An interactive view of where ACS annual $100K trade earners, OEWS wage baselines, and projected openings pressure show up across the country. Pick a trade, toggle states vs metros, and read the numbers honestly — market pressure is a directional signal, not a direct count of vacancies.
Updated May 25, 2026
Fetching the three BLS and Census artifacts in parallel.
Use the map to find patterns worth investigating: trades with stronger annual earnings evidence, states where projected openings pressure is higher, and metros where the local OEWS employment base is large enough to make the path visible. It is a research starting point for the quiz, switch briefs, apprenticeship pages, and paid guides.
It is not a promise of hiring demand, a live job board, a licensing answer, or a guarantee that a specific sponsor is accepting apprentices. Before applying, call the official sponsor, state apprenticeship office, or licensing authority and compare the live answer with the page source notes.
The map is an editorial triage tool, not a ranking machine. A strong cell tells us where a trade deserves closer human review: enough OEWS employment to make the local market visible, enough projected openings pressure to matter, and enough ACS annual-earnings evidence to justify asking whether the path can support an adult household. A weak cell does not mean the trade is impossible in that state or metro. It means the public labor-market data is thinner, noisier, or less favorable than the stronger comparison set.
The three data families answer different questions. OEWS helps estimate wage-and-salary employment and local wage baselines by occupation. ACS PUMS helps us see annual labor earnings, including people whose yearly income may reflect overtime, ownership, multiple jobs, or uneven seasonal hours. Projection data helps us judge whether openings pressure is broad enough to care about. None of those sources can tell you which sponsor is hiring this month, whether a school has evening seats, whether a local has a waitlist, whether a contractor has overtime, or whether your commute and childcare plan survives the first year.
That is why every serious reader should pair this map with a guide page, a switch brief, a state apprenticeship office, and direct sponsor calls. The map can point you toward a better question: "Why does this trade look stronger in this state?" or "Why does this metro have a large employment base but weak annual-earnings evidence?" The answer still has to be checked against current licensing rules, application calendars, wage sheets, tuition costs, tool requirements, background-screen policies, and the exact employer or union path you plan to pursue.
Prentice editors use the map to decide which pages need deeper review, not to crown a single best trade. A high-pressure state can still have slow sponsor intake. A weaker wage baseline can still work for a reader with local support, union access, prior experience, military benefits, or a reliable employer connection. A strong annual-earnings share can reflect overtime, travel, ownership, multiple income streams, or unusually senior workers. A large metro employment base can hide commute constraints, employer concentration, licensing quirks, or school capacity limits. The map is useful precisely because it surfaces those follow-up questions.
The safest way to read the visual is as a conversation starter. First, choose a trade that actually fits your body, schedule, temperament, and household budget. Second, compare state and metro signals for wages, employment base, projected openings pressure, and annual-earnings evidence. Third, open the relevant apprenticeship page or switch brief to see whether Prentice has traced sponsor lists, licensing notes, program counts, or state-specific warnings. Fourth, verify the current rule with the agency, union local, school, or employer before treating the pattern as actionable.
We also use the map to catch editorial gaps. If a trade looks unusually strong but the related guide lacks a clear explanation, that guide becomes a candidate for a rewrite. If a metro looks attractive because of employment size but the annual-earnings evidence is thin, the page should say that instead of over-promising. If a state has licensing language that could be misread, the licensing section needs a correction or a stronger disclaimer. If a data source changes its release schedule, geography definition, occupation code, or suppression rule, the methodology page is the place to document the change before article copy borrows the figure.
This is why the map remains paired with visible standards, contact routes, and a dated methodology. Readers can challenge stale numbers at editor@prentice.training, buyers can contact support for access or refunds, and the public page keeps the distinction between editorial judgment and raw government data visible. The design goal is not perfect prediction. It is a transparent, repeatable starting point for adults who need to compare risk before they spend money, quit a job, enroll in class, or wait for a sponsor call.
The strongest use case is comparison across several imperfect choices. A person choosing between electrician, HVAC, welding, automotive, cybersecurity, or software work should not stare at one number. They should compare pay floor, training duration, physical load, license friction, sponsor density, commute distance, application cadence, debt exposure, language accessibility, injury risk, and the likelihood that an employer will actually take a beginner. The map handles only a subset of that reality. The rest belongs in the guide narrative, the switch brief, and the reader's own calls.
Editors therefore treat surprising map results as assignments. A state with high pressure but weak wages may need an explanation about pressure without purchasing power. A metro with good wages but thin apprentice infrastructure may need a warning about hiring bottlenecks. A trade with strong annual earnings but heavy travel may need lifestyle context. A route with attractive pay but licensing complexity may need a board-specific source note. Those follow-up tasks are how a chart becomes an editorial system.
The map also needs a reader-safety lens. Color, rank, and interaction can make numbers feel more precise than they are. That is why the surrounding copy names suppression, projection, occupation-code mismatch, self-employment gaps, overtime distortion, travel premiums, local school capacity, sponsor intake, and licensing friction. A useful data page should slow readers down just enough to prevent false certainty while still helping them notice where a trade might deserve deeper research.