Measuring the Affordability of Water Supply, Sanitation, and Hygiene Services: A New Approach
Bibliographic record
Abstract
One common method for assessing the affordability of water supply, sanitation, and hygiene (WASH) services is to compare a household’s reported WASH expenditure, as a proportion of total household expenditure, to a predefined threshold. Another common method is to subtract this reported WASH expenditure from the household’s total income (or expenditure), and then compare that result against a minimum amount needed to purchase other basic goods and services. The innovative, alternative approach to determining affordability introduced in this paper borrows from the method commonly used to draw the monetary poverty line. This offers five advantages over the common methods of investigating the affordability of WASH services. First, it defines a “basket” of WASH services that accounts for the type and level of WASH services that a household receives (and that involves a threshold quality of service, deemed necessary for health and well-being). Second, it makes use of the actual costs of service, therefore moving away from household estimates of WASH expenditure that tend to be inadequate and rarely reflect actual costs. Third, it considers both initial fixed costs and recurring consumption costs, each of which pose their own unique challenges to affordability. Fourth, it makes use of household-level data on access to WASH services, which allows for the grouping of households into categories with distinct policy implications. Finally, this approach facilitates scenario analyses, whereby the impact of different pricing policies can be assessed. This approach is then applied to rural Nigeria, using data from the General Household Survey (GHS) 2015–16, to demonstrate its utility as a tool to better focus policy reform on the actual affordability constraints of the unserved.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".