I see the point for convenience in buying a car in cash and some people might like to store their wealth in physical banknotes or make transactions in cash. But for most that’s not advisable anyway and it makes it things too easy for criminals. Also, in many countries in Europe there is already a limit on how large cash transactions may be. So I guess I see the point for getting rid of the 500 note, but I’m not quite ready to side with economists like Kenneth Rogoff who argues for abolishing cash altogether.
The profits from seigniorage that come from this underworld business should be quite large. The drug lord Pablo Escobar stored his wealth in the ground in dollar notes in Colombia and he reportedly lost 10% of his wealth every year due to “depreciation”. So if these notes were just by rats (and not stolen and spent somewhere else), then this amounts to an annual 10% tax for the United States government on Escobar’s wealth.
A similar thing is at play when bank notes from one country are used in another. Whether they are held by the Russian mafia to store wealth or by Cambodian shopsellers to trade, dollars in circulation in another country are a direct source of income for the United States government. The reason is that the Federal Reserve can print more money and exchange it for goods without pushing up domestic inflation.
So countries looking for higher seigniorage revenues have an incentive to have high denomination banknotes to tempt criminals in other countries to use their currency. Maybe it’s not a coincidence that the countries with the highest denominations are Switzerland and Singapore? So seen from that angle, capping the denomination of banknotes is a coordination game among countries.
The ECB’s decision to end usage of the 500 euro note might be a good idea, but is difficult to implement properly. For once, the stop is not immediate and, secondly, the existing 500 euro notes in circulation will keep their worth infinitely.
They’ll stop giving them out by the end of 2018. So until then, people can convert as much of their cash into 500 notes as they wish.
Will there be a second market for 500 notes after 2018? A large number of 500 euro notes are outstanding.
People might hold on to them for years. But bank notes aren’t made to last forever, so they’ll probably be used until they are worn down and at that point somebody will exchange them at a bank for different denominations.
Update: Lawrence Summers thinks the ECB’s policy makes sense but is just a start:
First, the world should demand that Switzerland stops issuing SFr 1,000 franc notes. After Europe’s bold step, these notes will stand out as the hard-currency world’s highest denomination note by a wide margin. Switzerland has a long and unfortunate history with illicit finance. It would be tragic if it were to profit from criminal currency substitution.
And I would add that it’s not just “substitution”, but benefiting from it through seigniorage.
I wrote this essay for the St. Gallen Symposium’s essay competition whose topic in 2016 was “Alternatives to Economic Growth”. I’m sharing it here after slight editing. I thank several people who commented on drafts.
Some years ago, a leading professor in economic growth said in a class I was in:
“You see here how GDP per capita has grown over the last couple of centuries. People have become much richer over time, but there have been large differences across countries.”
This was not really new to me, but what he said next I have remembered well:
“Here’s a thought experiment. Say it’s 1700 and I give you two options: You can either choose to be a rich person who lives in a poor country or you can be someone who lives in a country that’s rich, but who’s poor.”
He argued that one would likely have been better off as a rich person in a poor country. The reason is that in this pre-industrial age, differences between countries were much less pronounced than differences within countries.1
He then said:
“Now ask yourself this question today: Would you rather be a rich person in a poor country or a poor person in a rich country? I bet you would choose to be in a rich country over being a rich person.”
It impressed me, because I had never thought about these developments from this angle. So what happened in the last centuries that led to this change?
For most of human history, there was little or no growth. Since the beginning of organized agriculture and the rise of cities, a large part of society lived off a meager diet of carbohydrates like gruels while a small urban elite dined on richer foods like meat.2 The productivity of people was so similar across countries that the economic power of an entity was thought to derive directly from the number of its subjects and states should aim to increase the number of people living within their borders.3 Innovation was not seen as an important source of progress. And there were even cases of technological regress, such as the Aborigines loosing the knowledge of using bow and arrow.4
But since the late 18th century there has been real sustained growth. In the course of this revolution, factories were build, the face of the earth was altered and trade networks spanned the globe, e.g. for cotton.5 Economies’ productive capacities per person increased in real terms between 1800 and 2010 by a factor of 11 in the United Kingdom, 21 for Germany, 9 in the Netherlands and 24 in the United States.6 Consider what that means: Could you imagine your living standard to be a tenth or twentieth of what it currently is?
