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Undercover Economist

The refugee crisis — match us if you can

‘However many refugees we decide to resettle, there’s no excuse for doing the process wastefully’

Writing in the 1930s, Lionel Robbins, head of LSE’s economics department, defined economics as “the science which studies human behaviour as a relationship between ends and scarce means which have alternative uses”. It’s the study of who gets what and why.

That typically means that economists study conventional markets: how prices work, how people respond to them and how the whole system might function or malfunction. But sometimes a market simply will not do. We don’t allocate children to state school places based on their parents’ willingness to pay. Most countries don’t sell passports to the highest bidder. We do not have a legal market in iced kidneys.

Whether we like it or not, the problem of who gets what and why remains. Sometimes it is grubbily resolved by the emergence of parallel markets — for example, children can be placed in desirable schools at taxpayer expense if their parents buy or rent expensive homes in the right areas.

Over the past few decades a small group of economists — most notably Nobel laureate Alvin Roth, author of Who Gets What — and Why (2015) — has been designing “matching mechanisms” to address allocation problems without resorting to traditional markets. A typical problem: matching teaching hospitals with trainee doctors. The doctors want good hospitals and the hospitals want good doctors. Each side will also have a focus on a particular field of medicine, and the doctors may have preferences over location. Some doctors may be dating fellow medics, who are themselves searching for a teaching hospital.

A good matching mechanism tries to satisfy as many of these preferences as possible. And it ends the need for people to second-guess the system. Bad matching mechanisms reward people who say that a compromise option is really their top preference. Such mind-games are alienating and unfair; in a well-designed matching system, they can be eliminated.

Roth and a growing number of his students and colleagues have designed matching mechanisms for schools and hospital placements, and even mechanisms to ensure the best match for donated kidneys. In each case a market is socially unacceptable but ad hoc or lottery-based allocations are also poor solutions. Nobody wants a random kidney, or to be assigned a place on the whim of a well-meaning bureaucrat who doesn’t really understand the situation.

By balancing competing demands, good matching mechanisms have alleviated real suffering in school systems and organ donation programmes. Now two young Oxford academics, Will Jones of the Refugee Studies Centre and Alexander Teytelboym of the Institute for New Economic Thinking, are trying to persuade governments to use matching mechanisms in the refugee crisis.

Most popular discussions of the crisis focus on how many refugees we in rich countries should accept. Yet other questions matter too. Once nations, or groups of countries, have decided to resettle a certain number of refugees from temporary camps, to which country should they go? Or within a country, to which area?

Different answers have been tried over the years, from randomly dispersing refugees to using the best guesses of officials, as they juggle the preferences of local communities with what they imagine the refugees might want.

In fact, this is a classic matching problem. Different areas have different capabilities. Some have housing but few school places; others have school places but few jobs; still others have an established community of refugees from a particular region. And refugee families have their own skills, needs and desires.

This is not so different a problem from allocating trainee doctors to teaching hospitals, or children to schools, or even kidneys to compatible recipients. In each case, we can get a better match through a matching mechanism. However many refugees we decide to resettle, there’s no excuse for doing the process wastefully.

There is no perfect mechanism for matching refugees to communities — there are too many variables at play — but there are some clear parameters: housing is a major constraint, as is the availability of medical care. Simple systems exist, or could be developed, that should make the process more efficient, stable and dignified.

One possibility is a mechanism called “top trading cycles”. This method invites each refugee family to point to their preferred local authority, while each local authority has its own waiting list based on refugee vulnerability. The trading cycles mechanism then looks for opportunities to allocate each family to their preferred location. The simplest case is that, for example, the family at the top of the Hackney waiting list wants to go to Hackney. But if the family at the top of Hackney’s list wants to go to Camden, the family at the top of Camden’s list wants to go to Edinburgh, and the family at the top of Edinburgh’s list wants to go to Hackney, all three families will get their wish.

Right now, the UK is a promising candidate to pioneer the use of one of these matching mechanisms to place refugees. The government has pledged to resettle 20,000 Syrian refugees now in temporary camps. Local authorities have volunteered to play their part. But to make the best possible matches between the needs of the refugees and the capabilities of these local authorities, it’s time to deploy a little economics.

Written for and first published at ft.com.

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Undercover Economist

A billion prices can’t be wrong

‘A “big data” approach to inflation is helping us understand the fundamental question of why recessions happen’

In the dying days of 2015 came news to set any geek’s pulse racing: the declaration of a “statistical emergency” by Mauricio Macri, the new president of Argentina. Macri’s move enabled Jorge Todesca, head of the statistics bureau, to suspend publication of some basic economic data. That might seem extreme but Argentina’s inflation numbers were widely discredited.

The International Monetary Fund censured Argentina in 2013 for its implausible numbers under previous president Cristina Fernández de Kirchner. Government statisticians say they were leaned on by her administration to report low inflation. Todesca himself used to be a private-sector economist, and, in 2011, his firm was fined half a million pesos for publishing numbers that contradicted the official version. (Half a million pesos was about $125,000 at the time; it is $35,000 these days, which rather proves the point.)

But one economist found a way to publish plausible inflation statistics without being prosecuted. His name is Alberto Cavallo, and he realised that by gathering price data published by online retailers, he could produce a credible estimate of Argentine inflation from the safety of Massachusetts. Cavallo’s estimate averaged more than 20 per cent a year between 2007 and 2011; the official figure was 8 per cent.

So began the Billion Prices Project and its commercial arm PriceStats, both collaborations between Cavallo and fellow MIT economics professor Roberto Rigobon. “Billion Prices” sounds hyperbolic but that is the number of prices collected each week by the project, from hundreds of retailers in more than 60 countries.

While the project confirmed that Argentina’s inflation numbers could not be trusted, it also showed that the US inflation numbers published by the US Bureau of Labor Statistics could be. Several maverick commentators had argued that hyperinflation would be the inevitable consequence of money printing at the Federal Reserve. When hyperinflation plainly failed to materialise, some critics suggested the BLS was hiding it — as if nobody would notice.

A second advantage, swiftly noted, was that the daily flow of data from PriceStats was a good predictor of official inflation statistics, which are typically published once a month. Cavallo and Rigobon like to point out that their US online price index started to fall the day after Lehman Brothers declared bankruptcy; the official Consumer Price Index took a month to respond at all, and two months to respond fully.

The BPP is also shedding light on some old economic mysteries. One is the problem of adjusting inflation for changes in quality. To some extent this is an intractable problem. The Edison phonograph cost $20 at the end of the 19th century; an iPod Nano costs about $145 today. What inflation rate does that imply over the past 117 years? There is simply no good answer to that question.

