The Economic Impacts of Rural Water Supply Infrastructures in Developing Countries: Empirical Evidence from Senegal
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it