Liquid Capital: Comparing the Industrial Organization of the Blood and Sperm Donation Markets
Bibliographic record
Abstract
Today, the blood and sperm donation markets are global, multi-billion dollar industries. Despite the altruistic rhetoric that commonly surrounds the donation of body fluids, donors are regularly paid for their “gifts,” and recipients can face remarkably high acquisition costs. For body fluid collection agencies, or ‘banks,’ which serve as intermediaries between donors and recipients, the markets for blood and sperm are quite robust. However, starkly different market outcomes have been seen to occur between the two industries and between countries. In this thesis, I employ an industrial organization approach to examine the strategic interaction between fluid collection firms and explore the efficiency of divergent regulatory regimes. I use the model of the blood donation industry presented by Nagurney and Dutta (2018) as a baseline framework for fluid donation, and sharpen new models for both industries based on my research of both industries. Through investigation of the blood and sperm markets, average donor characteristics, and varying regulatory approaches (using the United States and Canada as illustrative examples), I am able to map the effects of several key parameters on industry effectiveness. My results show that nonprofit firms in competition should provide super-optimal service levels to fluid donors in equilibrium. I also find that as donors become more intense in their preferences toward incentives, the “business stealing” effect that arises from competition between firms can mitigated by “product differentiation” (appealing to different donor niches). However, donor neutrality can benefit the market in alternative ways. These findings inform the policy recommendations that the United States retain its competitive market structure and continue to provide incentives for blood donation, but shift to a single-firm market which continues to provide incentives for sperm donation.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".