Overcoming obstacles to innovation: Can an educated workforce help?
Bibliographic record
Abstract
Firms face many obstacles in their pursuit of innovation. However, the mechanisms that enable firms to surmount these challenges and foster innovation are less understood. This study thus investigates whether the better performance of firms with higher human capital is due to their increased ability to overcome obstacles to innovation. Our estimation strategy accounts for the fact that facing obstacles is endogenous by correcting for the sample selection bias that is involved in determining which firms face obstacles. It appropriately estimates the impact of firms’ skill intensity on their propensity to innovate under two sets of circumstances—facing obstacles or not. Using a combination of rich survey and register data from over 2000 Danish firms for the period of 2006 to 2018, we also address several other biases that could affect our estimation of the impact of skill intensity on overcoming obstacles. Our results provide strong evidence that firms facing challenges in their innovation process are more likely to succeed when they have higher skill intensity. This applies to large and small firms as well as to firms in the service and manufacturing sectors , and it applies regardless of the type of innovation and, to some extent, which obstacles they face. Interestingly, we find that increasing skill intensity has no impact on the likelihood of innovation for firms that do not face obstacles. In contrast, firms that face obstacles can increase their likelihood of innovation by up to 25 %-points through higher skill intensity.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".