Bibliographic record
Abstract
Does attracting or losing jobs in high paying sectors have important spill-over effects on wages in other sectors?The answer to this question is central to a proper assessment of many trade and industrial policies.In this paper, we explore this question by examining how predictable changes in industrial composition in favor of high paying sectors affect wage determination at the industry-city level.In particular, we use US Census data over the years 1970 to 2000 to quantify the relationship between changes in industry-specific city-level wages and changes in industrial composition.Our finding is that the spill-over (i.e., general equilibrium) effects associated with changes in the fraction of jobs in high paying sectors are very substantial and persistent.Our point estimates indicate that the total effect on average wages of a change in industrial composition that favors high paying sectors is about 3.5 times greater than that obtained from a commonly used composition-adjustment approach which neglects general equilibrium effects.We interpret our results as being most likely driven by a variant of the mechanism recently emphasized in the heterogenous firm literature whereby changes in competitive pressure cause a reallocation of employment toward the most efficient firms.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".