EU Trade in Value Added
Bibliographic record
Abstract
The European Commission identified trade policy as a core component of the European Union's 2020 Strategy. The fast changing global economy, characterised by the dynamic creation of business opportunities and increasingly complex production chains, means that it is now even more important to fully understand how global value chains affect value added. Gathering comprehensive, reliable and comparable information on this is crucial to support evidence-based policymaking.\n\nGuided by that objective, the European Commission's Joint Research Centre (JRC) has produced this publication. It aims to be a valuable tool for trade policymakers.\n\nThe report features a series of indicators to illustrate in detail the EU value added dependence on the final demand of each EU Member State, and the value added in each Member State depending on the EU final demand as a whole. This is done using the World Input-Output Database (WIOD), 2016 release, as the main data source. This information has been complemented with data on labour compensation by skill and gender from other sources such as EUKLEMS. Besides, indicators have been also included to account for the inter-dependence between the EU and other world economies.\n\nMost indicators cover the period 2000-2014 but, due to data constraints, the indicators on labour compensation by skill and gender are only available from 2008 onwards. The geographical breakdown of the data includes the 28 EU Member States, Australia, Brazil, Canada, China, India, Indonesia, Japan, Mexico, Norway, Russia, South Korea, Switzerland, Turkey, Taiwan, the United States of America, and an aggregate “Rest of the World” region.\n\nThe information presented in this pocketbook is complemented with a software tool for analyses of global value chains, trade, income and employment. This tool enables a more detailed analysis of the different indicators related to global value chains and includes additional data management and visualization options.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.005 |
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".