But statistical agencies are always wrestling with smaller slices of the same problem. A new model of washing machine is introduced at a premium price, gradually discounted over the years and eventually sold at clearance prices and replaced with a swankier model. The same thing is happening over differing timescales with computers, summer dresses and cars. If the economic statisticians mishandle these cases, they will get their measure of inflation badly wrong; usually they rely on careful substitutes and clever theory, but success can never be assured.

Cavallo and Rigobon argue that the sheer volume of prices collected by the BPP helps resolve the problem. Every day, the project gathers the prices of hundreds of washing machines. By observing that the availability of the Scrub-O-Mat 9000 overlaps with that of the Cleanado XYZ, it’s possible to adjust as new products are introduced and old products discounted and then phased out.
This “big data” approach to inflation is also helping us to understand the fundamental question of why recessions happen. Without opening a big bag of macroeconomics at this stage in the column, one influential school of thought is that recessions happen (in part) because prices don’t adjust smoothly in the face of a slowdown. Like a small rock that starts an avalanche, this price rigidity causes big trouble. Unsold inventory builds up, retailers slash their orders, and manufacturers go bankrupt.

The trouble with the idea that price stickiness causes recessions is that, according to official inflation statistics, prices routinely change by amounts large or small, which suggests no price rigidity.

But it turns out that many small price changes are statistical illusions. For example, if a product is missing from four monthly inflation surveys and is 1 per cent more expensive when it returns in the fifth month, official statisticians will quite rightly smooth over the gap by imputing a 0.2 per cent rise per month. But it would be a mistake to take this as evidence that retailers did, in fact, repeatedly raise prices by 0.2 per cent. Collecting billions of prices removes the need to fill in these gaps, and in the BPP data very small price changes are rare. Prices will move by several per cent if they move at all. One might guess that in physical stores the cost of relabelling products is higher, and small price changes are even rarer.

The BPP’s big data approach has rescued the important macroeconomic idea of price stickiness. It is a reminder that we often gain from having a second opinion — or a billion of them.

Written for and first published at ft.com.

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Why everyone should give a TED talk and how to do it

I found out the hard way that bad public speaking is contagious. As a schoolboy I was pretty good at speeches, in a schoolboyish way. I won competitions; being a sharp, witty speaker was a defining part of who I felt myself to be.

Then I grew up and started a corporate job, and something strange happened. My talks sagged into “presentations”, burdened by humourless clip art and plodding bullet points. The reason? I was surrounded by people who were stuck in the same beige offices giving the same beige presentations. Like many workplaces, we had reached an unspoken consensus that giving bad talks was just the way things were done.

Aside from tradition — and it is a powerful one — why else are most talks bad talks? One reason is fear. Being afraid does not itself make a speech bad; fear can make a talk electrifying or touching. But most speakers take the coward’s way out. Afraid of running out of words, they overstuff their speeches. And they prop themselves up by projecting their speaking notes on the wall behind them, even though everyone knows that providing rolling spoilers for your speech is a terrible idea.

A second reason is lack of preparation. Most speakers rehearse neither their argument nor their performance. That is understandable. Practising in front of a mirror is painful. Practising in front of a friend is excruciating. Rehearsing offers all the discomfort of giving a speech without any of the rewards of doing so. But it will make the end result much better.

For these reasons, I think you should give a TED talk. Almost anyone can. All you need is 18 minutes, a topic and an audience — if only your cat. No matter how often or how rarely you usually speak in public, the act of trying to give a talk in the tradition of TED will change the way you think and feel about public speaking.

As with anything popular, TED talks have their critics, but it is hard to deny that the non-profit organisation behind the videoed presentations on subjects from science to business has helped reinvent the art of the public speech.

TED talks are vastly more entertaining than traditional lectures, while more thought provoking than most television. But that is TED from the point of view of the audience. From the view of an aspiring speaker, the lesson of TED is that most speakers could raise their game. A few TED talks are by professional politicians or entertainers such as Al Gore or David Blaine. Most are not.

There are more than 1,000 talks on the TED website with more than 1m views, typically delivered by writers, academics or entrepreneurs who have been giving mediocre talks as a matter of habit, and who have been suddenly challenged to stop being mediocre. Faced with the obligation to deliver the talk of their lives, they decided to do the work and take the necessary risks.

These speakers have been offered good advice by the organisers of TED, but that advice has never been a secret. It is now available to anyone in the form of TED Talks (buy in the UK) (buy in the US), a guide to public speaking from Chris Anderson, the TED boss. It is excellent; easily the best public speaking guide I have read. (I should admit a bias: I have spoken twice at TED events and benefited from the platform that TED provides.) Unlike many in the genre, Anderson’s book is not a comprehensive guide to going through the motions of wedding toasts and votes of thanks. Instead, it focuses on the stripped-down TED-style challenge: an audience, a speaker, plenty of time to prepare, and 18 minutes to say something worth hearing.

There is no formula for a great talk, insists Mr Anderson, but there are some common elements. First and most important: there is a point, an idea worth hearing about. Second, the talk has a “throughline” — meaning that most of what is said in some way supports that idea. There may be stories and jokes, even surprises — but everything is relevant.

Third, the speaker connects with those listening — perhaps through humour, stories, or simply making eye contact and speaking frankly. Finally, the speech explains concepts or advances arguments by starting from what the audience understand, and proceeding step by step through more surprising territory. It can be very hard for a speaker to appreciate just how much she knows that her audience do not. One reason to rehearse is that an audience can tell you when they get lost.

Most speakers are able to do some of this, some of the time — an interesting anecdote, a funny line, an educational explanation. We are social beings, after all. We have had a lot of practice talking.

Much of what turns a half-decent talk into a brilliant one is the ruthless excision of the fluff — the throat-clearing introduction, the platitudes, the digressions, the additional points that obscure the central message, and the “er, that’s about it” conclusion. With an audience of 60 people, for instance, every minute you waffle is an hour of other people’s time you are wasting. Sharpen up.

My only quibble is that the book offers less to a speaker who is short of preparation time. Because Mr Anderson is so keen to tell speakers how to prepare, he does not fully engage with the challenge of improvised speaking or debating.

Marco “Rubot” Rubio’s presidential dreams may have been snuffed out because he seemed over-rehearsed and unable to improvise. And Martin Luther King Jr’s greatest moment as a speaker — the second half of “I have a dream” — was unscripted. Sometimes the improvised response is more powerful than a prepared speech can ever be.

