1 00:00:11,039 --> 00:00:14,520 Speaker 1: Hello, and welcome to another episode of the Odd Thoughts Podcast. 2 00:00:14,600 --> 00:00:18,680 Speaker 1: I'm Tracy Allaway and I'm Joe. Wasn't all so, Joe. 3 00:00:18,960 --> 00:00:22,360 Speaker 1: I know we've been spending a lot of time on 4 00:00:22,440 --> 00:00:26,160 Speaker 1: the question of inflation and whether or not it's transitory, 5 00:00:26,160 --> 00:00:29,200 Speaker 1: but I feel like a lot of that discourse is 6 00:00:29,240 --> 00:00:34,360 Speaker 1: sort of happening at the expense of a greater focus 7 00:00:34,440 --> 00:00:36,280 Speaker 1: on the labor market. And I know that sounds like 8 00:00:36,280 --> 00:00:38,400 Speaker 1: a weird thing to say, but if you think that 9 00:00:39,000 --> 00:00:40,760 Speaker 1: what the FED is saying right now is that they're 10 00:00:40,800 --> 00:00:43,880 Speaker 1: going to keep rates very, very low until the labor 11 00:00:43,960 --> 00:00:48,960 Speaker 1: market fully recovers, or recovers even more than really we 12 00:00:49,000 --> 00:00:51,680 Speaker 1: should be digging into the labor market and what full 13 00:00:51,760 --> 00:00:55,480 Speaker 1: employment actually looks like, right, and that is I mean, 14 00:00:55,520 --> 00:00:58,280 Speaker 1: I think there's two questions, or there's a million questions, 15 00:00:58,320 --> 00:01:01,480 Speaker 1: but a as you say, what does quote full employment 16 00:01:01,720 --> 00:01:05,920 Speaker 1: unquote our maximum employment which the FED is established as 17 00:01:05,920 --> 00:01:08,120 Speaker 1: a precondition for rates liftoff, what does that look like? 18 00:01:08,280 --> 00:01:11,959 Speaker 1: That's one thing? And then two, why have we not 19 00:01:12,040 --> 00:01:14,520 Speaker 1: seen faster job market good growth? And that seems like 20 00:01:14,560 --> 00:01:16,800 Speaker 1: a funny question to ask because the labor market growth 21 00:01:16,800 --> 00:01:21,760 Speaker 1: has been incredibly fast since last year. Nonetheless, we do 22 00:01:21,800 --> 00:01:24,240 Speaker 1: seem to be in this weird mismatch where there's lots 23 00:01:24,240 --> 00:01:27,600 Speaker 1: of job openings and lots of people who are not employed, 24 00:01:28,200 --> 00:01:31,800 Speaker 1: and why haven't they Well, what are the reasons that 25 00:01:32,200 --> 00:01:34,240 Speaker 1: people who left the labor four US a year ago 26 00:01:34,680 --> 00:01:38,520 Speaker 1: haven't come back yet? Right? This is the mystery of 27 00:01:38,600 --> 00:01:41,080 Speaker 1: the labor market at the moment. So, on the one hand, 28 00:01:41,400 --> 00:01:45,360 Speaker 1: unemployment is higher than it was before the pandemic. I 29 00:01:45,400 --> 00:01:48,520 Speaker 1: think we're still something like four million jobs short of 30 00:01:48,560 --> 00:01:52,440 Speaker 1: where we were back in February. And if you look at, 31 00:01:52,480 --> 00:01:54,840 Speaker 1: you know, the line of where we would have been 32 00:01:54,920 --> 00:01:57,720 Speaker 1: had the pandemic never actually happened, I think we're about 33 00:01:57,760 --> 00:02:01,160 Speaker 1: seven million jobs short. And Yeah, at the same time, 34 00:02:01,720 --> 00:02:03,760 Speaker 1: you have a lot of companies, and you know, we've 35 00:02:03,760 --> 00:02:05,800 Speaker 1: spoken to at least one of them on the show 36 00:02:06,200 --> 00:02:09,079 Speaker 1: talking about the idea of a labor shortage, that they 37 00:02:09,120 --> 00:02:11,760 Speaker 1: can't get the right workers in the jobs that they 38 00:02:11,800 --> 00:02:15,760 Speaker 1: have open at the moment. And it turns out there's 39 00:02:15,800 --> 00:02:19,480 Speaker 1: really a perfect economic principle to capture this sort of 40 00:02:19,639 --> 00:02:23,800 Speaker 1: tension between job openings and the unemployment rate, and that 41 00:02:23,960 --> 00:02:27,760 Speaker 1: is something called the beverage curve. Yeah, you had a 42 00:02:27,800 --> 00:02:30,960 Speaker 1: great post on this recently. Thank you. Um So, the 43 00:02:31,000 --> 00:02:34,880 Speaker 1: beverage curve is basically the relationship. That's all I wanted 44 00:02:34,919 --> 00:02:36,959 Speaker 1: you to say, Joe, Yeah, I just I just wanted 45 00:02:36,960 --> 00:02:40,920 Speaker 1: to kick it back to you. Thanks. It's the relationship 46 00:02:41,000 --> 00:02:44,120 Speaker 1: between the unemployment rate and the job opening rate. And 47 00:02:44,360 --> 00:02:46,840 Speaker 1: you know, normally, if you look at the beverage curve 48 00:02:47,160 --> 00:02:51,440 Speaker 1: in a usual business cycle, it would be expected to 49 00:02:51,480 --> 00:02:55,160 Speaker 1: move in a sort of counter clockwise loop. So as 50 00:02:55,160 --> 00:02:59,960 Speaker 1: the unemployment rate initially jumps, you would expect the rate 51 00:03:00,080 --> 00:03:02,839 Speaker 1: of job openings to stay very small and then sort 52 00:03:02,880 --> 00:03:05,840 Speaker 1: of gradually recover and start moving to the left as 53 00:03:05,880 --> 00:03:09,440 Speaker 1: the economy healed. Uh, spoiler alert. That is not what 54 00:03:09,520 --> 00:03:13,360 Speaker 1: has happened in this particular business cycle. Instead, we've seen 55 00:03:13,600 --> 00:03:15,959 Speaker 1: something that is normally a curve. You know, the clue 56 00:03:16,040 --> 00:03:18,680 Speaker 1: is in the name, the beverage curve. It's basically morphed 57 00:03:18,720 --> 00:03:23,560 Speaker 1: into an up and down line, meaning that unemployment basically 58 00:03:23,639 --> 00:03:26,320 Speaker 1: isn't moving even as the number of job openings is 59 00:03:26,360 --> 00:03:30,480 Speaker 1: going higher and higher and higher. So something really appears 60 00:03:30,520 --> 00:03:33,120 Speaker 1: to have changed in the labor market here. And I 61 00:03:33,120 --> 00:03:35,800 Speaker 1: am pleased to say we have the perfect person to 62 00:03:35,960 --> 00:03:38,400 Speaker 1: discuss all of this he's actually the author of a 63 00:03:38,440 --> 00:03:42,120 Speaker 1: recent bulletin on the beverage curve on this exact topic, 64 00:03:42,200 --> 00:03:44,920 Speaker 1: we're going to be speaking with Thomas Lubeck. He's a 65 00:03:45,000 --> 00:03:49,080 Speaker 1: senior advisor in the research department at the Richmond fed So, Tom, 66 00:03:49,120 --> 00:03:51,800 Speaker 1: thanks so much for coming on. Okay, thanks so much 67 00:03:51,840 --> 00:03:54,760 Speaker 1: for having me, and thanks for the kind of introduction. Yeah, 68 00:03:54,840 --> 00:03:57,920 Speaker 1: so I was really interested in this paper. Maybe just 69 00:03:57,960 --> 00:04:00,200 Speaker 1: to begin with, you could lay out, you know, what 70 00:04:00,440 --> 00:04:03,600 Speaker 1: is the beverage curve and how would you expect it 71 00:04:03,680 --> 00:04:07,240 Speaker 1: to act in normal times. So I think you've already 72 00:04:07,280 --> 00:04:10,280 Speaker 1: introduced the concept of the beverage curve perfectly, So just 73 00:04:10,400 --> 00:04:15,560 Speaker 1: to restate what you discussed, the beverage curve is the 74 00:04:15,560 --> 00:04:19,400 Speaker 1: relationship between the unemployment rate and the job openings rate. 75 00:04:19,520 --> 00:04:24,640 Speaker 1: So job openings are open positions that businesses um want 76 00:04:24,680 --> 00:04:28,720 Speaker 1: to fill and looking to hire form. And this relationship 77 00:04:28,960 --> 00:04:32,040 Speaker 1: is it's a negative relationship. So when the unemployment rate 78 00:04:32,200 --> 00:04:38,520 Speaker 1: is high during a downturn, job openings are low because well, 79 00:04:38,560 --> 00:04:42,760 Speaker 1: the economy is um not working very well and firms 80 00:04:42,800 --> 00:04:46,440 Speaker 1: are reluctant to hire new workers because of the uncertainty 81 00:04:46,480 --> 00:04:49,480 Speaker 1: of how the economy might improve. But as the economy 82 00:04:49,640 --> 00:04:53,200 Speaker 1: improves um and the downturn turns into an upturn. The 83 00:04:53,279 --> 00:04:57,920 Speaker 1: unemployment rate falls because the job openings are being filled, 84 00:04:58,120 --> 00:05:03,200 Speaker 1: and as the economy improves, firm post more open positions, 85 00:05:03,240 --> 00:05:06,760 Speaker 1: so the job openings rate rises as the unemployment rate falls. 