economics consequences of terrorism: geography matters

Economic consequences of

terrorism: Geography matters

Omer Majeed

Panel :Professor Prema-chandra Athukorala

Professor Robert Breunig

Main results and hypothesis • Terrorism can impose significant costs on an economy. This

paper analyses the effect of geography on terrorism. In

particular, this paper hypothesises that a terrorist attack in the

financial hubs of a country will have significantly higher

economic costs than a similar attack in a remote part of the

country.

• In particular, this paper focuses on the case study of Pakistan

and Net Foreign Direct Investment (NFDI), finding that

terrorism in financial hubs of Pakistan has imposed a

significant cost on NFDI, but similar attacks in remote areas

have had insignificant impacts. This heterogeneity of the

geography of terrorism has long been ignored in the literature,

and as such is likely to be a significant contribution.

2

Outline

• Background and literature review

• Why geography matters

• Pakistan and Terrorism

• Data

• Methodology

• Results

• Conclusions and policy implications

3

Definition

• This paper uses the definition of Enders and

Sandler for terrorism: “Terrorism is the premeditated

use or threat to use violence by individuals or sub-

national groups to obtain a political or social

objective through the intimidation of a large

audience beyond that of the immediate victim”.

ENDERS, W. & SANDLER, T. 2006. The political

economy of terrorism, Cambridge University Press.

• Other definitions include that of UN and state

department.

4

Literature review

• Terrorism can impose significant economic costs.

Most important is the number of lives lost.

• Economic growth (Blomberg et al., 2004, Eckstein

and Tsiddon, 2004);

• Net Foreign Direct Investment (NFDI)

(Enders and Sandler, 1996);

• Trade (Nitsch and Schumacher, 2004) and

• Tourism (Enders et al., 1992).

5

Why geography matters

• This paper argues that the economic costs of a

terrorist incident vary by the geography of the

terrorist incident.

• In particular, this paper argues that a terrorist attack

in one of the financial hubs of a country will have a

significantly higher impact on the economy than a

similar attack in a remote area. For the purpose of

this paper, a financial hub is defined to be a high

economic activity area of the country.

6


• Five main reasons

• 1) A terrorist attack in a major city is likely to attract

bigger media coverage. This extra media coverage

is likely to dampen both consumer and business

confidence. This can be particularly relevant for

foreign investors as it can be hypothesized that FDI

has a high elasticity to terrorism.

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• 2) An attack in a major city is also likely to have a

bigger psychological impact, as this signals to

various stakeholders that the state may be weak

and that the terrorist may be well organised. Such

kinds of signals may force stakeholders to rationally

expect future terrorist attacks and as such they

would be forced to alter their behaviour. This may be

particularly bad for foreign investors.

8


• 3) There are more businesses, employees and

economic activity in a major city, compared to a

remote area. As such, disruption in a major city is

likely to cause higher economic costs than

disruptions in remote areas.

• 4) Financial institutions such as stockmarkets, banks

and other financial intermediaries tend to gravitate

towards financial hubs. A major attack near these

organisations is likely to cause a bigger negative

shock to the financial sector.

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• 5) Finally, there are more economic assets in a

financial hub. These include infrastructure, property,

and higher human and physical capital.

10


• 5) Finally, there are more economic assets in a

financial hub. These include infrastructure, property,

and higher human and physical capital.

11

Decision making by terrorists

• For the terrorist, the decision making is rational and

involves weighing the costs and benefits of attacking

a financial hub versus a remote area. Attacking a

financial hub gets terrorists more political leverage

but at the same time it is more difficult and more

costly

• Expected (benefit from attacking a financial hub –

cost of attack) > 0 (2.1)

• Expected (benefit from attacking a remote area –

cost of attack) > 0 (2.2)

12

Background and context for Pakistan

• After the terrorist attacks on September 11, 2001,

Pakistani military bases and land routes were used

by the US and NATO to attack the Taliban in

Afghanistan. As a consequence, the Taliban saw the

government of Pakistan as a puppet of the US and

started retaliating against the people and the state

of Pakistan.

• These organisations have successfully attacked

across all over Pakistan. These attacks include

major cities like Karachi, Lahore, Islamabad and

Rawalpindi. In addition remote areas of Pakistan

and minor cities have also been targeted.

13


• There are several terrorist organisations operating in

Pakistan and Afghanistan that have declared war on

the government of Pakistan. Some of these include

Tehreek-e-Taliban Pakistan (TTP), Lashkar-

eJhangvi (LeJ), Sipah-e-Muhammad Pakistan

(SMP) , Lashkar-e-Toiba (LeT) and the Balochistan

Liberation Army (BLA). Most of these organisations

are religious extremist organizations. Some of them

receive internal funding, while some of them receive

funding from overseas.

15

http://www.satp.org/satporgtp/countries/pakistan/terroristoutfits/ttp.htm





http://www.satp.org/satporgtp/countries/pakistan/terroristoutfits/lej.htm



http://www.satp.org/satporgtp/countries/pakistan/terroristoutfits/SMP.htm






http://www.satp.org/satporgtp/countries/india/states/jandk/terrorist_outfits/lashkar_e_toiba.htm






• This paper chooses NFDI for three reasons:

• Firstly, analysing NFDI gives this paper a base point

to compare with the growing literature on terrorism

and foreign direct investment (FDI) (Muckley, 2010,

Abadie and Gardeazabal, 2008, Enders and

Sandler, 1996, Enders et al., 2006, Bandyopadhyay

et al., 2011, Sandler and Enders, 2004, Mancuso et

al., 2010).

