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Undercover Economist

Five lessons from the US government’s ultimate innovators

Sunday July 29 is an important day in the history of innovation. It is the 60th anniversary of the founding of the US space agency Nasa, but that is only indirectly the reason. The incidental benefit of Nasa’s creation was that it stripped another young organisation of its funding, projects and purpose.

Founded in 1958, Arpa — the Advanced Research Projects Agency, part of the US Department of Defense — started the space race, but lost its role to Nasa a few months later and was described by Aviation Week as “a dead cat hanging in the fruit closet”.

But apparently cats really do have nine lives, because Arpa resurrected itself, and went on to play a foundational role in the creation of the internet, the Global Positioning System and, more recently, self-driving cars.

So what did Arpa do, does it deserve so much credit and, if so, can the trick be repeated in other fields such as clean energy or medicine? When it comes to an invention such as the internet, it is never easy to know whether success appeared by design or by luck. Still, here are five lessons I draw.

The first is that speed and flexibility are a vital part of the Arpa model. In fact, the agency has proved such a chameleon that some think it is a mistake even to speak of an “Arpa model”. The organisation can’t even figure out its name: it stuck “Defense” in front in 1972, took it off in the 1990s and is now back to being known as Darpa. The agency has proved able to ramp-up and pull back from projects with a speed that most organisations would find bewildering.

Consider the case of Robert Taylor. He was hired by Arpa in 1965 and made director of the information processing program a few months later, at the age of 34. He then hatched the idea of building a network to connect mainframe computers at campuses across the US.

Katie Hafner and Matthew Lyon’s history of the early internet, Where Wizards Stay Up Late (UK) (US), tells the story of what happened next. Taylor sauntered into the office of Arpa’s boss, Charles Herzfeld, and explained his thinking.

“Great idea,” Herzfeld said. “Get it going. You’ve got a million dollars more in your budget right now. Go.” That was the beginning of the internet. The network, Arpanet, was running less than four years later. The meeting itself had taken 20 minutes.

My second lesson is linked to the first. Arpa hired scientists rather than bureaucrats, and tempted free spirits to work for them by giving them tight control over large budgets, and a short tenure before they were released.

Many of the agency’s most influential programme managers only stayed for a couple of years. One was JR Licklider, a visionary psychologist who saw that the future of computing lay in a more intuitive real-time interface between humans and machines. Ivan Sutherland, his successor, and one of the fathers of computer graphics, stayed no longer. Taylor succeeded Mr Sutherland and remained for three years. The three men got a lot done in a short space of time.

Computer geeks at the time were fond of retelling a story they attributed to Soren Kierkegaard, about a man who fed wild ducks until they grew fat and tame. Arpa was deliberate about not taming its wild ducks.

A third principle is to create a vigorous marketplace for ideas. Arpa projects were distributed to a variety of universities and other institutions, practical prototypes were widely shared, and researchers brought together to learn from and pull apart each other’s work.

Shane Greenstein of Harvard Business School notes that as a result, the agency managed to gain some of the benefits of decentralisation while maintaining a degree of focus and discipline.

Fourth, find the gaps. Some organisations fund truly fundamental research very well, and other organisations — often private-sector firms — excel at producing polished products to meet well-defined demands. But the gap between blue sky research and a marketable end-product is not always well served, and Arpa has succeeded by identifying projects in the middle.

A recent paper by Pierre Azoulay, Erica Fuchs, Anna Goldstein and Michael Kearney emphasises that not every innovation problem can be solved with an Arpa-style agency. Nevertheless, there seems promise in the 21st-century efforts to create them for intelligence (Iarpa) and energy (Arpa-E).

Finally, don’t forget the mission. Arpa projects may have seemed speculative, but the agency kept its feet on the ground by focusing on US national security. There is, of course, an important role for pure theoretical research — but that is not the role of this type of agency. Arpa had a mission in mind, and trusted the scientists and engineers to deliver.

The clarity of the mission, quality of programme managers and the trust shown in them may explain some of the agency’s successes, especially in its glory days. That trust ran deep.

Dwight Eisenhower, who was US president when the agency was first established, asked after “my scientists” on his death bed in 1969 — calling them “one of the few groups that I encountered in Washington who seemed to be there to help the country and not help themselves”.

 

Written for and first published in the Financial Times on 27 July 2018.

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The Peter Principle is a joke taken seriously. Is it true?

There are a lot of idiots around — just look at the front benches of the House of Commons. But why do the idiots reach such elevated positions?

Many onlookers feel sure that the successors to David Davis, the UK’s former Brexit secretary, and Boris Johnson, ex-foreign secretary, cannot possibly be worse than the men they replace. Mr Davis was at least dignified in his resignation. Mr Johnson is widely regarded as the most disgraceful occupant of the post imaginable. And yet both men departed at the hour of their choosing: they were incompetent yet unsackable.

