Determinants of participation in charcoal production and its distributive impact on household welfare in rural Ethiopia
Bibliographic record
Abstract
Charcoal production is one of the main sources of households’ income in some part of rural Ethiopia. However, existing literature has rarely explored the presence of an entry barrier that might prevent the poorest from participating in this lucrative activity nor have they accounted for heterogeneity in the welfare effects of participation. To provide evidence for these issues, this study assesses the determinants of participation in charcoal production and its heterogeneous impact on household welfare using primary data from 390 households in selected rural villages of Awi zone, Ethiopia. This study uses a probit regression model to identify the determinants of participation and the quantile treatment effect (QTE) regression model to examine the welfare gap between participants and nonparticipants at different points of the welfare distribution and a decomposition technique to investigate whether the welfare gap is attributed to differences in characteristics or returns to these characteristics. Our probit model estimation result shows that poor households are less likely to participate in charcoal production, implying the existence of an entry barrier which may be attributed to the requirement of higher capital investment in the study area. Our QTE result suggests that participation affect welfare heterogeneously across the welfare distribution and the welfare gap is higher at the upper quantiles, suggesting that poor participants gain lower return from participation. Finally, our decomposition analysis reveals that the welfare gap exists due to coefficients effect. Although the results suggest the importance of policy to improve the participation of households in charcoal production for greater welfare, they also indicate the existence of uneven returns against poor participants.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".