Tim Harford The Undercover Economist

Undercover EconomistUndercover Economist

My weekly column in the Financial Times on Saturdays, explaining the economic ideas around us every day. This column was inspired by my book and began in 2005.

Undercover Economist

Superstar companies lose their lustre

Everyone seems to agree that we live in a “superstar economy” — one in which the leading companies (the likes of Apple, Amazon and Alphabet) are making dramatic strides both in productivity and in profitability. In response to this conventional wisdom the only question is: how should regulators respond?

There is a case that we should let the superstars keep doing what they do. Enjoy the fruits of their creativity. The smartphone! Internet search that works! Next-day delivery of next week’s landfill! For now, the superstars are innovative and efficient. In due course new monopolists will arise, motivated to seize the crown.

This case is not absurd, although my own instincts point the other way. Profit-minded monopolists can be innovative, but all too often they fail to adapt. More often markets deliver because they support a messy, pluralistic process of trial and error.

If that view is right, government intervention may be necessary to prevent the monopolies of today choking off the innovations of the future. One possibility is to break up the largest companies. Regulators might rule that a company cannot simultaneously own a platform while also offering products on it. This is the position taken in an artful argument published last week by US presidential hopeful Elizabeth Warren.

An alternative approach, set out this week by the UK’s Digital Competition Expert Panel, is to regulate the behaviour of the big digital groups rather than to change their structure. Regulators could insist on standards for data sharing and interconnection with established players, allowing smaller competitors to grow on or around the existing platforms.

I’m sympathetic to both approaches, although worry that it is easy for regulators to do more harm than good. Ms Warren’s rousing call to arms might easily lead to fruitless antitrust trench warfare. But set aside for a moment the argument about how to respond to the superstars and ask a different question. What if they are actually economic white dwarfs, dense but dim and fading?

That seems a strange conjecture. It appears to contradict the problem of laggard companies: research from the OECD suggests that many companies are far less productive than those at the frontier of their industrial sector, and that this gap is getting bigger. This suggests that superstars are blazing hot and the problem lies with the rest of the economy.

But much depends on what we mean by superstar companies. The OECD research focuses on highly productive concerns. Many of them are surprisingly small, with revenues in the tens of millions rather than the tens of billions.

Another alternative is to look at the most valuable 20 companies in the US economy, and the most valuable four in each of 60 or so different sectors, whether or not they are digital and whether or not they are highly productive. This is the approach taken by Germán Gutiérrez and Thomas Philippon, economists at New York University, in a new working paper. This perspective casts doubt on the very idea that the superstars of the US economy are particularly large or productive by postwar standards.

Compared with their forebears, these companies are unremarkable in both their sales and their employee numbers. They have a larger economic footprint than the leaders of 20 years ago, but smaller than 40 years ago. Alphabet, Amazon and Apple are impressive companies, but so were Intel, Microsoft and Walmart 20 years ago, or General Electric, General Motors and IBM before them.

Any large group contributes to the productivity of the economy as a whole in two ways: directly, by producing output, and indirectly, by drawing in resources from less productive players. Messrs Gutiérrez and Philippon reckon that the direct contribution of the leading companies is smaller than it used to be, while the indirect contribution has not increased by enough to compensate. Overall, they calculate that the contribution of the star companies to labour productivity growth in the US has fallen by 40 per cent since the year 2000.

This is a surprising result. Part of the explanation may lie in measurement: it is never easy to measure productivity, particularly in services and even more so in services provided free of charge in exchange for data and attention. But the “fading star” result may well be real, and surprising only because we often let the big digital groups stand as symbols of scale and market power. There is more to the US economy than Silicon Valley.

The US economy does seem to have a monopoly problem: concentration is increasing in most industries. Companies make money less by becoming more efficient, and more by exploiting their pricing power.

There is plenty for US antitrust authorities to get their teeth into. Perhaps they should take a look at Europe, where competition policy is more robust and politically independent, while EU markets have become more competitive and less profitable than those in the US. That may not be a coincidence.

Written for and first published in the Financial Times on 15 March 2019.

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Black holes in data affect health and wealth

Nearly seven decades ago, the noted psychologist Solomon Asch gave a simple task to 123 experimental subjects: to pick which one of three quite different lines was the same length as a “reference” line. Asch had a trick up his sleeve: he surrounded each subject with stooges who would unanimously pick the wrong line. Confused, the experimental subjects were often — not always — swayed by the error of those around them.

I’ve written before about these experiments, but there’s something I neglected to mention: not a single one of the stooges nor the experimental subjects was female.

If Asch had conducted all-women experiments, he would have discovered that women tend to conform to the group more often than men. Perhaps this omission doesn’t matter. Retellings of the Asch experiment have tended to exaggerate the conformity that was demonstrated, while glossing over the fact that it was an all-male study. The two biases may cancel each other out.

Still, it is a lesson in how easy it is to ignore important data — or to assume that they are comprehensive, when in fact they omit half the planet.

Invisible Women (US) (UK) a new book by Caroline Criado Perez, explores countless cases in which everything from the height of the top shelf to the functionality of an iPhone is predicated on the assumption that the user will be male. (Apple once released a “comprehensive” health app that could track your selenium intake but not menstruation.)

