Tim Harford The Undercover Economist

Undercover EconomistUndercover Economist

My weekly column in the FT Magazine on Saturday’s, explaining the economic ideas around us every day. This column was inspired by my book and began in 2005.

Undercover Economist

The pricing paradox: when diamonds aren’t on tap

‘Diamonds are costly because we desire them. But what if that isn’t true? What if they are desirable because they are costly?’

A glass of water costs very little; a diamond costs a lot. Yet there is nothing more useful than water, while the most prized uses of diamonds are decorative. This apparent paradox has tested some fine minds. Adam Smith’s answer to the paradox was that diamonds were expensive because it was hard work to find them and dig them up. That seems to strike close to the truth but it’s not the way that modern economics approaches the problem.

The usual name for this puzzle is the “paradox of value” or “the water-diamond” paradox but I now prefer to call it the “Button Gwinnett paradox”. (I hadn’t heard of Button Gwinnett until his life was described in a recent episode of the WNYC radio programme Radiolab.) The British-born Gwinnett moved to the colony of Georgia in the mid-1700s. He was a failed businessman, a serial debtor and a B-list politician in the independence movement. But, as it happened, he was one of the 56 signatories of the Declaration of Independence.

Gwinnett might seem a minor figure compared to some of the other men whose names sit beside his: John Hancock, Thomas Jefferson, John Adams and Benjamin Franklin. Despite that, a Button Gwinnett signature is vastly more valuable than a Jefferson or a Franklin. The simple reason for this is that collectors naturally wish to own the complete set of 56 signatures. Ben Franklin lived into his eighties and was a prolific correspondent, so there is no shortage of Franklin signatures.

Gwinnett died in a duel the year after signing the Declaration of Independence. His signature was recently discovered on the parish register of St Peter’s Church in Wolverhampton, where he was married. Most of the other signatures he left behind were on IOUs.

Benjamin Franklin may have been one of the most remarkable human beings in history but when collecting your set of Independence signatures, it’s the Button Gwinnett that will prove the final piece of the jigsaw. Anyone selling a Gwinnett will find few other sellers and many eager buyers.

Which brings us back to water and diamonds. Diamonds are expensive because at the point at which the supply of diamonds dries up, there are plenty of buyers willing to pay handsomely, and they compete with each other. Water is cheap in temperate climes because after satisfying our demand for drinking and cooking, then for washing and for irrigation, and finally for swimming around in, there is still plenty left. The value of the first litres of water may be incalculably high but the marginal value of one more litre is very low, and it’s this value that sets the price.

Everything so far has assumed that our desire for an object — a diamond, a glass of water, a Button Gwinnett signature — is a given. Diamonds are costly because we desire them, and not the other way around. But what if that isn’t true? What if diamonds are desirable because they are costly?

The economist Thorstein Veblen coined the term “conspicuous consumption” to describe situations where an object is attractive merely because it is expensive. The designer watch or car is valuable because, like a peacock’s tail, it is a credible indicator that you have resources to spare. What was the point of spending so much on that diamond engagement ring otherwise?

Another possibility is “pricing bias”. If we don’t really know a good suit or a good bottle of wine from a bad one, we tend to use the price to give us a clue. This is not strictly logical — after all, anyone can double the asking price of anything they are selling, so price is not by itself a reliable clue to quality. But pricing bias exists. Studies show that people will rate a wine more highly in a taste test if they think it is expensive; even placebo painkillers are more effective if the patients believe they are costly new drugs rather than cheap new drugs.

. . .

The final word on this should go to a team led by Laurie Santos at Yale’s Comparative Cognition Laboratory. Santos has spent some time teaching capuchin monkeys how to use money, to exchange it for food and to understand the idea that food can have a price that is high or low. In recent work with Robin Goldstein of UC Davis, Santos’s team has been trying to figure out whether the monkeys also display pricing bias.

It seems not. After a series of trials where monkeys were allowed to buy cheap or expensive jelly and ice lollies, they were then let loose on a free buffet to see if they gravitated towards the once costly items. They didn’t; unlike humans, the monkeys couldn’t care less what the item typically cost. They liked what they liked. In this, they differ not only from humans but also from starlings: Alex Kacelnik and Barnaby Marsh, zoologists at Oxford, have found that starlings prefer more costly food.

My guess is that the monkeys would have little interest in a Button Gwinnett signature. And those glossy advertisements for diamonds and designer handbags? They are evidently far too sophisticated for capuchin tastes.

Written for and first published at ft.com.

Undercover Economist

Man v machine (again)

‘The Luddite anxiety has been dormant for many years but has recently enjoyed a resurgence’

I’m writing these words in York, the city in which, two centuries ago, the British justice system meted out harsh punishments — including execution — to men found guilty of participating in Luddite attacks on spinning and weaving machines. By a curious coincidence, I’ve just read Walter Isaacson’s article in the FT explaining how wrong-headed the Luddites were. I’m not so sure.

“Back then, some believed technology would create unemployment,” writes Isaacson. “They were wrong.”

No doubt such befuddled people did exist, and they still do today. But this is a straw man: we can all see, as Isaacson does, that technology has made us richer while employment is as high as ever. (The least appreciated job-creating invention may well have been the washing machine, which helped turn housewives into women with salaries.)

