. And we 19 00:01:07,680 --> 00:01:10,479 Speaker 1: also know that the parts of the workforce that had 20 00:01:10,520 --> 00:01:14,560 Speaker 1: only recently benefited from that long slow recovery after the 21 00:01:14,640 --> 00:01:18,080 Speaker 1: last recession are the parts that are now being hit. First, 22 00:01:18,480 --> 00:01:22,560 Speaker 1: our US economy reporter Katia Dmitrieva has been one of 23 00:01:22,600 --> 00:01:25,480 Speaker 1: several Bloomberg reporters who've been trying to put a face 24 00:01:25,840 --> 00:01:29,000 Speaker 1: and a voice on some of those numbers. I'll talk 25 00:01:29,000 --> 00:01:31,200 Speaker 1: to her in a minute, and I'm also going to 26 00:01:31,240 --> 00:01:34,880 Speaker 1: be hearing from our Eurozone economist Maheva Kuza about the 27 00:01:35,000 --> 00:01:39,640 Speaker 1: contrasting situation in Europe. Unemployment there hasn't risen as fast 28 00:01:39,680 --> 00:01:42,679 Speaker 1: as in the US, but as many as forty million 29 00:01:42,720 --> 00:01:47,920 Speaker 1: workers are now having their wages indirectly paid by the state. First, though, 30 00:01:48,240 --> 00:01:51,040 Speaker 1: let's hear a bit more from Roy James about what's 31 00:01:51,040 --> 00:01:53,720 Speaker 1: happened to him since he lost his job. He was 32 00:01:53,720 --> 00:02:02,360 Speaker 1: talking to Bloomberg reporter Jeff Green. I was trying to 33 00:02:02,480 --> 00:02:09,720 Speaker 1: apply online. The system was impossible, also ready out for 34 00:02:11,480 --> 00:02:15,280 Speaker 1: eleve of weeks, and I didn't see nothing yet and 35 00:02:15,520 --> 00:02:22,680 Speaker 1: not even information. Right now, I'm already okay my to 36 00:02:22,720 --> 00:02:25,880 Speaker 1: pay my rent. I get a deal for the first month, 37 00:02:26,240 --> 00:02:28,560 Speaker 1: but for the second months, I didn't get a deal. 38 00:02:28,800 --> 00:02:32,280 Speaker 1: I had to pay on my rank, my grand yesterday. 39 00:02:33,040 --> 00:02:38,400 Speaker 1: About the time everything's gonna go back to normal, we 40 00:02:38,400 --> 00:02:41,400 Speaker 1: we have to pay everything. But that at the same time, 41 00:02:41,600 --> 00:02:44,400 Speaker 1: and we if we're not received the help that we're 42 00:02:44,400 --> 00:02:47,560 Speaker 1: supposed to receive from the government, from where I'm gonna 43 00:02:47,639 --> 00:02:50,359 Speaker 1: get the money to pay all those twos? But the 44 00:02:50,440 --> 00:02:53,160 Speaker 1: people need to help and there are a lot of people. No, 45 00:02:53,320 --> 00:02:58,320 Speaker 1: we're not talking about a couple of homebreds talking about millions. 46 00:02:59,680 --> 00:03:04,760 Speaker 1: So that was Roy James in Florida. Well, Katia Dmitrieva 47 00:03:05,160 --> 00:03:07,440 Speaker 1: has been part of the same project. She's a US 48 00:03:07,480 --> 00:03:11,840 Speaker 1: economy reporter and she's with me. Now, Katia, give me 49 00:03:11,880 --> 00:03:14,280 Speaker 1: some of the background the story you were trying to 50 00:03:14,360 --> 00:03:19,360 Speaker 1: tell by talking to the seven people featured in this article, 51 00:03:19,480 --> 00:03:21,320 Speaker 1: because I know you were in a way you're trying 52 00:03:21,360 --> 00:03:24,560 Speaker 1: to draw a contrast between what they're experiencing now and 53 00:03:24,680 --> 00:03:29,440 Speaker 1: what these groups were enjoying in the labor market, the 54 00:03:29,480 --> 00:03:32,040 Speaker 1: conditions that they were seeing even as recently as just 55 00:03:32,080 --> 00:03:34,880 Speaker 1: a few months ago. Yeah, that was one of the 56 00:03:34,880 --> 00:03:38,920 Speaker 1: things we wanted to explore, is what the labor market 57 00:03:39,520 --> 00:03:41,520 Speaker 1: looks like right now and what it feels like for 58 00:03:41,600 --> 00:03:45,000 Speaker 1: people on the ground. The first entry point