Water affordability challenges in Latin America and the Caribbean: Accounting for coping costs due to reliance on multiple, non-exclusive sources
Bibliographic record
Abstract
Standard water affordability measures that only account for expenditure on piped water are unlikely to adequately capture the situation of all users in developing countries, who often experience water service quality issues and must rely on coping strategies. Our analysis establishes a foundational framework for systematically incorporating coping costs into assessing affordability metrics. Moreover, we propose adjusting these metrics based on normative judgments regarding the necessity of these coping strategies. We exploit nationally representative household-level data from 18 countries in Latin America and the Caribbean, providing, for the first time, a regional perspective on water affordability We show that when coping costs, which disproportionately impact individuals in the lowest 20% income bracket, are considered, the share of income spent on water significantly exceeds conventionally accepted benchmarks. While our analysis does not reveal substantial differences between adjusted and unadjusted water affordability, our approach may yield more pronounced disparities in other developing countries. These findings, complemented by our identification of characteristics associated with water affordability challenges, provide relevant information for shaping policies aimed at guaranteeing safe and affordable access to water for all.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".