Beyond GDP, with Diane Coyle on Two Think Minimum

Scott Wallsten: Welcome back to Two Think Minimum, the podcast of the Technology Policy Institute. I’m Scott Wallsten, President of TPI. Today is Monday, December 1st, 2025. Our guest today is Diane Coyle, the Bennett Professor of Public Policy at the University of Cambridge, where she is Research Director at the Bennett Institute. She’s back for a second visit. We spoke with her in 2021 about her book Cogs and Monsters, where she argued that economics needed to reckon with increasing returns, network effects, and political feasibility. Her new book is The Measure of Progress: Counting What Really Matters. In it, she argues that the framework we use to measure the economy, developed in the 1940s, is increasingly inadequate for understanding a world of intangible assets, digital services, and global value chains.

GDP, she says, has become a distorting lens rather than a clear window into economic reality. And as AI reshapes how businesses operate and create value, the measurement problem is only getting worse. We’re making policy decisions about a technology we can’t properly measure, using statistics designed for a manufacturing economy.

Scott Wallsten: Diane, welcome back to the podcast. It’s great to have you.

Diane Coyle: It’s great to have another conversation with you.

Scott Wallsten: Let’s start off by talking about what may have changed or not. In 2014, you wrote a Brief But Affectionate History of GDP, and emphasized that GDP was still correlated with things that we care about, like jobs and income. Your new book is much more critical about GDP. Has it become more problematic in the last decade, or were you too charitable before and decided that the problems were already serious?

Diane Coyle: I think things have changed in the last decade. I guess I’d still argue that GDP is correlated with things that matter, the jobs and incomes. And we see that to the extent that slow growth and the affordability crisis are affecting the way people feel about how the economy is going and about politics. But the correlations are weaker, and that’s mainly because of the way that the economy has changed over that intervening period.

Even 10 years ago, a lot of the value created in the economy was intangible. In fact, that’s been the case for a long time. But the role of intangibles, the new technologies, seemed to me to have led to a bigger wedge, if you like, between what GDP does and can measure and the way that these intangible things—be it data, ideas, patents, all of those things—are driving both the creation of economic value and the distribution of economic value, who’s getting what out of the economy. So I do think things have changed.

The other thing that’s happened is that we had a major revision of the system of national accounts, the major international framework for defining and measuring GDP, approved earlier in 2025, but it’ll be implemented over the next few years. I guess I had higher hopes for how much change that would introduce, and actually, the change has been very incremental. So I thought the statistical community would get its act together a bit better than it has.

Scott Wallsten: What did you hope would happen compared to what actually happened with those revisions?

Diane Coyle: I hoped that there would be much more action on this intangibles front, because even in its own terms—you know, set aside the debates that people have about whether we’re measuring areas like natural capital properly, because that is clearly important for many economies. Set that aside. Even on its own terms of measuring productive activity in the market, GDP’s not doing very well, because the data collection about the very transnational digital sector just isn’t very good.

So we’re only just now beginning to get quite broad brush, quite aggregated data on value added and trade. In other words, these long chains of intermediate components that are traded across borders and end up in the final product. If you want to understand which countries are gaining from trade, you need that value-added approach. We’re only just getting it, it’s very broad brush. And just think of how the debate about tariffs and trade in the United States might have gone if we’d had that kind of information five years or ten years earlier.

Scott Wallsten: Do you think that information would have made a difference in the debate on tariffs? I mean, so much of it seems to be driven by ideology, unfortunately, at least in the U.S.

Diane Coyle: I’m sure it’s driven by ideology, but ideology can be informed or swayed by evidence, and understanding that many great American businesses rely on the imports of components would have been a factor, I’m sure.

Scott Wallsten: Let’s come back to intangibles for a minute. Carol Corrado and Chuck Hulten, for example, have said we just need to treat intangibles more like R&D and software as capital investments, and that that would raise measured productivity growth. But I think you don’t think that would get us far enough, or maybe you used to be more in favor of that kind of approach and now aren’t quite as aligned with that. How do you differ from what they say, and how should we be taking intangibles into account?

Diane Coyle: There are several ways to respond to that. One question in my mind is data and the role of data in driving AI. If you think about what creates productivity growth in the economy, and ultimately can lead to higher living standards, that’s about using technologies to do things in a different way—changing workflows, changing processes. In the jargon, process innovation rather than product innovation. And to do that using the new generation of digital technologies, AI and machine learning, you need the data.

