Hello, everyone, and welcome to the Oxford Martin School.
My name's Charles Godfrey, I'm the director of the Oxford Martin School and we're here because of the visionary bequest by James Martin.
I'm delighted to say that James widow, Jim Martens widow Lillian, is in the audience this afternoon with her daughter Gero,
and I'm delighted to welcome our speaker, Diane Coyle, this afternoon in our series on evolving economic thought.
Diane is currently the inaugural Bennett Professor of Public Policy and co-director of the Bennett Institute for
Public Policy at the University of Cambridge and has been there about 15 months was previously at Manchester.
Don has done a huge number of things, both in the academic world and in the public policy world.
I'll just mention a couple of the things that Dan is involved at the moment.
Member of the Natural Capital Committee,
chaired by the town expert advice to the National Infrastructure Commission and a member of the very influential Council of Economic
Advisers that was awarded a CVA in 2018 for contributions towards the public understanding of economics and how fields of public policy,
economics, technology, industrial strategy and global inequality.
And Dan is a fabulous author, and if you haven't read it, I do recommend GDP a brief but affectionate history,
and it takes real skill to write so wonderfully on that topic.
And I have to say I before I even read it, I was invited by the fact it doesn't have a picture of you in it,
but it does have a picture of Utah cabbage who contributes not at all to GDP, but greatly to welfare.
Sit down, please come and give your lecture. Good evening, everybody.
My first challenge is to make sure I've got this the right way up because although I write about technology, I'm not very good at operating it.
So hello all of you, and can you hear me properly at the back?
OK. It's a great pleasure to be here today, and thank you very much for the invitation to take part in this series.
And amongst my other interests, the state of economics has been one of them and I've been quite involved in the core curriculum project as well.
And so I was delighted to accept the invitation,
and I'm going to talk about my own work on digital technology, which I've been doing for many years now.
In fact, my first book about it came out 22 years ago, and when I started getting interested in the mid 1990s and digital technology,
I said to a very distinguished economist he was already very distinguished.
He's now is more distinguished. I can't see him in the room this evening, but I'd better not name him.
I said, the internet is wonderful. It's going to change all kinds of things.
And he said, Why is the internet interesting?
It's going to reduce transactions costs a bit, but we know how to handle transactions costs in our model.
So why are you bothering wasting your time on this, on this new technology? And in a way that sets my theme this evening?
Because what I want to argue is that the the characteristics,
the economic characteristics of digital technology mean that the way we need to think about doing economics has to change.
So that's the theme, and I'm going to start with this man.
Some of you will know him. Hal Varian, chief economist at Google.
And why does he lose sleep at night? And I ask this because I've heard him give a talk a couple of times,
claiming that there is nothing to worry about in terms of competition in digital markets.
Sure. Google has 98 percent of search outside China.
It's quite a lot of it out of 100 percent, but there's nothing to worry about,
he says, because any new technological upstarts that had a better search engine,
better search technology could very quickly knock Google out of that position and grab that market for itself.
And he says this is exactly what Google did to Yahoo back in the day.
And so he loses sleep over, not over.
Department of Justice investigations or European Commission investigations.
But over the fear that the next Google is out there and it's going to eat the existing Google now.
That's why he loses sleep. A lot of sceptics.
There are a lot of sceptics about this claim, of course, and you might have heard the term gaffer or Google,
Apple, Facebook, Amazon, Microsoft is sometimes there and sometimes not.
Uber is sometimes there, but you can't make a nice acronym with Uber.
So we call it gaffer and concerned about the dominance of digital companies in some of these markets has increased everywhere.
European Commission has been particularly active in pursuing some of the cases,
and the even the American antitrust authorities are now getting somewhat interested in it in this country.
We had a digital competition expert panel chaired by Jason Furman, who's an economist at the Harvard Kennedy School,
and I was a member of that as evidence of some trends towards concentration more widely in the US economy and maybe this one as well,
but especially in digital, because these markets do tend to be dominated by one company.
And there are reasons for that, and the reasons lie in the underlying economic characteristics of the technology.
And these are what they are. First of all, superstar features, this superstar name came from a paper by Sherman Rosen in the early or mid 80s,
where he was trying to explain the very high pay of a particular movie Star Wars food star.
And it operates on the demand side and the supply side. On the supply side, there are very high fixed costs and very low or zero marginal cost.
So increasing returns to scale and particularly depending on the level that those fixed costs, that that scale can be quite large.
And on the demand side, you often talking about experience goods so people don't know what they're like until
they have actually already consumed a movie watching a baseball match and so on.
And so once people start to hear by word of mouth or other forms of contact that something is good,
then even if by any objective characteristics is not better,
particularly than anything else in the market, the demand will congregate on those particular suppliers.
So those are the superstar features and they operate in these digital markets.
They're also what are called indirect network effects in the jargon. Network effects are familiar for everyday life.
If you want to make a phone call, then the more people on the phone network, the better it is for you.
Indirect just refers to the fact that in many digital markets, they are matching suppliers to consumers.
And so if you're a consumer of one of these goods, if you want to hire an Airbnb apartment,
the more people supplying the Airbnb apartments, the better it is for you.
And if you're a supplier who wants to rent out your apartment, the more consumers on the platform, the better it is for you.
And so these indirect network effects are also mutually reinforcing and encouraging of scale.
These platforms are very often matching their matching, quite varied supply,
like the range of apartments that you can get in the city with quite varied demand.
So they're quite heterogeneous and there's a matching offer going on there, and they have a particular price structure.
If one side of the market is more likely to go to other suppliers, so usually it's the consumer side of the market.
Then the other side has to subsidise them to stay on the platform.
So many of these cases, consumers don't pay anything and supplies the platform,
appetisers and search or apartment owners visiting their apartments, pay a commission or fee and a commission to the platform.
