Endogenous factor market distortion, risk aversion, and international trade under input uncertainty
Bibliographic record
Abstract
In the context of non‐diversifiable and sector‐specific risks in labour markets, we show that the resulting factor market distortion – attributable to an endogenous intersectoral wage differential – can provide a possible rationale that explains why larger wage dispersion prevails in developing nations. We also demonstrate how endogenous wage distortions spill over to capital markets, with capital‐poor economies offering lower rates of returns. In addition, we show that inequality in the distribution of wealth further deviates factor allocation away from first‐best and impairs intersectoral mobility of the poor. Ce mémoire montre que la distorsion dans le marché des facteurs qui résulte de risques non diversifiables et spécifiques à certains secteurs (et qui se traduit par un différentiel de salaire endogène entre secteurs) peut expliquer pourquoi on observe une dispersion plus grande des salaires dans les pays en voie de développement.On montre aussi comment des distorsions endogènes de salaires débordent vers les marchés de capitaux,ce qui fait que les pays pauvres en capital ont des rendements plus faibles.De plus, on montre que l'inégalité dans la distribution de la richesse contribue à faire dévier l'allocation des ressources de son optimum de premier ordre et nuit à la mobilité inter‐sectorielle des pauvres.
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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 | 0.005 |
| 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 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".