Public–private partnerships in the US and Canada: “There are no free lunches”
Bibliographic record
Abstract
Governments in many industrialized nations have made concerted efforts to reduce their immediate expenditures and to reduce the cost of major infrastructure projects. Public–private partnerships (P3s) are one emerging method that might do so. Despite the increased use of P3s, there is little independent research on the effectiveness of P3s as a public policy instrument. This article considers the major rationales for P3s, including cost savings and keeping project financing off government budgets. It then presents a transaction cost model that suggests that P3s can often be prone to conflict, high contracting costs, opportunism and failure. Evidence from six major infrastructure projects and a summary analysis of US prisons is then presented. These cases confirm that contracting costs have been high, as predicted by the model. Specifically, high contracting costs reflect the presence of complexity/uncertainty, asset specificity, the potential for ex post bilateral opportunism and a lack of contract management skills by governments. Given these circumstances, the private sector can behave opportunistically at the expense of the public sector as there has sometimes been a political imperative to prevent projects from terminating. Public partners have also behaved opportunistically after projects are in place. Unless public sector managers recognize that they must design contracts that both compensate private sector partners for risk and then ensure that they actually bear it, P3s have little chance of being efficient or effective service delivery mechanisms.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.010 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".