Glossary

Labor market terms, defined plainly.

Eighteen terms you will meet in any workforce data conversation — each with the caveat that actually matters, not just a definition.

Annual openings — Growth plus replacement

The number of job openings an occupation is expected to generate each year: new jobs from growth, plus openings from workers leaving the occupation. Officially published only at the national level, so any local figure is modeled.

CIP — Classification of Instructional Programs

The standard taxonomy for academic programs. Every postsecondary completion is reported against a CIP code, which is what makes program-to-occupation analysis possible.

Entry-level wage — Entry wage

Roughly the 10th percentile of the wage distribution for an occupation in an area. Far more relevant to a prospective student or a new hire than the median.

Labor force participation rate — Participation rate

The share of the working-age population that is employed or actively looking for work. A falling unemployment rate means little if participation is falling with it.

Location quotient — LQ

Local employment share in an industry or occupation divided by the national share. Above 1.2 indicates genuine concentration. It measures specialization, not growth — a region can be highly concentrated in a declining industry.

Modeled estimate — Modeled figure

A figure produced by applying a stated assumption to published data, because the figure itself is not officially published at that geography. Legitimate — provided it is labeled as modeled and the method is disclosed.

NAICS — North American Industry Classification System

The standard industry taxonomy, from 2-digit sectors down to 6-digit detail.

Pipeline balance — Supply–demand gap

Annual completions in a program family compared against modeled annual openings in the occupations it leads to. A directional balance, not a count of unfilled jobs — graduates move, and employers hire from elsewhere.

Program-to-occupation crosswalk — CIP–SOC crosswalk

The standard mapping from instructional programs to occupations. It is many-to-many: one occupation is fed by several programs and one program feeds several occupations — which is why naive supply–demand comparisons inflate demand.

Replacement demand — Openings from separations

Openings created by workers leaving an occupation permanently, rather than by growth. In most occupations it substantially exceeds growth demand, which is why “declining” occupations still hire.

Service area — Region

The set of counties an institution or agency is actually responsible for. It rarely matches a statistical boundary, which is why analysis on custom county sets matters.

Shift-share — Shift-share analysis

Decomposition of regional employment change into national growth, industry mix, and a competitive residual. The residual is the part attributable to local conditions.

SOC — Standard Occupational Classification

The standard occupational taxonomy. Wage, projection and skills data all key to it, which is what lets them be combined into one view of an occupation.

Staffing pattern — Industry–occupation mix

The share of an industry’s employment in each occupation. Applied to local industry employment, it estimates which occupations a target industry would demand in a region.

Suppression — Cell suppression

Statistical programs withhold estimates for cells small enough to risk identifying a respondent. It is why some county-level wages are simply unavailable — and why any source that always has a number is worth questioning.

Turnover rate — Worker turnover

The rate at which workers leave and are replaced in an industry or area. High turnover means openings stay high even where total employment is flat.

Vintage — Data release

The release a figure comes from — its reference period and publication date. Figures from different vintages are not directly comparable, and a citation without a vintage is incomplete.

Wage percentile — Wage distribution

The wage below which a given share of workers fall: the 10th percentile is the entry end, the 90th the experienced end. The spread tells you far more than the median alone.

Read the practical guide to labor market data