into this 59 00:03:45,480 --> 00:03:49,160 Speaker 1: was just putting a face behind the data and and 60 00:03:49,200 --> 00:03:52,320 Speaker 1: the second part is really just finding out what is 61 00:03:52,360 --> 00:03:54,800 Speaker 1: happening on the ground. One thing that we were really 62 00:03:54,840 --> 00:03:58,080 Speaker 1: curious about is what happens when people are looking for 63 00:03:58,120 --> 00:04:01,240 Speaker 1: a job right now, when they're when they're hunting, because 64 00:04:01,280 --> 00:04:04,640 Speaker 1: a few months ago, they would have been welcomed with 65 00:04:04,720 --> 00:04:09,080 Speaker 1: open arms by employers. Employers were desperate for workers, They 66 00:04:09,120 --> 00:04:12,560 Speaker 1: were willing to pay higher wages. They were willing to 67 00:04:12,600 --> 00:04:16,680 Speaker 1: offer benefits everything from childcare to maybe a bit more 68 00:04:17,040 --> 00:04:20,200 Speaker 1: um loose kind of hours if you want to stay 69 00:04:20,200 --> 00:04:24,680 Speaker 1: here your hours, for example, they were offering healthcare benefits 70 00:04:24,680 --> 00:04:28,000 Speaker 1: at a higher rate. And that's completely changed, and it 71 00:04:28,120 --> 00:04:32,919 Speaker 1: happened very very quickly. We've had a long jobs recovery 72 00:04:33,080 --> 00:04:36,919 Speaker 1: since the global financial crisis, some of these more marginalized 73 00:04:37,000 --> 00:04:42,599 Speaker 1: members of the labor market, minorities, women had really only 74 00:04:42,680 --> 00:04:45,679 Speaker 1: just been benefiting from that recovery the last few years. 75 00:04:46,240 --> 00:04:50,640 Speaker 1: Are we seeing those groups being hit first by this 76 00:04:50,800 --> 00:04:54,800 Speaker 1: crisis to kind of first in, first out dynamic. The 77 00:04:54,839 --> 00:04:57,840 Speaker 1: March data does show a take up in the unemployment 78 00:04:57,920 --> 00:05:03,000 Speaker 1: rate for African Americans, for Hispanics, for women, but it 79 00:05:03,120 --> 00:05:05,839 Speaker 1: also shows a tick up in the unemployment just more broadly. 80 00:05:06,279 --> 00:05:09,000 Speaker 1: What we do know from the data is that they 81 00:05:09,240 --> 00:05:14,680 Speaker 1: are more likely to be let go first um they're 82 00:05:14,680 --> 00:05:18,920 Speaker 1: in industries that happened to be a bit more precarious, 83 00:05:19,000 --> 00:05:21,200 Speaker 1: and so they're more likely to be let go. And 84 00:05:21,240 --> 00:05:25,000 Speaker 1: also there are in industries that were most affected first, 85 00:05:25,320 --> 00:05:30,480 Speaker 1: so retail stores, restaurants, So this is um people over 86 00:05:30,520 --> 00:05:33,000 Speaker 1: the age of fifty five. There was this sort of 87 00:05:33,800 --> 00:05:36,720 Speaker 1: rise in the participation rate in labor market amongst that 88 00:05:37,040 --> 00:05:40,960 Speaker 1: amongst that group, and um, of course we know across 89 00:05:40,960 --> 00:05:46,840 Speaker 1: the country restaurants just shuttered in in droves, and the 90 00:05:46,920 --> 00:05:49,320 Speaker 1: same with retail stores. We had a bunch of closures. 91 00:05:49,480 --> 00:05:52,920 Speaker 1: These are also minimum wage jobs, and a large portion 92 00:05:53,000 --> 00:05:56,039 Speaker 1: of those uh folks for let go as well. Yes, 93 00:05:56,120 --> 00:05:58,640 Speaker 1: it's interesting not only that I was listening to some 94 00:05:58,720 --> 00:06:01,680 Speaker 1: of these interviews, and they do all seem to have 95 00:06:01,720 --> 00:06:06,200 Speaker 1: been surprised by losing their jobs. They had somehow thought 96 00:06:06,200 --> 00:06:08,520 Speaker 1: that it was going to be temporary, or that their 97 00:06:08,560 --> 00:06:12,320 Speaker 1: employers would try and hold keep them on. UM. And 98 00:06:12,920 --> 00:06:15,720 Speaker 1: you also had spoken, you and