Sick with “shareholder value”: US pharma’s financialized business model during the pandemic
Bibliographic record
Abstract
US-based pharmaceutical companies claim that they need high drug prices to provide sufficient profits to fund investments in innovation. This argument is consistent with the corporate-finance principle of retaining profits and reinvesting in productive capabilities that is central to our “theory of innovative enterprise” (TIE). The problem is that, for decades, resource allocation at the largest US-based pharmaceutical companies, known as “Big Pharma,” has embraced the agency-theory argument that a company should be run to “maximize shareholder value” (MSV). We use TIE to expose MSV as an ideology of “predatory value extraction” (PVE), which legitimizes price gouging, downsizing the labor force, and tax dodging. In the body of the article, we document that the largest US-based pharmaceutical companies have become highly financialized, using all their profits, and often more, to distribute cash dividends and stock buybacks to shareholders. We then show the extent to which stock-based remuneration incentivizes senior pharmaceutical executives to engage in this financialized behavior. As cases in point, we consider the financialization of the two US-based companies, Pfizer and Moderna, that were centrally involved in the delivery of COVID-19 mRNA vaccines during the SARS-CoV-2 pandemic. We conclude our analysis by placing PVE in the US pharmaceutical industry in the broader context of the US healthcare system and the US economy.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.006 | 0.006 |
| 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".