86 00:05:07,480 --> 00:05:12,800 Speaker 1: And this is what we see in every recession, and 87 00:05:12,960 --> 00:05:17,480 Speaker 1: the beverage curve basically describes this relationship. So you can 88 00:05:17,520 --> 00:05:21,440 Speaker 1: think of it as a scattered plot of monthly data 89 00:05:21,480 --> 00:05:24,520 Speaker 1: on the unemployment rate and the job openings rate, and 90 00:05:24,600 --> 00:05:30,239 Speaker 1: they line up nicely around this downward sloping beverage curve. Now, 91 00:05:30,320 --> 00:05:34,920 Speaker 1: what is absolutely striking in the COVID recession and COVID 92 00:05:35,000 --> 00:05:39,880 Speaker 1: recovery is that the beverage curve that we've seen in 93 00:05:39,880 --> 00:05:44,120 Speaker 1: the last twenty years pre COVID has changed dramatically and 94 00:05:44,160 --> 00:05:48,320 Speaker 1: in two ways. It has shifted outward, and it is 95 00:05:48,360 --> 00:05:53,240 Speaker 1: also has become much more steep. So the outward shift 96 00:05:53,480 --> 00:05:57,719 Speaker 1: of the beverage curve um that occurred right after the 97 00:05:57,800 --> 00:06:02,280 Speaker 1: COVID shock hits, so essentially starting in March two thousand, 98 00:06:02,320 --> 00:06:06,640 Speaker 1: twenty April, when the unemployment rate shot up um and 99 00:06:06,680 --> 00:06:11,679 Speaker 1: then declined very very rapidly once the economy worked itself 100 00:06:11,960 --> 00:06:17,720 Speaker 1: through the initial COVID shock. So, but this is connected 101 00:06:17,839 --> 00:06:20,919 Speaker 1: with an outward shift in the beverage curve in the 102 00:06:20,960 --> 00:06:26,760 Speaker 1: following sense. So, at any given unemployment rate in normal times, um, 103 00:06:26,839 --> 00:06:30,360 Speaker 1: you can derive and shop openings rate that is required 104 00:06:30,400 --> 00:06:36,080 Speaker 1: to maintain a certain employment level or unemployment level. Now, 105 00:06:36,200 --> 00:06:39,680 Speaker 1: during the first few months of the COVID shock, the 106 00:06:39,760 --> 00:06:44,159 Speaker 1: unemployment rate stayed high but declined, but so did the 107 00:06:44,240 --> 00:06:48,360 Speaker 1: job openings rate. But the job openings rate was at 108 00:06:48,360 --> 00:06:51,720 Speaker 1: a level that actually we hadn't seen before for this 109 00:06:51,839 --> 00:06:57,080 Speaker 1: given unemployment rate. And this has been quite striking. I wanna, 110 00:06:57,160 --> 00:07:02,000 Speaker 1: obviously why dive into why this relationship is the way 111 00:07:02,000 --> 00:07:04,240 Speaker 1: it is right now. But actually before we get to that, 112 00:07:04,279 --> 00:07:07,320 Speaker 1: I actually have a more straightforward question, which is how 113 00:07:07,520 --> 00:07:10,320 Speaker 1: is the job openings rate derived? Where does that number 114 00:07:10,320 --> 00:07:12,920 Speaker 1: come from? Because with the unemployment rate, you know, you 115 00:07:12,960 --> 00:07:15,520 Speaker 1: imagine you ask a thousand people what percentage of you 116 00:07:15,880 --> 00:07:19,080 Speaker 1: want a job and what don't have one now, etcetera, 117 00:07:19,120 --> 00:07:21,000 Speaker 1: you can come up with one. How does how do 118 00:07:21,120 --> 00:07:25,200 Speaker 1: they determine the job opening trade? So it comes from 119 00:07:25,200 --> 00:07:28,400 Speaker 1: the so called trolls data, So the shop openings and 120 00:07:28,520 --> 00:07:32,280 Speaker 1: Labor Turnover survey that the Borreau of Labor Statistics UM 121 00:07:32,320 --> 00:07:36,920 Speaker 1: conducts and they ask businesses a set of questions where 122 00:07:36,960 --> 00:07:40,680 Speaker 1: they really dig deep into what a job opening is. 123 00:07:41,000 --> 00:07:44,080 Speaker 1: Search job opening is defined, it's a position that is 124 00:07:44,200 --> 00:07:48,560 Speaker 1: immediately ready to be filled, for which there's funding, and 125 00:07:48,600 --> 00:07:53,000 Speaker 1: for which firms actively engage in recruiting efforts. And the 126 00:07:53,080 --> 00:07:57,480 Speaker 1: survey goes out to businesses and they report their shop 127 00:07:57,520 --> 00:08:02,040 Speaker 1: openings to the Borough of Labor Statistics and this then 128 00:08:02,160 --> 00:08:04,520 Speaker 1: ends up as the shop openings right, so at both 129 00:08:04,560 --> 00:08:08,520 Speaker 1: total level, but also I'm relative to UM the labor 130 00:08:08,600 --> 00:08:12,920 Speaker 1: force for instance, So I'm gonna jump to UM. The 131 00:08:13,000 --> 00:08:15,840 Speaker 1: next obvious question that Joe just kind of foreshadowed, but 132 00:08:16,560 --> 00:08:18,640 Speaker 1: what do you think is happening here? So you know, 133 00:08:18,760 --> 00:08:21,560 Speaker 1: you mentioned this is sort of an unprecedented move in 134 00:08:21,600 --> 00:08:27,160 Speaker 1: a relationship that has historically held across many different types 135 00:08:27,280 --> 00:08:31,840 Speaker 1: of recessions in many different countries. What's your theory about 136 00:08:31,840 --> 00:08:36,720 Speaker 1: what's happening? So, as you know, we've seen historically high 137 00:08:36,760 --> 00:08:42,200 Speaker 1: shop openings rates, so UM we hit seven percent in 138 00:08:42,320 --> 00:08:45,280 Speaker 1: truly has come down a little bit from this and 139 00:08:45,320 --> 00:08:47,559 Speaker 1: the shop openings the level is about ten and a 140 00:08:47,640 --> 00:08:53,520 Speaker 1: half millions when the unemployment total unemployment is around seven millions, 141 00:08:53,520 --> 00:08:56,319 Speaker 1: so there's a huge gap, and this is the highest 142 00:08:56,360 --> 00:09:00,280 Speaker 1: gap ever measured. So at this point, I think it 143 00:09:00,360 --> 00:09:04,040 Speaker 1: is fair to say that we don't have one clear 144 00:09:04,080 --> 00:09:07,719 Speaker 1: explanation of why the shop openings rate is so high. 145 00:09:07,760 --> 00:09:11,760 Speaker 1: I think it's a combination of variety of things. We 146 00:09:11,880 --> 00:09:17,280 Speaker 1: have seen even before COVID um relatively high shop openings rates. 147 00:09:17,880 --> 00:09:22,319 Speaker 1: So even before COVID, we heard stories from all reports 148 00:09:22,400 --> 00:09:26,560 Speaker 1: from the business sector about about mismatch if you will, 149 00:09:26,679 --> 00:09:30,199 Speaker 1: that firms find it difficult to hire the right workers 150 00:09:30,840 --> 00:09:34,679 Speaker 1: because they don't have the right skills. Even if they interview, 151 00:09:34,920 --> 00:09:38,720 Speaker 1: they don't show up for um, the beginning of um 152 00:09:39,360 --> 00:09:42,120 Speaker 1: of a job, and all kinds of things that we've 153 00:09:42,120 --> 00:09:46,959 Speaker 1: heard anecdotally from firms. Now, in a sense, the COVID 154 00:09:47,000 --> 00:09:50,880 Speaker 1: shock scaled this problem up, so we know that the 155 00:09:50,920 --> 00:09:56,160 Speaker 1: economy shut down, the positions were still there. Funding for 156 00:09:56,200 --> 00:10:00,320 Speaker 1: these positions was there through various programs, the Payment Action 157 00:10:00,920 --> 00:10:06,559 Speaker 1: Program p p P first and foremost, and aggregate demand 158 00:10:06,679 --> 00:10:09,840 Speaker 1: held up incredibly well during this recession. So all of 159 00:10:09,880 --> 00:10:13,360 Speaker 1: these signs point towards a very strong demand for labor 160 00:10:13,760 --> 00:10:17,560 Speaker 1: that shows up in the job openings rate. Now, the 161 00:10:17,640 --> 00:10:21,600 Speaker 1: question is why is this so outsized? And I think 162 00:10:22,600 --> 00:10:27,040 Speaker 1: one of the answers to this question is that there 163 00:10:27,280 --> 00:10:32,520 Speaker 1: is a sense in amongst businesses that the labor market 164 00:10:32,559 --> 00:10:36,960 Speaker 1: is changing, um that the requirements and workers is changing, 165 00:10:37,600 --> 00:10:42,400 Speaker 1: and that in a way, businesses post open positions preemptively. 166 00:10:42,840 --> 00:10:47,319 Speaker 1: So one aspect of this is the high quid rate. 167 00:10:47,400 --> 00:10:50,040 Speaker 1: So we've seen historically high quid rates. So in the 168 00:10:50,120 --> 00:10:53,240 Speaker 1: quid rates, they also come from the UM Job Openings 169 00:10:53,240 --> 00:10:58,280 Speaker 1: and Labor Turnover Survey and they represent voluntary quids of workers, 170 00:10:58,400 --> 00:11:02,680 Speaker 1: presumably into other positions. So there's a very high turnover 171 00:11:02,679 --> 00:11:05,400 Speaker 1: in the labor market, a lot of churn. You can 172 00:11:05,400 --> 00:11:08,280 Speaker 1: think of it this way. So a quit is connected 173 00:11:08,559 --> 00:11:13,240 Speaker 1: with two shop openings. So if a worker quits to 174 00:11:13,280 --> 00:11:16,200 Speaker 1: take another position, well that fills one shop opening, but 175 00:11:16,280 --> 00:11:21,120 Speaker 1: it creates immediately a new shop opening for the firm 176 00:11:21,120 --> 00:11:24,920 Speaker 1: that was left behind, so to speak. And firms businesses 177 00:11:25,160 --> 00:11:29,040 Speaker 1: may respond to this preemptively and post open positions because 178 00:11:29,040 --> 00:11:31,719 Speaker 1: there is concerned that they may not find the right 179 00:11:31,760 --> 00:11:35,520 Speaker 1: workers or replace them. So this actually sort of gets 180 00:11:35,520 --> 00:11:39,840 Speaker 1: back to my previous question, which is if that's the 181 00:11:39,880 --> 00:11:44,560 Speaker 1: case as you describe, okay, firms are anticipating churn, they're 182 00:11:44,600 --> 00:11:49,760 Speaker 1: anticipating quits. Is the nature of posting job openings? Has 183 00:11:49,840 --> 00:11:54,000 Speaker 1: it changed such that at least part of the mismatch 184 00:11:54,240 --> 00:11:57,640 Speaker 1: or part of the changing shape of the beverage curve 185 00:11:58,320 --> 00:12:01,560 Speaker 1: is not simply a relate ship ship between employers and 186 00:12:01,600 --> 00:12:05,800 Speaker 1: employees changing, but a reflection at least in part of 187 00:12:05,840 --> 00:12:09,480 Speaker 1: the nature of how and when businesses say they're looking 188 00:12:09,520 --> 00:12:12,120 Speaker 1: for an employee, and there is certainly it's a very 189 00:12:12,160 --> 00:12:15,320 Speaker 1: good points so and this is certainly part of the story. 