Are factor biases and substitution identifiable? The Canadian evidence
Bibliographic record
Abstract
Revised productivity accounts recently released by Statistics Canada are used to estimate a Klump-McAdam-Willman normalized CES supply-side system for the half-century 1961-2012. The model permits distinct rates of factor-augmenting technical change for capital and labour that distinguish between short-term versus long-term effects, as well as a non-unitary elasticity of substitution and timevarying factor shares. The advantage of the Canadian data for this purpose is that they provide a unified treatment of measurement issues that have had to be improvised in the US and European data used by previous researchers. In contrast to previous results, we find that an elasticity of substitution and distinct factor biases of technological progress are not well determined by the model. For the Canadian data, the KMW model does not appear to provide a framework that overcomes the classic Diamond-McFadden- Rodriguez non-identification result. That impossibility theorem is manifested in our findings, not overcome by them. JEL Classification: C51, E23, E25, O30, O51
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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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.018 | 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".