We call economic growth an increase in the amount and quality of goods and services that society produces and consumes. This concept is frequently invoked by politicians or economists as the target that we should aim to maximize by directing our institutions – such as laws and cultural norms – towards it. But economic growth happened very unequally across the world. Europe and its overseas offshoots participated early and grew strongly, such that by the middle of the 20th century, disparities within countries were small relative to differences between countries. This was the state of the world that the professor had alluded to. And when I looked at the data, it was true that – in 1970 – I would have been almost three times better off as a Swiss person earning an average income than as a person in India in the top one percent of the income distribution.7
But again, something happened. The market-based reforms and the globalization starting in the 1970s and 1980s allowed capital and labor to flow more freely and led to large increases in living standards in developing countries. Especially in China and India, millions of people form a new middle class. This reduced global inequality which as measured by the Gini coefficient fell from 0.70 in 1990 to 0.62 in 2010.8
This made me think that maybe the professor’s verdict did not hold anymore. The gap might have closed and a rich person in a poor country might be equally well off – or maybe even better off – than a person with an average income in a rich country. Taking the previous example, in 1999, the top percent in India earned 30 percent more than the average earner in Switzerland.9
But while we should applaud the fact that differences between countries have decreased globally, inequality within countries has been rising. Thomas Piketty, in his best-selling 2014 book, warns of rising levels of income inequality and wealth inequality which in the Anglo-Saxon countries approaches levels not seen since the First World War.10 This point is made similarly by Angus Deaton, the winner of the 2015 Nobel Memorial Prize in Economic Sciences, who analyzes global trends in health and poverty and makes a case for optimism about the state of the world. However, he cautions that in some cases of “Great Escapes’’ from poverty, the new elites have held down the people rising after them.11 He evokes the picture of a ladder being pulled up. So maybe we are seeing a return to the previous state before modern economic growth with large differences within countries and less strong differences between countries. And while the impact of globalization on inequality is almost mechanic, an old fear is emerging again: automation.
Meet Baxter (panel 1 in Figure 1): He is a friendly general-purpose robot who can be taught simple manual tasks by moving around his mechanical arm. He is safe to have around and reliable. This makes him great for jobs such as lifting objects or sorting things. Other robots bake pancakes (panel 2), teach the prayer (panel 3), carry heavy loads across dangerous terrain (panel 4), pick grapes (panel 5) or shoot rockets (panel 6).
Foxconn, the main manufacturer of Apple’s iPhone, already employs 50,000 robots in its Chinese factories and is planning to automate large parts of its production. Google is testing self-driving cars and they might become a reality on our streets. And who needs a taxi driver then?
200 years ago, the Luddites in England worried about automation in the form of mechanical looms. Since then, people have warned periodically of the rate of progress outpacing people’s ability to adapt to technological change. Some even quipped that humans will be made as redundant as horses in the advent of the automobile. However, these warnings were usually exaggerated as previous waves of technological progress replaced old jobs with new ones, leaving most people better off in the long-run. The most common recommendation of how to deal with these disruptions is to encourage more education for people to adapt faster and to gain skills that are complements, not substitutes, to machines.
But maybe that is not enough, because this time might be different in an important aspect: Technology is also advancing in fields that have previously been thought of as safe from automation. Research on artificial intelligence and machine learning is making fast progress. IBM’s algorithm Deep Blue beat the world’s best chess player, Garry Kasparov, in 1997 and Google’s AlphaGo beat the best human player at the ancient game of Go, which was considered to be much more difficult for computers to excel at, in 2016. Other algorithms have been developed to recognize songs, judge people’s attractiveness and compose music. These fears of automation are also expressed by Erik Brynjolfsson and Andrew McAfee in a recent book in which they warn of a “Second Machine Age”.
And what should worry us most is the increasing concentration of capital – be it robots or factories – in the hands of few which could lead to rising within-country inequality. Researchers have shown that across the world, the share of income going to labor has been in decline since the 1980s.13 Part of this decline can be explained through a rise in automation and computer technology. When automation advances, capital owners benefit more than workers. In the limit case, few capital owners could own all robots, while the rest of humanity would depend on their charity.
These distributional pressures raise several concerns. The first problem with inequality of wealth and income is that it is in several ways self-perpetuating. Wealthy people likely achieve higher returns on their wealth through better investment advice and opportunities. And some rich people might also use illegal opportunities for higher returns by evading taxes. Gabriel Zucman documents how France missed taxes in 2011 of about one percent of its GDP due to tax evasion.14 Changing marriage patterns work similarly. When once a doctor might have married his assistant and a CEO his secretary, couples are now more alike in their education and incomes.