For businesses and for public bodies alike, understanding things like what is the business case for investing in data needs some better measurement. How does it create value? For an intangibles-intensive economy like the United States or United Kingdom, how do you think about what role data plays in that, and what investments in data ought to be happening? So we’re sort of swimming in the dark there. Nobody really knows, except for the relatively small number of companies that do have a lot of data and understand its value to their position in the market.

But it’s a broader question than that. There are countries—India would be one example—where they see what’s called the data layer of their digital infrastructure as being crucial to future economic prosperity, but also better public services for ordinary people. So data is one big gap, in my view. We don’t measure it very much at all. We don’t understand how it creates value.

Another one in a similar vein is organizational capital, or in other words, what is it that companies know or do about the way they organize their activities that makes them valuable. For a company like Walmart in the 1990s, a lot of the productivity gain in the United States came from—literally came from—Walmart’s introduction of much more sophisticated logistics. That’s an aspect of organizational capital. But it’s not really clear how to define and measure that. It’s an area that I’m thinking about at the moment. I think if we understood that better, we’d have a better handle on why some businesses are adopting AI and some not, why it’s driving productivity in some cases and not in others. So I think there are just gaps in the kinds of insights that would help us understand how to make the technology deliver growth—GDP growth—and make people better off.

Scott Wallsten: Tell me more about the kinds of things countries like India are doing. We don’t normally think of India as being on the forefront of data collection. What is it that we can learn from them? Or are we learning what not to do, for example, the problems that they’re having?

Diane Coyle: There are problems, but what they’re doing is creating what they term digital public infrastructure. There’s a lot of interest in other low- and middle-income countries in learning lessons from this example. India has created a digital identity system called Aadhaar. People identify themselves biometrically. That gives them access to public services, an increasing number of private services, which are enabled by the data layer in this technology, which is public databases.

This is unlocking access to public services, better quality public services, and the potential for the private sector through APIs to the data and identity layers to deliver their services better as well. So it’s seen as a potential driver of economic development. For sure, there are problems, and one of the issues about using AI in any public services is, what if it gets it wrong? Are there mechanisms for correcting the errors which are about to happen? The Indian press has had many examples of there not being scope to correct the errors. So certainly implementation problems.

But another example might be the PIX payment system in Brazil, which again has a digital identity component to it. So thinking about digital infrastructure and the value that can be created by data—for these two large middle-income economies, this is seen as a pathway to future economic growth.

Scott Wallsten: Do you think that a barrier to changes like that here or in more developed countries might be things like privacy issues? Because if you talk about people needing to give up biometric data to the government or anybody else, there are immediate privacy concerns, whether justified or not. And it sounds like that’s a big component of this new sort of data collection that other countries are trying. Not that that’s the only thing that’s possible, but that seems to be part of, at least, that approach.

Diane Coyle: Of course, there are privacy concerns in any digital technology, and I think we’re all aware of the fact that other people know much more about us, and arguably private large companies more than our governments. So I completely understand that there are concerns about the government being able to join up all of the information about an individual.

It doesn’t have to be structured that way. It can be done in privacy-preserving ways, and you can enable businesses or public agencies to access data about people without taking the data. They can address it where it sits, and it can be done in privacy-preserving ways. So the technology has improved a lot there.

I think, though, ultimately there’s a trade-off. Do we want our societies to be able to access the benefits, or do we care about privacy enough that we don’t want them?

Scott Wallsten: Let’s go back a little bit. At one point, you sort of agreed with this dashboard approach, a dashboard of indicators that would help this problem, and now you’ve moved away from that. In your book, you explain the things you think won’t work with it anymore, although you still seem to be sort of open to the idea. Explain what people mean when they talk about a dashboard of indicators, and why you think it’s not the right solution anymore.

Diane Coyle: I guess I was always a little bit ambivalent about dashboards. The reason for having a dashboard at all is that we all understand that you can’t capture everything about the economy in a single number. Compelling as that number is—you know, GDP growth is the headline indicator—but it doesn’t tell us anything about the distribution of that growth. It doesn’t tell us about the use of natural resources, or access to housing, or the quality of the infrastructure. There are all kinds of things that we might care about that aren’t captured in GDP. So for many years now, there’s been a case made to put together dashboards, and there are lots of dashboards.

