The Impact of Economic Crises on Political Representation in Public Communication: Evidence from the Eurozone

Abstract

External threats such as war have been shown to disrupt representation as politicians 'put politics aside' and cooperate across cleavages. This article examines whether a severe economic crisis can have a similar effect. It introduces a new approach that provides a spatial representation of how political parties represent societal actors in their public interactions, based on more than 140,000 machine coded news events from eleven eurozone countries between 2001 and 2011. The study shows that in bad economic times, there is a compression of political representation: parties' relationships with the societal groups they are closest to become less cooperative, while their relationships with the groups they are least close to become less conflictual.

Quantifying Political Relationships

“…However, this is not reflected in the scores for 2009, the year in which the Great Recession dominated the agenda and all party dyads exhibit neutral relationships. Notice that this is also true for the scores involving one party and one nonpartisan actor, while relations for nonpartisan dyads become more polarized (for a detailed analysis see Weschle 2017). Germany, 2001Germany, -2009 Black: two parties.…”

“…This makes it possible to analyze the relationship between two actors even if no direct interaction between them is observed. For example, political representation does not require direct communication, so we can infer how well a party i represents a societal actor j , no matter whether they directly interact or not (see Weschle 2017). On the practical side, this advantage is especially relevant if the data are only a partial observation of the network.…”

Elite Interactions and Voters’ Perceptions of Parties’ Policy Positions

“…To measure interparty cooperation and conflict as reported in the media, we apply the approach introduced by Weschle (2018), which uses a combination of large‐scale, machine‐coded event data and latent factor network models to produce a dyadic variable that quantifies the public relationships between political parties (also see Weschle 2019). Here, we provide a nontechnical explanation of this variable's construction.…”