The Effect of National Culture on CEO Compensation: Evidence from Europe and North America
Bibliographic record
Abstract
The purpose of this paper is to determine the extent to which culture, in six European and two North-American countries, affects CEO compensation. If differences in culture between countries can provide an explanation for cross-national differences in CEO compensation, it may increase multinational corporations understanding of how to design CEO compensations in the countries where they operate. Acquiring such knowledge would maximize the effect of their compensation plans. The study relates cultural dimensions to total CEO compensation and the ratio between variable compensation and total CEO compensation. Cultural data, which comprises the study’s theoretical foundation, is based on the GLOBE study (House et al., 2004). Of the GLOBE study’s nine cultural dimensions, the study examines the five dimensions found most relevant to CEO compensation practices; performance orientation, uncertainty avoidance, institutional collectivism, future orientation and power distance. The research has been conducted through a regression analysis of 240, both private and publically listed companies. Companies with a turnover above €49 million or at least 250 employees were randomly chosen in Sweden, Germany, Netherlands, United States, Canada, France, Ireland and United Kingdom. The study’s results shows that the cultural dimensions examined, to different extent do affect CEO compensation. The results show total CEO compensation to be negatively related to institutional collectivism, power distance and performance orientation. Further, total CEO compensation is positively related to future orientation. The proportion of variable compensation to total CEO compensation is negatively related to institutional collectivism and uncertainty avoidance. The proportion of variable compensation to total CEO compensation is positively related with future orientation. Thus we conclude that culture can contribute to understand cross-national differences in CEO compensation.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".