Am I obliged to vote? A regression discontinuity analysis of compulsory voting with ill-informed voters
Bibliographic record
Abstract
Abstract We study the impact of compulsory voting in Brazil, where voting is mandatory from age 18 to 70 and voluntary for those aged 16, 17 and 70+. Using a survey sample of 8008 respondents, we document voter confusion about how the age criterion applies. Some people falsely believe that what matters is one's age in an election year rather than on Election Day. Next, we perform a regression discontinuity (RD) analysis of compulsory voting among young voters with register-based data from six Brazilian elections (2008–2018). We find that the effect of compulsory voting is seriously underestimated if we focus solely on the discontinuities prescribed by the law. Our findings carry important implications for studies adopting the RD design where knowledge of the cutoff is expected of the units of interest (like those about compulsory voting) and confirm that compulsory voting is a strong institutional arrangement that promotes greater electoral participation.
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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 | 0.016 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 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".