1 00:00:02,720 --> 00:00:17,000 Speaker 1: Bloomberg Audio Studios, Podcasts, Radio News. 2 00:00:18,480 --> 00:00:21,360 Speaker 2: Hello and welcome to another episode of the All Thoughts Podcast. 3 00:00:21,440 --> 00:00:23,560 Speaker 3: I'm Tracy Alloway and I'm Joe Wisenthal. 4 00:00:23,880 --> 00:00:27,080 Speaker 2: Joe, I kind of missed this on Friday because I 5 00:00:27,240 --> 00:00:29,880 Speaker 2: was not feeling very well, so I was out sick. 6 00:00:30,000 --> 00:00:32,520 Speaker 2: But what was Friday morning like when you were sat 7 00:00:32,560 --> 00:00:33,920 Speaker 2: in front of your Bloomberg terminal. 8 00:00:34,200 --> 00:00:38,080 Speaker 3: Well, the jobs report was pretty shocking. Obviously Friday was 9 00:00:38,120 --> 00:00:41,080 Speaker 3: just a crazy day. Yeah, So the jobs report, obviously 10 00:00:42,600 --> 00:00:45,000 Speaker 3: it was bad. I mean, the last two months massively 11 00:00:45,040 --> 00:00:48,240 Speaker 3: revised down, all of this evidence of labor market momentum 12 00:00:48,479 --> 00:00:52,080 Speaker 3: perhaps seems to be stalling the tariff effect. Maybe we 13 00:00:52,200 --> 00:00:54,560 Speaker 3: really are and it's sort of some sort of terriff 14 00:00:54,600 --> 00:00:57,480 Speaker 3: and do slow down. The only job creation is in 15 00:00:57,560 --> 00:00:58,880 Speaker 3: healthcare and social service. 16 00:00:59,280 --> 00:01:02,400 Speaker 2: Not great, right, Okay, So I saw the initial jobs 17 00:01:02,560 --> 00:01:06,080 Speaker 2: report headline. So we had non farm payrolls coming in 18 00:01:06,240 --> 00:01:08,680 Speaker 2: at plus seventy three thousand, which I think was like 19 00:01:09,040 --> 00:01:12,920 Speaker 2: thirty percent lower than the average expectation. We had the 20 00:01:13,000 --> 00:01:15,360 Speaker 2: unemployment rate taking up to like four point three percent, 21 00:01:15,400 --> 00:01:19,760 Speaker 2: four point almost four point three percent. Oh, let me 22 00:01:19,880 --> 00:01:21,560 Speaker 2: let me sorry, I'm rounding up here. 23 00:01:21,600 --> 00:01:24,160 Speaker 3: Okay, Okay, okay, although maybe I shouldn't. 24 00:01:23,720 --> 00:01:26,600 Speaker 2: Do that on a podcast all about labor market statistics. 25 00:01:27,160 --> 00:01:29,400 Speaker 2: But the big thing that seemed to catch everyone's attention 26 00:01:29,600 --> 00:01:32,880 Speaker 2: was we also had these massive revisions to the report 27 00:01:32,920 --> 00:01:35,680 Speaker 2: for May and June, so we had a combined two 28 00:01:35,760 --> 00:01:39,880 Speaker 2: hundred and fifty eight thousand jobs basically lowered from the 29 00:01:40,000 --> 00:01:43,760 Speaker 2: initial reports. And this was like the biggest revision since 30 00:01:43,880 --> 00:01:47,400 Speaker 2: the depths of the pandemic. And then the headline that 31 00:01:47,440 --> 00:01:49,920 Speaker 2: really caught my eye while I was, you know, laying 32 00:01:49,960 --> 00:01:54,200 Speaker 2: in my sick bed on Friday afternoon, was Trump firing 33 00:01:54,640 --> 00:01:57,200 Speaker 2: the head of the Bureau of Labor Statistics, so the 34 00:01:57,240 --> 00:02:00,160 Speaker 2: agency responsible for putting out the non farm pay Roule 35 00:02:00,240 --> 00:02:01,480 Speaker 2: Report every month. 36 00:02:01,800 --> 00:02:04,160 Speaker 3: Yeah, this is I would say. The key thing is 37 00:02:04,200 --> 00:02:07,400 Speaker 3: that a we've been used to these, you know, seeing 38 00:02:07,440 --> 00:02:11,080 Speaker 3: significant revisions. We've been talking you in particular, have been 39 00:02:11,120 --> 00:02:14,200 Speaker 3: writing a lot in the newsletter about deteriorating quality of 40 00:02:14,280 --> 00:02:17,600 Speaker 3: labor statistics. Response rates to a lot of surveys have 41 00:02:17,680 --> 00:02:19,760 Speaker 3: gone down over time, kind of like we're seeing in 42 00:02:19,760 --> 00:02:23,280 Speaker 3: political opinion surveys and so forth. So there's already been 43 00:02:23,320 --> 00:02:26,760 Speaker 3: this sort of anxiety. You have a lot of people online, 44 00:02:26,800 --> 00:02:30,720 Speaker 3: including President Trumpet himself, you know, been like stoking sort 45 00:02:30,760 --> 00:02:34,080 Speaker 3: of these conspiracy theories about these revisions, these attempts to 46 00:02:34,160 --> 00:02:37,600 Speaker 3: actually be transparent, and then you get the first sort 47 00:02:37,639 --> 00:02:41,200 Speaker 3: of like genuinely negative report under this administration. It's the 48 00:02:41,200 --> 00:02:44,160 Speaker 3: first one that was like, Okay, this was bad and boom, 49 00:02:44,320 --> 00:02:48,040 Speaker 3: Trump fires the person responsible for it. Now again, this 50 00:02:48,160 --> 00:02:50,640 Speaker 3: is one of those things where sort of like Doge itself, 51 00:02:50,639 --> 00:02:54,680 Speaker 3: where it's like, I like the idea of government efficiency, right, yeah, sure, Doge. 52 00:02:54,760 --> 00:02:57,320 Speaker 3: I like the idea of the BLS sort of doing 53 00:02:57,360 --> 00:03:00,000 Speaker 3: a better job in some way or addressing these responses. 54 00:03:00,440 --> 00:03:04,120 Speaker 3: But like Doge, which you know, we all have seen 55 00:03:04,160 --> 00:03:06,040 Speaker 3: how that's turned out. I think there are a lot 56 00:03:06,040 --> 00:03:08,359 Speaker 3: of more than a lot of questions, more than a 57 00:03:08,400 --> 00:03:11,079 Speaker 3: lot of questions about whether firing to be less and 58 00:03:11,160 --> 00:03:14,040 Speaker 3: replacing the head with someone who's you know, a Trumpet 59 00:03:14,080 --> 00:03:18,560 Speaker 3: point t will you know, add a more credible transparent 60 00:03:18,680 --> 00:03:19,799 Speaker 3: data clauding right. 