Patents are preventing the manufacture of Covid-19 vaccines

 

The Economics Lesson Taught by the Covid Pandemic

 

Statistics, lies and the virus: Five lessons from a pandemic

In an age of disinformation, the value of rigorous data has never been more evident writes Tim Harford in The FT 

Will this year be 1954 all over again? Forgive me, I have become obsessed with 1954, not because it offers another example of a pandemic (that was 1957) or an economic disaster (there was a mild US downturn in 1953), but for more parochial reasons. 

Nineteen fifty-four saw the appearance of two contrasting visions for the world of statistics — visions that have shaped our politics, our media and our health. This year confronts us with a similar choice. 

The first of these visions was presented in How to Lie with Statistics, a book by a US journalist named Darrell Huff. Brisk, intelligent and witty, it is a little marvel of numerical communication. 

The book received rave reviews at the time, has been praised by many statisticians over the years and is said to be the best-selling work on the subject ever published. It is also an exercise in scorn: read it and you may be disinclined to believe a number-based claim ever again. 

There are good reasons for scepticism today. David Spiegelhalter, author of last year’s The Art of Statistics, laments some of the UK government’s coronavirus graphs and testing targets as “number theatre”, with “dreadful, awful” deployment of numbers as a political performance. 

“There is great damage done to the integrity and trustworthiness of statistics when they’re under the control of the spin doctors,” Spiegelhalter says. He is right. But we geeks must be careful — because the damage can come from our own side, too. 

For Huff and his followers, the reason to learn statistics is to catch the liars at their tricks. That sceptical mindset took Huff to a very unpleasant place, as we shall see. Once the cynicism sets in, it becomes hard to imagine that statistics could ever serve a useful purpose.  

But they can — and back in 1954, the alternative perspective was embodied in the publication of an academic paper by the British epidemiologists Richard Doll and Austin Bradford Hill. They marshalled some of the first compelling evidence that smoking cigarettes dramatically increases the risk of lung cancer. 

The data they assembled persuaded both men to quit smoking and helped save tens of millions of lives by prompting others to do likewise. This was no statistical trickery, but a contribution to public health that is almost impossible to exaggerate.  

You can appreciate, I hope, my obsession with these two contrasting accounts of statistics: one as a trick, one as a tool. Doll and Hill’s painstaking approach illuminates the world and saves lives into the bargain. 

Huff’s alternative seems clever but is the easy path: seductive, addictive and corrosive. Scepticism has its place, but easily curdles into cynicism and can be weaponized into something even more poisonous than that. 

The two worldviews soon began to collide. Huff’s How to Lie with Statistics seemed to be the perfect illustration of why ordinary, honest folk shouldn’t pay too much attention to the slippery experts and their dubious data. 

Such ideas were quickly picked up by the tobacco industry, with its darkly brilliant strategy of manufacturing doubt in the face of evidence such as that provided by Doll and Hill. 

As described in books such as Merchants of Doubt by Erik Conway and Naomi Oreskes, this industry perfected the tactics of spreading uncertainty: calling for more research, emphasising doubt and the need to avoid drastic steps, highlighting disagreements between experts and funding alternative lines of inquiry. The same tactics, and sometimes even the same personnel, were later deployed to cast doubt on climate science. 

These tactics are powerful in part because they echo the ideals of science. It is a short step from the Royal Society’s motto, “nullius in verba” (take nobody’s word for it), to the corrosive nihilism of “nobody knows anything”.  

So will 2020 be another 1954? From the point of view of statistics, we seem to be standing at another fork in the road. The disinformation is still out there, as the public understanding of Covid-19 has been muddied by conspiracy theorists, trolls and government spin doctors.  

Yet the information is out there too. The value of gathering and rigorously analysing data has rarely been more evident. Faced with a complete mystery at the start of the year, statisticians, scientists and epidemiologists have been working miracles. I hope that we choose the right fork, because the pandemic has lessons to teach us about statistics — and vice versa — if we are willing to learn. 

The numbers matter 

“One lesson this pandemic has driven home to me is the unbelievable importance of the statistics,” says Spiegelhalter. Without statistical information, we haven’t a hope of grasping what it means to face a new, mysterious, invisible and rapidly spreading virus. 

Once upon a time, we would have held posies to our noses and prayed to be spared; now, while we hope for advances from medical science, we can also coolly evaluate the risks. 

Without good data, for example, we would have no idea that this infection is 10,000 times deadlier for a 90-year-old than it is for a nine-year-old — even though we are far more likely to read about the deaths of young people than the elderly, simply because those deaths are surprising. It takes a statistical perspective to make it clear who is at risk and who is not. 

Good statistics, too, can tell us about the prevalence of the virus — and identify hotspots for further activity. Huff may have viewed statistics as a vector for the dark arts of persuasion, but when it comes to understanding an epidemic, they are one of the few tools we possess. 

Don’t take the numbers for granted 

But while we can use statistics to calculate risks and highlight dangers, it is all too easy to fail to ask the question “Where do these numbers come from?” By that, I don’t mean the now-standard request to cite sources, I mean the deeper origin of the data. For all his faults, Huff did not fail to ask the question. 

