Innovation is deemed crucial to sustained economic growth and welfare improvement. One may subsequently pose, as I do, that innovations require some sort of inequality before they can blossom. This does not mean that some people need to be kept poor so that others can innovate; it means that those individuals who have the potential to significantly improve things for society should be enabled (or left free) to act on that potential.
One of my favourite writers (and speakers), Milton Friedman, explains that experimentation, which is closely related to innovation, can bring tomorrow’s laggards above today’s mean. I’ve drawn the picture below to illustrate what he means (or at least how I understood he meant it):
Bringing tomorrow’s laggards above today’s mean
Thus, if we accept that today some inequities exist, which means that some are poorer than others, tomorrow the poorest (the “laggards”, on the left end of the graph) may be better off than the average today. The crucial insight is that inequalities are relative. Even though some may be better off relatively, everyone is better off absolutely.
Someone’s life expectancy is the expected number of years he or she will remain alive. It is an average that is computed for several groups of people of varying specificity, such as the entire global population, newborns in Ghana, or 15-year-old women in Europe. It is a statistic used in many debates, especially in those concerning a country’s (under)development. The statistic is always presented with much confidence, that is, no-one really doubts the accuracy and reliability, which becomes clear in thousands of articles, but let’s pick one:
I find such statements truly remarkable, since it is not at all straightforward that we can compute life expectancy statistics with great confidence and accuracy. A great deal of uncertainty enters the calculations in several ways, of which I would like to discuss a few: picking indicators, large prediction horizons, and lacking backtesting.