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Speaker 1: When the government is trying to get a handle on inflation,

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Speaker 1: it's the Federal Reserve that has the biggest lever to pull.

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Speaker 1: Think of the FED like a traffic cop instead of

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Speaker 1: a whistle and cone. The central Bank uses interest rates

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Speaker 1: to try and contain inflation. When rates go up, money

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Speaker 1: becomes expensive and people tend to spend and borrow less.

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Speaker 1: That slows the economy down. When rates go down, people

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Speaker 1: are more willing to spend since everything from credit card

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Speaker 1: fees to mortgage rates are cheaper. Unlike the traffic build

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Speaker 1: up on a road which anyone can see. The FED

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Speaker 1: has to get creative in order to manage the economy,

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Speaker 1: so it uses data to decide when and how to intervene.

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Speaker 1: But last year, when economists everywhere were expecting a full

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Speaker 1: blown recession, the FED was raising interest rates over and

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Speaker 1: over again. They needed to rain in inflation, and the

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Speaker 1: man in charge, chaired your own Powell. He kept pointing

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Speaker 1: to one category of data that was guiding the Fed's

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Speaker 1: to stay the labor market.

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Speaker 2: A labor market, A labor market remains very tight.

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Speaker 1: All this talk about the tight labor market made Claudia

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Speaker 1: Sam's ears perk up. She's an economist and a Bloomberg

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Speaker 1: Opinion contributor. She worked in the Obama White House and

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Speaker 1: spent twelve years at the FED. She'd been looking into

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Speaker 1: the labor market numbers herself, and the Fed's decisions left

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Speaker 1: her scratching her head.

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Speaker 3: They are making big decisions about the interest rates, the

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Speaker 3: mortgage rates we pay, the credit card interest rates, auto loans,

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Speaker 3: so we want them to be data driven, but they

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Speaker 3: can only do as good a job as the data.

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Speaker 1: They have, and that data they've been focused on, she's

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Speaker 1: had some serious questions about it. This morning, the government

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Speaker 1: released new GDP data that shows the US successfully avoided

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Speaker 1: a recession, even though almost every economist was predicting one.

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Speaker 1: But the data that the Federal Reserve examined as it

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Speaker 1: made policy decisions is complicated. On today's show, have policymakers

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Speaker 1: trusted data that might have been faulty? I talked to

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Speaker 1: Claudia sam about her findings, and I sit down with

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Speaker 1: Tracy Alloway and Joe Wisenthal from Bloomberg's Odd Lots podcast.

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Speaker 1: We talk about what's behind the numbers and why it's

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Speaker 1: important in.

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Speaker 4: An election year.

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Speaker 1: From Bloomberg's Washington Bureau, This is the Big Take DC

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Speaker 1: podcast I'm Your host Seleiah Mosen Claudia Sam decided her

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Speaker 1: concerns about the Federal Reserves data were worth voicing, so

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Speaker 1: in November she wrote an article for Bloomberg Opinion. It

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Speaker 1: had an eye catching headline, economists may have been flying

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Speaker 1: blind all along.

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Speaker 3: So the argument I was making when I said economists

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Speaker 3: or flying blind is the awareness that we need to

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Speaker 3: have in terms of the measures like how we try

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Speaker 3: and measure quote unquote reality, and then in our giving

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Speaker 3: policy advice.

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Speaker 4: How we measure quote unquote reality.

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Speaker 1: I know that sounds dense, but her point is that

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Speaker 1: as much as we'd love to think that the FED

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Speaker 1: is making its decisions based on hard numbers, you know, objective,

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Speaker 1: unbiased data, often it's not.

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Speaker 3: Data doesn't doesn't come down from heaven.

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Speaker 1: For example, let's look at that tight labor market that

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Speaker 1: FED Chair J. Powell kept mentioning. He said that the

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Speaker 1: labor market was tight, meaning more job openings than workers.

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Speaker 1: He cited numbers from the Job Openings and Labor Turnover

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Speaker 1: Survey JOLTS for short. Now that might sound straightforward, right,

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Speaker 1: measuring the number of open jobs.

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Speaker 4: Not so fast.

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Speaker 3: Now, there was a lot of conversation those of us

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Speaker 3: who have nothing better to do than study data. What

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Speaker 3: a job opening is could be changing over time because

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Speaker 3: of the pandemic.

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Speaker 1: The way employers list jobs is just different than it

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Speaker 1: was before.

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Speaker 3: Especially from work from home. You can put up multiple

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Speaker 3: ones different geographies because it doesn't matter.

