1 00:00:02,720 --> 00:00:07,200 Speaker 1: Bloomberg Audio Studios, podcasts, radio News. 2 00:00:09,080 --> 00:00:12,720 Speaker 2: On Friday, the US Bureau of Labor Statistics released its 3 00:00:12,800 --> 00:00:14,120 Speaker 2: monthly jobs report. 4 00:00:14,320 --> 00:00:17,680 Speaker 1: So the job's data was not good just put it lightly. 5 00:00:18,079 --> 00:00:20,759 Speaker 2: Molly Smith is an editor on the US Economy team 6 00:00:20,840 --> 00:00:21,480 Speaker 2: at Bloomberg. 7 00:00:21,800 --> 00:00:24,759 Speaker 1: Not only did we get a week number for the 8 00:00:24,880 --> 00:00:28,400 Speaker 1: jobs added in July, the bigger problem in this report 9 00:00:28,520 --> 00:00:31,120 Speaker 1: was that there were really big downward revisions the prior 10 00:00:31,240 --> 00:00:34,760 Speaker 1: two months. They were actually the biggest since the pandemic for. 11 00:00:34,720 --> 00:00:38,440 Speaker 2: The BLS, revisions are normal, it can take businesses a 12 00:00:38,479 --> 00:00:41,800 Speaker 2: while to respond to the agency's surveys, so a fuller 13 00:00:41,840 --> 00:00:44,200 Speaker 2: picture of the labor market comes with time. 14 00:00:44,600 --> 00:00:47,640 Speaker 1: But Trump and his administration have really seized on these 15 00:00:47,680 --> 00:00:51,400 Speaker 1: revisions as a sign that BLS as an institution is 16 00:00:51,440 --> 00:00:55,960 Speaker 1: incompetent and that its data is inaccurate. So Trump was 17 00:00:56,000 --> 00:00:57,520 Speaker 1: not happy with these numbers. 18 00:00:57,760 --> 00:01:00,720 Speaker 2: We had no confidence. I mean the numbers ridiculous. 19 00:01:00,960 --> 00:01:03,560 Speaker 1: You said that they were rigged to make him and 20 00:01:03,640 --> 00:01:07,000 Speaker 1: Republicans look bad. It's a scam in my opinion, And 21 00:01:07,360 --> 00:01:11,959 Speaker 1: he immediately fired Erica mcintarford, who's the commissioner of the 22 00:01:12,000 --> 00:01:16,160 Speaker 1: Peer of Labor Statistics. She was appointed by Biden in 23 00:01:16,360 --> 00:01:19,920 Speaker 1: twenty twenty three, confirmed on a bipartisan basis in twenty 24 00:01:19,959 --> 00:01:23,560 Speaker 1: twenty four, including then Senator J. D Vance, now our 25 00:01:23,640 --> 00:01:25,479 Speaker 1: vice president, voted to confirm her. 26 00:01:26,280 --> 00:01:28,120 Speaker 2: How did you react when you heard the news? 27 00:01:28,800 --> 00:01:31,840 Speaker 1: I was stunned, for sure. Remember I thought that we 28 00:01:31,880 --> 00:01:34,160 Speaker 1: had just gotten through the big rush of covering the 29 00:01:34,240 --> 00:01:37,720 Speaker 1: jobs report that day, and the day took on a 30 00:01:37,720 --> 00:01:40,520 Speaker 1: whole other meaning in the scope though of like being 31 00:01:40,560 --> 00:01:44,319 Speaker 1: a journalist. During Trump's presidency, it hasn't been uncommon to 32 00:01:44,319 --> 00:01:48,520 Speaker 1: see him go after figures who he deems to be 33 00:01:48,680 --> 00:01:51,280 Speaker 1: people who are after him, or saying things that don't 34 00:01:51,280 --> 00:01:55,560 Speaker 1: flatter him, even though again the numbers are completely a political, 35 00:01:55,720 --> 00:02:01,560 Speaker 1: non partisan and frankly, have nothing to do with him. 36 00:02:01,760 --> 00:02:04,760 Speaker 2: Molly says there's no evidence that the data coming out 37 00:02:04,760 --> 00:02:08,400 Speaker 2: of the BLS was inaccurate or manipulated for political reasons. 