The Economic Impacts of Rural Water Supply Infrastructures in Developing Countries: Empirical Evidence from Senegal
Bibliographic record
Abstract
Abstract The paper addresses the often-neglected economic impacts associated with the supply of hydraulic infrastructure in rural and under-serviced communities in developing countries. We rely on a rich panel dataset including 1319 Senegalese rural households collected in 2016 and 2020, during the deployment of the first phase of the Emergency Program for Community Development (PUDC). By combining propensity score matching (PSM), inverse probability weighting, difference-in-differences, and quantile regression, we find that access to piped water improves employment in the agricultural sector but has no significant impact on household expenditures. After controlling for attrition, through PSM, we find that the employment effect operates through access to a greater quantity of water and a reduction in the time women devote to water fetching chores. Moreover, when bundled with complementary infrastructure interventions such as the construction of rural roads, we find that access to water services generates an even higher impact. The quantile analysis shows that non-poor households seem to benefit more from the provided water supply infrastructure compared to poor households. Finally, when comparing the welfare effect of government-led PUDC water supply with that of community-led initiatives, our findings advocate for the widespread implementation of the former for reasons of cost-effectiveness.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".