Contract Design by Service Providers with Private Effort
Bibliographic record
Abstract
We investigate the performance of two commonly used pricing schemes—hourly-rate contract and two-part tariff—in service environments where the buyer’s valuation is invisible to the service provider and the provider’s effort may not be visible to the buyer. In the private effort environment, we further distinguish between situations where the contract may be based on the outcome or on the effort reported by the provider. We show that under the two-part tariff, when effort is private, the provider can achieve the same profit as under public effort by contracting on reported effort and will be worse off by contracting on outcome. Under the hourly-rate contract, compared with the public effort case, the provider may be better or worse off in keeping effort private and contracting on the reported effort, and the trade-off is affected by the degree of outcome uncertainty in a nontrivial way. We find that a provider’s profits under an hourly-rate contract are as good as under a two-part tariff over a sizable parameter regime when contracting on reported effort. The online appendix is available at https://doi.org/10.1287/mnsc.2017.2743 . This paper was accepted by Vishal Gaur, operations management.
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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 | 0.029 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".