190 00:12:15,400 --> 00:12:18,520 Speaker 1: So a lot of chop search, I would say, the majority, 191 00:12:18,600 --> 00:12:22,880 Speaker 1: but now has no moved online, which also has reduced 192 00:12:22,960 --> 00:12:25,840 Speaker 1: the cost of finding workers, right, And I think this 193 00:12:25,960 --> 00:12:28,120 Speaker 1: is also one of the drivers. And this is actually 194 00:12:28,120 --> 00:12:30,600 Speaker 1: what all economic models would tell us that if the 195 00:12:30,720 --> 00:12:35,320 Speaker 1: cost of UM searching for workers of chop search UM 196 00:12:35,520 --> 00:12:40,000 Speaker 1: is UM is slower than more vacancies, more open positions 197 00:12:40,040 --> 00:12:43,320 Speaker 1: are being posted because it simply is less costly. So 198 00:12:43,360 --> 00:12:44,760 Speaker 1: that makes me wonder, I mean, when I think what 199 00:12:44,920 --> 00:12:47,000 Speaker 1: people look at these curves or when they look at 200 00:12:47,000 --> 00:12:50,520 Speaker 1: these mismatches, and I my sense is that there's a 201 00:12:50,559 --> 00:12:52,400 Speaker 1: lot of reasons, but we'll get into that. We look 202 00:12:52,400 --> 00:12:55,000 Speaker 1: at these mismatches, and we say, oh, maybe it's people 203 00:12:55,080 --> 00:12:57,160 Speaker 1: have all these savings that they're not rushing back to work. 204 00:12:57,600 --> 00:13:03,200 Speaker 1: Maybe it's labor and gil's mismatch. Maybe it's unemployment insurance. 205 00:13:03,400 --> 00:13:07,400 Speaker 1: Maybe it's people lack childcare. Maybe it's people don't want 206 00:13:07,400 --> 00:13:10,600 Speaker 1: to go back into an office for fear of risking COVID. 207 00:13:10,840 --> 00:13:13,720 Speaker 1: At some element, there is also just as change, as 208 00:13:13,760 --> 00:13:16,960 Speaker 1: you point out being able to post a job opening online, 209 00:13:17,320 --> 00:13:20,320 Speaker 1: at least some of it might just be that, all 210 00:13:20,480 --> 00:13:25,439 Speaker 1: all things equal, firms maybe posting more openings today than 211 00:13:25,480 --> 00:13:28,240 Speaker 1: they would have had similar economic condition to say occurred 212 00:13:28,280 --> 00:13:33,719 Speaker 1: in the nine Yes, I think that's a very fair point. So, 213 00:13:33,920 --> 00:13:37,559 Speaker 1: but to be clear, so in a way, but it's 214 00:13:37,640 --> 00:13:41,960 Speaker 1: nice about this beverage curve idea that both the job 215 00:13:42,040 --> 00:13:44,959 Speaker 1: openings rate and the unemployment rate are normalized by by 216 00:13:45,000 --> 00:13:49,400 Speaker 1: the labor force. So essentially you can make this discussion 217 00:13:49,400 --> 00:13:53,080 Speaker 1: about labor force participation, which we know has declined and 218 00:13:53,240 --> 00:13:56,160 Speaker 1: is much below ware and it was a pre COVID, 219 00:13:56,559 --> 00:14:01,960 Speaker 1: but the beverage curve essentially nets out label first participation effects. 220 00:14:16,760 --> 00:14:20,160 Speaker 1: As we observe this relationship sort of breaking down, and 221 00:14:20,160 --> 00:14:24,920 Speaker 1: and maybe it's because of this job creation condition that 222 00:14:25,000 --> 00:14:28,720 Speaker 1: you just described, you know, the idea that employers are 223 00:14:28,720 --> 00:14:31,720 Speaker 1: expecting churn, and so they're posting more and more potential 224 00:14:31,840 --> 00:14:34,960 Speaker 1: jobs on various websites to sort of make up for that. 225 00:14:35,560 --> 00:14:39,400 Speaker 1: But we still we still like come back to this 226 00:14:39,560 --> 00:14:45,040 Speaker 1: problem or this issue of employers and employees struggling to 227 00:14:45,680 --> 00:14:48,800 Speaker 1: match with each other. And I'm wondering, do you see 228 00:14:48,800 --> 00:14:52,160 Speaker 1: that more as a problem of some sort of match 229 00:14:52,280 --> 00:14:58,040 Speaker 1: inefficiency the way we're assigning jobs or hiring people for jobs, 230 00:14:58,080 --> 00:15:01,000 Speaker 1: I should say, is just some are more inefficient than 231 00:15:01,040 --> 00:15:03,480 Speaker 1: it used to be. Or is it that we're seeing 232 00:15:04,080 --> 00:15:08,320 Speaker 1: more competition for workers and people are having to our 233 00:15:08,400 --> 00:15:11,800 Speaker 1: employers are having to compete for a sort of smaller 234 00:15:11,840 --> 00:15:15,880 Speaker 1: potential pool of employees. That's a very good point. I 235 00:15:15,920 --> 00:15:21,000 Speaker 1: think it's both of these factors. So when we start 236 00:15:21,040 --> 00:15:24,720 Speaker 1: back the club to the financial crisis, and this is 237 00:15:25,000 --> 00:15:28,600 Speaker 1: when when I became first interested in the beverage curve. 238 00:15:29,160 --> 00:15:33,000 Speaker 1: So during the financial crisis, the unemployment rate h ten percent, 239 00:15:33,680 --> 00:15:36,200 Speaker 1: stayed around between nine and ten for a while and 240 00:15:36,240 --> 00:15:39,520 Speaker 1: would not come down for a substantial period of time, 241 00:15:40,160 --> 00:15:42,840 Speaker 1: and then it declined rapidly as the economy was working 242 00:15:42,920 --> 00:15:46,560 Speaker 1: through this this unemployment overhang. But at that time we 243 00:15:46,560 --> 00:15:51,160 Speaker 1: were talking a lot about precisely this mismatch phenomenon. So 244 00:15:51,200 --> 00:15:56,480 Speaker 1: the mismatch phenomenon describes the idea that, well, simply speaking, 245 00:15:56,760 --> 00:16:00,040 Speaker 1: that workers and firms don't find each other um on 246 00:16:00,440 --> 00:16:04,640 Speaker 1: the matching market. So this could have geographic reasons. During 247 00:16:04,640 --> 00:16:07,480 Speaker 1: the financial crisis, some regions in the United States were 248 00:16:07,560 --> 00:16:10,880 Speaker 1: hit harder than others, which has an implication for job 249 00:16:11,000 --> 00:16:15,120 Speaker 1: openings and open positions. There was a sexual change away 250 00:16:15,120 --> 00:16:21,880 Speaker 1: from construction manufacturing to healthcare other services. So all of 251 00:16:21,920 --> 00:16:26,720 Speaker 1: these issues combine into mismatch, the idea that workers and 252 00:16:26,760 --> 00:16:31,479 Speaker 1: firms don't find each other. Now we see the same phenomenon, 253 00:16:31,600 --> 00:16:35,080 Speaker 1: if you will, in the beverage curve, in the sense 254 00:16:35,160 --> 00:16:40,000 Speaker 1: that this beverage curve relationship has shifted outward in the 255 00:16:40,240 --> 00:16:42,880 Speaker 1: in the scatter plot sense. So in other words, for 256 00:16:42,920 --> 00:16:46,640 Speaker 1: a given unemployment rate, now many more job openings need 257 00:16:46,720 --> 00:16:51,560 Speaker 1: to be posted to reduce the unemployment rate. So this 258 00:16:51,800 --> 00:16:55,640 Speaker 1: is I would say, prima fasci evidence that yes, there 259 00:16:55,720 --> 00:16:59,400 Speaker 1: is mismatch going on. Now, the question is is this 260 00:16:59,480 --> 00:17:02,040 Speaker 1: the same time type of mismatch that we've seen in 261 00:17:02,080 --> 00:17:05,639 Speaker 1: the Great Recession. And I would argue likely not, because 262 00:17:06,440 --> 00:17:09,480 Speaker 1: COVID hit most of the US in a similar way. 263 00:17:09,840 --> 00:17:15,760 Speaker 1: Now we don't see many regional differences, and we know 264 00:17:15,960 --> 00:17:19,240 Speaker 1: that the service sector was hit much harder by COVID 265 00:17:19,359 --> 00:17:23,480 Speaker 1: than UM the financial services sector, for instance, So there 266 00:17:23,520 --> 00:17:26,640 Speaker 1: may be some aspect to this, But I think overall speaking, 267 00:17:26,640 --> 00:17:29,760 Speaker 1: I think there is an aspect of mismatch going on 268 00:17:29,880 --> 00:17:33,159 Speaker 1: that we don't quite fully understand yet, which is connected 269 00:17:33,320 --> 00:17:38,040 Speaker 1: to this this NEXUSUS Tracy mentioned of workers being reluctant 270 00:17:38,080 --> 00:17:42,679 Speaker 1: to return to work, childcare issues, COVID is still still around, 271 00:17:43,000 --> 00:17:47,600 Speaker 1: and also potentially skills changing skills requirement. So but in 272 00:17:47,680 --> 00:17:52,240 Speaker 1: that sense, I think I would argue that what we've 273 00:17:52,359 --> 00:17:55,600 Speaker 1: seen in the second half of the last decades, so 274 00:17:55,720 --> 00:17:59,320 Speaker 1: between two thousand and fifteen two thousand and twenty, that 275 00:17:59,520 --> 00:18:03,800 Speaker 1: this may have been more of an outlier than was 276 00:18:03,920 --> 00:18:06,640 Speaker 1: acknowledged at that time. So we saw the labor force 277 00:18:06,680 --> 00:18:11,399 Speaker 1: participation going up despite all these demographic changes, so it 278 00:18:11,520 --> 00:18:13,600 Speaker 1: brought in a lot of people into the labor force, 279 00:18:14,640 --> 00:18:19,000 Speaker 1: and this has disappeared through COVID. So in terms of 280 00:18:19,080 --> 00:18:21,840 Speaker 1: labor force participation, I would argue that we are now 281 00:18:21,880 --> 00:18:26,240 Speaker 1: almost back to the pre two thousand fifteen trend based 282 00:18:26,240 --> 00:18:30,400 Speaker 1: on cham with demographics. So long story short, I think 283 00:18:30,400 --> 00:18:33,480 Speaker 1: there's a lot going on in the labor market that 284 00:18:33,560 --> 00:18:37,879 Speaker 1: we don't quite fully understand yet, um simply because we 285 00:18:37,960 --> 00:18:42,040 Speaker 1: are lacking the micro data as of this point. And 286 00:18:42,080 --> 00:18:46,080 Speaker 1: the beverage curve just gives this overall snapshot that something 287 00:18:46,160 --> 00:18:48,440 Speaker 1: is going on in the labor market, a structural change. 