Instead, Mr Anderson’s aim is to help readers give a full-blown TED talk, despite the hard work that entails. Fair enough. Preparing to give a high-stakes speech is like training for a marathon or studying for an exam: even if you only do it once, the process will teach you things you will always remember.

Written for and first published in the Financial Times.

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A short-cut to speeches

A TED-style talk takes weeks of preparation. What if you have hours, or minutes, to prepare?

• Say something worth hearing. “It’s not about you,” says Chris Anderson, who warns that business presentations are often sales pitches or boasts. He adds that the same information will land much better if it is “here’s what we’ve learnt” rather than “look how great we’ve been”.

• Less is more. Once you have found something worth saying, focus. Strip it down to a single core point. Everything about your speech — stories, jokes, statistics, graphics — should connect to that point.

• Your speaking notes should not intrude. Bullet points are a good idea if they are written on handheld cards, but not when projected on the wall behind you. If your speech is scripted, do not try to memorise it if you have no time, but become familiar with it. “There’s a big difference between being 90 per cent down in the script, and 60 per cent up and connected,” says Anderson.

• You are usually your own best visual aid. By all means use pictures, diagrams or video when they are good. But do not use substandard slides as wallpaper; when you have nothing to show, show nothing. Hit “B” to blank the screen and focus attention on you, or use empty slides.

• Practise. Even one run-through with a friend will help. Or find an empty room and record yourself on your phone. It is awkward but worth it.

• First and final impressions last. Improvised talks often suffer from a slow start and a limp finish. Think of a good opening and closing, and practise them. If you can start and finish strongly, you and your audience will both feel better.

11th of May, 2016Other WritingComments off
Undercover Economist

The odds are you won’t know when to quit

‘The truth is that there are no foolproof methods for knowing when to hold ’em and when to fold ’em’

There is a strong case to be made for persistence. As a child I was told the legend of Robert the Bruce. Cowering and hiding in some dank cave in Scotland, he felt like giving up his struggle against the English. Then he noticed a spider repeatedly failing to spin a web before eventually succeeding. Heartened, King Robert returned to give the English a sound thrashing in 1314. Even for an English boy, it was an inspiring tale. If at first you don’t succeed, try again.

But there is an equally strong case to be made against being stubborn. When Irving Fisher and John Maynard Keynes failed to predict the Wall Street Crash of 1929, the two great economists reacted differently. Fisher stuck to his guns; Keynes shrugged and changed direction. Fisher was ruined; Keynes died a millionaire. If at first you don’t succeed, do something different next time.

Do we tend to quit too soon or quit too late? Are we too stubborn or not determined enough? There has been much excitement recently around the idea of “grit” — a personality trait representing commitment to and enthusiasm for long-term goals, championed by psychologist Angela Duckworth. She argues, plausibly, that grit is more important than talent in predicting a successful life.

The idea is appealing in principle but one must ask what Duckworth’s brief “grit” questionnaire is really measuring. (Perhaps I am just sore because I took the questionnaire and discovered I have less grit than the average marshmallow.)

While Duckworth’s work suggests that perseverance is vital, other psychological research suggests that we sometimes persevere when we should not. Nobel laureate Daniel Kahneman, with the late Amos Tversky, discovered a tendency called “loss aversion”. Loss aversion is a disproportionate dislike of losses relative to gains, and it can lead us to cling on pig-headedly to bad decisions because we hate to stop playing when we’re behind.

My favourite study of loss aversion concerns players of the TV game show Deal or No Deal, in which players must periodically decide whether to keep gambling or accept an offer from the mysterious “Banker” to buy them out of the game. In one notorious Dutch episode, a contestant named Frank was offered €75,000 to stop; he kept playing and lost his next gamble. The Banker’s next offer was just €2,400, which was actually a fair offer. But at that point loss aversion kicked in. With the lost €75,000 in mind, Frank refused all further deals, kept gambling and kept losing. He eventually won just €10.

A study of Deal or No Deal by behavioural economists including Thierry Post and Richard Thaler found that while Frank’s fate was spectacular, his behaviour was statistically typical. People hate to quit if they feel they’re losing.

Loss aversion warps investment strategies in a similar way. We happily sold our stocks in Google and Apple but clung on to those in Enron and Lehman Brothers. The same tendency affects house prices: we hate to sell for less than we paid. Recent research by Alasdair Brown and Fuyu Yang finds that the same thing is true when people are offered the opportunity to cash in a bet on a sporting event that is still in progress. They are happy to cash out if their team is a goal up, even though that will cut their possible gains, but they will cling on if their team is a goal down even though they could cut their losses.

I was struck by a recent FT article by equity analyst Daniel Davies describing how a portfolio based on expert research recommendations would tend to do badly, but if the same portfolio had a “stop-loss” rule that simply jettisoned stocks after a 10 per cent loss, it would tend to do very well. The stop-loss rule cancelled out the instinctive tendency to hold on stubbornly to losers. Yet Warren Buffett seems to do very well by buying and holding.

The truth is that there are no foolproof methods for knowing when to hold ’em and when to fold ’em. But I have three suggestions. The first is to look resolutely away from sunk costs and towards future prospects. Whether you paid $70 or $130 for your Apple shares should be irrelevant to your decision to sell them today for $100. Bygone profits and losses are a distraction.

The second is to persevere flexibly rather than stubbornly. Angela Duckworth’s family follows a “hard thing” rule: the children have to choose an activity, such as music or athletics, that requires dedication and practice. They’re allowed to quit but only at a natural break point and only if they find an alternative “hard thing”. That seems to steer a course between the Scylla of obstinacy and the Charybdis of laziness.

The third is to view decisions as experiments. Signing up to learn the violin is an experiment; so is moving cities or careers. Of course, one can end an experiment too early or doggedly persist too long. But viewing a decision as an experiment gives a useful perspective because experiments are always designed to teach us something. We can keep asking: what have I learnt? And am I still learning? If a new project or activity keeps teaching us new things, it is probably worth continuing — even if the lessons are sometimes painful.

Written for an first published at ft.com.

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How to give a TED talk in a hurry

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4th of May, 2016VideoComments off
Undercover Economist

Could an income for all provide the ultimate safety net?

‘Though the idea of a basic income is far from mainstream, it has had astonishingly broad support’

Last week, I pondered how society should protect or compensate people whose jobs have been lost to the forces of globalisation or technological change. I did not, however, discuss the most obvious idea of all: that we should simply give people money — a basic income for everyone, regardless of what they do or what they need. It’s the ultimate social safety net.

