Does the Designation of Least Developed Country Status Promote Exports
Bibliographic record
Abstract
In this paper, we examine the extent to which developing countries export more as a result of being officially labelled as an LDC and consequently being eligible for a range of unilateral trade preferences. We estimate a gravity model of trade over the period of 1970 to 2013, in which identification is achieved by exploiting the particularities and asymmetries of "inclusion" and "graduation" criteria from LDC status. The main results show that inclusion in the official LDC list is associated with substantially higher exports. This is particularly the case for LDCs that also export manufactured and industrial goods and started to play a significant role after 1990. In addition, we evaluate the impact of developed countries' trade preferences on the exports of LDCs and the effectiveness of the trade preference schemes of the EU, the US, Canada, Japan, Australia, New Zealand, Norway and Turkey to better understand the mechanism at play. Unilateral preference regimes are, on average, not always beneficial in terms of increased export values for beneficiary developing countries but do have an impact on some sectors. They are mostly beneficial for agricultural goods and a few for manufactured goods, including textiles. As far as individual preference schemes are concerned, positive and statistically significant effects are found for the GSP schemes of Canada and Turkey. The positive effect of LDC status, however, is statistically significant and sizable even when controlling for trade preference schemes suggesting that other benefits of that status play a role in promoting exports.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".