Replication Data for: Descriptive Representation in an Era of Polarization

Studies of descriptive representation find that voters more positively evaluate representatives who share their ascriptive characteristics. I argue that this pattern can be upended when voters develop more positive affect towards outgroups. In the United States, Democrats have increasingly expressed more positive views towards marginalized groups, while Republicans’ attitudes about these groups have not shifted. Under such conditions, my argument predicts that the effect of representatives’ race and gender on constituent evaluations should vary more by constituents’ partisanship than by their own ascriptive characteristics. Applying a difference-in-differences design to 2008-2020 CCES data, I find that Democrats of all backgrounds now approve more highly of Congressmembers from historically marginalized groups, whereas Republicans’ approval is unrelated to Member identity. Democrats also give women and minority representatives leeway to diverge ideologically. These findings demonstrate that polarizing attitudes about race and gender can disrupt classic patterns in how constituents evaluate representatives.有关描述性代表制（descriptive representation）的研究表明，选民会对与自身拥有相同归属性特征（ascriptive characteristics）的代表给出更为积极的评价。本文提出，当选民对外群体（outgroups）产生更为积极的情感时，这一经典模式将会被颠覆。在美国，民主党人对边缘化群体（marginalized groups）的积极评价日益增多，而共和党人对此类群体的态度则未发生任何变化。在此背景下，本文的理论预测指出：代表的种族与性别对选民评价的影响，将更多地取决于选民自身的党派认同（partisanship），而非其本人的归属性特征。本文将双重差分法（difference-in-differences）应用于2008-2020年合作国会选举研究（Cooperative Congressional Election Study，CCES）数据后发现，各类背景的民主党人如今对来自历史边缘化群体的国会议员给出了更高的认可度，而共和党人的认可度则与议员身份特征无关。民主党人还会为女性与少数族裔代表留出意识形态层面的偏离空间。上述研究结果表明，针对种族与性别的极化态度，能够打破选民评价代表的经典模式。

搜集汇总

背景与挑战

背景概述

该数据集复现了研究《极化时代的描述性代表》的结果，基于2008-2020年CCES数据，采用双重差分设计，发现民主党选民对少数族裔和女性议员评价更高，而共和党选民评价与议员身份无关，表明党派极化改变了传统描述性代表的评价模式。