The measure of progress: counting what really matters

Summary

Podcast Summary: "The Measure of Progress: Counting What Really Matters"

LSE Public Lectures and Events – January 22, 2026
Guest: Professor Dame Diane Coyle (University of Cambridge)
Host: Richard Davis (LSE)

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Overview

This episode explores the limitations of our traditional economic measurements—especially GDP—and asks how, and why, we should measure progress differently in an age shaped by digital technologies, shifting forms of work, and new forms of value creation. Professor Diane Coyle, drawing on her new book The Measure of Progress, discusses the inadequacy of current statistics to capture the realities of modern economies, how these gaps affect policy, and suggests new frameworks for what we ought to count if we want to understand societal progress.

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Key Discussion Points & Insights

1. The Problem with Traditional Economic Measurement

• GDP’s Outdated Framework
  Diane Coyle argues that “the invisibility of the economy as it is now in statistics is extraordinary” (03:47), emphasizing that policy is still driven by measures designed for a radically different world.
• Why It Matters:
  • Statistics are foundational for governance and policy, but lack tracking of crucial aspects like supply chains and new business models.
  • Real-world surprises (e.g., untracked bottlenecks in energy/fertilizer) exemplify the inadequacy of current data (04:13).
• Metaphor:
  “We don’t have the right shaped holes. The framework just doesn’t fit how the economy has changed over the past 80 years” (04:47).

2. The Origins and Function of GDP and National Accounts

• Historical Roots:
  Economist Sir William Petty’s early work, Political Arithmetic, and Keynes’ WWII-era focus on measuring the economy’s capacity to fight the war (06:22–08:25).
• GDP as a Flow, Not a Stock:
  “It’s what happens during a given period of time... what’s in the tubes, not what’s in the containers.” (10:06–10:19)
• Shortcomings:
  GDP does not reflect the value of unpaid labor, free digital goods, or informal and hidden economies.

3. Digital Economy and Uncounted Value

• YouTube and DIY Repairs
  Using free online resources to repair something (e.g., a boiler) reduces GDP because no market transaction occurs (11:18–11:58).
• Self-Checkout and Automation:
  The shift to self-service at supermarkets blurs the line between paid market labor and unpaid consumer labor. Productivity might appear to rise, but actual labor input hasn’t changed—it’s just unpaid (12:03–13:33).
• Shadow Pricing for Digital Services:
  “There’s no real agreement about how you figure out what [free digital services] are worth. ... One method is to ask people their willingness to pay.” (14:16–16:25)

4. The Hidden and Informal Economy

• Measuring Informality:
  Digital data like mobile phone and satellite imagery is used, but lacks the rigor of official statistics. Access to private sector data is an ongoing challenge (18:47–19:43).
• Emerging methodology:
  “The challenge is that they’re held by private sector companies... not collected with the same kind of rigor...” (18:47–19:28).

5. Changing Nature of Firms and Production

• Factoryless Goods Production & Servitization:
  • Iconic brands (Apple, Nike) outsource manufacturing, while many companies profit from services attached to physical goods (e.g., Rolls Royce’s aero engine monitoring) (20:42–23:08).
  • “The solution is that there are some services that come bundled with the basic product that you’re buying.” (22:31)

6. Time and Hybrid Work

• Wednesday Golf & Hybrid Work:
  The shift toward hybrid and flexible work blurs the home/work boundary, complicating time-use measurement and productivity calculations (23:51–25:11).
• Production Boundary Problem:
  The strict separation of ‘economic’ and ‘household’ production is increasingly unrealistic (25:33–26:13).

7. Toward Better Measurement—Diane Coyle’s Proposals

• Comprehensive Wealth Balance Sheets:
  Go beyond GDP flows by including broader ‘capital’—physical, natural, human, and social—in national accounts to assess sustainability (26:56–29:20).
• Why It Matters for Policy:
  Without valuing these new forms of capital, policy incentives (like tax breaks for physical capital) are incomplete or biased (29:20–30:33).

  “There are some really important trade-offs and choices... If we build housing in a flood zone... in five years time, the value of those assets will be destroyed.” (30:09–30:33)

• Data Collection Challenges:
  Many advanced countries suffer from declining survey response rates. While the private sector collects huge amounts of data, there’s no structure for sharing it with statisticians (32:02–33:40).

8. Time as a Metric

• Time-Use as a Budget:
  “You do have a budget for time. We’ve got 24 hours, can’t save it... One way to think about being better off is: Do we enjoy how we’re spending our time?” (34:53–36:13)
• Measuring Value of Time:
  Diarization, smart devices, and task-based studies are potential tools (36:53).

9. What We Measure Defines What We Value

• “Measurement... is going to be about what we value; you’re not going to be able to come up with any kind of new system... without getting measurement right.” (37:32–38:19)
• Diane’s hope: More conscious, critical engagement with the construction and meaning of economic data—especially as AI and big data encode similar philosophical assumptions.

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Notable Quotes & Memorable Moments

• On GDP:
  “GDP is a real number. And I go, you really haven’t understood anything about how this number is put together. It’s no more real than all kinds of other numbers. … They’re philosophical categories, really.” (38:19)
• On Data Resources:
  “How bizarre is it that governments are cutting spending on government data at a time when the whole of the private sector is investing massively in data...” (32:02)
• On Household Value:
  “The whole Internet runs on a few pieces of key open source software that are run by volunteers. And if they stop being maintained, the whole modern economy goes down. The value of that is incredible.” (49:56)
• On the Trouble with Imputed Values:
  “Imputed rent... is a notional rent that owner-occupiers pay themselves to live in their house. It doesn’t exist... One effort I’ve seen comes up with I think 15% [of UK GDP] for imputations. So you do end up with this being a strange beast. It’s not a real thing at all.” (52:03–53:41)

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Selected Audience Q&A and Responses

Q: How to handle political resistance to new, lower growth metrics?

• A: Politicians may push back, especially if net growth figures are lower when including human, social, or natural capital—but there’s also public pressure to move beyond “GDP that isn’t doing anything for people.” (42:30)

Q: How to value unpaid care or household labor?

• A: Use market wages for similar paid work, or the opportunity cost of the person’s time. Data remains patchy and much more research is needed. (45:58)

Q: Thoughts on happiness as a measure of progress?

• A: Diane is skeptical, preferring to focus on economics—allocation and use of resources—while acknowledging happiness is “the outcome that we want.” (57:13)

Q: Is time-use as a measure vulnerable to the same subjectivity as GDP?

• A: All economic measures have aggregation and value challenges, but “knowing how much time people spend on [various activities] is important for the policy debate.” (62:28–65:08)

Q: Has the hidden or underground economy grown?