Inequality has real costs for the economy as a whole. Daron Acemoğlu and James Robinson argue that in the long run countries with inclusive institutions prosper and countries with extractive institutions suffer. A concern is that the middle class in countries like the United States might not feel included anymore or – to cite Charles Murray – society is “coming apart”. This point is also supported through research by Anne Coase and Angus Deaton, who show a worsening in the mortality of middle aged white men in the United States.
And inequality is not just a problem for society as a whole, but it impacts individual people. Researchers have shown that people are not only interested in their own well-being, but have an inherent wish for a fair distribution.15 Our bodies react with unhealthy heart rates to perceived unfair distributions and even monkeys were shown to react badly to unequal rewards.1617 What we learn is that questions of efficiency cannot be analyzed separately from questions of fairness and distribution.18 So instead, let us counteract these forces and work towards a society where people participate more broadly. I propose to rearrange our priorities from generating aggregate economic growth, to encouraging inclusive growth.
Does that mean that we should not value economic growth anymore and need a complete alternative to it? No. If everybody is poor, then there is no inequality. But it is important to acknowledge that the two are not independent of each other. And we do not have to choose one of these modes for the exclusion of the other, because making sure all parts of society are included also allows for more material wellbeing for everybody.
So how do we get there? A number of solutions have been proposed. And while there is no perfect and exact solution, some argue for higher minimum wages and some for unconditional basic incomes. Brynjolfsson and McAfee push for negative income taxes, which seems promising because it subsidizes human labor and recognizes that work gives people purpose.
We should also broaden our notion of what it means to work. To rephrase from John Adams, the second president of the United States, maybe our parents studied engineering for us to have the liberty to study computer science such that our children have the liberty to study music and art.19
In 1932, Bertrand Russell reflected on how to deal with changes in how we live and work:20
“Modern methods of production have given us the possibility of ease and security for all; we have chosen, instead, to have overwork for some and starvation for others. Hitherto we have continued to be as energetic as we were before there were machines; in this we have been foolish, but there is no reason to go on being foolish forever.”
But the actual first step is to acknowledge that there is a problem. We can make our world a more inclusive place and we can start building this society now. Let’s not wait.
The top 1% in India earned an average of 133,250 rupees per year in 1970 which amounts to 12,300 in 2005 purchasing power corrected US dollars. The average tax unit in Switzerland in 1970 earned the purchasing power equivalent of 34,800 US dollars in 2005 values. Sources: Facundo Alvaredo, Anthony B. Atkinson, Thomas Piketty, Emmanuel Saez and Gabriel Zucman, The World Wealth and Income Database, http://www.wid.world, 12.01.2016. and Penn World Table 8.1. ↩
Using the same sources as before, the top 1% in India earned an average of 229,679 rupees per year in 1999. And adjusted for purchasing power differences this is equivalent to 42,731 US dollars in 2005 values. The average income per tax unit in Switzerland in real 2010 CHF in 1999 was 66,100, corrected for purchasing power differences that would be 32820 US Dollars in 2005 values. ↩
See the talk (in German) by Armin Falk: “Fairness- und Gerechtigskeitsfragen lassen sich von Effizienzfragen nicht trennen.” (“Questions of fairness and justice cannot be separated from questions of efficiency.”) [34m55s] ↩
Original quote from McCullough, David (2001). John Adams, chapter 5:
I must study politics and war that my sons may have liberty to study mathematics and philosophy. My sons ought to study mathematics and philosophy, geography, natural history, naval architecture, navigation, commerce, and agriculture, in order to give their children a right to study painting, poetry, music, architecture, statuary, tapestry, and porcelain.
By chance, I came across the first part of the biography of John Maynard Keynes by Robert Skidelsky and started reading it, not quite expecting to finish. It’s very well written and much more profound than I had hoped for in a biography.
Right from the first sentence, the book shines:
“John Maynard Keynes was not just a man of establishments; but part of the elite of each establishment of which he was a member. […]
This position was largely achieved by the force of his dazzling intellect and by his practical genius. But he did not start life without considerable advantages which helped him slip easily into the parts for which his talents destined him. There was no nonsense about his being in the wrong place or having the wrong accent. Of his chief advantages was being born at Cambridge, into a community of dons, the son of John Neville and Florence Ada Keynes.
