Unpopular Opinion

Sunday 13 September 2020

Seeing Mussolini’s Italy in Modi’s India

There are uncanny parallels between the Italy of the 1920s and the India of the 2020s writes Ram Guha in Scroll India

I read a lot of biographies, these often set in other countries than my own. A book I have just finished is Benedetto Croce and Italian Fascism, by the Canadian scholar, Fabio Fernando Rizi. It uses the life of a great philosopher to tell a larger story of the times he passed through.

Reading Rizi’s book, I found uncanny parallels between the Italy of the 1920s and the India of the 2020s. The myth of Benito Mussolini, like the myth of Narenda Modi, was crafted by writers and propagandists “eager to sing paeans to the genius of the Duce”. These propagandists had begun to call the leader of fascism “the providential man”, “the man of massive faith”, or simply, “the Man of Providence”. Thus was created “the myth of the Duce, the chief who is always right, the leader who dares where others vacillate”.

In December 1925, the Italian State passed a new law, which came down hard on the press and its freedoms. The consequences of this law were that “within a few months, the most important papers came under Fascist control, one by one. Some owners were compelled to sell under economic or political pressure. All the liberal editors had to resign and were replaced by more accommodating men.”

Professed reverence for the laws

In the same year, Benedetto Croce characterised the ideology of the ruling party and of Mussolini as a “bizarre mixture of appeals to authority and to demagoguery, of professed reverence for the laws and of violation of the laws, of ultra-modern concepts and of musty old trash, abhorrence of culture and sterile attempts at producing a new one…” In this regard, the Italian State of the 1920s bears a striking resemblance to Modi’s regime today which speaks respectfully of the Constitution while blatantly violating its spirit and essence, which appeals to ancient wisdom while displaying a contempt for modern science, which claims to exalt ancient culture while manifesting an utter philistinism in practice.

While most independent-minded Italian intellectuals were forced into exile, Benedetto Croce stayed on in his homeland, offering an intellectual and moral opposition to fascism. As his biographer puts it, “[w]hereas the regime employed the mass media and the education system to promote the cult of Mussolini and to inculcate submission to authority, demanding from the new generations, in mystical union with the Duce, without asking questions, ‘to believe, to obey, to fight’, Croce, instead, offered a set of liberal values, preached freedom, defended the dignity of man, as a free agent, and urged individual decision and personal responsibility.”

Reading further into Rizi’s book, I found this passage: “By the end of 1926, liberal Italy had died. Mussolini had consolidated his power and created the legal instruments for the continuation of his dictatorship. Political parties had been outlawed, and freedom of the press destroyed. The opposition had been disarmed and Parliament reduced to impotence. By 1927 it had become almost impossible to undertake any political action; it was also dangerous to express critical opinions in personal letters or in public places. Civil employees could lose their jobs if they expressed views contrary to government policy.

“Besides a powerful and revitalized police division in the Ministry of the Interior, under the direct responsibility of the chief of police, a new and efficient secret police organization, ominously and mysteriously called OVRA, was created with the aim of repressing any sign of anti-fascism and controlling any expression of dissent. In a short while, it collected files on more than one hundred thousand people, including Fascist leaders, and built an impressive web of special agents, spies, and informers whose reach extended throughout the country and even abroad.”

As I was transcribing these words from Rizi’s book, news came in of the home ministry demanding, from the Finance Commission, a sum of Rs 50,000 crore to fund what it called “real-time surveillance” of citizens. This at a time when the states are being denied the money owed to them by the Centre; and while the home ministry has already dangerously abused its powers through the foisting of fake cases on independent thinkers, activists, and journalists.

And here is Rizi’s description of the Italian Parliament, c. 1929: “Parliament had become a rubber stamp of the government’s decisions. Speeches of the few remaining members of the opposition were ignored, or more often shouted down to jeers from the floor and from the public galleries.”
Patria and gloria

Fabio Fernando Rizi’s book focuses on one person in one country, and eschews comparative analysis. However, in passing, the author remarks that “Italian Fascism created an authoritarian regime, ever increasing its reach, but it did not have the time, perhaps did not even possess the strength, to build a totalitarian society.” This must be read as meaning only one thing; however awful Mussolini’s Italy was, it was not nearly as awful as Hitler’s Germany.