So once one side subsidises the other, all of these mean there's what what's called the chicken and egg problem, which is exactly what it sounds like.
It's really hard to get one of these platforms going and succeeding.
And there's some new work out by Annabel GAWA and Michael Cusumano suggesting that as many as four and five platforms fail.
I wouldn't be surprised if actually it was more than that. It's really hard to get to viable scale,
and the big American ones have very large sums of venture capital money covering their losses for a long period, and those losses can be large indeed.
And so all of this adds up to you need to be big.
You've got to get to a certain critical mass. Once you do, there's a characteristic dynamic.
So you've been bumping along for a while, you get to the critical point and then all of a sudden you're huge you.
That takes off very quickly. And if you're not big, then you're dead.
So those are the basic characteristics of these markets.
So on the supply side, increasing returns to scale on the demand side that often experience good information goods.
There are these large externalities in consumption in this form of these network effects.
If you do basic economics, we focus on the externalities in production. But these are in consumption.
There's great heterogeneity in the product. Every search is different or they're not, but you get the idea.
And even in a competitive market,
price is not going to equal marginal cost because marginal cost is so low and you've got to cover the fixed cost as well.
So that's what they're like. There's the basics.
There are also other features of these digital platforms that make it very hard to think about them,
certainly in the competition context, in the way that you do any other kind of business.
And one of this is the strategy called Envelopment. I love this.
I took this picture in Manchester of Uber deliverers Uber Eats deliveries and Deliveroo deliveries,
walking alongside each other as a form of healthy competition, obviously.
So if you've got a lot of uses on one side of one market, if you've got a lot of consumers using your taxi service,
which is what Uber is, then you can use those consumers to cross sell a completely different product.
So this isn't quite like the bundling that we're used to in oligopolistic market and economics.
The products have nothing to do with each other. It just happens to be that Uber decided in this case,
it would get into the food delivery business because it had a lot of users already on its app, and that was easy.
And so that means if you are thinking about competition,
if a company is dominant in one market or a large animal market, you not only need to think about that market,
you need to think about all the other ones that could get into and is scale is having a lot of uses,
a bull market and a barrier to entry to other people getting into other markets.
Or can you expect the dominant players to dive into anything that they think looks promising?
Uber's latest thing is dark kitchens capitalising on the delivery market.
They now want to buy up dark kitchens in big cities so that they can produce all different kinds of food and capture the whole supply chain there.
So they do. That's very common. And the other thing that's really striking is the data barrier,
and people sometimes call this the data flywheel or the data loop, and it's a self-reinforcing feedback mechanism.
If you are a big company and you've got a great service, you get a lot of customers.
If you have a lot of customers and you accumulate their data, you can use that data to improve your service and so it feeds on itself.
And so one of the things that we looked at in our report was whether there are ways in particular to break down this data loop.
But what all of these features add up to is that the kind of conventional analysis that we do in competition policy is much harder and in fact,
it doesn't work at all because in a conventional competition enquiry and I spent eight years doing this and you look at
the possibility for a merged company or a dominant company to increase its price by a small but significant amount.
And you see what substitution possibilities that all can your customers easily switch to something that's very similar or not?
And so it's called a snip test, which stands for small but significant.
Increase in price, and I can't remember the instance for the minute.
You can't do these in these markets, partly because you've got this phenomenon operating across different markets,
so you need to figure out exactly what the boundaries of the markets that you're looking at.
But also because of the asymmetry in price and a perfectly competitive platform may still be charging a zero price to its consumers.
And so that standard kind of test is really difficult to do.
An alternative would be to look at profitability, so the companies dominating a market, you would expect it to have higher than average profitability.
And sometimes you do this analysis in an enquiry. But these are companies which even until they're very large, are losing money.
So. It's a losing money and then charging zero prices, what's the problem?
You have to ask as a competition analyst, why is that a problem at all?
And there are these coaches that dynamics as well, that you're looking at potential competitors to a dominant company.
And it's the Halliburton question, the Hal Varian challenge. You know,
I'm really worried about this small guy because at some stage they could grow very quickly and capture this market conventional
competition and don't handle these contexts very well and have never really handled the dynamics of markets really well.
If you want to think about what the home of Google dominance might be, it's that there's innovation that's not going to happen because of it,
that it will dominate the market such that the better search engine can't ever
get into that market because of the data barrier or because of the sheer scale.
So we've got to ask what the dynamic costs of this kind of dominance.
And we are in a world not of static competition, which we're used to looking at in competition policy.
But the world of sugar and competition, the creative destruction that he wrote about so famously and there are indeed examples of digital overthrow.
So rather than look at competition in the market, we're looking at competition for the market in the terminology that will be introduced,
and there hasn't been examples of this kind of overthrow. I've given you a chart here of Facebook.
And if you look carefully, you'll see that actually it ends in 2014. So even before the recent Facebook scandals, there was some engagement metrics.
This suggests that Facebook has passed its peak and may not be the dominant social media platform.
And apparently, young people are using all kinds of things these days that we old folks sitting in this room have never even heard of.
So maybe this will be the same.
And there are earlier examples, too, like Microsoft Internet Explorer being the dominant browser and then being overturned,
or as this chart shows, Facebook itself overthrowing MySpace.
Look at MySpace. This deep research for this, and it turns out that Kim Kardashian is still on MySpace, so it isn't completely dead yet.
But this kind of dynamic analysis is difficult for competition authorities to do
because you have to think about what might happen to the sector in the future.
You got to try to forecast the technology and the consumer response to that technology.
So digital is different. It's got these increasing returns to scale. He's not alone.
You need dynamics, these externalities. It's also a public good is it's not rivalry.
In many cases, a piece of software is in technical terms of public good because once it's been written,
it can be used by any number of people, and that doesn't detract from the first user.
