Evidence on the impact of R&D and ICT investments on innovation and productivity in Italian firms
Bibliographic record
Abstract
Both research and development (R&D) and information and communication technology (ICT) investment have been identified as sources of relative innovation underperformance in Europe vis-à-vis the USA. In this article, we investigate the R&D and ICT investment at the firm level in an effort to assess their relative importance and to what extent they are complements or substitutes. We use data on a large unbalanced panel data sample of Italian manufacturing firms constructed from four consecutive waves of a survey of manufacturing firms, to estimate a version of the CDM model of R&D, innovation, and productivity [Crépon–Duguet–Mairesse 1998. Research, innovation and productivity: An econometric analysis at the firm level. Economics of Innovation and New Technology 7, no. 2: 115–58] that has been modified to include ICT investment and R&D as the two main inputs into innovation and productivity. We find that R&D and ICT are both strongly associated with innovation and productivity, with R&D being more important for innovation, and ICT investment being more important for productivity. For the median firm, rates of return to both investments are so high that they suggest considerably underinvestment in both these activities. We explore the possible complementarity between R&D and ICT in innovation and production, but find none, although we do find complementarity between R&D and worker skill in innovation.
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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 | 0.003 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".