CREATE Research Archive

Published Articles & Papers

2011

An Evaluation of INTERPOL's Cooperative-BasedCounterterrorism LinkagesTodd SandlerUniversity of Texas at Dallas, tsandler@utdallas.edu

Daniel G. ArceUniversity of Texas at Dallas, darce@utdallas.edu

Walter EndersUniversity of Alabama - Tuscaloosa, wenders@cba.ua.edu

Follow this and additional works at: http://research.create.usc.edu/published_papers

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Recommended CitationSandler, Todd; Arce, Daniel G.; and Enders, Walter, "An Evaluation of INTERPOL's Cooperative-Based Counterterrorism Linkages"(2011). Published Articles & Papers. Paper 148.http://research.create.usc.edu/published_papers/148

An Evaluation of INTERPOL’s Cooperative-based Counterterrorism Linkages by

Todd Sandler* Daniel G. Arce

School of Economic, Political & Policy Sciences, GR31 University of Texas at Dallas

800 W. Campbell Road Richardson, TX 75080-3021 USA

tsandler@utdallas.edu darce@utdallas.edu Tel. 1-972-883-6725 Fax 1-972-883-6486

and Walter Enders

Department of Economics and Finance University of Alabama

College of Business Administration Tuscaloosa, AL 35487 USA

wenders@cba.ua.edu Center for Global Collective Action Working Paper No. 10-001

ABSTRACT

This paper evaluates the payback from INTERPOL’s efforts to coordinate proactive

counterterrorism measures by its member countries to arrest terrorists and weaken their

ability to conduct operations. We use INTERPOL arrest data and utilization of

INTERPOL resources by member countries to compute counterfactual benefit measures,

which, when matched with costs, yield benefit-cost ratios. These ratios average around

200 over 12 alternative counterfactual scenarios, so that each dollar of INTERPOL

counterterrorism spending returns around $200. The paper also puts forward a

perspective on benefits derived from INTERPOL’s Stolen and Lost Travel Document

database. INTERPOL provides an inexpensive proactive measure against transnational

terrorism that, unlike military operations, does not result in backlash attacks.

Keywords: INTERPOL; counterterrorism; international cooperation; benefit-cost

analysis; game theory

Forthcoming in Journal of Law and Economics, Vol. 54, February 2011

*Sandler’s research was funded, in part, by the US Department of Homeland Security (DHS) through the Center for Risk and Economic Analysis of Terrorism Events (CREATE) at the University of Southern California, grant number 2007-ST-061-000001. However, any opinions, findings, and conclusions or recommendations are solely those of the authors and do not necessarily reflect the view of the DHS or CREATE. The authors gratefully acknowledge helpful comments from an anonymous referee.

An Evaluation of INTERPOL’s Cooperative-based Counterterrorism Linkages

1. Introduction

Formed in 1923, the International Criminal Police Organization (henceforth, INTERPOL) fosters

transnational police cooperation in the organization’s mission to curb a host of international

criminal activities. Currently, INTERPOL focuses on six crime areas: (i) corruption, (ii)

fugitives, (iii) drugs and organized crime, (iv) public safety and terrorism, (v) trafficking in

human beings, and (vi) financial and high-technology crime (INTERPOL 2008a). To address

these areas, INTERPOL uses its resources and linkages to assist and coordinate the crime

fighting efforts of its member countries. As an independent international organization,

INTERPOL is primarily supported by its 188 member nations through assessed dues based on

ability to pay or income considerations. Well over 80 percent of INTERPOL’s 2008 operating

expense of approximately 54.6 million euros comes from assessed dues, with the remainder

coming from voluntary contributions.1

INTERPOL has assumed a larger role in curbing transnational terrorist attacks that,

through their victims, perpetrators, or implications, impact two or more countries. Terrorist

incidents that begin in one country and end in another (e.g., many skyjackings) are transnational,

as are assassinations or kidnappings of foreigners by a domestic terrorist group (Enders and

Sandler 2006a, pp. 6–7). If, moreover, the perpetrators cross an international border or receive

training and support from abroad, then the terrorist incident is transnational. To direct its

counterterrorism activities, INTERPOL created a Public Safety and Terrorism (PST) sub-

directorate in October 2001 following the September 11, 2001 hijackings (henceforth, 9/11) in

the United States. In so doing, INTERPOL focused more of its crime-fighting resources on

counterterrorism. In particular, INTERPOL assumed a proactive role in fighting transnational

terrorism by bolstering international cooperative linkages among law enforcement agencies,

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INTERPOL, and its member countries’ National Central Bureaus (NCBs). Some of these

linkages are dependent on technology. The resulting proactive measures are aimed at capturing

terrorists, thwarting planned incidents, and reducing terrorist resources (INTERPOL 2008b).

NCBs consist of skilled law enforcement personnel from the countries concerned and are

controlled by the law enforcement structure of their country; they interface with INTERPOL at

its General Secretariat in Lyon, France and at its six regional bureaus (INTERPOL 2008a).

The primary purpose of this paper is to evaluate the payback from INTERPOL’s efforts

to coordinate proactive counterterrorism measures by its member countries. Such proactive

measures yield positive externalities as the terrorist threat to all at-risk nations is curtailed. The

presence of these positive side benefits means that such proactive measures are likely to be

undersupplied when provided independently by nations (Arce and Sandler 2005; Enders and

Sandler 1995; Sandler and Siqueira 2006). INTERPOL’s actions to link countries after 9/11 in

counterterrorism measures provide potentially large network externalities. Our effort to evaluate

INTERPOL’s antiterrorism contribution requires that its costs be apportioned to the

organization’s counterterrorism activities, since INTERPOL does much more than fight

terrorism. Our study also necessitates a difficult counterfactual calculation as to the number of

transnational terrorist events in the absence of INTERPOL’s actions. We base our

counterfactuals on the utilization of INTERPOL tools and resources – particularly, its facilitated

arrests of terrorist suspects and “hits” (i.e., positive matches) on its Stolen and Lost Travel

Document (SLTD) database. Thus, we link INTERPOL’s resources – its secure global police

communication link, its terrorism-related databases, its police support services, and its training

workshops – to the arrests of suspected terrorists that stemmed from INTERPOL-issued arrest

notices or diffusions (i.e., an NCB-issued arrest request) in 2006 and 2007. Based on past

transnational terrorist events, we then devise a means for translating arrests into fewer incidents

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and fewer casualties under 12 alternative arrest scenarios. These scenarios allow the reader to

see a range of benefit measures, based on conservative to extremely conservative

counterfactuals. Once we estimate the reduction in terrorist events attributed to INTERPOL’s

coordinating actions, we value these reductions in terms of gross domestic product (GDP)

savings and fewer casualties (i.e., reduced deaths and injuries). The GDP calculations are based

on an econometric model of Blomberg, Hess, and Orphanides (2004) that estimates the impact of

transnational terrorist events on income per capita growth. Finally, we can combine the benefit

and cost information to compute a benefit-cost ratio ( B C ), which indicates the return per dollar

of costs from INTERPOL’s fight against terrorism. We err on the side of caution by

emphasizing costs and downplaying benefits whenever a judgment must be made.

A second purpose here is to use our payback calculations to offer some policy

recommendations with respect to INTERPOL. In particular, our analysis shows that the

cooperative-based counterterrorism activities of INTERPOL have much to offer at a surprisingly

low cost, especially when compared with standard proactive and defensive responses (Sandler,

Arce, and Enders 2009). For example, US homeland security and US proactive responses cost

tens of billions of dollars each in recent years (Treverton et al. 2008). Currently, INTERPOL’s

counterterrorism activities cost just tens of millions of dollars. We show that INTERPOL’s

benefit-cost ratios range from a high of 370 to a low of 65 when GDP savings are included. For

the various arrest scenarios, the ratios have an average value of about 200, thereby returning

$200 for each dollar spent. If, instead, the ratios only include the value of reduced casualties,

then they range from a high of 162 to a low of 4.2, with an average of over 40. Throughout the

study, we raise caveats because we are trying to measure a very elusive entity that has no direct

observation. Despite these caveats, we believe that the payback from further use of INTERPOL

assets is very large. This should not be surprising because network externalities may result in

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increasing returns to scale as all linked members benefit. Unlike proactive military action by one

country or group of countries, which may breed new grievances and backlash terrorism,

INTERPOL-assisted arrests and vigilance are directed at suspects, while respecting the Universal

Declaration of Human Rights, and are thus much more benign.

The remainder of the paper contains eight sections. Section 2 provides preliminaries,

necessary to understand the role of INTERPOL in fighting transnational terrorism. In Section 3,

we present some theoretical considerations to explain the undersupply of proactive measures, as

well as the underutilization of INTERPOL’s assets. Section 4 identifies the costs of

INTERPOL’s counterterrorism efforts, while Section 5 presents the counterfactual scenarios for

computing the benefits of INTERPOL’s counterterrorism actions. In Section 6, we calculate the

benefits from INTERPOL resources, based on fewer casualties and saved GDP losses associated

with various arrest scenarios. Benefit-cost ratios are then computed in Section 7. Section 8

estimates reduced incidents coming from US instantaneous access at border crossings to

INTERPOL’s SLTD database. These additional benefits underscore the conservative nature of

the benefit-cost calculations in Section 7. Finally, Section 9 contains concluding remarks.

2. INTERPOL and Its Counterterrorism Efforts

To appreciate what INTERPOL contributes to global collective action against transnational

terrorism, the reader must first understand something about the organization and its

counterterrorism assets. The General Assembly governs INTERPOL and consists of member

countries’ delegates, who determine the institutional finances, policies, initiatives, and methods.2

Each member country casts a single (unweighted) vote in the General Assembly, which meets

but once a year. INTERPOL’s main mission is to facilitate as widely as possible transnational

police cooperation in its six priority crime areas, mentioned earlier. The Executive Committee

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implements the policies of the General Assembly and consists of 13 members, elected by the

General Assembly.

The Secretary General is nominated by the Executive Committee and confirmed by the

General Assembly based on one-country, one-vote secret ballot voting procedure. The Secretary

General directs the General Secretariat and its regional offices in their daily operation in

facilitating better police practices and efficiency worldwide. The General Secretariat and the

regional offices achieve INTERPOL’s mission by interfacing with member countries’ NCBs, the

United Nations, and all organizations, authorities and services, whose mission is to prevent or

combat international crime.

