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Speaker 1: Have you been hacked? Who got to you? Blink three times?

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Speaker 1: If you need rescuing. Look, there was the response I

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Speaker 1: got when I said the last job market report was strong,

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Speaker 1: And look, I get the unease. We're talking about administration

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Speaker 1: that lies all the time. This was a president who

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Speaker 1: fired the head of the Bureau of Labor Statistics after

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Speaker 1: the numbers didn't go his way. He tried to install

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Speaker 1: a crank in her place. Those are real reasons to worry.

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Speaker 1: So I'm not going to ask you to switch your

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Speaker 1: brain off and pluly trust the government. I sure don't.

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Speaker 1: In fact, quite the opposite. I'm here to help you

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Speaker 1: look more carefully. I want to explore how our economic

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Speaker 1: numbers are actually put together, to give you a sense

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Speaker 1: of what tampering might actually look like if it were occurring.

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Speaker 1: And I want to help you assess the evidence for yourself.

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Speaker 1: Because here's the thing. I still believe the jobs report.

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Speaker 1: It's not because I trust politicians, but because I do

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Speaker 1: know how these numbers are put together. I know how

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Speaker 1: all the things that have to do if they wanted

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Speaker 1: to corrupt the numbers, and I know what the warning

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Speaker 1: signs would look like if someone were trying. But I

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Speaker 1: get it today. It's not enough for me to tell

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Speaker 1: you that I trust these numbers. I want to equip

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Speaker 1: you so that you can make your own informed judgment.

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Speaker 1: To do that, we're going to have to answer six questions.

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Speaker 1: Why do these facts feel so uncomfortable? Why the bare

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Speaker 1: less scare was real, How the institution quietly held, why

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Speaker 1: I believe the numbers, what distorted data actually looks like.

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Speaker 1: And finally, once we believe the data, If we believe

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Speaker 1: the data, why you might be drawn to believe the

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Speaker 1: labor market right now looks reasonably strong. So let's start

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Speaker 1: with the first question, which is why do these facts

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Speaker 1: the job market looks strong feel uncomfortable. The latest government

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Speaker 1: reports said that non farm payrolls rose by one hundred

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Speaker 1: and seventy two thousand in May and that the unemployment

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Speaker 1: rate held steady at four point three percent. That's a

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Speaker 1: factual claim about what the Bureau of Labor Statistics says

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Speaker 1: it found. It's not yet to claim about whether those

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Speaker 1: numbers are good or bad. That interpretation's going to come later.

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Speaker 1: But for a lot of people, just hearing those numbers,

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Speaker 1: which sound pretty strong, was jarring. The reaction was instant.

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Speaker 1: It was impossible. They said it was fake, that it

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Speaker 1: was propaganda. Justin must have been God at Hey, this

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Speaker 1: instinct that's very human. We all like stories that make

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Speaker 1: the world feel coherent. That leads us to prefer facts

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Speaker 1: that slot neatly into the picture that's already sitting in

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Speaker 1: our heads. But economics begins precisely where that instinct ends.

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Speaker 1: Economic says, start with the reality, then build your story

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Speaker 1: to make sense of it. Don't start with a story,

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Speaker 1: and then decide which facts you're willing to admit that's

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Speaker 1: the problem, or to put it in more Platypus seeknoms

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Speaker 1: terms sometimes the world hands or platypus. What do I

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Speaker 1: mean by that? Well, the first time the Europeans saw

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Speaker 1: a platypus, a lot of them called it a fraud

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Speaker 1: instead of calling it the world's cutest animal. They saw

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Speaker 1: a duck bill and a beaver tail. They saw it

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Speaker 1: laid eggs. But it's also a man will come on.

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Speaker 1: A British scientist named George Shaw, a serious man, a

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Speaker 1: museum curator no less, He literally checked the pelt for stitches.

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Speaker 1: He thought someone had siwn a duck's beak onto a beaver.

