The Political Economy of Terrorism || Terrorized Economies

Terrorized EconomiesAuthor(s): Nicole V. Crain and W. Mark CrainSource: Public Choice, Vol. 128, No. 1/2, The Political Economy of Terrorism (Jul., 2006), pp.317-349Published by: SpringerStable URL: http://www.jstor.org/stable/30026647 .

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Public Choice (2006) 128:317-349 DOI 10.1007/s 11127-006-9056-6

ORIGINAL ARTICLE

Terrorized economies

Nicole V. Crain 0 W. Mark Crain

Received: 1 March 2005 / Accepted: 1 August 2005 C Springer Science + Business Media B.V. 2006

Abstract In this paper we estimate the macroeconomic consequences of terrorism using panel data for 147 countries for the period 1968-2002. The results reveal that the potential gains to a country from reducing terrorism are quite large, although the specific estimates depend on a country's population, base level of output, and investment. We present estimates of the impact of terrorism on GDP, GDP growth, investment, consumer spending, and tourism. These estimates of the marginal impact of terrorism provide a threshold against which a country's expenditures on anti-terrorism can be weighed.

Keywords Terrorism - International terrorism - Regional terrorism Homeland security Macroeconomic performance - Economic performance - Deterrence Public expenditure

The views and interpretations expressed in this paper are those of the authors and do not reflect the views and opinions of the National Defense University, the Department of Defense, or any other U.S. government agency.

1. Introduction

The four terrorist incidents on September 11, 2001 that killed 3,249 individuals and wounded 869 others on US soil accelerated academic research on terrorism and pushed the most sweeping reorganization in the US government in more than fifty years. Some researchers focus on patterns of terrorism (Enders & Sandler, 2002), the underlying causes of terrorism (Krueger & Maleckova, 2003), deterrence (Frey & Luechinger, 2003), or governmental

N. V. Crain Department of Economics, The National Defense University Industrial College of the Armed Forces, Washington, DC 20319, USA; Lafayette College, Easton, PA 18042, USA e-mail: [email protected]

W. M. Crain Department of Economics and Business, Lafayette College, Easton, PA 18042, USA e-mail: [email protected]

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318 Public Choice (2006) 128:317-349

responses to terrorism (Brown, Cummins, Lewis, & Wei, 2004; Mueller, 2004). Building further on the existing body of literature, other studies describe country, region-specific, or urban effects of terrorism (Abadie & Gareazabal, 2003; Fielding, 2003a,b; Konstantinos & Kutan, 2003; Eckstein & Tsiddon, 2004; Rossi-Hansberg, 2004).1 Blomberg, Hess, and Orphanides (2004) estimate the effects of terrorism on economic growth, and attempts to measure the costs of 9/11 are summarized in a report by the US Government Accounting Office (2002).

In this paper we estimate the macroeconomic consequences of terrorism using panel data for 147 countries for the period 1968-2002. We examine the levels of economic output, as opposed to growth rates in economic output, and include an estimate of the cost of unanticipated terrorist events. The analysis covers nearly 12,000 terrorist acts, and the results provide a foundation to compute the costs of terrorism and the benefits of anti-terrorism activities.2

To preview a few of the results, we find that the number of terrorist incidents and casualties from terrorist attacks have a substantial impact on economic performance. For example, if Germany deterred one terrorist incident (reducing its historically average rate from 19 to 18) the GDP gains would be $1.6 billion (in 2003 dollars). In the Philippines, a reduction from 9 to 8 incidents would increase its GDP by an estimated $122 million. As we describe in detail, these estimates of the GDP gains from deterrence depend on the extent of terrorism in a particular country because the impact of terrorism on GDP is not linear. These estimates of the economic gains from marginal reductions in terrorism provide a threshold against which a country's expenditures on anti-terrorism can be weighed.

The remainder of the paper is organized into four sections. Section 2 presents an overview of international terrorist activity: how we define and measure it, how the patterns have varied over the 35-year sample period, and the regional distribution of terrorism. Section 3 describes the empirical methodology and presents the results. The analysis drills down a bit further to seek estimates regarding if and by how much terrorism affects the largest components of GDP, namely capital investments and consumer spending. We also examine how much terrorism affects tourism, a sector that is presumably sensitive to terrorist activities.3 Section 4 applies the results by projecting the impact of terrorism in countries of varying sizes and economic circumstances. Section 5 summarizes the main findings and offers concluding comments.

2. Overview of global terrorist activities: 1968-2002

2.1. Defining and measuring terrorism

We examine the economic impact of international/transnational terrorism using the data series compiled by the ITERATE project, which is described in The Iterate 1968-2002 Data

1Krueger and Maleckova (2003) find little evidence for a link between poverty, education and terrorism

contradicting the notion - held by prominent observers - that providing hope for the future through aid

supporting educational reforms or youth employment opportunities can reduce terrorism. See also USAID, Foreign Aid in the National Interest, p. 106. Studies pre-dating 2001 include (Premo, 1981; Pion-Berlin, 1984; Zwick, 1984; Lopez, 1986; Enders & Sandler, 1991; Enders, Sandler & Cauley, 1990; Enders, Sandler & Parise, 1992; World Bank, 1994; Enders & Sandler, 2000).

2For the purposes of this paper, deterrence and anti-terror activities are broadly defined as activity leading to a reduction in the number of terrorist incidents and/or causalities. The efficacy of various methods employed to reduce terrorist activity is beyond the scope of this paper.

3See, for example, US GAO (2002), p. 26.

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Public Choice (2006) 128:317-349 319

Table 1 Global terrorist activity, 1968-2002

Year Number of incidents Total wounded Total killed Total casualties

Total 11,723 23,062 14,075 37,137 Mean per year 335 659 402 1,061

Codebook (Mickolus, Sandler, Murdock, & Flemming, 2003). Other sources of data for terrorist activities are available, but the ITERATE database contains the most comprehensive time-series, and these data have been used for different purposes in several academic studies (for examples, Sandler, 2000; Lai, 2003).4 The ITERATE project defines international/transnational terrorism as the use or threat of extra-normal violence for political purposes to influence the attitudes of a target group. Moreover, this target group is larger than the immediate victims, and the ramifications of the terrorists' actions transcend national boundaries.5

The ITERATE data series on terrorist activities starts in 1968, and our analysis concludes with the 2002 series.6 During this 35-year period, our sample includes 11,723 incidents of terrorism that resulted in 37,137 casualties (defined as the sum of individuals killed and wounded). Table 1 provides a broad overview of global terrorist activity during the period examined. Based on the sample means, the typical year saw 335 incidents, 659 individuals wounded, 402 individuals killed, and 1,061 casualties. The median values tell largely the same story, except in the case of deaths.7

Figure 1 plots the annual number of global terrorist incidents. A visual inspection of Figure 1 reveals no obvious 35-year trend in incidents, although incidents appear to be- gin a secular decline in the early 1990s. Regressing the natural log of incidents against a time trend variable (using the full panel of 147 countries, controlling for year dummy variables, fixed country effects, and first-order autocorrelation) yields a coefficient of - 0.042,

40Other data sources for terrorist activities include Pinkerton (available for various years), the US Depart- ment of State (annual editions), ICT at http://www.ict.org, and the Rand Terrorism Chronology 1968-1997 and RANDR-MIPT Terrorism Incident database (1998-Present). Jackman and Boyd (1979) examine the cost/benefit of using multiple data sources including the New York Times and Encyclopedia Britannica Year- book, concluding that using more than two sources yields little additional benefit. Krueger and Maleckova (2003) find a high correlation between the US Department of State data and the ITERATE data for the years that the two data series overlap.

5Specifically, "[I]nternational/transnational terrorism is the use, or threat of use, of anxiety-inducing, extra- normal violence for political purposes by any individual or group, whether acting for or in opposition to established governmental authority, when such action is intended to influence the attitudes and behavior of a target group wider than the immediate victims and when, through the nationality or foreign ties of its perpetrators, its location, the nature of its institutional or human victims, or the mechanics of its resolution, its ramifications transcend national boundaries. International terrorism is such action when carried out by individuals or groups controlled by a sovereign state, whereas transnational terrorism is carried out by basically autonomous non-state actors, whether or not they enjoy some degree of support from sympathetic states." (ITERATE Data Codebook, July 27, 2003, p. 2) The ITERATE definition differs only slightly from the US Department of State definition of terrorism; for example, ITERATE does not specify targets as noncombatant while the US Department of State does. The State Department definition of noncombatant, however, does not necessarily exclude either military personnel or military bases. Obviously, one can quibble over the best definition of terrorism. Some definitions of terrorism include categories defining either the purpose of the terrorist activity or the sponsor of the activity. 6The data through 1978 are available through ICPSR. The data from 1979 through 2002 are proprietary and available for purchase from Edward Mickolus.

7The sole change to the ITERATE data series was to update the US to include the US Department of State data for 2001 US casualties. The ITERATE data set did not include 9/11 casualty data.

