Producing done farmer. And Maria Del Rio Shann, owner,
who have written a very interesting paper supply and demand shocks in the covered nineteen pandemic for the latest issue,
which a special feature issued on the pandemic of Ox Jockstrap, edited by, amongst others, Cameron Hepburn.
And they'll be talking about that paper and also some of the associated words that they are doing that that doesn't come into the general issue.
So this is a double treat for us. We'll get the journal paper plus more.
And no doubt that they're thinking since they did that. I recruited Darren Farmer to Oxford sometime back.
I can't remember when it was done eight years ago, eight years ago to run the complex, the economics group in the Oxford Martin School from Santa Fe.
He's a leading world thinker on complex economics. He really is not only a top economist.
Ben has been in physics and maths as well.
And amongst the leading people in the world, an agent based modelling and breakthroughs in other areas of economics,
someone who's managed to combine interdisciplinary thinking from multiple perspectives into
economics to make real breakthroughs in economists understanding of the world and to show us how.
Tools that he and others have developed can be useful and no doubt we'll be seeing in the presentation today.
He's also dabbled in other areas, including wearable technology, and was a founder of the prediction company, which was sold to UBS in 2006.
They don't delighted to be with you on this.
And he'll be joined by Maria Del Rio Girona, who is a doctoral student who I met was from Mexico University Daftness.
Now it's on a map of Mexico. Wish that the B.S. before starting her HD.
And she's been focussing on data driven network models of labour markets.
So they'll talk for about half an hour or so. We'll have a short conversation and then we'll open it up to questions and answers.
So I encourage people to post their questions in the ask a question tab, which should be on the bottom right of your screen.
It's likely that you both joined and then we hand over to Maria and done.
OK, so I will start off and introduce the talk and then I'm going to make the handoff to Rhea.
I don't think I have to convince anybody that Cauvin covered pandemic is having a profound economic effect on world.
What we're gonna present here is, are the model we've made for it.
And so you'll get to see how it was done. Call the pandemic is actually unusual from an economic point of view in several respects.
The first one is that, ironically, it's actually the case where things in a sense work conceptually the way they should in economics.
That is, most big economic events, in my opinion, are endogenous.
They come from within the economy itself. The 2008 crisis would be a good example.
The Great Depression would be another good example. These things, business cycles are, in my opinion, another example.
But that's not the way economists normally look at them. The way economists normally look at them is they're exoticness events.
They're what they call shocks that hit the economy from outside the economy somewhere.
And then the economy responds to those outside events covered pandemic.
In my view, is the rare case where that point of view is the correct way to look at things.
And so you'll see that in the way our model is structured here.
We begin by trying to understand what those shocks are.
And a big challenge in our case was to actually predict the shocks before we knew what they were.
Normally in economic models, the shocks hit and then you measure them and then you look at how the economy responds.
Our efforts are unusual and we actually predicted the shocks before they occurred.
And then once they occur, you have to look at how the economy responds to it.
Now, the pandemic is also unusual in other respects. First of all, the shocks are big.
This is a profound event in economic history. The shocks are big and they're deep, but they're precise, so they don't hit everything, they hit.
The things you see happening. Restaurants, for example, restaurants are more or less gone.
I mean, so some sectors get hit profoundly.
Other sectors don't get directly hit at all. And another way in which this is unique is that everything happened really fast.
You'll see when Reia talks about remodelling a matter of a month. There were profound effects on the economy.
And so you really have to think at a fairly rapid timeframe. And the model that Maria will discuss, we both forecast the shocks.
And then we we had to make a whole new kind of economic model in which we look at
things as a function of time in which we're able to deal with really large shocks.
We had to think about the means of production.
How would the economy as a whole be affected by the effect of shocks in some sectors on the production of other sectors?
So we had to think about things like inventories.
So we had to do quite a lot of bespoke work to build the model that Marie is about to present to you.
I think it's an important step in a new direction of complexity, economic models that stimulate the economy from the bottom up.
And that take what happens in the economy as an emergent phenomenon of these lower level events.
So on that, I'm going to hand things over to Maria and let her present the model that we made, and then we can come back.
I can maybe participate. One question. Put it over to you, Maria. Okay, perfect.
Thank you very much. Thank you all.
I'm just sharing screen, hopefully you'll be able to see my screen.
Is that a good. No, perfect.
OK, so I'm going to do it with a brief overview of her work and it's on supply and demand shocks and the work with that later.
And this is all being done. I know it. So I'd like to start by asking who's the most vulnerable in the Kobe 19 pandemic?
I know if if you've been flooded than he was did say it's probably the year
there'll be people with pre-existing conditions such such as asthma and diabetes.
However, we think this is only part of the question because this is a people that are vulnerable because of the nature of the work of the states,
as they do every day. So we're talking about doctors that are more exposed to the virus and people that are exposed to the virus due to close
proximity or because they cannot work because their sector has been knocked down and then their income is compromised.
So we tried to see this as an early stage economic diagnostic to identify the vulnerable population.