• A: Hard to measure systematically, but likely significant; new digital and volunteer production (such as open source software) remains largely invisible in official stats. (49:32–49:56)

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Suggested Measures for Progress and Policy (49:56+)

• Time Use:
  “I would like to know how people are spending their time and what value do they gain from how they’re spending their time.”
• Living Standards, Human and Social Capital
• Comprehensive Wealth Accounts:
  Including natural, human, and social capital, not just physical assets.

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Timestamps for Key Segments

• [04:01] – Why the invisibility of new economic realities in statistics matters
• [06:02] – The origins of economic measurement: William Petty & WWII
• [10:06–11:58] – GDP as flow, and its limitations (DIY repairs, self-checkout)
• [14:16] – Shadow pricing in the digital economy
• [20:42] – Factoryless production, servitization, and modern firms
• [26:56] – Diane’s proposed solution: comprehensive wealth
• [34:53] – The importance of time-use as a core metric
• [38:19] – The philosophy and reality of economic measurement
• [49:56] – Q&A: What really matters, measuring the hidden economy, imputed value

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Conclusion & Closing Quote

Diane Coyle urges a rethinking of statistical frameworks so that what we measure—as societies and governments—aligns with what we truly value: human potential, sustainable well-being, and the multifaceted activities that define modern life. As Diane puts it:

“If people would start thinking more about where the numbers come from and what they mean, and are they telling us what we really want to know... that would be a step forward.” (38:19)

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Recommended: Buy and read Diane Coyle’s new book, come to LSE events, and critically engage with the statistics that shape our world.

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Skip to [26:56] for Diane Coyle’s own solutions and vision for the future of measurement if you’re short on time!

Transcript

[A]

Welcome to the LSE Events Podcast by the London School of Economics and Political Science. Get ready to hear from some of the most influential international figures in the social sciences.

[B]

Good evening, everyone. Welcome to the lse. It's great to see you all here. Hello there. See some. Some friendly faces in the crowd. My name is Richard Davis. I'm an economist here at the LSE and I'm delighted to welcome Diane Coyle to join us. I'll introduce our speaker properly shortly, but I've been given some exciting announcements to read out. First, phones. Please put your phones on. Silence. Second, in part, that's because we're recording this event. The event will be made available as a podcast and as a video, of course, along with all LSE events. There'll be lots of time for Q and A at the end, so please do get your questions ready for those joining us online. You're very welcome. I've got a screen in front of me with your questions, so get your questions ready. If you're online, please include your name and your affiliation. And at the end, there will be a book signing. Diane is here to talk about her book, the Measure Of Progress and the Gilded Acorn will be outside selling the book and Diane will be available to sign books. So why are we here? Why do we use ancient measures to understand the modern economy? Surely the entire world has changed in the past three or four years? With chat GPT in a nutshell, Professor Dame Diane Coyle argues that our measures are badly outdated, that this matters hugely for policy, and she offers us a route forward. Diane has a storied career in economics. She's currently the Bennett professor of Public Policy at the University of Cambridge. She's a writer. Early in her career, she was Economics editor of the Independent, and she is worryingly prolific for those of us that occasionally attempt to do books. She's done at least 10 books by my counting. They include this book, but also Cogs and what Economics Is and what It Should Be, gdp, A Brief But Affectionate History, and most closely amongst your others, related to this, I think, A Weightless World, which was your first book, in 97. She's a researcher. Her own research focuses on productivity, on the digital economy and AI. She's been hugely involved in policy. She started off at the HM treasury, she's currently a member of the Industrial Strategy Council. She's worked as a member of the Competition Commission, and she's held a range of public service roles, including at the BBC. And as a result of all this, in 2023, she was awarded a DBE for her services to economics and public policy. On a more personal note, I know that Diane has been hugely influential to many young economists myself, not so young anymore, but in my early stages as an economist, giving people a leg up, supporting people with the books that they do have written, their academic work. And she's also done things in her own way with the rigor of academia, but also the probing questionism, questioning that we get with the best journalism. So I'd like us all to formally welcome Diane to the lse. So this is a book all about measurement. It's a book all about value. Towards the end. You say the invisibility of the economy as it is now in statistics is extraordinary. Why does that matter? Why does it matter that the economy has become or is invisible to us in this statistical sense?

[C]

It matters for the policy decisions that governments take. The word statistics comes from state, that's the etymology of it. And governments, our government now says growth is its top priority. And we know that it's measuring growth by the increase in gdp. That has good things about it. But it might not be the only way. We want to think about how the economy is growing. And even below that headline aim, there are lots of policy questions that need good information. So if you cast your mind back to the pandemic or the energy shock, or cast your mind forward to the geopolitical events that are going on now, knowing about global supply chains and the potential vulnerabilities there, the bottlenecks, we were taken by surprise. Who knew that there were only two fertilizer factories in the UK when the energy crisis hit? And who knew that one of those produced all of the carbon dioxide used in food processing in the uk? So there are all kinds of bottlenecks that we don't know because those supply chains aren't mapped in the data. Things like the distinction made between manufacturing and services, I think we might come onto this in more detail is very unhelpful because a lot of companies that do manufacturing actually do a lot of services too. And so we have, I think, about this kind of game. If you've got children or grandchildren a certain age, there's a game where you've got to put the right shaped blocks in, into the right shape holes. And we don't have the right shaped holes. The framework just doesn't fit how the economy has changed over the past 80 years altogether, but the past 40 or 20 in particular.

[B]

Yeah, well, I want to dig into some of those problems, but let's jump back first in part because we share a little bit of educational history with a particular person that I think we should mention. Can you tell us a little bit about Sir William Petty, what he did and kind of why it matters?

[C]

I've got a slide of him. Oh. Of his book. So we're not the only people who cared about measurement. Here I am some years ago, holding a copy of William Petty's book, Political Arithmetic in Cheetham's library in Manchester. And the exciting thing about this photograph, and why I'm looking a bit smug, is that it was the copy used by Karl Marx and Friedrich Engels to study when they were writing the Communist Manifesto. Understanding what's happening to the economy turns out to be a big advantage, whether you're doing political economy analysis of a grand sweeping kind, or also during the Second World War. Understanding the capacity of the economy to produce and consumption needs is thought to have been a big advantage for the Allies in winning the Second World War. But William Petty was one of the first people to think about how would you aggregate the scale of the economy? In his case, it was to understand the size of the tax base and could the government of England afford to go to war with France, which it subsequently did. And so just this early pioneering effort to develop statistics, statistics for the state. He also attended Brasenose College, which I did too. And you also.

