Occupational employment and wages (OEWS)
U.S. Bureau of Labor Statistics
In a RepVector build, this dataset shows whether the money behind a territory is growing or contracting.
Employment and median annual wage for any occupation in a state, free.
Look up wages by occupation| Occupation | Employment | Median annual wage | Mean annual wage | Area | Year |
|---|---|---|---|---|---|
| All occupations | 1,105,580 | $51,340 | $67,270 | Columbus, OH Metro Area | 2,025 |
| All occupations | 5,550,180 | $49,380 | $64,390 | Ohio | 2,025 |
What this dataset is
Employment levels, mean annual wage and median annual wage for occupations in a metro area or state. Answers what it costs to hire a given role in a place, and how many people already do that work there. It is published by U.S. Bureau of Labor Statistics and can be queried at cbsa and state level, refreshed annual. Each row is an aggregate statistic for a geography, so results describe a market as a whole rather than a single account.
Why it is useful
- Distinguishes a market that is expanding from one that is merely large but flat.
- Gives a defensible reason for sequencing territories when quota is fixed and travel time is not.
- Anchors budget conversations in published income and output trends rather than anecdote.
Questions it helps answer
- Is this territory's income base growing faster than the state as a whole?
- Which markets have the economic headroom to absorb a price increase?
- Where is output shrinking enough to justify pulling coverage back?
How different verticals use it
RepVector is vertical-agnostic: this dataset is selected by what your question asks for, not by industry. These are common ways teams put it to work.
Tracks regional output to decide which territories can absorb capacity expansion.
Backs credit and expansion cases with published income and output trends.
Flags markets where the income base is growing ahead of the state average.
Explains payer-mix pressure that shows up later in utilisation data.
How RepVector uses it
- You describe a market in plain language. The geography resolver turns the place you named into the exact identifiers this dataset needs — city to county to state, plus DMA where relevant — so a query at cbsa runs without you knowing any codes.
- The planner selects Occupational employment and wages (OEWS) only when your question matches what it actually reports. It is never included to pad a result.
- Returned rows are normalised into the shared metric layer, so figures from U.S. Bureau of Labor Statistics line up with every other source in the same dashboard instead of sitting in an isolated table.
- Quality checks score coverage and recency. If U.S. Bureau of Labor Statistics suppresses or omits a value for your geography, the gap is reported as a gap — nothing is estimated or filled in.
- The result renders as charts, KPIs and, where the rows are entities, prioritised targets you can export to PDF, Excel, CSV or JSON.
What it does not tell you
- Reports aggregate trends with a reporting lag; it will not reflect a plant closure or opening from last quarter.
| Field | Key | Type |
|---|---|---|
| Occupation | occupation | string |
| Employment | employment | number |
| Median annual wage | medianAnnualWage | currency |
| Mean annual wage | meanAnnualWage | currency |
| Area | area | geo |
| Year | year | number |
How it fits with the rest of the catalog
Economic trend lines explain the direction of a market; business counts and spending data explain who inside it is transacting. RepVector queries every selected dataset against the same resolved geography and time window, then normalises the results into one metric layer so they can be charted side by side. These datasets are the most common companions to Occupational employment and wages (OEWS):
Other economy datasets
Common questions
What is OES / OEWS data?
The Bureau of Labor Statistics Occupational Employment and Wage Statistics program: annual employment counts and wage estimates for around 800 occupations, by state and metropolitan area.
How often is it updated?
Annually. The BLS publishes estimates each spring for the previous May reference period.
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