After reading Rizi’s intellectual biography of Benedetto Croce, I turned to David Gilmour’s magnificent book, The Pursuit of Italy, a wide-ranging and compellingly readable history of that country from the beginnings of time. Thirty of the four hundred pages of this book deal with Mussolini’s years in power. As with Rizi, much of what Gilmour said about Italy in the past chillingly resonated with what I am witnessing in my own country at present.

Consider thus these remarks: “In the 1930s the regime’s style became more ostentatious. There were more parades, more uniforms, more censorship, more hectoring, more speeches from the leader, more shouting, gesturing and grimacing from a balcony to vast crowds, which greeted Mussolini’s every reference to patria and gloria with chants of ‘Du-ce! Du-ce! Du-ce!’”.

Much the same could be said about Modi’s rule, especially after he won a second term in 2019, his every utterance greeted with “Mo-di! Mo-di! Mo-di!”.

Credit: Prakash SINGH / AFP

Why did the Italian demagogue enjoy such great popularity among the masses? Here is Gilmour’s answer: “Mussolini survived so long partly because he incarnated certain strands of italianata; he embodied the hopes, fears and generations that believed Italy had been cheated of its due, both by its liberal politicians and by its wartime allies, who had forced it to accept the ‘mutilated peace.’”

By the same token, Modi has successfully appealed to an alleged Golden Age in the distant past where Hindus were supreme in India and abroad, argued that Hindus had slipped from that pedestal owing to Muslim and British conquerors in the past, and pitted himself against conniving and corrupt Congress politicians who would drag Hindus and India down again.

Reading these books about Italy in the 1920s in the India of the 2020s, I was depressed by the many parallels; but also consoled by the few departures. Unlike Mussolini’s Italy, in Modi’s India, the Bharatiya Janata Party has had to contend with political opposition from other parties; admittedly an Opposition much attenuated at the Centre, but still fairly robust in half-a-dozen major states of the Union. The press has been tamed, but not entirely crushed. And while Mussolini’s Italy had only Benedetto Croce to call it to account, Modi’s India still has many writers and intellectuals speaking out courageously in defence of the founding principles of the Republic, and in all the languages of the Republic too.

In The Pursuit of Italy, after describing how Mussolini consolidated his rule, Gilmour remarks: “Fascism’s appeal was blunted, however, by its failure to provide prosperity. Italians might be deceived into thinking they were well governed but they could not be deceived into thinking they were well off.” Mussolini failed in providing jobs and prosperity; whereas Modi has, in fact, done far worse on the economic front, his ill-thought and quixotic policies annulling much of the progress that the Indian economy had made in the three decades since liberalisation.

Millions of young men today fanatically follow Narendra Modi. The fate that awaits them, and us, is anticipated in what Benedetto Croce said with regard to the millions of young men who fanatically followed Mussolini. After the Italian dictator had died and his regime had finally fallen, Croce wrote sadly of “the treasury of moral energies that the oppressive regime misguided, exploited and at the end had betrayed”.

Benito Mussolini and his fascists thought they would rule Italy forever. Narendra Modi and the BJP think likewise. These fantasies of eternal rule will not come to fruition; but so long as the present regime remains in power, it will continue to extract a horrendous cost – in economic, political, social, and moral terms. Italy took decades to recover from the ravages of Mussolini and his party; India may take even longer to recover from the ravages of Modi and his party.

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.

Thursday 10 September 2020

Facts v feelings: how to stop our emotions misleading us

The pandemic has shown how a lack of solid statistics can be dangerous. But even with the firmest of evidence, we often end up ignoring the facts we don’t like. By Tim Harford in The Guardian

By the spring of 2020, the high stakes involved in rigorous, timely and honest statistics had suddenly become all too clear. A new coronavirus was sweeping the world. Politicians had to make their most consequential decisions in decades, and fast. Many of those decisions depended on data detective work that epidemiologists, medical statisticians and economists were scrambling to conduct. Tens of millions of lives were potentially at risk. So were billions of people’s livelihoods.

In early April, countries around the world were a couple of weeks into lockdown, global deaths passed 60,000, and it was far from clear how the story would unfold. Perhaps the deepest economic depression since the 1930s was on its way, on the back of a mushrooming death toll. Perhaps, thanks to human ingenuity or good fortune, such apocalyptic fears would fade from memory. Many scenarios seemed plausible. And that’s the problem.