At the outset, we must emphasize that INTERPOL’s General Secretariat Headquarters

and Regional Offices do not arrest criminals or terrorists; rather, they help member countries

make arrests through the use of the organization’s linkages and resources. In the case of

terrorism, INTERPOL assists its member countries in their counterterrorism activities through its

secure communication network (I-24/7), its databases (e.g., SLTD, terrorist profiles, DNA, and

fingerprints), its investigative resources and specialized projects, its money laundering expertise,

its dissemination of best practices (INTERPOL 2008b), and secure communications among

police worldwide. I-24/7 connects all member countries to a secure global communication

system that provides continual access to INTERPOL’s databases. I-24/7 not only allows police

forces to share their data (INTERPOL 2007a), but also gives member countries access to

INTERPOL’s growing dataset on wanted terrorists. This network is used to issue Red Notices

seeking the arrest internationally of suspected criminals, including terrorists. Special

INTERPOL-UN Security Council Notices are issued to prevent international travel, possession

of firearms, and movement of money by suspected members or affiliates of al-Qaida or the

Taliban, while Orange Notices warn of dangerous disguised weapons or acts that present a public

6

hazard (INTERPOL 2008c). A warning of a new terrorist mode of attack could be issued by an

Orange Notice. In addition to Notices, NCBs can use I-24/7 to issue so-called “diffusions,”

which are wanted persons alerts that include information helping to identify the persons.

INTERPOL’s secure communication network is also used to assist in freezing terrorist assets,

issuing travel bans, and preventing arms trafficking. The MIND/FIND internet linkage offers

member countries instant global access to INTERPOL databases that can be applied at border

crossings and elsewhere to catch suspected terrorists. As of November 6, 2009, 46 member

countries, including the United States, have implemented MIND/FIND.

Public Safety and Terrorism (PST) is most responsible for coordinating INTERPOL’s

counterterrorism activities, including running best-practice workshops, the bioterrorism program

(BioT), and INTERPOL Weapons and Explosive Tracking System (IWETS). Both PST and

INTERPOL’s Command and Coordination Center are involved with Incident Response Teams

that are dispatched at countries’ requests to assist in the investigation of terrorist incidents. For

example, teams were dispatched to investigate the Bali nightclub bombings (October 2002), the

Madrid commuter train bombings (March 2004), the Amman Hotels bombings (November 2005)

and the Mumbai attacks (November 2008). For 2002-2008, Incident Response Teams were

dispatched 46 times.3 As part of PST, “the Fusion Task Force (FTF) was created in 2002 to

initiate a proactive, multi-disciplinary approach to assist member countries in terrorism-related

investigations” (INTERPOL 2008b). The FTF works with member countries to collect

information on terrorist groups and their members. This information is accessible to member

countries, unless a country-specific limitation applies, through INTERPOL global police

databases. The FTF focuses on six regions – Southeast Asia, Central Asia, South America,

Africa, the Middle East, and Europe. These task forces also track the movement of suspected

terrorists and post warnings on INTERPOL’s secure website. As a consequence, some Asian-

7

based terrorists have been apprehended in Europe as they flew into an airport hub in Paris.4 FTF

databases are accessed by member countries and have resulted in the arrest of wanted terrorists

and their supporters. These task forces also organize working group meetings to inform law

enforcement agents of regional terrorist risks. In recent years, the FTF has proven to be a

valuable asset in assisting countries to arrest individuals who were either planning or financing

terrorist attacks.5

Another important INTERPOL counterterrorism asset is the Command and Coordination

Center (CCC), which was first started on a temporary basis on 9/11. The CCC is the contact

point for member countries confronting a crisis situation, such as a terrorist attack. CCC analysts

are available 24/7 to answer member countries’ requests during crises and to coordinate the

delivery of special assistance in the four official languages – English, French, Spanish, and

Arabic – of INTERPOL. The CCC also facilitates the exchange of intelligence and the

transmission of all INTERPOL Notices.

In 2003, INTERPOL established the Fugitive Investigative Service (FIS) at the General

Secretariat in Lyon. The FIS offers operational support to member countries in their

international fugitive investigations, including those involving transnational terrorists. This

support includes not only best-practices conferences, but also a network for connecting fugitive

units worldwide. Such linkages facilitate the exchange of information and pooling of expertise.

A final counterterrorism asset is INTERPOL’s Bioterrorism Prevention Unit (BioT)

which augments national and international capabilities to recognize and offset bioterrorism

threats (INTERPOL 2007b). BioT promotes international awareness of bioterrorism through

information exchange, best practices, and workshops. This prevention unit also lobbies for

legislation worldwide that protects against bioterrorism attacks through safeguards and other

means. Funding for BioT has come from the Sloan Foundation, the European Commission, the

8

US State Department, and the Canadian government (INTERPOL 2007b).

In summary, INTERPOL provides a wide range of counterterrorism resources and tools

that can be used by member countries in arresting suspected terrorists and their supporters. Since

INTERPOL does not deploy agents to make the arrests, our measurement of the benefits from

INTERPOL’s counterterrorism activities is necessarily by inference. The most direct measure of

INTERPOL’s success in curbing transnational terrorism is from terrorism-related arrests, where

notices and/or diffusions had been issued. We know that arrests included individuals suspected

of financing, planning, or carrying out terrorist incidents. In only a few instances, do we know

the person arrested – e.g., on 18 August 2005, a suspected bomber in the 3/11 Madrid train

bombing was arrested in Serbia by border authorities who used I-24/7 and INTERPOL’s

databases. A Red Notice had been issued in March 2004 for this suspected terrorist (INTERPOL

2005). In addition, we do not know the final disposition of those arrested – e.g., whether they

were convicted.

3. Theoretical Considerations

There is an important distinction between defensive and protective countermeasures against

transnational terrorism. Defensive actions harden potential targets at home – physical and

human. Such actions dissuade terrorists by reducing their anticipated net gains from attacks.

This follows because defensive measures raise terrorist attack costs and lower their anticipated

benefits. Expected benefits fall because defensive measures reduce terrorists’ logistical success

rates and their gain if successful. A country’s defensive measures have a downside since they

induce the terrorists to find softer targets abroad. The literature shows that defensive measures

against transnational terrorism by one country result in a negative externality to another country

as the attack is displaced abroad (Enders and Sandler 2006a; Sandler and Lapan 1988).

9

Following 9/11, Enders and Sandler (2006b) established that homeland security in North

America and Europe shifted terrorist attacks to the Middle East and Asia where targets were

softer. As countries choose their defensive measures independently, a “defense race” can ensue

with too much being spent as nations compete to transfer attacks abroad (Sandler 2005).

In contrast, proactive or offensive measures directly attack the terrorists, their resource

base (e.g., training camps, finances, or planners), or their supporters. Effective proactive

measures that weaken a transnational terrorist group provide positive externalities to all at-risk

countries. When a country chooses its proactive response to a transnational terrorist threat, the

country equates its perceived marginal benefits to its marginal proactive costs, thereby ignoring

the marginal benefits that its response confers on other countries (Bandyopadhyay and Sandler

2010; Sandler and Siqueira 2006). This then results in too little proactive measures from a social

viewpoint being supplied against the common transnational terrorist threat. As such, the

terrorists profit from the failure of nations to internalize the associated positive externality. In

essence, proactive measures are a pure public good to all potential target countries. Those

countries, whose people or property are most in jeopardy at home or abroad, are most inclined to

take offensive measures, with the other nations free riding on their actions.

There is, thus, a clear role for an international institution, such as INTERPOL, to bolster

these proactive measures by coordinating an international response to transnational terrorism.

Secretary General Ronald K. Noble recognized this need following 9/11 and redirected some of

INTERPOL’s resources into efforts to coordinate counterterrorism actions against al-Qaida and

other transnational terrorist groups (e.g., Jemaah Islamiyah). Moreover, INTERPOL correctly

focused on proactive, rather than defensive, measures since only the former is undersupplied.

We now present a stylized game representation of INTERPOL to explain why some

member countries take advantage of INTERPOL’s resources while others do not. This

10

representation suggests that INTERPOL has reduced the suboptimality associated with proactive

measures but has not eliminated it. To show this outcome, we assume two sets of INTERPOL

members: those that do not perceive a net benefit from participating and those that do perceive a

net benefit from participating. We begin with the former countries which view their benefits, bi,

as less than their costs, ci, from participating. These costs include not only the investment in the

necessary linkages, but also any perceived loss in autonomy from sharing information. A

stylized game is presented in matrix a of Table 1 for six such countries. The number of these

countries is immaterial to our point. Nations that do not view themselves at risk from

transnational terrorism will envision a small bi from utilizing some or all of INTERPOL

proactive assets. With bi small and ci increasing with new technological requirements or

autonomy concerns about information sharing, these countries will likely view their ci as greater

than bi.

In matrix a, we assume six symmetric countries whose 5ib = and whose 8.ic = Each of

these countries has two strategies – not to use INTERPOL’s assets (not be proactive) or to use

INTERPOL’s assets (be proactive). Everything is presented from country i’s viewpoint so that

the payoffs are those of i, under alternative action scenarios of the other five countries. If

country i is not proactive and no other country is proactive, then i obtains 0. If, however, i is not

proactive and one other country is proactive, then country i gains a payoff of 5 in the second cell

on the top row. In the top row, country i gains a benefit of 5 from every proactive country

because INTERPOL’s coordinated proactive response is a pure public good. Thus, the payoffs

in the top row of matrix a increase by 5 with each proactive country.

[Table 1 near here]

In the bottom row of matrix a, country i is proactive and confers a benefit of 5 on itself

and on the other five countries at a cost of 8 to itself. When country i proacts alone, it nets

11

( )3 5 8 .− = − If, however, a second country engages in INTERPOL’s coordinated proactive

measures, then country i receives 2, which is the difference between 10 in benefits (5 from its

actions and 5 from those of the other country) less its costs of 8. The other entries are computed

in a similar fashion. Country i has a dominant strategy not to be proactive, since the payoffs in

the top row are higher than the corresponding payoffs in the bottom row. As all six countries

choose their dominant strategy, the Nash equilibrium results, with no one participating in the

INTERPOL proactive link. The social optimum, however, requires all six countries to

participate for a gain of 22 apiece. The underlying game is a Prisoner’s Dilemma with a

disappointing payoff of 0. Nevertheless, country i will not unilaterally change its strategy at the

Nash equilibrium because 0 3.> −

Next, we focus on those countries that do perceive a net benefit from utilizing

INTERPOL’s proactive assets, despite the associated costs. Without loss of generality, we

assume there to be three (symmetric) countries in this position with, say, 11and 8i ib c= = ; any

( ) 0i ib c− > will do. The benefits are high because these countries are the target of more

transnational terrorist attacks at home or abroad, as in the case of the United States,6 the United

Kingdom, and France. Given the public nature of proactive measures, every proactive country

again confers a benefit of 11 on the countries that view terrorist risks as they do. In the top row

of matrix b, country i receives 0 when no one else utilizes INTERPOL’s assets. Country i

receives 11 or 22 depending on whether one or two countries engage in INTERPOL-coordinated

proactive measures. In the bottom row, country i nets 3 when it acts alone, while it nets another

11 for each country that joins its proactive efforts. The dominant strategy for this three-country

game is for nation i and, therefore, the other two nations to jointly act, thereby giving each of the

three nations a gain of 25 apiece.

