Agricultural Trade Policy Modelling: Insights from a Meta-Analysis of Doha Development Agenda Outcomes
Bibliographic record
Abstract
In a meta-analysis of trade policy models, Hess and von Cramon-Taubadel (2008) use over 5800 simulated welfare effects from 110 studies of potential Doha Development Agenda outcomes to identify characteristics of models, data and policy experiments that influence simulation results. This meta-analysis, which is recapitulated here, produces plausible results and explains a significant proportion of the variation in simulated welfare effects. However, due to insufficient documentation and the complexity of the general and partial equilibrium models in the literature sample, many explanatory variables employed in this analysis are binary. This precludes more detailed analysis of their impacts across models. Therefore, a partial equilibrium model and a single country CGE for Canada are employed to generate synthetic meta-data. Simulation scenarios are based on random combinations of base data, elasticities and tariff changes selected from plausible ranges obtained from the literature sample. The synthetic meta-data has the advantage that the values of explanatory variables are measured exactly. This makes it possible to explore more complex issues of functional form and interaction between variables in the meta-analysis. The results indicate for both models that first- and second-order polynomials provide sufficient approximations of the model response. Especially in the CGE model, interaction terms between elasticities and policy variables are important. We conclude that meta-analysis can provide insights into the behaviour of trade policy models beyond what is possible with conventional sensitivity analysis and qualitative reviews.
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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 | 0.118 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.031 |
| Bibliometrics | 0.011 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".