your colleagues to quite 99 00:06:15,760 --> 00:06:18,760 Speaker 1: a lot of young people. And we know historically that 100 00:06:18,920 --> 00:06:23,520 Speaker 1: young people can have the most lasting effects from a 101 00:06:23,520 --> 00:06:27,080 Speaker 1: recession if they lose their first job or are not 102 00:06:27,200 --> 00:06:29,360 Speaker 1: unable to get on the job's ladder at all after 103 00:06:29,440 --> 00:06:32,680 Speaker 1: leaving high school or college because of a recession. You 104 00:06:32,760 --> 00:06:34,680 Speaker 1: might think, well, that's just a few months delay, but 105 00:06:34,720 --> 00:06:37,320 Speaker 1: actually the numbers suggest people can have a permanent hit 106 00:06:37,360 --> 00:06:40,600 Speaker 1: to their income from missing out in that first step 107 00:06:41,600 --> 00:06:45,080 Speaker 1: in their career. And I think catchier. You and your 108 00:06:45,120 --> 00:06:49,599 Speaker 1: your colleagues interviewed a few people like that, including a 109 00:06:49,680 --> 00:06:55,280 Speaker 1: woman in Ohio, Danny or thiece who Viviana Further your 110 00:06:55,320 --> 00:06:57,800 Speaker 1: colleague met. Do you how much do you know about her? 111 00:06:57,880 --> 00:07:03,200 Speaker 1: What's what's her story? Yeah, Danny is a twenty five 112 00:07:03,279 --> 00:07:07,440 Speaker 1: year old in Cleveland, and she was furloughed from her 113 00:07:07,440 --> 00:07:11,480 Speaker 1: part time job as a teaching assistant at a Montessori school. 114 00:07:12,200 --> 00:07:15,320 Speaker 1: And it kind of shows this chain reaction that happens 115 00:07:15,440 --> 00:07:19,040 Speaker 1: because when she lost her job, she basically lost her 116 00:07:19,360 --> 00:07:22,680 Speaker 1: main source of income and how she was paying for 117 00:07:22,760 --> 00:07:26,520 Speaker 1: her school. And she was supposed to complete her associate's 118 00:07:26,560 --> 00:07:29,040 Speaker 1: degree this summer and that would have let her do 119 00:07:29,440 --> 00:07:33,280 Speaker 1: uh Montessori teacher training and then also transferred to a 120 00:07:33,320 --> 00:07:37,160 Speaker 1: b A. So the knock on effect of that job 121 00:07:37,240 --> 00:07:40,480 Speaker 1: loss means that she cannot complete her associate's degree, she 122 00:07:40,520 --> 00:07:45,160 Speaker 1: can't move on to do this really crucial teacher training, 123 00:07:45,760 --> 00:07:48,680 Speaker 1: and essentially she has delayed the start of her career. 124 00:07:48,960 --> 00:07:52,840 Speaker 1: So here's Dannie Ortiz now. But it was just so 125 00:07:52,880 --> 00:07:56,000 Speaker 1: out of nowhere. So it just happened so sudden, you know, 126 00:07:56,120 --> 00:07:59,040 Speaker 1: you didn't have time to prepare. It was like, Okay, 127 00:07:59,120 --> 00:08:02,640 Speaker 1: here's your last check, and you won't get four more 128 00:08:02,800 --> 00:08:05,440 Speaker 1: for the rest of the year. And at least for me, 129 00:08:05,760 --> 00:08:10,560 Speaker 1: I am okay money wise, but that's not gonna last forever. 130 00:08:11,640 --> 00:08:15,840 Speaker 1: It's gonna last me at least two more months. Maybe 131 00:08:16,440 --> 00:08:19,680 Speaker 1: this summer I was supposed to take the Monastori training 132 00:08:20,600 --> 00:08:22,800 Speaker 1: and so that I could have my own classroom in 133 00:08:22,880 --> 00:08:26,920 Speaker 1: two years. But you know that all changed. Um so 134 00:08:27,120 --> 00:08:30,280 Speaker 1: just like just like career wise, like I've had to 135 00:08:30,440 --> 00:08:33,280 Speaker 1: reconsider things, not just schooling, but like the training that 136 00:08:33,320 --> 00:08:37,240 Speaker 1: I wanted to do this year. To me, it sounded 137 00:08:37,240 --> 00:08:41,280 Speaker 1: a lot like what happened to um millennials when they 138 00:08:41,320 --> 00:08:44,360 Speaker 1: graduated into the