Replication Data for: The Power of Characters: Evaluating Machine Learning Modified Bayesian Improved Surname Geocoding Inference of Race in Redistricting

Identifying racial disparities in policy and politics is a pressing area of research within the United States. Where early work made use of identifying potentially noisy correlations between county or precinct demographics and election outcomes, the advent of Bayesian Improved Surname Geocoding (BISG) vastly improved estimation of race by employing voter lists. Machine Learning (ML) modified BISG in turn offers accuracy gains over the static – and potentially outdated – surname dictionaries present in traditional BISG. However, it is unclear the extent to which ML might substantively alter the policy and political implications of redistricting given its improvements in voter race estimation. Therefore, we ascertain the potential gains of ML modified BISG in improving the estimation of race for the purpose of redistricting majority-minority districts. We evaluate a ML modified BISG program against traditional BISG estimates in correctly estimating the race of voters for creating majority-minority congressional districts within North Carolina and Georgia, and in state assembly districts in Wisconsin. Our results demonstrate that ML modified BISG offers substantive gains over traditional BISG, especially in diverse political geographic units. Further, we find meaningful improvements in accuracy when estimating majority-minority district racial composition. We conclude with recommendations on when and how to use the two methods, in addition how to ensure transparency and confidence in BISG related research.

在美国，识别政策与政治领域中的种族差异是一个紧迫的研究方向。早期研究通过识别县或选区人口统计数据与选举结果之间可能存在噪声的相关性开展工作，而贝叶斯改进姓氏地理编码（Bayesian Improved Surname Geocoding, BISG）的出现则通过利用选民名单极大地提升了种族估计的准确性。机器学习（Machine Learning, ML）改进的BISG进一步克服了传统BISG中静态且可能过时的姓氏词典的局限，从而获得了更高的准确性。然而，鉴于ML在选民种族估计方面的改进，目前尚不清楚其在多大程度上能实质性改变重新划分选区的政策与政治影响。因此，我们旨在确定ML改进的BISG在提升种族估计准确性方面的潜在收益，以服务于多数-少数族裔选区的重新划分工作。我们对比评估了ML改进的BISG程序与传统BISG估计方法在准确预测选民种族方面的表现——这些预测用于在北卡罗来纳州和佐治亚州创建多数-少数族裔国会选区，以及在威斯康星州创建多数-少数族裔州议会选区。研究结果表明，ML改进的BISG相较于传统BISG具有实质性优势，尤其在多样化的政治地理单元中表现突出。我们最终提出了关于何时及如何使用这两种方法的建议，此外还包括如何确保BISG相关研究的透明度与可信度。