So to do this, we. Yes.
How is the economy affected? And on the supply side, we see that some industries cannot work because they're not considered essential.
So pops in restaurants. But May and some parts of manufacturing that work clothes.
And then if people cannot work remotely, they just cannot work at all on the demand side.
We see that some industries weren't close, but people changed their consumption preference.
And so, for example, we see Transperth that what's considered essential.
But people didn't use transport as much. So we split this and demand and supply.
And on the supply side, as we said, we wanted to calculate everything with data pre the pandemic.
So we looked at for the US how the composition between industries and occupations.
So which industries employ because for which occupations? We also had data from the tunnel and government release data on their essential industries.
So we could see which industries were going to be closed or not due to the pandemic on the occupations side.
We have data on the work of these. They do. And then we can label the work activities as can be done remotely or not.
And we can come up with a remote labour index. And in this way, we can say which industries can work and which occupations can work.
Basically, you can work if your industry is considered essential or you can work from home.
And if those things don't happen, you just cannot work or cannot produce.
So just to take a look at the remote labour index here on the person talks and we have room with labour index.
If it's worn, it means you can do everything from home. Zero means you can't do anything.
And we see education has a high remotely were indexed, as we expect, and construction, a low labour index.
And this is from the occupations side. But we have to do audits between occupations and industries.
So from the industry side, we have finance and insurance, have a height remote labour index.
As you'd expect, agriculture, forestry and fishing have a low remote labour index.
OK. And then we're gonna couple this with essentialness. Right.
So we also have those data on how which industries are essential.
And so in this plot, you have the remote labour index on the horizontal axis and the fraction of employed in essential industries.
And we have different occupations here. And the people we're worried about are those that are not in essential industries or their industry clothes.
And also they cannot work remotely. So we're looking at the bottom left quartile.
And, for example, roofers, rock splitter dishwashers, those are the people that's cut work remotely.
OK. And then we put this together and we say, well, around how many people can it work?
So we have this this charts of the people that are in non-essential jobs and people that cannot work remotely.
And in conjunction, it's around 90 percent of the US population. What we predicted.
OK. So that's all from the supply side. But now let's talk about demand. So in demands is wanted to do everything pre pandemic.
We looked and we found this study from the CBO and two thousand six that talked about a severe influenza pandemic similar to that of the Spanish flu.
They predicted the shocks. So they predict that agriculture, construction, manufacturing would have around a 10 percent decrease.
And then you see transportation having a large shock of six to seven percent, a combination arts and recreation of 80 percent.
And then health care actually a slight increase. That's something that that happen it since it was just the very small sector of health care.
And then we can merge the supply show and the demand shock and we can see, for example,
cooks, waiters, they have a large supply shock because they're not considered essential.
It can't work. But they also have a strong demand shock because people don't want to go to restaurants.
Airline pilots, they're consider essential transport.
But people don't want to fly. So they also have a heart shock and then other patients have different shock.
So office clerks, for example, they're sort of mix their effect. Less.
OK. And as we have this duality between industry and occupation, we can do the same thing.
So this are now the shocks for the industries. We have a supply and demand shock for the supply shock we see again.
Entertainment to rest around their head. Both sorry. Both supply and demand.
Transport is not hit by supply, but it's highly hit by demand.
And then when it comes to manufacturing, demand is low. But supply it very varies law depending on on how the lockdown happened.
OK. So can we do a quick you know, this are just shocks and I'm going to talk later then about second order effects.
But just looking at the shocks, what are we talking about? How much is the economic cost?
So first we look at employment. So people that cannot work. And I have to say, this does not translate into unemployment because this for No.
And there's contracts and this other stuff. But we said 90 percent of people cannot work because of supply of 13 percent because of this demand.
Of course, there are some people that are mixed in here, like cooks and restaurants that are hit by both.
So in total, it's actually what we estimate is 23 percent of the people cannot work.
And then we said, well, let's say, you know, those people,
let's assume they wouldn't get their wage out of the total wages that are paid normally how much wages wouldn't be paid.
And that was a 16 percent notice that it's quite lower than this.
Twenty three. So this is already ringing a bell. And then we look at the value added.
Right. We have industries and we can see how the shock, different interest rates add up.
And that's a 20 percent in value added.
Let me go a bit more into this employment and wage thing with the population into quartiles, into wage quartiles.
So the lowest quartile is what you'd say, Mermin, the poor. It's people that earn below the wage is below 25 percent of all the wages.
Top quartile is the rich people there. The wage is higher than 75 percent of all the population.
Now, what we see is that if you're in the lowest quartile, this a forty one percent probability that you won't be able to work.
And this is quite high when you compare it to if you're rich. There's only six percent chance that you won't be able to work.
And now we also looked at the total wage lost.
So we said the 16 percent. But how is it divided? How much is in the lowest quartile?
And that's a thirty one percent. So we actually see out of all the quartos, even in wage, in wage of quantity.
It's buried by the poor. So this is what we have to think about safety nets and unemployment benefits because while this is happening.
