Welcome to Macro Matters, the weekly economics and political podcast from the ASI Research Institute, hosted by Paul Diggle and Stephanie Kelly.
This week - what lessons can economists learn from how a virus spreads, and what pitfalls should they avoid? Our guest, the distinguished economist Paul Ormerod, suggests that the macro-modellers of the Covid-19 virus might have missed a trick; put simply, they didn’t properly take into account the fact that people faced with a situation – any situation – are highly likely to change their behaviour as a result. And if economics is all about human behaviour, should economists have been as closely involved as epidemiologists were in modelling the approach to managing the virus back in March?
In the second part of the podcast, Paul and Stephanie discuss the search for a vaccine, and how the Research Institute has tried to extend the kind of thinking that Paul Ormerod was talking about by building a framework to measure vaccine development and output.
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You can read Stephanie’s article on the search for a vaccine here.
Hello and welcome to Macro Matters, the Economics and Politics podcast from the Aberdeen Standard Investments Research Institute. My name is Paul Diggle. I'm an economist at Aberdeen Standard and co-host the podcast.
This week we are very fortunate to be talking to Paul Ormerod. Paul is an economist and author of several best-selling economics books. An entrepreneur who's founded a number of economic consulting businesses and an academic researcher. And I first encountered Paul when I read his 1994 book The Death of Economics and I was at school at the time. And that book argued that conventional economics offers a misleading view of how the world operates. And much of Paul's subsequent work has been developing alternative approaches to doing economics and indeed, during the coronavirus crisis pool has taken an active role in the policy debates around lockdowns and approaches to dealing with the fallout from the virus.
I actually worked with Paul between 2008 and 2010, where among other things we co-wrote a pamphlet called Be Bold for Growth in which we question the limits of economics in areas like forecasting long term growth and exerting fine-tuned control over the macro economy. So I'm really looking forward to talking to Paul about the coronavirus crisis and how it may change or challenge the economics profession. Paul, welcome to the podcast. It's lovely to have you with us. Perhaps the place to start is by asking you if you think that that this crisis is the fundamental challenge to economics and the discipline of economics in the way that the financial crisis was often thought to be the failure to predict the financial crisis was seen as a sort of existential threat. And is this crisis like that? Is it a big change for the profession?
I actually think it's really completely different. Because I mean, you mentioned the book I wrote and it’s nearly 30 years since I wrote it. And economics at the micro level, or the level of understanding the individual, I think has moved on in a very positive way in those decades. It's on the macro side that that it's gone backwards. And my criticism of economics in the COVID crisis is that they weren't sufficiently involved from the outset, which was the mathematical models that the epidemiologists use. Although they're obviously difficult for laypeople to understand, for economists, they're quite straightforward. That’s basically either something called agent-based models, which economists use a lot, or simple, ordinary differential equations. And again, economists are extensively trained in these.
And if you remember that the lockdown was triggered by a forecast from an Imperial College epidemiologists suggesting there could be half a million deaths without a lockdown. Now the fundamental insight of economics is about individual behaviour, that if the incentives facing individuals changes, their behaviour will change. What we know from the history of the world is that when confronted by a pandemic, people will take steps to shield themselves, the behaviour won't remain the same, it might be difficult to predict exactly how they will change, but they will change that behaviour. And the epidemiological models simply did not contain this term. And we can see it if I could just perhaps finish on this rather long answer. We can see it in the Imperial model was applied to Sweden in early April. And of course, Sweden, although it's had some restrictions, as essentially being lock-down free. And that predicted there would be 80,000 deaths in Sweden by the end of July. And we're now at the end of September, and there hasn't even been 6000. And because people have changed their behaviour, people who are vulnerable shield. And so economists I think were negligent in not getting actively involved in this debate, and pointing out a major flaw in the epidemiological models which were driving policy decisions and still are today.
So I wonder how that that take sits alongside the view that a lot of critics of the economics profession at the post-financial crisis era - economists had been too involved in the minutiae of policy decisions and sort of pulling levers to control the micro economy. And this time, so the argument is that policy decisions that involve complex trade-offs and economic impacts, didn't sufficiently involve economists or how is there a tension in that?