12

We now examine the game from the collective viewpoint of all nine countries where

5ib = for the group of six and 11ib = for the group of three. Moreover, all nine countries face

8.ic = The proactive response from the group of three gives a free-rider benefit of ( )15 3 5= ×

to the group of six. In consequence, a constant of 15 is added to the payoffs in all 12 cells in

matrix a. The dominant strategy in the transformed matrix a (not shown) is not to participate,

insofar as each payoff in the top row remains higher by 3 than the corresponding payoff in the

bottom row. As a consequence, these countries gain just 15 and confer no benefits on the group

of three, whose payoff matrix remains unchanged from that in matrix b.

Thus, it is likely to have some member countries of INTERPOL not taking advantage of

the counterterrorism assets that it provided after 9/11. Currently, only 46 members utilize the

instantaneous access to INTERPOL databases offered by MIND/FIND technology owing to high

costs that likely involve autonomy worries. Nonetheless, INTERPOL has greatly improved

social welfare because of the countries that use its resources, without coming near to achieving a

social optimum. These gains in social welfare are apt to produce very large benefit-cost ratios,

given the public nature of the benefits and the relatively small costs involved.

There are a few factors that will raise the share of INTERPOL member countries utilizing

its proactive assets over time. First, as the prime-target countries harden their targets at home,

terrorist attacks will shift abroad. This shift will make these soft-target countries come to value

proactive measures greater, thereby raising bi relative to ci. Second, prime-target countries may

wish to subsidize countries where they have vulnerable interests, thereby lowering these

countries’ cis. Given that INTERPOL offers a network externality, rich target countries have

much to gain from subsidizing other countries’ proactive participation, which weakens the

common terrorist threat. In the stylized game in Table 1, the three participating countries would

gain 11 from each country in the nonparticipating group that begins to use INTERPOL’s

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linkages. These potentially large gains provide incentives for the three participating countries to

subsidize the efforts of the nonparticipants, thereby reducing their costs. Third, for some

countries, ci is high because of the value that they place on autonomy over their security. As

these countries come to realize that INTERPOL does not greatly infringe on this autonomy,

perceived ci may fall and participation may increase.

4. Costs of INTERPOL’s Counterterrorism Efforts

Insofar as INTERPOL engages in five priority crime areas other than terrorism, we cannot assign

the entire costs of INTERPOL to counterterrorism activities. The INTERPOL website gives the

overall budget and the assessment percentages, from which we can ascertain the dues of each

member country. The website does not provide the breakdown by activity or sub-directorate, so

that we cannot determine INTERPOL’s counterterrorism spending per se. We, thus, contacted

the General Secretariat in Lyon and were invited to come and discuss our data needs, which we

did in October 2008. INTERPOL’s counterterrorism resources involve the PST sub-directorate,

I-24/7, BioT, Fugitive Investigative Service, CCC, and CCC major event spending. The Fusion

Task Force’s costs and workshops expense are part of PST spending. INTERPOL staff members

sent us Excel sheets with the requested budget information, summarized in Table 2 for 2006,

2007, and 2008. In all cases, we used the actual expenditures rather than the budgeted allocation,

since the budget allowed for some fungibility among categories. All spending values are in

euros, except for the next-to-last row where we used the average exchange ratios ($/euros) for

the relevant years to convert INTERPOL’s counterterrorism expenditures into US dollars. These

totals are approximately 23 percent and 26 percent of INTERPOL’s operating expense for 2006

and 2007, respectively, which seem reasonable given the other crime-fighting activities of

INTERPOL. These total operating expenses are given in the last row of Table 2.

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[Table 2 near here]

We list the partial counterterrorism costs in 2008 for the reader’s interest. In 2008, the

expenditure on I-24/7 is smaller than in earlier years, because the figure, provided to us, only

includes operating expense and excludes associated IT and other infrastructure cost. We cannot

do our B C estimates for 2008 because we do not have the arrest data for that year.

Some caveats are in order. Although the PST is primarily focused on counterterrorism,

PST serves a few other functions in terms of public safety and police training. Member countries

use both I-24/7 and CCC for things other than counterterrorism. For example, the CCC is also

used to address natural disasters, such as a tsunami or an earthquake. I-24/7 can also facilitate

the arrest of a pedophile or a drug trafficker. Moreover, the Fugitive Investigative Service helps

to capture some nonterrorists. By including their entire costs as part of INTERPOL

counterterrorism spending, we are surely erring on the side of overestimating this spending. This

overestimation is intentional because we want any biases in our study to be working against the

hypothesis that INTERPOL’s counterterrorism B C is large, so that the reader has greater

confidence in the conservative nature of our estimated ratios. There are no finer budget

breakdowns that would allow us to better pinpoint INTERPOL’s counterterrorism spending.

5. Counterfactual Scenarios

The most challenging calculation for this study is to compute INTERPOL’s counterterrorism

benefits, since it involves a counterfactual: i.e., how many more transnational terrorist incidents

and associated casualties would there have been had INTERPOL coordinating actions not taken

place. This is challenging because this counterfactual is obviously not observable. Thus, we

must use the members’ utilization of INTERPOL’s counterterrorism assets to estimate the fewer

number of transnational terrorist events. Ultimately, all INTERPOL assets, indicated in Sections

15

2 and 4, are used to arrest terrorists. We, therefore, pin our counterfactual exercise on

INTERPOL-assisted arrests coming from INTERPOL-issued notices or diffusions, including

following alerts of escapes. INTERPOL provided us with the arrest data in two forms: all arrests

and terrorist-related arrests. Obviously, only the latter is germane to our study. In Table 3,

INTERPOL-assisted terrorist-related arrests are broken down by region and by notices and

diffusions for 2006 and 2007. In 2006, there were 74 terrorist-related arrests, while in 2007,

there were 104 terrorist-related arrests. Two-thirds of these arrests took place in Europe in 2006,

while just under three-quarters of these arrests took place in Europe in 2007. This unequal

regional distribution is probably due to two factors: European countries’ proclivity for utilizing

INTERPOL linkages, and the presence of major airport hubs in Europe. A terrorist apprehended

in London may be en route to somewhere else and may belong to al-Qaida or some other terrorist

group. The location of the arrest does not necessarily indicate the terrorist’s planned venue of

attack, nor the terrorist’s network affiliation. Unfortunately, we do not have information about

those arrested.

[Table 3 near here]

To lend credence to our counterfactual computation of benefits, we include myriad

scenarios to show the robustness of our B C estimates. The mean of these B C estimates

represent a central tendency. We rely on International Terrorism: Attributes of Terrorist Events

(ITERATE) data to compute our various scenarios (Mickolus et al. 2008). In particular, we use

ITERATE to translate INTERPOL arrest data into fewer transnational terrorist attacks, reduced

injuries, and fewer deaths. We utilize three time periods – 1968–2007, 2000–2007, and 2006–

2007 – as baselines for our scenarios. In Table 4, these three time periods underlie our

calculations of the average number of nonterrorists injured per incident, the average number of

nonterrorist deaths per incident, and the average size of a terrorist attack force.

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[Table 4 near here]

For 1968–2007, we apply ITERATE data to make these three calculations for all

incidents, for all incidents with no missing values, and for all incidents with 9/11 included. If an

incident does not have figures for casualties, deaths, or the attack force size, then it is excluded

from the second set of calculations. The average attack force size increases greatly when

incidents with missing values are dropped because media accounts of simple incidents – a bomb

in a trash can – often do not record the attack force size, which usually involves just one or two

terrorists. Media coverage of more complex hijackings or armed attacks frequently includes the

size of the attack force, which involves 4 to 6 persons. Hence, an average force of 1.52 terrorists

is too low, while an average force of 6.13 terrorists is too high. In the third row of Table 4, we

include the two hijacked airplanes that crashed into the World Trade Center. The huge number

of injuries and deaths for these two incidents make them obvious outliers. Since the exact

casualties for these two events are not apportioned to the two incidents in ITERATE, we use the

figures given in Wikipedia.7 The other two 9/11 skyjackings are included in all of our

calculations insofar as the exact casualty counts are known and are not large outliers. We make

similar per-incident injuries, deaths, and attack force calculations for 2000–2007 and 2006–2007,

except that 9/11 is not relevant for the latter period. There are more injuries and deaths per

incident for these latter baseline years. This follows because, prior to the mid-80s, the left-wing

terrorists dominated and they did not strive for large casualty counts. The rise of fundamentalist

terrorists and the greater use of suicide missions raise casualty figures – especially injuries – in

recent years (Sandler, Arce, and Enders 2009).

Our working hypothesis is that each INTERPOL-assisted arrest may result in at most one

fewer transnational terrorist incident. Our much more conservative counterfactual hypothesis is

that the average sized attack force must be apprehended to limit incidents by one in a given year.

17

We do not presume a decrease of one incident in perpetuity for the various scenarios, because

terrorist organizations will recruit new operatives. Capturing one or more terrorist cell members

will surely disrupt a planned incident until new terrorists can be trained or recruited for the

mission. Our presumption is that arrests under various scenarios result in less incidents that year,

because it takes time for terrorists to put the mission back on track. Our hypothesized

counterfactuals are then applied to the three baseline periods in Table 4 to produce 12 scenarios

of fewer incidents, injuries, and deaths for 2006 and 2007. Implicitly, we assume that these

arrests would not have been made in that year without the notice or diffusion.8

The previous paragraph raises the worry of an intertemporal substitution, whereby fewer

terrorist events today, owing to INTERPOL-assisted arrests, are merely replaced by terrorist

incidents tomorrow. Hence, we examined the total number of transnational terrorist incidents in

recent years using ITERATE data. There were 83 incidents in 2006, 93 incidents in 2007, and

92 incidents in 2008. This distribution does not suggest an intertemporal substitution.

Moreover, there is less transnational terrorism in 2006 and 2007, compared to 2004 and 2005

when there were 233 and 108 incidents, respectively.

