The Opportunities and Limitations of Monopsony Power in Healthcare: Evidence from the United States and Canada
Bibliographic record
Abstract
Perhaps more than any other sector of the economy, healthcare depends on government resources.As a result, many healthcare systems rely on the use of government monopsony power to decrease spending.The United States is a notable exception, where prices in large portions of the healthcare sector are set without government involvement.In this paper we examine the economic implications of a greater use of monopsony power in the United States.We present a model of monopsony power and test its predictions using price differences between the United States and Canada -a country that represents an example of a "Medicare for All" style system.Overall, we find that wage differences for medical providers across the two countries are primarily driven by the broader labor market while price difference for prescription drugs are more directly the result of buyer power.We discuss theoretical reasons why a Canadian monopsonist may be more willing to exploit its buyer power over prescription drugs rather than provider wages and why a U.S. monopsonist might not be willing to do the same
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".