The United States is rare among nations in its highly decentralized process for negotiating collective bargaining agreements with local teachers’ unions. To determine whether partisanship can predict these highly localized decisions, I construct an original database of Pennsylvania collective bargaining agreements (CBAs) merged with publicly available voter registration records to predict the presence of high-profile contract items. Using spatial autoregression and probit regression, I reveal that the partisanship of a school district is a significant predictor for fewer seniority protections but not for lower salaries. These partisan relationships can guide both district administrators and union leaders in future negotiations.

在与地方教师工会谈判集体谈判协议的高度分散的过程中，美国在各国中很少见。为了确定党派关系是否可以预测这些高度本地化的决定，我构建了宾夕法尼亚州集体谈判协议（CBA）的原始数据库，该数据库与可公开获得的选民登记记录合并，以预测备受瞩目的合同项目的存在。使用空间自回归和概率回归，我发现学区的党派关系是显着的预测指标，可以减少工龄保障，但不能降低薪水。这些党派关系可以指导地区行政人员和工会领导人未来的谈判。