An epidemiologist, John Ioannidis, wrote in mid-March that Covid-19 “might be a once-in-a-century evidence fiasco”. The data detectives are doing their best – but they’re having to work with data that’s patchy, inconsistent and woefully inadequate for making life-and-death decisions with the confidence we would like.

Details of this fiasco will, no doubt, be studied for years to come. But some things already seem clear. At the beginning of the crisis, politics seem to have impeded the free flow of honest statistics. Although the claim is contested, Taiwan complained that in late December 2019 it had given important clues about human-to-human transmission to the World Health Organization – but as late as mid-January, the WHO was reassuringly tweeting that China had found no evidence of human-to-human transmission. (Taiwan is not a member of the WHO, because China claims sovereignty over the territory and demands that it should not be treated as an independent state. It’s possible that this geopolitical obstacle led to the alleged delay.)

Did this matter? Almost certainly; with cases doubling every two or three days, we will never know what might have been different with an extra couple of weeks of warning. It’s clear that many leaders took a while to appreciate the potential gravity of the threat. President Trump, for instance, announced in late February: “It’s going to disappear. One day it’s like a miracle, it will disappear.” Four weeks later, with 1,300 Americans dead and more confirmed cases in the US than any other country, Trump was still talking hopefully about getting everybody to church at Easter.

As I write, debates are raging. Can rapid testing, isolation and contact tracing contain outbreaks indefinitely, or merely delay their spread? Should we worry more about small indoor gatherings or large outdoor ones? Does closing schools help to prevent the spread of the virus, or do more harm as children go to stay with vulnerable grandparents? How much does wearing masks help? These and many other questions can be answered only by good data about who has been infected, and when.

But in the early months of the pandemic, a vast number of infections were not being registered in official statistics, owing to a lack of tests. And the tests that were being conducted were giving a skewed picture, being focused on medical staff, critically ill patients, and – let’s face it – rich, famous people. It took several months to build a picture of how many mild or asymptomatic cases there are, and hence how deadly the virus really is. As the death toll rose exponentially in March, doubling every two days in the UK, there was no time to wait and see. Leaders put economies into an induced coma – more than 3 million Americans filed jobless claims in a single week in late March, five times the previous record. The following week was even worse: more than 6.5m claims were filed. Were the potential health consequences really catastrophic enough to justify sweeping away so many people’s incomes? It seemed so – but epidemiologists could only make their best guesses with very limited information.

It’s hard to imagine a more extraordinary illustration of how much we usually take accurate, systematically gathered numbers for granted. The statistics for a huge range of important issues that predate the coronavirus have been painstakingly assembled over the years by diligent statisticians, and often made available to download, free of charge, anywhere in the world. Yet we are spoiled by such luxury, casually dismissing “lies, damned lies and statistics”. The case of Covid-19 reminds us how desperate the situation can become when the statistics simply aren’t there.

When it comes to interpreting the world around us, we need to realise that our feelings can trump our expertise. This explains why we buy things we don’t need, fall for the wrong kind of romantic partner, or vote for politicians who betray our trust. In particular, it explains why we so often buy into statistical claims that even a moment’s thought would tell us cannot be true. Sometimes, we want to be fooled.

Psychologist Ziva Kunda found this effect in the lab, when she showed experimental subjects an article laying out the evidence that coffee or other sources of caffeine could increase the risk to women of developing breast cysts. Most people found the article pretty convincing. Women who drank a lot of coffee did not.

We often find ways to dismiss evidence that we don’t like. And the opposite is true, too: when evidence seems to support our preconceptions, we are less likely to look too closely for flaws. It is not easy to master our emotions while assessing information that matters to us, not least because our emotions can lead us astray in different directions.

We don’t need to become emotionless processors of numerical information – just noticing our emotions and taking them into account may often be enough to improve our judgment. Rather than requiring superhuman control of our emotions, we need simply to develop good habits. Ask yourself: how does this information make me feel? Do I feel vindicated or smug? Anxious, angry or afraid? Am I in denial, scrambling to find a reason to dismiss the claim?

