Labour Productivity in European Non-Financial Corporations: The Roles of Country, Sector, and Size
Bibliographic record
Abstract
This study aims to investigate the determinants of labour productivity across European non-financial entities using aggregated data from the Bank for the Accounts of Companies Harmonized (BACH) database. Focusing on six European Union countries (Belgium, France, Italy, Portugal, Poland, and Spain). Annual information from 2010 to 2023 is used (the last available year), including three size classes (small, medium-sized and larger entities) per division (two-digit code) by year and by country, totalling 14,188 observations. The combination of sectors and class sizes varies from 191 to 208 by country. It uses gross value added per employee as a proxy for labour productivity. Using a fixed-effects estimator and panel data regression techniques, the analysis reveals that labour productivity explanatory factors, particularly firm size, profitability, financialisation, leverage, and tangibility, have heterogeneous and sometimes contradictory effects across countries, sectors, and size classes. Larger firms generally tend to have higher levels of labour productivity, although this feature is not consistent among countries. Size and profitability more consistently exert a strong positive influence, whereas financialisation and leverage typically show negative or nonlinear effects. The results highlight the structural diversity of the European corporate landscape and challenge the adequacy of one-size-fits-all policy measures, contributing to the literature on productivity and offering further insights to policymakers by integrating cross-sectional, sectoral, and size-specific perspectives on labour efficiency within the EU context.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".