Labor market intelligence

Know what your region's labor market is actually doing.

Employment and wages, occupational demand, program completions and workforce supply gaps — resolved to your region, in minutes, from evidence that is already assembled and current. Used by colleges and universities, municipalities and public agencies, employers, and the analysts who advise them.

Twelve years of history, every county Refreshed nightly No per-seat licensing
Talent Supply Pipeline — Tucson, AZ metro 5 program families
Health professions -740 Computer & IT -330 Mechanic & repair -335 Business & mgmt +340 Construction trades -220
Annual completions Modeled annual openings Gap shown at right · illustrative figures

Questions it answers on the spot

  • Which industries here are actually growing?
  • What does this role really pay in our region?
  • Where are the workforce supply gaps?
  • Who is training for these occupations?
  • Is this occupation in demand and high-wage?
  • How do we compare to our peer regions?
  • Where do our workers commute from?
  • Who gets trained, and who gets hired?

The problem

Decisions get made on evidence nobody can trace.

A dean needs to know whether a new program will place graduates. A city needs to know which industries to build a strategy around. An employer needs to know what a role really pays here. The answer usually arrives as a number in a slide deck with no vintage, no geography and no method — or from a licensed dashboard that will not say where its estimate came from. Neither survives a hard question from a board, a council or a grant reviewer.

Regional, not national

National growth rates say nothing about whether your county hires. Futurescope resolves every figure to the region you actually care about — county, metro, or a custom set of counties you define.

Supply against demand

Completions on their own are half the picture. The pipeline view puts your annual completions next to modeled annual openings by program family, and shows where the balance sits.

Defensible on the record

Every number is attributable and dated. Modeled figures are labeled as modeled and show their method — so the evidence holds up when it is challenged.

The platform

Thirteen modules, one evidence base.

Each module answers a question somebody in your institution already gets asked — and answers it for your service area, not the nation.

01

Labor Market Overview

How is this region doing right now, and where is it heading?

02

Industry Data & Trends

Which industries are here, how concentrated, growing or shrinking?

03

Occupational Data & Demand

Who works here, what they earn, and what is projected.

04

Similar Occupations

What work is closest on skill profile, what it pays, where the gap is.

05

Academic Program Data

What is being produced by program, award level, institution and geography.

06

Talent Supply Pipeline

Completions against modeled annual openings, by program family.

07

Pipeline Diversity

Who completes each program, against the resident population and who is hired.

08

Wage Benchmarking

Full wage distribution, indexed against the nation and peer areas.

See all thirteen modules

What you get back

Answers with the working shown.

Ask which industries in your region are both concentrated and growing, what a program's graduates can expect to earn against the national distribution, or how many people your service area actually trains against how many it hires. The platform answers, then tells you exactly which series it used.

  • Location quotient and shift-share on any industry set
  • Full wage distribution, 10th to 90th percentile, indexed to nation and peers
  • Adjacent occupations ranked by standardized skill profile
  • CSV export on every table, Word export on every report
Industry employment — 2015 to 2024, with modeled outlook10-yr trend
38 48 58 67 772015201820212024
Health care Manufacturing Retail trade
Wage distribution vs. national — Registered NursesPercentiles
10th percentile $61,400 25th percentile $71,900 Median $84,200 75th percentile $101,300 90th percentile $124,700

Illustrative. Every wage figure in the platform carries its vintage and geography.

How it works

From service area to board packet.

  1. We configure your region

    Your region — a county, a metro, or a custom set of counties — plus the peer set you benchmark against, and your own institution if you are an educational one. Set up once, and every module resolves to it.

  2. You ask the question

    Filter by NAICS, SOC, CIP, award level, entry education, wage floor or year. Every filter is a real dimension in the underlying statistics, not a bucketed approximation.

  3. The platform answers, with citations

    Figures arrive with their citation and vintage attached. Anything modeled says so and links to the method note that explains how it was produced.

  4. You export the evidence

    CSV for the analysis, print-clean vector charts for the deck, and a written narrative report that carries its source notes into Word — ready for a curriculum committee, a council packet or a grant submission.

Depth

Deep enough to answer the follow-up question.

Most tools hand you a headline number and stop there. Futurescope carries the full distribution behind it, twelve years of history, detail down to county level, and a method note for anything that was derived rather than published.

So when somebody asks "compared to what?" or "how do you know?" — and somebody always does — the answer is already on the screen.

What the platform covers

Minutes
To an answer, not weeks
12 yrs
Of history on every series
3,000+
Counties, metros and regions
Nightly
Kept current for you

Common questions

What people ask before a demo.

What is labor market data — and what counts as good labor market data?

Labor market data describes who is employed where, in what industries and occupations, what they are paid, how that is changing, and what is being trained. Good labor market data is attributable: every number can be traced and dated, and anything derived says how it was derived.

Futurescope is built entirely on official statistics. There is no proprietary black box, no synthetic records and no demo dataset. Where we model a figure that is not officially published, the interface labels it as modeled and shows the method.

Read the full guide to labor market data →

Who actually uses this, and what for?

Colleges and universities — program review, new-program justification, portfolio decisions, Perkins and grant reporting, accreditation evidence. Workforce boards and economic development organizations — regional plans, in-demand occupation lists, sector strategy, target-industry analysis. Municipalities, counties and other public agencies — economic development strategy, grant applications, comprehensive plans, council briefings. Employers — compensation benchmarking, talent supply, location comparison. Analysts, researchers and consultants — a reconciled multi-year panel with documented methods, ready to interrogate.

What they have in common is that somebody will question the number, and they need to be able to answer.

Do you use scraped job postings?

No. Job-posting data is useful for reading demand signals month to month, but it is a sample of what employers advertise online, not a measure of employment, and no vendor publishes the sampling frame. Our figures come from official statistical programs with published methodology — employer records that cover nearly every establishment, and large probability surveys.

That is a deliberate trade. You get figures you can defend in an accreditation review, a grant application or a board meeting. You do not get real-time posting counts.

What geographies are covered?

The nation, all 50 states and DC, every metropolitan and micropolitan statistical area, combined statistical areas, and every county — plus custom regions built from any set of counties. Coverage at the smallest geographies is limited by what is officially published; where a cell is suppressed, the platform says so rather than filling it in silently.

How current is the data?

As current as the underlying statistics allow, and refreshed for you automatically — monthly labor market series and weekly unemployment claims update nightly, employment and wage panels weekly, and the reference layers monthly. Every module shows the vintage of the data behind it, so you always know how fresh a figure is before you cite it.

The full refresh schedule is on the data integrity page.

All frequently asked questions

Bring your own region to the demo.

Thirty minutes, your service area, your programs, your CIP codes. You will see the actual figures for your county — not a canned dataset — and you keep the export.