recession in two thousand and eight, two nine, 139 00:08:44,880 --> 00:08:48,520 Speaker 1: as far as two and they were faced with a 140 00:08:48,559 --> 00:08:51,040 Speaker 1: few jobs and the jobs that they were able to get, 141 00:08:51,480 --> 00:08:54,320 Speaker 1: the power was really with the employer. They could they 142 00:08:54,320 --> 00:08:56,880 Speaker 1: could pay them what they wanted, um, often not not 143 00:08:57,000 --> 00:09:00,640 Speaker 1: the best benefits. So basically the polar opposite of of 144 00:09:00,679 --> 00:09:03,800 Speaker 1: what we were seeing up until a couple of months ago. UM. 145 00:09:03,960 --> 00:09:07,840 Speaker 1: The point you made actually about the surprise that these 146 00:09:07,880 --> 00:09:10,280 Speaker 1: folks had when they lost their jobs. I think it's 147 00:09:10,280 --> 00:09:15,640 Speaker 1: really important because the economic expansion completely changed the dynamic 148 00:09:15,679 --> 00:09:19,680 Speaker 1: of the workforce. You went from something that Danny Ortiz 149 00:09:19,760 --> 00:09:23,520 Speaker 1: is probably gonna face now, graduating into a market that 150 00:09:23,880 --> 00:09:27,800 Speaker 1: maybe doesn't want her um, And instead they were going 151 00:09:27,800 --> 00:09:31,520 Speaker 1: into a market where people were desperate for workers, all 152 00:09:31,600 --> 00:09:34,640 Speaker 1: kinds of workers, people without a college degree, people who 153 00:09:34,679 --> 00:09:37,680 Speaker 1: had some college um, people who didn't even want to 154 00:09:37,679 --> 00:09:41,360 Speaker 1: go to college. UM, African Americans who, like I mentioned, 155 00:09:41,400 --> 00:09:44,600 Speaker 1: their unemployment rate went down. Jobs were offered to them. 156 00:09:44,960 --> 00:09:48,560 Speaker 1: So it's it's worth emphasizing how surprising it was because 157 00:09:48,600 --> 00:09:51,319 Speaker 1: employers were really bending over backwards up until a few 158 00:09:51,320 --> 00:09:57,440 Speaker 1: months ago to keep people on hand. Katia, Finally, was 159 00:09:57,480 --> 00:10:00,560 Speaker 1: anything surprising to you in the report voting that you 160 00:10:00,640 --> 00:10:03,559 Speaker 1: did in the last week or recently? You know, it 161 00:10:03,720 --> 00:10:06,040 Speaker 1: was the main takeaway bin for you. I think one 162 00:10:06,040 --> 00:10:08,199 Speaker 1: of the more interesting parts in the reporting and in 163 00:10:08,360 --> 00:10:11,280 Speaker 1: speaking with some of these people that are in this 164 00:10:11,360 --> 00:10:16,640 Speaker 1: story is just how finding out just how precarious the 165 00:10:16,720 --> 00:10:20,640 Speaker 1: labor market was for them even before the coronavirus. You know, 166 00:10:20,720 --> 00:10:24,680 Speaker 1: I think I think we often talk about the coronavirus 167 00:10:24,720 --> 00:10:27,400 Speaker 1: as this black swan event that caused a lot of 168 00:10:27,440 --> 00:10:31,760 Speaker 1: these issues, But the reality is that as as good 169 00:10:31,800 --> 00:10:35,280 Speaker 1: as the gains were beforehand, you know, wages did rise 170 00:10:35,640 --> 00:10:39,280 Speaker 1: and people were able to find jobs easier, but a 171 00:10:39,280 --> 00:10:43,400 Speaker 1: lot of the protections weren't in place either, and the 172 00:10:43,440 --> 00:10:47,400 Speaker 1: wages often didn't compensate people for the kind of for 173 00:10:47,440 --> 00:10:49,520 Speaker 1: the kind of work they were doing. So, if anything, 174 00:10:50,200 --> 00:10:53,679 Speaker 1: I think that's the question going forward is after all 175 00:10:53,720 --> 00:10:57,240 Speaker 1: of this is over, when the economy quote unquote reopens, 176 00:10:57,280 --> 00:10:59,800 Speaker 1: when people start going back to work, what will the 177 00:11:00,040 --> 00:11:05,160 Speaker 1: brief worth look like? Basically, um, maybe