Scott Wallsten: That, for me, is one of the problems. There are lots of dashboards.

Diane Coyle: Yes. There’s something sort of arbitrary about what you put in your dashboard. And then anyway, there’s that question about if you’re trying to make decisions, whether it’s in business or in government, on the basis of all these various indicators, you’ve got to trade them off against each other, or weight them together in some way, so you end up anyway with a single number. It’s a go, no-go decision about something. So I’ve always had that ambivalence about it.

And people don’t understand that those weights actually do imply judgment. There are judgment calls in those weights, and they’re not always very clear, very transparent.

So I still think, I guess, that there are several indicators that any government’s going to want to know about. Whether you call it a dashboard or whether you just call it keeping on top of the economic statistics, it doesn’t really matter.

The way my thinking has changed is toward having what I would call a balance sheet alongside the profit and loss account of the GDP numbers. Having a sense of what’s the economy’s balance sheet? What are the assets available to an economy to grow, not just this quarter and next quarter, but in a sustained way over a long period?

That balance sheet has lots of things in it. It has the obvious physical capital, the housing stock, the infrastructure. You can have the mineral resources of the economy. You could also add other natural resources that are not priced, like the air quality and water quality, which matter for people’s health and ultimately the economy. You can add human capital, so what are the skills and health of your workforce? You could add what’s called social capital or institutional capital, the kind of thing for which the Nobel Prize was awarded last time, about how do people organize themselves collectively to make the economy work well. So you could make it really quite broad like that—all of these things that the economic literature tells us are important for future economic growth.

And there’s an economic theory behind it about how you weight all these things together, about the way you turn it into monetary terms, so that you can compare—you know, we’re doing great on physical capital but not so good on infrastructure, and we’re doing well on human capital and not so good on natural capital. So it’s a systematic framework, which you could say is a kind of dashboard, because you can get quite a lot into it, but it’s a dashboard of things informed by economic theory about what makes economies grow.

Scott Wallsten: So is part of this problem that with statistics like GDP, we’re focusing too much on flows and not on stocks, not on assets? We sort of ignore them in our national accounts.

Diane Coyle: Yeah, which, you know, of course no business would do, or no well-run business would do. Because if you’re depleting your assets, then your growth will come to an end.

Scott Wallsten: A lot of the problems that you talk about, aside from stocks versus flows, come down to prices that we aren’t measuring, or the prices don’t accurately reflect scarcity, and we don’t know values of non-market goods. Is it possible to—can one argue that we really should be focusing more on fixing prices than trying to fix the problems that are a result of bad pricing methods?

Diane Coyle: That’s a really interesting question. Part of my beef is just about data collection. There’s a lot of data that we just don’t collect, and the agencies are underfunded to be able to do it, and they’re not using new techniques and so on. So set aside those practical problems. What’s the biggest challenge?

I do think it’s interesting to think about prices. One is what you just alluded to, that there are non-market goods which matter. So how can we put a value on those? We try and use something that economists call shadow prices. How do you estimate those? Well, there’s a range of techniques to do so. You’re never going to get that feeling of solidity that you get from looking at a price in a market and saying it’s so many dollars. But there are methods for doing that. One of my colleagues here has been trying to do exactly that. It’s a big task, but setting up the figures in the first place was a big task, so we shouldn’t let that stop us.

There’s another set of challenges. The way that price indices are constructed is to assume that goods substitute for each other. And that means that you get into the very familiar debates about whose inflation measure are we looking at, because in reality you can’t substitute access to Netflix for buying meat in the supermarket.

Scott Wallsten: Right.

Diane Coyle: So that’s slightly bogus anyway, and there’s a long literature on should we have distributional price indices, which I completely sympathize with. But then there’s also this issue that for lots of goods, they’re bundled together, they’re complements, not substitutes.

I came across this forcefully looking at telecommunication services prices in the United Kingdom. The telecoms engineers said to the Office of National Statistics, your price index is nonsense, it’s been flat for ten years, and we know that there’s been amazing technological advance and huge increases in data flowing over the network.

And so when we looked at how to improve it, we came up with a range of methods that showed, rather than flat, something between a 40 percent and a 90 percent decline. So big decline anyway, but what’s the right number between minus 40 and minus 90?