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Speaker 1: So a company might list the same job in several

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Speaker 1: different cities. It doesn't cost them anything, but it does

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Speaker 1: mean that the numbers are getting inflated. So when economists

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Speaker 1: at the FED were looking at the number of open

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Speaker 1: jobs and basing their assumptions off of what was typical,

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Speaker 1: they were at risk of ignoring one key factor.

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Speaker 3: The world wasn't typical.

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Speaker 1: I wanted to understand just what's going on here and

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Speaker 1: whether it was an issue beyond this one job survey.

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Speaker 1: So I sat down with two of my colleagues.

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Speaker 5: I'm Tracy Alloway, I am the co host of the

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Speaker 5: auth Lots podcast.

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Speaker 2: And I'm Jill Wasenthal, also the co host of the

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Speaker 2: Outlots podcast.

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Speaker 1: Joe and Tracy read Sam's article and they agreed with her.

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Speaker 1: They do not trust that jolt data.

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Speaker 2: Pre COVID jolts was a bottom shelf economic indicator. It

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Speaker 2: was the well drinks of you know, it's like some

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Speaker 2: nerds like to pour over it because there is information

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Speaker 2: on it. It was not a market mover.

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Speaker 1: If Joelts was a bottom shelf well drink to them

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Speaker 1: pre COVID, it was basically a cheap shot of bad tequila.

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Speaker 1: Once the pandemic hit.

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Speaker 2: You just don't know that the patterns of history related

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Speaker 2: to things like job openings, related to things like claims

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Speaker 2: quit really mean the same thing in this environment as

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Speaker 2: they might have in past cycles.

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Speaker 5: If it was a business cycle, it was the weirdest

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Speaker 5: business cycle ever. Companies aren't behaving differently to how they

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Speaker 5: used to. There's the idea of labor hoarding. People are

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Speaker 5: so scarred from the pandemic period that they just want

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Speaker 5: to make sure they're not caught out again with a

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Speaker 5: labor shortage, so they're just hiring who they can, or

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Speaker 5: they're putting out ads to see who responds. I mean,

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Speaker 5: it's pretty easy to place an ad on some digital

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Speaker 5: job site nowadays. It doesn't really cost that much, so

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Speaker 5: why not try and see who you get?

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Speaker 1: So the pandemic through all our old markers of normal

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Speaker 1: out the window that left the Jolt survey and pretty

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Speaker 1: and steady ground. But COVID didn't just mess with jolts.

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Speaker 1: It also did another thing that influences all sorts of

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Speaker 1: important data points that FED economists rely on survey responses.

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Speaker 5: We know they have declined in recent years, so I

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Speaker 5: think something like the Housing Survey gets like half of

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Speaker 5: the people it surveys actually responding nowadays, and that's down

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Speaker 5: from two thirds.

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Speaker 1: We reached out to the Bureau of Labor Statistics and

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Speaker 1: the Census Bureau for comment for this episode, and they

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Speaker 1: both acknowledged declining response rates as a critical problem that

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Speaker 1: they're trying to address. It's a problem that only got

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Speaker 1: worse during the pandemic. All this matters because if your

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Speaker 1: survey only captures half of the people you contact.

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Speaker 5: You're going to have to question whether or not that

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Speaker 5: fifty percent is reflective of the actual American experience. And

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Speaker 5: of course, the irony is that most advanced economies are

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Speaker 5: collecting more data than ever. We're doing more soft surveys

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Speaker 5: than ever, but the response rates are trending down and

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Speaker 5: the quality of that data is questionable.

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Speaker 1: We'll get to why Americans are getting survey shy, what

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Speaker 1: the FED is doing to fix it, and what it

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Speaker 1: all means with a twenty twenty four election.

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Speaker 4: After the break, we're back.

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Speaker 1: Part of what made Claudia Sam argue that economists may

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Speaker 1: have been flying blind is lower response rates to government surveys,

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Speaker 1: and that decline is actually a symptom of a much

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Speaker 1: bigger problem.

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Speaker 3: We've seen a growing distrust in government. And you know,

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Speaker 3: I can understand if you don't trust the government, if

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Speaker 3: they show up and be like, hey, tell us all

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Speaker 3: about your wealth and you're dead and how much income

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Speaker 3: you make, which for a these are very sensitive topics.

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Speaker 1: Pere research found that two thirds of adults think the

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Speaker 1: risk of responding to a survey outweighs the benefits. They're

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Speaker 1: concerned about things like privacy and not so concerned about

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Speaker 1: the consequences of low response rate data.