Well, I think I think, you know, I'm just saying I think the economists themselves held back and seem to take like everybody else take the epidemiological models, as science - when I say in fact there is a major difference in that we can we can discuss how behaviour ought to have been built in. Those might be different views on that. But there has to be some behavioural response in these epidemiological models. No - behaviour doesn't remain fixed in the, in the face of, of an epidemic, the financial crisis was really completely different.
And that was what I was saying earlier, I think that was more a flaw of models at the macro level, where I think economics has gone backwards in the last 25-30 years. Because at the micro level, economics now paints a much more realistic and complex picture of how individuals really do take decisions than it did 30 years ago, when you know, as an approximation, if you like the stereotype of the rational economic person was still completely dominant. And macro-economics in the last 30-35 years, I've had a very curious task of importing into macroeconomics, the idea of an a completely omniscient, rational decision maker, at the same time that micro economics has been modifying that out of all recognition.
And it was the idea that in some way, there was a rational agent, which would guarantee equilibrium. And in fact, it was really got quite absurd in that, you know, the dominant is still around. But the, the dominant models of policy and academia, at the time of the financial crisis, something called dynamic stochastic general equilibrium models, the time they only had one decision, one person, one unit in the whole model, the so called representative agent who was purported to represent the entire economy in a single decision maker. Now, of course, if you've only got one agent, you can't have credit, and you can't have debt. Now, the individual can't be in debt to itself. And so it’s like a fundamental aspect of the financial crisis was inherently missing. Now, of course, since then, there's been a huge amount of work done in that framework to try to accommodate it. But I don't think it's very successful. And it was, that very strange tension was in a much more realistic picture of people's behaviour being developed by micro economists, and the macro people importing into their models, a view of behaviour, which the micro people had modified severely even 25-30 years ago.
So one of those alternative models - alternatives to the classic dynamic static equilibrium models is, as you were saying, Agent-based models and network models. And of course, the original agent-based model, as I understand it was of a viral spread between agents who sort of met and then and you have this contagion. And that analogy is sort of carried over into economic behaviour in agent-based models. So my question is, is this a sort of a very strong opportunity to advance the cause of things like agent-based modelling and alternative approaches to doing macro-economics that are built on those more realistic micro foundations?
Well, yes, I've been interested in these for a long time until, say, at least 10 years prior to the financial crisis. First of all, I mean, the epidemiological models are either differential equations or are agent-based models. And so the basically analytical here, very similar, I think there is an opportunity, but I think agent-based model is they haven't really gained the traction, although they're not people - the Bank of England have written papers in support of them, and they haven't really gained the traction that they could have done.
The great advantage of, if you like, standard economics is the as a theory of how an agent behaves. And this is true under any circumstances. It's a general theory of behaviour. And the agent-based ones require you to actually like calibrate behaviour to a specific circumstance. So a model is not completely general, but it's specific to a particular problem. Now, I think this is a great strength, but it's met with resistance amongst economists precisely for this reason, saying, well, essentially, you've got you've chosen this particular rule of behaviour. Well, why didn't you look at others? And I think there's actually, if you don't mind, it seems a sort of rather tenuous connection, I think, but it's important. I think, not enough effort has been devoted by agent base modellers to how you establish that your model is valid. Now that you've chosen: how do you produce evidence? - now there are ways to do this, but I think they've not put enough importance on it. And stepping back if we look back to say March and April with the epidemiological models. Now are these models validated? If we gone through the same process, then we'd say maybe, but they are missing a big chunk of how people might respond. And the agent-based models in economics haven't really got the traction because I don't think people have really spent enough time, if you like persuading people that these are scientifically valid.
So a lot of your work as well as been about borrowing from the sciences and importing the tools of say, physics or biology into economics. And as you've been describing agent-based modelling, it has potentially been one of those avenues. And I suppose it's interesting that you've sort of come full circle in a way and then argue that well, just as we economists borrowed from those sciences, now, the management of a biological sort of catastrophe and crisis should rely more on economist input as well in Policymaking?
No, I think that's a good way of putting it. And I think one of the things that this COVID crisis is exposed is really how blinkered most academics are no, they do live in their own little silos, whether they're economists or epidemiological modellers, or whatever. And they focus is not, again, to us to think about economics, there's no real incentive for them to, if you like, spread out and to look at multidisciplinary approaches, you know, within the framework in which research funds and promotions are allocated, regardless of the economic discipline, whereas this crisis has shown the great importance of multidisciplinary approaches.
