Moody's Talks - Inside Economics - No Jobs Friday

Episode Date: October 3, 2025

The Inside Economics team welcomes Lisa Simon, Chief Economist at Revelio Labs, for an unusual jobs Friday podcast as the ongoing government shutdown prevented the release of the September employment report. Lisa details the new public labor statistics data that Revelio Labs began publishing recently in the wake of turmoil at the Bureau of Labor Statistics. The team discusses how private data sources can help fill in the gaps left by the temporary absence of government data and also dissects the current state of the labor market.Guest: Lisa Simon – Chief Economics, Revelio LabsFor more about Lisa Simon, click here: https://www.reveliolabs.com/author/lisa-k-simon/Explore the risks and realities shaping the economy in our new webinar, now streaming for free.U.S. Economic Outlook: Under Unprecedented UncertaintyWatch here: https://events.moodys.com/mc68453-wbn-2025-mau25777-us-macro-outlook-precipice-recession?mkt_tok=OT…Hosts: Mark Zandi – Chief Economist, Moody’s Analytics, Cris deRitis – Deputy Chief Economist, Moody’s Analytics, and Marisa DiNatale – Senior Director - Head of Global Forecasting, Moody’s Analytics Questions or Comments, please email us at [e-mail]. We would love to hear from you.  To stay informed and follow the insights of Moody's Analytics economists, visit Economic View. Hosted by Simplecast, an AdsWizz company. See pcm.adswizz.com for information about our collection and use of personal data for advertising.

Welcome to Inside Economics. I'm Mark Zandi, the chief economist of Moody's Analytics, and I'm joined by my two trusty co-host, Chris DeReedy and Natalie. Hi, guys. Good morning. Hey, Mark. Good to see you. Good to see it.

Chris, I see you've got a tie on. What's going on? Yeah, I'm in Washington. I was here for the labor market report. I was ready, you know, but other plans developed. Developed, yeah. Yep, Friday. Hey, do you have a little bit of withdrawal?

I certainly did. No jobs numbers today. Yeah, everyone's scrambling to look for any other piece of data to fill the void, right? That sounds like a tease. I think it's a good segue. But we're not going to segue, yeah, because we've got to bring in Dante. Hey, Dante. Oh, look.

Hi, Mark. How are you doing? Good. Good. How are you doing? Doing well. Did you have withdrawal this morning with no jobs numbers? It was a weird morning.

I felt like I had some free time that I don't usually have, so it's kind of a strange day, yeah. Can I ask you a question? It might be a personal one. Sure. I'll tell you a little bit about myself and then I. I'm going to ask you if you feel this name way. Okay. Like, I get a little butterfly in my pit of my stomach every job Friday.

Like, I'm actually nervous about the number. You know, do you feel the same way or is it just me? Well, I do because, you know, I obviously make a forecast. And if my forecast is bad, you typically, you know, make it known to the world that it was bad. So I try to hope for the best every morning, you know, every month. What was your forecast for this month? This best one. Zero.

It's zero. Yeah. Oh, wow. I was pretty cool. I think it's the first time I've ever forecast at zero. Yeah. Chris, do you get butterflies on Jobs Friday or you're pretty stoic? Just Chris.

Definitely not regularly, right? Maybe recently. But, no, not really invested to that level. Really? Oh, gosh. Revisions, Mark. Revisions. The first print is.

you know, meaningless. That's a good point. Yeah. Well, you're much better suited to be a cheap economy than me. Yeah, I get excited. I mean, one way or another. Yeah. You get excited.

Right, when it comes out, it's my 5.30 in the morning. So usually by the time I wake up, it's already out. So I always reach for my phone and there's that anticipation of, because there's always a news alert with it on there. I don't have to go looking for it, right? And it's always I flip my phone around. And do you mean you're not Economic View Alert first? You're looking at a news alert.

Is that what you're telling me? I'm sorry, John. I don't have my analysis. I don't even think that's a thing we do. It could be in your email, you know? No, it's right on the home screen of my phone. Like Wall Street Journal or New York Times tells me what the jobs number is. Well, you know, we have a guest and we're going to bring her in in just a second.

And very apropos to Jobs Friday. But I do, you know, I want to just let listeners know a little bit about, this is now inside, inside economics, you know, people think our podcasts are scripted somehow, that we carefully spend all this time putting the questions together, vetting them with the guests, having the guests come back, some of the guests are divas that they want to change the, the, the questions or object to the questions and we go back and forth and we spend a lot of time on that. Is that how things go here, Chris? Nothing could be further from the truth. This is not scripted, right? People think this is scripted? Now what I do, yeah, people think it's scripted, yeah. I don't know, take, I think that's a compliment actually, because they, right? To some degree. They think, you know, it's very, we were spending all this energy and time on it. But one of the reasons why I like the podcast is because I spend no energy and time on it.

It's a lot of fun. It's just a lot of fun. And I learn a lot as we go along here. But what I do do so people know is like a couple, three days before the podcast, I'll send an email with a paragraph saying, hey, you know, this, we'd like you to talk a little about yourself, talk about, you know, you're at the company or the organization you work with. And here are a few things that, you know, we'll probably talk about if you're okay with it. And if there's anything else you want to chat about, let me know. I also say, hey, you want to generally play the game, the stats game.

And people say, well, what the heck? They're very nervous when they say, stats game. Really? What's that? Then I, you know, I lay their fears and everyone, you know, has a great time. But that's the extent of it, right? that's the full extent of it. So, you know, and look, that's a good time to bring in our guest, Lisa, Simon.

Lisa, welcome to Inside Economics. Thanks so much for having me. And Lisa is the chief economist, Revelyo Labs. And Lisa, did I describe the process accurate? Very accurately. And I don't think it was three days before. I think it was yesterday. That's right.

It's usually right of hours before, Mark. Yeah. Damn. I was, you know, you're right. I've gotten a little lax. with regard to that. But it's good to have you aboard. And it's really good timing, right? Because today's Job Friday in Revelyo Labs has done a lot of great work estimating employment and other things in the labor market. But let me stop. Let me turn this back to you. Can you just give us a sense of

you and your path to being chief economist? And a little bit about Revilleio, and I'm sure I'm mispronouncing the name. It's Ravillo Labs. Yeah. Yeah, yeah, yeah. But I was just saying, You know, it really should be Revealio because the Harry Potter audiobooks on the spell, so you say Revelyo. I've told our founder this before, but no, it's Reveloor Labs. Revelyo. Okay, so tell us about yourself in Revelyo. Yeah, so, you know, chief economist is such a misnomer for what I actually do at Revelyo. I'm, you know, head of PR, head of academic sales, and really a microeconomist through and through.

