Using Licensing Data for Workforce Planning
Licensing data is one of the most underused resources in workforce planning. Every state board maintains detailed records on who holds licenses, when they were issued, when they expire, and whether they’re active. That data, when aggregated and analyzed, tells you where talent is concentrated, where supply is tightening, and where expansion opportunities exist. Organizations that integrate licensing data into their workforce strategy make better hiring, expansion, and compensation decisions because they’re working from supply-side data rather than guessing.
What can licensing data actually tell you?
At its core, a professional license database is a census of qualified workers. Every active license represents someone who has met the education, examination, and regulatory requirements to practice in that state. This makes licensing data uniquely valuable for several workforce planning questions.
Talent supply by geography
By counting active licenses per state and per profession, you can map where qualified professionals are concentrated and where they’re scarce.
Example: RN density per 100,000 population (selected states)
| State | Active RNs | Population (est.) | RNs per 100K |
|---|---|---|---|
| Massachusetts | 131,000 | 7.0M | 1,871 |
| South Dakota | 18,500 | 0.92M | 2,011 |
| California | 448,000 | 39.0M | 1,149 |
| Texas | 308,000 | 30.5M | 1,010 |
| Georgia | 124,000 | 11.0M | 1,127 |
Source: State board of nursing reports and NCSBN workforce data, 2025 estimates.
These density figures directly affect hiring difficulty. States with lower per-capita licensee counts typically have tighter labor markets, longer time-to-fill, and higher compensation demands. Employers expanding into Texas or Georgia should expect a more competitive hiring environment than in Massachusetts or South Dakota, at least for nursing.
Pipeline indicators
New license issuances per year serve as a leading indicator of talent pipeline strength. If a state’s nursing board issued 15,000 new RN licenses last year but only 12,000 this year, that 20% decline signals a future supply constraint, often 12-18 months before it shows up in job market conditions.
What to track:
- New licenses issued per year by state and profession
- Pass rates for licensing exams (NCLEX for nursing, state exams for real estate, SAFE test for MLO)
- Number of approved educational programs in the state
- Program enrollment and graduation data (available from NCSBN for nursing, state boards for other professions)
Declining exam pass rates, in particular, deserve attention. The NCLEX pass rate for first-time BSN candidates has fluctuated in recent years, with some states seeing noticeable dips. A drop in pass rates means fewer new licensees entering the market even if program enrollment holds steady.
Attrition and retention signals
License renewal rates indicate how many professionals are staying in the field versus leaving. If a state board reports that 90% of eligible RNs renewed their licenses, that’s a healthy retention rate. If that number drops to 80%, it suggests increasing attrition, whether from retirement, burnout, career changes, or relocation.
Caveat: renewal rates aren’t a perfect attrition measure. Some professionals let licenses lapse in one state because they’ve moved to another, not because they’ve left the profession. Multi-state data analysis helps account for this.
BLS data provides complementary information. The Occupational Employment and Wage Statistics (OEWS) program publishes employment counts by occupation and geography annually. Cross-referencing BLS employment figures with state licensing counts reveals the gap between licensed professionals and actively employed ones.
How do organizations apply this to hiring strategy?
Workforce planning teams can use licensing data at three decision points: where to recruit, how to compete, and when to invest in pipeline.
Where to recruit
If you’re hiring licensed professionals, licensing data shows you where the candidates are. This sounds obvious, but many organizations recruit based on where they’re located rather than where the talent lives.
Example: A healthcare system in the Southeast needing to hire 50 RNs might focus recruitment in their local metro area. But licensing data might show that neighboring states have higher per-capita RN counts and active compact participation, making cross-state recruitment feasible and efficient. Travel nurse agencies use exactly this kind of analysis to match supply with demand.
For multi-state employers, the NLC compact membership list is particularly relevant. Recruiting from compact states means candidates can start working in your compact state immediately without waiting for a new license to process.
