Dear August, there are lots of different ways of measuring income distribution and lots of difference income concepts over which you can measure inequality. As we said in the book, we averaged three years of figures published by the UN which were basically the same as the ones published by the World Bank. They came originally from the Luxembourg Income Study (LIS) which was set up to help produce internationally comparable figures. In our use of data we have an absolute rule that we take the data as published in our source. If we started to pick and choose data points that would be an important potential source of bias. As an example, look at figure 6.4 showing infant mortality. We included Singapore even though we cannot believe it has the lowest infant mortality in the world and in strongly detracts from the relationship with inequality.
I have since become aware that OECD quote very different figures for Japan which come from a different Japanese income survey from the other one, but I have not found out what the difference is in what the two surveys measure. However, my impression is that the OECD figures have not been through the same careful processing to maximise international comparability as those on LIS. But this is not a major problem for our thesis. If you take Japan out of our index graph (the one where we put al the health and social problems together), or put Japan almost anywhere else, there is still a strong relation between inequality and our index of health and social problems.
Australian research on trends in income inequality: As I have often said, there is a background rate of improvement in life expectancy - we gain two or three years with every decade that passes and in most developed countries that has been going on for over 100 years and there are no adequate explanations of it - though I am sure it used to be driven substantially be the long term (not short term) effects of economic growth. The forces behind this are not only unknown, but have changed. Since about 1975 death rates have been falling at older ages where little progress had previously been made. Time trends will be powerfully effected by the background rate of improvement, but the fact that there are now about 200 studies looking at health in relation to inequality shows that inequality must be a factor yet it cannot be anything like the most important factor. The life expectancy and inequality relationships in our book are one of the weaker ones we discuss, and what inequality seems to do is to make small differences in the background rate of improvement in life expectancy enjoyed by almost all rich countries. To correlate century long changes over time would mean controlling for the unknown background rate of change. And what the residual would then be correlated with are basically two periods of change. Inequality was high in most of the English speaking countries until about 1930, it then fell almost continuously till sometime in the 1970s and started rising again. In doing time series analysis, no one knows what lag periods to use: they should probably be different for every cause of death and every age group: we do know for example that health in later life is strongly influenced by early childhood experience. The fact that most older people dying now were probably born in a period of increasing equality yet die in a period of increasing inequality points to some of the difficulties in doing time series.
I don't have any doubt that Angus Deaton is wrong about the issue being poverty. Many studies have controlled not only for poverty, but also the effect of individual incomes across society. And absolute poverty could never explain why the effects of inequality go all the way across society. Nor could they exlpain the total lack of relation between most of our outcomes and Gross National Income per head among the richaest countries - see for instance Figures 1.1 and 1.3.
We will have to spend too much time in the future defending our thesis from fairly simple criticism even though we only shows what many people have believed for hundreds of years is true.
I am attaching a paper - a commentary - on the difficulty of understanding what is driving the long term improvement in health. You will understand that I cannot spare enough time to answer too many individual questions.
With best wishes, Richard
Och angående Tino
Sanandajis påstående att inequality is positively correlated with life expectancy då man använder sig av OECDs Gini-statistik, skriver Wilkinson följande:
Dear August, - just to add to what I said earlier, this morning we looked at our Index of Health and Social Problems in relation to the OECD Ginis you mentioned. The correlation is only a little weaker - close to 0.7 - and almost entirely because Japan is an outlier on these figures. You might like to look at the Powerpoint slide of that relationship which I have attached. We will add something on this to our FAQs. Add it to your blog etc if you want, but the most important thing is that the OECD figs do not make much difference to the correlation - except for Japan.
Wilkinsons nya Gini-index (Index of health and social problems in relation to inequality):
Wilkinson svarar kort på Tinos specifika kommentarer kring life expectancy och inequality (se tex. kommentarsfältet till detta blogginlägg):
Sorry - far to busy to follow up this stuff. I know the right will be doing everything to get rid of our material but we don't have time for blogs etc now. I notice country names are not given and assume he has included countries which are not among the richest in the world and so should control for GNP before looking at inequality. Should also try looking at mortality among infants and working age populations.
Wilkinson ger ett mer utförligt svar på Tinos gini-index:
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.
Wilkinsons svarar på Tinos senaste inlägg där det hävdas att Wilkinson nu ''erkänt'' att ''there is no statistically significant relationship between life expectancy and inequality'':
You are clutching at straws. You forget the 200 papers on inequality and health covering rich and poor countries, regions of Russia and China, S. America, the analyses of the 50 US states, plus the reasons why you might sometimes loose a simply cross-sectional relationship (to the naive delight of critics) and our knowledge of life-long causal mechanisms.
You might also like to read a recent meta-analysis of multilevel models of the relation between income distribution and health which covered individual data on 60 million people. With it I attach our editorial comment - Both from the British Medical Journal.
I cannot spare the time to continue with this.
Här har jag, för den intresserade, laddat upp alla filer (index etc.) som Wilkinson bifogade i sina mail.