Ariel Rubinstein on @rodrikdani 's new book. (Proud to say both have done 5books interviews, Ariel on Game Theory, Dani on Globalization) https://t.co/x0rX4UQIFR
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. The second ingredient is anti-pluralism. 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 isn’t 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 (my 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 even independent of globalization. More precisely: There cannot be a 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.
It’s the time of the year for “best of the year” articles. I’m a sucker for these lists, so here’s mine. It draws not from books published in 2016, but from those I read in 2016. In reverse order:
“The Party”, by Richard McGregor. Take-away: The Chinese Communist Party is the operation system that all other Chinese institutions run on.
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.
And:
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
Reached top
Deaths
Share
1950-1989
16581
425
2.5%
1990-2009
30738
340
1.1%
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?
The first three points in this post by Olivier Blanchard are an interesting summary of what almost all macroeconomists can agree on. (Through Rüdiger Bachmann)
In the book, Kindleberger shows that there’s a pattern common to these events and that financial crises aren’t all that rare if you zoom out enough:
Speculative excess, referred to concisely as a mania, and revulsion from such excess in the form of a crisis, crash, or panic can be shown to be, if not inevitable, at least historically common. (p4)
A common sequence is followed:
What happens, basically, is that some event changes the economic outlook. New opportunities for profits are seized, and overdone, in ways so closely resembling irrationality as to constitute a mania. Once the excessive character of the upswing is realized, the financial system experiences a sort of “distress,” in the course of which the rush to reverse the expansion process may become so precipitous as to resemble panic. In the manic phase, people of wealth or credit switch out or borrow to buy real or illiquid financial assets. In panic, the reverse movement takes place, from real or financial assets to money, or repayment of debt, with a crash in the prices of […] whatever has been the subject of the mania. (p5)
And:
[…] [I]rrationality may exist insofar as economic actors choose the wrong model, fail to take account of a particular and crucial bit of information, or go so far as to suppress information that does not conform to the model implicitly adopted. (p29)
Kindleberger then writes:
The end of a period of rising prices leads to distress if investors or speculators have become used to rising prices and the paper profits implicit in them. (p103)
Causa remota of the crisis is speculation and extended credit; causa proxima is some incident which snaps the confidence of the system, makes people think of the dangers of failure, and leads them to move [from the object of speculation] back into cash. […] Prices fall. Expectations are reversed. […] The credit system itself appears shaky, and the race for liquidity is on. (p107-108)
How to avoid financial crises or deal with them?
Kindleberger identifies a rise in the leverage in the economy as the culprit:
Speculative manias gather speed through an expansion of money and credit or perhaps, in some cases, get started because of an initial expansion of money and credit. (p52)
There’s plenty of research showing that credit plays an important role for financial crises. Kaminsky and Reinhart (1999) and Schularick and Taylor (2012) provide cross-country statistical evidence that financial crises are preceded by credit booms. Mian and Sufi (2009) similarly show that the parts of the United States in which the credit supply to less financially healthy (“subprime”) households increased more strongly, also experienced more mortgage defaults during the financial crisis of 2007 onwards. This leads them to write in their book:
As it turns out, we think debt is dangerous. (p12)
And in their research (pdf) with Emil Verner, they argue it’s the supply of credit rather than demand for it that drives these cycles of debt accumulation. López-Salido, Stein and Zakrajšek also show that optimistic credit conditions predict economic downturns.
So maybe the regulator should stop credit bonanzas before they become dangerous. The central bank could “lean against the wind” in good times by sucking liquidity out of credit markets. George Akerlof and Robert Shiller put it like this:
But financial markets must also be targeted [by the central bank]. (“Animal Spirits”, p96)
Kindleberger’s response (which I found the most interesting thought of the book) is that what counts as money isn’t obvious and that it’s therefore also difficult to control the credit supply:
The problem is that “money” is an elusive construct, difficult to pin down and to fix in some desired quantity for the economy. As a historical generalization, it can be said that every time the authorities stabilize or control some quantity of money \(M\), either in absolute volume or growing along a predetermined trend line, in moments of euphoria more will be produced. (p57)
My contention is that the process is endless: fix any \(M_1\) and the market will create new forms of money in periods of boom to get around the limit and create the necessity to fix a new variable \(M_j\). (p58)
He goes through a range of possibilities of what he calls the
[…] virtually infinite set of possibilities of expanding credit on a fixed money base. (p68)
Instead - he argues - the central bank should step in when the crisis occurs:
If one cannot control expansion of credit in boom, one should at least try to halt contraction of credit in crisis. (p165)
He argues forcefully that the central bank should act as lender of last resort. This means that the central bank expands the money supply in times of crisis and provides liquidity to banks.
In a word, our conclusion is that money supply should be fixed over the long run but be elastic during the short-run crisis. The lender of last resort should exist, but his presence should be doubted. (p12)
Kindleberger is aware of the moral hazard problem: If banks know that they’ll be bailed out, then they might behave recklessly. But he says there’s no alternative (his emphasis):
The dominant argument against the a priori view that panics can be cured by being left alone is that they almost never are left alone. (p143)
He says that it shouldn’t be certain whether banks will be bailed out:
Ambiguity as to whether there will be a lender of last resort, and who it will be, may be optimal in a close-knit society. (p174)
He thinks central banks should decide on an ad-hoc basis:
The rule is that there is no rule. (p176)
Nothing we can do?
I find it discouraging to think that we live in the 21st century, but we can’t properly control the money or the credit supply and have to resort to ambiguity on whether banks will be saved to control them. I’m all for bending the rules in times of crisis, but isn’t there more we could do to not get there in the first place?
Anat Admati and Martin Hellwig argue that
banks should be required to finance more through stocks and less through deposits and bonds:
Whatever else we do, imposing significant restrictions on bank’s borrowing is a simple and highly cost-effective way to reduce risks to the economy without imposing any significant cost on society. (“The Bankers’ New Clothes”, p10)
The benefit of this that the owner of the bank stocks would bear losses which could avert the danger of a bank going bankrupt or the threat of a bank run.
Similarly, Atif Mian says about nominal debt:
The key characteristic of debt, which makes it so destructive at times for the macroeconomy is the inability of a debt contract to share risk between the borrower and the lender. And in particular when I say “share risk”, it’s really the downside risk that we’re talking about.
[...]
We want to move away from a world where debt is the predominant contract. (link)
Doomed if we do, doomed if we don’t
But Hans-Joachim Voth writes:
“The optimal number of financial crises is not zero” (download pdf)
This is based on the evidence by Roman Rancière, Aaron Tornell and Frank Westermann that countries that experience more drastic contractions in credit tend to do better economically in the long run than countries who cripple their financial institutions and hence have stable but inefficient financial systems.
Other researchers studying a longer time horizon than Rancière et al. document that we traded lower real volatility against fewer but more harmful crises.
Rancière et al. also write:
We would like to emphasize that the fact that systemic risk can be good for growth does not mean that it is necessarily good for welfare. (“Systemic crises and growth”, p404)
And that is because we don’t like what follows financial crises if they lead to an emotional scarring that brings political polarization, a loss of trust and a lasting unwillingness to bear risks.
Liberalized financial markets were probably good for growth. But if they mean severe rare crises, then maybe it wasn’t a good choice and we should return to a world of more boring, safer banking. And we might even want to give up some of our future prosperity for that.