[Table 5 near here]

In Table 5, Scenario 1 for 2006 involves 74 fewer incidents if each arrest gives one fewer

terrorist incident. This then results in ( )161 74 2.17= × fewer injuries and ( )77 74 1.04= ×

fewer deaths, using the ITERATE-based averages from Table 4. Analogous computations are

made for 2007. Scenario 2 follows similarly, except that 9/11 casualties are included so that

injuries fall by ( )196 74 2.65= × and deaths by ( )92 74 1.24= × . Other calculations for

Scenario 2 follow in an identical manner. Scenario 3 requires the removal of 1.52 terrorists for

one fewer incidents, so that incidents dropped by ( )49 74 1.52= in 2006. The averages from

18

the first row of Table 4 are applied for Scenario 3’s calculations in Table 5. Scenario 4

corresponds to no missing value averages and the necessary removal of 6.13 terrorists to reduce

incidents by one, so that ( )12 74 6.13= fewer incidents are hypothesized in 2006. These fewer

incidents are then multiplied by 2.21 and 1.05 – the per-incident averages associated with no

missing values – to give 27 and 13, respectively.

The eight remaining scenarios are computed in the same way for 2006 and 2007. The

somewhat different scenario is Scenario 9, where we include 9/11 and the large 4.76 attack force.

A similar scenario for 1968–2007 would not have added much variation since casualty rates per

incident did not differ much owing to the larger time period that smoothes out differences. There

are two fewer scenarios for 2006–2007 because there is no 9/11 outlier to worry about. All

calculations follow as before and require no further explanation.

Each of the three baselines results in different hypothesized INTERPOL-assisted

reductions in incidents and casualties. Generally, the smallest savings in benefits come from the

1968–2007 calculations, while the greatest come from the 2000–2007 baseline. Also there are

larger derived benefits from the fewer incidents and casualties in 2007, compared with 2006,

because there were more INTERPOL-assisted arrests in 2007.

Before moving to the benefit-cost calculations, we should address a final concern

because, as mentioned earlier, a large share of INTERPOL-assisted arrests occurred in Europe.

In particular, the size of the terrorist attack force may differ between Europe and the rest of the

world, so that our worldwide attack force averages in Table 4 may bias our results to high

benefit-cost ratios. Thus, we used ITERATE data to compute the size of terrorist attack forces in

Europe for various scenarios.9 We discuss only the values for 1968–2007 because the other

periods’ values follow the same tendency. European attack forces averaged 1.01 terrorists for all

incidents and 4.15 terrorists for incidents with no missing values. Using these values, instead of

19

1.52 and 6.13, would, ceteris paribus, increase benefits because fewer terrorists must be arrested

for there to be one fewer incident. Hence, our use of worldwide averages underestimates

benefits and works against high benefit-cost ratios. We prefer the worldwide averages because

we do not know which country the arrested terrorist really planned to attack.

6. Benefit Measures

We must devise benefit measures that can be fitted into the twelve scenarios for each of the two

sample years. There are two basic drivers of benefits from reduced terrorism: (i) the value of

fewer injuries and deaths and (ii) the reduced losses in GDP.

6.1. Valuing Saved Casualties

We derive a proxy for the injury component of terrorist incidents by appealing to a study

conducted by Abenhaim, Dab, and Salmi (1992) of the injury and psychological consequences of

21 terrorist attacks – 20 bombings and 1 machine-gun attack – that occurred in France during

1982–1987. These authors sent surveys to the population of 324 civilian victims, registered by

the police as having been hospitalized. Abenhaim, Dab, and Salmi (1992) reported an overall

participation rate of 86 percent. Psychological symptoms were identified from assessing

respondents’ self-reported symptoms on a diagnostic portion of the survey. We use their survey

data to create a composite distribution of injuries to apply to the number of wounded, given in

Table 5 for the various scenarios. Although transnational terrorism includes more than

bombings, the French survey on assessing injuries is an excellent proxy because over half of all

transnational terrorist attacks are bombings, which are the most likely attacks to result in injuries.

The distribution of physical injuries and psychological trauma found by Abenhaim, Dab,

and Salmi (1992) for a composite terrorism attack is displayed in Table 6. For example, about

20

47 percent of the injured had hearing loss, 15.4 percent had severe burns, and so on. The injury

total is greater than 100 percent because victims may experience multiple wounds. In Table 6,

the disability weight for each type of injury is taken from Mathers, Lopez, and Murray (2003).

Avoided injuries are converted into avoided deaths by summing over the product of the

percentage of each injury type, jI , and the associated disability weight, jD . The latter measures

the relative value of a health state on a [0,1] scale, where 0jD = is full health, 1jD = is death,

and ( )0,1jD ∈ is an injury. For example, a hearing loss has a disability weight of 0.12 and a

head trauma has a disability weight of 0.35.10 The weighted value of incidence of injury is

j jj

I D∑ , which constitutes the injury equivalent of death. If, for example, all injuries were

deaths, then j j jj j

I D I=∑ ∑ for which jI denotes the type of death rather than the type of

injury. Given the number of injuries, N, avoided for a particular scenario, j jj

N I D×∑ is then

the number of fewer injuries avoided, as measured in terms of deaths avoided. In particular,

j jj

I D∑ is approximately 0.57, implying that every injury avoided in a composite terrorist event

translates into just under 0.57 deaths avoided. The total number of “injuries-as-deaths” avoided

for each scenario is multiplied by $2 million to derive the injury benefits reported later in Section

7. We use a value of life of $2 million because that was the average compensation paid to those

who lost a family member in 9/11 (Sandler, Arce, and Enders 2009).

[Table 6 near here]

We note that our injury calculation is biased downward because it only accounts for the

psychological trauma experienced by those directly affected by the attack. There is now

substantial evidence that major terrorist events have significant psychological impact on the

21

general population. For example, Galea et al. (2002) found that, within two months following

9/11, 7.5 percent of respondents in Manhattan reported symptoms consistent with post traumatic

stress disorder (PTSD) and 9.7 percent indicated 9/11-related depression. Similarly, Miguel-

Tobal et al. (2006) indicated that, in the first three months following the Madrid train bombings,

8.4 percent of the city’s respondents reported either bomb-related PTSD or depression and 1.4

percent reported both disorders. The higher percentages for New Yorkers is attributed to the

larger number of eyewitnesses to the 9/11 attacks.

6.2. Valuing Saved GDP

We use a two-step procedure to measure GDP savings from INTERPOL-assisted thwarted

incidents. We first estimate the historical costs of terrorism using the actual number of observed

incidents. Specifically, we follow the procedure developed by Blomberg, Hess, and Orphanides

(henceforth BHO) (2004) to measure the GDP cost of terrorism. We then project how much

higher the GDP costs would have been had INTERPOL not helped thwart any incidents.

To estimate the actual GDP losses from terrorism, BHO (2004) constructed a panel of

177 countries for their 1968–2000 sample period. For our purposes, the key finding of their

study is that a nation’s per capita GDP growth falls by 0.04545 percentage points (1.5/33 ≈

0.04545) for each year in which a terrorist incident occurs. If, therefore, we use simple

compounding, a nation with at least one terrorist incident in each of the 33 years of their study

will suffer a cost equal to 1.5 percentage points of its real per capita GDP. Let ity equal real per

capita income in country i during period t and let itT equal the terrorism measure for country i

during t. Because BHO find that 1 0.0004545it it ity y T−Δ = , the one-year per capita GDP cost of

terrorism is 10.0004545it it ity T y −Δ = × . Multiplying this figure by the population of country i and

22

summing over all countries yields the total GDP cost of terrorism in period t.

To avoid the idiosyncrasies associated with the choice of any single reference year, we

use five-year historical averages to measure a nation’s real GDP. We obtain real per capita GDP

(measured in year 2000 US dollars) and population data from the World Bank (2008) for each

nation with at least one terrorist incident during 2003–2007. In the left-hand column of Table 7,

we list all countries experiencing one or more terrorist attacks during 2003–2007. The second

column in Table 7 indicates the simple average of real GDP in millions of US dollars over the

same period. The values are constructed by multiplying real per capita GDP by population. In

the third column, the number of years of transnational terrorist events (Ti) in country i is

displayed for 2003–2007. The terrorism data come from ITERATE, which is the same data used

by BHO.

[Table 7 near here]

The BHO methodology can be readily applied to our exercise. For example, over 2003–

2007, Albania’s real GDP averaged $4,799 million and Albania experienced only one year with a

terrorist attack. Given the estimate from BHO, Albania’s loss from terrorism that year is $2.181

million (= 4,799 × 0.0004545); over 2003–2007, its average annual loss is one-fifth of this

amount or $436,200. Since Algeria experienced at least one incident in three of the five years,

Algeria’s average losses are estimated to be (3/5) × $68,501 × 0.0004545 = $18.680 million.11

Column 4 of Table 7 shows the estimated average annual cost of terrorism for each nation. As

shown in the last row of Table 7, the sum of all costs is in excess of $6,534 million.

On first sight, these losses might appear to be high; however, the total direct losses from

the 9/11 attacks were $48.7 billion (Enders and Sandler 2006a). Hence, our $6.5 billion

worldwide annual loss appears quite conservative. As pointed out by BHO (2004), most of the

23

indirect losses from terrorism come from a reallocation of resources away from the private sector

toward increased government spending. Although enhanced defense expenditures may be

necessary to prevent subsequent terrorist attacks, the additional resources diverted to defense

cause a growth-retarding crowding-out of private sector investment.

We next apply the calculations from the twelve scenarios reported in Table 5 to these

historical cost estimates. Under the assumptions of Scenario 1 for 2006, Table 5 indicates there

would have been 74 additional terrorist incidents. ITERATE reports the actual number of

incidents in 2006 to be 83. If, therefore, these incidents had not been thwarted under Scenario 1,

then there would have been approximately 47 [≈74/(74 + 83)] percent more incidents. The fifth

column of Table 7 projects the 47 percent cost increase that would have been incurred had these

incidents not been thwarted. As such, we project that Albania’s GDP losses would have been

about $206,000 (= $436,200 × .4713) higher. The figures are identical for Scenarios 2, 5, 6, and

10. As shown in the last row of Table 7, the sum of the GDP savings from Scenario 1 is

$3,080.04 million. Under the assumptions of Scenario 3, only 49 incidents are thwarted, so that

the sixth column reports a 37 [≈ 49/(49 + 83)] percent historical cost saving from thwarted

incidents. The remainder of the table reports the savings from each of the various scenarios.

