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Quantifying fear: the social impact of terrorism1
Juan Prieto-RodrguezUniversidad de Oviedo, Avda. del Cristo s/n, 33071 Oviedo
Tel: +34 985 103768. E-mail: jprietor@uniovi.es
Juan Gabriel Rodrguez*Universidad Rey Juan Carlos, Campus de Viclvaro, 28032 Madrid
Tel: +34 91 4887948. E-mail: juangabriel.rodriguez@urjc.es
Rafael SalasUniversidad Complutense de Madrid, Campus de Somosaguas, 28223 Madrid
Tel: +34 91 3942512. E-mail: r.salas@ccee.ucm.es
Javier Suarez-PandielloUniversidad de Oviedo, Avda. del Cristo s/n, 33071 Oviedo
Tel: +34 985 103768. E-mail: jspandi@uniovi.es
Abstract
This paper proposes a methodology to measure social impact of terrorism. We define a
multidimensional terrorism index based not only on deaths but also on other variables
such as injuries, bombs and kidnappings. The weight of each terrorist activity is given
by its social impact, which is estimated through its relevance in the media. For this task
we build up a new data set from the four most important newspapers in Spain, namely,
El Pas, El Mundo, ABC and La Vanguardia. Finally, we evaluate the social impact of
ETA terrorism in Spain from 1993 through 2004.
Keywords: terrorism, multidimensional index and social impact weight.
JEL Codes: C20, D00 and Z13.
*Corresponding author
1We are grateful for assistance with the data base from Jorge Reones. We also acknowledge useful
comments and suggestions by the participants in the Lisbon Conference on Defence and Security 2008.
This research has benefited from the Spanish Ministry of Science and Technology Projects SEJ2007-64700/ECON and SEJ2006-15172/ECON. The usual disclaimer applies.
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The calculated use of violence or the threat of violence to inculcate fear;
intended to coerce or to intimidate governments or societies in the
pursuit of goals that are generally political, religious, or ideological.
(Definition of terrorism by the U.S. Department of Defense)
1. INTRODUCTIONSince September 11th terrorism has became a global problem and a worldwide concern.
In fact, terrorist activities are one of the most important worries for those societies that
suffer from this problem (for example, Ireland and Spain). Though terrorism is easy to
observe, its measurement is a difficult task (see Frey and Luechinger, 2005).
The traditional measurement of terrorism based on the number of terrorist events and/or
casualties (see, for example, the time series study in Enders and Sandler, 2002) does not
consider the consequences for people in terms of economic or utility losses. However,
sophisticated techniques also have problems. Impact studies that measure individual
losses in terms of monetary revenue (see, among others, Enders and Sandler, 1991;
Enders et al. 1992; Enders and Sandler, 1996; Caplan, 2002; Drakos and Kutan, 2003
and Abadie and Gardeazbal, 2003) exclude non-market values so, in this manner, they
may underestimate the phenomenon of terrorism. The hedonic market approach relies
on the assumption that labor and housing markets are in equilibrium meanwhile the
averting behavior approach assumes perfect substitutability between individual and
public expenditures for the mitigation of terrorism effects. Moreover, both approaches
cannot capture the negative external effects of terrorism over non-use values.2 A
2 The non-use values are the following: existence value; option value; education value; and, prestige
value.
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different method, the contingent valuation survey includes non-use values (see, for
instance, Viscusi and Zeckhauser, 2003); however, strategic response biases are
possible. Moreover, this method relies on the individual valuation of a specific public
good. This evaluation is a demanding cognitive task so superficial responses without
adequate consideration of substitutes and the budget constraint may result. The political
reaction of voters may capture individuals evaluation of anti-terrorist policies (see, for
example, Nacos 1994). The problem with this method is that voters may solely evaluate
a government by its outcomes. In this case, citizens could support a particular
government that has not undertaken any policy to fight against terrorism if terrorism
declines due to other factors. Another method to measure terrorism in the literature is
the induced change in happiness.3 This approach captures non-use values though utility
losses may reflect not only the effects of terrorism but also government reactions.
