The economic effects of a pandemic

A little over ten years ago I was approached by some health experts who wanted to look at the economic effects of a influenza pandemic. They needed someone with a macroeconomic model to look at the general equilibrium impacts. In the 1990s I had led a small team that constructed a model called COMPACT, and these health experts and I completed a paper that was subsequently published in Health Economics. We reference to other studies that had been done earlier in that paper.

The current coronavirus outbreak will have different characteristics to the pandemic we studied, and hopefully it will not become a pandemic at all. (In terms of mortality it seems to be somewhere in between the ‘base case’ and ‘severe case’ we looked at in our work.) But I think there were some general lessons from the exercise we did that will be relevant if this particular coronavirus does become a global pandemic. One proviso is that a key assumption we made about the pandemic is that it was mainly a 3 month affair, and obviously what I have to say is dependent on it being short-lived.

It is worth saying at the start that the bottom line of all this for me is that the economics are secondary to the health consequences for any pandemic that has a significant fatality rate (as coronavirus so far appears to have). The economics are important in their own right and as a warning to avoid drastic measures that do not influence the number of deaths, but beyond that there is no meaningful trade-off between preventing deaths and losing some percent of GDP for less than half the year. 

Let me start with the least important impact from an economic point of view, and that is the fall in production due to workers taking more time off sick. It is least important in part because firms have ways of compensating for this, particularly if illness is spread over the quarter. For example those who have been sick and come back to work can work overtime. This will raise costs and might lead to some temporary inflation, but the central bank should ignore this.

This ‘direct’ impact of the pandemic will reduce GDP in that quarter by a few percentage points. The precise number will depend on what proportion of the population that get sick, on what the fatality rate in the UK turns out to be, and how many people miss work in an attempt not to get the disease. The impact on GDP for the whole year following the pandemic is much less at around 1% or 2%, partly because output after the pandemic quarter is higher as firms replenish diminished stocks and meet postponed demand.

All this assumes schools do not close once the pandemic takes hold. School closures can amplify the reduction in labour supply if some workers are forced to take time off to look after children. On the basis of the assumptions we made, if schools close for around 4 weeks that can multiply the GDP impacts above by as much as a factor of 3, and if they close for a whole quarter by twice that. If that seems large, remember nationwide school closures impact everyone with children and not just those with the disease.

But even with all schools closed for 3 months and many people avoiding work when they were not sick, the largest impact we got for GDP loss over a year was less than 5%. That is a one quarter very severe recession, but there is no reason why the economy cannot bounce back to full strength once the pandemic is over. Unlike a normal recession, information on the cause of the output loss, and therefore when it should end, is clear.

All this assumes that consumers who have not yet got the disease do not alter their behaviour. For a pandemic that spreads gradually this seems unlikely. The most important lesson I learnt from doing this study is that the pandemic need not just be a supply shock. It can also be a demand shock that can hit specific sectors very hard, depending on how consumers behave. This is because a lot of our consumption nowadays can be called social, by which I mean doing things that bring you into contact with other people. Things like going to the pub, to restaurants, to football matches or travel. Other sectors that provide consumption services that involve personal contact (e.g haircuts) and can easily be postponed may also be hit.

If people start worrying about getting the disease sufficiently to cut back on this social consumption, the economic impact will be more severe than any numbers discussed so far. One reason it is severe is that it is partly a permanent loss. Maybe you will have a few more meals out once the pandemic is over to make up for what you missed when you stayed home, but there is likely to be a net fall in your consumption of meals out over the year. What I realised when I did the analysis was just how much of our consumption was social.

This is why the biggest impacts on GDP occur when we have people reducing their social consumption in an effort not to get the disease. However falls in social consumption do not scale up all scenarios by the same amount, for the simple reason that supply and demand are complimentary. If school closures and people taking more time off work increase the size of the supply shock, the demand shock has less scope to do damage. The largest fall in annual GDP in all the variants we looked at was 6%.

Could conventional monetary or fiscal policy offset the fall in social consumption? Only partially, because the drop in consumption is focused on specific sectors. What is more important, and what we didn’t explore in the exercise, is what would happen if the banks failed to provide bridging finance for the firms having to deal with a sudden fall in demand. The banks may judge that some businesses that are already indebted may not be able to cope with any additional short term loans, leading to business closures during the pandemic.

