1 00:00:02,720 --> 00:00:09,880 Speaker 1: Bloomberg Audio Studios, podcasts, radio news. The headline is the 2 00:00:09,960 --> 00:00:12,879 Speaker 1: labor market appears to be stabilizing. 3 00:00:12,600 --> 00:00:15,560 Speaker 2: Today, the Bureau of Labor Statistics came out with one 4 00:00:15,560 --> 00:00:19,200 Speaker 2: of the year's most anticipated data dumps, a report on 5 00:00:19,360 --> 00:00:22,840 Speaker 2: hiring and firing in January and a revision of the 6 00:00:22,920 --> 00:00:26,880 Speaker 2: jobs numbers from last year. Molly Smith, a Bloomberg US 7 00:00:26,920 --> 00:00:32,519 Speaker 2: Economy editor, says that heading into today, economists' expectations had 8 00:00:32,520 --> 00:00:33,320 Speaker 2: been low. 9 00:00:33,520 --> 00:00:36,680 Speaker 1: We keep seeing these huge job cut announcements coming out 10 00:00:36,720 --> 00:00:39,080 Speaker 1: of big companies, and that seems to be making a 11 00:00:39,120 --> 00:00:42,200 Speaker 1: lot of big news and making people really anxious. 12 00:00:41,800 --> 00:00:44,440 Speaker 3: About the job market. But you get a report like 13 00:00:44,479 --> 00:00:45,320 Speaker 3: this and it's. 14 00:00:45,159 --> 00:00:48,000 Speaker 1: Like, well, there was a lot of really positive things 15 00:00:48,000 --> 00:00:48,600 Speaker 1: that happened. 16 00:00:48,720 --> 00:00:51,840 Speaker 2: The US economy added one hundred and thirty thousand jobs 17 00:00:51,920 --> 00:00:55,279 Speaker 2: last month, according to the BLS report, far more than 18 00:00:55,360 --> 00:00:59,880 Speaker 2: economists were projecting, and the unemployment rate actually dropped to 19 00:01:00,120 --> 00:01:04,800 Speaker 2: four point three percent. The takeaway the twenty twenty six 20 00:01:04,920 --> 00:01:07,280 Speaker 2: labor market could be stronger than we thought. 21 00:01:07,720 --> 00:01:10,560 Speaker 1: To see the way that the January numbers came in, 22 00:01:10,959 --> 00:01:13,560 Speaker 1: not just the beat and hiring, but also the drop 23 00:01:13,600 --> 00:01:16,760 Speaker 1: and the unemployment rate, which was not expected. Really just 24 00:01:16,840 --> 00:01:19,880 Speaker 1: showed that the labor market seems to be gaining its footing. 25 00:01:20,080 --> 00:01:23,160 Speaker 2: But Mollie says, the other takeaway is that the labor 26 00:01:23,200 --> 00:01:27,320 Speaker 2: market of twenty twenty five was weaker than originally reported. 27 00:01:27,680 --> 00:01:31,120 Speaker 1: The pace of hiring originally last year was around forty 28 00:01:31,200 --> 00:01:34,039 Speaker 1: nine thousand jobs added per month on average. 29 00:01:34,480 --> 00:01:37,040 Speaker 3: The revisions today show that was just fifteen thousand. 30 00:01:37,160 --> 00:01:39,920 Speaker 1: In the world of economics, that's of basically nothing, so 31 00:01:40,120 --> 00:01:42,520 Speaker 1: that's really not a great number. 32 00:01:43,480 --> 00:01:46,240 Speaker 2: The Federal Reserve will be weighing all these numbers as 33 00:01:46,240 --> 00:01:49,400 Speaker 2: it sets rates in the months ahead, and Mollie says 34 00:01:49,640 --> 00:01:52,240 Speaker 2: the odds are now looking better that it will stay 35 00:01:52,320 --> 00:01:53,120 Speaker 2: the course. 36 00:01:53,120 --> 00:01:53,760 Speaker 3: For the Fed. 37 00:01:54,280 --> 00:01:57,760 Speaker 1: This means that they're fairly justified in holding interest rates 38 00:01:57,960 --> 00:02:00,280 Speaker 1: right now. You know, there really is no rush to 39 00:02:00,320 --> 00:02:03,000 Speaker 1: cut interest rates when you have an economy, a job 40 00:02:03,040 --> 00:02:05,800 Speaker 1: market that seems to be studying the way that it is, 41 00:02:06,120 --> 00:02:08,400 Speaker 1: and it looked like in hindsight that the time that 42 00:02:08,520 --> 00:02:11,480 Speaker 1: the cuts that they already made were fairly well timed. 