The Bureau of Labor Statistics (BLS) is the top source for employment, wages, and labor trends in the U.S. It collects, analyzes, and publishes massive amounts of data. And much of it can be mapped. If you’re using GIS to study jobs, industries, wages, or economic trends, BLS should be on your list.
Let’s look at what BLS offers, how it’s structured, and how to use it in a mapping context.
What Is the Bureau of Labor Statistics?
BLS is a U.S. federal agency under the Department of Labor.
It provides key economic indicators:
- Employment and unemployment
- Wages and earnings
- Job openings and turnover
- Inflation and prices
- Productivity and work hours
- Occupational outlook
BLS data informs policy, planning, business decisions, and academic research. It’s also free and public.
Key BLS Programs Useful for GIS
Local Area Unemployment Statistics (LAUS)
- Monthly data on labor force, employment, and unemployment.
- Available for states, counties, metro areas, and cities.
- Great for local economic health analysis.
Quarterly Census of Employment and Wages (QCEW)
- Covers nearly all U.S. jobs, by industry.
- Includes employment levels and average wages.
- Available at county and metro levels, sometimes zip codes.
- Based on employer-reported data, not a survey.
Occupational Employment and Wage Statistics (OEWS)
- Employment and wage estimates for hundreds of occupations.
- Data available for states and metro areas.
- Useful for job market analysis and workforce planning.
Job Openings and Labor Turnover Survey (JOLTS)
- National and regional data on hiring, quits, and layoffs.
- Less geographic detail, but shows labor market trends.
Consumer Price Index (CPI)
- Measures inflation, not employment.
- Regional CPI can be mapped to show cost of living differences.
Geographic Coverage
BLS data is mostly tied to standard geographic units:
- State
- County
- Metropolitan and micropolitan areas
- Zip code (in QCEW)
Most datasets include geographic codes or names, which can be joined to shapefiles. This allows easy use in GIS platforms like Atlas, QGIS, or ArcGIS.
Accessing BLS Data
You can get BLS data in several ways:
- BLS website (bls.gov) – Interactive tools, downloads, and charts.
- BLS Public Data API – Pull data programmatically by area, industry, or series.
- CSV downloads – From program-specific pages like LAUS or QCEW.
- Data tools – For job projections or inflation calculators.
- Geocoded QCEW files – Available from third parties, often with shapefiles.
You’ll often need to match area names or codes to geographic boundaries for mapping.
How to Use BLS in GIS Projects
Map Unemployment Rates
LAUS data can show where unemployment is high or low. This can help target job training or aid programs.
Map Wage Levels by Industry
QCEW data can break down average wages in manufacturing, tech, healthcare, and more—by county or metro.
Compare Occupations
Use OEWS to map high-paying or fast-growing occupations in different cities.
Analyze Economic Trends Over Time
BLS data is updated monthly or quarterly. That makes it ideal for tracking trends like job recovery after economic shocks.
Overlay with Other Data
Pair labor data with demographics, education, or housing to get a full picture of community conditions.
Examples of GIS Use Cases
- Mapping counties with high job growth in logistics or healthcare.
- Analyzing wage disparities across metro regions.
- Targeting workforce development based on occupation shortages.
- Identifying rural areas with persistent unemployment.
- Tracking inflation impact using regional CPI.
BLS data brings depth to economic maps.
Tips for Working with BLS Data in Atlas
- Use clean geographic joins – Match county FIPS codes or metro names accurately.
- Check time series – Use consistent date ranges when comparing over time.
- Convert wages to real terms – Adjust for inflation if comparing across years.
- Watch for missing data – Some small geographies or industries may be suppressed.
- Combine with ACS – Use BLS for jobs and ACS for population or commuting.
Limits to Know
Not all BLS data is available at fine geographic levels. Zip code and tract-level data is rare. Most data stops at county or metro. Some industries or occupations are excluded to protect privacy. Also, definitions vary across programs—check the metadata and footnotes.
