Yeah I actually do cite that piece in the appendix ‘GDP as a proxy for welfare’ where I list more literature like this. So yeah, it’s not a perfect measure but it’s the one we have and ‘all models are wrong but some are useful’ and GDP is quite a powerful predictor of all kinds of outcomes:
In a 2016 paper, Jones and Klenow used measures of consumption, leisure, inequality, and mortality, to create a consumption-equivalent welfare measure that allows comparisons across time for a given country, as well as across countries.[6]
This measure of human welfare suggests that the true level of welfare of some countries differs markedly from the level that might be suggested by their GDP per capita. For example, France’s GDP per capita is around 60% of US GDP per capita.[7] However, France has lower inequality, lower mortality, and more leisure time than the US. Thus, on the Jones and Klenow measure of welfare, France’s welfare per person is 92% of US welfare per person.[8]
Although GDP per capita is distinct from this expanded welfare metric, the correlation between GDP per capita and this expanded welfare metric is very strong at 0.96, though there is substantial variation across countries, and welfare is more dispersed (standard deviation of 1.51 in logs) than is income (standard deviation of 1.27 in logs).[9]
GDP per capita is also very strongly correlated with the Human Development Index, another expanded welfare metric.[10] If measures such as these are accurate, this shows that income per head explains most of the observed cross-national variation in welfare. It is a distinct question whether economic growth explains most of the observed variation across individuals in welfare. It is, however, clear that it explains a substantial fraction of the variation across individuals.
Although GDP per capita is distinct from this expanded welfare metric, the correlation between GDP per capita and this expanded welfare metric is very strong at 0.96, though there is substantial variation across countries, and welfare is more dispersed (standard deviation of 1.51 in logs) than is income (standard deviation of 1.27 in logs).[9]
I checked the paper and it looks like they’re comparing welfare by “how much more would person X from the US have to consume to move to another country i?” Which results in equations like this:
which says what the factor λsimplei , should be in terms of differences in life expectancy, consumption, lessure and inequality. So I suppose it isn’t suprising that it’s quite correlated with GDP, given the individual correlations at play here, but I am suprised that it is so strongly correlated since I’d expect e.g. life expectancy vs gdp to correlate at maybe 0.8[1]. Which is a fair bit weaker than a 0.96 correlation!
Good point. I grabbed the dataset of gdp per capita vs life expectancy for almost all nations from OurWorldInData, log transformed GDP per capita and got a correlation of 0.85.
Yeah I actually do cite that piece in the appendix ‘GDP as a proxy for welfare’ where I list more literature like this. So yeah, it’s not a perfect measure but it’s the one we have and ‘all models are wrong but some are useful’ and GDP is quite a powerful predictor of all kinds of outcomes:
In a 2016 paper, Jones and Klenow used measures of consumption, leisure, inequality, and mortality, to create a consumption-equivalent welfare measure that allows comparisons across time for a given country, as well as across countries.[6]
This measure of human welfare suggests that the true level of welfare of some countries differs markedly from the level that might be suggested by their GDP per capita. For example, France’s GDP per capita is around 60% of US GDP per capita.[7] However, France has lower inequality, lower mortality, and more leisure time than the US. Thus, on the Jones and Klenow measure of welfare, France’s welfare per person is 92% of US welfare per person.[8]
Although GDP per capita is distinct from this expanded welfare metric, the correlation between GDP per capita and this expanded welfare metric is very strong at 0.96, though there is substantial variation across countries, and welfare is more dispersed (standard deviation of 1.51 in logs) than is income (standard deviation of 1.27 in logs).[9]
GDP per capita is also very strongly correlated with the Human Development Index, another expanded welfare metric.[10] If measures such as these are accurate, this shows that income per head explains most of the observed cross-national variation in welfare. It is a distinct question whether economic growth explains most of the observed variation across individuals in welfare. It is, however, clear that it explains a substantial fraction of the variation across individuals.
I checked the paper and it looks like they’re comparing welfare by “how much more would person X from the US have to consume to move to another country i?” Which results in equations like this:
which says what the factor λsimplei , should be in terms of differences in life expectancy, consumption, lessure and inequality. So I suppose it isn’t suprising that it’s quite correlated with GDP, given the individual correlations at play here, but I am suprised that it is so strongly correlated since I’d expect e.g. life expectancy vs gdp to correlate at maybe 0.8[1]. Which is a fair bit weaker than a 0.96 correlation!
I checked. It’s 0.67.
This seems to come from European countries.
Good point. I grabbed the dataset of gdp per capita vs life expectancy for almost all nations from OurWorldInData, log transformed GDP per capita and got a correlation of 0.85.