The structure of informal water markets: Insights from spatial monitoring in Lodwar, Kenya
Bibliographic record
Abstract
Public water utilities have struggled to keep pace with rapid urbanization, particularly in towns and small to medium-sized cities of low-income regions. Informal water markets have proliferated to fill gaps in piped water coverage and service delivery through a wide range of water vending activities (from private water sources to tanker trucks and handcart operators that distribute water). Despite the prevalence and persistence of water vending, the structure, impacts, and evolution of informal water markets in these settings remain poorly understood, especially the interaction between private vendors and public utilities. We seek to improve our understanding of mobile, distributing vendors (tankers, motorcycles) by advancing high-frequency, spatially explicit monitoring of water vendor transactions in Lodwar, Kenya. We examine both the market and spatial structure of the informal water supply system and then draw inferences about their impacts and evolution. We find that vendors that use motorcycles are not making profits from transporting water. We also identify many linkages between the formal and informal systems. For example, purchases of bulk water by water vendors account for 28% of the public water utility’s revenue. We also find that while most consumers of vended water are located outside of the piped water service area, many households and institutions inside the service area still purchase from private water vendors due to concerns about reliability and quality. These results highlight the complementarities between public utilities and private water vending and the corresponding importance of mapping water vending networks to support planning, policy, and investment and to protect consumers.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".