Making better sense of the digital age
- Digital technologies including virtual goods, data centres and AI are reshaping the world we live in
- But official statistics miss out much of their impact, making it hard for economists to take account of them
- Competition regulators also need to rethink the basis on which they intervene
Professor Diane Coyle thinks the field of economics needs a paradigm shift.
The rise of the internet and the digitisation of our working and private lives have led to huge changes. But, Prof Coyle says, her profession lacks the tools to keep pace and its models consequently miss many critical aspects of 21st-century life, putting practitioners at risk of giving misleading and potentially damaging advice.
The University of Cambridge academic comes to the problem with a wealth of experience, including stints at the UK Treasury, the Competition Commission, the BBC’s governing trust and a spell advising Google’s artificial intelligence sister company DeepMind.
Her latest book is Cogs and Monsters: What Economics Is, and What It Should Be. The title represents a pushback against the idea that economists can assume people are self-interested individuals making decisions without influence from others - the ‘cogs’ in their traditional theories.
The ‘monsters’ references the unknown dynamics that technological changes have unleashed.
The book also takes aim at other assumptions that don't always hold true in the digital age, such as consumption of a product necessarily reducing its supply.
And it suggests regulators need to pay greater attention to how companies have used the internet and cloud computing to radically cut the cost of providing products and services to each user – potentially to the point of shutting newcomers out of the market.
Prof Coyle met Baillie Gifford investment manager Gary Robinson in London in February for a wide-ranging discussion.
Robinson: How are some of the changes that we're seeing affecting the statistics that economists have relied on as barometers?
Coyle: If you look at the official statistics, the digital sector’s almost invisible. Officials are only just starting to get their heads around how to change definitions and standards to measure what’s happening.
Think about a smartphone, which has become a substitute for radios, watches, diaries, roadmaps, and a whole host of other things that we used to buy as separate products.
What should we measure? Is it in terms of road atlases or the ability to get from A to B? Clearly, it’s the latter. But in that case, how would you begin to measure that? You don’t know what the data flows are. You don’t know which apps people are using. You don’t know how much time they’re spending on different things.
One thing I’m looking at now is the productivity puzzle. UK productivity has flatlined since the mid-2000s. Some of that is real, like the debt overhang from the financial crisis or the population ageing, or levelling-up issues.
But some of it is about how we measure prices. There are sectors like communications and software where it looks like there’s been really low productivity. However, if you say that to software engineers, data scientists or telecoms engineers, they laugh at you because there has been so much technological change.
Robinson: So can we say with a high level of confidence that inflation has been overstated and productivity growth understated?
Coyle: We can say with confidence that productivity growth has been understated a bit, and inflation has been overstated a bit. But I don’t really know the extent of that compared to all the other things. It’s like one of those Agatha Christie novels where everybody is the murderer. There are many culprits.
Robinson: How do we move forward on this topic?
Coyle: One side benefit of the big data revolution is that it’s making us think much more about the sourcing of data. What are its biases? How did it get classified and defined in the first place?
We’ve got people talking about how important data is – this cliché about it being the new oil, which I don’t like. But it gives an indication of how important it’s become.
Yet we’ve not funded enough public data collection. The government should be putting much more effort into thinking about what data needs to be available to people. We should be thinking about national platforms for data.
Robinson: Why are you sceptical about the comparisons between data and oil? It strikes me there are some similarities in the sense that countries with more access to data can drive forward on AI at a much faster rate.
Coyle: I agree with that. The US and China have got the big tech companies, and they have the data. So they’re speeding ahead in AI.
The bit I don’t like about the analogy is that oil is something that only I can use. If I burn it, it’s gone. Data has what economists call non-rival character: once it’s there, it’s available for anybody to use, and it doesn’t get depleted. Lots of people can use it at the same time.
Robinson: I think it would probably be fair to say that there was excitement that the internet had the potential to be a truly democratising force. Do you think it’s failed to live up to that expectation?
Coyle: Well, it’s gone wrong, hasn’t it? It’s brought us online harms and surveillance by either companies or governments. So the question is, why has that gone wrong? And I think part of it is a failure to enforce competition effectively.
When the internet was built, it had common standards, interoperability baked into it. As the current big tech giants came along, they pushed that out and created their own walled gardens and started hoarding the data and then creating different technology platforms and reducing interoperability.
Competition authorities around the world were slow to latch onto that. That’s now changing, but we’ve got this position where the incumbents are incredibly powerful. And in pretty much every jurisdiction, there’s a debate about how to address that market power and the political power that goes with it.
