Frontiers in Political Science (Jul 2022)
Puzzles in affective polarization research: Party attitudes, partisan social distance, and multiple party identification
Abstract
Affective polarization refers to people having favorable attitudes toward their preferred political parties, or inparties, along with their supporters, and negative attitudes toward other parties, or outparties, and their supporters. Originally an American concept, there is now growing interest in studying (AP) in European countries characterized by multiparty systems. So far, researchers have primarily focused on like-dislike ratings when measuring affect, which has relegated another important aspect to the background, namely attitudes toward ordinary supporters of parties. Open questions also remain relating to how political ingroups and outgroups should be conceptualized in situations with large numbers of relevant political parties. We examine these puzzles using data from an online panel in Finland. First, we measure partisan social distance, or feelings toward interacting with supporters of different parties, in addition to commonly used like-dislike ratings of parties. We find that social distance differs from party like-dislike ratings in that respondents are less likely to report animosity toward outparty supporters. Second, we measure multiple party identification based on party support and closeness, and find that people commonly have not one, but many potential inparties. Finally, we build two individual-level AP measures and apply them using both like-dislike ratings and social distance scales. One of the measures is based on identifying a single inparty, while the other takes the possibility of multiple inparties into account. We find that choosing which type of attitude to measure is more consequential for the outcome than how partisanship is operationalized. Our results and discussion clarify relationships between AP and related constructs, and highlight the necessity to consider the political and social context when measuring AP and interpreting results.
Keywords