In the early days of the coronavirus epidemic, helpful-seeming misinformation spread even faster than the virus itself. One viral post – circulating on Facebook and email newsgroups – all-too-confidently explained how to distinguish between Covid-19 and a cold, reassured people that the virus was destroyed by warm weather, and incorrectly advised that iced water was to be avoided, while warm water kills any virus. The post, sometimes attributed to “my friend’s uncle”, sometimes to “Stanford hospital board” or some blameless and uninvolved paediatrician, was occasionally accurate but generally speculative and misleading. But still people – normally sensible people – shared it again and again and again. Why? Because they wanted to help others. They felt confused, they saw apparently useful advice, and they felt impelled to share. That impulse was only human, and it was well-meaning – but it was not wise.

Protestors in Edinburgh demonstrating against Covid-19 prevention measures. Photograph: Jeff J Mitchell/Getty Images

Before I repeat any statistical claim, I first try to take note of how it makes me feel. It’s not a foolproof method against tricking myself, but it’s a habit that does little harm, and is sometimes a great deal of help. Our emotions are powerful. We can’t make them vanish, and nor should we want to. But we can, and should, try to notice when they are clouding our judgment.

In 1997, the economists Linda Babcock and George Loewenstein ran an experiment in which participants were given evidence from a real court case about a motorbike accident. They were then randomly assigned to play the role of plaintiff’s attorney (arguing that the injured motorcyclist should receive $100,000 in damages) or defence attorney (arguing that the case should be dismissed or the damages should be low).

The experimental subjects were given a financial incentive to argue their side of the case persuasively, and to reach an advantageous settlement with the other side. They were also given a separate financial incentive to accurately guess what the damages the judge in the real case had actually awarded. Their predictions should have been unrelated to their role-playing, but their judgment was strongly influenced by what they hoped would be true.

Psychologists call this “motivated reasoning”. Motivated reasoning is thinking through a topic with the aim, conscious or unconscious, of reaching a particular kind of conclusion. In a football game, we see the fouls committed by the other team but overlook the sins of our own side. We are more likely to notice what we want to notice. Experts are not immune to motivated reasoning. Under some circumstances their expertise can even become a disadvantage. The French satirist Molière once wrote: “A learned fool is more foolish than an ignorant one.” Benjamin Franklin commented: “So convenient a thing is it to be a reasonable creature, since it enables us to find or make a reason for everything one has a mind to.”

Modern social science agrees with Molière and Franklin: people with deeper expertise are better equipped to spot deception, but if they fall into the trap of motivated reasoning, they are able to muster more reasons to believe whatever they really wish to believe.

One recent review of the evidence concluded that this tendency to evaluate evidence and test arguments in a way that is biased towards our own preconceptions is not only common, but just as common among intelligent people. Being smart or educated is no defence. In some circumstances, it may even be a weakness.

One illustration of this is a study published in 2006 by two political scientists, Charles Taber and Milton Lodge. They wanted to examine the way Americans reasoned about controversial political issues. The two they chose were gun control and affirmative action.

Taber and Lodge asked their experimental participants to read a number of arguments on either side, and to evaluate the strength and weakness of each argument. One might hope that being asked to review these pros and cons might give people more of a shared appreciation of opposing viewpoints; instead, the new information pulled people further apart.

This was because people mined the information they were given for ways to support their existing beliefs. When invited to search for more information, people would seek out data that backed their preconceived ideas. When invited to assess the strength of an opposing argument, they would spend considerable time thinking up ways to shoot it down.

This isn’t the only study to reach this sort of conclusion, but what’s particularly intriguing about Taber and Lodge’s experiment is that expertise made matters worse. More sophisticated participants in the experiment found more material to back up their preconceptions. More surprisingly, they found less material that contradicted them – as though they were using their expertise actively to avoid uncomfortable information. They produced more arguments in favour of their own views, and picked up more flaws in the other side’s arguments. They were vastly better equipped to reach the conclusion they had wanted to reach all along.

Of all the emotional responses we might have, the most politically relevant are motivated by partisanship. People with a strong political affiliation want to be on the right side of things. We see a claim, and our response is immediately shaped by whether we believe “that’s what people like me think”.

Consider this claim about climate change: “Human activity is causing the Earth’s climate to warm up, posing serious risks to our way of life.” Many of us have an emotional reaction to a claim like that; it’s not like a claim about the distance to Mars. Believing it or denying it is part of our identity; it says something about who we are, who our friends are, and the sort of world we want to live in. If I put a claim about climate change in a news headline, or in a graph designed to be shared on social media, it will attract attention and engagement not because it is true or false, but because of the way people feel about it.

