Does employee’s diversity help innovation?: Evidence from Canadian firms
Bibliographic record
Abstract
Labour is commonly perceived as a uniform input within the literature on knowledge production. Nevertheless, the ethnic diversity of employees can also exert an influence on knowledge generation. Organisational behaviour (OB) theories have identified decision-making and social categorisation as two fundamental processes that can shape the effects of diversity on innovation. Ethnically diverse employees may contribute to innovation through their distinct ideas rooted in their diverse cultural backgrounds. Conversely, they might impede innovation due to potential conflicts in behaviour. In this research, we explore the impact of ethnic diversity among employees on both product and process innovations, using data from the Canadian Workplace and Employee Surveys (WES). Our mixed logit model estimation outcomes substantiate the positive contribution of ethnic diversity on innovation, even after controlling for employee and firm characteristics. These results remain robust when we account for potential endogeneity issues. Furthermore, our findings suggest that ethnically diverse employees are particularly effective in fostering innovation within firms that possess substantial organisational capital and offer comprehensive training programmes. Across various industries, it appears that manufacturing, transportation, and select service sectors have reaped the greatest benefits from ethnic diversity to 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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".