The value of property rights and environmental policy in Brazil: Evidence from a new database on land prices
Bibliographic record
Abstract
Lack of property rights is associated with lower investment, development, and welfare. In the Brazilian Amazon, insecure property rights have historically led to civil conflicts and deforestation, which would be expected to provide incentives for landowners to seek formal title. In this paper, we construct a novel database of land prices in Brazil to measure the market value of formal title to land and compliance with environmental regulation. Using online advertisements of land sale offers scraped from a widely used seller’s platform, we first estimate a hedonic model that regresses the last offer price on property attributes such as farm-level agricultural production, land characteristics, structure amenities, and capital equipment included in the offer, as well as spatial and temporal fixed effects. We use this hedonic model to examine how property rights and environmental compliance capitalize into land prices across the Amazon and Cerrado biomes. Our main results imply low net benefits from property rights and low net benefits from compliance with the central Brazilian regulation that aims to maintain forest cover, the Forest Code. Finally, we estimate a duration model that follows the sequence of weekly offers for a specific property until it sells. Our findings show that parcels compliant with the Forest Code sell 46 % faster in the Amazon, while entitled properties in the Cerrado sell 9 % faster, unless they are compliant with the Forest Code, which requires a substantial portion of the property to be under native vegetation cover.
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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 | 0.002 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".