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320 Public Choice (2006) 128:317-349

600

500

400

300

200

100

0

1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Fig. 1 Number of terrorist incidents, 1968-2002

which is significant at the five-percent confidence level. However, this estimate of roughly a 4 percent annual decline in terrorist incidents over the 35-year period is sensitive to the specification.8

Figure 2 plots the annual number of casualties (killed and wounded) that resulted from terrorist activities. Again, these data exhibit no obvious temporal pattern.9 The exceptional spike in 2001 clearly reflects the four incidents that occurred in the US on September 11 of that year.

The data reveal considerable year-to-year variation in the extent of terrorist activity. In 1991 the world experienced the largest number of incidents at 568, or 233 more incidents than the annual mean for the 35-year period, and 409 associated casualties. The fewest incidents occurred in 2001, but those 50 incidents generated the largest annual number of casualties at 4,577 - substantially more than the annual mean of 1,061. The single country that had the highest number of terrorist incidents was Germany with 181 incidents in 1993, and those incidents caused 75 casualties including eight deaths.

2.2. Regional distribution

By far the region with the most terrorist incidents during this time period was West Europe, with 28 percent of global incidents (or 3,362 incidents).10 The fewest terrorist incidents occurred in South Asia, with four percent of global incidents.

8For example, the coefficient on the time-trend variable is insignificant if we omit the year dummy variables. A random effects model yields a positive coefficient on the time-trend variable equal to 0.038, which is

significant at the five-percent level. The time-trend variable in the random effects model is insignificant without the year dummy variables. See also Enders and Sandler (2000), for a discussion and estimates of the decline in transnational terrorism.

9Regressing the natural log of casualties against a time-trend variable (using the full panel of 147 countries, controlling for year dummy variables, fixed country effects, and first-order autocorrelation) yields a coefficient of -0.008, but it is not significant at the five percent confidence level. Other specifications similarly indicated no significant time-trend in the casualty data. Enders and Sandler (2000) find that casualties per incident have increased relative to earlier periods, while incidents have declined.

loThe eight regional assignments were made following the Easterly and Sewadeh designations available at: http://www.worldbank.org/research/growth/GDNdata.htm. Note, again, that this data series is limited to international terrorism and that there may be a reporting bias in that LDCs and authoritarian countries may have incidents underreported.

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Public Choice (2006) 128:317-349 321

5000 4500

4000

3500

3000

2500

2000

1500

1000

500

0

1968 1970 1972 1974 1976 1978 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Fig. 2 Number of casualties from terrorist incidents, 1968-2002

Latin America and Caribbean 22%

East Asia and Pacific 8%

East Europe and Central Asia

5%

Middle East and North Africa 21% Sub Saraha Africa

6%

North Americal 6%

South Asia 4%

West Europe 28%

Fig. 3 Regional distribution of terrorist incidents (1968-2002)

3. Estimates of the economic impact of terrorism

We now turn to examine the relationships between these indicators of terrorist activities and country economic performance. We first estimate the effects of terrorism on a nation's output (GDP) and then the effects on its major components, capital investment and consumption expenditures. Finally, we examine the impact on international tourism expenditures.

3.1. Specification issues and variable descriptions

The basic model used to estimate the impact of terrorism on economic performance is specified in Equation (1).

GDPper Capitai,t = ~(Terrorist Activity)i,t + -(X)i,t

+ ai + zt + Ei,t (1)

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322 Public Choice (2006) 128:317-349

The sample used to estimate the parameters in Equation (1) is organized as a panel dataset that includes 147 countries for the 35-year period that starts in 1968 and ends in 2002. The variable subscripts i and t in Equation (1) denote an observation in country i (= 1, ..., 147) in year t (= 1968, .., 2002). The sample includes all countries for which the data are available on terrorist incidents and GDP per capita. As we clarify in more detail below, the exact sample of countries included in a specific regression model will depend on the set of variables included. That is, observations on some variables are missing for some countries in specific years and are therefore dropped from the panel dataset in those model estimations.

The dependent variable, GDP per Capita, is real GDP per capita, denominated in constant 1995 US dollars (source: World Bank, 2004). The main explanatory variables of interest are the measures of terrorist activity. We estimate Equation (1) using the four measures of terrorist activity from the ITERATE dataset that were described in Section 2 above: the number of terrorists incidents; the number of individuals killed in terrorist incidents; the number of individuals wounded in terrorist incidents; and the number of casualties from terrorist incidents. The number of casualties in terrorist incidents is the sum of individuals killed and wounded. These four terrorist activity variables are denominated in terms of a country's population, specifically per 100,000 population - for example, the number of incidents per 100,000 population or the number of individuals killed by terrorists per 100,000 population. This means that a terrorist incident in a country with 50 million residents (roughly the sample mean) has a larger variable value than an incident in a country with 288 million residents (the US).

We include a number of economic and demographic control variables, represented by the vector X in Equation (1) (source: World Bank, 2004). These control variables are drawn from the voluminous empirical literature that examines differences in economic levels across countries and over time (for surveys, see Hall & Jones, 1997; Barro & Sala-i-Martin, 1995; Barro, 1997). The set of controls included in X are the level of fixed capital investment per capita, country population, foreign trade as a share of GDP, primary school enrollment as a share of the eligible population, government expenditures as a share of GDP, domestic credit provided by the banking sector as a share of GDP, and communications infrastructure. We use the number of telephone mainlines per 1,000 people as a proxy for a country's communications infrastructure. Most of the models reported below include country fixed- effects (ai) variables and year fixed-effects (rt) variables. Finally, the natural logarithmic transformations of the variables are used to estimate Equation (1). Appendix A provides summary statistics for all the variables used in the analysis, and lists the countries included in the sample.

In the remainder of this section, we describe the statistical reliability of the findings - that is, the degree to which the models and specific coefficients are significant at acceptable confidence levels. In Section 4 that follows we concentrate on the economic significance of the estimates - that is, the magnitude of the economic consequences of terrorism.

3.2. Results for GDP per capita

As an initial look at the impact of terrorist activity on GDP per capita, Table 2 reports the estimated coefficients on the four terrorist activity variables when the control variables are omitted. All models reported in Table 2 include the year and country fixed-effects variables and make a correction for detected first-order autocorrelation. We report these results both as a baseline for comparing the subsequent results that add the full set of control variables, and because they provide our most inclusive sample of countries (= 147). When we

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Public Choice (2006) 128:317-349 323

add the control variables to the models, missing values for one or more of these variables reduce the sample size. In essence, the results in Table 2 use the largest possible sample size, but sacrifice overall explanatory power attributed to the absence of reasonable control variables.

Table 2 reports two models for each of the four variables measuring terrorist activity, the contemporaneous level and the level in the prior year. Two coefficients are statistically significant at the 5-percent level in Table 2, the number of (contemporaneous) incidents and the number of individuals killed in the prior year. As expected, the explanatory power of these initial models that omit the vector of control variables is generally low.

In a second set of preliminary models, we introduce a metric that reflects unanticipated levels of terrorist activity. Unanticipated levels are derived as the residual value from the amount of terrorist activity predicted by the time-series trend in each country." The intu- ition behind trying to capture unanticipated terrorist activities is that residents might come to expect some level of terrorist activity based on past trends. This expected level becomes an unfortunate, but routine part of daily life. Individuals adapt their behavior and take precau- tionary measures commensurate with the expected level based on the historical experience within a country. That is, individuals adjust their personal and work-related behavior in re- sponse to anticipated vulnerability. In this framework, unpredicted levels of terrorism would matter for further adjustments, not simply the levels anticipated and factored into current behavioral plans.

Table 3 reports the results for unanticipated terrorism variables. Two models are shown for each of the four terrorism indicators, one with the unanticipated level of terrorist activity alone, and one with both the unanticipated level and the lagged value of terrorist activity.

For the reason noted above, Table 3 lists the models initially estimated with the country fixed effects and year fixed-effects variables and without the set of control variables. The estimated coefficient for three of the four variables for unanticipated terrorist activity is significant at the one-percent or five-percent level of confidence. (The estimated coefficient for the unanticipated number killed is significant at the 10-percent level of confidence.) Three of the lagged variables for terrorist activity - incidents, casualties, and number killed - are significant at the five-percent or the one-percent confidence level.

Table 4 presents the parameter estimates using the full model shown in Equation (1), that is, when the seven economic and demographic control variables are included.