So the fact that I get to comment on the macroeconomy now makes me deeply nervous and uncomfortable. No, but yeah, I have a PhD in economics. I wrote that thesis back in Munich and Germany, which is where I'm originally from, on labor and education. So microeconomics determinants of individual labor market outcomes. I had to look that out this morning, what the title. love my thesis, always. And then I got to spend two years at Stanford working with Susan Athe on problems around machine learning for causal inference and applying that to questions on the labor market.

Cool. Yeah. So that was, you know, great learning experience. And then I was fully prepared. Machine learning, just, I mean, that's kind of, that's AI, right? right right before everybody was calling it yeah right this is like you know 2018 2019 yeah exactly

and then I was fully prepared to take a job at the OECD and go back to Europe but my husband got a gig at BU in Boston which is where we're at now and I had to search for a new job and I came across a small startup that was looking for an economist a labor economist working with very similar data that I had previously worked with, spoke to the founder and, yeah, the rest of his history.

I joined Revelliol Labs four years ago when the company was really like 15 people or so, and now we're over 70, and yeah, I... Oh, congratulations on the success. That's fantastic. Yeah, I know. It's been a fun ride. And Ravelli, tell us about what you do there. What is the company all about? Yeah, we're a workforce intelligence company. The goal is really to understand employment at any company.

So take any company of, I think we track something like 30 million companies globally, and we want to know what's their head count, how does it break down, what's their ratio of software engineers to marketing, gender, ethnicity, where are they located and we track that through scraping and standardizing a ton of public employment data.

So the biggest source are professional online profiles. We also scripe job postings, sentiment data, salary data. And because we're so many economists at the company, I think we do a better job than most companies to really make sure that things are

representative of local labor markets and macro markets as well now more recently. And you've come really, you and your company have really come into the fore recently because of all the issues regarding to government data, the cuts in funding and jobs at the BLS, and Bureau of Labor Statistics and other government agencies. And so there's been increasing questions about the reliability. And as we can see, with this government shutdown and we're not getting numbers, that just makes it more, your data more valuable in terms of trying to understand what's going on.

Before we kind of dive into your data and what it's saying, because obviously one of what it's saying, because now your data, along with a couple of other data sources, that's the data, right? That's the data that we have. What is your sense of what's going on with the government statistical agencies? I mean, I got a question this morning, did an interview, and they were asked, Should we trust the data, the government data? If you had, I'm asking you that question, should we trust the government data?

Yeah, I think, you know, it's obviously right now great for our company that there's not a jobs report because we're sort of the ones looking like saving the day, but I actually don't want to live in a world where we don't have government statistics. That's very scary. And I think, you know, the BLS hat has. it's issues. I think they've been severely underfunded for, you know, several years. And of course, latest revisions were pretty big and significant and, you know, so remarkable because in the wrong direction, which we know BLS revisions always tend to be pro-cyclical. So they always get

worse when things are bad. But revisions, as we all know, are part of life working with numbers. and I personally haven't had reasons to believe that the numbers are completely incredible. I think people put way too much stock in the one particular Jobs Friday number. I mean, I get the same butterflies in my stomach every Jobs Friday looking at what it is, but that number probably gets way too much weight in terms of attention and the trends are what matters way more. but I haven't had reasons to think that the data is completely incredible. Having said that, of course, there's like, because of the underfunding that's been going on for

years, the BLS just hasn't had the capacity to innovate on methodologies at all, really, in sort of the past decade or so. I think in an ideal world where that agency is better funded, they have the capacity to maybe partner with a company like ourselves, like ADP, anything that sort of provides more real-time data that isn't survey-based and, you know, survey based on a fairly small survey with, you know, terrible response rates and so forth,

all the known problems of survey-based. research. So that's, you know, that would be the ideal world, I think, where both can happen in partnerships with the private government. But I think we need government data to get really the foundational statistics of the full distribution of the labor force and what that looks like. Because our metrics also rely to some degree on government statistics for rewading and making the the data representative of the workforce. Having said that I just got a question actually as to how long we could be doing what we're doing without the government.

The truth is actually for a fair while. So, you know, it's not like you need to reweight the data constantly, right? With the news data. So that's probably a once a year sort of process. And even in the absence, we would probably be okay to use things like company reported like 10K head counts to reweight, probably with a lot more work, but it wouldn't be completely impossible. Okay.

Let's come back to that. And what you're alluding to is that you collect all this data, but it's not entirely representative of kind of the distribution of jobs across the economy. And you, as you said, reweight your data to be more consistent with the weighting that's evident in the broader economy. You need that government data to do that. You don't need to do that every month. You're saying, if I do that once a year, I'm good.

You know, that feel pretty complex. Correct. Yeah, just calculating sampling rates, yes. Yeah, got it. Before we go into that, let me turn to Dante. Let me ask you the same question. Should we trust the data, Dante? Would you answer the same way as Lisa answered the question?

That's the way I answered the question. I would, yeah. I don't have a reason. It's not to lead you in any way, but go ahead. No, I don't have a reason not to trust it, right? I mean, I think there are obviously issues with accuracy. There have always been issues with accuracy, and they've perhaps gotten a little bit worse recently, but I think there's still not a reason not to trust it.

It's still sort of the gold standard of labor market data that we have. The only thing I'd say, though, is it's not a question of trusting whether the data is being properly calculated and estimated. this really question of the quality of the data eroding to a point where is it real, how much noise is there and how much information is there? So, for example, CPI, the Consumer Price Index, the Bureau of Labor Six puts that together at the start of the year. And I'm speaking from memory, so I don't have these numbers exactly. But 10% of the goods and services that were included in the CPI were imputed, those prices.