The Luddites themselves had a more subtle view than Isaacson suggests, and one which is as relevant as ever. They believed that the machines were altering economic power in the textile industry, favouring factory owners and low-skilled labourers at the expense of skilled craftsmen. They wanted to defend their interests and they did so violently. As the historian Eric Hobsbawm put it, their frame-breaking activity was “collective bargaining by riot” and “simply a technique of trade unionism” in the days before formal unions existed.

To put it another way, the Luddites weren’t idiots who thought that machines would destroy jobs in general; they were skilled workers who thought that machines would devalue their specific jobs and their specific skills. They were right about that, and sufficiently determined that stopping them required more than 10,000 troops at a time when the British army might have preferred to focus on Napoleon.

The Luddite anxiety has been dormant for many years but has recently enjoyed a resurgence. This is partly because journalists fear for their own jobs. Technological change has hit us in several ways — by moving attention online, where (so far) it is harder to charge money for subscriptions or advertising; by empowering unpaid writers to reach a large audience through blogging; and even by introducing robo-hacks, algorithms that can and do extract data from corporate reports and turn them into financial journalism written in plain(ish) English. No wonder human journalists have started writing about the economic damage the robots may wreak.

Another reason for the robo-panic is concern about the economic situation in general. Bored of blaming bankers, we blame robots too, and not entirely without reason. Inequality has risen sharply over the past 30 years. Many economists believe that this is partly because technological change has favoured a few highly skilled workers (and perhaps also more mundane trades such as cleaning) at the expense of the middle classes.

Finally, there is the observation that computers continue to develop at an exponential pace and are starting to make inroads in hitherto unexpected places — witness the self-driving car, voice-activated personal assistants and automated language translation. It is a long way from the spinning jenny to Siri.

What are we to make of all this? One view is that this is business as usual. We’ve had dramatic technological change for the past 300 years but it’s fine: we adapt, we still have jobs, we are incomparably richer — and the big headache of modernity isn’t unemployment but climate change.

A second view is that this time is radically different: the robots will, before long, render many people economically valueless — simply incapable of earning a living wage in a market economy. There will be plenty of money around but it will flow to the owners of the machines, and maybe also to the government through taxation. In principle, all could be well in such a future but it would require a radical reimagining of how an economy could work. The state, not the market, would be the arbiter of who gets what. Such a world is probably not imminent but, by 2050, who knows?

 . . . 

The third perspective is what we might call the neo-Luddite view: that technology may not destroy jobs in aggregate but rather changes the demand for skills in ways that are real and troubling. Median incomes in the US have been stagnant for decades. There are many explanations for that, including globalisation and the decline of collective bargaining, but technological change is foremost among them.

If the neo-Luddites are right, then the challenge in front of us is simply to adapt. Individual workers, companies and the political system will have to deal with wrenching economic changes as old industries are destroyed and new ones created. That seems a plausible view of the near future.

But there is a final perspective that doesn’t get as much attention as it might: it’s that technological change is too slow, not too fast. The robo-booster theory implies a short-term surge in jobs, as all those lovely new machines are designed and built and installed, followed by a long-term surge in productivity as the robots make the economy ruthlessly efficient. It is hard to see much sign of either trend in the economic statistics. Productivity, in particular, has been disappointing in the US and utterly dismal in the UK. Where are the robots when we need them?

Written for and first published at ft.com.

Undercover Economist

Boom or bust for bitcoin?

Bitcoin appeals to libertarians on the basis that governments cannot arbitrarily make more of it

In a moment, I’ll gaze into the crystal ball and foretell the future of the world’s most famous cryptocurrency, bitcoin. I should first explain what’s happening now.

It was developed in 2008 by an unknown programmer or programmers. Confusingly, bitcoin is both a payment technology and a financial asset. The asset called bitcoin has no intrinsic value but it has a market price that fluctuates wildly. Like digital gold, it appeals to libertarians on the basis that governments cannot arbitrarily make more of it.

The payment technology called bitcoin is what you might get if you ran the Visa network over a peer-to-peer network of computers. In case that description doesn’t help, it’s a way of sending money anywhere in the world but instead of relying on the authority of a financial intermediary such as Visa or Western Union, it uses a decentralised network to verify that the transaction has occurred. The record of all previous transactions is called the blockchain; it, too, is stored on a decentralised network. The entire process relies on cryptographic techniques to prevent fraud, which is why bitcoin and other currencies like it are called cryptocurrencies.

This may all seem very esoteric but the internet was esoteric once and it turns out to have become important. So what lies ahead for bitcoin?

Here’s one scenario.

Bitcoin has enjoyed many booms and busts in value, and later in 2015, the price surges again. This will be the biggest yet, drawing more and more people into the market. As the dotcom bubble and railway mania proved, even revolutionary technologies can be overvalued; with Bitcoins selling for $2,000, $5,000 and eventually $10,000 each, nemesis is around the corner.

The first sign of trouble will be the scams. A recent research paper by computer scientists Marie Vasek and Tyler Moore identified almost 200 bitcoin scams, in which about 13,000 victims lost $11m. Such scams will only become more common as the stakes become higher and the pool of naive investors deeper. Soon they will be the stuff of mainstream consumer rights phone-ins.

Arguably, scams are a sign that Bitcoin has matured — after all, nobody proposes abandoning the dollar because con artists like to be paid in dollars. But they are just a foretaste of what is to come — Bitcoin will be gutted by predatory monopolists.

