Influence of educational composition on firm innovation and performance
Bibliographic record
Abstract
Despite a strong focus on innovation from both politicians and managers, our current understanding of the relationship between managers’ education level and the educational composition of the workforce in terms of innovation and their joint association with performance remains limited. For example, while most studies include workforce quality as a control, and recognize its importance, there is still little knowledge about how it influences the innovation process. The data and estimation framework used in this study enable for a detailed analysis of the importance of both managers’ and workers’ skill intensity and workers’ skill diversity throughout the sequential innovation process that transforms ideas into valuable innovation in three stages. In addition, the availability of panel data allows us to account for various econometric issues, such as sample selection bias, reverse causality, simultaneity, and measurement errors. We conclude that educated managers, together with a balanced workforce across education length and field enhance both innovation and performance. Even more interestingly, we show that the educational level of managers influences the innovation process primarily through the recruitment of more educated workers with diverse educations. And the importance of including the indirect effect of educational composition through R&D decisions on the likelihood of innovation.
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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 | 0.001 | 0.001 |
| 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".