Does Local Politics Drive Tropical Land-Use Change? Property-Level Evidence From the Amazon
Bibliographic record
Abstract
Abstract Land conversion to agriculture is a defining environmental challenge for tropical regions. We construct a novel panel dataset of land-use changes on the properties of municipal politicians and campaign donors in the Brazilian Amazon to assess channels through which local politics may drive land conversion. Estimating event studies around close mayoral elections, we find that large landholders significantly increase soy cultivation while the candidate they donated to is in office. This suggests that landholders invest in political influence to overcome barriers to agricultural intensification. In turn, mayors who receive landholder donations govern in favour of agriculture—increasing spending on agricultural promotion and distribution of rural credit. While agricultural promotion ‘returns the favour’ for mayors’ donors, it is not precisely targeted. We document large spillovers onto lands not registered to donors, resulting in increased environmental violations in these areas. Results reveal how patronage and special interests drive land-use change in the Amazon.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".