people will get 178 00:11:05,200 --> 00:11:07,800 Speaker 1: that kind of stability where they can't just be let 179 00:11:07,840 --> 00:11:10,400 Speaker 1: go at a moment's notice and lose their healthcare and benefits, 180 00:11:11,280 --> 00:11:13,960 Speaker 1: or will it be eroded further, which is what we 181 00:11:13,960 --> 00:11:16,920 Speaker 1: saw in the two thousand eight financial crisis. So I 182 00:11:16,960 --> 00:11:20,240 Speaker 1: think that's a big question going forward, and there's definitely 183 00:11:20,320 --> 00:11:21,840 Speaker 1: one that we're going to be talking about a lot. 184 00:11:21,880 --> 00:11:25,040 Speaker 1: And you here asked across the world now, not just 185 00:11:25,240 --> 00:11:27,800 Speaker 1: in the U. S. Catier Dmitrieva, thank you very much 186 00:11:28,800 --> 00:11:42,079 Speaker 1: thanks so much so, the International Labor Organization estimated this 187 00:11:42,120 --> 00:11:45,600 Speaker 1: week that working out as worldwide would be at least 188 00:11:45,640 --> 00:11:50,040 Speaker 1: ten percent lower this quarter than before the pandemic started, 189 00:11:50,080 --> 00:11:52,920 Speaker 1: which is the equivalent of three hundred and five million 190 00:11:53,200 --> 00:11:57,559 Speaker 1: full time jobs. Now, countries probably don't have a choice 191 00:11:57,679 --> 00:12:00,280 Speaker 1: about whether to have that kind of decline in an 192 00:12:00,280 --> 00:12:03,679 Speaker 1: employment in their countries if they want to contain the virus, 193 00:12:03,679 --> 00:12:07,160 Speaker 1: but the decisions that governments take can affect whether people 194 00:12:07,200 --> 00:12:11,120 Speaker 1: at the sharp end of that decline can formally stay 195 00:12:11,120 --> 00:12:15,160 Speaker 1: in their jobs during this time, earning wages rather than 196 00:12:15,240 --> 00:12:18,040 Speaker 1: joining the ranks of the unemployed. In the US, as 197 00:12:18,040 --> 00:12:21,559 Speaker 1: we've heard, unemployment has already sowed and that could yet 198 00:12:21,640 --> 00:12:25,440 Speaker 1: happen across Europe. But we've also seen governments step in 199 00:12:25,520 --> 00:12:28,880 Speaker 1: to cover wages directly, to the point where now a 200 00:12:29,040 --> 00:12:33,200 Speaker 1: mind boggling number of people are getting their wages indirectly 201 00:12:33,280 --> 00:12:37,480 Speaker 1: paid by the state. Our Eurozone economist may Vakuza, who 202 00:12:37,480 --> 00:12:39,839 Speaker 1: has been on the program before, has been looking at 203 00:12:39,840 --> 00:12:42,720 Speaker 1: the numbers. May ver, good to have you back. How 204 00:12:42,760 --> 00:12:47,079 Speaker 1: many people across the Eurozone are currently being paid by 205 00:12:47,120 --> 00:12:51,280 Speaker 1: the state by your calculation, So looking just at the 206 00:12:51,440 --> 00:12:56,000 Speaker 1: four biggest countries so Founds, Germany, Italian, Spain. It's about 207 00:12:56,040 --> 00:12:59,760 Speaker 1: thirty million people who have found for those short term 208 00:13:00,000 --> 00:13:04,480 Speaker 1: employment schemes, the partial unemployment schemes where they are still 209 00:13:04,520 --> 00:13:07,640 Speaker 1: employed by a company but there's no activity and their 210 00:13:07,679 --> 00:13:10,760 Speaker 1: wages are paid bases states. And what does that meant? 