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Great expectations: The Darwinian wars of economic and epidemiological forecasting

Great expectations: The Darwinian wars of economic and epidemiological forecasting

A key concept in modern economics is, to use the jargon term, rational expectations.

The idea has dominated orthodox macroeconomics over the past 30 years. Not all economists have been persuaded of its merits by any means, but nevertheless, its influence has extended far beyond academia, into finance ministries and central banks around the world.

The basic idea is simple, even though the maths of the macroeconomic models which use rational expectations can rapidly become hair-raising.

When forming a view about the future, an individual chooses the model which best describes how the economy works. It is then simply a matter of running the model and using the values which it outputs as your expectations.

An obvious criticism seems to be that economic forecasts are very often wrong, but this is easily handled by the rational expectations enthusiasts. Each individual forecast in a sequence of months or years can be wrong. The key thing is that the errors over time cancel out. On average over time, the forecasts are correct.

The idea is not as absurd as it may appear to the layperson. For example, the Federal Reserve Bank of Philadelphia has published the Survey of Professional Forecasters since 1968.

This does what it describes on the label. It takes a wide range of forecasts by academic, commercial, financial and governmental bodies. Information on the average forecast for, say, GDP growth one year ahead is published, as well as details of the spread around the average.

Remarkably — and exactly in line with rational expectations — comparing the predictions with the actual growth over many years, the errors do indeed balance out. Spectacular errors have been made for individual years, but the over and under-predictions cancel out over time.

A more telling attack is that economists themselves do not seem to agree on what constitutes the correct model of the macroeconomy. Different groups have different models.

The standard defence is that the best model will eventually prove its superiority and will drive the others out of existence. But the problem here is that this has just not happened.

The concept of rational expectations can be applied directly to the predictions of the epidemiological models. These purport to describe how a virus spreads. So to form a view about the future, make a set of assumptions about the key inputs, and use the forecasts generated by the model.

It should be much easier in epidemiology for the best model to eliminate its competitors than it is in economics. Economics has a wide range of variables to predict, such as inflation, GDP, unemployment, public borrowing, interest rates. The focus of epidemic predictions is much narrower and their models are in general mathematically simpler than those of economics.

The Covid pandemic set up a competition between epidemiological modelling groups of fierce Darwinian intensity. The efforts of many years of academic debate have been concentrated into a handful of months.

But no group seems to have admitted yet that its model is not up to scratch — and huge differences in forecasts persist.

As published in City AM Wednesday 29th July 2020
Image: Opposites via Pxfuel
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Why you should read the small print on alarmist Covid-19 death projections

Why you should read the small print on alarmist Covid-19 death projections

Another day, another lurid, headline-grabbing number of deaths to expect from Covid-19.

This time, it was a study from the Academy of Medical Sciences. A second wave, we were warned, could kill 120,000 this winter in hospitals alone.

To be fair, this study was a projection rather than a forecast. A forecast is what is thought to be the most likely outcome.  A projection looks at what could happen under a particular set of conditions.

The Academy essentially assumed that behaviour would revert to the same as it was before the crisis, with people acting as though the virus had never appeared.

The researchers made their assumptions clear. But entirely predictably, the media seized on the 120,000 figure for deaths. The qualifications made around that number faded into the background.

The plain fact is that the assumptions of the report were wholly implausible. Even if lockdown were lifted completely, behaviour is not going to immediately revert to exactly what it was before the Covid crisis. Will people shake hands? Of course not. The concept of  a typical way of life has irrevocably changed.

The entire history of the world can be cited in evidence of this proposition. In the face of an epidemic, people alter their behaviour. They do not need to be told to do so by governments.

Of course, how much behaviours will change is ultimately a matter of judgement. But it is one where the social sciences, including economics, can make a valuable contribution. Epidemiology is currently too important a subject to be left in the hands of the epidemiologists.

It is something of a mystery why the numbers churned out by various epidemiologists retain any credibility. Their models in general take no account of behavioural change when a pandemic occurs.