 

He retells a cautionary tale that has become known as “Stamp’s Law” after the economist Josiah Stamp — warning that no matter how much a government may enjoy amassing statistics, “raise them to the nth power, take the cube root and prepare wonderful diagrams”, it was all too easy to forget that the underlying numbers would always come from a local official, “who just puts down what he damn pleases”. 

The cynicism is palpable, but there is insight here too. Statistics are not simply downloaded from an internet database or pasted from a scientific report. Ultimately, they came from somewhere: somebody counted or measured something, ideally systematically and with care. These efforts at systematic counting and measurement require money and expertise — they are not to be taken for granted. 

In my new book, How to Make the World Add Up, I introduce the idea of “statistical bedrock” — data sources such as the census and the national income accounts that are the results of painstaking data collection and analysis, often by official statisticians who get little thanks for their pains and are all too frequently the target of threats, smears or persecution. 

 

In Argentina, for example, long-serving statistician Graciela Bevacqua was ordered to “round down” inflation figures, then demoted in 2007 for producing a number that was too high. She was later fined $250,000 for false advertising — her crime being to have helped produce an independent estimate of inflation. 

In 2011, Andreas Georgiou was brought in to head Greece’s statistical agency at a time when it was regarded as being about as trustworthy as the country’s giant wooden horses. When he started producing estimates of Greece’s deficit that international observers finally found credible, he was prosecuted for his “crimes” and threatened with life imprisonment. Honest statisticians are braver — and more invaluable — than we know.  

In the UK, we don’t habitually threaten our statisticians — but we do underrate them. “The Office for National Statistics is doing enormously valuable work that frankly nobody has ever taken notice of,” says Spiegelhalter, pointing to weekly death figures as an example. “Now we deeply appreciate it.”  

Quite so. This statistical bedrock is essential, and when it is missing, we find ourselves sinking into a quagmire of confusion. 

The foundations of our statistical understanding of the world are often gathered in response to a crisis. For example, nowadays we take it for granted that there is such a thing as an “unemployment rate”, but a hundred years ago nobody could have told you how many people were searching for work. Severe recessions made the question politically pertinent, so governments began to collect the data. 

More recently, the financial crisis hit. We discovered that our data about the banking system was patchy and slow, and regulators took steps to improve it. 

So it is with the Sars-Cov-2 virus. At first, we had little more than a few data points from Wuhan, showing an alarmingly high death rate of 15 per cent — six deaths in 41 cases. Quickly, epidemiologists started sorting through the data, trying to establish how exaggerated that case fatality rate was by the fact that the confirmed cases were mostly people in intensive care. Quirks of circumstance — such as the Diamond Princess cruise ship, in which almost everyone was tested — provided more insight. 

Johns Hopkins University in the US launched a dashboard of data resources, as did the Covid Tracking Project, an initiative from the Atlantic magazine. An elusive and mysterious threat became legible through the power of this data.  

That is not to say that all is well. Nature recently reported on “a coronavirus data crisis” in the US, in which “political meddling, disorganization and years of neglect of public-health data management mean the country is flying blind”.  

Nor is the US alone. Spain simply stopped reporting certain Covid deaths in early June, making its figures unusable. And while the UK now has an impressively large capacity for viral testing, it was fatally slow to accelerate this in the critical early weeks of the pandemic. 

Ministers repeatedly deceived the public about the number of tests being carried out by using misleading definitions of what was happening. For weeks during lockdown, the government was unable to say how many people were being tested each day. 

Huge improvements have been made since then. The UK’s Office for National Statistics has been impressively flexible during the crisis, for example in organising systematic weekly testing of a representative sample of the population. This allows us to estimate the true prevalence of the virus. Several countries, particularly in east Asia, provide accessible, usable data about recent infections to allow people to avoid hotspots. 

These things do not happen by accident: they require us to invest in the infrastructure to collect and analyse the data. On the evidence of this pandemic, such investment is overdue, in the US, the UK and many other places. 

Even the experts see what they expect to see 

Jonas Olofsson, a psychologist who studies our perceptions of smell, once told me of a classic experiment in the field. Researchers gave people a whiff of scent and asked them for their reactions to it. In some cases, the experimental subjects were told: “This is the aroma of a gourmet cheese.” Others were told: “This is the smell of armpits.” 

In truth, the scent was both: an aromatic molecule present both in runny cheese and in bodily crevices. But the reactions of delight or disgust were shaped dramatically by what people expected. 

Statistics should, one would hope, deliver a more objective view of the world than an ambiguous aroma. But while solid data offers us insights we cannot gain in any other way, the numbers never speak for themselves. They, too, are shaped by our emotions, our politics and, perhaps above all, our preconceptions. 

A striking example is the decision, on March 23 this year, to introduce a lockdown in the UK. In hindsight, that was too late. 

“Locking down a week earlier would have saved thousands of lives,” says Kit Yates, author of The Maths of Life and Death — a view now shared by influential epidemiologist Neil Ferguson and by David King, chair of the “Independent Sage” group of scientists. 

The logic is straightforward enough: at the time, cases were doubling every three to four days. If a lockdown had stopped that process in its tracks a week earlier, it would have prevented two doublings and saved three-quarters of the 65,000 people who died in the first wave of the epidemic, as measured by the excess death toll. 

