On the linkage between Gross Value Added by Economic Activities and the Overall Gross Value Added in EU-27
Bibliographic record
Abstract
Abstract The financial and economic crisis led to a significant recession in the EU-27 in 2009, followed by a rebound in 2010. The existing economic situation requires a rethinking of the economic development policies, focused on analyzing the indicator of gross value added, created in production. This may lead to a new growth model, based on economic activities with higher value added. This paper investigates the linkage between overall gross value added in EU-27 and gross value added by economic activities, as described by NACE Rev. 2, from first quarter 2010 to second quarter 2020. The article attempts to include a wide range of statistical analysis and models for a complete assessment of the subject. Therefore, in order to achieve the objective, we choose to investigate the presence of causality relationships using VAR/SVAR models and Granger causality test, which reflect the presence of long and short-term relationships between certain selected variables. Through the assessment, we discovered a strong bi-directional causality between overall gross value added and the gross value added by industry and by distributive trades, transport, accommodation and food services, based on which we estimated a linear regression. The findings should present interest for policymakers, in order to assess perspectives to economic growth.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".