MASTER THESIS - THE ECONOMIC CONSEQUENCES OF TERRORISM - Thibaut GRANCHER

05/09/2016
THE ECONOMIC
CONSEQUENCES OF
TERRORISM
By Thibaut Grancher
MASTER 2
DEVELOPMENT ECONOMICS &
INTERNATIONAL PROJECT MANAGEMENT
University Paris-Est Créteil

1
ABSTRACT / RÉSUMÉ
The economic consequences of terrorism
After the Charlie Hebdo attack of January 2015, France was once again victim of terrorism the
13th November 2015, later, the 22nd March 2016, Belgium was also hit by the terrorism, causing
lots of casualties and important damages. After these attacks, lots of measures taken by
countries to deal with the threat ask questions today on their economic viability and on
economic consequences of terrorism. Among these, in France, the extension of the state of
emergency, the Vigipirate plan and the Sentinelle operation reinforcement. To analyse the
economic consequences of terrorism and measures taken to deal with it, three channels are
studied in this paper, the household consumption, the tourism industry and the evolution of
military expenditures through three variables resulting from terrorism, the frequency of attacks,
the number of dead and injured people per year. It asserts that the actions taken after the attacks
play a determinant role about their consequences.
*************
Les conséquences économiques du terrorisme
Après l’attaque de Charlie Hebdo en janvier 2015, la France a de nouveau été frappée par le
terrorisme le 13 novembre 2015, plus tard, le 22 mars 2016, la Belgique est aussi touchée,
causant de nombreuses victimes et des dommages importants. Après ces attaques, de
nombreuses mesures qui ont été prises par les gouvernements pour limiter la menace interrogent
aujourd’hui sur leur viabilité et sur les conséquences économiques du terrorisme. Parmi celles-
ci, nous trouvons, en France, la prolongation de l’état d’urgence, le renforcement du plan
Vigipirate et de l’opération Sentinelle. Pour analyser les conséquences économiques du
terrorisme et des mesures engagées pour lutter contre, trois domaines sont étudiés, la
consommation des ménages, l’industrie du tourisme et l’évolution des dépenses militaires, à
travers trois variables directement liées au terrorisme, la fréquence des attaques, le nombre de
morts et de blessés par année. Il en ressort que les actions engagées après une attaque terroriste
jouent un rôle déterminant quant aux conséquences économiques de l’attaque.

2
AKNOWLEDGMENT
Before going further in this paper, I would like to express my gratitude to my master thesis
director, the Professor and Head of the Economic Department of the OECD, Patrick Lenain. I
thank him to have given me the possibility to make my thesis on the economic consequences
of terrorism, advised me, and shared his expertise with me. I am also grateful for his availability,
especially in view of his responsibilities.
I also would like to thank the deputy and its close associates of the National Assembly with
whom I work for the time they made available to give me the possibility to complete my master
thesis on time. I am also grateful for the experience I acquired alongside them, particularly on
the terrorism issue.
STATEMENT
Although, I initially wanted to analyse the economic impact of public spending on defence, the
choice to study the economic consequences of terrorism was made through a dialogue with my
thesis supervisor, Patrick Lenain. Considering all the researches made on the impact of defence
expenditures, I prefer to analyse the economic consequences of terrorism to bring something
new. Indeed, although it is a topical subject, only a few researchers studied its impact. This
topic presented several advantages. In one hand, it permitted me to link my professional
experience within the Ministry of Defence, my work for the Institute for Higher National
Defence Studies and my experience of the National Assembly to my economic skills, acquired
during my years at the University Paris-Est Créteil.
Through this work, I improved my reasoning and analytical skills. This research enabled me to
put my theoretical knowledge into practice and to broaden them, principally in econometrics. I
also completed my knowledge on terrorism issues in analysing what other authors found about
them. Finally, this thesis permitted me to develop my critical analysis.

