Age Differences in Leadership Positions Across Cultures
Bibliographic record
Abstract
In most countries around the world, the population is rapidly aging. A by-product of these demographic shifts is that older adults will likely occupy more positions of power and influence in our societies than ever before. Further, cultural differences might shape how these transitions unfold around the globe. Across two studies, we investigated whether business and political leaders differed in age across various cultures. Study 1 ( N = 1,034) showed that business leaders were significantly older in Eastern (e.g., China, India, and Japan) cultures than Western (e.g., United States, Sweden, and United Kingdom) cultures, even while controlling for population structure (e.g., percentage of elderly in the society), gross domestic product (GDP), and wealth distribution across the population (GINI). Study 2 ( N = 1,268) conceptually replicated these findings by showing that political leaders were once again older in Eastern vs. Western cultures. Furthermore, cultural tightness mediated the relationship between culture and older leadership. These findings highlight how cultural differences impact not only our preferences, but also important outcomes in consequential domains such as business and politics. Potential explanations for why cultural tightness may be related to differences in leader age across cultures are discussed. To build on these findings, future research should assess the potential causal mechanisms underlying the cultural effect on leader age, and explore the various practical implications of this effect.
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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 | 0.000 | 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) | 0.001 | 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".