Short-run subsidies, take-up, and long-run demand for off-grid solar for the poor: Evidence from large-scale randomized trials in Rwanda
Bibliographic record
Abstract
More than a billion people lack access to modern electricity and instead rely on kerosene and other dirty lighting sources, grid expansion is not expected to keep pace with population growth, and both contribute to climate change. Moreover, pneumonia is the leading cause of death for under-fives in the world and kerosene smoke is a significant risk factor. For-profit distribution of low-cost solar LEDs has been touted as an answer, but adoption remains low, especially by the poorest. This study estimates demand curves for both the initial price of low-cost solar LEDs as well as the subsequent user fee for repeated purchases, while also estimating the impact of shortrun subsidies, or a free trial period, on long-run demand. We find uptake is highly sensitive to price with most households purchasing at zero price and none at full cost. Furthermore, using unique objective big data on long-term usage we show that households that received lights for free use their lights as much as those that paid a positive price, disproving the notion, in this context, that consumers will not use goods they received for free. Finally, we find short-term subsidies for user fees actually increases long-term demand in the context of repeated purchases.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".