And just to put even another concerning picture out there, we hear you have the labour shock.
So you have Cookes and Bear Stearns being hardly hit and wage on the horizontal axis.
And we colour coded occupations by their exposure to infection. So this is how do they deal with how regular they deal with the season infection?
And what's surprising is that from the high wage occupations, airline pilots are basically the only ones that have a high labour shock.
However, those many low wage occupations that have a very high labour shock.
And then if you look at the low wage occupations that have a low labour shock, they tend to be lightly coloured.
So you see janitors, maids and house cleaners. Well, this is basically something is OK, basically, if if you tend to be in the lowest quartile,
you either cannot work or if you're working well, maybe you're being exposed to the virus in higher doses.
So this is also a bit worrying. OK. But this was all about getting the shocks out there.
But then, you know, OK, this we have this study mid April.
But this pandemic is still ongoing. How can we take the second order effects and all the complicated things the economy is doing?
So to do that, we need to incorporate the supply change in a macroeconomic model.
So here you can see notes are sectors of the economy.
We see agriculture here at the top and final demand and consumption.
And what you can think, for example, is, let's say a shock hits manufacturing, electronic.
They start producing. Well, this might hit ITC, for example.
Of course, I.T. might also be hit by demand because people want I.T. or less or more depending on on it.
And we have a lot of propagation here. So to do is follow up work.
We did a one dynamic model, so it's model and time steps.
And this allows us to put complicated rules. Then we have a unique production function that distinguish between critical and non-critical inputs.
We consider both supply and demand shocks and those important difference in how those behave.
We also had inventories and a consumption function that considers an epidemic impact.
I'm going to go into detail into this. So how does this dynamic look?
Very broadly, but just very broadly, we have an initial steady state. We have a lock down and we have the first sort of shocks that start propagating.
So we have, you know, computers are not being built. So people cannot work in I.T., for example, or something like that.
And then and then there's a post lockdown economy where, you know, people.
Can work again and supply shocks are removed, but consumption is still not removed entirely because, well, we haven't gone back to normal yet.
Right. Like, not everyone's going to restaurants. And then finally, in a post pandemic world, the the some of the man shocks are removed.
So what about this critical inputs? Well, we are some mostly some experts made some survey and they asked,
can product can production continue in industry X if input Y is not available for two months.
And this is what we call critical or not critical inputs.
And our production function considers this Ferdo model.
Now, there's a difference between demand and supply shocks us and said supply shocks disappear after locked down.
So here we see three sectors. Electricity is not hit paper. Men are shocked and factoring is hit on the supply side.
But after Lockdown's, they can go back to normal instead. Restaurant after lockdown.
They decrease. But people are still wary of their virus. So this just goes along slowly.
And this is also consumption function. That depends on the epidemics, right?
People are still scared of the virus or worried about their income.
And this affects it. And it won't go to the end until we reach the pandemic.
Until the pandemic ends. They won't go away. So this is also a thing or model allows us to do OK.
So we did this and we predicted through time.
Lockdown starts to come later. Two months later, resume lockdown ended.
What's the girls up for? For different industries. And on the black solid blue line is the deprecate, the aggregate of all the industries.
So for the first quartile, if you compare 2010 with 2019, we predicted a twenty one percent decrease in GDP.
We're talking that most forecasts were around 16 percent. Actually, Bank of England with more pessimistic, they assumed a 30 30 percent decrease.
It turns out that when the data came out, this was a 22 percent and of course, are forecast were done before the data came.
So there's actually quite a nice result. We're quite happy with this.
We looked a bit more into into how things work when we compare to the Washington data.
So here it's unemployment. People that are upper unemployments and white or model would predict.
And this are different sectors of the economy. And we see that we have we have errors for sure.
But in general, they seem to have averaged out on the aggregate in such a way that we managed to do a decent prediction.
One very interesting thing about our model is that when you look about how the economy interacts,
there can be coordination failures in the supply chain.
So we played in, you know, just put demand shocks and then only put supply shocks and then put both.
And something really interesting was that apparently it's worse only to put supply shocks and not demand and supply.
And we're curious about this. And what happens is that sometimes when you when people keep on buying stuff, they end.
Inventories are running out for those industries whose production is compromised
because of the lockdown as people start buying some of those critical inputs.
Other industries kind of work and we have this bottlenecks.
So but perhaps you could think of I.T. they they need computers and other special equipment,
but then people at home, they start needing more of this stuff because they're working remotely.
They start buying some of this essential stuff. And then I can no longer work.
And because I can no longer work, then. Other industries can no longer work.
And so something we need to more into.
But it's something it's in your results and it's something we believe is intrinsic for a model that acknowledges how complex the economy is.
Finally, let's talk a bit about economics and epidemiology. We know there decoupled, right.
The virus is spread by people and the economy is run by people. So we did some very basic epidemiology.
And this is something we're working to refine and do better with a more detailed epidemic analysis.
But just on a on a brief basis, we consider different scenario.