61 00:03:20,120 --> 00:03:22,440 Speaker 2: One thing that we have learned from all our episodes 62 00:03:22,520 --> 00:03:24,720 Speaker 2: on data collection in the US is that it is 63 00:03:24,800 --> 00:03:29,000 Speaker 2: actually a really really insanely labor intensive thing to do. 64 00:03:29,120 --> 00:03:31,320 Speaker 2: You have to actually call up a bunch of people 65 00:03:32,080 --> 00:03:34,080 Speaker 2: homeowners for instance, or you have to call up a 66 00:03:34,080 --> 00:03:36,560 Speaker 2: bunch of businesses and ask questions. You have to go 67 00:03:36,640 --> 00:03:40,800 Speaker 2: out into the field and gather individual prices. And we 68 00:03:40,800 --> 00:03:43,400 Speaker 2: can have a debate over whether or not you could 69 00:03:43,440 --> 00:03:46,600 Speaker 2: maybe use new technology or price scanning data to make 70 00:03:46,640 --> 00:03:48,960 Speaker 2: all of that more efficient, but for the time being, 71 00:03:49,040 --> 00:03:51,400 Speaker 2: that's how it's done, and so you need people to 72 00:03:51,440 --> 00:03:54,120 Speaker 2: do it. And if DOGE comes in and eliminates, you know, 73 00:03:54,600 --> 00:03:56,720 Speaker 2: a big chunk of the budget, then it becomes harder 74 00:03:56,760 --> 00:03:57,200 Speaker 2: to do. 75 00:03:57,400 --> 00:03:59,640 Speaker 3: Or if it just gets more costly and the budget's 76 00:03:59,640 --> 00:04:01,320 Speaker 3: gone up, and then the one thing I would say 77 00:04:01,520 --> 00:04:04,480 Speaker 3: is that in our conversations, and again you have talked 78 00:04:04,480 --> 00:04:07,840 Speaker 3: to the BLS many times, they are really good. They're professionals, 79 00:04:08,000 --> 00:04:09,640 Speaker 3: and they take it very seriously. 80 00:04:09,720 --> 00:04:13,000 Speaker 2: They're very responsive, however, very transparent. We could talk about 81 00:04:13,000 --> 00:04:15,280 Speaker 2: this a little bit, but perhaps less responsive than they 82 00:04:15,360 --> 00:04:18,440 Speaker 2: used to be in recent weeks. So anyway, we should 83 00:04:18,440 --> 00:04:20,719 Speaker 2: talk about all of this. I think it's really important. 84 00:04:20,760 --> 00:04:22,880 Speaker 2: And as you said, there are big questions like what 85 00:04:22,960 --> 00:04:25,840 Speaker 2: happens if a president ouse a BLS chief and then 86 00:04:25,920 --> 00:04:28,440 Speaker 2: introduces a new one. We have the perfect guests. Someone 87 00:04:28,520 --> 00:04:33,440 Speaker 2: we've spoken to before about the decline of America's data infrastructure. 88 00:04:33,600 --> 00:04:35,560 Speaker 2: We're going to be speaking with Bill Beach. He is, 89 00:04:35,600 --> 00:04:39,120 Speaker 2: of course, the former Commissioner of Labor Statistics, former head 90 00:04:39,160 --> 00:04:41,080 Speaker 2: of the BLS. So Bill, thank you so much for 91 00:04:41,120 --> 00:04:42,159 Speaker 2: coming back on all thoughts. 92 00:04:43,080 --> 00:04:45,200 Speaker 4: Yeah, it's just really a pleasure to be back with you. 93 00:04:45,320 --> 00:04:48,240 Speaker 4: I wish we were talking about some other subject besides 94 00:04:49,120 --> 00:04:52,120 Speaker 4: this termination. I mean, it's just yeah, shocked. 95 00:04:52,279 --> 00:04:56,640 Speaker 2: Well, on that note, Friday, Trump tweets or posts that 96 00:04:56,680 --> 00:05:00,599 Speaker 2: he wants to fire Erica mc Andterfer, the Commissioner of 97 00:05:00,640 --> 00:05:02,960 Speaker 2: Labor Statistics. What was going through your mind when you 98 00:05:02,960 --> 00:05:03,640 Speaker 2: saw that news. 99 00:05:04,000 --> 00:05:07,240 Speaker 4: I was dumbcast. I just finished a luncheon meeting at 100 00:05:07,279 --> 00:05:09,479 Speaker 4: a very nice restaurant, and I was sitting in my 101 00:05:09,520 --> 00:05:12,920 Speaker 4: pickup truck and I just couldn't believe. And then I 102 00:05:12,960 --> 00:05:17,119 Speaker 4: sat there for probably thirty forty minutes, car running and 103 00:05:17,320 --> 00:05:21,440 Speaker 4: answering emails, people just sorting it out. It isn't that 104 00:05:21,720 --> 00:05:23,920 Speaker 4: he didn't have the authority to do so. He does 105 00:05:23,960 --> 00:05:26,479 Speaker 4: have the authority. We all, you know, everyone in the 106 00:05:26,520 --> 00:05:29,160 Speaker 4: executive branch serves as the pleasure of the president, except 107 00:05:29,200 --> 00:05:32,960 Speaker 4: those that have been specifically exempted by statute, and where 108 00:05:32,960 --> 00:05:35,320 Speaker 4: that statute has been signed by the President. I think 109 00:05:35,360 --> 00:05:38,320 Speaker 4: what was shocking about this is not that she was dismissed. 