Digital adoption and human capital upscaling: a regional study of the manufacturing sector
Bibliographic record
Abstract
Abstract We study the effect of the diffusion of digitalization, measured as the level of expenditures in digital technologies, on labor demand within the manufacturing sector. We exploit unique information from a focus study of the quarterly survey of Unioncamere Piemonte (one of Italy’s most industrialized and technologically advanced regions) to measure the extent to which planned digital technologies investments impact hiring propensity, differentiated by educational level. Based on a representative sample of non-micro firms, our findings suggest a positive relationship between digital investments and the probability of hiring highly educated workers, mainly driven by the demand for individuals with a post-secondary technical institute (ITS) diploma and post-MSc qualifications or a PhD in STEM fields. Conversely, we also find that digital investments negatively influence the probability of hiring low-educated individuals, primarily referring to the demand for workers with secondary education. Our results reveal firms’ human capital upscaling dynamics powered by digitalization processes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".