3
INTRODUCTION
With the terrorist attacks hitting France these two last years, lots of measures were taken by the
government to deal with the threat, such prolongation of the state of emergency, the Vigipirate
plan and the Sentinelle operation reinforcement. All the actions taken in a difficult economic
context cause us to reflect about their economic consequences. In order to analyse it, I decided
to focus my research on five developed countries with a similar macroeconomic structure, and
hit successively since the beginning of 2000s, the United States, Spain, the United Kingdom,
France and Belgium. On September 11, 2001, 19 militants associated to the Islamic extremist
group Al Qaeda, hijack four airliners to smash into the World Trade Center in New York, the
Pentagon outside Washington and in Pennsylvania. Over 3,000 people were killed and 10,000
others were injured, numerous buildings were damaged. Although, hitting also developing
countries, terrorist attacks took more and more importance in succeeding in developed countries
as Spain, the 11, March 2004. This day, terrorists, affiliated to Al-Qaeda activate 10 bombs
located on four trains in three Madrid train stations in the rush hour. This killed 191 people and
wounded around 2000 others. Later, the 7th
July 2005, the worst bombing since the World War
2 hits the United Kingdom, when four young men set off bombs on a bus in central London and
on three underground cars killing 52 people and injuring about 700 others. In 2015, France is
touched by several terrorist attacks, from the 7 to 9 January, 3 terrorists equipped with automatic
weapons kill 17 people, to the 13th November, where 10 terrorists used automatic weapons and
suicide attacks to murder 130 people and injure 413 others. In 2016, terrorist attacks continue
to strike Europe. The 22nd March, the terrorism hits Belgium, three suicide-attacks strike
Brussels airport at Zaventem and Maelbeek underground station, killing 28 people and injuring
around 340 others. Recently, France was another time touched when a terrorist drove his truck
into crowds celebrating Bastille Day at the Promenade des Anglais killing 84 people and
wounding 121 others.
The choice I have made to select data from 2001 to 2015 explains by the changing of perception
about terrorism after the attacks of the 11th September 2001 and the repetition of widespread
terrorist attacks in developed countries in the years following.
In current debates on the impact of terrorism, the consumption of households representing
55.2% of the GDP in France and 58.3% in the World1
. In taking in account the theory saying
that the uncertainty push consumers to save money rather spending it, it is interesting to analyse
the impact of terrorism on people consumption to observe if there is a national resilience or not.
For that I decided to analyse the impact of terrorism on consumption in using different data
than traditionally used. I used in this part the characteristics of terrorist attacks as independent
variables as the frequency of attacks, the number of dead and injured people. Concerning the
dependent variables I decided to use data on the household expenditures and the consumer
confidence. Although related to household expenditures, the index I will use on the consumer
confidence will permit me to analyse the psychological impact that have terrorist attacks on
people and to look how it could affect the economy in the future.
Also, one of the major concerns of professionals is the impact of attacks on tourism activities.
After the attacks of the 13th
November 2015, scars are always visible in Paris with a decrease
of the tourist activity. This observation does not concern exclusively Paris, we saw it in other
cities. If we compare the number of flight ticket reservations to Nice with data from the last
1
Household final consumption expenditures, etc. (% of GDP) - World Development Indicators (WDI)

4
year to estimate the tourism activity of the city, we observe a reduction of the tourism activity
of 9,4% between 14th
to 23rd
July and forecasts from the 1st
August to the 30th
September
announce a decrease of this activity of 20%2
. To analyse the impact of attacks on tourism
industry on the second part of my research, I decided to use three dependent variables. The first
one is the total contribution of travel and tourism on GDP, with as independent variables the
domestic and foreign tourism spending indicators to complete those on terrorism, the frequency
of attacks, the number of fatalities and injuries. This will enable me to evaluate the evolution
of the tourism activity after terrorist attacks. In the second subpart, I will analyse the impact of
terrorism on tourism flows in using two dependent variables the number of international tourist
arrivals in our countries sample and the number of resident tourist departures to international
destinations.
Related to the tourism and household consumption, it is interesting to analyse the rise of military
spending. Indeed, it is often reproached to this spending to affect negatively peace dividends,
especially in reducing the spending in other sectors as the education, the health etc. However,
this expenditures can have a good effect in reinforcing the confidence of our citizens or tourists
who are or plan to come in France. To estimate it I decided to divide my third part in two
subparts. In the first one I will analyse the impact of terrorist attacks on the evolution of military
expenditures in terms of GDP. Then I will study the effect of military expenditures on consumer
confidence and on the tourism flows, through the departures of resident tourists and the arrivals
of international tourists in our countries sample.
2
Look ForwardKeys

