Tim Harford The Undercover Economist

Articles published in June, 2016

How do you make the Olympics pay? Fudge the figures

‘Hosting the games is not unlike building a church for one single, glorious wedding celebration’

Rio de Janeiro is about to host the Olympic Games. Good luck to it. Brazil is burdened by a political struggle that has seen President Dilma Rousseff impeached, a sharp economic downturn and a public health crisis in the form of the Zika virus. On top of that, Rio has to stage the Olympics.

Don’t get me wrong: I loved the London 2012 Olympics. It was a superb spectacle in its own right and there’s an impressive legacy — some great sporting facilities, a lovely park and new housing in a city that desperately needs it. I just doubt that it was worth what it cost. Very few Olympic Games are.

This shouldn’t really be a surprise: hosting the games is not unlike building a church for one single, glorious wedding celebration. The expensive facilities will only be fully used for a short time. They will then either be underutilised or, at best, cleverly reworked at some expense. It’s possible to adjust and dye a wedding dress so that it can be worn again but this is a pricey way to get a posh frock.

A new survey by economists Robert Baade and Victor Matheson provides a good starting point to understand the problem. Every host of the summer Olympics from Seoul in 1988 to Rio in 2016 has spent many billions of dollars on the games. The cheapest by some margin was Atlanta in 1996, which cost the equivalent of $3.6bn in today’s money; the most expensive was Beijing in 2008, a national vanity project that cost an astonishing $45bn. A reasonable assumption is that today it costs at least $10bn to host a summer Olympics — the last to be cheaper was Sydney in 2000. (These numbers are collated by Baade and Matheson.)

Given likely costs of more than $10bn, an Olympic Games is all but guaranteed to lose money. A host city might expect roughly $4bn in revenue: $1bn from ticket sales, $1bn from sponsors, $1bn from local broadcast rights and $1bn as a share of the International Olympic Committee’s global broadcast deals.

How on earth to make the numbers stack up, then?

The answer is simple: fudge them. The London Olympics and Paralympics provide a shining example. Afterwards, the government reported that the final cost of £8.77bn (around $13bn) was more than £500m “under budget”, which is true after a fashion, since the budget had been revised to £9bn several years before. But the original estimate for the games, back in 2005, was £2.4bn. If you can say that an event that cost almost four times the original estimate was “under budget”, you can say anything you like. At least the London organising committee didn’t emulate its counterpart at Nagano, the host of the 1998 Winter Olympics, and burn its financial records after the event.

Organising committees are always sure to hire consultants to produce enormous estimates of the spillover benefits of hosting the games. These consultant reports generally ignore substitution effects — for example, that a dollar spent on an Olympic ticket might well be a dollar not spent on some other local attraction. They also gloss over the risk of “crowding out”, where, fearing crowds and high prices, some tourists avoid Olympic cities. The UK had fewer visitors during July and August in 2012 than in the same months in 2011; Beijing’s hotels suffered a fall in occupancy during the 2008 Olympics.

Faced with PR fluff, the rule of thumb among serious economists is to divide all such benefits by 10 to get a more realistic figure. Most rigorous studies have found very modest spillover benefits at best.

In the case of the Sydney Olympics, researchers concluded that the wider Australian economy had actually been damaged by hosting the games.

But what if it’s not enough just to claim that hosting the games makes sense? What if the host city actually hopes to turn a profit, rather than merely forecast one — or, at least, to produce broader benefits that justify the expense? That’s a tall order, but here are three suggestions.

First, make sure the games take place during a recession, so that the spending can boost aggregate demand. Rio did get this one right but, of course, nobody can forecast recessions eight months ahead of time, let alone eight years.

Second, be a hidden gem, so the games serve to spotlight your qualities and boost tourism for many years afterwards. Rio, London, Beijing, Athens and Sydney hardly qualify here but Barcelona did: in 1990 the city was half as popular as Madrid with tourists but, by 2010, it had outstripped its rival. The Olympics can perhaps take some credit. Utah, similarly, enjoyed greater success as a destination for skiers after the Salt Lake City winter games. (Candidates for the 2024 games include Paris and Rome, not exactly well-kept secrets — and Budapest, which is a more plausible candidate to earn a lasting boost from tourism.)

Third, and most importantly, launch a cut-price bid in the wake of a disastrous games. Los Angeles in 1984 achieved the near-impossible and turned a profit because, after the ruinously expensive Montreal event of 1976, LA was the sole bidder for the games. They were hosted in the Los Angeles Coliseum, an ageing stadium that had been second-hand even when it first hosted the Olympics in 1932.