For an idea that is so far from mainstream political practice, the payment of a basic income has had astonishingly broad support, from Martin Luther King Jr to Milton Friedman. It’s on the lips of the policy wonk community too: the Freakonomics podcast recently devoted an episode to the case for a universal basic income. The Royal Society for Arts, a venerable British think-tank, has published a report enthusiastically supporting the idea. Dutch journalist Rutger Bregman is just as keen, as outlined in his recent, eloquent book Utopia for Realists.

Policy experiments are also on the way. The charity GiveDirectly has just announced plans to run a randomised trial in which 6,000 Kenyans will receive a basic income for more than a decade. Various Silicon Valley types — with one eye on the looming Robot Job Apocalypse — are making serious-sounding noises about running experiments too. Pilots are planned in Canada and Finland, and the Swiss have a referendum on the topic in June.

Could a basic income really work? The answer is yes. But the plan may be more painful than some of its advocates are willing to admit.

First, let’s establish what we’re talking about. A universal basic income is a cash payment from the state, paid to everyone unconditionally. For the sake of being concrete, let’s call it £10 a day. That seems like a lot of money to be giving to absolutely everyone, but it’s within the bounds of reason. Such a payment would cost £234bn a year across 64 million UK residents, so it could be largely paid for by scrapping all social security spending, which is £217bn.

There are lots of other proposals that one might call a basic income. Leftwing advocates might want far more than £10 a day but that would require a huge expansion of the state, with much higher taxes. The more libertarian proponents of the idea might also approve of a higher basic income, in exchange for a rolling back of state-provided services. Privatising the entire health and education system in the UK would free up £240bn, easily enough to double the basic income to £20 a day for every man, woman and child. But that money would need to cover school fees and medical bills.

All this is within the bounds of affordability. But is it desirable? Here are two big question marks over the idea.

The first is whether people would simply stop working. Several large experiments conducted in the US and Canada in the late 1970s and early 1980s suggest that a minimum income would encourage people to reduce their hours a little. If such slacking-off undermined the tax base, the entire project could become both economically and politically unsustainable.

But the tax base is probably safe enough, because the people who might be tempted to quit work and live on £10 a day are not the people whose taxes pay for most state spending. In the UK, the richest 15 per cent of taxpayers — people who pay at least some tax at the 40 per cent rate — supply about two-thirds of income tax revenue. Few of these people are likely to find the basic income a tempting inducement to leave the labour force.

In some cases, we might celebrate a decision to stop work. Some people volunteer; others care for children or relatives; some might use the income to fund themselves as they stay in education or retrain. Some, alas, might use the money to stay alive as they write poetry.

The second objection is more worrying: if the welfare state is to be replaced by a basic income, it will provide far too little for some. A tenner a day is less than half the new UK state pension, so it’s hard to imagine pensioners embracing the idea with much gusto.

On the other hand, if the basic income is to be supplemented by a raft of special cases — people with disabilities, people with expensive rent, people who are elderly — then it may become as complex as the tangle of benefit entitlements it aims to replace, or hugely expensive, or both.

Andrew Hood of the Institute for Fiscal Studies says that compared with current welfare benefits, a basic income would “either be a lot less generous or a lot more expensive”. Take your pick.

In the end, the idea appeals to three types of people: those who are comfortable with a dramatic increase in the size of the state, those who are willing to see needy people lose large sums relative to the status quo, and those who can’t add up.

A basic income makes perfect sense once we arrive at an economy where millions work for low wages while automation produces a bountiful economy all around them. The debate turns on whether that world has already arrived.

Written for and first published at ft.com.

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3rd of May, 2016Undercover EconomistComments off
Undercover Economist

Tata Steel, Port Talbot and how to manage industrial decline

‘The wounds of a large industrial closure run deep. The entire economic ecosystem of an area can collapse’

The possible closure of Tata Steel’s operations in Port Talbot casts a deep shadow over the area. There’s something familiar about this depressing story. The shipyards of the Clyde and the cotton mills of Manchester have faded. The coalfields of Derbyshire, Nottinghamshire and South Yorkshire were all around me as I grew up in Chesterfield during the miners’ strike of 1984-85. Now the mining jobs are gone. Further afield, there are the job losses in the automobile production lines of Detroit, for the shoemakers of Kobe in Japan, or at Eastman Kodak in Rochester, New York.

So what should be done when communities are wounded by such blows? One tempting answer is “everything”; that the government should nationalise troubled operations or adopt similar big-bazooka tactics, such as high trade barriers or large subsidies. It’s easy enough to make the emotional case for this but the practical case isn’t so plausible. Would nationalisation have saved Kodak’s film business? Is Manchester the place for a 21st-century Cottonopolis?

Sometimes, government can help restructure a troubled business — as with the Obama administration’s interventions in the case of General Motors, or the long but ultimately successful nationalisation of Rolls-Royce in the 1970s.

However, taxpayers are always at risk of being saddled with the role of supporting industries in inescapable structural decline. The political economy of these cases is skewed towards preservation rather than creative destruction. Old industries under stress have much to gain from government support, and can point to people who need help. There is no constituency for jobs that have not yet been created.

So a different answer is that we should do nothing. This laissez-faire reasoning points out that economic change inevitably creates losers but, ultimately, society is better off. We cannot resist change, only adjust. Former autoworkers, steelworkers, and coalminers need to pick themselves up and move to where fresh jobs are available, or retrain.

There is a logic to this argument but it glosses over the deep wounds of a large industrial closure. It isn’t just that workers lose jobs. The entire economic ecosystem of an area can collapse. Newly jobless workers find that their homes are worthless, their pensions too sometimes.

And workers with the kind of skills that are under pressure from technology or trade may find that they move from one sinking lifeboat to another, with their new jobs under threat from the same forces that destroyed the old ones. More radically, retraining — maybe as a neurosurgeon or data scientist — would solve that problem, but then so would discovering a Rembrandt in the attic.

Between the ideologically pure answers of “do everything” and “do nothing”, we have the current consensus, “do something”. But what?

There are three broad approaches to looking after the losers from economic change: try to bring new jobs to people; try to help the people change to find new jobs; just send money.

Bringing new jobs to people is the most natural idea, but regional regeneration is difficult. Depressed communities often stay depressed. A Sheffield Hallam University study from 2014, The State of the Coalfields, found that 30 years after the miners’ strike, coalfield communities have lower employment rates and higher reliance on disability benefits. The track record of place-based regeneration policies is patchy and sobering.

