Competition and Coordination in Two‐Tier Public Service Systems under Government Fiscal Policy
Bibliographic record
Abstract
As a result of government budgetary limits and rapid market growth, many public service systems—such as health care—are characterized by extensive customer wait times that have become a serious problem. This problem might be solved by allowing private firms to enter these markets, which would provide customers with a choice between a free (governmental) public service provider (SP) and a fee‐charging (or “toll”) private SP. In such a two‐tier service system, the two SPs are differentiated by service quality and cost efficiency. This study focuses on the competition and coordination issues for two‐tier service systems with customers who are sensitive to both service quality and delay. The free system attempts to maximize its expected total customer utility with limited capacity, whereas the toll system attempts to maximize its profit. Neither goal is aligned with the social welfare goal of the public service. To achieve the social welfare goal, the government plays a crucial role in coordinating the two‐tier service system via the budget, the tradeoff of social members' goals, and tax‐subsidy policies. Using a mixed duopoly game, we establish Nash equilibrium strategies and identify the conditions for the existence of the two‐tier service system. We employ several interesting and counter‐intuitive managerial insights generated by the model to show that the public service can be delivered more efficiently via customer choice and SP competition. In addition, we show that a relatively low tax‐subsidy rate can almost perfectly coordinate the two SPs to achieve most of the maximum possible benefit of the two‐tier service system.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".