Most job tasks, most activities, involve a panoply of skills. Brains and brawn. Technical expertise and expert judgment. Or “perspiration and inspiration”, in the words of Mark Twain. In general, all of these tasks need to be done to accomplish the work. So eliminating one set of them doesn’t mean there’s nothing left to do and in economic terms, automating a subset of the tasks increases the economic value of what’s remaining. It complements those workers. (link)
He also ventures into the marriage market, unequal child investment (the Trump family gets a laugh) and even artificial intelligence and the technological singularity.
Fundamentally, when we’re concerned about automation we’re talking about a concern about rising productivity, right? We’re not talking about shielding ourselves from a bomb that’s falling onto our city. The bomb is exploding productivity. It’s that we’re actually being able to do more and more with less. That may create a distributional problem, as I’ve highlighted in this talk. But it doesn’t create a wealth problem. It means we have lots and lots of wealth. (link)
[T]o make the inflation rate meaningful, we must condense this distribution of prices to a measure of, as statisticians would call it, “central tendency.” However, reasonable people can differ on the proper measure because the distribution of price changes has long “tails.”
Within this basket, the distribution of price changes is usually approximately symmetric, […]. The interesting exception is during the Great Recession period, when commodity prices fell sharply, bringing a strong negative skewness for the first time since the mid-1980s. […] In this period when the economy seemed to be in tremendous flux, the headline, average CPI moved little. However, the skewness—and the tails of the price distribution—changed quite a bit.
Why don’t easy to use, helpful online banking accounts like the one described here exist?
Suppose you could design the interface for your own investing software. […] What would you put on that first landing page?
For mine, I’d intentionally not show most of what shows up on DIY brokerage sites today:
The shares/price/value of each position I hold
Whether those positions are in a gain or a loss
The historical performance
Market news related to my holdings
These aren’t just useless for making forward-looking decisions – they’re actively harmful.
New UBS Public Paper by Dominic Rohner (download pdf) on conflicts and institutions.
Over the years, my brain has banished Chinese. I dream in English. I talk to myself in English. And memories—not only those about America but also those about China; not only those carried with me but also those archived with the wish to forget—are sorted in English.
29% of German economists think Italy should exit the eurozone (in German).
In Germany, the book the “Knigge” describes the etiquette that the upper-class ought to follow. The first version was published by the nobleman Adolph Freiherr Knigge in 1788 as “Über den Umgang mit den Menschen” (roughly “How to interact with people”). Many updated Knigges have since been published and they reflect the values of their times. I read the version by Kurt von Weißenfeld “Der moderne Knigge” from 1950:
I liked the part on children: Children who’re kept busy with good activities are well-behaved children. Sensible ways to engage children must be taylored to their abilities and interests (“Interessenwelt”). (p19)
Don’t take your issues out on your children. (p20)
On relationships, a sentence that might have come from Esther Perel: Where there is closeness, there is soon tightness. (p29)
A surprising amount of text is devoted to how a wife should treat a husband who comes home in a bad mood. Apparently, wife who doesn’t remain silent when the husband is in a bad mood is a “dumb goose” (p30). The supposedly correct response is for the wife to go to another room and leave cigarettes and cognac with him (p31).
One of a wife’s duties is to install “half a dozen” ash trays in every room and empty them twice a day. She should also fake enjoying her husbands hobbies, such as playing cards.
And then there’s this:
“Everybody considers Mrs Else enchanting”
“But enchanting she is only for others. At home she doesn’t even bother to put on a dress or make her hair. Such a “grabby” woman easily repels her husband, just as a too dashing man repels a woman.
To top it all off, Weißenfeld delves into the 1788 Knigge’s treatment of women and how society has progressed and is now “egalitarian”.
In the book, Müller argues that populists set themselves apart by claiming to be the sole representative of the real people. And while all populists are against the elites and the establishments, that is only a necessary, not a sufficient condition for popularism. In addition, anti-pluralism is key. So populists aren’t democratic, as democracy is necessarily pluralistic. Müller says that in a sensible debate about populism, one must talk about what to expect from democracy.
Parties used to be based on distinct social identities. […] Partisanship didn’t detract from, but increased, the legitimacy of the political system. Parties were not one mechanism among others that made ‘mass democracy’ acceptable: they were the principal means of transmitting popular will and opinion from civil society to the state. […] [P]oliticians could not simply go in search of support from the people as whole or adopt what Mair terms ‘the politics of “what works”’. The question was not ‘what works?’ but ‘what works for us?’ And that self-interest on the part of multiple constituencies was precisely what made democracy work as a whole.
Citing Mair, Müller writes in his book that parties are increasingly responsible, but have become less responsive.
It’s an argument against outsourcing our decision-making to a meritocratic technocracy. We would hope that non-elected institutions such as constitutional courts, central banks or nudge units can decide what’s best for us, but more delegation comes at a cost.
