BLS_GENDER

Build an extractable slideshow comparing men's and women's occupational earnings using unweighted and employment-weighted distributions.

Syntax

BLS_GENDER(year, minimumEmployment, selector1, selector2, ...)

Argument	Description
`year`	An annual year, or `LATEST`. The latest available annual year is used when omitted.
`minimumEmployment`	Minimum employment, in thousands, required for both men and women in an included occupation. The default is zero.
`selector1...`	Optional occupation codes or case-insensitive name fragments. Multiple selectors use OR logic.

The command returns an extractable BLSGenderOperator. Its JSON representation is the complete animated slideshow.

Complete example

Gender=BLS_GENDER(2025,100)
SLIDESHOW(Gender)

This includes only detailed occupations in which both men and women have at least 100,000 workers. Because BLS data can be revised, the number of qualifying occupations may change.

What the slideshow contains

Study definition and methodology
Table of included occupations, earnings, employment, and women-to-men earnings ratios
Women-versus-men occupational scatterplot with an equal-earnings line
Men's unweighted histogram and fitted density
Women's unweighted histogram and fitted density
Merged unweighted histograms and fitted densities
Men's employment-weighted histogram and fitted density
Women's employment-weighted histogram and fitted density
Merged employment-weighted histograms and fitted densities
Summary comparing the unweighted and weighted results

Available extracts

Extract	Returns
`data` or `datalist`	The four aligned cross-sectional series.
`men` or `men_wages`	Men's median weekly earnings by occupation.
`women` or `women_wages`	Women's median weekly earnings by occupation.
`men_employment`	Men's employment weights.
`women_employment`	Women's employment weights.
`occupation_count` or `occupations`	Number of included occupations.
`table`	Detailed occupational comparison table.
`comparison` or `scatter`	Women-versus-men earnings scatterplot.
`men_fit`	Men's unweighted histogram and fitted density.
`women_fit`	Women's unweighted histogram and fitted density.
`men_weighted_fit`	Men's employment-weighted histogram and fitted density.
`women_weighted_fit`	Women's employment-weighted histogram and fitted density.
`unweighted` or `unweighted_merged`	Merged unweighted histograms and fits.
`weighted` or `weighted_merged`	Merged employment-weighted histograms and fits.
`summary`	Summary comparison panel.
`slideshow`	The complete animated presentation.
`json`	Serialized slideshow JSON.

Extraction example

Gender=BLS_GENDER(2025,100)
Data=EX(Gender,data)
Table=EX(Gender,table)
Comparison=EX(Gender,comparison)

MenFit=EX(Gender,men_fit)
WomenFit=EX(Gender,women_fit)
Unweighted=EX(Gender,unweighted)

MenWeighted=EX(Gender,men_weighted_fit)
WomenWeighted=EX(Gender,women_weighted_fit)
Weighted=EX(Gender,weighted)

Summary=EX(Gender,summary)

Unweighted versus employment-weighted

View	Question answered	Weight assigned
Unweighted	What does the distribution of earnings look like across occupational categories?	Each included occupation receives one observation.
Employment-weighted	What earnings distribution is implied by where men and women are employed?	Each occupation is weighted by the number of workers of that sex.

The weighted calculation does not treat wage × employment as a new wage observation. Employment supplies the frequency weight attached to each occupation's median wage. The weights are sex-specific because men and women are distributed differently across occupations.

Statistical interpretation

Occupations—not individual workers—are the observations in the unweighted distribution.
The fitted density is a compact description of the observed distribution, not a causal model.
The employment-weighted distribution reflects both occupational wages and the placement of workers across occupations.
Medians cannot be aggregated as if they were individual wage records; the weighted view is an approximation based on occupational medians.
The analysis does not use the BLS standard errors currently available for some occupational estimates.

Best use

Use BLS_GENDER to show why a single aggregate earnings statistic can conceal two distinct forces: differences in earnings associated with occupations and differences in where men and women work. The paired unweighted and weighted slides make that composition effect visible.

Related commands

LOOP_BLS_OCCUPATIONS—retrieve the underlying historical or cross-sectional BLS data.
FIT_DIST—fit unweighted or weighted distributions.
MERGE_CHARTS—merge the men's and women's charts.
SLIDESHOW—display the operator's complete presentation.
EXTRACT—retrieve a component for a custom analysis.