Replication Data for: Errors and Calibration in Mail Ballot Signature Rejections

Many U.S. states require election workers to check signatures on ballots returned by mail, causing tens of thousands of ballots to be rejected at each general election. This paper applies Bayes’ theorem, along with findings from prior research, to show how rejection rates are likely to vary under plausible assumptions about signature checker accuracy and the prevalence of invalid signatures. This approach yields a pair of predictions. On the one hand, given findings from research on handwriting forensics, the Bayesian analysis implies election workers are more likely to wrongly reject valid ballots for purported signature mismatch than to correctly reject invalidly signed returns. On the other hand, research on election workers as problem-solvers suggests they may try to minimize the wrongful rejection of ballots. I find empirical support for each prediction, including signs that election workers learn from past errors to calibrate signature rejection rates downwards. This suggests election worker discretion may be an unexpected source of resilience against laws otherwise liable to disenfranchise eligible voters, although I also discuss conditions under which such discretion may conflict with democratic norms.

美国多个州要求选举工作人员核对邮寄选票的签名，这导致每届大选都有数万张选票被驳回。本文结合贝叶斯定理（Bayes’ theorem）与既往研究结论，分析在签名核验准确率、无效签名占比的合理假设下，选票驳回率可能出现的变化。该分析得出两项预测结论：一方面，结合笔迹鉴定（handwriting forensics）的研究结果，贝叶斯分析表明，相较于正确驳回签名无效的选票，选举工作人员更易因所谓的签名不符而错误否决有效选票；另一方面，将选举工作人员视为问题解决者的相关研究显示，他们会尽力减少对选票的错误驳回。本文为两项预测均找到了实证支撑，其中包括选举工作人员会从过往失误中吸取教训，下调签名核验驳回率的相关迹象。这表明，选举工作人员的自由裁量权或成为抵御原本可能剥夺合格选民选举权的法律的意外韧性来源，不过本文也探讨了此类自由裁量权与民主规范产生冲突的情形。