Inequality kills

Published on Real-World Economics Review Blog, by Merijn Knibbe, October 23, 2010.

Inequality considered: what do agent based macro data on wealth and health tell us about the consequences of inequality?

1. Introduction

Inequality is back into the limelight. Of course, we already knew that, especially in Anglo Saxon countries, neo liberal policies had caused an increase in inequality (just consult consecutive versions of the UN World Development reports {Human Development Report 2009, M Economy and inequality}) … // 

… 4. Inequality kills, in the Netherlands. What kinds of method should have been used to prove this internationally?

October 11th, the Dutch Centraal Bureau voor de Statistiek (CBS) published three studies on differences in income, wealth and health (Brakel en Knoops, 2010; Bruggink, 2010; Wingen, Berger-Van Sijl, Kunst and Otten, 2010). These studies are based on ‘agent based statistics’ on income and (perceived) pathology. The ‘individual tax number’ of Dutch citizens makes it possible to match different sets of individual data on  incomes, wealth and health, which  in turn makes it possible to compare ‘standardized household’ incomes with health and life expectancy. This is as good as it gets. Brakel and Knoops state that (my translation): “Internationally, no other studies comparing healthy life expectancy between people with a lower income and people with a higher income (household income, M.K.) are known”. The results of these studies are clear: inequality kills, inequality is sickening. People with an income below the poverty threshold (10.020 Euro) have a lower life expectancy (6 years) and a lower ‘healthy life expectancy’ (16 years, that is: sixteen years.). In the Netherlands, a reasonably equal country with a well organized health care system and excellent pre- and post natal care, which boasts the tallest people of the world (a sensitive indicator of health during childhood) people who are ‘not poor’ (i.e. with an income higher than 10.020,–) can expect sixteen healthy years more than poor people. And the differences get larger when we look at rich people: wealth, as well as health, as well as the combination of health and wealth have independent positive relationships with health and life expectancy. High income, high wealth people have much longer and very much healthier lives than low income, low wealth people. Methodological improvement no. 1: it’s not enough to look at income inequality, wealth inequality counts too, as well as the combination of wealth and income (to be sure: Saunders does mention that wealth inequality in the Scandinavian states is higher than in the U.K., which to me was a real surprise). Methodological improvement no 2: we do not only have to look at averages per country but also at differences within countries: are differences in, for instance, life expectancy or teenage births between the lowest and the highest quintile higher in the (unequal) USA or U.K. than in the (more equal) Netherlands or Japan? The reason to do this is of course the well known ‘omitted explaining variable’ problem. If there are omitted variables which influence the average level of life expectancy it is better to look at the differences between income groups (though there may also been omitted variables which influence differences). Just looking at averages might hide the real differences.

Where does this leave us?

The results of all these studies (including Saunders and Gordon) are clear. Inequality increased during the last decades – but surely not everywhere. (France is, according to Gordon, a clear exception). Inequality is, beyond any doubt, statistically connected with social pathologies like large differences in individual health, though we do have to take demographic, economic, geographical and cultural differences into account when we want to understand and explain these differences (nothing new here, in fact).  Even in rather equal and well organized countries, there are large differences in life expectancy and even larger differences in health between income groups, not only when we compare the poor with the rich but even when we compare the poor with the ‘not poor’. Being poor is bad for you. But is it also, as Pickett and Wilkinson imply, also bad for the others? I think we indeed do need more precise data while we do have to take complexity into account. But Saunders clearly proofs that there are societies which can manage equality as well as low levels of social pathology and a high life expectancy. And can he mention one example of the opposite? The USA? Russia? Mexico? Israel? Portugal (lots of homicides, over there)? Also: has somebody already calculated the costs of sixteen more unhealthy years?

P.S. when preparing this blog I stumbled upon what is in all probability a Tinbergen article (CBS, 1946). In this article, income inequality in Denmark, the U.K, the USA, France and the Netherlands between (about) 1915 and (about) 1942 is measured with Pareto’s alpha (The ‘smell test’: yes, Tinbergen, who during World War II worked at the CBS). It’s not a new discussion. The results: Inequality decreased everywhere after WWI and again after about 1930. The 1920’s increase was specific to the USA. Underscoring Saunders ‘cultural’ explanation of inequality: between 1921 and 1939 Denmark was by far the most equal country of the lot. And now that I think of it: the ‘Saez approach’ to economics has a very distinct Tinbergen-smell to it … (full text).

(From: Peter Söderbaum “A financial crisis on top of the ecological crisis: Ending the monopoly of neoclassical economics”, real-world economics review, issue no. 49, 12 March 2009, pp. 8-19).

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