The Bitcoin system has always relied on a crowd of people putting their computers to work verifying transactions and writing them into the blockchain, a task which costs money and energy. In a rather confusing analogy with gold, these people are called “miners” and they are compensated in Bitcoins, of course. Yet there is a basic inconsistency at the heart of this system, as the economist Kevin Dowd has observed: Bitcoin mining needs to be done by a decentralised crowd but is more efficiently done by large arrays of computers owned by a few players. Or possibly just a single one.

Even today, Bitcoin mining is a game for the big boys. As the Bitcoin mining industry becomes a tight, self-serving oligopoly, the stage is set for Bitcoin counterfeiting on a massive scale. In 2018, 10 years after the invention of Bitcoin, the system collapses under the weight of its own contradictions.

It’s an intriguing story — but of course, it is just a story. We could give it a name: “BitCon”.

. . .

If you don’t believe that, I have another story for you. The title is “Daisy Chains”. Throughout 2015 and 2016, the price of Bitcoins continues to collapse. Speculators lose interest and some of the big miners sell off their computers at a heavy loss. The spotlight moves elsewhere but the true believers in the power of decentralised blockchain processing continue to develop the system.

Bitcoins aren’t the only things that can be transferred using a peer-verified network, after all — you could transfer the digital lock to a smart car; or a financial contract, with pay-offs and penalties automatically adjudicated and paid for by the blockchain. The question is whether the effort of doing all this is more efficient than the current centralised systems using interbank payments.

The answer is yes but only in certain circumstances. A blockchain is a ledger of every digital transaction ever made on the system. This proves far too unwieldy for a universal means of payment. Yet specialised niche systems evolve: by 2018, block-chain processing is common for remittances; by 2019, block-chain processing pays for and controls self-driving taxis. You can even download an out-of-the-box blockchain app for your local babysitting circle — or your prostitution ring. Blockchain approaches don’t replace Western Union and Visa everywhere but they squeeze margins and make inroads for certain applications.

The only disappointment for the true Bitcoin enthusiasts is that Bitcoin itself, the currency that started it all, fails to catch on. Most people prefer a trusted brand. When a standard of value is used on these disparate blockchain processes, the most popular by far is “FedCoin” — more commonly known by its correct name, the US dollar.

Two stories about the future, and most likely neither one will come true. These are interesting times for cryptocurrencies.

Written for and first published at ft.com.

Undercover Economist

Battle for the web’s ‘last mile’

The fact that a few large players have such influence over vital services should make us all queasy

The cable companies who own the wires that plug us into the internet – particularly the “last mile” along your street and into your house – have a great deal of market power. Small wonder, then, that the notion of “net neutrality” is appealing: the term is usually characterised as the idea that all data transmitted over the internet should be treated equally. After all, why should Google get a zippier connection than a small rival? Why should Netflix have to pay an additional fee to Big Cable when customers have already paid handsomely to be connected?

Advocates of net neutrality won a famous victory a few weeks ago, when the US Federal Communications Commission announced plans to regulate cable companies as utilities. The aim of this was to enforce net neutrality rules after a vibrant grass-roots campaign.

Small wonder that the campaign became so popular. The idea that cable companies could partition the internet into slow lanes and fast lanes is infuriating. Customers have already paid for access, and they don’t take kindly to the prospect of “throttling” – deliberately degrading a service to extort money from content providers.

This kind of product sabotage is far older than the internet itself. The French engineer and economist Jules Dupuit wrote back in 1849 that third-class railway carriages had no roofs, not to save money but to “prevent the passengers who can pay the second-class fare from travelling third class”. Throttling, 19th-century style.

But imagine that a law was introduced stipulating “railway neutrality” – that all passengers must be treated equally. That might not mean a better deal for poorer passengers. We might hope that everyone would ride in comfort at third-class prices, and that is not impossible. But a train company with a monopoly might prefer to operate only the first-class carriages at first-class prices. Poorer passengers would get no service at all. Product sabotage is infuriating but the alternative – a monopolist who screws every customer equally – is not necessarily preferable.

It is easy to think of outrageous scenarios in which a cable company might exploit market power – favouring campaign videos from politicians who do its bidding, or shutting down rivals who pose a competitive threat.

But it is also easy to think of good reasons to treat different kinds of content differently. An online back-up service for big data sets might prefer a discount for a connection that will run only at quieter times of day. Stream the World Cup final and you’ll want to guarantee uninterrupted coverage; sell the highlights as a download and you might accept a cheaper, more volatile connection if it saves money.

With a mandatory uniform price, the online back-up might be too expensive to operate, the live stream too slow to satisfy customers, and the video download getting a faster connection than it really needs. (There is a formal economic model of this effect courtesy of Benjamin Hermalin and Michael Katz but it seems intuitive to me.)

What about the idea that customers have already paid for their internet content, so cable companies shouldn’t be able to demand cash from content providers too? That is not how things work elsewhere. In a shopping mall, customers enter for free and retailers pay to be there. (They pay very different rents, too.) At an industry convention, both the delegates and the exhibitors will pay. There is nothing sacred about the idea that one side of the market pays nothing. Customers may even benefit if content providers must pay, since then the cable company might wish to slash prices to attract them and increase its leverage with the content providers.

