We explore whether differences of terrorism risk perception across all European countries reflect their underlying differences in terrorism risk, which we decompose into a long term and innovation component. We employ longitudinal country-level data on terrorism risk concern and our modeling approach is motivated by the Bayesian framework. We conclude that the observed risk perception variation is significantly explained by the long term terrorism countries face, while the cyclical part of terrorism activity does not affect risk perception.

Cathérine Müller

Konstantinos Drakos

English

Research Papers in Economics

Economics of Security Working Paper Series 
Economics of Security is an initiative managed by DIW Berlin 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Konstantinos Drakos and Cathérine Müller 
 
Terrorism Risk Concern in Europe 
June 2010 
 
 
 
 
Economics of Security Working Paper 37
This publication is an output of EUSECON, a research project supported by the European 
Commission’s Seventh Framework Programme. Economics of Security Working Paper Series 
Economics of Security is an initiative managed by DIW Berlin 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Correct citation: Drakos, K. and Müller, C. (2010). “Terrorism Risk Concern in Europe”. 
Economics of Security Working Paper 37, Berlin: Economics of Security. 
 
 
First published in 2010 
 
© Konstantinos Drakos and Cathérine Müller 2010 
 
ISSN: 1868-0488 
 
 
 
For further information, please contact: 
 
Economics of Security, c/o Department of International Economics, German Institute for 
Economic Research (DIW Berlin), Mohrenstr. 58, 10117 Berlin, Germany. 
 
Tel: +49 (0)30 89 789-277 
 
Email: eusecon@diw.de  
 
Website: www.economics-of-security.eu 1 
 
Terrorism risk concern in Europe 
 
Konstantinos Drakos
*† 
Department of Accounting and Finance 
Athens University of Economics and 
Business 
 
                   
Cathérine Müller 
Department of International  
Economics DIW Berlin, 
 Humboldt-University Berlin 
 
 
 
 
Abstract  
   
We explore whether differences of terrorism risk perception across all European countries reflect their 
underlying  differences  in  terrorism  risk,  which  we  decompose  into  a  long  term  and  innovation 
component. We employ longitudinal country-level data on terrorism risk concern and our modeling 
approach is motivated by the Bayesian framework. We conclude that the observed risk perception 
variation is significantly explained by the long term terrorism countries face, while the cyclical part of 
terrorism activity does not affect risk perception.    
 
 
                                                           
* Corresponding author, kdrakos@aueb.gr, Address: 76 Patission Street, 10434, Athens, Greece.  
† The authors acknowledge financial support by the European Commission’s 7
th Framework Programme (“A New 
Agenda for European Security Economics”). We would like to thank participants at the meeting of the Network 
for the Economic Analysis of Terrorism (4
th NEAT, Brussels, 2009) for their useful comments and suggestions.      2 
 
1. Introduction 
We  conduct  the  first  longitudinal  analysis  on  a  pan-European  level,  investigating  whether 
differences in terrorism risk are reflected on terrorism perception, motivated by the Bayesian 
framework  (Viscusi  and  O'Connor  1984;  Viscusi  1985;  Viscusi  et  al.  1987;  Rogers  1997). 
Pinning down the drivers of terrorism concern is important since it is known to affect various 
economic and non-economic aspects of behavior (Elster 1998; Schuster et al. 2001; Becker and 
Rubinstein 2004; Berrebi and Klor 2006; Frey et al. 2007).   
2. Data 
Data on terrorism concern for 2003-2008 (broken down to six month intervals Spring-Autumn) 
were obtained from the Eurobarometer (ZA: 3904, 3938, 4056, 4229, 4411, 4414, 4506, 4526, 
4530, 4565, 4744) which is a harmonized survey of representative samples for Austria, Belgium, 
Bulgaria,  Croatia,  Cyprus,  Czech  Republic,  Denmark,  Estonia,  Finland,  France,  Germany, 
Greece,  Hungary,  Ireland,  Italy,  Latvia,  Lithuania,  Luxemburg,  Malta,  Netherlands,  Poland, 
Portugal,  Romania,  Slovakia,  Slovenia,  Spain,  Sweden,  Turkey,  Great  Britain  and  Northern 
Ireland. We use responses from the question:  
“What  do  you  think  are  the  two  most  important  issues  facing  (OUR  COUNTRY)  at  the 
moment?” 
Terrorism  risk  concern  , it trc   is  calculated  as  the  proportion  of  respondents  that 
mentioned terrorism in the above question:   
 
