Most economists, left or right, care about human development. By human development, they mean more than simply increases in income. They refer to a greater ability for individuals to choose the lives they deem most fulfilling under continually weakening constraints. Regardless of their political leanings, most economists will also be concerned with the inequalities in human development.
This sort of inequality is hard to measure. Generally, we concentrate on income inequality to measure those inequalities. This tends to create false impressions about how equal the world has grown since the early 19th century. In fact, numerous indicators suggest that the world is now more equal than it was in the past!
Concentrating solely on incomes is bound to have shortcomings. The issue with income is that the levels capture both the opportunities available to workers and the decisions of workers. For example, it is well-known that after a certain wage level, workers will use wage increases to substitute leisure for paid work time.
This is known as the backward bending labor supply curve. However, the curve is not the same for everyone. Some workers simply decide to work more than others (or have incentives to do so). This is why we observe rising inequality in working hours in richer countries. In a situation like this, how can we assess the inequality in human development (i.e. inequality in our ability to make choices)?
Measures such as the human development index (HDI) use a broader set of indicators to capture human development. One such indicator is life expectancy at birth. It is taken as a proxy for how healthy our lives are. The intuition is that the healthier we are, the more we are able to make choices. If life expectancy at birth can be taken as a reliable indicator of health outcomes broadly defined, inequalities in life expectancy will be relevant to inequality in human development.
What do such measures say? Using demographic data accessible to all, Sam Peltzman made the exercise of measuring inequality in life expectancy in a 2009 article in the Journal of Economic Perspectives. He calculated the Gini coefficient for that indicator since the late 19th century for many countries and as far back as 1750 for a few countries such as Sweden and Germany. The Gini coefficient takes a value of zero if there is perfect equality and a value of one if there is perfect inequality.
What does his exercise yield? The Gini coefficient for Sweden, England, France, Germany and the United States stood between 0.4 and 0.5 for most of the 19th century. However, there was a clear downward trend in mortality inequality so that by 1900, the level had fallen to a range between 0.3 and 0.4. By 1950, the drop had continued and stood instead between 0.1 and 0.2. Today it is closer to 0.1. Similar declines are observed in countries like India, Brazil and Japan over the course of the 20th century.
In fact, Peltzman points out that in some countries like India and Brazil, “mortality is distributed more than income.” This is a momentous collapse in the inequality in life expectancy. Peltzman made a similar exercise using life expectancy for American states starting in 1910 and found a marked decline in life expectancy inequality within the United States.
What used to be a major source of inequality is now a minor source of inequality in human development. The unhealthy focus on income inequality makes us blind to these great developments in human well-being. This is not to say that analysis of income (or wealth) inequality should be abandoned. However, it ought to be complemented with other indicators of inequality. A great number of indicators would constitute a dashboard for sober analysis. At the very least, it would give us the capacity to appreciate how we are living in a more equal and richer world than we used to before.