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.