If you doubt this, ponder the findings of a Gallup poll conducted in 2015. It found a huge gap between how much Democrats and Republicans in the US worried about climate change. What rational reason could there be for that?

Scientific evidence is scientific evidence. Our beliefs around climate change shouldn’t skew left and right. But they do. This gap became wider the more education people had. Among those with no college education, 45% of Democrats and 23% of Republicans worried “a great deal” about climate change. Yet among those with a college education, the figures were 50% of Democrats and 8% of Republicans. A similar pattern holds if you measure scientific literacy: more scientifically literate Republicans and Democrats are further apart than those who know very little about science.

If emotion didn’t come into it, surely more education and more information would help people to come to an agreement about what the truth is – or at least, the current best theory? But giving people more information seems actively to polarise them on the question of climate change. This fact alone tells us how important our emotions are. People are straining to reach the conclusion that fits with their other beliefs and values – and the more they know, the more ammunition they have to reach the conclusion they hope to reach.

Anti-carbon tax protesters in Australia in 2011. Photograph: Torsten Blackwood/AFP/Getty Images

In the case of climate change, there is an objective truth, even if we are unable to discern it with perfect certainty. But as you are one individual among nearly 8 billion on the planet, the environmental consequences of what you happen to think are irrelevant. With a handful of exceptions – say, if you’re the president of China – climate change is going to take its course regardless of what you say or do. From a self-centred point of view, the practical cost of being wrong is close to zero. The social consequences of your beliefs, however, are real and immediate.

Imagine that you’re a barley farmer in Montana, and hot, dry summers are ruining your crop with increasing frequency. Climate change matters to you. And yet rural Montana is a conservative place, and the words “climate change” are politically charged. Anyway, what can you personally do about it?

Here’s how one farmer, Erik Somerfeld, threads that needle, as described by the journalist Ari LeVaux: “In the field, looking at his withering crop, Somerfeld was unequivocal about the cause of his damaged crop – ‘climate change’. But back at the bar, with his friends, his language changed. He dropped those taboo words in favour of ‘erratic weather’ and ‘drier, hotter summers’ – a not-uncommon conversational tactic in farm country these days.”

If Somerfeld lived in Portland, Oregon, or Brighton, East Sussex, he wouldn’t need to be so circumspect at his local tavern – he’d be likely to have friends who took climate change very seriously indeed. But then those friends would quickly ostracise someone else in the social group who went around loudly claiming that climate change is a Chinese hoax.

So perhaps it is not so surprising after all to find educated Americans poles apart on the topic of climate change. Hundreds of thousands of years of human evolution have wired us to care deeply about fitting in with those around us. This helps to explain the findings of Taber and Lodge that better-informed people are actually more at risk of motivated reasoning on politically partisan topics: the more persuasively we can make the case for what our friends already believe, the more our friends will respect us.

It’s far easier to lead ourselves astray when the practical consequences of being wrong are small or non-existent, while the social consequences of being “wrong” are severe. It’s no coincidence that this describes many controversies that divide along partisan lines.

It’s tempting to assume that motivated reasoning is just something that happens to other people. I have political principles; you’re politically biased; he’s a fringe conspiracy theorist. But we would be wiser to acknowledge that we all think with our hearts rather than our heads sometimes.

Kris De Meyer, a neuroscientist at King’s College, London, shows his students a message describing an environmental activist’s problem with climate change denialism:

To summarise the climate deniers’ activities, I think we can say that:

(1) Their efforts have been aggressive while ours have been defensive.

(2) The deniers’ activities are rather orderly – almost as if they had a plan working for them.

I think the denialist forces can be characterised as dedicated opportunists. They are quick to act and seem to be totally unprincipled in the type of information they use to attack the scientific community. There is no question, though, that we have been inept in getting our side of the story, good though it may be, across to the news media and the public.

The students, all committed believers in climate change, outraged at the smokescreen laid down by the cynical and anti-scientific deniers, nod in recognition. Then De Meyer reveals the source of the text. It’s not a recent email. It’s taken, sometimes word for word, from an infamous internal memo written by a cigarette marketing executive in 1968. The memo is complaining not about “climate deniers” but about “anti-cigarette forces”, but otherwise, few changes were required.

You can use the same language, the same arguments, and perhaps even have the same conviction that you’re right, whether you’re arguing (rightly) that climate change is real or (wrongly) that the cigarette-cancer link is not.