Much of this imbalance has nothing to do with data, but some of it does — and this “gender data gap” is particularly important because good statistics are one of the only windows we have into the lives of an entire population, rather than just a handful of friends.

Consider the UN Sustainable Development Goals, admirable targets to improve the lives of 7.5bn people. Yet as development economists Mayra Buvinic and Ruth Levine have pointed out, while one of these goals is gender equality, we lack much of the data on whether that goal is being achieved.

Some missing numbers — for instance, on sex-trafficking — are obvious. Others are more subtle, such as the ubiquitous choice to measure the income not of individuals but of households. Does that household income come from a man, a woman or both?

We shouldn’t assume that the balance between “wallet and purse” is irrelevant. Economist Shelly Lundberg and colleagues studied what happened when in 1977, child benefit in the UK was switched from being a tax credit (usually to the father) to a cash payment to the mother. That measurably increased spending on women’s and children’s clothes relative to men’s. The UK’s new universal credit is payable to a single “head of household”; that curious decision may well favour men. Given the data we have, it will be hard to tell.

The story is often told of the accidental discovery of sildenafil (Viagra). Intended as a treatment for angina, the clinical trial revealed a side-effect: magnificent erections. Had the original trial included women, we might have fortuitously discovered a treatment for severe period pain. As it was, men got their miracle drug but women are still waiting. We can’t confidently prescribe sildenafil as a safe and effective treatment for period pain because, as Ms Criado Perez reports, only a small and suggestive trial has yet been funded.

There are many data gaps out there — statistician David Hand calls them “dark data”. There are the unpublished studies that produce less interesting or lucrative results than published ones. There are the voters who are coy about confessing their voting intentions to pollsters — the “shy Tory” effect. There are the psychology experiments that study only “WEIRD” subjects — Western, Educated and from Industrialised Rich Democracies.

We have plenty of statistics on shares and currencies, but not much on debt and derivatives. What Gillian Tett called the submerged iceberg of financial markets got less attention, until it turned out to have holed the entire financial system below the waterline in 2007.

There is no simple way to shine a light on all this dark data. There is a reason why it is easier to collect statistics in a rich country than a poor one, and why fluent speakers of English are more likely to fill in the UK census form. Collecting data on who bears the burden of “life admin” is harder than collecting that on primary paid occupations. But what we count and what we fail to count is often the result of an unexamined choice. We can make better choices, both by involving ordinary citizens in survey design, and by trying to get more women and minority groups into economics and statistics.

It is hardly encouraging that the Office for National Statistics has just been found wanting in a sexual discrimination case, but there is hope. The president of the Royal Statistical Society, the managing director of the International Monetary Fund and both heads of the UK Government Economic Service are all women. Still, plugging data gaps takes time, and considerably more thought than I once gave to Solomon Asch’s curious experimental line-up.

 

Written for and first published in the Financial Times on 8 March 2019.

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Why happiness is easy to venerate, hard to generate

In 1972, the teenage king of Bhutan, Jigme Singye Wangchuck, declared that “gross national happiness is more important than gross domestic product”. The sound bite has been echoed approvingly down the years, although the king may just have been making excuses. Bhutanese GDP per person was then the grinding poverty of about a dollar a day. If I were king of such a country, I’d be tempted to change the subject, too.

Clearly he had a point. Most of us would rather be poor and happy than rich and depressed. If so, gross national happiness seems a fine goal. But it is one thing for a monarch to announce that happiness is important. It’s quite another to make people happy. Shangri-La does not move from fiction to reality just because we desire it.

Bhutan has not always lived up to its own hype. Same-sex intercourse is illegal, which suggests a country with a less-than-expansive view of whose happiness matters. Three decades ago, around 100,000 of the Nepali-speaking Lhotshampa minority fled Bhutan to escape military persecution during a campaign of ethnic cleansing on a colossal scale. One-sixth of the entire population of Bhutan ended up in refugee camps in Nepal.

Even setting aside this enormity, it’s hard to see that Bhutan paid much more than lip service to gross national happiness. They hosted conferences, but according to a recent IMF working paper, nobody in the government collected systematic indicators on happiness until 2005. The World Happiness Report ranks Bhutan at 97th out of 156 countries, down from 84th a few years ago. Happiness is easy to venerate, but hard to generate.

Perhaps I am doing a disservice to a small kingdom wedged between sparring regional powers. It can’t be easy. And Bhutan has a lesson to teach us all — maybe we should think a little less about over-arching goals and a little more about specifics.

Consider some of the issues that are notoriously bypassed by GDP, the most common measure of economic activity: digital services are hard to value, while by design GDP omits any consideration of inequality or environmental damage. Unpaid work — of which men do a great deal, and women a great deal more — is also left out.

But if our aim is (for example) to reduce carbon emissions, we don’t achieve it by moaning about GDP. We achieve it with specific policies such as carbon taxes and investments in public transport and a renewable-friendly electric grid. Neither gender equality nor respect for unpaid work would be automatically improved by any change in the way national income accounts are computed.

