Speaker 1: Hello, odd last listeners, you'll be enjoying an episode of
Speaker 1: one of our fellow podcasts on the Bloomberg Network, Big
Speaker 1: Take DC.
Speaker 2: YEP, hosted by Soliah Mosen. This episode features one of
Speaker 2: our favorite guest economist, Claudia Salm. You might remember that
Speaker 2: we spoke to her back in November about the outlook
Speaker 2: for the US economy and Soleia digs even deeper into
Speaker 2: what's driving things like sentiment consumer surveys versus the hard data.
Speaker 2: Right now, take a listen.
Speaker 3: When the government is trying to get a handle on inflation,
Speaker 3: it's the Federal Reserve that has the biggest lever to pull.
Speaker 3: Think of the FED like a traffic cop instead of
Speaker 3: a whistle and cone. The Central Bank uses interest rates
Speaker 3: to try and contain inflation. When rates go up, money
Speaker 3: becomes expensive and people tend to spend and borrow less.
Speaker 3: That slows the economy down. When rates go down, people
Speaker 3: are more willing to spend since everything from credit card
Speaker 3: fees to mortgage rates are cheaper. Unlike the traffic build
Speaker 3: up on a road which anyone can see. The Fed
Speaker 3: has to get creative in order to manage the economy,
Speaker 3: so it uses data to decide when and how to intervene.
Speaker 3: But last year, when economists everywhere were expecting a full
Speaker 3: blown recession, the FED was raising interest rates.
Speaker 4: Over and over again.
Speaker 3: They needed to rain in inflation, and the man in
Speaker 3: charge chared your own Powell. He kept pointing to one
Speaker 3: category of data that was guiding the Fed's decision, the
Speaker 3: labor market.
Speaker 1: Of labor market, A labor market remains very tight.
Speaker 3: All this talk about the tight labor market made Claudia
Speaker 3: Sam's ears perk up. She's an economist and a Bloomberg
Speaker 3: Opinion contributor. She worked in the Obama White House and
Speaker 3: spent twelve years at the FED. She'd been looking into
Speaker 3: the labor market numbers herself, and the Fed's decisions left
Speaker 3: her scratching her head.
Speaker 5: They are making big decisions about the interest rates, the
Speaker 5: mortgage rates we pay, the credit card interest rates, auto loans.
Speaker 5: So we want them to be data driven, but they
Speaker 5: can only do as good a job as the data.
Speaker 3: They have, and that data they've been focused on, she's
Speaker 3: had some serious questions about it.
Speaker 4: This morning, the government.
Speaker 6: Released new GDP data that shows the US successfully avoided
Speaker 6: a recession, even though almost every economist was predicting one,
Speaker 6: but the data that the Federal Reserve examined as it
Speaker 6: made policy decisions is complicated.
Speaker 3: On today's show, have policymakers trusted data that might have
Speaker 3: been faulty? I talked to Claudia Sam about her findings,
Speaker 3: and I sit down with Tracy Alloway and Joe Wisenthal
Speaker 3: from Bloomberg's Odd Lots podcast. We talk about what's behind
Speaker 3: the numbers and why it's important in an election year.
Speaker 3: From Bloomberg's Washington Bureau. This is the Big Take DC podcast.
Speaker 3: I'm your host Seleiah Mosen. Claudia Sam decided her concerns
Speaker 3: about the Federal Reserves data were worth voicing, so in
Speaker 3: November she wrote an article for Bloomberg Opinion. It had
Speaker 3: an eye catching headline, economists may have been flying blind
Speaker 3: all along.
Speaker 5: So the argument I was making when I said economists
Speaker 5: or flying blind is the awareness that we need to
Speaker 5: have in terms of the measures like how we try
Speaker 5: and measure quote unquote reality, and then in our giving
Speaker 5: policy advice.
Speaker 4: How we measure quote unquote reality.
Speaker 3: I know that sounds dense, but her point is that
Speaker 3: as much as we'd love to think that the FED
Speaker 3: is making its decisions based on hard numbers, you know, objective,
Speaker 3: unbiased data.
Speaker 4: Often it's not.
Speaker 5: Data doesn't doesn't come down from heaven.
Speaker 3: For example, let's look at that tight labor market that
Speaker 3: FED Chair J.
Speaker 4: Powell kept mentioning.
Speaker 3: He said that the labor market was tight, meaning more
Speaker 3: job openings than workers. He cited numbers from the Job
Speaker 3: Openings and Labor Turnover Survey JOLTS for short. Now that
Speaker 3: might sound straightforward, right, measuring the number of open jobs
Speaker 3: not so fast.