This paper proposes a class of indices to measure terrorism. In particular, we propose a
multidimensional index of terrorism based on a set of dimensions or factors that
generate relevant social impact. The basic variables that we consider are the following:
number of people murdered or injured; type of attack; scale of terrorism action; and,
number of kidnapped people. In the construction of our index we assume a certain
degree of substitution among different dimensions or variables. As a consequence, we
discard lexicographic orderings. Moreover, we weight dimensions according to social
valuation. The valuation that society makes of terrorist activities is not directly
observable so we must consider a proxy. We take, in particular, the relevance of
terrorist activities in newspapers as a proxy for social valuation. In this manner, we
capture utility losses due to terrorism. Furthermore, non-market values and non-use
3 The relationship between economics and happiness is analyzed, among others, by Frey and Stutzer,
2002 and Bruni and Porta, 2005.
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values are, in principle, included and the contamination of government reactions is
avoided. Nevertheless, the reliability of our measure will depend on the gravity of the
gap between public opinion and coverage given to terrorist acts by newspapers.
To measure the degree of terrorism impact in newspapers, we have built up a new data
set with the incidence of ETA activities in the four most important newspapers in Spain,
namely, El Pas, El Mundo, ABC and La Vanguardia. This data set covers the period
1993-2004. Therefore, it provides information for a complete political cycle including
three general elections and their corresponding four legislative periods: two
governments of the Partido Socialista Obrero Espaol (PSOE, henceforth) and two
governments of the Partido Popular (PP, henceforth).4
Once we have estimated the weight for each dimension, the evolution of ETA terrorism
in Spain is analyzed. In general terms, we find that ETA terrorism has non-
monotonically decreased since 1994.
The paper is organized as follows: we proposed a multidimensional index of social
impact of terrorism in Section 2; the data sets used to compute this index and the
regression models to empirically determine the weight assigned to each dimension are
presented in Section 3; the social impact in Spain of ETA activities during the period
1993-2004 is displayed in Section 4; we comment the usefulness of the proposed index
in Section 5; and finally, in Section 6, we discuss the main results.
4See Barros and Gil-Arana (2006) for a study on ETA activity during the last 30 years.
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2. SOCIAL IMPACT OF TERRORISM: A THEORETICAL PROPOSAL FOR AN INDEXWe develop in this section a theoretical proposal for measuring social impact of
terrorism. This proposal should be eligible for any country or region and any time. A
first decision to be made is to choose between an ordinal and cardinal approach. We
focus our attention on cardinal approaches since we are interested in comparisons
between periods and regions based on total levels of terrorism activity and not on
relative terms.
Once this option has been taken, we must decide between a uni-dimensional and
multidimensional approach. The problem with a uni-dimensional index of terrorism, for
example one exclusively based on casualties, is that other dimensions of terrorism are
missed. In fact, people may suffer from great terrorist threats even if there are no
casualties. As said above, measurement of terrorism based on the number of terrorist
casualties does not consider the consequences for people in terms of economic or utility
losses. Consequently, estimations of terrorism based in just one dimension may be
biased. We believe, accordingly, that any possible dimension of terrorism must be
considered so we advocate for a multidimensional approach. Moreover, we show below
that the uni-dimensional approach is a particular case of our proposal. That is, both
methodologies converge when the coefficients for all dimensions other than one are not
statistically different from zero. In Section 4 we estimate the empirical relevance of
including variables other than killed people for ETA terrorism in Spain.
An important issue in multidimensional measurement is the choice of dimensions. One
possible criterion to choose relevant dimensions is orthogonally. To compute this
orthogonally we could apply multivariate analysis using information on different
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terrorism dimensions.5 However, it is difficult to find orthogonal dimensions in the
terrorism framework. Variables like the number of people murdered or injured, bombs,
scale of terrorism action and the number of kidnapped people do not seem to be
orthogonal at all. Therefore, we work directly with observable variables which can be,
in principle, substitutes or complements. We assume that dimensions are substitutes,
think for example about the relationship between total number of killed people and
injured people. Nevertheless, some complementarities are possible when dealing with
directly observable terrorism variables.6 The degree of substitutability between
dimensions is calculated through a regression approach in Section 3.
