OWNERSHIP, COMPENSATION AND BOARD DIVERSITY AS INNOVATION DRIVERS: A COMPARISON OF U.S. AND CANADIAN FIRMS
Bibliographic record
Abstract
Using a large sample of North American firms, from 1999 to 2016, we investigate the effect of corporate governance structures, specifically ownership, board characteristics, and executive compensation contracts on innovation intensity and output. We consider both R[Formula: see text]D expenditures and patents as innovation proxies and evaluate consequences of the economic downturns of 2000 and 2008. We find that R[Formula: see text]D investment increases with ownership by institutional blockholders and with the number of institutional owners, confirming the key role institutions play in innovation activities of firms. We observe higher R[Formula: see text]D levels for firms with more independent boards, more females board members and more outside directorships held by directors. We report that firms with CEO/chair of the board duality have lower R[Formula: see text]D intensity, as do firms with higher ownership by directors and with a higher mean board age. Innovation is negatively related to CEO salary levels, but positively related to the ratio of incentives to total compensation, confirming that incentives contribute to aligning shareholders and management interests, which leads to better long-term decisions. However, those incentives reduce the number of patents. We do not find any systematic changes in R[Formula: see text]D for the 2000 recession, however there is an increase for the 2008 financial crisis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".