Implementing the Trade Facilitation Agreement (TFA): estimates of reduction in time at customs for the United Nations' vulnerable economies
Bibliographic record
Abstract
All members of the WTO participate in the Trade Facilitation Agreement (TFA) that is to reduce border and documentary compliance in customs. Successful implementation should benefit all countries, the developing countries and more particularly the three categories of vulnerable countries receiving special status at the UN: Least Developed Countries (LDCs), the Landlocked Developing Countries (LLDCs) and the Small Island Developing States (SIDS). This paper gives plausible estimates (in the sense of realizable at the country and group levels) of reduction in trade costs from a successful implementing of the TFA. The paper starts with a presentation of the TFA noting its two principal characteristics. First, the TFA is a rules-based bottom-up approach built into the agreement that takes into account countries' implementation capabilities, an important feature for the three groups of UN vulnerable countries. Second, the TFA provisions are monitorable (e.g. provisions like the publication of information, advance rulings, appeal or review of decisions, transparency, and border agency cooperation). In preparation for the agreement, the OECD has assembled large amount of indicators of the state of implementation of provisions in the TFA summarized in a TFI (Trade Facilitation Index). TFI values for 2019 are then used to evaluate econometrically the impact of implementing TFA on the waiting-time reduction at customs for a sample of 160 countries. Average ad-valorem equivalents (AVEs) of reduction of time in customs estimates for each UN-grouping (LDCs, LLDCs, and SIDS) show averages in the range 2.1%-2.9% for imports and 1.9%-2-7% for exports. Larger gains are obtained for a more ambitious implementation of the TFA. Importantly, gains are larger for each of the three groupings than for other developing countries, a corroboration that the UN vulnerable categories capture an aspect of vulnerability.
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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 | 0.007 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 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".