If the jobs won’t move, perhaps the workers can? An influential 1992 study by economists Olivier Blanchard and Lawrence Katz found that the US labour market once worked this way. While a shock could have a lasting effect on a local economy, the unemployment rate itself would subside, “not because employment picks up, but because workers leave the state”.

But new research from Mai Dao, Davide Furceri and Prakash Loungani at the IMF finds that US workers move less than they did back in the 1980s. Instead of moving, they are more likely to stay put and stay jobless. (Mobility has improved in the European Union, albeit from much lower levels.)

We don’t really know why mobility is falling in the US. Maybe because dual-income households find it harder to move — but then the same pattern is seen for single people. Housing costs increasingly prevent poor people from moving to booming areas such as New York and London in search of work.

“My guess is that there’s no one reason for the fall in mobility,” says Betsey Stevenson of the University of Michigan, also formerly chief economist at the US labour department. Stevenson, like many economists, argues that education must be a huge part of the answer to economic shocks. The jobs have changed, and so must we.

Education is, indeed, a remarkable thing. Lawrence Katz has observed that between 1979 and 2012, the wage gap between a US household of two college graduates and a household of two high school graduates grew by around $30,000 — a sum that dwarfs most shifts in the economic landscape. But it is easy to be glib about retraining: governments are tempted to use training programmes as a way to make work and shift people off the welfare rolls.

So a final answer as to how to compensate the losers is the simplest: give them money. That is a strategy that offers both more, and less, than it might seem at first glance. But that is a topic for next week.

Written for and first published at ft.com.

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What I’ve been reading in April

Utopia for Realists by Rutger Bregman.  A short book arguing in favour of a universal basic income (and a few other things, but the UBI stuff is easily the most interesting). I’m not totally convinced by the proposal – the arithmetic of a UBI calls for some painful choices – but it’s an excellent read and full of well-told stories and details I didn’t know. (UK) (US)

The Art of Travel by Alain de Botton – re-reading my favourite de Botton book. A fascinating and original reflection on how and why we travel – and whether we might find what we seek closer to home. (UK) (US)

The Creative Habit by Twyla Tharp. A practical guide to living a creative life, by a brilliant, driven and entrepreneurial choreographer. I love the story of Twyla’s near-disastrous collaboration with Billy Joel, and often retell the story myself. (UK) (US)

You Are Not So Smart by David McRaney – a fun guide to cognitive errors and logical fallacies. I’ve been adoring the podcast of the same name. (UK) (US)

Out on the Wire by Jessica Abel. Okay, here’s the deal: everyone I know in radio is reading this book to learn how to be the next Ira Glass, Jad Abumrad or Roman Mars. And it’s good – very good. (UK) (US)

Or you may fancy one of my own books – for example, Adapt.


25th of April, 2016MarginaliaComments off

How Politicians Poisoned Statistics

We have more data — and the tools to analyse and share them — than ever before. So why is the truth so hard to pin down?

In January 2015, a few months before the British general election, a proud newspaper resigned itself to the view that little good could come from the use of statistics by politicians. An editorial in the Guardian argued that in a campaign that would be “the most fact-blitzed in history”, numerical claims would settle no arguments and persuade no voters. Not only were numbers useless for winning power, it added, they were useless for wielding it, too. Numbers could tell us little. “The project of replacing a clash of ideas with a policy calculus was always dubious,” concluded the newspaper. “Anyone still hankering for it should admit their number’s up.”

This statistical capitulation was a dismaying read for anyone still wedded to the idea — apparently a quaint one — that gathering statistical information might help us understand and improve our world. But the Guardian’s cynicism can hardly be a surprise. It is a natural response to the rise of “statistical bullshit” — the casual slinging around of numbers not because they are true, or false, but to sell a message.

Politicians weren’t always so ready to use numbers as part of the sales pitch. Recall Ronald Reagan’s famous suggestion to voters on the eve of his landslide defeat of President Carter: “Ask yourself, ‘Are you better off now than you were four years ago?’” Reagan didn’t add any statistical garnish. He knew that voters would reach their own conclusions.

The British election campaign of spring last year, by contrast, was characterised by a relentless statistical crossfire. The shadow chancellor of the day, Ed Balls, declared that a couple with children (he didn’t say which couple) had lost £1,800 thanks to the government’s increase in value added tax. David Cameron, the prime minister, countered that 94 per cent of working households were better off thanks to recent tax changes, while the then deputy prime minister Nick Clegg was proud to say that 27 million people were £825 better off in terms of the income tax they paid.

Could any of this be true? Yes — all three claims were. But Ed Balls had reached his figure by summing up extra VAT payments over several years, a strange method. If you offer to hire someone for £100,000, and then later admit you meant £25,000 a year for a four-year contract, you haven’t really lied — but neither have you really told the truth. And Balls had looked only at one tax. Why not also consider income tax, which the government had cut? Clegg boasted about income-tax cuts but ignored the larger rise in VAT. And Cameron asked to be evaluated only on his pre-election giveaway budget rather than the tax rises he had introduced earlier in the parliament — the equivalent of punching someone on the nose, then giving them a bunch of flowers and pointing out that, in floral terms, they were ahead on the deal.

Each claim was narrowly true but broadly misleading. Not only did the clashing numbers confuse but none of them helped answer the crucial question of whether Cameron and Clegg had made good decisions in office.

To ask whether the claims were true is to fall into a trap. None of these politicians had any interest in playing that game. They were engaged in another pastime entirely.

Thirty years ago, the Princeton philosopher Harry Frankfurt published an essay in an obscure academic journal, Raritan. The essay’s title was “On Bullshit”. (Much later, it was republished as a slim volume that became a bestseller.) Frankfurt was on a quest to understand the meaning of bullshit — what was it, how did it differ from lies, and why was there so much of it about?

Frankfurt concluded that the difference between the liar and the bullshitter was that the liar cared about the truth — cared so much that he wanted to obscure it — while the bullshitter did not. The bullshitter, said Frankfurt, was indifferent to whether the statements he uttered were true or not. “He just picks them out, or makes them up, to suit his purpose.”

Statistical bullshit is a special case of bullshit in general, and it appears to be on the rise. This is partly because social media — a natural vector for statements made purely for effect — are also on the rise. On Instagram and Twitter we like to share attention-grabbing graphics, surprising headlines and figures that resonate with how we already see the world. Unfortunately, very few claims are eye-catching, surprising or emotionally resonant because they are true and fair. Statistical bullshit spreads easily these days; all it takes is a click.