This is all a roundabout way of saying through the example of competition policy,
that the conventional ways we have of thinking about production in the economy don't really apply in this sector.
And for all that, academic economists do all kinds of research looking at digital companies and increasing returns to scale and so on.
And they're not used in policy economics. The applied tools are not there yet.
And we, as a research community,
haven't given competition authorities the way to think about how do I know whether Facebook is really past his best and somebody
else is coming in with a better search engine that in five years time might have 98 percent of the market and six billion users?
We don't know how to answer that question. And a lot of what we revert to is our instinct about how markets operate doesn't apply and these markets.
So this is one of an example of a general purpose technology, and they fulfil this definition.
They enable radical innovation in both products and services and in processes of production.
And these apply across a wide range of activities in the economy. They start out in a particular sector.
And before you know it, they're applying to a wide range.
They lead to a major reorganisation of the structure of the economy because they change input costs dramatically.
They also require a lot of additional investments. So it's the same kind of dynamic.
They start out very slowly. And then eventually they start to have quite dramatic impacts.
And part of the reason for the slow start is micro-scale is because they need a lot of investments in infrastructure and organisation,
in the skills to operate them and so on.
So they're slow to diffuse the when really well known example of this is a paper Paul David wrote in 1980 about the spread of computer technology,
digital technology and its first wave, comparing it to the electric dynamo.
And he pointed out that that innovation had required new kinds of factories to be built because you
only get the productivity benefits of using electricity to power the factory rather than steam.
If you have low, flat factories with assembly lines rather than the tall ones,
with the shaft being driven by one steam engine driving lots of machines on different stories of the building,
so whole building's had to come along, the grid had to be built. The because the factories were in greenfield sites.
Ultimately, you ended up with so suburbanisation and the electrification of the economy and the creation of household electronic goods.
So that had an effect on women's activities and their ability to go out and work and paid employment.
So the ultimate impacts were substantial, but the impact on real GDP growth.
First of all, to the long time shows through in productivity figures.
And even then, it was actually quite a small number. And the economic history looking at the GDP impact of general purpose technologies
shows that it's not large numbers in terms of the increment to growth,
but they have very dramatic impacts. So this is doing this,
and before long it's going to be really stupid to talk about the digital economy and the ways that we wouldn't talk about the electricity economy.
It just is the economy and you get the slow and fast pace of change.
How dramatic is it going to be? Well, the answer is very,
and to see that you need to look just at the prices and the top left panel here shows you the price of computation calculated by William,
not a very well-known paper, and it's a log scale.
So it shows you the issues in Moore's law because of that dramatic fall that begins around 1940.
Most famously, that you get a doubling of the computing power or halving of the price every every 18 months or two years.
How does that translate into actual prices? People pay for things. The other charts come from some of my recent research with co-authors,
and the top right hand one is the price that you pay for access to cloud computing facilities.
And you can see that it's come down quite dramatically in this chart, which runs from 2010 up to last year.
This is not a log scale, but if you look at the figures, something that started out at 40 cents has is now less than 10 cents.
And what this means is that companies that used to invest in servers and hire
people to operate those and have I.T. departments don't need to do that now.
If they've got one already, they might still have kept it. But more and more companies, pretty much all start ups, don't invest in their own servers,
and they use cloud computing companies like Amazon Web Services or Microsoft Azure or Google's Cloud Services.
You might recognise these names from the earlier part of my talk this evening.
So it's cheap to do, and I have talked to executives who say, Well, we used to have this department and it cost tens of thousands of pounds a year.
But now, if I want to do something, I put it on the company credit card and it's just a few pounds.
So it's a dramatic fall in price, an increase in capability,
and it means that companies don't need to have skilled data scientists to use A.I. they can use algorithms that are provided by these cloud providers.
The second example is the price of telecommunications services. And the darkness runs from 2010 to 2015.
We've updated it a bit pattern hasn't changed. The top line The Blue Line shows the official price index for telecommunication services over
a period when communications technology has changed dramatically speeds the compression,
the latency and the amount of data that people are using and transmitting have fundamentally changed over that period.
So telecoms engineers came to own us and my colleagues and said, What is going on here?
Why is this index showing no decrease in the price? And one answer was that the index left out new data services.
So we put those in and that gives you the red line. So rather than having zero decrease, you get a decrease for third in the price over five years.
And the bottom line, the purple one, shows a thought experiment of saying,
Suppose we take the revenues of the companies and we divide that by the volume of data in bits,
and that shows you a 90 percent decline over that five year period, and that's gone down even further since.
And this is pretty logical thing to do because there will be bits of data.
The component services actually do have different prices per bit, but they're converging quite rapidly,
and the more they converge, the closer the price index will get to the purple line.
So here too. You've got communications services falling substantially in price about 90 percent over a five year period.
And so when things are almost free or really cheap, people use them a lot and that's what companies have been doing.
I might maybe next and there's a lot of hype about A.I.
There are many economists. I'm one of them who argue it's the next general purpose technology.
It's not just more digital. There's a lot of hype about it.
And you've probably seen things like this on the internet,
which show you that it's actually really hard for a computer to distinguish between a chihuahua and a blueberry muffin.
Actually, it's not that funny, because it turns out that for some specific tasks,
including image recognition, the air has been better than we have since 2015.
So this is one of the images. This is the image that competition and by 2015.
The error rate for the algorithm is much lower and the error rate for humans.
So it turns out that we're not very good at the blueberry muffin chihuahua test either.
In fact, you can get some quite sophisticated algorithms for free.
And I found a load of adverts for a code to build your own robot dogs.
So these are this is number eight, 11 12 in the catalogue. As a teenager near you making robots like this right now.