211 00:13:10,800 --> 00:13:13,400 Speaker 1: I mean you look at what the equivalent might have 212 00:13:13,440 --> 00:13:16,559 Speaker 1: been in terms of the unemployment rate if you hadn't 213 00:13:16,559 --> 00:13:21,520 Speaker 1: seen governments introduced those wage support schemes quite early on, 214 00:13:21,840 --> 00:13:26,680 Speaker 1: at the same time as lockdowns began. So at the moment, 215 00:13:26,760 --> 00:13:30,880 Speaker 1: are about ten million unemployed across your economies as Big four. 216 00:13:31,720 --> 00:13:34,959 Speaker 1: If you had thirty million people who are now short 217 00:13:35,080 --> 00:13:38,080 Speaker 1: term in the short term and employment schemes, that would 218 00:13:38,360 --> 00:13:42,320 Speaker 1: mean forty million unemployed. It would multiply the unemployment rate 219 00:13:42,679 --> 00:13:47,800 Speaker 1: by by about four so to about thirty percent. If 220 00:13:47,800 --> 00:13:50,480 Speaker 1: you had on top of that those the other workers 221 00:13:50,480 --> 00:13:54,199 Speaker 1: that can't go into the unemployment, the short term unemployment, 222 00:13:54,240 --> 00:13:57,439 Speaker 1: you could get easier each forty percent unemployment rate at 223 00:13:57,480 --> 00:14:00,520 Speaker 1: the at the peak of the of the down, so 224 00:14:00,720 --> 00:14:04,559 Speaker 1: around now start of me it's probably the big for 225 00:14:04,720 --> 00:14:08,600 Speaker 1: tip percent from eight percent in February. We're recording this 226 00:14:08,800 --> 00:14:11,839 Speaker 1: before we've got the April employment numbers for the US, 227 00:14:11,920 --> 00:14:17,319 Speaker 1: but most forecasters are expecting well over twenty million decline 228 00:14:17,559 --> 00:14:22,080 Speaker 1: in employment and potentially an unemployment rate in the US 229 00:14:22,200 --> 00:14:25,200 Speaker 1: heading to over the next few weeks if it's not 230 00:14:25,240 --> 00:14:28,520 Speaker 1: there already. So what you're saying is it could be. 231 00:14:28,600 --> 00:14:31,840 Speaker 1: It could have been maybe even double that in Europe 232 00:14:32,000 --> 00:14:35,040 Speaker 1: if you've not had this support programs in place, But 233 00:14:35,160 --> 00:14:37,920 Speaker 1: now it's going to be maybe in the region of 234 00:14:38,400 --> 00:14:42,560 Speaker 1: twelve or fourteen percent, depending on depending on what happens. Yeah, 235 00:14:42,800 --> 00:14:45,200 Speaker 1: that's about where is it. That's that's a sort of 236 00:14:45,280 --> 00:14:47,360 Speaker 1: runch at the moment. It's difficult to know how much. 237 00:14:47,400 --> 00:14:50,400 Speaker 1: In fact, so if you look at a much unemployment 238 00:14:50,520 --> 00:14:55,760 Speaker 1: rate in your area, it has increased only marginally. And 239 00:14:55,840 --> 00:14:58,440 Speaker 1: the main reason from seven points hip percent for your 240 00:14:58,480 --> 00:15:01,320 Speaker 1: areas or to seven points opposite, I think. And the 241 00:15:01,360 --> 00:15:05,840 Speaker 1: main reason is that um to be registered as unemployed 242 00:15:05,920 --> 00:15:09,840 Speaker 1: according to the high definition, you have to be not working, 243 00:15:10,240 --> 00:15:12,480 Speaker 1: you have to be actively looking for a job, and 244 00:15:12,520 --> 00:15:14,680 Speaker 1: you have to be available to start a job. And 245 00:15:14,720 --> 00:15:18,320 Speaker 1: because people are in lockdowns, they're not necessarily available to 246 00:15:18,360 --> 00:15:21,880 Speaker 1: start a job because they can't move. So the headline 247 00:15:22,000 --> 00:15:26,840 Speaker 1: unemployment rate won't tell you the full extent of the impact. 248 00:15:27,160 --> 00:15:29,760 Speaker 1: But when you look at claimant numbers, so people who 249 00:15:29,760 --> 00:15:32,640 Speaker 1: have failed to get the unemployment benefits, then you can 250 00:15:32,680 --> 00:15:36,160 Speaker 1: see some increase. In Spain, for instance, that is reason 251 00:15:36,280 --> 00:15:39,360 Speaker 1: too um more or less where it was at the 252 00:15:39,440 --> 00:15:42,400 Speaker 1: end of twenty sixteen, so quite a lot. In France 253 00:15:42,480 --> 00:15:46,320 Speaker 1: it has increased as well, but still it hasn't increased 254 00:15:46,320 --> 00:15:49,200 Speaker 1: as much as you could have expected. And you can 255 00:15:49,240 --> 00:15:52,960 Speaker 1: see as well in the