Government Occupations

Overview

The Government Occupations dataset provides government-sourced labor market information for occupations and US geographic areas. Use this dataset to analyze occupational employment, earnings, growth, job openings, hires, separations, and other labor market measures.

The dataset is available through the government_occupations dataset in Lightcast MCP.

Purpose

The Government Occupations dataset provides a labor market baseline for understanding employment, earnings, and workforce trends across occupations and geographic areas. Use this dataset to analyze employment, earnings, job openings, growth, workforce activity, and educational attainment.

What the Dataset Measures

Business Meaning	Description
Occupational Employment	Number of jobs and residence-based employment for an occupation.
Workforce Earnings	Median and percentile earnings for an occupation.
Employment Growth	Changes in occupational employment over time.
Job Openings	Total openings and replacement openings for an occupation.
Workforce Activity	Hires, separations, jobs created, and jobs destroyed.
Education Attainment	Percentage of workers in an occupation at a selected education attainment level.

Common Use Cases

Occupational Employment Analysis

Measure employment for an occupation in a specific geographic area.

Occupational Earnings Analysis

Analyze median earnings and earnings distributions.

Employment Growth Analysis

Understand changes in occupational employment.

Job Openings Analysis

Measure occupational openings and replacement needs.

Hiring Activity Analysis

Analyze hires and other measures of labor market activity.

Workforce Separations Analysis

Understand occupational separations in a geographic area.

Education Attainment Analysis

Understand the education attainment levels associated with an occupation.

Example Questions

01/How many pharmacist jobs existed in Florida in 2016, and how many are projected?

Compare historical and projected employment for pharmacists in Florida. Related metric: lmi.jobs.

02/How many replacement openings are expected for marketing managers?

Understand replacement needs for marketing managers. Related metric: lmi.replacements.

03/Is employment for an occupation growing or declining in a region?

Track employment changes for an occupation across regions and time periods. Related metric: lmi.growth.

04/What are the 90th percentile earnings for an occupation in a region?

Compare earnings at the 90th percentile across occupations or regions. Related metric: lmi.earnings.

05/How many hires or separations occurred for an occupation?

Measure hiring and separation activity for an occupation and region. Related metrics: lmi.hires, lmi.separations.

Available Dimensions

Dimension	Description
`occupation_soc`	Standard Occupational Classification (SOC) is used to identify occupations.
`location_area_us`	US geographic areas, including states, metropolitan statistical areas (MSAs), and counties.
`demographic_age_band`	Age group of people represented in the occupational data.
`demographic_gender`	Gender of people represented in the occupational data.
`demographic_race`	Race of people represented in the occupational data.
`demographic_ethnicity`	Ethnicity of people represented in the occupational data.
`education_attainment_level`	Education attainment level associated with the occupation.

For dimension definitions and filtering guidance, see Dimensions.

Age, gender, race, and ethnicity dimensions are supported only with lmi.jobs. The education_attainment_level dimension is supported only with lmi.education_attainment_percent and requires occupation_soc at level 4 (detailed occupation). Education attainment cannot be filtered by geographic area.

Available Metrics

Metric	Description
`lmi.jobs`	Number of occupied positions in the occupation.
`lmi.median_earnings`	Median annual earnings for the occupation.
`lmi.growth`	Net change in occupied positions for a given year.
`lmi.replacements`	Vacated positions that need replacement and are not due to employment growth.
`lmi.openings`	Total openings from employment growth and replacement needs for a given year.
`lmi.residence_jobs`	Number of people living in an area who hold the occupation.
`lmi.created_jobs`	Number of jobs created by firms.
`lmi.destroyed_jobs`	Number of jobs destroyed by firms.
`lmi.hires`	Number of hires.
`lmi.separations`	Number of separations, including quits and fires.
`lmi.earnings`	Annual earnings values used for percentile distribution analysis.
`lmi.education_attainment_percent`	Percentage of workers in the occupation at the selected education attainment level.

The distribution tool supports the 10th, 25th, 50th, 75th, and 90th percentiles for lmi.earnings.

For metric definitions and usage guidance, see Metrics.

lmi.jobs measures occupied positions, not unique workers. A person can hold more than one position.

Supported MCP Tools

Tool	Supported	Purpose
`distribution`	Yes	Analyze occupational earnings distributions using `lmi.earnings`.
`estimate`	Yes	Retrieve occupational employment, earnings, growth, openings, workforce activity, and education attainment metrics.
`count`	No	Not supported for this dataset.
`list`	No	Not supported for this dataset.
`timeseries`	Yes	Retrieve supported metrics over yearly intervals.
`top`	Yes	Rank supported dimensions by available metrics.

The timeseries tool uses the same metrics as estimate and requires a time filter. It supports yearly intervals only.

The top tool ranks supported dimensions by available metrics. It does not support education_attainment_level as a ranking dimension.