Results-based financing : evidence from performance-based financing in the health sector
Bibliographic record
Abstract
Results-based approaches have been a focus of recent discussions in international development.This paper is a contribution to answering the question if results-based approaches can help to make development aid and domestic funds more effective and to investigating the experiences made with results-based funding.It focuses on performance-based financing (PBF), which is a type of results-based financing (RBF), as opposed to results-based aid (RBA).RBF is defined as any programme where the principal sets financial or other incentives for an agent to deliver predefined outputs or outcomes and rewards the achievement of these results upon verification (Musgrove 2010).In RBF in development cooperation, the principal is usually a national or sub-national government body of a developing country.The agent is an implementing agency (in the case of performance-based financing) or an individual (in the case of a conditional cash transfer -CCT).RBF may be funded by domestic funds, by donor funds or by a combination of both (Klingebiel 2012).If RBF targets the supply side, it is also called performance-based financing and aims at setting incentives for service providers to deliver good performance.Indicators are set by the principal -often together with the agent.Payment takes place against achievement of these predefined indicators.The present paper evaluates the experiences made with PBF in the health sector in order to answer the following research question: Can performance-based financing be an appropriate tool to make funding in the health sector more effective and efficient?This paper tries to answer this question by investigating the targeting mechanisms, the incentive structure, the effectiveness and the efficiency of performance-based financing in the health sector.It studies the experiences and data from PBF programmes in 13 developing countries in Africa, Asia and South America. German Development Institute / Deutsches Institut für Entwicklungspolitik (DIE)The available qualitative and quantitative evaluations of the schemes studied in this paper suggest that PBF may be more effective in improving healthcare supply and healthcare coverage than other funding schemes.This applies mainly to the targeted indicators.However, there is little evidence that these improvements in health outputs and outcomes are achieved through the resultsorientation of the programmes as opposed to additional funding and other contextual factors, because rigorous impact evaluations are still lacking.Evidence of the impact of PBF on the quality of healthcare delivery and on the efficiency of PBF is also insufficient.Even though there is some suggestive evidence that PBF may be more cost-effective than other funding schemes, a lack of crucial financial information makes it difficult to evaluate the efficiency of PBF.All in all, better and more monitoring of experiences as well as more research are needed in order to evaluate the potential of PBF in particular, and of RBF in general.In the future research agenda, efforts should particularly focus on investigating the incentive structure of RBF more thoroughlyincluding non-monetary and perverse incentives -, on evaluating the effectiveness and efficiency of schemes more rigorously, and on studying the long-term effects of RBF.
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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 | 0.078 | 0.247 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".