Once a multidimensional cardinal approach has been adopted, there are two main
possible ways to define a terrorism index: the axiomatic approach and the information
theory approach. In the first case, a set of axioms is imposed over the measure. In the
second case, aggregation of dimensions for terrorism assessments adopts the
Information Theory approach (see Theil, 1967). In this paper we explore the last
approach which is formally presented next.7
Let }...,,2,1{ nN= be the set of terrorist attacks in a period of time and }...,,2,1{ dD =
the set of indicators or dimensions, whether they are of a quantitative or qualitative
nature. An outcome matrixX is an n x dmatrix whose elementxij is the outcome of
5 Some of these techniques are Principal Components Analysis, Factor Analysis and Cluster Analysis.
6For instance, the magnitude of a bomb seems to be positively correlated with casualties. However, we
do not have information about the magnitude of a bomb, only about the type of bomb.
7 Measurement of multidimensional poverty applies a somehow related methodology (see Maasoumi
1986). In this field, all relevant attributes of well-being are assumed to be perfectly substitutable, though
some scholars have recently suggested the existence of a partial trade-off between attributes (see
Bourguignon and Chakravarty 2003, Tsui 2002).
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the terrorism attack i in terms of the dimension j. The domain of outcome matrices
denoted by is restricted to matrices of nonnegative integer numbers. Then, a
multidimensional terrorism index T () for a given period of time is a mapping from
matrixXto a number in the set of real-valued numbers:
= :)()( XfXT .
This index must fulfillthe following normalization property: T(X) = 0 wheneverxij = 0
for all i andj. However, this index cannot be normalized to 1 for the case terrorism is
maximum because, in principle, there is no upper bound for terrorist activity.
Now, we discuss the theoretical properties of this index trying to explain its relevance.
A first possibility for T(X) could be the lexicographic ordering of social preferences.
Given two scenariosXand Y(for example, two countries or two periods of time for the
same country) the lexicographic ordering is as follows:
=
=
=
djyy
yy
y
ddjj
2211
11
x,x
...
x,x
x
T(Y)T(X)
where = =
n
iijj xx
1denotes aggregate value for anyj dimension. We assumej is ordered
according to certain social hierarchical relevance, such as j = 1 is the number of killed
people in a terrorist attack,j = 2 is the number of injured people and so on. In words, as
long as the first dimension ofX is larger than that of Y, X shows a higher degree of
terrorism, and this is so even if the rest of dimensions are larger forY. But as soon as the
first dimension become equal only the second dimension is relevant.
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Unfortunately, the lexicographic ordering is just an extreme possibility. There are two
main problems with this ordering. First, it assumes that there is no substitution between
dimensions at all. For example, one murder is always worst than any amount of injured
people. Second, it is not marginally affected by small variations inxij, that is, it is not
continuous because it may exhibit jumps.
To avoid these problems we adopt a different approach. The idea is to replace the n
pieces of information on the values of terrorist attacks for the various dimensions by a
composite indicator ),...,( 1 cdcc xxx = which is a vector of d scalars, one for each
dimension. For this, the vector ),...,( 1 njj xx corresponding to the dimensionj is replaced
by the scalar cjx . This scalar may be considered as representing the level that dimension
j derives from the terrorist attacks that have occurred in a society during a given period
of time. In this paper, we consider that jcj xx = though other alternatives are possible
because terrorist attacks are not necessarily alike. For instance, we could weight more
those terrorist attacks that take place before an election (see Barros et al., 2006 and
Berrebi and Klor, 2006), or those which occur at the beginning of the period when
people have not got used to them yet. To make things simple we assume that terrorist
attacks are equally important so they will receive the same weight. Nevertheless, we
introduce a dummy for each year in the proposed regression model to control for the
year when terrorist attacks take place (see Section 3).
Next, we have to select appropriate weights for the dimensions in the composite vector
cx . We propose to weight each dimension by its presence in the media. That is, we
consider that the main purpose of terrorist activity is to make terrorists goals notorious
to society. However, the valuation that society makes of terrorist activities is not
directly observable so we proxy the social relevance of each dimension by its presence
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in television, newspapers, reviews and so on. The advantage of this weighting procedure
is that utility losses of terrorism are considered. Moreover, non-market values and non-
use values are captured without the contamination of government reactions. The
estimation procedure of these weights is presented in Section 3.
