Wilkinson writes:
"These problems look serious only to the uninitiated, unaware of the vast literature. There are now about 200 peer reviewed analyses of the relation between various measures of health and inequality in different settings (see attached review). There have been times in the past where, because of rapid changes in income distribution, cross-sectional relationships have temporarily disappeared only to reappear when the new levels of inequality have had time to work their way through to affect culture and then health. As I said, death rates at older ages which now dominate life expectancy, are likely to be influenced by inequality throughout life. Although I started off working on health in relation to inequality, we now know that health is more weakly related to inequality than many of the other outcomes we look at.
Kate has quickly run through our data to see how the outcomes relate to the OECD inequality figures (see attached file). There are a few changes, some stronger, some weaker, but the vast majority much as before - see attached table. Inevitably things change, there are lag periods and there are different measures of income and income distribution. She has also included correlations for some of the relationships in question using the new inequality measures in the UN Human Dev Rpt 2009."
What you will notice in his two graphs is that Life expectancy never had a statistically significant relationship to Gini! This is even if you use his 21 cherry picked countries, and even if you use the UN HDI index instead of the OECD.
I encourage everyone to go ahead and klick on the tables. You will see with your own eyes that inequality is not related in a statistically significant way with life expectancy. P value 0.24 means that it is not even statistically significant at a 10% level (usually you require 5%).
Wilkinson is through this table ADMITTING that his main variable, inequality, does not have a robust tie to the main measure of health, life expectancy.
Mission accomplished.
He refuses to admit this in words however, choosing to avoid the subject, and claim you have to be "uninitiated" to want statistically significant results between simple and straightforward variables that his theory predicts.
Wilkinson want to remove Japan from the sample. Even if he does this it is not statistically significant at a 5% level. And what justification does he have to arbitrarily remove one country from the sample, a country with 127 million inhabitants? Can I remove the U.S from the sample when doing obesity? There is a name for this: data mining. There is another name, but let us not get nasty.
I can tell you that this is not how you do science. If you are an undergraduate student, and remove one country with no justification from your small sample in order to get closer to statistical significance, your professor will likely fail you.
Does Wilkinson admit in his lectures and in his book that life expectancy is not linked to inequality?
If he doesn't, puts up those famous diagrams and gives the audience the opposite impression ("Inequality Kills") he is committing academic fraud.
Instead we get to know the number of studies does about the issue. There is a "vast literature". Ooh, I am impressed! What he does not tell you is that many of the of those studies find no effect (I linked to a few as examples in earlier posts). The number of studies tell us nothing. There are hundreds of studies made on taxes and growth, but there is no accepted robust relationship.
Would you believe me if I told you: Taxes are proven to be bad for growth. I have no robust evidence for this, but here are references to 200 studies with mixed findings and endogeniety problems. With so many studies it must be true!
(I don't quite understand why The Spirit Level pushed all those nice convincing cross country correlations in the first place, if the argument now is not the cross country relationship, but the "vast literature". Oh well. I must be very "uninitiated" to ask these rude questions).
Of claiming something is true because there are many studies about it will not impress academics. Wilkinston, in choosing to ignore addressing the causality problem in his variables, and in ignoring statistical significance as a standard, has decided he does not need academic credibility. He will target an unsophisticated audience who don't ask too many difficult questions, such as Social Democratic party activist in need for their ideology to be supported by "science".
By the way, notice yesterday he told me to look at mortality for working age adults. "look at mortality among... the working age populations". I did, no statistically significant relationship.
He does not mention why. Ignoring this problem, he changes his story, and today writes that inequality will instead influence the old.
What is good about life expectancy is that you can account for both mortality among the old and middle ages. But OK. Let us use his latest standard: the life expectancy of the old.
I look at remaining life expectancy at 65 for men and women (the OECD does not have a table for both sexes, and since there are many more women alive at 65 we can't average them easily).
In both cases, the relationship with inequality is not statistically significant, and of the wrong sign (more unequal live slightly longer).
For women:
For men:
Now, since I am super-nice, I will graph life expectancy at 65 in 2005 with inequality 2 decades ago, for all countries that there is data.
For women:
For men:
Nothing, the opposite sign of what The Spirit Level claims again, and not statistically significant.
How many specifications have I run so far? 20? I have done the UN, the OECD, the young, the old. Except infant mortality, a robust relationship just isn't there.
This sounds like a good life though. I can find not statistically significant variables, and tour around the world selling stories to naive Social Democrats.
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