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Speaker 4: Some says they should be, but you need.

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Speaker 3: To rebuild that relationship and help people understand know what

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Speaker 3: you tell us, Like policy makers, this is important and

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Speaker 3: if we don't know what's going on in your life,

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Speaker 3: then it's almost guaranteed that the policy just can't address

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Speaker 3: the issues.

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Speaker 1: We're in an election year one when Americans list the

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Speaker 1: economy as the top issue driving their votes. So flawed

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Speaker 1: assumptions about the economy based on sketchy data carry a

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Speaker 1: risk as people decide who they want as president, and

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Speaker 1: so does data that doesn't fully capture voters lived experiences.

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Speaker 1: I asked Joe and Tracy from Odd Lots about all

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Speaker 1: that it kind of feels like the worst timing to

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Speaker 1: have bad data or questionable data when there's such a

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Speaker 1: consequential election at hand. So I'm curious what you guys think.

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Speaker 1: How do you think potentially flawed data is going to

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Speaker 1: affect all of this.

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Speaker 2: One of my favorite surveys to read through is the

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Speaker 2: NFIB Small Business Optimism Survey, and there's one chart that

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Speaker 2: really catches my eye in which the NFIB itself disambiguates

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Speaker 2: between what they call the hard data and the soft data.

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Speaker 2: So the hard data is like, we're your sales higher

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Speaker 2: or lower in the last three months. It's not really

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Speaker 2: an opinion question either it was or what I was

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Speaker 2: it into your answer. And then there's the soft data.

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Speaker 2: It's like, do you feel confident enough to invest in

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Speaker 2: this environment. What's really interesting is that the hard data

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Speaker 2: and soft data really do converge during past Republican administrations

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Speaker 2: and really do diverge during democratic administration, So there's a

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Speaker 2: huge gap right now within the NFIB between their soft

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Speaker 2: and hard data. So I do think that there is

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Speaker 2: a split. It's sort of how people perceive the economy

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Speaker 2: versus how people perceive their own household finances. That is

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Speaker 2: sort of interesting. How do people vote on this? You know,

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Speaker 2: it's hard to say to that point.

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Speaker 5: I kind of think about it on a sort of

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Speaker 5: personal versus like absolute basis, which is you do see

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Speaker 5: a lot of self reporting, so people talking about their

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Speaker 5: own financial circumstances, or to Joe's point, about small businesses,

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Speaker 5: their own small business circumstance, they will say it's going

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Speaker 5: relatively well, and you can see some of that born

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Speaker 5: out in the hard data. But when they talk about

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Speaker 5: the economy in aggregate, that's when you tend to see

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Speaker 5: a lot more negative sentiment, and there is a sort

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Speaker 5: of weird cognitive dissonance there. We can talk about like

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Speaker 5: whether that might be down to partisanship, down to the media,

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Speaker 5: things like that. But I do think the interesting question

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Speaker 5: is if everyone keeps saying they think the economy is

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Speaker 5: doing terribly, is that actually going to manifest in a

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Speaker 5: slowdown in growth or even a contraction at some point,

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Speaker 5: we haven't seen that yet.

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Speaker 1: That's significant a lot of people. This time last year,

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Speaker 1: we're looking at government data and saying we're headed straight

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Speaker 1: for recession. But it turns out all this flawed data

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Speaker 1: isn't just affecting the Fed's decisions. It also goes the

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Speaker 1: other way, as in FED decisions like raising interest rates

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Speaker 1: also shape the narratives that economists construct about the state

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Speaker 1: of the economy.

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Speaker 5: The consensus position going into twenty twenty three was that

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Speaker 5: we work on to see a recession, that it was

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Speaker 5: impossible to have the extent of the rate hikes that

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Speaker 5: we had seen without having some sort of slowing or

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Speaker 5: negative effect on the economy.

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Speaker 2: The story is, you make money more expensive, that decreases

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Speaker 2: the ability to invest and borrow. That causes people to

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Speaker 2: lose their jobs. Lost jobs mean less demand. Less demand

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Speaker 2: means lower prices. That is the basic causal chain between

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Speaker 2: how higher rates causes low inflation. It's sort of the

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Speaker 2: standard popular telling of how economics work.

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Speaker 5: The idea that prices could come down without spiking unemployment

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Speaker 5: was just absolutely outrageous sort of this time last year,

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Speaker 5: and yet what we've seen is exactly that.

