World Scientific Reference on Handbook of the Economics of Wine
Bibliographic record
Abstract
The European Union’s (EU) long-standing financial support for its wine industry has been nontrivial but very difficult to estimate. The Organization for Economic Cooperation and Development’s (OECD) generic producer support estimate methodology has been able to capture some of the supports, but it excludes such measures as subsidized distillation of low-quality wine, grants to promote wine generically, protection via import tariffs, and grubbing-up premiums. Nor does the OECD disaggregate EU supports to individual member countries. This chapter provides a new set of a complete estimates of support to EU wine producers. It also reveals how unevenly those supports are spread across EU member countries. The new estimates suggest that during 2007–2012, annual assistance amounted to approximately 700 euros per hectare of vines or 0.15 euros per liter of wine produced in the EU as measured at the winery gate. That is equivalent to a nominal rate of direct plus indirect producer assistance of approximately 20%. (JEL Classifications: F14, H25, L66, Q18).
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".