administrative data. In Spain, for instance, 256 00:15:53,040 --> 00:15:57,400 Speaker 1: about nine hundred thousand people have lost their jobs due 257 00:15:57,560 --> 00:16:00,360 Speaker 1: to the pandemic, so between mid March the end of 258 00:16:00,400 --> 00:16:05,920 Speaker 1: paper um, most of them almost eight hundred thousand are 259 00:16:06,080 --> 00:16:11,320 Speaker 1: temporary workers and very few permanent workers. And in contrast, 260 00:16:11,360 --> 00:16:14,160 Speaker 1: you have had an increased in the short term and 261 00:16:14,200 --> 00:16:19,120 Speaker 1: employed so these followed workers schemes of about three million 262 00:16:19,360 --> 00:16:22,560 Speaker 1: in Spain, so you can see that there is still 263 00:16:22,720 --> 00:16:26,240 Speaker 1: an impact on the labor market. It's not necessarily something 264 00:16:26,320 --> 00:16:28,960 Speaker 1: you can see very well in the data yet, because 265 00:16:29,000 --> 00:16:34,720 Speaker 1: of these problems of connecting the statistics during the lockdowns, 266 00:16:34,760 --> 00:16:38,240 Speaker 1: but the impact is a lot smaller than it would 267 00:16:38,240 --> 00:16:44,360 Speaker 1: have been without the fellowed workers schemes, and we've seen 268 00:16:44,400 --> 00:16:46,560 Speaker 1: that also in the US. We see it everywhere. Actually, 269 00:16:46,640 --> 00:16:49,120 Speaker 1: this gap between a number of people losing their jobs 270 00:16:49,120 --> 00:16:51,280 Speaker 1: and the number of people who you see joining unemployment 271 00:16:51,360 --> 00:16:53,760 Speaker 1: roles for exactly that reason. I think it's it's about 272 00:16:54,240 --> 00:16:57,560 Speaker 1: forty or fifty of people losing their jobs in the 273 00:16:57,680 --> 00:17:02,480 Speaker 1: US are not necessarily joining the formally unemployed. People outside 274 00:17:02,560 --> 00:17:05,760 Speaker 1: the Europe might say, well, this isn't so different. You know, 275 00:17:05,840 --> 00:17:08,240 Speaker 1: we think of Europe as being a place of big 276 00:17:08,280 --> 00:17:10,840 Speaker 1: government and they already have lots of people working in 277 00:17:10,880 --> 00:17:15,120 Speaker 1: the public sector. Um, maybe this isn't such a psychological 278 00:17:15,280 --> 00:17:18,879 Speaker 1: shift for European governments. But I mean, what do you 279 00:17:18,920 --> 00:17:22,880 Speaker 1: think is the implications of the government having intervened this 280 00:17:23,040 --> 00:17:25,000 Speaker 1: heavily in the labor market? I mean, is it going 281 00:17:25,040 --> 00:17:27,359 Speaker 1: to be hard to unwind? And is it going to 282 00:17:27,440 --> 00:17:32,879 Speaker 1: be impossibly expensive even for these notoriously big spending European government. 283 00:17:34,000 --> 00:17:36,920 Speaker 1: So I don't think it's going to be helped to unwind. 284 00:17:36,920 --> 00:17:41,480 Speaker 1: When activity returns, those workers will go back to work, 285 00:17:41,600 --> 00:17:44,560 Speaker 1: And in a way, it's actually that should be easier 286 00:17:44,600 --> 00:17:49,600 Speaker 1: to unwind than normal unemployment, than people moving onto the 287 00:17:49,760 --> 00:17:52,480 Speaker 1: unemployment benefits and then they have to find a new job. 288 00:17:52,960 --> 00:17:55,240 Speaker 1: Those workers they still have a job, they are still 289 00:17:55,320 --> 00:17:58,480 Speaker 1: linked to a company. It's just that there's no activity 290 00:17:58,520 --> 00:18:00,960 Speaker 1: for this company at the moment. So I think it 291 00:18:01,000 --> 00:18:06,119 Speaker 1: should be relatively straightforward for um those workers to go 292 00:18:06,200 --> 00:18:09,520 Speaker 1: back to their normal activity and go back to their 293 00:18:09,800 --> 00:18:12,800 Speaker 1: employers pay roll rather than being paid by the States. 