Physician Incentives and the Rise in C-Sections: Evidence from Canada
Bibliographic record
Abstract
More than one in four births are delivered by Cesarean section across the OECD where fee-for-service remuneration schemes generally compensate C-sections more generously than vaginal deliveries. In this paper, we exploit unique features of the Canadian health care system to investigate if physicians respond to financial incentives in obstetric care. Previous studies have investigated physicians' behavioral response to incentives using data from institutional contexts in which they can sort across remuneration schemes and patient types. The single payer and universal coverage nature of Medicare in Canada mitigates the threat that our estimates are contaminated by such a selection bias. Using administrative data from nearly five million hospital records, we find that doubling the compensation received for a C-section relative to a vaginal delivery increases by 5.6 percentage points the likelihood that a birth is delivered by C-section, all else equal. This result is mostly driven by obstetricians, rather than by general practitioners. We also find that physicians' response to financial incentives is greater among patients over 34, which may reflect physicians' greater informational advantage on the risks of different delivery methods for this category of mothers.
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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 | 0.002 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".