On‐the‐job‐search, wage dispersion and trade liberalization
Bibliographic record
Abstract
Abstract This paper builds a dynamic, general equilibrium, open economy model that allows for ex ante homogeneous workers to experience different wage growth before and after trade liberalization. Key features of our model include a product market composed of heterogeneous firms and a labour market characterized by directed, on‐the‐job‐search and dynamic contracts. We characterize the complementarity of these two sources of wage dispersion in an open economy setting where trade liberalization affects the endogenous equilibrium set of producers and the structure of wages. We find that worker job‐to‐job transitions, through on‐the‐job search, increases the impact of trade liberalization on wage dispersion by 4%. Résumé Recherche d’emploi en cours d’emploi, dispersion des salaires et libéralisation du commerce. Ce mémoire construit un modèle dynamique d’équilibre général d’une économie ouverte qui permet à des travailleurs ex ante homogènes de faire l’expérience de croissance différente des salaires avant et après la libéralisation du commerce. Les caractéristiques importantes du modèle incluent un marché du produit composé de firmes hétérogènes et un marché du travail caractérisé par une recherche dirigée d’emploi en cours d’emploi et des contrats de salaires dynamiques. On caractérise la complémentarité de ces deux sources de dispersion des salaires dans une économie ouverte où la libéralisation du commerce affecte l’équilibre endogène de l’ensemble des producteurs et la structure des salaires. On découvre que les transitions d’emploi à emploi des travailleurs, via la recherche d’emploi en cours d’emploi, accroîssent l’impact de la libéralisation du commerce sur la dispersion des salaires de 4 %.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".