That might be an overestimate of the effect, since people were already voluntarily pulling back from social interactions. Yet there is little doubt that if a lockdown was to happen at all, an earlier one would have been more effective. And, says Yates, since the infection rate took just days to double before lockdown but long weeks to halve once it started, “We would have got out of lockdown so much sooner . . . Every week before lockdown cost us five to eight weeks at the back end of the lockdown.” 

Why, then, was the lockdown so late? No doubt there were political dimensions to that decision, but senior scientific advisers to the government seemed to believe that the UK still had plenty of time. On March 12, prime minister Boris Johnson was flanked by Chris Whitty, the government’s chief medical adviser, and Patrick Vallance, chief scientific adviser, in the first big set-piece press conference. Italy had just suffered its 1,000th Covid death and Vallance noted that the UK was about four weeks behind Italy on the epidemic curve. 

With hindsight, this was wrong: now that late-registered deaths have been tallied, we know that the UK passed the same landmark on lockdown day, March 23, just 11 days later.  

It seems that in early March the government did not realise how little time it had. As late as March 16, Johnson declared that infections were doubling every five to six days. 

The trouble, says Yates, is that UK data on cases and deaths suggested that things were moving much faster than that, doubling every three or four days — a huge difference. What exactly went wrong is unclear — but my bet is that it was a cheese-or-armpit problem. 

Some influential epidemiologists had produced sophisticated models suggesting that a doubling time of five to six days seemed the best estimate, based on data from the early weeks of the epidemic in China. These models seemed persuasive to the government’s scientific advisers, says Yates: “If anything, they did too good a job.” 

Yates argues that the epidemiological models that influenced the government’s thinking about doubling times were sufficiently detailed and convincing that when the patchy, ambiguous, early UK data contradicted them, it was hard to readjust. We all see what we expect to see. 

The result, in this case, was a delay to lockdown: that led to a much longer lockdown, many thousands of preventable deaths and needless extra damage to people’s livelihoods. The data is invaluable but, unless we can overcome our own cognitive filters, the data is not enough. 

The best insights come from combining statistics with personal experience 

The expert who made the biggest impression on me during this crisis was not the one with the biggest name or the biggest ego. It was Nathalie MacDermott, an infectious-disease specialist at King’s College London, who in mid-February calmly debunked the more lurid public fears about how deadly the new coronavirus was. 

Then, with equal calm, she explained to me that the virus was very likely to become a pandemic, that barring extraordinary measures we could expect it to infect more than half the world’s population, and that the true fatality rate was uncertain but seemed to be something between 0.5 and 1 per cent. In hindsight, she was broadly right about everything that mattered. MacDermott’s educated guesses pierced through the fog of complex modelling and data-poor speculation. 

I was curious as to how she did it, so I asked her. “People who have spent a lot of their time really closely studying the data sometimes struggle to pull their head out and look at what’s happening around them,” she said. “I trust data as well, but sometimes when we don’t have the data, we need to look around and interpret what’s happening.” 

MacDermott worked in Liberia in 2014 on the front line of an Ebola outbreak that killed more than 11,000 people. At the time, international organisations were sanguine about the risks, while the local authorities were in crisis. When she arrived in Liberia, the treatment centres were overwhelmed, with patients lying on the floor, bleeding freely from multiple areas and dying by the hour. 

The horrendous experience has shaped her assessment of subsequent risks: on the one hand, Sars-Cov-2 is far less deadly than Ebola; on the other, she has seen the experts move too slowly while waiting for definitive proof of a risk. 

“From my background working with Ebola, I’d rather be overprepared than underprepared because I’m in a position of denial,” she said. 

There is a broader lesson here. We can try to understand the world through statistics, which at their best provide a broad and representative overview that encompasses far more than we could personally perceive. Or we can try to understand the world up close, through individual experience. Both perspectives have their advantages and disadvantages. 

Muhammad Yunus, a microfinance pioneer and Nobel laureate, has praised the “worm’s eye view” over the “bird’s eye view”, which is a clever sound bite. But birds see a lot too. Ideally, we want both the rich detail of personal experience and the broader, low-resolution view that comes from the spreadsheet. Insight comes when we can combine the two — which is what MacDermott did. 

Everything can be polarised 

Reporting on the numbers behind the Brexit referendum, the vote on Scottish independence, several general elections and the rise of Donald Trump, there was poison in the air: many claims were made in bad faith, indifferent to the truth or even embracing the most palpable lies in an effort to divert attention from the issues. Fact-checking in an environment where people didn’t care about the facts, only whether their side was winning, was a thankless experience. 

For a while, one of the consolations of doing data-driven journalism during the pandemic was that it felt blessedly free of such political tribalism. People were eager to hear the facts after all; the truth mattered; data and expertise were seen to be helpful. The virus, after all, could not be distracted by a lie on a bus.  

That did not last. America polarised quickly, with mask-wearing becoming a badge of political identity — and more generally the Democrats seeking to underline the threat posed by the virus, with Republicans following President Trump in dismissing it as overblown.  

