The Effects of Competition on Prescription Payments in Retail Pharmacy Markets
Bibliographic record
Abstract
Using pharmacy claims from New Hampshire between 2009 and 2011, I study the extent to which pharmacy competition affects prescription payments. I measure pharmacy competition by the distance to nearby rivals, as well as a fixed‐travel‐time Herfindahl–Hirschman index (HHI) (Dunn and Shapiro ). After controlling for various factors, including insurer, pharmacy, drug, and area characteristics, I find higher average drug prices in more concentrated seller (pharmacy) markets, but lower prices in more concentrated buyer (insurer) markets. Ceteris paribus, pharmacies with high market power (concentration in the 90th percentile) charge 2.78% more than those with low market power (concentration in the 10th percentile). The distance effect is more pronounced if a nearby pharmacy belongs to the same national chain. In addition, I show heterogeneous distance effects across different drug types and areas. My analysis contributes to the empirical literature on competition measures by adding new evidence from the retail pharmaceutical market.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".