The Spirit Level dishonest about the state of research

The Journal of Economic Literature is the most prestigious journal for summarizing the findings of a field of research. Princeton Economist Agnus Deaton surveyed the literature on inequality and health. Deaton is not just anyone, there is talk about eventually awarding him the Nobel prize.

Deaton starts his conclusions by writing:

“The stories about income inequality affecting health are stronger than the evidence”

He further concludes:


But who are you going to trust, one of the world’s leading Health Economists in the Journal of Economic Literature, or Wilkinson and Pickets own(!) meta-study, and their claims on their book selling tour?

The Spirit Level makes a big deal about the association between inequality and health within American States. But in another paper Deaton and Lubotsky have shown that:

(from abstract) “Conditional on the fraction black, neither city nor state mortality rates are correlated with income inequality”

Here is the share of minorities in 2000 and Gini in 1999.


I did the regression to make sure: Controlling for the share of African Americans Gini has no statistically significant effect on life expectancy within American States (p value 0.431).

The Spirit Level authors are not only wrong, they are dishonest. They must be aware that their correlation within states has been proven to be due to demographic factors. They must be aware that the more heavy-weight surveys of the literature conclude that there is no robust relationship between inequality and life expectancy.

But they know that they audience does not know this, and exploit the trust of the readers to sell them a false view of the state of the science.

Scientific Nobel Prizes per capita among the OECD

Relying on Wikipedia (I believe it is reliable for this purpose, since it in turn uses the Nobel web page), I have calculated the number of scientific Nobel prizes per capita in the post war period. Because the period is 1945-2009 I have used the average population 1950-2009 from the OECD Factbook. Note that Wikipedia double counts people with affiliation to more than one country.

To make it about what everyone agrees are pure sciences, the Nobel Memorial Prize in Economic Sciences is not included. The figures are thus based on only Physics, Chemistry and Medicine.


Not surprisingly The U.S crushes the E.U.15, getting 9.8 per ten million people, compared to 4.7 for the E.U.15. The U.S alone got close to half of all scientific Nobel prizes in the post-war period, more if Economics is included. (I once did the same exercise for U.S alone, including Economics, restricting the country affiliation to what the Nobel home page wrote, and measured those with 2 affiliations as half. The U.S got 60% of all prizes).

It may be just a coincidence, but a good news for Europeans is that the E.U.15 reduced the massive gap with the U.S slightly in the 2000s compared to the 1990s. Not adjusting for population the U.S got 117% more prizes in the 1990s, and “just” 84% more in the 2000s.

What I am really impressed by is Switzerland, who has by far most prizes per capita in the world in the post war period. There is a famous quote from the movie The Third Man that “In Switzerland they had brotherly love – they had 500 years of democracy and peace, and what did that produce? The cuckoo clock.”

This is wrong, and an example of the representativeness bias. (Already by the time the movie was made, tiny Switzerland had won 8 scientific Nobel prizes).

Sweden does very well also. Someone could blame this on home bias. The Literature prize for example has an absurd number of Swedish and Nordic winners. Sweden has 6 Literature Nobel winners and the Nordic countries in total have 14! In comparison China has 1 literature prize, the United States 12. For Economics Ohlin deserved it, Myrdal probably did not. I don’t have the knowledge to judge the 3 core sciences, but hopefully they are more objective. I personally think even without home bias Sweden would do very well, they are a creative people with a strong tradition in engineering and science, especially before the welfare state started to erode some of their norms and education standards.

The Swiss case aside stereotypes are confirmed. Germany is a science powerhouse, Italy is not, to put it kindly, and France is in between.

Japan and South Korea do poorly, in line with the stereotypes that Asians are smart but not creative. However a more likely explanation is the very long lag between scientific work achievement and prize, in much of the studied period Japan and South Korea were quite underdeveloped.

Breaking: Wilkinson admits there is no statistically significant relationship between life expectancy and inequality!

Blogger August has done some nice journalism.

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.