Pecuniary and Non-Pecuniary Incentives in Prescription Pharmaceuticals: The Case of Statins
Bibliographic record
Abstract
Abstract Health insurance companies seek to influence the type of care patients receive in order to increase value in relation to cost. Traditional health insurance relies primarily on price mechanisms to affect patients' and doctors' choices, whereas managed care plans such as HMOs, as the name implies, affect choices directly thorough various forms of managed care. I investigate the effect of pecuniary and non-pecuniary incentives used by health insurance companies to influence prescription decisions in an important class of pharmaceuticals, cholesterol-lowering drugs called Statins, using a discrete-choice demand model on patient-level data. My results suggest that HMOs are significantly more successful at influencing drug choice than traditional indemnity insurers. In conjunction with volume-contingent discounts given by drug producers, this could explain part of the cost-effectiveness differential between HMOs and traditional indemnity insurers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.000 | 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 teacher head, 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".