Part of this displays the awful dynamics of British politics today. But it also reflects something more universal. In 1969, Laurence Peter, a professor of education, and Raymond Hull, a playwright, published a management book that became a bestseller and an enduring classic: The Peter Principle. (UK) (US)

The Peter principle states that “every employee tends to rise to his level of incompetence”. If someone is good at her job, she’ll be promoted into a job that demands different skills. If she’s good at the new job too, she’ll be promoted again, requiring yet another set of skills. One day, she will arrive at a job for which she is wholly unsuited, and there she will stick. Since when did a manager ever get sacked for anything?

The Peter Principle is satire: it mocks management and it mocks books about management. It is striking, then, that most people take it quite seriously. The Harvard Business Review has published numerous straight-faced responses.

Two questions, then: is the Peter principle true? If so, what can we do about it?

We should acknowledge that the Peter principle may be an illusion. The top jobs are difficult to do well; they can make monkeys out of capable people. We shouldn’t be surprised if it is easy to call to mind examples of business or political leaders who have struggled. That would not by itself mean there are systematic errors in the way people rise to the top.

People were not promoted for behaviour that might seem correlated with managerial ability — in particular, those who collaborated with others were not rewarded for doing so

But this year, the economists Alan Benson, Danielle Li and Kelly Shue published what may be the first detailed empirical investigation of the Peter principle. Profs Benson, Li and Shue used data from a company that provides performance management software to sales teams; the data cover 214 firms, more than 53,000 workers and more than 1,500 promotions. This data set was ideal for identifying highly effective sales staff, and also provided enough information to evaluate what happened to a team once a hotshot salesperson was promoted into the role of team leader.

The authors of the paper discovered that the best salespeople were more likely to be promoted, and that they were then terrible managers. The better they had been in sales, the worse their teams performed once they arrived in a managerial role.

What’s more, people were not promoted for behaviour that might seem correlated with managerial ability — in particular, those who collaborated with others were not rewarded for doing so. What mattered were sales, pure and simple.

In short, Professor Peter was right. Brilliant people are promoted until they become awful managers. Perhaps employers are using the prospect of promotion as an incentive, and are willing to accept the collateral damage caused by all these terrible managers. If so, they are probably making a mistake. It would be better just to encourage the star salespeople with cash bonuses instead.

There is a more radical approach. One of my favourite IG Nobel Prizes (the IG Nobels reward “achievements that first make people laugh, then make them think”) was awarded to two physicists and a sociologist — Alessandro Pluchino, Andrea Rapisarda and Cesare Garofalo — who wondered how organisations might evade the logic of the Peter principle.

If performance at one level of a hierarchy is uncorrelated with performance at the next level up, the best strategy is simply to promote the very worst people. Nobody knows whether they will make good managers, but at least they will no longer be dreadful staff — or as Dogbert in the cartoon strip Dilbert put it back in 1995: “Leadership is nature’s way of removing morons from the productive flow.”

There are two difficulties with this approach: first, it may be too extreme to assume that no skills at all carry over from one job to the next; second, if the reward for failure is promotion, then the likely response is an organisation full of people bent on sabotage. So Profs Pluchino, Rapisarda and Garofalo suggest a compromise: promote people at random.

This may be the best response to a world where leaders stick around until they are ready to depart. But there is an obvious alternative: when people are not up to the demands of their job, we should not wait for them to resign. They should be sacked — or, perhaps better, demoted back to the roles where they once flourished. A mistake is regrettable, but stubbornly sticking to the mistake is far worse. Just ask the British electorate.

 

Written for and first published in the Financial Times on 20 July 2018.

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The cool tools that are reshaping economics

If Hollywood is to be believed, every mad scientist who ever lived has a laboratory full of bubbling flasks, flashing consoles and glowing orbs. Science writer Philip Ball — who has visited countless research labs — tells me that reality is not so very different: the gear may be more subdued, but the gear is always there.

Science depends on tools, often instruments to detect or measure that which was previously undetectable — think of Galileo’s telescope or Newton’s prisms. Nobel Prizes have often been awarded to the physicists who developed such tools: the cloud chamber (1927); the electron microscope (1986); and LIGO, the laser interferometer gravitational-wave observatory (2017).

What, then, of economics? Economics has its own quasi-Nobel Prize, but it is a stretch to find a single example of a prize being awarded for the development of new tools or instruments. Simon Kuznets (laureate in 1971) probably has the best such claim, for developing the ideas behind the gross domestic product measure. Alas, GDP is a broad aggregate with limitations that Kuznets himself understood all too well.

The great Alfred Marshall described economics as being the study of humanity “in the ordinary business of life”. Unfortunately, in Marshall’s day — he died in 1924 — there was no way to observe the ordinary business of life, except perhaps as an anthropologist. Economists spent a lot of time in armchairs, thinking hard about theory rather than measurement.