Speaker 5: Now, there was a lot of conversation those of us
Speaker 5: who have nothing better to do than study data. What
Speaker 5: a job opening is could be changing over time.
Speaker 3: Because of the pandemic. The way employers list jobs is
Speaker 3: just different than it was before.
Speaker 5: Especially from work from home. You can put up multiple
Speaker 5: ones for different geographies because it doesn't matter, So.
Speaker 3: A company might list the same job in several different cities.
Speaker 3: It doesn't cost them anything. But it does mean that
Speaker 3: the numbers are getting inflated.
Speaker 4: So when economists at the.
Speaker 3: FED were looking at the number of open jobs and
Speaker 3: basing their assumptions off of what was typical, they were
Speaker 3: at risk of ignoring one key factor.
Speaker 5: The world wasn't typical.
Speaker 3: I wanted to understand just what's going on here and
Speaker 3: whether it was an issue beyond this one job survey.
Speaker 3: So I sat down with two of my colleagues.
Speaker 2: I'm Tracy Alloway, I am the co host of the
Speaker 2: auth Lots podcast.
Speaker 1: And I'm Jill Wasenthal, also the co host of the
Speaker 1: aud Lots podcast.
Speaker 3: Joe and Tracy read Sam's article and they agreed with her.
Speaker 3: They do not trust that Jolts data.
Speaker 1: Pre COVID, Jolts was a bottom shelf economic indicator. It
Speaker 1: was the well drinks of you know, it's like some
Speaker 1: nerds like to pour over it because there is information
Speaker 1: on it, but it was not a market mover.
Speaker 3: If Jolts was a bottom shelf well drink to them,
Speaker 3: pre COVID, it was basically a cheap shot of bad tequila.
Speaker 3: Once the pandemic hit.
Speaker 1: You just don't know that the patterns of history related
Speaker 1: to things like job openings, related to things like claims
Speaker 1: quit really mean the same thing in this environment as
Speaker 1: they might have in past cycles.
Speaker 2: If it was a business cycle, it was the weirdest
Speaker 2: business cycle ever. Companies are behaving differently to how they
Speaker 2: used to. There's the idea of labor hoarding. People are
Speaker 2: so scarred from the pandemic period that they just want
Speaker 2: to make sure they're not caught out again with a
Speaker 2: labor shortage. So they're just hiring who they can, or
Speaker 2: they're putting out ads to see who responds. I mean,
Speaker 2: it's pretty easy to place an ad on some digital
Speaker 2: job site nowadays. It doesn't really cost that much, so
Speaker 2: why not try and see who you get?
Speaker 3: So the pandemic through all our old markers of normal
Speaker 3: out the window. That left the Jolt survey and pretty
Speaker 3: and steady ground. But COVID didn't just mess with jolts.
Speaker 3: It also did another thing that influences all sorts of
Speaker 3: important data points that fed economists rely on survey responses.
Speaker 2: We know they have declined in recent years, so I
Speaker 2: think something like the Housing Survey gets like half of
Speaker 2: the people its surveys actually responding nowadays, and that's down
Speaker 2: from two thirds.
Speaker 3: We reached out to the Bureau of Labor Statistics and
Speaker 3: the Census Bureau for comment for this episode, and they
Speaker 3: both acknowledge declining response rates as a critical problem that
Speaker 3: they're trying to address. It's a problem that only got
Speaker 3: worse during the pandemic. All This matters because if your
Speaker 3: survey only captures half of the people you contact, you're.
Speaker 2: Gonna have to question whether or not that fifty percent
Speaker 2: is reflective of the actual American experience. And of course
Speaker 2: the irony is that most advanced economies are collecting more
Speaker 2: data than ever. We're doing more soft surveys than ever,
Speaker 2: but the response rates are trending down and the quality
Speaker 2: of that data is questionable.
Speaker 3: We'll get to why Americans are getting survey shy, what
Speaker 3: the FED is doing to fix it, and what it
Speaker 3: all means with a twenty twenty four election.
Speaker 4: After the break, we're back. Part of what.
Speaker 3: Made Claudia Sam argue that economists may have been flying
Speaker 3: blind is lower response rates to government surveys, and that
Speaker 3: decline is actually a symptom of a much bigger problem.
Speaker 5: We've seen a growing distrust in government, and I can
Speaker 5: understand if you don't trust the government if they show
Speaker 5: up and be like, hey, tell us all about your
Speaker 5: wealth and your dead and how much income you make.