[B]

Yeah, he's. He's a very annoying person if you sort of compare yourself to him. I think he also invented the first catamaran. He invented, like, he sort of basically invented the photocopier. He was the first person to do that thing where you attach a kind of machine to a hand and you write a letter and it writes another letter. He just, like, invented so many different things. So he was one of the first people that had this. This motivation that. And he was a policy maker later in his life. We need good statistics to do good policy. I guess the other big kind of big moment, really in measurement comes after the Second World War with the emergence of gdp. So can you just give us a flavor of that, why it happened and what it meant to be, before we start then, taking it down as a concept.

[C]

The later efforts to measure the economy as a whole started in the earlier 20th century. People have heard of Simon Kuznets, who did this in the United States, somebody called Colin Clark, who was also attached to Brasenose College. There must be something about it. And counting did this for the United Kingdom. The Great Depression was a real incentive for governments to understand what was going on in the economy. But during the war. Keynes wrote a pamphlet called how to Pay for the War which bemoaned the fact that there were not good up to date measures of exactly those things. I was just mentioning the production capacity, the ability to produce material for the war, how much consumption would have to be held down. So it was an implementation of Keynes demand management approach which had been published just before the war in the general theory in statistics as a tool of being able to conduct the war better and understand the US and UK's financial position better. So that was how the current national accounts framework started. It was taken forward by pupils of Keynes in the years immediately after the Second World War. And the whole United nations framework for setting a standard that all countries are supposed to abide by developed after that. So the demand side was the first part of it. And then they added the production side of the economy as well. And the income side of the economy because it's a quadruple bookkeeping system. Quadruple entry. You've got the three income, expenditure and output all supposed to add up. Of course they don't in practice.

[B]

Yeah. And it's just to get everyone on the same page before we start, start critiquing it.

[D]

You've got.

[B]

Because we're going to talk about capital and wealth, but this is not a measure of a stock. As economists, we might like to call it a flow. It's kind of a measure of activity. It's kind of what's happening in a given year.

[C]

It's year by year or quarter by quarter. Now we even measure it month by month. But the Phillips machine here in the lse, and we have one in Cambridge as well, is what's in the tubes, not what's in the containers.

[B]

Yeah. So it's like it's what happens during a given period of time that takes us to gdp, that is GDP that takes us to growth, which is the change in gdp, as you mentioned, it's the government's overarching mission. It's the name of the team that I run here. So if there are problems with this thing, they're kind of big problems for us all. In the book you have a little vignette which I think we should dig into, where you talk about your boiler breaks. And if your boiler breaks, then what you do, and what many people in the audience will do is immediately look to YouTube and to find a video of how you can mend your boiler. I'm sure we've all done that. Maybe not for our boiler, maybe for something else. In some sense the world is better. You're happy, you've got a warm house when you're going to get a cold house. But GDP could have be lower. Can you explain why that is? And it kind of leads us into some big, bigger problems.

[C]

Well, of course, the other thing you have is the amazing sense of pride of being able to fix the boiler. What you're doing, though, is you take a free service, YouTube. There are things that we pay for. We pay our electricity bill, we pay our Internet service provider or our phone provider. But the plumbing service, the plumbing knowledge provided is free. And you are then substituting your free labour, part of your household labour, for paid labour in the market. So the immediate effect of that is that you're reducing GDP by the amount that you would have paid a plumber to do the work instead.

[B]

Because GDP only includes the market transaction, it doesn't include the work in the home.

[C]

And so one of the things that's happening with digital technologies is that some of the activity that used to be in the market is shifting out of the market. And there are a couple of problems with this. I think I've got a slide of another. So here's the other example that we're all probably aware of now, and that's going shopping. And this used to involve very little capital and paid labor. So. So in the 50s and 60s, you'd go to your green grocer and you'd say, I'd like half a pound of butter, please. And he would fetch it from behind the counter and you'd have a nice chat along the way. We then got supermarkets with some capital installed, but there were still people to put your groceries in a bag. And then the people putting the groceries in the bag got done away with. And now we have these automated checkouts where our unpaid labour has substituting for all that. So there's more capital involved, there's software involved, and the extreme, although I think they're not succeeding at the Amazon Go shops, where it's all capital and software and actually a lot of the labor has been done away with. So there's this transition and this is taking activities from the market paid for in GDP out of the market, and it affects how you interpret what's going on. So productivity for supermarkets might look as though it's going up with the automatic checkouts, but actually you're still doing the scanning, putting things in the bag. That's still the same amount of physical labor involved there. So until we get robots doing it for us, I'm not sure that there is a real productivity gain there.

[B]

And we know that productivity is one of the big questions for advanced economies, particularly the UK economy. And it's something that people spend a lot of time working on. So if they're not measuring it correctly, that work might be going to waste. You've done. So the YouTube example is like this. It's like open source software. It's something that's out there, we value it, we don't pay for it. The price is zero, basically. And that's one of the big, big challenges we have. Can you explain the notion of shadow pricing and maybe some of the work you've done on trying to kind of tease out what hidden prices should be, what sort of willingness to pay should be. For some of these digital services, there's.

[C]

No real agreement about how you figure out what they are worth. And people do it in different ways. You can think about it as a sort of barter transaction where we're offering attention and we're being shown advertising. So you can think about what's the size of the advertising market and does that help you measure the unpaired, the free services. But one of the things that I've done in this country, and Eric Brunelson is best known for doing it in the United States, is actually asking people, how much do you value this particular free service? We ran a survey in February 2020 and then in a stroke of luck, the lockdowns happened and we ran it again in May 2020. So we got some nice, a nice natural expression in how those valuations changed. And actually it was all quite plausible. So not surprisingly, the value people attributed to online shopping increased quite substantially. One thing we did was as well as a lot of free digital goods. And so the value of using Google Maps went down for obvious reasons because people weren't moving around. But the value of online shopping and other online services went up. We included some non digital services too, like public parks, and their value also went up. So I'm particularly interested in measuring what's going on in the digital economy so that we understand that. But this does open up questions about what? About other unpaid services and should we not put all of those in? And if you're putting all of those in and there are new things coming along all the time and I'm answering these surveys and I might say, oh yes, I would, I'd pay £10amonth to have access to search and the same amount to have access to public parks. You need a budget constraint, and that budget constraint needs to be how much time you have to spend doing all of these things. So I think there are some really interesting open questions for economists to think about in doing that. But at the moment it's one of the most promising ways we have of thinking about what are the positive benefits, benefits of all of these free digital services.

[B]

But I mean something that's come up recently, I'm not sure when the, the. There's a rude term that refers to the degradation of online digital services. Some of you may have come across it.

[C]

We're allowed to say the word we're not on national television.

[B]

It's the insurification hypothesis. Is it Corey? Corey Doctorates. It's really, really interesting. I encourage people to look at it despite the title, excuse me, it's his title, not mine. Because if we're not, it's very easy to critique and you know, there are lots of problems with social media and so on. That's the main thing we hear in the press about digital services. But actually we all use them and the research shows that they're actually hugely valued. And if this, it seems, if that hypothesis is right, that actually there's this kind of degradation going on, actually there's this kind of loss of, I'm not sure whether it be GDP or wealth, possibly both that we could miss because you know, something that we value, again we're not looking at it properly where it's invisible to us.