When he was five his great-grandmother Jane Elizabeth Ford wrote to him, ‘You will be expected to be very clever, having lived always in Cambridge.’” (p1)
For an economics student, Keynes is worth reading about in any way, but the more I read about Keynes the more fascinating he becomes.
Skidelsky wrote this three-volume biography over almost twenty years with this first volume coming out in 1983 and the last volume in 2000. In this first part, Skidelsky covers Keynes’ upbringing in Cambridge, his education at Eton and Cambridge, his time in London with his Bloomsbury friends and his time at the Treasury during and after World War I.
How about this:
“He never did take an economics degree. In fact, his total professional training came to little more than eight weeks.” (p166)
“Like most economists at the time, Keynes started teaching economics without having taken a university degree in the subject. […] Compared with today, there was little to learn, and that was not difficult. […] In this way he acquired a firm understanding of a fairly limited range of theory.” (p206)
I was surprised to read that Keynes never traveled further east than Egypt.
Skidelsky explains in length the intellectual development in Cambridge which included Keynes father John Neville Keynes who he sums up with:
“In producing one book on pure, and a second on applied, logic, Neville had circumnavigated the range of his intellectual interests. He was thirty-eight. He lived another sixty years. Apart from a few contributions […] and the odd essay, his pen was henceforth confined to revising previous work, writing his diary and letters, and drafting minutes. […]
Perhaps he is to be admired, rather than pitied, for keeping silent when he had nothing to say.” (p64)
People at the time struggled with the gradual loss of theology during the transition from the Victorian to Edwardian period and searched for something to replace it. The names of the people involved are quite familiar to students of economics:
“Both [Marshall and Sidgwick] inherited the problems of collapsing theology and both engaged in essentially the same enterprise: the attempt to find authoritative theology-substitutes. […] Sidgwick was mainly a classicist; Marshall was mainly a mathematician. […] For many intellectuals brought up on Christianity still felt the need for authoritative guidance on how to conduct their lives – which they did not get from economics.” (p32)
“The difference was that Sidgwick had a need, which Keynes never had, to find a way to bring all these things into a rational, coherent, relationship with each other.” (p34)
There’s also a part on the Keynes’s family’s finances:
“The Keynes life-style was sustained by an income which was never less than comfortable, and grew more so.” (p55)
“As Maynard grew up his parents grew steadily more affluent. Capital and earnings went up, while prices went down. […]
But what strikes one today is how secure his position was. He just went on getting richer without great effort on his part. That is what the Victorians meant by progress.
Neville found his affluence all the more agreeable because his enjoyment of it was unclouded by any sense of guilt.” (p56)
This reminds me of Piketty’s “historical fact” (with the real return on wealth and the growth rate of incomes). Piketty cites Jane Austen, who implicitly states that real rates are about 5% which allows for a comfortable life of existing wealth.
It is amazing, how much of Keynes’ conversation is documented and how much of his life can be reconstructed. I recently came across this article which states that we are left with 20,000 of Goethe’s letters. How many of us keep records of our emails or our Facebook and Whatsapp conversations?
I liked this part on Keynes’ mind:
“One never feels that he had a sense of a single current of history carrying the world forward to the natural order described by the classical economists, or some other kind of utopia. Rather he was always impressed, some would say over-impressed, by the fragility of the civilisation inherited from the Victorians, by the feeling that it was an exceptional episode in human history.” (p92)
And this reminds me of many people I know who are very good at what they do:
“Once again [writing an essay at Eton] he was showing his ability to get totally absorbed in a subject remote from his official interests.” (p113)
Reading about Bloomsbury I’m reminded of my time at UCL when I often passed Keynes’ house at Gordon Square. Consider these bits about the Bloomsbury Group for example:
“For it was [G. E.] Moore who tried to redefine the content of ethical discussion by insisting that the primary question was not ‘what ought I to do’ but ‘what is good’; and that the primary question could be answered only by reference to some conception of the good life. The virtues, Moore said, have no value in themselves. They are valuable only as a means to what is good, and must be rationally proportioned to it. If Bloomsbury can be defined by a common attitude of mind – as it surely can – this is it.” (p245)
“Bloomsbury, it is true, was devoid of Christian belief. […] And there is no doubt that it encouraged, thought it did not entail, political passivity.” (p246)
“Bloomsberries, as they called themselves, might be curious about outsiders. They were also frightened of them, and could be chilling to them.