In March, we all remember the Imperial College study claiming that without lockdown there would be 500,000 deaths in the UK. This was ludicrous — and economists such as Gerard Lyons and I quickly attacked it.

In April, a very similar model was run on Swedish data. It claimed that there would be 40,000 deaths by the beginning of July. In fact, even with no lockdown, there were only some 5,000, the majority of which took place in care homes.

Forecasts such as these make economic forecasts seem like Platonic ideals of precision.

This is far from a mere spat between scientific disciplines. Poor models and their resulting projections can lead to poor policy decisions. They generate a wholly unwarranted climate of fear among the population. This reduces economic activity, meaning less money available to fund health services, and greater poverty with its associated illnesses such as depression.

The number of deaths in England and Wales peaked on 8 April. The average time from infection to death tells us that the number of cases peaked in the week 18–25 March.  Lockdown was only introduced on the evening of 23 March. This shows that behaviour had already altered dramatically.

No more credence should be given to epidemiological projections which do not assume behavioural change.

As published in City AM Wednesday 22nd July 2020
Image: UK Government Coronavirus by Gustave iii via Wikimedia CC BY-SA 4.0
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Office clusters are as crucial to productivity as they ever were

Office clusters are as crucial to productivity as they ever were

The Prime Minister is now demanding that offices reopen to revive economic activity in the centres of towns and cities.

But there is not much sign of a return to work.

The preferences of the workforce are an important factor in the very slow pace of return. Fears expressed about the safety of public transport may or may not be genuine, but it is certainly true that many prefer to avoid the time spent commuting and enjoy the extra leisure time this brings.

But why do offices cluster together in urban centres anyway?

It is easy to see that in the old days industries such as steel and coal clustered geographically. One was a key supplier of the other. Being near at hand minimised transport costs.

Today’s offices span a wide range of diverse industries, from consulting to law to oil companies. The reasons why they locate in close proximity are more subtle.

The views of economists on this are still shaped by the writings of Alfred Marshall. He established the faculty of economics at Cambridge in 1903 and was then probably the world’s leading economist.

Marshall described the tendency of businesses to cluster near each other as “agglomeration”. He gave three key reasons why this colocation is observed.

In addition to the savings on the costs of transporting the materials needed in industrial processes, Marshall developed a theory of labour market pooling, in which firms located near one another can share labour.

Further, he believed that “intellectual spillovers” were important. Firms locate near each other in order to learn and speed up the process of innovation. Think of Silicon Valley, formed nearly a century after Marshall wrote.

A large number of detailed studies in recent decades confirm that these are not just mere theories. They have strong empirical support. The Harvard economist Ed Glaeser, for example calculated that in the US in the 2000s each of Marshall’s three reasons were of roughly equal importance.

There have been very distinct benefits to agglomeration. Throughout the developed world, the greater the density of employment in an area, the higher is its productivity. Head offices contain more highly skilled staff and so will be more productive than the average. But in city centres, their productivity is even higher than their skill levels suggest they should be.

Has Covid-19 changed all this? Or more specifically, has the crisis enabled people to see that new technology could overturn two centuries of experience in urban centres in industrialised countries?

Certainly, tech platforms such as LinkedIn offer the potential for efficient hiring of relevant skills and for employees to discover opportunities through their networks. But new recruits need to be integrated. And younger people probably still need a combination of social and remote interaction to develop their own professional networks.

It is less clear that remote working can encourage innovation in the same way. Much of the informal contacts needed for this cannot be captured by video conferences.

Yes, there will be an increase in working from home. But Marshall’s insights into the benefits of agglomeration still hold true.

As published in City AM Wednesday 15th July 2020
Image: The City of London via Wikimedia CC BY-SA
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The costs of lockdown could far outweigh the benefits

The costs of lockdown could far outweigh the benefits

Radical leaders such as Jacinda Ardern in New Zealand and Nicola Sturgeon in Scotland have gained plaudits through their relentless focus on eliminating Covid-19.