It is in this light that we should view the collapse of stock markets around the world. In macroeconomic terms this is a one-off shock, so Martin Sandbu is right that the recent stock market reaction looks overblown. But if many businesses are at financial risk from the temporary drop in social consumption, that implies a rise in the equity risk premia, which helps account for the size of the stock market collapse we have seen. (I say 'helps' deliberately, as much of the impact will be on smaller businesses that do not find their way into the main stock market indices.)  

If I was running the central bank or government, I would have already started having conversations with banks about not forcing firms into bankruptcy during any pandemic. 

But economics can also influence health outcomes, and not just in terms of NHS resources. For a minority of self employed workers there will be no sick-pay and those without a financial cushion will be put under stress. One of the concerns as far as the spread of the pandemic is concerned is that workers will not be able to afford to self-isolate if they have the disease. So if I was in government I would be thinking of setting up something like a sick-leave fund that such workers could apply to if they get coronavirus symptoms.

The government also needs to think about keeping public services and utilities running when workers in those services start falling ill. In fact there are a whole host of things the government should now be doing to prepare for a pandemic. It is at times like these that we really need governments to act fast and think ahead. Do we in the UK, and US citizens, have confidence that the government will do what is required? One lesson of coronavirus may be never put into power politicians that have a habit of ignoring experts.

Miles on Haldane on Economics in Crises

Anything that says economics is in crisis always gets a lot of attention, particularly after Brexit (because economists are so pessimistic about its outcome), and Andy Haldane’s public comments were no exception. But former Monetary Policy Committee colleague David Miles has hit back, saying Haldane is wrong and economics is not in crisis. David is right, but (perhaps inevitably) he slightly overstates his case.

First an obvious point that is beyond dispute. Economics is much more than macroeconomics and finance. Look at an economics department, and you will typically find less than 20% are macroeconomists, and in some departments there can be just a single macroeconomist. Those working on labour economics, experimental economics, behavioural economics, public economics, microeconomic theory and applied microeconomics, econometric theory, industrial economics and so on would not have felt their sub-discipline was remotely challenged by the financial crisis.

David Miles is also right that economists have not found it difficult to explain the basic story of the financial crisis from the tools that they already had at their disposal. Here I will tell again a story about an ESRC seminar held at the Bank of England about whether other subjects like the physical sciences could tell economists anything useful post-crisis. It was by invitation only, Andy Haldane was there throughout, and for some reason I was there and asked to give my impressions at the end. In the background document there was a picture a bit like this.

UK Bank leverage: ratio of total assets to shareholder claims. (Source Bank of England Financial Stability Report June 2012) Added by popular request 17/1/17 [3]: 

I made what I hope is a correct observation. Show most economists a version of this chart just before the crisis, and they would have become very concerned. Some might have had their concern reduced by assurances and stories about how new risk management techniques made the huge increase in leverage seen in the years just before the crisis perfectly safe, but I think most would not. In particular, many macroeconomists would have said what about systemic risk?

The problem before the financial crisis was that hardly anyone looked at this data. There is one institution that surely would have looked at this like this data, and that was the Bank of England. As Peter Doyle writes:

“ .. it was not “economics” that missed the GFC, but, dare I say it (and amongst some others), the Bank of England.”

If there is a discussion of the increase in bank leverage and the consequent risks to the economy in any Inflation Reports in 2006 and 2007 I missed it. I do not think we have been given a real account of why the Bank missed what was going on: who looked at the data, who discussed it etc. I think we should know, if only for history’s sake.

What I think David Miles could have said but didn’t is that macroeconomists were at fault in taking the financial sector for granted, and therefore typically not including key finance to real interactions in their models. [1] As a result, the crisis has inspired a wave of new research that tries to make up for that, but this involves using existing ideas and applying them to macroeconomic models. There has also been new work using new techniques that has tried to look at network effects, which Andy Haldane mentions here. Whether this work could be usefully applied much more widely, as he suggests, is not yet clear, and to say that until that happens there is a crisis in economics is just silly.

The failure to forecast that consumers after the Brexit vote would reduce their savings ratio is a typical kind of forecasting error. Would they have done this anyway, and if not what about the Brexit vote and its aftermath inspired it, we will probably never know for sure. This kind of mistake happens all the time in macro forecasting, which is why comparisons to weather forecasting and Michael Fish are not really apt. [2] That is what David Miles means by saying it is a non-event.