Table 8 relies on the identical methodology to calculate the GDP savings from Scenarios 1-12,

based on the 2007 terrorist data. The entries in Table 8 are higher than those in Table 7 since

the number of incidents reduced for 2007 exceeds that for 2006 (see Table 5).12

[Table 8 near here]

Consistent with our other benefit estimates, the GDP calculations are prone to

underestimation. This follows because we include only the GDP losses in the venue country.

Transnational terrorists often attack another country’s assets within the venue country (e.g., the

24

US embassy in Tehran or Tanzania), so that there will be GDP consequences abroad, not taken

into account. The resulting underestimation downplays benefits and adds credibility to our

counterfactual exercise.

7. Benefit-Cost Calculations

In Table 9, we display the benefits associated with the use of INTERPOL assets in millions of

US dollars for each of the twelve scenarios for 2006 and 2007. In the GDP savings columns of

Table 9, we record the totals from the bottom row of Tables 7 and 8 in the appropriate scenario

and year. Thus, for example, $3,080 million is entered for Scenarios 1-2, 5-6, and 10 for 2006.

Next, we calculate and enter the benefits from reduced injuries, as discussed in Section 6.1. The

value of injuries equals $1.14 million (= .057 × $2 million) times the fewer injuries associated

with each scenario, as reported in Table 5 for 2006 and 2007. For Scenario 1 in 2006, the 161

fewer injuries are valued at $183.4 million in Table 9. Other benefits from reduced injuries are

computed in a similar fashion. Death benefits simply equal $2 million times the fewer deaths for

each scenario in Table 5.

[Table 9 near here]

The three sources of benefits are summed for each scenario in a given year and then are

placed under benefits for 2006 and 2007 in Table 10. Costs are taken from Table 2 and entered

in Table 10. These costs do not vary by scenario. Both benefits and costs are in millions of US

dollars. For each year, we present two alternative benefit-cost ratios. B C includes benefits in

terms of saved GDP and fewer casualties, attributable to INTERPOL-assisted arrests. The much

smaller B C includes just benefits in terms of fewer casualties. Leaving out GDP reduces the

ratio on average to a fifth of B C .

25

[Table 10 near here]

With the exception of B C for Scenario 4, the benefit-cost ratios are quite large and vary

from a high of 370.3 to a low of 4.2. A ratio of, say, 196.2 indicates that each dollar spent on

INTERPOL’s counterterrorism efforts has a potential payback of almost $200. Given the wide

range of scenarios, it is instructive to get some central tendency for these ratios over the twelve

alternative underlying assumptions. In 2006, B C has an average value of 204.3, while B C

has an average value of 41.4. Similar averages characterize 2007: B C averages 195.6 and

B C averages 47.3. Such large ratios indicate that there is an extremely high payback to further

investment in INTERPOL counterterrorism activities. These ratios stand in stark contrast to

those for increased country-directed defensive and proactive measures that possess ratios

between 0.04 and 0.30, indicative of negative returns (Sandler, Arce, and Enders 2009).

The savings in GDP from INTERPOL linkages and technology overwhelm the benefit-

cost calculations because relatively few people die in transnational terrorism, so that fewer

incidents result in relatively modest savings in casualties. Even when GDP savings are

eliminated, the benefit-cost ratios are more that 40 to 1 on average. Compared with the tens of

billions of US dollars spent on defensive homeland security, INTERPOL countermeasures

against terrorism are a great value at less than $20 million. The high payback results because of

the network externalities that benefit all members. As more member countries utilize

INTERPOL databases and resources, more arrests will be made and the benefit-cost ratios should

increase further.

8. US Benefits from INTERPOL’s SLTD Database

In a hearing entitled, “Stolen Passports: A Terrorist’s First Class Ticket,” the Committee on

26

International Relations of the US House of Representatives (2004) characterized SLTDs as worth

their weight in gold and a serious threat to national security. Fraudulent passports were used by

terrorists involved in 9/11, the 2004 Madrid bombings, and the 2005 London bombings.13

Moreover, SLTDs from visa waiver countries (VWCs) are regarded as the most valuable,

because travelers from such countries are not required to undergo the rigorous screening process

normally associated with visa applications at US consulates. Table 11 identifies VWCs.

Examples of terrorists found to have VWC passports include convicted 9/11 terrorist Zacarias

Moussaoui, Richard Reid (the “shoe bomber”), Ahmed Ajaj (WTC bombing of 1993), and Wali

Khan (convicted in the Manila airline bombing plot, Operation Bojinka, with Ramzi Yousef).

[Table 11 near here]

Furthermore, stolen blank passports often suffer from a two-number problem. That is, an

individual’s passport number is often different from the “book,” “inventory control,” or “lot”

number used for record-keeping purposes in the passport production process. It is therefore

possible for a traveler to have a stolen passport with a legitimate passport number that has been

borrowed from someone else. In this case, the lot number can be used to identify a passport as

stolen. For example, in 2004, more than 14,000 blank French passports were stolen by the

truckload in three separate instances. During the same year, the US Department of Homeland

Security determined that several thousand blank German temporary passports had been lost or

stolen, and that Germany had not reported some of this information to the United States.

Subsequent to 9/11, the Departments of State and Homeland Security created a

centralized SLTD clearinghouse, and the resulting Consular Lost and Stolen Passport (CLASP)

database was made available at all US ports of entry. Surprisingly, only secondary inspectors at

ports of entry had direct access to CLASP. As of 2004, the use of SLTDs from VWCs remained

a major risk. The United States had, for example, no formal agreement with France for the

27

exchange of SLTD information. To overcome bilateral gaps associated with SLTDs, the

General Accounting Office recommended “business-to-business” access to the SLTD database

created by INTERPOL. The United States transferred in late April 2004 limited data on over

300,000 SLTDs to INTERPOL. Business-to-business access was online for primary inspectors

at US ports of entry in October 2007.

FIND refers to the Fixed INTERPOL Database on SLTDs and MIND refers to a mobile

database that is basically a local copy of FIND. The information provided by 145 member

countries includes document number, document type (passport, identity card, etc.), type of fraud

(e.g., stolen blank or stolen from the bearer), the lot number (if passports are stolen blank),

country and place of theft/loss, date of theft/loss, country of issuance, and status of investigation.

Other information includes when the information was recorded and how long it would be

maintained in the system. All member countries have the ability to restrict selected countries

from accessing their information on the INTERPOL database. At the same time, a country’s

benefits may arise from accessing information provided by countries where relations are not so

cordial. An example of a lost opportunity is Ramzi Yousef, who entered the US prior to the

1993 World Trade Center bombings on a stolen Iraqi passport.

Given that we only have comparable data on SLTD hits or matches at border crossings

over time for the United States, our focus will be on a counterfactual based on hits using the US

database versus hits when the INTERPOL database came online at US ports of entry. Even

under this restriction, the impact of INTERPOL’s technology-based solution is hard to ignore.

Table 11 compares the interception of fraudulent passports from VWCs using the US database

from January to June 2005 versus hits from MIND/FIND for January to June 2008. INTERPOL

technology resulted in 951 more hits over a six-month period.

An extreme upper bound on the US value of MIND/FIND would be the percentage of

28

terrorism-related hits required to justify all of INTERPOL’s counterterrorism spending in 2007.

Let T be the number of additional terrorists that the United States interdicts via MIND/FIND hits.

We have established that the total number of additional hits per year is 1,902 (= 951 × 2). If

1.52 is the average number of operatives used per terrorist incident, then T/1.52 is the number of

terrorist events averted. From Section 6.1, there are 2.17 injuries per event with an injuries-as-

deaths value of 0.57. Additionally, 1.04 nonterrorist deaths occur per event. This then implies

that (T/1.52) × (2.17 × 0.57) + (T/1.52) × 1.04 = 1.485 × T deaths are avoided. At $2 million

per death, this provides $2,995,000 × T value of lives saved. To justify INTERPOL’s entire

counterterrorism spending for 2007 of $16,646,352, the United States must only catch 5.5564

terrorists using MIND/FIND. Given the 1,902 additional MIND/FIND hits, this means that only

0.29 percent of these hits would need to be terrorism related. If, instead, the average attack force

consists of the upper bound of 6.13 terrorists, then 1.16 percent of US’s MIND/FIND hits would

need to be terrorism-related to justify the entire counterterrorism spending of INTERPOL. These

figures are much higher than necessary because: (i) our benefit figure is only for the United

States; (ii) the entire INTERPOL counterterrorism spending is being justified; (iii) VWC hits are

only 55 percent of the hits received by the United States for the first six months of 2008; (iv) the

remaining VWC hits include criminals other than terrorists, implying additional US benefits; and

(v) an equivalent proprietary database would require the United States to successfully negotiate

bilateral SLTD agreements with all participating countries in the INTERPOL database.

9. Concluding Remarks

The counterfactual exercise that we have undertaken is supremely difficult. We have based our

29

benefit-cost ratios on some conservative assumptions that intentionally downplay benefits and

emphasize costs (e.g., assigning all of the costs of I-24/7 and CCC to counterterrorism) to give

conservative benefit-cost ratios. Nevertheless, our ratios show that a dollar spent on INTERPOL

counterterrorism assets gives, on average, a payback of $200 to $40, depending on the inclusion

of GDP savings. INTERPOL’s benefit-cost ratios are so high because its efforts to coordinate

proactive counterterrorism measures yield benefits that help all at-risk members. Since such

actions will be undersupplied owing to the associated purely public benefits, coordinated

proactive countermeasures can realize large network externalities. INTERPOL-assisted actions

are extremely cheap, compared with military actions or defensive measures. INTERPOL’s

cooperative-based proactive measures leading to arrests of terrorists or preventing international

travel, possession of firearms, and movement of money by suspected terrorists do not have the

same potential for backlash attacks that military actions can have (Rosendorff and Sandler 2004).

The redirecting of some of INTERPOL’s assets to fighting terrorism by Secretary General Noble

shows a good grasp of the need to provide undersupplied collective action. Ironically,

INTERPOL is achieving the network externalities that the terrorists have been exploiting since

al-Qaida linked diverse terrorist groups in a network and shared training and other assets.

