EU Exports to the World: Effects on Employment
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 trade flows affect employment. Gathering comprehensive, reliable and comparable information on this is crucial to support evidencebased policymaking. Guided by that objective, the European Commission's Joint Research Centre (JRC) and the Commission's Directorate General for Trade have collaborated to produce this publication. It aims to be a valuable tool for trade policymakers. Following up the first edition (Arto et al, 2015), the report features a series of indicators to illustrate in detail the relationship between trade and employment for the EU as a whole and for each EU Member State using the new Input-Output Database (WIOD), 2016 release (Timmer et al, 2015, 2016), as the main data source. This information has been complemented with data on employment by age, skill and gender from other sources such as EUKLEMS. All the indicators relate to the EU exports to the rest of the world so as to reflect the scope of EU trade policymaking. Most indicators are available as off 2000 but, due to data constraints, the indicators on employment split by skill, gender and age are only available from 2008 to 2014. 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. On the basis of the number of jobs embodied in every million EUR worth of exports in 2014 and more recent data on international trade in goods and services, this report also provides projections elaborated by the JRC for 2017 using a different methodology, so they should be taken with caution. The information presented in this pocketbook is complemented with an electronic version allowing downloads of the tables with the complete time series (2000-2014 and 2017).
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".