Multilateral development banks investment behaviour in water and sanitation: Findings and lessons from 60 years of investment projects in Africa and Asia
Bibliographic record
Abstract
Abstract Multilateral development banks (MDBs) play a pivotal role in financing water and sanitation infrastructure projects and thus have a major impact on the development of basic services. Although information about the MDBs' investments is publicly available, it is dispersed and not easily comparable. A comprehensive compilation of MDBs' water and sanitation investments has long been lacking. To address this gap, we assess water and sanitation financing by the three MDBs most relevant to Africa and Asia between 1960 and 2020: the World Bank, the African Development Bank, and the Asian Development Bank. We compile a new dataset by drawing on 3,639 water and sanitation projects and assess territorial trends, technology choices, distribution of financial burdens, and reforms to institutional arrangements. We find that MDBs' investments align with changing patterns of urbanization and increasingly finance sanitation infrastructures including non-sewered technologies. However, our results also suggest that institutional reforms have addressed utility efficiency through investment in equipment and skills rather than through increased commercialization and private sector participation. The leverage effect of MDB investment on private financing is negligible, whereas co-financing from local governments dominates.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".