288 00:18:49,000 --> 00:18:51,480 Speaker 1: So as you mentioned, I mean, if you look and 289 00:18:51,480 --> 00:18:53,679 Speaker 1: I'm looking right now at b LS dot gov their 290 00:18:53,720 --> 00:18:55,959 Speaker 1: beverage curve, they have a nice chart of it over 291 00:18:56,000 --> 00:18:59,360 Speaker 1: a different time period. And as you mentioned, we did 292 00:18:59,359 --> 00:19:02,240 Speaker 1: see a shift outwards in the beverage curve after the 293 00:19:02,280 --> 00:19:04,520 Speaker 1: Great Financial Crisis, so that was already a bit of 294 00:19:04,520 --> 00:19:07,639 Speaker 1: a change. And then of course this current shape of 295 00:19:07,680 --> 00:19:10,240 Speaker 1: the beverage curve, it's blown out way past that. So 296 00:19:10,280 --> 00:19:13,760 Speaker 1: it's clearly to a very uh high degree. And one 297 00:19:13,840 --> 00:19:17,840 Speaker 1: of the possibilities is the so called mismatch and maybe 298 00:19:17,840 --> 00:19:21,480 Speaker 1: that's geographical. Some people think, uh, this is a very 299 00:19:21,480 --> 00:19:24,879 Speaker 1: popular theory after the GFC that it had to do 300 00:19:24,920 --> 00:19:27,520 Speaker 1: with skills or the lack of skills or mismatch. But 301 00:19:27,600 --> 00:19:30,399 Speaker 1: you know, you mentioned construction and so forth. But on 302 00:19:30,440 --> 00:19:34,000 Speaker 1: the other hand, you know that led to people arguably 303 00:19:34,560 --> 00:19:38,200 Speaker 1: prematurely thinking that we had hit maximum employment or that 304 00:19:38,520 --> 00:19:40,840 Speaker 1: you know, at this point there's nothing more that monetary 305 00:19:40,880 --> 00:19:43,480 Speaker 1: policy can do or fiscal policy because at this point 306 00:19:43,560 --> 00:19:46,760 Speaker 1: we just need to upskill workers, etcetera. Instead, what we 307 00:19:46,840 --> 00:19:50,040 Speaker 1: saw is that the unemployment rate just kept going down. 308 00:19:50,119 --> 00:19:52,480 Speaker 1: It was slow, but I think we got down like 309 00:19:52,520 --> 00:19:55,760 Speaker 1: three point four percent prior to COVID. So what is 310 00:19:55,880 --> 00:19:57,639 Speaker 1: in your view what are in your views some of 311 00:19:57,640 --> 00:20:01,040 Speaker 1: the policy up shots from this s idea of mismarriach 312 00:20:01,160 --> 00:20:04,400 Speaker 1: What what is? What is mismartrich define however you want 313 00:20:04,400 --> 00:20:09,240 Speaker 1: to define it? Tell policymakers. So I think my preferred 314 00:20:09,280 --> 00:20:12,399 Speaker 1: angle in terms of thinking about mismatches is really a 315 00:20:12,440 --> 00:20:16,359 Speaker 1: skill story. So does the workforce has the right scat 316 00:20:16,440 --> 00:20:20,639 Speaker 1: of set sort of skills for the requirements that firms 317 00:20:20,640 --> 00:20:24,639 Speaker 1: in the changing economic environment want to have? And of 318 00:20:24,640 --> 00:20:26,920 Speaker 1: course there's a lot of research shots of policy discussions 319 00:20:26,960 --> 00:20:29,200 Speaker 1: about it, but I think the overall sense is that 320 00:20:29,359 --> 00:20:33,960 Speaker 1: the labor force is lagging behind this in terms of 321 00:20:34,320 --> 00:20:38,959 Speaker 1: um stem skills. Um if you will, no, but do 322 00:20:39,000 --> 00:20:42,320 Speaker 1: your to your point. I think it is also fair 323 00:20:42,359 --> 00:20:47,440 Speaker 1: to say that we work caught by surprise how well 324 00:20:47,720 --> 00:20:51,199 Speaker 1: the labor market performed UM in the second half of 325 00:20:51,240 --> 00:20:54,560 Speaker 1: the last last decade, which brought in a lot of 326 00:20:54,600 --> 00:20:59,480 Speaker 1: people from the from the sidelines. So and in that sense, yes, 327 00:21:00,080 --> 00:21:04,720 Speaker 1: growing economy UM growing. I don't above trend can bring 328 00:21:04,800 --> 00:21:08,760 Speaker 1: in additional workers from the sidelines. But much of this UM, 329 00:21:08,800 --> 00:21:11,320 Speaker 1: I think was driven by an expansion in services, a 330 00:21:11,400 --> 00:21:17,600 Speaker 1: shift away from manufacturing to some extent, healthcare sector expanded. 331 00:21:17,960 --> 00:21:20,119 Speaker 1: So I think there is a structural reason for this. 332 00:21:21,480 --> 00:21:23,520 Speaker 1: Just to play devil's advocate for a second, could you 333 00:21:23,560 --> 00:21:27,320 Speaker 1: flip that slightly and argue that maybe the labor market 334 00:21:27,400 --> 00:21:30,600 Speaker 1: or the economy isn't offering up the type of jobs 335 00:21:31,119 --> 00:21:34,439 Speaker 1: that people want. And you know, I say this after 336 00:21:34,480 --> 00:21:36,560 Speaker 1: the experience of the pandemic, when we had a lot 337 00:21:36,600 --> 00:21:40,439 Speaker 1: of anecdotes about people who simply didn't want to go 338 00:21:40,480 --> 00:21:43,280 Speaker 1: back to their service jobs and maybe didn't have to 339 00:21:43,600 --> 00:21:47,680 Speaker 1: because they had higher, you know, unemployment payouts and maybe 340 00:21:47,680 --> 00:21:51,040 Speaker 1: they were day trading game stop stock in their basements 341 00:21:51,320 --> 00:21:54,680 Speaker 1: or cryptocurrency or whatever and didn't feel as much pressure 342 00:21:54,720 --> 00:21:58,520 Speaker 1: to go back to a job that they didn't like 343 00:21:59,359 --> 00:22:02,720 Speaker 1: or value. Is that something that's potentially on your radar 344 00:22:02,760 --> 00:22:09,240 Speaker 1: here it is because it is connected to UM well, 345 00:22:09,320 --> 00:22:11,399 Speaker 1: the outside option of the workers, if you will to 346 00:22:11,440 --> 00:22:14,840 Speaker 1: use the technical term, or the willingness of workers to 347 00:22:15,880 --> 00:22:20,000 Speaker 1: work in low wage shops, if if you will so, 348 00:22:20,040 --> 00:22:23,560 Speaker 1: and yes, I mean this is definitely something that we 349 00:22:23,640 --> 00:22:28,480 Speaker 1: see in the data already that particularly employment in Lesion 350 00:22:28,520 --> 00:22:32,280 Speaker 1: hospitality in the service sector is lagging much behind the 351 00:22:32,280 --> 00:22:35,879 Speaker 1: financial services UM sector, for for instance, And there there 352 00:22:35,880 --> 00:22:40,760 Speaker 1: are good reasons for this. So all the support programs 353 00:22:40,880 --> 00:22:46,240 Speaker 1: UM during the COVID shock and the recovery UM certainly 354 00:22:46,400 --> 00:22:49,480 Speaker 1: changed the financial position of workers. There was no need 355 00:22:49,800 --> 00:22:54,199 Speaker 1: to look for a shop that may not offer the 356 00:22:54,280 --> 00:22:58,480 Speaker 1: same benefits in the same wage that one could get 357 00:22:58,480 --> 00:23:02,800 Speaker 1: through the government support program. So now this is starting 358 00:23:02,800 --> 00:23:06,800 Speaker 1: to disappear. So to take you up on the devil's 359 00:23:06,840 --> 00:23:11,000 Speaker 1: advocate question, so I think if we want to rest 360 00:23:11,000 --> 00:23:15,280 Speaker 1: out the remainder of the slack and the labor market, 361 00:23:15,280 --> 00:23:17,639 Speaker 1: if you will, so, from the gap between four and 362 00:23:17,680 --> 00:23:20,760 Speaker 1: a half percent where we are now to below four percent, 363 00:23:21,359 --> 00:23:24,760 Speaker 1: I think this is probably where it is coming from. 364 00:23:24,800 --> 00:23:27,880 Speaker 1: But to your point, I think this is certainly an 365 00:23:27,960 --> 00:23:30,680 Speaker 1: issue that is on the radar, namely in terms of 366 00:23:30,720 --> 00:23:35,800 Speaker 1: what is maximum employment in this economy. This seems to 367 00:23:35,880 --> 00:23:39,120 Speaker 1: be the question that the FED. And by the way, 368 00:23:39,200 --> 00:23:41,320 Speaker 1: I think it's interesting people should know. We are recording 369 00:23:41,359 --> 00:23:45,040 