[C]

So if you're in the competition and markets authority and you're thinking about market power in digital markets, that degradation in the consumer benefit over time is really interesting number. So these kinds of studies are valuable.

[B]

Yeah, yeah. What about the hidden economy in a more in almost a simpler sense. So in, in my own work I've been to economies, for example the drc, Democratic Republic of the Congo that have highly, highly informal economies and because of that there's not really a way to collect statistics and amongst other things it's very, very difficult to raise tax. So it's very difficult for those economies to develop. Is there any hope? I mean we're going to, we're going to jump in a second to your solution but that's a kind of different type of hidden economy. It's because you don't kind of have the same state capacity to do that, to do that measurement. Is there anything because so much stuff is now digitized that we can use to help create better statistics in emerging economies and low income economies.

[C]

Yeah, people are using mobile phone data, they're using satellite data. Increasingly the digital companies have user data from economies all over the world. I Mean the challenge is that they're held by private sector companies and even if they make it available and that can be tricky getting access to that, they're not collected with the same kind of rigor in terms of sampling and reliability that official statistics are. So I think that's an open agenda. How do we use the new methodologies and still have the kind of rigor that goes into official statistics or at least when they're properly resourced.

[B]

So this is things like people use satellite imagery to go over slightly Orwellian but kind of look down on countries. You can do things like look at light use during the night to tell if there are factories running and things.

[C]

Like this changes in construction, you can, if it's fine enough resolution, you can look at number of cranes and understand building. So those kinds of physical manifestations.

[B]

Yeah. So you can, we can, we can do better to spot, spot gdp a big chunk. So there's a, there's, I guess there's a change that I think most people will be aware of in the book that drives the problem. And that's this in part, it's the digital economy. You know, we all, we all use these products. A big chunk of it is also actually changes in the nature of the firm, which I think unless people work in one of these firms that's doing this, they might not have been exposed to it. It was new to me. Could you. And there are some really jargonistic terms here which for once are not come up with by economists. They're from business, I think. Can you tell us a bit about factory less goods production and servitization and how those things affect measurements?

[C]

There's a mini quiz here, so put your hands up. Does Apple manufacture iPhones? No.

[B]

Everyone knows that one.

[C]

Does Mercedes manufacture Mercedes cars?

[B]

Surely does.

[C]

No, not that particular model. That's completely outsourced. Does Rolls Royce manufacture aero engines? Yes. Does it make most of its money from manufacturing aero engines?

[E]

No.

[C]

Good audience. So the first two are examples of factoryless goods production, which means what it says, although it is an ugly term and it's pretty common in some iconic cases. You know, Nike shoes would be another example. But also quite common in some sectors of the economy. So some in autos, some, some in pharmaceuticals, some in electronics and up to about 20% of production in those sectors is factoryless goods production. That's the firm level version of the outsourcing, the global production chains that we are much more familiar with. I suppose Rolls Royce is an example of servitization, most high value manufacturing now makes, adds most of the value, makes most of the profit and revenue from the services that are sold around the manufactured item. So Rolls Royce, you've probably been, has this fantastic huge factory in Derby. Really impressive. The wind tunnels where they test these engines is, is amazing. But they, the services they sell, the monitoring of the engines in real time as aircraft fly, you know, they essentially lease the engines and are responsible for, for maintaining them, spotting problems in advance and so on. So they, they make a lot of their profits from that. And that's a little hut in the corner of the site with row of people, rows of people sitting at computers. There was an example in the newspaper the other day. There's a company in Glasgow, a startup that makes miniature, miniature satellites. And a lot of people are interested in the miniature satellites, but more people, to the surprise of the founders, are interested in the data and the data services that they can now sell around the satellites. So it's again, quite a widespread phenomenon and you see this in the term solutions everywhere. Everybody's offering you a solution and the solution is that there are some services that come bundled with the basic product that you're buying.

[B]

So we've got the rise of the digital economy, free goods, we've got changes in the nature of production, the nature of the modern firm. I want to come on to your solutions in a sec. But a third trend that is going to lead to one of those solutions again. There was a striking thing in the book, just something I remembered from reading it, which is that Wednesday afternoon golf has massively increased, according to one researcher. Could you say a bit about how the nature, the way we're spending our time has changed and the problems that gives us in terms of measurement, it's.

[C]

Hybrid working, of course, that explains the Wednesday golf phenomenon. Whereas people always used to go to the golf course on Friday afternoon, they'd clock off a bit early and go and play golf. Now there's a Wednesday phenomenon. The proportion of people who are spending part of their time working in a hybrid way has obviously increased dramatically with the pandemic. And so you see this blurring of your household production time. You can stick the washing on when you're doing your work, your leisure time and your work time. If you don't get something finished, you're at home, so you carry on doing that. And, you know, still, I think, quite an unresolved debate about what the consequences are for the economy, for productivity, for individuals. They like it, they like the flexibility. They report higher productivity themselves. For organizations, it hasn't delivered higher Productivity and I think this is an interesting lesson for AI use as well. There's a gap between something that makes individuals more productive and whatever it is that makes organizations more productive. But the technology generally is allowing, is creating much more fluidity in how we as individuals spend our time and it has implications for how we understand productivity and what happens to firm productivity as well.

[B]

So I guess, I mean this is my interpretation of what you've written and what you're saying is even if you thought that it was the right thing to do, to have this boundary, and a lot of people think it's odd and when you actually read it, if you, if you do go and read the UN kind of descriptions of how national accounts should work, the kind of.

[C]

Product, only about 400 pages, so.

[B]

But there's a bit about the production boundary which is really odd. And you, and you read it and you think, yeah, no, this is philosophy, this is not a hard, you know, this could be done in a different way is what you said. Even if you thought that boundary was right, what you're saying is there used to be this boundary between home and work and now they're just completely intermingled because I might do a bit of home at work and then a bit of work at home. So even if you like that boundary, it's almost impossible to measure now, increasingly so.

[C]

And that's a reversion of course to the pre industrial days when that boundary didn't exist either. It was only when people started going out to work in the factories and the offices that the boundary became a feasible concept at all.

[B]

Yeah, yeah. So we've got a huge set problems with the existing, the existing data system that we have set up, the existing data frameworks that we have set up. What's the olution? You have two big buckets really in the book. One is about how we value things and what assets we should be looking at and the other is about time and how we, how we think about time. Maybe we could start with, with capital and moving from, you might say thin or minimalistic definite definitions of capital to a more comprehensive measure.