110 00:05:38,320 --> 00:05:41,640 Speaker 4: That's shocking enough. It was the possibility that we could 111 00:05:41,720 --> 00:05:46,560 Speaker 4: have now a sustained attack on official economic statistics, and 112 00:05:46,640 --> 00:05:50,360 Speaker 4: that would undermine confidence in those statistics and put us 113 00:05:50,400 --> 00:05:52,720 Speaker 4: in a path that other countries have followed. I mean 114 00:05:53,000 --> 00:05:56,320 Speaker 4: instantly people went to worst case. After a while, that 115 00:05:56,440 --> 00:05:58,240 Speaker 4: sort of settled down, and we began to look at 116 00:05:58,279 --> 00:06:02,280 Speaker 4: the reasons for the dismissal, which then subsequently said on 117 00:06:02,320 --> 00:06:05,600 Speaker 4: Twitter or x that they were they were unfounded. And 118 00:06:05,800 --> 00:06:09,359 Speaker 4: i haven't dropped that position since, so I'm happy to 119 00:06:09,400 --> 00:06:09,960 Speaker 4: talk about it. 120 00:06:10,279 --> 00:06:12,279 Speaker 3: Well, why don't we just back up for a second. 121 00:06:12,320 --> 00:06:14,880 Speaker 3: We talked to you before, because you've been sounding the 122 00:06:14,920 --> 00:06:18,479 Speaker 3: alarm about the capacity constraints to collect good data. But 123 00:06:19,040 --> 00:06:22,359 Speaker 3: just for people who haven't listened to or haven't followed you, 124 00:06:22,360 --> 00:06:24,840 Speaker 3: you were a Trump appointing. I mean, you served at 125 00:06:24,839 --> 00:06:28,000 Speaker 3: the head of the BLS in twenty seventeen, and so 126 00:06:28,080 --> 00:06:29,560 Speaker 3: I do you know, and it's tart of like, okay, 127 00:06:29,560 --> 00:06:31,240 Speaker 3: your credibility. Why don't you just give us a little 128 00:06:31,279 --> 00:06:35,440 Speaker 3: bit of background about who you are, your position, and 129 00:06:35,480 --> 00:06:38,440 Speaker 3: the concerns that you've been raising for some time about 130 00:06:38,480 --> 00:06:39,960 Speaker 3: the constraints at the BLS. 131 00:06:40,240 --> 00:06:42,919 Speaker 4: So I was really honored to be the Commissioner of 132 00:06:42,960 --> 00:06:47,400 Speaker 4: Labor Statistics from twenty nineteen until twenty twenty three. So 133 00:06:47,440 --> 00:06:50,200 Speaker 4: I served a little less than two years under President Trump. 134 00:06:50,760 --> 00:06:55,840 Speaker 4: He nominated me. It is a presidential nomination, Senate confirmed positions, 135 00:06:56,040 --> 00:06:59,200 Speaker 4: it has a statutory for your term. And then I 136 00:06:59,240 --> 00:07:02,159 Speaker 4: served the rest of my under President Biden. So I 137 00:07:02,279 --> 00:07:05,360 Speaker 4: was in both terms. I think that's kind of important 138 00:07:05,440 --> 00:07:11,680 Speaker 4: because the BLS Commissioner has been exempt from the normal 139 00:07:11,800 --> 00:07:15,960 Speaker 4: turning over of the presidency by party. And that's because 140 00:07:16,000 --> 00:07:18,400 Speaker 4: the view as well, these data are so important they 141 00:07:18,400 --> 00:07:21,480 Speaker 4: should not even be a part of the political appointment 142 00:07:21,560 --> 00:07:23,640 Speaker 4: process except when they come to. It's sort of like 143 00:07:24,240 --> 00:07:26,200 Speaker 4: you know, the Federal Reserve chair or something of that 144 00:07:26,320 --> 00:07:28,240 Speaker 4: nature has just seen as we want to keep it 145 00:07:28,240 --> 00:07:31,880 Speaker 4: out of politics. So I served over that period of time. 146 00:07:32,480 --> 00:07:34,440 Speaker 4: You know, that was the COVID period, so there were 147 00:07:34,480 --> 00:07:36,680 Speaker 4: all kinds of real challenges from that, but I think 148 00:07:36,760 --> 00:07:39,560 Speaker 4: the main thing that I drew from that period is 149 00:07:39,640 --> 00:07:43,480 Speaker 4: how important it is crucial, almost were the crisis level, 150 00:07:43,800 --> 00:07:47,280 Speaker 4: to modernize the way we collect data. The response rates 151 00:07:47,320 --> 00:07:50,600 Speaker 4: on the surveys are falling dramatically. I've made that point 152 00:07:50,640 --> 00:07:54,400 Speaker 4: on this program, and our costs arising dramatically, which makes 153 00:07:54,440 --> 00:07:57,600 Speaker 4: it really difficult to conduct those surveys in the future 154 00:07:57,640 --> 00:07:59,840 Speaker 4: and the way we have in the past, and then 155 00:08:00,280 --> 00:08:02,920 Speaker 4: the future is kind of going in the direction of 156 00:08:03,000 --> 00:08:07,160 Speaker 4: the employment survey, and I just said surveys are following 157 00:08:07,160 --> 00:08:09,920 Speaker 4: with the employment surveys a little different. It's not an 158 00:08:09,960 --> 00:08:13,600 Speaker 4: in person survey, it is an electronic survey, and we 159 00:08:13,680 --> 00:08:17,920 Speaker 4: need to invest more in the electronic side of statistical 160 00:08:18,120 --> 00:08:21,440 Speaker 4: creation or production, blended data, etc. I could do a 161 00:08:21,440 --> 00:08:24,520 Speaker 4: whole program on this, but your point was well made. 162 00:08:24,760 --> 00:08:27,400 Speaker 4: BLS has had some challenges in front of it, as 163 00:08:27,520 --> 00:08:32,200 Speaker 4: has the entire statistical system. Unfortunately, for this particular episode, 164 00:08:32,240 --> 00:08:36,559 Speaker 4: those challenges were really not I don't say relevant. They're relevant, 165 00:08:36,559 --> 00:08:38,600 Speaker 4: of course, but they're really not the reason why we 166 00:08:38,920 --> 00:08:41,160 Speaker 4: should have had a change at the top. 167 00:08:41,920 --> 00:08:44,880 Speaker 2: Well, speaking of the employment report, talk to us about 168 00:08:44,880 --> 00:08:48,760 Speaker 2: what exactly happens when the BLS is publishing the initial 169 00:08:48,800 --> 00:08:51,920 Speaker 2: non farm payrolls and then what happens, you know, in 170 00:08:52,080 --> 00:08:55,680 Speaker 2: the couple months or so before it publishes the revision, 171 00:08:55,760 --> 00:08:58,440 Speaker 2: and why does it seem we are getting these large 172 00:08:58,480 --> 00:09:01,000 Speaker 2: gaps at least in recent months and years. 