Modelling Non-Tariff Barriers as an Exporter Cost
Bibliographic record
Abstract
Non-tariff measures (NTMs) are a prominent feature of many recent free trade agreement (FTA) negotiations, including the Trans-Pacific Partnership (TPP), the Canada-EU FTA and BREXIT. The implementation of NTMs within computable general equilibrium (CGE) models has been relatively simple to date, with modelers generally incorporating NTMs as tariff equivalents via export or import taxes or as import-augmenting technological (or iceberg) change. Our study compares and contrasts two new methods with the traditional mechanisms used. The first new method is the willingness to pay method, recently developed by Walmsley and Minor (2015). The second method, introduced here, provides a new methodology for adjusting the exporters’ production costs directly, henceforth referred to as the export cost method. We find that the choice of mechanism can have important consequences for estimates of the impact of changes in NTMs on variables such as GDP and trade. More careful consideration of the NTMs being investigated, the estimates being utilized, and the CGE mechanisms being used, could therefore improve analysis. We find some similarities between the willingness to pay and trade tax methods on the one hand, and the iceberg and new export cost methods on the other, suggesting that the traditional methods may provide reasonable approximations of the impact of NTMs on certain key variables, such as the short run impact on GDP. However, further analysis reveals that the approaches can elicit very different changes in trade, factor prices and terms of trade, leading to larger differences in real GDP over time.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".