[C]

So you already mentioned that GDP is a flow concept. How much happens in a certain period of time that doesn't tell you anything about how sustainable, sustainable the growth that you're experiencing is. And so just as companies need a balance sheet to tell them that the economy needs a balance sheet for us to understand if today's consumption is only happening at the expense of tomorrow's because we're depleting assets always in the national Accounts there have been physical assets like machines, infrastructure, not always very well measured, but they're there. But much more recently we've started to add some natural capital because since the Second World War we've been using a lot of nature for free and depleting those kinds of resources. So obvious sustainability questions there. I would argue for an even broader concept where we would add human capital, which includes the health of the population and as well as skill level, innate abilities and so on. And potentially what economists would call organizational or institutional or social capital, which is how do good institutions of the kind that Nobel Prize winners like Daruna Simogli write about functioning law courts, cooperative neighbourhoods, effective firms with good managerial practices. We know from a lot of economic research that these make a big difference to productivity and growth. So should we also try to have a balance sheet that tells us about those? So it's called comprehensive wealth. It's a really broad balance sheet for the economy that introduces this idea of sustainability. Longer term, the pushback I get from national accountants and statisticians is, is that that's really complicated, I'm sure. But given that we know it matters, I don't think that's a great excuse for not trying to do better. And you know, GDP is really complicated. As I just said, the handbook about how you do it is hundreds of pages long. So it's a bit of a feeble excuse to say this is just too hard to do. I think we definitely need to try and do it. The tricky bit of it is then valuing those things. So let's start with physical constructs and then start to work on how do we value all of those assets.

[B]

Yeah, and I mean that would have big implications for policy if you could do that correctly. Because we fundamentally, it's baked in now after 80 years or whatever it is that we just value physical capital, we want physical capital. More capital leads to, by which I mean machines, plant and machinery, a new factory for a firm or whatever it is, leads to more output that's good for the economy. And because of that there are lots of policies that relate to those things, tax breaks and so on, which incentivize more of that. Whereas these other things that we don't measure and don't have as a focus, don't receive those, those nudges or those incentives towards people investing in them.

[C]

But there are some really important trade offs and choices. If we build housing in a flood zone, in a floodplain, that will increase GDP and it will create jobs and it will help solve the housing problem. But in five years time, the value of those assets will be destroyed and people will lose their homes and there'll be all of the costs of rebuilding that we have to pay bear. So there's a sort of time trade off there very, very immediately.

[B]

Yeah, yeah. I'm going to open up to the audience in a couple of minutes, so get your questions ready for Diane, please. Two more, I think, from me, the first one is a practical one, and I'm with you, that it's not, in some sense it's not good enough for the national statisticians to just say, you know, all of that sounds quite complicated, but taking their own remit, looking at their own remit of what they're doing at the moment. We also, many people may have read about this, but a good number of you may have not. We have this system of inadequate statistics, as you clearly said. But even that system is in crisis. And the crisis is because even our numbers that we think of as hard numbers that get reported on the front page of the FT and every other newspaper, gdp, the unemployment rate, the inflation numbers, those are all based on surveys or on selection samples of data. And the response rate for those surveys is declining over time and has declined quite rapidly recently. So now I completely take your point. Those statistics aren't good enough. We need these other ones, but we're not even able to get the data for those ones at the moment. What ideas are there out there in order to gather together the data we're going to need for this new system?

[C]

There are, I guess, a few points to make. One is how bizarre is it that governments, not just in the UK but elsewhere, are cutting spending on government gathering data that informs policy at a time when the whole of the private sector is investing massively in data because they appreciate that that's going to help them increase their revenues and profits and make decisions. So I find that strange anyway. And a lot of agencies have had budget cuts, including the Office of National Statistics here. There is an issue about price, private sector data, we know, we talked about mobile phones and satellite. There's the data that digital companies hold, there's scanner data that all the stores hold. There is a lot of data, sensor data, but no structure for that becoming readily ingested or economic model for that going to the Office of National Statistics and its equivalence. I think that kind of provision of data is part of the social license to operate for the private sector. They will benefit from it too. And we haven't got any kind of political consensus or public consensus about that. So that's a big debate that needs to be had. And then the technology itself offers some scope for making efficiencies. So things like checking data quality, it's not going to address survey fatigue. You know, we all probably get emails asking us to do surveys three times a day. But there are other techniques that we can look to so that the resource that's available can be concentrated on the areas where it's needed.

[B]

So it's really in a way, it's not in a country like the UK or another advanced country to recapitulate in different what you said in a different way. It's not like the example I mentioned of in fact formal economy where stuff just going on and it's not being measured. Actually the data. Let's take consumer expenditure, probably the data for consumer expenditure, albeit on lots of different systems, on lots of different bank systems, lots of different shop systems, that data actually exists for yesterday. And so it is at least feasible to gather that together and produce a better, better statistic.

[C]

Absolutely. Who uses cash?

[B]

It's all recorded being measured by somebody. Okay, final, final question then. I'm going to open it up. So do you get your questions ready? Final theme and I guess for me the most radical one I think because you know, I'd heard of people talking about different types of capital before human capital important and so on is really when you get into time and how we think about time and how we measure our use of time and whether we should actually think about a budget for time. So can you explain your thoughts there?

[C]

Well, you do have a budget for time. We've got 24 hours, got to use it up, can't save it. And so one way to think about are we as individuals getting better off is do we enjoy how we're spending our time and does it give us what we need? And so it's paid work and unpaid work and how much do you enjoy each of those activities and do you have leisure time and so on. So you can think about an individual's time budget and assign well being values to each way that each mode of spending your time on productivity. On the production side, the thing that technology does that drives productivity is it makes things faster. Steamships are faster than sailing ships. Manufacturing processes with robots that much faster than they used to be. So just in time production actually puts it in the name and was a huge source of productivity growth in manufacturing. And so thinking about speeding up those kinds of activities and on the other hand thinking about spending a lot of high value, high quality time on things that need it. So a trivial example would be, you know, going to the hairdresser and I want to have an hour with a really excellent hairdresser who's going to do the head massage and the whole works versus going around the corner to the barber for a quick trim.

[B]

We all prefer the former.

[C]

Or, you know, you're in hospital, either you want to have the test done really quickly and get the results back instantly, or if you're ill, you want to have devoted attention of lots of really highly skilled professionals for lots of time. And, and so either way, time becomes a metric of efficiency or quality in production. And so from both sides, it seems to me this would be a really interesting metric to think about.

[B]

And are these things surveyable or measurable in some way? Could we just could. A lot of people volunteer their smart watches, for example.

[C]

There have been different methods used. It's often a diary method. You fill in an online diary if you're at home. Time use at work is not surveyed very much, but that would be really interesting as well. And online, certainly it's pretty straightforward to measure the time people take to do certain tasks. And for generative AI, that's one of the key metrics people are using. How long can you leave the AI to do something by itself and you save all that time.