,
,
,
it
it
it
n
trc
N

 

                                         (1) 3 
 
Where    i   denotes  country,    t   time  period,  n  number  of  respondents  mentioning 
terrorism, and  N  the total number of survey participants.    
We proxy terrorism risk by the following metric (Eckstein and Tsiddon 2004):  
, , , , log (1 ) i t i t i t i t terrindex deathrate woundedrate attackrate                  (2) 
Where: 
,
,
count of terrorist attacks
100 thousand inhabitants
it
it
attackrate ,  ,
,
count of fatal casualties
100 thousand inhabitants
it
it
deathrate  and 
,
,
count of wounded 
100 thousand inhabitants
it
it
woundedrate . 
This metric takes into account, not only the count of attacks, but also the severity of attacks. Data 
for the period 1994-2007 on terrorist events are obtained from the Global Terrorism Database 
(http://www.start.umd.edu/start/). Data on population were obtained from Eurostat.  
3. Modeling terrorism risk concern  
The Bayesian framework is our departure point where risk perception is a weighted average of 
the reference risk  ,
p
it trc , based on prior beliefs (ex ante perceived risk), and the arrival of new 
information  ,
s
it r
,
 corresponding to the sample risk inferred from the information (Viscusi and 
O'Connor  1984;  Viscusi  1985,  1989;  Smith  and  Michaels  1987;  Smith  and  Johnson  1988; 
Loewenstein and Mather 1990; Smith et al. 1990; Evans and Viscusi 1991; Liu et al. 1998; 
Viscusi and Evans 1998; Smith et al. 2001):  
, 1 , 2 ,
ps
i t i t i t trc w trc w r                              (3) 
Where  12 , ww  are positive constants.  4 
 
In equation (3) one has to deal with the latent nature of the prior concern and the measurement of 
sample  risk.  To  this  end  we  assume  that  the  public’s  prior  terrorism  concerns  reflect  a 
fundamental  characteristic,  and  are  shaped  by  the  country’s  overall  past  experience  with 
terrorism. Hence the first building block is that priors are a function of a country’s long term 
history of terrorism risk  , it ltr :  
,,
p
i t i t trc ltr                                (4)  
Where β > 0.   
The  sample  risk  is  derived  as  the  difference  between  current  terrorism  risk  and  long-term 
terrorism risk:  
, , ,
s
i t i t i t r terrindex ltr                              (5) 
Hence:  
, 1 , 2 , , i t i t i t i t trc w ltr w terrindex ltr                        (6) 
Now  equation  (6)  is  operational  provided  that  long-term  terrorism  risk  and  innovations  of 
terrorism risk are available. We derive these quantities by employing a standard time series 
decomposition of   , it terrindex , into a long-run trend  , it  and a cyclical component  , it c , in 
an additive manner (see Harvey 1985; Clark 1987):  
, , , i t i t i t terrindex c                                        (7) 
We decompose the terrorism index employing 3 alternative smoothing specifications: 
moving averages (using windows of 1.5 or 2.5 years) or exponential smoothing, using a non-
linear optimizer to choose the smoothing parameter    which minimizes the sum of squared 
residuals. Thus, the trend component  , it  for each country is: 5 
 
, 2 , 1 , 3
, 3
i t i t i t ma
it
terrindex terrindex terrindex
, 
, 4 , 3 , 2 , 1 , 5
, 5
i t i t i t i t i t ma
it
terrindex terrindex terrindex terrindex terrindex
, and  
exp
, , 1 , 2 * 1 * i t i t i t terrindex terrindex . 
The estimation of the long term terrorism risk  , it  permits us to compute the cyclical 
component  , it c , as the deviation of the current terrorism risk from the trend. Thus we explore 
the Bayesian property of terrorism concern by testing whether prior beliefs and new information 
have a positive and significant impact (δ’s >0):   
, 0 1 , 1 2 , 1 ,
jj
i t i t i t i t trc c                          (8) 
Where the superscript  j  denotes each  alternative smoothing method and  , it  is a 
random disturbance. The  's  are parameters estimated by the following alternative techniques: 
(i) pooled OLS with cluster-robust standard errors, (ii) Fixed-Effects, and (iii) Random-Effects. 
Also note that the trend and cycle variables are included in the model with a one period lag to 
ensure that the specification matches agents’ available information set at the time of forming 
their terrorism risk concern.       
4.  Empirical results  
Table 1 presents the empirical results from projecting risk concern on the long-term terrorism 
risk and its cyclical component, with cluster-robust standard errors.  In addition, we conduct a 
battery of diagnostic tests for the sphericity of the error term  , it , with special reference to two 
types  of no-autocorrelation violations.  In particular, we explore the possibility that the error 6 
 
