The role of industrial classification in the micro‐macro integration: The case of the banking business in the 1997 North American industrial classification system
Bibliographic record
Abstract
Although economic classification is not part of the Ruggles's prodigious contributions to the System of National Accounts, it is certainly meant to help achieve the integration and linking of macrodata with microdata. Unfortunately, economic classification is a component of the statistical infrastructure that often remains unquestioned by the existing industrial organization literature. This paper fills this gap using the banking business under the 1997 North American Industry Classification System (NAICS) as an example. More specifically, the paper ascertains the extent to which NAICS succeeds at combining the various activities performed by Canadian banks into homogeneous industries. Assuming that producing units within the same industry should display more similar cost structure than those in less similar industries, we find that NAICS—at least for the banking sector—is successful at identifying and grouping producing units into homogeneous economic activities. This result is particularly helpful for empirical research that relies on microdata to draw inferences on the structures, conduct and economic performance of the banking sector as whole.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.017 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".