Why foreign agricultural investment fails? Five lessons from Ethiopia
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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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".