The parts are cheap, the algorithms are free. You can put them together. They will be roaming the streets.
Folks are before we know it. So there is a lot of hype. The abilities of A.I. currently are quite narrow.
But it's also really cheap and it's going to change again.
Change what people do in some ways. That's not all.
If you look at other areas outside the immediate digital territory like drug discovery, prices are falling faster there.
And this is a chart from the National Institutes of Health showing that the costs of
sequencing the genome are falling faster than predicted the trajectory of Moore's law.
So this is like digital, but even more so, and this is a logarithmic scale.
So this is really dramatic change in the price. And in a sense, drug discovery is becoming an AI technology.
A lot of it's being done by A.I. All of this is about the production side of the economy and how that changes with digital.
What about the consumption side? What about us? How do we feel about it? Is this how we feel about it?
And judging by popular literature and a lot of the news?
Yeah, we're pretty upset about this.
And there are all kinds of stories about aling bias and the use of algorithms in the criminal justice system leading to undesired outcomes.
But, you know, all new technologies make people afraid. And this is a cartoon from the age of electricity.
And it wasn't Frankenstein in a vacuum. People were generally pretty sure that electricity was going to kill you.
So here's somebody in Victorian London caught up in the wires and killed by the light.
And so there's a fear about new technologies. And once people become familiar enough, they just can't get enough of them.
And there's some work that's been done by Eric Brynjolfsson and his colleagues,
and we're repeating this now for the UK and testing different methodologies about how much people really, really love digital technology.
These are estimates of the willingness to accept giving up Facebook for a certain period of time or search engines or email.
So this is probably too small to read search engines willingness to accept giving up all search engines for a year in 2016,
almost $15000 or email almost $6000.
I mean, some of us might pay to give up email, but we're not the average and so on.
So it doesn't say that you can ask these up.
It's not clear how you aggregate these or what kinds of time budget constraints you need to think about, which is what we want to explore.
But these are big numbers. And if you could add them up, you'd have, I don't know,
two thirds of median US income as the value that people say that they place on these technologies, just reflecting the fact that we use them a lot.
So I'm not switching to UK data, but this is Ofcom's tracker of how much time people spend online.
It goes from 2007 to 2017, and we are.
The question is how many hours of a typical week would you say you spend online at home, at your workplace or place of education or anywhere else?
So it's everything. It's looking at Twitter. When you're on your mobile phone answering emails at work, it's everything,
but it's 24 hours a week online, so we're using it a lot and that's doubled over that 10 year period.
So we're doing a lot of things online now we are doing banking, travel agency, education, entertainment, social media communication,
accessing information as a syndrome and lot of what we would default to assuming about consumption of goods and services doesn't hold either,
because if you think about the basic economic theory that we have our indifference curves.
I was just a local school safety and I'm told they don't teach in different schools anymore.
It's a sign of how old I am, but we have this construct of utility and individuals have utility derive from their preferences.
We have seen think of some fixed preferences that are known in some way as I know,
or we can figure it out from food preferences, from their behaviour.
So we've got the utility concept and we aggregate individual utilities to get social welfare in some way.
And there's a whole huge literature about how you might do that aggregation, but is the utility constraint?
I've never seen the utility. I don't know what one is. This is a constraint that doesn't really allow for new goods and big changes in behaviour.
And there's an assumption that we know now will we knew in 2005 when our preferences for buying smartphones was going to be?
And that's obviously untrue. It's also not the case that preferences are individual and not fixed,
and they're not an individual either, as these guys would have told you in the 1950s.
The whole advertising industry is based on the malleability of preferences and also social influence and preferences.
So the social aspect isn't just that there are network effects for some of these technologies, such as talking about before,
but that people like Jony Ive persuade us that we really want an iPhone as a job
category of YouTube influences and social media have become absolutely pervasive.
I think, anyway, economics is in a bit of a mess about how we think about choice, and on the one hand,
we assume that people are better than the A.I., the algorithms because we are assumed to optimise in the same way in a reasoning way.
You can restrict information search for sure, but there's a rational calculation in some form going on,
and we're better than the robots because they can only do it in their narrow domains in which they're programmed.
Whereas we operate in one domains and on the other hand, you've got the behave.
That's the standard economic assumption, and the competing behavioural assumption in effect assumes that we're not as smart
as pigeons because there's a whole branch of study biological markers theory,
which finds that creatures from even fungi to pigeons to monkeys, sea creatures or any creatures in certain contexts behave like rational,
calculating individuals optimising in the information environment to which they find themselves.
And yet, the behavioural economics assumes that we don't do that in lots of domains,
so we don't really understand in which domains to apply which kind of model.
So on the production side,
are defaults set of assumptions and hold on the consumption side or default set of assumptions is a bit of a mess in this digital economy,
which is becoming increasingly pervasive.
And there's a sense in which economists know that, of course, we know that the economy is not like the Phillips machine.
This is the Cambridge one. They they kinda opened the COVID doors so I could photograph it for you.
We know that the economy is not like that.
We've put the machine in the cupboard, but we haven't updated the everyday mental furniture in a really important sense.
And it's in the sense a lot of public policy economics is based in some way on the basic welfare theorems in economics,
and they are these people irrational, self-interested, Typekit fixed preferences.
They use all the available information. There are no increasing returns to scale.
There are no externalities. There are complete markets, including for future goods, good survival, not non-renewable.
And they are owned by a stranger, their property rights and transactions can be enforced.
And so from what I've been saying about digital markets, the digital economy,
maybe the Russian self-interested, we don't have fixed preferences, though.
Maybe we use all available information, but we've got increasing returns to scale across a really wide swathe of the economy,
loads of externalities even more than we were used to thinking about in the old economy.
We don't have complete markets for future goods, and many digital goods are non-removable.
