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Abstract
Agenda-setting scholars have claimed that the typical punctuated pattern of governmental attention is a consequence of disproportionate information processing. Yet these claims remain unsubstantiated. We tackle this challenge by considering mass media coverage as a source of information for political actors and by examining the relationship between preceding media information and subsequent governmental attention. Employing data capturing U.S. media attention and congressional hearings (1996–2006), we find that the effects of media attention on congressional attention are conditioned by the presence of “media storms”—sudden and large surges in media attention to a given topic. A one-story increase in media attention has a greater effect on congressional attention in the context of a media storm, since media storms surpass a key threshold for catching policymakers’ attention. We find evidence that the influence of media attention on political attention is nonlinear; agenda-setting operates differently when the media are in storm mode.
Notes
2. We redid the models including a three-month average for MIP, as we do for the media variables (see further discussion). This did not change our findings in any way.
3. Models that include both fixed effects and a lagged dependent variable are sometimes criticized for producing biased and inconsistent estimates. This criticism, however, applies specifically to situations with large N (units) and small/midsize T (over-time observations). In our situation, we deal with a small/midsize N (16 topics) and a relatively large T (120 months). In those situations, fixed-effects estimators are consistent and can be applied (Baltagi, 2001, p. 130). We consider our model choice to be a fairly conservative one, taking into consideration both the temporal dependency of observations (through the lagged dependent variable) and topic-level heterogeneity (through the fixed-effects dummies).
4. Following Walgrave and colleagues (Citation2008), for the media attention and media congestion measures, we consider multiple lags—that is, the average levels of each variable, attention and congestion, in the previous months—assuming that media might not only have a more direct (1-month lag) impact, but also a longer-term, delayed impact. The models we present next include the three-lag average of media attention. We also estimated models with a single lag and two-lag averages. The results of those models resemble the ones of the model with a three-lag average, but have a slightly worse model fit. We use the average and not separate lags for reasons of parsimony and clarity: especially when it comes to constructing the interaction between media attention and media storms, using single lags would result in the inclusion of a large number of interaction terms that might be difficult to interpret. For the main effects model, we redid the model with separate lags. Results suggest that jointly these lags exert a significant influence on the dependent variable (F(1,1896) = 35.49; p < .001), as does the averaged coefficient.
Additional information
Notes on contributors
Stefaan Walgrave
Anne Hardy is a researcher at the Department of Political Science, University of Antwerp.
Amber E. Boydstun
Stefaan Walgrave is Professor, Department of Political Science, University of Antwerp. Amber E. Boydstun is Associate Professor, Department of Political Science, University of California,
Rens Vliegenthart
Davis. Rens Vliegenthart is Professor of Media and Society, Department of Communication Science and Amsterdam School of Communication Research (ASCoR), University of Amsterdam.
Anne Hardy
Anne Hardy is a researcher at the Department of Political Science, University of Antwerp.