Crosswalk Complexity in Classification Bridges

Context

When classification systems change, whether from SIC to NAICS, ICD-9 to ICD-10, or any taxonomy migration, historical data must be bridged to enable longitudinal analysis.

Problem

The relationship between old and new classification codes is rarely one-to-one. Codes split, merge, and shift in meaning. Naive crosswalks create false precision or lose important distinctions.

The Mapping Patterns

One-to-one: Rare. The old category maps cleanly to exactly one new category. Simple but uncommon.

One-to-many (splits): A single old code divides into multiple new codes. Example: one SIC manufacturing code becomes three NAICS codes distinguishing different production methods.

Many-to-one (merges): Multiple old codes combine into a single new category. Historical granularity is lost.

Many-to-many: Most complex. Old codes split and merge simultaneously. No clean mapping exists.

Solution Approaches

Unweighted crosswalks: List all possible mappings without preference. Useful for reference but requires analyst judgment for each use case.

Weighted crosswalks: Assign probability weights based on empirical data (e.g., what proportion of establishments with old code X actually have new code Y). More actionable but weights may not transfer across contexts.

Harmonized categories: Create a coarser intermediate classification that both systems can map to unambiguously. Sacrifices granularity for consistency.

Time-split analysis: Analyze pre-transition and post-transition periods separately. Acknowledge the break in series rather than creating false continuity.

Consequences

Without careful bridging: Time series appear to show dramatic shifts that are actually classification artifacts. Researchers draw false conclusions about economic change.

With proper bridging: Longitudinal analysis remains valid but with acknowledged uncertainty bands around transition periods.

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