So a lockdown or a pre lockdown manufacturing and then different opening, let's say manufacturing open.
All except consumer facing, et cetera, et cetera. So blue bar.
So the increase in GDP, of course, even if you open everything because there was the lockdown,
then you you won't recover 20 thing, but you've recovered most of it.
And we split the R serios the reproduction number, you know, if it's above one.
This is where basically we start to get worried because one people transmits it to more than one person.
So we have exponential growth. And what we see is we we divided this number into consumption, transport, school and work.
And we also took work. We consider work in which industry they work and we have data on how exposed.
Well, they're people they could be, so we desegregated this event further.
And we actually found that maybe there's some compromise when it comes to supply shocks on all except consumer facing right now.
This is something we ought to be careful about because, as we say, the supply shocks are removed with a lock down, that demand shocks are not removed.
So you still see a huge drop. And this is because on this, the epidemic is it's over.
We're still going to we're not going to return to a back to normal. We might never even return for back to normal.
But people are not going to spend as much in restaurants and pubs and in barbers and other stuff.
If we still have an epidemic, I'm going. So just with that, I'll conclude and live to doing.
The economic costs are distributed differently amongst income classes.
We see they they bear higher costs on the poor are shocks can be issue like macroeconomic model.
So this is something they're out there. We're very happy if you use our model. But if you have another model, please go ahead.
Do we have second order effects that are complex? We observe this coordination failure in the supply chain and, you know, further work.
We have to learn how to live with the virus. We have to find the sweet spot between having the economy open, but also mitigating the pandemic.
And there's going to be a hammer and dance. So just with that, I'd like to say thank you.
I think it's month seven of the pandemic. So I'm hope you're holding in there and sending everyone a remote pod.
Thanks. Thanks, Maria.
Oh, handed over to you. Thank you. Well, I think you did a great job.
So I just take questions from the audience. Great.
So just before we get on to the questions from the audience.
I have a few myself.
The first is, did you do much analysis of the impact by different age groups, so age cohorts of the employment, income and other effects?
Because a lot of what I'm picking up is that it's highly variable. Right?
I mean, I guess the short answer is no. Then at first on just the.
I think. But yeah, it's it's one of the things we're looking forward to, too.
And also that's also very important when you look at epidemics as well. Yes.
Yeah. Yeah. And vulnerability's and I think it will be as we go back.
The other question is, did you do some differences between country strategies?
I mean, would this look very different in Germany to the U.K., to the U.S.?
And I guess in the US, the obvious experiment is between different states who have adopted very different strategies in the way they've managed this.
Yeah, maybe I can take that one. Now, we debated which country to do.
We had the best information about shots for the U.S. We haven't used tell Italian with the simple indices.
And, you know, we live in the UK.
So we initially said, okay, we'll do it for the U.S. if we had more detailed information from the U.S., as you mentioned.
And there's actually 50 different policies going on with the pandemic when the economy is all out of sync in the data collection is terrible.
And basically, there's a massive headache of the fact that we don't have good data for any one country.
So the model had to be a bit of a pastiche of information we took from different countries and then modified need it.
And so do you feel that the results are mainly applicable for the U.K., or do you think that General Generalisable, the results are quite general.
And if we had more more personal power, we would we could have done most of the countries in the world.
We've set up the model now. So it uses something called the world.
It put up a database which covers about 85 or 90 percent of GDP and covers maybe at this point 50 countries.
So we can really we could do all of those countries that would have that would make the model for the UK more accurate because then we'd
be modelling the rest of the world rather than right now we have to take the rest of the world into an exorbitant input and inaccuracy.
And there is just more work. And, you know, we basically did this whole modelling effort without any official support,
just sort of grabbing people off of whatever they were doing, the press gang style and working very intensely.
You know, I think I'm quite proud of the fact that we did it quickly and we did.
And we recommended a policy that wasn't too far away from what was enacted.
And we came out with predictions that were pretty much in line with what's happened since then.
And I guess a related question is these trade offs between short and medium term.
So it might be that short term pain.
I mean, that's certainly the arguments about short term pain is that you have short term pain, a medium term gain.
And that's what strategies in places like China, South Korea, Japan, Taiwan, Mongolia, Vietnam would seem to vindicate it.
So I guess you don't really have that temporal dimension.
So it's difficult for you to say anything about that?
No, actually, we do have the temporal dimension and we also I mean, really didn't say too much about it,
but we built an epidemiological model now to answer the question that you're raising.
We do after a couple of the epidemiological model to the economic model, as we did.
But you have to have a good enough epidemiological model that you believe the result.
And we we found we were totally pleased with anybody, epidemiological model, including our own.
You know, that's not necessarily the fault of the epidemiologist, which is a unique virus,
and that it transmitted both through the air and through droplets. And so if the transmission mechanism is different than what people are focussed on,
you got TV on one side, which is like the airborne small particles that we're to worry about.
The room with people about. Another is the droplets that don't go more than four metres.
And as long as somebody who doesn't spit on you, you're okay. So.