That depends on—do you weight the components by the revenues the telecoms companies get from different services, when actually they manage to charge different prices for a legacy text message versus a WhatsApp message, which is free, so that’s a bit of an artifact. Or do you assume that every byte of data over the network is equal, and you have a volume-based measure? But the bytes are not all equal—we care much more about some than we do about others. And the fundamental issue is that it’s not the networks, and it’s not the data centers, and it’s not the ISPs, it’s the content that matters. But you can’t get the content without the bundle.

Scott Wallsten: Right.

Diane Coyle: And so I think it’s going to be really interesting to think harder about what we think our price indices are doing when we translate from dollar values to real terms or volume terms measures of GDP growth, which is what economists focus on.

Scott Wallsten: But some of this is putting together consumer surplus with what we measure as economic flows. The telecommunications example is a good one. When somebody’s connection goes from 1 megabit per second to 10 megabits per second, that’s a big deal, they get a lot more value from it. But going from 100 megabits per second to a gigabit per second is a bigger change but might deliver less incremental value to them. Is thinking about this in terms of consumer surplus, or maybe we should say total surplus, a productive approach, or is that something different?

Diane Coyle: Well, I think you’re correct, but we already do that, because we already have price indices whose aim is to measure what it would take to keep our standard of living, our utility level, constant. So we’re doing it already, and so we should think more about it.

There are economists like Erik Brynjolfsson who argue that we should be looking at the consumer surplus value of all the free digital goods and adding that to GDP. He’s got a measure called GDP-B. I think that has the issue that we’ve not got an infinite amount of time, so he needs to figure out a time budget constraint to put in there. But there are very respected voices arguing for making the consumer surplus calculation more explicit.

Scott Wallsten: You’ve talked about the problems of flying blind with our inadequate data collection and measurement issues. What do you think is the biggest overlooked or least understood issue right now because of these problems?

Diane Coyle: You want me to pick a single thing?

Scott Wallsten: It doesn’t have to be a single thing. It could be a host of things.

Diane Coyle: I think I would single out the way that business model changes are reflected or not in the statistics, and so our misinterpretation of what the statistics we have might be telling us. So that’s everything from global value chains and understanding where the value is being added along those chains, which I already talked about, but also platform business models, what’s called factoryless goods production, servitization—these are terrible jargon terms—whereby companies that you think of as manufacturers are actually getting most of their revenues from services.

This manifests itself in different ways. Sometimes it cuts across sector definitions, so manufacturing versus services will be one. But there are some definitions of sectors that don’t capture the way that companies do or don’t operate in different statistically defined sectors of the economy. We don’t measure ecosystems, which most technology companies operate in, be it biotech or AI. So there are lots of mismatches between the way many businesses operate now and the statistical framework that we put around what we’re measuring that make it hard for policymakers to understand what would better help value-added companies grow.

Scott Wallsten: Do you think these would help explain the productivity trends that we have trouble understanding, why productivity increases are so low now?

Diane Coyle: Yes and no. To some extent, I think there are a lot of measurement artifacts related to the kinds of things that I talk about in the book. On the other hand, we also have aging populations, and we’ve got very unequal income distributions that are likely to have some productivity impacts. So there are real causes, there are real headwinds on productivity, as well as not measuring it well.

But with the telecoms example, we added 0.2 percentage points to GDP growth for each of the years, and when your GDP growth is less than 2 percent, that’s really worth having. And that’s one service.

Scott Wallsten: Right, and that’s a huge amount for one service.

You actively advise policymakers on these issues. Do you find them being receptive to these ideas? Do you think that there’s potential for real change, even though the international standards that you talked about earlier—the changes were disappointing?

Diane Coyle: I’m afraid I’m going to give you another yes and no answer. The United Nations, almost immediately after announcing the new standards, announced a high-level committee of experts to have another look at measuring beyond GDP, so that’s still going on at the moment. They’ve just put out an interim report that recommends a dashboard. We’re going back around that loop.

On the one hand, politicians understand that things have not gone well in the economy. We’ve got very polarized voters, we’ve got a lot of people who are struggling to make ends meet, and that reflects some discontent. It’s hard to look at phenomena like obesity as linked to the food industry, or the cost of pharmaceuticals, and say the market economy is working well for everybody. So I think that’s well understood.

