Normalized CES Supply Systems: Replication of Klump, McAdam, and Willman (2007)
Bibliographic record
Abstract
In light of the literature that it has spawned, Klump, McAdam, and Willman (Factor substitution and factor-augmenting technical progress in the United States: A normalized supply-side system approach. Review of Economics and Statistics 2007; 89(1): 183–192) can be seen to be an iconic empirical implementation that has helped resurrect interest in CES functional forms and parametric supply-side systems. This replication revisits their analysis using alternative software, confirming their results substantively and, in large measure, numerically. Contributions include a more explicit consideration of the nested testing structure than has appeared previously, and the appropriate means of imposing and testing the special case of logarithmic growth in technology. As well, plots of the likelihood serve to emphasize that maxima in the neighborhood of a unitary elasticity of substitution are often a spurious artifact of the singularity of the model at this point, of which empirical researchers should beware. JEL Classification: C22, 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 | 0.009 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".