38 00:02:09,000 --> 00:02:13,920 Speaker 2: The BLS is designed to be independent, nonpartisan. It's tasked 39 00:02:13,919 --> 00:02:18,960 Speaker 2: with collecting and releasing data that reflect reality. But after 40 00:02:19,000 --> 00:02:24,040 Speaker 2: the firing of Erica mcintarfur critics are concerned that could change. 41 00:02:26,560 --> 00:02:28,800 Speaker 2: I'm Sarah Holder, and this is the big take from 42 00:02:28,840 --> 00:02:32,320 Speaker 2: Bloomberg News Today. On the show What Firing the head 43 00:02:32,360 --> 00:02:35,160 Speaker 2: of the Bureau of Labor Statistics could mean for the 44 00:02:35,200 --> 00:02:45,079 Speaker 2: integrity of US economic data. Molly, Let's take a step back. 45 00:02:45,280 --> 00:02:47,760 Speaker 2: What kind of data is the Bureau of Labor Statistics 46 00:02:47,800 --> 00:02:51,080 Speaker 2: responsible for and how is that data typically used to 47 00:02:51,280 --> 00:02:52,639 Speaker 2: make decisions about the economy. 48 00:02:53,000 --> 00:02:56,320 Speaker 1: BLS puts out some of the most important numbers on 49 00:02:56,560 --> 00:03:01,480 Speaker 1: employment and inflation in the US, and BLS, in addition 50 00:03:01,520 --> 00:03:04,040 Speaker 1: to some of its counterparts at the Census Bureau and 51 00:03:04,160 --> 00:03:08,359 Speaker 1: Bureau of Economic Analysis, have a gold standard reputation globally 52 00:03:08,400 --> 00:03:13,480 Speaker 1: for producing some of the really the most quality and nonpartisan, 53 00:03:13,680 --> 00:03:17,400 Speaker 1: free of political influence statistics in the world. And some 54 00:03:17,480 --> 00:03:20,200 Speaker 1: of the data that we're talking about here the Jobs Report, 55 00:03:20,600 --> 00:03:24,880 Speaker 1: also the Consumer Price Index, the widely followed CPI for 56 00:03:24,960 --> 00:03:29,160 Speaker 1: the inflation data. These are really important reports for businesses 57 00:03:29,400 --> 00:03:34,760 Speaker 1: and governments at all levels for setting information about wages, prices, 58 00:03:34,880 --> 00:03:39,280 Speaker 1: hiring decisions, adjusting social security benefits. It really has a 59 00:03:39,320 --> 00:03:42,920 Speaker 1: whole range of uses to, you know, people at all 60 00:03:43,000 --> 00:03:44,400 Speaker 1: levels of business and government. 61 00:03:45,680 --> 00:03:47,280 Speaker 2: I wanted to dig into some of that data that 62 00:03:47,320 --> 00:03:50,320 Speaker 2: the BLS was releasing this year. What was the BLS 63 00:03:50,400 --> 00:03:54,560 Speaker 2: data showing about the early months of Trump's presidency at 64 00:03:54,600 --> 00:03:55,720 Speaker 2: the US economy. 65 00:03:56,000 --> 00:04:00,000 Speaker 1: So so far, the labor market has really been fairly resilient, 66 00:04:00,120 --> 00:04:03,800 Speaker 1: and throughout Trump's presidency and even coming out of the 67 00:04:03,840 --> 00:04:06,280 Speaker 1: COVID years as well. It's been a lot of the 68 00:04:06,360 --> 00:04:10,400 Speaker 1: reason why the Federal Reserve has not felt the need 69 00:04:10,440 --> 00:04:12,880 Speaker 1: to cut interest rates yet because there hasn't really been 70 00:04:13,240 --> 00:04:17,040 Speaker 1: a market deterioration in the labor market. That unemployment is 71 00:04:17,120 --> 00:04:21,200 Speaker 1: still pretty low, job gains are still pretty decent. Granted, 72 00:04:21,240 --> 00:04:23,960 Speaker 1: this report, though, really changed a lot of how we 73 00:04:24,040 --> 00:04:26,760 