Finally, what is the proposed model or aggregation function for the index T()? In
principle, we could think about the translog function or the constant elasticity of
substitution function (CES) because of their flexibility to support different alternatives
about substitution between variables. Unfortunately, the estimation of these functions
requires the specification of each component cjx in logarithms so zero values are not
allowed. All terrorist activities take this value from time to time (fortunately!),
consequently, we need to rely on a different model. A first candidate is the Cobb-
Douglas function which defines imperfect substitution between terrorism dimensions
but it is not possible for the same reason above. We eventually propose a linear and
quadratic aggregation functions. In the linear case, the terrorism index is the following:
=
=
d
j
cjjxXT1
)(
where j is the weight of dimension j. This functional form assumes perfect
substitution between dimensions though they have different weights. Because of this
disadvantage we propose a quadratic model. In this case, the terrorism index is the
following:
2112
2
222
2
111
1
)( cccc
d
j
cjj xxxxxXT +++==
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where dimensions 1 and 2 are the number of killed people and injured people,
respectively. Note that the rest of explicative variables (j = 3, ..., d) will be dummies so
it does not make sense to extend the quadratic form to these variables. The advantage of
this functional form is that the marginal rate of substitution is not constant (see next
section).
3. ESTIMATION OFWEIGHTSTo make our index T() applicable we need to estimate the weight for each dimension.
As said in the previous section, we propose to weight each dimension by its presence in
the media as a proxy for social valuation. Accordingly, we build up a data set with
information about terrorist activities in newspapers. For this empirical exercise, we
focus on a long term terrorist problem in Spain since the last years of Francos era, the
terrorist activities of ETA. Since the 11th March 2004 train bombing was not an ETA
attack, it is excluded from our study.8
3.1.DATABASE
The data set Terrorism in Western Europe: Events Data (Engene, 2006) is a valuable
source of information for analysing patterns of terrorism in Western Europe. From this
data set, we obtain information for the following dimensions of terrorism: total number
of killed people; total number of injured people; type of attack (letter bomb, car bomb,
other bomb, rocket or grenade attack, armed attack and other attack); kidnappings; and,
8International terrorism is analysed, among others, in Engene (2004), Bellany (2007) and Barros et al.
(2007).
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type of target (military, police, public service, political institution, business and civil).
The descriptive statistics for these variables are shown in the appendix (see Table A.1.)
Moreover, using information contained in this data set to locate ETA activities through
time, we have collected daily data from the four most important newspapers in Spain,
namely, El Pas, El Mundo, ABC and La Vanguardia. The first three newspapers are
national meanwhile the last one is local, in particular, Catalan. Moreover, the ordering
of national newspapers attending to their conservatism is the following: ABC; El
Mundo; and, El Pas. We have considered the following twelve variables: photograph in
the cover page; percentage of the news about terrorism on the cover page; percentage of
the news about terrorism in the editorial section; total number of pages; photograph on
the first interior page; percentage of the news about terrorism on the first interior page;
photograph on the second interior page; percentage of the news about terrorism on the
second interior page; photograph on the third interior page; percentage of the news
about terrorism on the third interior page; percentage of the news about terrorism on the
second day editorial; and, second day total number of pages. The descriptive statistics
for these variables are shown in the Table A.2. (see appendix).
Information about ETA activities has been collected for a complete political cycle
including three general elections and their corresponding four legislative periods: two
governments of the PSOE from 1993 through 1996 and from 2004 through 2008; and
two governments of the PP from 1996 through 2000 and from 2000 through 2004. Note
that the data set only provides information for the first year of PSOEs second
government though there was a truce from the 22nd of March 2006 through the 30th of
December 2006.
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3.2.REGRESSIONANALYSIS
The first step in the estimation of weights is to define an appropriate dependent variable.
This variable has to represent the social valuation of terrorist activities. For this we
apply Factor analysis to the data collected from Spanish newspapers. In model 1 we
only consider information about the following day, in particular: photograph on the
cover page; percentage of the news about terrorism on the cover page; percentage of the
news about terrorism on the editorial section; and, total number of pages. The results for
this model are shown in Table 1.
Table 1. Factor analysis of model 1.