Should all content providers be able to connect free of charge? This may not be the best rule for consumers nor the best way to promote innovation. The best defence of such a rule is that it seems to have worked well in the past and, with so much at stake, a change would be risky – not a terrible argument but hardly cast-iron.

Nevertheless I am grateful to the advocates of net neutrality, because they have brought into sharp focus the importance of market power on the internet – both of content providers such as Google and Facebook, and the cable companies who connect us to them. The ability to connect to the internet has become a basic part of living a full economic, social and political life. We use the internet to make our voices heard, to spend money, to access services, to find out the news, to connect with our friends. Increasingly our fridges, cars and pacemakers will use it too. The fact that a few very large players have such influence over such vital services should make us all queasy.

Fast lanes and slow lanes are a symptom of this market power but the underlying cause is much more important. The US needs more internet service providers, and the obvious way to get them is to force cable companies to unbundle the “last mile” and lease it to new entrants.

Alas, in the celebrated statement announcing a defence of net neutrality, the FCC also specifically ruled out taking that pro-competitive step. The share prices of cable companies? They went up.

Written for and first published at ft.com.

Undercover Economist

Overconfidence man

We don’t have a good sense of our own fallibility. Checking my answers, it was the one I felt the most certain of that I got wrong

In 1913 Robert Millikan published the results of one of the most famous experiments in the history of physics: the “oil drop” experiment that revealed both the electric charge on an electron and, indirectly, the mass of the electron too. The experiment led in part to a Nobel Prize for Millikan but it is simple enough for a school kid to carry it out. I was one of countless thousands who did just that as a child, although I found it hard to get my answers quite as neat as Millikan’s.

We now know that even Millikan didn’t get his answers quite as neat as he claimed he did. He systematically omitted observations that didn’t suit him, and lied about those omissions. Historians of science argue about the seriousness of this cherry-picking, ethically and practically. What seems clear is that if the scientific world had seen all of Millikan’s results, it would have had less confidence that his answer was right.

This would have been no bad thing, because Millikan’s answer was too low. The error wasn’t huge — about 0.6 per cent — but it was vast relative to his stated confidence in the result. (For the technically minded, Millikan’s answer is several standard deviations away from modern estimates: that’s plenty big enough.)

There is a lesson here for all of us about overconfidence. Think for a moment: how old was President Kennedy when he was assassinated? How high is the summit of Mount Kilimanjaro? What was the average speed of the winner of last year’s Monaco F1 Grand Prix? Most people do not know the exact answers to these questions but we can all take a guess.

Let me take a guess myself. JFK was a young president but I’m pretty sure he was over 40 when elected. I’m going to say that when he died he was older than 40 but younger than 60. I climbed Kilimanjaro many years ago and I remember it being 6,090-ish metres high. Let’s say, more than 6,000m but less than 6,300m. As for the racing cars, I think they can do a couple of hundred miles an hour but I know that Monaco is a slow and twisty track. I’ll estimate that the average speed was above 80mph but below 150mph.

Psychologists have conducted experiments asking people to answer such questions with upper and lower bounds for their answers. We don’t do very well. Asked to produce wide margins of error, such that 98 per cent of answers fall within that margin, people usually miss the target 20-40 per cent of the time; asked to produce a tighter margin, such that half the answers are correct, people miss the target two-thirds of the time.

We don’t have a good sense of our own fallibility. Despite the fact that I am well aware of such research, when I went back to check my own answers, it was the one I felt most certain of that I got wrong: Kilimanjaro is just 5,895m high. It seemed bigger at the time.

But there’s another issue here. The charismatic Nobel laureate Richard Feynman pointed out in the early 1970s that the process of fixing Millikan’s error with better measurements was a strange one: “One is a little bit bigger than Millikan’s, and the next one’s a little bit bigger than that, and the next one’s a little bit bigger than that, until finally they settle down to a number which is higher. Why didn’t they discover the new number was higher right away?”

What was probably happening was that whenever a number was close to Millikan’s, it was accepted without too much scrutiny. When a number seemed off it would be viewed with scepticism and reasons would be found to discard it. And since Millikan’s estimate was too low, those suspect measurements would typically be larger than Millikan’s. Accepting them was a long and gradual process.

Feynman added that scientists have learnt their lesson and don’t make such mistakes any more. Perhaps that’s true, although a paper published by the decision scientists Max Henrion and Baruch Fischhoff, almost 15 years after Feynman’s lecture, found that same pattern of gradual convergence in other estimates of physical constants such as Avogadro’s number and Planck’s constant. From the perspective of the 1980s, convergence continued throughout the 1950s and 1960s and sometimes into the 1970s.

Perhaps that drift continues today even in physics. Surely it continues in messier fields of academic inquiry such as medicine, psychology and economics. The lessons seem clear enough. First, to be open to ourselves and to others about the messy fringes of our experiments and data; they may not change our conclusions but they should reduce our overconfidence in those conclusions. Second, to think hard about the ways in which our conclusions may be wrong. Third, to seek diversity: diversity of views and of data-gathering methods. Once we look at the same problem from several angles, we have more chances to spot our errors.

But humans being what they are, this problem isn’t likely to go away. It’s very easy to fool ourselves at the best of times. It’s particularly easy to fool ourselves when we already think we have the answer.