And when Bobby Kennedy movingly commented that GDP “does not include the beauty of our poetry or the strength of our marriages”, he was quite right. Nevertheless I fail to see how any solution follows, whether for connubial harmony or American verse.

The specifics matter when it comes to happiness, too. Broad research into the causes of national happiness has tended to produce banal conclusions: we tend to compare ourselves to others, unemployment makes us miserable, and we hate being ill. There is nothing here to suggest that we need to overhaul commonplace policies such as redistributive taxation, the avoidance of recessions, and support for public health.

Just as with GDP itself, it is only when we move to the specifics that gross national happiness becomes useful. Richard Layard, one of the leading happiness researchers, argues that mental illness is a leading cause of misery, and that it can be treated very cost-effectively. That seems useful enough to me.

What is not useful is the sense that measuring GDP is the problem, and measuring gross national happiness is the solution. Few societies have ever really focused on either. We should all be happy about that.

 

Written for and first published in the Financial Times on 1 March 2018.

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Just because you’re paranoid, doesn’t mean the algorithms aren’t out to get you

If you do not like the price you’re being offered when you shop, do not take it personally: many of the prices we see online are being set by algorithms that respond to demand and may also try to guess your personal willingness to pay. What’s next? A logical next step is that computers will start conspiring against us. That may sound paranoid, but a new study by four economists at the University of Bologna shows how this can happen.

The researchers allowed two simple artificial intelligence algorithms to compete against each other in a setting where they simultaneously set prices and reaped profits accordingly. The algorithms taught themselves to collude, raising prices from the cut-throat competitive level towards what a monopolist would choose. Price cuts were met with price wars, after which collusion would return. Just because you’re paranoid, it doesn’t mean the computers are not out to get you.

This is not a surprising result for anyone who — like me — squandered their youth studying the theory of industrial competition. Robert Axelrod’s book The Evolution of Cooperation (US) (UK) published in 1984, described a tournament in which computers played a “prisoner’s dilemma”, a scenario analogous to two competing sellers. The best approaches used the threat of punishment to sustain co-operation. They were also simple: not something that a machine-learning system would struggle to discover.

An obvious question is, who — if anyone — should be prosecuted for price fixing when the bots work out how to do it without being told to do so, and without communicating with each other? In the US, where the Federal Trade Commission has been pondering the prospect, the answer seems to be no one, because only explicit collusive agreements are illegal. The bots would only be abetting a crime if they started scheming together. Tacit collusion, apparently, would be fine.

This is a reminder that algorithms can misbehave in all kinds of intriguing ways. None of us can quite shake the image of a Skynet scenario, in which an AI triggers a nuclear war and then uses Arnold Schwarzenegger as the model for a time-travelling robot assassin on a mission to suppress human resistance. At least that strategy is refreshingly direct. The true scope of algorithmic mischief is much subtler and much wider.

We are rightly concerned about algorithms that practice racial or sexual discrimination, by accident or design. I am struck by how quickly tales of racist algorithms have gone from novelty to cliché. The stories may fade but the issue is not going away.

Algorithms that simply magnify human errors now appear almost quaint. In 2012, the Financial Times had a headline, “Knight Capital glitch loss hits $461m”; those were innocent times.

Then there were those T-shirts selling on Amazon a few years ago, offering offensive slogans such as “Keep Calm and Hit Her”, and bizarre ones such as “Keep Calm and Skim Me”. Hundreds of thousands of slogans were assembled by an algorithm and, if any appealed, the vendor would print them on demand. “We didn’t do it, it was the algorithm,” was a weak defence in 2013, but at least it was novel. That is no longer true.

We are also realising that the algorithms can amplify other human weaknesses — witness recommendation engines on YouTube and Facebook that seem to amplify disinformation or lead people down the dark tunnels of conspiracy thinking or self-harm.

By no means are all malevolent programs an accident; some are designed with mischief in mind. Bots can be used to generate or spread misinformation. Jamie Bartlett, author of The Dark Net (US) (UK), warns of a future of ultra-personalised propaganda. It is one thing when your internet-enabled fridge knows you’re hungry and orders yoghurt. It’s another when the fridge starts playing you hard-right adverts because they work best when you’re grumpy and low on blood sugar. And unless we radically improve both our electoral laws and our digital systems nobody need ever know that a particular message was whispered in your ear as you searched for cookies.

Obviously, both the law and regulators must be nimble. But ponder, too, the challenges for corporate public relations and social responsibility departments. The latter is about being a good corporate citizen; PR is about seeming to be so. But who takes corporate responsibility for a harmful or tasteless decision made by an algorithm?

It is not an entirely new problem. Before there was tacit collusion between algorithms, there was tacit collusion between sales directors. Before companies blamed rogue algorithms for embarrassing episodes, they could blame rogue employees, or their suppliers. Can we really blame the bank whose cleaning subcontractor underpays the cleaning staff? Or the sportswear brand opposed to sweatshop conditions, whose suppliers quietly hire children and pay them pennies?

The natural answer is: we can and we do, but subcontracting is a source of both deniability and complexity. Subcontracting to algorithms complicates matters, too. But we are going to have to figure it out.

 

Written for and first published in the Financial Times on 22 February 2019.