Speaker 1: this Monday in November twenty two, and we started recording 370 00:23:45,040 --> 00:23:49,080 Speaker 1: at nine am, right at the moment literally we started 371 00:23:49,080 --> 00:23:52,440 Speaker 1: recording right at the moment that it got officially announced 372 00:23:52,440 --> 00:23:56,080 Speaker 1: that President Biden would be renominating Powell. So, assuming he 373 00:23:56,119 --> 00:23:59,879 Speaker 1: gets confirmed, this is the question that Powell will have 374 00:24:00,240 --> 00:24:03,120 Speaker 1: have to answer. How do you think about this question 375 00:24:03,359 --> 00:24:07,760 Speaker 1: of maximum employment and this idea of we wanted you know, 376 00:24:07,880 --> 00:24:10,280 Speaker 1: as you mentioned in the second half of the tens, 377 00:24:10,640 --> 00:24:13,720 Speaker 1: economists were taken by a surprise by how far how 378 00:24:13,760 --> 00:24:16,439 Speaker 1: strong the labor market could get, how many people it 379 00:24:16,480 --> 00:24:19,240 Speaker 1: could bring in, what it could do for labor force participation, 380 00:24:19,720 --> 00:24:24,280 Speaker 1: how low the unemployment rate could get without triggering meaningful 381 00:24:24,400 --> 00:24:28,280 Speaker 1: increase in inflation, and so forth. So on this question 382 00:24:28,480 --> 00:24:30,960 Speaker 1: of you know, obviously there's some head scratching that's going 383 00:24:31,000 --> 00:24:34,240 Speaker 1: to happen. The FET is obviously reluctant to put any 384 00:24:34,240 --> 00:24:38,000 Speaker 1: sort of number on maximum employment. It kind of feels 385 00:24:38,040 --> 00:24:40,480 Speaker 1: like we'll know when we see a type of thing 386 00:24:40,800 --> 00:24:44,600 Speaker 1: inherently subjective, but from your perspective, and from your research, 387 00:24:45,080 --> 00:24:48,679 Speaker 1: how should the FED be thinking about answering that question 388 00:24:48,720 --> 00:24:52,120 Speaker 1: of what what success on the employment front looks like. 389 00:24:54,080 --> 00:24:59,119 Speaker 1: So the Federati surfact stipulates maximum employment UM as the 390 00:24:59,160 --> 00:25:03,800 Speaker 1: first mend that into FAT has always interpreted this well, 391 00:25:03,880 --> 00:25:07,840 Speaker 1: first a little bit more narrower and then a broader 392 00:25:07,880 --> 00:25:12,720 Speaker 1: goal in terms of an unemployment rate that is sustainable 393 00:25:13,240 --> 00:25:18,639 Speaker 1: without generating additional inflation, and where the labor market and 394 00:25:18,640 --> 00:25:24,880 Speaker 1: the turn the labor market, very loosely speaking, are in equilibrium. 395 00:25:24,920 --> 00:25:29,320 Speaker 1: So when I went to college then graduate school, so 396 00:25:29,400 --> 00:25:32,000 Speaker 1: we were thinking of maximum employment as an unemployment rate 397 00:25:32,040 --> 00:25:34,480 Speaker 1: of five percent, which we dropped to four and a 398 00:25:34,560 --> 00:25:39,160 Speaker 1: half percent in the two thousands. So this unemployment rate 399 00:25:39,359 --> 00:25:43,879 Speaker 1: tied to maximum employment can change, of course, and in 400 00:25:44,119 --> 00:25:47,160 Speaker 1: a sense, this is where where we were surprised that 401 00:25:47,320 --> 00:25:52,880 Speaker 1: the unemployment rate could sustain low inflation rates below four 402 00:25:52,960 --> 00:25:56,040 Speaker 1: percent for a very very long time. And I think 403 00:25:56,040 --> 00:25:59,359 Speaker 1: it's fair to say that this experience of below four 404 00:25:59,400 --> 00:26:03,520 Speaker 1: percent and employment really informs the current discussion right now. 405 00:26:03,840 --> 00:26:07,800 Speaker 1: But I think we've the experience of firm the two 406 00:26:07,800 --> 00:26:11,240 Speaker 1: thousand tents and the recovery from the global financial crisis 407 00:26:11,920 --> 00:26:17,280 Speaker 1: also taught us that the unemployment rate, the headline unemployment rate, 408 00:26:17,960 --> 00:26:22,440 Speaker 1: may not be the single best gauge for what maximum unemployment. 409 00:26:22,560 --> 00:26:25,720 Speaker 1: Maximum employment means. So for one, I mean it is 410 00:26:25,760 --> 00:26:30,600 Speaker 1: a survey based measure, so it is measured reliably as 411 00:26:30,640 --> 00:26:34,240 Speaker 1: much as it can reliably be measured, but it does 412 00:26:34,320 --> 00:26:39,280 Speaker 1: not fully encompass all of the chop search that is 413 00:26:39,320 --> 00:26:42,800 Speaker 1: going on. So, and this is why policymakers in my 414 00:26:43,080 --> 00:26:48,920 Speaker 1: in my reading, have shifted to looking at additional metrics, 415 00:26:49,200 --> 00:26:53,000 Speaker 1: so the employment population ratio, which may be just as 416 00:26:53,040 --> 00:26:56,280 Speaker 1: good on a measure of full employment, then the labor 417 00:26:56,320 --> 00:27:01,160 Speaker 1: force participation rate, as well as O based measures such 418 00:27:01,160 --> 00:27:04,399 Speaker 1: as the drop openings in labor turnover survey. So I 419 00:27:04,800 --> 00:27:07,960 Speaker 1: am inclined to agree with you that we know it 420 00:27:08,040 --> 00:27:11,840 Speaker 1: when we see it when we have maximum employment. So 421 00:27:11,880 --> 00:27:13,720 Speaker 1: at this point I think there's still room to go 422 00:27:14,280 --> 00:27:17,240 Speaker 1: until we hit four pc. No. The flip side of 423 00:27:17,320 --> 00:27:22,320 Speaker 1: this is, of course, our view of maximum employment was 424 00:27:22,400 --> 00:27:26,399 Speaker 1: informed by the fact that the inflation rate um stayed 425 00:27:26,440 --> 00:27:29,480 Speaker 1: well contained above two percent for a long time in 426 00:27:29,520 --> 00:27:33,040 Speaker 1: the two thousand tents. So much of the thinking that 427 00:27:33,240 --> 00:27:37,639 Speaker 1: goes into what is maximum employment is actually related to 428 00:27:37,680 --> 00:27:42,520 Speaker 1: the inflation rate, because we did see historical episodes where um, 429 00:27:42,560 --> 00:27:47,200 Speaker 1: when unemployment was below it's its natural rate, if you will, 430 00:27:47,240 --> 00:27:50,040 Speaker 1: inflation would go up. This didn't happen in the two 431 00:27:50,040 --> 00:27:54,480 Speaker 1: thousand tents. This kind of leads into something that I 432 00:27:54,520 --> 00:27:58,119 Speaker 1: wanted to ask, which is, when would you expect to 433 00:27:58,160 --> 00:28:03,280 Speaker 1: see this competence shan for workers or the sort of 434 00:28:03,320 --> 00:28:07,080 Speaker 1: inefficiency in matching. When would you expect to see that 435 00:28:07,200 --> 00:28:11,560 Speaker 1: feed into wage increases? And is it possible that if 436 00:28:11,600 --> 00:28:16,439 Speaker 1: we've seen a historic break in the beverage curve, you know, 437 00:28:16,520 --> 00:28:19,880 Speaker 1: the relationship between the unemployment rate and the job opening rate, 438 00:28:20,280 --> 00:28:23,080 Speaker 1: then is it possible that maybe we're going to see 439 00:28:23,160 --> 00:28:29,400 Speaker 1: something vastly, wildly different when it comes to wages. So 440 00:28:29,520 --> 00:28:36,880 Speaker 1: at this point, we haven't seen much of wage inflation 441 00:28:37,359 --> 00:28:44,040 Speaker 1: at a level where I think policymakers and economists would 442 00:28:44,120 --> 00:28:48,800 Speaker 1: become uncomfortable. So nominal wages have been going up, but 443 00:28:48,880 --> 00:28:51,640 Speaker 1: not excessively so, and actually to the point that real 444 00:28:51,720 --> 00:28:56,400 Speaker 1: wages have been falling because of the inflation numbers that 445 00:28:56,520 --> 00:29:00,400 Speaker 1: we have been seeing. What we do see is some 446 00:29:00,600 --> 00:29:07,200 Speaker 1: higher wage increases in some sectors um so lesion, hospitality. 