Managing contractual uncertainty for drinking water services in rural Mali
Bibliographic record
Abstract
Contracts allocate rights, obligations, and risks between various parties to achieve specific results. In response to slow progress to deliver safe drinking water to rural populations, governments across Africa are increasingly introducing contracts with professional service providers. Contracts for drinking water service provision are designed to align operational outcomes with financial objectives. Yet, professional drinking water service providers generally miss revenue goals despite meeting their contractually agreed service delivery obligations. This mismatch between contractual expectations and outcomes in implementation speaks to wider issues of contract incompleteness. Contract theory indicates that renegotiation is a critical mechanism to adapt an incomplete contract design to uncertainties in operational and financial performance materializing during contract implementation. In this article we examine how contract incompleteness affects the sustainability of professional rural water service delivery and explore how and to what extent an incomplete contract might be addressed. Applying contract theory to a professional service delivery model operating in rural Mali, the empirical analysis uses qualitative methods to provide new insights on the process and consequences of contract renegotiation. Results of our case study show that contract renegotiation is conditional on the original parties’ agreement to adapt the contract and also requires the involvement of external actors and capital to address the shortcomings of an incomplete contract design. In the Mali case, we find that the contract is incomplete because of the inability to enforce local water demand, hindering progress to revenue targets aligned with commercial finance. This condition is likely to hold in most rural contexts requiring contracting models for rural water services to combine public and philanthropic funding and private finance to deliver on desired outcomes.
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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 | 0.021 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.011 | 0.007 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 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".