When do Agricultural Exports Help the Rural Poor? A Political-economy Approach
Bibliographic record
Abstract
Many economists have argued that agricultural exports should be one of the best ways to reduce rural poverty in developing countries, through the creation of productive employment in the rural areas. Non-economists have tended to be sceptical, often seeing such exports as competitive with food crops and thus potentially threatening to an adequate supply of food. The historical record includes many cases in which the prospect of profitable agricultural exports prompted the rich/powerful to appropriate land formerly occupied by lower income agricultural workers, often squatters or people with traditional land rights. That record, as currently understood, leaves it unclear whether such exports have more frequently brought benefits to the rural poor or hurt them. An adequate model of the poverty effects of agricultural exports must thus take account of how control of land (and labour as well) may be shifted among groups without compensation as it becomes more valuable. Two major issues/questions are of current interest. First, have the unjust mechanisms whereby the rich wrested valuable resources from the poor in the past become less common? Second, is there evidence that the sort of labour-intensive agricultural exports most likely to benefit the poor are growing fast enough to suggest an important poverty effect at present and in the future? More in-depth research is needed to clarify both points. For the present, it appears unlikely that agricultural exports will be a major source of poverty reduction for the rural poor in the Third World taken as a whole.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".