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 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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".