Export competition issues after Nairobi
Bibliographic record
Abstract
What is the economic significance of the Nairobi outcome in agriculture? Export competitionAs far as export competition is concerned, the use of export subsidies has declined significantly compared to the early 1990s when the EU alone would spend more than 10 billion euros a year.Today, the use of this instrument has practically disappeared.As highlighted by Díaz-Bonilla and Hepburn, several product groups such as grains and oilseeds that were the main recipients of subsidies have not received such support in recent years, and several countries with export subsidy entitlements only used a small proportion of their allowed levels.This decline is largely due to policy reforms such as the dismantling of market price support schemes in the EU, but also to the fact that agricultural and food prices increased significantly between 2000 and 2011, reducing the need to dispose of production surpluses in world markets.However, with prices trending downwards since 2011 (as a result of slowing demand for commodities in major economies such as China, falling oil prices, and a robust supply-side response to high prices for farm goods) maintaining the possibility to use such instruments could have resulted in significant increases in the use of export subsidies.4 Laborde and Díaz-Bonilla (2015) have found for example that if countries were to increase such support gradually up to their maximum allowed entitlement, the amounts spent could reach US$11.5 billion with significant downward effects on world prices, agricultural investment, and the wages of unskilled workers in rural areas, highlighting the negative impact of such subsidies on poverty alleviation and food security in developing countries.5 Furthermore, with market price-support schemes becoming more prevalent in several large developing countries, the new disciplines on export subsidies may prove critical to protect farmers from the disposal of unwanted surplus farm products originating in other parts of the developing world.6 For all these reasons, the Nairobi outcome clearly represents a significant achievement, at least in the medium to long term.In the short term, some distortions may, however, persist not least due to longer transition periods granted to Canada, the EU, Norway, and Switzerland for processed products, dairy, and swine meat.In the case of the EU, the Nairobi decision also allows for a phasing out of sugar quotas by 2017 to be consistent with the outcome of previous WTO dispute rulings and subsequent reforms of the EU's Common Agricultural Policy.In a similar vein, the flexibilities granted to developing countries to maintain article 9.4 subsidies covering transport and marketing costs until 2023 may affect several 4 See Díaz-Bonilla and Hepburn in this volume. 5See Laborde, David; Eugenio Díaz
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.143 | 0.031 |
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".