For comparison, column (1) lists the results of a model that only includes the seven control variables. Column (2) adds the two terrorism incidents variables (Unanticipated Incidents and Lagged Incidents). The coefficients on these variables are significant at the five-percent level. Importantly, the estimated economic impact of terrorist incidents triples once the full set of control variables is included in the analysis. The estimated coefficients for the other terrorism indicator variables are presented in columns (3), (4), and (5). The coefficient for the unanticipated number of wounded is significant at the ten-percent level, and its magnitude doubles compared to prior models that omitted the control variables.12

Table 5 presents a final look at the impact of terrorism on per capita GDP, in this case av- eraging each of the variables over five-year sub-periods. For example, period one is the mean GDP per capita for the years 1968-1972, period two is the mean for the years 1973-1978,

I I Specifically, we estimate the following time-series model for each of the 147 countries: In (Incidents per 100,000 population) t = Constant +/ (Time)t + ut, where the error term is the unanticipated level in year t for country i.

12For comparison, in Appendix B we present the results of estimating Equation (1) only using time-series data for the United States.

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324 Public Choice (2006) 128:317-349

Table

2 GDP

impact

of terrorism:

Preliminary

models

without

control

variables

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Incidents

per

100,000

-0.001

(2.50)*

pop.

(current

year)

Incidents

per

100,000

-0.000

(1.81)

pop.

(prior

year)

Casualties

per

100,000

-0.000

(1.91)

pop.

(current

year)

Casualties

per

100,000

-0.000

(1.63)

pop

(prior

year)

Wounded

per

100,000

-0.000

(1.53)

pop.

(current

year)

Wounded

per

100,000

-0.000

(1.25)

pop.

(prior

year)

Killed

per

100,000

pop.

-0.000

(1.02)

(current

year)

Killed

per

100,000

pop.

-0.001

(2.09)*

(prior

year)

Observations

4354

4249

4341

4236

4343

4238

4349

4244

Number

of countries

147

147

147

147

147

147

147

147

F stat

10.12

10.38

17.83

17.47

16.90

15.74

12.80

13.53

R-sq

within

0.08

0.08

0.13

0.12

0.12

0.11

0.10

0.10

R-sq

between

0.03

0.04

0.03

0.04

0.03

0.04

0.03

0.04

Dependent

variable

= natural

log

of GDP

per

capita

(in

1995

US$)

Absolute

value

of t statistics

in parentheses

Coefficients

for country

and

year

fixed

effects

not

shown

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

*Significant

at 5%

**Significant

at 1%

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Public Choice (2006) 128:317-349 325

Table

3 GDP

impact

of terrorism:

Preliminary

models

without

control

variables

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Unanticipated

incidents

per

-0.001

(2.39)*

-0.001

(3.71)**

100,000

pop.

(current

year)

Incidents

per

100,000

pop.

-0.001

(3.36)**

(prior

year)

Unanticipated

casualties

per

-0.001

(1.91)

-0.001

(2.74)**

100,000

pop.

(current

year)

Casualties

per

100,000

pop.

-0.001

(2.42)*

(prior

year)

Unanticipated

wounded

per

-0.000

(1.44)

-0.001

(2.09)*

100,000

pop.

(current

year)

Wounded

per

100,000

pop.

-0.001

(1.86)

(prior

year)

Unanticipated

killed

per

-0.000

(0.86)

-0.001

(1.90)

100,000

pop.

(prior

year)

Killed

per

100,000

pop.

(prior

-0.001

(2.67)**

year) Observations

4329

4224

3901

3804

3909

3811

4183

4080

Number

of countries

146

146

131

131

131

131

141

141

F stat

10.12

10.56

9.28

9.18

9.41

9.57

9.84

10.1

R-sq

within

0.08

0.08

0.08

0.08

0.08

0.08

0.08

0.08

R-sq

between

0.03

0.04

0.02

0.03

0.02

0.03

0.03

0.04

Dependent

variable

= natural

log

of GDP

per

capita

(in

1995

US$)

Absolute

value

of t statistics

in parentheses

Coefficients

for country

and

year

fixed

effects

not

shown

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

*Significant

at 5%

**Significant

at 1%

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326 Public Choice (2006) 128:317-349

Table

4 GDP

impact

of terrorism:

Models

include

the

economic

and

demographic

control

variables

(1)

(2)

(3)

(4)

(5)

Unanticipated

incidents

per

100,000

pop.

(current

year)

-0.003

(2.21)*

Incidents

per

100,000

pop.

(prior

year)

-0.003

(2.20)*

Unanticipated

casualties

per

100,000

pop.

(current

year)

-0.002

(1.43)

Casualties

per

100,000

pop.

(prior

year)

-0.001

(1.07)

Unanticipated

wounded

per

100,000

pop.

(current

year)

-0.002

(1.87)

Wounded

per

100,000

pop.

(prior

year)

-0.002

(1.30)

Unanticipated

killed

per

100,000

pop.

(current

year)

-0.001

(0.97)

Killed

per

100,000

pop.

(prior

year)

-0.000

(0.12)

Fixed

capital

per

capita

0.235

(18.10)**

0.244

(16.65)**

0.250

(16.78)**

0.248

(16.74)**

0.237

(18.25)**

Population

-0.106

(4.98)**

0.148

(11.01)**

0.130

(9.24)**

0.130

(9.26)**

-0.115

(5.32)**

Foreign

trade

0.043

(1.81)

0.184

(7.59)**

0.176

(7.03)**

0.177

(7.08)**

0.042

(1.79)

Education

level

0.071

(1.61)

0.287

(6.16)**

0.356

(7.15)**

0.356

(7.15)**

0.070

(1.59)

Government

expenditure

(% of GDP)

-0.123

(5.03)**

-0.078

(2.84)**

-0.090

(3.22)**

-0.088

(3.15)**

-0.126

(5.18)**

Credit

0.006

(0.56)

0.021

(1.64)

0.022

(1.64)

0.021

(1.58)

0.013

(1.10)

Telephone

mainlines

0.355

(26.64)**

0.353

(23.44)**

0.350

(22.97)**

0.351

(23.05)**

0.352

(26.47)**

Observations

830

830

790

790

824

Number

of countries

99

99

91

91

97

F stat

3058.49

2281.52

2193.80

2206.00

2789.36

R-sq

within

0.99

0.99

0.99

0.99

0.99

R-sq

between

0.90

0.88

0.89

0.89

0.90

R-sq

overall

0.90

0.87

0.88

0.88

0.89

Dependent

variable

= natural

Log

of GDP

per

capita

(in

1995

US$)

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

Absolute

value

of t statistics

in parentheses

Coefficients

for country

and

year

fixed

effects

not

shown

*Significant

at 5%

**Significant

at 1%

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Public Choice (2006) 128:317-349 327

and so on through the 1998-2002 period. Each terrorism variable and the economic and demographic control variables are likewise averaged over these seven, five-year sub-periods. The purpose here is to check the sensitivity of the parameter estimates by smoothing the short-term, year-to-year fluctuations. The models in Table 5 include the full set of control variables, and we report the coefficients from fixed-effects and random-effects models.

All of the coefficients for the terrorist activity variables are significant at the one-percent or five-percent confidence level in both the fixed effects and the random effects models. In essence, the results using the panel data organized as five-year averages indicate a clear, negative relationship between terrorist activities and per capita GDP.

The results of the various specifications reported in Tables 2-5 all focus on the relationships between terrorist activities and total GDP per capita in a country. Total GDP, of course, includes both private sector and public sector output. We also estimated identical models using only private sector GDP per capita as the dependent variable. The purpose of examining only private sector GDP is to investigate whether some of the negative impact of terrorism might be offset by increased government spending - for example, on deterrence-related programs, or on programs that provided relief to the victims of terrorism.

The results of estimating the impact of terrorism on private sector GDP are provided in Appendix C. The findings (for both the annual panel-data sample and the five-year averaged sample) are quite similar to the models that use total GDP. One possible explanation is simply that terrorism has adverse effects that are roughly comparable on activities performed by the public and private sectors. An alternative conjecture is that government responses to terrorism involve a reallocation of public sector resources, some of which are diverted away from government programs that hinder overall economic performance. The alternative policy responses to terrorism clearly merits further investigation, but this issue goes beyond the scope of this paper.13

As a final methodological note on the GDP impact of terrorism, the model specified in Equation (1) examines the levels of economic output, as opposed to growth rates in economic output. Our preference for the model based on levels derives from its straightforward neoclassical underpinnings. Put simply, we attempt to model the negative effects of terrorism as a downward shift in the aggregate production function. This stands in contrast to a model where the negative effects result in a permanent change in a country's rate of growth. Nonetheless, for the sake of comparison we present in Appendix D the results of various models that estimate the impact of terrorism on the growth rate in GDP per capita. One justification for the specifications that examine changes in GDP per capita - one that remains within the neoclassical framework - is that the observed changes reflect an economy's transitory adjustments to a new growth path, and not permanent changes in the growth rate. In the growth rate models reported in Appendix D the number of incidents is significant at the five-percent confidence level in the total GDP growth model, and at the ten-percent confidence level in the private sector GDP growth model.

