WEBVTT - Here's Why Some Economic Data Matters More Than Others

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

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<v Speaker 1>this is Here's Why, where we take one news story

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<v Speaker 1>and explain it in just a few minutes with our

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

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<v Speaker 1>the numbers that tell us about the state of the economy.

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<v Speaker 2>The August data was at least what in manufacturing PMI,

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<v Speaker 2>it was disappointing.

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<v Speaker 1>Flash PMI survey data for Genie signals a slowing pace

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<v Speaker 1>of economic growth. The latest payrolls report coming in below estimates.

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<v Speaker 2>US job's data, new data, data, data, data, data data.

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<v Speaker 1>There's a deluge of data available for major economies. But

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<v Speaker 1>to misquote George Orwell, some data is more equal than others.

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<v Speaker 1>Think about the many different ways that we measure inflation

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<v Speaker 1>or the labor market, job openings, job creation, unemployment all

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<v Speaker 1>tell you something different. And everything from economic growth to

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<v Speaker 1>part just saying manager index surveys can get significantly revised

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<v Speaker 1>between the first and last versions. So here's why some

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<v Speaker 1>economic data matters more than others. We'll also tell you

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<v Speaker 1>how to separate the signal from the noise. Joining me

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<v Speaker 1>now is our Global economy reporter and a current and

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<v Speaker 1>a great to have you with us. You're a man

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<v Speaker 1>who knows your numbers. There's always this question of when

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<v Speaker 1>we get data, whether it's telling us what was happening

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<v Speaker 1>in the past, or what's happening right now, or giving

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<v Speaker 1>us a hint as to what's going to potentially happen

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<v Speaker 1>in the future. How do we attach different levels of

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<v Speaker 1>importance to those timeframes.

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<v Speaker 2>Yeah, so some of the numbers we get are very

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<v Speaker 2>backward looking, like, for example, when you hear people talking

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<v Speaker 2>about GDP data on the news headlines, that's typically telling

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<v Speaker 2>you where the economy was maybe a quarter ago. So

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

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<v Speaker 2>change quickly. Economists like to talk about what they call

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<v Speaker 2>high frequency indicators, data points that are giving more timely

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

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

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<v Speaker 2>a good indicator of consumer confidence. You might keep an

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<v Speaker 2>eye also on what's going on with boring fancing at

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<v Speaker 2>from banks. If banks are lending lots of money, that

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<v Speaker 2>suggests that there's animal spirits and a willingness to invest

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<v Speaker 2>out there. By Cuparts and maybe for would be homeowners

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

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<v Speaker 2>lending money, then it suggests that perhaps things are more

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<v Speaker 2>subdued them you might have expected. So some of the numbers,

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

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<v Speaker 2>just to treat them as such. If you want to

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<v Speaker 2>timely read, keep an eye on the more high frequency indicators.

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<v Speaker 1>Yeah, I mean animal spirits. Depending on what kind of

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<v Speaker 1>animal you're thinking about, I suppose tells you different things

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<v Speaker 1>about it. How do we explain the contradictions that we

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<v Speaker 1>sometimes see in the numbers? Sometimes they don't make so

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<v Speaker 1>much sense lining up one against another. If we think

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<v Speaker 1>about an example of maybe inflation.

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<v Speaker 2>So over the past few years, if you want to

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<v Speaker 2>talk about the advanced economy world, there's been the worst

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<v Speaker 2>outbreak of inflation in decades that impacted everyone's living stand

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<v Speaker 2>So interest rates go up and the cost of a

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<v Speaker 2>mortgage and alone go through the roof as a result.

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

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<v Speaker 2>So the pacer inflation has slowed dramatically in many economies.