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Speaker 1: So the problem wasn't that the platypus was fake. The

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Speaker 1: problem was that it didn't fit the model in the

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Speaker 1: BRIT's heads, so they denied the reality sitting right there

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Speaker 1: in front of them. And I think that's what's happening

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Speaker 1: right now with the labor market. The jobs report doesn't

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Speaker 1: fit many people's preferred story, so rather than update the story,

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Speaker 1: they reject the evidence. That's bad science, it's bad economics,

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Speaker 1: and in politics, as in life, it gets very dangerous,

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Speaker 1: very quickly. But it's also true that the appeal of

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Speaker 1: this type of emotional response has been heightened by the

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Speaker 1: very real concerns coming from the Trump administration and its

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Speaker 1: treatment or mistreatment of information. And I think it's insulting

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Speaker 1: that you are trying to test my knowledge of economics

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Speaker 1: and the decisions that this president has made. The bar

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Speaker 1: Less scare was real, and that's our second point. It

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Speaker 1: was real. There are good reasons to be suspicious these days.

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Speaker 1: You're not going crazy. Last year, after a week jobs report,

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Speaker 1: President Trump fired the commissioner of the Bureau of Labor Statistics,

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Speaker 1: our chief dork Erica mcanniffer, and accuse the agency of

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Speaker 1: dishonesty with absolutely no evidence and no evidence has subsequently

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Speaker 1: come to light. This was a big deal for me,

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Speaker 1: earth shattering on I remember it very well. I was

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Speaker 1: asleep in a hotel room in Tokyo and my better

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Speaker 1: half literally shook me awake at six am to tell

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Speaker 1: me he does he fired the BLS commissioner. That's how

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Speaker 1: big of a deal it was. It was literally unprecedented

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Speaker 1: because the Bureau of Labor Statistics, it's one of the

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Speaker 1: country's core truth telling agencies. It measures so much of

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Speaker 1: what matters unemployment and jobs, and inflation and productivity, the

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Speaker 1: basic plumbing of economic reality that we all rely on.

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Speaker 1: This story got worse, if you remember, the administration put

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Speaker 1: forward a bloke by the name of E. J. And Tony,

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Speaker 1: a partisan hack, wildly unqualified to run the Bureau of

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Speaker 1: Labor Statistics. He had no knowledge of running a bureau,

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Speaker 1: no experience studying labor, and no background collecting statistics. Oh

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Speaker 1: he did have one qualification. He was at the Capitol

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Speaker 1: on January sixth. So if you saw that whole thing

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Speaker 1: play out and you thought, hang on, maybe they're trying

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Speaker 1: to leticize the numbers, you were right. You should worry

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Speaker 1: when leaders attack the statisticians. The very notion of truth.

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Speaker 1: You should worry when they sacked people for publishing unwelcome facts.

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Speaker 1: You should worry when they try to install loyalists in

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Speaker 1: truth telling institutions. But that still leaves us with the

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Speaker 1: next question. You see, the scare was real, but what

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Speaker 1: happened next? Did the institution collapse or did it hold?

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Speaker 1: Here's where I want to tell you about how the

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Speaker 1: institution quietly held. The public heard about the firing, I

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Speaker 1: bet you did. They heard about the crackpot nominee. But

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Speaker 1: most people missed was what came next. The institution kept running,

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Speaker 1: the worst nominee was withdrawn and the replacement who's been

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Speaker 1: nominated as a serious, sober minded statistician with a genuine

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Speaker 1: commitment to the truth. The problem here is a problem

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Speaker 1: of how we do news. The attempted sabotage that was

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Speaker 1: front page news, the institution quietly holding. It wasn't dramatic,

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Speaker 1: it wasn't noisy, It didn't get headlines. Public servants do

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Speaker 1: their job, somehow never ends up on the front page

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Speaker 1: of the paper. So none of the major networks really

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Speaker 1: told that story, although we did here on Platipus Economics.

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Speaker 1: I'm coming back to talk about it again, because I

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Speaker 1: think this is a story that the platypus economics crowd

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Speaker 1: cares about. And so while everyone was understandably staring at

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Speaker 1: the circus, the Bureau of Labor Statistics kept producing data

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Speaker 1: the way that it always does. There was career staff,

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Speaker 1: there were routine processes, standard release schedules, overlapping internal checks.

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Speaker 1: The acting leadership stayed in the hands of the long

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Speaker 1: term professionals, so the cranks never got near the suit

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Speaker 1: of power. The President was ultimately forced to withdraw his

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Speaker 1: nomination of the Charlotte and e J ANDTNI. The replacement nominee,

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Speaker 1: Brett Matsimoto, looks like exactly the sort of person a

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Speaker 1: normal White House would nominate for this job. In fact,

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Speaker 1: he could be appointed by Democrat or publican. He's a

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Speaker 1: career public servant, a statistician, a social scientist, someone whose

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Speaker 1: professional life has been about measurement, statistics and footnotes, not messaging.