3.3. The impact on investment and consumption

The analysis next turns to a slightly different question: how does terrorism affect a country's economic activity? We explore two potential and perhaps obvious avenues, capital investment and consumption. For example, the risk of terrorism is likely to affect the expected rate of return on capital and thereby deter the flow of financial capital into new investments. These

13Mueller (2004) addresses the related issue of how governments respond to terrorism in a constitutional perspective.

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328 Public Choice (2006) 128:317-349

Table

5 GDP

impact

of terrorism:

Five-year

averaged

data

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Incidents

per

100,000

pop.

-0.007

(2.54)*

-0.009

(2.63)**

Casualties

per

100,000

pop.

-0.004

(2.13)*

-0.007

(2.82)**

Wounded

per

100,000

pop.

-0.004

(2.21)*

-0.008

(2.95)**

Killed

per

100,000

pop.

-0.004

(2.09)*

-0.007

(2.73)**

Observations

498

498

498

498

498

498

498

498

Number

of countries

107

107

107

107

107

107

107

107

F stat

69

68

68

68

Wald

Chi-sq

1665

1667

1665

1668

R-sq

within

0.72

0.62

0.72

0.62

0.72

0.62

0.72

0.62

R-sq

between

0.36

0.92

0.35

0.92

0.36

0.92

0.36

0.92

R-sq

overall

0.40

0.94

0.40

0.93

0.40

0.94

0.40

0.93

Model

Type

FE

RE

FE

RE

FE

RE

FE

RE

Dependent

variable

= natural

log

of GDP

per

capita

(in

1995

US$)

FE

= fixed-effects

model

RE

= random-effects

model

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

Absolute

value

of t statistics

in parentheses

Models

include

the

economic

and

demographic

control

variables

Coefficients

for

control

variables

and

period

fixed

effects

not

shown

* Significant

at 5%

**Significant

at 1%

Springer



Public Choice (2006) 128:317-349 329

risks might come either in the form of direct destruction of capital assets - from say a bomb attack - or indirectly in the form of a reduced product demand. Either way, a reduction in the expected rate of return on capital in a country would cause financial assets to flow to other countries, or into alternatives other than productive, fixed capital investments. The impact of terrorism on fixed capital investment per capita (denominated in 1995 US dollars) is shown in Table 6 using models that include the variables measuring unanticipated terrorist activity and the lagged values of terrorist activity.

The results in Table 6 indicate that only the coefficient on the unanticipated level of incidents is significant at the five-percent level of confidence.

Table 7 presents the models using final consumption expenditures per capita (denominated in 1995 US dollars) as the dependent variable.

The coefficients for the unanticipated number of incidents are significant at the five percent confidence level, and the unanticipated number of wounded is significant at the one percent confidence level. The coefficients for unanticipated casualties, and lagged incidents, casualties, and wounded are significant at the ten-percent confidence level.

3.4. The impact on international tourism expenditures

As a final look at the impact of terrorism on economic activity we look at international tourism expenditures, a sector that presumably would be highly sensitive to terrorist activity. These results are reported in Table 8. The only terrorism coefficient that is significant at the five-percent confidence level is for the unanticipated number of incidents and, as might be expected, its estimated impact is substantial.

4. Illustrating the economic impact: The gains from deterring terrorism

This section uses the empirical findings to gauge the positive impact on GDP and capital investment from reducing terrorism. Of course, the results from the empirical analysis are symmetrical; they can be applied just as readily to project the expected reduction in a nation's economy from terrorist events. It is important to emphasize that the projected impact on GDP and capital investment in this section are converted into 2003 US dollars.

The effects on GDP and capital investment from reductions in terrorism are shown in Tables 9 through 12 below, using the parameter estimates presented in the previous section. Table 9A illustrates the percentage and dollar gains from a one-incident reduction in terrorism for eight country sizes. Here the projections apply to countries with a per capita GDP of $3,212, which equals the median GDP per capita in the sample. For example, Table 9A shows that if a country's population is 250 million, a reduction from three incidents (the sample mean) to two incidents results in a GDP gain of $2.9 billion.14 In a country whose population is one billion, the same one-incident decrease from three to two incidents yields an $11.7 billion GDP gain. Not surprisingly, the total GDP gain from a one-incident reduction in terrorism increases with population. The median country population in the sample is about 10 million, and a one-incident decrease to two incidents yields a $120 million gain in a country of this size. Note that in all three cases this reduction (from three incidents to two) results in a real GDP gain of 0.36 percent.

14The dollar estimates shown in Tables 9 through 12 are rounded for ease of presentation.

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330 Public Choice (2006) 128:317-349

Table

6 Fixed

capital

investment:

impact

of terrorism

(1)

(2)

(3)

(4)

Unanticipated

incidents

per

100,000

pop.

(current

year)

-0.006

(2.86)**

Incidents

per

100,000

pop.

(prior

year)

-0.000

(0.29)

Unanticipated

casualties

per

100,000

pop.

(current

year)

-0.000

(0.12)

Casualties

per

100,000

pop.

(prior

year)

0.001

(0.50)

Unanticipated

wounded

per

100,000

pop.

(current

year)

-0.001

(0.55)

Wounded

per

100,000

pop.

(prior

year)

-0.003

(1.23)

Unanticipated

killed

per

100,000

pop.

(current

year)

-0.000

(0.12)

Killed

per

100,000

pop.

(prior

year)

0.002

(0.69)

Observations

3024

2869

2855

2956

Number

of countries

123

114

114

120

F stat

3.22

3.36

3.30

3.53

R-sq

within

0.04

0.04

0.04

0.04

R-sq

between

0.07

0.07

0.08

0.07

Dependent

variable

= natural

log

of fixed

capital

investment

per

capita

(in 1995

US$)

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

Absolute

value

of t statistics

in parentheses

Coefficients

for country

and

year

fixed

effects

not

shown

*Significant

at 5%

**Significant

at 1%

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Public Choice (2006) 128:317-349 331

Table

7 Final

consumption

expenditures:

Impact

of terrorism

(1)

(2)

(3)

(4)

Unanticipated

incidents

per

100,000

pop.

(current

year)

-0.003

(2.17)*

Incidents

per

100,000

pop.

(prior

year)

-0.002

(1.65)

Unanticipated

casualties

per

100,000

pop.

(current

year)

-0.002

(1.72)

Casualties

per

100,000

pop.

(prior

year)

-0.001

(1.01)

Unanticipated

wounded

per

100,000

pop.

(current

year)

-0.004

(2.84)**

Wounded

per

100,000

pop.

(prior

year)

-0.002

(1.81)

Unanticipated

killed

per

100,000

pop.

(current

year)

-0.001

(0.73)

Killed

per

100,000

pop.

(prior

year)

0.001

(1.27)

Observations

739

699

699

733

Number

of countries

88

80

80

86

F stat

1849

1932

1938.76

2345

R-sq

within

0.98

0.99

0.99

0.99

R-sq

between

0.87

0.89

0.89

0.91

R-sq

overall

0.86

0.88

0.88

0.90

Dependent

variable

= natural

log

of final

consumption

expenditures

per

capita

(in

1995

US$)

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

Absolute

value

of t statistics

in parentheses

Control

variables

include:

fixed

capital,

population,

trade,

education,

government

expenditures,

credit,

and

telephone

mainlines

Coefficients

for

control

variables,

country

and

fixed

year

effects

not

shown

*Significant

at 5%

**Significant

at 1%

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332 Public Choice (2006) 128:317-349

Table

8 International

tourism

expenditures:

impact

of terrorism

(1)

(2)

(3)

(4)

(5)

Unanticipated

incidents

per

100,000

pop.

(current

year)

-0.011

(2.79)**

Incidents

per

100,000

pop.

(prior

year)

-0.002

(0.46)

Unanticipated

casualties

per

100,000

pop.

(current

year)

0.004

(1.01)

Casualties

per

100,000

pop.

(prior

year)

-0.003

(0.79)

Unanticipated

wounded

per

100,000

pop.

(current

year)

-0.001

(0.32)

Wounded

per

100,000

pop.

(prior

year)

-0.004

(0.90)

Unanticipated

killed

per

100,000

pop.

(current

year)

0.001

(0.23)

Killed

per

100,000

pop.

