Latest / Monetary Policy Explained with Fexingo: Central Banks, Money Supply, and Interest Rates / Why Central Banks Care About Your Online Shopping Habits
Transcript
- Lucas: Okay, Luna, so we talk a lot about how central banks watch the economy. They look at inflation reports, jobs numbers, GDP, all that. But what if I told you that they're now watching something much more immediate — like, literally, your morning coffee purchase? Luna: You mean they're tracking our actual spending? Like, from our credit cards? Lucas: Exactly. And not in some distant future — this is happening right now. A growing number of central banks are using real-time payment data from networks like Visa and Mastercard to see how the economy is moving, almost by the day. Luna: That's a big shift from the old way of doing things, right? I mean, we used to wait weeks for the monthly retail sales report. Lucas: Right. And that's the key point. Traditional economic data is like looking at a rearview mirror — it tells you where you've been. But with payment data, central banks can see the road ahead, or at least the immediate present. The Bank of England, for example, has been experimenting with this since around 2016. They partnered with a company called Falkirk, which aggregates anonymized data from payment processors. Luna: Anonymized — that's a crucial word. So they're not seeing individual names, just patterns? Lucas: Exactly. They get aggregated, anonymized information. No one at the bank can see that you, Luna, bought a latte at 8:15 this morning. They just see that, say, spending on coffee shops in London rose 3 percent in the last week. Luna: But how is that different from the old surveys? I mean, we already have consumer confidence surveys and retail sales data. Lucas: So, the big difference is speed and granularity. Traditional retail sales data from the Office for National Statistics comes out with a lag of several weeks. And it's a sample — they survey a bunch of businesses and extrapolate. With payment data, you get near real time information, sometimes with just a two-day lag. Plus, you can slice it by region, by sector, by even time of day. During the pandemic, this was a game-changer. The Bank of England used this data to track the immediate impact of lockdowns on spending — they could see the plunge in hospitality spending almost as it happened. Luna: I remember that. It was like watching a cliff dive. But what about the Federal Reserve? Are they doing the same thing? Lucas: The Fed has been a bit more cautious, but they've been exploring it too. They have something called the 'Daily Treasury Statement' which gives them a lot of payment data, but they're also looking at private-sector data. There was a paper from some Fed economists a couple of years ago that looked at using debit card data to estimate GDP in real time. And I know the New York Fed has done some work with anonymized payroll data too. Luna: So, is this just about speed, or is it actually changing how they make decisions? Lucas: That's the million-dollar question. So far, it's mostly been a supplement to traditional data, not a replacement. Central bankers are cautious by nature — they don't want to make a rate hike based on a blip in one week's credit card data. But it's becoming a bigger part of the picture. For example, when the Bank of England was deciding on rate moves in 2020 and 2021, they were looking at these high-frequency indicators to gauge the strength of the recovery. Luna: It feels like a double-edged sword, though. Because with more data, you might get more noise. How do they separate the signal from the noise? Lucas: Good question. And that's where the statistical work comes in. They have to adjust for seasonal patterns, for one-off events, for differences between payment methods. Not everyone uses a credit card — a lot of people still pay cash. So they have to figure out how to extrapolate from that sample. It's not trivial. But I think the consensus is that even with the noise, having a real-time read on the economy is better than waiting for a lagged survey. Luna: And what about privacy? I get that it's anonymized, but people might still feel uneasy about the central bank knowing their spending habits. Lucas: Yeah, that's a real concern. The central banks are very careful to emphasize that they only see aggregated data, and they have strict protocols. The Bank of England, for instance, uses a third-party aggregator so they never see raw transaction data. But I think the bigger issue is that the private sector — like Visa and Mastercard — already have this data, and they use it for their own purposes. So the question is, should central banks have access to it? And if so, under what safeguards? Luna: I mean, it's one thing for a private company to use your data to target ads. It's another for a government institution to use it to set interest rates, which affect everyone's mortgage payments. Lucas: Exactly. And that's why this is such an important conversation. It's