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<v Speaker 2>Coming back to the area where central banks like to be,

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<v Speaker 2>that's a good news story. But if you walk into

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<v Speaker 2>the shop having heard it on the news, headlinds, you're

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<v Speaker 2>still paying much higher prices than you wore only a

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<v Speaker 2>couple of years ago. So I think, say in the US,

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<v Speaker 2>for example, basket of groceries maybe twenty odd percent higher

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<v Speaker 2>than what they wore before the inflation crisis struck out.

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<v Speaker 2>And that's where you get into the difference between the

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<v Speaker 2>rate of inflation, which is what the economists measure every month,

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<v Speaker 2>versus the actual price level that you're paying in the store.

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<v Speaker 2>And I think there is a disconnected and confusion there

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

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<v Speaker 2>are coming down now. To be clear, for prices to

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<v Speaker 2>come down, that would need deflation. And when an economy

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<v Speaker 2>is in deflation, it typically suggests that it has some

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<v Speaker 2>real problems going on. So it's a tricky you want

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<v Speaker 2>it moment. It's a tough pill for households to swallow.

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<v Speaker 2>We're at a point where inflation is slow, but for

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<v Speaker 2>prices to start falling that would require something of a

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<v Speaker 2>deeper shock to the economy.

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<v Speaker 1>Yeah, and indeed, most of the conversations that you'll have

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<v Speaker 1>with people will be about how expensive things are consistently

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<v Speaker 1>rather necessarily how much they've gone up by. Another quirk

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<v Speaker 1>that we follow very closely here at Bloomberg is data revisions.

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<v Speaker 1>So we get sometimes several iterations of the same number.

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<v Speaker 1>Why do we see sometimes very big revisions in the data?

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<v Speaker 2>There can be bigger revisions. We recently had a larger

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

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<v Speaker 2>as I say, a lot of these readings are snapshots

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<v Speaker 2>in time. They are incomplete. It might be on a

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<v Speaker 2>monthly basis, or maybe a quarterly basis, and as the

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<v Speaker 2>months of the year ago goes by, and maybe after

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<v Speaker 2>another year or so. The kind of agencies whoutalitates the

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<v Speaker 2>statistic agencies and the government economic agencies pull all the

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<v Speaker 2>numbers together when they have a more complete picture. And

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<v Speaker 2>that's when you were able to say, oh, we overstated

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<v Speaker 2>something there, or we underestimated something there, and they make

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

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<v Speaker 2>the US employment data, and this is true of employment

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<v Speaker 2>data anywhere, can be subject to material revisions. Which has

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<v Speaker 2>had recent revisions to US jobs at it which suggest

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<v Speaker 2>over eight hundred thousand less jobs than originally counted in

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<v Speaker 2>the system. That speaks to a weekly labor market than

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<v Speaker 2>was broadly expected at a jobs market still low kind

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<v Speaker 2>to us. But it goes to show you that revisions

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<v Speaker 2>can have a material impact.

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<v Speaker 1>Yeah, and look, it also speaks to the idea of

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<v Speaker 1>getting the right data and data that is accurate, and revisions,

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<v Speaker 1>I suppose, get us closer to what is a better

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<v Speaker 1>picture of what's going on in something like the jobs

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<v Speaker 1>market as well. There's an old joke about you put

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<v Speaker 1>ten economists in a room and you get eleven opinions.

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<v Speaker 1>How much can numbers be open to interpretation?

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<v Speaker 2>There is a degree of interpretation because it could suit

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<v Speaker 2>someone's investment thesis. They will want to read numbers whatever

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<v Speaker 2>way it is to back the argument they're making. Numbers

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<v Speaker 2>can be interpreted to meet somebody's political buyas or political outlook.

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<v Speaker 2>For example, so when we had the recent interest rate

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<v Speaker 2>cut in the US, for example, you had one side

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<v Speaker 2>of politics here saying it shows that the inflation story

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<v Speaker 2>is under control and the Fed is at a point

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<v Speaker 2>that work and bring down interest rates. That's good for

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<v Speaker 2>costs of living. But of course you had the other

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<v Speaker 2>side of the political avide here making the point that

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<v Speaker 2>interest rates coming down because the economy is losing jobs

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<v Speaker 2>and the jobs market is weakning, So everything can be

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<v Speaker 2>interpreted in different ways, but ultimately, one of the good

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<v Speaker 2>things about economics is the numbers and the data and

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<v Speaker 2>the statistics do not lie.