Some economists now make progress using old tools from other fields. MIT’s Esther Duflo, winner of the prestigious John Bates Clark medal, answers economic questions using randomised controlled trials. RCTs are typically dated back to Austin Bradford Hill’s 1948 trial of streptomycin for tuberculosis. (Hill was trained as an economist, so perhaps we can score that one for the profession.)

But the holy grail is to be able to observe the ordinary business of life in detail, in real time, and at scale — ideally all three at once. That was once an impossible goal, but three new developments put that goal within reach.

The first is the availability of high-resolution satellite images. In the mid-1990s, an economist named Alex Pfaff realised that these images could be used to answer questions about the connection between development projects and deforestation in the Amazon.

Hundreds of others have followed suit. Satellites can easily measure illumination at night, a simple way to track economic activity and patterns of urban development. It is also possible to measure various kinds of air pollution, and to observe the growth of crops. Algorithms are starting to extract subtle information at scale: how many Ethiopian homes have tin roofs? Which roads in Kenya are in good condition? And ever-cheaper small satellites are taking detailed photographs of everywhere, every day.

An even bigger change is that economists are using administrative data. I realise that “economists are using administrative data” is a contender for the most boring sentence uttered in 2018. But over the past two decades or so, this has been a quietly revolutionary move.

Administrative data are the numbers generated by governments or private companies for the purposes of getting things done. Schools keep track of attendance and grades. Tax authorities know your (declared) income — but also where you live, your age, and perhaps who your children are.

As such records have become digitised, they can be used to answer serious questions in research. For example, tax data can tell us the extent to which the children of rich or poor parents grow up to be rich or poor themselves. These detailed data are now at the forefront of empirical economic research.

According to Dave Donaldson, who like Prof Duflo is a John Bates Clark medallist at MIT: “In my field, international trade, I rarely see a paper that doesn’t use customs-level data. Every shipment generates a record which will specify what it is, where it came from, where it’s going, and the tax paid.”

A third measurement tool is the mobile phone. Every time a call is placed, the phone company generates a record of who called whom, when, for how long, and where the phones were, sometimes to within less than a hundred metres. With that kind of “metadata”, economists and other researchers can ask questions such as: how rapidly are people moving around, and to what extent is that correlated with the spread of an epidemic? Is a city’s transport infrastructure working well? How quickly are refugees integrating into a new society?

This is both an opportunity and a challenge for economists. Data scientist and economist Josh Blumenstock told me that “anyone who graduated with an economics PhD more than five years ago has no idea how to handle this data, and is frantically scrambling.”

Surely the scramble will produce results. At last, it is possible not just to theorise about Marshall’s “ordinary business of life”, but to observe it. Our tools are letting us see something new — and what we can see determines what we can think.

 
Written for and first published in the Financial Times on 13 July 2018.

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What does a robot accountant look like?

What shall we do when the robots take our jobs? Last week’s column discussed mass technological unemployment, and readers were quick to write in with variants on the same common sense suggestion: tax the robots.

“Give a robot a deemed income equivalent to the wage of the human it is replacing, with a default of the average income,” wrote one. A similar suggestion: “If one robot displaced five people earning a modest £20,000 each, then that robot could be said to have an economic value of £100,000 per annum.” Both correspondents proposed an income tax or similar levy on this robot-generated output.

I’m grateful for all constructive comments, but particularly grateful for these because I think they are wrong in a fascinating and instructive way. The fault is mine: I set a trap every time I talk about “robots” and “jobs”. That is not how automation happens.

Consider an idea dreamt up in 1978, released in October 1979, and so revolutionary that the journalist Steven Levy could write just five years later: “There are corporate executives, wholesalers, retailers, and small business owners who talk about their business lives in two time periods: before and after the electronic spreadsheet.”

Spreadsheet software redefined what it meant to be an accountant. Spreadsheets were once a literal thing: two-page spreads in a paper ledger. Fill them in, and make sure all the rows and columns add up. The output of several spreadsheets would then be the input for some larger, master spreadsheet. Making an alteration might require hours of work with a pencil, eraser, and desk calculator.

Once a computer programmer named Dan Bricklin came up with the idea of putting the piece of paper inside a computer, it is easy to see why digital spreadsheets caught on almost overnight.

But did the spreadsheet steal jobs? Yes and no. It certainly put a sudden end to a particular kind of task — the task of calculating, filling in, checking and correcting numbers on paper spreadsheets. National Public Radio’s Planet Money programme concluded that in the 35 years after Mr Bricklin’s VisiCalc was launched, the US lost 400,000 jobs for book-keepers and accounting clerks.

Meanwhile, 600,000 jobs appeared for other kinds of accountant. Accountancy had become cheaper and more powerful, so people demanded more of it.

What does a robot accountant look like? Not C-3PO with a pencil sharpener, that’s for sure. One might say that Microsoft Excel is a robot accounting clerk. A more plausible answer is that there is no such thing as a robot accountant. One day we may have androids sophisticated enough to do everything human accountants do now, but by then the very concept of an “accountant” will have changed beyond recognition.

