The impact of political assassinations on turnout: Evidence from Colombia
Bibliographic record
Abstract
Although a growing literature has investigated the effects of various types of civil war violence on political behavior, no study has examined the impact of assassinations targeting politicians. This is a critical omission, as violence against local politicians is prevalent across civil war contexts and may be the most consequential form of violence for political participation by affecting both candidate supply and voter demand. Using an original dataset of nearly 2,000 killings of Colombian local politicians between 1980 and 2023, we estimate the impact of this violence on voter turnout. Taking municipalities where assassination attempts failed as a comparison group, we find that political assassinations significantly decrease voter turnout in both the short and medium terms, with effects persisting in various elections even after the signing of a peace agreement. These findings contrast with many studies suggesting that other forms of civil war violence enhance political participation during the postconflict period or after a truce or peace agreement. Our results suggest that different forms of violence can have distinct effects on political behavior, underscoring the need to theorize how the targeting, nature, and context of violence condition its effects. This echoes calls for more nuanced studies on the behavioral impacts of violence. Our findings also have implications for understanding democracy amid rising violence against political leaders in countries affected by organized crime, such as Mexico and Brazil; polarized contexts, such as the United States; and weakly institutionalized democracies, such as South Africa, Indonesia, and the Philippines.
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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 | 0.001 | 0.003 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".