(prior

year)

-0.003

(0.67)

Population

-0.232

(5.86)**

-0.235

(5.95)**

-0.186

(4.60)**

-0.188

(4.72)**

-0.230

(5.68)**

Foreign

trade

0.461

(6.23)**

0.468

(6.34)**

0.526

(6.84)**

0.529

(6.91)**

0.459

(6.09)**

Education

level

0.236

(1.76)

0.241

(1.80)

0.069

(0.47)

0.080

(0.55)

0.216

(1.55)

Telephone

mainlines

0.584

(13.41)**

0.587

(13.48)**

0.592

(13.77)**

0.593

(13.87)**

0.585

(13.26)**

Observations

1223

1223

1093

1102

1196

Number

of countries

128

128

114

115

125

F stat

56.87

51.49

56.73

58.43

49.99

R-sq

within

0.46

0.46

0.52

0.52

0.46

R-sq

between

0.78

0.78

0.79

0.79

0.78

R-sq

overall

0.74

0.75

0.76

0.76

0.74

Dependent

variable

= natural

log

of international

tourism

expenditures

per

capita

(in

1995

US$)

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

Absolute

value

of t statistics

in parentheses

Coefficients

for country

and

year

fixed-effects

not

shown

*Significant

at 5%

**Significant

at 1%

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Public Choice (2006) 128:317-349 333

Table

9 GDP

impact

of deterrence:

number

of terrorist

incidents

Reduce

number

of terrorist

incidents

from:

2 to 1

3 to 2

4 to 3

5 to 4

6 to 5

7 to 6

8 to 7

9 to 8

10 to 9

Percentage

gain

in real

GDP:

0.62%

0.36%

0.26%

0.20%

0.16%

0.14%

0.12%

0.11%

0.09%

Population

Total

GDP

(Billion,

2003

US$)

$Gain

in total

GDP

(Billion,

2003

US$)

Scenario

A: Assumes

a GDP

per

capita

of $3,212

(Median

GDP

PC

in the

sample

converted

into

2003

US

$)

1 Billion

3,212

20.04

11.72

8.32

6.45

5.27

4.46

3.86

3.40

3.05

250

Million

803

5.01

2.93

2.08

1.61

1.32

1.11

0.97

0.85

0.76

100

Million

321

2.00

1.17

0.83

0.65

0.53

0.45

0.39

0.34

0.30

50 Million

161

1.00

0.59

0.42

0.32

0.26

0.22

0.19

0.17

0.15

25 Million

80

0.50

0.29

0.21

0.16

0.13

0.11

0.10

0.09

0.08

10 Million

32

0.20

0.12

0.08

0.06

0.05

0.04

0.04

0.03

0.03

5 Million

16

0.10

0.06

0.04

0.03

0.03

0.02

0.02

0.02

0.02

1 Million

3

0.02

0.01

0.01

0.01

0.01

0.00

0.00

0.00

0.00

Scenario

B: Assumes

a GDP

per

capita

of $10,144

(Mean

GDP

PC

in the

sample

converted

into

2003

US$)

250

Million

2,536

15.82

9.25

6.57

5.09

4.16

3.52

3.05

2.69

2.40

100

Million

1,014

6.33

3.70

2.63

2.04

1.66

1.41

1.22

1.08

0.96

50 Million

507

3.16

1.85

1.31

1.02

0.83

0.70

0.61

0.54

0.48

25 Million

254

1.58

0.93

0.66

0.51

0.42

0.35

0.30

0.27

0.24

10 Million

101

0.63

0.37

0.26

0.20

0.17

0.14

0.12

0.11

0.10

5 Million

51

0.32

0.19

0.13

0.10

0.08

0.07

0.06

0.05

0.05

1 Million

10

0.06

0.04

0.03

0.02

0.02

0.01

0.01

0.01

0.01

(Continued

on next

page)

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334 Public Choice (2006) 128:317-349

Table

9 (Continued)

Reduce

number

of terrorist

incidents

from:

2 to 1

3 to 2

4 to 3

5 to 4

6 to 5

7 to 6

8 to 7

9 to 8

10 to 9

Percentage

gain

in real

GDP:

0.62%

0.36%

0.26%

0.20%

0.16%

0.14%

0.12%

0.11%

0.09%

Population

Total

GDP

(Billion,

2003

US$)

$Gain

in total

GDP

(Billion,

2003

US$)

Scenario

C: Assumes

a GDP

per

capita

of $35,000

250

Million

8,750

54.6

31.9

22.7

17.6

14.4

12.1

10.5

9.3

8.3

100

Million

3,500

21.8

12.8

9.06

7.03

5.7

4.9

4.2

3.7

3.3

50 Million

1,750

10.9

6.4

4.5

3.5

2.9

2.4

2.1

1.9

1.7

25 Million

875

5.5

3.2

2.3

1.8

1.4

1.2

1.1

0.93

0.83

10 Million

350

2.2

1.3

0.91

0.70

0.57

0.49

0.42

0.37

0.33

5 Million

175

1.1

0.64

0.45

0.35

0.29

0.24

0.21

0.19

0.17

1 Million

35

0.22

0.13

0.09

0.07

0.06

0.05

0.04

0.04

0.03

Uses

parameter

estimate

for

incidents

= -0.009,

shown

in Table

5, column

(2)

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Public Choice (2006) 128:317-349 335

Table 10 International terrorism, selected countries

Reduce incidents Increase incidents Gain/loss in

Pop (mil.) per year from: per year from: GDP (mil.)*

Argentina 38 11 to 10 10 to 11 $257 Colombia 44 13 to 12 12 to 13 $87 Egypt 66 5 to 4 4 to 5 $221 France 59 18 to 17 17 to 18 $1,161 Germany 82 19 to 18 18 to 19 $1,605 India 1,048 5 to 4 4 to 5 $1,132 Indonesia 212 2 to 1 1 to 2 $1,533 Italy 58 11 to 10 10 to 11 $1,298 Philippines 80 9 to 8 8 to 9 $122

Spain 41 1 to 0 0 to 1 $92,000 UK 59 18 to 17 17 to 18 $828 US 288 3 to 2 2 to 3 $40,626 World To zero for 2002 From zero to 2002 total $3,600,000

*The gain/loss column represents negative numbers if incidents are increasing GDP gains from deterring incident(s) or GDP losses from additional incident(s)

The projections in Tables 9B and 9C are identical to Table 9A except that these tables compute the impact using higher values for a nation's per capita GDP. The estimates in Table 9B use GDP per capita equal to the sample mean (= $10,144). Naturally, as a country's GDP per capita increases, the gains from deterring terrorist activity will increase. For example, with a population of 250 million, the GDP gain from reducing incidents from three to two rises to $9.2 billion. The sample population mean is about 50 million, in which case a reduction in incidents from three to two yields a gain of $1.8 billion (again, assuming a per capita GDP of $10,144).

Table 9C outlines the consequences of reducing terrorist incidents in countries whose GDP per capita is relatively high, in this case $35,000 or roughly that of France, Ireland, the US, and Singapore. A one incident decline from three to two incidents results in GDP gains of $31.9 billion in a country with 250 million people, and $6.4 billion in a country with 50 million people.

As a final note, the GDP gains from reducing terrorist incidents by one incident in Tables 9A, 9B, and 9C are not linear. For example, a reduction from ten to nine terrorist incidents does not yield the same gain as a reduction from two to one incident.

Table 10 provides estimates of the economic gains from reducing one terrorist incident in 12 selected countries. Table 10 also provides an aggregate estimate for the world, which equals $3.6 trillion (in 2003 dollars).

Tables 11A, 11B, and 11C analyze the impact of casualties from terrorist activity rather than incidents, but in all other material respects the set up for the analysis is identical to Tables 9A, 9B, and 9C. Table 11A depicts the incremental gains from a 10-person reduction in casualties from 95 to 85, from 85 to 75, and so on. Here the analysis uses a GDP per capita equal to the sample median of $3,212 per capita in 2003 US dollars. Reductions from 25 to 15 casualties, a range roughly equal to the sample mean, results in an $11.5 billion GDP gain for a country of one billion. The same casualty reduction yields a $2.9 billion gain if the population is 250 million, or $570 million for a country with 50 million inhabitants.

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336 Public Choice (2006) 128:317-349

Using the sample mean for GDP per capita, $10,144, raises the gains as shown in Table 11B. In a country whose population is 250 million, a reduction from 25 to 15 casualties increases GDP by $9.1 billion. With a population of 50 million, this reduction increases GDP by $1.8 billion.

Table 11C illustrates the consequences of decreasing terrorist casualties in countries whose GDP per capita is $35,000. In a country with a population of 250 million, reducing casualties from 25 to 15 produces a gain of $31.3 billion; for a population of 50 million, the projected gain is $6.3 billion.

Tables 12A and 12B illustrate the impact of deterrence on fixed capital investment. If individuals can predict a level of terrorist incidents, then behavioral adjustments are possible. Those incidents not predicted by temporal patterns (the residual) significantly affect the investment in fixed capital. The differences in Tables 12A and 12B result from varying the assumption about the baseline level of fixed capital investment. As was the case with GDP in the previous tables, the projected increases in fixed capital investment expand in countries with a higher base level of investments.

Table 12A uses the sample mean of fixed investment per capita $2,143. For a country with a population of 1 billion residents, the analysis projects a $5.2 billion increase in fixed capital investment as a result of an unanticipated reduction in terrorist incidents from three to two. The models project a $1.3 billion increase in capital investment if the population is 250 million, and a $300 million increase in capital investment in a country of 50 million.