So it is misleading of me to write of “robots” taking “jobs”. What actually happens is that specific tasks are automated, rather than the broad bundle of tasks that together constitute a human “job”. Automating tasks means reshaping jobs. The process can create jobs or destroy them, and will usually do both.

In their recent book, The Future of The Professions (UK) (US), Daniel and Richard Susskind offer numerous examples of the spreadsheet dynamic in action: algorithms that scan mammograms and spot trouble that humans miss; online tutorials that monitor students and alert teachers to where the child is struggling; “document assembly systems” that quiz clients and then draft legal contracts.

Another example. If I didn’t have the use of email, internet search and a mobile phone, I would need to employ someone as a personal assistant. But I have had these tools for a long time, so I have never had a secretary. Should I have to pay the never-employed secretary’s tax bill, because I own a smartphone?

White-collar anxiety about automation is new, but the problem is old. Mike Mulligan and His Steam Shovel (UK) (US) is a Depression-era children’s book about technological obsolescence. (It is wonderful.) The steam shovel’s name is Mary Anne; she “could dig as much in a day as a hundred men could dig in a week”. So is Mary Anne a “digging robot” who destroyed 500 jobs? Yes; no; maybe. After a while the question seems ridiculous. Nor would many sensible people argue that Mike Mulligan, Mary Anne’s owner, be liable for 500 tax bills.

As any tax wonk can tell you, whatever we choose to tax — land, capital, profits, value-added, imports, wealth, greenhouse gas emissions — inevitably turns out to be a more ambiguous concept than it might appear, especially since ambiguity is often tax efficient.

But the category of “robot” is particularly difficult to define, and therefore to tax. We cannot tax the androids who march into our workplaces, stand by while we clear our desks, then sit down to replace us: they do not exist and it is hard to see why they ever would.

In a world of mass technological unemployment we are certainly going to need to tax something other than labour income alone. There are several plausible candidates. “Robots” is not one of them.

 

Written for and first published in the Financial Times on 6 July 2018.

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Fifty Things That Made The Modern Economy – UK Paperback

I’m delighted to report that “Fifty Things That Made The Modern Economy” is out in paperback in the UK this week. (The US edition – Fifty Inventions That Shaped the Modern Economy – is out at the end of August. Sorry you have to wait…)

I had such fun writing this book and people seem to be enjoying reading it, which is great. One of the joys of the book was the ability to leap across time and topic, pick up under-rated technologies and explored the unexpected consequences of the things we invent.

“Packed with fascinating detail… Harford has an engagingly wry style and his book is a superb introduction to some of the most vital products of human ingenuity”, said the The Sunday Times.

“It’s great fun to dip into… Harford succeeds in teaching… without resorting to technical terminology and intimidating charts and tables. Such a feat requires a kind of inventiveness in itself.” That was the  The Wall Street Journal.

You can read more reviews and get more information about where to buy the book here.

30th of July, 2018Other WritingComments off
Undercover Economist

The secret to happiness after the robot takeover

We all seem to be worried about the robots taking over these days — and they don’t need to take all the jobs to be horrendously disruptive. A situation where 30 to 40 per cent of the working age population was economically useless would be tough enough. They might be taxi drivers replaced by a self-driving car, hedge fund managers replaced by an algorithm, or financial journalists replaced by a chatbot on Instagram.

By “economically useless” I mean people unable to secure work at anything approaching a living wage. For all their value as citizens, friends, parents, and their intrinsic worth as human beings, they would simply have no role in the economic system.

I’m not sure how likely this is — I would bet against it happening soon — but it is never too early to prepare for what might be a utopia, or a catastrophe. And an intriguing debate has broken out over how to look after disadvantaged workers both now and in this robot future. Should everyone be given free money? Or should everyone receive the guarantee of a decently-paid job?

Various non-profits, polemicists and even Silicon Valley types have thrown their weight behind the “free money” idea in the form of a universal basic income, while US senators including Bernie Sanders, Elizabeth Warren, Cory Booker and Kirsten Gillibrand have been pushing for trials of a jobs guarantee.

Basic income or basic jobs? There are countless details for the policy wonks to argue over, but what interests me at the moment is the psychology. In a world of mass technological unemployment, would either of these two remedies make us happy?

Author Rutger Bregman (UK) (US) describes a basic income in glowing terms, as “venture capital for everyone”. He sees the cash as liberation from abusive working conditions, and a potential launch pad to creative and fulfilling projects.

Yet the economist Edward Glaeser views a basic income as a “horror” for the recipients. “You’re telling them their lives are not going to be ones of contribution,” he remarked in a recent interview with the EconTalk podcast. “Their lives aren’t going to be producing a product that anyone values.”

