The structural dynamics of the pharmaceutical industry.” The Industrial Geographer 1(2
Bibliographic record
Abstract
It is axiomatic that prescription drug prices are higher in the United States than Canada. While many politicians and consumer advocates consider this prima facie evidence of drug company greed, economists are less quick to judge. The monopoly offered by patents generates the profits that are necessary to motivate innovation. Still, the evi-dence is rather overwhelming that drug prices vastly exceed their marginal production costs. The existence of price controls in many nations creates an ex-aggerated version of price discrimina-tion in the pharmaceutical industry and this, in turn, offers a unique ability to directly measure the loss in effi-ciency that results from the market structure of this industry. We begin by offering prima facie evi-dence that pharmaceutical industry profitability is inefficiently high and continue by describing the Guell (1995, 1998) methodology for estimating pharmaceutical static inefficiency. We note that reducing static inefficiency, that which arises at the production-sale stage, comes at a cost of creating dy-namic inefficiency, that which arises when too little is invested in research and development. We proceed by noting that the Food and Drug Administra-tion’s ban on the re-importation of pre-scription drugs in the United States is an example of price discrimination that allows us to use Canadian controlled drug prices to function as an upper-bound estimate of marginal cost. We conclude by using these 2002 Canadian prices to update dead weight loss calcu-lations found in Guell (1995, 1998) in which the 1993 United Kingdom drug price data reported by the U.S. Con-gress’s General Accounting Office was used to create dead weight loss esti-mates. The Prima Facie Case There has been a great deal of concern in recent years about the rising costs of prescription drugs in the United States. The popular sentiment is that Ameri-
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 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".