What is hardly ever said, so I make no apologies for doing so once more, is that macroeconomic theory has in some ways ‘had a good crisis’. Basic Keynesian macroeconomic theory says you don’t worry about borrowing in a recession because interest rates will not rise, and they have not. New Keynesian theory says creating loads of new money will not lead to runaway inflation and it has not. Above all else, macroeconomic theory and most evidence said that the turn to austerity in 2010 would delay or weaken the recovery and that is exactly what happened. As Paul Krugman often says, it is quite rare for macroeconomics to be so fundamentally tested, and it passed that test. We should be talking not about a phoney crisis in economics, but why policy makers today have ignored economics, and thereby lost their citizens' the equivalent of a lot of money.

[1] In the COMPACT model I built in the early 1990s, credit conditions played an important role in consumption decisions, reflecting the work of John Muellbauer. But as I set out here, proposals to continue the model and develop further financial/real linkages were rejected by economists and the ESRC because it was not a DSGE model.

[2] Weather forecasts for the next few days are more accurate than macro forecasts, although perhaps longer term forecasts are more comparable. But more fundamentally, while the weather is a highly complex system like the economy. It is made up of physical processes that are predictable in a way human behaviour will never be. As a result, I doubt that simply having more data will have much impact on the ability to forecast the economy.

[3] Total asset are the size of the bank's balance sheet. Shareholder claims are the part of those assets that belong to shareholder, and which therefore represent a cushion that can absorb losses without the bank facing bankruptcy. So at the peak of the financial crisis, banks had over 60 times as many assets as that cushion. That makes a bank very vulnerable to loss on those assets.

Economics, DSGE and Reality: a personal story

As I do not win prizes very often, I thought I would use the occasion of this one to write something much more personal than I normally allow myself. But this mini autobiography has a theme involving something quite topical: the relationship between academic macroeconomics and reality, and in particular the debate over DSGE modelling and the lack of economics in current policymaking. [1]

I first learnt economics at Cambridge, a department which at that time was hopelessly split between different factions or ‘schools of thought’. I thought if this is what being an academic is all about I want nothing to do with it, and instead of doing a PhD went to work at the UK Treasury. The one useful thing about economics that Cambridge taught me (with some help from tutorials with Mervyn King) was that mainstream economics contained too much wisdom to be dismissed as fundamentally flawed, but also (with the help of John Eatwell) that economics of all kinds could easily be bent by ideology.

My idea that by working at the Treasury I could avoid clashes between different schools of thought was of course naive. Although the institution I joined had a well developed and empirically orientated Keynesian framework [2], it immediately came under attack from monetarists, and once again we had different schools using different models and talking past each other. I needed more knowledge to understand competing claims, and the Treasury kindly paid for me to do a masters at Birkbeck, with the only condition being that I subsequently return to the Treasury for at least 2 years. Birkbeck at the time was also a very diverse department (incl John Muellbauer, Richard Portes, Ron Smith, Ben Fine and Laurence Harris), but unlike Cambridge a faculty where the dedication to teaching trumped factional warfare.

I returned to the Treasury, which while I was away saw the election of Margaret Thatcher and its (correct) advice about the impact of monetarism completely rejected. I was, largely by accident, immediately thrust into controversy: first by being given the job of preparing a published paper evaluating the empirical evidence for monetarism, and then by internally evaluating the economic effects of the 1981 budget. (I talk about each here and here.) I left for a job at NIESR exactly two years after I returned from Birkbeck. It was partly that experience that informed this post about giving advice: when your advice is simply ignored, there is no point giving it.

NIESR was like a halfway house between academia and the Treasury: research, but with forecasting rather than teaching. I became very involved in building structural econometric models and doing empirical research to back them up. I built the first version of what is now called NIGEM (a world model widely used by policy making and financial institutions), and with Stephen Hall incorporated rational expectations and other New Classical elements into their domestic model.

At its best, NIESR was an interface between academic macro and policy. It worked very well just before 1990, where with colleagues I showed that entering the ERM at an overvalued exchange rate would lead to a UK recession. A well respected Financial Times journalist responded that we had won the intellectual argument, but he was still going with his heart that we should enter at 2.95 DM/£. The Conservative government did likewise, and the recession of 1992 inevitably followed.

This was the first public occasion where academic research that I had organised could have made a big difference to UK policy and people’s lives, but like previous occasions it did not do so because others were using simplistic and perhaps politically motivated reasoning. It was also the first occasion that I saw close up academics who had not done similar research but who had influence use that influence to support simplistic reasoning. It is difficult to understate the impact that had on me: being centrally involved in a policy debate, losing that debate for partly political reasons, and subsequently seeing your analysis vindicated but at the cost of people becoming unemployed.

