The Changing Structure of Global Value Chains: Are Central Hubs Key for Productivity?
Bibliographic record
Abstract
This article uses "centrality" metrics reflecting position within Global Value Chains (GVCs) to identify central hubs and peripheral European economies and sectors. We find evidence of large changes in the structure of European production networks, with rising importance of Eastern European economies coinciding with the timing of their EU accession. Using cross-country firm-level data from ORBIS, we find that changing structure of GVCs can play a role in the catch-up of firms, but the effects are heterogeneous across firms and countries. First, becoming more central is associated with faster productivity growth of firms in post-2004 EU members. Second, the average productivity (centrality weighted) of buyers/suppliers matters for the productivity of firms overall in other European economies, and particularly non-frontier (initially less productive) firms in both groups of countries. The results for post-2004 EU members suggest that policies to encourage integration into GVCs are particularly important for the productivity of emerging or less integrated economies, whereas for more advanced economies a more sophisticated policy is needed that encourages the formation of linkages with productive, frontier foreign firms and economies.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".