(Here’s an example of this tendency that, for personal reasons, I can’t help but be sensitive about. My left-leaning, environmentally conscious friends are justifiably critical of ad hominem attacks on climate scientists. You know the kind of thing: claims that scientists are inventing data because of their political biases, or because they’re scrambling for funding from big government. In short, smearing the person rather than engaging with the evidence.

Yet the same friends are happy to embrace and amplify the same kind of tactics when they are used to attack my fellow economists: that we are inventing data because of our political biases, or scrambling for funding from big business. I tried to point out the parallel to one thoughtful person, and got nowhere. She was completely unable to comprehend what I was talking about. I’d call this a double standard, but that would be unfair – it would suggest that it was deliberate. It’s not. It’s an unconscious bias that’s easy to see in others and very hard to see in ourselves.)

Our emotional reaction to a statistical or scientific claim isn’t a side issue. Our emotions can, and often do, shape our beliefs more than any logic. We are capable of persuading ourselves to believe strange things, and to doubt solid evidence, in service of our political partisanship, our desire to keep drinking coffee, our unwillingness to face up to the reality of our HIV diagnosis, or any other cause that invokes an emotional response.

But we shouldn’t despair. We can learn to control our emotions – that is part of the process of growing up. The first simple step is to notice those emotions. When you see a statistical claim, pay attention to your own reaction. If you feel outrage, triumph, denial, pause for a moment. Then reflect. You don’t need to be an emotionless robot, but you could and should think as well as feel.

Most of us do not actively wish to delude ourselves, even when that might be socially advantageous. We have motives to reach certain conclusions, but facts matter, too. Lots of people would like to be movie stars, billionaires or immune to hangovers, but very few people believe that they actually are. Wishful thinking has limits. The more we get into the habit of counting to three and noticing our knee-jerk reactions, the closer to the truth we are likely to get.

For example, one survey, conducted by a team of academics, found that most people were perfectly able to distinguish serious journalism from fake news, and also agreed that it was important to amplify the truth, not lies. Yet the same people would happily share headlines such as “Over 500 ‘Migrant Caravaners’ Arrested With Suicide Vests”, because at the moment at which they clicked “share”, they weren’t stopping to think. They weren’t thinking, “Is this true?”, and they weren’t thinking, “Do I think the truth is important?”

Instead, as they skimmed the internet in that state of constant distraction that we all recognise, they were carried away with their emotions and their partisanship. The good news is that simply pausing for a moment to reflect was all it took to filter out a lot of the misinformation. It doesn’t take much; we can all do it. All we need to do is acquire the habit of stopping to think.

Inflammatory memes or tub-thumping speeches invite us to leap to the wrong conclusion without thinking. That’s why we need to be calm. And that is also why so much persuasion is designed to arouse us – our lust, our desire, our sympathy or our anger. When was the last time Donald Trump, or for that matter Greenpeace, tweeted something designed to make you pause in calm reflection? Today’s persuaders don’t want you to stop and think. They want you to hurry up and feel. Don’t be rushed.

The UK is one of the most corrupt nations on Earth

Fortunes are being made by political favourites, while Brexit could cement London’s reputation for money laundering writes George Monbiot in The Guardian

‘Awarding coronavirus contracts to unusual companies, without advertising, transparency or competition now appears to have been adopted as the norm.’ Photograph: Andrew Milligan/PA

Fear, shame, embarrassment: these brakes no longer apply. The government has discovered that it can bluster through any scandal. No minister need resign. No one need apologise. No one need explain.

As public outrage grows over the billions of pounds of coronavirus contracts issued by the government without competition, it seems determined only to award more of them. Never mind that the consulting company Deloitte, whose personnel circulate in and out of government, has been strongly criticised for the disastrous system it devised to supply protective equipment to the NHS. It has now been granted a massive new contract to test the population for Covid-19.

Never mind that some of these contracts have reportedly cost taxpayers £800 for every protective overall delivered. Never mind that at least two multi-million pound contracts appear to have been issued to dormant companies. Awarding contracts to unusual companies, without advertising, transparency or competition now appears to have been adopted as the norm. Several of the firms that have benefited from this largesse are closely linked to senior figures in the government.