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Speaker 1: In other words, the anti and Tony. There's no flashy

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Speaker 1: nonsense here. He never attended a coup. He's just a

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Speaker 1: bloke who quietly spent his years trying to count things

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Speaker 1: doing correctly made I love this stuff. It's extraordinary it's

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Speaker 1: the quiet beauty of serious people doing serious work. But

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Speaker 1: for us, the point is that we avoided the worst

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Speaker 1: story that so many of us feared. More than that,

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Speaker 1: the Bureau of Labor Statistics today remains in very good hands.

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Speaker 1: The bureau itself is fine, and so as statistics. The

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Speaker 1: president who tried to damage it, that's another matter entirely,

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Speaker 1: and frankly, I'm still mad about it. Trump tried to

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Speaker 1: bully the institution. He did real damage to public trust,

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Speaker 1: and that's why so many people are asking so many

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Speaker 1: questions right now. He is the reason why your first

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Speaker 1: reaction might have been did they cook the books? Problems?

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Speaker 1: Not you, it's him. Trust me. I'm like a spite person.

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Speaker 1: And if Trump had gotten his way, maybe the books

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Speaker 1: would be cooked. But he didn't. Our official economic statistics survived,

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Speaker 1: and that survival is part of why I'm willing to

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Speaker 1: make the next argument. So let's turn to that next argument,

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Speaker 1: the fourth question, Why I believe the numbers that came

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Speaker 1: out last week and the month before and the month

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Speaker 1: before that. Here's the starting point. I know how these

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Speaker 1: data are put together. I know there are dozens of

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Speaker 1: people and processes that sit behind between the presidents whim

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Speaker 1: and the final published number. I know exactly how big

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Speaker 1: the conspiracy would have to be to fake this thing,

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Speaker 1: and I know this administration leaks like a sieve. At

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Speaker 1: a personal level, I literally personally know many of the

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Speaker 1: people who put these numbers together. I know their integrity.

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Speaker 1: I know that they'd call me and they'd tell me

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Speaker 1: if things were going wrong, And I know what the

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Speaker 1: tells would be in the data if the processes were rigged.

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Speaker 1: That's why I believe the numbers. But that's not good

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Speaker 1: enough for you. You need to know how to check

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Speaker 1: for yourself. So let me give you a few clues. First.

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Speaker 1: The Job's report isn't just one number from one source.

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Speaker 1: It actually combines two separate surveys. There's the household survey,

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Speaker 1: which is called the current Population Survey, that gives us

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Speaker 1: the unemployment rate and broader information about who's work and

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Speaker 1: who's looking for work and who's dropped out of the

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Speaker 1: labor force. And then there's the payroll survey called the

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Speaker 1: Current Employment Statistics Survey, which measures the number of people

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Speaker 1: who are on employer's payrolls. So what we have is

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Speaker 1: two different instruments measuring three closely related things, and that's

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Speaker 1: really useful. It should help make you more confident because

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Speaker 1: we're not hanging the whole story just a one survey,

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Speaker 1: one method, one lever, one set of statisticians. Okay. Second,

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Speaker 1: we can actually look inside these data from the outside.

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Speaker 1: The results from each individual survey that each person fills

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Speaker 1: in and the household survey they posted online. We call

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Speaker 1: it microdata. Real researchers can and do dig in every

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Speaker 1: single month. They test their data, they challenge it, they

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Speaker 1: cross check it. No one has found anything fishy so far.

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Speaker 1: And Third, the payroll data the other data, so it's

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Speaker 1: not just floating in the breeds. Each benchmarked, that is

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Speaker 1: to say, it's checked against broader administrative records. There's a

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Speaker 1: complicated survey called the Quarterly Censors of Employment and Wages,

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Speaker 1: And basically what they do is they get the unemployment

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Speaker 1: insurance records from every state. Now, it's very hard for

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Speaker 1: a federal government to falsify state records, and so in

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Speaker 1: plain English, the monthly payrolls estimate gets checked against a

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Speaker 1: much more complete count later on that comes from the states,

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Speaker 1: not from the Feds. Fourth, what's wonderful about the United

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Speaker 1: States is we've also got private sector smell tests beyond

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Speaker 1: the employment data collected by the Bureau of Labor Statistics.