Even though our benefit-cost ratios are high, we are confident that we have not

overestimated them. For example, many of our estimates are based on a single death coming

from an avoided incident. In recent years, suicide attacks are on the rise; such attacks kill twelve

people on average (Pape 2006). Higher average death tolls will greatly increase our benefit-cost

ratios. The trend in recent years is toward higher death tolls – see the casualties for 2000–2007

and 2006–2007 per incident in Table 4. If this trend continues, then the potential payback on

INTERPOL-assisted arrests will increase far beyond our estimates. Moreover, we ignore that the

arrest of a terrorist may yield multi-year paybacks. As defensive measures by rich countries shift

30

(diffuse) the risks of transnational terrorism to more countries, the benefit-cost ratios associated

with a coordinated proactive approach will increase as the terrorism risk is reduced to everyone.

Quite simply, INTERPOL presents large paybacks on small expenditures as it addresses the

shortfall of collective proactive measures. This is a smart way to fight transnational terrorism.

31

Footnotes

1. Some budget information on total budgets, share of voluntary contributions, and

country assessment rates can be found on the INTERPOL website at http://www.interpol.int.

Much of the budget information used in this study was given to us by the General Secretariat of

INTERPOL. In particular, budgets for the organization’s sub-directorates and operations (e.g.,

Command and Coordination Center or Fugitive Investigative Services) are not available online.

2. On the organization of INTERPOL, see INTERPOL (2008a).

3. Please note that not all Incident Response Teams are terrorism related. Data provided

to the authors by INTERPOL.

4. Some examples of this kind were presented to the authors at a briefing, made by a task

force member at the General Secretariat in October 2008.

5. In a private communication from an analyst from the Central Asia FTF, the authors

were given numerous examples of people whose arrests were facilitated by the FTF. These

individuals were either suspected terrorists or individuals who planned, financed, or supported

terrorist attacks. In some cases, an entire cell planning an attack was arrested.

6. Although relatively few transnational terrorist attacks occur on US soil, just over 40

percent of these attacks are against US assets (Sandler and Enders 2004, pp. 302–4).

7. There were 2628 deaths at the World Trade Center and around 6200 injured.

(Wikipedia 2009).

8. This is a necessary assumption for our counterfactual exercise and may not always be

the case. This is why we have more conservative scenarios.

9. We used INTERPOL’s classification for European countries.

10. When a one-to-one relationship between disability and type of injury does not exist,

we use the conventions applied in Sandler, Arce, and Enders (2009, p. 33).

32

11. These values are slightly different than those in the table owing to rounding.

12. An alternative methodology is to apply the 2006 GDP data to the scenarios for 2006

and apply the 2007 GDP data to the scenarios for 2007. Because GDP growth has been positive

for nearly every country in the study, the methodology reported here tends to underestimate the

GDP savings.

13. The events and facts in this section are drawn from Department of Homeland

Security (2004a,b), General Accounting Office (2006, 2007), US House of Representatives

(2004), and US Senate Judiciary Committee (2007).

33

References

Abenhaim, Lucien, William Dab, and L. Rachid Salmi. 1992. Study of Civilian Victims of

Terrorist Attacks (France 1982-1987). Journal of Clinical Epidemiology 45:103–9

Arce, Daniel G. and Todd Sandler. 2005. Counterterrorism: A Game-Theoretic Analysis.

Journal of Conflict Resolution 49:183–200.

Bandyopadhyay, Subhayu and Todd Sandler. 2010. The Interplay between Preemptive and

Defensive Counterterrorism Measures: A Two-Stage Game. Economica 77:

forthcoming.

Blomberg, S. Brock, Gregory D. Hess, and Athanasios Orphanides. 2004. The Macroeconomic

Consequences of Terrorism. Journal of Monetary Economics 51:1007–32.

Department of Homeland Security. 2004a. An Evaluation of the Security Implications of the

Visa Waiver Program. OIG-05-07, December. Washington, D.C.: Office of

Inspections, Evaluations, & Special Reviews.

______. 2004b. A Review of the Use of Stolen Passports from Visa Waiver Countries to Enter

the United States. OIG-04-26, April. Washington, D.C.: Office of Inspections,

Evaluations, & Special Reviews.

Enders, Walter and Todd Sandler. 1995. Terrorism: Theory and Application. Pp. 1:213–49 in

Handbook of Defense Economics, edited by Keith Hartley and Todd Sandler.

Amsterdam: North-Holland.

______. 2006a. The Political Economy of Terrorism. Cambridge, U.K.: Cambridge University

Press.

______. 2006b. Distribution of Transnational Terrorism among Countries by Income Class and

Geography after 9/11. International Studies Quarterly 50:367–93.

Galea, Sandro, Jennifer Ahern, Heidi Resnick, Dean Kilpatrick, Michael Bucuvalas, Joel Gold,

34

and David Vlahov. 2002. Psychological Sequelae of the September 11 Terrorist Attacks

in New York City. New England Journal of Medicine 346:982–7.

General Accounting Office. 2006. Border Security. Stronger Actions Needed to Assess and

Mitigate Risks of the Visa Waiver Program. GAO-06-854, July. Washington, D.C.:

General Accounting Office.

______. 2007. Homeland Security. Progress Has Been Made to Address Vulnerabilities

Exposed by 9/11, but Continued Federal Action is Needed to Further Mitigate Security

Risks. GAO-07-375, January. Washington, D.C.: General Accounting Office.

INTERPOL. 2003. INTERPOL Secretary General Calls for International Network of Fugitive

Investigators.

http://www.interpol.int/public/ICPO/PressReleases/PR2003/PR200303.asp.

______. 2005. Co-operation between INTERPOL National Central Bureaus Leads to Arrest of

Madrid Train Bombings Suspect.

http://www.interpol.int/public/News/2005/Madrid20050818.asp.

______. 2007a. Connecting Police: I-24/7. Fact Sheet, COM/FS/2007-11/GI-03. Lyon:

INTERPOL.

______. 2007b. Bioterrorism. Fact Sheet, COM/FS/2007-09/TE-02. Lyon: INTERPOL.

______. 2008a. INTERPOL: An Overview. Fact Sheet, COM/FS/2008-03/GI-01. Lyon:

INTERPOL.

______. 2008b. Terrorism. Fact Sheet, COM/FS/2008-03/TE-01. Lyon: INTERPOL.

______. 2008c. Notices. Fact Sheet, COM/FS/2008-03/GI-02. Lyon: INTERPOL.

______. 2008d. 2007 INTERPOL Annual Report.

http://www.interpol.int/Public/ICPO/InterpolAtWork/iaw2007.pdf.

______. 2009. 2008 INTERPOL Annual Report.

35

http://www.interpol.int/Public/ICPO/InterpolAtWork/iaw2008.pdf.

Mathers, Colin D., Alan D. Lopez, and Christopher J. L. Murray. 2003. The Burden of Disease

and Mortality by Condition: Data, Methods, and Results for 2001.

http://files.dcp2.org/pdf/GBD/GBD03.pdf.

Mickolus, Edward F., Todd Sandler, Jean M. Murdock, and Peter A. Flemming. 2008.

International Terrorism: Attributes of Terrorist Events, 1968-2007 (ITERATE). Dunn

Loring, VA: Vinyard Software.

Miguel-Tobal, Juan J., Antonio Cano-Vindel, Hector Gonzalez-Ordi, Iciar Iruarrizaga, Sasha

Rudenstine, David Vlahov, and Sandro Galea. 2006. PTSD and Depression after the

Madrid March 11 Train Bombings. Journal of Traumatic Studies 19:69–80.

Pape, Robert A. 2006. Dying to Win: The Strategic Logic of Suicide Terrorism. New York:

Random House Trade Paperback.

Rosendorff, B. Peter and Todd Sandler. 2004. Too Much of a Good Thing? The Proactive

Dilemma. Journal of Conflict Resolution 48:657–71.

Sandler, Todd. 2005. Collective Versus Unilateral Responses to Terrorism. Public Choice

124:75–93.

Sandler, Todd, Daniel G. Arce, and Walter Enders. 2009. Transnational Terrorism. Pp. 516–62

in Global Crises, Global Solutions, Second Edition, edited by Bjorn Lomborg.

Cambridge, U.K.: Cambridge University Press.

Sandler, Todd and Walter Enders. 2004. An Economic Perspective on Transnational Terrorism.

European Journal of Political Economy 20:301–16.

Sandler, Todd and Harvey E. Lapan. 1988. The Calculus of Dissent: An Analysis of Terrorists’

Choice of Targets. Synthése 76:245–61.

Sandler, Todd and Kevin Siqueira. 2006. Global Terrorism: Deterrence Versus Pre-emption.

36

Canadian Journal of Economics 39:1370–87.

Siqueira, Kevin and Todd Sandler. 2007. Terrorist Backlash, Terrorism Mitigation, and Policy

Delegation. Journal of Public Economics 91:1800–15.

Treverton, Gregory F., Justin L. Adams, James Dertouzos, Arindam Dutta, Susan S.

Everingham, and Eric V. Larson. 2008. The Costs of Responding to the Terrorist

Threat: The U.S. Case. Pp. 48–80 in Terrorism, Economic Development, and Political

Openness, edited by Philip Keefer and Norman Loayza. Cambridge, U.K.: Cambridge

University Press.

United States House of Representatives. 2004. Stolen Passports: A Terrorist’s First Class

Ticket. Hearing Before the Committee on International Relations, Serial No. 108-117,

June 29. Washington, D.C.: U.S. House of Representatives.

United States Senate Judiciary Committee. 2007. Statement of Ronald K. Noble, Secretary

General of INTERPOL before the Subcommittee on Terrorism, Technology, and

Homeland Security. U.S. Senate, May 2. Washington, D.C.: U.S. Senate.

Wikipedia. 2009. September 11 Attacks.

http://en.wikipedia.org/wiki/September_11_attacks.

World Bank. 2008. World Development Indicators Online. http://www.worldbank.org.

Number of other proactive countries in group of six

0 1 2 3 4 5

country i is not proactive Nash

0 5 10 15 20 25

country i is proactive –3 2 7 12 17 Social optimum

22

a. Prisoner’s Dilemma, bi = 5 and ci = 8 Number of other proactive countries

0 1 2

country i is not proactive 0 11 22

country i is proactive 3 14 25

b. bi = 11 and ci = 8

Table 1. Alternative underlying games for INTERPOL member countries

Table 2. INTERPOL costs assigned to counterterrorism (in euros)

2006 2007 2008 Public Safety & Terrorism (PST) 342,914 536,679 340,930

I-24/7 Network 8,336,077 8,813,803 4,980,615a

Bioterrorism Prevention 686,396 1,102,427 844,905

Fugitive Investigative Service 215,772 181,409 183,608b

Command and Coordination Center (CCC) 1,176,123 1,511,513 1,462,773b

CCC Crisis Major Event 0 358 355,402b

Total counterterrorism expense in euros 10,757,282 12,146,189 8,168,233

Total counterterrorism expense in US dollarsc 13,506,843 16,647,566 12,013,837 INTERPOL operating expense in eurosd 46,991,000 47,222,000 54,621,000 aThe I-24/7 figure for 2008 only includes operating expense and does not include IT and other infrastructure expense. These support expenses are included for the external audited numbers for 2006 and 2007. bBased on year-to-date spending through October 2008 which was 83% of anticipated spending. Hence, the cumulative spending in October 2008 was divided by .83 to give this entry. cBased on the average exchange rate for relevant year, using Eurostat rates of 1.2556 $/euro for 2006, 1.3706 $/euro, and 1.4708 $/euro. dSource: INTERPOL (2008d, 2009).

