Democratic Peace and Electoral Accountability
Bibliographic record
Abstract
Democracies rarely engage in conflicts with one another, though they are not averse to fighting autocracies. We exploit the existence in many countries of executive term limits to show that electoral accountability is the key reason behind this “democratic peace ” phenomenon. We construct a new dataset of term limits for a sample of 177 countries over the 1816–2001 period, and combine this information with a large dataset of interstate conflicts. Our empirical analysis shows that, although democracies are significantly less likely to fight each other, democracies with leaders who face binding term limits are as conflict prone as autocracies. The study of electoral calendars confirms the importance of re-election incentives: in democracies with two-term limits, conflicts are less likely to occur during the executive’s first mandate than in the last one. Our findings support the Kantian idea that elections act as a discipline device, deterring leaders from engaging in costly conflicts. (JEL: F00, C72, D72) The editor in charge of this paper was Fabrizio Zilibotti.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".