Eigenvalue Difference Proportion Cumulative
Factor1 2.46068 2.11617 0.9716 0.9716
Factor2 0.34451 0.45422 0.1360 1.1077
Factor3 -0.10970 0.05324 -0.0433 1.0643
Factor4 -0.16295 . -0.0643 1
VariableWeightFactor 1 Uniqueness
Cover photograph 0.7526 0.4336
% Cover 0.8935 0.2016
% Editorial 0.7066 0.5007
Total number of pages 0.7724 0.4034
N 537
LR test [independent vs. saturated chi2(66)] 1262.83
Kaiser-Meyer-Olkin measure 0.6888
AIC (Factor1) 199.4424
BIC (Factor1) 216.5863
The eigenvalue of a particular factor captures its variance. Accordingly, the column of
proportions gives the part of total variance that is explained by each factor. Thus, the
first factor accounts for more than 97% of total variance meanwhile the second factor
accounts for 13% of total variance and so on. The last two eigenvalues are negative
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because the matrix has not full rank, that is, although there are 4 factors the
dimensionality of the factor space is smaller. Given these results we have taken the
factor 1 as our dependent variable. Note that the weight of each variable of information
in the construction of Factor 1 is provided below the eigenvalues. Moreover, it is shown
the Kaiser-Meyer-Olkin measure of sampling adequacy. This is an index for comparing
the magnitudes of observed correlation coefficients with the magnitudes of partial
correlation coefficients. Large values for the Kaiser-Meyer-Olkin measure indicate that
a factor analysis for a set of variables is a good idea.
We also consider an extensive model (model 2) which includes information about the
second day. Results are shown in the appendix (see Table A.3.). It is apparent from the
results in Table 1 and Table A.3. that the first factor explains the major part of total
variance. In this sense the result of model 1 is robust. Moreover, the Kaiser-Meyer-
Olkin measure increases from 0.69 to 0.82. However, the required level of information,
twelve variables instead of four, significantly reduces the net benefit of extending the
model. For these two reasons we compute the dependent variable, social impact, by the
Factor 1 in model 1.
Once social impact has been estimated by Factor analysis, we regress this dependent
variable on the terrorism dimensions specified above. The estimated coefficient of a
particular terrorism dimension will represent the part of social impact that such
dimension explains. We have also included in the regression a dummy for each year to
control for time and a dummy variable for El Pas, El Mundo and La Vanguardia to
control for the difference between newspapers. In Table 2 the results for the linear and
quadratic specifications are shown. Note that the variables other attacks, civil targetand
ABCare used as references so they are not included in the regression.
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Table 2. Regression of model 1.
Social impact (model 1) Lineal Quadratic
Total killed 0.22748*** 0.62002***
(0.038) (0.088)
Total killed squared --- -0.06931***
--- (0.016)
Total injured 0.01313*** 0.04791***
(0.003) (0.009)
Total injured squared --- -0.00039***
--- (0.000)
Total killed * Total injured --- -0.00569***
--- (0.002)
Letter bomb 0.42541* 0.49863**
(0.224) (0.218)
Car bomb 0.46301*** 0.46053***
(0.135) (0.133)
Other bomb 0.04976 0.0472
(0.148) (0.147)
Rocket or grenade attack -0.26524 -0.10021
(0.374) (0.364)
Armed attack 0.71809*** 0.64656***
(0.140) (0.136)
Kidnapping 0.78329*** 0.86321***
(0.256) (0.249)
Military target 0.25213* 0.17283
(0.133) (0.130)
Police target -0.09364 -0.23505**
(0.104) (0.107)
Public service target -0.01358 0.07702
(0.128) (0.129)
Political institutions target 0.41084*** 0.34341***
(0.108) (0.107)
Business target -0.27707 -0.23694
(0.197) (0.191)
El Mundo -0.27326*** -0.26716***(0.084) (0.081)
El Pas -0.35276*** -0.35538***
(0.085) (0.082)
La Vanguardia -0.49669*** -0.49014***
(0.083) (0.081)
Constant -0.46497** -0.62766***
(0.201) (0.198)
N
R2
F
537
0.4929
19.064
537
0.527
19.481
***: Significant at the 1% level. **: Significant at the 5% level. *: Significant at the1% level. Standard deviations in parentheses.
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First, the quadratic specification achieves a better fit than the linear specification.
Besides the square terms and the cross effect are negative and statistically significant at
1%. A negative value for the square terms implies certain saturation in the social impact
of total killed and injured people. In fact, these two variables reach their maximum
social impact at 4 killed people and 60 injured people, respectively.