They weren't actually someone going out and picking up the can of peas and see what the price is. they imputed from other data. That's up to 35 percent, I think, in the most recent survey. So when you ask, do you trust that data? It's not that I trust that they, someone put a finger on the scale and change the methodology for how it's calculated. But, you know, I, it is question, there is a greater question as to the quality and the trustworthiness of the information just because of the fact that they now have to impute because they don't have resources to go out and actually collect the data. Is that, is that bare, Dante? Yeah, that's fair. And I think, obviously, that was a known problem. And pre-shutdown, they were, I think, trying to address that problem, at least in part right there. There had been some funding to hire some additional part-time data collectors specifically for the CPI, I think, to try to address that. Obviously, now with the shutdown, you don't know what that looks like afterwards. But yeah, I think, you know, they were aware of it. Everyone was aware of it that you don't want to continue on that way where, you know, a third of the prices that you're using to calculate the CPR imputed, right? But you need money to be able to sort of fix that.

problem. We're saying that here? No, I think it's just the survey response rates and just the lack of funding for the past 10 years that have increased the, you know, noise in the data. But I don't, I don't have any reason to distrust it from a personnel perspective at any of these statistical agencies. Chris, anything? No, I agree, certainly. I agree. Yeah. I'm still having trouble with Chris's tie. I don't know about you guys. Maybe we could ask him to take it along or something. He looks like a, you know, he's in D.C. and he looks like a politician, doesn't he?

Yeah. It looks like he's running for office or something. It's beautiful wins or not. You're right. Look at that. That's amazing. I haven't done that in about, well, before the pandemic. Back when you were ties.

I'm bringing it back. It's coming back, Mark. All right. Hey, Lisa, let's get back to your data. So, can you, we're a very nerdy bunch here. So don't worry about nerding us now. Yeah, and the audience is, I always warn people, you should, you know, if you want to listen to the podcast, you've got to be a little on the nerdy side. So we're really into the data. Can you just provide more granularity with regard to exactly how you do this?

Because every month you're going out scraping, you're bringing data back in. Now you're coming up with estimates of, you know, job creation during the month. So just give us a sense of how that's done. Yeah, absolutely. Happy to. So the main underlying data is professional. online profile data. So we essentially scrape the universe of public professional online profile data. The majority of those come from LinkedIn. And so that's that's sort of the source of the information. Can I ask on that one, can anyone go to LinkedIn and scrape it? Or do you have some kind of...

I mean, it's not, it's not easy, but it's because it's, you know, when in an unblocked state from, from LinkedIn, so you have to be incognito. And Google yourself what you're able to see about yourself, and that's basically what we're able to see. So it's public information. The beauty of that is that a lot of Americans have LinkedIn profiles. So I think we have something like 120 million profiles for U.S. workers. so that's, you know, very close to the full workforce.

We do a little bit of cleaning and throwing out of sort of low information people and, you know, part-time worker. Well, not part-time, but like, you know, interns and, of course, non-farm and all the, you know, everything that shouldn't be in there. That also wouldn't be captured by the establishment survey. So after all the cleaning, we're left with something like 105 million, profiles, which is still pretty sizable and what we consider highest signal profiles. And then the first step is, of course, to reweight. Well, I mean, a lot of cleaning and obviously standardizing and, you know, we need to company map and know what industry people are in and occupations and so forth.

That's sort of our bread and butter of how we usually get company headcounts and all of that. But for our macro statistics, we're actually reweighting the data. more aggressively, basically, than what we usually do for company headcounts. Company headcounts usually our weights are optimized to get company headcounts right, which is different from a macro headcount. Let me give you the example of a janitor at a small company. You know, janitors are probably severely underrepresented in our data, maybe let's say by a factor of five or so,

so we're only observing 20% of janitors. if we multiplied every janitor that we do observe in the data by five at like small companies you'd end up with you know outblown company head count sizes but at the macro level of course that's exactly what you want to do and so that's how we get the representativeness so we use the oEWS from the BLS to reweigh our data at both the NICs and SOC codes code level to to be representative of the US economy So that's the first major adjustment that we have to do. Because, of course, as we know, LinkedIn data isn't fully representative of the full economy. Are there some occupations industries where you just don't have any representation or not? Well, the beauty is that nowhere, it's not like we don't have any representation in any particular industry, especially not at like the two, three digit next level that we're, you know, doing analysis on. We always have some data somewhere.

It's just that some, you know, error bars will basically be bigger because the upweighing needs to be a little bit bigger. But that's, you know, with the sheer volume of profiles that we get, we really do have some representation in all industries. But of course, the obvious sectors like information, professional and business services are, are better representative than manufacturing construction very clearly, right? Economists are probably overrepresented. Economists. A lot of economists. Yeah.

Yeah. The other issue with professional online profile data is reporting lag. So people don't tend to update their professional online profiles in real time, which is a real challenge for trying to get. you know, timely work for statistics. So we really need to get the inflows and outflows usually out of companies, but in this case, out of the economy right this month.

And the median reporting lack and the data is something like three months. So that's really the one model that does a lot of heavy lifting in our prediction is to get that right. and that's a Bayesian structural time series model that basically learns. So the good thing is that we've been doing this for seven years now and we've been getting updates to LinkedIn profiles for that long. So we know on average how long it takes people to update their profile,

what basically the cumulative distribution function is until all the updates are reported. And that's something that we use here to basically correct for the fact that people, you know, if you lose your job tomorrow, you're not going on LinkedIn tomorrow to say your job has ended, but you will probably wait. And that's also why we'll always have revisions in our data, because quite frankly, we probably won't have like the full realization of what happened in the economy until like, you know, two years from an hour or so. Like, of course, these changes are small, but people will always go

back and actually say, hey, well, actually, I did that thing at, you know, wherever it was in, in 2023. Let me put that on my CV. So it's actually a bit of a forecast, right? Because you're saying, hey, I got this link, I'm scraped. There was a change in this LinkedIn profile. And I'm going to, because of the lags, I'm using a model to forecast when that actually shows up in the data.

Yeah, we call it now cast, but sure, yeah. it's basically a forecast also because quite often we have to stop data collection a little bit before the end of the month depending on how soon jobs Friday comes around so we're always publishing the day before jobs Friday so yeah it's it's definitely a nowcast people always ask us whether we do forecasts of the economy I'm like well I find it hard enough to get today right so never mind about tomorrow that's our job so we get Made the big bucks. Yeah.