5
TABLE OF CONTENTS
INTRODUCTION.......................................................................................................................... 3
LITTERATURE REVIEW............................................................................................................ 6
EMPIRICAL METHOD ............................................................................................................... 9
EMPIRICAL RESULTS ............................................................................................................. 11
0. Descriptive statistics....................................................................................................... 11
1. Impact of terrorism on consumption........................................................................... 14
A. Household expenditures............................................................................................... 14
B. Consumer confidence................................................................................................... 16
2. Impact of terrorism on tourism industry.................................................................... 17
A. Tourism state................................................................................................................. 17
B. Tourism flows............................................................................................................... 20
a. Impact on international tourist arrivals................................................................... 20
b. Impact on resident tourist departures...................................................................... 22
3. Military expenditures in response to attacks ............................................................. 24
A. Impact of terrorism on military expenditures ............................................................. 24
B. Impact on consumer confidence and tourism flows................................................... 25
CONCLUSION............................................................................................................................. 27
REFERENCES ............................................................................................................................ 28
APPENDIX................................................................................................................................... 29

6
LITTERATURE REVIEW
Since decades, countries all around the world are victims of terrorism. Considered as located
facts in the fifties and sixties, the terrorism became considered, to the world’s eyes, as an
international problem after the attack hitting the United States in 2001. All these tragedies
launched several debates in the societies that some researchers, political leaders or specialists
tried to resolve. Among these ones, the following questions: who are the terrorists, why do they
attack us and what will be the consequences for our countries? To these questions, I will try to
answer the last one in analysing the economic consequences. However, we will see briefly how
researchers analysed the two first questions to better understand what terrorism is and what are
its aims.
Defining the terrorism is an ambiguous component in studies, there is no universal definition
about it. However definitions are generally similar, the U.S. Department of State defines in
1983 the terrorism as “premeditated, politically motivated violence perpetrated against non-
combatant targets by subnational groups or clandestine agents, usually intended to influence an
audience”. The term non-combatant refers to all people who at the time of the accident are
unarmed and / or not on duty. The international terrorism is considered as a terrorism “involving
citizens or the territory of more than one country”. Today the Us Code3 defines the terrorism as
all “involve violent acts or acts dangerous to human life that violate Federal or State law; appear
to be intended to intimidate or coerce a civilian population; to influence the policy of a
government by intimidation or coercion; or to affect the conduct of a government by mass
destruction, assassination, or kidnapping”, considering international terrorism as the one which
“transcend national boundaries in terms of the means by which they are accomplished, the
persons they appear intended to intimidate or coerce, or the locale in which their perpetrators
operate or seek asylum”.
After the last terrorist’s attacks, we assisted to speeches condemning these acts and explaining
why they happened. Some simple explanations providing from leaders as Barack Obama, David
Cameron, François Hollande and others, always present today in the debates, highlighted the
economic deprivation situations and the lack of education of terrorists. Nevertheless, popular
explanations for terrorism as the poverty, the lack of education or the idea they “hate of our
way of life and freedom” have no basis. In 2005, Chen and Revallion estimated that a half of
the world population lived on $2 a day even less, Barro and Lee in 2000 estimated that 1 billion
in the world had a primary school education or less and that 785 millions of adults were
illiterates. If the lack of education and poverty tend to terrorist activities the world would know
so much more terrorists attacks than today. The 9/11 Commission Report proved it for the 11th
September attacks.
In a context of extension of the state of emergency in France, after Nice attack, it is very
important to understand the root of terrorism to avoid taking counterproductive set of actions,
demystify terrorism and permit the society to move with risks related. According to Alan B.
Krueger (2008) the risk after a terrorist attack is to limit civil liberties, what could push people
to act more violently.
In its book, Krueger (2008) demonstrates terrorists, as a group, are generally better educated
and from richer families than those of the same group of age in the country in which they are
3
FBI website, the terrorism category