The prominent infectious-disease expert Anthony Fauci does not strike me as a partisan figure — but the US electorate thinks otherwise. He is trusted by 32 per cent of Republicans and 78 per cent of Democrats. 

The strangest illustration comes from the Twitter account of the Republican politician Herman Cain, which late in August tweeted: “It looks like the virus is not as deadly as the mainstream media first made it out to be.” Cain, sadly, died of Covid-19 in July — but it seems that political polarisation is a force stronger than death. 

Not every issue is politically polarised, but when something is dragged into the political arena, partisans often prioritise tribal belonging over considerations of truth. One can see this clearly, for example, in the way that highly educated Republicans and Democrats are further apart on the risks of climate change than less-educated Republicans and Democrats. 

Rather than bringing some kind of consensus, more years of education simply seem to provide people with the cognitive tools they require to reach the politically convenient conclusion. From climate change to gun control to certain vaccines, there are questions for which the answer is not a matter of evidence but a matter of group identity. 

In this context, the strategy that the tobacco industry pioneered in the 1950s is especially powerful. Emphasise uncertainty, expert disagreement and doubt and you will find a willing audience. If nobody really knows the truth, then people can believe whatever they want. 

All of which brings us back to Darrell Huff, statistical sceptic and author of How to Lie with Statistics. While his incisive criticism of statistical trickery has made him a hero to many of my fellow nerds, his career took a darker turn, with scepticism providing the mask for disinformation. 

Huff worked on a tobacco-funded sequel, How to Lie with Smoking Statistics, casting doubt on the scientific evidence that cigarettes were dangerous. (Mercifully, it was not published.)  

Huff also appeared in front of a US Senate committee that was pondering mandating health warnings on cigarette packaging. He explained to the lawmakers that there was a statistical correlation between babies and storks (which, it turns out, there is) even though the true origin of babies is rather different. The connection between smoking and cancer, he argued, was similarly tenuous.  

Huff’s statistical scepticism turned him into the ancestor of today’s contrarian trolls, spouting bullshit while claiming to be the straight-talking voice of common sense. It should be a warning to us all. There is a place in anyone’s cognitive toolkit for healthy scepticism, but that scepticism can all too easily turn into a refusal to look at any evidence at all.

This crisis has reminded us of the lure of partisanship, cynicism and manufactured doubt. But surely it has also demonstrated the power of honest statistics. Statisticians, epidemiologists and other scientists have been producing inspiring work in the footsteps of Doll and Hill. I suggest we set aside How to Lie with Statistics and pay attention. 

Carefully gathering the data we need, analysing it openly and truthfully, sharing knowledge and unlocking the puzzles that nature throws at us — this is the only chance we have to defeat the virus and, more broadly, an essential tool for understanding a complex and fascinating world.

Any Covid-19 vaccine must be treated as a global public good

David Pilling in The Financial Times

Imagine if, in a year’s time, 300m doses of a safe and effective Covid-19 vaccine have been manufactured in Donald Trump’s America, Xi Jinping’s China or Boris Johnson’s Britain. Who is going to get them? What are the chances that a nurse in India, or a doctor in Brazil, let alone a bus driver in Nigeria or a diabetic in Tanzania, will be given priority? The answer must be virtually nil. 

The ugly battle between nations over limited supplies of tests and personal protective equipment will be a sideshow compared to the scramble over a vaccine. Yet if a vaccine is to be anything like the silver bullet that some imagine, it will have to be available to the world’s poor as well as to its rich.  

Any vaccine should be deployed to create the maximum possible benefit to public health. That will mean prioritising doctors, nurses and other frontline workers, as well as those most vulnerable to the disease, no matter where they live or how much they can afford. 

It will also mean deploying initially limited quantities of vaccine in order to snuff out clusters of infection by encircling them with a “curtain” of immunised people — as was done successfully against Ebola last year in the Democratic Republic of Congo.  

With Covid-19, this looks like a pipe dream. Far from bringing the world together, the pandemic has exposed a crisis of international disunity. The World Health Organization is only as good as its member states allow. That it finds itself squeezed between China and the US when humanity is facing its worst pandemic in 100 years, is a sign of the broken international order. 

How, under such circumstances, can we possibly conceive of a vaccine policy that is global, ethical and effective? 

There are precedents. The principle of access to medicines was established with the HIV-Aids pandemic, in which life-saving medicines were originally priced far above the ability of patients in Africa and other parts of the developing world to pay. 

But in 2001, in the so-called Doha declaration on Trade-Related Aspects of Intellectual Property Rights, the World Trade Organization made it clear that governments could override patents in public health emergencies. Largely as a result, a tiered pricing system has developed in which drug companies make profits in richer countries while allowing medicines to be sold more cheaply in poorer ones. 

There are also tried-and-tested methods of funding immunisation campaigns that have saved literally millions of lives in Africa, Asia and Latin America. Gavi, the Vaccine Alliance, was founded in 2000 to address market failures. It guarantees the purchase of a set number of vaccine doses so that companies can manufacture existing, or develop new, vaccines knowing there will be a market for their product. 

Along similar lines, 40 governments this month pledged $8bn to speed up the development, production and equitable deployment of Covid-19 vaccines, as well as diagnostics and therapeutics. There are already more than 80 candidates for a Covid-19 vaccine, with some of these now in human trials.  