You might even question the last one,
if you think about the contestation of intellectual property and the way that the concept of property and physical markets has
been transferred to intangible intellectual property in digital markets and the some of the strange consequences of that.
Now some economists in the room might be twitching now and saying, We know this is nothing new in this, and in a sense, that's absolutely true.
There are lots of people in academic economics who know that these assumptions don't
hold and are researching digital markets and increasing returns to scale and so on.
But in public policy. They're not operating on the academic front here.
They're operating on what they were taught in their courses. A number of years ago.
And that matters because there are lots of economists in government.
This is a Treasury chart going up to 15, you can see that lovely upward trend in the number of economists employed in the government economic service.
I've put in a Red Cross to represent the Brexit surge.
It's up to about 18:00 now, and a lot of other people who are not economists and government has studied some economics and they have internalised.
They've been socialised into thinking in that conventional way,
which ultimately says the default is that markets work that quotes free markets are the default way to organise the economy.
And if you spot next another team, you can address that in an individual way and devise a government policy to fix it.
So my argument is that the interdependence in the economy,
which has always been there to some degree but is getting much greater thanks to digital, means that that should not be the default assumption.
And we should switch it around. Well, Elmo wrote his Ph.D. thesis got published in 1950, and he made this very point.
And actually a lot of what I do involves reading things people wrote in 1940 or 1950 and going, you know,
they've really had a point with that simplifying premise that these types of
interdependence in supply and demand a negligible or non-existent is misleading.
Such an assumption is not neutral, he said.
And he made this very point that the assumption that the absence of interdependence points you towards the benchmark free market model.
And that's circular reasoning because you've assumed interdependence and therefore
you conclude that markets of people operating you've assumed independence.
So you conclude that markets people operating independently are the way to organise the economy.
If you think about the interdependence, you might come to different types of conclusion.
So I just want to think a bit about what it would mean to take economic welfare
seriously and ditch that framework that we're all taught and our courses.
And there are some really good straws in the wind, I think.
So the top left hand one is an example from a fantastic paper in the Oxford Review of Economic Policy.
I can see at least one of the co-authors here in the room, an example of using market design to take account of interdependencies at the start.
So you're using a choice mechanism, a market process.
But you are right from the start taking account of externalities and interdependencies and people's behaviour.
So that's a very exciting area of research.
The top right hand example comes from Jim O'Neill, who was appointed by the government to look at the problem of antimicrobial resistance.
The report came out a couple of years ago, and it was praised by the pharmaceutical industry,
which has since made absolutely zero progress on developing new generation antibiotics.
And so early this year, Jim O'Neill came out and said the market is not going to do this.
We need a public provider who will produce this kind of pharmaceutical product and that is
taking the high fixed costs and the non-life rivalry of many of these types of good seriously.
So I would argue that once there is a public policy we should be thinking about very seriously is public provision of digital goods.
Why would we bother to leave it to Google and Facebook? Why would you not think about some public provision?
Why would you not think about nationalising Facebook if only it were ours?
So the characteristics that I start out by describing actually make public ownership, which of course has lots of problems.
Public provision a very sensible alternative to think about bottom left hand corner
is an Austrian and homework was about non-state non-market governance structures,
institutional structures for dealing with the production and allocation, and exactly the kinds of goods that I've been talking about.
Where trust is, trust needs to be high, when the goods are known rival,
whether interdependencies between people and it's not a free market, it's not Whitehall telling you what to do.
Maybe you should be thinking about different models of economic institutions.
And then finally, and I can't resist talking about measurement, which is what I do in my day job.
Thinking about economic statistics and GDP seems to me fundamentally the wrong measure anyway,
because it's a value added construct and anybody who's done economics will know that you add up all the production in the.
On me, all of the income, all expenditure, but on the production side,
you edit all up and you net out the intermediate stages of consumption because the argument is you double counting.
You don't want to count both the flour going into the bread and the bread at the same time.
You've got a net out the intermediate products. What this means is that in a sense,
the whole process of disintermediation and breaking up the value chains has gone on over the past 20 years and is
continuing apace with things like cloud computing is sort of invisible in the statistics when letting it all out.
The structure of the economy has changed in terms of those extended production chains has changed profoundly.
And actually, that's what Adam Smith said. It was the driving force of capitalism that you the greater the scope of your market people to specialise.
The more narrow the specialisation, then the better off it would be.
And we've kind of taken that out. And so I'm very interested in thinking about just entirely different ways to think about what's making us better.
How do we take account of those really high values? People seem to be placing on things that they do have to pay for, and that in some sense,
don't get captured in a very obvious way in the GDP and conventional statistics.
There's lots of stuff we're not measuring about the economy. We're not measuring how much consumers use these digital services,
the time they spend and maybe a new time use survey that covers that, but it's not there yet.
We don't know how many people use digital platforms they know, but we don't.
We don't know the extent to which companies are adopting cloud computing. We know if they use any, but not how much or what services they using.
We don't know how much companies are using AI. We don't know how much data there is.
There's no data on data. Talks about it being a fundamental economic asset.
I to right at the start about the way it was a barrier to entry in digital markets,
but we didn't know what there is and we don't know how to value it, even if we didn't know what there is in terms of volume, in terms of bits of data.
So I'm starting a project trying to think about how would you categorise the valuation of different types of data set?
We have no idea about cross-border flows.
If a manufacturer in this country emails a blueprint to a contract manufacturer in Malaysia, we don't know what the value of that is.
We don't know what they're doing. We don't know what the transfer pricing is. We don't know how much data is crossing borders.
If a company here uses a cloud computing service,
we don't know if there's any export or import involved because we don't know we think the century goes to.
It could be here in the UK or it could be in Belgium. We don't know what prices people are paying for things.
The price of a digital camera is still recorded.
