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 Green Revolution in India and the arguments on the effect of withdrawing fertilizer subsidies during structural adjustment in Nigeria, and finally the paper presents new data on the effect of crop data subsidies in Malawi. 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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".