Consider a widely shared list of homicide “statistics” attributed to the “Crime Statistics Bureau — San Francisco”, asserting that 81 per cent of white homicide victims were killed by “blacks”. It takes little effort to establish that the Crime Statistics Bureau of San Francisco does not exist, and not much more digging to discover that the data are utterly false. Most murder victims in the United States are killed by people of their own race; the FBI’s crime statistics from 2014 suggest that more than 80 per cent of white murder victims were killed by other white people.

Somebody, somewhere, invented the image in the hope that it would spread, and spread it did, helped by a tweet from Donald Trump, the current frontrunner for the Republican presidential nomination, that was retweeted more than 8,000 times. One can only speculate as to why Trump lent his megaphone to bogus statistics, but when challenged on Fox News by the political commentator Bill O’Reilly, he replied, “Hey, Bill, Bill, am I gonna check every statistic?”

Harry Frankfurt’s description of the bullshitter would seem to fit Trump perfectly: “He does not care whether the things he says describe reality correctly.”

While we can’t rule out the possibility that Trump knew the truth and was actively trying to deceive his followers, a simpler explanation is that he wanted to win attention and to say something that would resonate with them. One might also guess that he did not check whether the numbers were true because he did not much care one way or the other. This is not a game of true and false. This is a game of politics.

While much statistical bullshit is careless, it can also be finely crafted. “The notion of carefully wrought bullshit involves … a certain inner strain,” wrote Harry Frankfurt but, nevertheless, the bullshit produced by spin-doctors can be meticulous. More conventional politicians than Trump may not much care about the truth but they do care about being caught lying.

Carefully wrought bullshit was much in evidence during last year’s British general election campaign. I needed to stick my nose in and take a good sniff on a regular basis because I was fact-checking on behalf of the BBC’s More or Less programme. Again and again I would find myself being asked on air, “Is that claim true?” and finding that the only reasonable answer began with “It’s complicated”.

Take Ed Miliband’s claim before the last election that “people are £1,600 a year worse off” than they were when the coalition government came to power. Was that claim true? Arguably, yes.

But we need to be clear that by “people”, the then Labour leader was excluding half the adult population. He was not referring to pensioners, benefit recipients, part-time workers or the self-employed. He meant only full-time employees, and, more specifically, only their earnings before taxes and benefits.

Even this narrower question of what was happening to full-time earnings is a surprisingly slippery one. We need to take an average, of course. But what kind of average? Labour looked at the change in median wages, which were stagnating in nominal terms and falling after inflation was taken into account.

That seems reasonable — but the median is a problematic measure in this case. Imagine nine people, the lowest-paid with a wage of £1, the next with a wage of £2, up to the highest-paid person with a wage of £9. The median wage is the wage of the person in the middle: it’s £5.

Now imagine that everyone receives a promotion and a pay rise of £1. The lowly worker with a wage of £1 sees his pay packet double to £2. The next worker up was earning £2 and now she gets £3. And so on. But there’s also a change in the composition of the workforce: the best-paid worker retires and a new apprentice is hired at a wage of £1. What’s happened to people’s pay? In a sense, it has stagnated. The pattern of wages hasn’t changed at all and the median is still £5.

But if you asked the individual workers about their experiences, they would all tell you that they had received a generous pay rise. (The exceptions are the newly hired apprentice and the recent retiree.) While this example is hypothetical, at the time Miliband made his comments something similar was happening in the real labour market. The median wage was stagnating — but among people who had worked for the same employer for at least a year, the median worker was receiving a pay rise, albeit a modest one.

Another source of confusion: if wages for the low-paid and the high-paid are rising but wages in the middle are sagging, then the median wage can fall, even though the median wage increase is healthy. The UK labour market has long been prone to this kind of “job polarisation”, where demand for jobs is strongest for the highest and lowest-paid in the economy. Job polarisation means that the median pay rise can be sizeable even if median pay has not risen.

Confused? Good. The world is a complicated place; it defies description by sound bite statistics. No single number could ever answer Ronald Reagan’s question — “Are you better off now than you were four years ago?” — for everyone in a country.

So, to produce Labour’s figure of “£1,600 worse off”, the party’s press office had to ignore the self-employed, the part-timers, the non-workers, compositional effects and job polarisation. They even changed the basis of their calculation over time, switching between different measures of wages and different measures of inflation, yet miraculously managing to produce a consistent answer of £1,600. Sometimes it’s easier to make the calculation produce the number you want than it is to reprint all your election flyers.

Very few claims are eye-catching, surprising or emotionally resonant because they are true and fair

Such careful statistical spin-doctoring might seem a world away from Trump’s reckless retweeting of racially charged lies. But in one sense they were very similar: a political use of statistics conducted with little interest in understanding or describing reality. Miliband’s project was not “What is the truth?” but “What can I say without being shown up as a liar?”

Unlike the state of the UK job market, his incentives were easy to understand. Miliband needed to hammer home a talking point that made the government look bad. As Harry Frankfurt wrote back in the 1980s, the bullshitter “is neither on the side of the true nor on the side of the false. His eye is not on the facts at all … except insofar as they may be pertinent to his interest in getting away with what he says.”

Such complexities put fact-checkers in an awkward position. Should they say that Ed Miliband had lied? No: he had not. Should they say, instead, that he had been deceptive or misleading? Again, no: it was reasonable to say that living standards had indeed been disappointing under the coalition government.

Nevertheless, there was a lot going on in the British economy that the figure omitted — much of it rather more flattering to the government. Full Fact, an independent fact-checking organisation, carefully worked through the paper trail and linked to all the relevant claims. But it was powerless to produce a fair and representative snapshot of the British labour market that had as much power as Ed Miliband’s seven-word sound bite. No such snapshot exists. Truth is usually a lot more complicated than statistical bullshit.

On July 16 2015, the UK health secretary Jeremy Hunt declared: “Around 6,000 people lose their lives every year because we do not have a proper seven-day service in hospitals. You are 15 per cent more likely to die if you are admitted on a Sunday compared to being admitted on a Wednesday.”

This was a statistic with a purpose. Hunt wanted to change doctors’ contracts with the aim of getting more weekend work out of them, and bluntly declared that the doctors’ union, the British Medical Association, was out of touch and that he would not let it block his plans: “I can give them 6,000 reasons why.”