Every week, Boris Johnson looks more like George I, under whose government vast fortunes were made by political favourites, through monopoly contracts for military procurement. Any pretence of fiscal rectitude or democratic accountability has been abandoned. With four more years and the support of the billionaire press, who cares?

The way the government handles public money looks to me like an open invitation to corruption. While it is hard to show that any individual deal is corrupt, the framework under which this money is dispensed invites the perception.

When you connect the words corruption and the United Kingdom, people tend to respond with shock and anger. Corruption, we believe, is something that happens abroad. Indeed, if you check the rankings published by Transparency International or the Basel Institute, the UK looks like one of the world’s cleanest countries. But this is an artefact of the narrow criteria they use.

As Jason Hickel points out in his book The Divide, theft by officials in poorer nations amounts to between $20bn and $40bn a year. It’s a lot of money, and it harms wellbeing and democracy in those countries. But this figure is dwarfed by the illicit flows of money from poor and middling nations that are organised by multinational companies and banks. The US research group Global Financial Integrity estimates that $1.1tn a year flows illegally out of poorer nations, stolen from them through tax evasion and the transfer of money within corporations. This practice costs sub-Saharan Africa around 6% of its GDP.

The looters rely on secrecy regimes to process and hide their stolen money. The corporate tax haven index published by the Tax Justice Network shows that the three countries that have done most to facilitate this theft are the British Virgin Islands, Bermuda and the Cayman Islands. All of them are British territories. Jersey, a British dependency, comes seventh on the list. These places are effectively satellites of the City of London. But because they are overseas, the City can benefit from “nefarious activities … while allowing the British government to maintain distance when scandals arise”, says the network. The City of London’s astonishing exemption from the UK’s freedom of information laws creates an extra ring of secrecy.

The UK also appears to be the money-laundering capital of the world. In a devastating article, Oliver Bullough revealed how easy it has become to hide your stolen loot and fraudulent schemes here, using a giant loophole in company law: no one checks the ownership details you enter when creating your company. You can, literally, call yourself Mickey Mouse, with a registered address on Mars, and get away with it. Bullough discovered owners on the Companies House site called “Xxx Stalin” and “Mr Mmmmmm Xxxxxxxxxxx”, whose address was given as “Mmmmmmm, Mmmmmm, Mmm, MMM”. One investigation found that 4,000 company owners, according to their submitted details, were under the age of two.

By giving false identities, company owners in the UK can engage in the industrial processing of dirty money with no fear of getting caught. Even when the UK’s company registration system was revealed as instrumental to the world’s biggest known money-laundering scheme, the Danske Bank scandal, the government turned a blind eye.

A new and terrifying book by the Financial Times journalist Tom Burgis, Kleptopia, follows a global current of dirty money, and the murders and kidnappings required to sustain it. Again and again, he found, this money, though it might originate in Russia, Africa or the Middle East, travels through London. The murders and kidnappings don’t happen here, of course: our bankers have clean cuffs and manicured nails. The National Crime Agency estimates that money laundering costs the UK £100bn a year. But it makes much more. With the money come people fleeing the consequences of their crimes, welcomed into this country through the government’s “golden visa” scheme: a red carpet laid out for the very rich.

None of this features in the official definitions of corruption. Corruption is what little people do. But kleptocrats in other countries are merely clients of the bigger thieves in London. Processing everyone else’s corruption is the basis of much of the wealth of this country. When you start to understand this, the contention by the author of Gomorrah, Roberto Saviano, that the UK is the most corrupt nation on Earth, begins to make sense.

These activities are a perpetuation of colonial looting: a means by which vast riches are siphoned out of poorer countries and into the hands of the super-rich. The UK’s great and unequal wealth was built on colonial robbery: the land and labour stolen in Ireland, America and Africa, the humans stolen by slavery, the $45t n bled from India.

Just as we distanced ourselves from British slave plantations in the Caribbean, somehow believing that they had nothing to do with us, now we distance ourselves from British organised crime, much of which also happens in the Caribbean. The more you learn, the more you realise that this is what it’s really about: grand larceny is the pole around which British politics revolve.

A no-deal Brexit, which Boris Johnson seems to favour, is likely to cement the UK’s position as the global entrepot for organised crime. When the EU’s feeble restraints are removed, under a government that seems entirely uninterested in basic accountability, the message we send to the rest of the world will be even clearer than it is today: come here to wash your loot.