173 00:09:01,200 --> 00:09:06,560 Speaker 4: Right, So the employment survey, the jobs numbers come from 174 00:09:06,720 --> 00:09:10,680 Speaker 4: a survey of businesses. The unemployment rate comes from a 175 00:09:10,679 --> 00:09:13,760 Speaker 4: survey of households. So there's two surveys involved in every 176 00:09:13,760 --> 00:09:17,080 Speaker 4: First Friday report. What we're talking about now is the 177 00:09:17,120 --> 00:09:19,120 Speaker 4: survey that goes out to businesses, and it goes out 178 00:09:19,120 --> 00:09:22,440 Speaker 4: to hundreds of thousands of businesses. Well, of course there's 179 00:09:22,480 --> 00:09:25,920 Speaker 4: still a probability sample, since there's well in excess of 180 00:09:25,960 --> 00:09:31,840 Speaker 4: twelve million businesses in our Census of business, our business register. Well, 181 00:09:32,040 --> 00:09:36,280 Speaker 4: those businesses are supposed to turn their surveys in at 182 00:09:36,280 --> 00:09:39,960 Speaker 4: the end of the month basically, but only about sixty 183 00:09:40,000 --> 00:09:43,200 Speaker 4: eight percent usually that's the average do so, and so 184 00:09:43,559 --> 00:09:47,160 Speaker 4: BLS keeps the window open for two more months, so 185 00:09:47,360 --> 00:09:49,199 Speaker 4: sixty eight percent or so at the end of the 186 00:09:49,280 --> 00:09:53,120 Speaker 4: first month they make the first estimate. Then at the 187 00:09:53,200 --> 00:09:55,600 Speaker 4: end of the second month we've get about eighty three 188 00:09:55,679 --> 00:10:00,560 Speaker 4: percent completion, and they revise that number of the first month, 189 00:10:00,640 --> 00:10:02,920 Speaker 4: and then by the third month we're into the nineties. 190 00:10:03,000 --> 00:10:05,360 Speaker 4: Usually end up around ninety three to ninety four percent 191 00:10:05,400 --> 00:10:07,800 Speaker 4: of all the sent out returns. Come back to us 192 00:10:07,840 --> 00:10:12,679 Speaker 4: with information. So it's a wonderful survey. The revisions are 193 00:10:12,720 --> 00:10:16,520 Speaker 4: done because we get more information from businesses. Oftentimes big 194 00:10:16,559 --> 00:10:19,280 Speaker 4: businesses answer first they have the capacity to do so, 195 00:10:19,320 --> 00:10:22,559 Speaker 4: and then we get smaller entities, state and local governments, 196 00:10:23,040 --> 00:10:27,720 Speaker 4: smaller businesses answer in those next two months. We always 197 00:10:27,800 --> 00:10:32,800 Speaker 4: have revisions. There's there's even when we say the number 198 00:10:32,840 --> 00:10:35,600 Speaker 4: did not change, it doesn't mean we didn't have revisions. 199 00:10:35,600 --> 00:10:38,240 Speaker 4: They just canceled each other out. There's just all these 200 00:10:38,280 --> 00:10:42,240 Speaker 4: revisions are coming in. The revisions have been high recently, 201 00:10:42,280 --> 00:10:45,240 Speaker 4: but this is not a typical of a period when 202 00:10:45,280 --> 00:10:48,400 Speaker 4: the economy is either going back to growth or going 203 00:10:48,520 --> 00:10:53,040 Speaker 4: down to subsidence. So oftentimes turning points in the economy 204 00:10:53,360 --> 00:10:58,360 Speaker 4: are accompanied by changes larger changes in the revisions. Particularly 205 00:10:58,440 --> 00:11:01,959 Speaker 4: in this case where we SUSPEC effect. Smaller businesses are 206 00:11:01,960 --> 00:11:04,680 Speaker 4: feeling the effects of the supply shock coming from the 207 00:11:04,720 --> 00:11:09,199 Speaker 4: tariff policy and from immigration, and that these supply shocks 208 00:11:09,240 --> 00:11:12,480 Speaker 4: are affecting smaller businesses more than they are larger businesses, 209 00:11:13,040 --> 00:11:16,120 Speaker 4: and state and local governments are being affected by another factor, 210 00:11:16,160 --> 00:11:19,280 Speaker 4: and that is the expiration of the COVID era money. 211 00:11:19,679 --> 00:11:22,360 Speaker 4: I think that was actually the root of a lot 212 00:11:22,400 --> 00:11:25,200 Speaker 4: of the changes that you saw on Friday. State and 213 00:11:25,240 --> 00:11:28,360 Speaker 4: local governments are not able to hire at the levels 214 00:11:28,400 --> 00:11:30,800 Speaker 4: they were going to hire at in previous years or 215 00:11:30,960 --> 00:11:33,679 Speaker 4: did hire at in previous years because they did not 216 00:11:33,880 --> 00:11:37,319 Speaker 4: have the subsidies that they had in previous years. So 217 00:11:37,640 --> 00:11:42,880 Speaker 4: these are important revisions. Actually, the research shows, and this 218 00:11:42,960 --> 00:11:46,760 Speaker 4: may shock you, that BLS is getting much better at 219 00:11:46,800 --> 00:11:50,640 Speaker 4: its first estimate than it was thirty years ago. In fact, 220 00:11:50,640 --> 00:11:55,120 Speaker 4: it's really kind of almost a steady progress towards greater accuracy. 221 00:11:55,840 --> 00:12:00,599 Speaker 4: So the big revisions are indicative of not air of 222 00:12:00,679 --> 00:12:04,480 Speaker 4: more information, and the bigger the revisions, the more likely 223 00:12:04,520 --> 00:12:06,080 Speaker 4: we are in a turning point. 