Abstract
Do terror attacks have an impact on public opinion, even if the terror attacks happen far away? We exploit the fact that the fourth round of the European Social Survey was conducted in several West European countries at the time of the 2008 terror attacks in Mumbai, India, in order to identify the causal effect of the Mumbai attacks on public opinion. We identify a clear jump in fear of terrorism at home as a consequence of the terror attacks, but despite the increase in fear of terrorism, we find no significant effect of the attack on support for illiberal interrogation techniques or for liberal immigration policies. We do find indications of a shift in conservative direction on the left–right scale, but this shift is not significant in all time windows. Our findings suggest that a terror attack needs to have a very large impact on the fear of terrorism before people change their policy preferences.
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Notes
But see Frey et al. (2009), who study how terrorism affects survey respondents’ reported life satisfaction.
For instance, Becker and Rubinstein (2010) argue that some people will invest effort to reduce the discrepancy between their subjective beliefs about the likelihood of terrorism and the objective danger, rather than changing the demand for a “terror-infected” good—the “terror-infected” good in our case being civil liberty.
The Madrid attack took place only 3 days before the parliamentary election, an election where the incumbent conservative government suffered heavy defeats. Thus, the conservative shift on the left–right scale appears to have been completely reversed by the conservative government’s handling of the attack (see Montalvo 2011, for an analysis of the electoral impact of the attack).
The procedures for assigning interview dates vary somewhat across countries, but the process is generally as follows: the interviewers are allotted a number of sampled units with whom they attempt to make appointments, either face-to-face or by phone (but phone calls to make appointments are acceptable only if the country can provide evidence that the response rate will not be damaged by appointments being made by phone rather than face-to-face). At least four unsuccessful personal visits must be attempted before the sampled unit is abandoned. All interviews have to be made face-to-face. One of the most important goals is to achieve a high response rate and much of the work is tailored to obtain this goal. We find it unlikely that the procedures for obtaining a high response rate should systematically influence our results. See www.europeansocialsurvey.org for detailed fieldwork and data documentation.
We control for gender, age, income, education level, whether the respondent is born in the respective country, and whether the respondent lives in an urban area. Strictly speaking, we do not need to include all of these controls since there is a significant difference in means between treatment and control groups only for age (see Table A.1). However, including exogenous controls might increase precision, but this is not the case here, possibly because the number of observations is reduced when we include controls.
We chose 2 months later rather than 1 month later because 1 month later is in the middle of the Christmas vacation, a period during which fewer respondents were interviewed.
Multilevel modeling is especially useful when one wants to examine variation across groups, as the estimates of country-specific coefficients represent a compromise between estimating separate coefficients for each country and one coefficient for all countries (see Gelman and Hill 2007 for an introduction to multilevel modeling). In practice, this implies that the country-specific estimates for those coefficients that are allowed to vary across countries represent a compromise between the mean across countries and the country-specific data. This compromise is guided by the sample size so that the estimates for countries with small samples are driven more by the cross-country relationship.
We estimate linear models since we find that the estimated cross-national variation in the treatment effect estimated by the multilevel linear probability model appears more plausible in light of the descriptive data. It is well known that the multilevel logit/probit model is less accurate than the linear multilevel model when the sample size at level two is fairly small (see, for example, Hox 2002: 176). We would like to stress, however, that the multilevel probit’s estimate of the average (cross-national) treatment effect is identical to the estimate from the multilevel linear model, and that all conclusions from the single-level models presented earlier in the paper are similar whether we estimate logit, probit or linear probability models. The only apparent problem with the probit model is its estimate of the degree of cross-national variation in the multilevel models.
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Acknowledgements
We would like to thank Andreas Kotsadam, Niklas Jakobsson, Bjarne Strøm, and participants at the Political Behavior Seminar at the Institute for Social Research (ISF) for useful comments. The usual disclaimer applies.
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Finseraas, H., Listhaug, O. It can happen here: the impact of the Mumbai terror attacks on public opinion in Western Europe. Public Choice 156, 213–228 (2013). https://doi.org/10.1007/s11127-011-9895-7
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DOI: https://doi.org/10.1007/s11127-011-9895-7