Measuring Social Impact of Terrorism%5B2%5D

8/3/2019 Measuring Social Impact of Terrorism%5B2%5D

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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: [email protected]

Juan Gabriel Rodrguez*Universidad Rey Juan Carlos, Campus de Viclvaro, 28032 Madrid


Rafael SalasUniversidad Complutense de Madrid, Campus de Somosaguas, 28223 Madrid


Javier Suarez-PandielloUniversidad de Oviedo, Avda. del Cristo s/n, 33071 Oviedo


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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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.

REFERENCES

Abadie, A. and J. Gardeazbal (2003): The economic cost of conflict: A case study of

the Basque Country,American Economic Review, 93: 113-132.

Barros, C. P. and L. A. Gil-Alana (2006): ETA: a persistent phenomenon, Defence

and Peace Economics, 17 (2): 95-116.

Barros, C. P., J. Passos and L. A. Gil-Alana (2006): The timing of ETA terrorism,

Journal of Policy Modelling, 28 (3): 335-346.

Barros, C. P., J. R. Faria and L. A. Gil-Alana (2007): Terrorism against American

citizens in Africa: Related to poverty?,Journal of Policy Modelling, 30 (1): 55-

69.

Bellany, I. (2007): Terrorism: facts from figures, Defence and Peace Economics, 18

(2): 101-112

Berrebi, C. (2007): Evidence about the link between education, poverty and terrorismamong palestinians,Peace Economics, Peace Science and Public Policy, 13(1):Article 2.

Berrebi, C. and E. F. Klor (2006): "On Terrorism and Electoral Outcomes: Theory and

Evidence from the Israeli-Palestinian Conflict", Journal of Conflict Resolution,

50: 899-925.

Bourguignon, F. and S. Chakravarty (2003): "The measurement of multidimensional

poverty",Journal of Economic Inequality, 1(1): 25-49.

Bruni, L. and P.L. Porta (2005):Economics and Happiness, Oxford University Press.


22/27

22

Caplan, B. (2001a): Rational ignorance versus rational irrationality, Kyklos, 54(1): 3-

26.

Caplan, B. (2001b): Rational irrationality and the microfoundations of political

failure,Public Choice, 107(3/4): 311-331.

Caplan, B. (2002): How does war shock the economy, Journal of InternationalMoney and Finance, 21: 145-162.

Caplan, B. (2008): Terrorism: The relevance of the rational choice model, Public

Choice, forthcoming.

Chen, A. H. and T. F. Siems (2004): The effects of terrorism on global capital

markets,European Journal of Political Economy, 20(2): 349-366.

Drakos, K. And A.M. Kutan (2003): Regional effects of terrorism on tourism: three

Mediterranean countries,Journal of Conflict Resolution, 47 (5): 621-641.

Enders, W. and T. Sandler (1991): Causality between transnational terrorism andtourism: the case of Spain, Terrorism, 14: 49-58.

Enders, W., T. Sandler and G. F. Parise (1992): An econometric analysis of the impact

of terrorism on tourism,Kyklos, 45: 531-554.

Enders, W. and T. Sandler (1996): Terrorism and foreign direct investment in Spain

and Greece,Kyklos, 49(3): 331-352.

Enders, W. and T. Sandler (2002): Patterns of transnational terrorism, 1970-99:

alternative time series estimates,International Studies Quarterly, 46: 145-165.

Engene, J.O. (2004): Terrorism in Western Europe: Explaining the trends since 1950,

Cheltenham UK/Northampton MA, USA: Edward Elgar.

Engene, J.O. (2006): Terrorism in Western Europe: Events data, University of

Bergen.

Epanechnikov, V.A. (1969): Nonparametric estimation of a multivariate probability

density, Theory of Probability and its Applications,14:153-158.

Frey, B. S. and A. Stutzer (2002): What can economists learn from happiness

research?,Journal of Economic Literature, 40 (2): 402-435.

Frey, B. S. and S. Luechinger (2005): Measuring terrorism inLaw and the State, eds.

A. Marciano and J-M. Josselin, pp. 142-181. Edward Elgar Publishing Ld.,Cheltenham, UK.

Frey, B. S., S. Luechinger and A. Stutzer (2007): Calculating tragedy: Assessing the

costs of terrorism,Journal of Economic Surveys, 21(1): 1-24.

Higgs, R. and A. Kilduff (1993): Public opinion: a powerful predictor of U.S. defence

spending,Defence and Peace Economics, 4 (3): 227238.

Krueger, A. and J. Malekov (2003): Education, poverty and terrorism: Is there a

causal connection?,Journal of Economic Perspectives, 17(4): 119-44.

Llorca-Vivero, R. (2008): Terrorism and international tourism: new evidence,Defence and Peace Economics, 19 (2): 169188.


23/27

23

Maasoumi, E. (1986): The measurement and decomposition of multi-dimensional

inequality,Econometrica,54, 991-997.

Murdoch, J. and T. Sandler (2002): Civil wars and economic growth: a regional

comparison,Defence and Peace Economics, 13 (6): 451464.

Nacos, B. L. (1994): Terrorism and the Media. New York: Columbia University Press.

Nadaraya, E.A. (1964): On estimating regression, Theory Probability Applied, 10:186190.

Olson, M. (1996): Big bills left on the sidewalk: Why some nations are rich and others

are poor,Journal of Economic Perspectives, 10: 3-24.

Prez - Fornis, C., M. D. Gadea and E. Pardos (2004): Gasto en defensa y renta en los

pases de la Alianza Atlntica (1960-2002), Hacienda Pblica Espaola Revista de Economa Pblica, 170(3): 137-153.

Riera, A., A. M. Ripoll and J. M. Sbert (2007): Estimacin del valor estadstico de lavida en Espaa: una aplicacin del mtodo de salarios hednicos, Hacienda

Pblica Espaola Revista de Economa Pblica, 181(2): 29-48.

Rohner, D. and B. Frey (2007): Blood and ink! The common-interest game between

terrorists and the media,Public Choice, 127(1): 129-145.

Sez, J. L. (2002): Economa y poltica en la duracin de los Gobiernos: El caso de

Espaa (1977-2001), Hacienda Pblica Espaola Revista de Economa

Pblica, 161(2): 69-98.

Theil, H. (1967):Economics and Information Theory. Amsterdam: North Holland.

Tsui, K. (2002): Multidimensional poverty Indices, Social Choice and Welfare, 19:69-93.

Viscusi, W. K. and R. J. Zeckhauser (2003): Sacrificing civil liberties to reduce

terrorism risks,Journal of Risk and Uncertainty, 26: 99-120.

Watson, G.S. (1964): Smooth regression analysis, Sankhya Series A, 26: 10116.


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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 0.42719 0.30491% Editorials 0.14022 0.15661


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 0.8181 0.3307

% Editorial 0.6504 0.577


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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