Speaker 1: thought about the labor market, especially because just a few 74 00:04:26,839 --> 00:04:30,120 Speaker 1: days before, we had FED Jared Jerome Powell saying that 75 00:04:30,120 --> 00:04:33,880 Speaker 1: the labor market is solid, it's broadly balanced, very good, 76 00:04:34,520 --> 00:04:37,360 Speaker 1: and to then see this report two days later really 77 00:04:37,400 --> 00:04:40,680 Speaker 1: cast a lot of doubt on those characterizations. Remember, there 78 00:04:40,680 --> 00:04:44,480 Speaker 1: were also two FED governors who wanted to cut interest 79 00:04:44,560 --> 00:04:47,280 Speaker 1: rates at that meeting, and they said it was because 80 00:04:47,360 --> 00:04:50,000 Speaker 1: the labor market was much weaker under the surface than 81 00:04:50,000 --> 00:04:53,160 Speaker 1: what the data suggested, and one of them, in particular, 82 00:04:53,360 --> 00:04:56,680 Speaker 1: FED Governor Chris Waller, had said that there were expected 83 00:04:56,760 --> 00:04:59,360 Speaker 1: data revisions that were going to show. 84 00:04:59,160 --> 00:05:04,520 Speaker 2: That we're anticipating what eventually happened, which was a revision 85 00:05:04,560 --> 00:05:07,160 Speaker 2: to the past few months of data coming out of 86 00:05:07,160 --> 00:05:11,000 Speaker 2: the jobs report that showed that things were weaker under 87 00:05:11,000 --> 00:05:11,880 Speaker 2: the surface. 88 00:05:12,040 --> 00:05:14,600 Speaker 1: Right, Yeah, and there has been some pattern of that 89 00:05:14,680 --> 00:05:17,160 Speaker 1: in the latest jobs report. You have to understand, this 90 00:05:17,200 --> 00:05:20,320 Speaker 1: is a report that has so many statistics in it. 91 00:05:20,320 --> 00:05:23,640 Speaker 1: It's maybe like thirty something pages with a lot of numbers, 92 00:05:23,640 --> 00:05:26,840 Speaker 1: a lot of rows on every page. And you look 93 00:05:26,880 --> 00:05:29,000 Speaker 1: at the headline numbers for the most part, Right, we 94 00:05:29,040 --> 00:05:31,800 Speaker 1: look at non farm payrolls, what happened in the month, 95 00:05:32,240 --> 00:05:35,200 Speaker 1: We look at the unemployment rate, and we look at 96 00:05:35,360 --> 00:05:38,880 Speaker 1: the change in wages of wage gains. Those are the 97 00:05:38,960 --> 00:05:43,240 Speaker 1: three primary numbers, but there's so much more beyond that 98 00:05:43,240 --> 00:05:46,719 Speaker 1: that come from the jobs report. So that's what a 99 00:05:46,760 --> 00:05:49,479 Speaker 1: lot of people have been taking notice of that. Maybe 100 00:05:49,480 --> 00:05:52,200 Speaker 1: the headline numbers look good, but under the surface there's 101 00:05:52,240 --> 00:05:53,159 Speaker 1: reason for concern. 102 00:05:54,040 --> 00:05:57,640 Speaker 2: So for several months, even as Trump's trade policies created 103 00:05:57,760 --> 00:06:02,320 Speaker 2: uncertainty that rock the markets, those headline jobs numbers weren't 104 00:06:02,400 --> 00:06:07,160 Speaker 2: raising any major alarms. But in the July report that changed. 105 00:06:07,800 --> 00:06:11,240 Speaker 2: What kinds of reactions did you see immediately after the 106 00:06:11,320 --> 00:06:14,279 Speaker 2: report was published? What did it mean about the job market? 