Speaker 1: Bloomberg Audio Studios, Podcasts, radio News. Hello Stephen here, it's
Speaker 1: been a year since we launched Here's Why, and in
Speaker 1: that time we've brought you stories from around the world
Speaker 1: about the global economy and how it's changing. To mark
Speaker 1: our first birthday, we wanted to bring you one of
Speaker 1: our favorite episodes with our global economics reporter, end a Current.
Speaker 1: I'll be back next week with a brand new episode.
Speaker 1: In the meantime, enjoy, I'm Stephen Carol and this is
Speaker 1: Here's Why, where we take one news story and explain
Speaker 1: it in just a few minutes with our experts here
Speaker 1: at Bloomberg. It's the lifeblood of the finance world, the
Speaker 1: numbers that tell us about the state of the economy.
Speaker 2: The August data was at least what in manufacturing PMI
Speaker 2: it was disappointing.
Speaker 1: Flash PMI survey data for JINE signals a slowing pace
Speaker 1: of economic growth. The latest payrolls report coming in below
Speaker 1: estimates US jubsdsa.
Speaker 2: New data data data, data, data data.
Speaker 1: There's a deluge of data available for major economies. But
Speaker 1: to misquote George Orwell, some data is more equal than others.
Speaker 1: Think about them many different ways that we measure inflation
Speaker 1: or the labor market, job openings, job creation, unemployment all
Speaker 1: tell you something different, and everything from economic growth to
Speaker 1: purchasing manager index surveys can get significantly revised between the
Speaker 1: first and last versions. So here's why some economic data
Speaker 1: matters more than others. We'll also tell you how to
Speaker 1: separate the signal from the noise. Joining me now is
Speaker 1: our global economy reporter and a current and a great
Speaker 1: to have you with us. You're a man who knows
Speaker 1: your numbers. There's always this question of when we get data,
Speaker 1: whether it's telling us what was happening in the past
Speaker 1: or what's happening right now, or giving us a hint
Speaker 1: as to what's going to potentially happen in the future,
Speaker 1: how do we attach different levels of importance to those timeframes.
Speaker 2: Yeah, so some of the numbers we get are very
Speaker 2: backward looking, like, for example, when you hear people talking
Speaker 2: about GDP data on the news headlines, that's typically telling
Speaker 2: you where the economy was maybe a quarter ago, So
Speaker 2: in economic terms, that's kind of ancient history. Conditions can
Speaker 2: change quickly. Economists like to talk about what they call
Speaker 2: high frequency indicators, data points that are given more timely
Speaker 2: read and what's happening and there. For example, you might
Speaker 2: look at retail sales, retail spending on Main Street. That's
Speaker 2: a good indicator of consumer confidence. You might keep an
Speaker 2: eye also on what's going on with boring financing at
Speaker 2: from banks. If banks are lending lots of money, that
Speaker 2: suggests that there is animal spirits and a willingness to
Speaker 2: invest out there by cuparts and maybe for would be
Speaker 2: homeowners buy a home, that's a good signal. If you're
Speaker 2: not lending money, then it suggests that perhaps things are
Speaker 2: more subdued them you might have expected. So some of
Speaker 2: the numbers, as you say, it can be quite backward looking.
Speaker 2: It's better just to treat them as such. If you
Speaker 2: want to timely read, keep an eye on the more
Speaker 2: high frequency indicators.
Speaker 1: Yeah, I mean animal spirits. Depending on what kind of
Speaker 1: animal you're thinking about, I suppose tells you different things
Speaker 1: about it. How do we explain the contradictions that we
Speaker 1: sometimes see in the numbers? Sometimes they don't make so
Speaker 1: much sense lining up one against another. If we think
Speaker 1: about an example of maybe inflation so.
Speaker 2: Over the past few years. If you want to talk
Speaker 2: about the advanced economy world there's been the worst outbreak
Speaker 2: of inflation in decades that impacted everyone's living standards, So
Speaker 2: interest rates go up, and the cost of a mortgage
Speaker 2: and alone go through the roof as a result. Now
Speaker 2: we're in a phase whereby this inflation is well entrenched,
Speaker 2: so the pace of inflation has slowed dramatically in many economies,
Speaker 2: coming back to the area where central banks like to be.
Speaker 2: That's a good news story. But if you walk into
Speaker 2: the shop having heard it on the news, headlinds, you're
Speaker 2: still paying much higher prices than you wore only a
Speaker 2: couple of years ago. So I think, say in the US,
Speaker 2: for example, basket of groceries maybe twenty odd percent higher
Speaker 2: than what they wore before the inflation crisis struck out.
Speaker 2: And that's where you get into the difference between the
Speaker 2: rate of inflation, which is what the economists measure every month,
Speaker 2: versus the actual price level that you're paying in the store.
Speaker 2: And I think there is a disconnected and confusion there.
Speaker 2: People hear inflation's coming off, that doesn't mean prices are
Speaker 2: coming down now. To be clear, for prices to come down,
Speaker 2: that would need deflation, And when an economy is in deflation,
Speaker 2: it typically suggests that it has some real problems going on.
Speaker 2: So it's a tricky one at the moment. It's a
Speaker 2: tough pill for households to swallow. But we're at a
Speaker 2: point where inflation is slow, but for prices to start
Speaker 2: falling that would require something of a deeper shock to
Speaker 2: the economy.
Speaker 1: Yeah, and indeed, most of the conversations that you'll have
Speaker 1: with people will be about how expensive things are consistently
Speaker 1: rather necessarily how much they've gone up by. Another quirk
Speaker 1: that we follow very closely here at Bloomberg is data revisions.
Speaker 1: So we get sometimes several iterations of the same number.
