Counterterrorism Evaluation and Citizens: More Than about Policing?
Abstract
:1. Introduction
2. Past Research on Public Attitudes towards Terrorism and Hypotheses Formulation
3. Methodology
4. Results and Analysis
5. Conclusions and Implications
Funding
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
- Operationalisation of control variables.
- Gender—(0) Male and (1) female categories.
- Age—In years.
- Political Ideology—Ideological views of respondents from 1 (left) to 10 (right).
- Country Voice Counts in the EU—using the question “Please tell me to what extent you agree or disagree with the following statement: “My voice counts in the European Union” 1/Totally agree, 2/Tend to agree, 3/Tend to disagree and 4/Totally disagree”.
1 | The main concern reported by EU member states relates to jihadist terrorism. A large majority of fatalities and casualties arising from terrorist attacks in Europe are classified as Islamic terrorism in the related period. Furthermore, a large proportion of arrests for terrorism-related offences are linked to jihadist terrorism (Europol 2019). |
2 | Popular scepticism towards many of these matters moreover outstrips the electoral weight of such parties (Rodrik 2018). |
3 | A decriminalisation policy to grant greater consideration to the social causes of crime. |
4 | This effect is only true when terrorist violence is substantial. |
5 | This scepticism towards globalisation has moreover contributed to the rise of populism and growing public support for radical political parties (see Inglehart and Norris 2016). |
6 | Police presence and fairness also affect public confidence in policing (Morrell et al. 2019). |
7 | Populist political parties and factions in mainstream political parties are particularly “encouraging” the connection and inter alia pointing to the lack of EU external and national effective borders (see, for example, Zalan 2015). |
8 | The standard deviation for each weighted score is between brackets. |
9 | This method is appropriate since my concern is with examining whether several outcome variables have similar patterns of responses and therefore are all associated with a latent variable (i.e., here, public attitudes towards policing terrorism) (Tabachnick and Fidell 2013). |
10 | As one examines how national economic, societal and political contexts shape individual differences in attitudes towards counterterrorism, a two-level hierarchical linear model is employed. This method is appropriate since the concern is with explaining variation at both the individual and national levels. A multi-level model enables us to explore causal heterogeneity and test the generalisability of findings across different national contexts (Steenbergen and Jones 2002). This model also corrects for the dependence of observations within countries—intraclass correlations—and makes adjustments both within and between the parameter estimates for the clustered nature of the data (Snijders and Bosker 1999). The dependent variable (composite score of terrorism, arms and human trafficking) has been transformed (square root transformation) so as to improve model fitting and better respect regression assumptions. |
11 | This is tantamount to an expectation of competence that is consistent with a prevalent definition of trust across different disciplines—the willingness of trustors to make themselves vulnerable to a trustee (PytlikZillig and Kimbrough 2015). |
12 | The result of the Kayser–Meyer–Oklin test was 0.71 (0.72 for the year 2017), exceeding the recommended value of 0.5, and the Bartlett’s test of Sphericity reached statistical significance (<0.01), which supported the use of factor analysis (Bartlett 1954; Kaiser 1974). The terrorism policing assessment scale has a reasonable internal consistency (α = 0.80 and 0.84 in 2015 and 2017, respectively). |
13 | Here, we draw upon expert survey data rather than manifesto project data as the former tend to be more reliable, flexible and provide immediately usable information (see, for example, Mikhaylov et al. 2012; Benoit and Laver 2007). The use of expert survey or mass survey data tends to prevail in the most recent empirical political research. |
