The Value of Free Water: Analyzing South Africas Free Basic Water Policy
Bibliographic record
Abstract
This paper analyzes South Africa’s Free Basic Water Policy, under which households receive a free water allowance equal to the World Health Organization’s recommended minimum of 6 kiloliters per month. I structurally estimate residential water demand, evaluate the welfare e¤ects of free water, and provide optimal price schedules derived from a social planner’s problem. I use a unique dataset of monthly metered billing data for 60,000 households for 2002-2008 from a particularly disadvantaged suburb of Pretoria. The dataset contains rich price variation across 18 di¤erent nonlinear tari ¤ schedules, and includes a policy experiment which removed the free allowance. I …nd that without government subsidy, the mean monthly consumption would decrease from 12.6 to 5.6 kiloliters, which is below the clean water consumption recommended by the WHO. However, it is possible to reallocate the current subsidy to form an optimal tari¤ without a free allowance, which would increase welfare while leaving the water provider’s revenue unchanged. This optimal tari ¤ would also reduce the number of households consuming below the WHO-recommended level. I would like to thank Patrick Bajari and Amil Petrin for their advice and support, and Tom Holmes, Pinar Keskin, Kyoo il Kim, Péter Kondor, Michael Kremer, Minjung Park, Chris Timmins, Gergely Ujhelyi, several friends and
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".