Can cash incentives modify groundwater pumping behaviors? Evidence from an experiment in Punjab
Bibliographic record
Abstract
Abstract As groundwater levels steadily decline in India, authorities are concerned about reducing extraction for irrigation purposes without jeopardizing food security. Very low or zero prices for electricity and water in agriculture is partly responsible for overextraction, but charging higher prices is politically not feasible. In this study, we describe the results of a pilot scheme implemented in Punjab, India, where farmers who enrolled were allocated a monthly entitlement of electricity units and compensated for unused electricity. Eight hours of uninterrupted daytime electricity supply were also provided under the scheme instead of the usual mix of daytime and night‐time supply. Analyzing data from a cross‐sectional farm household survey and instrumenting for enrollment, we find that self‐reported hours of irrigation for enrolled farmers were significantly lower than for non‐enrolled ones, with no impact on rice yields. We also find a reduction in monthly electricity consumption at electricity‐feeder level due to the pilot scheme using the synthetic control method. Our results suggest that the combination of daytime electricity provision and cash incentives for unused electricity has the potential to incentivize farmers to reduce electricity consumption and irrigation hours by at least 7.5% and up to 30% without impacting paddy yields.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".