224 00:12:06,440 --> 00:12:08,840 Speaker 3: There's so much that was in that answer that I 225 00:12:08,880 --> 00:12:12,120 Speaker 3: found to be very helpful and clarifying, so thank you. 226 00:12:12,160 --> 00:12:15,200 Speaker 3: I mean, just this idea, you know, a that actually 227 00:12:15,240 --> 00:12:18,040 Speaker 3: over time, contrary to what people on Twitter might think, 228 00:12:18,120 --> 00:12:20,880 Speaker 3: that the equality of that first pass has actually gone 229 00:12:20,960 --> 00:12:24,160 Speaker 3: up is very striking. 230 00:12:24,360 --> 00:12:24,520 Speaker 1: You know. 231 00:12:24,960 --> 00:12:28,400 Speaker 3: Let's stipulate that, Okay, the BLIS could use some upgrading. 232 00:12:28,440 --> 00:12:32,119 Speaker 3: As you mentioned that there are these capacity constraints, non response, 233 00:12:32,200 --> 00:12:34,680 Speaker 3: the cost of going out. Maybe it needs to adopt 234 00:12:34,720 --> 00:12:37,839 Speaker 3: more technology in some way to solve for this. Does 235 00:12:37,880 --> 00:12:42,360 Speaker 3: the BLS currently have that capacity, either budgetary wise or 236 00:12:42,440 --> 00:12:46,600 Speaker 3: statutory authority such that a commissioner could just do that 237 00:12:47,240 --> 00:12:50,040 Speaker 3: whatever it is that ideal state, or would it need 238 00:12:50,080 --> 00:12:52,560 Speaker 3: either some sort of budgetary allocation or an Act of 239 00:12:52,600 --> 00:12:56,040 Speaker 3: Congress to get to the point where it's you know, 240 00:12:56,120 --> 00:12:58,120 Speaker 3: getting to the level that we are happy with. 241 00:12:58,160 --> 00:13:01,520 Speaker 4: Again, it has the authority to make the changes, it 242 00:13:01,559 --> 00:13:04,880 Speaker 4: doesn't have the budget to do so. And it isn't 243 00:13:04,920 --> 00:13:07,680 Speaker 4: that BLS just needs more money, right. As a matter 244 00:13:07,720 --> 00:13:09,800 Speaker 4: of fact, I think Congress, if it paid more attention, 245 00:13:10,240 --> 00:13:12,720 Speaker 4: would say, well, what are you spending your money on now? 246 00:13:12,800 --> 00:13:16,400 Speaker 4: And let's make sure that the low priority is canceled 247 00:13:16,520 --> 00:13:18,559 Speaker 4: so that we can support the high priority. I think 248 00:13:18,679 --> 00:13:21,480 Speaker 4: BLS would be very happy with that. But we do 249 00:13:21,559 --> 00:13:24,360 Speaker 4: need more money. And why Well, if you're going to 250 00:13:24,480 --> 00:13:27,680 Speaker 4: change an official statistic, the unemployment rate, let's just take that. 251 00:13:29,040 --> 00:13:31,199 Speaker 4: You want to be really careful, right, You want to 252 00:13:31,280 --> 00:13:34,200 Speaker 4: run tests, you want to test out your new idea. 253 00:13:34,559 --> 00:13:37,480 Speaker 4: You don't want to have a count the number of 254 00:13:37,520 --> 00:13:39,360 Speaker 4: geese in the air, and that's the unemployment rate. You 255 00:13:39,400 --> 00:13:41,520 Speaker 4: want to make sure that that is a particularly good 256 00:13:41,600 --> 00:13:45,520 Speaker 4: indicator of the employment market, and you want to test 257 00:13:45,559 --> 00:13:48,360 Speaker 4: it in real time as well as experimental time. You 258 00:13:48,440 --> 00:13:51,400 Speaker 4: in real time, so run it parallel. We these tests 259 00:13:51,840 --> 00:13:56,480 Speaker 4: expand the costs of your unemployment statistics program because you're 260 00:13:56,520 --> 00:14:03,040 Speaker 4: running essentially two systems simultaneously. That to modify the current 261 00:14:03,080 --> 00:14:07,080 Speaker 4: population survey, which is the survey of households, would cost 262 00:14:07,200 --> 00:14:11,200 Speaker 4: around fifteen million to twenty million in experimental costs over 263 00:14:11,240 --> 00:14:14,400 Speaker 4: a two year period. It's not great. The program itself 264 00:14:14,440 --> 00:14:17,360 Speaker 4: costs about forty five million a year, but it does 265 00:14:17,480 --> 00:14:19,520 Speaker 4: add to the cost for those two years or so. 266 00:14:19,920 --> 00:14:22,600 Speaker 4: Congress could could easily do that. I mean, oh my gosh, 267 00:14:22,680 --> 00:14:25,000 Speaker 4: Congress is spending a lot more money than that on 268 00:14:25,080 --> 00:14:28,280 Speaker 4: things that we might disagree with. But they need to 269 00:14:28,320 --> 00:14:30,840 Speaker 4: allocate a little bit of money to CPS for that. 270 00:14:31,040 --> 00:14:33,880 Speaker 4: Does it have the authority? Here's a really interesting story. 271 00:14:34,400 --> 00:14:38,480 Speaker 4: During COVID, we canceled all travel, all conferences, you know, 272 00:14:39,080 --> 00:14:41,720 Speaker 4: and I was surprised at how many millions of dollars 273 00:14:42,160 --> 00:14:46,360 Speaker 4: went into travel and conferences and meetings. So this became 274 00:14:46,400 --> 00:14:49,320 Speaker 4: for me couch money during the COVID years, and I 275 00:14:49,400 --> 00:14:52,480 Speaker 4: was able to spend that money, you know, on important 276 00:14:52,520 --> 00:14:54,520 Speaker 4: things I thought, So I did a lot of things, 277 00:14:54,560 --> 00:14:56,720 Speaker 4: like I've built a new data center to get us 278 00:14:56,760 --> 00:14:58,520 Speaker 4: out of the basement of one hundred and four year 279 00:14:58,560 --> 00:15:03,000 Speaker 4: old building out to a wonderful above ground local location. 