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Speaker 1: Bloomberg Audio Studios, podcasts, radio news. I'm Stephen Carol and

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Speaker 1: this is Here's Why, where we take one news story

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Speaker 1: and explain it in just a few minutes with our

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Speaker 1: experts here at Bloomberg. It's the lifeblood of the finance world,

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Speaker 1: the numbers that tell us about the state of the economy.

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Speaker 2: The August data was at least what in manufacturing PMI,

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Speaker 2: it was disappointing.

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Speaker 1: Flash PMI survey data for Genie signals a slowing pace

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Speaker 1: of economic growth. The latest payrolls report coming in below estimates.

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Speaker 2: US job's data, new data, data, data, data, data data.

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Speaker 1: There's a deluge of data available for major economies. But

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Speaker 1: to misquote George Orwell, some data is more equal than others.

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Speaker 1: Think about the many different ways that we measure inflation

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Speaker 1: or the labor market, job openings, job creation, unemployment all

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Speaker 1: tell you something different. And everything from economic growth to

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Speaker 1: part just saying manager index surveys can get significantly revised

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Speaker 1: between the first and last versions. So here's why some

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Speaker 1: economic data matters more than others. We'll also tell you

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Speaker 1: how to separate the signal from the noise. Joining me

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Speaker 1: now is our Global economy reporter and a current and

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Speaker 1: a great to have you with us. You're a man

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Speaker 1: who knows your numbers. There's always this question of when

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Speaker 1: we get data, whether it's telling us what was happening

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Speaker 1: in the past, or what's happening right now, or giving

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Speaker 1: us a hint as to what's going to potentially happen

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Speaker 1: in the future. How do we attach different levels of

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Speaker 1: importance to those timeframes.

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Speaker 2: Yeah, so some of the numbers we get are very

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Speaker 2: backward looking, like, for example, when you hear people talking

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Speaker 2: about GDP data on the news headlines, that's typically telling

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Speaker 2: you where the economy was maybe a quarter ago. So

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Speaker 2: in economic terms, that's kind of ancient history. Conditions can

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Speaker 2: change quickly. Economists like to talk about what they call

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Speaker 2: high frequency indicators, data points that are giving more timely

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Speaker 2: read and what's happening and there. For example, you might look,

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Speaker 2: I've got retail sales, retail spending on Main Street. That's

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Speaker 2: a good indicator of consumer confidence. You might keep an

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Speaker 2: eye also on what's going on with boring fancing at

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Speaker 2: from banks. If banks are lending lots of money, that

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Speaker 2: suggests that there's animal spirits and a willingness to invest

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Speaker 2: out there. By Cuparts and maybe for would be homeowners

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Speaker 2: buy a home, that's a good signal. If you're not

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Speaker 2: lending money, then it suggests that perhaps things are more

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Speaker 2: subdued them you might have expected. So some of the numbers,

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Speaker 2: as you say, can be quite backward looking. It's better

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Speaker 2: just to treat them as such. If you want to

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Speaker 2: timely read, keep an eye on the more high frequency indicators.

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Speaker 1: Yeah, I mean animal spirits. Depending on what kind of

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Speaker 1: animal you're thinking about, I suppose tells you different things

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Speaker 1: about it. How do we explain the contradictions that we

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Speaker 1: sometimes see in the numbers? Sometimes they don't make so

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Speaker 1: much sense lining up one against another. If we think

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Speaker 1: about an example of maybe inflation.

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Speaker 2: So over the past few years, if you want to

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Speaker 2: talk about the advanced economy world, there's been the worst

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Speaker 2: outbreak of inflation in decades that impacted everyone's living stand

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Speaker 2: So interest rates go up and the cost of a

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Speaker 2: mortgage and alone go through the roof as a result.

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Speaker 2: Now we're in a phase whereby this inflation is well entrenched.

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Speaker 2: So the pacer inflation has slowed dramatically in many economies.

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Speaker 2: Coming back to the area where central banks like to be,

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Speaker 2: that's a good news story. But if you walk into

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Speaker 2: the shop having heard it on the news, headlinds, you're

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Speaker 2: still paying much higher prices than you wore only a

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Speaker 2: couple of years ago. So I think, say in the US,

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Speaker 2: for example, basket of groceries maybe twenty odd percent higher

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Speaker 2: than what they wore before the inflation crisis struck out.

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Speaker 2: And that's where you get into the difference between the

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Speaker 2: rate of inflation, which is what the economists measure every month,

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Speaker 2: versus the actual price level that you're paying in the store.

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Speaker 2: And I think there is a disconnected and confusion there

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Speaker 2: people here. Inflation is coming off, that doesn't mean prices

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Speaker 2: are coming down now. To be clear, for prices to

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Speaker 2: come down, that would need deflation. And when an economy

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Speaker 2: is in deflation, it typically suggests that it has some

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Speaker 2: real problems going on. So it's a tricky you want

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Speaker 2: it moment. It's a tough pill for households to swallow.

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Speaker 2: We're at a point where inflation is slow, but for

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Speaker 2: prices to start falling that would require something of a

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Speaker 2: deeper shock to the economy.

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Speaker 1: Yeah, and indeed, most of the conversations that you'll have

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Speaker 1: with people will be about how expensive things are consistently

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Speaker 1: rather necessarily how much they've gone up by. Another quirk

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Speaker 1: that we follow very closely here at Bloomberg is data revisions.

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Speaker 1: So we get sometimes several iterations of the same number.

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Speaker 1: Why do we see sometimes very big revisions in the data?