43 00:02:14,760 --> 00:02:16,960 Speaker 2: I'm Sarah Holder, and this is the big take from 44 00:02:16,960 --> 00:02:20,880 Speaker 2: Bloomberg News Today. On the show, the US labor market 45 00:02:20,960 --> 00:02:25,320 Speaker 2: shows signs of stabilizing after a really slow year. What 46 00:02:25,360 --> 00:02:29,280 Speaker 2: today's jobs numbers mean for job seekers, employers, and the 47 00:02:29,360 --> 00:02:37,840 Speaker 2: fet So let's talk about what the report revealed about 48 00:02:37,919 --> 00:02:42,000 Speaker 2: the state of the labor market right now. Those January numbers. 49 00:02:42,040 --> 00:02:44,360 Speaker 2: Where are the bright spots and where are there areas 50 00:02:44,440 --> 00:02:46,720 Speaker 2: of concern in the January jobs report? 51 00:02:46,880 --> 00:02:48,440 Speaker 3: So I guess you. 52 00:02:48,440 --> 00:02:50,320 Speaker 1: Could call this a bright spot, but also an area 53 00:02:50,320 --> 00:02:53,840 Speaker 1: of concern was that healthcare continues to dominate hiring and 54 00:02:53,880 --> 00:02:56,519 Speaker 1: actually have like the most amount of jobs added since 55 00:02:56,560 --> 00:03:00,919 Speaker 1: twenty twenty, and healthcare dominated hiring last too, really was 56 00:03:00,960 --> 00:03:03,880 Speaker 1: the majority of all job growth. Ideally, you'd want to 57 00:03:03,880 --> 00:03:07,839 Speaker 1: see more breadth of hiring and seeing this across more industries. 58 00:03:08,160 --> 00:03:10,959 Speaker 1: There were some other industries though, that did add jobs, 59 00:03:11,000 --> 00:03:14,000 Speaker 1: Manufacturing notably first time in a long time that we 60 00:03:14,040 --> 00:03:17,440 Speaker 1: saw job gains there. We saw federal government continued to 61 00:03:17,440 --> 00:03:20,760 Speaker 1: cut jobs, not really surprising. That's more or less the 62 00:03:20,800 --> 00:03:25,080 Speaker 1: industry breakdown. Looking to some other positive aspects, we also 63 00:03:25,160 --> 00:03:28,600 Speaker 1: get another survey in the jobs report, that is the 64 00:03:28,680 --> 00:03:32,680 Speaker 1: Survey of households that showed that more people voluntarily quit 65 00:03:32,760 --> 00:03:34,920 Speaker 1: their jobs, which usually is a sign that you feel 66 00:03:34,960 --> 00:03:37,240 Speaker 1: pretty confident in your ability to find a new one. 67 00:03:37,400 --> 00:03:38,160 Speaker 3: That number has been. 68 00:03:38,080 --> 00:03:40,240 Speaker 1: Pretty low for the most part. You know, people will 69 00:03:40,280 --> 00:03:43,360 Speaker 1: feel fairly insecure, don't really think this is a good time, 70 00:03:43,400 --> 00:03:44,880 Speaker 1: and you know, just up and leave. 71 00:03:44,720 --> 00:03:47,280 Speaker 2: Your job, right the big freeze, Right, that's what people 72 00:03:47,320 --> 00:03:49,600 Speaker 2: were talking about. People too afraid to leave their jobs. 73 00:03:49,680 --> 00:03:51,160 Speaker 2: That seems to be changing perhaps. 74 00:03:51,280 --> 00:03:53,720 Speaker 1: I mean, you know, another thing economists will always tell 75 00:03:53,760 --> 00:03:56,400 Speaker 1: you is only one month of data, but if sustained, 76 00:03:56,560 --> 00:03:58,920 Speaker 1: that could be a sign that maybe things are turning. 77 00:03:59,200 --> 00:04:02,360 Speaker 1: You saw far less people reported that they're working part 78 00:04:02,360 --> 00:04:05,440 Speaker 1: time for economic reasons, so that's something that had been climbing. 79 00:04:05,840 --> 00:04:08,360 Speaker 1: Also a sign, you know, of some financial distress. So 80 00:04:08,440 --> 00:04:10,160 Speaker 1: that was positive to see that fall. 81 00:04:10,680 --> 00:04:12,400 Speaker 2: And one of the other things that was on people's 82 00:04:12,400 --> 00:04:14,680 Speaker 2: mind hetting into this jobs report, where are these headline 83 00:04:14,680 --> 00:04:18,360 Speaker 2: grabbing layoffs? Amazon announced it would be cutting about sixteen 84 00:04:18,360 --> 00:04:21,880 Speaker 2: thousand jobs in January. The Washington Post, also owned by 85 00:04:21,920 --> 00:04:25,600 Speaker 2: Amazon founder Jeff Bezos, fired more than three hundred journalists. 86 00:04:25,920 --> 00:04:29,480 Speaker 2: How are those layoffs showing up in the job market 87 00:04:29,520 --> 00:04:32,839 Speaker 2: right now and are there signs of some industries contracting 88 00:04:32,920 --> 00:04:35,120 Speaker 2: or were these kind of outliers. 