Output and wages with inequality averse agents
Bibliographic record
Abstract
We analyse a two-task work environment with risk-neutral but inequality averse individuals.For the agent employed in task 2 effort is verifiable, while in task 1 it is not.Accordingly, agent 1 receives an incentive contract that, owing to his wealth constraint, leads to a rent that the other agent resents.We show that greater inequality aversion unambiguously decreases total output and therefore average labour productivity.More specifically, inequality aversion reduces effort, wage, and payoff of agent 1. Effects on wage and effort of agent 2 depend on whether effort levels across tasks are substitutes or complements in the firm's output function.JEL classification: D2, J3 Produit et salaires quand les agents ont de l'aversion pour l'ine´galite´.Les auteurs analysent un environnement de travail a`deux taˆches ou`les individus ne sont pas inquie´te´s par le risque mais ont une aversion pour l'ine´galite´.Pour l'agent employe´a`la taˆche 2, le niveau d'effort est ve´rifiable, alors que pour la taˆche 1, il ne l'est pas.En conse´quence, l'agent 1 rec¸oit un contrat d'incitation qui, compte tenu de sa contrainte de richesse, entraıˆne une rente qui donne lieu a`du ressentiment chez les autres agents.On montre qu'une plus grande aversion a`l'ine´galite´entraıˆne sans ambiguı¨te´une chute de la production totale et donc de la productivite´moyenne du travail.Plus spe´cifiquement, l'aversion a`l'ine´galite´re´duit l'effort, le salaire et les gains de l'agent 1. L'effet sur le salaire et l'effort de l'agent 2 de´pend de la nature de la relation entre les niveaux d'effort dans les deux taˆches (substituts ou comple´ments) dans la fonction de production de l'entreprise.We would like to thank two anonymous referees for their valuable comments.Fluet is also affiliated with CIRANO.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".