terms exhibit time series autocorrelation:  , , 1 ,0 i t i t Cov   ∀  i . The second considers the 
possibility  for  cross-sectional  dependence:  ,, ,0 i t j t Cov   ∀  ij .  This  type  of  error 
dependence  would  be  relevant  in  the  case  where  terrorism  risk  concern  across  European 
countries  was  subject  to  cross-country  correlated  shocks.  Testing  for  these  two  types  of 
correlation, we find significant cross-sectional error dependence (using the Pesaran’s CD test, 
Pesaran 2004), but no serial correlation (Wooldridge 2002).   
 -----Table 1----- 
Then  we  re-estimate  model  parameters  with  robust  standard  errors,  accounting  for  error 
structures with cross-sectional correlation, applying the Driscoll and Kraay (1998) algorithm. 
Table 2 reports the relevant estimation results. As a further robustness check we also provide 
results including time dummies. Controlling for cross-sectional dependence yields qualitatively 
similar results as the models assuming spherical errors. Irrespectively of the smoothing method, 
concerns about terrorism are at best marginally affected by the cyclical component. The main 
driver  through  all  specifications  is  the  trend  component  of  terrorism  risk.  This  implies  that 
terrorism concern cannot be tackled on a short term basis. Rather, people observe the evolution 
of the terrorism risk and evaluate its long-term trend.  
-----Table 2----- 
5.  Conclusions 
We decomposed country terrorism risk into long and short run components, and investigated 
whether  they  account  for  risk  perception  across  European  countries.  We  conclude  that  risk 
perception variation is only explained by the long term terrorism countries face. In contrast, the 
cyclical  part  of  terrorism  activity  does  not  affect  risk  perception.  Future  research  could  be 7 
 
directed towards a micro level analysis accounting for respondents’ heterogeneity captured by 
their personal characteristics. Moreover, one could also explore location and timing effects of 
terrorist incidents as determinants of terrorism risk concern.              8 
 
References  
 
Becker, G. and Rubinstein, Y. (2004): “Fear and the response to terrorism: an economic 
analysis,” University of Chicago, online 
http://econ.tau.ac.il/papers/faculty/beckerrubinstein_may11.pdf. 
 
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Frey, B. S., Luechinger, S. and Stutzer, A. (2007): “Calculating Tragedy: Assessing The Costs 
Of Terrorism,” Journal of Economic Surveys 21(2) 1-24.  
 
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 9 
 
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MA: The MIT Press. 10 
 
Tables  
 
 
Table 1. Terrorism risk concern and decomposed terrorism index
(a)  
Estimation 
method 
Pooled OLS with cluster-robust 
standard errors   Fixed Effects   Random Effects  
Regressor  MA3 
Filter
(b) 
MA5  
Filter 
EXP.  
Filter 
MA3  
Filter 
MA5  
Filter 
EXP.  
Filter 
MA3  
Filter 
MA5  
Filter 
EXP.  
Filter 
trend 
0.503
*** 
(0.141) 
0.510
** 
(0.187) 
0.410
*** 
(0.137) 
0.131
** 
(0.056) 
0.126 
(0.099) 
0.426
*** 
(0.118) 
0.172
* 
(0.093) 
0.194
* 
(0.101) 
0.419
*** 
(0.073) 
cycle 
-0.016 
(0.045) 
0.013 
(0.045) 
0.135 
(0.084) 
-0.005 
(0.055) 
0.011 
(0.054) 
0.044 
(0.031) 
-0.006 
(0.045) 
0.011 
(0.046) 
0.050 
(0.040) 
Intercept  
0.070
*** 
(0.015) 
0.068
*** 
(0.014) 
0.071
*** 
(0.017) 
0.086
*** 
(0.002) 
0.086
*** 
(0.005) 
0.070
*** 
(0.006) 
0.080
*** 
(0.017) 
0.079
*** 
(0.016) 
0.067
*** 
(0.011) 
  Diagnostics 
Serial 
correlation test 
(c) 
0.885    0.821     0.376     0.885      0. 821    0.376      0.885       0. 821    0.376   
Cross-sectional 
independence 
test 
(d) 
0.000  0.000  0.000  0.000  0.000  0.000  0.000  0. 000  0.000 
R-squared 
(within)  -  -  -  0.031  0.018  0.117  0.031  0.018  0.117 
R-squared 
(between)  -  -  -  0.403  0.378  0.199  0.403  0.378  0.201 
R-squared 
(overall) 
0.275  0.284  0.200  0.275  0.283  0.184  0.275  0.284  0.186 
Observations  288  288  288  288  288  288  288  288  288 
Overall 
significance (p-
value) 
0.000  0.000  0.000  0.003  0.100  0.001  0.173  0.159  0.000 
Notes: (a) *, **, *** denote significance at the 10, 5 and 1 percent level respectively, (b) MA3, MA5 and EXP Filter stand for the 
moving average smoother with 3 and 5 observations, and the exponential smoother respectively, (c) Wooldridge (2002), (d) Pesaran 
(2004).  11 
 