So, okay. Dante, I know you're down into the DNA of this as well. We've been looking at the data. We've got Lisa here. Anything you want to bring up? Any questions you might have for her regarding to the methodology being used? Yeah, I would say, I mean,

you obviously highlighted some of the challenges that you have in, taking the data and producing a sort of monthly jobs number, right? That maybe isn't what the data is sort of best suited for. And I know you publish lots of data beyond just a jobs number, right? You know, you started publishing a full set of sort of labor data. So among that sort of national level data that you're publishing, what sort of what do you feel the best about? What do you think sort of the data set is best suited to tell us? And sort of are there pieces of it that you feel sort of less certain about?

Listen, I mean, is it well suited to do this? I think it is well suited in the sense that there is some real signal. that we're getting in every month, how many people started new jobs and quit jobs. Of course, you know, there's a ton of imputation. But, you know, I think it's as well suited as, you know, a survey of, you know, a third of the population. So I do have confidence in our employment number. So this month, the September number came in at 60. thousand jobs had it. Oh, you gave it away. Sorry, sorry. I was going to tease it a little bit more,

but okay. I'm so sorry. Forget I said that, but I think, is it like, do I absolutely believe at 60,000 jobs? I don't know. You know, the ADP report obviously said negative 32,000, to me that all sounds like zero anyway, to be, to be honest. But more importantly, if you look at our trend over the past six months or so, I think that's really where you get real information. And it's a very clear direction in which the economy is headed when you look at that. If you want a little bit more of a forward-looking view, we also capture job postings. Very classically, it tends to be more of a forward-looking view. There's a little bit of, you know, with job postings, you always have to be careful that they're real job postings.

we've been looking at a lot of, you know, go to job postings. But given that they're down, and I think they've been down for like six consecutive, even 12 consecutive months now, you know, that trend is also very clear. We also do salaries from new job postings,

which gives you this nice, like, basically speed of wage inflation. So what are employers willing to pay for new hires, right? And especially if that growth rate dips below, like what, what workers are earning. I think that's always a very interesting tell-tale sign. And then we've actually also added tracking the layoff notices from the Warren Act this month to our RPLS numbers.

And that's up for the first time in a while as well. So slight uptick in layoffs, which we haven't seen. Interesting. Yeah. So, you know, I think you always want to like triangulate all your statistics anyway, right? the same way that we look at both unemployment and the employment number, as well as jolts and claims and all of it. You want to do the same with us, but I have, yeah, I have confidence that, you know, the signals there.

Yeah, so what I did is I immediately took your number. As you said, 60,000, your estimate for the month of September. And I took the ADP, which is another longstanding source of private sector data minus, what was it, minus 32? I just took, I took the, you know, the average 15 and that feels like if I were going to put the number, that would be 15. That feels like what I would do. Very good. Yeah, we also did this something similar where we actually just ran a very simple regression of ADP's series, our series, regressed on BLS and predicted out what the BLS would have said today,

and that number was 37 for us. 47. Yeah, interesting. Is there, well, two questions before we actually, because I want to go back to the numbers and get a deeper sense of what they were saying. But two quick questions. One, on ADP, what do you think about ADP's data? And two, is there any other private sector

data out there that you're looking at that might be helpful in understanding what's going on the labor market, similar to your own and in ADP's data? Yeah, you know, I mean, ADP has been doing, as you very well know, what they're doing for a long time. And I think, you know, they're, they have near-perfect data in a small set of industries. So, you know, I think their waiting is probably even more important than what we do. Yeah, so, you know, I think they do a great job, and I have like the, like, now more than ever the utmost respect of, you know, doing what they're doing.

You know, I noticed that they did just, I think, recompute their weights where I think their number would have actually been like zero this month, but they had like a slight recomputation, I think, of the weights that pulled their number down further. Fair enough. And in terms of other... Yeah, any others out there you're looking at?

Yeah, you know, I always really like the glass store, like employee confidence in Nex that Daniel Zau brings out. I think that's just like a nice, yeah, just like pulse on how workers are are feeling. We also collect that data, but I think they do that really, you know, prepare that data really nicely to see how

employees are feeling. And I think that's also been down for like six months in a row now. If it's not longer, like an old time low. Yeah, I'll take to look. I hadn't heard about that. I'll say. Yeah, yeah. It's basically the, so Glaster has a

business outlook rating that employees can leave for their employers. And yeah, it's basically the business outlook metric at the macro level. Got it. Well, let's go to the data. The 60K, the number for September, I know you break that down in lots of different ways, industry, occupation, region. Can you just give us a sense of what's driving that 60K?

Why, what's behind it? Yeah. You know, usual suspect industries are really driving a lot of the strengths. So education and health, I think are really like, I mean, really the only like meaningful industry that is adding jobs. Which, you know, I'm always torn between people saying that, you know, of course that's problematic in the sense that it's a very, like, those are very specific industries that you can't just sort of go into if you don't have the right education and credentials. but at the same time, those industries do just employ huge shares of the US workforce. So, you know, if they're hiring, that's good.

And if they're positively contributing to job growth, even better. Some of the other sectors, retail trade, is actually doing okay. I noticed that. I was surprised by that, right? Yeah, month over month. But actually, when you look year over year, it's down pretty significantly. So I think it's like short-term okay, structurally depressed, I would put it.

And then construction is up a little bit, but I think, you know, that's sort of the last year right before winter hits and construction doesn't, doesn't hire anymore. So that that's probably going away as well. Although your date is seasonally adjusted, though, right? Of course. Yeah, it's seasonally adjusted. Yeah, yeah. No, of course, of course, but more than last year.

And what about government? I think that it was not, was it down? Government's down. Yeah, yeah, government is down. The one that's down the most is leisure and hospitality. I think that's another really interesting index for consumer confidence because a lot of like leisure and hospitality is obviously restaurants and how people are spending their money on like on fun. And if people aren't spending money on fun, that's that's always a bad sign. And I'm, you know, I think it can be a forward-looking indicator. And then the other, yeah, it's like all the other big professional services industries that are also down. So tech, finance, and yeah, professional business services all down in employment. So, I mean, if you hear that entry level, like grads can't find entry level jobs, like, yeah, all the usual industries that you want to go into aren't hiring. So no surprises.

So the two sectors that kind of drove the train or health. I guess education health care and retail trade. And the rest of these sectors were up, maybe up a little bit. Basically zero, I think. Basically zero. Yeah. Okay, so very close to the bill. What about a cross-occupation?

