Real Wage — BLS OES × BEA RPP

Real Wage is the nominal wage divided by the state's Regional Price Parity, expressed as a percentage of the national average:

Real Wage = Nominal Wage ÷ (RPP / 100)
RPP = state's price level relative to the US average (US = 100)

Source: BLS OES (May release) + BEA RPP (annual). Latest data year stamped on every job/calculator page.

This converts a nominal salary into purchasing-power terms. A $120K salary in California (RPP ~113) becomes $106K real; the same nominal in Mississippi (RPP ~88) becomes $136K real. Our Real Take-Home Pay calculator applies this on top of federal/state/local tax + FICA.

Percentile interpretation (P10–P90)

BLS OES publishes hourly + annual wages at five percentiles by occupation × geography. We surface them as:

  • P10 — entry-level / first-year floor.
  • P25 — early-career range.
  • P50 (median) — typical mid-career compensation.
  • P75 — experienced / senior tier.
  • P90 — top decile, often reflects high-COL metros, specialty practice, or supervisory roles.

P90 is censored at $239,200 in BLS publications (top-coded). For occupations where total compensation routinely exceeds this — particularly tech and physician specialties — we surface a separately-labeled "uncapped" estimate where reliable employer or BLS occupational employment supplement data exists.

Career Transition ROI

Net present value over a horizon of 10 years (default), discounted at 5% (default, editable):

NPV = Σₜ (post_wage_t − pre_wage_t) / (1 + r)^t − tuition − forgone_wage

post_wage and pre_wage use BLS P50 (median) by default — switchable to P25 (conservative) or P75 (optimistic) on the calculator.

Tuition is the program's published price (or the median across our named program list for that path). Forgone wage is the wage you'd have earned during the program, minus part-time work assumption (default 50% of pre_wage).

License reciprocity coding

For each profession we code states as:

  • Member — currently active in the compact / reciprocity agreement.
  • Pending — bill introduced or signed; not yet effective. We track the bill number and effective date.
  • Considering — committee discussions or working group, no bill yet.
  • Not participating — no current activity.

Coding is sourced from each compact commission's official tracker (NLC for nursing, PT Compact, NASDTEC for teaching, ARELLO for real estate). We re-sync quarterly and on major announcements; changelog records every change.

What we don't model (yet)

  • Equity / RSU compensation in non-tech occupations (data is sparse).
  • Locality pay adjustments below the state level (planned for top-50 MSAs in Phase 2).
  • International wage / cost comparisons (out of scope — DeepComps is US-only).
  • Pension / deferred compensation in detail (we flag occupations where it materially changes the picture, but don't NPV it).

Corrections Policy

Compensation, license, and ROI numbers drive real decisions. We treat correction reports as a primary editorial signal, not customer service.

What counts as a correction

  • Factual data error — a published wage, percentile, RPP value, NPV result, or compact-state status that doesn't match the cited source.
  • Stale source — we're citing a release that has since been superseded (e.g., a newer BLS OES vintage is out and the page hasn't been re-synced).
  • Formula reproduction failure — running the math we publish doesn't reproduce the number we display.
  • Source mismatch — a page cites a dataset but the displayed value is actually computed from a different one.
  • Misclassification — an occupation, license, or state is coded into the wrong category (e.g., NLC member vs. pending).

Differences of interpretation (e.g., your locality data shows higher wages than BLS OES state aggregates) aren't corrections — those are methodology suggestions, and we welcome them via data-source suggestions, but we don't adjust the published number unless the source itself is wrong.

How to submit a correction

  1. Email corrections@deepcomps.com.
  2. Include the page URL and the section heading.
  3. State the value as displayed and the value you believe is correct.
  4. Cite a source (BLS / BEA / O*NET / NCSBN release URL or document) so we can reproduce the discrepancy.

Our response timeline

  • Acknowledged within 2 business days — you receive a tracking reference and the analyst assigned (see Editorial Team).
  • Resolved within 5 business days for clear factual errors. Data-pipeline corrections (re-running ETL across affected pages) may take up to 10 business days; we'll tell you if so.
  • Material errors (wage off by >5%, license-status reversal, or NPV sign change) trigger an immediate page-level correction notice and a changelog entry. The notice stays for 60 days.
  • Minor errors (typos, formatting, broken citations) are fixed silently and noted in the next monthly changelog rollup.

What gets disclosed on the page

When we issue a material correction, the affected page carries a "Corrected" timestamp under the title for 60 days, with a one-line summary of what changed and the prior value. The full correction history of every data-driven page is permanently linked in the changelog. We don't silently overwrite numbers that previously appeared on a page; the prior value stays accessible in the changelog entry.

Disputes

If you've submitted a correction and believe our resolution is wrong, escalate to editorial@deepcomps.com. The Compensation Methodology Lead reviews disputed resolutions within 10 business days. We will not retract a published number to settle a dispute without a verifiable source backing the change.

What we won't correct

  • Numbers that match the cited source but you believe should reflect a different one — submit as a data-source suggestion instead.
  • Subjective interpretation in narrative text that's not tied to a published figure (e.g., "registered nurses see strong purchasing power in low-RPP states" — we'll consider revisions but won't treat these as factual corrections).
  • Anonymous or unsourced claims about prevailing wages, employer-specific pay, or locality differentials below state level — we cite federal aggregates, not employer-reported data.