 
Table 2. Terrorism risk concern and decomposed terrorism index correcting for cross-sectional dependence 
Estimation 
method 
Pooled OLS with 
Driscoll-Kraay s. e. 
FE with 
Driscoll-
Kraay s.e. 
Pooled OLS with Driscoll-
Kraay s. e.  
FE with Driscoll-
Kraay s.e.  
Regressor  
MA3 
Filter  
MA5 
Filter  EXP. Filter  MA3 Filter  MA5 Filter  EXP. Filter 
trend 
0.503
*** 
(0.036) 
0.510
*** 
(0.057) 
0.426
*** 
(0.085) 
0.497
*** 
(0.038) 
0.505
*** 
(0.060) 
0.418
*** 
(0.077) 
cycle 
-0.016 
(0.068) 
0.013 
(0.085) 
0.044 
(0.034) 
-0.006 
(0.067) 
0.023 
(0.084) 
0.050 
(0.031) 
Time dummies  -  -  -  Included  Included  Included 
Intercept  
0.070
*** 
(0.005) 
0.068
*** 
(0.005) 
0.070
*** 
(0.008) 
0.100
*** 
(0.002) 
0.093
*** 
(0.005) 
0.094
*** 
(0.004) 
Diagnostics 
R-squared   0.275  0.284  0.117  0.298  0.307  0.212 
Observations  288  288  288  288  288  288 
Overall 
significance  
(p-value) 
 0.000  0.000  0.000  0.000  0.000  0.000 
 
 

A Bayesian Perspective on Biases in Risk Perceptions,”

A national survey of stress reactions after the September 11, 2001, terrorist attacks,” New England

Adaptive Responses to Chemical Labeling: Are Workers Bayesian Decision

An Investigation of the Rationality of Consumer Valuations of Multiple Health Risks,”

Calculating Tragedy: Assessing The Costs Of Terrorism,”

Can Public Information Programs Affect Risk Perceptions?”

Consistent Covariance Matrix Estimation with Spatially Dependent Panel Data,”

Do Smokers Respond to Health Shocks?”

Dynamic processes in Risk Perception,”

Econometric Analysis of Cross Section and Panel Data”

Emotions and economic theory,”

Estimation of Revealed Probabilities and Utility Functions for Product Safety Decisions,”

Estimation of State-Dependent Utility Functions Using Survey Data,”

Fear and the response to terrorism: an economic analysis,”

General diagnostic tests for cross section dependence in panels,” Cambridge Working Papers in Economics 0435.

How Did Households Interpret Chernobyl? A Bayesian Analysis of Risk Perception,”

How Do Risk Perceptions Respond to Information: The Case of Radon,”

Information and Risk Perception: A Dynamic Adjustment Process,” Risk Analysis

Macroeconomic consequences of terror: Theory and the case of Israel,”

On Terrorism and Electoral Outcomes: Theory and Evidence from the Israeli-Palestinian Conflict,”

Prospective Reference Theory: Toward an Explanation of the Paradoxes,”

The Cyclical Component of U.S.

The Dynamics of Risk Perception: How Does Perceived Risk Respond to Risk Events,” Risk Analysis 17(6),

Trends and Cycles in Macroeconomic Time

Terrorism Risk Concern in Europe

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Terrorism Risk Concern in Europe

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