The North American Industry Classification System and Its Implications for Accounting Research*
Bibliographic record
Abstract
Abstract Industry classification is an important component of the methodological infrastructure of accounting research. Researchers have generally used the Standard Industrial Classification (SIC) system for assigning firms to industries. In 1999, the major statistical agencies of Canada, Mexico, and the United States began implementing the North American Industry Classification System (NAICS). The new scheme changes industry classification by introducing production as the basis for grouping firms, creating 358 new industries, extensively rearranging SIC categories, and establishing uniformity across all NAFTA nations. We examine the implications of the change for accounting research. We first assess NAICS's effectiveness in forming industry groups. Following Guenther and Rosman 1994, we use financial ratio variances to measure intra‐industry homogeneity and find that NAICS offers some improvement over the SIC system in defining manufacturing, transportation, and service industries. We also evaluate whether NAICS might have an impact on empirical research by reproducing part of Lang and Lundholm's 1996 study of information‐transfer and industry effects. Using SIC delineations, they focus on whether industry conditions or the level of competition is the main source of uncertainty resolved by earnings announcements. Across all levels of aggregation, we find inferences are similar using either SIC or NAICS. How‐ever, we also observe that the regression coefficients in Lang and Lundholm's model show smaller intra‐industry dispersion for NAICS, relative to SIC, definitions. Overall, the results suggest that NAICS definitions lead to more cohesive industries. Because of this, researchers may encounter some differences in using NAICS‐industry definitions, rather than SIC, but these will depend on research design and industry composition of the sample.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.030 | 0.094 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.015 | 0.054 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".