14 | Introducing country-level independent variables one by one in the regression models does not modify our results. |
15 | In other words, the direction of the coefficients in Table 4 indicates that the more people think that things are going in the wrong direction in their country (or the more they believe that their voice does not count in the EU), the more they are of the opinion that the police and other law enforcement authorities in their country are not doing enough to fight terrorism (or arms and human trafficking). |
16 | Interaction terms should only be tested if constitutive terms are statistically significant, as recommended by Brambor et al. (2006). Due to space limitation, full results are not presented here for the models integrating the interaction terms. These are, however, available from the corresponding author upon request. |
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“To what extent do you agree or disagree with the following statements: The police and other law enforcement authorities in (OUR COUNTRY) are doing enough to fight…? (1 to 5 scale, where 1 = Totally Agree, 2 = Tend to Agree and, 3 = Don’t Know, 4 = Tend to Disagree and 5 = Totally Disagree” | |||||
---|---|---|---|---|---|
28 Countries | 2.64 1 (1.28)/2.59 2 (1.31) | Spain | 2.65 (1.38)/2.27 (1.27) | Netherlands | 2.48 (1.12)/2.20 (1.14) |
Belgium | 2.50 (1.21)/2.51 (1.26) | Finland | 2.08 (0.99)/2.07 (1.08) | Bulgaria | 3.13 (1.44)/3.08 (1.44) |
Croatia | 2.72 (1.31)/2.75 (1.35) | France | 2.65 (1.25)/2.81 (1.31) | Poland | 2.61 (1.13)/2.54 (1.06) |
Luxembourg | 2.57 (1.13)/2.35 (1.09) | United Kingdom | 2.53 (1.25)/2.59 (1.30) | Portugal | 2.97 (1.20)/2.50 (1.12) |
Cyprus | 3.00 (1.39)/2.73 (1.32) | Hungary | 2.57 (1.35)/2.22 (1.26) | Sweden | 2.39 (1.19)/2.48 (1.30) |
Czech Republic | 2.90 (1.27)/2.54 (1.18) | Greece | 2.79 (1.35)/2.90 (1.39) | Slovenia | 3.13 (1.25)/3.05 (1.37) |
Germany | 2.63 (1.30)/2.80 (1.37) | Ireland | 2.61 (1.29)/2.96 (1.34) | Slovakia | 3.03 (1.24)/3.01 (1.30) |
Denmark | 1.88 (0.99)/1.99 (1.12) | Italy | 2.71 (1.33)/2.48 (1.34) | Austria | 2.48 (1.30)/2.47 (1.35) |
Estonia | 2.45 (1.01)/2.40 (1.05) | Lithuania | 2.80 (1.11)/2.68 (1.13) | Romania | 2.86 (1.39)/2.61 (1.32) |
Latvia | 2.66 (1.10)/2.61 (1.13) | Malta | 2.76 (1.31)/2.82 (1.19) |
Varimax Rotated Factor Loadings | |
---|---|
Item | Factor 1: Terrorism Policing |
“Terrorism” 1 | 0.83 (0.84) |
“Arms Trafficking” | 0.86 (0.89) |
“Human Trafficking” | 0.85 (0.88) 2 |
Eigenvalues | 2.15 (2.27) |
Percentage of variance explained | 72 (76) |
α | 0.80 (0.84) |
Estimates 2015 | Estimates 2017 | |
---|---|---|
Fixed Effects ● Constant | 2.746 ** (0.044) | 2.648 ** (0.051) |
Variance Components ● Individual level ● Country level | 1.527 ** (0.012) 0.070 ** (0.016) | 1.566 ** (0.001) 0.076 ** (0.002) |
−2 × log likelihood | 45,182.870 | 45,428.090 |
Model 1 b 2015 | Model 1 2017 | Model 2 2015 | Model 2 2017 | |
---|---|---|---|---|
Constant a | 2.623 ** (0.039) | 2.592 ** (0.056) | 2.870 ** (0.022) | 2.860 ** (0.028) |
Education | −0.001 (0.018) | −0.037 * (0.017) | 0.013 (0.007) | −0.004 (0.008) |
Gender | 0.020 * (0.008) | 0.005 (0.008) | 0.010 ** (0.003) | 0.004 (0.003) |
Age | 0.037 ** (0.009) | 0.034 ** (0.009) | 0.030 ** (0.004) | 0.027 ** (0.004) |
Ideology | 0.026 ** (0.008) | 0.040 ** (0.008) | −0.003 (0.003) | 0.004 (0.003) |
Occasional Financial Difficulties | −0.032 * (0.013) | 0.006 (0.015) | −0.013 * (.006) | 0.009 (0.006) |
Almost No and No Financial Difficulties | −0.030 * (0.014) | −0.005 (0.016) | −0.017 ** (0.006) | −0.000 (0.007) |
Middle Class | −0.030 ** (0.009) | −0.029 ** (0.010) | −0.010 * (0.004) | −0.010 * (0.004) |
Higher Class | −0.014 (0.009) | −0.024 * (0.010) | −0.002 (0.004) | −0.005 (0.004) |
Right Country Direction | −0.172 ** (0.009) | −0.258 ** (0.009) | −0.088 ** (0.004) | −0.127 ** (0.004) |