89 00:04:35,720 --> 00:04:39,960 Speaker 1: It's hard to reconcile the two because you see these 90 00:04:40,240 --> 00:04:43,719 Speaker 1: announcements of layoffs from these big companies you mentioned Amazon 91 00:04:43,880 --> 00:04:48,799 Speaker 1: Ups was another, and they haven't really translated into actual 92 00:04:49,040 --> 00:04:52,360 Speaker 1: layoffs in the aggregate for the most part. One thing 93 00:04:52,400 --> 00:04:56,000 Speaker 1: to note is that announcements are simply announcements. They're not 94 00:04:56,080 --> 00:04:59,400 Speaker 1: necessarily meaning that people are going to be fired right 95 00:04:59,400 --> 00:05:02,040 Speaker 1: then and there. Course at the post they unfortunately were. 96 00:05:02,760 --> 00:05:05,040 Speaker 1: But in a lot of these other companies that can 97 00:05:05,080 --> 00:05:08,479 Speaker 1: be spread out for months, you know when those actual 98 00:05:08,600 --> 00:05:12,120 Speaker 1: layoffs might happen. So even though we have had a 99 00:05:12,120 --> 00:05:15,480 Speaker 1: lot of these announcements, they haven't in the data shown 100 00:05:15,560 --> 00:05:16,279 Speaker 1: up as far as. 101 00:05:16,200 --> 00:05:19,479 Speaker 3: Actual layoffs will go. Yeah, that's that's really helpful. 102 00:05:19,520 --> 00:05:22,760 Speaker 2: So those kinds of announcements could show up in you know, 103 00:05:22,880 --> 00:05:25,360 Speaker 2: a March report or an April. 104 00:05:25,000 --> 00:05:26,800 Speaker 3: Report, or they might not show up at all. 105 00:05:26,880 --> 00:05:29,640 Speaker 1: Maybe it was an announcement that was you know, at 106 00:05:29,720 --> 00:05:31,800 Speaker 1: one point in January. Who knows, maybe things change and 107 00:05:31,839 --> 00:05:33,000 Speaker 1: they weren't actually enacted. 108 00:05:33,880 --> 00:05:34,680 Speaker 3: When you look back. 109 00:05:34,560 --> 00:05:37,440 Speaker 2: Over decades, job creation has often been tied to a 110 00:05:37,440 --> 00:05:41,560 Speaker 2: growing economy. Right, is the dynamic any different now? Given 111 00:05:41,560 --> 00:05:44,160 Speaker 2: the growth of AI, worries that AI is coming for 112 00:05:44,240 --> 00:05:48,360 Speaker 2: Americans jobs, should these numbers be viewed through a different lens? 113 00:05:48,400 --> 00:05:48,720 Speaker 2: At all. 114 00:05:49,120 --> 00:05:51,479 Speaker 1: I mean, obviously we had a great report today, but 115 00:05:51,520 --> 00:05:54,480 Speaker 1: I think in general it's absolutely fair to say that, 116 00:05:54,600 --> 00:05:57,400 Speaker 1: you know, the labor market has absolutely slowed down from 117 00:05:57,440 --> 00:05:59,960 Speaker 1: you know, those post pandemic peaks, and that we have 118 00:06:00,200 --> 00:06:02,560 Speaker 1: seen one that has been gradually cooling now for a 119 00:06:02,640 --> 00:06:06,039 Speaker 1: number of years. Today's does not change that, and it's 120 00:06:06,120 --> 00:06:10,000 Speaker 1: difficult to then reconcile that with what has been really 121 00:06:10,040 --> 00:06:13,520 Speaker 1: strong GDP numbers. That you see an economy that is 122 00:06:13,560 --> 00:06:17,440 Speaker 1: expanding at some of the fastest paces in years, yet 123 00:06:17,520 --> 00:06:21,440 Speaker 1: the labor market has been fairly slow. The reason why 124 00:06:21,480 --> 00:06:23,200 Speaker 1: you can look at those things is because the way 125 00:06:23,240 --> 00:06:25,839 Speaker 1: that GDP is actually calculated has nothing to do with 126 00:06:25,880 --> 00:06:29,480 Speaker 1: the chop market, which is also why it's a difficult 127 00:06:29,480 --> 00:06:33,840 Speaker 1: way to measure the US economy. So the economy has 128 00:06:33,880 --> 00:06:37,000 Speaker 1: been growing as fast as it is in the last 129 00:06:37,080 --> 00:06:41,160 Speaker 1: few quarters largely because of a virtual in trade policy. 