Easing the burden of non-tariff barriers: a regional and firm-level data analysis
Bibliographic record
Abstract
This article aims at providing a firm-level analysis of non-tariff barriers' (NTBs) categories based on the importance of exports for domestic firms across diverse regions in the world. It exploits cross-sectional data from the World Bank enterprise surveys of 10,266 firms across 81 countries covering the period from 2006 to 2014. The study focuses on four NTBs: customs and trade regulations, tax rate, tax administration, and business licensing and permits. Firms were analysed according to levels of exports and locations. The results show that tax rate and business licensing and permits are more likely to be rated as a severe barrier. The tax administration and customs and trade regulations are more probable to be ranked as minor or no obstacle to trade. The business licensing and permits and tax rate are more likely to be ranked as a severe barrier for the firms within the 51%-75% level of exports. In addition, the majority of the firms with 26%-50% of exports are more likely to rank tax administration and customs and trade regulation as severe barriers.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".