447 00:29:07,240 --> 00:29:10,880 Speaker 1: There we do see higher wage growth, higher nominal wage growth, 448 00:29:11,200 --> 00:29:14,160 Speaker 1: And we also see the chop switchers, which brings me 449 00:29:14,240 --> 00:29:18,120 Speaker 1: back to UM the quid rate and the historically elevated 450 00:29:18,200 --> 00:29:21,080 Speaker 1: level of quid rate. So chop switchers have seen high 451 00:29:21,120 --> 00:29:23,960 Speaker 1: wage increases, which presumably is one of the reasons why 452 00:29:24,000 --> 00:29:28,480 Speaker 1: they switch shops. So but at the same time, we 453 00:29:28,560 --> 00:29:33,440 Speaker 1: know that the wage data are um lagging behind in 454 00:29:33,480 --> 00:29:38,760 Speaker 1: the sense that so many wages are determined one year ahead, 455 00:29:39,520 --> 00:29:43,360 Speaker 1: and now many companies are having wage discussions at the 456 00:29:43,440 --> 00:29:46,920 Speaker 1: end of the year. So what is the compensation picture 457 00:29:46,920 --> 00:29:50,760 Speaker 1: going to look like for two thousand twenty two given 458 00:29:50,760 --> 00:29:54,400 Speaker 1: the high levels of inflation we have had. So I 459 00:29:54,560 --> 00:29:59,040 Speaker 1: think going into the next year, UM, I am starting 460 00:29:59,080 --> 00:30:01,360 Speaker 1: to become a list little bit more nervous that we 461 00:30:01,440 --> 00:30:22,880 Speaker 1: may see much higher nominal wage inflation numbers. Let me 462 00:30:23,240 --> 00:30:27,120 Speaker 1: I want to go back to something important and again, uh, 463 00:30:27,280 --> 00:30:30,160 Speaker 1: this mismatch idea, because ultimately that really is what the 464 00:30:30,240 --> 00:30:32,840 Speaker 1: chart is showing us. On some level, the things are 465 00:30:32,960 --> 00:30:36,920 Speaker 1: very there is some uh, some big change to what 466 00:30:37,080 --> 00:30:41,160 Speaker 1: extent is these things you know you mentioned for example, 467 00:30:41,240 --> 00:30:44,840 Speaker 1: and you know numerous policy makers talk about say skills 468 00:30:44,920 --> 00:30:47,880 Speaker 1: and stem skills and tech skills and coding skills in 469 00:30:47,920 --> 00:30:51,160 Speaker 1: particular is always being in short supply, and there's probably 470 00:30:51,160 --> 00:30:55,160 Speaker 1: not a major business in the entire country that doesn't 471 00:30:55,200 --> 00:30:58,200 Speaker 1: have numerous job openings for technical skills. But one thing 472 00:30:58,200 --> 00:31:01,920 Speaker 1: that we did see at the end of the tens 473 00:31:02,320 --> 00:31:05,640 Speaker 1: in addition to bring some people back into the labor forces, 474 00:31:06,080 --> 00:31:10,560 Speaker 1: more anecdotal stories about skills training where the company itself 475 00:31:11,000 --> 00:31:14,480 Speaker 1: would pay for skills training or fund skills training, or 476 00:31:14,880 --> 00:31:19,000 Speaker 1: also where the companies would hire people who are previously 477 00:31:19,120 --> 00:31:23,920 Speaker 1: incarcerated and so looking beyond the normal pool of applicants 478 00:31:24,320 --> 00:31:27,200 Speaker 1: to bring in people back into the workforce. Should theory 479 00:31:27,760 --> 00:31:30,960 Speaker 1: has a very sort of positive long term effect to 480 00:31:31,000 --> 00:31:34,680 Speaker 1: what extent should these mismatches essentially be or can they 481 00:31:34,760 --> 00:31:38,280 Speaker 1: essentially be dealt with by the market that if you know, 482 00:31:38,560 --> 00:31:42,800 Speaker 1: FED recognized can recognize the persistence of mismatches, but ultimately, 483 00:31:42,920 --> 00:31:47,480 Speaker 1: if there are whether it's skills or geography, these are 484 00:31:47,600 --> 00:31:51,880 Speaker 1: things that employers and employees will ultimately solve if there 485 00:31:51,960 --> 00:31:55,840 Speaker 1: is a price opportunity there. Yeah, I mean I very 486 00:31:55,880 --> 00:31:57,520 Speaker 1: much agree with you on this point. I mean, in 487 00:31:57,920 --> 00:32:02,320 Speaker 1: the end, these mismatches will be dealt with by um 488 00:32:02,480 --> 00:32:07,520 Speaker 1: changes and employment relationships and how workers and their employers 489 00:32:08,400 --> 00:32:12,160 Speaker 1: reorient their relationship. I mean, I think the prime example 490 00:32:12,280 --> 00:32:15,720 Speaker 1: for this is um working from home on a hybrid 491 00:32:16,280 --> 00:32:21,080 Speaker 1: working environment, which decouples the location of the business from 492 00:32:21,120 --> 00:32:24,880 Speaker 1: the location of the workers. So so the geographic mismatch 493 00:32:25,400 --> 00:32:28,760 Speaker 1: part of the mismach story I think would probably largely 494 00:32:28,760 --> 00:32:31,560 Speaker 1: go where because I could, you know, as we're having 495 00:32:31,560 --> 00:32:34,280 Speaker 1: this interview right now, I mean, I'm sitting here in Richmond, 496 00:32:34,640 --> 00:32:38,200 Speaker 1: UM and you're in other locations all over the world, 497 00:32:38,760 --> 00:32:42,560 Speaker 1: So that part, I think can be mitigated. So one thing, 498 00:32:42,800 --> 00:32:46,200 Speaker 1: so with respect to the US labor markets, So one 499 00:32:46,240 --> 00:32:50,680 Speaker 1: thing that struck me and my background is in Germany 500 00:32:50,680 --> 00:32:53,160 Speaker 1: and growing up in Germany, is the is the lack 501 00:32:53,240 --> 00:32:57,120 Speaker 1: of apprenticeship programs and on the job training programs, which 502 00:32:57,160 --> 00:33:01,120 Speaker 1: is very formalized UM and very institutional liced in in 503 00:33:00,880 --> 00:33:05,240 Speaker 1: in Germany. And you don't see this to the same extent, 504 00:33:05,280 --> 00:33:08,040 Speaker 1: actually to a much lower extent here in the United States. 505 00:33:09,200 --> 00:33:12,400 Speaker 1: So and I think this is one of those labor 506 00:33:12,480 --> 00:33:16,680 Speaker 1: market policies that UM I think policymakers should try to 507 00:33:16,720 --> 00:33:21,560 Speaker 1: pay more attention too. I'm under drop training, um upskilling 508 00:33:21,600 --> 00:33:25,640 Speaker 1: of the worker force and essentially worker training. But I 509 00:33:25,680 --> 00:33:30,840 Speaker 1: think the broader issue is can we already discern structural 510 00:33:30,920 --> 00:33:37,000 Speaker 1: changes in the labor market that would completely decouple us 511 00:33:37,160 --> 00:33:40,760 Speaker 1: from you know, previous working arrangements. And I think the 512 00:33:40,800 --> 00:33:44,040 Speaker 1: first signs are there. Um. So, I think the COVID 513 00:33:44,880 --> 00:33:49,360 Speaker 1: um shock has accelerated this work towards more of the 514 00:33:49,440 --> 00:33:55,920 Speaker 1: worker as an entrepreneur rather than as a salaried wage employee. So, Thomas, 515 00:33:56,040 --> 00:33:59,120 Speaker 1: I'm I'm aware that you've been publishing quite a lot 516 00:33:59,280 --> 00:34:02,320 Speaker 1: at the Richmond FED and I mean you've sort of 517 00:34:02,360 --> 00:34:06,520 Speaker 1: taken on the very very big questions of the economic 518 00:34:06,600 --> 00:34:10,200 Speaker 1: experience of the pandemic, including how to actually model um 519 00:34:10,400 --> 00:34:14,160 Speaker 1: COVID nineteen and things like that. But what's been the 520 00:34:14,200 --> 00:34:18,960 Speaker 1: most interesting work that you've undertaken over the past year 521 00:34:19,080 --> 00:34:22,520 Speaker 1: or two, or perhaps the most surprising thing that you've 522 00:34:22,560 --> 00:34:25,680 Speaker 1: seen other than the beverage curve of course, Um, yeah, 523 00:34:25,719 --> 00:34:27,560 Speaker 1: thanks for your kind words. I mean I would have 524 00:34:27,600 --> 00:34:32,640 Speaker 1: answered the beverage curve because because this is this is 525 00:34:32,680 --> 00:34:35,640 Speaker 1: truly striking. I mean I started working on this during 526 00:34:35,640 --> 00:34:41,319 Speaker 1: the financial crisis, and at that time, the shifts in 527 00:34:41,320 --> 00:34:45,160 Speaker 1: the beverage curve seemed completely out of this, out of 528 00:34:45,200 --> 00:34:49,879 Speaker 1: line with historical record and then I just plotted late 529 00:34:49,960 --> 00:34:54,480 Speaker 1: last year the beverage curve and whoa, suddenly that the 530 00:34:55,200 --> 00:34:57,680 Speaker 1: change in the beverage curve was even much more dramatic. 531 00:34:58,440 --> 00:35:00,960 Speaker 1: But in terms of other other wor work. UM. So 532 00:35:01,640 --> 00:35:06,040 Speaker 1: one aspect that that occupies my thinking a lot is UM. Well, 533 00:35:06,080 --> 00:35:08,920 Speaker 1: what we call our star or the natural real rate 534 00:35:08,960 --> 00:35:14,120 Speaker 1: of interest, which basically anchors almost all of our policy discussions. 535 00:35:15,160 --> 00:35:20,040 Speaker 1: And what we can say is that the natural real 536 00:35:20,200 --> 00:35:23,160 Speaker 1: rate of interest, So when all is said and done, 537 00:35:23,840 --> 00:35:26,520 Speaker 1: the level of the real interest rate that the economy 538 00:35:26,600 --> 00:35:31,920 Speaker 1: would naturally gravitate towards has declined over the last twenty 539 00:35:32,080 --> 00:35:36,319 Speaker 1: thirty forty years. The precise level of where our star 540 00:35:36,560 --> 00:35:41,240 Speaker 1: is UM, Yeah, there's much uncertainty about this, and this 541 00:35:41,920 --> 00:35:46,479 Speaker 1: fundamentally informs the policy question. So in the long run, 542 00:35:47,120 --> 00:35:52,799 Speaker 1: where should a natural normal level of UM policy accommodation 543 00:35:52,880 --> 00:35:56,080 Speaker 1: or the federal funds rate? But I think we're far 544 00:35:56,120 --> 00:36:00,399 Speaker 1: away from UM having a good answer to this at 545 00:36:00,400 --> 00:36:03,040 Speaker 1: this point. What about just this sort of you you know, 546 00:36:03,440 --> 00:36:08,120 Speaker 1: usefulness of these indicators are star neutral rate, real rate? 547 00:36:08,440 --> 00:36:10,960 Speaker 1: You know this is prior to COVID and I always 548 00:36:11,000 --> 00:36:14,439 Speaker 1: forget which hero was either I want to say chair. 