I didn't have a chance to look at that. Anything stand out in terms of occupation? Yeah, the one thing that struck me was that legal was down a lot, which I, you know, was part of me was thinking whether the reckoning, the AI reckoning of the lawyers is here. yet. Yeah, that's the one that struck with the stable. In regionally, anything stand out for you? You know, as much as we had sort of a lot of strength in the Northeast in our August report,

that seems to have flipped this month. So actually the northeastern states more negative and then sort of, yeah, quite moderate. changes across the country. So nothing really that stood out like super strongly there. Hey Marcy, did you look at the Revelliolio Labs data? Did you take a point?

No, I think that Lisa covered the big ones that I noticed. But Lisa, do you track, you said you tracked salaries that are advertised in job openings. Is there like a number? Do you have like a sort of a salary growth number that we're at year over year?

Yeah, month over month. Oh, year over year I don't have. Oh, I actually did I note that down. That's okay. I'm just, you know, I'm just wondering how that compares to like the ECI or average hourly earnings in BLS if it shows any different trend. Yeah, yeah, no, it's, so month over month, it's down 0.3%. And I believe that's pretty much in line with average hourly earnings. So, you know, not great, but not horrible. And it was worse earlier this year where actually new salaries were growing a lot slower than existing salaries.

I think the year-over-year growth is 2%. That's what I got it down just before. But yeah, I actually don't, I'm not entirely sure how that compares to Yeah, I mean, that's quite a bit weaker. But I mean, you could also argue these are new salaries being offered, so it's a little different from what

the concepts that BLS is tracking with those other. No, absolutely. Yeah, this is for sure, like the, we think of it as the flow of new salaries. We do reweight all the job postings every month to actually represent the entire workforce. So in that sense, we're taking out the variation in actual hiring every month. So we're pretending like every industry and occupation was hiring in measures. but then like given that what would they be prepared to pay? Hey Dante, I know you're down into the DNA of this data too. Anything that stood out for you that hasn't been brought up already?

No, I think that covered the big things that I was looking at, yeah. Why don't we do this, guys? Why don't we play the stats game? I browbeat Lisa into playing. She's got a stat. And then we'll come back and talk about what's going on in the labor market. We've got the data. Now let's go back and talk.

about what's going on the labor market and maybe a little bit about the outlook, you know, where we're headed, you know, where we think we're headed. So let's play the game. The stats game, we each put forward a stat. The rest of us try to figure that out through clues, deductive reasoning and questions. The best stats, one that's not so easy that we get it right away, one that's not so hard we never get it. And if it's apropos to the topic at hand, jobs, that would be great. Okay, so Marissa, you're up. What's your stat? The stat is four. Four percent? Just four.

The number four. It's like Sesame Street. Yeah, the number four. Labor market related? Yeah. Is it a ratio? The number four. Is it an index?

Is it an index value? Sorry, what? You all spoke at the same day. It's not a great rate. Sorry. A percent. No. Can you give us a hint?

Not time? It's the number of times something has happened. Oh. Government shutdowns in the past 10 years. No, that's about. No. We've had like 20 in the line.

Lisa is on the right track. Is labor market relation? It's related to government shutdown? The number of times the BLS report didn't appear. Whoa. Very good. Lisa, man, that's impressive. Thanks.

Did Dante give that to you? I had no clue, so I don't know. Was that ever? Is that what time period is that? So that's as far back as I can research it, which is the early 90s. So four times, you know, I went back through all the releases. I chat GPTed it, tried to triangulate it through other sources. But it seems like there's been four times since the early 90s where the jobs report,

was delayed due to a government shutdown. So twice in late 95 into early 96, it was delayed. And then once in 2013 and then, of course, today. And it, it, in the early, in the late 90s, it actually, you know, it affected the collection of the previous month, too. The shutdown lasted so long that not only was that month's job jobs report not released, but that it ate into, the collection period for the CPS and the CES. So they had to, in one case, they extended the period by a day or two. So they extended that reference week to try to get more responses. And, you know, the problem that they run into is that people forget, right? When you're asking, typically they're asking, what did you do last week? You know, how many hours did you work?

And tell me about all the people in your household that are working. As that gets pushed forward, people tend to forget exactly some of these details. So they actually track the response rates and the accuracy rates and the revisions. And the further you get from that reference week, the worse the data gets because people just have a recall problem. This could be an issue, right? Because the BLS, the Bureau of Labor Statistics, does its survey, the payroll household survey in the week that includes the 12th of the month. That's a Sunday. not this Sunday, but the following Sunday.

So it's conceivable, the shutdown, if it extends on for more than a couple weeks, it's going to disrupt that. And you're saying they'll do the survey after the fact, so towards the end of October, and they'll ask the respondents, the companies, to tell them what employment was as of the 12th, during the week that included 12th. They'll go back and say, tell me what that was. I think it's less of a issue for the payroll survey because that's all automated records, all automated payroll records, right? It's more of an issue on the household survey where you're calling individuals and asking them to tell you about their activity during a certain week. That's where it gets more dodgy. And what happens if, and this happened back in 2018-19, that was a five-week shutdown the longest in history. What happened there? BLS wasn't shut down then.

So, right, if it's already been appropriated, BLS won't shut down. But in these four cases, there were no appropriations. So they just stopped working, right? Just like now. Oh, my goodness. But it's conceivable, if we have a five-week shutdown now because BLS is closed, there may not be a service. We could be missing a month.

Right. Yes. So they would have to decide what to do. Do they try to collect any data and just hope? that people remember what they were doing, right? Or did they just skip an entire month of, never had that situation? No, my goodness.

No. Marissa, you and I talked about that too, right? For some data, like for the CPI, right, you can't really go back, right? They're collecting prices in real time, so you can't go back and say, what was prices a month ago, right? So you almost have to skip something like that where you can't ask somebody what they were doing a month ago, right?

You can't go record prices from a month ago. So, yeah, if it lasts long enough, I think you'd definitely end up with some holes in some of the data. Boy. Yeah. So the Federal Reserve is going to meet at the end of this month and could have no data. It's possible. To make a decision.

They'll rely on Revelo and ADP and all these other data sources, right? Yeah. Well, that's inflation, but yeah, they won't know inflation. Maybe at least we can work on that somehow. Lisa, you want to go next? Sure. Myostat is $139,000. 691.