So. OK. So that was a tough problem. Now, let me just mention another thing that we've found.
I think our model is very good at short for maximum fat because we're modelling all these
mechanical effects like running out of inventory and the flow of goods across sectors in time.
Then other models don't do. Why the short term? Our model pretty good long term.
It's harder because long term stuff depends on things like people's expectations.
How is the pandemic going to affect everybody's savings rate and what are they going to do when it lifts?
Are they going to spend all the money that they save, which has to do with their expectations about the future because they might be unemployed?
And the expectation is that getting another job is going to determine how much they're going to hear their opinions.
And so there's a lot of the longer term questions that really depend on the way people think.
And those are always the harder to model.
So it's a big challenge to get this right over the long term and really understand it rebound effects properly.
Just to add onto that one. One important thing about the virus is that it's exponential growth.
So when you talk short term, medium term, the thing is,
the closer you get it to the initial conditions, the initial outbreak, the better it's going to be.
Well, when you're in an economy, you know, the lockdown doesn't really matter when you put it.
So that should give you enough of an idea of the Trade-Off of you should start the locked out early on.
But the thing that's really interesting about our model is that it's based on data, on work, on how people work and how people interact.
And since, you know, people working are the basis of the economy in a way that consults how it is affected.
But people working and interrupting also tells us how the virus spreads.
So if we nail that one thing along with, you know, everything else in the very complex economy, then we have a pretty good shot.
So I think the fact that we have a dynamic model, what Times says what we can do everything, we're out of equilibrium.
It just gives a unique advantage that we really hope to exploit. And do you feel that from all of that, you have some advice that, you know,
where do you feel the position that you could say something to the UK government or any governments about strategy on the results of your model?
Well, we did. We actually spread it to make them aware of it.
And, you know, one of our main messages of media said at the end is that some industries can be open
with relatively little epidemiological scene and play a profound role in the economy.
In fact, it's essential that the upstream industry indiscreetly the supply of stuff to other industries to make things.
Those need to get open because otherwise the economy gets bottlenecks and things could actually be worse than opening.
To put this closing downstream industry, it doesn't help if you don't open up upstream.
And. So we really we could see very clearly specific things about that.
And we gave brothers friendly advice. OK, well, I've got more questions, but I see the six in the box.
So let's let's turn to those. And I see people haven't voted as far as I can see, where there's one vote, three votes.
So let's start with the three vote question. OK.
That's a technical question. What programming language did you use to create the model?
And do you believe open source models are better than commercial models? Of course.
But by the way, this is being recorded. So I guess, you know, just be aware it's being recorded.
If you're not comfortable with it being recorded, then you should involves questions, I suppose.
Sure. No. So it's within a mix.
Most of the first analysis is done in Python. The second one is done in ah.
We have everything aligned. So if you click on well we can distribute it on the second paper.
It's an interactive so we can actually play with a lot on and put your own scenarios and see, you know what our epidemiology.
A lot of what's the ah Cerro. And what's the consequences. And it's all open source.
It's own get help. All our our our out out there. And yeah.
We're, we're just big on open source and having thanks as clear as I can be.
And yeah, we could live in that share of mobile. OK.
This has got three votes. What are the policy implications of these findings in terms of the redistributive
options to protect welfare and demand from McCRUDDEN three supporters?
So to protect what you're talking about. Well, to protect welfare and demand welfare and demand.
So I think, as Maria stressed in her talk, one of the things we see is that policies.
If we just let things roll, the poor bear the brunt of the pandemic and they buried on both sides, most likely more likely to get sick.
I think that's pretty clear from the data so far. And they're much more likely to be unemployed.
And they're actually paying lion's share even in absolute terms in terms of lost wages.
So so our model makes that very clear. And so we want to fight against that.
We have the policies that overturn that. You know, I think economists are in pretty broad agreement about this,
that you really need policies that protect people, protect displaced people, in particular, protect poor people.
You know, it's ultimately good for them, but it's also good for GDP in general,
because if you lose a lot of people from the economy, you're in danger of not getting them back.
And one of the lessons that we've learnt during the Great Depression is that, you know,
if there's a Keynesian feedback loop, once you have a lot of people unemployed, I mean, those people aren't demanding goods.
They aren't buying things, which means that people have no incentive to produce them, which means that people stay unemployed.
And you can get stuck in that loop for decades.
And so it's essential that we do things to jolt the economy back out of that loop and really do want to add some spending.
I mean, I just one shot on that food budget. We also have the more you protect the people, the more people are okay.
Staying at home.
You've seen huge problems in developed countries where you have and former labour and where governments that implement the safety nets,
where people keep on working, people keep going out.
So all in all, we need to protect those people because they're losing their income, because if not, they're exposed to the virus.
But and as I was going said, even from a GDP scenario, we don't want to lose them or you get this food.
But about the ones we got in the Great Depression. But even when it comes to the epidemiology, you want to pay people to stay at home.
I think I think that's that's one of the things we have to emphasise that.
Yeah. But we have to support people for being at home as well. OK.
