Leader age and international conflict: A regression discontinuity analysis
Bibliographic record
Abstract
Abstract Does leader age matter for the likelihood of interstate conflict? Many studies in biology, psychology, and physiology have found that aggression tends to decline with age throughout the adult lifespan, particularly in males. Moreover, a number of major international conflicts have been attributed to young leaders, including the conquests of Alexander the Great and the ambitious military campaigns of Napoleon. However, the exact nature of the relationship between leader age and international conflict has been difficult to study because of the endogeneity problem. Leaders do not come to power randomly. Rather, many domestic and international factors influence who becomes the leader of a country, and some of these factors could correlate with the chances of interstate conflict. For instance, wary democratic publics might favor older leaders when future international conflict seems likely, inducing a relationship between older leaders and interstate conflict. This article overcomes such confounding by using a regression discontinuity design. Specifically, it looks at close elections of national leaders who had large differences in age. It finds that when older candidates barely defeated younger ones, countries were much less likely to engage in military conflict. Its sample is also fairly representative of democracies more broadly, meaning that the findings likely hold true for cases outside the sample. The results demonstrate the important role that individuals play in shaping world politics. They also illustrate the value of design-based inference for learning about important questions in the study of international relations and peace science.
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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 | 0.019 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".