[B]

Final, final thing, thing, and then it's over to you guys. You say right at the start, quite powerfully, and I wondered if you could give us your thoughts on it now.

[D]

How.

[B]

Actually, this question, it might seem like a technical question, it might seem a bit of a kind of geeky type of question. And there are these other big swirling things out there, like we need a new model of politics, we need a new political economy, and so on. But actually you're quite clear and you say, look, measurement, what you measure is what you value. So ultimately this is going to be about what we value and you're not going to be able to come up with any kind of new system, improved system of these, perhaps higher aims, you might say, without getting measurement. Right. So what are your hopes for, for the improvement in data and how that can feed into a kind of improved political economy, whether it's needed for an improved political economy?

[C]

Well, we talked a while ago about the origins of the current system of national accounts. And I see them as very much part of that post war Keynesian consensus about how the economy ought to operate for people and the measures, the metrics that you needed to deliver that. And it's been a long time in the breaking down and I Think it's clearly not functioning well. Now, I suppose my first aim would be to get people to think about the data. How are these categories defined? As economists, we're so used to just downloading them and running regressions with them without thinking about how were they constructed. So people would say to me, well, GDP is a real number. And I go, you really haven't understood anything about how this number is put together. It's no more real than all kinds of other numbers. So these are, I mean, you said, as you said, they're philosophical categories really. And so I'm coming at it from 10 years of thinking about, from an economics perspective, improvements in the statistics that we have. So I certainly don't have the answer to what's the new big picture. But I think if people would start thinking more about where the numbers come from and what they mean, and are they telling us what we really want to know about? And this overlaps a lot with what's going on with AI because of course, the data constructs that are being used to run generative models and make decisions in areas that have huge impact on people's lives equally raises those very profound questions about what are you defining and measuring and therefore doing to people with your system of measurement.

[B]

Yeah, yeah. Thank you. Okay, over to you guys, please. If you have a question, please raise your hand. There's three in a row at the back there. Can we start at the edge there? There's a lady and a gentleman next to.

[F]

First of all, thank you very much for joining us today. So my name is Hanan McKeen. I'm a researcher here at LSE and I'm currently working on a study on measuring growth across Africa. And one of the key observations is that growth levels in terms of GDP have been lagging across the continent at around 3% since 2015. And applying the new framework that you suggest by incorporating human capital, social and institutional capital is likely to bring those figures even lower, which is going to be problematic to politicians. So my question is, given how politicized economic growth and GDP is, do you anticipate any pushback against your. From politicians, against your framework? And if so, how do you address this?

[A]

Thank you.

[B]

That's a, That's a fantastic question.

[D]

Question.

[B]

We've already got quite a few online and quite a few in the room. So I'm going to propose we'll take them as a couple. So can we take the question from the gentleman that was sitting next to you and then we'll go to the other side of the room. And online.

[D]

Yeah, thank you very much, Professor. My name is Zhi Hao and I'm actually studying for PhD in rural Hollywood University right now. So my question is. So given, given the situation, you also talk about like AI development and also the pandemic time that like the productivity of people and also the working mode and like to also people working remotely. So do you think, do you think pandemic actually delayed the development of AI or actually accelerated development? Because usually I talk with my friends or like other people from different industries. So some people saying that during the pandemic, pandemic, we actually, we should have actually all the things we are using right now about AI tools back in like 2020 or 21. But there are lots of people also saying that actually pandemic has actually accelerated all the things up and to, for example, like people working remotely. So the things can be actually accelerated development. So what do you think of this issue? Thank you. Thank you.

[B]

Great. Yeah, take those two.

[C]

So on the first question, for many countries on the African continent, if you start to look at things like depletion of natural resources, then you might well get lower figures, lower net growth figures than GDP growth. So at a political level, I completely accept that there might be pushback against that. The counterweight, I think, is that many politicians know that GDP growth isn't doing anything for people. And we've seen that within the UK as well. This sense that it's not my gdp, the widespread sense that the market economy is not working for a lot of people, that lives have been static and incomes haven't been, or living standards haven't been going up, and that I think, is the political counterweight. So at some point, I would hope that there's a political jump possible to a set of metrics that does allow you to not just measure what's happening, but improve what's happening in people's lives, because you've got the right guiding signals from the data may be wildly optimistic, and sometimes I'm much less optimistic about it. On did the pandemic accelerate AI? So I think a lot of the technical foundations for what we've seen now with generative AI were laid before the pandemic. And I'm not sure that I think it accelerated generative AI in itself, but it clearly accelerated lots of other behaviors, including familiarity with using online services and, you know, as I said before, switching away from cash to using electronic payments all the time. So there was clearly some impact on the world of digital in general, whether it's generative AI in particular. I'm much less sure about that. I'm not sure what the counterfactual would be, but in, I think in the counterfactual world we would have generative AI much as we do now.

[B]

There are three gentlemen in a row there. Let's go to them. But at first I'm going to go online while we get the mic to them. So we've got two questions, both sort of specific questions asking for a steer on what people have done. First one's from Anne. It seems your valuation of unpaid production, the application to domestic labour and unpaid care work. Have you attempted to value that kind of unpaid paid production or do you know of attempts to do so? So the care economy, domestic labor economy, and then what you talk, you talk down in the book about the importance of the next generation. Lovely picture. Here's one from the next generation of economists. I hope we've got Thomas Clark, who's a year 12 student, who says similar is a question, is there a way, a valid way to measure the negative effects that GDP growth can have on the environment?

[A]

Hi, I'm interrupting this event to tell you about another awesome LSE podcast that we think you'd enjoy. Lseiq asks social scientists and other experts to answer one intelligent question like why do people believe in conspiracy theories? Or can we afford the super rich? Come check us out. Just search for lseiq wherever you get your podcasts. Now back to the event.

[C]

Great questions on unpaid production. I haven't done that work, but there are terrific economists who do. Typically the approach taken has been to use market wages applied to the time spent on household activity. And those could be the market wages earned for somebody doing the same kind of activity. Or it could be the market wage earned by the person doing the activity if they went out to work. And those are different numbers, but that's typically the way that it's done. Doing that depends on having up to date data on how people are spending their time. And it would benefit from having a much clearer view about what kind of capital in the home people need. There's a wonderful Hans Rosling video about the washing machine being the best invention of the 20th century because it saved women so much time that they then had free to do things that they valued more. So, you know, I think having a much richer set of data on what's going on in households, particularly as digital changes, that would be really useful on the negative effect on, on GDP growth. So at the moment we don't get very much of the negative effect. What you need really is not gross domestic product but net domestic product and net off the monetary value of depreciation of the natural assets, depletion of the natural assets that are being used in economic activity. And some of that is measured, but not all of it. Measuring physical changes is relatively straightforward, although not easy. The value that you assign to that is much harder. And we have a project going on at the moment looking at how do you estimate the shadow prices, the sort of true economic value of changes in the quantity and quality of natural assets. Another quite optimistic project. I'm not sure we'll ever get the right answer, but we know that assigning a value of zero is that to that is not the right answer. So it's worth trying.