549 00:36:14,560 --> 00:36:18,840 Speaker 1: Powell gave a speech sort of questioning whether any of this, 550 00:36:19,800 --> 00:36:22,680 Speaker 1: any of these numbers, what the neutral rate is, that 551 00:36:22,760 --> 00:36:25,480 Speaker 1: this is really knowable in real time? And I took 552 00:36:25,520 --> 00:36:27,640 Speaker 1: it to me and that maybe there's just some question 553 00:36:27,680 --> 00:36:30,640 Speaker 1: about like to what degree should we be using these 554 00:36:30,640 --> 00:36:34,359 Speaker 1: to build models that inform policy in real time? How 555 00:36:34,360 --> 00:36:38,120 Speaker 1: do you feel about the confidence that economists should have 556 00:36:38,600 --> 00:36:41,280 Speaker 1: in being able to derive a number like our star 557 00:36:41,400 --> 00:36:44,359 Speaker 1: and actually use it to make a decision from one 558 00:36:44,400 --> 00:36:48,360 Speaker 1: meeting to the next. Um, Yeah, thanks for the questions. 559 00:36:48,360 --> 00:36:49,759 Speaker 1: So I mean, as I said, I mean there's much 560 00:36:49,840 --> 00:36:54,279 Speaker 1: uncertainty about um, the levels of these natural rates, be 561 00:36:54,440 --> 00:36:59,440 Speaker 1: it you still in a sort of unemployment bepat our star. Ultimately, 562 00:37:00,040 --> 00:37:03,080 Speaker 1: think all of the models that we have in mind, 563 00:37:03,080 --> 00:37:06,200 Speaker 1: and I'm not just thinking about, you know, the academic 564 00:37:06,200 --> 00:37:09,920 Speaker 1: models that academic economists built, but also the implicit models 565 00:37:09,960 --> 00:37:14,040 Speaker 1: that policymakers have in their mind. They ultimately come down 566 00:37:14,080 --> 00:37:20,120 Speaker 1: to the question, what is the long run equilibrium outcome 567 00:37:20,280 --> 00:37:22,960 Speaker 1: in the economy where we are at maximum employment, where 568 00:37:23,000 --> 00:37:26,279 Speaker 1: we fit our inflation target, and what the long run 569 00:37:26,400 --> 00:37:30,680 Speaker 1: interest rate should be. So and and once you take 570 00:37:30,840 --> 00:37:34,080 Speaker 1: this issue on board, this immediately leads you to the question, 571 00:37:34,440 --> 00:37:36,680 Speaker 1: what can we figure out what our star or you 572 00:37:36,880 --> 00:37:40,479 Speaker 1: star UM is so and and and to be fair, 573 00:37:40,640 --> 00:37:46,160 Speaker 1: these are fundamentally unobservable, so we have to use some 574 00:37:46,280 --> 00:37:50,360 Speaker 1: statistical methods technical tricks to tease them out from the data. 575 00:37:50,480 --> 00:37:53,680 Speaker 1: But I think that they are informative. Nevertheless, so even 576 00:37:53,719 --> 00:37:56,000 Speaker 1: if it is just that the level that our star 577 00:37:56,520 --> 00:37:59,720 Speaker 1: is much lower than it was in the ninety eighties, 578 00:38:00,239 --> 00:38:05,319 Speaker 1: which puts us into a different policy environment. So just 579 00:38:05,400 --> 00:38:08,560 Speaker 1: on that note, I have a slightly I guess, strange question. 580 00:38:08,800 --> 00:38:12,560 Speaker 1: But you know, one of the things that has been 581 00:38:12,600 --> 00:38:15,719 Speaker 1: repeated in many of our conversations on all lots over 582 00:38:15,760 --> 00:38:17,600 Speaker 1: the past year or so is the idea that a 583 00:38:17,600 --> 00:38:20,919 Speaker 1: lot of economic principles that we seem to have taken 584 00:38:20,960 --> 00:38:23,799 Speaker 1: for granted, actually no one seems to know how they 585 00:38:23,880 --> 00:38:26,640 Speaker 1: really work. So, you know, something weird is going on 586 00:38:26,880 --> 00:38:30,480 Speaker 1: with the labor market, something weird is going on with inflation. 587 00:38:31,000 --> 00:38:33,880 Speaker 1: I think our star. You know, people have been questioning 588 00:38:34,040 --> 00:38:37,279 Speaker 1: UM the natural rate of interest idea for a while now. 589 00:38:37,520 --> 00:38:40,760 Speaker 1: I know you've done some academic work on the output gap, 590 00:38:41,040 --> 00:38:44,800 Speaker 1: UM not being particularly useful for monetary policy. Is this 591 00:38:44,920 --> 00:38:50,480 Speaker 1: sort of a moment of doubt for economic researchers, or 592 00:38:50,920 --> 00:38:54,560 Speaker 1: does it encourage you to, you know, examine these issues 593 00:38:54,680 --> 00:38:58,040 Speaker 1: even more and seek out answers. It feels almost like 594 00:38:58,080 --> 00:39:00,640 Speaker 1: a sort of existential crisis, or at least if you 595 00:39:00,640 --> 00:39:03,840 Speaker 1: read some of the commentary around higher than expected c 596 00:39:04,000 --> 00:39:06,120 Speaker 1: p i UM from a week or two ago, it 597 00:39:06,200 --> 00:39:10,759 Speaker 1: feels like existential crisis for the FAT at least. Yeah, 598 00:39:10,760 --> 00:39:14,680 Speaker 1: I would not put it in such standard terms. That's fair. 599 00:39:14,880 --> 00:39:18,200 Speaker 1: I just I gotta ask. Yeah, so in the sense 600 00:39:18,239 --> 00:39:21,480 Speaker 1: that I think we do have a much better understanding 601 00:39:21,520 --> 00:39:27,879 Speaker 1: of how the economy works UM compared to forty years ago. 602 00:39:28,560 --> 00:39:32,120 Speaker 1: But I think what we've become more aware is that 603 00:39:32,160 --> 00:39:38,480 Speaker 1: there's much more uncertainty about these unobservable variables. And and 604 00:39:38,520 --> 00:39:41,399 Speaker 1: and again, going back to the COVID shock, we were 605 00:39:41,520 --> 00:39:47,359 Speaker 1: hit by an completely unprecedented UM shocked that shut down 606 00:39:47,440 --> 00:39:52,120 Speaker 1: the economy the economy is worldwide to an unprecedented level. 607 00:39:52,520 --> 00:39:57,080 Speaker 1: And we've recovered from this incredibly quickly in terms of 608 00:39:57,120 --> 00:40:03,520 Speaker 1: the macroeconomic data. And yes, while the beverage curve may 609 00:40:03,560 --> 00:40:07,000 Speaker 1: look different from what we would have expected had had 610 00:40:07,080 --> 00:40:11,360 Speaker 1: COVID not happened, UM, it still follows the same underlying principles. 611 00:40:12,160 --> 00:40:15,600 Speaker 1: So when times are good or improving, the unemployment rate falls, 612 00:40:15,880 --> 00:40:19,279 Speaker 1: chop openings go up. But what the beverage curve tells 613 00:40:19,320 --> 00:40:21,200 Speaker 1: us in that sense it is it is you know, 614 00:40:21,239 --> 00:40:24,759 Speaker 1: a visualization device of the state of the economy. Is yes, 615 00:40:24,800 --> 00:40:28,359 Speaker 1: but there's something else going on now. We don't know 616 00:40:28,840 --> 00:40:33,000 Speaker 1: exactly what is going on, so more research will be 617 00:40:33,000 --> 00:40:36,160 Speaker 1: poured into it. So when I presented the beverage curve 618 00:40:36,560 --> 00:40:39,880 Speaker 1: to our internal audience and outside audiences, I introduced it 619 00:40:39,960 --> 00:40:46,440 Speaker 1: as um chop security for labor economists. So more research 620 00:40:46,600 --> 00:40:52,200 Speaker 1: needs to be done. But I think overall, our economic 621 00:40:52,320 --> 00:40:55,960 Speaker 1: models and our economic thinking actually had held up pretty well, 622 00:40:56,520 --> 00:41:02,240 Speaker 1: um during the COVID shock. Thomas, it's been great speaking 623 00:41:02,239 --> 00:41:05,680 Speaker 1: with you, and uh again plug to the beverage curve paper, 624 00:41:05,680 --> 00:41:08,279 Speaker 1: which you can find on the Richmond Fed website. Thank 625 00:41:08,280 --> 00:41:11,239 Speaker 1: you so much. Okay, thank you so much. It's a 626 00:41:11,239 --> 00:41:25,680 Speaker 1: pleasure talking to you. Thanks Thomas. That was great, So Joe, 627 00:41:25,760 --> 00:41:28,560 Speaker 1: I thought that was really interesting, and again I think, um, 628 00:41:28,560 --> 00:41:31,360 Speaker 1: you know all of well, the two major issues inflation 629 00:41:31,560 --> 00:41:33,640 Speaker 1: and the labor market are of course tied together, but 630 00:41:33,719 --> 00:41:37,840 Speaker 1: it does feel like the focus of the majority of 631 00:41:37,840 --> 00:41:41,200 Speaker 1: attention has been on the inflation question, um, and people 632 00:41:41,239 --> 00:41:44,080 Speaker 1: are only really talking about the labor market insofar as 633 00:41:44,120 --> 00:41:47,600 Speaker 1: it might lead to wage increases. So I thought it 634 00:41:47,640 --> 00:41:50,240 Speaker 1: was quite um, it was quite good to dig into, 635 00:41:50,480 --> 00:41:54,080 Speaker 1: you know, the beverage curve and a particularly wonky corner 636 00:41:54,280 --> 00:41:59,400 Speaker 1: of economic research. Yeah, it really is. If anyone who 637 00:41:59,480 --> 00:42:01,840 Speaker 1: just pulls it up and goes to the BLS beverage 638 00:42:01,840 --> 00:42:06,640 Speaker 1: curved chart, you just instantly see how wild. I mean, 639 00:42:06,760 --> 00:42:11,200 Speaker 1: look like COVID data is going to leave this imprint 640 00:42:11,280 --> 00:42:12,920 Speaker 1: on all the church that we look at for like 641 00:42:12,960 --> 00:42:15,960 Speaker 1: decades because it's just so weird, whether it's the surge, 642 00:42:16,200 --> 00:42:18,880 Speaker 1: the raw surge in the unemployment rate, the pure speed 643 00:42:18,880 --> 00:42:21,759 Speaker 1: of the bounce back, the collapse, everything is just so weird. 