Surely both of them have a point. A similar disagreement exists regarding the psychological effect of a basic jobs guarantee, with advocates emphasising the dignity of work, while sceptics fear a Sisyphean exercise in punching the clock to do a fake job.

So what does the evidence suggest? Neither a jobs guarantee nor a basic income has been tried at scale in a modern economy, so we are forced to make educated guesses. We know that joblessness makes us miserable.

In the words of Warwick university economist Andrew Oswald: “There is overwhelming statistical evidence that involuntary unemployment produces extreme unhappiness.” What’s more, adds Prof Oswald, most of this unhappiness seems to be because of a loss of prestige, identity or self-worth. Money is only a small part of it. This suggests that the advocates of a jobs guarantee may be on to something.

In this context, it’s worth noting two recent studies of lottery winners in the Netherlands and Sweden, both of which find that big winners tend to scale back their hours rather than quitting their jobs. We seem to find something in our jobs worth holding on to.

Yet many of the trappings of work frustrate us. Researchers led by Daniel Kahneman and Alan Krueger asked people to reflect on the emotions they felt as they recalled episodes in the previous day. The most negative episodes were the evening commute, the morning commute, and work itself.

Things were better if people got to chat to colleagues while working, but (unsurprisingly) they were worse for low status jobs, or jobs for which people felt overqualified. None of which suggests that people will enjoy working on a guaranteed-job scheme.

Psychologists have found that we like and benefit from feeling in control. That is a mark in favour of a universal basic income: being unconditional, it is likely to enhance our feelings of control. The money would be ours, by right, to do with as we wish. A job guarantee might work the other way: it makes money conditional on punching the clock.

On the other hand (again!), we like to keep busy. Harvard researchers Matthew Killingsworth and Daniel Gilbert (UK) (US) have found that “a wandering mind is an unhappy mind”. And social contact is generally good for our wellbeing. Maybe guaranteed jobs would help keep us active and socially connected.

The truth is, we don’t really know. I would hesitate to pronounce with confidence about which policy might ultimately be better for our wellbeing. It is good to see that the more thoughtful advocates of either policy — or both policies simultaneously — are asking for large-scale trials to learn more.

Meanwhile, I am confident that we would all benefit from an economy that creates real jobs which are sociable, engaging, and decently paid. Grand reforms of the welfare system notwithstanding, none of us should be giving up on making work work better.

 

Written for and first published in the Financial Times on 29 June 2018.

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The topsy turvy logic of Trump’s trade tirades

When the US president attacks Canada’s prime minister as “dishonest and weak”, before staging a love-in with the dictator of North Korea, you know that the journey through the looking glass is complete.

But for economics nerds, the puzzle isn’t that Donald Trump is making concessions to a rogue state while slapping hefty tariffs on the steel and aluminium produced by his allies. It is that the entire debate about trade is upside-down and back-to-front.

Mr Trump complains of “Trade Abuse”, saying that other countries “impose massive Tariffs and Trade Barriers” while “sending their product into our country tax free”. This is narrowly true, broadly false and wholly absurd.

The narrow truth in Mr Trump’s tweet is that there are some unconscionably high tariffs around. Beyond a small quota, Canada’s average tariff on dairy imports is well over 200 per cent. The broad falsehood is the idea that only the US levies low tariffs. American tariffs are indeed low — the World Trade Organisation estimates that its weighted average tariff rate is 2.4 per cent. But at 3.1 per cent, average Canadian tariffs are only slightly higher, as are those of the EU (and therefore France, Germany, Italy and the UK). Japan’s tariffs are lower than the US.

Real obstacles to trade are higher than this, partly because there are regulatory differences that are hard to quantify, and partly because by looking at a weighted average tariff, we put less weight on any trade that has been squeezed by trade barriers. Still, tariffs between rich countries are low and there is nothing obviously unfair about the situation. If Canada’s average tariff of 3.1 per cent is cause for a trade war, it is hard to imagine a victory for either side being anything other than pyrrhic.

Now let us ponder the absurdity, the real topsy-turvy part of the entire argument. Most people — including Mr Trump — intuitively believe that most victims of Canada’s huge dairy tariff are American. They aren’t. They’re Canadian.

Any Canadian who drinks milk or eats cheese pays a higher price because inexpensive dairy imports are being taxed at the border.

And there’s a more subtle cost to Canadians, perhaps best understood by imagining a simplified Canadian economy with only two goods, milk and cars. The more milk these Canadians import, the more cars they will have to export to pay for it. A tariff on dairy imports will have exactly the same effect as a tax on car exports. The economist Abba Lerner proved this in 1936.

In the real world the dairy tariff acts as a tax on Canadian exports of anything from maple syrup to aeroplanes. Intuitively, it seems like an obstacle directed against foreign importers. But it makes just as much sense to see it as the result of an internal power struggle that Canadian dairy farmers have won — and Canadian miners, manufacturers and milkshake drinkers have lost.