And indeed, I mean, going back following on from your last question, one thing I've been very keen on there for at least 20 years or more is precisely this idea of networks, which is getting traction, and saying that in many situations, not always, but when people are making decisions, they're not simply gathering information, and comparing it to their own tastes and preferences, and then making the one which is closest to them with it not meeting it subject to any constraints they face, like what their income levels are, but they can be influenced by simply observing the behaviour of others. And that influence group will vary from decision to decision. So for financial services, you might be influenced by your uncle or whatever. But in terms of other contexts, now, whether - this is a study we did I think when you were at when we were working together - I'm not sure where you were involved in it on binge drinking, you know, that's much more of a your influence there and more by, you know, your peers and friends rather than sort of different networks. But the idea that tastes, that your views, your tastes and preferences are not fixed, but very, in some circumstances, potentially very malleable. With respect by observing the behaviour of others, I think that is getting traction in economics. And it's an important addition to, if you like to see, if you like purely incentive driven rational behaviour, although ironic, a look at some of the articles that appear in the major economic journals about networks. And even that been a people are trying to, you know, optimise that particular network. So economists can't resist that.
But actually, the financial crisis showed overwhelmingly what was done by the Bank of England and natural sciences from that side, that that was a classic example of a network effect that were cascades now that the solvency of one bank affected the solvency although the perceived solvency of others. And that was something which was entirely missing from most of the economic models, the idea of a cascade of failure. And this is something which has now become not necessarily mainstream, but much more widely accepted, you know, so no - things have moved on in this respect. And this is something I've been not very keen on for many, many years.
So perhaps taking the conversation down a slightly different route. And another one of your, your areas of interest has been in big data and alternative data sources. During the coronavirus crisis, there has been this explosion in big data and real time data sources that economists use to monitor Yeah, we wanted the huge contraction and then the sharp rebound as well sort of in near real time. And it was data provided by the likes of Google or Open Table showing you where people were booking restaurants. I mean, have you found this data useful? And do you think there are drawbacks to using this? I mean, does it have privacy implications? Is it a tough tool to use and interpret or by contrast, is this a big important breakthrough in economics?
Well, no, I was very pleased because for the moment, while for the past few years, I've been a visiting professor in the Computer Science department at UCL. And essentially, I mean, I mean, this is a simplification but essentially, people shouldn't be frightened by these machine learning and AI tools that are basically an extension of econometrics.
So, you know, they're not something completely different. So they are readily comprehensible. And they are readily available in programmes which economists tend not to use, and every other scientific discipline uses it heavily, or routines like Python, and many of these algorithms are readily accessible in the same way that say, you know, ordinary least-squares algorithms are accessible too, you know, in econometric packages. So these tools are increasingly available.
And there was a an important article, a big paper in the Journal of Economic literature, which, of course, is a widely read, it's an American Economic Association journal, widely read by economists. And the title was simply was text as data. And this is what I've been working on, you're mentioning gathering data. And I'll mention that in a minute. Now … first of all, I think, yes, I mean, the fact that we can gather things in real time, so you can measure say traffic flow, traffic density, footfall, you can measure these things, the Bank of England has been very keen about these things. I mean, actually, I mean, this is rather irreverent. But I like to think of central banks as like, the old security organs in in the Soviet Union that know, they have to know what's going on, you know, they can't pretend like the politicians.
And so they have to be willing to use new tools. And so, but there are there are limits on it. I mean, I did, there was a story I remember a number of years ago about how Google searches for flu, we're very good at predicting a behaviour on flu. But I did a study with an American academic, when we showed that's only true providing going back to the network point, that the search is genuinely independent, and they're not being driven. There are ways of distinguishing this. They're not being driven by the fact that people are searching for it simply because it's popular. They're not searching, you know. So if you say, right, I'm googling flu, because I think I might get it, I might have got it. Rather than saying, I'm googling flu. You know, because everybody else's, I wonder what they're looking for, you know, you do get quite different outcomes.
