Part of the Survivorship bias (and the reason it is sometimes called an observation selection effect) is that countries that lose out, lose most of their records, or never had their economic records. So you can't really use all relevant data because the data's gone. Example: Try tracking Germany's stockmarket 1940-1945 (necessary for any annualized global growth rate). It's pretty hard, and all we can really say is 'It fell. A lot.' Which is hardly helpful. The studies I've read seem to indicate that global growth is overestimated by about 1% because of this.
I don't think that you'd lower the productivity growth, because it is accurate *assuming* no freak events occur like WWIII, or a war which took out the US as a sovereign entity- just increase the margin in which you have confidence.


~Maru
Erik Reuter wrote:
No, if it is a significant effect which lowers the productivity
growth, then you don't widen the margin of error. You would lower the
productivity growth estimate across the board. Which puts SS in worse
financial shape. Unless you are arguing that the countries that were
left out actually had higher productivity growth than the ones that were
included.

Personally, I don't think it is a big effect. The usual way to guard
against survivorship bias is to use all relevant data. In this case, you
would choose your samples at the beginning of the period, rather than
the end of the period. To see how one expects productivity to grow in a
country with a well-developed economy and free market, one should start
by going back to, say 1900, and identifying all of the countries in
existence at that time which had a well developed free market economy.
Then include all of them in the model.

If you think there is significant survivorship bias in the study I
referenced, then you should be able to point to a number of countries
with well developed free markets in 1900 that were not included.

--
Erik Reuter   http://www.erikreuter.net/
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