The same is true for Mr Trump’s new steel and aluminium tariffs. Ostensibly an attack on perfidious foreigners, the tariffs hurt any American who directly or indirectly uses steel or aluminium, all 327m of them. And by obstructing US imports they obstruct US exports, too.

This simplified analysis leaves out some important facts. It is unclear, however, that the omissions change the argument.

There is the China shock: some US communities were damaged by the inflow of cheap Chinese imports beginning in the late 1990s. The damage was local, but deep and lasting. Still, overall US consumers benefited enormously from these Chinese imports, and in any case it’s hard to blame Canada.

There is the US trade deficit. This is the result of the world’s insatiable desire to invest in US assets, coupled with the American consumer’s preference to spend rather than save. It has little to do with tariffs on milk powder or anything else.

There is a case for “infant industry” protection — using trade barriers to shield a new industry from competition while it finds its feet. The trick is harder than it sounds, and often fails, but some emerging Asian economies have used it to great effect. But what is the infant industry in the US? It is the fading industrial titans, not the infants, that usually manage to lobby for protection.

There is the need for rules to determine what counts as a trade barrier or a subsidy. These rules rely on international institutions and norms. A happy side effect of the institutions is that they help defang lobby groups eager to blame foreigners while picking the pocket of consumers. The US wrote many of these rules. As a lone bully it is just conceivable that it might do better without them, but it is more likely simply to lose less heavily than some.

All these complications are real, but they do not change the fundamental nature of the argument about trade, which was best summarised by the British economist Joan Robinson. In 1937 she pointed out that, except as a narrow negotiating ploy, it made little sense to meet tariffs with tariffs: “It would be just as sensible to drop rocks into our harbours because other nations have rocky coasts.”

 

Written for and first published in the Financial Times on 15 June 2018.

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Football’s minnows demonstrate how poor countries can catch up

Is the rest of the world catching up with the leading countries? That depends. If you are talking about economic productivity, the answer is unclear. If you are talking about football results, that is another story.

Putting aside football for now (it will not be gone for long) let us focus instead on the living standards of everyone on the planet. “Convergence” — the idea that poor countries grow faster than rich ones — is an important idea.

In a very poor country, the return on a few simple investments should be very high. Adding a paved road between two towns makes a bigger difference than adding a new lane to a road that already exists. The same is true for power lines, railways and ports. So capital should flow to poorer countries and they should grow faster than rich countries.

That is the theory, at least, and it seems plausible when one ponders the dazzling growth of postwar Japan and Germany, South Korea in the 1970s and 1980s, China, and oft-overlooked success stories such as Ethiopia.

When poor countries grow quickly, people escape from poverty and global inequality tends to fall. So if convergence was the natural state of affairs it would be good news. Alas, in the words of economist Dani Rodrik: “empirical work has not been kind to this proposition”. The crude historical fact is that the world economy sharply diverged between 1820 and 1990, with today’s rich nations expanding their share of world income from 20 per cent to 70 per cent.

That trend has been sharply reversed since then, says Richard Baldwin, an economist and author of The Great Convergence (UK) (US). But, as Prof Baldwin notes, the catch-up is highly concentrated. Between 1970 and 2010, six major industrialising countries, China, Korea, India, Poland, Indonesia and Thailand, expanded from having close to zero to more than one-quarter of world manufacturing output. The G7 has slipped from two-thirds to 50 per cent. The rest of the world has been treading water.

So economists have largely abandoned the idea of convergence as a universal phenomenon. They speak instead of “conditional convergence”. Convergence will not save Venezuela nor North Korea from catastrophic governments, but it will happen if you get the right combination of policy, institutions and economic pixie dust. Quite what that combination is and why some nations struggle to achieve it remains the trillion-dollar question.

Prof Rodrik has found evidence that unconditional convergence does happen, not for economies as a whole but for specific manufacturing sectors in those economies, such as “macaroni and noodles” or “knitted or crocheted apparel” or “plastic sacks and bags”.

If such a sector is well behind the global cutting edge, it can expect labour productivity to grow by 4-8 per cent a year, enough to double every decade or two. This tendency holds regardless of what else might be happening in the economy. Why?

The likely answer is that such manufacturing sectors get drawn into global supply chains. They can learn quickly, and must do so to respond to the incessant threat of competition. They will be doing business with suppliers and customers who can provide swift feedback and instruction. In the modern global economy, certain kinds of know-how travel fast, small tasks are unbundled, and part-finished goods and components shuttle back and forth across borders. Any enterprise plugged into this process will improve quickly. It may be more closely integrated into global supply chains than its own local economy, which might not keep up.

All of which brings us back to football. Two economists, Melanie Krause and Stefan Szymanski, decided to examine whether the unconditional convergence hypothesis holds for international men’s football, as it does for manufacturing sectors. (Prof Szymanski is the co-author, with the Financial Times’s Simon Kuper, of Soccernomics. (UK) (US)) Football, after all, offers a long data set and some clear measures of performance. International football’s governing body, Fifa, has more members than the UN.