7
originally from. However, he underlines the difficulty to assess it considering the strong
heterogeneity of the group. Terrorist organisations do not pursue the same objectives and so do
not recruit people on the same criteria. Generally, more educated people and from richer
families are more radicalised and supportive of terrorism than the most disadvantages ones.
Indeed, people with a little education or illiterates are often unable to express their opinions
about policies issues.
Always according to Krueger, a range of socioeconomic indicators, often used, are unrelated
with the implication in terrorist acts as the illiteracy rate, the infant mortality, the GDP per
capita. International terrorists are more likely to come from moderate income countries than
poor ones. There is many examples of countries with low living standards which provide more
liberties and political rights to their citizens than rich countries as Saudi Arabia. The increase
of living standards does not permit to reduce terrorism. When we look at the origins of foreign
fighters in Iraq, for instance, we observe they are motivated by the lack of civil liberties or the
religion. For the Islamic States the religion is in the main reason for which foreign fighters fight
for it.
When an attack happens, one of the questions we can ask is about the economic consequences
of this attack. Each attack lead to policies to deal with it. Some economists estimate terrorism
affects negatively the economy and other think it could lead to a stronger growth. However,
most part of them explain the economic consequences by the possible overreaction of economic
actors.
Patrick Lenain, Marcos Bonturi and Vincent Koen (2001) describe the necessary policy
response after a terrorist attack to avoid a short term negative economic impact. They also
underline the medium term policies in the crisis management to restore confidence, safeguard
the financial system and avoid the depressions. Analysing the economic consequences of the
11th
September 2001 terrorist attacks, they present measures taken such the management of
liquidities with a financial support on loans and guarantees, the governmental interventions,
limited in time and scope, to cover risks related to the terrorism with a rise of insurance
industry’s premium and a reduction of coverage. They analyse the effects associated to the
tightening of borders crossing procedures on costs of trading and the long-lasting detrimental
consequences on the economic growth, estimating that an increase of 1% in trade costs could
reduce the flow trade from 2 to 3%. They insist on the necessity to well-balanced the efficiency
and the security at the borders. They introduce the economic negative consequences of an
increase of public spending on homeland security and military operations. According to their
estimations, an increase of 1% of military or security expenditures will decrease the GDP by
0.7% after 5 years.
Some economists evoke little economic consequences caused by terrorism such Becker and
Murphy (2001) and Krueger (2001). They underline the little impact of the bulk of physical and
human capital available for the production. They consider the human capital as primarily
responsible of the high level of GDP in modern countries, this is why it is important to protect
people possessing the knowledge and the competencies to produce. The physical capital is less
important taking in account that the human capital can rebuild it. According to Becker and
Murphy and Krueger, not enough people die to really impact the economy. The second point is
the capacity of businesses and people to adapt their behaviour to different contexts they meet.
After the 11th September, 2001, we saw a movement of firms located in lower Manhattan to
hotels etc. The third point is the expansion of sectors such the defence and counterterrorism
ones.