Speaker 5: Much for a lot, these are very sensitive topics.
Speaker 3: Peer research found that two thirds of adults think the
Speaker 3: risk of responding to a survey outweighs the benefits they're
Speaker 3: concerned about things like privacy and not so concerned about
Speaker 3: the consequences of low response rate data.
Speaker 4: Some says they should be, Well, you need.
Speaker 5: To rebuild that relationship and help people understand know what
Speaker 5: you tell us. Like policymakers, this is important and if
Speaker 5: we don't know what's going on in your life, then
Speaker 5: it's almost guaranteed that the policy just can't address the issues.
Speaker 3: We're in an election year, and when Americans list the
Speaker 3: economy as the top issue driving their votes, so flawed
Speaker 3: assumptions about the economy based on sketchy data carry a
Speaker 3: risk as people decide who they want as president, and
Speaker 3: so does data that doesn't fully capture voters lived experiences.
Speaker 3: I asked Joe and Tracy from Odd Lots about all
Speaker 3: that it kind of feels like the worst timing to
Speaker 3: have bad data or questionable data when there's such a
Speaker 3: consequential election at hand. So I'm curious what you guys think.
Speaker 3: How do you think potentially flawed data is going to.
Speaker 4: Affect all of this.
Speaker 1: One of my favorite surveys to read through is the
Speaker 1: NFIB Small Business Optimism Survey, and there's one chart that
Speaker 1: really catches my eye in which the NFIB itself disambiguates
Speaker 1: between what they call the hard data and the soft data.
Speaker 1: So the hard data is like, we're your sales higher
Speaker 1: or lower in the last three months, it's not really
Speaker 1: an opinion question either was or what was itenc you?
Speaker 1: And then there's the soft data, it's like, do you
Speaker 1: feel confident enough to invest in this environment. What's really
Speaker 1: interesting is that the hard data and soft data really
Speaker 1: do converge during past Republican administrations and really do diverge
Speaker 1: during democratic administration. So there's a huge gap right now
Speaker 1: within the NFIB between their soft and hard data. So
Speaker 1: I do think that there is a split in sort
Speaker 1: of how people perceive the economy versus how people perceive
Speaker 1: their own household finances. That is sort of interesting. How
Speaker 1: do people vote on this? You know, it's hard to say.
Speaker 4: To that point.
Speaker 2: I kind of think about it on a sort of
Speaker 2: personal versus like absolute basis, which is you do see
Speaker 2: a lot of self reporting, so people talking about their
Speaker 2: own financial circumstances, or to Joe's point about small businesses,
Speaker 2: their own small business circumstance, they will say it's going
Speaker 2: relatively well, and you can see some of that born
Speaker 2: out in the hard data, but when they talk about
Speaker 2: the economy in aggregate, that's when you tend to see
Speaker 2: a lot more negative sentiment, and there is a sort
Speaker 2: of weird cognitive dissonance there. We can talk about whether
Speaker 2: that might be down to partisanship, down to the media
Speaker 2: of things like that, But I do think the interesting
Speaker 2: question is if everyone keeps saying they think the economy
Speaker 2: is doing terribly, is that actually going to manifest in
Speaker 2: a slowdown in growth or even a contraction at some point.
Speaker 2: We haven't seen that yet.
Speaker 3: That's significant a lot of people this time last year,
Speaker 3: we're looking at government data and saying we're headed straight
Speaker 3: for a recession. But it turns out all this flawed
Speaker 3: data isn't just affecting the fed's decisions. It also goes
Speaker 3: the other way, as in FED decisions like raising interest
Speaker 3: rates also shape the narratives that economists construct about the
Speaker 3: state of the economy.
Speaker 2: The consensus position going into twenty twenty three was that
Speaker 2: we work on to see a recession, that it was
Speaker 2: impossible to have the extent of the rate hikes that
Speaker 2: we had seen without having some sort of slowing or
Speaker 2: negative effect on the economy.
Speaker 1: The story is you make money more expensive, that decreases
Speaker 1: the ability to invest in borrow. That causes people to
Speaker 1: lose their jobs. Lost jobs mean less demand. Less demand
Speaker 1: means lower prices. That is the basic causal chain between
Speaker 1: how higher rates causes low inflation. It's sort of the
Speaker 1: standard popular telling of how economics work.
Speaker 2: The idea that prices could come down without spiking unemployment
Speaker 2: was just absolutely outrageous sort of this time last year,
Speaker 2: and yet what we've seen is exactly that.
