Design of Off-Grid Lighting Business Models to Serve the Poor: Field Experiments and Structural Analysis
Bibliographic record
Abstract
A significant proportion of the world’s population has no access to grid-based electricity and so relies on off-grid lighting solutions. Rechargeable lamp technology is gaining popularity as an alternative off-grid lighting model in developing countries. In this paper, we explore consumer behavior and the operational inefficiencies that result under this model. Specifically, we are interested in (i) measuring the impact of inconvenience (of traveling to recharge the lamp) along with the impact of liquidity constraints (because of poverty) on lamp usage and (ii) evaluating the efficacy of strategies that address these factors. We build a structural model of consumers’ recharge decisions that incorporates several operational features of the low-income regions. We conducted large-scale field experiments in Rwanda in partnership with a local rechargeable lamp operator and use the resultant data to estimate and test our model. We find that the complete removal of inconvenience and liquidity constraints from the current business model results in 73% and 126% increases in both recharges and revenue, thereby suggesting that these constraints are major sources of inefficiency. By implementing simple operations-based strategies—such as starting more recharge centers, visiting consumers periodically to collect their lamps for recharge, and allowing consumers to partially recharge their lamps and pay flexibly for the recharge—more than half the benefit of completely eliminating the inefficiencies can be attained. By contrast, the price- and capacity-based strategies that vary the economic variables (i.e., the amount paid per recharge and the amount of light obtained in return) but not the operational model perform far worse than the aforementioned strategies. Overall, our analysis emphasizes the importance of managing operations effectively even in markets with cash-constrained consumers, in which firms may have a natural tendency to focus more on reducing prices. This paper was accepted by Vishal Gaur, operations management. Funding: This work was supported by the International Growth Centre; Wharton School, University of Pennsylvania; and Institut Européen d’Administration des Affaires; The Environment and Energy Partnership; Grand Challenges Canada (Stars in Global Health); INSEAD’s Emerging Markets Institute; INSEAD’s Randomized Controlled Trials Laboratory; the INSEAD–Wharton Alliance, and International Growth Centre. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4844 .
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".