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<v Speaker 1>So if you're looking for the most quality data or

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<v Speaker 1>the best things to look out for when you're trying

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<v Speaker 1>to assess numbers as they're being published, what are the

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<v Speaker 1>sort of things that you think about when you're trying

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<v Speaker 1>to parse what a certain number means for the economy.

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<v Speaker 2>You have to look for the numbers that really speak

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<v Speaker 2>to what's happening in both people's lives and in companies' lives.

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<v Speaker 2>So that would be figures around corporate investment, business investment.

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<v Speaker 2>What's happening there with companies? Have they got the confidence

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<v Speaker 2>of God and expand and hire new staff. Then obviously

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<v Speaker 2>you have to keep an eye on what's happening with

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<v Speaker 2>household credit. Are people taking out mortgages to buy a

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<v Speaker 2>house or those who have a mortgage taking out a

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<v Speaker 2>loan to renovate the house. That speaks to, of course

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<v Speaker 2>confidence in terms of consumer confidence that those all around us.

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<v Speaker 2>And then of course you have the very timely, monthly

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<v Speaker 2>or even quarterly readings in terms of inflation, what is

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<v Speaker 2>going on with the price that we're paying for goods

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<v Speaker 2>and services. I mentioned the jobs out earlier. That's obviously

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<v Speaker 2>a very critical one, and then all of that creates

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<v Speaker 2>the jigsaw that is known as a GDPGIC. So that's

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<v Speaker 2>backward looking, but it's a health check and it tells

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<v Speaker 2>you worth economy has been And.

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<v Speaker 1>You've covered economies all over the world for Bloomberg. I

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<v Speaker 1>wonder do you have a favorite piece of data? Is

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<v Speaker 1>the one that still you get excited about trying to

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<v Speaker 1>read into the details of well.

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<v Speaker 2>I used to get excited about. There was a phase

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<v Speaker 2>when satellite data on China was a big thing, especially

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<v Speaker 2>among hedge funds. It was popular that someone had the

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<v Speaker 2>latest satellite footage of some industrial expansion or development somewhere,

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<v Speaker 2>maybe some housing property site being developed, and they were

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<v Speaker 2>claiming that they were getting an early read and what's

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

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<v Speaker 2>phase now. During the pandemic, there was a huge rush

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<v Speaker 2>on high frequency indicators, so people wanted to know what's

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<v Speaker 2>happening with cinema tickets and what's happening with eating out

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<v Speaker 2>and what's happening.

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<v Speaker 1>With Samwiches from Press.

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<v Speaker 2>I remember that one. All of this and usage of

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<v Speaker 2>the subways and truth, we're back to where we started.

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<v Speaker 2>We're looking at the official data that comes out of

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<v Speaker 2>the agencies, and people are keeping an eye on as

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<v Speaker 2>I mentioned earlier, spending data, keeping an eye lending data,

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<v Speaker 2>jobs data, inflation data. I think the satellites and SANDWIDG

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<v Speaker 2>indexes were all very interesting, but we've come back to

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<v Speaker 2>what we know and trust.

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<v Speaker 1>Most, return to the classics and the current our Global

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<v Speaker 1>Economy reporter. Thanks very much for joining us for more

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<v Speaker 1>explanations like this from our team of twenty seven hundred

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<v Speaker 1>journalists and analysts around the world. Search for Quick Take

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<v Speaker 1>on the Bloomberg website or Bloomberg Business app. I'm Stephen Caroll.

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

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<v Speaker 1>Thanks for listening, Z