Tab

le 3

. IN

TE

RPO

L te

rror

ist-

rela

ted

arre

st d

ata

for

2006

and

200

7

2006

2007

R

egio

n A

rres

ts/N

otic

es

Arr

ests

/Dif

fusi

ons

A

rres

ts/N

otic

es

Arr

ests

/Dif

fusi

ons

Afr

ica

2

Am

eric

as (

Nor

th &

Sou

th)

3

2

6

Asi

a Pa

cifi

c

3

Eur

ope

10

39

21

55

Nor

th A

fric

a/M

iddl

e E

ast

8

3

1

Unk

now

n 2

10

2

11

Tot

al

15

59

37

67

Sou

rce:

IN

TE

RP

OL

Gen

eral

Sec

reta

riat

Table 4. Number of casualties and number of terrorist operatives per incidents

Time interval Incidents considered

Nonterrorists injured per

incident

Nonterrorists killed per incident

Number of terrorists in attack force

1968–2007 All incidents 2.17 1.04 1.52 Incidents with no missing valuesa 2.21 1.05 6.13 All incidents with 9/11 2.65b 1.24c 2000–2007 All incidents 6.86 3.54 1.54 Incidents with no missing valuesa 7.36 3.69 4.76 All incidents plus 9/11 12.36b 5.92c 2006–2007 All incidents 3.73 2.63 1.63 Incidents with no missing valuesa 4.07 2.79 5.24

aFor the per-incident calculations, we excluded incidents where the relevant casualty count or the number of terrorists is missing. b Includes the estimated 6291 injuries from the four 9/11 hijackings. It makes almost no difference if incidents with missing values are excluded. c Includes 2729 deaths from the World Trade Center, the 52 deaths from United Airlines 93, and the 236 deaths from American Airlines 77. It makes almost no difference if incidents with missing values are excluded.

Tab

le 5

. Sc

enar

ios

for

bene

fit-

cost

cal

cula

tions

bas

ed o

n ar

rest

not

ices

and

dif

fusi

ons

20

06

20

07

Sce

nari

os

Few

er

Inci

dent

s Fe

wer

In

juri

es

Few

er

Dea

ths

Fe

wer

In

cide

nts

Few

er

Inju

ries

Fe

wer

D

eath

s 19

68–2

007

Sc

enar

io 1

(al

l inc

iden

ts)

74

161

77

10

4 2

26

108

Scen

ario

2 (

9/11

cas

ualti

es in

clud

ed)

74

196

92

10

4 2

76

129

Scen

ario

3 (

atta

ck f

orce

of

1.52

nee

ded)

a 49

10

6 5

1

68

150

7

1 Sc

enar

io 4

(at

tack

for

ce o

f 6.

13 n

eede

d)b

12

27

13

1

7

38

18

2000

–200

7

Scen

ario

5 (

all i

ncid

ents

) 74

50

8 26

2

104

713

36

8 Sc

enar

io 6

(9/

11 c

asua

lties

incl

uded

) 74

91

5 43

8

104

1285

61

6 Sc

enar

io 7

(at

tack

for

ce o

f 1.

54 n

eede

d)a

48

329

170

6

8 4

66

244

Scen

ario

8 (

atta

ck f

orce

of

4.76

nee

ded)

b 16

11

8 5

9

22

162

8

1 Sc

enar

io 9

(9/

11 a

nd a

ttack

for

ce o

f 4.

76)b

16

198

95

2

2 2

72

130

2006

–200

7

Scen

ario

10

(all

inci

dent

s)

74

276

195

10

4 3

88

274

Scen

ario

11

(atta

ck f

orce

of

1.63

nee

ded)

a 45

16

8 11

8

64

239

16

8 Sc

enar

io 1

2 (a

ttack

for

ce o

f 5.

24 n

eede

d)b

14

57

39

2

0

81

55

a The

num

ber

of a

rres

ts is

div

ided

by

the

aver

age

per-

inci

dent

num

ber

of te

rror

ists

in th

e at

tack

for

ce, w

ith th

e un

ders

tand

ing

that

this

is th

e nu

mbe

r of

arr

ests

req

uire

d to

red

uce

the

num

ber

of in

cide

nts

by o

ne.

b The

larg

er r

educ

tion

in in

cide

nts

refl

ects

the

larg

er p

er-i

ncid

ent n

umbe

r of

terr

oris

ts w

hen

only

inci

dent

s w

ith n

o m

issi

ng

valu

es f

or th

e at

tack

for

ce s

ize

are

incl

uded

. T

his

is a

ver

y co

nser

vativ

e m

easu

re o

f th

e re

quir

ed n

umbe

r of

terr

oris

ts

enga

ged

per

inci

dent

s, s

ince

a lo

t of

mis

sing

val

ues

are

likel

y to

be

one

to tw

o te

rror

ists

.

Tab

le 6

. D

istr

ibut

ion

of in

juri

es a

nd tr

aum

a in

a c

ompo

site

terr

oris

t atta

ck

P

hysi

cal I

njur

ies

P

sych

olog

ical

Tra

uma

H

eari

ng

Los

s Se

vere

B

urns

H

ead

Tra

uma

Eye

In

jury

R

espi

rato

ry

Impa

irm

ent

Frac

ture

s an

d/

or A

mpu

tatio

ns

PT

SD

c M

ajor

D

epre

ssio

n P

erce

nt o

f In

juri

esa

I

j 46

.9%

15

.4%

15

.0%

13

.0%

67

.0%

45

.0%

15.1

%

13.3

%

Dis

abil

ity

Wei

ghtb

D

j 0.

12

0.26

0.

35

0.11

0.

28

0.35

0.11

0.

76

a Sour

ce: A

benh

aim

, Dab

, and

Sal

mi (

1992

) b So

urce

: Mat

hers

, Lop

ez, a

nd M

urra

y (2

003)

c PT

SD s

tand

s fo

r po

st tr

aum

atic

str

ess

diso

rder

T

able

7.

Los

t GD

P du

e to

Tra

nsna

tiona

l Ter

rori

sm A

ttack

s: S

cena

rios

for

200

6 R

eal G

DP

a

His

tori

cal

Sce

nari

os

Sce

nari

o S

cena

rio

Sce

nari

o S

cena

rios

S

cena

rio

Sce

nari

o (M

illio

ns

Tib

Cos

t 1,

2, 5

, 6, 1

0 3

4 7

8, 9

11

12

C

ount

ry

of U

S $)

Mill

ions

of

Rea

l US

Dol

lars

c A

lban

ia

4,79

9 1

0.43

6 0.

206

0.16

2 0.

055

0.16

0 0.

071

0.15

3 0.

063

Alg

eria

68

,501

3

18.6

82

8.80

6 6.

935

2.36

0 6.

845

3.01

9 6.

568

2.69

6 A

rgen

tina

314,

833

2

57

.242

26

.980

21

.249

7.

231

20.9

74

9.25

1 20

.124

8.

262

Aus

tria

20

9,72

6 1

19.0

66

8.98

7 7.

078

2.40

8 6.

986

3.08

1 6.

703

2.75

2 A

zerb

aija

n 10

,835

1

0.98

5 0.

464

0.36

6 0.

124

0.36

1 0.

159

0.34

6 0.

142

Bah

rain

10

,017

1

0.91

1 0.

429

0.33

8 0.

115

0.33

4 0.

147

0.32

0 0.

131

Ban

glad

esh

61,7

73

1 5.

616

2.64

7 2.

085

0.70

9 2.

058

0.90

8 1.

974

0.81

1 B

elgi

um

251,

495

1 22

.863

10

.776

8.

487

2.88

8 8.

377

3.69

5 8.

038

3.30

0 B

oliv

ia

9,81

8 1

0.89

3 0.

421

0.33

1 0.

113

0.32

7 0.

144

0.31

4 0.

129

Bos

nia/

Her

z.

6,83

6 2

1.24

3 0.

586

0.46

1 0.

157

0.45

5 0.

201

0.43

7 0.

179

Bra

zil

741,

349

1 67

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31

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25

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8.

513

24.6

94

10.8

92

23.6

94

9.72

7 B

urun

di

802

2 0.

146

0.06

9 0.

054

0.01

8 0.

053

0.02

4 0.

051

0.02

1 C

ambo

dia

5,71

3 1

0.51

9 0.

245

0.19

3 0.

066

0.19

0 0.

084

0.18

3 0.

075

Cha

d 2,

570

1 0.

234

0.11

0 0.

087

0.03

0 0.

086

0.03

8 0.

082

0.03

4 C

hile

92

,750

1

8.43

2 3.

974

3.13

0 1.

065

3.09

0 1.

363

2.96

4 1.

217

Chi

na

1,92

8,69

2 1

175.

336

82.6

42

65.0

87

22.1

48

64.2

45

28.3

37

61.6

41

25.3

06

Col

ombi

a 10

0,45

4 2

18.2

64

8.60

9 6.

780

2.30

7 6.

692

2.95

2 6.

421

2.63

6 C

ongo

, Rep

3,

922

3 1.

070

0.50

4 0.

397

0.13

5 0.

392

0.17

3 0.

376

0.15

4 C

ote

d'Iv

orie

10

,350

1

0.94

1 0.

443

0.34

9 0.

119

0.34

5 0.

152

0.33

1 0.

136

Cyp

rus

10,9

44

1 0.

995

0.46

9 0.

369

0.12

6 0.

365

0.16

1 0.

350

0.14

4 C

zech

Rep

68

,569

1

6.23

4 2.

938

2.31

4 0.

787

2.28

4 1.

007

2.19

1 0.

900

Den

mar

k 17

1,22

9 1

15.5

66

7.33

7 5.

778

1.96

6 5.

704

2.51

6 5.

473

2.24

7 E

cuad

or

20,4

03

1 1.

855

0.87

4 0.