Speaker 1: Why do we see sometimes very big revisions in the data.
Speaker 2: It's mostly because, as I say, a lot of these
Speaker 2: readings are snapshots in time. They are incomplete. It might
Speaker 2: be on a monthly basis, or maybe a quarterly basis,
Speaker 2: and as the months of the year ago goes by,
Speaker 2: and maybe after another year or so. The kind of
Speaker 2: agencies whutalitates, the statistic agencies and the government economic agencies
Speaker 2: put all the numbers together when they have a more
Speaker 2: complete picture, and that's when they are able to say, oh,
Speaker 2: we overstated something there, or we underestimated something there and
Speaker 2: they make changes to what we're previously now. And so,
Speaker 2: for example, the US employment data, and this is true
Speaker 2: of employment data anywhere, can be subject to material revisions.
Speaker 2: Which has had recent revisions to US jobs at it
Speaker 2: whichhou suggest there were eight hundred thousand less jobs than
Speaker 2: originally counted in the system. That speaks to a weekly
Speaker 2: labor market than was broadly expected. Now the jobs market
Speaker 2: still low kind in the US, but it goes to
Speaker 2: show you that revisions can have a material impact.
Speaker 1: Yeah, and look, it also speaks to the idea of
Speaker 1: getting the right data and data that is accurate, and revisions,
Speaker 1: I suppose get us closer to what is a better
Speaker 1: picture of what's going on in something like the jobs
Speaker 1: market as well. There's an old joke about you put
Speaker 1: ten economists in a room and you get eleven opinions.
Speaker 1: How much can numbers be open to interpretation?
Speaker 2: There is a degree of interpretation because it could suit
Speaker 2: someone's investment thesis. They will want to read numbers whatever
Speaker 2: way it is to back the argument they're making. Numbers
Speaker 2: can be interpreted to meet somebody's political bias or political outlook.
Speaker 2: For example, so when we had the recent interest rate
Speaker 2: cut in the US, for example, you had one side
Speaker 2: of politics here saying it shows that the inflation story
Speaker 2: is under control and the FED is at a point
Speaker 2: that work can bring down interest rates. That's good for
Speaker 2: costs of living. But of course you had the other
Speaker 2: side of the political a ide here making the point
Speaker 2: that interest rates coming down because the economy is losing
Speaker 2: jobs and the jobs market is weakning. So everything can
Speaker 2: be interpreted in different ways, but ultimately, one of the
Speaker 2: good things with economics is the numbers and the data
Speaker 2: and the statistics do not lie.
Speaker 1: So if you're looking for the most quality data or
Speaker 1: the best things to look out for when you're trying
Speaker 1: to assess numbers as they're being published, what are the
Speaker 1: sort of things that you think about when you're trying
Speaker 1: to parse what a certain number means to the economy.
Speaker 2: You have to look for the numbers that really speak
Speaker 2: to what's happening in both people's lives and in companies' lives.
Speaker 2: So that would be figures around corporate investment, business investment.
Speaker 2: What's happening there with companies. Have they got the confidence
Speaker 2: to go out and expand and hire new staff, Then
Speaker 2: obviously you have to keep an eye on what's happening
Speaker 2: with household credit. Are people taking out mortgages to buy
Speaker 2: a house or those who have a mortgage taking out
Speaker 2: a loan to renovate the house. That speaks to, of
Speaker 2: course confidence in terms of consumer confidence of those all
Speaker 2: around us. And then of course you have the very
Speaker 2: timely monthly or even quarterly readings in terms of inflation,
Speaker 2: what is going on with the price that we're paying
Speaker 2: for goods and services? I mentioned the jobs at earlier,
Speaker 2: that's obviously a very critical one. And then all of
Speaker 2: that creates the jigsaw that is known as a GDPGIC.
Speaker 2: So that's backward looking, but it's a health check and
Speaker 2: it tells you worth economy has been And.
Speaker 1: You've covered economies all over the world for Bloomberg. I
Speaker 1: wonder do you have a favorite piece of data? Is
Speaker 1: there one that still you get excited about trying to
Speaker 1: read into the details of well.
Speaker 2: I used to get excited about. There was a phase
Speaker 2: when satellite data on China was a big thing, especially
Speaker 2: among hedge funds. Was popular that someone had the latest
Speaker 2: satellite footage of some industrial expansion or development somewhere, maybe
Speaker 2: some housing property site being developed, and they were claiming
Speaker 2: that they were getting an early read and what's happening
Speaker 2: in China's economy. But I think we've passed that phase now.
Speaker 2: During the pandemic, there was a huge rush on high
Speaker 2: frequency indicators, so people wanted to know what's happening with
Speaker 2: cinema tickets, and what's happening with eating out, and what's
Speaker 2: happening with.
Speaker 1: Samwiches from press.
Speaker 2: I remember that one all of this and usage of
Speaker 2: the subways and truth, we're back to where we started.
Speaker 2: We're looking at the official data that comes out of
Speaker 2: the agencies, and people are keeping an eye on as
Speaker 2: I mentioned earlier, spending data, keeping an eye lending data,
Speaker 2: jobs data, inflation data. I think the satellites and SANDWIDG
Speaker 2: indexes were all very interesting, but we've come back to
Speaker 2: what we know and trust.
Speaker 1: Most returned to the classics and the current. Our Global
Speaker 1: Economy reporter, thanks very much for joining us for more
Speaker 1: explanations like this from our team of twenty seven hundred
Speaker 1: journalists and analysts around the world. World search for Quick
Speaker 1: Take on the Bloomberg website or Bloomberg Business app. I'm
Speaker 1: Stephen Carol. This is Here's why I'll be back next
Speaker 1: week with more thanks for listening,