Knowledge that Transforms

Analysis of longitudinal earning data indicates higher earnings growth for scientists of the same experience but more recent vintage. Theoretical justification for such a relation is suggested, and the implied biases in cross-section data are noted. Because of a basic identification problem, an alternative interpretation, time-experience interaction, is also considered. The general conclusion is that earning growth is not uniform or neutral. There is no simple mechanical method by which lifetime profiles can be inferred from single cross-section data.