Written for and first published at ft.com.

Undercover Economist

Is it possible to just click with someone?

‘Whether the computer reckons you’re a love match or not isn’t something that anyone should take seriously’

I’ve occasionally wondered whether the secret to love is mathematics, and I’m not the only one. Mathematics is full of perky ideas about matching or sorting that have a veneer of romantic promise. But for all their beauty and cleverness, one often feels that such ideas are a far better introduction to mathematics than they are to dating and mating.

Consider the Gale-Shapley algorithm, which dates from 1962 but won Lloyd Shapley a Nobel Memorial Prize in economics just a couple of years ago. The algorithm is a way of assigning matching pairs in a stable way. By “stable”, we mean that no two people would do better ignoring the algorithm and instead making a side-arrangement with each other. The Gale-Shapley algorithm can be used for matching students to university places, or kidney donors to kidney recipients. However, it is most famously described as a way of allocating romantic partners. It is, alas, ill suited to this task, since it skips over the possibility of homosexuality, bisexuality, polyamory or even something as simple as divorce. (1962 is on the phone . . . it wants its algorithm back.)

But if pure mathematics cannot help, surely statistics can? Internet dating promises to move us away from abstractions to the more gritty reality of data. Simply type in everything you have to offer, in great detail, and let the computer algorithm find your match. What could be simpler or more efficient?

Perhaps we should be a little cautious before buying into the hype. After all, such promises have been made before. The journalist Matt Novak has unearthed an article from 1924’s Science and Invention magazine in which the magazine’s publisher Hugo Gernsback explained that humans would soon enjoy the same scientific matchmaking approach then lavished on horses. The science included the “electrical sphygmograph” (it takes your pulse) and a “body odor test” (sniffing a hose attached to a large glass capsule that contains your beau or belle).

Then, in the 1960s, enterprising Harvard students set up “Operation Match”. It was a matchmaking service powered by a punch-card IBM computer. Despite breathless media coverage, this was no more scientific than Gernsback’s sphygmograph. According to Dan Slater’s Love in the Time of Algorithms, the men who founded Operation Match were hoping for the first pick of the women themselves.

One subscriber expressed the advantages and limitations of digital dating very well: “I approve of it as a way to meet people, although I have no faith in the questionnaire’s ability to match compatible people.”

Quite so. Operation Match was a numbers game in the crudest sense. It was an easy way to reach lots of nearby singles. There should be no pretence that the computer could actually pair up couples who were ideally suited to each other.

Perhaps we simply need more data? OkCupid, a dating site with geek appeal and a witty, naughty tone, allows you to answer thousands of questions: anything from “Do you like the taste of beer?” to “Would you ever read your partner’s email?” Users typically answer several hundred such questions, as well as indicating what answer they would hope for from a would-be date, and how important they feel the question is.

Again, media reaction has been credulous. Every now and then we hear of nerds who are living the dream, playing OkCupid’s algorithms with such virtuosity that love is theirs to command. Wired magazine introduced us to Chris McKinlay, “the math genius who hacked OkCupid”. McKinlay, we are told, downloaded a dataset containing 20,000 women’s profiles and six million questionnaire answers, optimised his own profile and unleashed an army of software bots to draw women in. He was a data-driven love-magnet.

But OkCupid’s own research suggests this is all rather futile. In one controversial experiment, it took a collection of pairs of users who were a poor match, according to the OkCupid algorithm — and then told them instead that they were highly compatible. One might expect that these not-really-compatible couples would find that their conversations quickly fizzled. In fact, they did scarcely less well than couples where the algorithm genuinely predicted a match. In short, whether the computer reckons you’re a love match or not isn’t a piece of information that anyone should take seriously.

. . .

Hannah Fry, author of The Mathematics of Love, expresses the problem neatly. The algorithm, she says, “is doing exactly what it was designed to do: deliver singles who meet your specifications. The problem here is that you don’t really know what you want.”

Quite so. The list of qualities that we might want in a partner — “fascinating, sexy, fun, handsome, hilarious” — are a poor match for the list of qualities one could share with a computer database — “likes beer, boardgames, Malcolm Gladwell and redheads”. If the computer cannot pose the right questions it is hardly likely to produce the right answers.

As for Chris McKinlay, no doubt we all wish him well. He announced his engagement to Christine Tien Wang — the 88th woman he met in person after spending months in the middle of a perfect dating storm. His experience suggests that just as with Operation Match, the matching process is nonsense and the secret to finding love is to date a lot of people.

Written for and first published at ft.com.

Undercover Economist

Why the high street is overdosing on caffeine

‘If Starbucks opens a café just round the corner from another Starbucks, is that really about selling more coffee?’

“New Starbucks Opens in Restroom of Existing Starbucks”, announced The Onion, satirically, in 1998. It was a glimpse of the future: there were fewer than 2,000 Starbucks outlets back then and there are more than 21,000 now. They are also highly concentrated in some places. Seoul has nearly 300 Starbucks cafés, London has about 200 — a quarter of all the Starbucks outlets in the UK — and midtown Manhattan alone has 100. It raises the question: how many Starbucks shopfronts are too many?

Such concerns predate the latte boom. In the late 1970s, Douglas Adams (also satirically) posited the Shoe Event Horizon. This is the point at which so much of the retail landscape is given over to shoe shops that utter economic collapse is inevitable.