The best way to host a profitable Olympics is to do it twice, both times on the cheap.

Written for and first published at ft.com.

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How to fuel a rewarding culture

‘Money matters, but sometimes we find financial incentives to be insulting or grubby’

Here’s an age-old management conundrum: who should be rewarded for high performance, and how? As Diane Coyle, the economist and former adviser to the UK Treasury, recently observed in this newspaper, the answer to the question is usually self-serving. Simple and easily monitored jobs, such as flipping burgers, are natural candidates for performance incentives. Yet somehow it’s the inhabitants of the C-suite who tend to pick up bonuses, despite the fact that their complex, hard-to-measure jobs are poorly suited to the crude nature of performance-related pay.

But let’s assume that managers really want to answer the question. The answer is deliciously complex. Money matters, but sometimes we find financial incentives to be insulting or grubby. And we can respond keenly to non-financial rewards such as praise, status or the satisfaction in a job well done.

So managers might try running experiments to see what works in a particular situation. There is a long tradition of this, going back to Harvard professor Elton Mayo’s productivity trials at Western Electric’s Hawthorne works in the 1920s and early 1930s.

The Hawthorne experiments themselves, alas, were flawed and have been mythologised. But more modern experiments are revealing some intriguing results. I reported a few years ago on the curious alliance between “Farmer Smith”, owner of a large British fruit farm, and three economists, Oriana Bandiera, Iwan Barankay and Imran Rasul. Bandiera and her colleagues designed and tested different incentive schemes on Farmer Smith’s farms. (The deal: he got higher productivity; they got the data.)

The fruit farm experiments show that financial incentives do matter, at least for casual immigrant labour on fruit farms. First, a piece-rate scheme boosted productivity by 50 per cent; then, performance pay for the front-line managers ensured that work was no longer assigned as a favour to friends, and productivity increased another 20 per cent; then, a tournament encouraged workers to sort themselves into productive teams, and productivity increased by a further 20 per cent.

In another study by Bandiera (with Nava Ashraf and Kelsey Jack), hair stylists in Zambia’s capital Lusaka were recruited to sell condoms and give advice on HIV prevention. It turned out that celebrating the top performers at a public ceremony proved a far better approach than providing financial incentives to sell more condoms.

But sometimes neither a public ceremony nor a financial incentive is appropriate. Consider the case of long-haul airline captains. Unlike part-time condom agents or fruit pickers, these senior pilots have high-status, six-figure salaries and powerful unions to defend their pay and conditions. Nevertheless, a recent experiment conducted by Greer Gosnell, John List and Robert Metcalfe examines what can be done to influence the behaviour of these star players.

Gosnell, List and Metcalfe teamed up with a commercial airline that wanted to encourage captains to save fuel. Broadly, there are three ways to do this: before take-off, by carefully calculating fuel requirements; after landing, by switching off some engines while taxiing; and during the flight, by carefully adjusting the flap settings and negotiating the most efficient altitude, speed and course with air traffic control. The airline’s own data suggested that captains could potentially save 3 to 6 per cent on fuel — a substantial financial and environmental gain. But how to incentivise them?

Gosnell, List and Metcalfe designed an experiment that did not rely on paying bonuses. Instead, the captains were told that their company was running an experiment with the aim of saving fuel, and that the researchers would maintain anonymity for all the captains. There would be no financial incentives and no league tables.

Instead, the captains were split randomly into four groups. The “information” group received monthly feedback reports detailing how often they had saved fuel before, during and after each flight. The “target” group received the same reports but were also set targets to improve their performance. (The reward for hitting the target was a hearty “well done!”) The “incentives” group were told that for each target they hit, £10 would be donated to the charity of their choice — a total donation of £240 was possible if all three targets were hit across the eight months of the study. A control group was simply told that a study into fuel efficiency was taking place.

The most obvious outcome was that there was a large and lasting “observer effect”. Merely telling captains that the experiment was happening prodded them into being more careful and saving a lot of fuel. It is always possible that the sudden switch to fuel-saving behaviour had a cause that was nothing to do with the experiment but there are no apparent alternative explanations.

The second outcome was that all three treatments saved fuel compared with the control group but setting targets (with or without the charitable donation) had a particularly notable effect. And the third outcome was that captains who hit their targets were substantially more satisfied with their jobs.