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Is life a bet, or an experiment?

Since the official Brexit policy of the UK government now seems to be “accidents happen”, it is a nourishing age for those who dine on uncertainty. Most of us, however, started feeling queasy long ago. If you’re looking for guidance as to how to digest the unpredictabilities of life, why not turn to a poker player for advice?

Annie Duke, author of Thinking In Bets (UK) (US), says that “wrapping our arms around uncertainty and giving it a big hug will help us become better decision makers”. Fair enough. If we’re going to have to eat our uncertainty broccoli anyway, it may be best to swallow it down before it goes cold and limp.

As the winner of several million dollars as a professional poker player, Ms Duke has some credibility on this point. But is she right?

One quibble is that games have a more tightly-defined spectrum of uncertainty than reality does. Edward Thorp, a mathematician who achieved considerable success at blackjack and as a hedge fund manager, found (UK) (US) that the true risks he faced in the casinos were not an unlucky turn of the card, but crooked dealers and poisoned coffee. Not for nothing does Nassim Taleb, author of The Black Swan (UK) (US) warn of the “ludic fallacy” — treating the unknown risks of life as though they were the known risks of a game of chance.

Still, poker is a more instructive game than many others. John von Neumann, the brilliant mathematician who laid down the foundations of game theory in the late 1920s, was a poker player. Poker was no mere computational problem like chess, he said: “Real life consists of bluffing, of little tactics of deception, of asking yourself what is the other man going to think I mean to do, and that is what games are about in my theory.”

So what does poker teach us about “wrapping our arms around uncertainty”? Ms Duke offers us several lessons.

One is that, since luck matters as well as skill, bad decisions can have good outcomes, and vice versa. If you drive drunk, you will probably get home without killing anyone, but that would not make drunk driving a good decision.

A second lesson is that we should always be willing to ask ourselves, “do I want to bet on that?” — it’s easy to be overconfident if there are no obvious consequences for being wrong. A bet forces us to think about the odds and the possibility that someone else may know better.

So there is much to be said for thinking in bets. Yet we should not overlook an alternative approach to uncertainty: thinking in experiments. An experimental thinker views the uncertainties of the world as something to be resolved through tentative trial and error. Try something modest or reversible; an experiment doesn’t need to be a double-blind, randomised controlled trial to yield useful information. If it works, do more of it.

Some decisions are by their nature irreversible. Each big poker hand is a one-shot proposition that does not offer much scope for experiment. The same could be said of some investments: if you think shares in Tesla are cheap, there is little to be said for buying just one share and watching it to find out what happens to its price. An experiment cannot help; instead you must figure out whether the odds are in your favour, and take the plunge.

But other decisions are more experimental. In these cases, the choices can be made in stages, with each step designed to reveal some information. From Marvel’s decision to publish the Spider-Man comics (that went well) to Google’s launch of the G+ social network (that didn’t) a company can see what works and then either redouble its efforts or abandon the project. For an individual, anything from a new hobby to a new career can be treated as an experiment.

Many of the decisions we make are reversible. Only our stubbornness makes them permanent. Thinking in bets forces a commitment, which is sometimes helpful but sometimes not. Thinking in experiments allows us to learn.

I thought of all this when reading of poker professional John Hennigan’s $30,000 bet that he could move to Des Moines, Iowa, and live there, just for a few weeks. As Ms Duke tells the story, Mr Hennigan was bored out of his mind within days, and paid $15,000 to buy himself out of the bet and move back to Las Vegas. That’s thinking in bets at its worst: an idle thought (“should I move to Des Moines?”) became a high-stakes zero-sum game. Fun — if you like that kind of thing.

A few years ago, my family were agonising over a similar question (“Should we move to Oxford?”). We vacillated and made lists of pros and cons. What resolved the uncertainty was realising the decision could be an experiment: rather than selling up, we could rent a place in the city for a year to see how things worked out. Running this experiment created some additional costs, but we have never regretted doing it.

Thinking in bets is a rigorous and admirable habit, but not everything has to be a high-stakes poker hand. If you can make it work, thinking in experiments is less painful.

 

 

Written for and first published in the Financial Times on 15 Feb 2019.

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Why inflation is good for us

Not long ago, I wrote a column in defence of central banks. Some readers were quick to disagree. Central banks had failed to maintain “the real value of our fiat currencies”, wrote one, urging me to ponder how inflation had eroded the true value of savings over the decades.

A glance at Venezuela, where inflation over the past year has been more than 100,000 per cent and the economy is breaking down, reminds us that this is no idle complaint. Central banks must keep inflation under control. But what does “under control” mean? How much inflation is too much? And — a question only an economist could ask — how much inflation is too little? It goes without saying that hyperinflation is an economic catastrophe, so the first thing to check is whether hyperinflation is likely in an advanced economy — or indeed a competently governed country of any sort. It is not.

In 2012, economists Steve Hanke and Nicholas Krus assembled a list of every confirmed episode of hyperinflation in history. There weren’t very many: 56 in total, mostly in the 20th century, to which we might add recent outbreaks in Zimbabwe, Iran, Venezuela and perhaps North Korea. France suffered a bout in the 1790s, but most instances of hyperinflation occurred either in central European states after the first world war (including the infamous crisis in Weimar Germany), or during or immediately after the second world war (including Hungary, history’s worst example of hyperinflation), or in the eastern bloc as the Soviet Union disintegrated.