And so you’ve got to make sure the searches are independent. But yes, I think this is a big step forward. But my current interest has been this pressure for some time is how do we know it's straightforward enough, it's well established? How do we turn text into quantitative data series? Or how do we identify emotions, for example, in text, and I've been we've published I started doing this in just before the Brexit referendum in 2016, we published something called a London feel-good factor when we just use machine learning techniques on Twitter to measure feeling. And it's got a number of interesting properties that it might because it's based in London. So you might imagine, immediately after the Brexit referendum, it was very, very negative feeling, but didn't last long.
And when Trump was elected in later that year, there's a down drop down, but after a few days, it bounced back, it does track the economy really rather well. I think quite interestingly, it's not dropped dramatically during the COVID crisis. And so we've looked at several range of emotions on which, for example, uncertainty or fear, and these haven't really dropped away. And it's showing what we know is that the COVID crisis is really something quite different. It's not like the financial crisis, where there's fundamental questions about the balance sheets of major financial institutions, which is we know it's a major, major problem. It's a sort of supply side shock. But no, once a danger passes or a vaccine’s found or something, you know .. the economy will bounce back very strongly as it may well be doing already.
And so it's not really impacted on people's emotion the same way. Just to give another point, going back to 2016. There was then in the run up to the Brexit referendum, there was notorious “project fear”. When the Treasury predicted that if there was a leave vote, by the end of 2016, there would be a major recession and unemployment would rise by half a million. Now, of course, that didn't happen. But the interesting thing is that the Bank of England immediately prior to Brexit, were thinking of raising interest rates, because he thought the economy's growing strongly and the risk of inflation. And because of these forecasts, they didn't so they influenced interest rates. Now in real time, we could see that certainly individual sentiment was in fact, as it happens, it was rising. It didn't fall. It was stronger, as you move through 2016 than it been in stronger in September than it was in June, stronger in October than it was in August. And this sort of information, you know, could be used by central banks in terms it's another way of getting a handle on real time movements in the economy. So yes, I do think In short, that's a long answer. But in short, yes, there are major opportunities of using big data in economics. And economists should take the trouble to learn about these algorithms and start using them quite widely. They're not something separate. They're simply if they look at numbers, econometrics, they'll feel more comfortable.
So maybe a final question is then on questions of economic geography. Again, this is one of your areas of interest in and research, the death of cities has been much heralded as one possible consequence of this crisis and the sort of reduction in the amount that we actually visit the office. congregations of people in city centres have these large positive productivity externalities, agglomeration, externalities that boost productivity. So if the future is indeed less density in city centres is - does that necessarily mean a permanently lower productivity growth path for the economy?
No, I think this, this is an important point. And I don't have the complete answer. But of course, you're right. And this goes back to the idea of agglomeration effects goes all the way back to Alfred Marshall, who founded the Cambridge economics department back in 1903 – he was a very good mathematician, but he didn't have the tools to do it. But it's all in the textbooks that he wrote, then he persuaded Keynes to become an economist. So we saw that and you're right.
And one of the key things he identified was the ability for innovation to spread. And Silicon Valley is the prime example. You know, people move between firms, people copy ideas, and it boosts the productivity of the whole region. And you're right, if this, if human density becomes lower, then productivity, long term productivity will suffer, I think we can see some recognition of this increasingly amongst large firms. So we'll go back to the previous model, something's happened, people work fewer hours. And this is just perfectly natural. You know, when I was young, my father worked, and almost everybody else did routinely worked on Saturday morning, went into work. Now that stopped, not years and years ago, that was an evolution. And so we may be seeing another evolutionary step now, but increasingly saying, well, you know, you do lose these productivity benefits, you do lose informal contacts. And there's an increasing concern, as you say, that if you like innovation is going to be stifled if people are working remotely rather than being at home. And certainly, from my own perspective, I mean, interviews like this are fine. But if you actually having a discussion, if we were having a one to one discussion about these things, in my view, my experience for us to be in the same room, and then you know, things would spark off and we'd make more progress more rapidly than we would on a video call. And I think this is a sentiment, which will, it'll take time, but I think that's one which will gradually get the upper hand.
Great. Well, thank you, Paul. It's wonderful to talk to you. I much appreciate your time.
