Reducing Wait Times at Customs to Boost Trade: How Implementing the Trade Facilitation Agreement Can Expand Trade among AfCFTA Countries?
Bibliographic record
Abstract
Abstract All WTO members participate in the Trade Facilitation Agreement (TFA), a rules-based bottom-up approach built on monitorable provisions (e.g., the publication of information, advance rulings, appeal or review of decisions, transparency and border agency cooperation) aimed at reducing time in customs. The paper draws on the OECD indicators of the state of implementation of provisions in the TFA summarised in a TFI (Trade Facilitation Index) to estimate the reduction in waiting time at customs for a large sample of 160 countries. Implementing the TFA could be a significant complement to the African Continental Free Trade Area (AfCFTA)'s objectives. The paper's estimates suggest that a realistic implementation of TFA measures could reduce time in customs for imports by 3.7 days and by 1.9 days for exports. Using extraneous estimates from customs-level transactions, this translates to a reduction tariff Ad-Valorem Equivalent (AVE) in the range 3.5%–7% for imports and 8% extra growth for exports. The large differences in interests across AfCFTA participants—landlocked-coastal, resource-rich and resource-poor, large-small—suggest large gains from reducing tariffs on intra-African trade. However, tariff reductions face the zero-sum hurdle of negotiations involving rent transfers across and within countries. By avoiding rent-transfer issues, this paper suggests that taking seriously the TFA provisions would be a powerful complement to the AfCFTA's tariff-reduction agenda.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".