Hyperinflation does not strike at random, and it does not happen because central banks briefly slumber. It must be manufactured by the relentless printing of money, generally as the last resort in the face of political dysfunction alongside a severe fiscal crisis. Perhaps it is rash to say so, but I think we can set aside fears of hyperinflation in an advanced economy today. If it ever does happen, it will be only one element in a far more comprehensive economic disaster.

But the readers who emailed to complain about inflation did not express concerns about hyperinflation. They are worried about low-level ambient inflation, the kind that central banks not only tolerate, but actively seek. Central banks do not try to maintain “the real value of our fiat currencies”, but to erode them, typically by 2 per cent a year. They have often been explicitly instructed to do so by elected politicians.

Are those instructions wise? Even 2 per cent inflation will halve the value of money in 36 years. That seems bad, but let’s try to pin down why it might matter. It may help to remember what it is that we expect any good currency to do.

First, we want it to serve as a medium of exchange, allowing me to pick up a loaf of bread without having to persuade the baker to swap it for a copy of The Undercover Economist Strikes Back. Inflation at 2 per cent a year — or 5, or even 20 — does not prevent money serving as a medium of exchange.

The second, and perhaps most fundamental, role of money is as a stable unit of account. It helps us understand the economic forces around us, whether a particular product is expensive or cheap, without resorting to a calculator. Hyperinflation destroys that. “Are we ruined or in clover?” asks a character in an Erich Maria Remarque novel set in the Weimar hyperinflation. No one knows. But moderate inflation will not boggle minds on a trip to the shop.

Inflation does more obvious damage to money’s third role, which is as a store of value. If you stick your currency under the mattress then inflation will hurt you. It will also hurt if you have a non-indexed pension, or cannot find a high-interest savings account. Normally, however, a well-functioning financial sector offers returns to compensate for inflation.

That has not been the case since the 2008 financial crisis, of course. But the pain savers are feeling is not because central banks have carelessly let inflation take off. It is the result of a deliberate policy of low interest rates to stimulate spending and investment. Perhaps this policy is a mistake, perhaps not. But it would be wrong to view it as a dereliction of duty.

Another source of pain is governments’ fondness for using inflation as a way to grab a bit more revenue, by taxing nominal interest payments. It’s an insult for savers, but let’s be realistic: the tax would be levied somehow anyway. Inflation is sometimes the taxman’s chisel, but he has other tools.

It is surprisingly difficult to find any serious costs to low levels of inflation. In contrast, the benefits are easily stated: more room to stimulate the economy in a recession, and more room for real wages to adjust if they must. Olivier Blanchard, during his time as chief economist of the IMF, even floated the idea that the inflation target should be 4 per cent, not 2 per cent. He is by no means alone in that opinion. Like a low dose of aspirin, a low dose of inflation is unlikely to do much harm — and it can prevent an economic heart attack.

 

Written for and first published in the Financial Times on 8 Feb 2019.

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How high should the top rate of tax be, and who should pay it?

What should the top rate of income tax be? Should it be 70 per cent, as has been informally suggested by the young star of the US Democratic party, Congresswoman Alexandria Ocasio-Cortez? That instinctively feels too high to me. But, as an economist with sporadic hopes of making logical arguments based on evidence, I admit that “instinctively feels too high” is a weak response.

What about 50 per cent, the official policy of the UK’s opposition Labour party at the last general election? Or zero, the optimal top rate that emerged from a thought experiment posed by the late James Mirrlees, a Nobel laureate in economics?

An alternative is to tax wealth instead of income, as US Democratic Senator Elizabeth Warren has proposed. But — at least in principle — there is not much difference between a small annual tax on total wealth and a large tax on the nominal return generated by that wealth.

To make the case for a top rate of tax above 70 per cent, it helps to believe four things.

The first is that taxable income itself won’t evaporate in the face of a high rate, as it did in the UK when the top tax rate was briefly raised from 40 to 50 per cent in 2010, then cut to 45 per cent. Most high earners found it easy to realise income early, or late, and avoid the 50 per cent rate. A permanent increase is harder to avoid; so is an increase that is enforced with determined (or draconian) measures; as is an increase levied by a large economy with global legislative reach such as the US. In smaller economies such as the UK’s, the very rich are more likely to take themselves elsewhere for any given tax rate.

One academic paper produced by Emmanuel Saez (a star in the study of inequality) and Peter Diamond (a Nobel laureate and colleague of Mirrlees) estimated that the combined rate of tax on the income of high earners could be 73 per cent in the US without proving counter-productive. Another paper, published in the same journal, by Gregory Mankiw and co-authors, put the optimal top rate at just under 50 per cent instead. The difference lies in the assumptions.