“I just couldn’t believe the impact we had on job satisfaction,” says Metcalfe. Far from annoying the captains, the fact that the company was taking an interest in fuel saving, and acknowledging success, seemed to delight them.

No performance scheme will fit every occasion but the fuel-saving study does suggest an approach worth trying more broadly. If you want people to do a good job, tell them what success looks like to you — and that you’ve noticed when they’ve achieved it.

Written for and first published at ft.com.

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The dubious power of power poses

‘Many notable results in psychology are now being questioned because later research has reached different conclusions’

Imagine that a group of researchers set out to explore the idea that adopting a “power pose” could make a real difference to how we thought and acted. High-power poses include standing with hands on hips, feet planted confidently apart, or lounging back in a chair with feet on table and hands behind head; low-power poses include slumped shoulders and folded arms.

The researchers asked 200 people to adopt such poses, then tested the levels of two hormones in their saliva: testosterone, associated with high status, and cortisol, associated with stress.

The astonishing findings? Well, actually, there were no astonishing findings: the power poses seemed to make no difference worth mentioning. High-power poses were correlated with slightly lower testosterone and slightly higher cortisol — the opposite of what might be expected, but tiny and statistically indistinguishable from chance.

Now imagine that a second group of researchers re-examined the same hypothesis. There were some small variations, and the study was smaller (42 participants). The new study did produce remarkable findings: high-power poses boosted testosterone and lowered cortisol. Low-power poses had the opposite effect. The scale of the effect was described as “a whopping significant difference” by one of the researchers — more formally, the sizes were both practically large and statistically significant. (Statistical significance is a test of whether the result might easily have been a fluke; it’s possible to have small but statistically significant results, or large but statistically insignificant results.)

Faced with both these research findings, published in reputable journals, what should we think? The natural conclusion is that the second study was a fluke, and that standing in a bold pose for a couple of minutes makes no difference to hormone levels. Being open-minded people, we might also be intrigued by the faint possibility that the second study had uncovered a genuine and important result.

This is a hypothetical scenario, I should emphasise. It hasn’t happened. The studies did take place but not in this order. The smaller study was conducted by Amy Cuddy of Harvard and Dana Carney and Andy Yap of Columbia. It inspired a book, and a TED talk that has been watched 34 million times. The larger study was conducted by a team led by Eva Ranehill. But the smaller Cuddy-Carney Yap study didn’t come second; it was conducted first. The Ranehill team’s study came later.

This story will sound familiar to some. Many notable results in psychology are now being questioned because later research has reached different conclusions. Last year, the “Reproducibility Project”, a large collaborative effort reproducing 100 studies in psychology, published the unnerving finding that only 36 per cent of the replication attempts had produced statistically significant results.

But it is not easy to know quite what to make of that percentage. Failing to find a statistically significant effect in a replication does not simply discredit the original work. For example, some replications find similar effects to the original studies without achieving statistical significance. That means the replication provides (faint) support for the original study rather than evidence against it.

Wharton psychologist Uri Simonsohn suggests a replication attempt should use a substantially larger sample than the original, so it is likely to estimate effects more precisely. If the replication fails to find an effect, that’s not proof there’s no effect; it does suggest, however, that the original study was a fluke.

Columbia University statistician Andrew Gelman suggests a simple rule of thumb that I followed in the opening paragraphs of this column: mentally reverse the order of the studies. Imagine the “replication” came first, and the “original” study came later. Being published first should not be a privileged position from which our conclusions can only be budged with extraordinary evidence. Gelman’s rule of thumb helps us avoid doggedly sticking to the status quo.

But perhaps the most important lesson is to remember that while “statistical significance” sounds scientific, it’s hardly a cast-iron endorsement of a result. The theory behind statistical significance assumes that a single pre-chosen hypothesis will be tested. In practice, researchers rarely pre-specify their hypothesis. They can test dozens, or hundreds — and sooner or later a pattern will emerge, if only by chance.

Imagine testing the idea that vitamin supplements boost childhood achievement. OK. But only for girls? Only for boys? Only for children suffering a poor diet? Only for under-10s?

An unscrupulous researcher can grind through the myriad combinations until a statistically significant pattern appears. But, says Gelman, there is no reason to think such unethical behaviour is common. More likely, researchers gather the data, look informally at the patterns they see, and only then choose a few hypotheses to test. They will tell themselves — correctly — that they’re being led by the data. That’s fine. But nobody should take seriously a test of statistical significance that emerges from such a research process: it will bring up fluke after fluke.

