Social Impact Bonds: Promises Versus Facts. What Does the Recent Scientific Literature Tell Us?
Bibliographic record
Abstract
Interest in Social Impact Bonds (SIBs) and similar performance-based investments has been spreading around the world ever since the first SIB was issued in the United Kingdom in 2010. At the same time, such investments have given rise to questions regarding the complexity and cost of the contracting mechanisms involved, the possible contradictions between the various objectives pursued, and even the validity of the theoretical premises underpinning them (Albertson et al., 2018a). The objective of this paper is to take stock of the existing knowledge about SIBs and similar performance-based investments, through an overview of recent scientific literature. Relying on a targeted review of the literature and empirical studies, the idea is to compile the arguments that have been mobilized to support, nuance or possibly undermine the implementation of SIBs and similar financing tools. The objective is to provide answers to the question: Do SIBs work? And if yes, under which conditions? After explaining what SIBs are, how they function and how they have developed, the authors propose a synthesis of the arguments from recent scientific literature both supporting and criticizing SIBs. The article concludes that despite the possible improvements and opportunities of these bonds, caution is advised in applying them for as long as empirical evidence is insufficient with regard to their effectiveness and the conditions under which they might be considered appropriate.
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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 | 0.009 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.009 | 0.017 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".