The second thing one needs to believe is that the rich will barely miss any extra income if tax rates rise. The truth of this is unknowable, although another famous study from yet more Nobel laureates, Daniel Kahneman and Angus Deaton, suggests that money will not improve your everyday mood and wellbeing after an income of $75,000 a year or so. To reach their conclusions about the 73 per cent rate, Professors Diamond and Saez assume that a dollar is 25 times more valuable to a person on about $50,000 a year than to a person on $500,000. That is not an insane assumption, but it’s an assumption nonetheless.

If you accept these first two beliefs, the economic case for a high top rate of tax follows. A high rate maximises revenue if the tax base doesn’t shrink too much, and revenue maximisation is a reasonable goal if it’s true that the rich would barely notice the lost income.

But this argument ranges far beyond economics. If you like high tax rates, the third thing it helps to believe is that inequality is intrinsically corrosive. Perhaps it undermines democracy. Perhaps it causes stress, envy or resentment. The empirical evidence is not much help here; it is sketchy and often seems tendentious. Causal channels are unclear: does inequality lead to a hollowed-out state? Or does a hollowed-out state enable inequality? Perhaps a thought-experiment is more helpful here: how would you feel about a policy that simply confiscated resources from the super-rich and destroyed them? Would such a policy be a criminal waste and a grotesque infringement of liberty, or a helpful rebalancing of the scales?

Then there’s a fourth, often unstated, belief: that the rich have so much money that a high rate of tax will raise serious revenue. That depends on who you regard as “rich”. Ms Ocasio-Cortez mentioned a threshold of $10m. Profs Diamond and Saez focused on the highest earning 1 per cent of taxpayers, implying that the band would apply above around $500,000 a year. The Labour party wanted its highest rates to apply on incomes over £100,000.

These are very different definitions of “rich” and they have very different implications for revenue. For example, Ms Ocasio-Cortez’s income threshold of $10m is higher than that required to get into the top 0.01 per cent of the US income distribution: about 16,000 families. This tiny slice of the US population receives a less-than-tiny 5 per cent of total US income — which nevertheless implies that 95 per cent of income is earned by those making less.

The super-rich are a tempting target, but a serious attempt to raise revenue cannot stop with them. Whether we are talking about income or wealth, the lion’s share lies not with the billionaires but with the comfortably off. It is nice to talk about taxing somebody else’s money, but in a world of chronic budget deficits and worsening demographics, the ethics and economics of higher tax rates are unlikely to remain someone else’s problem.

 
Written for and first published in the Financial Times on 1 Feb 2019.

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Undercover Economist

“Blue Monday” pseudoscience should teach us to be more curious

Given that it is the purest bullshit, the “Blue Monday” meme is showing surprising longevity. While the US this week celebrated Martin Luther King Jr day, the British were reading about what purports to be the most depressing day of the year.

The fantasy that the third Monday of January is Blue Monday was dreamt up by Sky Travel, a holiday company that no longer exists. It is based on an equation linking weather, debt and other factors that is transparently absurd, and was given the faintest air of academic rigour by being endorsed by a psychologist with the title “Dr”. This scheme to sell more package holidays was launched in 2005 — 14 years ago, for goodness’ sake — and is still being used to sell package holidays.

It endures despite some blistering reporting from Ben Goldacre, a psychiatrist, writer, and researcher in evidence-based medicine. A single internet search, a moment’s glance at Wikipedia, should be enough to give anyone pause before citing Blue Monday. Yet we continue; it seems we can’t help ourselves.

Blue Monday is particularly popular, but it is by no means the longest-lived myth. I’ve seen lies about EU cabbage regulations that date back to the mid-20th century and were originally lies about the US government: six or seven decades of misinformation, circulating under the radar to pop up again in the age of social media.

Why do such ideas endure? What do they tell us about our attitude to science, evidence or the truth itself?

The obvious response is that we are too credulous: we’ll believe anything. I’m not so sure. There are plenty of things we should believe, but which many people do not — for example, that Neil Armstrong walked on the moon, that smoking dramatically increases the risk of lung cancer, that carbon dioxide emissions are changing the climate, and that routine vaccines are far more likely to prevent harm than cause it. The risk of believing anything must be weighed against the risk of believing nothing.

In the case of Blue Monday, the basic problem seems to be that nobody cares enough to ask a couple of simple questions. When “experts” “officially” say that it is a depressing day, which experts? What reasons do they give for making the claim? One or two clicks on a search engine reveal the answer: no experts believe this and no good reason has ever been given.

But in the case of climate change or vaccine denial — or, dare I say it, the curious belief in numbers written on the side of a big red bus — the problem is not that nobody cares. It is that people care passionately. They care so passionately that they will go to great lengths to dismiss contrary evidence. The scepticism isn’t lazy; it is energetic. And it’s something we should recognise in ourselves: who can honestly say they have never flipped a newspaper page, turned over the channel, or found someone else to talk to at a party, in search of an opinion that we can agree with?

So should we be more trusting, or more sceptical? Onora O’Neill, whose 2002 Reith Lectures were on the subject of “trust”, sharpens the question — as we might hope a philosopher would. Rather than trying to measure or increase some vague measure of “trust”, she says, we should be aiming for a better ability to trust what is trustworthy and to mistrust what is not.