Second, kidnapping reaches the largest coefficient. We could think, therefore, that the
best strategy for a terrorist group like ETA, in terms of social impact, is kidnapping
instead of killing people. In fact, kidnapping also allows terrorist groups to collect
money. However, we think that terrorist groups like ETA do not widely adopt this
strategy because kidnapping is a great source of risk and cost for the gang.
Moreover, the fact that the variable armed attack has also a larger coefficient than total
killed people in the quadratic specification reinforces our multidimensional approach, in
the sense that total killed people does not include the whole terrorism phenomenon.
Next section shows the difference between the proposed multidimensional index and a
measure based exclusively on killed people.
Third, newspapers have a different sensitivity towards terrorism. As said above, the
variable ABC is used as reference so the negative sign of the coefficients for El Pas, El
Mundo and La Vanguardia implies that these newspapers are less sensitive towards
terrorism than ABC. Thus, the ordering of the Spanish newspapers according to this
sensitivity is the following: ABC; El Mundo; El Pas; and, La Vanguardia. The more
conservative is a national newspaper, the greater the sensitivity towards terrorism. Note
that the inclusion of a dummy variable for the type of newspaper allows us to control
for newspapers ideology. Unfortunately, it does not permit to control for the gap
between public opinion and coverage given to terrorist acts by newspapers. Media
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coverage of terrorist events and publics attitudes and perceptions may be different if we
face a common-interest game. In this case, terrorists get free publicity for their cause
and media make money as reports of terror attacks increase newspaper sales (see
Rohner and Frey, 2007).
Fourth, we assumed in Section 2 that total number of killed and injured people were
substitutive variables. Moreover, the quadratic specification of our model implied a
non-constant marginal rate of substitution between both dimensions. Both assumptions
are confirmed in Table 3 where the estimations for the marginal rate of substitution at
different quantiles are shown. Bear in mind that the marginal incidence measures are the
derivative of social impact with respect to the number of killed and injured people.
Meanwhile, the marginal rate of substitution is the ratio of both variables. The rate of
substitution for the mean is 12.22.
Finally, we have also computed a regression for social impact in model 2. The results
are shown in the appendix (see Table A.4.). In general terms, estimations in Table 2 are
replicated for a more extensive definition of social impact.
Table 3. Marginal Rate of Substitution.
Quantile
Marginalincidence of killed
People
Marginalincidence of
Injured People
Marginal Rate ofSubstitution
5% 0.6200 0.0479 12.9413
10% 0.6200 0.0479 12.9413
25% 0.6200 0.0479 12.9413
50% 0.4814 0.0422 11.4021
75% 0.4757 0.0414 11.4794
90% 0.2916 0.0295 9.8803
95% 0.2574 0.0248 10.3677
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4. SOCIAL IMPACT OF ETATERRORISM IN SPAINThe final step is to calculate the multidimensional index T(). For this, we have used the
estimated coefficients in Table 2, after eliminating the variables of control (dummies for
time and newspapers). To make comparisons, we have also computed a terrorism index
based exclusively on the total number of killed people (lexicographic ordering). The
number of attacks, deaths and the value of index Tby year are shown in the appendix
(see Table A.5.). Moreover, we have applied nonparametric techniques to smooth the
diary evolution of deaths and index T. In particular, we have used the Nadaraya-Watson
nonparametric smoother (Nadaraya, 1964 and Watson, 1964) with the Epanechnikov
kernel (Epanechnikov, 1969).
In Figure 1 we show the evolution of both indices of terrorism. Note that the index T
has no dimensions so the scale in the vertical axis corresponds to the number of killed
people. Moreover, there was a truce from 16th of September 1998 through 3rd of
December 1999. The vertical lines in Figure 1 represent this period of time.
Two facts become apparent from Figure 1. The first fact is that both measures need not
to coincide in their diagnosis. That is, one index can indicate that terrorism is increasing
meanwhile the other can indicate the opposite (see, for example, the first half of 1995,
the beginning of 1997 and 2004). A more important fact is that the killed people index
does not include the whole information about terrorist activities so it smoothes
terrorism. This causes a more linear evolution of the killed people index in comparison
with the proposed index T() which shows higher crests and deeper valleys. For the
whole period we observe that ETA terrorism has non-monotonically decreased since
1994.
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Figure 1. Evolution of ETA terrorism in Spain (1993-2004).
5. ECONOMIC APPLICATIONSIn previous sections we have described the contents and main properties of the proposed
index of terrorism. Next, we suggest some fields where this index can be applied.