My time at NIESR convinced me that I would find teaching more fulfilling than forecasting, so I moved to academia. The publications I had produced at NIESR were sufficient to allow me to become a professor. I went to Strathclyde University at Glasgow partly because they agreed to give temporary funding to two colleagues at NIESR to come with me so we could bid to build a new UK model. [3] At the time the UK’s social science research funding body, the ESRC, allocated a significant proportion of its funds to support econometric macromodels, subject to competitions every 4 years. It also funded a Bureau at Warwick university that analysed and compared the main UK models. This Bureau at its best allowed a strong link between academia and policy debate.

Our bid was successful, and in the model called COMPACT I would argue we built the first UK large scale structural econometric model which was New Keynesian but which also incorporated innovative features like an influence of (exogenous) financial conditions on intertemporal consumption decisions. [4] We deliberately avoided forecasting, but I was very pleased to work with the IPPR in providing model based economic analysis in regular articles in their new journal, many written with Rebecca Driver.

Our efforts impressed the academics on the ESRC board that allocated funds, and we won another 4 years funding, and both projects were subsequently rated outstanding by academic assessors. But the writing was on the wall for this kind of modelling in the UK, because it did not fit the ‘it has to be DSGE’ edict from the US. A third round of funding, which wanted to add more influences from the financial sector into the model using ideas based on work by Stiglitz and Greenwald, was rejected because our approach was ‘old fashioned’ i.e not DSGE. (The irony given events some 20 years later is immense, and helped inform this paper.)

As my modelling work had always been heavily theory based, I had no problem moving with the tide, and now at Exeter university with Campbell Leith we began a very successful stream of work looking at monetary and fiscal policy interactions using DSGE models. [5] We obtained a series of ESRC grants for this work, again all subsequently rated as outstanding. Having to ensure everything was microfounded I think created more heat than light, but I learnt a great deal from this work which would prove invaluable over the last decade.

The work on exchange rates got revitalised with Gordon Brown’s 5 tests for Euro entry, and although the exchange rate with the Euro was around 1.6 at the time, the work I submitted to the Treasury implied an equilibrium rate closer to 1.4. When the work was eventually published it had fallen to around 1.4, and stayed there for some years. Yet as I note here, that work again used an ’old fashioned’ (non DSGE) framework, so it was of no interest to journals, and I never had time to translate it (something Obstfeld and Rogoff subsequently did, but ignoring all that had gone before). I also advised the Bank of England on building its ‘crossover’ DSGE/econometric model (described here).

Although my main work in the 2000s was on monetary and fiscal policy, the DSGE framework meant I had no need to follow evolving macro data, in contrast to the earlier modelling work. With Campbell and Tatiana I did use that work to help argue for an independent fiscal council in the UK, a cause I first argued for in 1996. This time Conservative policymakers were listening, and our paper helped make the case for the OBR.

My work on monetary and fiscal interaction also became highly relevant after the financial crisis when interest rates hit their lower bound. In what I hope by now is a familiar story, governments from around the world first went with what macroeconomic theory and evidence would prescribe, and then in 2010 dramatically went the opposite way. The latter event was undoubtedly the underlying motivation for me starting to write this blog (coupled with the difficulty I had getting anything I wrote published in the Financial Times or Guardian).

When I was asked to write an academic article on the fiscal policy record of the Labour government, I discovered not just that the Coalition government’s constant refrain was simply wrong, but also that the Labour opposition seemed uninterested in what I found. Given what I found only validated what was obvious from key data series, I began to ask why no one in the media appeared to have done this, or was interested (beyond making fun) in what I had found. Once I started looking at what and how the media reported, I realised this was just one of many areas where basic economic analysis was just being ignored, which led to my inventing the term mediamacro.

You can see from all this why I have a love/hate relationship to microfoundations and DSGE. It does produce insights, and also ended the school of thought mentality within mainstream macro, but more traditional forms of macromodelling also had virtues that were lost with DSGE. Which is why those who believe microfounded modelling is a dead end are wrong: it is an essential part of macro but just should not be all academic macro. What I think this criticism can do is two things: revitalise non-microfounded analysis, and also stop editors taking what I have called ‘microfoundations purists’ too seriously.

