Bonus culture: Competitive pay, screening, and multitasking. Working paper 18936
Bibliographic record
Abstract
Econometric Society Winter Meetings for helpful comments. Both authors gratefully acknowledge …nancial support from the ERC programme grant FP7/2007-2013 No. 249429, “Cognition and Decision-Making: Laws, Norms and Contracts”. Bénabou also gratefully acknowledges support from the Canadian Institute This paper analyzes the impact of labor market competition and skill-biased technical change on the structure of compensation. The model combines multitasking and screening, embedded into a Hotelling-like framework. Competition for the most talented workers leads to an escalating reliance on performance pay and other high-powered incentives, thereby shifting e¤ort away from less easily contractible tasks such as long-term investments, risk management and within-…rm cooperation. Under perfect competition, the resulting e ¢ ciency loss can be much larger than that imposed by a single …rm or principal, who distorts incentives downward in order to extract rents. More generally, as declining market frictions lead employers to compete more aggressively, the monopsonistic underincentivization of low-skill agents …rst decreases, then gives way to a growing overincentivization of high-skill ones. Aggregate welfare is thus hill-shaped with respect to the competitiveness of the labor market, while inequality tends to rise monotonically. Bonus caps and income taxes can help
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".