The Capitalization of Area Payments into Farmland Rents: Micro Evidence from the New EU Member States
Bibliographic record
Abstract
This study investigates the impact of the Single Area Payment Scheme (SAPS) on farmland rental rates in the New EU Member States. Using a unique set of farm level panel data with 20,930 observations for 2004 and 2005 we are able to control for important sources of endogeneity. According to our results, the SAPS has a positive and statistically significant impact on land rents in the EU. However, the estimated incidence is smaller than predicted theoretically. Land rents capture only 19 cents of the marginal SAPS EUR, and around 10% of the SAPS benefit nonfarming landowners through higher farmland rental prices. As the share of rented land is higher in corporate farms than individual ones, family farms benefit more from the SAPS than corporate farms do. Dans la présente étude, nous avons étudié les répercussions du Régime de paiement unique à la surface (RPUS) sur les taux de location de terres agricoles dans les nouveaux États membres de l’Union européenne. À l’aide d’un ensemble unique de données de panel sur les exploitations agricoles renfermant quelque 20 930 observations recueillies en 2004 et en 2005, nous avons pu maîtriser des sources d’endogénéité importantes. Nos résultats montrent que le RPUS a des répercussions positives et statistiquement significatives sur les loyers fonciers dans les pays de l’Union européenne. L’incidence estimative est toutefois inférieure à la valeur prévue théoriquement. Les loyers fonciers ne s’emparent que de 19 cents l’euro du RPUS marginal, et près de 10 p. 100 du RPUS profitent aux propriétaires fonciers non exploitants en raison des prix de location de terres plus élevés. Comme la part des terres louées est plus élevée dans le cas des fermes constituées en société que dans le cas des fermes individuelles, les fermes familiales profitent davantage du RPUS.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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 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".