The transatlantic productivity gap: Is R&D the main culprit?
Bibliographic record
Abstract
Abstract The literature has pointed to different causes to explain the productivity gap between the EU and the US in the last decades. This paper tests the hypothesis that the lower European productivity performance in comparison with the US can be explained not only by a lower level of corporate R&D investment but also by a lower capacity to translate R&D investment into productivity gains. The proposed microeconometric estimates are based on a unique longitudinal database covering the period 1990–2008 and comprising 1,809 US and EU companies for a total of 16,079 observations. Consistent with previous literature, we find robust evidence of a significant impact of R&D on productivity; however, using different estimation techniques, the R&D coefficients for the US firms always turn out to be significantly higher. To see to what extent these transatlantic differences in the R&D/productivity relationship may be related to the different sectoral structures in the US and the EU, we differentiated the analysis by sectors. The result is that bothin manufacturing, services and high‐tech manufacturing sectors US firms are more able to translate their R&D investments into productivity increases.
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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 | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".