It's very precise. 139,000. That's called it 140,000. Job-related. Job-related. It's a number that relates to number of things happening in 2024. So it's like the sum of things in 2024. Oh, boy.

That just complicates things enormously in my life. Let me give another hint. It's related to your number, like two episodes ago, Dante. Oh, God. What was that? Oh, gosh. Dante can't remember what happened yesterday. I was out of the facts.

I have no idea. Okay. Think two weeks ago. Think break even right. Oh. And then think what influence is that the most? Is that total labor force growth so far this year? No, it's not, but it's, um...

Well, was 140,000, was that average monthly job growth last year in 2024? No. It is not. It's a, it pertains to a group of people. Immigrant? Immigrants? Oh, is, so 140K is the, is, it's a change in, I guess. Yeah, it's something new.

Employment, if foreign born employment, over the last year? Almost. Okay, go ahead. Put us out of our misery. It's the number of new H-1B visas granted in 2024. Whoa, that sounds low.

I'm being very close to my heart. Are you on H-1B? Not anymore. Luckily, got my green card like two months ago. Yeah, congratulations. Yeah. So there were 140,000 H-1Bs last year. Is that typical, Lisa?

You know? Pretty typical. It's a little higher than, It was in 2023, but that number's been basically stable. So it fluctuates a little bit. Basically, there's a cap, right? It can't go higher than $85,000 for private businesses, and then all the rest just gets filled up by the non-cap,

which are basically universities and non-profits. And then obviously that'll break down completely with the new fees. There's $100,000 fee, I guess, on new applications. for H1B. And you're saying that's a pretty steep fee. Yeah, I mean, only, sure, you know, some tech companies might want to pay that to their senior most hires, but like the entry level H-1B, I think is probably a thing of the past now.

Right, right, right. Oh, that's a good one. Very good. I'm sorry we kind of... Little obscure. I apologize. We fell down on the job, though. You know, Chris is kind of sylon over there.

He's not really pretending. He doesn't want to say anything that might derail his election campaign. I mean, when I talk about immigration. That's what it is. No comment. No comment. No comment. Yeah, exactly.

All right, let's do one more. Chris, do you have a good one? Got to go. What Dante has, I'm sure has the next one. Let's go with you. Let's go with Chris. Let's go. Because Christy has been kind of quiet over there.

All right. All right. 946,426. Oh, these specific numbers. Is this related to the government shutdown? No. Is it jobs related? Yes.

900,000. Is it? Yeah. That's a lot of people. Oh, is it people? It is. It is? Okay.

Is it related? Is it from an employee? Is it from a survey? Is it a survey-based number? Yes. Payroll survey? Well, it No.

No. No, it's not a government source. Oh, is it a Challenger? It is Challenger grade in Christmas. So this is the year to date announced job cuts, correct? But it's right. How does that compare?

That's up 55% from last year. Really? So it's big. It's, yeah, it's the highest, I think, since, well, certainly since the pandemic. We had 2 million or so. And then, yeah, it's a big number.

Those are announced layoffs, right? These are announced layoffs, correct. In global, right? Because they could be in global. Yeah. Lisa, do you pay much attention to the Challenger Gray and Christmas data at all? No, I don't. I was asked by a journalist about it.

Yes. Yeah. Come again. No, I don't, but we collect the Warren data ourselves. So I imagine part of that goes into what they do. Well, there's a, yeah, in fact, announced. You mentioned the warrant data before. Do you want to, for the listener, describe what that is and what you think it's saying?

Yeah, it's announcements of mass layoffs. So the Warren Act basically requires businesses of a certain size to give 60-day advance notice to their respective states to if they're firing more than. I forget the exact number, but it's more than a particular share of their workforce. And basically giving people, both giving people as well as states the chance to prepare people for job transitions coming up. And that number, that, you know, like layoffs have been fairly low and going down. And actually September we saw the first time both. layoff announcements, so people notified of layoffs, as well as layoffs effective,

which typically is like a 60-day lag take up in September for the first time. Interesting. Okay, well, I think is, Dante, are you okay if we skip yours this week or this month? It's fine. I didn't put as much effort into it as Marissa did, you know, going back through 30 years of release the date. So I'm okay if we skip it. Okay, just tell us what it was. Just I'm curious.

It was the labor market differential from the conference board. It's a good one. Oh, damn. It's the lowest in 2017. Yeah. So it's people are not feeling. Explain it. Go ahead and explain.

That's a good one. Yes. The differential is the, it looks at the share of people that say the jobs are plentiful minus the share of people who say the jobs are hard to get. Right. So if that number is big, if that gap is large, that means people are feeling positive about the labor market.

If that gap is small or negative, that means that people are feeling pretty bad. So if you go back, you know, it was at an all-time high in 2022 of 47. point one, and it's all the way down to 7.8, which is the lowest since 2017. You know, again, it's been lower historically. It's been negative, you know, during recessions, but that's a pretty low number in a time when we're not actively in a recession, perhaps. In my recollection is that's a very good kind of predictor of the change in the unemployment rate. So that affects pretty close. So that would suggest if we got the September data, we would have seen an increase in unemployment.

Who knows, but that's what it would suggest. Yeah. Depends on the break even rate. That's right. There you go. Well, let's turn to that now. And Lisa, let me just open into question. You know, given all the data that you're looking at,

how do you characterize the labor market, the strength of the labor market? How do you think about it? It's like in, I wouldn't know. So it's not in free fall. I don't think we're there, but it's sort of like, yeah, stalling, I suppose is the sputtering maybe is the right objective. Right. Well, one way of thinking about that is, you know, what is the so-called break-even job growth? So what job growth month-to-month would you need to maintain kind of stable unemployment? And also what I call the underlying rate of job growth,

you know, abstracting from the vagaries of the data, the ups. As you were pointing out, any hard-to-read a lot in any given month, but if you look over the past six months or so, you get a better read more information. Do you have a sense of what the underlying rate of job growth is at this point and what the break-even job growth is at this point? Yeah, I really want to dig into what the break-even rate is with, I just don't think we actually have such a great metric for immigration right now. You know, there's obviously estimates. So that's actually something we're digging,

trying to dig in with our data because you can see where people are coming from. So it wouldn't necessarily be sort of foreign-born workers coming into the country, but foreign-educated workers. But it gives you a pretty precise. That's why I was bringing that H-1B number. And, you know, the, yeah, immigration has been,

has been declining at least legal immigration. And that would bring down that break-even rates more. So, yeah, who knows what the actual number of jobs is that we're needing to add. But that's definitely something that we want to spend a bit more time on. What about underlying job growth? What do you think underlying job growth is? I mean, again, abstracting from the vagaries in the data. Zero?