Its weight in the consumer price index has been going down because people aren't buying them very much anymore.
But nowhere are we putting the zero price that we're all paying for taking photographs and looking at them on our smartphone.
So the price indices that we use to calculate real GDP and real productivity are completely wrong.
We're not even doing some of the basics,
and I thought I'd try to look up a trade in robots because robots are becoming thing and I found in the tariff schedule actually as a heading.
Industrial robots, not elsewhere, specified or included.
So if you're a car manufacturer and you've got a robot arm, you might describe it as a machine tool for making cars.
If it's not specified anywhere else, you will put it in this category of industrial robots.
We also count toy robots really carefully. We've got really good statistics on these little guys in the trade figures.
And the reason is because non-human figures have different tariff rate than human figures.
And it's been changed. That shows if you know the story that used to be human figures for toys versus animal figures for toys,
and it was a famous court case fought over Star Wars characters and whether they were humans or animals.
So the category is now being extended. For example, robots, devils or angels or aliens should be in that as well.
So we don't have some quite basic stuff. So what's the new agenda for economics?
We need some badly, need some policy and empirical approaches to these issues.
Theoretically, the people research them, but the competition authority doesn't know how to do it.
Somebody needs to start doing that applied work and figuring out, you know,
what is the potential harm to consumers of a new search engine not being able to get into the market?
And we need to think a lot about the public provision.
As I was arguing an alternative the regulation of information goods, the public good and non rival the market will under provide them.
How are we going to regulate this? How are we going to think about regulating and.
Lateral property better. We need to think more about social capital and governance.
As I was asking about Alan Ostrom was the right business models and governance and governance mechanisms for these types of companies.
What do you think about the dynamics and what gets the economy onto one kind of dynamic rather than another?
Both Shiller has an interesting book out this autumn called Narrative Economics, and it's exactly about this point.
If you've got self-fulfilling phenomena or averting phenomena, what is it that tips you from one to the other determines which path you get onto?
And the argument is, the narrative is part of the story. You've got to align people's behaviour, and the narrative is what helps to do that.
And I think you really fundamentally in my mind about what is it that makes people better.
We've got this wealth economic construct that actually we've known for a long time isn't fit for purpose and
less and less so with the economic characteristics that are spreading across the economy through digital.
So that would be my economic research agenda. So to sum up, we've got an economics that is all the things on the left, linear static,
constant returns, et cetera, and an economy that's exactly the opposite.
We've got a construct of economics, the point you towards the market thinking about the market as your default, your benchmark.
Whereas we should be thinking about institutional structures as our benchmarks,
whether that be market design or Ostrom style institutions or public provision, let's think seriously about institutional structures.
This involves some changing of methods as well, and economists who hear about big data will always reply We we do.
Data is what we do, and we might have not have millions and millions of records, but we've had a very large datasets.
But there were new data sources. The digital economy itself is a source of data is quite hard to access the data generating process.
The statistical properties aren't always well understood. A lot of it's held in these companies silos.
We need to figure out how to get that through the top for the public good because there's public
value being left on the table by the fact that these silos are completely separate from each other.
I want to be able to join up my smartwatch and my shopping data online GDP data for my own purposes.
I want something to find an app for me that does that matching and tells me how I can make myself better off.
We need computational approaches. You can't do a lot of this and analytically the way that we've been used to in economics.
So to give one example, we have people maximise their utility subject to budget constraint of money.
If you want to add a budget constraint of time, which is key with these digital goods, it becomes algebraically intractable.
You've got to do some other way when you reintroduce more economic history because we are in a technological
revolution and looking back at past ones is a good way to think about how to approach the one we're going through.
They'll be huge differences,
but it points you towards thinking about those much more distant changes that I was talking about with the interesting example.
I think narratives is an idea of really worth taking seriously. We have very quantitative and economics.
I think we need to recognise the merits of qualitative methods.
Going to talk to companies is absolutely illuminating and you find out lots of things about how they operate,
and they've got no idea what their marginal costs are and don't know what you're talking about.
But above all, I think it's a new mindset. It's a new default that we go to.
And in this room, we can pat ourselves on the back if they're already thinking about this.
But for the policy world, this is quite a big change in mindset.
Still, too many economists in in government who who default to the market, as I said, I've been writing about this for a long time.
This is just the advertising interlude at the end.
So the way this world came out in 1997, and it's free on my website if anybody wants to read it and a lot of these issues,
I think, were clear already back then the measurement issues, the sustainability issues I haven't really talked about.
And a new book coming out early in 2020, which tries to apply some of the things I've been talking about this evening to public policy.
And hopefully by the time we get to the next generation of students having their jobs and government, things will be a little bit different.
Probably going a bit over my time. So I'm going to stop now. Thank you all very much. I think we've got a bit of time for Q&A.
Just to remind you before questions, this is being videos I do remember when you ask a question that is going out live,
who would like to ask the first question? Question there.
Thank you so much for a great talk and for really focussing on an area that I think is we're going through such a transition.
And I know in hindsight we'll look back and go, Wow, I can't believe we went through that,
but you know, we didn't feel it while we were going through it. That's true generally, anyway.
So we're moving from a tangible world to an intangible world.
And one of the things that strikes me is economics is a huge thing and whether capitalism works and how it works within that,
but it interacts with all these other things like laws. You're talking about IP laws and competition laws and things, taxation, right?
And how we tax intangible assets versus tangible pensions, education systems,
health care systems, they're all systems and they all interact and we can fix one.
But if we do it in isolation of the others, we're going to have an asymmetry and it's not going to work.
So how do you embed this into this greater kind of multi system?
My goodness, if I had a quick answer to that, I'd be making a fortune from advising governments about it.
It's obviously really hard.
Any changing one system is hard, and we're in a context where we've got an overlapping set of systems, including the environmental system.