Despite bitter opposition and strike action from doctors, Hunt’s policy remained firm over the following months. Yet the numbers he cited to support it did not. In parliament in October, Hunt was sticking to the 15 per cent figure, but the 6,000 deaths had almost doubled: “According to an independent study conducted by the BMJ, there are 11,000 excess deaths because we do not staff our hospitals properly at weekends.”

Arithmetically, this was puzzling: how could the elevated risk of death stay the same but the number of deaths double? To add to the suspicions about Hunt’s mathematics, the editor-in-chief of the British Medical Journal, Fiona Godlee, promptly responded that the health secretary had publicly misrepresented the BMJ research.

Undaunted, the health secretary bounced back in January with the same policy and some fresh facts: “At the moment we have an NHS where if you have a stroke at the weekends, you’re 20 per cent more likely to die. That can’t be acceptable.”

All this is finely wrought bullshit — a series of ever-shifting claims that can be easily repeated but are difficult to unpick. As Hunt jumped from one form of words to another, he skipped lightly ahead of fact-checkers as they tried to pin him down. Full Fact concluded that Hunt’s statement about 11,000 excess deaths had been untrue, and asked him to correct the parliamentary record. His office responded with a spectacular piece of bullshit, saying (I paraphrase) that whether or not the claim about 11,000 excess deaths was true, similar claims could be made that were.

So, is it true? Do 6,000 people — or 11,000 — die needlessly in NHS hospitals because of poor weekend care? Nobody knows for sure; Jeremy Hunt certainly does not. It’s not enough to show that people admitted to hospital at the weekend are at an increased risk of dying there. We need to understand why — a question that is essential for good policy but inconvenient for politicians.

One possible explanation for the elevated death rate for weekend admissions is that the NHS provides patchy care and people die as a result. That is the interpretation presented as bald fact by Jeremy Hunt. But a more straightforward explanation is that people are only admitted to hospital at the weekend if they are seriously ill. Less urgent cases wait until weekdays. If weekend patients are sicker, it is hardly a surprise that they are more likely to die. Allowing non-urgent cases into NHS hospitals at weekends wouldn’t save any lives, but it would certainly make the statistics look more flattering. Of course, epidemiologists try to correct for the fact that weekend patients tend to be more seriously ill, but few experts have any confidence that they have succeeded.

A more subtle explanation is that shortfalls in the palliative care system may create the illusion that hospitals are dangerous. Sometimes a patient is certain to die, but the question is where — in a hospital or a palliative hospice? If hospice care is patchy at weekends then a patient may instead be admitted to hospital and die there. That would certainly reflect poor weekend care. It would also add to the tally of excess weekend hospital deaths, because during the week that patient would have been admitted to, and died in, a palliative hospice. But it is not true that the death was avoidable.

Does it seem like we’re getting stuck in the details? Well, yes, perhaps we are. But improving NHS care requires an interest in the details. If there is a problem in palliative care hospices, it will not be fixed by improving staffing in hospitals.

“Even if you accept that there’s a difference in death rates,” says John Appleby, the chief economist of the King’s Fund health think-tank, “nobody is able to say why it is. Is it lack of diagnostic services? Lack of consultants? We’re jumping too quickly from a statistic to a solution.”

When one claim is discredited, Jeremy Hunt’s office simply asserts that another one can be found to take its place

This matters — the NHS has a limited budget. There are many things we might want to spend money on, which is why we have the National Institute for Health and Care Excellence (Nice) to weigh up the likely benefits of new treatments and decide which offer the best value for money.

Would Jeremy Hunt’s push towards a seven-day NHS pass the Nice cost-benefit threshold? Probably not. Our best guess comes from a 2015 study by health economists Rachel Meacock, Tim Doran and Matt Sutton, which estimates that the NHS has many cheaper ways to save lives. A more comprehensive assessment might reach a different conclusion but we don’t have one because the Department for Health, oddly, hasn’t carried out a formal health impact assessment of the policy it is trying to implement.

This is a depressing situation. The government has devoted considerable effort to producing a killer number: Jeremy Hunt’s “6,000 reasons” why he won’t let the British Medical Association stand in his way. It continues to produce statistical claims that spring up like hydra heads: when one claim is discredited, Hunt’s office simply asserts that another one can be found to take its place. Yet the government doesn’t seem to have bothered to gather the statistics that would actually answer the question of how the NHS could work better.

This is the real tragedy. It’s not that politicians spin things their way — of course they do. That is politics. It’s that politicians have grown so used to misusing numbers as weapons that they have forgotten that used properly, they are tools.

You complain that your report would be dry. The dryer the better. Statistics should be the dryest of all reading,” wrote the great medical statistician William Farr in a letter in 1861. Farr sounds like a caricature of a statistician, and his prescription — convey the greatest possible volume of information with the smallest possible amount of editorial colour — seems absurdly ill-suited to the modern world.

But there is a middle ground between the statistical bullshitter, who pays no attention to the truth, and William Farr, for whom the truth must be presented without adornment. That middle ground is embodied by the recipient of William Farr’s letter advising dryness. She was the first woman to be elected to the Royal Statistical Society: Florence Nightingale.

Nightingale is the most celebrated nurse in British history, famous for her lamplit patrols of the Barrack Hospital in Scutari, now a district of Istanbul. The hospital was a death trap, with thousands of soldiers from the Crimean front succumbing to typhus, cholera and dysentery as they tried to recover from their wounds in cramped conditions next to the sewers. Nightingale, who did her best, initially believed that the death toll was due to lack of food and supplies. Then, in the spring of 1855, a sanitary commission sent from London cleaned up the hospital, whitewashing the walls, carting away filth and dead animals and flushing out the sewers. The death rate fell sharply.

Nightingale returned to Britain and reviewed the statistics, concluding that she had paid too little attention to sanitation and that most military and medical professions were making the same mistake, leading to hundreds of thousands of deaths. She began to campaign for better public health measures, tighter laws on hygiene in rented properties, and improvements to sanitation in barracks and hospitals across the country. In doing so, a mere nurse had to convince the country’s medical and military establishments, led by England’s chief medical officer, John Simon, that they had been doing things wrong all their lives.

A key weapon in this lopsided battle was statistical evidence. But Nightingale disagreed with Farr on how that evidence should be presented. “The dryer the better” would not serve her purposes. Instead, in 1857, she crafted what has become known as the Rose Diagram, a beautiful array of coloured wedges showing the deaths from infectious diseases before and after the sanitary improvements at Scutari.

When challenged by Bill O’Reilly on Fox News, Trump replied, ‘Hey Bill, Bill, am I gonna check every statistic?’

