Gender, Assets, and Agricultural Development: Lessons from Eight Projects
Bibliographic record
Abstract
Ownership of assets is important for poverty alleviation, and women’s control of assets is associated with positive development outcomes at the household and individual levels. This research was undertaken to provide guidance for agricultural development programs on how to incorporate gender and assets in the design, implementation, and evaluation of interventions. This paper synthesizes the findings of eight mixed-method evaluations of the impacts of agricultural development projects on individual and household assets in seven countries in Africa and South Asia. The results show that assets both affect and are affected by projects, indicating that it is both feasible and important to consider assets in the design, implementation, and evaluation of agricultural development projects. All projects were associated with increases in asset levels and other benefits at the household level; however, only four projects documented significant, positive impacts in women’s ownership or control of assets relative to a control group, and of those only one project provided evidence of a reduction in the gender asset gap. The quantitative and qualitative findings suggest ways that greater attention to gender and assets by researchers and development implementers could improve outcomes for women in future projects.
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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 | 0.001 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".