107 00:06:14,400 --> 00:06:15,480 Speaker 2: The effects of the trade war? 108 00:06:16,040 --> 00:06:18,440 Speaker 1: So I think that this was just more showing that 109 00:06:18,480 --> 00:06:22,400 Speaker 1: the labor market broadly is weakening, and certainly a lot 110 00:06:22,440 --> 00:06:25,400 Speaker 1: weaker than we thought, particularly through the earlier months of 111 00:06:25,440 --> 00:06:29,159 Speaker 1: the summer. Remember coming out of COVID, it was so 112 00:06:29,160 --> 00:06:32,960 Speaker 1: so strong, like unsustainably strong, you know, it inevitably had 113 00:06:32,960 --> 00:06:35,200 Speaker 1: to come back to earth. And now it's just more 114 00:06:35,279 --> 00:06:38,320 Speaker 1: of seeing that, you know, consumers have been dealing with 115 00:06:38,520 --> 00:06:41,720 Speaker 1: years and years of inflation, and that how is that 116 00:06:41,800 --> 00:06:45,440 Speaker 1: now maybe starting to affect the job market more broadly? 117 00:06:45,680 --> 00:06:49,160 Speaker 1: And certainly Trump's policies are playing a part in this too, 118 00:06:49,320 --> 00:06:52,320 Speaker 1: But it wasn't really one category that stood out and 119 00:06:52,400 --> 00:06:54,479 Speaker 1: was like boom, that's tariffs right there. 120 00:06:54,880 --> 00:06:57,960 Speaker 2: In the hours after the job's report dropped on Friday morning, 121 00:06:58,360 --> 00:07:01,080 Speaker 2: markets slumpt the worst day on the nast that one 122 00:07:01,160 --> 00:07:04,159 Speaker 2: hundred since April, the west week since May, and that 123 00:07:04,240 --> 00:07:07,440 Speaker 2: says we get that so called resilient jobs market with 124 00:07:07,560 --> 00:07:12,200 Speaker 2: some cracks showing. Just after two pm Eastern on Friday afternoon, 125 00:07:12,720 --> 00:07:15,560 Speaker 2: Trump posted on truth Social that he was directing his 126 00:07:15,680 --> 00:07:19,440 Speaker 2: team to fire Erica mcintarfer, and that set off its 127 00:07:19,480 --> 00:07:20,560 Speaker 2: own chain reaction. 128 00:07:21,160 --> 00:07:25,360 Speaker 1: I think you've seen economists and statisticians from both sides 129 00:07:25,440 --> 00:07:28,520 Speaker 1: of the Aisle of You know, both political ideologies come 130 00:07:28,600 --> 00:07:32,000 Speaker 1: to mcintarfur's defense as well as that of the BLS 131 00:07:32,040 --> 00:07:35,480 Speaker 1: as an institution, to say that she's a very well 132 00:07:35,560 --> 00:07:39,320 Speaker 1: respected economist who had served the Biden administration, but she's 133 00:07:39,360 --> 00:07:42,160 Speaker 1: also was in the Census Bureau. She was at the 134 00:07:42,320 --> 00:07:45,480 Speaker 1: Department of Treasury over the course of twenty years under 135 00:07:45,600 --> 00:07:49,080 Speaker 1: presidents of both parties. So she has a very very 136 00:07:49,120 --> 00:07:53,520 Speaker 1: good reputation in the community. And to see economists, again 137 00:07:53,720 --> 00:07:58,480 Speaker 1: of both political ideologies say that this does not reflect 138 00:07:58,520 --> 00:08:02,040 Speaker 1: on her as a person or her competencies whatsoever, that 139 00:08:02,120 --> 00:08:04,680 Speaker 1: this is a normal part of the data collection process. 