And in 1972, the US Federal Trade Commission issued an entirely non-satirical complaint against the leading manufacturers of breakfast cereal, alleging that they were behaving anti-competitively by packing the shelves with frivolous variations on the basic cereals. That case dragged on for years before eventually being closed down by congressional action.

The intuition behind these complaints is straightforward. If Starbucks opens a café just round the corner — or in some cases, across the road — from another Starbucks, could that really be about selling more coffee, or is it about creating a retail landscape so caffeinated that no rival could survive? Similarly, the arrival on the supermarket shelves of Cinnamon Burst Cheerios might seem reasonable enough, were they not already laden with Apple Cinnamon Cheerios and Cheerios Protein Cinnamon Almond and 12 other variants on the Cheerios brand.

Conceptually, there is little difference between having outlets that are physically close together and having products that differ only in subtle ways. But it is hard to be sure exactly why a company is packing its offering so densely, at the risk of cannibalising its own sales.

A crush of products or outlets may be because apparently similar offerings reflect differences that matter to consumers. I do not much care whether I am eating Corn Flakes or Shreddies — the overall effect seems much the same to me — but others may care very much indeed. It might well be that in midtown Manhattan, few people will bother walking an extra block to get coffee, so if Starbucks wants customers it needs to be on every corner.

But an alternative explanation is that large companies deliberately open too many stores, or launch too many products, because they wish to pre-empt competitors. Firms could always slash prices instead to keep the competition away but that may not be quite as effective — a competitor might reasonably expect any price war to be temporary. It is less easy to un-launch a new product or shut down a brand-new outlet. A saturated market is likely to stay saturated for a while, then, and that should make proliferation a more credible and effective deterrent than low prices.

A recent paper by two economists from Yale, Mitsuru Igami and Nathan Yang, studies this question in the market for fast-food burgers. Igami and Yang used old telephone directories to track the expansion of the big burger chains into local markets across Canada from 1970 to 2005. After performing some fancy analysis, they concluded that big burger chains did seem to be trying to pre-empt competition. If Igami and Yang’s model is to be believed, McDonald’s was opening more outlets, more quickly than would otherwise have been profitable.

It is the consumer who must ultimately pay for these densely packed outlets and products. But perhaps the price is worthwhile. The econometrician Jerry Hausman once attempted to measure the value to consumers of Apple Cinnamon Cheerios. He concluded that it was tens of millions of dollars a year — not much in the context of an economy of $17tn a year, but not nothing either. Perhaps competitors were shut out of the market by Apple Cinnamon Cheerios but that doesn’t mean that consumers didn’t value them.

 . . . 

It may be helpful to consider what life would be like if every café, cereal brand or fast-food joint were owned by a separate company. Steven Salop, an economist at Georgetown University, produced an elegant economic analysis of this scenario in 1979. He found that even a market full of independents will seem a little too crowded. This is because firms will keep showing up and looking for customers until there is not enough demand to cover their costs. The last entrepreneur to enter is the one that just breaks even, scraping together enough customers to pay for the cost of setting up the business. She is indifferent to whether she is in business or doing something else entirely. However, every other entrepreneur in the crowded market is wishing that she had stayed away.

Whether the products are shoes or cereal, lattes or cheeseburgers, markets will often seem wastefully crowded. That perception is largely an illusion, but not entirely. In big city markets, there really are too many cereals, too many cafés and too many fast-food restaurants. But even if they were all mom-and-pop independents, that might still be true.

Written for and first published at ft.com.

Undercover Economist

Making a lottery out of the law

‘The cure for “bad statistics” isn’t “no statistics” — it’s using statistical tools properly’

The chances of winning the UK’s National Lottery are absurdly low — almost 14 million to one against. When you next read that somebody has won the jackpot, should you conclude that he tampered with the draw? Surely not. Yet this line of obviously fallacious reasoning has led to so many shaky convictions that it has acquired a forensic nickname: “the prosecutor’s fallacy”.

Consider the awful case of Sally Clark. After her two sons each died in infancy, she was accused of their murder. The jury was told by an expert witness that the chance of both children in the same family dying of natural causes was 73 million to one against. That number may have weighed heavily on the jury when it convicted Clark in 1999.

As the Royal Statistical Society pointed out after the conviction, a tragic coincidence may well be far more likely than that. The figure of 73 million to one assumes that cot deaths are independent events. Since siblings share genes, and bedrooms too, it is quite possible that both children may be at risk of death for the same (unknown) reason.

A second issue is that probabilities may be sliced up in all sorts of ways. Clark’s sons were said to be at lower risk of cot death because she was a middle-class non-smoker; this factor went into the 73-million-to-one calculation. But they were at higher risk because they were male, and this factor was omitted. Which factors should be included and which should be left out?

The most fundamental error would be to conclude that if the chance of two cot deaths in one household is 73 million to one against, then the probability of Clark’s innocence was also 73 million to one against. The same reasoning could jail every National Lottery winner for fraud.

Lottery wins are rare but they happen, because lots of people play the lottery. Lots of people have babies too, which means that unusual, awful things will sometimes happen to those babies. The court’s job is to weigh up the competing explanations, rather than musing in isolation that one explanation is unlikely. Clark served three years for murder before eventually being acquitted on appeal; she drank herself to death at the age of 42.