Restoring trust in the claims of science, statistics, or expertise — while stoking a healthy scepticism of snake oil and pseudoscience — is not something that can be left to any one part of society. If experts — and for that matter, journalists — wish to be trusted, they must provide evidence of their trustworthiness. But the non-experts among us could also do more to keep ourselves well-informed.

How you demonstrate trustworthiness depends on who you are and what you hope to be trusted to do. A good starting point is the list of principles for “intelligent openness” set out a few years ago by the Royal Society in a report, Science As An Open Enterprise. (Baroness O’Neill was one of the report’s authors.)

Intelligent openness requires that the data used to make scientific claims are accessible, understandable, usable and assessable. “Accessible” implies publication online at minimal cost. “Understandable” means claims made in plain language, as clearly as possible. “Usable” may mean supplying data in a format easily analysed by computers, and it also suggests that the conclusions be framed in a way that is relevant to everyday concerns. “Assessable” means that anyone with the time and expertise has the detail required to rigorously test the idea if they wish.

That is something scientists, statisticians, economists and other “experts” can do. What the rest of us owe them — and more importantly, owe ourselves — is to ask a few questions before we spread an idea on social media or rely on it to govern our votes, our diets, or our attitudes to each other.

One or two smart questions, or a moment double-checking with an internet search — that is often all it takes to provide the context we need to make a wiser judgment. It shouldn’t be too much to ask of ourselves.

 
Written for and first published in the Financial Times on 25 January 2019.

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Undercover Economist

Lessons from the wreck of the Torrey Canyon

On Saturday March 18 1967, around half past six in the morning, the first officer of the Torrey Canyon realised that his vessel was in the wrong place. The 300-metre ship was hurrying north past the Scilly Isles, 22 miles off the tip of Cornwall in the south west of England, with more than 119,000 tonnes of crude oil. The aim was to pass west of the islands, but the ship was further east than expected.

The officer changed course, but when the sleep-deprived captain Pastrengo Rugiati, was awoken, he countermanded the order. A two-hour detour might mean days of waiting for the right tides, so Capt Rugiati decided instead to carry on through the treacherous channel between the Scilly Isles and the mainland.

Most serious accidents have multiple causes. A series of mistakes or pieces of bad luck line up to allow disaster. The Torrey Canyon was hampered by an unforgiving schedule, barely adequate charts, unhelpful winds and currents, confusion over the autopilot, and the unexpected appearance of fishing boats in the intended course. But reading Richard Petrow’s contemporary account of the Torrey Canyon disaster, a clear lesson is that Capt Rugiati was too slow to adjust. He had a plan, and saw far too late that the plan was doomed to failure — and with it, his ship.

Some accident investigators call this “plan continuation bias”. Airline pilots sometimes call it “get-there-itis”. The goal appears within touching distance; it’s now or never. Tunnel vision sets in. The idea of a pause or a change of approach becomes not just aggravating, expensive or embarrassing — it becomes literally unthinkable.

In such circumstances aeroplanes have crashed after trying to land in bad weather because the destination airport was so temptingly close. Patients have died of oxygen starvation because doctors and nurses fixated on clearing blocked airways rather than checking whether an oxygen pump was working. And the Torrey Canyon ran aground, producing the world’s first major oil tanker disaster.

We’ve all experienced “get-there-itis”. For me, it tends to emerge when dealing with family logistics. One child needs to go somewhere, another must be picked up from school. Then it turns out that someone needs to be at home to receive a delivery; the car is in for a service; the babysitter calls to cancel.

The plan seems feasible at first, but as complications mount, it starts to resemble an increasingly precarious assembly of stages and steps, lift-swaps and rendezvous, a Rube Goldberg fever-dream of an itinerary. If I’m lucky, someone finds the mental space to see clearly the fragility of it all. Someone suggests a cancellation or two, replacing the entire time-and-motion nightmare with something radically simpler.

It’s that moment of clarity that is so often missing. Haste makes things worse, as when La La Land was mistakenly announced as the winner of the Oscar for best picture two years ago. When Warren Beatty opened the envelope he’d been given at the Academy Awards ceremony, live on stage in front of Hollywood’s most powerful stars and a TV audience of tens of millions, what he saw in front of him didn’t make a lot of sense. That was because he’d been given the wrong envelope.

With hindsight he should have walked off stage and asked for clarification — but of course, he felt under pressure to continue with the plan, which was to read whatever was in the envelope. In the end it was his co-host Faye Dunaway who blurted out the wrong film name; she had even less time than Mr Beatty to stop and think.

Is there a solution? In their book Meltdown (US) (UK), Chris Clearfield and András Tilcsik argue that even in fast-moving situations, successful teams will find a way to check on each other and reassess the situation. The simpler solution, although it is not always possible, is to slow down.

For those of you wondering whether this column is really about Brexit, you may draw your own conclusions. But Theresa May— who at the final hurdle has managed to get her signature policy crushingly rejected by the UK parliament — is not the only one who has been suffering from get-there-itis.

