Not all crops are equal: the impacts of agricultural investment on job creation by crop type and investor type
Bibliographic record
Abstract
Significant agricultural investment has taken place across Africa over the most recent two decades. An expanding set of literature analyzes these investments, often using case study and comparative approaches. While this is important, not all agricultural investments are equal, yet they are often described as being such. Few studies utilize large data sets to conduct quantitative, comparative research. This paper examines investments in Ethiopia, using quantitative data of 102 investments that took place in one region between 1998 and 2018. Using this unique dataset, we conduct a comparative assessment of investments, analyzing traits such as crop choice, job creation, job type, implementation status, and investor type (Ethiopian, foreign, diaspora). We find that Ethiopian investors are granted larger average leases of land, though are fewer in number in comparison to foreign and diaspora investors. The same category of investors (Ethiopians) have higher implementation status but have created the least per hectare permanent and seasonal jobs. The regression analysis, however, shows that there is no statistically significant difference among the three types of investors in terms of per hectare job creation. From the investment types, horticulture/flowers created the most employment per hectare, followed by vegetables and fruits production. This evidence contests common narratives about agricultural investment and provides a basis for decision makers to better enable positive outcomes, such as greater job creation.
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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 | 0.000 | 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 | 0.000 |
| 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".