Bloomsbury was a particular expression of, and gave direction to, the ‘revolt against the Victorians’.” (p248)
I was most touched by the parts on Keynes’ and his Bloomsbury friends’ response to World War I.
“Although the Archduke […] had been assassinated on 28 June, only a month later was there a first reference [in Keynes diaries] to the worsening international situation. Characteristically it was in the context of Stock Exchange speculation. […] Next day Germany invaded Belgium. On 4 August 1914 England declared war on Germany, and Bloomsbury’s – and Maynard’s – world collapsed.” (p285)
Keynes gradually came to oppose British participation in the war and so did his Bloomsbury friends. He considered quitting his job at the Treasury, but he thought it was better to be inside the circle of knowledge. He wrote to his mother that he would resign from the Treasury only if they started
I liked this interview with David Card and Alan Krueger which I found here. Some good bits:
Krueger: […] So I encourage economists to use a variety of different research styles. What I think on the margin is more informative for economics is the type of quasi-experimental design that David and I emphasize in the book.
But the other thing I would say, which I think is underappreciated, is the great value of just simple measurement. Pure measurement. And many of the great advances in science, as well as in the social sciences, have come about because we got better telescopes or better microscopes, simply better measurement techniques.
In economics, the national income and product accounts is a good example. Collecting data on time use is another good example. And I think we underinvest in learning methods for collecting data—both survey data, administrative data, data that you can collect naturally through sensors and other means.
This reminds me of a talk by Hal Varian in Bonn last year, in which he said that one of the new frontiers in social science is to make use the data that is created when we use our smartphone or shop online.
And I knew that Scandinavia was famous for its administrative matched data, but I didn’t know that Germany stands out, too:
Krueger: We’ve long been behind Scandinavia, which has provided linked data for decades. And we’re now behind Germany, where a lot of interesting work is being done.
And this was interesting, although a bit general:
Krueger: And we haven’t caught up in terms of training students to collect original survey data. I’ve long thought we should have a course in economic methods — […] — and cover the topics that applied researchers really rely upon, but typically are forced to learn on their own. Index numbers, for example.
In 300 BC, a new plow was developed in China. It required the effort of only one oxen, where before several were needed for the same work. It had a heavier design, but overall reduced the necessary effort.
The stunning thing is that other parts of the world, and in particular Europe, did not use this better design until the 17th century AD. When it arrived, the adoption of this type of plow was important for Europe’s agricultural revolution.
Although the invention seems obvious and all necessary materials had been around for a long time, people did not come up with it. Maybe technological progress isn’t really as linear and inevitable as it seems in retrospect?
I found this in “491” by Charles C. Mann. He uses this example of the Europeans failure to invent or adopt the Chinese plow to put into perspective that the Maya’s used the wheel for toys, but not to grind maize or to carry burdens. Mann takes this from Robert Temple’s “The Genius of China” and expanding on Mann’s original citation we find (with added emphasis):
“Of all the advantages which China had for centuries over the rest of the world, the greatest was perhaps the superiority of its plows. Nothing underlines the backwardness of the West more than the fact that for thousands of years, millions of human beings plowed the earth in a manner which was so inefficient, so wasteful of effort, and so utterly exhausting that this deficiency of plowing may rank as mankind’s single greatest waste of time and energy.” (p17)
“For farmers, this was like going from the bow and arrow to the gun.” (p19)
“The increased friction meant that huge multiple teams of oxen were required, whereas Chinese plowmen could make do with a single ox, and rarely more than two. Europeans had to pool their resources, and waste valuable time and money in getting hold of six to eight oxen to plow the simplest field. […] It is no exaggeration to say that China was in the position of America or Western Europe today, and Europe was in the position of, say, Morocco.” (p20)
The following would be interesting to study:
One could first look at why people didn’t adopt the better plow much sooner. Maybe we have records on which cities or regions adopted this plow first. Do places, that adopted first, differ? I might expect larger urban places with more diverse populations that traded heavily to adopt this innovation first. This paper leads me to think that “openness to disruption” might have helped.
And there could be occasions when the arrival of the new technology could be treated as a “quasi-experiment”. Can we see the effects of new technology in action? How did output, wages and profits react?