Sure enough, Profs Krause and Szymanski found that the strength of international football teams is converging. The minnows are acquiring bite, and the old cliché, “there are no easy games in international football”, is far truer today than it was in 1950.

Perhaps we should not be surprised. As with manufacturing, the standard of competition is fierce, performance metrics unforgiving, and the very best ideas will be copied. As an additional spur to progress, elite football offers a global labour market: a strong player from a weak national team will spend most of his time at a top club side in the company of world-class dietitians, trainers, and teammates. His home nation will enjoy the benefits.

It is tempting to draw grand conclusions from all this, about the increasing importance of knowledge in globalisation; about the bracing effects of robust international competition; about the benefits of being open to international migrants. But perhaps it is better to just watch the football. In an age of distressing reality-TV politics, here, at least, is a competitive spectacle we can all enjoy.

 

 
Written for and first published in the Financial Times on 22 June 2018.

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Undercover Economist

The progressive case for auctions for everything

Korean pop sensations BTS are coming to London in October, which means the Undercover Economist spent a morning recently with multiple browser tabs open, trying in vain to secure tickets from a variety of websites that were claiming to sell them. Grey-market tickets of unknown provenance are now available at high prices.

The struggle for BTS tickets is hardly unique. The World Cup is coming up, and then there’s Wimbledon, and don’t even get me started on Hamilton.

Economists have had a solution to such frustrations for so long that it is becoming our cliché. The problem is that a limited supply of BTS tickets is priced too cheaply relative to demand. Auctioning off the tickets to the highest bidders would fix that problem, and all but eliminate touts and scalpers: if true BTS fans have already submitted their highest bids in the auction, there is little profit left for re-sellers.

Of course, some people find auctions distasteful, and BTS may not want to be tainted by that. But leaving such qualms aside, the auction design problem itself is more intriguing than it might appear.

What seems at first to be a single commodity — concert tickets — is actually a cluster of related products. Standing, seated, on Wednesday, on Thursday, far back, close up, two tickets for the diehard fans among us or five for the whole family — different combinations of these products might have appealed, depending on their absolute prices and their prices relative to each other. And BTS might have been willing to perform an extra show on the Tuesday, too, if there was enough demand.

Elizabeth Baldwin and Paul Klemperer, economists at Oxford university, have designed auction software that can easily accommodate such complexity. I could have submitted a bid for tickets along the lines of: “Five tickets at up to £50, or two tickets at up to £100, an extra £5 if it’s Thursday rather than Wednesday and up to £20 a head more for the VIP package.”

Looking at hundreds of thousands of such bids, aggregated by a computer, BTS could sell tickets to the keenest bidders, but could also adjust the mix on offer by installing some seats in an area previously reserved for standing, or arranging an extra tour date. It could easily allow favourable terms for fans with proven loyalty.

A more important (and likely) use of such a “product mix” auction would be a treasury raising money from the bond markets. How much money might be borrowed, and at what maturities, would depend on the demand. It is more efficient to combine several separate bond auctions into a single process.

The Bank of England has used “product mix auctions” to inject liquidity into the banking system. They could be used in most situations where a range of similar goods might be bought or sold in different mixes, depending on relative prices.

I confess to having a soft spot for auctions; I wrote a thesis on the subject two decades ago, and Prof Klemperer was my supervisor. But even I hesitate at the radicalism of some economists — such as Eric Posner and Glen Weyl, authors of a new book, Radical Markets. (UK) (US)

One of several eye-catching ideas in the book is, in effect, that everyone’s big possessions — your car, your house — should be up for auction all the time. I would put a price on my car — say, £5,000. It would go into a searchable public register, and if you thought the price was attractive, you could buy it. Having named the price, I would have no right to refuse.

If my car had a sentimental value, I could put a higher price on it to ensure that I kept it. (£5,000 is several times its value already.) But I would hesitate to name a crazy price like £5m, because under Messrs Posner and Weyl’s system, my taxes would be determined by the self-declared value of my assets.

This system has enormous potential — simple, fair, progressive taxes and a more dynamic economy. It would be much easier to develop new infrastructure, build new homes, buy your neighbour’s garden, and pour concrete all over twee villages to build monorails or airport runways.

I once tried to buy a scrap of underused backyard in Hackney. The owner agreed a price, then on reflection, tripled it, and the deal fell through. Neither of us will ever know whether a good trade was scuppered by our posturing.

Under Messrs Posner and Weyl’s system, such questions would not arise: mutually beneficial trades would not be derailed. And compulsory purchase would no longer be a piece of contested bureaucratic coercion: homeowners would name their price (and thus their tax bill) and the developers of Heathrow airport, the Hyperloop, or the latest stunt skyscraper would decide whether or not to pay it.