Some see popularism as a “necessary corrective” in democracies. But Müller instead considers populists a danger to democracy. And the response to populism cannot be to shut populists out of the debate, but the answer itself must be democratic.
He also offers an interesting discussion of the normative status of pluralism which he thinks is not a first-order normative value such as freedom or equality. Instead it follows from the freedom and equality of a diverse people that their different views must be respected.
My favorite quote from the book is this (in my crude translation):1
In times of globalization – that is, porous or blurring borders – populists suggest with their “We” unambiguous affiliations and clear boundaries (“our western civilization”) – and all “true” Germans will know what is meant by that. Democracy, however, does not do well with unambiguousness and that is independent of globalization. More to the point: There is no democratic foundation of borders. Because for defining borders through the demos, one would have to know who the sovereign people is – and that is exactly the question.
In einer Zeit der Globalisierung – sprich: durchlässiger oder gar verwischender Grenzen – suggerieren die Populisten mit ihrem “Wir” eindeutige Zugehörigkeit und klare Grenzen (“unser Abendland”) – und alle “wahren” Deutschen wissen dann schon, was gemeint ist.) Die Demokratie tut sich hingegen mit Eindeutigkeiten schwer, und zwar ganz unabhängig von der Globalisierung. Genauer gesagt: Sie kann Grenzen gar nicht demokratisch begründen. Denn um die Grenzen durch den Demos zu bestimmen, müsste man ja schon wissen, wer das entscheidungsberechtigte Volk ist – und genau das war die Frage. (p21-22)
Scott Alexander reviews Stefano DellaVigna and Devin Pope’s “Predicting Experimental Results” paper with an interesting reference:
I think that, as in Superforecasting, the best explanation is a separate “rationality” skill which is somewhat predicted by high IQ and scientific training, but not identical to either of them.
Another important caveat: predictive tasks are different than interpretative tasks. Ability to predict how an experiment will go without having any data differs from ability to crunch data in a complicated field and conclude that eg saturated fat causes/doesn’t cause heart attacks.
The distinction between punditry and expertise is pretty fuzzy.
The Free Europe Press mailed numerous books to dissidents in Eastern Europe, sneaking their materials past the censors wherever they could. By the end of the Cold War, “it was estimated that over ten million Western books and magazines had infiltrated the Communist half of Europe through the book-mailing program.”
How much did these efforts cost? In the case of the Congress for Cultural Freedom, surprisingly little.
We return to see this at various points in the book, but meanwhile I can promise that this book stands unashamedly with the tradition and against any modern, or postmodern, scepticism about the value of reflection.
It is quite difficult to detect any universal pattern at all: flexibility rules. Human beings can grow to make killing fields, and they can grow to make gardens.
It was 1967. It was the Age of Aquarius. It was the zenith of an extraordinary period of cultural creativity in the Anglophone world that had produced a musical fusion bomb composed of Celtic folk harmonies, the twelve-bar blues of the Mississippi delta, and a few sitar riffs knocked off, in the Great British Orientalist tradition, from Ravi Shankar. On both sides of the Atlantic, four increasingly shaggy Liverpudlians bestrode the charts.
“Im Westen nichts Neues”, by Erich Maria Remarque. Soldiers in the ditch used the same swear words and played the same card games as us.
I like its discussion of the ebb and flow of our favorite methods, but I think they miss something here:
Before, economists would try to predict things using only a few inputs. With machine learning, the data speak for themselves; the machine learns which inputs generate the most accurate predictions.
Non-structural, statistical time-series forecasting (factor VARs anyone?) is nothing new. The way more interesting application of machine learning for economists is to measure concepts that were previously out of our reach and to use such new data in our causal studies. See, for example, here or here.
New paper by Leo Bursztyn, Philipp Ager and Hans‐Joachim Voth (pdf):
How can [fighter pilots] be motivated to [risk their lives]? We study the role of awards and of status competition. We collect and compile new, detailed data on monthly victory scores of over 5,000 German pilots during World War II. Our results suggest that awards may have been an important incentive. Crucially, we find evidence of status competition: When the daily bulletin of the German armed forces mentioned the accomplishments of a particular fighter [pilot], his former peers perform markedly better. …
The authors write:
In an average month, [a] … German pilot scored 0.55 victories and faced a risk of 3.4% of exiting the sample permanently, synonymous in almost all cases with death.
How does that compare to other extreme activities?
The chance of dying while making it to a peak above 7000m in the Himalayas is also about 1-2%. Here are the numbers (totals for both members and hired personnel) according to the Himalayan Database assembled by Elizabeth Hawley (see p. 125):1
So pilots could not well expect to survive and – due to institutional change – those that survived weren’t seen as heroes and those that died weren’t revered as martyrs.
It would be interesting to learn more about the pilots’ motivations and their expectations of the end of the war and the time after. Did they bet on reaping the benefits of their status after the war? Or were they driven by duty, revenging the death of their friends and the wish to outshine their peers?