This one's got two votes. I noticed in the journal paper that the demand in the health care sector was up 15 percent in the US.
I've read and heard talks about the health care amongst us have decreased by 30 percent.
You've got this picture front of arts and health care facilities and they're telling you health does not compensate for this.
What's the discrepancy between your increased demand and those that argue it's actually gone down?
And I've also seen demand and I suppose part of the answer is a different type of fund, health care.
But that, too. So that's that's certainly one of the things we didn't get right.
We didn't anticipate that in the US. It's a peculiarity of the US.
We think, you know, the the normal kinds of health care thing procedures were largely postponed.
They wanted to reserve Kassidy for the hospital for public patients. They didn't want people coming in to get exposed to Corbitt in the hospital.
So, in fact, the sign was flipped from what we would have thought based on.
Could we just assume that the normal stuff would go on?
So that's been the fact that I think played out to different levels and different countries and that we didn't properly anticipate.
Just to add on to that, in the Appendix E., I believe of the paper, we have a like a more detailed response on what the aid that things actually were.
And the other thing is the way we wanted to do this is everything was what things?
Pre pandemic. Right. And the CBO study was there, one we found available that had those predictions.
And I think also I thought that's one of the things we need to improve on.
And CBO does need to revise that one. OK.
Thinking back to one of the last slides about the impact of opening different sectors of the economy.
Do you know why opening school increases are so much? And why is the impact having schools open or closed on the economy?
From Pipper. Maybe.
OK, so schools, you know, obviously very important issue.
There's a lot of devil's in the details of that question because we had to make some assumptions about how schools worked and now.
And part of the whole problem is that people are adapting their behaviour all the time.
So as people find new ways of doing things, then then things change.
I'm in New York actually at the moment. My son is enrolled in a school private school here in New York.
Greek school. And. And they've organised things so that kids are in small pods.
So they're pods of five students. And he's really only interacting in a way that can infect other people with those five students.
So that if there's a case, they can shut that pod down and still keep the rest of the school going.
Measures like that are going to have a fairly dramatic effect on how much our zeros affected by opening and closing schools.
I think we're also seeing a question priorities, of course. If schools are closed, then a lot of people can't go to work because they need take care.
So it's important, I don't think that came through properly in our paper that schools get a lot of other things.
So there really are quite a high priority to keep open, both because kids need school,
but also because it has a dramatic effect on the rest of the labour force.
OK, I guess we'll just she she needs to reconnect, so we'll come back to that if she has anything.
OK. This is a question I got for votes.
It has been pointed out that high earning population groups have the ability to work remotely, have ended up with more disposable income.
And that's part of why they're spending less on food and transport and so on.
And so I have savings rates for these people being higher over the last six months than would otherwise have been.
And would you expect that to be translated into splurge on non-essential luxury goods post locked?
And how how are these excess savings is going to be consumed and what impact will that have on?
That's from Alex Clark. Yeah, so good set of questions, we wrestle hard with those questions.
Indeed, the model predicts and we did observe that there is more savings for high affluence groups during the pandemic.
And I think there is also some evidence of some splurging as it ends.
Although one of the things that we, you know,
didn't quite know how to deal with and that you see going on in our model is that we just assumed a complete on lockdown peson certain point.
And whereas, you know, worse, that's ambiguous at this point, how long the pandemic is going to last.
And when are we really going to see this splurge? I think it's probably spread out over a longer period of time than our model would anticipate.
There's also, you know, again, it also depends on people's long term view of the economy.
If people are going, gosh, we can have another Great Depression, things could be bad for a long time,
then they're more likely to hang onto those savings and not put them back in.
So there is a Trade-Off there that, you know, our model predicts some splurge spending,
but but not unrestricted because people are still worried that this could go on forever.
And even if a pandemic ends, the economic pain could last a long time.
Scholars of the Great Depression are well aware of that. Go.
So don't go out and buy any stocks in luxury brands just yet.
How much uncertainty is there in the amount different occupations or sectors contribute to transmission?
And did this uncertainty propagate through into your recommendations from Chris W.
So let me make. How much uncertainty? When you say transmission does transmission, you mean economic transmission or epidemiological transmission?
Chris, in my class. OK. So I'll try and answer that question.
Yeah, absolutely. Try to both questions. Economic transit transmission.
Well, there's always uncertainty. It's an economic model. And we're not talking celestial mechanics here.
So there's always uncertainty in these things. But I think, you know, you could see the amount of pain.
You can certainly see what happened if you reduce the flow by a certain.
But this actually gets back to a question about production functions, which maybe I would have emphasised a little more than Maria did.
There's two questions. One is, OK, who can work? But the other question is how much will they produce?
Given that we have certain groups working, mothers not working. How much will that affect how much they produce?
And so you get thrust back to one of the nasty problems, you know, fundamental problems in economics of the production function.
If you have a certain set of inputs, how much will you produce?
And what you can see as their model is very sensitive to the assumptions that are made about the production function and that people are
making models with different versions of production functions as if those production functions were handed by God and were the truth.