So having heard from Paul or more out there about the state of the economics profession during this crisis, Stephanie and I are now going to with appropriate humility, turn our attention to something that is usually well outside the wheelhouse of economists and that is a vaccine. And what we want to do is describe how we, as economic forecasters, who are clearly non-experts in the process of vaccine development, nonetheless, try to come to a considered view about how to condition our economic outlook on a plausible baseline expectation for what happens vis-à-vis vaccine. So let's start by hearing from Bill Gates, who is of course through his philanthropic work deeply involved in this area describing the state of coronavirus vaccine development.
Bill Gates clip
There are six efforts that are the furthest along which will be in phase three trials by the end of September. I do think several of them will be successful not just at the 50% level, but the 80 90% level in terms of blocking transmission and sickness.
So Stephanie, why don't we start with you telling us why the the outlook for vaccine is such a crucial part of the overall economic outlook at the moment.
I think we only need to look around at our lives today versus how they were a year ago. And think about the year that we've been through as individuals but also as a total economy, in terms of the impacts of COVID has had on our ability to go out, to spend, to interact, all of these things, I think are pretty crucial to the economic outlook. And that's reflected in the massive downgrade to economic forecasts that came about as a result of COVID. And indeed, the long term effects that we think they're likely to be the reason the vaccine matters is because the vaccine, in a way holds the key to unlocking parts of the economy that will remain challenging. Even in a world where you have effective track and trace, even in a world where you have some parts of the economy open to essentially return to 2019 means trying to find a path where you aren't putting the health system at risk. And the vaccine is a crucial component of that. It's not a silver bullet. But it is certainly a component that would expedite the reopening of parts of the economy that continue to remain either, you know, fully closed or constrained in a significant way.
So we have to incorporate a vaccine view really, today formulate an economic view, but it's not just a question of Will there be a vaccine or wait, that's a much more complicated series of sort of sub questions about development efficacy rollout take up? So could you talk us through how we tried to break down the problem into its constituent parts?
I mean, part of this is also worth saying upfront, recognising what you don't know, but at least trying to have a framework so that as information comes in, you can update and, you know, somewhat mechanically assess the number of inoculations that are likely. And by understanding the number of inoculations, that means that we can then start to get to grips with what the impact can be on the economy of having a vaccine in place and how many people are getting it. So in terms of what that looks like, just very briefly, the first question is, you know, does a vaccine get approved that has questions in it about efficacy and safety ones that we really can't know, until we have phase three results? We don't have any phase three results, we still don't have a lot of phase two results at the moment. Once we have a vaccine that is effective, and relatively safe and approved, at least for pre authorization, then you're talking about well, okay, so you have an approved vaccine, but how do you get the vaccine to people?
So that's twofold. One is manufacturing the vaccine. And then the second is rolling it out to the population. And that's around public health questions, public health campaigns. And then that fundamentally leads to the fourth element, we need to think of virus, which is after it's been manufactured, after it's been rolled out, who's taking it off? And are there issues with things like anti vaccine and groups, those kinds of elements can really affect the amount of vaccine that as much as you might make it, if it's not getting inside people, it doesn't have the effect on the economy that we're talking about. And then once we have that sense of the combination of the likelihood of a vaccine, the efficacy of that vaccine, the manufacturing capacity, the rollout, and the take up, you can then estimate the number of people who get inoculated in a given year. And for us, we looked to kind of mid-2021, as our day for when we were thinking about how many people could feasibly be inoculated by mid-2021.
I suppose the way I thought of it is that we were sort of trying to take an approach, which was there's all these sort of nodes to before you get to giving people a vaccine. And we sort of tried to assign probabilities or decide what those nodes were and then assign probabilities to reach this, this probability-weighted outcome of how many people are vaccinated by what date, but how did we go about sort of making sensible, plausible assumptions for each of those sub questions, things like the efficacy of the vaccine, manufacturing capacity of the vaccine, take up of the vaccine.
So on the question of efficacy, I think was one of the most challenging, because it did involve a lot of economists in the Research Institute, trying to read medical journals. I'm here, you know, as you said, right at the outset, we have to have some degree of humility here. And we are economists, despite what we might think we can't do everything (!) But nonetheless, the ability to read an academic paper and make realistic assessments about what's the common number that's coming off, what's the number that experts and then I think this is where really need to focus on what vaccine experts talk about not what are politicians saying, but what are vaccine experts talking about?