294 00:18:13,320 --> 00:18:17,400 Speaker 1: So I'm not too worried about the difficulty in unwinding. 295 00:18:17,880 --> 00:18:22,200 Speaker 1: But it is clear that the programs have been generally 296 00:18:22,280 --> 00:18:27,560 Speaker 1: made more generous, more easy to access, and most most 297 00:18:27,760 --> 00:18:30,600 Speaker 1: governments initially had said it would be for March and April, 298 00:18:30,920 --> 00:18:33,199 Speaker 1: but now they have to increase, and they have started 299 00:18:33,240 --> 00:18:37,480 Speaker 1: increasing um or extending to May as well, of course, 300 00:18:37,560 --> 00:18:40,240 Speaker 1: because the lugduns are still in place and they're only 301 00:18:40,280 --> 00:18:45,320 Speaker 1: going to be gradually removed, so it's going to cost more, 302 00:18:45,680 --> 00:18:48,640 Speaker 1: but it will come to an end when activity resumes 303 00:18:48,680 --> 00:18:51,480 Speaker 1: and returns. They will go back to work. But is 304 00:18:51,520 --> 00:18:53,560 Speaker 1: it is it worth the money? Do you think? You 305 00:18:53,600 --> 00:18:56,000 Speaker 1: make the good point that is the European Commission has 306 00:18:56,040 --> 00:19:00,960 Speaker 1: helped to provide a backup so that country which perhaps 307 00:19:01,000 --> 00:19:04,960 Speaker 1: didn't feel they had the fiscal capacity the budget to 308 00:19:05,119 --> 00:19:08,560 Speaker 1: do this could could do it. As far as your concern, 309 00:19:08,680 --> 00:19:12,719 Speaker 1: just this, these tens of billions, money well spent. In 310 00:19:12,760 --> 00:19:15,600 Speaker 1: many countries, it's not much more expensive than people claiming 311 00:19:15,680 --> 00:19:18,840 Speaker 1: unemployment benefits. In Spain, for instance, it is exactly the 312 00:19:18,880 --> 00:19:22,480 Speaker 1: same money that people will receive as unemployment benefits. So 313 00:19:23,520 --> 00:19:26,679 Speaker 1: if those workers would have been fired and moved to 314 00:19:26,760 --> 00:19:30,640 Speaker 1: the to the claiman account, then you know it's it's 315 00:19:30,640 --> 00:19:32,879 Speaker 1: the same amount, and at least they are keeping a 316 00:19:32,880 --> 00:19:37,000 Speaker 1: direct link to the labor market and you're not destroying 317 00:19:37,359 --> 00:19:40,040 Speaker 1: it's quite a un it's an investment. It's it's a 318 00:19:40,119 --> 00:19:43,439 Speaker 1: productive capacity that's matching between workers and firms, and when 319 00:19:43,480 --> 00:19:45,400 Speaker 1: you lose them, you have to spend more effort, more 320 00:19:45,480 --> 00:19:49,600 Speaker 1: time finding the right people and training them. So I 321 00:19:49,680 --> 00:19:53,600 Speaker 1: think it's money well spent. Difinity maybe because I thank 322 00:19:53,600 --> 00:19:55,359 Speaker 1: you very much. It's great to talk to you again. 323 00:19:55,920 --> 00:20:04,639 Speaker 1: Thank you so thanks for listening to Stephanomics. We'll be 324 00:20:04,720 --> 00:20:08,200 Speaker 1: back next week with more about the way COVID nineteen 325 00:20:08,560 --> 00:20:11,959 Speaker 1: is turning the world economy upside down. Remember you can 326 00:20:12,000 --> 00:20:15,119 Speaker 1: always find us on the Bloomberg Terminal, website, app or 327 00:20:15,160 --> 00:20:17,800 Speaker 1: wherever you get your podcasts. And for more news and 328 00:20:17,800 --> 00:20:22,760 Speaker 1: analysis from Bloomberg Economics, follow at Economics on Twitter. This 329 00:20:22,880 --> 00:20:26,240 Speaker 1: episode was produced by Magnus Hendrickson, with special thanks to 330 00:20:26,400 --> 00:20:33,159 Speaker 1: Roy James, Danny Orkiece, Jeff Green, Viviana Hertadol Kidmitrieva, and 331 00:20:33,320 --> 00:20:37,560 Speaker 1: Mava Kusan. Scott Lanman is the executive producer of Stephanomics 332 00:20:37,840 --> 00:20:40,600 Speaker 1: and the head of Bloomberg Podcast is Francesca Levi.