It is widely theorized that echo chambers contribute to polarization, yet behavioral evidence of partisan selective exposure in the real world is surprisingly tenuous. Why do partisans have polarized perceptions even though they have relatively balanced media diets? We argue that partisans vary in terms of when they pay attention to the news, not just in terms of the ideological media sources they follow. By leveraging national election surveys across seven decades, as well as the sudden change in the economic news environment that was induced by the collapse of Lehman Brothers, we show that partisans vary their political attentiveness and media consumption in response to whether news events are congenial to their party. These findings suggest that partisans can subject themselves to biased information flows even if their media diets are balanced. The temporal dynamics of selective exposure carry important implications for mass polarization.
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By polarization, we refer to polarization of politically-relevant perceptions broadly. Depending on the type of information partisans consume, polarization can occur in the realm of policy attitudes (policy polarization) and/or feelings toward other partisans (affective polarization). Another prominent explanation for the growing polarization is partisan motivated reasoning, in which partisan identification biases the way people interpret political information (e.g. Bartels 2002)
Based on this conceptualization, we use general news consumption and political attentiveness as our dependent variables.
The discrepancy may arise from the fact that the real world contains many more distractions than controlled settings do. See also Guess (2020) on the explanation for this gap.
In particular, we want to highlight their argument on how non-political media consumption would dilute the effect of partisan news.
The data and code needed to replicate the results (and all of the supplemental and robustness checks) are available on https://doi.org/10.7910/DVN/J62BZM.
This includes all the ANES time series surveys collected between 1952 and 2016 except 1974, which did not include our dependent variable.
The raw data can be found at http://www.gallup.com/interactives/185273/presidential-job-approval-center.aspx.
We focus on “interest in the elections” variable because only this item was measured consistently throughout the ANES time-series data. Other indicators of political interest and media consumption were measured more sparsely.
This unexpected bankruptcy of the investment bank is widely deemed to be the starting point of the Great Recession.
In Appendix D, we demonstrate that the public did receive the “treatment” by showing people’s perception that the country is heading “in the right direction” sharply dropped after the cutoff and continued to go down in the following weeks (see Fig. D1).
Those without internet access were not asked about online news use and therefore were omitted from the analyses on news exposure. This decision does not render our results invalid because internet access is highly unlikely to be affected by the “treatment” (the economic meltdown), although the findings here are generalizable only to U.S. adults with internet access (78% of the full sample had internet access). We chose to drop these individuals instead of coding their internet news use as 0 because doing so would introduce a floor effect. Nonetheless, we find very similar results when their online news use was treated as non-missing zeros.
Media consumption questions were not asked in Wave 3 (April 2, 2008 - August 28, 2008), and Wave 4 was collected during the “treatment” period (August 29, 2008 November 4, 2008), hence omitted in the analysis.
This timeline roughly matches the short-lived post-convention period, during which both partisan groups were becoming more attentive. We did not choose a wider bandwidth of the control group because doing so could make our estimates more vulnerable to history confounds (e.g., convention effects). We found similar, though less precise, estimates when we narrowed the control band width to 3 days or to just 1 day before the “treatment”. Appendix E details the robustness checks (see Tables E3 to E6).
We define multiple treatment groups because we do not have clear theoretical expectations for the immediacy and longevity of the partisan gap in overtime changes in news consumption. On one hand, partisans may have switched political interest on or off immediately after the Lehman debacle, and then defaulted back to the usual campaign mode in the long run. On the other hand, because the economic turmoil continued throughout the rest of the campaign, the gap may have grown even wider as the financial crisis continued to worsen.
We assumed that the treatment would not have changed people’S party ID, given its stability (Green, Schickler and Palmquist 2002). We found no significant difference in the proportion of partisans in the sample around the cutoff date (see Appendix D).
The survey items for the outcome variables measured respondents’ behaviors “in the past week.” Thus, responses from those interviewed in Week 1 (between 9/15 and 9/21) could reflect what they did on Sep-tember 14 or earlier. While we do not drop them for transparency, Week 1 should be considered an interim period between the pre-treatment and post-treatment windows. When Week 1 is excluded, the average of the interaction terms is statistically significant in all but Column 5.
Similar to general news exposure indices, offline news consumption was measured by counting the number of sources people received news from (Columns 5 and 6) as well as by calculating the average of the number of days people used each channel of communication for news (Columns 7 and 8) outside the internet.
For example, Democrats’ weekly online news consumption increased by about 0.15 sources (standard error = 0.05) relative to Republicans, following the treatment event. To put this figure in context, consider that it rivals the observed difference between those who voted and those who did not vote during the 2008 primaries (difference = 0.14 sources) and amounts to half of the difference between those with and without a college degree (difference = 0.30 sources).
We note, though, that reluctance to respond to a political survey in and of itself can be seen as a strong sign of people tuning out of politics.
We thank anonymous reviewers for suggesting this analysis. We used party ID reported in Wave 1 for endogeneity concerns.
One suggested mechanism is a two-step communication flow in which those who watch partisan media affect others who do not watch partisan media. See Druckman, Levendusky and McLain 2018.
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An earlier version of this paper—then titled “Switching on and off: Rethinking Partisan Selective Exposure”—was presented at the 2018 Annual Meeting of the International Communication and the 2017 Annual Meeting of the American Political Science Association. It received the Top Paper Award from the Political Communication Division of the International Communication Association. We thank participants of the seminars, Michael Delli Carpini, Josh Clinton, D.J. Flynn, Andrew Guess, Shanto Iyengar, Matt Levendusky, and Brendan Nyhan for their advice and feedback. The Annenberg Public Policy Center of the University of Pennsylvania provided the Telephone and Online Panel components of the 2008 National Annenberg Election Survey (NAES) Data.
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Kim, J.W., Kim, E. Temporal Selective Exposure: How Partisans Choose When to Follow Politics. Polit Behav 43, 1663–1683 (2021). https://doi.org/10.1007/s11109-021-09690-1