644 00:42:21,800 --> 00:42:24,360 Speaker 1: But you look at this and it's obviously very different. 645 00:42:25,200 --> 00:42:27,799 Speaker 1: And I do think that, like, I don't think there's 646 00:42:27,840 --> 00:42:31,880 Speaker 1: a totally satisfactory answer yet to why do we seem 647 00:42:31,920 --> 00:42:35,839 Speaker 1: to have this very robust demand for labor and yet 648 00:42:36,280 --> 00:42:39,880 Speaker 1: we continue to have, um, this big whole you know, 649 00:42:39,920 --> 00:42:41,960 Speaker 1: at least five or six million people if you were 650 00:42:41,960 --> 00:42:44,680 Speaker 1: on the employed than they were a pre crisis. It's 651 00:42:44,680 --> 00:42:48,280 Speaker 1: a pretty it still remains a pretty big puzzle. Yeah, 652 00:42:48,360 --> 00:42:51,160 Speaker 1: and I like to Thomas's description of it as a 653 00:42:51,160 --> 00:42:55,160 Speaker 1: full employment plan for economists, but I mean on that 654 00:42:57,040 --> 00:43:00,359 Speaker 1: on that wider point though, I mean the idea that 655 00:43:00,480 --> 00:43:03,760 Speaker 1: maybe maybe we know more than we did before about 656 00:43:03,800 --> 00:43:07,000 Speaker 1: the way the economy works, but we sort of don't 657 00:43:07,120 --> 00:43:11,239 Speaker 1: have a good way of measuring those unknown economic variables 658 00:43:11,320 --> 00:43:16,279 Speaker 1: like maximum employment and um our star. I think that's 659 00:43:16,360 --> 00:43:19,000 Speaker 1: quite maybe that's the way to think about it, Like 660 00:43:19,040 --> 00:43:22,400 Speaker 1: it's not just that no one knows how anything works anymore, 661 00:43:22,440 --> 00:43:25,760 Speaker 1: which would be quite frightening, but it's becoming increasingly hard 662 00:43:25,880 --> 00:43:29,680 Speaker 1: to um to gauge those particular ideas. Maybe, you know what, 663 00:43:29,840 --> 00:43:33,880 Speaker 1: while we're here, I imagine some people maybe this is 664 00:43:34,440 --> 00:43:37,560 Speaker 1: no while we're here, And I imagine maybe some listeners 665 00:43:37,880 --> 00:43:39,799 Speaker 1: who listen to this episode may work at the FED 666 00:43:39,880 --> 00:43:41,359 Speaker 1: or something like that, because we had a guest at 667 00:43:41,360 --> 00:43:45,000 Speaker 1: the Richmond Fed. So I have a request to those listeners, Tracy, 668 00:43:45,040 --> 00:43:48,600 Speaker 1: you don't like the Beige Book, do you ever? Uh? So, 669 00:43:48,640 --> 00:43:51,400 Speaker 1: they it's like the regional survey and they always and 670 00:43:51,800 --> 00:43:57,000 Speaker 1: um our guests just mentioned it. It's like business leaders said, uh, 671 00:43:57,040 --> 00:43:59,400 Speaker 1: you know, we're seeing labor market Titan is having trouble 672 00:43:59,480 --> 00:44:03,720 Speaker 1: hiring this. I want a Beige Book for workers anecdotes 673 00:44:04,520 --> 00:44:08,400 Speaker 1: of the labor market, but from the from the worker perspective, 674 00:44:09,120 --> 00:44:12,240 Speaker 1: just asking people questions. Okay, we get the actual data, 675 00:44:12,320 --> 00:44:15,839 Speaker 1: but talking about what was your experience? What was your 676 00:44:15,840 --> 00:44:20,720 Speaker 1: experience applying for a job that you're ostensibly qualified for? 677 00:44:21,239 --> 00:44:25,960 Speaker 1: Did companies actually call you back? Was the description actually 678 00:44:26,000 --> 00:44:28,440 Speaker 1: how it was. I think employers are going to get 679 00:44:28,440 --> 00:44:30,959 Speaker 1: to sort of like give their anecdotal takes that aren't 680 00:44:30,960 --> 00:44:32,839 Speaker 1: really based in data on like oh, you know, like 681 00:44:33,040 --> 00:44:35,439 Speaker 1: we can't find the people or the workers aren't showing 682 00:44:35,480 --> 00:44:37,440 Speaker 1: up after we offer them the job. I think we 683 00:44:37,600 --> 00:44:40,480 Speaker 1: deserve a Beijing Book from the workers perspective, and I 684 00:44:40,480 --> 00:44:42,799 Speaker 1: think that might help us answer some of these questions stuff. 685 00:44:42,800 --> 00:44:45,520 Speaker 1: Anyone who's listening. Maybe we could get some funding for that. 686 00:44:46,320 --> 00:44:48,279 Speaker 1: I think that's a great idea. And I mean I 687 00:44:48,320 --> 00:44:52,839 Speaker 1: want to know whether or not there is a wealth effect, Yeah, 688 00:44:53,040 --> 00:44:57,560 Speaker 1: greater wealth effects going on from like stock trading from crypto. Yeah. 689 00:44:57,920 --> 00:45:00,280 Speaker 1: This would be the kind of thing where like instead 690 00:45:00,320 --> 00:45:02,919 Speaker 1: of just having to speculate, I mean, anecdotes can only 691 00:45:02,920 --> 00:45:04,400 Speaker 1: go if you go so far. And I think we 692 00:45:04,440 --> 00:45:06,279 Speaker 1: have to be careful. And one reason we have to 693 00:45:06,280 --> 00:45:09,520 Speaker 1: be careful about anecdotes is because you know, employers started 694 00:45:09,520 --> 00:45:12,839 Speaker 1: calling the labor market tight like fifteen, then we had 695 00:45:12,880 --> 00:45:15,560 Speaker 1: like five more years of unemployment, right, so you have 696 00:45:15,640 --> 00:45:18,320 Speaker 1: to be careful of anecdotes. But all of these things 697 00:45:18,320 --> 00:45:21,120 Speaker 1: like oh, are you taking the time off from work 698 00:45:21,120 --> 00:45:23,520 Speaker 1: because you want to trade crypto or something like, let's 699 00:45:23,560 --> 00:45:26,520 Speaker 1: ask people in a systematic manner and have it be 700 00:45:26,600 --> 00:45:28,960 Speaker 1: something that we record rather than just sort of like 701 00:45:29,000 --> 00:45:31,759 Speaker 1: speculate on totally. And I also I also want to 702 00:45:31,760 --> 00:45:35,240 Speaker 1: see how people react to or what they say about, 703 00:45:35,280 --> 00:45:39,839 Speaker 1: like the new sort of UM hiring websites and things 704 00:45:39,880 --> 00:45:42,120 Speaker 1: like that, because I gather it's been it's been a 705 00:45:42,120 --> 00:45:44,319 Speaker 1: while since I've applied to a job on on one 706 00:45:44,360 --> 00:45:46,799 Speaker 1: of those um so, thank you Bloomberg. But like my 707 00:45:46,880 --> 00:45:49,279 Speaker 1: impression of them is that you kind of have to 708 00:45:49,320 --> 00:45:51,800 Speaker 1: take all these boxes and it can be really really 709 00:45:51,800 --> 00:45:56,320 Speaker 1: frustrating if like one aspect of your resume is slightly 710 00:45:56,480 --> 00:45:59,640 Speaker 1: out of line with whatever the request is from the 711 00:45:59,680 --> 00:46:03,040 Speaker 1: potential employer or the monster dot conform or whatever. And 712 00:46:03,080 --> 00:46:06,560 Speaker 1: I suspect that's also frustrating some of these matching efforts. Yeah, 713 00:46:06,640 --> 00:46:10,640 Speaker 1: totally that again, and that speaks to Thomas's point, which 714 00:46:10,680 --> 00:46:13,319 Speaker 1: is like, Okay, like these websites have made it a 715 00:46:13,320 --> 00:46:16,920 Speaker 1: lot easier to post job openings, their behavioral effects on 716 00:46:17,000 --> 00:46:21,960 Speaker 1: the employment side where it's like listing is maybe different 717 00:46:21,960 --> 00:46:25,719 Speaker 1: than listings in you know or something like that. And 718 00:46:25,880 --> 00:46:29,120 Speaker 1: so again it feels like there's a lot of ambiguity 719 00:46:29,239 --> 00:46:32,560 Speaker 1: in all of this, and I yes, interviewing someone interviewing 720 00:46:32,560 --> 00:46:35,160 Speaker 1: more people about their experience on these sites. Do they 721 00:46:35,160 --> 00:46:38,040 Speaker 1: actually get callbacks for jobs that they're qualified for that 722 00:46:38,080 --> 00:46:40,920 Speaker 1: they continually see posted? I think would be very useful. 723 00:46:40,960 --> 00:46:44,120 Speaker 1: So hopefully as someone listening, call us up, name it 724 00:46:44,280 --> 00:46:50,160 Speaker 1: the odd odd Survey, great idea, Boka, the Odd Book, 725 00:46:50,320 --> 00:46:54,480 Speaker 1: the Odd Book that is at ring to it all? Right, Um, 726 00:46:54,560 --> 00:46:58,239 Speaker 1: let's leave in there. Let's leave it there. This has 727 00:46:58,280 --> 00:47:01,840 Speaker 1: been another episode of the Odds Podcast. I'm Tracy Alloway. 728 00:47:01,920 --> 00:47:05,000 Speaker 1: You can follow me on Twitter at Tracy Alloway and 729 00:47:05,040 --> 00:47:07,160 Speaker 1: I'm Joe Why Isn't All? You can follow me on 730 00:47:07,200 --> 00:47:11,080 Speaker 1: Twitter at the Stalwart. Follow our producer on Twitter, Laura Carlson. 731 00:47:11,239 --> 00:47:14,840 Speaker 1: She's at Laura M. Carlson. Follow the Bloomberg head of podcast, 732 00:47:14,880 --> 00:47:18,480 Speaker 1: Francesco Levi at Francesca Today and check out all of 733 00:47:18,520 --> 00:47:22,400 Speaker 1: our podcasts on Twitter under the handle at podcasts. Thanks 734 00:47:22,400 --> 00:47:22,880 Speaker 1: for listening.