130 00:06:41,520 --> 00:06:43,920 Speaker 1: You know that there had been such a huge rush 131 00:06:44,200 --> 00:06:47,520 Speaker 1: at the beginning of twenty twenty five to import as 132 00:06:47,560 --> 00:06:50,520 Speaker 1: much as companies could ahead of those expected tariffs that 133 00:06:50,560 --> 00:06:54,560 Speaker 1: did come in April, and the way that GDP is calculated, 134 00:06:54,800 --> 00:06:57,840 Speaker 1: that would then add to growth when you don't have 135 00:06:58,360 --> 00:07:01,960 Speaker 1: import activity as strong all else equal, So that's been 136 00:07:02,000 --> 00:07:06,120 Speaker 1: a lot of what's been keeping GDP so elevated, and 137 00:07:06,160 --> 00:07:08,400 Speaker 1: that's why it's hard to then look at the job 138 00:07:08,440 --> 00:07:11,840 Speaker 1: market next to that and see how the two compare well. 139 00:07:11,880 --> 00:07:13,920 Speaker 2: I also want to look closer at some of the revisions. 140 00:07:14,440 --> 00:07:17,560 Speaker 2: Annual revisions are released every January. The report we got 141 00:07:17,600 --> 00:07:20,640 Speaker 2: today showed that last year's job market was weaker than 142 00:07:20,800 --> 00:07:24,160 Speaker 2: originally reported. How much weaker and how do we know? 143 00:07:24,680 --> 00:07:27,880 Speaker 1: So this is where it gets. Also, there's just so 144 00:07:27,920 --> 00:07:30,440 Speaker 1: many layers to this. There's a few different kinds of 145 00:07:30,480 --> 00:07:34,520 Speaker 1: revisions that BLS carries out. There's one that was the 146 00:07:34,560 --> 00:07:37,720 Speaker 1: main headline revision. We call it the benchmark revision, and 147 00:07:37,760 --> 00:07:41,480 Speaker 1: that has to do with basically incorporating a more accurate, 148 00:07:41,480 --> 00:07:46,120 Speaker 1: but less timely employment series that is based on actual. 149 00:07:45,920 --> 00:07:48,640 Speaker 3: Unemployment insurance records. So that's it just has a bit 150 00:07:48,680 --> 00:07:49,080 Speaker 3: of a lag. 151 00:07:49,120 --> 00:07:53,000 Speaker 1: It's a quarterly series, so that one updated payrolls through 152 00:07:53,120 --> 00:07:55,800 Speaker 1: March of twenty twenty five, and then there's another set 153 00:07:55,800 --> 00:07:59,760 Speaker 1: of revisions that updates how BLS accounts for businesses that 154 00:07:59,840 --> 00:08:02,800 Speaker 1: own open and close. The net number between those two 155 00:08:03,120 --> 00:08:05,840 Speaker 1: that impacts the rest of twenty twenty five, as well 156 00:08:05,840 --> 00:08:08,880 Speaker 1: as you know, a model that then influenced payrolls beyond that, 157 00:08:09,440 --> 00:08:13,320 Speaker 1: and then there's also an adjustment of how BLS factors 158 00:08:13,320 --> 00:08:14,760 Speaker 1: for seasonal adjustment factors. 159 00:08:15,120 --> 00:08:17,560 Speaker 3: So all of those things combined, it's a lot going on. 160 00:08:17,920 --> 00:08:19,360 Speaker 3: I think the easiest way to think. 161 00:08:19,240 --> 00:08:22,640 Speaker 1: About this is that over the course of twenty twenty five, 162 00:08:23,320 --> 00:08:26,560 Speaker 1: the average pace of monthly job growth was now fifteen 163 00:08:26,640 --> 00:08:31,840 Speaker 1: thousand versus initially reported forty nine thousand. That essentially incorporates 164 00:08:32,200 --> 00:08:34,200 Speaker 1: all of the revisions together, and I think that's the 165 00:08:34,200 --> 00:08:35,520 Speaker 1: easiest way to think about it. 166 00:08:35,880 --> 00:08:38,840 Speaker 2: How does that stack up historically? How should we think 167 00:08:38,880 --> 00:08:40,160 Speaker 2: about that number? 168 00:08:40,240 --> 00:08:42,559 Speaker 3: How low is that? It's low? 169 00:08:43,040 --> 00:08:46,000 Speaker 1: It's like I'm trying to that's essentially like the same 170 00:08:46,000 --> 00:08:49,040 Speaker 1: as like there really was no hiring, Like that's what 171 00:08:49,080 --> 00:08:54,760 Speaker 1: we would call anemic, you know, un dynamic, barely chugging 172 00:08:54,840 --> 00:08:57,600 Speaker 1: along like there was like not really a whole lot happening. 173 00:08:57,960 --> 00:09:00,840 Speaker 2: Molly, you've walked us through all the different kinds of 174 00:09:01,080 --> 00:09:03,680 Speaker 2: revisions that we're looking at in conversation right now. Can 175 00:09:03,760 --> 00:09:06,720 Speaker 2: you remind us why these revisions happen every year? What 176 00:09:06,880 --> 00:09:10,840 Speaker 2: new information does the BLS incorporate in this data, right. 