140 00:08:04,840 --> 00:08:08,800 Speaker 1: And what Trump has done here has now undermined confidence 141 00:08:08,920 --> 00:08:12,840 Speaker 1: in the institution, which really, what good or statistics if 142 00:08:12,840 --> 00:08:15,160 Speaker 1: we can't trust them. Is he now going to put 143 00:08:15,240 --> 00:08:18,760 Speaker 1: somebody in place at the top that makes us doubt 144 00:08:18,840 --> 00:08:22,280 Speaker 1: whether the figures are really free of political influence? That's 145 00:08:22,360 --> 00:08:24,120 Speaker 1: really now what the concern is. 146 00:08:27,040 --> 00:08:29,840 Speaker 2: We dig into that concern and what this all means 147 00:08:29,840 --> 00:08:44,200 Speaker 2: for policymakers like the FED after the break. The Bureau 148 00:08:44,240 --> 00:08:47,920 Speaker 2: of Labor Statistics is responsible for collecting and analyzing some 149 00:08:47,960 --> 00:08:51,400 Speaker 2: of the most foundational data to the US economy. The 150 00:08:51,440 --> 00:08:54,880 Speaker 2: Federal Reserve, for example, looks to the BLS's inflation and 151 00:08:54,960 --> 00:08:59,160 Speaker 2: unemployment figures to make interest rate decisions. So I asked 152 00:08:59,200 --> 00:09:03,560 Speaker 2: Bloomberg's US Economy editor Mollie Smith, how Trump's firing of 153 00:09:03,600 --> 00:09:07,200 Speaker 2: the head of this critical agency could undermine public trust 154 00:09:07,320 --> 00:09:08,760 Speaker 2: in the data it produces. 155 00:09:09,000 --> 00:09:11,760 Speaker 1: I think what this does is really plants a seed 156 00:09:12,040 --> 00:09:15,280 Speaker 1: that there's reason now to think that the numbers may 157 00:09:15,360 --> 00:09:18,400 Speaker 1: not be free of political influence going forward, which is 158 00:09:18,400 --> 00:09:21,680 Speaker 1: something that we've all understood to be true up until 159 00:09:21,679 --> 00:09:25,160 Speaker 1: this point, and that the BLS will tell you that 160 00:09:25,679 --> 00:09:29,280 Speaker 1: the data that it produces is nonpartisan and independent, and 161 00:09:29,480 --> 00:09:31,400 Speaker 1: that is so much of where the value in it 162 00:09:31,440 --> 00:09:35,560 Speaker 1: comes from. And if we lose that understanding, then what 163 00:09:35,760 --> 00:09:37,240 Speaker 1: good are these numbers anymore? 164 00:09:38,160 --> 00:09:42,200 Speaker 2: People across the political spectrum reacted to the move with concern. 165 00:09:42,760 --> 00:09:47,079 Speaker 2: On Friday, shortly after Trump's announcement, Republican Senator Tom Tillis 166 00:09:47,080 --> 00:09:51,000 Speaker 2: told reporters he wanted more information about the president's motivations. 167 00:09:51,520 --> 00:09:54,360 Speaker 2: It was just fired because the president or whoever decided 168 00:09:54,400 --> 00:09:58,000 Speaker 2: to buire the director, just did them because they didn't 169 00:09:58,040 --> 00:09:58,559 Speaker 2: like the numbers. 170 00:09:58,600 --> 00:09:59,400 Speaker 1: They had to grow up. 171 00:10:00,000 --> 00:10:04,000 Speaker 2: Former Treasury Secretary Larry Summers disputed Trump's accusation of data 172 00:10:04,040 --> 00:10:06,760 Speaker 2: manipulation on ABC's This Week. 173 00:10:06,880 --> 00:10:09,680 Speaker 1: This is a preposterous charge. 174 00:10:09,920 --> 00:10:14,320 Speaker 2: The Trump administration has continued to defend mcintarfur's firing, saying 175 00:10:14,320 --> 00:10:16,840 Speaker 2: the move comes on the heels of the biggest downward 176 00:10:16,880 --> 00:10:21,160 Speaker 2: revision in years. Here's Trump's National Economic Council Director Kevin 177 00:10:21,240 --> 00:10:23,760 Speaker 2: Hassett on NBC's Meet the Press. 178 00:10:23,679 --> 00:10:25,280 Speaker 1: And what we need is a fresh out of eyes 179 00:10:25,360 --> 00:10:27,800 Speaker 1: over at the BLS. 180 00:10:28,880 --> 00:10:31,400 Speaker 2: In the short term. Who is running the agency? And 181 00:10:31,440 --> 00:10:33,800 Speaker 2: will we get in August jobs report? 