One of the things that doesn’t translate well from offline to online is the aggregation of data about you. East Germany’s secret police force, the Stasi, had this term: Gläsernen Mensch, meaning transparent people, because their ambition was to know everything about you.
In real life, we only know what we need to know about somebody. Your bank only needs to know that you are entitled to take out money. As a lecturer, I only need to know the academic record of my student.
But these sorts of restrictions on access or permissions don’t seem to have entirely translated into online. Google, Amazon or whoever can aggregate a lot of data about you and use it to make you buy more things and earn them money. Governments can also integrate lots of data about you.
Robinson: To what extent do you think this can be solved by the companies themselves?
Coyle: I don’t think they want to. They are very powerful, and they’re making lots of money. So their incentive to change is to do just enough to stop more intrusive regulation.
They are saying, ‘come along and regulate us now’, because they recognise that the climate has changed. But I don’t think they’ve got strong incentives themselves to do anything like introduce interoperable and transferrable data.
Robinson: Do you think these companies have been a net benefit for consumer welfare overall?
Coyle: Oh, gosh, I don’t know the answer to that. What do you think?
Robinson: It’s a difficult one. You’ve mentioned the power these companies have over consumer data and the imbalance that creates.
But on the other hand, the increasing returns to the scale these businesses benefit from have translated into a lot of good. They’ve helped to fund very high levels of research and development, which has then been translated into products that have been, in effect, given away to consumers. So I would probably fall in the net positive camp.
Coyle: I suppose one way to think about it is, would you want to do without them? And I certainly wouldn’t.
I did a piece of research that asked people how much they would need to be paid to give up a free digital service for 12 months. And there were some variations. People really value search and personal email. The median valuation for something like Facebook, Amazon or other kinds of apps is lower, but there are super-users who really value them.
That makes the regulatory questions quite hard because you upset that at your peril.
Robinson: Are the wheels in motion with regards to the changes in competition law?
Coyle: I think they are. In the US, the Biden administration is being much more muscular about tackling the power of these big American companies and has appointed a prominent critic of Amazon to head up the Federal Trade Commission. The Chinese government, for different kinds of reasons, has been tackling market power there. Here we’ve got the Digital Markets Unit being set up. Legislation is due later this year to give that a legal footing. And the EU’s got the Digital Market Act.
So there is going to be more regulation. But it remains to be seen if it will change anything.
Robinson: I want to ask a little bit about complexity theory. A lot of the challenges in dealing with today’s economy stem from the fact that it’s a complex adaptive system involving agents with different preferences that interact with one another and they respond to feedback.
Why haven’t we seen this new type of approach to economics embraced to a greater extent?
Coyle: My hypothesis is that mainstream economics is very heavily invested in analytic solutions using algebra, calculus and, increasingly, a branch of mathematics called topology. So we have a set of tools that we like to use, and we like to be able to write down the models rather than say: ‘I’m now going to go to my computer and simulate this.’
It’s beginning to change. There’s a lot more interest in agent-based modelling and complexity models than there used to be. So I think it’s partly the traditional thing in a discipline of a mainstream cohort that was trained the way it was trained. And you only get change with new generations who’ve learnt the new tools.
Robinson: Is there the potential for machine learning to deliver better economic predictions?
Coyle: We’ve obviously got a lot of challenges for which AI could be a useful tool. Machine learning systems have this capacity to take vast amounts of data, identify patterns that other techniques are not so good at doing and deliver better outcomes.
There are all kinds of marvellous opportunities like medical diagnosis or identifying fraud in banking systems. But I have a couple of concerns.
One is that you have to be really clear about what you mean by better. So, if it’s diagnosing cancer, you know what you’re trying to optimise for. But’s not always so clear in policy contexts.
The other is accountability. We have a lot of areas of policy where people have very different ideas of what are the principles behind the decisions. So in criminal justice, it might be you want retribution, or it might be that you want rehabilitation. And those are very different kinds of aims.
You can fudge that in a machine learning system by putting different weights on things, but they’re so complicated. How do you explain that? How do you deliver the political accountability for that?
And how actually do you accommodate areas where fudge is actually the way to get consensus in a democratic society? It’s got a bad reputation, but sometimes you just want to paper over irreconcilable conflicts so that we live peacefully together.
Prof Coyle will discuss Cogs and Monsters at the Hay Festival at Hay-on-Wye on 31 May 2022.
Cogs and Monsters: What Economics Is, and What It Should Be is published by Princeton University Press.
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