Given this dreadful case, one might hope that the legal system would school itself on solid statistical reasoning. Not all judges seem to agree: in 2010, the UK Court of Appeal ruled against the use of Bayes’ Theorem as a tool for evaluating how to put together a collage of evidence.

As an example of Bayes’ Theorem, consider a local man who is stopped at random because he is wearing a distinctive hat beloved of the neighbourhood gang of drug dealers. Ninety-eight per cent of the gang wear the hat but only 5 per cent of the local population do. Only one in 1,000 locals is in the gang. Given only this information, how likely is the man to be a member of the gang? The answer is about 2 per cent. If you randomly stop 1,000 people, you would (on average) stop one gang member and 50 hat-wearing innocents.

We should ask some searching questions about the numbers in my example. Who says that 5 per cent of the local population wear the special hat? What does it really mean to say that the man was stopped “at random”, and do we believe that? The Court of Appeal may have felt it was spurious to put numbers on inherently imprecise judgments; numbers can be deceptive, after all. But the cure for “bad statistics” isn’t “no statistics” — it’s using statistical tools properly.

Professor Colin Aitken, the Royal Statistical Society’s lead man on statistics and the law, comments that Bayes’ Theorem “is just a statement of logic. It’s irrefutable.” It makes as much sense to forbid it as it does to forbid arithmetic.

 . . . 

These statistical missteps aren’t a uniquely British problem. Lucia de Berk, a paediatric nurse, was thought to be the most prolific serial killer in the history of the Netherlands after a cluster of deaths occurred while she was on duty. The court was told that the chance this was a coincidence was 342 million to one against. That’s wrong: statistically, there seems to be nothing conclusive at all about this cluster. (The death toll at the unit in question was actually higher before de Berk started working there.)

De Berk was eventually cleared on appeal after six years behind bars; Richard Gill, a British statistician based in the Netherlands, took a prominent role in the campaign for her release. Professor Gill has now turned his attention to the case of Ben Geen, a British nurse currently serving a 30-year sentence for murdering patients in Banbury, Oxfordshire. In his view, Geen’s case is a “carbon copy” of the de Berk one.

Of course, it is the controversial cases that grab everyone’s attention, so it is difficult to know whether statistical blunders in the courtroom are commonplace or rare, and whether they are decisive or merely part of the cut and thrust of legal argument. But I have some confidence in the following statement: a little bit of statistical education for the legal profession would go a long way.

Written for and first published at ft.com.

Undercover Economist

The great data debate

‘The idea that we can somehow measure “the thing that matters most” is quite absurd’

As he appeals to the British public to vote him in as prime minister, the leader of the opposition proposes collecting new data to provide a better picture of how the country is doing. “Wellbeing can’t be measured by money or traded in markets,” he says. He adds, “We measure all kinds of things but the only thing we don’t measure is the thing that matters most.”

All of the preceding paragraph is true, except for one detail: the first quotation is from David Cameron, then leader of the opposition, in 2006. The second is from Ed Miliband, the current leader of the opposition, a couple of weeks ago. Both men are united, it seems, by a feeling that the most familiar economic measuring stick, GDP (Gross Domestic Product), just isn’t up to the job. Cameron wanted to gather data on wellbeing or happiness; Miliband wants a “cost of living” index. Few reasonable people can object to gathering timely and authoritative economic and social statistics, yet Miliband and Cameron have managed the impressive feat of being cynical and naive at the same time.

The cynical motives in both cases are plain enough — as were, for example, Nicolas Sarkozy’s when, as French president, he commissioned some alternative economic measures that just happened to be more flattering to France. As the leader of a party with a reputation for liking free markets and low taxes, Cameron wanted to soften his image and suggest a broader, more caring perspective. Miliband is trying to replace a government that is presiding over a sudden uptick in GDP, so naturally he wishes to point the spotlight somewhere else.

The naivety requires more statistical digging to uncover, and it’s in three parts. The first point is that many of these data already exist. The Office for National Statistics asks questions about wellbeing as part of the Labour Force Survey. The ONS also publishes regular data on inflation, while wage data are in the Annual Survey of Hours and Earnings. Neither Cameron nor Miliband was really asking the statisticians at the ONS to do something new, just to do it more often or in more detail.

The second point is that no mainstream politician has ever regarded GDP (or its cousin Gross National Product) as the only worthwhile policy objective, although we are often invited to draw that conclusion. Robert Kennedy’s famous complaint that GNP counts “napalm” and “nuclear warheads” but not “the health of our children” or “the strength of our marriages” was wonderful rhetoric — but surely nobody believes that if only the statisticians had collected different data, divorce would be prevented and the Vietnam war would never have happened.

An acerbic comment in Nature last year complained that, “Despite the destruction wrought by the Deepwater Horizon oil spill in 2010 and Hurricane Sandy in 2012, both events boosted US GDP because they stimulated rebuilding.” But this is only a problem if the Deepwater Horizon spill was in some way caused by the collection of GDP data.

If politicians truly sought to maximise GDP they would immediately abolish all planning restrictions, all barriers to immigration and a good chunk of the welfare state. These ideas are political suicide, which proves that GDP is not the sole objective of public policy — it’s just a way to try to measure the size of the economy.