177 00:09:10,720 --> 00:09:13,040 Speaker 1: So the big one is what I had referred to 178 00:09:13,280 --> 00:09:16,160 Speaker 1: as that series that is more accurate but less timely. 179 00:09:16,200 --> 00:09:20,120 Speaker 1: It's called the Quarterly Census of Employment and Wages QCW 180 00:09:20,360 --> 00:09:23,640 Speaker 1: for sure, and that is really what a lot of 181 00:09:23,679 --> 00:09:26,520 Speaker 1: people would say is probably like the one of the 182 00:09:26,559 --> 00:09:29,840 Speaker 1: more accurate series of employment that we get. You know, 183 00:09:29,920 --> 00:09:33,240 Speaker 1: this is just how statistics work, and that if you 184 00:09:33,280 --> 00:09:37,120 Speaker 1: want to balance speed and accuracy, you have to accept 185 00:09:37,200 --> 00:09:41,280 Speaker 1: that as you get more data, that numbers are going 186 00:09:41,320 --> 00:09:44,600 Speaker 1: to be different. And that's just a trade off that 187 00:09:44,679 --> 00:09:47,080 Speaker 1: you have to be comfortable with. You want to see 188 00:09:47,080 --> 00:09:49,600 Speaker 1: the first Friday of the following month what the job's 189 00:09:49,640 --> 00:09:52,880 Speaker 1: number was. People don't have the patience to wait longer 190 00:09:53,120 --> 00:09:56,080 Speaker 1: for when more data will come in. So if you're 191 00:09:56,080 --> 00:09:58,560 Speaker 1: going to demand that kind of speed, you have to 192 00:09:58,600 --> 00:10:03,080 Speaker 1: then accept that as more data comes in in subsequent months, quarters, 193 00:10:03,200 --> 00:10:06,280 Speaker 1: even years, that the numbers are going to be revised, 194 00:10:06,320 --> 00:10:09,680 Speaker 1: and in time that does make the numbers more accurate. 195 00:10:10,080 --> 00:10:12,439 Speaker 2: I mean that speaks to something else that's significant about 196 00:10:12,440 --> 00:10:15,400 Speaker 2: this report is that the data is backward looking at 197 00:10:15,400 --> 00:10:17,040 Speaker 2: this point. So what does it mean for how we 198 00:10:17,080 --> 00:10:20,000 Speaker 2: should be viewing the labor market today? That there was, 199 00:10:20,320 --> 00:10:23,200 Speaker 2: you know, barely chugging along job growth in twenty twenty five. 200 00:10:23,320 --> 00:10:24,720 Speaker 2: What does that mean for twenty twenty six. 201 00:10:25,120 --> 00:10:26,840 Speaker 1: Well, it sets the bar a little low for where 202 00:10:26,840 --> 00:10:29,600 Speaker 1: we're starting from, that's for sure. But if you look 203 00:10:29,640 --> 00:10:32,400 Speaker 1: at say that now, the average pace of monthly job 204 00:10:32,440 --> 00:10:36,080 Speaker 1: growth in twenty twenty five was fifteen thousand compared to 205 00:10:36,080 --> 00:10:39,600 Speaker 1: today's number was one hundred and thirty thousand. Just comparing 206 00:10:39,640 --> 00:10:41,840 Speaker 1: those two, obviously there's a lot more going on that 207 00:10:41,880 --> 00:10:44,120 Speaker 1: seems like more than stabilizing to me. I mean, that's 208 00:10:44,120 --> 00:10:45,880 Speaker 1: like a pretty huge search. 209 00:10:46,000 --> 00:10:48,800 Speaker 2: But guess we'll know next year how that number is revised. 210 00:10:48,800 --> 00:10:50,920 Speaker 1: Well, even see next month how that number is revised 211 00:10:50,920 --> 00:10:54,040 Speaker 1: and the following month, and again this happens multiple times, 212 00:10:54,280 --> 00:10:57,320 Speaker 1: so we'll see how that one thirty numbers sticks. 213 00:10:57,400 --> 00:11:00,760 Speaker 3: But as it stands today, I think you can say that. 214 00:11:00,760 --> 00:11:03,000 Speaker 1: Powell did have the right idea when he spoke at 215 00:11:03,000 --> 00:11:06,160 Speaker 1: the January FED meeting that the labor market does appear 216 00:11:06,200 --> 00:11:09,400 Speaker 1: to be stabilizing. It's not just the unemployment rate that 217 00:11:09,520 --> 00:11:12,479 Speaker 1: perhaps hiring two is maybe picking. 