[B]

Yeah. And if Thomas is looking for a source on that, there's stuff on. On he can google you and and six capitals and stuff touching on environment will come up there, won't it? For sure.

[D]

Yeah.

[B]

Good. Okay, thank you.

[E]

Thank you. The title of your book is Counting what Really Matters. And you have given us a few clues about what you think really matters, like what's going on in households. And one I really liked was Measuring Living Standards. But if you had to pick a handful of measures or I might say two handfuls of measures, what do you you think the UK should measure so that it becomes a better place? Hi, thank you very much, Diane. That was fantastic. I was struck by your comments on how the parts of the economy are disappearing. The YouTube stuff and people at Amazon go there's no one needing to be there at the supermarket checkouts and made me think about the hidden economy and whether the hidden economy is growing and if there's any way or if anyone's doing any work on that. Obviously people do. There's all these creators on YouTube and I don't know how much of this is measured or how they even measure it.

[G]

Dan, thanks for all the years of work you've inspired me with. I've had a bee in the bonnet with the concept of imputed rents ever since I came across it. It doesn't exist. We make it up and it makes up 10.6% of UK GP. So I just wanted to ask you bluntly, how much of GDP in this country do we actually make up out of thin air? And importantly, how do you change the value of that made up number over time?

[C]

Okay, biggies. If I had to choose just a handful of measures so I would go for the time use. I would like to know how people are spending their time and what value do they gain from how they're spending their time. I came across this line. There's a book by an economist called Ian Steedman called Consumption Takes Time. And it's one of the only things I've ever found that addresses this question. And he says, you don't wake up in the morning thinking what shall I spend today? You wake up thinking what shall I do today? And I think that's quite a profound truth. So if I had to pick one thing, I'd go for proper time use studies across all of our time, not just leisure time and household production, on the hidden economy, there's a sort of classic type of study which thinks about this in terms of the underground economy, people trying to avoid taxes and uses, physical measures such as electricity use or you mentioned lighting. You know, looking at the gap between what electricity tells you about economic growth and what the GDP figures tell you. I don't know of anything that systematically tried to look at these movements across the production boundary. And you know, they're just pockets, markets, things like the production of open source software. People here probably know that the whole Internet runs on a few pieces of key open source software that are run by volunteers. And if they stop being maintained, the whole modern economy goes down. The value of that is incredible. One economist I know who does that kind of work is Shane Greenstein, Harvard Business School. But it's quite rare. So there are just pockets here. And I would find it really hard to put an overall number on how important this is, except to say I think it's really big and we just don't know. We just don't know how much is.

[B]

Completely made up, which is Will's question.

[C]

How much is completely made up? Imputed rent, for those who don't know, is a notional rent that owner occupies pay themselves to live in their house. And the rationale for doing that is that, that different countries have different patterns of renting versus owner occupation. And it changes over time as well. So for comparability across time and across countries, the decision was made to put an imputation for rent into gdp. And it's a large number and you know, it isn't a thing. We don't actually pay ourselves that money. The government sector goes into gdp. That was done because. Because during the war the Keynes and the other people working on this did not want it to look like the military effort was reducing the economy. And so wartime spending and all government spending went into gdp. There are lots of arbitrary decisions. One effort I've seen to put a total number on that comes up with I think it's 15% or so altogether for imputations in GDP. Some of the proposals for looking at the digital stuff, free digital stuff that's going on would add more imputations to that. So you do end up with this being a strange beast. It's not a real thing at all. So the delusion is that GDP is an actual object. It is an idea. The definitions have been fought over. They've changed over time. And it not like trying to measure how high is the mountain or how tall, how fast is this tree growing. It's not a physical measure at all.

[B]

Okay, we'll have another round of questions in the room. Let's come to the front here. There are three people kind of near one another starting the gentleman with the yellow shirt. Let me. Excuse me, sir. Let me be fair to people online, then just go to them quickly again, I think you can deal with relatively briefly, Darren. One is from Antonio, who's a visitor, Italian visitor, the lse. Can big data help us in measuring income or output? And then from Freddie Online says, when we think about productivity, do we think about quantity of resources or quality? What do you prefer? Do economists think of that? Do economists share the opinion that we need to adjust for those things?

[C]

So the first one I can answer quickly. Yes, if only we could get the big data. And this goes back to the conversation we had earlier about who's got that big data. So it could certainly help on quantity versus quality. You need both. And my answer here is, how much time do we have? We've got this construct of productivity which is at the firm level, the value added provided by the firm, the difference between what it earns in revenue and its costs. And then as economists, we divide those by price indices to get the real value added and the real level of inputs used. And then we adjust the price indices for quality change. And so businessmen find it very hard to get their head around this productivity concept that economists use because we are introducing things that are called real but are not real at all. And they do incorporate quality change. So I've forgotten the formulation of the question, but actually productivity is a really complicated thing to try and think about would be my answer there.

[B]

Let's go to the front here.

[H]

Yeah, hi, it's Simon Glynn from Zero Ideas, which is a climate change, sorry, climate policy research charity. Mike, it seemed to me, listening to you, that you're asking what you're asking for in the. In public accounting seems quite analogous to what ESG is doing in corporate accounting. And I wonder what you thought of that analogy and if it's valid that that raises all of sorts, sorts of concerns for me because there's, there's a lot of, you know, measurement difficulties. There's a huge distraction of effort and money and everything else going into ESG metrics and it's, it's, it's not a wild success. So are you asking to do the same thing in public accounting or how to have what, what do you learn from ESG for what you're saying?

[B]

And then the gentleman next to you and the judgment behind three next.

[G]

Hi there, my name is Akaksha. I am a year 12 student currently studying A level economics. Our last lesson we were learning about like GDP as like measures of different things and we came across the Happy Planet index. We watched a 15 minute TED talk on it as well, talking about like how, like how happy a country is, the more progress, what economic growth they have. What are your thoughts on like GDP as a measure of happiness and if so, do you think happiness can be used to measure like progress in an economy?

[B]

Yeah. Did you want to. No, no. Let's take those two then and then we'll go get them. Can we get the MIC sort of background to the back? There's one to the back.