The deepest piece of naivety is the idea that — in Ed Miliband’s words — we can measure the one single “thing that matters most”. ONS data on median wages are a case in point. According to one measure, the median wage for people in full-time employment rose just 0.1 per cent in the past tax year — well below the rate of inflation. According to another way of calculating exactly the same number, median wages rose by 4.1 per cent, well above the rate of inflation. (The median is the wage earned by someone slap in the middle of the sample.)

How can that be? The lower measure is the median for the entire sample. The higher measure looks at the median wage of people who’ve been in the same job for the entire year — the vast majority. The two numbers would differ if — for example — some high-income people retired and some low-income people joined the labour force (school-leavers? immigrants?). It’s possible for most people to enjoy a decent pay rise while median wages stagnate, and that may be what is happening now. One rather narrow question — “how are things going for people in full-time employment in the middle of the income distribution?” — turns out to have two very different answers. Each one is perfectly justifiable.

We haven’t even got into questions of part-timers, the self-employed, the poorest, the richest, pensioners or benefit recipients. The idea that we can somehow measure “the thing that matters most” is quite absurd.

It’s the duty of our official statisticians to provide a range of timely and objective statistics that will lead to better decisions. That is why so many different types of data must be gathered, analysed and published. It is a hard job, which is why the ONS has better things to do than help our schoolboy politicians score points off each other.

Written for and first published af ft.com.

Undercover Economist

The power of saying no

‘Every time we say yes to a request, we are also saying no to anything else we might accomplish with the time’

Every year I seem to have the same resolution: say “no” more often. Despite my black belt in economics-fu, it’s an endless challenge. But economics does tell us a little about why “no” is such a difficult word, why it’s so important — and how to become better at saying it.

Let’s start with why it’s hard to say “no”. One reason is something we economists, with our love of simple, intuitive language, call “hyperbolic discounting”. What this means is that the present moment is exaggerated in our thoughts. When somebody asks, “Will you volunteer to be school governor?” it is momentarily uncomfortable to refuse, even if it will save much more trouble later. To say “yes” is to warm ourselves in a brief glow of immediate gratitude, heedless of the later cost.

A psychological tactic to get around this problem is to try to feel the pain of “yes” immediately, rather than at some point to be specified later. If only we could feel instantly and viscerally our eventual annoyance at having to keep our promises, we might make fewer foolish promises in the first place.

One trick is to ask, “If I had to do this today, would I agree to it?” It’s not a bad rule of thumb, since any future commitment, no matter how far away it might be, will eventually become an imminent problem.

Here’s a more extreme version of the same principle. Adopt a rule that no new task can be deferred: if accepted, it must be the new priority. Last come, first served. The immediate consequence is that no project may be taken on unless it’s worth dropping everything to work on it.

This is, of course, absurd. Yet there is a bit of mad genius in it, if I do say so myself. Anyone who sticks to the “last come, first served” rule will find their task list bracingly brief and focused.

There is a far broader economic principle at work in the judicious use of the word “no”. It’s the idea that everything has an opportunity cost. The opportunity cost of anything is whatever you had to give up to get it. Opportunity cost is one of those concepts in economics that seem simple but confuse everyone, including trained economists.

Consider the following puzzle, a variant of which was set by Paul J Ferraro and Laura O Taylor to economists at a major academic conference back in 2005. Imagine that you have a free ticket (which you cannot resell) to see Radiohead performing. But, by a staggering coincidence, you could also go to see Lady Gaga — there are tickets on sale for £40. You’d be willing to pay £50 to see Lady Gaga on any given night, and her concert is the best alternative to seeing Radiohead. Assume there are no other costs of seeing either gig. What is the opportunity cost of seeing Radiohead? (a) £0, (b) £10, (c) £40 or (d) £50.

If you’re not sure of your answer, never fear: the correct answer (below), was also the one least favoured by the economists.

However dizzying the idea of opportunity cost may be, it’s something we must wrap our heads around. Will I write a book review? Will I chair a panel discussion on a topic of interest? Will I give a talk to some students? In isolation, these are perfectly reasonable requests. But viewing them in isolation is a mistake: it is only when viewed through the lens of opportunity cost that the stakes become clearer.

Will I write a book review and thus not write a chapter of my own book? Will I give a talk to some students, and therefore not read a bedtime story to my son? Will I participate in the panel discussion instead of having a conversation over dinner with my wife?

The insight here is that every time we say “yes” to a request, we are also saying “no” to anything else we might accomplish with the time. It pays to take a moment to think about what those things might be.

Saying “no” is still awkward and takes some determination. Nobody wants to turn down requests for help. But there is one final trick that those of us with family commitments can try. All those lessons about opportunity cost have taught me that every “no” to a request from an acquaintance is also a “yes” to my family. Yes, I will be home for bedtime. Yes, I will switch off my computer at the weekend.

And so from time to time, as I compose my apologetic “sorry, no”, I type my wife’s email address in the “bcc” field. The awkward email to the stranger is also a tiny little love letter to her.

Answer: Going to see Lady Gaga would cost £40 but you’re willing to pay £50 any time to see her; therefore the net benefit of seeing Gaga is £10. If you use your free Radiohead ticket instead, you’re giving up that benefit, so the opportunity cost of seeing Radiohead is £10.

Written for and first published at ft.com.

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