The Wage Response to Shocks: The Role of Inter-Occupational Labour Adjustment
Bibliographic record
Abstract
How do cities' average wages adjust to a shock, e.g. trade-induced or technology-induced, shifting the industrial and/or occupational composition of employment? This question is commonly answered using a shift-share analysis, which imputes the effects of composition changes on cities' average wages in a purely mechanical way. This paper argues that a shift-share approach can be misleading if compositional shifts spill over into industrial and occupational wages. Using data on Germany, this paper finds that the spillover effects from a shift in industrial and occupational composition are large and imply a total effect on cities' average wages of 2.8 times the effect that is predicted by a shift-share exercise. In addition, results indicate that in the case of Germany, accounting for adjustments along the occupational mix is crucial to identify these spillovers, even if one is only interested in the effect of an industry-specific shock.
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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 | 0.008 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| 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".