Not surprisingly, countries with a larger base level of fixed capital investments per capita reap larger benefits from an unanticipated decline in incidents, as illustrated in Table 12B. If per capita fixed capital is $8,000, or roughly the US level in 2003, and population is 250 million, the gain from deterring one unanticipated act and thus experiencing only 2 incidents in a given year is $4.9 billion. And if population is 50 million, a decline of one unanticipated incident from three to two leads to an estimated $1 billion increase in fixed capital investment.

5. Concluding comments

Following the events of September 11, 2001 the risks associated with terrorism permeate practically every discussion of global economic forecasts, to say nothing about the policy deliberations over preventive measures designed to deter terrorist threats. This paper informs these discussions by examining the historical experience for a large number of countries over a 35-year period. We use observations on nearly 12,000 terrorist acts to examine the impact of terrorism on GDP, fixed capital investment, consumption expenditures, and tourism.

The results reveal that the potential gains to a country from reducing terrorism are quite large, although the specific estimates depend on a country's population, base level of output, and investment. We illustrate the size of these effects by computing the benefits of a one-incident reduction in terrorist incidents and a ten-unit reduction in casualties. Of course, the parameter estimates can be applied to myriad country circumstances. In the US, for example, a reduction in incidents from three to two leads to gains in GDP of roughly $40 billion. Most striking is the estimated world cost of terrorism, or the benefit of elim- inating all international terrorism. If there were no terrorist incidents in 2002, world GDP would have been $3.6 trillion higher than it was that year. That is, terrorism cost the world

SSpringer



Public Choice (2006) 128:317-349 337

Table

11

GDP

Impact

of deterrence:

number

of casualties

from

terrorist

incidents Reduce

number

of casualties

from

terrorist

incidents

from:

15 to 5

25 to 15

35 to 25

45 to 35

55 to 45

65 to 55

75 to 65

85 to 75

95 to 85

Percentage

gain

real

GDP

0.77%

0.36%

0.24%

0.18%

0.14%

0.12%

0.10%

0.09%

0.08%

Population

Total

GDP

(Billion,

2003

US$)

$Gain

in total

GDP

(Billion,

2003

US

$)

Scenario

A: Assumes

a GDP

per

capita

of $3,212

(Median

GDP

PC

in the

sample

converted

into

2003

US

$)

1 Billion

3,212

24.7

11.5

7.6

5.7

4.5

3.8

3.2

2.8

2.5

250

Million

803

6.2

2.9

1.9

1.4

1.1

0.9

0.8

0.7

0.6

100

Million

321

2.5

1.1

0.8

0.6

0.5

0.4

0.3

0.3

0.3

50 Million

161

1.24

0.57

0.38

0.28

0.23

0.19

0.16

0.14

0.13

25 Million

80

0.62

0.29

0.19

0.14

0.11

0.09

0.08

0.07

0.06

10 Million

32

0.25

0.12

0.08

0.06

0.05

0.04

0.03

0.03

0.03

5 Million

16

0.12

0.06

0.04

0.03

0.02

0.02

0.02

0.01

0.01

1 Million

3

0.025

0.011

0.008

0.006

0.005

0.004

0.003

0.003

0.003

Scenario

B: Assumes

a GDP

per

capita

of $10,144

(Mean

GDP

PC

in the

sample

converted

into

2003

US$)

250

Million

2,536

19.5

9.1

6.0

4.5

3.6

3.0

2.5

2.2

2.0

100

Million

1,014

7.8

3.6

2.4

1.8

1.4

1.2

1.0

0.9

0.8

50 Million

507

3.9

1.8

1.2

0.9

0.7

0.6

0.5

0.4

0.4

25 Million

254

2.0

0.9

0.6

0.4

0.4

0.3

0.3

0.2

0.2

10 Million

101

0.8

0.4

0.2

0.18

0.14

0.12

0.10

0.09

0.08

5 Million

51

0.39

0.18

0.12

0.09

0.07

0.06

0.05

0.04

0.04

1 Million

10

0.078

0.036

0.024

0.018

0.014

0.012

0.010

0.009

0.008

(Continued

on next

page)

Springer



338 Public Choice (2006) 128:317-349

Table

11

(Continued)

Reduce

number

of casualties

from

terrorist

incidents

from:

15 to 5

25 to 15

35 to 25

45 to 35

55 to 45

65 to 55

75 to 65

85 to 75

95 to 85

Percentage

gain

real

GDP

0.77%

0.36%

0.24%

0.18%

0.14%

0.12%

0.10%

0.09%

0.08%

Population

Total

GDP

(Billion,

2003

US$)

$Gain

in total

GDP

(Billion,

2003

US

$)

Scenario

C: Assumes

a GDP

per

capita

of $35,000

250

Million

8,750

67.3

31.3

20.6

15.4

12.3

10.2

8.8

7.7

6.8

100

Million

3,500

26.9

12.5

8.2

6.2

4.9

4.1

3.5

3.1

2.7

50 Million

1,750

13.5

6.3

4.1

3.1

2.5

2.1

1.6

1.5

1.4

25 Million

875

6.7

3.1

2.1

1.5

1.2

1.0

0.9

0.8

0.7

10 Million

350

2.70

1.25

0.82

0.62

0.49

0.41

0.35

0.31

= 0.27

5 Million

175

1.35

0.63

0.41

0.31

0.25

0.21

0.18

0.15

0.14

1 Million

35

0.269

0.125

0.082

0.062

0.049

0.041

0.035

0.031

0.027

Uses

parameter

estimate

for

incidents

= -0.007,

shown

in Table

5, column

(4)

Springer



Public Choice (2006) 128:317-349 339

Table

12

Fixed

capital

investment

impact

of deterrence:

unanticipated

number

of terrorist

incidents

Unanticipated

reduction

in number

of terrorist

incidents

from:

2to

1

3 to 2

4 to 3

5 to 4

6 to 5

7 to 6

8 to 7

9 to 8

10 to 9

Percentage

gain

in real

fixed

capital

investment

0.42%

0.24%

0.17%

0.13%

0.11%

0.09%

0.08%

0.07%

0.06%

Population

Total

fixed

capital

investment

(Billion,

2003

US$)

$Gain

in total

fixed

capital

investment

(Billion,

2003

US$)

Scenario

A: Assumes

a fixed

capital

investment

per

capita

of $2,143

(the

mean

capital

investment

per

capita

in the

sample

converted

into

2003

US

$)

1 Billion

2,143

8.9

5.2

3.7

2.9

2.3

2.0

1.7

1.5

1.4

250

Million

536

2.2

1.3

0.9

0.7

0.6

0.5

0.4

0.4

0.3

100

Million

214

0.9

0.5

0.4

0.3

0.2

0.2

0.2

0.2

0.1

50 Million

107

0.4

0.3

0.2

0.1

0.1

0.1

0.1

0.1

0.1

25 Million

54

0.22

0.13

0.09

0.07

0.06

0.05

0.04

0.04

0.03

10 Million

21

0.09

0.05

0.04

0.03

0.02

0.02

0.02

0.02

0.01

5 Million

11

0.045

0.026

0.018

0.014

0.012

0.010

0.009

0.008

0.007

1 Million

2

0.009

0.005

0.004

0.003

0.002

0.002

0.002

0.002

0.001

Scenario

B: Assumes

a fixed

capital

investment

per

capita

of $8,000

250

Million

2,000

8.3

4.9

3.5

2.7

2.2

1.9

1.6

1.4

1.3

100

Million

800

3.3

2.0

1.4

1.1

0.9

0.7

0.6

0.6

0.5

50 Million

400

1.7

1.0

0.7

0.5

0.4

0.4

0.3

0.3

0.3

25 Million

200

0.8

0.5

0.3

0.3

0.2

0.2

0.2

0.1

0.1

10 Million

80

0.33

0.20

0.14

0.11

0.09

0.07

0.06

0.06

0.05

5 Million

40

0.166

0.097

0.069

0.054

0.044

0.037

0.032

0.028

0.025

1 Million

8

0.033

0.019

0.014

0.011

0.009

0.007

0.006

0.006

0.005

Uses

parameter

estimate

for

incidents

= -0.006,

shown

in Table

6, column

(1)

Springer



340 Public Choice (2006) 128:317-349

more than the GDP of the UK, Italy, and Argentina combined, or about one-third of US GDP.

The empirical analysis also suggests that unanticipated reductions in incidents have significant positive economic effects. A reduction of one unanticipated incident in the US (from three to two) would predictably add nearly $5 billion in fixed capital investment to the US economy.

In this paper we dwell on the measurable, macroeconomic costs of terrorism and, sym- metrically, the economic gains associated with deterrence. These estimates obviously do not include the benefit of avoiding pain and suffering, or other emotional benefits of deterrence, which would only add to the value of deterrence. However, we have attempted to provide at least a starting point for assessing the resources that countries might sensibly dedicate to anti-terrorism activities.

Acknowledgements We are grateful to Dennis Mueller, and NDU faculty and student seminar participants for detailed commentary on an earlier draft of the paper. We also thank Charles Rowley, Ann Rathbone and other participants of the Conference on the Political Economy of Terrorism for comments that improved the paper.

