Operationalizing Work Requirements at Scale

The Framework

Converting tacit knowledge about what jobs require into structured, comparable data across an entire economy.

Why This Matters

Policy decisions require data. Disability determinations compare claimants’ functional abilities against “work that exists nationally.” But work requirements are largely tacit, understood by practitioners, embedded in practice, rarely documented systematically. Without structured data, these decisions rely on outdated references or ad hoc judgment.

The BLS Occupational Requirements Survey demonstrates one approach to making tacit work knowledge explicit at national scale.

Key Components

Standardized collection protocols: Field economists use consistent frameworks, strength level definitions, SVP scales, environmental condition categories, ensuring comparable data across thousands of establishments.

Hierarchical scoping: Requirements attach to critical tasks supporting critical job functions, not everything a worker might do. This prevents over-specification while capturing what’s essential.

Distributional estimates: Rather than single values per occupation, the ORS reports what percentage of workers require each level. A job may be “light work” for 68% of workers and “medium work” for 32%, the distribution preserves variation information that a single classification would lose.

Derived classifications: High-level categories (strength levels, SVP tiers) are composites of underlying measurements. The derivation logic is transparent and documented.

Validation testing: Interview-based collection was tested against direct observation, revealing tradeoffs between scalability and behavioral grounding.

When to Apply This Thinking

Any domain where tacit, context-dependent knowledge needs to be captured systematically:

• Competency frameworks for roles
• Skills taxonomies for labor markets
• Requirements specifications in regulated industries
• Training needs analysis

The core challenge recurs: local knowledge is rich but doesn’t aggregate; standardized measurement enables comparison but loses nuance. The ORS approach, standardized collection, distributional reporting, transparent derivation, offers one pattern for navigating this tension.

Limitations

The framework produces statistical portraits of occupations, not descriptions of particular jobs. Individual variation gets smoothed into percentages. The classification logic, however transparent, still embeds design choices that shape what’s visible in the data.

And interview-based collection captures requirements as understood by respondents, which may differ from requirements as enacted in practice.

Related: 06-molecule—tacit-vs-explicit-knowledge, 06-molecule—knowledge-graph-construction, 02-molecule—faceted-classification