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Composition-Adjusted Wage Growth: A Robust Measure from Microdata

Author

Listed:
  • Bo E. Honore
  • Luojia Hu

Abstract

Wage growth is a key indicator of labor market conditions, but common measures often conflate individual wage changes with shifts in workforce composition. This paper develops a composition-adjusted measure of wage growth using nonparametric decomposition and program evaluation methods. The adjusted measure tracks unadjusted growth in stable periods but diverges during disruptions: during the Covid-19 pandemic, wage growth falls from 12% to 6% after adjustment. The method accommodates rich covariates, is robust to data quality issues such as rounding, heaping and top-coding, and enables distributional and subgroup analysis using micro data, offering more accurate views of underlying wage dynamics.

Suggested Citation

  • Bo E. Honore & Luojia Hu, 2025. "Composition-Adjusted Wage Growth: A Robust Measure from Microdata," Working Paper Series WP 2025-14, Federal Reserve Bank of Chicago.
    • Handle: RePEc:fip:fedhwp:101717
    DOI: 10.21033/wp-2025-14
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    Keywords

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    JEL classification:

    • J31 - Labor and Demographic Economics - - Wages, Compensation, and Labor Costs - - - Wage Level and Structure; Wage Differentials
    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C18 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Methodolical Issues: General

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