Am I Eligible to Register? Registration Rules, Eligibility Uncertainty, and Youth Voter Turnout
Bibliographic record
Abstract
Abstract Is a lack of information about eligibility rules partly responsible for the particularly low youth voter turnout in U.S. elections? In a context where new voters usually have to register several weeks before Election Day and where registration rules vary by state, this article argues that there is substantial uncertainty among young Americans about their eligibility to register and vote in elections. It uses a natural experiment that leverages the as-if random assignment of a person’s 18th birthday around a state registration deadline to identify the causal effect of uncertainty about whether someone has to be 18 by the registration deadline or by Election Day to register and vote in an election on youth voter turnout. Drawing on fine-grained data from 19 U.S. state voter files, the study finds a sharp discontinuity in turnout in nine states. There are smaller or no effects in states with same day registration, a later registration deadline closer to Election Day or explicit information that 17-year-olds are eligible to register. Moreover, the effect persists over time, with people who are discouraged from voting due to eligibility uncertainty significantly less likely to vote in future elections. These findings have important implications for our understanding of youth turnout, election reforms, habit formation, and the study of citizens’ information and beliefs about electoral rules with administrative data.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".