There are various technical solutions to this problem. But a little common sense also goes a long way. When a study of 42 subjects inspires 34 million people, it’s not unreasonable to go back and check the results.

Written for and first published at ft.com.

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Worth the wait?

‘If you miss your plane or your train, it hardly matters that the queue was a nice place to chill’

I love queues. Not that I love queueing — I may be English but I’m not that English. But from a safe distance, queues are fascinating. They’re less fun if they cause you to miss your flight. In mid-May, two-hour queues for security at Chicago’s Midway airport had just that effect. Jeh Johnson, the US Secretary for Homeland Security, offered travellers some meditative advice: “Contemplate increased wait times as you travel.” I’d hope we can do a little better than mindful meditation.

There are three very different perspectives on queues: psychological, engineering and economic.

The psychological perspective tells us that much of what makes queues unpleasant is nothing to do with the waiting time. If a queue carries risk (you may or may not make your flight), then it is far more stressful. So are queues that are confrontational, unfair or require constant monitoring for queue-jumpers or the sudden opening up of new lines.

A single serpentine queue, secure against cheats, can be a perfectly civilised place to stand and check email or read a paperback. With a bit of cleverness, the queue may be a pleasure — as at well-designed theme parks — or an unobtrusive virtual version, as when you collect a ticket from the supermarket deli counter and do some shopping while waiting for your number.

There are, however, limits to the psychological approach. When the Eyjafjallajökull eruption shut down air travel across Europe in 2010, I found myself queueing for train tickets in Stockholm Central Station, along with almost everyone else in Sweden. Thankfully, the queue had a counter system: simply take a ticket, and wait for your turn. I sat in a café, sipping espresso and typing on a laptop as I waited. But, after a pleasant three-quarters of an hour, I did some mental arithmetic, and realised that the queue was approximately 14 hours long. In the end, if you miss your plane or your train, it hardly matters that the queue itself was a nice place to chill.

When psychology fails, engineering must take the strain. A well-engineered queue copes gracefully with periods of high demand, and balances the cost of waiting against the expense of overproviding idle service staff.

Queue engineers understand that queues can have strange properties. Imagine the queue at a busy post office. During the mid-morning lull, roughly one person a minute arrives and one person a minute can be served. The queue will fluctuate — and, alas, there will never be a negative number of people in the queue — but we can expect it to stay fairly short. Then, during lunch hour, extra people arrive and the queue starts to lengthen — two people, then four, five, 10. As the rush subsides, the capacity of the ticket office again begins to match the inflow of customers: one person arrives each minute, and one person is served each minute.

Annoyingly, even though the inflow and outflow of people from the queue is the same as it was in the morning, the afternoon queue is about 10 people long. It will stay 10 people long until the capacity of the ticket office is greater than the inflow of customers. Once a serious queue has formed, it needs attention or it can linger indefinitely.

That brings us to the economic perspective on queues. Queues are a terrible, inefficient waste of time. If the resource in question is genuinely limited, then the existence of a queue shows that it is being underpriced. If everyone had to pay to join a queue, the queue itself would be shorter, because some people would decide not to bother. Those who did queue would earn back their entry fee in time saved, while the person selling tickets for the queue would make some cash.

In other cases, however, capacity should expand to keep the queue short. Imagine a line so long that most passengers would pay $50 to skip it — probably a good description of the two-hour queues at Midway. Hiring extra Homeland Security staff would save $50 worth of frustration for every extra person they scan from the line.

. . .

How many people could an extra security team see? One per minute, perhaps? Fifty dollars a minute would surely pay for some extra personnel. The problem is that the security team is unlikely actually to receive the $50. In an alternate universe, passengers would have a whip round, hire more agents, and the line would move just fine.

But in the world in which we live, queues remain. Part of the cost is imposed on foreigners, whose annoyance barely registers on the system. (This is particularly true of immigration checks.) For example, on a recent trip from South America to London, I chose to change at Madrid rather than at Miami because I’ve had terrible experiences at Miami. That’s bad for the US economy but security screeners, customs officers and immigration officials respond to political signals, not market ones. The US political system is hardly likely to dance to my tune.

Looking on the bright side, I hear that Reagan National Airport, often used by members of Congress as they fly in and out of Washington DC, works like a charm.

Written for and first published at ft.com.

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