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