218 00:11:12,280 --> 00:11:13,200 Speaker 3: Up a little bit as well. 219 00:11:17,480 --> 00:11:20,360 Speaker 2: So the January jobs report held good signs about the 220 00:11:20,440 --> 00:11:23,319 Speaker 2: labor market in the year ahead. But with layoffs in 221 00:11:23,400 --> 00:11:28,200 Speaker 2: the news and affordability a growing concern, how will the public, politicians, 222 00:11:28,400 --> 00:11:41,800 Speaker 2: and the Fed respond that's next. This month's sunny jobs 223 00:11:41,800 --> 00:11:46,920 Speaker 2: report doesn't address Americans' persistent concerns about inflation and affordability, 224 00:11:47,760 --> 00:11:50,160 Speaker 2: and it's not likely to diminish the sense of a 225 00:11:50,280 --> 00:11:53,440 Speaker 2: vibe session the idea that many people feel like the 226 00:11:53,480 --> 00:11:57,120 Speaker 2: economy isn't working for them, regardless of what the economic 227 00:11:57,200 --> 00:12:00,880 Speaker 2: data show. So I asked Bloomberg US econom editor Mollie 228 00:12:00,920 --> 00:12:05,320 Speaker 2: Smith how she reconciles the strong January jobs numbers with 229 00:12:05,440 --> 00:12:08,360 Speaker 2: people's current perceptions of the labor market. 230 00:12:09,520 --> 00:12:14,160 Speaker 1: This is what's been really challenging about squaring survey data 231 00:12:14,200 --> 00:12:17,640 Speaker 1: with what we call hard data, and that the surveys 232 00:12:17,679 --> 00:12:21,680 Speaker 1: are consistently far more negative than what the actual numbers show. 233 00:12:22,160 --> 00:12:24,840 Speaker 1: There is a survey that the government conducts that it's 234 00:12:24,880 --> 00:12:28,080 Speaker 1: a hypothetical. It asks if you had to pay for 235 00:12:28,120 --> 00:12:30,840 Speaker 1: an emergency four hundred dollars expense, could you do it? 236 00:12:31,320 --> 00:12:33,320 Speaker 1: And that's something that people have tracked for a long 237 00:12:33,360 --> 00:12:36,240 Speaker 1: time and how that share has like declined by and 238 00:12:36,320 --> 00:12:39,720 Speaker 1: large for a while. But there was another survey provider 239 00:12:39,760 --> 00:12:43,640 Speaker 1: who actually asked people, did you have an expense and 240 00:12:43,720 --> 00:12:45,560 Speaker 1: were you able to pay it? Now do you think 241 00:12:45,600 --> 00:12:48,240 Speaker 1: you can? But were you able to? What actually happened? 242 00:12:48,760 --> 00:12:50,960 Speaker 1: And by and large people were able to pay it. 243 00:12:51,040 --> 00:12:53,760 Speaker 1: So I think some of that just goes to show 244 00:12:53,800 --> 00:12:57,200 Speaker 1: that people maybe are more resilient than they report to 245 00:12:57,240 --> 00:12:59,640 Speaker 1: be and will find a way, you know, we did. 246 00:12:59,679 --> 00:13:02,280 Speaker 2: The other serve that I always look at is how 247 00:13:02,480 --> 00:13:04,920 Speaker 2: likely do you think it would be to find another 248 00:13:05,040 --> 00:13:07,600 Speaker 2: job if you were fired tomorrow? 249 00:13:07,840 --> 00:13:08,040 Speaker 3: Right? 250 00:13:08,200 --> 00:13:13,840 Speaker 2: And that number when it's low, feels really anxiety inducing 251 00:13:13,880 --> 00:13:15,600 Speaker 2: about the state of the economy. But as you said, 252 00:13:15,640 --> 00:13:18,720 Speaker 2: you need to kind of match that with the actual 253 00:13:18,800 --> 00:13:21,959 Speaker 2: odds of finding another job, or people's actual success rates 254 00:13:22,000 --> 00:13:22,840 Speaker 2: and finding a job. 255 00:13:22,720 --> 00:13:24,920 Speaker 1: Right, like did you actually quit your job and try 256 00:13:24,920 --> 00:13:27,440 Speaker 1: to find another one or that's just your case sitting 257 00:13:27,520 --> 00:13:30,200 Speaker 1: here employed today, if you took a guess if you 258 00:13:30,240 --> 00:13:32,120 Speaker 1: were in that situation. And I think that's where you 259 00:13:32,160 --> 00:13:35,040 Speaker 1: see a huge divergence in some of these numbers. That's 260 00:13:35,080 --> 00:13:37,960 Speaker 1: also why the economy as a whole is also still 261 00:13:37,960 --> 00:13:41,000 Speaker 1: doing really well. That we look at these sentiment surveys, 262 00:13:41,040 --> 00:13:44,360 Speaker 1: particularly for what it means for consumer spending, which you 263 00:13:44,400 --> 00:13:47,760 Speaker 1: would think based on these surveys that people aren't spending 264 00:13:47,760 --> 00:13:50,080 Speaker 1: any money at all, but that's not at all what 265 00:13:50,120 --> 00:13:53,480 Speaker 1: the actual data suggest, and that spending is still holding 266 00:13:53,559 --> 00:13:56,199 Speaker 1: up really well, as is the overall economy. 