Here's a prayer for you. Got a pencil? … [Protect me from
knowing what I don't need to know. Protect me from even
knowing that there are things to know that I don't know.
Protect me from knowing that I decided not to know about
the things I decided not to know about. Amen.] There's
another prayer that goes with it. [Lord, lord, lord. Protect
me from the consequences of the above prayer.]
Douglas Adams, Mostly Harmless
Rightly or wrongly, 95 percent of applied econometrics is concerned with averages. If, for example, a training program raises average earnings enough to offset the costs, we are happy. The focus on averages is partly because it's hard enough to produce good estimates of average causal effects. And if the dependent variable is a dummy for something like employment, the mean describes the entire distribution. But many variables, such as earnings and test scores, have continuous distributions. These distributions can change in ways not revealed by an examination of averages; for example, they can spread out or become more compressed. Applied economists increasingly want to know what is happening to an entire distribution, to the relative winners and losers, as well as to averages.
Policy makers and labor economists have been especially concerned with changes in the wage distribution. We know, for example, that flat average real wages are only a small part of what's been going on in the labor market for the past 25 years. Upper earnings quantiles have been increasing, while lower quantiles have been falling. In other words, the rich are getting richer and the poor are getting poorer. Recently,