Why foreign agricultural investment fails? Five lessons from Ethiopia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract In the past two decades, foreign direct investment (FDI) in emerging economies has witnessed substantial growth in the agricultural sector. Globally, more than a quarter of these investments have failed. Beyond case studies, the factors that contribute to these failures have been subject to limited research. To address this research gap, this article draws on a unique data set of 106 investments in Ethiopia, from which failures were identified and detailed case studies analysed to identify the causes of failure. Drawing on the literature on institutional voids, our analysis shows that the high rates of failure in the agricultural sector are often caused by insufficient planning at the proposal stage, assumptions about the availability of expertise, socio‐political and environmental risks, insufficient financing and/or a changing investment landscape and underestimation and/or misunderstanding regarding the limits of extractive approaches. These lessons suggest that while FDI in the agricultural sector has potential, the working approaches require significant transformation. We offer a set of strategic recommendations to mitigate the risk of investment failure in agricultural investment.
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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 | 0.001 | 0.000 |
| 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 it