Better the Devil You Know: An Online Field Experiment on News Consumption
Abstract: This paper investigates the causal link between the public’s self-selective exposure to like-minded partisan media and polarization. I first present a parsimonious model to formalize a traditionally neglected channel through which media selection leads to reduced polarization. In a world where the media heavily distorts signals with its own partisan preferences, familiarity with media biases is vitally important. By choosing like-minded partisan media, news consumers are exposed to familiar news sources. This may enable them to arrive at better estimates of the underlying truth, which can contribute to an alleviation of polarization. The predictions of this model are supported by experimental evidence collected from a South Korean mobile news application that I created and used to set up an RCT. The users of the app were given access to curated articles on key political issues and were regularly asked about their views on those issues. Some randomly selected users were allowed to select the news source from which to read an article; others were given randomly selected articles. The users who selected their news sources showed larger changes in their policy views and were less likely to have radical policy views—an alleviation of polarization—in comparison with those who read randomly provided articles. The belief updating and media selection patterns are consistent with the model’s predictions, suggesting that the mechanism explained in the model is plausible. The findings suggest that the designers of news curation algorithms and their regulators should consider the readers’ familiarity with news sources and its consequences on polarization.
Social Learning of Political Elites: A Natural Experiment in Iceland (Joint with Matt Lowe)
Abstract: Many legislative chambers are segregated along party lines, limiting cross-party interaction. Would there be less polarization if politicians were physically integrated? This paper tackles this question by exploiting random seating in Iceland’s national Parliament. Since almost all voting is along party lines, we use a text-based measure of language similarity to proxy for the similarity of beliefs between any two politicians. Using this measure, we find an in-coalition effect: language similarity is greater for two politicians that share the same political coalition (government coalition or opposition) than for two politicians that do not, suggesting that the measure captures meaningful partisan differences in language. Next, we find that when two MPs randomly sit next to each other, their language similarity in the next parliamentary session (when no longer sitting together) is significantly higher, an effect that is roughly 16 to 25 percent of the size of the in-coalition effect. The persistence of effects suggests that politicians are learning from their neighbors, not just facing transient social pressure. However, this learning does not reflect the exchange of ideas “across the aisle”. The effects are large for neighbors in the same coalition group, at 29 to 53 percent of the in-coalition effect, with no evidence of learning from neighbors in the other group. Based on this evidence, integration of legislative chambers would likely slow down, but not prevent, the ingroup homogenization of political language.
Research in Progress
Donald Trump, Father of Five, Future Creator of the Wall: Candidate Information and Polarization of the US Voters (Joint with Cory Smith)
Abstract: In this paper we investigate whether voters react differently to different types of candidate information. Specifically, can partisan polarization of voters be alleviated by providing biographic information about candidates to which voters can relate, rather than information about the candidates’ policy stances? In a collaboration with BallotReady, a social venture for voter participation, we conduct an online field experiment in several US states during the 2018 general election. We randomize the type of information about candidates that we send to voters, along with reminder messages for impending election. From surveys, we collect information about the extent of split-ticket voting and subjective evaluation of candidates’ quality and favorability. We also observe whether and how much the voters seek to learn about other-party candidates. We track their clicks on URL links to webpages that contain candidate information and record the time they spend on those webpages.
Social Learning from Locals: Natural Experiments in the Parliaments of Norway and Sweden (Joint with Matt Lowe)
Abstract: Group identity is multi-dimensional, and individuals may be more likely to learn from an outgroup member (along one dimension) when that outgroup member shares group membership along another dimension. We test this hypothesis in a political context with two salient group affiliations—region and party. In Norway and Sweden, Members of Parliament from the same region sit together. In this context, cross-party learning may be possible, since cross-party neighbors are still ingroup members along one dimension—that of region. To identify effects of political peers on voting, co-sponsorship, and language, we exploit the rule-based seating within region—in Sweden seating is determined by age and tenure, and in Norway it is determined by vote share. In both cases we employ a regression discontinuity design to identify social learning between politicians.