280 00:15:03,160 --> 00:15:06,280 Speaker 4: But I also started a number of research programs. 281 00:15:06,320 --> 00:15:06,520 Speaker 2: You know. 282 00:15:06,600 --> 00:15:10,280 Speaker 4: I made thirty one big modifications to do the CPI. 283 00:15:11,240 --> 00:15:15,240 Speaker 4: We started a whole new approach on looking at consumer expenditures, 284 00:15:15,360 --> 00:15:21,960 Speaker 4: and I subsidized some experimentation on the Jobs Report well 285 00:15:22,000 --> 00:15:24,600 Speaker 4: as it as COVID was over, we started to get 286 00:15:24,800 --> 00:15:30,080 Speaker 4: congressionally mandated programs that then sucked up all that counch money. 287 00:15:30,960 --> 00:15:33,960 Speaker 4: If we had a little bit of extra, we I 288 00:15:33,960 --> 00:15:36,160 Speaker 4: mean be only asked a little bit of extra to 289 00:15:36,200 --> 00:15:39,280 Speaker 4: do these things. Let's just think of the innovation that 290 00:15:39,400 --> 00:15:41,920 Speaker 4: they would happen. I think we would quickly be out 291 00:15:42,000 --> 00:15:47,160 Speaker 4: of the problems that we are facing from a methodological standpoint, 292 00:15:47,280 --> 00:15:50,320 Speaker 4: and that might then reduce the political pressure that we 293 00:15:50,360 --> 00:15:52,240 Speaker 4: saw that was so evident on Friday. 294 00:15:52,600 --> 00:15:56,280 Speaker 2: So we're recording this August fourth, nine, five am in 295 00:15:56,320 --> 00:15:58,920 Speaker 2: the morning and thirty three seconds. I feel like I 296 00:15:58,960 --> 00:16:02,080 Speaker 2: need to be that specific nowadays with the newsflow, so 297 00:16:02,120 --> 00:16:04,800 Speaker 2: who knows what happens between now and when this episode 298 00:16:04,880 --> 00:16:08,840 Speaker 2: actually comes out, but we are expecting the administration to 299 00:16:08,880 --> 00:16:13,360 Speaker 2: announce a new BLS head in the coming days. What 300 00:16:13,560 --> 00:16:16,840 Speaker 2: happens now, you know, there's a Trump appointee for a 301 00:16:16,880 --> 00:16:21,360 Speaker 2: new statistics commissioner after the President has specifically stated that 302 00:16:21,440 --> 00:16:26,400 Speaker 2: he thinks the previous commissioner was politically motivated in some way. 303 00:16:26,880 --> 00:16:29,520 Speaker 2: Are people still going to believe the numbers that are 304 00:16:29,560 --> 00:16:31,400 Speaker 2: coming out? And how do you think that's actually going 305 00:16:31,440 --> 00:16:36,160 Speaker 2: to play internally at the BLS, at an organization whose whole, 306 00:16:36,320 --> 00:16:40,480 Speaker 2: you know, Raison Deutch is to find, you know, factual 307 00:16:40,680 --> 00:16:43,760 Speaker 2: numbers and statistics to portray the American economy. 308 00:16:44,680 --> 00:16:46,720 Speaker 4: Well, I think there has been damage and it will 309 00:16:46,760 --> 00:16:49,840 Speaker 4: take time to recover from that damage. The last time, 310 00:16:49,960 --> 00:16:54,600 Speaker 4: there was a serious political effort on BLSS during the 311 00:16:54,680 --> 00:16:59,280 Speaker 4: Nixon administration and it took some time for BLS to 312 00:16:59,360 --> 00:17:02,160 Speaker 4: recover from that, though the action taken by the President 313 00:17:02,200 --> 00:17:05,399 Speaker 4: at that time visa BLS was not, I think as 314 00:17:05,880 --> 00:17:09,919 Speaker 4: significant as this one. So he just imagined that the 315 00:17:09,920 --> 00:17:14,080 Speaker 4: President decides to appoint Saint Peter, you know, as the 316 00:17:14,119 --> 00:17:17,760 Speaker 4: new BLS commissioner, and of course Saint Peter has a 317 00:17:17,800 --> 00:17:22,120 Speaker 4: reputation for honesty and for clarity of thought. Still it'll 318 00:17:22,119 --> 00:17:24,720 Speaker 4: be the case because Saint Peter won't have any control 319 00:17:24,840 --> 00:17:27,480 Speaker 4: over the way that the data are collected, the data 320 00:17:27,560 --> 00:17:30,960 Speaker 4: are assembled, or the estimates that are done. That's all 321 00:17:31,000 --> 00:17:33,760 Speaker 4: done outside of the of the knowledge of the commissioner. 322 00:17:33,760 --> 00:17:36,080 Speaker 4: Commissioner has no control over that. In fact, you're locked 323 00:17:36,080 --> 00:17:39,159 Speaker 4: out of that whole process. So it will be the 324 00:17:39,160 --> 00:17:42,480 Speaker 4: case that Saint Peter will have a month come when 325 00:17:42,640 --> 00:17:45,880 Speaker 4: the unemployment rate will be disappointingly low, could even be negative. 326 00:17:46,800 --> 00:17:50,560 Speaker 4: But I think given what has just happened, people will say, well, 327 00:17:51,880 --> 00:17:55,040 Speaker 4: Saint Peter probably influenced that number and it's not as 328 00:17:55,080 --> 00:17:58,520 Speaker 4: bad as it really is. So for a while, that 329 00:17:58,680 --> 00:18:02,679 Speaker 4: suspicion that the estimate that's in ount it's really not 330 00:18:02,800 --> 00:18:06,320 Speaker 4: the real estimate, will be in the minds of some people, 331 00:18:06,359 --> 00:18:08,760 Speaker 4: not in my mind, because I know that these people 332 00:18:08,760 --> 00:18:11,399 Speaker 4: who work there, the professionals, the full time staff, the 333 00:18:11,440 --> 00:18:15,439 Speaker 4: patriots that work there, the loyal Americans who are just 334 00:18:15,920 --> 00:18:20,400 Speaker 4: extremely diligent in doing an absolutely objective job. Those people 