182 00:10:33,960 --> 00:10:36,959 Speaker 1: So yes, the man who was the deputy commissioner is 183 00:10:37,000 --> 00:10:40,600 Speaker 1: now acting commissioner in the interim, and I think even 184 00:10:40,640 --> 00:10:44,120 Speaker 1: before the August employment report, we're all probably looking more 185 00:10:44,200 --> 00:10:48,160 Speaker 1: to the July CPI report first. So that's what's going 186 00:10:48,240 --> 00:10:50,800 Speaker 1: to be coming out over the course of the next week. 187 00:10:50,920 --> 00:10:52,800 Speaker 1: I mean, who knows. We also could have a new 188 00:10:52,840 --> 00:10:56,199 Speaker 1: commissioner before then. Trump did say Sunday night that he'd 189 00:10:56,240 --> 00:10:59,480 Speaker 1: be looking to name somebody in the course of the 190 00:10:59,520 --> 00:11:02,680 Speaker 1: next few days. Granted though that person is subject to 191 00:11:02,720 --> 00:11:06,240 Speaker 1: Senate confirmation, which I'm sure is not going to happen 192 00:11:06,240 --> 00:11:08,600 Speaker 1: in the next week. So it will take some time 193 00:11:08,640 --> 00:11:09,680 Speaker 1: to see how this plays out. 194 00:11:10,720 --> 00:11:13,480 Speaker 2: In the meantime, the July jobs report could have its 195 00:11:13,520 --> 00:11:16,920 Speaker 2: own real world implications. When the Federal Reserve meets in 196 00:11:16,960 --> 00:11:20,640 Speaker 2: September to discuss interest rates, it'll use that BLS data 197 00:11:20,720 --> 00:11:22,079 Speaker 2: to inform its decisions. 198 00:11:22,400 --> 00:11:25,520 Speaker 1: Trump has made his intentions very clear he wants interest 199 00:11:25,600 --> 00:11:29,920 Speaker 1: rates lower. Ironically, the Chili jobs report, for all of 200 00:11:29,960 --> 00:11:32,960 Speaker 1: the flaws that the President pointed out, actually helps advance 201 00:11:33,080 --> 00:11:37,160 Speaker 1: that directive that it was so poor that people immediately 202 00:11:37,200 --> 00:11:40,199 Speaker 1: brushed to put bets that the Fed would lower rates 203 00:11:40,200 --> 00:11:44,520 Speaker 1: in September. So, if anything, actually the worst jobs numbers 204 00:11:44,880 --> 00:11:46,760 Speaker 1: help advance the idea that the Fed is going to 205 00:11:46,800 --> 00:11:47,679 Speaker 1: cut interest rates. 206 00:11:48,800 --> 00:11:52,760 Speaker 2: The Federal Reserve is another historically a political agency that 207 00:11:52,800 --> 00:11:56,679 Speaker 2: has become a major target for President Trump. He's repeatedly 208 00:11:56,720 --> 00:12:00,760 Speaker 2: threatened to replace FED Chair Jerome Powell over his interest decisions, 209 00:12:00,960 --> 00:12:04,800 Speaker 2: and on Friday, just after Trump fired Macintarfur from BLS, 210 00:12:05,280 --> 00:12:09,040 Speaker 2: a member of the Fed's Board of Governors resigned. Replacing 211 00:12:09,040 --> 00:12:12,079 Speaker 2: that board member gives Trump an opportunity to appoint someone 212 00:12:12,160 --> 00:12:15,959 Speaker 2: new who shares his views on lowering rates, and it's 213 00:12:15,960 --> 00:12:18,760 Speaker 2: possible that the new board member could be Trump's preferred 214 00:12:18,800 --> 00:12:23,160 Speaker 2: pick to ultimately replace Powell as FED chair. I asked 215 00:12:23,240 --> 00:12:27,040 Speaker 2: Mollie what these coinciding events might mean for the economy 216 00:12:27,480 --> 00:12:30,040 Speaker 2: and for the integrity of our economic data. 217 00:12:30,679 --> 00:12:34,880 Speaker 1: Well, it gives Trump now this opportunity to put two 218 00:12:34,920 --> 00:12:37,720 Speaker 1: people in place who are going to be responsible for 219 00:12:38,200 --> 00:12:42,439 Speaker 1: overseeing really important economic data and making decisions that shape 220 00:12:42,440 --> 00:12:46,560 Speaker 1: monetary policy. The US has a really sprawling statistical system. 