Her opposite number Jeremy Corbyn, the leader of the Labour party, is obsessed with winning the snap general election that he has no power to call. The hard Brexiters are so fixated on an immediate and extreme Brexit that they seem happy to risk disgrace if they succeed, and no Brexit at all if they fail. The EU’s negotiating triumph may yet be a pyrrhic victory. Even the pro-European parliamentarians, with whom I have considerable sympathy, are now fumbling as they scramble for the contradictory goals of a soft Brexit or another referendum. For each faction, the goal seems so close, the blinkers go on, and the ship hits the rocks.

In an attempt at damage control, the Torrey Canyon was bombed by the Royal Navy. The thickest oil slicks ended up on the beaches of France. Make of that what you will.

Capt Rugiati was haunted by his failure — a broken man, cowering from press attention. If only he’d taken the time to slow down and think again.

 
Written for and first published in the Financial Times on 18 January 2019.

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Trump, May and the necessary art of brinkmanship

Brinkmanship is an old idea, but not such an old word. It was first used in 1956, after US Secretary of State John Foster Dulles opined that “the ability to get to the verge without getting into the war is the necessary art . . . if you are scared to go to the brink, you are lost.”

Adlai Stevenson, the Democratic presidential nominee, began to use the term “brinkmanship” in response. He did not intend it as compliment.

Yet we find ourselves surrounded on all sides by leaders who think they have mastered this “necessary art”. The stakes are blessedly lower, but still high enough to deserve examination. In the US, Donald Trump has failed to deliver on his promise to get Mexico to pay for his border wall, and has partly shut down the federal government until Congress agrees that the US taxpayer will fund it instead. Voters will reach their own conclusions as to who is to blame.

In the UK, Theresa May wants parliament to vote for the unappetising Brexit deal she has negotiated with the EU. She offers two simultaneous and mutually exclusive threats, confronting hardliners with the prospect of no Brexit at all, while warning the EU and British moderates that there will be a chaotic “no deal” outcome instead.

Whether we are talking about Brexit, a border wall, or the early stages of the Vietnam war, each situation is different. Yet it is worth pondering similarities in the structure of the problem.

These threats may seem empty. Dulles did not want nuclear war. Mrs May does not want six-day-long traffic jams on the way into Dover. Nevertheless the threat may be made credible enough to achieve results. How?

One option is to use a doomsday machine, made famous by Stanley Kubrick’s dark comedy Dr Strangelove. The doomsday machine is credible because it is automatic. It cannot be switched off, only obeyed. The risks are obvious; in the movie, the doomsday machine destroys civilisation.

Mrs May’s doomsday machine was the Article 50 divorce process, which we were told could not be halted once begun. Without parliamentary approval of a deal, this legal doomsday machine would deliver a disruptive no-deal by default. Triggering Article 50 weakened the prime minister’s negotiating hand with the EU but strengthened it when dealing with those MPs who seem open to reason.

Yet it now transpires that the machine has an off-switch after all. The UK government can simply revoke its notification to leave. Mrs May therefore managed to hobble her bargaining position with the EU while leaving herself hostage to her own party.

The second tactic for gaining credibility is the “madman” strategy: if you are insane, or can fake insanity, then insane threats seem plausible. The strategy was flawlessly executed by Sheriff Bart in the film Blazing Saddles, who managed to escape being lynched by racists by threatening to shoot himself. That achievement is hard to replicate, though. As Bart tells himself, “you are so talented. And they are so dumb!”

Mr Trump is erratic enough to make the madman tactic seem plausible, although he has also frequently backed down. Mrs May does a good line in stubbornness, and is trying hard to make a chaotic no-deal seem as if it is an inescapable force of nature, like an earthquake or a flood. Yet it seems unlikely that she would embrace the chaos when, with a stroke of her pen, she could call it all off. Some leading Brexiters, however, have perfected the madman pose; they’ve convinced me that they simply do not care. Perhaps I’ve been fooled by a brilliant bluff. Perhaps.

There is a third way to make threats credible: create the risk of an accident. Thomas Schelling, cold war strategist and Nobel laureate economist, described handcuffing yourself to your opponent then cavorting on the edge of a cliff. You’re not suicidal, but you are willing to create the risk that things will go terribly wrong. If your counterpart fears that risk more than you, you may extract concessions.

As Schelling and his fellow strategists knew, in situations such as the Cuban missile crisis there was always a risk that something would get out of hand, and all of us would slip off the cliff together. It was this that made world-ending threats plausible.

If you are finding all this discomfiting, you are not alone. Somehow we have managed to produce a situation where democratically elected politicians are threatening substantial harm to their own countries as a bargaining tactic. The tactic is credible because accidents happen. At least we can comfort ourselves that long-range bombers are not involved.

How did we get here? Recall the final scene of Dr Strangelove. With Armageddon inevitable, Strangelove reassures the all-male leadership of the US that they could survive in underground cities. The survival of the human race would be ensured by a ratio of 10 “highly stimulating” women to each man. Everyone seems rather cheered by this thought.

Brinkmanship does not work if it does not create a risk of harm. Yet the people practising the strategy may not be the ones who will experience it.

 

Written for and first published in the Financial Times on 11 January 2019.

My book “Fifty Things That Made the Modern Economy” (UK) / “Fifty Inventions That Shaped The Modern Economy” (US) is out now in paperback – feel free to order online or through your local bookshop.

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