Occupational Labour Demand and the Sources of Non‐neutral Technical Change*
Bibliographic record
Abstract
Abstract This article introduces a two‐step empirical approach for examining both the nature and sources of non‐neutral technical change across multiple occupations. First, conventional labour‐demand parameter estimates and unbiased tests for neutrality are obtained in the context of a flexible cost system. The resulting input‐specific indices of technical change, unconstrained with respect to time path, facilitate subsequent evaluation of proposed sources. In our application to employment decisions of airline firms, we find labour‐saving technical change that is non‐neutral across occupations. We also document occupation‐specific responses to aircraft technology adoption, route system developments and an unprecedented range of technical change elements. Would it not be sensible to start by trying to identify the form which the growth of conventional input efficiency has taken and then proceed to tackle the intriguing, but quite distinct, question of the sources of such growth? As it is now, the typical modus operandi implicitly involves an effort to dispose of both issues by a single stroke. David and van de Klundert (1965)
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".