Willingness to Pay for Solar Lanterns: Does the Trial Period Play a Role?
Bibliographic record
Abstract
Abstract Where electricity access is limited, solar lanterns are a viable and relatively inexpensive source of basic lighting for households. However, the creation of commercially viable business models for solar lanterns is difficult because the customers are poor and make decisions under tight liquidity constraints. To understand the economics of technology adoption in the case of solar lanterns, we conduct a field experiment on willingness to pay (WTP) for solar lanterns in rural Uttar Pradesh. Applying the Becker–DeGroot–Marschak method of eliciting WTP, we evaluate the ability of a trial period and postponed payment to increase sales. We find no evidence for the effectiveness of the trial period and only weak evidence for the positive effect of postponed payment. Overall, WTP for the product among the customers is low. There is no clear evidence for concerns about the uncertain quality of the product, liquidity constraints, or present‐bias. In this context, policies to subsidize very small solar lanterns would not correct a market failure, as people appear to have only a limited interest in the product.
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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 | 0.043 | 0.126 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.025 | 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".