The bit that doesn’t follow is that politicians say, we need to really fund our statistical agencies to get to the bottom of what is going on and collect better data and develop new methods for doing it. On the contrary, there’s less and less funding going into the official agencies. So I think we’re in a world now where we need private sector companies that have lots of data to step up and do some public service in terms of their own analysis and telling us what’s going on.

Scott Wallsten: That’s interesting. You think that the solution isn’t going to come just from government, that somehow companies need to have incentives to contribute to—I don’t know what the right way to put it is—contribute to national statistics?

Diane Coyle: I do. I think it ought to be part of their social license to operate, actually. It’s not going to come from government at the moment, that’s pretty clear.

I’ve said to tech companies when I’ve talked to them, look, the talk about AI is all about psycho chatbots trying to urge you to commit suicide, or the jobs apocalypse. If you want to get any other message out there, you need to be sharing with us some of the insights that you can get from your data.

And we are starting to see that a little bit. I would like to see them be more open and share with official agencies as well, because obviously when it comes directly from them, you have to wonder about what they’re not telling us, or how are they shaping what they’re telling us. But I do think there is a kind of social responsibility on companies to reflect back to the citizens they are serving with their products and services insights into what’s going on.

Scott Wallsten: In Cogs and Monsters, you talked a lot about political feasibility. Does that perspective feed into this view, that the private sector needs to take more responsibility because you see it not being politically feasible for governments to do it?

Diane Coyle: Well, it would suggest that I’m being delusionally optimistic if I think the private sector is going to do this. No, I don’t know how we get to that nirvana of widely shared insights into the economy coming from the private sector. But something has to replace the old way of doing it, because the standard surveys that agencies send out to businesses to fill out—they’ve got terrible response rates, and we are getting a decreasingly detailed and timely view of what’s happening in the private sector.

Scott Wallsten: Now, this question—I feel like it’s a little too cynical, but there are so many attacks on statistical agencies around the world, and maybe no more so than in the United States right now. Are you at all concerned that these critiques give some legitimacy to an argument that you don’t agree with, which is to people who want to discredit statistical agencies?

Diane Coyle: I suppose that’s the danger. On the other hand, I do think the agencies have been very conservative. They have continued to insist, including through the revision process for the system of national accounts, that the way they were doing things was basically fine. They’ve not been very open to arguments about doing things in different ways. Very cautious, and wanting to carry on doing things the way they’ve always done things. So yes, in that sense, the critique does open them to attack.

Scott Wallsten: And then maybe trying to put more of an optimistic spin on it, sometimes we think big changes can’t happen without some kind of crisis, or at least a forcing event. Could these attacks open the way for changes that ultimately are productive? I mean, I’m asking—there’s so many hypotheticals built into this question, I don’t know that it’s fair.

Diane Coyle: I guess the big reason for optimism is that there are new data sources and new data techniques, and lots of people doing research on this. There are a lot of economists now starting to do research on statistics, and when I started—which would have been in around 2012 or so, I guess—it was a very solitary occupation. But that community of researchers has grown. People understand the structure of the economy is changing, and that the concepts that underpin the measurements and the processes of measurement need to move on. So I do think we will see quite a lot of intellectual progress on how to do it in the next few years.

Scott Wallsten: And in that vein, looking forward—you talked a little bit about AI already, and of course no conversation, I think, is even legally allowed anymore if you don’t talk about AI. But you’ve said recently, and I forget exactly how you put it, that we’re basically in a thick conceptual fog about AI. And now you’ve said that these companies should be more open or contribute more data. What do we want to know? What should we measure differently if we want to make good policy decisions on AI?

Diane Coyle: Well, there are two things I’m interested in, so let me tell you about those two things.

One is, what are the systems being designed to do? They have either an explicit objective function in machine learning or an implicit objective function—frontier models that are set to attain certain benchmarks or outcomes. I would like to know much more about what the models are being trained to do well, what they’re being optimized for. And particularly when they’re being used in high-impact areas—be it healthcare or criminal justice or whatever—that becomes very important, and that’s an area I’m really interested in.

The other area is, what are people doing with it? So much more about the applications that are being built on top of frontier models, and how companies are using that. There are starting to be ad hoc surveys in different countries looking at the use of AI by business, not so much by individuals. They tend to be about “are you using it or not” and not so much “how much are you using it” or “what are you doing with it”—so it’s the extensive, not intensive margin.