335 00:18:20,480 --> 00:18:22,879 Speaker 4: are still in place. But if you don't have my 336 00:18:23,000 --> 00:18:25,960 Speaker 4: level of knowledge, or a reasonable level of knowledge of 337 00:18:26,000 --> 00:18:30,399 Speaker 4: how internally BLS works, you're going to be subject to 338 00:18:30,480 --> 00:18:35,080 Speaker 4: these falsehoods and accusations, and that will be damaging, That 339 00:18:35,160 --> 00:18:38,280 Speaker 4: will reduce investment, that will reduce economic activity, That will 340 00:18:38,280 --> 00:18:42,360 Speaker 4: make at least that will create greater uncertainty about what's 341 00:18:42,400 --> 00:18:47,040 Speaker 4: happening inside the US economy. Policymakers will be less clear 342 00:18:47,240 --> 00:18:49,240 Speaker 4: in the direction that they take, So there will be 343 00:18:49,280 --> 00:18:52,320 Speaker 4: a time when we need to recover. No matter who's appointed, 344 00:18:52,920 --> 00:18:55,719 Speaker 4: I hope that it will be a short lived time. 345 00:18:56,000 --> 00:18:59,680 Speaker 4: And my guess is, given the reputation of BLS, if 346 00:18:59,800 --> 00:19:04,320 Speaker 4: someone you know, really reputable is appointed, then yeah, I 347 00:19:04,359 --> 00:19:06,920 Speaker 4: don't think the period of transition will be that a lot. 348 00:19:07,440 --> 00:19:10,120 Speaker 2: All right, Well, Bill Beach really appreciate you coming back 349 00:19:10,160 --> 00:19:13,119 Speaker 2: on the show. Thank you so much for being on 350 00:19:13,200 --> 00:19:13,680 Speaker 2: Off Box. 351 00:19:13,920 --> 00:19:14,920 Speaker 3: Thanks Bill, that was great. 352 00:19:15,119 --> 00:19:16,480 Speaker 4: Thank you very much for asking me. 353 00:19:29,920 --> 00:19:31,840 Speaker 2: Joe so good to get Bill back on to talk 354 00:19:31,840 --> 00:19:34,400 Speaker 2: about this. Really the perfect guest. There are so many, 355 00:19:34,560 --> 00:19:38,800 Speaker 2: I guess ironies involved in this whole conversation. The big one, 356 00:19:38,840 --> 00:19:41,919 Speaker 2: of course, is this idea that like, well, if Trump 357 00:19:41,960 --> 00:19:44,400 Speaker 2: wants to convince everyone that the US economy is doing 358 00:19:44,440 --> 00:19:47,720 Speaker 2: fantastic and job growth is great, then firing the head 359 00:19:47,720 --> 00:19:52,080 Speaker 2: of statistics and having everyone distrust, you know, the subsequent numbers, 360 00:19:52,160 --> 00:19:55,919 Speaker 2: because the subsequent head might be a political appointee who's 361 00:19:55,960 --> 00:19:58,399 Speaker 2: you know, a loyalist to Trump. It seems like you're 362 00:19:58,440 --> 00:19:59,800 Speaker 2: sort of shooting yourself in the foot. 363 00:20:00,080 --> 00:20:03,720 Speaker 3: The other huge contradiction is that he says Powell is 364 00:20:03,760 --> 00:20:06,200 Speaker 3: too late. Yea wait, this is the other huge contradiction, 365 00:20:06,280 --> 00:20:08,399 Speaker 3: which is every day he slams Powell, he's like, oh, 366 00:20:08,440 --> 00:20:11,600 Speaker 3: you're too late, you need to cut rates. Well, if 367 00:20:11,640 --> 00:20:14,520 Speaker 3: the job numbers were weak, then I could understand this 368 00:20:14,680 --> 00:20:17,080 Speaker 3: argument to some extent, oh the job But he's also 369 00:20:17,920 --> 00:20:20,840 Speaker 3: casting expersions on the negative jobs numbers. So how is 370 00:20:20,880 --> 00:20:23,119 Speaker 3: Powell too late? I mean, this is I mean, it's 371 00:20:23,160 --> 00:20:27,000 Speaker 3: an irretrievable contradiction in the two criticism of both of 372 00:20:27,000 --> 00:20:28,120 Speaker 3: the Fed and the BLS. 373 00:20:28,160 --> 00:20:31,120 Speaker 2: It's definitely a contradiction. I don't think, however, that Trump 374 00:20:31,240 --> 00:20:36,440 Speaker 2: necessarily draws a direct connection between the unemployment rate and interest. 375 00:20:36,160 --> 00:20:36,760 Speaker 3: Rates as well. 376 00:20:37,600 --> 00:20:40,600 Speaker 2: You know, he seems to just like low interest rates. 377 00:20:40,359 --> 00:20:43,280 Speaker 3: And he seems to perceive that low interest rates are 378 00:20:43,359 --> 00:20:48,360 Speaker 3: basically a reward for an improving economy. But yes, but I. 379 00:20:48,280 --> 00:20:51,520 Speaker 2: Mean, lots of other contradictions. You're absolutely right. So another 380 00:20:51,600 --> 00:20:53,760 Speaker 2: one is this idea that Okay, a lot of the 381 00:20:53,760 --> 00:20:56,880 Speaker 2: weakness came through on the revision side. The revisions are 382 00:20:56,960 --> 00:21:00,199 Speaker 2: mostly coming from small businesses who reply late. And if 383 00:21:00,240 --> 00:21:02,639 Speaker 2: you think about the Trump administration, you know, they always 384 00:21:02,680 --> 00:21:06,920 Speaker 2: sort of portray themselves as a good business environment for 385 00:21:07,200 --> 00:21:10,720 Speaker 2: smaller businesses, and it might be that that scenario that's 386 00:21:10,720 --> 00:21:11,880 Speaker 2: seeing some weakness. 