The effects of state‐level pharmacist regulations on generic substitution of prescription drugs
Bibliographic record
Abstract
Substituting generic for brand name drugs whenever possible has been proposed to control prescription drug expenditure growth in the United States. This work investigates two types of state laws that regulate the procedures under which pharmacists substitute bioequivalent generic versions of brand name drugs. Mandatory substitution laws require pharmacists to use the generic as a default, and presumed consent laws allow them to assume that the patient agrees to the substitution. Both situations can be overruled by the patient. Using plausibly exogenous changes in states' laws, we use difference-in-differences and a discrete choice model to show that although the mandatory switching laws have little effect, the presumed consent laws reduce consumers' probability of purchasing brand name drugs by 3.2% points. The differential effectiveness of the laws is likely caused by pharmacists' profit motives. These results offer important implications for policies that seek to reduce drug expenditures by incentivizing the use of generic drugs.
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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 | 0.001 | 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) | 0.000 | 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".