Regulation of Privatized Public Service Systems
Bibliographic record
Abstract
To alleviate the financial shortage for public service provision, a government agency may jointly finance, own, and run a service system with a private firm (in the manner of a joint venture) or delegate service provision to the firm subject to regulation in service price or wait time. We model the service system as a queueing system in which customers are heterogeneous in service valuation and sensitive to price and delay. While the government aims to maximize social welfare, the firm's goal is to maximize profit. Hence, the joint venture has the objective of a mix of profit maximization and social welfare creation. Under the regulation, two types of interaction between the government and the firm, that is, sequential move (in the absence of the government's myopic adjustment) and simultaneous move (in the presence of myopic adjustment), are considered. We find that while wait time regulation is more efficient than price regulation in the presence of myopic adjustment, the relationship is reversed in the absence of myopic adjustment. Somewhat surprisingly, price regulation with myopic adjustment may backfire. However, in some instances, the government must take a large share in a joint venture to achieve the same performance under price regulation without myopic adjustment. Our work uncovers whether the government adopts myopic adjustment plays a critical role in choosing the regulation instrument.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".