Disentangling Age, Time, and Cohort Effects in Income Inequality: A Proxy Machine Learning Approach

A canonical finding from earlier research is that the cross-sectional variance of income increases sharply with age Deaton and Paxson (1994). However, the trend in this age profile is not separately identified from time and cohort trends. Conventional methods solve this identification problem by ruling out "time effects." This strong assumption is rejected by the data. We propose a new proxy variable machine learning approach to disentangle age, time and cohort effects. Using this method, we estimate a significantly smaller slope of the age profile of income variance for the US than conventional methods, as well as less erratic slopes for 11 other countries.