Replication Data for: Campaign Communication and Legislative Leadership

Do policy priorities that candidates emphasize during election campaigns predict their subsequent legislative activities? We study this question by assembling novel data on legislative leadership posts held by Japanese politicians and using a fine-tuned transformer-based machine learning model to classify policy areas in over 46,900 statements from 1,270 candidate manifestos across five elections. We find that a higher emphasis on a policy issue increases the probability of securing a legislative post in the same area. This relationship remains consistent across multiple elections and persists even when accounting for candidates’ previous legislative leadership roles. We also discover greater congruence in distributive policy areas. Our findings indicate that campaigns provide meaningful signals of policy priorities.

候选人在竞选活动中强调的政策优先级，能否预示其后续的立法履职行为？本研究针对该问题展开分析：我们首先构建了关于日本政客所担任立法领导职位的全新数据集，并采用基于微调Transformer（Transformer）的机器学习模型，对五次选举中1270份候选人竞选纲领内的逾46900条声明进行政策领域分类。研究发现，候选人对某一政策议题的强调程度越高，其在该政策领域获得立法职位的概率便越高。该关联在多次选举中均保持稳定，即便在控制候选人既往立法领导职位因素的情况下依然成立。我们还发现，在分配型政策领域中，二者的契合度更高。本研究结果表明，竞选活动能够有效传递候选人政策优先级的清晰信号。