Reading Metro-Area Wage Data from OEWS

Why Metro Area Wage Data from OEWS Beats a National Figure in Most Reports

A vocational expert cites the national median wage for retail sales workers in a wage-earning-capacity report. The claimant lives forty minutes outside a mid-sized metro, in a labor market where retail wages run meaningfully below the national figure. Opposing counsel doesn't need to be a labor economist to make the point in deposition: "Why did you use a number that describes wages somewhere else, when my client has never worked outside this county?" The report's earning-capacity conclusion doesn't change because the math was wrong — it changes because the geography was wrong, and geography is one of the first things a skilled cross-examiner tests in a labor market survey.

Median wage figures shift by location. The Bureau of Labor Statistics' Occupational Employment and Wage Statistics (OEWS) program publishes wage estimates at the national, state, and metropolitan (MSA) level for the same occupation codes — and picking the wrong one is one of the most common, most preventable weaknesses in a vocational report. By the end of this piece, you'll know when metro-area OEWS data is the right tool, how to read an MSA wage table correctly, and how to cite the geography and reference year so the figure holds up under scrutiny.

What OEWS Metro-Area Estimates Actually Measure

OEWS is a federal-state cooperative survey program that estimates employment and wages by detailed Standard Occupational Classification (SOC) code, published at the national level, the state level, and the metropolitan statistical area (MSA) level. The metro-area tables are not a different survey — they're the same underlying establishment data, tabulated to a smaller geography.

That matters for how much weight you put on any single MSA figure. OEWS estimates are built from a probability sample of roughly 1.1 million establishments nationally, drawn in semiannual panels of about 186,000 to 189,000 each. That sample supports reliable national and state estimates for most occupations. At the metro level, the same total sample is spread across hundreds of MSAs, so a large metro with a deep establishment base in a given occupation will generally produce a more stable estimate than a small MSA with few employers reporting that SOC code. When an occupation is thin in a given metro, BLS may suppress the estimate or publish a wider wage range rather than a precise median — that's a feature of the sampling design, not an error in your pull.

Every occupation is tied to the same SOC code lookup you'd use for national data, so crosswalking a job title to its code is the first step regardless of geography — see SOC code lookup by job title for that process.

National, State, or Metro: Choosing the Right Geography

None of the three geographies is universally "correct." Each answers a different question:

• National data is appropriate for broad transferable-skills screening — comparing wage levels across a set of candidate occupations before narrowing to the claimant's actual location.
• State data is appropriate when a jurisdiction's fee schedule, agency guidance, or court practice specifically calls for state-level figures, or when the claimant's labor market genuinely spans the state (common in smaller or less urbanized states).
• Metro-area (MSA) data is appropriate when the claimant's realistic reemployment options sit within a defined commuting area, and that area's wage levels diverge from the state or national figure — which is the ordinary case in a labor market survey built around a specific person's location.

There is no single national rule dictating which geography a report must use — practice varies by jurisdiction, by referral source, and by whether the audience is a workers' comp board, an LTD carrier, or a court. Confirm with the relevant agency or attorney of record which geography that jurisdiction expects before finalizing a report, and note the choice explicitly in your methodology section rather than leaving it implicit.

A Worked Example: Reading an OEWS Metro Wage Table

Here's how the comparison plays out mechanically. Suppose — purely as an illustration, not a published figure — an OEWS table lists a median hourly wage for SOC 43-9061 (Office Clerks, General) at $17.80 nationally, $16.90 for the relevant state, and $19.40 for the claimant's specific MSA. The metro figure sits above both the state and national medians, which might reflect a higher local cost of living, a tighter local labor market for that occupation, or simply a different employer mix within that metro.

If the claimant's realistic job search radius is that metro area, the $19.40 figure — not $17.80 — belongs in the wage-earning-capacity calculation, because it describes the wages actually available where the claimant would be applying. Using the national figure here would understate the claimant's earning capacity; using it in the reverse direction (metro below national) would overstate it. Either error is exactly the kind of detail a hostile cross-examination is built to surface.

The same table typically breaks the wage out by percentile (10th, 25th, median, 75th, 90th), which lets you situate a claimant's transferable skill level within the distribution rather than defaulting to the median for every case — see BLS OEWS wage data by SOC code for how to read the full percentile structure, and median wage by occupation and location for how the same occupation code moves across geographies side by side.

Metro Wage Data Alone Isn't Job Availability

A wage figure tells you what a job pays where it exists. It does not tell you that the job exists in numbers the claimant could realistically reach.

OEWS metro estimates describe wages for occupations currently employed within that MSA — they don't confirm current job openings, hiring volume, or whether a specific employer is within a reasonable commute for someone with the claimant's mobility restrictions. A defensible labor market survey pairs the wage figure with actual evidence of job availability within a commuting distance appropriate to the claimant's medical restrictions and access to transportation. That's a separate research step, not something the wage table substitutes for — see commuting distance and job availability in a workers' comp survey and the broader methodology in labor market survey for vocational rehabilitation.

Citing OEWS Metro Data So It Survives Cross-Examination

A citation that will hold up under questioning names four things: the SOC code, the specific MSA (by its OMB-designated name, not an informal regional label), the wage statistic used (median, or a specific percentile — never just "the average"), and the OEWS release year. OEWS estimates are refreshed on a regular release cycle, and metro boundaries themselves are periodically redefined by the Office of Management and Budget, so a figure pulled two release cycles ago may reference a different geography than the current one. State the reference year explicitly in the report, and confirm the current release and current MSA delineation before relying on a number pulled for an earlier report on the same claimant.

Put It in the Calculation, Not Just the Narrative

Reading the metro table correctly is only half the job — the number still has to flow cleanly into a wage-earning-capacity calculation that shows pre-injury versus post-injury earning capacity, with the geography and year documented at the point the figure enters the math. The Wage-Earning-Capacity Calculator Workbook is built to hold that citation alongside the calculation, so the source geography travels with the number instead of getting lost between the research and the report. Download the template and drop your next MSA-level figure straight into a structured, citable calculation.