Economic Experiments as a Tool for Agricultural Policy Evaluation: Insights from the European CAP
Bibliographic record
Abstract
This article assesses the potential contribution of economic experiments to evidence‐based policy making in the field of agriculture, with a special focus on the European Union (EU)'s Common Agricultural Policy (CAP). CAP evaluation mostly relies on standard tools such as farm and market simulation models, calibrated with EU‐wide statistical data; statistical and econometric analysis of survey data; and a range of qualitative methods such as interviews with stakeholders, focus group, or Internet‐based public consultation. Yet, the CAP has changed considerably over the past decades, requiring adaptations of its evaluation toolbox. A detailed review of existing studies using economic experiments for designing and evaluating agricultural policies provides the backbone for a comprehensive assessment of the complementarity of experimental approaches with standard evaluation tools. Based on conclusions drawn from a workshop organized with experts, academics, and policy makers of Directorate‐General for Agriculture and Rural Development of the European Commission, the article further provides recommendations aiming at facilitating inclusion of economic experiments into the CAP evaluation toolbox. Cet article étudie la contribution potentielle des méthodes expérimentales pour l'élaboration de politiques agricoles fondée sur des données probantes, en considérant en particulier le cas de la politique agricole commune de l'UE (PAC). L'évaluation de la PAC repose principalement sur des outils standard tels que des modèles de simulation d'exploitations et de marchés agricoles, calibrés avec des données récoltées à l'échelle de l'UE; des analyses statistiques et économétriques sur des données d'enquête; et une série de méthodes qualitatives telles que des entretiens avec les parties prenantes, des groupes de discussion ou des consultations publiques sur Internet. Pourtant, la PAC a considérablement changé ces dernières décennies, ce qui nécessite d'adapter ses méthodes d'évaluation. Un examen détaillé des études existantes mobilisant les approches expérimentales pour la conception et l'évaluation de politiques agricoles permet d'analyser la complémentarité des méthodes expérimentales avec les outils d'évaluation standards. A partir des conclusions d'un atelier organisé avec des experts, des universitaires et des décideurs politiques de la Direction générale de l'agriculture et du développement rural de la Commission européenne, cet article propose en outre des recommandations pour faciliter l'intégration des approches expérimentales dans les méthodes d'évaluation de la PAC.
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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 | 0.171 | 0.205 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.011 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".