There's a lot of uncertainty in and I think none of the production functions do a really good job of seeing what's happening.
I think this is one of the advantages of our model. We because we did a survey amongst industry experts about what mattered.
I think we have a better production function than others.
And I think that's one of the reasons we made good predictions. So there's uncertainty on the economic side, particularly in the production function.
Now, on the epidemiological side, there's huge uncertainty because in order to make a model of what the epidemiological consequence of a particular
industry being retired or being at low output and people not going to work or or really to say it better,
the epidemiological consequences of sending them back to work. You have to make.
You have to have a reasonable model about how much people are going to infect each other.
When they go back to work, and that really depends on a lot of details about the workplace and a lot of
details about the way the covered 19 virus is transmitted that are unknown.
So we had to do our best to make guesses about those things.
We were helped by, you know, a remarkable database in the US that does give a lot of information,
like average proximity of workers in the workplace and even has information about likelihood of exposure to infection.
Well, that's typically assuming that they're working with sick people. And so we make some guesses at that.
And but there's a lot of uncertainty because we don't have good models for those things.
So I think that's the biggest uncertainty in the model.
Thank you. Here's a question from a Ninka. Do you think of his results and you sort of answered this already?
We've got Maria back, which is good. Do you think your results are reliable?
Only in the UK. Yeah.
I mean, as we said, we made the UK is our target. So we can have some numbers to put in.
We could have done this for any country. And and if we'd had more person power, we could have done it for 50 countries.
And that would have been certainly better and more informative.
It would've been interesting because then we could have done as each country as it as the pandemic swept
around the world because you call it went from China to Europe to America to South America and so on.
As it swept around the world, we would have been able to see better how the different parts of the economy were asynchronously,
causing supply and demand bottlenecks. And it would have been a very interesting thing to do if we had had the person power to do that.
Just just trot on to that other shocks. We have them for the U.S. and we have them for the UK at the white level and depending on life.
Man, oh, man. How we might be increasing to Italy and Germany and Spain where we have other essential industries.
But if you're planning to use are that are interesting and just email us and we try and see what we can do.
But at least for the UK and the US, the shocks are there and people can use them.
Great. Maria, did you want to come back on any of the previous questions?
Well, I don't know what happened, but just connected. Yeah, I don't know.
But my computer froze. Actually, I wasn't able to hear the questions that I might be up there.
I mean, if you go to the answered section of the Oscar. Oh, I see.
You'll see. Yeah. OK. So a couple more questions that are coming up while you look at that.
I'll just pose these other questions first. We're beginning to come close to time.
How would you score the U.S. and U.K. in terms of acting in line with your conclusions, today's line?
Well, the U.S. and U.K. get lousy scores in because it's really the epidemiological side.
Well, the epidemiological side is it's been poorly managed in both countries.
In both cases, the response should have been aggressive early on. And the emphasis should have been on contact tracing and testing at scale.
And had we implemented massive efforts in both of those early on, we could have averted a huge economic dip.
So the amount of money we would have spent to do that would have been saved by factors of one hundred or something.
On the other side now, our model doesn't explicitly go into that because we don't have a model on how contact tracing would work.
And we weren't focussed on the, you know, what if scenario of sand.
Now, that said, economically, the U.K. did implement some pretty sensible policies that help blunt the force of the epidemic,
like furloughing and basically trying to keep workers in place so that we wouldn't have to totally reconstruct the economy once locked down.
And so I think the UK did reasonably well there in the UK.
You know, there were massive unemployment, there was massive unemployment, but there has been an economic stimulus.
So that economic stimulus is not as much as our model would say would be required to get a better result.
So I give them both those for. But the UK did a bit better economic as a question from Lillian Moxon.
What is the most important or surprising thing you've learnt from the models so far?
Good question, Lily. Well, the bottleneck effects that Maria mentions surprised us at some point to show you something.
It was very counterintuitive that if you have only demand shock, you get a certain other economic.
If you have supply shocks only you get a certain amount of economic pain.
And that's actually worse than supply shocks and demand shocks.
I didn't get those backward.
And that's because of the peculiar bottleneck effect where if you haven't carefully sorted out the way you're rationally intermediate goods,
they shut down the economy. Industries that are really important may not have the inputs they need to produce within.
And so you get a bottleneck that partially shuts the economy down.
And we've never heard of other models showing that the facts are.
Model is unique in that your model in both in remodelling them dynamically through time.
And so I think this was the most surprising thing that happened in our model.
Really? Did you want to do anything? No, I agree.
That said, I guess we must highlight that, you know, if the economy's ever out of equilibrium, it's an epidemic.
Right. Like, toilet paper ran out. People wanted to work. They couldn't work.
There were so many complex dynamics. And economic models are all most of them have not been out of equilibrium.
And the fact that just creating an out of equilibrium, a lot of we found this stuff.
It makes me wonder how many things there are out there in the economy that we have not seen because we have not explored out of equilibrium models.