The Rose Diagram isn’t a dry presentation of statistical truth. It tells a story. Its structure divides the death toll into two periods — before the sanitary improvements, and after. In doing so, it highlights a sharp break that is less than clear in the raw data. And the Rose Diagram also gently obscures other possible interpretations of the numbers — that, for example, the death toll dropped not because of improved hygiene but because winter was over. The Rose Diagram is a marketing pitch for an idea. The idea was true and vital, and Nightingale’s campaign was successful. One of her biographers, Hugh Small, argues that the Rose Diagram ushered in health improvements that raised life expectancy in the UK by 20 years and saved millions of lives.

What makes Nightingale’s story so striking is that she was able to see that statistics could be tools and weapons at the same time. She educated herself using the data, before giving it the makeover it required to convince others. Though the Rose Diagram is a long way from “the dryest of all reading”, it is also a long way from bullshit. Florence Nightingale realised that the truth about public health was so vital that it could not simply be recited in a monotone. It needed to sing.

The idea that a graph could change the world seems hard to imagine today. Cynicism has set in about statistics. Many journalists draw no distinction between a systematic review of peer-reviewed evidence and a survey whipped up in an afternoon to sell biscuits or package holidays: it’s all described as “new research”. Politicians treat statistics not as the foundation of their argument but as decoration — “spray-on evidence” is the phrase used by jaded civil servants. But a freshly painted policy without foundations will not last long before the cracks show through.

“Politicians need to remember: there is a real world and you want to try to change it,” says Will Moy, the director of Full Fact. “At some stage you need to engage with the real world — and that is where the statistics come in handy.”

That should be no problem, because it has never been easier to gather and analyse informative statistics. Nightingale and Farr could not have imagined the data that modern medical researchers have at their fingertips. The gold standard of statistical evidence is the randomised controlled trial, because using a randomly chosen control group protects against biased or optimistic interpretations of the evidence. Hundreds of thousands of such trials have been published, most of them within the past 25 years. In non-medical areas such as education, development aid and prison reform, randomised trials are rapidly catching on: thousands have been conducted. The British government, too, has been supporting policy trials — for example, the Education Endowment Foundation, set up with £125m of government funds just five years ago, has already backed more than 100 evaluations of educational approaches in English schools. It favours randomised trials wherever possible.

The frustrating thing is that politicians seem quite happy to ignore evidence — even when they have helped to support the researchers who produced it. For example, when the chancellor George Osborne announced in his budget last month that all English schools were to become academies, making them independent of the local government, he did so on the basis of faith alone. The Sutton Trust, an educational charity which funds numerous research projects, warned that on the question of whether academies had fulfilled their original mission of improving failing schools in poorer areas, “our evidence suggests a mixed picture”. Researchers at the LSE’s Centre for Economic Performance had a blunter description of Osborne’s new policy: “a non-evidence based shot in the dark”.

This should be no surprise. Politicians typically use statistics like a stage magician uses smoke and mirrors. Over time, they can come to view numbers with contempt. Voters and journalists will do likewise. No wonder the Guardian gave up on the idea that political arguments might be settled by anything so mundane as evidence. The spin-doctors have poisoned the statistical well.

But despite all this despair, the facts still matter. There isn’t a policy question in the world that can be settled by statistics alone but, in almost every case, understanding the statistical background is a tremendous help. Hetan Shah, the executive director of the Royal Statistical Society, has lost count of the number of times someone has teased him with the old saying about “lies, damned lies and statistics”. He points out that while it’s easy to lie with statistics, it’s even easier to lie without them.

Perhaps the lies aren’t the real enemy here. Lies can be refuted; liars can be exposed. But bullshit? Bullshit is a stickier problem. Bullshit corrodes the very idea that the truth is out there, waiting to be discovered by a careful mind. It undermines the notion that the truth matters. As Harry Frankfurt himself wrote, the bullshitter “does not reject the authority of the truth, as the liar does, and oppose himself to it. He pays no attention to it at all. By virtue of this, bullshit is a greater enemy of the truth than lies are.”


Written for and first published in the FT Magazine

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20th of April, 2016HighlightsOther WritingComments off

Three pieces of Brexit Bullshit

A referendum on UK membership of the European Union is scheduled for June 23: dodgy statistics ahoy.

“Ten Commandments — 179 words. Gettysburg address — 286 words. US Declaration of Independence — 1,300 words. EU regulations on the sale of cabbage — 26,911 words”

Variants of this claim have been circulating online and in print. It turns out that the “cabbage memo” is a longstanding urban myth that can be traced back to the US during the second world war. Variants have been used to berate bureaucrats on both sides of the Atlantic ever since.

Part of the bullshit here is that nobody ever stops to ask how many words might be appropriate for rules on fresh produce. Red Tractor Assurance, the British farm and food standards scheme, publishes 56 different protocols on fresh produce alone. The cabbage protocol is 28 pages long; there is a separate 28-page protocol on pak choi and choi sum. None of this has anything to do with the EU.

Three million jobs depend on the EU

This claim is popular among “Remain” advocates — most famously the former deputy prime minister Nick Clegg. What makes this claim bullshit is that it could easily be true, or utterly false, and it all hangs on the definition of “depend”.

The claim is that “up to 3.2 million jobs” were directly linked to exports of goods and services to other EU countries. That number passes a quick reality check: it’s about 10 per cent of UK jobs, and UK exports to the EU are about 10 per cent of the UK economy.

But even if “up to” 3.2 million jobs depend on trade with the EU, that does not mean they depend on membership of the EU. Nobody proposes — or expects — that trade with the EU will just stop. Three million jobs might well be destroyed if continental Europe was to sink beneath the waves like Atlantis, but that is not what the referendum is about.

EU membership costs £55m a day

This one is from Ukip leader Nigel Farage, who says membership amounts to more than £20bn a year. In fact, the UK paid £14.3bn to the EU in 2014 and got £6bn back. The net membership fee, then, was £8.3bn, less than half Farage’s number.

But even the correct number is little use without context. It is, for example, just over 1 per cent of UK public spending. Not nothing, but not everything either. And non-member states such as Norway and Switzerland pay large sums to the EU to retain access to the single market, so Brexit would not make this bill disappear.

The membership fee is small relative to the plausible costs and benefits of EU membership, positive or negative. If EU membership is good for Britain then £8.3bn is cheap. And if the EU is holding Britain back, then a few billion on membership is the least of our worries.


Written as a sidebar for “How Politicians Poisoned Statistics“, and first published in the FT Magazine.

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