How to compete on compensation
BLS salary data by state and metro area, combined with licensing density data, reveals where employers need to pay more to attract scarce talent.
States with low licensee-to-population ratios and high demand (growing population, aging demographics) typically command higher salaries. States with high licensee density and moderate demand may offer more favorable compensation economics for employers.
Practical application: If you’re setting salary bands for a new location, pull BLS median wages for that metro area and compare against the licensee density. Low density relative to peer metros suggests you’ll need to offer above-median compensation to be competitive.
When to invest in pipeline
Organizations with long-term workforce needs (health systems, large staffing agencies, regional brokerage firms) benefit from investing in the talent pipeline itself: partnerships with educational institutions, scholarship programs, or tuition assistance.
Licensing data helps identify where pipeline investment will pay off. States with declining new license issuances or falling exam pass rates are where pipeline development is most critical. Waiting until the talent shortage is acute means competing with every other employer for the same shrinking pool.
What data sources are available?
The challenge with licensing data isn’t that it doesn’t exist. It’s that it’s fragmented across dozens of state-specific sources.
| Data Source | Coverage | Access | Cost |
|---|---|---|---|
| Individual state board databases | Single state, single profession | Free (manual lookup) | Free |
| NURSYS (NCSBN) | Nursing, all participating states | Verification fees apply | Per-query fee |
| NMLS | MLO, all states | Free (consumer access) | Free for basic lookup |
| BLS OEWS | All occupations, all states | Free (data.bls.gov) | Free |
| NCSBN Workforce Survey | Nursing, national | Published reports | Free (reports) |
| ARELLO | Real estate, participating states | Member access | Varies |
| License Guide API | Multi-profession, all states | API access | See /api/ |
The fragmentation problem: Manually collecting data from 50 state nursing boards, 50 state real estate commissions, and the NMLS creates a significant data aggregation challenge. Each source has different data formats, update frequencies, and access methods.
The License Guide API addresses this by aggregating licensing data across professions and states into a single interface. For workforce planning teams, this means spending time on analysis rather than data collection. See our methodology page for details on how data is sourced and validated.
How do you build a licensing data dashboard?
A workforce planning dashboard that incorporates licensing data should track these metrics at minimum:
Supply-side metrics:
- Active licensees by state and profession (updated quarterly)
- New license issuances (trailing 12 months vs. prior year)
- Renewal rates by state
- Compact license adoption rates
- Licensee density per capita by state
Demand-side metrics (from internal data):
- Open positions by state and profession
- Time-to-fill by state
- Offer acceptance rates by state
- Turnover rate by state
- Projected headcount needs (next 12-24 months)
Combined indicators:
- Supply-demand gap by state (licensee density vs. open positions)
- Compensation competitiveness by state (your offers vs. BLS median)
- Pipeline health score by state (new issuances trend + program count + pass rates)
An honest limitation: building this dashboard requires ongoing data maintenance. Licensing data updates on different schedules (state boards may publish annual or biennial reports), and some data points require manual collection. Budget 10-20 hours per quarter for data updates unless you’re using automated data feeds.
Where is this heading?
Three trends are making licensing data more valuable for workforce planning:
Growing data accessibility. More state boards are publishing data online, and aggregation services (including the License Guide API) are making cross-state analysis easier. Five years ago, building a 50-state licensing data set required months of manual research. Today, it can be done in days.
Compact expansion. As more professions adopt interstate compacts, the relationship between licensing data and workforce mobility becomes more important. Compact participation data helps employers predict where talent will flow and where friction will remain.
Predictive analytics. Organizations are beginning to use licensing data in predictive models, combining it with demographic data, education pipeline data, and economic indicators to forecast supply 3-5 years out. This is still early-stage for most organizations, but the data foundations are being built now.
For employers looking to integrate licensing data into their workforce planning process, the starting point is understanding what data is available and where. Our guides provide profession-specific context, and the License Guide API documentation details the data endpoints available for programmatic access. Questions about building licensing data into your analytics stack? Reach out to our team.