But I think that gives us more of a handle on: is this technology really going to add value in the economy and drive productivity, or is it going to turn out to be more marginal than that? I think both that there’s massive hype about AI and that it will turn out to restructure business processes quite a lot in the end.

Scott Wallsten: Anthropic released some public data for a week, I guess, in August, with prompts and linking it to various occupations. I’ve used it in some research, and I know others have too. And OpenAI did something similar. There’s an NBER paper about how it’s used, although that data itself, I believe, isn’t public. Is that the kind of information that you’d like to see more of, or do you want to see more of what’s behind the curtain, rather than how people use what they release?

Diane Coyle: I’m interested in both behind the curtain and in front of the curtain. The bit behind the curtain, I guess, is exactly this point about model design, really. There are very telling examples in the medical literature about how what appeared to be quite small changes in decision algorithms for ranking patients actually lead to very big differences in treatment outcomes and recommendations. So I think as the use in some contexts like that is going, without doubt, going to increase a lot, it behooves us to understand better what’s driving those differences.

Scott Wallsten: With any government agency—anybody really, of course—has a limited budget in what they can focus on. If you’re talking to the U.S. Census, BEA, for example, where would you tell them to spend their resources first? What needs to change? What should they be learning more about?

Diane Coyle: I think they should start by looking at the cost savings they can make using the new techniques. Things like quality assurance of the statistics, or actually conducting surveys, which are better done by AIs than by humans because the AIs are really patient. There are some opportunities to save costs, and that releases the budget constraint a little bit.

And then I would focus on—my book highlights several areas where we know that we’ve got real gaps in measurement that speak very directly to policy concerns, and I think the policy concerns should help you prioritize what you do. I guess I think they should spend less and find ways of making more efficient the standard national accounts collection they do, which is a big machine. It takes a lot of resource in the agencies, and that’s what they love, it’s what they least want to give up, but that’s where they need to make some savings to release money.

Scott Wallsten: That’s interesting. Every organization has a way that they like to do things and doesn’t want to change or has an interest in maintaining it. But I also think of the people who work in these agencies as extremely dedicated to what they do, and very apolitical. Do you think that they’re open to rethinking the ways in which they approach data collection and processing?

Diane Coyle: I think they’re going to be forced to, because—there are obvious pressures in the United States that are quite distinctive, but in the UK and other countries the budget pressures are just enormous, and I think they’re going to have to change the way that they do things as a result of that. Our Office of National Statistics has recently put out a statement saying, we’re just going to have to stop doing some of these things because we’ve got to focus on our core business.

Scott Wallsten: Historically, how did that work? You talked about how in earlier days, when we were more agrarian societies, the statistics focused on agriculture almost entirely. How did—what was the reaction among agencies to changing what they did then? Because it must have been similar. They were sort of set up to collect incredibly detailed data on crops and agriculture, and they had to change. Was there opposition at the time?

Diane Coyle: It seems to have been the result of political pressure, actually. There’s a very nice book by the historian Eli Cook about this, where he talks about the fact that parliamentarians couldn’t get the information they needed. They could walk through central London and see the poverty and squalor, but there were no statistics on the consequences of the Industrial Revolution that was going on all around them.

And so they had inquiries, and they published what were called blue books, special reports about phenomena. And that kind of pressure seems to have changed the way that the agencies operated. And then if you roll forward a few decades into the Depression, I think it’s pretty clear that governments wanting to get a handle on what was going on drove Simon Kuznets on your side of the Atlantic and a colleague, Colin Clark, on my side of the Atlantic to start putting together aggregate figures on the economy. So it does ultimately boil down to—this is taxpayers’ money, what do the politicians who represent us think is important for the agencies to do?

Scott Wallsten: And just finally, are you optimistic that we’ll see any of these changes in the nearer-ish future?

Diane Coyle: Yes, I do. I am optimistic. I think the technical possibilities are there, I think the pressures are mounting, and we can all see that the economy is changing around us, and many politicians in many countries aren’t quite sure what they ought to be doing about it. So I think we’re in one of those periods of transition, like agriculture to industry.

Scott Wallsten: All right, well, thank you so much for taking the time to talk with us. It’s really interesting, it’s fascinating.

Diane Coyle: Always a pleasure, and I’m always really pleased when somebody gets interested in economic measurement.

Scott Wallsten: Me too. Thanks.