221 00:12:46,720 --> 00:12:50,960 Speaker 1: It's thirteen principal agencies in total, spread throughout the government 222 00:12:51,080 --> 00:12:55,240 Speaker 1: that compile statistics on everything from education to health to 223 00:12:55,360 --> 00:12:59,520 Speaker 1: agriculture in the economy and goes far beyond unemployment rate 224 00:12:59,640 --> 00:13:02,079 Speaker 1: and inflation and a lot of the numbers that we 225 00:13:02,520 --> 00:13:06,800 Speaker 1: care about primarily here in Financial News. The trust in 226 00:13:06,880 --> 00:13:09,600 Speaker 1: the data and to understand that it's not meant to 227 00:13:09,640 --> 00:13:13,560 Speaker 1: serve anything but a public good is really paramount to 228 00:13:14,280 --> 00:13:16,720 Speaker 1: the foundation of what federal statistics are. 229 00:13:17,440 --> 00:13:19,760 Speaker 2: And did Trump's act of firing the head of the 230 00:13:19,800 --> 00:13:22,600 Speaker 2: BLS shake that foundation totally? 231 00:13:22,760 --> 00:13:25,520 Speaker 1: Yeah? I mean this is really where the crux of 232 00:13:25,559 --> 00:13:29,120 Speaker 1: this centers around. Is the BLS still going to be 233 00:13:29,240 --> 00:13:33,720 Speaker 1: respected as a non partisan institution going forward that can 234 00:13:33,760 --> 00:13:37,640 Speaker 1: we still trust its data will be free of political influence, 235 00:13:37,679 --> 00:13:40,200 Speaker 1: and I think a lot of the consensus around that 236 00:13:40,320 --> 00:13:44,240 Speaker 1: so far is yes that Remember, the commissioner is one 237 00:13:44,320 --> 00:13:48,520 Speaker 1: person in it's an agency of roughly two thousand people, 238 00:13:48,960 --> 00:13:51,880 Speaker 1: similar to the FED. That it's not like FED shared 239 00:13:51,920 --> 00:13:55,200 Speaker 1: Jerome Powell is the sole person who has control over 240 00:13:55,240 --> 00:13:58,320 Speaker 1: what interest rates are. It's a collective process in both 241 00:13:58,360 --> 00:14:02,160 Speaker 1: of these institutions. But the second that you make a 242 00:14:02,240 --> 00:14:07,280 Speaker 1: position that is perceived to be a political political a 243 00:14:07,320 --> 00:14:09,160 Speaker 1: lot of that trust comes into question. 244 00:14:10,040 --> 00:14:12,040 Speaker 2: Well, Mollie, thank you so much for joining us. 245 00:14:12,160 --> 00:14:12,920 Speaker 1: Thanks for having me. 246 00:14:16,440 --> 00:14:19,360 Speaker 2: This is the Big Take from Bloomberg News. I'm Sarah Holder. 247 00:14:19,640 --> 00:14:22,240 Speaker 2: To get more from The Big Take and unlimited access 248 00:14:22,280 --> 00:14:26,040 Speaker 2: to all of Bloomberg dot com, subscribe today at Bloomberg 249 00:14:26,080 --> 00:14:29,840 Speaker 2: dot com slash podcast offer. If you liked this episode, 250 00:14:29,960 --> 00:14:32,240 Speaker 2: make sure to follow and review The Big Take wherever 251 00:14:32,320 --> 00:14:35,040 Speaker 2: you listen to podcasts. It helps people find the show. 252 00:14:35,920 --> 00:14:38,120 Speaker 2: Thanks for listening. We'll be back tomorrow.