As for macroeconomic advice and policy, you can see that austerity is not the first time good advice has been ignored at considerable cost. And for the few that sometimes tell me I should ‘stick with the economics’, you can see why given my experience I find that rather difficult to do. It is a bit like asking a chef to ignore how bad the service is in his restaurant, and just stick with the cooking. [6]

[1] This exercise in introspection is also prompted by having just returned from a conference in Cambridge, where I first studied economics. I must also admit that the Wikipedia page on me is terrible, and I have never felt it kosher to edit it myself, so this is a more informative alternative.

[2] Old, not new Keynesian, and still attached to incomes policies. And with a phobia about floating rates that could easily become ‘the end is nigh’ stuff (hence 1976 IMF).

[3] I hope neither regret their brave decision: Julia Darby is now a professor at Strathclyde and John Ireland is a deputy director in the Scottish Government.

[4] Consumption was of the Blanchard Yaari type, which allowed feedback from wealth to consumption. It was not all microfounded and therefore internally consistent, but it did attempt to track individual data series.

[5] The work continued when Campbell went to Glasgow, but I also began working with Tatiana Kirsanova at Exeter. I kept COMPACT going enough to be able to contribute to this article looking at flu pandemics, but even there one referee argued that the analysis did not use a ‘proper’ (i.e DSGE) model.

[6] At which point I show my true macro credentials in choosing analogies based on restaurants.  

The day macroeconomics changed

It is of course ludicrous, but who cares. The day of the Boston Fed conference in 1978 is fast taking on a symbolic significance. It is the day that Lucas and Sargent changed how macroeconomics was done. Or, if you are Paul Romer, it is the day that the old guard spurned the ideas of the newcomers, and ensured we had a New Classical revolution in macro rather than a New Classical evolution. Or if you are Ray Fair (HT Mark Thoma), who was at the conference, it is the day that macroeconomics started to go wrong.

Ray Fair is a bit of a hero of mine. When I left the National Institute to become a formal academic, I had the goal (with the essential help of two excellent and courageous colleagues) of constructing a new econometric model of the UK economy, which would incorporate the latest theory: in essence, it would be New Keynesian, but with additional features like allowing variable credit conditions to influence consumption. Unlike a DSGE it would as far as possible involve econometric estimation. I had previously worked with the Treasury’s model, and then set up what is now NIGEM at the National Institute by adapting a global model used by the Treasury, and finally I had been in charge of developing the Institute’s domestic model. But creating a new model from scratch within two years was something else, and although the academics on the ESRC board gave me the money to do it, I could sense that some of them thought it could not be done. In believing (correctly) that it could, Ray Fair was one of the people who inspired me.

I agree with Ray Fair that what he calls Cowles Commission (CC) type models, and I call Structural Econometric Model (SEM) type models, together with the single equation econometric estimation that lies behind them, still have a lot to offer, and that academic macro should not have turned its back on them. Having spent the last fifteen years working with DSGE models, I am more positive about their role than Fair is. Unlike Fair, I want “more bells and whistles on DSGE models”. I also disagree about rational expectations: the UK model I built had rational expectations in all the key relationships.

Three years ago, when Andy Haldane suggested that DSGE models were partly to blame for the financial crisis, I wrote a post that was critical of Haldane. What I thought then, and continue to believe, is that the Bank had the information and resources to know what was happening to bank leverage, and it should not be using DSGE models as an excuse for not being more public about their concerns at the time.

However, if we broaden this out from the Bank to the wider academic community, I think he has a legitimate point. I have talked before about the work that Carroll and Muellbauer have done which shows that you have to think about credit conditions if you want to explain the pre-crisis time series for UK or US consumption. DSGE models could avoid this problem, but more traditional structural econometric (aka CC) models would find it harder to do so. So perhaps if academic macro had given greater priority to explaining these time series, it would have been better prepared for understanding the impact of the financial crisis.

What about the claim that only internally consistent DSGE models can give reliable policy advice? For another project, I have been rereading an AEJ Macro paper written in 2008 by Chari et al, where they argue that New Keynesian models are not yet useful for policy analysis because they are not properly microfounded. They write “One tradition, which we prefer, is to keep the model very simple, keep the number of parameters small and well-motivated by micro facts, and put up with the reality that such a model neither can nor should fit most aspects of the data. Such a model can still be very useful in clarifying how to think about policy.” That is where you end up if you take a purist view about internal consistency, the Lucas critique and all that. It in essence amounts to the following approach: if I cannot understand something, it is best to assume it does not exist.