And so one answer is that if you look back at the historical examples of what happens
of conflict and period of chaos and contestation and quite a political conflict,
and then eventually new arrangements and systems emerge out of that.
The worry about apart from the discomfort of waiting for that this time is that some of
those systems seem to be approaching catastrophic for the environment in particular.
So you want to change the energy system pretty pronto. And I just don't know how you do it.
It's really hard in government. We don't have government structures that enable it.
When we did our report and the competition aspect of digital economy,
we recommended a new regulatory body that would have particular oversight over companies deemed to have strategic market power at the same time.
Frances Cairncross did a report on the news industry and the way it's been hollowed out by digital.
And she recommended a new regulator. Ofcom already has some responsibilities.
Competition and Markets Authority already has some responsibilities. DCMS put out a white paper saying, We need a regulator for online harms.
You've already got six digital regulators there, and it's not at all obvious to me how Whitehall,
never mind the Conservative Party that's in government at the moment, Will will settle on them.
How will they arrange that and actually what is the vital management for that? So part of the question isn't about economics at all.
It's about the political science fundamentally of what's the right level of governance for certain decisions and how do you do that?
Joining up that we are profoundly bad at doing.
We have government silos and I don't know how you bring this on is you've got to change the budget lines.
It's tough. Thank you. Good, thank you, John.
I've got a couple of questions that relate to the point that so much of what we buy now is free.
Okay. The first one is what is the inflation rate generally in this environment?
I know you've asked before. What does it mean for central bank policy? Should central banks be sending negative interest rates?
But clearly, if they do that, the whole financial system doesn't work.
On the other question relates to a similar theme, but basically, I saw some estimates coming out of the U.S.,
which said that if you try and value the internet and you put a user charge on it, the value of the internet is something like $500 billion.
And to put that in context, world GDP is 80 on the value of the entire energy industry.
20. So if that's true, surely geopolitics are going to come in here.
Governments are going to try and grab this for taxpayers or to do something with it.
Well, my government would love to start taxing some of this,
but digital companies are just as good as any other multinational and moving their profits to places where they don't get taxed.
So I'm not a macro economist and I'm no expert on what the Bank of England ought to be doing.
We still have money. You can still count nominal GDP.
And I think part of the error is to think not only that, you can calculate real GDP in a meaningful way given your point about price indices,
but that you can then compare that to potential real GDP.
I don't think we've got any idea really what what that is. For all of these intangible sectors.
So if I were them,
I would be looking at nominal GDP and actually I would be raising interest rates because the banking sector isn't functioning with low interest rates,
never mind negative ones. And it point seatbelts, not using monetary policy as a means of demand management, I think at the moment.
That was a huge caveat. That is probably 30 years since I've done any macroeconomics.
Eric, terrific talk. Diane, as one of our leading experts on metrics, I'd like to ask you about a metric that policymakers obsess over productivity.
You know, it has a clear meaning and is useful concept in a world of goods that you can draw upon your foot in this world.
What does it mean? Does it mean anything? Do we need a new concept? We need a new way of thinking about that whole notion.
I think you're right. We need a new concept, and the one that I've been playing with is about time.
Because in a service based economy, never mind digital and intangible and a service based economy, you either want something to happen really quickly.
You want your train to take you as fast as possible on the journey.
You've got to have a blood test done. You want that to be done as quickly as possible or you want the opposite.
You want it to be as long as it needs to be in really high quality.
So if you're in the ICU, you want to have the devoted attention of a skilled nurse for 24 hours a day.
So those would be examples of productivity. Not at all. Not at all.
Captured by the current productivity metrics and services. So I think the concept needs to change and even physical goods,
the quality change that gets embedded in those now means that it's very hard to think about what productivity figures need.
Thank you. Over there, David.
Hi, Dan, you managed to do the whole top without mentioning the greatest externality facing us, which obviously we need to think about pricing.
We need to think about how we deal with it. It's a global problem.
It's got huge numbers of and then I'd thought about consequences that would be happening over time.
I mean, it's very hard to think that these are the key problems, given that we've got a much bigger problem overlying it.
And I do wonder about how you would think about integrating climate change into your kind of analysis here.
It's a good challenge, and I think about it in a separate mental bucket is the honest answer.
So as well as trying to measure digital and GDP, I try to think about the balance sheet of the economy,
including natural assets and the health of the climate.
And so we have a programme looking at the moment of natural capital and social capital.
And you know, if you don't have a balance sheet in some broad sense, you're not thinking about sustainability.
It doesn't answer the detail of your question in any way.
But if you think about it in terms of metrics, then thinking about assets seems to be one way to go about it.
But you know, as you note, people in the room are much more expert than I am on this.
A lot of thought has been going into thinking about policy approaches to solving the climate change problem.
And it's not really an economic question, it seems to me now it's the political, again,
a political science question about why it's so hard for any of those policies to gain traction.
And the one that looks most appealing to every politician I've ever spoken to is that we find some wonderful new technology that fixes it.
Don, one of the things I didn't say in my introduction is that you've been a member of the BBC Trust.
Does the argument about the public provision of information and some of the digital goods?
Does it have traction when applied to the BBC in policy circles in this country?
Yes. And that's one of the experiences that informs my thinking about this and whether we
should have a public service search engine or public service social media company.
Because although the BBC has been attacked by free market politicians and funding has been cut,
so that isn't the same solid consensus about it as I would have been 30 years ago.
It's very popular, it's very high quality. Its services are really widely used.
And I spent my eight years on the BBC Trust being concerned about its impact on the commercial market,
and it's always been very careful not to foreclose markets to commercial rivals.
But we've got a really healthy media market with a really big public player in it,
and I think it means that the competition hasn't been about large number of viewers and the lowest common denominator programming.
