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TheAncientGeek comments on Political ideas meant to provoke thought - Less Wrong
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As Taleb points out here it's not even clear that socialism promotes less inequality.
Apple's and oranges. Virtually nowhere is socialist in the one party state sense.
The point is that Europe is more socialist than the US.
Europe is also more equal than the US. The counterargument put forward to that is that the Iron Curtain countries were not particularly egalitarian. My countercounterargument is that social democracy is not commensurable with single party state socialism.
That is precisely the claim being disputed. In particular, as Taleb points out in the document I quoted in the great-grandparent, when you stop trying to use static measures of inequality and instead base it on the amount of turnover at the top, you see that Europe is much more unequal (almost an oligarchy) than the US.
Europe is more equal on empirical measures such as the Gini Coefficient.
The comment in Talebs aphorisms does not refute that, because it is not evidence based. Instead, Taleb is making some sort of circular, ideology driven argument...that Europe is "socialist" and under "socialism" the state runs everything., therefore no healthy competition, therefore stasis..but no. in Socially Democratic Europe, the government does not intervene in the boardroom.
What's more, the empirical evidence actually contradicts Talebs untested expectation:
"according to the latest Global 500 CEO Departures™ study by global public relations firm Weber Shandwick, departing European chief executives were also more likely to be forced out of office than North American and Asia Pacific CEOs during this 2007 time period."
http://www.reputationrx.com/Default.aspx/CEOTURNOVER/GLOBAL500CEODEPARTURES%E2%84%A2andCEODEPARTURESSTUDY%E2%84%A2
Here is Taleb's paper about the problems with measures like the Gini Coefficient.
If I understand Taleb correctly, his objection is that if X's distribution's upper tail tends to a power law with small enough (negated) exponent α, then sample proportions of X going to the distribution's top end are inconsistent under aggregation, and suffer a bias that decreases with sample size. And since the Gini coefficient is such a measure, it has these problems.
However, Taleb & Douady give me the impression that the quantitative effect of these problems is substantial only when α is appreciably less than 2. (The sole graphical example for which T&D mention a specific α, their figure 1, uses α = 1.1). But I have a hard time seeing how α can really be that small for income & wealth, because that'd imply mean income & mean wealth aren't well-defined in the population, which must be false because no one actually has, or is earning, infinitely many dollars or euros.
[Edit after E_N's response: changed "a bias that rises with sample size" to "a bias that decreases with sample size", I got that the wrong way round.]
Um no. They're not well defined over the distribution, they will certainly be well defined over a finite population.
You seem to be confused about how distributions with infinite means work. Here's a good exercise: get some coins and flip them to obtain data in a St. Petersburg distribution notice that even though the distribution has infinite mean all your data points are still finite (and quite small).
I'm lost. A statistical distribution characterizes a population (whether the population is an abstract construction or a literal concrete population); if the mean isn't well-defined for the population it oughtn't be well-defined for the distribution allegedly characterizing the population.
Taking annual income for concreteness, the support of a power law distribution would include, for example, $69 quadrillion. But no one actually earns so much (global economic activity, denominated in dollars, is simply too small), so the support of the actual annual income distribution must exclude $69 quadrillion. Consequently the actual annual income distribution and the power law distribution cannot actually be the same distribution; they have different support.
In the case of the St. Petersburg distribution one defines an abstract data-generating process which, by construction, implies a particular distribution with infinite mean. In the case of people's incomes or wealth, by contrast, we know that the output of the data-generating process is constrained from above by the size of the economy, so the resulting population (and the distribution representing that population) must have finite mean income and finite mean wealth. (It's as if we were talking about an imperfect real-life instantiation of the St. Petersburg process where we knew the casino had a limited amount of money.)
No, I think the argument Eugine is refering to is that more companies in the SAP 500 weren't there 50 years ago then corresponding European indexes. It's a data driven argument. I'm however not sure that it measures "equality".
Me neither. Turnover in companies isn't the same as turnover of CEOs....and the relationship between CEo stability and oligarchy, in the polsci sense, is rather murky too.