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Speaker 1: The payroll Company ADP has counts of how many checks

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Speaker 1: it's printed. Bank of America has account linked data, home

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Speaker 1: based tracks, small business and hourly worker data. Paychecks has

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Speaker 1: its own small business employment measure. All of these measures

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Speaker 1: are imperfect. None of them are as precise as BLS measure,

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Speaker 1: and none of them can tell you what happened last

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Speaker 1: month better than the official measure. But they're useful enough

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Speaker 1: that if the government numbers were being systematically falsified or

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Speaker 1: messed with, that broader ecosystem would start to smell funny

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Speaker 1: and the official numbers would look very different from everything

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Speaker 1: else that we're seeing. That's one of the wonderful things

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Speaker 1: about American the American statistical system. We're a big economy,

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Speaker 1: lots of ways of measuring things, and so our statistics

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Speaker 1: don't just dwell alone in the dark. You can think

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Speaker 1: about our economic statistics is like a ship with multiple

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Speaker 1: navigation systems. We've got GPS, we got radar, and when

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Speaker 1: you don't trust the GPS, just look up at the stars.

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Speaker 1: They'll help you. Each one of them's imperfect, but if

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Speaker 1: they all say you're in the same ocean, you probably

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Speaker 1: are in that ocean. And if one of them starts

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Speaker 1: to say you're in the Sahara, well the others are

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Speaker 1: going to look a little different, and that's your alarm bell. Now.

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Speaker 1: I want to add one really quite important nuance to this.

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Speaker 1: These data, the government data, like all data, involve scientific judgments.

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Speaker 1: If you've ever sat through a status class, you know

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Speaker 1: this stuff is hard. Sampling is hard, Waiting is hard,

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Speaker 1: seasonal adjustment is hard. Good statisticians can and do argue

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Speaker 1: about how to make the numbers more accurate. That's healthy

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Speaker 1: and that's science. I play in that sandbox sometimes, but

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Speaker 1: that's a completely different claim from saying the numbers are dishonest.

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Speaker 1: So the point I want to make is debating about

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Speaker 1: how to improve imperfect data. That's one thing, and it's

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Speaker 1: something you're going to hear me talk about a lot,

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Speaker 1: but loose talk about politically corrupted numbers is an altogether

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Speaker 1: different thing that should only be done when you've got

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Speaker 1: more than just a suspicion. And if you care about truth,

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Speaker 1: if you care about the scientific method, you should fight

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Speaker 1: back against that confusion. Because the first is an argument

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Speaker 1: about how to make the data more accurately reflect reality,

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Speaker 1: and it's an argument worth having. The second is an

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Speaker 1: attack on reality itself. And so for now, what might

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Speaker 1: be the most useful question is what would actually look

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Speaker 1: like if our government really was distorting the data. I

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Speaker 1: don't want us to be complacent. Now. I've been an

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Speaker 1: economists for a while and I've seen authoritarian regimes mess

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Speaker 1: with their numbers, which means I can tell you a

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Speaker 1: little bit about what history tells us we ought to

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Speaker 1: be looking out for. Turns out, it's actually pretty uncommon

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Speaker 1: to literally invent a number entirely out of thin air. Honestly,

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Speaker 1: it's just really hard to do without looking like a clown.

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Speaker 1: The real playbook that the authoritarians use is more subtle,

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Speaker 1: and I think more dangerous, because it's a harder set

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Speaker 1: of stories to tell. Instead of making up numbers, they

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Speaker 1: attack the credibility of their statisticians. They sack or intimidate

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Speaker 1: those who publish unwelcome facts. They try to reduce the

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Speaker 1: transparency of the process by which the numbers are put together.

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Speaker 1: They suddenly change methods all of a sudden, for no

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Speaker 1: reason whatsoever. They create discontinuities so measurement in one month

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Speaker 1: is different measurement in the next, and that makes it

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Speaker 1: very hard to compare what was happening before the regime

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Speaker 1: came to powered what happened after you stop publishing the

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Speaker 1: data on a monthly level and you move it to quarterly,

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Speaker 1: you stop publishing data you don't like and say the

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Speaker 1: problem must be that the data were wrong, because our

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Speaker 1: policies are certainly working. And look the president, he started

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Speaker 1: at the top of his list, and he was working

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Speaker 1: his way through that list, and he didn't get all

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Speaker 1: the way through. EJ. And Tony, if he'd been appointed,

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Speaker 1: was willing to keep fighting to degrade our data, but

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Speaker 1: he was stopped. Now, I remain vigilant, and I hope

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Speaker 1: you do too. So no, I'm not naive. I'm not

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Speaker 1: saying that governments never distort data. I'm saying that I

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Speaker 1: know what it looks like, and what I see right

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Speaker 1: now is an administration that has flirted with that danger

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Speaker 1: but hasn't, at least so far, for just anything that

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Speaker 1: looks remotely like a fabricated job's number. The administration, to

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Speaker 1: be clear, lies, It lies when it wakes up, it

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Speaker 1: lies when it goes to bed. And that lies in between.