not just about the technology — it's about the governance of that data. And I think central banks are aware of that. They've been pretty transparent about what they're doing, which is good. But as these tools get more sophisticated, the potential for misuse grows. So there's a balance to be struck. Luna: So, what's the next step? Are we going to see central banks using this data to make real-time policy decisions, like adjusting rates every month? Lucas: I don't think we'll see monthly rate decisions based on this alone. But we might see more 'high-frequency' guidance from policymakers. For example, they might issue statements that reference 'recent spending trends' based on this data. And it could help them respond faster to shocks, like another pandemic or a sudden supply chain disruption. The bottom line is, this is a paradigm shift in how central banks see the economy. Luna: It's like they're getting a new set of glasses. Everything becomes clearer, but you have to learn to adjust to the new vision. Lucas: That's a great analogy. And speaking of adjusting to new visions, you know, this whole conversation reminds me that we keep this podcast ad-free. And honestly, if today's episode was worth a coffee to you, that's the link — buy me a coffee dot com slash fexingo. It's a small way to keep the show going. Luna: Yeah, it really helps us keep the lights on. And we appreciate every bit of support. Lucas: So, going back to the data — another interesting angle is how this real-time data is changing the way central banks measure inflation. For a long time, inflation was measured by sending people to stores to check prices. Now, with online transaction data, they can track prices across millions of products in real time. Luna: Wow, that's a huge change. So, does that mean the official CPI numbers might be replaced by something more immediate? Lucas: Not fully replaced, but definitely supplemented. There are already experiments with 'daily inflation' indices using online prices. The Billion Prices Project at MIT, for example, has been doing this for years. And central banks are starting to incorporate these into their analysis. The idea is that you can see inflation pressures emerging much faster than waiting for the monthly CPI report. Luna: But doesn't that create a problem? What if online prices differ from in-store prices? You might get a distorted view. Lucas: That's a valid concern. That's why they can't just replace traditional measures. But as a complement, it's powerful. For instance, during the early days of the pandemic, there were shortages of certain goods, and online prices spiked quickly. Traditional CPI data took months to reflect that. With online data, policymakers could see it almost immediately and potentially respond. Luna: So, is it fair to say that central banks are becoming more like data scientists than traditional economists? Lucas: That's a great observation. There's definitely a shift in the skills they're hiring for. They need people who can handle big data, machine learning, and real-time analytics. But they still need the economists to interpret what the data means. It's a blend of both. And I think that's a good thing — it brings fresh perspectives to old problems. Luna: I guess the challenge is that with so much data, you might be tempted to react to short-term fluctuations that are just noise. Lucas: Right, and that's the danger of real-time data. Central banks are well aware of that. They talk about 'looking through' short-term volatility. So even with this new tool, they're likely to maintain a longer-term perspective. But the point is, they can spot turning points faster. And in a crisis, that could be crucial. Luna: It's fascinating how central banks are adapting to the digital age. I never thought about how my debit card swipes could influence monetary policy. Lucas: It's a quiet revolution. And it's only going to grow. As we move toward digital currencies and more cashless payments, this data will become even more comprehensive. Some central banks are even exploring the idea of using their own digital currency transaction data — though that's a whole other can of worms. Luna: So, what should our listeners take away from this? I mean, beyond the fact that big brother might be watching their spending. Lucas: I think the key takeaway is that the way we measure the economy is changing. It's becoming more dynamic, more responsive. And that could lead to better, timelier policy decisions. But it also raises important questions about privacy and data governance that we need to keep an eye on. So, the next time you tap your card, just think — you might be generating a data point that helps set interest rates. Luna: That's a wild thought. And on that note, I think we've covered a lot. Thanks for joining us, everyone. Lucas: And remember, if you found this valuable, you could support us at buy me a coffee dot com slash fexingo. We'll be back tomorrow with more. Until then, keep spending, and keep the data flowing.