Right. It seems to me like, you know, one of the things that's been done tells us, OK,
we have to do safety nets, we have to manage the pandemic, but we also need better economic tools.
If more people had been doing this, maybe it would have been better prepared.
So I think I think, you know, that when you said that was one of the unexpected things.
I'll go back to a quick question.
Someone said we have Alex Clark when you expect a splurge of non-essential Luxon goods post pandemic lockdown in the paper.
We have one revision with some demand shocks that they say they postpone it.
So some things don't work like that haircut. You know, you don't get three haircuts after the pandemic, but cars might be that way.
So that's the Sutent. Some things you have to take into account. But I'm mostly concerned that our man consumption will change for the longer term.
I'm not sure we're going to back normal sort of to set up.
OK, well, then like to another question, which is what contribution can a green stimulus package make to the recovery?
Yeah, well, it's a great opportunity to pump money into because the economy needs to be stimulated,
replacing climate change, which is an even bigger problem than the cold pandemic and a much more sustained problem.
So it's a perfect chance to stimulate the economy by stimulating industry that will help us deal with climate change.
Now, that said, I think it's important just to stimulate the economy.
So we shouldn't. You know, it's it's not the time to be doing things that might hurt the economy in the long run, but in the short run.
But, you know, I have another paper that's under review at the moment where we are arguing that
massive jump in renewables is actually going to make energy cheaper and that,
in fact, even if there weren't monitoring, we should be massively going into solar and wind because they're going to be energy cheaper.
You're going to save that money. And so why not step on the gas now would probably pump stimulus into making it happen.
Okay, let's take two final questions and then a Medal of Arts amongst some of it.
And a kind of loss of GDP in 2021 eventually play out as another loss of demand.
But the lag effect later in 2025 or 2027 or later.
Yeah, that's a difficult question. I would say we haven't really addressed.
I don't think we're seeing. So let me make sure I understand the question.
So the question is, are we are we going to see long term GDP effects in 2025?
I guess laggards lagged effects in investments and savings and so on lag effects, and it doesn't mean saving.
So this kind of goes back to what I was saying before, which is that you have to look at people's expectations over the long term.
And there is a danger of a feedback loop.
Part of the Keynesian piece by piece is that once the economy gets depressed, people expect a depressed economy.
And so they depress their investments.
And so we stayed out until we definitely need to worry about that very persistent effect happening and pump stimulus in the greatness of that.
So. So, yeah, I think that's a big word.
Final question. How does the emergence of the second wave disrupt the economy?
And do you have a sense of the magnitude of the economic shock that a second wave could provoke from your modelling work?
Yeah, you know, it depends on how big the second wave is.
If the second waves as big or bigger than the first wave is a similarly large economic shock and lets people decide to handle things differently,
I think during the course of time we've got a lot of debate about.
Thirties. And so, you know,
there's a Swedish approach and let a few people that's people die and keep the economy going and there is a Danish approach that let's be very safe.
And one of the things we saw is when we compare Denmark and Sweden both took a big economic hit.
We've only slightly worked for Denmark and Sweden and Denmark took a substantially smaller identikit.
So I think it really depends on the Trade-Off you make because in the second wave, you may make the Trade-Off a bit differently.
I also think in the second wave we're learning how to adapt.
We want to totally idiotic things that the British government did was they did not have people weren't wearing masks early on in the pandemic.
I mean, this is a no brainer. And in New York, everybody wears a mask.
They've been doing it for a long time. Crazy, right?
So so now people will be worrying now more and we'll have a more sensible policy.
So we may be able to function economically at a better level while keeping more industries going,
because we're just making obvious epidemiological adaptations.
We're learning as we as we go. Hopefully we're learning. Not everyone's learning about portions.
One wonders about Boris. It's not the only one.
If only he was. Thanks so much to Maria and Sudan and to everyone that's joined in the studio.
It's wonderful that you one million has been so involved in the Oxford Martin School from before its inception, actually in two thousand and six.
The Oxford Martin School is devoted to thinking about the big challenges of the future and bringing great minds together,
thinking about interdisciplinary teams and drawing on different disciplines. And I think the pandemic has really highlighted all of this.
There can be no bigger issue for us to focus on at the moment.
It absolutely requires people to draw on different disciplines, not least the medical and immunological, but also social sciences and not economics.
And I think, Maria Dunn, we've seen a wonderful example of how this can be brought together,
providing some answers and, of course, many more questions, which is what always happens with great thinking.
Tomorrow, there's a talk uncovered in Africa think that the amongst schools hosting and then next week on the 13th of October at five p.m.,
Marianna Mason-Cox is talking about the big failure of small government covered 19 public sector capacity.
Also as part of the series on SREP. And I'm sure that some camera will be back for that.
So to all of you, thank you for connecting on whatever time zone you on that one.
Benefits of the pandemic has gone.
We have a wider global audience than it gets in the lecture theatre at times, although back to can be connected to remotely.
So thanks to all of you. Thanks to don't worry particularly.
Stay safe. Stay well and stay engaged. Quite a lot of care like.