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

Author

Listed:
  • David Bruns-Smith
  • Emi Nakamura
  • Jón Steinsson

Abstract

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.

Suggested Citation

  • David Bruns-Smith & Emi Nakamura & Jón Steinsson, 2025. "Disentangling Age, Time, and Cohort Effects in Income Inequality: A Proxy Machine Learning Approach," NBER Working Papers 34380, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:34380
    Note: EFG LS ME
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    JEL classification:

    • E20 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment - - - General (includes Measurement and Data)
    • J20 - Labor and Demographic Economics - - Demand and Supply of Labor - - - General

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