Not everyone would see these as advantages; I do. But the idea of posting a public selling price for all my property makes even an auction fan like me a little squeamish.

It is not necessary to go so far to ensure a more dynamic economy. Before we consider an auction for villages, kidneys, immigration visas, or my garden shed, I hope that we will at least get round to auctioning off tickets to see BTS.

 

Written for and first published in the Financial Times on 8 June 2018.

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Undercover Economist

A simple way for computers to improve our economic forecasts

I am not one of those clever people who claims to have seen the 2008 financial crisis coming, but by this time 10 years ago I could see that the fallout was going to be bad. Banking crises are always damaging, and this was a big one. The depth of the recession and the long-lasting hit to productivity came as no surprise to me. I knew it would happen.

Or did I? This is the story I tell myself, but if I am honest I do not really know. I did not keep a diary, and so must rely on my memory — which, it turns out, is not a reliable servant. In 1972, the psychologists Baruch Fischhoff and Ruth Beyth conducted a survey in which they asked for predictions about Richard Nixon’s imminent presidential visit to China and Russia. How likely was it that Nixon and Mao Zedong would meet? What were the chances that the US would grant diplomatic recognition to China?

Professors Fischhoff and Beyth wanted to know how people would later remember their forecasts. Since their subjects had taken the unusual step of writing down a specific probability for each of 15 outcomes, one might have hoped for accuracy. But no — the subjects flattered themselves hopelessly. The Fischhoff-Beyth paper was titled, “I knew it would happen”.

This is a reminder of what a difficult task we face when we try to make big-picture macroeconomic and geopolitical forecasts. To start with, the world is a complicated place, which makes predictions challenging. For many of the subjects that interest us, there is a substantial delay between the forecast and the outcome, and this delayed feedback makes it harder to learn from our successes and failures. Even worse, as Profs Fischhoff and Beyth discovered, we systematically misremember what we once believed. Small wonder that forecasters turn to computers for help.

We have also known for a long time — since work in the 1950s by the late psychologist Paul Meehl — that simple statistical rules often outperform expert intuition. Meehl’s initial work focused on clinical cases — for example, faced with a patient suffering chest pains, could a two or three-point checklist beat the judgment of an expert doctor? The experts did not fare well.

However, Meehl’s rules, like more modern machine learning systems, require data to work. It is all very well for Amazon to forecast what impact a price drop may have on the demand for a book — and some of the most successful hedge funds use algorithmically-driven strategies — but trying to forecast the chance of Italy leaving the eurozone, or Donald Trump’s impeachment, is not as simple. Faced with an unprecedented situation, machines are no better than we are. And they may be worse.

Much of what we know about forecasting in a complex world, we know from the research of the psychologist Philip Tetlock. In the 1980s, Prof Tetlock began to build on the Fischhoff-Beyth research by soliciting specific and often long-term forecasts from a wide variety of forecasters — initially hundreds. The early results, described in Prof Tetlock’s book Expert Political Judgement (UK) (US), were not encouraging. Yet his idea of evaluating large numbers of forecasters over an extended period of time has blossomed, and some successful forecasters have emerged – as described in Tetlock’s later book, Superforecasting (UK) (US).

The latest step in this research is a “Hybrid Forecasting Tournament”, sponsored by the US Intelligence Advanced Research Projects Activity, designed to explore ways in which humans and machine learning systems can co-operate to produce better forecasts. We await the results.

If the computers do produce some insight, it may be because they can tap into data that we could hardly have imagined using before. Satellite imaging can now track the growth of crops or the stockpiling of commodities such as oil. Computers can guess at human sentiment by analysing web searches for terms such as “job seekers allowance”, mentions of “recession” in news stories, and positive emotions in tweets.

And there are stranger correlations, too. A study by economists Kasey Buckles, Daniel Hungerman and Steven Lugauer showed that a few quarters before an economic downturn in the US, the rate of conceptions also falls. Conceptions themselves may be deducible by computers tracking sales of pregnancy tests and folic acid.

Back in 1991, a psychologist named Harold Zullow published research suggesting that the emotional content of songs in the Billboard Hot 100 chart could predict recessions. Hits containing “pessimistic rumination” (“I heard it through the grapevine / Not much longer would you be mine”) tended to predict an economic downturn. His successor is a young economist named Hisam Sabouni, who reckons that a computer-aided analysis of Spotify streaming gives him an edge in forecasting stock market movements and consumer sentiment.

Will any of this prove useful for forecasting significant economic and political events? Perhaps. But for now, here is an easy way to use a computer to help you forecast: open up a spreadsheet, note down what you believe today, and regularly revisit and reflect. The simplest forecasting tip of all is to keep score.

 

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

My book “Messy: How To Be Creative and Resilient in a Tidy-Minded World” is now available in paperback both in the US and the UK – or through your local bookshop.

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