Productivity, Competitiveness, and Territories of the Italian Medium-Sized Companies
Bibliographic record
Abstract
The medium-sized firms (MEs) are the cutting-edge of the Italian manufacturing sector. They have a crucial role in influencing the behavior of the local systems whose they are part (2/3 of the total firms are located in industrial districts). This study investigates the drivers of Italian MEs’ productivity, a fundamental aspect for assessing their ability to compete successfully. The classical approach (i.e. TFP) in measuring productivity is inapplicable to MEs, whose business model is characterized by: i) specialized production at the leading technological edge; ii) organization based on vertical and horizontal supply chains, where the major players are small companies, specialized on single production phase; iii) marketing strategy focused on market niches, which are created/dominated thanks to product differentiation and continuous innovation and where MEs impose premium prices. The empirical evidence shows that: i) the RTS are not constant, but decreasing and size and productivity are inversely related; ii) the quality of the workforce is the major driver of productivity: companies that employ a low-salary workforce are less productive than those that use more skilled and costlier workers; iii) territories matter: knowledge-intensive service firms as well as infrastructures and managerial skills have a positive impact on productivity.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".