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Speaker 2: There can be bigger revisions. We recently had a larger

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Speaker 2: vision to US employment data, for example. It's mostly because,

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Speaker 2: as I say, a lot of these readings are snapshots

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Speaker 2: in time. They are incomplete. It might be on a

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Speaker 2: monthly basis, or maybe a quarterly basis, and as the

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Speaker 2: months of the year ago goes by, and maybe after

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Speaker 2: another year or so. The kind of agencies whoutalitates the

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Speaker 2: statistic agencies and the government economic agencies pull all the

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Speaker 2: numbers together when they have a more complete picture. And

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Speaker 2: that's when you were able to say, oh, we overstated

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Speaker 2: something there, or we underestimated something there, and they make

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Speaker 2: changes to what we're previously now. And so, for example,

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Speaker 2: the US employment data, and this is true of employment

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Speaker 2: data anywhere, can be subject to material revisions. Which has

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Speaker 2: had recent revisions to US jobs at it which suggest

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Speaker 2: over eight hundred thousand less jobs than originally counted in

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Speaker 2: the system. That speaks to a weekly labor market than

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Speaker 2: was broadly expected at a jobs market still low kind

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Speaker 2: to us. But it goes to show you that revisions

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Speaker 2: can have a material impact.

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Speaker 1: Yeah, and look, it also speaks to the idea of

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Speaker 1: getting the right data and data that is accurate, and revisions,

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Speaker 1: I suppose, get us closer to what is a better

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Speaker 1: picture of what's going on in something like the jobs

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Speaker 1: market as well. There's an old joke about you put

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Speaker 1: ten economists in a room and you get eleven opinions.

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Speaker 1: How much can numbers be open to interpretation?

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Speaker 2: There is a degree of interpretation because it could suit

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Speaker 2: someone's investment thesis. They will want to read numbers whatever

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Speaker 2: way it is to back the argument they're making. Numbers

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Speaker 2: can be interpreted to meet somebody's political buyas or political outlook.

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Speaker 2: For example, so when we had the recent interest rate

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Speaker 2: cut in the US, for example, you had one side

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Speaker 2: of politics here saying it shows that the inflation story

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Speaker 2: is under control and the Fed is at a point

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Speaker 2: that work and bring down interest rates. That's good for

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Speaker 2: costs of living. But of course you had the other

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Speaker 2: side of the political avide here making the point that

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Speaker 2: interest rates coming down because the economy is losing jobs

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Speaker 2: and the jobs market is weakning, So everything can be

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Speaker 2: interpreted in different ways, but ultimately, one of the good

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Speaker 2: things about economics is the numbers and the data and

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Speaker 2: the statistics do not lie.

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Speaker 1: So if you're looking for the most quality data or

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Speaker 1: the best things to look out for when you're trying

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Speaker 1: to assess numbers as they're being published, what are the

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Speaker 1: sort of things that you think about when you're trying

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Speaker 1: to parse what a certain number means for the economy.

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Speaker 2: You have to look for the numbers that really speak

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Speaker 2: to what's happening in both people's lives and in companies' lives.

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Speaker 2: So that would be figures around corporate investment, business investment.

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Speaker 2: What's happening there with companies? Have they got the confidence

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Speaker 2: of God and expand and hire new staff. Then obviously

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Speaker 2: you have to keep an eye on what's happening with

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Speaker 2: household credit. Are people taking out mortgages to buy a

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Speaker 2: house or those who have a mortgage taking out a

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Speaker 2: loan to renovate the house. That speaks to, of course

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Speaker 2: confidence in terms of consumer confidence that those all around us.

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Speaker 2: And then of course you have the very timely, monthly

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Speaker 2: or even quarterly readings in terms of inflation, what is

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Speaker 2: going on with the price that we're paying for goods

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Speaker 2: and services. I mentioned the jobs out earlier. That's obviously

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Speaker 2: a very critical one, and then all of that creates

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Speaker 2: the jigsaw that is known as a GDPGIC. So that's

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Speaker 2: backward looking, but it's a health check and it tells

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Speaker 2: you worth economy has been And.

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Speaker 1: You've covered economies all over the world for Bloomberg. I

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Speaker 1: wonder do you have a favorite piece of data? Is

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Speaker 1: the one that still you get excited about trying to

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Speaker 1: read into the details of well.

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Speaker 2: I used to get excited about. There was a phase

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Speaker 2: when satellite data on China was a big thing, especially

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Speaker 2: among hedge funds. It was popular that someone had the

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Speaker 2: latest satellite footage of some industrial expansion or development somewhere,

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Speaker 2: maybe some housing property site being developed, and they were

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Speaker 2: claiming that they were getting an early read and what's

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Speaker 2: happening in China's economy. But I think we've passed that

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Speaker 2: phase now. During the pandemic, there was a huge rush

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Speaker 2: on high frequency indicators, so people wanted to know what's

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Speaker 2: happening with cinema tickets and what's happening with eating out

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Speaker 2: and what's happening.

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Speaker 1: With Samwiches from Press.

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Speaker 2: I remember that one. All of this and usage of

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Speaker 2: the subways and truth, we're back to where we started.

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Speaker 2: We're looking at the official data that comes out of

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Speaker 2: the agencies, and people are keeping an eye on as

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Speaker 2: I mentioned earlier, spending data, keeping an eye lending data,

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Speaker 2: jobs data, inflation data. I think the satellites and SANDWIDG

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Speaker 2: indexes were all very interesting, but we've come back to

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Speaker 2: what we know and trust.

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Speaker 1: Most, return to the classics and the current our Global

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Speaker 1: Economy reporter. Thanks very much for joining us for more

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Speaker 1: explanations like this from our team of twenty seven hundred

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Speaker 1: journalists and analysts around the world. Search for Quick Take

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Speaker 1: on the Bloomberg website or Bloomberg Business app. I'm Stephen Caroll.

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Speaker 1: This is here's why. I'll be back next week with more.

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Speaker 1: Thanks for listening, Z