Averting Expenditures and Willingness to Pay for Electricity Supply Reliability
Bibliographic record
Abstract
Abstract Nepal has suffered from the worst electricity shortages in South Asia. This study is an attempt to measure the willingness to pay for an improved service using a model of revealed preference. Respondents are asked about the actions they are taking to reduce the impact on their household or business of scheduled and unscheduled outages and more stable voltage. We estimate the averting expenditures that were being incurred to compensate for the lack of reliability of the electricity service. The estimated cost of the averting actions as a percentage of the electricity bills is 53 % for households, 47 % for small businesses, 46 % for medium businesses, and 35 % for large businesses. Based on the estimations, we find that in 2017 the annual benefit from improving the reliability of the electricity service would be approximately US$ 188 million with a present value over 20 years of US$ 1.6 billion.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".