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Speaker 1: I'm not defending a word they say. What I want

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Speaker 1: you to do is distinguish between what the White House

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Speaker 1: says what the official statisticians say. That, in turn brings

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Speaker 1: us to a different question. If the numbers are honest,

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Speaker 1: the jobs numbers, what do they actually mean? And why

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Speaker 1: does this labor market look reasonably strong? So we saw

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Speaker 1: a payroll gain of one hundred and seventy two thousand

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Speaker 1: in May, followed by upward reversions that put the past

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Speaker 1: couple of months a little bit higher as well. Those

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Speaker 1: are simply facts saying that this run of numbers is

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Speaker 1: good news and that you should feel more upbeat about this

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Speaker 1: labor market. That's an interpretation. That's my interpretation. And look,

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Speaker 1: a reasonable person can disagree about how upbeat to be.

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Speaker 1: Different people can put different weights on different parts of

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Speaker 1: the report. That's fine, But here's my read, and I'm

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Speaker 1: going to show you my work. Payroll growth of one

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Speaker 1: hundred and seventy two thousand would have looked pretty middling

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Speaker 1: during the big snapback from COVID, that's correct. Back then,

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Speaker 1: millions of people were flooding back into ad economy that

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Speaker 1: had been temporarily switched off, but that comparison, that's the

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Speaker 1: wrong benchmark for today. Back then, the economy was climbing

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Speaker 1: out of a crater. You should have expected large monthly

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Speaker 1: gains when you're rebuilding after a moment like that. Today

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Speaker 1: we're back to normal, But actually more than that, we're

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Speaker 1: in a new normal because the Trump administration's immigration crackdown

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Speaker 1: has led population growth to virtually stop for the first

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Speaker 1: time in decades. That in turn means that the economy

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Speaker 1: doesn't need the same gigantic pace of job creation to

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Speaker 1: keep the labor market healthy and so, compared to today's benchmark,

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Speaker 1: a benchmark that's relevant for this low immigration world we're

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Speaker 1: in right now, one hundred and seventy two thousand jobs

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Speaker 1: is a pretty solid number, and it didn't come alone.

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Speaker 1: It came out to similar numbers in March and April,

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Speaker 1: and in fact we've seen through this period the unemployment

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Speaker 1: rate held pretty steady at about four and a quarter percent.

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Speaker 1: This doesn't mean everything's perfect. It doesn't mean every work

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Speaker 1: is thriving. Macro data and personal experience can diverge one

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Speaker 1: person's labor market, your labor market, even it's not the

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Speaker 1: whole labor market. Both things can be true at once.

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Speaker 1: You can struggle and the aggregate can be doing okay.

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Speaker 1: But if you want to ask me whether this report

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Speaker 1: says that recession panic should cool down, whether it says

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Speaker 1: the labor market is holding up better than many feared,

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Speaker 1: whether it says the economy is still adding jobs at

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Speaker 1: a healthy clip relative to the supply of available workers,

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Speaker 1: my answer is yes, that's what makes these pretty good numbers. Look,

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Speaker 1: here's the thing. If I only ever give you facts

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Speaker 1: that make you feel good, I'm not telling you the

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Speaker 1: whole truth. That's a tell that I'm not doing my job,

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Speaker 1: and you should listen to me less. That's a test

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Speaker 1: you should applied any commentator in economics, in politics, and

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Speaker 1: indeed in any domain. A healthy society depends on institutions

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Speaker 1: that tell us uncomfortable truths. None of us get to

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Speaker 1: keep the part of reality that flatters our worldview and

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Speaker 1: throw away the rest. That's not economics, that's not science,

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Speaker 1: and over time, it's not democracy either, because if every

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Speaker 1: inconvenient fact is dismissed as propaganda, then we don't just

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Speaker 1: lose one job's report. We lose your ability to spot

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Speaker 1: the platypus