So we spent quite a lot of time looking not only at the Oxford vaccine, but also at the multitude of vaccines being assessed at the moment being developed and trying to see what are the estimates in the phase one, phase two for efficacy, and then we kind of revised down a little bit, you know, just a sense that usually those phase two trials are in kind of regular kind of healthy populations. Once you start stretching to phase three, and you have older populations, you have different racial backgrounds, socioeconomic backgrounds, all of those elements can have an impact on what how effective the vaccine is, because different people respond differently to vaccine. So that's really the first step was how effective is it? Well, let's look at the medical journals. But then let's kind of be human, relatively humble about it, and if anything, kind of marked down so we ended up with vaccine approval probability at 60% with efficacy of about 70%.
So why don't we could continue on that and speak a bit less theoretically about the framework we use actually tease out the actual assumptions that we that we embedded in our forecast. So vaccine approval, efficacy, you've spoken about what did we decide in terms of manufacturing capacity and rollout?
So I think manufacturing and rollout is more regular, we'll have stuff for economists but as it comes down to what can companies do, what can .. what are they saying they can do? And then what do we know about kind of health systems and public health in general, and here, we assess that by mid-2021, that you could potentially see about 2 billion doses manufactured.
Now, that doesn't specifically assume one vaccine is developed, I think of anything, it probably assumes a number of vaccines are developed, for example, the Oxford vaccine, the estimate is that they could develop 2 billion by the end of 2021. But then you've got the Moderna vaccine, where their estimates are around a billion by the end of 2021. You've got Pfizer vaccine, again, quite high estimates. And so when we looked across the swathe of vaccines being developed, we kind of said, okay, what's a reasonable number based off of what the manufacturers are telling us? So we ended up at about 2 billion and then for rollout similarly, we kind of asked the question of how much how quickly can the vaccine be taken and given to populations. And we know that there can be inefficiencies, and different countries vary a lot here. So it's worth bearing in mind, our analysis was based off of G 20. So we were looking at an aggregate view. But the reality is that some countries are better set off to rapidly distribute a vaccine. Generally speaking, it's easier if it's a smaller country, just because there's less, there's less of a complexity of pathways to try and get vaccine out. But we estimated that to the general population, we think you could get away it's about 88%, and the rollout in general population of about 90%. So it's, it's basically a pending for some inefficiencies coming into the system. And without being overly specific about what those look like. Because at an aggregate level at G 20 level issues and inefficiencies are different in different countries.
And I suppose another consideration as you sort of move down the the wealth level of countries, you possibly encounter further problems or difficulties in terms of roll out, especially if the vaccine is one that involves sort of been kept very cold and frozen, very low temperatures, which some of the candidates do, there's a lot of technical hurdles to overcome there. But perhaps the final question, then is, What impact did it all have on our actual economic forecasts? And how much did we think that lockdown could be eased in return to a normal life can actually resume now that we've sort of conditioned our forecast on this the expectation for a vaccine as you as you've described it?
So I think it's fair to say that, you know, a billion people being inoculated by mid-2021, was the estimate that we came out with, which is significant, if you think that our entire population in the G 20 is about 5 billion. So it's significant. But it's not clearly .. by no means is it a majority of people being inoculated. And so although it's helpful, it's not enough. And I think this is where what we really want to do to further refine this and more clearly build it into individual countries taking into account those individual factors is to actually carry out the same framework, don't ask those same questions we've talked about approval, about efficacy about take-up and rollout. But do that for a country to country level, because that will give us a better sense of the specific, you know, impact as we move forward, especially because we'll get more information on the vaccine. For now, we see it as clearly helpful from a growth perspective, but by no means a silver bullet. And we continue, we didn't, for example, change the ultimate shape of the growth outlook, we had kind of over a three year basis, at least in terms of the shape of that. It didn't fundamentally shift that though it is of course a source of a source of potential strength for forecast going forward.
Steph, thank you very much, that's really useful. So that's all we have time for this week. If you're enjoying macro matters, please subscribe or leave us a comment or review, but only remains for me to say until next time, goodbye and good luck out there.
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