Impact of Diversity and Inclusion on Firm Performance: Moderating Role of Institutional Ownership
Bibliographic record
Abstract
We investigate the impact of diversity and inclusion (D&I) on firm performance for the period 2017–2021. While the existing literature examines the relationship between diversity and firm performance, little is known about the combined effects of D&I on firm performance. This study aims to utilize the most widely used data source, the Global Diversity and Inclusion (D&I) Index, provided by the LSEG workspace. Using 8089 firm-year observations from a sample of globally listed firms and an OLS regression model, we find that firms with a higher D&I score have better firm performance, as measured by Tobin’s Q. Our moderating analysis shows that the impact of D&I on firm performance is more pronounced for firms with higher institutional ownership. We also split institutional ownership into domestic and foreign institutional ownership and show that the influence of D&I on firm performance differs between domestic and foreign institutional ownership. Our result is robust when we use an alternative proxy for firm performance and consider the findings without US firms in the sample. The overall findings indicate that considering a diverse and inclusive workforce is worthwhile for key stakeholders when making policy decisions.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".