Apparently it was the Dutch who first brought the plow back from China and then brought it to England as Dutch laborers working there. And from there it next went to America and France. And then:
“By the 1770s it was the cheapest and best plow available. […] There was no single more important element in the European agricultural revolution.” (p20)
I started from the “Dresden” theme and changed it a bit, by removing some of the unnecessary information like affiliations and using less strong colors. Also I find a black side at the end (in the PowerPoint style) quite useful to end presentations. I took the blue color from colorbrewer2 (recommended here).
I enjoy backpacking greatly and I often use the Lonely Planet’s travel guides to do so. I read Maureen and Tony Wheeler’s history of how they came to write the first Lonely Planet in which they tell how they travelled from London to Australia. This made me curious to read the original guide from 1975 and it turns out the electronic version is available freely on Amazon. Here are my thoughts on it:
Many countries back then turned you away at the border, if you looked like a hippie.
There’s a lot of talk about where to get the best dope.
Bali was back then a real island paradise.
The change of which parts where safe to travel to then and now is quite drastic. Back then, Iran, Afghanistan and Pakistan were safe to go to. People thought of them as a little boring and mostly rushed through. But Kabul, especially, was exciting. Again, mostly for the drugs. Compare that to Southeast Asia back then, were it was only safe to go to Singapore, Malaysia, Thailand and Burma.
Nowadays, the guides are more careful with their language.
Prices in nominal terms seem to be ridiculously cheap to us, but in real terms they were also very cheap.
Selling blood was a good source of income while travelling. Kuwait had the highest prices. Is this still the case?
The highway through Yugoslavia (the “Autoput”) was what my father told be about it: quite dangerous.
Money and communication were a much, much bigger hassle back then.
How good are people at forecasting political or economic events? Why are some people better than others?
Philip Tetlock and Dan Gardner have written “Superforecasting” based on a tournament started in 2011 in which they have 2800 people predict a number of events. They then scored how they did and analyze the results.
Tetlock is famous for his 2005 book “Expert Political Judgment” in which he summarizes a 20 year study in which pundits, researchers, analysts and political experts forecasted events. He finds overall disappointing forecasting performance, but is able to draw a clear line between “foxes” (who are good forecasters) and “hedgehogs” (who are not). For this metaphor, he draws on an essay by Isaiah Berlin with reference to the ancient idea of: “The Fox knows many things, but the hedgehog knows one thing well.”
Hedgehogs are guided by the one thing they know – their ideology – and they form their forecasts to fit into their way of thinking. But foxes consider different possible explanations.
I was intrigued when I first read Tetlock’s 2005 book, because it seemed to play with the debate in economics on how “structural” vs. “reduced-form” our research should be. A structural model is backed by theory and tries to explain why things happen. A reduced-form model imposes less theory and tries to find patterns in data and predict what comes next, but it usually cannot explain why things happened.
Tetlock and Gardner’s new book does not resolve this conflict. They argue, again, that those people are good at prediction who produce good ballpark estimates (what they call “fermi-tizing”) and are carefully adjusting their probability estimates when new information becomes available. I liked this bit:
“Confidence and accuracy are positively correlated. But research shows we exaggerate the size of the correlation.” (p138)
“[…] granularity predicts accuracy […].” (p145)
They criticize after-the-fact explanations with: “Yeah, but any story would fit.” This is the basic criticism of structural models. Any number of models could fit your data points. How do we know which is right?
“Religion is not the only way to satisfy the yearning for meaning. Psychologists find that many atheists also see meaning in the significant events in their lives, and a majority of atheists said they believe in fate, defined as the view that “events happen for a reason and that there is an underlying order to life that determines how events turn out.” Meaning is a basic human need. As much research shows, the ability to find it is a marker of a healthy, resilient mind.” (p148)
In my opinion, the authors don’t take the necessity for models serious enough: We need models and we want them. And, actually, we will always have a model in our mind, even if we don’t make it explicit and admit it. Even Nate Silver (who is famous for his accuracy in prediction) says:
“For example, I highly prefer […] regression-based modeling to machine learning, where you can’t really explain anything. To me, the whole value is in the explanation.”
And in fact the authors become more humble near the end:
“In reality, accuracy is often only one of many goals. Sometimes it is irrelevant. […] ‘kto, kogo?’” (p245)
This last reference is Lenin saying: “Who did what to whom?”
They describe how good forecasters average the estimates they derive from different methods. For example, taking the outside view “how likely is it that negotiations with terrorists ever work?” and then the inside view “what are the motivations of the Nigerian government and what drives Boko Haram?”.
