The Political Economy of Agricultural Statistics and Input Subsidies: Evidence from <scp>I</scp>ndia, <scp>N</scp>igeria and <scp>M</scp>alawi
Bibliographic record
Abstract
The political economy of agricultural policies – why certain interventions may be preferred by political leaders rather than others – is well recognized. This paper explores a perspective that has previously been neglected: the political economy of the agricultural statistics. In developing economies, the data on agricultural production are weak. Because these data are assembled using competing methods and assumptions, the final series are subject to political pressure, particularly when the government is subsidizing agricultural inputs. This paper draws on debates on the evidence of a G reen R evolution in I ndia and the arguments on the effect of withdrawing fertilizer subsidies during structural adjustment in N igeria, and finally the paper presents new data on the effect of crop data subsidies in M alawi. The recent agricultural census (2006/7) indicates a maize output of 2.1 million metric tonnes, compared to the previously widely circulated figures of 3.4 million metric tonnes. The paper suggests that ‘data’ are themselves a product of agricultural policies.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".