Then there is manufacturing. Lack of diagnostics and PPE has exposed the flaws of a just-in-time system that builds in no redundancy. Vaccine capacity must be built up now, even if that means some of it will go to waste. Nor can existing capacity simply be given over to a putative Covid-19 vaccine. That could unwittingly unleash outbreaks of previously controlled diseases, such as mumps or rubella.  

Manufacturing will also have to be dispersed geographically to ensure a vaccine can be deployed globally. 

Most vaccines are international collaborations. One against Ebola was discovered in Canada, developed in the US and manufactured in Germany. It is unlikely — and certainly undesirable — that any one country will be able to claim a Covid-19 vaccine all to itself. 

Even if a successful candidate is developed, not everyone will want to take it. 

Heidi Larson, director of the Vaccine Confidence Project, says surveys show that up to 9 per cent of British people, 18 per cent of Austrians and 20 per cent of Swiss would not agree to be immunised. Trust in vaccines is generally higher in the developing world, where the impact of infectious disease is more obvious. But here too there could be resistance, particularly if people suspect they are being used as guinea pigs. 

The vaccine against a fictional pandemic in the 2011 film Contagion is distributed through a lottery based on birth date. When a vaccine against a real-life Covid-19 is found, it must be deployed as a global public good. 

Health experts estimate it will cost some $20bn to vaccinate everyone on earth, equivalent to roughly two hours of global output. This is the best bargain in the world. Let us hope the world can recognise it.

Should we be scared of the coronavirus debt mountain?

The pandemic has necessitated huge borrowing – but post-crisis austerity would be the very worst way to deal with it 

 

‘A world in which coronavirus debts are repaid by a wealth tax would look very different from one in which benefits are slashed and VAT is raised.’ Photograph: Ben Birchall/PA


We do not know how this pandemic will end. We do know that we will be poorer when it’s over: GDP is plunging around the world.

We also know that there will be a towering pile of IOUs left from the bills run up during the crisis. When it is over we will have to figure out how to repay them – or whether to repay them at all. That question will decide the complexion of our politics, and the quality of our public infrastructure and services for years to come. Unless we tackle this issue, coronavirus debts will be the battering ram for a new campaign of austerity.

The scale of the challenge is huge. Hard cases like Italy grab the headlines. Its debt currently stands at 135% of GDP. As a result of the crisis it will likely rise to 155%. But it is no longer an extreme outlier. According to the IMF, the debt ratio of the average advanced economy will exceed 120% next year. In the US, the debt to GDP ratio may soon surpass that at the end of the second world war.

These numbers are impressive, daunting even. They offer an open door to conservative scaremongering. The first move in that tradition of debt politics is to invoke the tenuous analogy to a household. In this picture, debts are a burden on the profligate; a moral obligation that must be honoured on pain of national bankruptcy and ruin.

There are some circumstances in which this analogy is apt, specifically when you are an impoverished and desperate country dependent on foreign creditors who will lend to you only in the currency of another country, most commonly that of the US. Many poorer countries are in this position. Few rich countries are. Indeed, one of the definitions of being an advanced economy is that you are not.

Advanced economies borrow in their own currency and overwhelmingly from their own citizens. For them, the household analogy is profoundly misleading. In fact, those seeking to rebut the misconceptions of the household analogy sometimes say we merely owe government debts to ourselves.

That is a liberating thought. It makes clear that we are not in the position of a subordinate debtor nation. But it has a dizzying circularity to it. If we are our own creditors, are we not also our own debtors – master and slave at the same time? Ultimately, it is a bon mot that relies on treating the economic nation as a unit. That may look like liberation, but it is an illusion achieved by removing the real politics of debt – which are about class, not nationality.

Historically, government debts were assets owned by the middle and upper classes, the famous rentiers. And taxes were overwhelmingly indirect and thus fell disproportionately on lower incomes.

Today, the richest still own a disproportionate share of government debt. But the liabilities of the government are now widely distributed. They are staple investments for pension funds and insurers. Government debt is not simply a burden; it is a highly useful financial asset, offering modest interest rates in exchange for safety. It is all the more useful for the fact that the government lives for ever and will generate revenue for ever through taxation. So it enables very long-term planning.

The tax base today is much broader than it was a century ago. But who pays taxes – and who does not – remains one of the most urgent questions of the moment. A world in which coronavirus debts are repaid by a wealth tax or a global crackdown on corporate tax havens would look very different from one in which benefits are slashed and VAT is raised. And it is very possible that debt service will be taken out of other spending, whether that be schools, pensions or national defence.

As the great Austrian economist Joseph Schumpeter remarked in the aftermath of the first world war, “the budget is the skeleton of the state stripped of all misleading ideologies”, the truest reflection of the distribution of power and influence.
It is a distributional issue. But not only that. Debts may also affect the size of the cake itself. As we know only too well, a regime of austerity that keeps taxes high and government spending low is not conducive to rapid economic growth. And yet for debt to be sustainable, what we need is growth in GDP – to be precise, growth in nominal GDP, which includes real economic growth and inflation. Inflation matters because it acts as a tax on debts that are owed in money that is progressively losing its value. Price stability, the objective of monetary policy since the 1970s, no doubt has benefits for everyone, but most of all the creditor class.