But this comes at an obvious economic cost. Tourism is some 15 per cent of New Zealand’s GDP, and major destinations such as Queenstown in the Southern Alps have been devastated. Here in the UK north of the border, Scottish businesses are increasingly frantic about the economic damage done by the rigorous lockdown.

Health experts and epidemiologists have not helped. They have remained firmly enclosed in their own silos of expertise, unable or unwilling to see the broader picture.

But good policy is not made in a vacuum. A key concept in economics is that of trade-offs.

An obvious health related example is road accidents. As a society, we trade off the 1,800 deaths and 250,000 injuries a year in road accidents against the benefits of using vehicles.

Trade-offs between alternatives have always been central to our economic policy and political debate. Lockdown is no different to any other policy. It has both benefits and costs.

The benefits of lockdown were never in doubt: the policy was intended to save lives. But there is now increasing awareness of the potential economic costs. Ironically, some of those most shrill in favour of lockdown are now crying out for jobs to be saved.

David Miles, an Imperial College economist and former member of the Monetary Policy Committee, along with two medical specialists published last week a valuable assessment of the overall costs and benefits of lockdown.

Miles argues that we must normalise how we view Covid-19. Its costs and risks are comparable to other health problems, such as cancer, heart problems and diabetes, where governments have made resource decisions for decades.

The lockdown is a public health policy, and Miles and his colleagues value its impact using the standard tools developed by the National Institute for Health and Care Excellence to guide healthcare decisions in the UK public health system.

A key concept in this is QALYs — quality of life adjusted years. Essentially, the benefits of any policy in terms of QALYs are compared with its costs.

Economists have developed a broad consensus on the value of saving a single quality of life adjusted year. Macabre though it may seem, some metric like this is necessary in order to have any meaningful assessment of the costs and benefits of different decisions.

Miles’ conclusions are stark. The estimate of lives saved by lockdown in his analysis is deliberately chosen to be the one at the very top end of the range of such estimates. This way, his team cannot be said to be underestimating the benefits of lockdown.

Even so, in the authors’ own words: “we find that having extended the lockdown for as long as three months consistently generates costs that are greater — and often dramatically greater — than likely benefits”.

Gerard Lyons and I warned in early April that, while it was necessary to introduce lockdown, it needed to be relaxed rather swiftly because of the costs it would entail. The work of Miles and his colleagues confirms, in impressive detail, this view.

As published in City AM Wednesday 8th July 2020
Image: Playground by Jlbirman1 via Wikimedia CC BY-SA
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Innovation is the only way to recover from the Covid crisis

Innovation is the only way to recover from the Covid crisis

One silver lining of the Covid-19 crisis has been a surge in innovation.

Enterprising firms have invented both new products and different ways of delivering existing ones.

Innovation is the life blood of any prosperous economy. Innovation is much more than a scientific invention. It turns inventions into things of practical and affordable use to people.

The ability to deliver innovation in a sustained way is the one single quality which distinguishes capitalism from all other forms of social and economic organisation.

Yet our understanding of it remains imperfect.

MIT Nobel laureate Robert Solow laid the foundations for the modern theory of economic growth over 60 years ago. His neat mathematical model postulated that growth was caused by increases in the amount and quality of both capital and labour used in the productive process, and by innovation.

But when the theoretical model was applied to real world data, it created a problem for economics. The increases in the inputs which could be readily measured — capital and labour — could only explain a small fraction of the growth which had taken place.

By implication, most of the huge growth experienced in the west was due to innovation. But innovation itself was not explained in the Solow model.

Despite various attempts to do better, including an ingenious one which won the Nobel Prize for another MIT economist Paul Romer, economists are still far short of a convincing explanation of innovation.

Matt Ridley, the author and scientific polymath, has made a valuable contribution in his recently published book How Innovation Works.

