Brewing inclusivity: foreign agribusiness and local food security – the case of Heineken in Ethiopia
Bibliographic record
Abstract
Purpose Inclusive agribusiness models are recognized as a vital strategy for addressing development challenges by enabling smallholder farmers to profitably engage in agricultural value chains. Hence, the purpose of this study is to explore the impact of a proclaimed inclusive agribusiness model on farmers’ productivity, asset stock and dietary diversity and its indirect effects on the local community in the Arsi Zone, Ethiopia. Design/methodology/approach This study used a mixed research approach. Survey data from 251 households were analysed using endogenous switching regression and propensity score matching to compare participant and non-participant households in terms of productivity, asset acquisitions and dietary diversity status. To understand the indirect effects on the wider community, interviews with key informants and focus group discussions with participants and non-participants were conducted. Findings Contracted farmers registered increased malt barley productivity and asset stocks. However, with regard to dietary diversity, there was no significant difference between participating and non-participating farmers. Interviews revealed that this was due to spending on priorities other than food and less diverse food availability in rural markets. Research limitations/implications Inclusive business approaches can positively contribute to smallholder farmers’ productivity and income, yet this does not automatically translate into improved household diet diversity in rural areas. For this to occur, local food availability and accessibility should be taken into consideration. In addition, evaluating the impact of an inclusive business approach on a small minority (i.e. contract farmers) risks overlooking the impact on the majority, who are not reached by these business arrangements. Originality/value This study contributes to the literature and debates on private sector-led development by illustrating the impact of presumably inclusive agribusiness on local food security. The unique feature is that this study also considers wider community effects.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".