This is the awesome dilemma we will face in the aftermath of Covid-19. This is the battle for which we must brace. Not right now, but once the immediate crisis has passed. After the financial crisis of 2007-08, it was in 2010 that the push for belt-tightening began. Like revenge, austerity is a dish best served cold.

Progressive politics cannot, of course, shrink from a battle about budgetary priorities. But it should resist fighting on the terms set by austerian debt-fear. In the circumstances of the UK or the US, alarmism about debt is false. And how false is being demonstrated by the crisis itself.

There is one mechanism through which we can ensure we truly owe the debts to ourselves. That mechanism is the central bank. Its principal job is to manage public debt – and at a moment of crisis central banks do what they must. They buy government debts or, in what amounts to the same thing, they open overdraft accounts for the government.

That has two effects that, acting together, have the potential to negate debt as a political issue. Central bank intervention lowers the interest rate. If interest rates are held down, debt service need not be an onerous burden. At the same time, the central bank purchases remove government IOUs from private portfolios and put them on the balance sheet of the central bank. There, they are literally claims by the public upon itself. 

When the central bank buys the debt it does so by creating money. Under ordinary circumstances one might worry about that causing inflation. But given the recession we face that is a risk worth running. Indeed modest inflation would help us by taking a bite out of the real value of the debt.

Of course, ensuring that the central banks continue their crisis-fighting methods into the recovery period will itself require a political battle. Fearmongering about inflation is the close cousin of fearmongering about debt. We should resist both blackmails. We have the institutions and techniques to neutralise the coronavirus debt problem. We owe it to ourselves to use them.

Now the world faces two pandemics – one medical, one financial

Coronavirus fears are feeding financial and economic anxiety and vice versa. Breaking the cycle will not be easy, but it is possible writes Robert Shiller in The Guardian  

 

The normally busy Schiphol airport in the Netherlands. Photograph: Patrick van Katwijk/Getty Images


We are feeling the anxiety effects of not one pandemic but two. First, there is the Covid-19 pandemic, which makes us anxious because we, or people we love, anywhere in the world, could soon become gravely ill and even die. And, second, there is a pandemic of anxiety about the economic consequences of the first.

These two pandemics are interrelated but are not the same phenomenon. In the second pandemic, stories of fear have gone viral and we often think of them constantly. The stock market has been dropping like a rock, apparently in response to stories of Covid-19 depleting our lifetime savings unless we take some action. But, unlike Covid-19, the source of our anxiety is that we are unsure what action to take.

It is not good news when two pandemics are at work simultaneously. One can feed the other. Business closures, soaring unemployment, and loss of income fuel financial anxiety, which may, in turn, deter people, desperate for work, from taking adequate precautions against the spread of the disease.

Moreover, it is not good news when two contagions are, indeed, global pandemics. When a drop in demand is confined to one country, the loss is partially spread abroad, while demand for the country’s exports is not diminished much. But this time, that natural safety valve will not work, because the recession threatens nearly all countries.

Many people seem to assume that the financial anxiety is nothing more than a direct byproduct of the Covid-19 crisis – a perfectly logical reaction to the disease pandemic. But anxiety is not perfectly logical. The pandemic of financial anxiety, spreading through panicked reaction to price drops and changing narratives, has a life of its own.

The effects financial anxiety has on the stock market may be mediated by a phenomenon that the psychologist Paul Slovic of the University of Oregon and his colleagues call the “affect heuristic.” When people are emotionally upset because of a tragic event, they react with fear even in circumstances where there is no reason to fear.

In a joint paper with William Goetzmann and Dasol Kim, we found that nearby earthquakes affect people’s judgment of the probability of a 1929- or 1987-size stock market crash. If there was a substantial quake within 30 miles (48km) during the previous 30 days, respondents’ assessment of the probability of a crash was significantly higher. That is the affect heuristic at work.

It might make more sense to expect a stock market drop from a disease pandemic than from a recent earthquake, but maybe not a crash of the magnitude seen recently. If it were widely believed that a treatment could limit the intensity of the Covid-19 pandemic to a matter of months, or even that it would last a year or two, that would suggest the stock market risk is not so great for a long-term investor. One could buy, hold, and wait it out.

But a contagion of financial anxiety works differently than a contagion of disease. It is fuelled in part by people noticing others’ lack of confidence, reflected in price declines, and others’ emotional reaction to the declines. A negative bubble in the stock market occurs when people see prices falling, and, trying to discover why, start amplifying stories that explain the decline. Then, prices fall on subsequent days, and again and again.

Observing successive decreases in stock prices creates a powerful feeling of regret for those who have not sold, together with a fear that one might sell at the bottom. This regret and fear prime people’s interest in both pandemic narratives. Where the market goes from there depends on their nature and evolution.

To see this, consider that the stock market in the US did not crater when, in September-October 1918, the news media first started covering the Spanish flu pandemic that eventually claimed 675,000 US lives (and over 50 million worldwide). Instead, monthly prices in the US market were on an uptrend from September 1918 to July 1919.