267 00:13:58,360 --> 00:14:00,360 Speaker 2: So, I mean, given the fact that how people feel 268 00:14:00,400 --> 00:14:04,640 Speaker 2: about the economy really matters politically, I'm wondering what this 269 00:14:04,760 --> 00:14:07,280 Speaker 2: report means for the Trump administration. 270 00:14:07,600 --> 00:14:09,959 Speaker 1: Well, he did just tweet a post that, you know, 271 00:14:10,120 --> 00:14:13,400 Speaker 1: great job numbers today, you know, the Golden Ages upon us, 272 00:14:13,480 --> 00:14:16,120 Speaker 1: everything looks great, which you know is a little bit 273 00:14:16,160 --> 00:14:19,040 Speaker 1: of like selective attention. I would say on his part. 274 00:14:19,160 --> 00:14:22,080 Speaker 2: He was president all last year that too, but we 275 00:14:22,160 --> 00:14:24,320 Speaker 2: had a positive aspect of the report to focus on it. 276 00:14:24,320 --> 00:14:26,280 Speaker 1: I'm not saying that he was wrong to focus on that, 277 00:14:26,320 --> 00:14:28,800 Speaker 1: but I'm saying it's a little bit selective and maybe 278 00:14:29,280 --> 00:14:32,120 Speaker 1: excluding the part of where he presided over twenty. 279 00:14:31,920 --> 00:14:34,160 Speaker 2: Twenty five, right, right, I mean in August, the Bureau 280 00:14:34,200 --> 00:14:37,480 Speaker 2: of Labor Statistics revised some of its data downward, saying 281 00:14:37,480 --> 00:14:40,520 Speaker 2: the US economy added fewer jobs than it had previously said, 282 00:14:40,680 --> 00:14:44,239 Speaker 2: and Trump called the numbers rigged. He fired the BLS commissioner. 283 00:14:44,320 --> 00:14:46,560 Speaker 2: We talked about it on the podcast, but he hasn't 284 00:14:46,560 --> 00:14:47,320 Speaker 2: had that kind. 285 00:14:47,200 --> 00:14:49,760 Speaker 3: Of reaction this time, not that I've seen so far. 286 00:14:49,840 --> 00:14:51,480 Speaker 3: I mean, the day is young. Who knows. 287 00:14:51,520 --> 00:14:53,640 Speaker 1: There's a lot of positives to take from this. But 288 00:14:54,320 --> 00:14:57,520 Speaker 1: you know, for somebody who has been very critical of 289 00:14:57,520 --> 00:15:00,200 Speaker 1: this data agency, of the revisions that they make, what 290 00:15:00,240 --> 00:15:03,640 Speaker 1: it says about the labor market as a whole, it 291 00:15:03,720 --> 00:15:05,600 Speaker 1: seems to be omitting that entire chunk. 292 00:15:05,400 --> 00:15:05,920 Speaker 3: Of the equation. 293 00:15:06,320 --> 00:15:09,360 Speaker 2: So we're also getting a report on the consumer Price Index, 294 00:15:09,640 --> 00:15:14,360 Speaker 2: which measures inflation this Friday. How do today's jobs numbers, 295 00:15:14,400 --> 00:15:17,600 Speaker 2: which are generally better than expected for January change, How 296 00:15:17,600 --> 00:15:19,840 Speaker 2: we're going to view those inflation numbers? How are you 297 00:15:19,840 --> 00:15:21,760 Speaker 2: going to put those two numbers in conversation. 298 00:15:22,120 --> 00:15:24,920 Speaker 1: Well, something else that we saw in this job's report 299 00:15:24,960 --> 00:15:27,600 Speaker 1: today that we didn't get into was wage growth, which 300 00:15:27,720 --> 00:15:31,160 Speaker 1: was a bit stronger than expected. So you know, usually 301 00:15:31,160 --> 00:15:34,240 Speaker 1: that is the real engine of course, of like consumer spending, 302 00:15:34,280 --> 00:15:37,000 Speaker 1: of like what your pay is, and to see then 303 00:15:37,280 --> 00:15:41,000 Speaker 1: how that might factor into demand for goods and services 304 00:15:41,080 --> 00:15:43,720 Speaker 1: and how that could infect their prices in January. 305 00:15:43,760 --> 00:15:45,840 Speaker 3: I think that would be the correlation we would look for. 306 00:15:46,440 --> 00:15:48,200 Speaker 2: And I mean a body that will be looking very 307 00:15:48,240 --> 00:15:50,880 Speaker 2: closely at the CPI data and the jobs data. 308 00:15:50,840 --> 00:15:52,360 Speaker 3: Is the Federal Reserve. 