Obviously, this is not a closed list of issues, but only a sort of examples exploring where
the use of our proposal can be useful.
Evaluation of terrorism costs
In the economic literature, some studies have recently assessed the costs of terrorism.
They adopt a macroeconomic perspective (see Caplan, 2002 and Abadie and
Gardeazbal, 2003) or a microeconomic perspective based on the quality of life lost (see
Frey et al., 2007) or the actual loss of human life (Riera et al., 2007). We think that our
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index, insofar as it approximates the social relevance of terrorism, can be used as a
complementary tool in this field.
Economic impact of terrorism in development
Another issue that has received increasing attention is the analysis of the determinants
for development (see for example Olson, 1996 and Murdoch and Sandler, 2002). A line
of research that has given some fruits in this field explores the impact of terrorism in
development. In particular, terrorism has been analyzed through its effects on capital
markets (Chen and Siems, 2004), foreign investment (Enders and Sandler, 1996) and
economic activities linked to specific sectors, typically tourism (Enders et al., 1992 and
Llorca-Vivero, 2008). A terrorism index like the one we are proposing may help
researchers to explain these phenomena. For example, economic activities like tourism
and foreign investment crucially depend on the perception that foreign people have on
countrys security. Accordingly, it might be a good idea to study the incidence of
foreign public opinion on economic growth. The difference with respect our study
would be the source of information: foreign newspapers.
Terrorism, institutions and public choice
A third field where our indicator can be applied is Institutional Economics. More
precisely, the proposed terrorism index can be used to study the roots of institutions and
processes of collective choice. A good example of this is the number of studies devoted
to analyze the connections between economic variables like poverty, cultural variables
like education and terrorism (see, among others, Berreby, 2007 and Krueger and
Malekov, 2003). Another example from a more theoretical view is the number of
studies that question the irrationality of terrorism (Caplan, 2001a, 2001b and 2008) or
its effects on the duration of Governments (Saez, 2002).
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Terrorism and public economics
Finally, another research guide that can be supported by an instrument such as the one
proposed here is the analysis of terrorism effects on the allocation of public spending.
Thus, terrorism affects public spending not only through protection of citizens (defence
and police spending) (Higgs and Kilduff, 1993 and Prez-Fornis et al., 2004), but also
through the support of victims and reconstruction of damages.
In all these cases, possibly more, we think that a multidimensional index based on the
social valuation of terrorism such as the one suggested here, can help to understand the
economic roots and effects of this social scourge.
6.CONCLUSIONS
This paper proposes a multidimensional index of terrorism. We construct this measure
by aggregating the different dimensions of terrorism, namely, killed people, injured
people, bombs, kidnappings and targets. For this aggregation we estimate the weight of
each dimension by regressing the social impact of terrorism on these dimensions.
However, social impact is not directly observable so we consider information in the
media as a proxy. Finally, we apply our index to the terrorism of ETA in Spain and
compare it with the evolution of the total number of killed people. The comparison
shows that the effort to compute the proposed index is worthwhile.
Let us finish this paper with the following comment. The nature and political objectives
of national and international terrorist groups are usually different. Thus, national
terrorism is usually linked to territorial defined political objectives, independence in
most of the cases. On the contrary, global terrorism is usually focused on factors like
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21
religion and the socio-economic organization of a nation. However, both kinds of
terrorism manifest themselves through their impact on the citizens state of mind. The
methodology proposed here measures the social impact of terrorism so, in principle, it
can be applied in the measurement of both kinds of terrorist activities.
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Appendix
Table A.1.Descriptive statistics of the dependent and explicative variables
Variable Mean Std. Dev.
Social Impact (from Factor analysis) 0.02127 0.93458
Total killed 0.68691 0.97433
Total injured 3.24099 11.23905
Letter bomb 0.02657 0.16096
Car bomb 0.29791 0.45778
Other bomb 0.29981 0.45861
Rocket or grenade attack 0.00190 0.04356
Armed attack 0.27894 0.44890
Kidnapping 0.02657 0.16096
military target 0.10436 0.30602
police target 0.15939 0.36639
public service target 0.07780 0.26811
political institutions target 0.19355 0.39545
business target 0.03985 0.19579
El Mundo 0.25237 0.43479
El Pais 0.24288 0.42923
La Vanguardia 0.26186 0.44006
Table A.2.Descriptive statistics of the factor variables
Variable Mean Std. Dev.
