Quality upgrading of Italian manufactures: evidence from firms� prices and strategies
Bibliographic record
Abstract
Even before the global crisis, the Italian economy was in difficulties internationally, but slow growth and a declining share of world trade were accompanied by a perceptible process of manufacturing transformation. This paper, using data from the Bank of Italy�s survey of manufacturers, measures a crucial aspect of the transformation, namely quality upgrading, from 2000 to 2006. The gauge of upgrading, not used in earlier literature, is the portion of price changes representing the return to value creation, both tangible (new products and improvement of existing ones) and intangible (branding policies). We find evidence of upgrading capable of explaining a quarter of the firms� average annual price increases (about 0.5 out of 2 percentage points), with roughly equal effects from the tangible and the intangible components. The analysis also shows that strategies of product upgrading helped foster job creation and sales growth.
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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".