Neither Right Nor Wrong Country Direction | −0.072 ** (0.009) | −0.085 ** (0.009) | −0.037 ** (0.004) | −0.037 ** (0.004) |
Voice Counts in the EU | 0.161 ** (0.009) | 0.151 ** (0.009) | 0.076 ** (0.004) | 0.082 ** (0.004) |
Press Freedom | 0.123 * (0.045) | 0.026 (0.078) | 0.048 (0.025) | −0.043 (0.039) |
Unemployment | −0.051 (0.044) | −0.067 (0.066) | 0.030 (0.025) | 0.006 (0.033) |
Terrorism Impact | 0.018 (0.047) | 0.057 (0.073) | 0.017 (0.026) | 0.038 (0.036) |
Gini Coefficient | −0.034 (0.041) | 0.037 (0.070) | −0.014 (0.023) | 0.034 (0.035) |
Size of the immigrant community | −0.001 (0.029) | 0.007 (0.045) | −0.001 (0.016) | −0.019 (0.022) |
Crime | −0.146 (0.075) | −0.027 (0.050) | −0.034 (0.043) | 0.019 (0.052) |
Positioning of Party Leaderships on Multiculturalism | −0.005 (0.032) | 0.105 (0.071) | 0.003 (0.018) | 0.072 (0.036) |
N (Nations) | 28 | 28 | 28 | 28 |
N (Individuals) | 28,082 | 28,093 | 28,082 | 28,093 |
−2 × log likelihood | 35,593.920 | 35,248.590 | 17,745.170 | 18,182.910 |
Model 1 b 2015 | Model 1 2015 | Model 1 2017 | Model 1 2017 | |
---|---|---|---|---|
Constant a | 2.621 ** (0.039) | 2.623 ** (0.039) | 2.592 ** (0.056) | 2.591 ** (0.057) |
Education | −0.002 (0.018) | −0.001 (0.018) | −0.037 * (0.017) | −0.0370 * (0.017) |
Gender | 0.020 * (0.008) | 0.020 * (0.008) | 0.005 (0.008) | 0.005 (0.008) |
Age | 0.037 ** (0.009) | 0.037 ** (0.009) | 0.034 ** (0.009) | 0.034 ** (0.009) |
Ideology | 0.026 ** (0.008) | 0.026 ** (0.008) | 0.040 ** (0.008) | 0.039 ** (0.008) |
Occasional Financial Difficulties | −0.031 * (0.013) | −0.032 * (0.013) | 0.006 (0.015) | 0.007 (0.015) |
Almost No and No Financial Difficulties | −0.029 (0.015) | −0.030 * (0.014) | −0.005 (0.016) | −0.005 (0.016) |
Middle Class | −0.030 ** (0.009) | −0.031 ** (0.009) | −0.029 ** (0.009) | −0.029 ** (0.009) |
Higher Class | −0.014 (0.009) | −0.016 (0.009) | −0.025* (0.009) | −0.028 ** (0.009) |
Right Country Direction | −0.173 ** (0.009) | −0.172 ** (0.009) | −0.258 ** (0.009) | −0.258 ** (0.009) |
Neither Right Nor Wrong Country Direction | −0.071 ** (0.009) | −0.072 ** (0.009) | −0.085 ** (0.009) | −0.085 ** (0.009) |
Voice Counts in the EU | 0.161 ** (0.009) | 0.161 ** (0.009) | 0.151 ** (0.009) | 0.151 ** (0.009) |
Press Freedom | 0.123 * (0.045) | 0.123 * (0.045) | 0.026 (0.078) | 0.027 (0.078) |
Unemployment | −0.051 (0.044) | −0.051 (0.044) | −0.068 (0.066) | −0.068 (0.066) |
Terrorism Impact | 0.018 (0.047) | 0.018 (0.047) | 0.058 (0.073) | 0.057 (0.073) |
Gini Coefficient | −0.034 (0.041) | −0.034 (0.041) | 0.037 (0.070) | 0.037 (0.070) |
Size of the immigrant community | −0.001 (0.029) | −0.001 (0.029) | 0.007 (0.045) | 0.006 (0.045) |
Crime | −0.146 (0.075) | −0.146 (0.075) | −0.028 (0.050) | −0.027 (0.050) |
Positioning of Party Leaderships on Multiculturalism | −0.005 (0.032) | −0.005 (0.032) | 0.106 (0.071) | 0.105 (0.071) |
Right Country Direction X Almost No and No Financial Difficulties | 0.007 (0.008) | – | – | – |
Right Country Direction X Middle Class | – | −0.011 (0.008) | −0.006 (0.008) | – |
Right Country Direction X Higher Class | – | – | – | 0.014 (0.008) |
N (Nations) | 28 | 28 | 28 | 28 |
N (Individuals) | 28,082 | 28,082 | 28,093 | 28,093 |
−2 × log likelihood | 35,593.610 | 35,593.070 | 35,248.360 | 35,246.980 |
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Balestrini, P.P. Counterterrorism Evaluation and Citizens: More Than about Policing? Soc. Sci. 2021, 10, 298. https://doi.org/10.3390/socsci10080298
Balestrini PP. Counterterrorism Evaluation and Citizens: More Than about Policing? Social Sciences. 2021; 10(8):298. https://doi.org/10.3390/socsci10080298
Chicago/Turabian StyleBalestrini, Pierre Philippe. 2021. "Counterterrorism Evaluation and Citizens: More Than about Policing?" Social Sciences 10, no. 8: 298. https://doi.org/10.3390/socsci10080298
APA StyleBalestrini, P. P. (2021). Counterterrorism Evaluation and Citizens: More Than about Policing? Social Sciences, 10(8), 298. https://doi.org/10.3390/socsci10080298