• Secondly, FDI is likely to be sensitive to terrorism.

• Data availability.

16

Data

• For a database of terrorist attacks this paper uses

Global Terrorism Database (GTD).

• For NFDI this paper uses CPEIC database, using

the US GDP deflator to convert nominal NFDI into

real values, with 2009 used as the base year. This is

monthly data and the sample period is between July

2001 and December 2011.

• To capture terrorism I created a causality list

which was the number of people killed plus

number of people wounded in a terrorist attack.

17

Data

18

Data: structural break and stationarity

Chow Break Point Test

Null Hypothesis: No breaks at specified breakpoints

F-statistic 13.67 Prob. F(3,119) 0.00

Chow Breakpoint Test: 2008M08

• Structural break in NFDI series, using Chow test.

The null hypothesis is that there are no structural

breaks in the data. The F-statistic is based on the

comparison of the restricted and unrestricted sum

of squared residuals.

• DF and Phillip-Perrson confirm that all series are

stationary.

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Data: Regions

• Terrorist attacks were disaggregated by geography

into three categories as following:

• i) major cities which included Karachi, Lahore,

Islamabad and Rawalpindi. These are the main

cities of Pakistan, as well as the financial hubs and

government centres;

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• ii) remote areas included Khyber Pakhtunkhwa (KP),

Balochistan, the Pakistani part of Kashmir, Gilgit-

Baltistan and tribal agencies on the Pakistan-Afghan

border in the North-West of Pakistan. Based on

contribution by GDP, these regions add very little to

the Pakistan’s economy and are considered remote;

and

• iii) medium zones consisted of the remainder of i

and ii.

• There were a total of 14199 casualties due to

terrorism between July 2001 and December 2011 for

Pakistan, of which 9213 were in major cities, 3431

were in remote areas and the remainder in the

medium zone. 21

Data

Variable Obs Mean Std. Dev. Min Max

NFDI 126 234.54 249.75 -15.12 1371.33

Remote Areas 126 27.23 44.69 0.00 243.00

Major City 126 73.12 114.19 0.00 643.00

Medium Zones 126 7.85 26.17 0.00 223.00

22

Methodology: Vector Autoregression

• Reasons:

• i) It can take into account reverse causality between

variables and is widely used in the literature;

• ii) by using impulse response functions (IRF) we

can evaluate how a shock in one variable impacts

other variables, and whether this impact is long

lasting, i.e. look at long term and short effects; and

• iii) it can be used to calculate total reductions on

NFDI caused by terrorism

(Enders and Sandler, 1996).

23


24


• Likelihood ratio (LR) was used to get lag length. It

chose 12 lags, which takes account of seasonality.

• Results robust to AIC. These criteria have better

small sample properties.

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Results: Impulse response functions

26

-40

0

40

0 1 2 3 4 5 6 7 8

order1, remote, NFDI

A. Terrorism in Remote Areas and NFDI

95% CI orthogonalized irf

step

Graphs by irfname, impulse variable, and response variable


27

-40

0

40

0 1 2 3 4 5 6 7 8

order1, major_city, NFDI

B. Terrorism in Major Cities and NFDI


step



• A standardised attack in a major city decreases

NFDI by around $40.94 million 2009 US dollars in

one months’ time. This result is statistically

significant at the 95 per cent level.

• All other impacts are statistically insignificant at 95

per cent level.

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29

-40

0

40

0 1 2 3 4 5 6 7 8

order1, medium_zone, NFDI

C. Terrorism in Medium Zone areas and NFDI


step



30

-20

0

20

40

0 1 2 3 4 5 6 7 8

order1, NFDI, major_city

Terrorism reaction to NFDI


step



• Another interesting dynamic between terrorism and

NFDI is that terrorism reacts to increased NFDI in

major cities. A one standard deviation increase in

NFDI in major cities results in terrorist attacks in

one, four and five months on average after the

increase.

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Results: Robustness test

• As a robustness test this paper uses two methods.

In the first method we see if our results are sensitive

to the ordering of the variables. Secondly we

combine data from terrorist attacks in all regions into

one regression and examine whether the results are

sensitive to specification.

• Our results remain robust to these two tests.

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Results: Accumulated effect

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34


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• Over the entire sample, this paper finds that

terrorism in major cities of Pakistan caused a

decline of $3.0 billion in 2009 US dollars, or about

10.7 per cent. These results are similar to Enders

and Sandler’s (1996) results on the effect of

terrorism in Spain and Greece.

Conclusion and policy implications

• There are two main policy implications from this

research and they are as following: i) terrorists gain

more media coverage and impose a bigger cost on

the state by attacking the financial hubs of the

country. Given this, financial hubs are more

vulnerable to terrorism and should be better

protected; and

• ii) terrorists react to foreign presence. In particular

this paper demonstrates that terrorists had the

tendency to increase attacks in major cities due to

increased FDI in Pakistan.

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Conclusion and policy implications

• It would be incorrect to conclude from this research

that security apparatus should focus on the financial

hubs only and ignore remote areas of the country. If

this happens, then the terrorists can use the vacuum

to launch attacks on the main sectors of the

economy. The example of how terrorists in a

remote-land-locked country managed to use the

vacuum in Afghanistan to launch attack on the main

financial hub of the world is still prominent in every

one’s memory.

37

Questions?

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