387 00:21:12,240 --> 00:21:15,400 Speaker 3: I thought your question about sort of internal morale, and 388 00:21:15,760 --> 00:21:19,440 Speaker 3: as Bill said, you know, an agency staffed with patriots, 389 00:21:20,000 --> 00:21:22,359 Speaker 3: I mean, I just you know, is Bilska going to 390 00:21:22,400 --> 00:21:25,080 Speaker 3: be continue to be a destination for people who are 391 00:21:25,400 --> 00:21:28,359 Speaker 3: patriots right or take their job seriously in the future 392 00:21:28,840 --> 00:21:31,520 Speaker 3: is a really interesting question. Also, the idea that like 393 00:21:31,560 --> 00:21:34,080 Speaker 3: maybe we could like get high data quality for like 394 00:21:34,080 --> 00:21:37,239 Speaker 3: twenty million dollars. Given how much that's worth, it's just 395 00:21:37,359 --> 00:21:39,440 Speaker 3: like drives you crazy if that's really all it took. 396 00:21:39,680 --> 00:21:41,600 Speaker 3: But also I really like this idea that it would 397 00:21:41,600 --> 00:21:44,440 Speaker 3: have to always be done in parallel with the current thing, 398 00:21:44,800 --> 00:21:47,880 Speaker 3: such that the new data is sort of provably backwards 399 00:21:47,960 --> 00:21:50,280 Speaker 3: compatible with the old data. Right, so if you're going 400 00:21:50,320 --> 00:21:53,000 Speaker 3: to adopt a new methodology, you need some allocation, run 401 00:21:53,040 --> 00:21:56,119 Speaker 3: two surveys at once, et cetera. Anyway, even in a 402 00:21:56,200 --> 00:21:58,880 Speaker 3: very short conversation. Learned a lot from Bill. Absolutely. 403 00:21:58,960 --> 00:22:00,439 Speaker 2: I do think it's worth pointing out out that there 404 00:22:00,440 --> 00:22:03,800 Speaker 2: are other statistical agencies in the world that are experimenting 405 00:22:04,040 --> 00:22:08,320 Speaker 2: with new technology for data sources. The UK Statistics Agency 406 00:22:08,400 --> 00:22:10,600 Speaker 2: in particular, they're going to start using you know, price 407 00:22:10,640 --> 00:22:12,400 Speaker 2: scanning data for CPI. 408 00:22:12,800 --> 00:22:15,040 Speaker 3: Wait, have you heard about the whole thing though, with 409 00:22:15,119 --> 00:22:17,840 Speaker 3: how they moved the Ons to Wales and no one 410 00:22:17,880 --> 00:22:20,280 Speaker 3: wanted to go out there, And have you heard about this? 411 00:22:20,800 --> 00:22:23,680 Speaker 2: I have Wales. It's all that bad. 412 00:22:23,600 --> 00:22:27,640 Speaker 3: Attribute the largest of the government across. Everyone loves that idea. 413 00:22:27,720 --> 00:22:30,280 Speaker 3: Let's not have it all be concentrated in London. They're like, okay, 414 00:22:30,320 --> 00:22:32,600 Speaker 3: we're going to make everyone move to Wales and now 415 00:22:32,640 --> 00:22:35,639 Speaker 3: it's a lot harder for the Ons to hire anyone. Anyway, 416 00:22:35,680 --> 00:22:38,360 Speaker 3: it's just a little interesting story out Maybe we'll cover 417 00:22:38,440 --> 00:22:38,840 Speaker 3: it at. 418 00:22:38,680 --> 00:22:41,000 Speaker 2: Some point why people don't want to move to Wales. 419 00:22:41,160 --> 00:22:44,840 Speaker 3: No interesting, it's interesting, right, Like there's intuitively I love 420 00:22:44,920 --> 00:22:46,720 Speaker 3: this idea of like, oh, why should all of the 421 00:22:46,760 --> 00:22:49,920 Speaker 3: government spending on public sector stuff at the federal level 422 00:22:50,680 --> 00:22:53,800 Speaker 3: in the richest city in the UK, or why should 423 00:22:53,840 --> 00:22:56,200 Speaker 3: it all be in dc et cetera. Should this idea 424 00:22:56,200 --> 00:22:58,240 Speaker 3: of diffuse anyway, it's a separate thing. It's actually some 425 00:22:58,400 --> 00:22:59,960 Speaker 3: interesting challenges arise for. 426 00:23:00,440 --> 00:23:01,560 Speaker 2: Okay, shall we leave it there? 427 00:23:01,600 --> 00:23:02,320 Speaker 3: Yeah's leave it there. 428 00:23:02,400 --> 00:23:04,520 Speaker 2: This has been another episode of the oud Lots podcast. 429 00:23:04,560 --> 00:23:07,680 Speaker 2: I'm Tracy Alloway. You can follow me at Tracy Alloway. 430 00:23:07,480 --> 00:23:10,440 Speaker 3: And I'm Joe Wisenthal. You can follow me at the Stalwart. 431 00:23:10,480 --> 00:23:13,640 Speaker 3: Follow our guest William Beach, He's at Beach WW four 432 00:23:13,800 --> 00:23:17,440 Speaker 3: five three. Follow our producers Carmen Rodriguez at Kerman Ermann, 433 00:23:17,520 --> 00:23:20,480 Speaker 3: Dashel Bennett at Dashbot, and Kale Brooks and Kale Brooks. 434 00:23:20,880 --> 00:23:23,359 Speaker 3: More odd Lots content go to Bloomberg dot com slash 435 00:23:23,440 --> 00:23:26,000 Speaker 3: odd Lots, where we have a daily newsletter and all 436 00:23:26,080 --> 00:23:28,280 Speaker 3: of our episodes, and you can chat about all of 437 00:23:28,320 --> 00:23:31,680 Speaker 3: these topics twenty four seven in our discord Discord dot 438 00:23:31,760 --> 00:23:33,040 Speaker 3: gg slash oud locks. 439 00:23:33,200 --> 00:23:35,480 Speaker 2: And if you enjoy odd Lots, if you like it 440 00:23:35,640 --> 00:23:38,800 Speaker 2: when we talk about the future of economic statistic collection 441 00:23:39,000 --> 00:23:41,200 Speaker 2: in the US, then please leave us a positive review 442 00:23:41,240 --> 00:23:44,280 Speaker 2: on your or the UK, and please leave us a 443 00:23:44,359 --> 00:23:47,560 Speaker 2: positive review on your favorite podcast platform. And remember, if 444 00:23:47,600 --> 00:23:50,080 Speaker 2: you are a Bloomberg subscriber, you can listen to all 445 00:23:50,119 --> 00:23:52,680 Speaker 2: of our episodes absolutely add free. All you need to 446 00:23:52,760 --> 00:23:55,200 Speaker 2: do is find the Bloomberg channel on Apple Podcasts and 447 00:23:55,320 --> 00:24:00,720 Speaker 2: follow the instructions there. Thanks for listening in