There's been competition about quality as well. And so an about innovation.
We are one of the few net exporters of popular and classical music around the world.
Sweden is one of the US is the other. And that's partly because we've got a public service broadcaster with a mission to encourage new material,
new creation inside this country so that it gets done and then it gets exported.
So I think it's a great parallel, actually. Thank you.
Question over there. Yeah. You talked a lot about costs going down with these new technologies.
My sense is that if you think of ICTs overall,
then the household budget or individual budgets have just been steadily growing
in terms of the amount of money that these services costs to individuals.
And you also talked about kind of how we change this or how what kind of choices people make.
It seems to me that that is not going to change. I mean, it's not going to increase vastly,
but driving down the costs for these large parts of a household or individual
budgets that have kind of grown over time isn't going to change anytime quickly,
even if it's not going to go up very much. I think there are two things going on.
One is that the price households pay per byte data.
Has been going down substantially, but the amount of the budget that they spend on information goods has been rising.
And that's the kind of shift in consumption patterns that we've seen over and over again over time.
And you know, when people have low incomes,
they spend more on food and shelter and that diversifies into other kinds of goods and then into other kinds of services.
And so there's no reason to expect that that share will go down.
But I think that's different from the price per byte of information, if you like.
Distinguish between two of them. Thank you. Other any further questions, one,
they can you can you make some comments on how this relates to our thinking about inequality in the sense that you know,
the way a lot of this technology is working?
On one hand, you're getting ever more billionaires and millionaires and a lot of the sort of wealth going towards them.
And and but on the other hand, lots of poor people are getting access to lots of technology very, very cheaply.
How do we sort of think about global inequality and the impact of all this discussion on the inequality we care about you?
So, so clearly, these are both going on at the same time, access to goods plus plus and inequality.
And so the short answer is it's complicated.
One is that there are genuine things like superstar effects.
So for some individuals, those will operate and make them very well off.
That is also. A sort of locational phenomenon in that the digital economy is enhancing forces of geographic agglomeration into big cities.
So productivity and incomes are higher in big cities, and that seems to be accelerating, at least if the US data is anything to go by.
So that's happening. There's also the thing that we often overlook in the economic discussion,
which is the politics of it and a lot of the inequalities is driven by politics,
by tax cuts for the wealthy, by bashing the unions and weakening labour market power by all kinds of institutional structures that can be changed.
And I have a Ph.D. student who's looking at exactly this issue about the difference in the incomes
of workers who were in industrial jobs got downsized and how that compares to green countries.
And it's very different.
So we have in our heads that the US model, there's something kind of deterministic about what the technology and trade effects are doing.
It gets refracted through politics as well, and that's a big part of the story, I think.
Thank you, gentlemen in a blue jumper. You mentioned the importance or perhaps the new importance of economic history.
I wonder whether there's any parallel or you can draw any between the the way in which public policy responded at the various stages of,
say, the industrial revolution. I'm thinking, say, canals, speed, electricity, late 19th century when there's a lot of disarray about all of that,
as you as you said, how quickly did public policy respond to that?
It depends which bit you're talking about, and some of it was quite variable and some areas of deregulation,
financial deregulation, financial changes and regulation seem to happen relatively quickly.
Things like the limited liability company came in relatively early in the period.
Things like safety, regulation or standards, which involve coordinating a lot of people in places much more slowly.
The one interesting panel is the data collection, and it took a long time for the government to start collecting data on the
industrial economy and for a very long period during the industrial revolution,
there were fantastic agricultural statistics. But all of the information people had about the industrial economy child labour were
number of mines and so on came through blue book reports commissioned by parliaments.
They were sort of one-off reports on what was happening. So even for policymakers to understand what's going on by having statistics is pretty.
It's pretty slow. The categories people have categories in, they had to collect the data and new technologies, often dangerous to start with.
I showed the cartoons for electricity, but it was dangerous. Air travel was dangerous to start with.
And I think an interesting question now is whether a tolerance for any kind of danger has reduced and whether autonomous vehicles will ever
really happen outside quite tightly due fenced areas because we don't have a tolerance for machines killing people in the way that we used to.
Thank you. That, Sarah. So thank you, Diana, for just a wonderful talk.
Your slide of the 1500 civil servants in Britain that learnt and old economics,
which you think can't address these, these problems, perhaps let alone the problems that David would add.
How do you address that? I mean, obviously, they should all buy your book and read it.
But but seriously, you you know you're Moore's law slides have their equivalent for the need for policy innovation to deal with this.
So any ideas on on what we do with government? I think the training need isn't so much my new book or that.
Of course, I hope we'll buy it as understanding the technologies that they're dealing with.
And there's not a lot of expertise in even regulators on these technologies,
and partly because the digital companies themselves buy so much of the expertise.
I think it's about the climate of ideas more than targeting the individuals.
And we've had a long period where a climate of ideas has been dominated by a free market benchmark.
It was actually a process encouraged by certain economists and think tanks and embedded by the Thatcher and Reagan revolutions.
And that has to change, I think, and I think it is, I see all kinds of straws in the wind.
Maybe I'm a deluded optimist, but I think that is changing and that will cause all those policymakers to through social influence,
change the way they think as well and reflect differently on the way they approach policy issues.
Thank you. Before asking you to thank done again. Two announcements, first of all.
Please join us for a glass of wine next door. In a moment.
And also just a bit of advertising.
Next week, we have a double act Colin Meyer and Paul Collier, both from Oxford, talking about the future of the corporation, economy and society.
And the following week we have Eric Fine, hooker Eric. I don't know the title of your talk.
Do you yet have new economic and moral foundations for the new moral and economic foundations for the Anthropocene?
So please come along to both of them, but please join me now for thanking God.
Thank you for the great.