Why didn’t the market crash? One likely explanation is that world war one, which was approaching its end after the last major battle, the second battle of the Marne, in July-August 1918, crowded out the influenza story, especially after the armistice in November of that year. The war story was likely more contagious than the flu story.

Another reason is that epidemiology was only in its infancy then. Outbreaks were not as forecastable, and the public did not fully believe experts’ advice, with people’s adherence to social-distancing measures “sloppy”. Moreover, it was generally believed that economic crises were banking crises, and there was no banking crisis in the US, where the Federal Reserve System, established just a few years earlier, in 1913, was widely heralded as eliminating that risk.

But perhaps the most important reason the financial narrative was muted during the 1918 influenza epidemic is that far fewer people owned stocks a century ago, and saving for retirement was not the concern it is today, in part because people didn’t live as long and more routinely depended on family if they did.

This time, of course, is different. We see buyers’ panics at local grocery stores, in contrast to 1918, when wartime shortages were regular occurrences. With the Great Recession just behind us, we certainly are well aware of the possibility of major drops in asset prices. Instead of a tragic world war, this time the US is preoccupied with its own political polarisation, and there are many angry narratives about the federal government’s mishandling of the crisis.

Predicting the stock market at a time like this is hard. To do so well, we would have to predict the direct effects on the economy of the Covid-19 pandemic, as well as all the real and psychological effects of the pandemic of financial anxiety. The two are different but inseparable.

Our politics isn’t designed to protect the public from Covid-19

The politics of denial, first honed in the tobacco industry, has serious consequences for a floundering Johnson government writes George Monbiot in The Guardian 

 

Protesters outside Downing Street. Photograph: Dan Kitwood/Getty Images


The worst possible people are in charge at the worst possible time. In the UK, the US and Australia, the politics of the governing parties have been built on the dismissal and denial of risk. Just as these politics have delayed the necessary responses to climate breakdown, ecological collapse, air and water pollution, obesity and consumer debt, so they appear to have delayed the effective containment of Covid-19.

I believe it is no coincidence that these three governments have responded later than comparable nations have, and with measures that seemed woefully unmatched to the scale of the crisis. The UK’s remarkable slowness to mobilise, followed by its potentially catastrophic strategy – fiercely criticised by independent experts and now abandoned – to create herd immunity, and its continued failure to test and track effectively or to provide protective equipment for health workers, could help to cause large numbers of unnecessary deaths. But to have responded promptly and sufficiently would have meant jettisoning an entire structure of political thought developed in these countries over the past half century. 

Politics is best understood as public relations for particular interests. The interests come first; politics is the means by which they are justified and promoted. On the left, the dominant interest groups can be very large – everyone who uses public services, for instance. On the right they tend to be much smaller. In the US, the UK and Australia, they are very small indeed: mostly multimillionaires and a very particular group of companies: those whose profits depend on the cavalier treatment of people and planet.

Over the past 20 years, I have researched the remarkably powerful but mostly hidden role of tobacco and oil companies in shaping public policy in these three nations. I’ve seen how the tobacco companies covertly funded an infrastructure of persuasion to deny the impacts of smoking. This infrastructure was then used, often by the same professional lobbyists, to pour doubt on climate science and attack researchers and environmental campaigners.

I showed how these companies funded rightwing thinktanks and university professors to launch attacks on public health policy in general and create a new narrative of risk, tested on focus groups and honed in the media. They reframed responsible government as the “nanny state”, the “health police” and “elf ’n’ safety zealots”. They dismissed scientific findings and predictions as “unfounded fears”, “risk aversion” and “scaremongering”. Public protections were recast as “red tape”, “interference” and “state control”. Government itself was presented as a mortal threat to our freedom.

Their purpose was to render governments less willing and able to respond to public health and environmental crises. The groups these corporations helped to fund – thinktanks and policy units, lobbyists and political action committees – were then used by other interests: private health companies hoping to break up the NHS, pesticide manufacturers seeking to strike down regulatory controls, junk food manufacturers resisting advertising restrictions, billionaires seeking to avoid tax. Between them, these groups refined the justifying ideology for fragmenting and privatising public services, shrinking the state and crippling its ability to govern.

Now, in these three nations, this infrastructure is the government. No 10 Downing Street has been filled with people from groups strongly associated with attacks on regulation and state intervention – such as Munira Mirza, who co-founded the Manifesto Club; Chloe Westley from the TaxPayers’ Alliance; and of course Dominic Cummings, who was hired by Matthew Elliott, the founder of the TaxPayers’ Alliance, to run Vote Leave. 

When Boris Johnson formed his first government, the Institute of Economic Affairs (IEA), which has been funded by the tobacco industry, boasted that 14 of its frontbenchers, including the home secretary, the foreign secretary and the chancellor, were “alumni of IEA initiatives”. The foreign secretary, Dominic Raab, has published one book and launched another through the IEA, which he has thanked for helping him “in waging the war of ideas”. The health secretary, Matt Hancock, in a previous role, sought to turn an IEA document into government policy. He has accepted significant donations from the organisation’s chairman, Neil Record. The home secretary, Priti Patel, was formerly a tobacco lobbyist. One in five new Conservative MPs have worked in lobbying or public relations for corporate interests.