Speaker 3: Janet Yellen, who serves as President Joe Biden's Treasury secretary,
Speaker 3: caught it a soft landing, no pun intended. In other words,
Speaker 3: if the economy is a plane, it didn't crash. So
Speaker 3: what does all this mean about Palm's argument? How can
Speaker 3: we make sense of the data we have and the
Speaker 3: stories economists are telling us about it.
Speaker 1: Maybe one way to think about it is, if you're
Speaker 1: going to extend the flying analogy, it's terrible weather and
Speaker 1: it's cloudy, and it's raining, and there's wind from multiple directions,
Speaker 1: and they're landing in an area with a lot of
Speaker 1: snow and a valley. It's really tough to know what's
Speaker 1: going on and what's striking is the degree of narratives
Speaker 1: that I could tell you right now, but what's happening
Speaker 1: with the economy. I could tell you a story about
Speaker 1: how inflation is coming down and the labor market is
Speaker 1: still robust and we're on pace for self landing. I
Speaker 1: could say there are certain measures of inflation that aren't
Speaker 1: coming down as much, and there are signs that the
Speaker 1: labor market is actually weakening. I could say, look at
Speaker 1: what's going on with financial market speculation and say, look, actually,
Speaker 1: we haven't extinguished the inflationary embers at all in this economy.
Speaker 1: And so any one of those narratives, someone could convincingly
Speaker 1: make the case it is extremely hard for the FED
Speaker 1: to really know what's going on.
Speaker 2: Yeah, there was no uncertainty, there would be no market basically,
Speaker 2: and not to labor the flying analogy, but I think
Speaker 2: the trick is that, yes, it's stormy outside, but you're
Speaker 2: flying a plane. You have all these different indicators. You know,
Speaker 2: you can look out the windscreen and see what the
Speaker 2: weather actually looks like. You can look at your instruments
Speaker 2: and measure a wind shear or whatever. You sort of
Speaker 2: have to figure out which of your instruments to listen
Speaker 2: to at this moment in time, and it's tricky because
Speaker 2: it's not the usual flying environment. Gosh, I'm getting sick
Speaker 2: of this analogy, but it is a weird business cycle.
Speaker 2: Going back to what we were saying.
Speaker 3: Earlier, Some was very clear in her article the Federal
Speaker 3: Reserve is doing the best it can.
Speaker 5: We're trying to get a sense on a twenty trillion
Speaker 5: dollar plus economy with you know, one hundreds of millions
Speaker 5: of people working, and we're like trying to measure a
Speaker 5: moving target.
Speaker 3: But she does think the government overall could do more
Speaker 3: to restore trust so that people are more willing to
Speaker 3: respond to surveys, and she's also been involved in efforts
Speaker 3: to bridge the gap between that hard and soft data
Speaker 3: that Joe mentioned by relying on both.
Speaker 5: There are ways to use administrative data where you could
Speaker 5: put together like surveys where we ask people things. It
Speaker 5: would be really hard to go measure somewhere else, But
Speaker 5: then maybe from the Internal Revenue Service, we know they're
Speaker 5: income and it's definitely easier than figuring out how to
Speaker 5: get people to trust the government more.
Speaker 3: Some says there's an urgent need to address these problems
Speaker 3: before they get worse.
Speaker 5: Statisticians have looked at this, and you know people that
Speaker 5: research in this area, and they still feel comfortable with
Speaker 5: the degree of quality accuracy, Like there are ways to
Speaker 5: get a sense of the reliability, and they're still in
Speaker 5: a place where it's like, Okay, we feel comfortable with these,
Speaker 5: and yet survey response rates that continue to go down. Right,
Speaker 5: at some point, you cross a threshold of being reliable.
Speaker 3: Thanks for listening to The Big Take DC podcast from
Speaker 3: Bloomberg News. I'm Seleiah Mosen. This episode was produced by
Speaker 3: Alex Suguiera, Julia Press, and Naomi Shaven. It was fact
Speaker 3: checked by Stacy Renee. A special thanks to Kate Davidson
Speaker 3: and Matt Bosler. Blake Maples is our mixed engineer, and
Speaker 3: our story editors are Michael Shepherd and Wendy Benjaminson. Nicole
Speaker 3: Beemster Bower is our executive producer. Sage Bauman is our
Speaker 3: head of podcasts. If you like what you heard, please
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Speaker 4: Thanks for tuning in. I'll be back next week.