The Role of Social Capital in the Adoption of Firewood Efficient Stoves in the Northern Peruvian Andes
Bibliographic record
Abstract
This paper explores rural households’ adoption of a new cooking technology in the Northern Peruvian Andes. It exploits a development intervention which distributed and installed, at no cost, firewood efficient stoves in the rural communities of Chalaco District. Using first hand data, collected from the beneficiary villages, this research investigates how village technology adoption patterns and village social capital mutually interact and influence the individual household’s adoption decision. The results in this paper indicate that the effect of village adoption patterns on the household’s adoption decision is significantly higher in villages with stronger social capital and that the marginal impact of social capital may be negative if village success in adoption is relatively low. It is also shown that only the proportion of adopters that did not experience problems with their own stoves has a positive impact on individual household adoption through its interaction with social capital, while the reverse is true for the village proportion of adopters experiencing problems with the new cooking technology. In this study measures of social capital were collected prior to the intervention; therefore, reverse causality should not be a critical issue in identifying the effects of this social variable. Village unobservable factors are not likely to drive the observed patterns in the data; this paper also shows that village success in adoption has a negative effect on the decision to uninstall the stove among beneficiary non users and that this effect is also increasing in village social capital. The results point to the importance of village social structures in the success of development interventions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".