The impact of beekeeping on household income: evidence from north-western Ethiopia
Bibliographic record
Abstract
The existing literature acknowledges the benefits of beekeeping as a livelihood diversification strategy and income source for farmers across the world. However, the impact of beekeeping on income at household level has rarely been quantified. Furthermore, the few existing studies provide conflicting evidence and the methods quantifying the impact of participating in beekeeping are not rigorous. In this study, we identify key determinants of such participation and quantify the impact of beekeeping on household income. We use a cross-sectional data set collected from 392 randomly selected households in north-western Ethiopia, employing the endogenous switching regression model with estimated treatment effects. Unlike the methods used by previous studies, the approach adopted here enabled the control of observed and unobserved heterogeneities that affect not only the decision to participate in beekeeping, but also income differences among households. The results show that there are important differences between beekeepers and non-beekeepers in terms of their skills and resource endowments. After these differences were controlled for, beekeeping participation was found to increase income by 3,418 Ethiopian Birr (ETB) per person, namely a 51% increase. Furthermore, it was estimated that households not participating in beekeeping could have increased their income by ETB 442 per person (an 11% increase) had they become beekeepers. These findings indicate that income gains from beekeeping participation are 22-44 percentage points higher than benefits reported by previous studies. Capitalising on the existing beekeeping policy, targeted beekeeping extension to farmers could contribute to closing gaps in skills and resource endowments and, hence, minimising differences in income.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".