Innovativity: A Comparison Across Seven European Countries
Bibliographic record
Abstract
This paper proposes a framework to account for innovation similar to the usual accounting framework in production analysis and a measure of innovativity comparable to that of total factor productivity. This innovation accounting framework is illustrated using micro-aggregated firm data from the first Community Innovation Surveys (CIS1) for seven European countries: Belgium, Denmark, Ireland, Germany, the Netherlands, Norway and Italy for the year 1992. Based on the estimation of a generalized Tobit model and measuring innovation as the share of total sales due to improved or new products, it compares the propensity to innovate, and the innovation intensity conditional and unconditional on being innovative, across the seven countries and low-and high-tech manufacturing sectors. Even with relatively few explanatory variables our innovation framework already accounts for sizeable differences in country innovation intensity. It also shows that differences in innovativity across countries can be nonetheless very large.
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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 | 0.013 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".