But that only works because Tetlock’s forecasts are quite specific. They’re relevant, yet they exclude a large number of things. Of the top of my head, here’s a list of what they didn’t forecast:
Long-run events: “What will the Gini coefficient in the United States be in 2060?”, “Will China have at least 80% of the number of airport carriers of the United States in 2100?”, “Will the growth rate of German real GDP per capita be above 1% per annum from 2020-2060?”, “How likely is it that there will be a virus that kills at least 10% of the global population within 3 years from 2020-2150?”
High-frequency events: “How should we trade this stock in the next 10 seconds?”
Predictions or classifications involving a large number of objects: “Can we identify clusters in these 3 million product descriptions?”, “Do these 10 million pictures show a cat?”
The first of these events might be the most relevant of all, but they are also the most difficult to form an expectation about. The questions are unanswerable if we don’t want to wait and if we did wait Tetlock’s superforecasters might well be good at forecasting them. So I have to grant them that.
The second kind (“high-frequency prediction”), I actually find the least relevant and interesting. I think here this would really just be number-crunching, pattern-matching, so “reduced-form” in its purest form and means writing or applying algorithms to do the work. Still, we don’t really learn anything about this kind of forecasting from Tetlock’s books, but it’s what a lot of people in finance think of when they hear “prediction”.
The third has recently become more relevant, but much more so in the realm of machine learning analysts and statisticians. They are the kind of problems one might find on kaggle. Again, they’re prediction but Tetlock’s recipes don’t work here.
I like the idea of ballpark estimates and “fermitization”, but something there irritated me. Isn’t their whole point about taking all information into account and not sticking with narratives, but to instead make careful probabilistic estimates? Tetlock and Gardner discuss the example of how many piano-tuners there are in Chicago. They then go through a textbook example of how to answer a consulting interview question. They come up with an estimate of 62 and present a highly dubious empirical estimate of 80. A number of things strike me as odd: First, they next go on to say how the empirical frequencies of events should be our baseline. So shouldn’t we first have googled for it and seen, “ok, there seem to be about 80 of these guys in Chicago”. Then, in the next step, we could think about where our estimate might have gone wrong. Maybe not everybody of them has a website? Maybe we didn’t find all? Maybe there’s double counting? And then we could adjust for that. Or, you could do both, their Fermi “structural” estimate and the Googling “reduced-form” estimate and then average both using weights that depending on how relatively certain you are.
Their iterated statement that we need to measure what we are talking about, reminds me of Thomas Piketty’s, Abhijit Banerjee and Esther Duflo’s and Angus Deaton’s books who also spend large portions of their texts arguing that we need to have good data about the things we care about. I completely agree.
I also liked their discussion on how all human beings need narratives and how that might even be good for our mental health and resilience. And I do suppose I would be miserable as a superforecaster. I already devour large amounts of news, blogs and more every day, but I dread getting updates by Google News about all the topics that I would have to cover. In fact, I did consider taking part in Tetlock’s superforecasting experiment. Back in 2011, it went through the blogs and I came across it. I looked at it a bit and I thought I might enjoy it, but really I didn’t want to commit so much time to something like that. With hindsight, I’m glad I didn’t participate.
He also discusses Keynes’ citation:
“When the facts change, I change my mind. What do you do, Sir?”
This sounds like a really foxish, Bayesian statement. I recently came across the assessment by Marc Blaug (in the introduction to his book) that Keynes was initially a Fox and became a Hedgehog. Tetlock then presents the nice twist that it’s unknown if Keynes really stated that. But he was ready to admit it, because it wasn’t fundamental to his (Tetlock’s) identity.
I also like the idea of a “pre-mortem” (p202), so thinking about reasons that my project might fail. (But as for research projects, maybe it’s better to actively resist this, otherwise you never get going.)
He ends with a plea for opposing parties to get together and use their different view to come up with a better forecast:
“The key is precision.” (p268)
The problem here is that we are talking about conditional vs. unconditional forecasts. Different groups want to change that condition. Also, some forecasts are political – such as those concerning GDP or population size – where the forecast itself might even have an impact on what will happen.
Last, I also agree with Tetlocks thank you note:
“[…] I thank my coauthor […] and editor […] who helped me tell my story far better than I could have – and who had to wrestle for two years against my professional propensity to “complexify” fundamentally simple points.” (p288)
When you compare Tetlocks two books on this topic, this last is much more pleasant to read without loosing in accuracy or depth.