Gap Heterogeneity

The gender earnings gap varies substantially across occupation, race/ethnicity, education, and region. All figures are raw hourly gaps from 2023 ACS.

By Occupation

Raw Gap by Broad Occupation (ACS 2023)

Horizontal bars sorted by gap magnitude

Gap = (male median hourly − female median hourly) / male median hourly × 100.

Occupation Raw Gap %
Healthcare30.59
Sales / Office28.63
Management / Professional25.27
Service20.06
Military5.47

By Race / Ethnicity

Raw Gap by Race / Ethnicity

Horizontal bars sorted by gap magnitude

Race / Ethnicity Raw Gap %
Asian20.39
White non-Hispanic19.30
Hispanic13.04
AIAN10.06
Black5.64

The gap is widest among Asian (20.4%) and White non-Hispanic (19.3%) workers. Among Black workers, the raw gap is only 5.6%—reflecting both lower male earnings and relatively higher female earnings in that group.

By Education

Raw Gap by Education Level

Bar chart by highest degree attained

Education Level Raw Gap %
Less than HS18.10
HS Diploma19.30
Some College22.09
Associate’s19.01
Bachelor’s23.15
Master’s27.42
Professional20.55
Doctorate17.69

The gap is widest among workers with a Master’s degree (27.4%) and narrowest among doctorate holders (17.7%). The gap generally widens with education through the Master’s level.

By Region

Raw Gap by Census Region

Four Census regions

Region Raw Gap %
South17.49
Midwest16.90
West16.06
Northeast15.93

Method Comparison (2023)

Method ACS CPS SIPP
Raw gap %16.8115.7715.09
OLS adjusted %13.2316.9610.91
DML adjusted %17.8819.6112.89
Oaxaca unexplained % (supplemental)87.8190.8885.60

DML (double/debiased machine learning with elastic net) produces larger adjusted gaps than OLS in all three datasets, suggesting OLS may understate the residual gap. The Oaxaca row is shown as a decomposition cross-check rather than a headline gap estimate.