689

0.23

4 0.

680

0.30

0 0.

652

0.26

8

Egy

pt,

121,

831

5 55

.378

26

.102

20

.557

6.

995

20.2

91

8.95

0 19

.469

7.

993

Eri

trea

75

1 1

0.06

8 0.

032

0.02

5 0.

009

0.02

5 0.

011

0.02

4 0.

010

Eth

iopi

a 11

,223

1

1.02

0 0.

481

0.37

9 0.

129

0.37

4 0.

165

0.35

9 0.

147

Fran

ce

1,44

1,08

0 3

393.

022

185.

246

145.

894

49.6

45

144.

008

63.5

19

138.

172

56.7

25

Geo

rgia

4,

440

2 0.

807

0.38

1 0.

300

0.10

2 0.

296

0.13

0 0.

284

0.11

7 G

erm

any

1,98

1,75

0 3

540.

477

254.

747

200.

632

68.2

71

198.

038

87.3

50

190.

012

78.0

07

Gre

ece

178,

734

3 48

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22

.976

18

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6.

157

17.8

61

7.87

8 17

.137

7.

035

Gua

tem

ala

22,7

25

1 2.

066

0.97

4 0.

767

0.26

1 0.

757

0.33

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726

0.29

8 H

aiti

3,80

4 3

1.03

7 0.

489

0.38

5 0.

131

0.38

0 0.

168

0.36

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150

Indi

a 65

0,94

0 4

236.

705

111.

568

87.8

68

29.9

00

86.7

32

38.2

55

83.2

17

34.1

64

Indo

nesi

a 20

8,88

2 3

56.9

68

26.8

51

21.1

47

7.19

6 20

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9.

207

20.0

28

8.22

2 Ir

an

134,

639

2 24

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11

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9.

087

3.09

2 8.

970

3.95

6 8.

606

3.53

3 Ir

aq

18,1

62

5 8.

255

3.89

1 3.

065

1.04

3 3.

025

1.33

4 2.

902

1.19

2 Is

rael

13

4,74

5 4

48.9

98

23.0

95

18.1

89

6.18

9 17

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7.

919

17.2

26

7.07

2 It

aly

1,14

4,65

5 2

208.

119

98.0

94

77.2

56

26.2

89

76.2

57

33.6

35

73.1

67

30.0

38

Jord

an

11,3

76

4 4.

137

1.95

0 1.

536

0.52

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516

0.66

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454

0.59

7 K

enya

15

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1

1.38

8 0.

654

0.51

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175

0.50

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224

0.48

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200

Kuw

ait

50,4

50

2 9.

173

4.32

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405

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361

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225

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yrgy

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n 1,

672

1 0.

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056

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053

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eban

on

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521

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023

0.89

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941

0.79

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ibya

40

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2

7.36

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471

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930

2.69

9 1.

190

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063

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agas

car

4,36

5 1

0.39

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187

0.14

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050

0.14

5 0.

064

0.14

0 0.

057

Mal

aysi

a 11

2,34

1 2

20.4

26

9.62

7 7.

582

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484

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181

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8 M

aldi

ves

952

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auri

tani

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5 M

exic

o 64

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58.1

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oroc

co

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063

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ethe

rlan

ds

411,

545

1 37

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eria

61

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264

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183

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932

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6 N

orw

ay

187,

900

1 17

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8.

051

6.34

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158

6.25

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761

6.00

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465

Paki

stan

94

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42.8

66

20.2

04

15.9

12

5.41

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6.

928

15.0

70

6.18

7 Pe

ru

66,4

60

1 6.

042

2.84

8 2.

243

0.76

3 2.

214

0.97

6 2.

124

0.87

2 Ph

ilipp

ines

95

,130

2

17.2

96

8.15

2 6.

421

2.18

5 6.

338

2.79

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081

2.49

6 R

ussi

an F

ed

353,

493

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0.67

9 75

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59

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20

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58

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25

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56

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23

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Sa

udi A

rabi

a 22

4,47

1 3

61.2

19

28.8

55

22.7

25

7.73

3 22

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605

TO

TA

LS

$6,5

34.6

8 $3

,080

.040

$2

,425

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$8

25.4

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94.3

86

$1,0

56.1

10

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97.3

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$943

.150

a R

eal G

DP

equa

ls f

ive

year

ave

rage

of

GD

P, v

alue

d in

mill

ions

of

year

200

0 U

S do

llars

. IT

ER

AT

E li

sts

inci

dent

s fo

r C

uba,

the

Gaz

a St

rip,

Q

atar

, Som

alia

, and

Yug

osla

via.

Sin

ce W

orld

Ban

k re

al G

DP

data

is n

ot a

vaila

ble

for

thes

e ar

eas,

we

have

exc

lude

d th

em f

rom

the

calc

ulat

ions

. Fi

gure

s fo

r B

ahra

in, I

raq,

and

Kuw

ait a

re th

e th

ree-

year

ave

rage

s of

200

3-20

05.

b T i e

qual

s th

e nu

mbe

r of

yea

rs w

ith a

t lea

st o

ne te

rror

ist i

ncid

ent.

c A

ll sc

enar

ios

are

valu

ed in

mill

ions

of

year

200

0 U

S do

llars

.

Tab

le 8

. L

ost G

DP

due

to T

rans

natio

nal T

erro

rism

Atta

cks:

Sce

nari

os f

or 2

007

Rea

l GD

Pa

H

isto

rica

l S

cena

rios

S

cena

rio

Sce

nari

o S

cena

rios

S

cena

rio

Sce

nari

o (M

illio

ns

Tib

Cos

t 1,

2, 5

, 6, 1

0 3,

7

4 8,

9

11

12

Cou

ntry

of

US

$)

M

illio

ns o

f R

eal U

S D

olla

rsc

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ania

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799

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077

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eria

68

,501

3

18.6

82

9.81

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842

2.86

1 3.

543

7.56

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278

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entin

a 31

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57.2

42

30.0

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28

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ustr

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rael

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ya

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occo

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way

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stan

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K

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en

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1.69

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TO

TA

LS

$6,5

34.6

8 $3

,432

.357

$2

,742

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$1

,000

.807

$1

,239

.336

$2

,646

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$1

,146

.435

a R

eal G

DP

equa

ls f

ive

year

ave

rage

of

GD

P, v

alue

d in

mill

ions

of

year

200

0 U

S do

llars

. IT

ER

AT

E li

sts

inci

dent

s fo

r C

uba,

the

Gaz

a St

rip,

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ar, S

omal

ia, a

nd Y

ugos

lavi

a. S

ince

Wor

ld B

ank

real

GD

P da

ta is

not

ava

ilabl

e fo

r th

ese

area

s, w

e ha

ve e

xclu

ded

them

fro

m

the

calc

ulat

ions

. Fi

gure

s fo

r B

ahra

in, I

raq,

and

Kuw

ait a

re th

e th

ree-

year

ave

rage

s of

200

3-20

05.

b T i e

qual

s th

e nu

mbe

r of

yea

rs w

ith a

t lea

st o

ne te

rror

ist i

ncid

ent.

c A

ll sc

enar

ios

are

valu

ed in

mill

ions

of

year

200

0 U

S do

llars

.

Table 9. Benefit calculations based on arrest notices and diffusions (in millions of US dollars) 2006 2007

Scenarios GDP

Savings Injuries Benefits

Death Benefits

GDP Savings

Injuries Benefits

Death Benefits

Scenario 1 3080.0 183.4 154.0 3432.4 257.5 216.0 Scenario 2 3080.0 233.3 184.0 3432.4 314.4 258.0 Scenario 3 2425.8 120.8 102.0 2743.0 170.9 142.0 Scenario 4 825.4 30.8 26.0 1000.8 43.3 36.0 Scenario 5 3080.0 578.7 524.0 3432.4 812.3 736.0 Scenario 6 3080.0 1042.4 876.0 3432.4 1463.9 1232.0 Scenario 7 2394.4 374.8 340.0 2743.0 530.9 488.0 Scenario 8 1056.1 134.4 118.0 1239.3 184.6 162.0 Scenario 9 1056.1 225.6 190.0 1239.3 309.9 260.0 Scenario 10 3080.0 314.4 390.0 3432.4 442.0 548.0 Scenario 11 2297.3 191.4 236.0 2647.0 272.3 336.0 Scenario 12 943.2 64.9 78.0 1146.4 92.3 110.0 Note: See Table 4 for descriptions of the scenarios.

Table 10. INTERPOL counterterrorism benefits, costs, and benefit-cost ratios 2006 2007

Benefits Costs B/C B C Benefits Costs B/C B C Scenario 1 3417.4 13.5 253.0 25.0 3905.9 16.6 235.3 28.5 Scenario 2 3487.3 13.5 258.3 30.2 4004.4 16.6 241.2 34.5 Scenario 3 2648.6 13.5 196.2 16.5 3055.9 16.6 184.1 18.8 Scenario 4 882.2 13.5 65.3 4.2 1080.1 16.6 65.1 4.8 Scenario 5 4182.7 13.5 309.8 81.7 4976.3 16.6 299.8 93.3 Scenario 6 4998.4 13.5 370.3 142.1 6128.3 16.6 369.2 162.4 Scenario 7 3109.2 13.5 230.3 52.9 3761.9 16.6 226.6 61.4 Scenario 8 1308.5 13.5 96.9 18.7 1216.7 16.6 73.3 20.9 Scenario 9 1471.7 13.5 109.1 30.8 1809.2 16.6 109.0 34.3 Scenario 10 3784.4 13.5 280.3 52.2 4422.4 16.6 266.4 59.6 Scenario 11 2724.7 13.5 201.8 31.7 3255.3 16.6 196.1 36.6 Scenario 12 1086.1 13.5 80.5 10.6 1348.7 16.6 81.2 12.2 Notes: Benefits and costs are in millions of US dollars. B/C includes casualties and GDP benefits. B C includes just casualties’ benefits.

Table 11. Number of fraudulent passport intercepts at US entry points, Visa Waiver Countries

Country of Issuance US’s Own Database

Hits, January-June 2005a INTERPOL SLTD Hits

January-June 2008b Austria 5 35

Belgium 5 31 France 67 1 Italy 52 164 Germany 6 91 Ireland 3 53 Japan 29 48 Netherlands 9 65 Norway 4 23 Portugal 19 2 Singapore 24 n/a Slovenia 14 1 Spain 19 148 Sweden 2 117 Switzerland 2 36 United Kingdom 38 434 Total 298 1,249 a Source: General Accounting Office (2006) b Source: INTERPOL data