“Beyond GDP? Welfare across Countries and Time”, 2016 (; backlinks; similar):
We propose a summary statistic for the economic well-being of people in a country. Our measure incorporates consumption, leisure, mortality, and inequality, first for a narrow set of countries using detailed micro data, and then more broadly using multi-country datasets. While welfare is highly correlated with GDP per capita, deviations are often large. Western Europe looks considerably closer to the United States, emerging Asia has not caught up as much, and many developing countries are further behind. Each component we introduce plays an important role in accounting for these differences, with mortality being most important.
Key Point 1: GDP per person is an excellent indicator of welfare across the broad range of countries: the two measures have a correlation of 0.98. Nevertheless, for any given country, the difference between the two measures can be important. Across 13 countries, the median deviation is about 35%.
Figure 5 illustrates this first point. The top panel plots the welfare measure, λ, against GDP per person. What emerges prominently is that the two measures are highly correlated, with a correlation coefficient (for the logs) of 0.98. Thus per capita GDP is a good proxy for welfare under our assumptions. At the same time, there are clear departures from the 45° line. In particular, many countries with very low GDP per capita exhibit even lower welfare. As a result, welfare is more dispersed (standard deviation of 1.51 in logs) than is income (standard deviation of 1.27 in logs).
The bottom panel provides a closer look at the deviations. This figure plots the ratio of welfare to per capita GDP across countries. The European countries have welfare measures 22% higher than their incomes. The remaining countries, in contrast, have welfare levels that are typically 25–50% below their incomes. The way to reconcile these large deviations with the high correlation between welfare and income is that the “scales” are so different. Incomes vary by more than a factor of 64 in our sample, ie. 6,300%, whereas the deviations are on the order of 25–50%.