Impact of privatization on social welfare in the service supply chain
Bibliographic record
Abstract
Emerging economies have developed different service supply chain configurations due to government divestment. Despite high demand for public services, governments typically lack capital to invest. Consequently, public services quality can decline, thereby affecting social welfare overall. Therefore, public firms partner with private firms to offer public services. Additionally, the private sector offers services to the public and maximizes profits. Therefore, any private sector involvement in public services should be carefully evaluated. Hence, it is important to understand how public and private entities have different objectives that affect SW and CS in terms of social welfare. This study uses the healthcare sector as a motivational example and develops and solves three models that consider different decisions: (1) All public firms provide services; (2) All private firms provide services; and (3) Public and private partners provide services. Additionally, social welfare maximization has been considered in a competitive environment. The results show that consumer surplus and welfare are higher when public firms only offer services. Private-public partnerships result in lower SW and CS. The CS and SW are higher under competition and lower without. Furthermore, another interesting result is that profit, CS, and SW are the same for Model AB and Model BA. Finally, we guide managers on privatizing service supply chains.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 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".