Is it zero? Are we close to zero? Yeah, probably. Yeah, I would, yeah. Right. Okay. And because unemployment has been ticking, it's still low, 4.3% and ticking higher.

It's probably not zero, to be honest. Yeah, it's, I mean, is it zero? Yeah, I honestly don't have a good answer to this. Yeah. Yeah. Yeah. Okay. Of course, what's your break-even estimate in your,

estimate of underlying job growth. So probably I'll give you a range, 25 to 50. K would be the break-even. Break-even. Oh, that's really good. And maybe on the lower end of that. Yeah, I'm pessimistic, I guess. But I think that underlying job growth is close to zero.

Close to zero. Marcelle? I think break-even is probably 50-ish-ish. Right. And heavy on the is. And then I think underlying is, I think it is close to zero. I think it would be anywhere from zero to 20K, which is to me essentially zero if it's 20K. Yeah.

Social and zero. Yeah. Dante? Yeah, I mean, I think break-even's, you know, 50 is probably the best guess I have right now for break-even. I don't think I'm quite as pessimistic on underlying job growth. I think it is still positive. I don't think it's strong. but, you know, 20, 25, 30,000 maybe somewhere in that neighborhood.

Yeah. I think break-even is, I'm going to say a range, 50 to 75K, and I think underlying is 0 to 25, you know, something along with those lines. Because the unemployment rate is ticking higher, so the break-even has got to be higher than the underlying. So not by a lot because it's not ticking that higher now. Hey, Lisa, in that context, at break-even is, let's say it's around 50, 75K, you know, how upset should one get if we start getting negative job numbers?

I mean, obviously, it's suggestive of an easing labor market, but the labor market's still pretty good, 4.3% of employment rate. Should we, is there any reason fundamental to be more nervous or worried that we're getting a negative side in front of the number? Or does that really matter? I mean, you know, it accumulates, of course, right? It's like month over month. And yeah, you know, I think it's also highly segmented in where obviously we talked about like, you know, the narrow base of the strength. So I think especially with younger people having such a hard time getting into the labor market, just from like a company perspective, companies will get into. real trouble, you know, succession planning at some point.

You know, here I'm outing myself again as not a true macroeconomist because I'm bringing it down to like a unit of analysis that I'm more familiar with. But that, no, no. Yeah, right? Yeah, lots micro guys can learn from you micro people. So go ahead. Feel free. Yeah, no.

So I think we'll get into real trouble as like the, the, younger share of the workforce isn't finding jobs and there's just nobody to take up like more middle management positions and the this is obviously like a medium term perspective not tomorrow just so many other like reasons to be concerned about about the economy with obviously tariffs and immigration and everything else right right um well you know if someone asked me about the job market the state of the job market. I'd say, you guys said sputtering. What was the other word you used?

Stalling, I guess. Stalling. Yeah, I think those are fair. I'd say it's punk, you know, just not going anywhere fast. Yeah, yeah. And this is kind of a tension between businesses not hiring. They're just not hiring. Hiring rates are about as low as they get.

They're like recession, where you see them in recessions. But they're not laying off, right? Exactly. As you plan out there, they've ticked up a little bit, but just that's what, you know, who knows? It's still very low. So that means the job market's just not going anywhere. You've got the push in the pool, no hiring, no firing, and you're kind of just stuck there. So the question in my mind is, what's behind that, fundamentally, do you think, behind the no hiring and behind the no layoffs?

Because that, you know, obviously is critical to understanding what's dead ahead, what's going to happen. Which of these push and pools are going to win the day here? Do you have any sense of that or a view on that? I mean, it's just such a dear in the headlight moment. And I've been saying that particular sentence for longer than I care to admit now. But I think now it's really true where people just don't know what's going on with. Like the uncertainty is just through the roof with tariffs, with, you know, again, immigration. And I think it's like, let's just wait it out a little bit longer before we start hiring again.

sense is just, you know, really, really strong. Of course, we have, you know, factors going in that might push that in the other direction with interest rates coming down and maybe that might entice, you know, a little bit more hiring eventually. So I don't know whether it'll get worse first. Like, I think it might get worse first before it gets better, is my sense, yeah. So does that explain the deer in the headlights phenomenon, both the no hiring and the no firing. Like I'm like, I'm so confused. I don't know what the heck's going on.

I don't know where we're headed. You know, certainty is just so omnipresent. I'm just, I'm going to sit on my hands. I'm not doing nothing. I'm not going to hire. I'm not going to fire.

I'm just going to see how this plays out. Yeah, exactly. And employees are also being just extremely careful where, you know, job switching is just just down so much, right? And, you know, everybody's very glad to have the job that they're, they're having. And, yeah, it's just,

just not much happening. I was at a talent intelligence conference last week in Amsterdam, and I'm always reminded that the Europeans know that problem pretty well, not from the fact that no hiring and firing is going on, but the fact that tenure times are just so much longer with labor force, like just labor protection. So I'm like, well, companies probably need to look to Europe, how to like talent management.

now with people just sticking around for so long, like what do you do with these people that you might not actually need for that particular role, but you can reuse them in other places and things like that. Very interesting. Hey, Dante, did you like the way I characterized it, this tension between no hiring, no firing, and kind of the, that's resulted in a labor market

that's kind of stuck in the mud punk? What do you think is behind? Do you agree with that kind of framing? And, you know, what do you think is behind? it. I mean, I think it's this, people are uncertain, right, which leads to the no hiring part. You know, the no firing feels a little more tenuous to me. I feel like we have for a long time been using the excuse that, you know, people are hoarding labor. They're still sort of scarred from the aftermath of the pandemic and how hard it was to find workers. And, you know, so they're

just sort of holding on because they don't want to go through that pain down the road. But at some point, that argument feels like it has to give up, right? If you're really feeling that pessimistic about the economy and where it's headed, you know, at some point, you're probably going to make that move to to lay off at a higher rate. So it just feels to me like the hiring piece makes a lot more sense to me right now. The fact that we still have seen layoff stay so low is a little more confusing to me just because that argument seems like it's got to give up. So what is the explanation? I don't know. That's the problem. Yeah. Maybe it is labor horror. Maybe people are. That's what they're doing. I don't know. But I don't have another good explanation for it. So I don't know where

else to, how else to describe it? You think it's better job matching post-grade resignation? Yeah, I mean, that could be part of it, right? That you had lots of these good fits, and so you're even more reluctant to give those people up, right? You really don't want to lay people off because you feel like you have the right people and that they're going to be valuable to you once things turn around, but it still feels like the bottom line will get in the way of that at some point, right? So I guess in our forecast, you know, if you look at our forecast, we're saying, if you think about our job forecast in the context of this tension between hiring and firing, we're actually saying that the hiring, both will pick up, but the hiring is going to pick up more than the firing. And we're going to start to get not a lot of job growth, but at least some job growth. That, you know, we can't stay.

