Global Value Chains and Productivity Growth: Does Intangible Capital Matter?
Bibliographic record
Abstract
This article investigates the impact of participation in global value chains (GVCs) on productivity growth considering the mediating effect of investment in intangible assets. We explore the existence of synergies between intangible capital accumulation and GVC participation and their influence on productivity in a sample of nine European economies in 1998-2013. The analysis relates the macroeconomic literature on the impact of intangibles and GVCs on productivity growth to microeconomic studies about the functions of intangibles along the value chain. The existence of complementarities between intangibles and GVC participation and their productivity effects are tested in an augmented production function framework. We find: a) positive and statistically significant productivity impact of backward participation; b) the marginal effect of GVC participation on growth is greater in countries-industries with higher intensity of intangible capital; c) non-R&D intangibles, and particularly organizational capital, exert a significant conditional effect on backward participation strengthening the productivity returns of global production activity.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".