Labour Supply, Work Effort and Contract Choice: Theory and Evidence on Physicians
Bibliographic record
Abstract
We develop and estimate a generalized labour supply model that incorporates work effort into the standard consumption-leisure trade-off. We allow workers a choice between two contracts: a piece rate contract, wherein he is paid per unit of service provided, and a mixed contract, wherein he receives an hourly wage and a reduced piece rate. This setting gives rise to a non-convex budget set and an efficient budget constraint (the upper envelope of contract-specific budget sets). We apply our model to data collected on specialist physicians working in the Province of Quebec (Canada). Our data set contains information on each physician’s labour supply and their work effort (clinical services provided per hour worked). It also covers a period of policy reform under which physicians could choose between two compensation systems: the traditional fee-for-service, under which physicians receive a fee for each service provided, and mixed remuneration, under which physicians receive a per diem as well as a reduced fee-for-service. We estimate the model using a discrete choice approach. We use our estimates to simulate elasticities and the effects of ex ante reforms on physician contracts. Our results show that physician services and effort are much more sensitive to contractual changes than is their time spent at work. Our results also suggest that a mandatory reform, forcing all physicians to adopt the mixed remuneration system, would have had substantially larger effects on physician behaviour than those observed under the voluntary reform.
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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 | 0.011 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".