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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".