Appendix A.1.: World terrorist incidents and casualties by year (1968-2002)

Number of Total Total Total Mean casualties Year incidents wounded killed casualties per incident

1968 119 196 34 230 1.9 1969 177 186 26 212 1.2 1970 341 203 125 328 1.0 1971 296 215 34 249 0.8 1972 451 357 138 495 1.1 1973 336 533 116 649 1.9 1974 422 1042 325 1367 3.2 1975 335 675 237 912 2.7 1976 449 581 362 943 2.1 1977 330 439 224 663 2.0 1978 278 471 377 848 3.1 1979 317 293 176 469 1.5 1980 503 856 343 1199 2.4 1981 454 1320 424 1744 3.8 1982 413 540 218 758 1.8 1983 407 1094 776 1870 4.6 1984 453 748 592 1340 3.0 1985 514 880 832 1712 3.3 1986 521 1256 343 1599 3.1 1987 482 899 266 1165 2.4

(Continued on next page)

!Springer



Public Choice (2006) 128:317-349 341

(Continued)

Number of Total Total Total Mean casualties Year incidents wounded killed casualties per incident

1988 411 446 483 929 2.3 1989 350 670 320 990 2.8 1990 357 420 251 671 1.9 1991 568 255 154 409 0.7 1992 305 639 177 816 2.7 1993 487 678 294 972 2.0 1994 309 830 463 1293 4.2 1995 284 820 285 1105 3.9 1996 201 1408 504 1912 9.5 1997 176 642 268 910 5.2 1998 86 212 399 611 7.1 1999 263 156 111 267 1.0 2000 155 405 249 654 4.2 2001 50 1080 3547 4627 92.5 2002 123 1617 652 2269 18.4 Total 11,723 23,062 14,125 37,187 Min 50 156 26 212 0.7 Max 568 1,617 3,547 4,627 92.5 Mean 335 659 404 1,062 Median 336 639 285 912

Appendix A.2. Summary statistics for panel dataset (for the estimation samples that include all control variables)

Variable N Mean Median Standard deviation Min Max

Incidents 934 3.4 1 9.8 0 181 Wounded 932 6.7 0 28.8 0 350 Killed 933 3.3 0 17.3 0 332 Casualties 932 10.0 0 37.9 0 354 Incidents (per 100,000 Population) 934 0.02 0.002 0.06 0 0.61 Killed (per 100,000 Population) 933 0.02 0 0.11 0 1.5630 Wounded (per 100,000 Population) 932 0.04 0 0.30 0 4.2045 Casualties (per 100,000 Population) 932 0.06 0 0.38 0 5.7019 Fixed capital investment per capita 934 1,775 595 2,372 0 12,685 (1995 US$) GDP per capita (1995 US$) 934 8,402 2,660 10,998 85 48,454 GDP (billions of 1995 US$) 934 305 51.5 973 0.640 8,990 Population (millions) 934 53.1 10.4 161 0.218 1,250 Trade as a share of GDP 934 67.6 61.8 34.7 11.6 233.5

Primary education 934 99.1 101.5 17.5 22.9 146.1 Government expenditure as share 934 28.5 27.8 10.8 6.4 72.8

of GDP Credit 934 64.7 53.9 43.0 0.7 272.4

Telephone mainlines 934 191.4 95.0 202.4 0.5 726.7

Consumption expenditures per capita 832 7,076 2,242 8,619 89 34,425 (1995 US$) International tourism expenditures per capita 845 162 36 301 0 2,789 (1995 US$)

Springer



342 Public Choice (2006) 128:317-349

Appendix A.3: Summary statistics for five-year averaged panel dataset (for the estimation samples that include all control variables)

Variable N Mean Median Standard deviation Min Max

Incidents 498 3.3 1 6.3 0 47.2 Wounded 498 6.9 0.2 21.5 0 223.6 Killed 498 4.2 0.2 31.6 0 686.4 Casualties 498 11.1 0.6 45.8 0 860.2 Incidents (per 100,000 Population) 498 0.026 0.007 0.053 0 0.497 Killed (per 100,000 Population) 498 0.025 0.001 0.085 0 0.988 Wounded (per 100,000 Population) 498 0.059 0.001 0.315 0 4.395 Casualties (per 100,000 Population) 498 0.083 0.004 0.385 0 5.2 Fixed capital investment per 498 1,630 485 2251 0.19 12,075

capita (1995 US$) GDP per capita (1995 US$) 498 7611 2340 10224 97 46,330 Population (millions) 498 43.8 9.99 132 .204 1,250 Trade as a share of GDP 498 63.9 56.4 33.9 11.1 219

Primary Education 498 96.2 100.7 19.9 17.5 164 Government expenditure as share of GDP 498 27.8 26.4 11.3 7.6 81 Credit 498 60 49 41 3 284

Telephone mainlines 498 154 61.82 184 0.5 716 Growth rate in real GDP per capita 491 0.013 0.014 0.048 -0.41 0.23

(five-year annualized rate) Growth rate in private sector real GDP per capita 462 0.015 0.018 0.029 -0.12 0.13

(five-year annualized rate)

Appendix A.4.: Countries included in the data sample

Albania Georgia Niger Algeria Germany Nigeria Angola Ghana Norway Antigua and Barbuda Greece Oman

Argentina Grenada Pakistan Armenia Guatemala Panama Australia Guinea Papua New Guinea Austria Guyana Paraguay Azerbaijan Haiti Peru

Bahamas, The Honduras Philippines Bahrain Hong Kong, China Poland

Bangladesh Hungary Portugal Barbados Iceland Puerto Rico

Belgium India Romania Bolivia Indonesia Russian Federation Bosnia and Herzegovina Iran, Islamic Rep. Rwanda Botswana Ireland Saudi Arabia Brazil Israel Senegal Bulgaria Italy Seychelles Burkina Faso Jamaica Sierra Leone Burundi Japan Singapore Cambodia Jordan Solomon Islands Cameroon Kenya South Africa

(Continued on next page)

Springer



Public Choice (2006) 128:317-349 343

(Continued):

Canada Korea, Rep. Spain Central African Republic Kuwait Sri Lanka Chad Kyrgyz Republic Sudan Chile Lao PDR Suriname China Latvia Swaziland Colombia Lebanon Sweden

Congo, Rep. Lesotho Switzerland Costa Rica Liberia Syrian Arab Republic Cote d'Ivoire Lithuania Tajikistan Croatia Luxembourg Tanzania

Cyprus Macedonia, FYR Thailand Czech Republic Malawi Togo Denmark Malaysia Trinidad and Tobago Djibouti Mali Tunisia Dominica Malta Turkey Dominican Republic Mauritania Uganda Ecuador Mexico Ukraine

Egypt, Arab Rep. Moldova United Arab Emirates El Salvador Morocco United Kingdom Equatorial Guinea Mozambique United States Eritrea Namibia Uruguay Estonia Nepal Venezuela, RB

Ethiopia Netherlands Vietnam

Fiji New Caledonia Yemen, Rep. France New Zealand Zambia Gabon Nicaragua Zimbabwe

Springer



344 Public Choice (2006) 128:317-349

Appendix

B: Parameter

estimates

using

US

time-series

data

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Unanticipated

incidents

per

-0.006

(2.79)**

-0.008

(3.51)**

100,000

pop.

(current

year)

Incidents

per

100,000

pop.

-0.001

(0.33)

0.002

(0.35)

(prior

year)

Unanticipated

casualties

(per

-0.002

(1.18)

-0.002

(1.69)

100,000

population)

Casualties

per

100,000

pop.

-0.002

(1.39)

-0.002

(1.84)

(prior

year)

Unanticipated

wounded

(per

-0.002

(1.29)

-0.001

(1.26)

100,000

population)

Wounded

per

100,000

pop.

-0.001

(0.93)

-0.001

(1.06)

(prior

year)

Unanticipated

killed

(per

-0.001

(1.12)

-0.001(0.77)

100,000

population)

Killed

per

100,000

pop.

(prior

-0.003

(2.09)*

-0.002

(1.57)

year) population

-3.518

(3.56)**

-2.84

(2.58)*

-3.083

(2.90)**

-2.427

(2.02)

Trade

as a share

of GDP

0.163

(2.54)*

0.145

(2.38)*

0.158

(2.55)*

0.145

(2.43)*

Credit

0.224

(2.74)*

0.212

(2.27)*

0.230

(2.77)*

0.191

(1.91)

Time

trend

0.020

(34.01)

**

0.049

(5.72)**

0.043

(4.47)**

0.020

(59.7)**

0.045

(4.80)**

0.020

(55.9)**

0.020

(51.1)**

0.039

(3.69)**

Observations

34

32

32

34

32

34

34

32

Years

dropped

from

sample

68

68,01,02

68,01,02

68

68,01,02

68

68

68,01,02

F stat

1098.1

1274.5

808.3

1349.2

1159.2

1418.0

875.2

496.6

Dependent

Variable

= Natural

Log

of US

GDP

per

Capita

(in 1995

US$)

t statistics

in parentheses

The

US

models

are

estimated

using

Newey-White

standard

errors

with

an AR(1)

lag

structure

*Significant

at 5%

**Significant

at 1%

Springer



Public Choice (2006) 128:317-349 345

Appendix

C.1.:

Models

estimated

using

private

sector

GDP

per

capita

annual

panel

data

(1)

(2)

(3)

(4)

(5)

Unanticipated

incidents

per

100,000

pop.

