High Commodity Prices and the EU's Single Payment Scheme: Some Consequences of Double‐Dipping
Bibliographic record
Abstract
When negotiating agricultural policies, decisions are generally based on historical net farm income figures, which are derived from the then current price and production data. In 2003, when the Single Payment Scheme (SPS) was introduced in the EU, agricultural commodity prices were “low.” As a result the SPS is very generous when viewed in light of today's “high” agricultural commodities prices: farmers are double‐dipping. We provide an empirical assessment of the implications for compensatory payments under the SPS in combination with high commodity prices for two major EU crops: wheat and barley. Yearly compensatory payments for these two crops alone exceed $10 billion. The lesson of this result for agricultural policy reform is that the payments under the SPS should have been made more flexible and tied to future (farm gate) prices, i.e., so payouts would usually vary with changing market conditions, while still being decoupled from production decisions. Au moment de négocier des politiques agricoles, les décisions sont généralement fondées sur les données historiques sur le revenu agricole net, lesquelles sont dérivées des prix et des données de production d'alors. En 2003, année où l'UE a mis en place le régime de paiement unique (RPU), les prix des produits agricoles étaient ≪faibles≫. Actuellement, le RPU est très généreux en raison des prix ≪élevés≫ des produits agricoles: les producteurs cumulent les avantages. Nous présentons une estimation empirique des répercussions sur les paiements compensatoires du RPU combinées aux prix élevés des produits de base pour deux grandes cultures de l'UE, à savoir le blé et l'orge. Pour ces deux cultures seulement, les paiements compensatoires annuels dépassent les dix milliards de dollars. La leçon est que les paiements versés dans le cadre du RPU auraient dûêtre plus souples et liés aux prix (à la ferme) futurs, de sorte que les versements varieraient généralement en fonction des conditions changeantes du marché tout en demeurant indépendants de la production.
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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 | 0.010 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".