309 00:15:52,600 --> 00:15:57,640 Speaker 2: Of course, what does this job's report mean for the 310 00:15:57,720 --> 00:16:00,680 Speaker 2: chances that they cut rates next meeting? 311 00:16:00,880 --> 00:16:03,600 Speaker 1: Oh, that was already about zero to say, like a 312 00:16:03,640 --> 00:16:06,360 Speaker 1: cut form March. That wasn't going to happen coming into 313 00:16:06,440 --> 00:16:10,600 Speaker 1: this report. The expectation was more around June for the 314 00:16:10,640 --> 00:16:13,400 Speaker 1: cut tappen at that meeting, But after today, basically that 315 00:16:13,520 --> 00:16:16,320 Speaker 1: shows that again that like what Pale has been saying 316 00:16:16,320 --> 00:16:18,800 Speaker 1: that like policy is well positioned right now, we feel 317 00:16:18,800 --> 00:16:20,960 Speaker 1: like we're in a good place, we can adjust if needed, 318 00:16:21,000 --> 00:16:23,000 Speaker 1: and that there is no urgency to cut. 319 00:16:23,440 --> 00:16:25,560 Speaker 3: All of that very much validated today. 320 00:16:25,880 --> 00:16:28,080 Speaker 1: You see that the way Trump reacted to the numbers, 321 00:16:28,120 --> 00:16:30,640 Speaker 1: saying that you know, we had such great job numbers, 322 00:16:30,640 --> 00:16:32,560 Speaker 1: as you know, we should be playing among the lowest 323 00:16:32,600 --> 00:16:35,240 Speaker 1: interest rates in the world. It's a little bit more 324 00:16:35,320 --> 00:16:37,960 Speaker 1: complicated than that that. You know, if you're seeing the 325 00:16:38,040 --> 00:16:40,640 Speaker 1: job market grow that the way it is, you wouldn't 326 00:16:40,640 --> 00:16:43,080 Speaker 1: think you would need lower interest rates to support it. 327 00:16:43,560 --> 00:16:44,960 Speaker 1: And that's how the FED is looking at this. 328 00:16:45,720 --> 00:16:48,800 Speaker 2: In May, we could see a new FED chair. Kevin 329 00:16:48,840 --> 00:16:53,240 Speaker 2: worsh is Trump's pick for the job. Has worsh responded 330 00:16:53,280 --> 00:16:55,960 Speaker 2: to these numbers at all. How might this change his thinking? 331 00:16:56,120 --> 00:16:58,960 Speaker 3: I think it's going to make things very complicated for him. 332 00:16:59,000 --> 00:16:59,400 Speaker 3: You know that. 333 00:17:00,080 --> 00:17:02,360 Speaker 1: Again, there's a lot of time between now and May. 334 00:17:02,720 --> 00:17:05,640 Speaker 1: He also has to be confirmed first. But basically, he 335 00:17:05,760 --> 00:17:08,720 Speaker 1: took this job with the understanding that the president wants 336 00:17:08,760 --> 00:17:11,520 Speaker 1: him to cut interest rates. Obviously it's not just his decision. 337 00:17:11,560 --> 00:17:16,159 Speaker 1: There's a whole body of policy makers. But certainly the 338 00:17:16,240 --> 00:17:19,439 Speaker 1: chair tries to rally a consensus and speaks for the 339 00:17:19,600 --> 00:17:23,600 Speaker 1: entire Central Bank. So Trump did say like that the 340 00:17:23,720 --> 00:17:26,120 Speaker 1: understanding of like warshaking this job is that he's going 341 00:17:26,119 --> 00:17:28,440 Speaker 1: to cut rates, and of course Warsh knows that too. 342 00:17:28,640 --> 00:17:30,560 Speaker 1: The whole country knows that. The whole world knows that 343 00:17:30,880 --> 00:17:34,119 Speaker 1: this is what Trump wants. The numbers make that again, 344 00:17:34,640 --> 00:17:37,480 Speaker 1: as we said here today, a bit more complicated. This 345 00:17:37,520 --> 00:17:39,720 Speaker 1: doesn't look like an economy right now that is calling 346 00:17:40,200 --> 00:17:42,399 Speaker 1: for any immediate cut to interest rates. 347 00:17:47,560 --> 00:17:50,520 Speaker 2: This is the Big Take from Bloomberg News. I'm Sarah Holder. 348 00:17:50,880 --> 00:17:53,480 Speaker 2: To get more from the Big Take and unlimited access 349 00:17:53,520 --> 00:17:57,400 Speaker 2: to all of Bloomberg dot com, subscribe today at Bloomberg 350 00:17:57,440 --> 00:18:01,479 Speaker 2: dot Com Slash Podcast Offer. Thanks for listening. We'll be 351 00:18:01,480 --> 00:18:02,080 Speaker 2: back tomorrow,