Modern politics is impossible to understand without grasping the pollution paradox. The greater the risk to public health and wellbeing a company presents, the more money it must spend on politics – to ensure it isn’t regulated out of existence. Political spending comes to be dominated by the dirtiest companies, ensuring that they wield the greatest influence, crowding out their cleaner rivals. While nobody has a commercial interest in the spread of coronavirus, the nature and tenor of the governments these interests have built impedes state attempts to respond quickly and appropriately.

Brexit (remember that?) could be interpreted as an effort to bridge the great split within the Conservatives, caused by the rising power of dirty money. The party became divided between an older, conservative base, with a strong aversion to novelty and change, and its polar opposite: the risk-taking radical right. Leaving the European Union permits a reconciliation of these very different interests, simultaneously threatening food standards and environmental protections, as well as price controls on medicines and other crucial regulations, while raising barriers to immigration and integration with other nations. It invokes ancient myths of empire, destiny and exceptionalism while potentially exposing us to the harshest of international trade conditions. It is likely further to weaken the state’s capacity to respond to the many crises we face. 

The theory on which this form of government is founded can seem plausible and logically consistent. Then reality hits, and we find ourselves in the worst place from which to respond to crisis, with governments that have an ingrained disregard for public safety and a reflexive resort to denial. When disasters arrive, its exponents find themselves wandering nonplussed through the wastelands, unable to reconcile what they see with what they believe. Witness Scott Morrison’s response to the Australian fires and Boris Johnson’s belated engagement with the British floods. It is what we see today, as the Trump, Johnson and Morrison governments flounder in the face of this pandemic. They are called upon to govern, but they know only that government is the enemy.

Humanity's 'inexorable' population growth is so rapid that even a global catastrophe wouldn't stop it

Steve Conor in The Independent

The global human population is “locked in” to an inexorable rise this century and will not be easily shifted, even by apocalyptic events such as a third world war or lethal pandemic, a study has found.

There is no “quick fix” to the population time-bomb, because there are now so many people even unimaginable global disasters won't stop growth, scientists have concluded.

Although measures designed to reduce human fertility in the parts of the world where the population growth is fastest will eventually have a long-term impact on numbers, this has to go hand-in-hand with policies aimed at reducing the consumption of natural resources, they said.

Two prominent ecologists, who normally study animal populations in the wild, have concluded that the number of people in the world today will present one of the most daunting problems for sustainable living on the planet in the coming century – even if every country adopts a draconian “one child” policy.

“The inexorable demographic momentum of the global human population is rapidly eroding Earth’s life-support system,” say Professor Corey Bradshaw of the University of Adelaide and Professor Barry Brook of the University of Tasmania in their study, published in the journal Proceedings of the National Academy of Sciences.

“Assuming a continuation of current trends in mortality reduction, even a rapid transition to a worldwide one-child policy leads to a population similar to today’s by 2100,” they say.

“Even a catastrophic mass mortality event of 2bn deaths over a hypothetical window in the mid-21st century would still yield around 8.5bn people by 2100,” they add.

There are currently about 7.1bn people on Earth, and demographers estimate that this number could rise to about 9bn by 2050 - and as many as 25bn by 2100, although this is based on current fertility rates, which are expected to fall over the coming decades.

The number of people in the world today will present one of the most daunting problems for sustainable living on the planet in the coming century (Getty)

Professor Bradshaw told The Independent that the study was designed to look at human numbers with the insight of an ecologist studying natural impacts on animals to determine whether factors such pandemics and world wars could dramatically influence the population projections.

“We basically found that the human population size is so large that it has its own momentum. It’s like a speeding car travelling at 150mph. You can slam on the brakes but it still takes time to stop,” Professor Bradshaw said.

“Global population has risen so fast over the past century that roughly 14 per cent of all the human beings that have ever lived are still alive today – that’s a sobering statistic,” he said.

“We examined various scenarios for global human population change to the year 2100 by adjusting fertility and mortality rates to determine the plausible range of population sizes at the end of the century.

“Even a worldwide one-child policy like China’s, implemented over the coming century, or catastrophic mortality events like global conflict or a disease pandemic, would still likely result in 5bn to 10bn people in 2100,” he added.

The researchers devised nine different scenarios that could influence human numbers this century, ranging from “business as usual” with existing fertility rates, to an unlikely one-child-per-family policy throughout the world, to broad-scale global catastrophes in which billions die.

“We were surprised that a five-year WWIII scenario mimicking the same proportion of people killed in the First World War and Second World War combined, barely registered a blip on the human population trajectory this century,” said Professor Brook.

Measures to control fertility through family planning policies will eventually have an impact on reducing the pressure on limited resources, but not immediately, he said.

“Our great-great-great-great-grandchildren might ultimately benefit from such planning, but people alive today will not,” Professor Brook said.

Simon Ross, the chief executive of the charity Population Matters, said that introducing modern family planning to the developing world would cost less than $4bn – about one third of the UK’s annual aid budget.

“So, while fertility reduction is not a quick fix, it is relatively cheap, reliable, and popular with most, with generally positive side effects. We welcome the recognition of the potential of family planning and reproductive education to alleviate resource availability in the longer term,” Mr Ross said.