(current

year)

-0.003

(1.88)

Incidents

per

100,000

pop.

(prior

year)

-0.004

(2.22)*

Unanticipated

casualties

per

100,000

pop.

(current

year)

-0.002

(1.68)

Casualties

per

100,000

pop.

(prior

year)

-0.003

(2.13)*

Unanticipated

Wounded

per

100,000

pop.

(current

year)

-0.003

(2.05)*

Wounded

per

100,000

pop.

(prior

year)

-0.004

(2.29)*

Unanticipated

Killed

per

100,000

pop.

(current

year)

-0.003

(1.73)

Killed

per

100,000

pop.

(prior

year)

-0.003

(1.81)

Fixed

capital

per

capita

0.201

(10.05)**

0.202

(10.10)**

0.206

(10.13)**

0.203

(10.03)**

0.205

(10.19)**

Population

0.267

(11.95)**

0.266

(12.02)**

0.251

(10.47)**

0.251

(10.52)**

0.268

(11.94)**

Foreign

Trade

0.053

(1.66)

0.052

(1.62)

0.043

(1.31)

0.046

(1.40)

0.048

(1.49)

Education

Level

0.163

(2.49)*

0.150

(2.30)*

0.202

(2.85)**

0.202

(2.84)**

0.150

(2.30)*

Government

Expenditure

(% of GDP)

-0.301

(8.23)**

-0.293

(8.03)**

-0.309

(8.27)**

-0.305

(8.17)**

-0.302

(8.26)**

Credit

0.032

(1.98)*

0.031

(1.93)

0.030

(1.75)

0.028

(1.67)

0.033

(1.98)*

Telephone

Mainlines

0.283

(13.27)**

0.286

(13.50)**

0.278

(12.93)**

0.279

(13.01)**

0.282

(13.23)**

Observations

788

788

752

752

782

Number

of Countries

97

97

89

89

95

F stat

450.27

423.41

364.26

370.51

395.13

R-sq

within

0.93

0.93

0.92

0.92

0.93

R-sq

between

0.67

0.67

0.69

0.69

0.67

R-sq

overall

0.66

0.67

0.68

0.68

0.66

Dependent

Variable

= natural

log

of private

sector

gdp

per

capita

(in 1995

US$)

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

Absolute

value

of t statistics

in parentheses

Coefficients

for

country

and

year

fixed

effects

not

shown

*Significant

at 5%

**Significant

at 1%

Springer



346 Public Choice (2006) 128:317-349

Appendix

C.2.:Models

estimated

using

private

sector

GDP

per

capita

Five-year

averaged

data

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Incidents

per

100,000

pop.

-0.006

(1.90)

-0.008

(2.08)*

Casualties

per

100,000

pop.

-0.004

(1.70)

-0.007

(2.44)*

Wounded

per

100,000

pop.

-0.004

(1.74)

-0.007

(2.57)**

Killed

per

100,000

pop.

-0.004

(1.59)

-0.006

(2.24)*

Observations

497

497

497

497

497

497

497

497

Number

of Countries

107

107

107

107

107

107

107

107

F stat

52

52

52

52

Wald

Chi-sq

1448

1484

1443

1484

R-sq

within

0.66

0.56

0.66

0.56

0.66

0.56

0.66

0.56

R-sq

between

0.36

0.92

0.36

0.92

0.36

0.92

0.36

0.92

R-sq

overall

0.41

0.93

0.41

0.93

0.41

0.93

0.41

0.93

Model

Type

FE

RE

FE

RE

FE

RE

FE

RE

Dependent

Variable

= Natural

Log

of Private

Sector

GDP

per

Capita

(in 1995

US$)

FE

= Fixed-Effects

Model

RE

= Random-Effects

Model

All

independent

variables

are

entered

into

the

regression

as natural

log

transformations

Absolute

value

of t statistics

in parentheses

Models

include

the

economic

and

demographic

control

variables

Coefficients

for control

variables

and

period

fixed

effects

not

shown

*Significant

at 5%

**Significant

at 1%

Springer



Public Choice (2006) 128:317-349 347

Appendix

D: Impact

of terrorism

on country

growth

rates:

Five-year

averaged

data

Growth

Rate

in Total

GDP

per

Capita

Growth

Rate

in Private

GDP

per

Capita

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

Incidents

per

100,000

pop.

-0.001

(2.03)*

-0.002

(1.91)

Casualties

per

100,000

-0.000

(1.22)

-0.001

(1.15)

pop. Killed

per

100,000

pop.

-0.000

(0.30)

-0.001

(0.91)

Wounded

per

100,000

pop.

-0.001

(1.62)

-0.001

(1.14)

Fixed

capital

per

capita

0.008

(3.46)**

0.008

(3.45)**

0.008

(3.47)**

0.008

(3.45)**

0.006

(1.47)

0.006

(1.49)

0.006

(1.49)

0.006

(1.48)

Population

0.004

(1.97)*

0.004

(2.00)*

0.004

(1.89)

0.004

(2.05)*

0.005

(1.62)

0.005

(1.66)

0.005

(1.61)

0.005

(1.65)

Foreign

Trade

0.018

(3.80)**

0.018

(3.71)**

0.017

(3.67)**

0.017

(3.70)**

0.023

(2.81)**

0.022

(2.73)**

0.022

(2.70)**

0.022

(2.70)**

Education

Level

0.006

(0.81)

0.005

(0.68)

0.004

(0.57)

0.005

(0.67)

0.018

(1.48)

0.016

(1.37)

0.016

(1.34)

0.016

(1.32)

Government

expenditure

-0.013

(2.37)*

-0.013

(2.30)*

-0.013

(2.26)*

-0.012

(2.23)*

-0.029

(2.98)**

-0.028

(2.88)**

-0.028

(2.87)**

-0.027

(2.81)**

(%

of GDP)

Credit

-0.008

(2.87)**

-0.008

(2.93)**

-0.008

(2.94)**

-0.008

(2.90)**

-0.010

(1.98)*

-0.010

(2.07)*

-0.011

(2.10)*

-0.010

(2.04)*

Telephone

Mainlines

-0.007

(2.44)*

-0.007

(2.43)*

-0.007

(2.51)*

-0.006

(2.35)*

-0.003

(0.63)

-0.003

(0.66)

-0.003

(0.70)

-0.003

(0.63)

Landlocked

Country

(Yes

-0.003

(0.58)

-0.003

(0.45)

-0.003

(0.51)

-0.002

(0.41)

-0.002

(0.27)

-0.001

(0.15)

-0.001

(0.16)

-0.001

(0.14)

= 1)

Geographic

Location

-0.014

(2.15)*

-0.014

(2.04)*

-0.014

(2.06)*

-0.013

(1.99)*

-0.014

(1.31)

-0.012

(1.20)

-0.012

(1.21)

-0.012

(1.17)

(Tropical

= 1)

Legal

Origin:

British

(Yes

-0.003

(0.32)

-0.003

(0.34)

-0.003

(0.44)

-0.002

(0.31)

-0.008

(0.67)

-0.008

(0.70)

-0.008

(0.70)

-0.008

(0.70)

= 1)

Legal

Origin:

French

(Yes

-0.005

(0.67)

-0.006

(0.76)

-0.006

(0.84)

-0.005

(0.74)

-0.003

(0.30)

-0.004

(0.40)

-0.005

(0.42)

-0.004

(0.40)

= 1)

Observations

491

491

491

491

462

462

462

462

Number

of Countries

104

104

104

104

104

104

104

104

Wald

Chi-sq

122.07

119.16

117.51

120.90

60.69

58.37

57.89

58.34

R-sq

within

0.17

0.16

0.16

0.17

0.06

0.05

0.05

0.05

R-sq

between

0.31

0.32

0.33

0.31

0.28

0.28

0.28

0.28

R-sq

overall

0.26

0.26

0.26

0.26

0.15

0.15

0.15

0.15

Dependent

Variable

= Annualized

Growth

Rate

in Real

GDP

per

Capita

Absolute

value

of z statistics

in parentheses

Coefficients

for period

fixed

effects

and

regional

dummy

variables

not

shown

*Significant

at 5%

**Significant

at 1%

Springer



348 Public Choice (2006) 128:317-349

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