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Speaker 1: Janet Yellen, who serves as President. Joe Biden's Treasury secretary

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Speaker 1: called it a soft landing, no pun intended. In other words,

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Speaker 1: if the economy is a plane, it didn't crash. So

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Speaker 1: what does all this mean about Palm's argument? How can

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Speaker 1: we make sense of the data we have and the

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Speaker 1: stories economists are telling us about it.

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Speaker 2: Maybe one way to think about it is, if you're

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Speaker 2: going to extend the flying analogy, it's terrible weather, and

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Speaker 2: it's cloudy, and it's raining, and there's wind from multiple directions,

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Speaker 2: and they're landing in an area with a lot of

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Speaker 2: snow and a valley. It's really tough to know what's

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Speaker 2: going on. And what's striking is the degree of narratives

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Speaker 2: that I could tell you right now. But what's happening

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Speaker 2: with the economy. I could tell you a story about

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Speaker 2: how inflation is coming down and the labor market is

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Speaker 2: still robust, and we're on pace for self landing. I

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Speaker 2: could say there are certain measures of inflation that aren't

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Speaker 2: coming down as much, and there are signs that the

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Speaker 2: labor market is actually weakening. I could say look at

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Speaker 2: what's going on with financial market speculation and say, look,

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Speaker 2: actually we haven't extinguished the inflationary embers at all in

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Speaker 2: this economy, and so any one of those narratives someone

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Speaker 2: could convincingly make the case it is extremely hard for

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Speaker 2: the Fed to really know what's going on.

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Speaker 5: Yeah, if there was no uncertainty, there would be no

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Speaker 5: market basically, and not to labor the flying analogy, but

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Speaker 5: I think the trick is that, yes, it's stormy outside,

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Speaker 5: but you're flying a plane. You have all these different indicators.

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Speaker 5: You know, you can look out the windscreen and see

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Speaker 5: what the weather actually looks like. You can look at

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Speaker 5: your instruments and measure windshar or whatever. You sort of

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Speaker 5: have to figure out which of your instruments to listen

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Speaker 5: to at this moment in time. And it's tricky because

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Speaker 5: it's not the usual flying environment. Gosh, I'm getting sick

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Speaker 5: of this analogy. But it is a weird business cycle.

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Speaker 5: Going back to what we were saying.

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Speaker 1: Earlier, Some was very clear in her article the Federal

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Speaker 1: Reserve is doing the best it can.

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Speaker 3: We're trying to get a sense on a twenty trillion

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Speaker 3: dollar plus economy with you know, one hundreds of millions

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Speaker 3: of people working, and we're like trying to measure a

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Speaker 3: moving target.

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Speaker 1: But she does think the government overall could do more

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Speaker 1: to restore trust so that people are more willing to

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Speaker 1: respond to surveys, and she's also been involved in efforts

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Speaker 1: to bridge the gap between that hard and soft data

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Speaker 1: that Joe mentioned by relying on both.

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Speaker 3: There are ways to use administrative data where you could

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Speaker 3: put together like surveys where we ask people things. It

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Speaker 3: would be really hard to go measure somewhere else, but

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Speaker 3: then maybe from the Internal Revenue Service, we know they're

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Speaker 3: income and it's definitely easier than figuring out how to

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Speaker 3: get people to trust the government more.

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Speaker 1: Sam says there's an urgent need to address these problems

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Speaker 1: before they get worse.

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Speaker 3: Statisticians have looked at this, and you know, people that

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Speaker 3: research in this area and they still feel comfortable with

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Speaker 3: the degree of quality accuracy, Like there are ways to

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Speaker 3: get a sense of the reliability, and they're still in

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Speaker 3: a place where it's like, Okay, we feel comfortable with these,

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Speaker 3: and yet survey responsorates that continue to go down. Right,

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Speaker 3: at some point you cross a threshold of being reliable.

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Speaker 1: Thanks for listening to The Big Take DC podcast from

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Speaker 1: Bloomberg News. I'm Salaia Mosen. This episode was produced by

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Speaker 1: Alex Sugia, Julia Press, and Naomi Shaven. It was fact

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Speaker 1: checked by Stacy Renee. A special thanks to Kate Davidson

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Speaker 1: and Matt Bosler. Blake Maples is our mixed engineer, and

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Speaker 1: our story editors are Michael Shepherd and Wendy Benjaminson. Nicole

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Speaker 1: Beemster Bower is executive producer. Sage Bauman is our head

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Speaker 4: I'll be back next week.