Ridley describes how major innovations arose in a wide range of sectors, such as energy, public health, food, transport and computing.

From this mass of detailed, empirical description, he synthesises some general principles, the vital ingredients for success.

A classic image of innovation is Archimedes jumping out of his bath shouting “Eureka!” But Ridley makes clear that such moments are exceptionally rare, even if the story is true.  This is for two reasons.

First, innovation is almost always a gradual process. It involves re-combinations of existing ideas and methods of production rather than single revolutionary events.

Second, innovation is, as Ridley puts it, a team sport. The myth of the isolated genius is deeply ingrained, but it is a myth. Innovation requires collaborations and building on what went before. Even Isaac Newton, one of the greatest minds in world history, acknowledged that he “stood on the shoulders of giants”.

Innovation also requires an acceptance of failure.  When Edison perfected the electric light bulb, he had tried 6,000 different materials for the filament before discovering what really worked.

The book ranges far wider than just the science. For example, Ridley argues that the EU has evolved into a system in which innovation simply cannot flourish. Of Europe’s 100 most valuable companies, not a single one was formed in the past 40 years. What a massive contrast with America.

Innovation is key to a successful recovery from the Covid crisis — and Ridley’s book offers excellent insights on how to make it happen.

As published in City AM Wednesday 1st July 2020
Image: Silver Cloud by lfranks via Pixabay
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The government should have been working on multiple tracing apps all along

The government should have been working on multiple tracing apps all along

The NHS contract tracing app has been scrapped in favour of a system developed by Google and Apple.

Although health secretary Matt Hancock has been heavily criticised for this failure, the UK is by no means alone.

For example, Denmark, Germany and Italy each tried to build their own app, based on the same type of centralised system as was attempted in the UK. But they have already ditched their efforts and taken up the decentralised approach of Apple and Google.

Australia is widely perceived as having had a “good” Covid-19 crisis. But the same cannot be said of its tracing app. It seems to have had serious problems working with iPhones at all.  The Aussies, too, are now taking the Google/Apple approach.

The simple fact is that most technological innovations fail.

The government can be criticised legitimately for not appreciating this fundamental feature of new technology. But it is a more subtle critique than merely pointing to the failure itself.

Given the importance of the tracing app, it would have been perfectly reasonable for the government to have pursued parallel tracks. At the same time as trying to develop its own NHSX app, it could have been collaborating with Apple and Google too.

Critics might have tried to pan this as an example of waste. But there is rarely such a thing as wasteful competition.

Spending on two completely different approaches at the same time would have been a hedge against the uncertainties which are inherent in the development of new technology.  No matter how smart you are, or how much prior information you gather, you just do not know whether an innovation really will work.

The tech companies themselves protect against this uncertainty by holding far more cash than conventional economic theory regards as rational. At the start of the Covid crisis, Apple, Microsoft and Google’s parent company Alphabet between them held over $450bn in cash or marketable securities.

Pharmaceutical companies face a similar challenge  Most new drugs fail. They fail when they are still in the lab, and they fail once they go out for testing to get regulatory approval.

In America, for example, there are three phases to the test process, each more demanding than the last.

The time scales are long. Andrew Lo, an MIT polymath, and his colleagues published a paper last year in the journal Biostatistics. They gathered a sample of over 400,000 clinical trials carried out between 2000 and 2015. Even after all the initial development work in the lab was completed, the typical successful drug took 8.3 years to obtain approval.

This puts into perspective the current frantic efforts to develop treatments and vaccines for Covid-19.

The probability of obtaining regulatory approval varies widely across categories. But overall, when a candidate drug enters phase one trials, its chances of eventual success are less than 10 per cent.

The government should embrace the idea that money spent on technology or drugs which fail is not money wasted. Indeed, the real mistake is not to risk enough, to stake everything on a single project.

This is the true failure of NHSX.

As published in City AM Wednesday 24th June 2020
Image: Covid tracing app by Gerd Altmann via Pixabay
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