[C]

I think the analogy for comprehensive wealth and ESG is not, not complete. You know, there's, there seem to be some surface similarities. You're trying to get companies to take account of what their production is doing to their social and economic and environmental context, but that does not take account of all of the externalities involved in thinking about anything social or environmental. I think it's actually more feasible at a more aggregated level to think about this and to take, take account of the externalities and spillovers that you would not do at the individual level. The other point is that because it's meant to be a complete framework, you're not picking out individual metrics in a way that companies are. Obviously if you've got a whole range of metrics it becomes a bit harder to game them, although not impossible. So I think there are similarities obviously, but they're not, it's not very directly comparable if that reassures you at all on the happiness question as human beings who like our fellow human beings, that's what we want, that's the outcome that we want. I really struggle with the idea of using happiness as a guide for policy because it doesn't really help. It doesn't really inform policymakers about the things that, that affect people's happiness. And GDP and incomes are definitely one of the things that affects people's happiness in quite a direct way. Incomes going up over time does make people happier over time, although not one for one. And I would much rather stick to the economics and stay away from the psychology. So I'm very happy for psychologists to think about what makes people happy. I think as economists we ought to be thinking about the use and allocation of resources and how policymakers make good decisions about that.

[B]

Okay, let's have just a couple more questions. There's the gentleman in the middle wearing glasses with his hand up.

[C]

And then that doesn't narrow it down entirely.

[B]

No, but he's the only one with his hand up that works. And then in a blue shirt and then we'll. I'll have to draw it so it close. Yeah, you wizzy. Oh, no, it's coming forward. Okay, pass the mic.

[I]

Yeah, stand up because the acoustics are a little bit. Diane, I know you know this. Robert F. Kennedy said in 1968, the University of Kansas gross national product counts air pollution and cigarette advertising and ambulances to clear our highways of carnage. You know that, you wrote it in your book. But after 40 years. No, not 40 years, almost 60 years. Why have we still not learned that GDP is not the way to measure the economy or progress? And I wanted to pose a meta question to you. Oh, also, do you talk about bullshit jobs being measured? David Graeber's famous term that would also distort GDP. When Blair came to power in New Labour in 97, he talked about having evidence led policy as a way forward. And it seems to me that we've ended up actually going towards having policy led evidence. That is evidence which is designed to support a policy in government. And say, for instance, Mayor Sadiq Khan arguably persuaded Imperial College to create statistics to justify the Ulez expansion scheme a couple of years ago around the number of people who died as a result of air pollution. And we've also quite selectively used statistics. For instance, with carbon emissions, we say we've reduced carbon emissions in the UK when we've actually exported them to China and Southeast Asia and so on. So I wonder, is there a meta problem? Even if we had the right statistics about productivity wealth creation in this country, if our politicians and leaders still want to distort the figures, how much progress have we made?

[B]

Interesting question and. Shirt.

[J]

Yeah, hi, thank you very much for the talk. How much would you value? Would you say the value of an hour is consistent over the entire lifespan? Is an hour watching TV reading a young professional the same as five hours or worth of 50s, five hours watching TV, you're in a care home. And I suppose the wider question is how would we make sure that using times and main metric does spawn the same sort of subjective questions that you attribute to gdp?

[C]

Sorry, I didn't hear the last bit of that.

[J]

I guess it's more of a case of gdp. You have a lot of subjective choices about what you include or you don't. How would you make sure that using time as a base metric but doesn't also fall victim to the idea of like, how would you make things comparable?

[B]

How would you compare my time?

[J]

Is it as vulnerable to subjective choice?

[C]

Yeah, yeah.

[B]

So politics and economics and your time and my time.

[C]

So I mean, the example you gave reflects how an evaluation study was conducted. And of course that's a lot of what economists do and we argue about it all the time and we try to figure out if we've done the econometrics right and identified causal relationships properly. You still need to measure the air pollution and the respiratory illness and to be able to answer that question at all. And I think it's an important question. So my baseline is let's get good statistics about air quality and illness. And then there is a separate layer of arguments which is what you're alluding to there. There was a lot of other things in your question, the, the point about happiness. To come back to this gentleman's question, actually GDP growth is really strongly related to happiness and over time across countries. And so although there are these negatives, often called regrettables, that are included in it, it doesn't completely devalue the whole measure. So I'm not arguing that we stop looking at GDP growth, though I would to like, like to improve it. The first part of the book is all about how do you improve the GDP measure? That we have to inform those kinds of decisions. How do you compare time? I think in a way this is an aggregation problem that we have anyway and we just stop thinking about it with gdp. So there are, you know, proposals to calculate democratic gdp, democratic inflation numbers, but typically we don't do that. We just average across the whole population. And I think, you know, as you point implicitly saying in your question, doing so is probably harder thinking about in some fundamental sense, how do people value their time. And I think the valuation questions are in general really difficult with any economic statistics, including the conventional economic statistics that we have. But I still would suggest that thinking about how people spend their time is a good thing to do. Listening to the radio this morning, there was A fantastic discussion with teenage kids about did they value spending five hours on social media or not. So there are some very deep questions in that. But knowing how much time they spend on social media is important for the policy debate that we're having at the moment.

[B]

Brilliant. In related work, Pedro Gomez at Birkbeck tells us that Friday is the new Saturday, which means that Thursday night is Friday night. So I don't want to keep people from their Friday night. So we will draw it to a close there. I have three vital announcements to make, though, two invitations. So those of you taking notes, please, please take notes. 2 March, Britain has elections in May, which will be huge in my view, both economically and politically. And so we have an event here at the LSE on 2 March, devolution economics, with economic leaders from Scotland and from Wales. And I have arranged drinks and nibbles from Scotland and from Wales. So part put the 2nd of March in your diary, please, if you're interested in economics. It's some of the biggest question the country faces. Monday, the 2nd of March, I think, not yet on the LSE site. 26th of March, we're launching a new thing and I would really like all of you guys to come because you're clearly interested in economics. I'm hoping I might even do it on the stage. I can twist Dan's arms to take part in this. I've obviously, I'm always influenced by the quality of the questions and so I put it to the lse. We should just do an event where we don't talk at all. We just start with the questions. And so we're going to do that for the first time on the 26th of March. So I need it to be full and I need there to be good questions. So please come along. So I'm going to arrange maybe six economists. Ask me anything, ask me anything. And you just have to go for it. Will you take part if it works.

[C]

With the diary, I'm free. And if there are nibbles from Wales and Scotland.

[B]

Okay, I can raise that. Final one is a quick fire questions for Diane, whether these things help grow the economy or shrink the economy based on this new set of measures we're going to have that include human capital and social capital. So buying books.

[C]

Oh, increase reading books, Increase human capital.

[B]

Goes up. Coming to events and talking with people after.

[C]

Increase social capital.

[B]

Okay. So with those things, buy books, read books, come to LSE events. Diane will be outside afterwards with her book and I'd just like to thank her for such a stimulating conversation.

[A]

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