If break even is actually 50, 75K, we can't stay here at zero to 25K for very, you know, forever. You know, we can for a while, but not forever. So that's kind of the implicit dynamic that underpins our forecast. Marissa, do you have a view on this about hiring and firing what's behind it, the lack of hiring and firing? Yeah, I think, I agree. Certainly the lack of hiring is this uncertainty. I think right now business profit margins are probably getting squeezed because businesses, we know a lot of businesses are trying to absorb the cost of the cost of the,

tariffs, right? They want to try not to pass that on to consumers for as long as they can, but eventually margins will come in and they're going to have to either pass that on to consumers or they're going to have to start laying people off if they want to continue to absorb some of that. And I do agree with Lisa. I do think that the pandemic and the advent of remote work has made job matching a lot better. So I think companies are probably in a position. where they feel like they have, by and large, the right people. And we know that firing people is a very costly process, especially when you have to then turn around and hire people back, right? Just the process of recruiting and onboarding and training and all the legal ramifications that may go into that.

Businesses have been flush, right? Like, they've been doing really, really well. So it would, it would stand to reason that they have cash and they have. the ability to prevent layoffs for a long time. But if things get bad enough, that will be the next shoot-a-drop. I think you can only not hire for, you know, a long period of time. You can put job openings out there and just not fill them. But eventually, if things get bad enough, they're going to have to start laying people off. So I just think that could be around the corner or it could be diverted if things kind of turn around. But where we are right now, it's hard to see, you know, the trajectory is not a good trajectory right now.

Yeah. Chris have you on this? Yeah, I agree with the uncertainty factor, certainly leading to that frozen type of environment, but I'll throw in another factor here, which is artificial intelligence, right? The technological changes that are going on, certainly, I think you can explain why There's some reluctance on the hiring front. Let's see what this technology looks like or how it pans out, especially in this current environment where there is so much uncertainty. And then on the firing, I think, to build out or actually get that artificial intelligence embedded,

you need to use your existing staff. You need that experience, right? Those people are valuable to firms in order to get that technology embedded. And then the wildcard is, well, what happens later on, right, in terms of, you know, If indeed that's a very successful experiment, could we see more layoffs further down the line of some of those more experienced, costlier workers being replaced by AI? Or not? Or are we opening up new products and services with this new technology? So I think that also kind of enters the mix here in terms of why you might see that stall speed on the surface. But underneath, there's probably a lot more dynamics going on within these firms in terms of how they're repositioning to use AI.

Yeah, makes sense. I'll throw out one other potential factor behind the lack of firing. Could it be the aging out of the baby boom generation? I mean, the fact is a lot of these companies, most companies, they're like a lot of people like me, you know, a lot of gray hair. A lot of people are retiring each and every day because that's a big cohort. And you don't need to fire anybody. You just wait until people leave. Right. The attrition rates are also low, right?

Is it low? Is attrition rates low? I haven't seen. Yeah. So I think people aren't retiring. That's a point. Okay. That doesn't explain it. Okay. Darnan, I thought I could contribute to what's going on. But Dante, I was teasing you because I'm confused too about how this dynamic is going to play out. But I do agree with what Marissa said that, you know, I think businesses have done everything except layoff, right? They've stopped hiring. They've cut hours. They've cut back on temp. Help. So if there is any weakening in demand, then layoffs are next. And once that happens,

you know, no layoffs are the firewall between no recession, what we have now in no recession. And as a layoffs begin to pick up, then we're going in. That's why it feels like the economy is, it's okay. It's growing. It's not in recession. but it feels very tenuous to me. It feels very tenuous. You want to disagree with that kind of characteristic? It feels very narrow to me. It feels like we're hanging our hat on just very, you know,

specific slices of the economy. And that's what makes me nervous. You know, it's like all the job growth is in health care. And all the stock market gains are coming from AI. And a lot of the business investment that we're seeing is investment in AI. So it just seems like... it's not diverse enough to make me feel comfortable that it's long-lasting or resilient. The spending isn't from the top percent.

Like every segment that you look, right, you can say a lot of this is just coming from this one sliver of whatever it is, people or spenders or businesses. And that's what makes me the most nervous. And that contributes to like a bad, you know, bad mood in a class. the nation. Most people are left out. People feel like they have. Yeah. Yeah.

Right. Right. Well, we'll have to say I was in Dallas this past week, you know, visiting, I had an event at the Fed and the FDIC and we had a dinner with clients. And we do this. I had these dinners around the country with, with folks, with clients. And historically, Dallas was the one place I'd go and I get cheer about.

It's like, it's like boom times. You know, this time I came away depressed. I was cheering them up, which is really bizarre. So I found that just one little anecdote, a little, you know, just an anecdote. But I thought that was a little weird. Usually I go to San Francisco, that's where I get depressed. Then I go to Dallas and they bring me back up. But not this time.

It failed to do that. Anyway, hey, Lisa, it was so good to have you on. I really appreciate it. Thank you for all the work that you're doing and the data that you're producing. It's, you know, incredibly informative and valuable. And we're going to obviously rely on that a lot more going forward. Whether the BLS gets back on the job or not. But, yeah, thanks so much for having me.

It was a pleasure. Yeah. Any last parting words to folks? Any words of wisdom before we call it a podcast? Not that you need to have any. I'm just, I'm just fishing. No, no. No, sorry.

Sorry. Okay. Right, go to. Well, all right. Well, with that, dear listener, I want to thank you for listening in, and we will talk to you next week. Take care now. Bye-bye.