Cover photograph 0.57169 0.49529
% Cover 0.42719 0.30491% Editorials 0.14022 0.15661
Total number of pages 3.80447 3.20286
First interior page photograph 0.94896 0.22029
% first interior page 0.87600 0.20650
Second interior page photograph 0.74488 0.43634
% Second interior page 0.71248 0.40977
Third interior page photograph 0.50466 0.50044
% third interior page 0.48678 0.46370
% second day editorials 0.15604 0.28968
Second day total number of pages 1.97816 2.70367
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Table A.3.
Factor analysis of model 2.
Eigenvalue Difference Proportion Cumulative
Factor1 5.51367 4.40069 0.7138 0.7138
Factor2 1.11297 0.37551 0.1441 0.8579Factor3 0.73747 0.28102 0.0955 0.9534
Factor4 0.45645 0.04154 0.0591 1.0125
Factor5 0.4149 0.31655 0.0537 1.0662
Factor6 0.09835 0.12312 0.0127 1.0789
Factor7 -0.02477 0.03194 -0.0032 1.0757
Factor8 -0.05671 0.02472 -0.0073 1.0684
Factor9 -0.08143 0.0244 -0.0105 1.0578
Factor10 -0.10583 0.01563 -0.0137 1.0441
Factor11 -0.12146 0.09807 -0.0157 1.0284
Factor12 -0.21953 . -0.0284 1
Variable Factor1 Uniqueness
Cover photograph 0.6543 0.5719
% Cover 0.8181 0.3307
% Editorial 0.6504 0.577
Total number of pages 0.8001 0.3598
First interior page photo 0.2756 0.9241
% first interior page 0.5346 0.7142
Second interior page photo 0.7634 0.4172
% Second interior page 0.7888 0.3778
Third interior page photo 0.8044 0.3529
% third interior page 0.8234 0.322
% second day editorial 0.3431 0.8823Second day total number ofpages 0.586 0.6566
N 537LR test [independent vs. saturatedchi2(66)] 4769.01
Kaiser-Meyer-Olkin measure 0.8150
AIC (Factor1) 2099.245BIC (Factor1) 2150.451
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Table A.4. Regression of model 2.
Social impact (model 2) Lineal Quadratic
Total killed 0.24340*** 0.64576***
(0.038) (0.089)
Total killed squared --- -0.06886***
--- (0.016)
Total injured 0.01264*** 0.05781***
(0.003) (0.009)
Total injured squared --- -0.00051***
--- (0.000)
Total killed * Total injured --- -0.00690***
--- (0.002)
Letter bomb 0.28526 0.37267*
(0.228) (0.220)
Car bomb 0.43928*** 0.41836***
(0.138) (0.135)
Other bomb 0.10644 0.0738
(0.152) (0.149)
Rocket or grenade attack -0.32302 -0.13739
(0.718) (0.689)
Armed attack 0.73931*** 0.67114***
(0.143) (0.138)
Kidnapping 0.81077*** 0.90410***
(0.261) (0.251)
Military target 0.19802 0.10822
(0.136) (0.131)
Police target -0.12392 -0.26248**
(0.106) (0.108)
Public service target -0.14652 -0.0327
(0.134) (0.134)
Political institutions target 0.36595*** 0.30206***
(0.112) (0.110)
Business target -0.27711 -0.21882
(0.201) (0.193)
El Mundo -0.37360*** -0.36738***(0.087) (0.083)
El Pas -0.51043*** -0.51101***
(0.088) (0.084)
La Vanguardia -0.68079*** -0.67303***
(0.086) (0.082)
Constant -0.28539 -0.45265**
(0.210) (0.205)
NR2F
5270.516120.51
5270.557921.625
***: Significant at the 1% level. **: Significant at the 5% level. *: Significant at the 1%level. Standard deviations in parentheses.
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Table A.5.
Number of attacks, deaths and value of index T.
Year Attacks Deaths Accumulated value ofT
1993
199419951996
19971998
199920002001200220032004
6
41010
166
--2218894
2
6105
95
--2613332
6.4
7.313.911.2
18.18.9
--33.223.07.05.92.7
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