8
George Horwich (2000) illustrates the little effect of terrorism in comparing its effects with
those of natural disasters as the Kobe earthquake of 1995 hitting Japan. He concludes natural
disasters are more detrimental than terrorist attacks. The Kobe earthquake resulted on 100,000
buildings destroyed, 250,000 damaged, 6,500 people dead and 300,000 homeless people. After
15 months, the situation recovered and the manufacturing sector came back to its pre-
earthquake level. Generally, cities victims of terrorist attacks tend also to recover quickly.
The high effect of terrorism is generally explained as resulting of the significant impact of
terrorism on specific industries. Lenain, Bonturi and Koen (2001) underline the loss in capital
and in demand in all OECD countries and the United States for airlines companies, the reduction
of orders immediately for aircraft manufacturers, the slowing down of tourism industry through
the hotels, restaurants, travel agencies reservations in United States and in other OECD
countries, the decline of the activity in the retail sector, the reduction of the mail traffic etc.
The fact that people and businesses can overreact is another factor influenced by the level of
confidence. For instance, the level of consumption can decrease with the fear of consumers to
be victims of another terrorist attack. Lenain, Bonturi and Koen (2001) introduce the perception
of the government capacity to protect the country by economic actors and observe a decrease
of people and businesses confidence after the 11th
September 2001 and the Iraqi invasion of
Kuwait in 1990. They forecast a negative impact on the United-States real GDP of 0.5% in
2001 and 1.2% in 2002 and estimate the cumulative loss for the end of 2003 at $500 billion.
From a purely business point of view the lack of confidence can be observed by the fall of stock
prices. Lenain, Bonturi and Koen found a reduction of stock prices in United-States and also in
the Euro area and the United-Kingdom.
To this lack of confidence, we can add the impact caused by the deterioration of people well-
being. Andrew Clark and Elena Stancanelli (2016) analyse the effect caused by Boston attacks
in 2013 in analysing the evolution of the well-being and the allocation of time of American
people before and after the attacks. The authors observe a reduction of the well-being of 1.5
points on a scale of 6 in the whole population, with a difference between genders. Contrary to
women, who knew a big decrease of their well-being, men tend to have their well-being level
stable. Authors explain it by a different degree of risk aversion between genders. The stress
contributes also to the decline of the well-being. This study completes this of Gary Stanley
Becker and Yona Rubinstein (2011) analysing the negative impact of the fear on the
consumption of goods and services.
After a terrorist attack, lots of measures are taken by the governments. Among these ones,
Krueger (2001) introduces the interventions against the immigration. In United-States and the
United-Kingdom, the immigration constitutes an important source of economic growth
considering the high skilled labour which composes it. Tightening procedures for foreigner’s
visas can have a negative impact on the economy. For countries welcoming low skilled people
the situation is different.
The uncertainty caused by a terrorist attack is another big effect. Nicholas Bloom (2009)
collects data on daily movements on stock market, each month, for the S&P 100. He observes
a high volatility on the stock market after the 11th
September 2001, slowing down hirings and
investments by companies. After the 11th
September 2001, Lenain, Bonturi and Koen (2001)
also observe a very short term uncertainty in the financial market until the end of 2001 through
variations on equity indices, government bond prices, the short term interest rates, the exchange
rates and the price of commodities. For Hines and Jaramillo (2004), after a disaster, what we

9
can compare to a terrorist attack, investments increase at a short term to replace the capital but
savings decrease at the same time, reducing investments at a long term.
Alberto Abadie and Javier Gardeazabal (2001) analyse the impact of terrorism in comparing
the GDP per capita for the Basque region from 1955 to 1997 with regions without terrorism.
They observe a negative impact on the GDP per capita, declining of 10% during the period in
the Basque region compared to the reference group. In 2008, the same authors continue their
analysis in studying the economic impact of terrorism for Israel, Ireland and the Basque region.
Georges Andrew Karolyi and Rodolfo Martell (2005) analyse the effect of terrorism acts
targeting companies on the stock values. They underline an impact focused on the company
touched by a terrorist attack and not affecting the sector in which they are. This study is very
interesting because the authors analyse the impact of the attacks in dividing them in several
categories such attacks with detonations of explosives, attacks with the use of automatic
weapons and attacks with the kidnapping of executives. They conclude that attacks in
democratic and wealthier countries, aiming to kidnap executives, have the biggest effect on the
stock market. This underlines the importance of the human capital loss in terrorist attacks.
EMPIRICAL METHOD
To analyse the economic consequences of terrorism, I decided to use six dependent variables
(Table 2, Appendix). The household expenditure represents the final consumption spending
made by resident to meet their daily needs, expressed in terms of annual growth rates. The
consumer confidence index evaluates the confidence of consumers in function of their
responses to a survey on their households plans for major purchases and their economic
situation, this variable is expressed as a long term average with 100 as basis. The tourism
contribution to GDP corresponds to the percentage of GDP generated by the tourism industry
per year. The number of arrivals of international tourists and the number of departures of
resident tourists to international destinations represent the number of people who arrive in a
country and leave it for tourism activities per year. To finish with the dependent variables, the
military expenditure in terms of GDP corresponding to the part of expenses allocated to the
military sector in terms of GDP per annum.
Concerning the explanatory variables (Table 3, Appendix), I decided to use three main variables
reflecting directly the terrorism activity. The frequency of terrorist attacks in one hand,
represents the number of all terrorist attacks per year, including also failed attempts. The
number of fatalities represents the number of dead people per year caused by terrorist attacks.
Then the number of injuries equals to the total of people injured per year after terrorist attacks.
Impacting not alone the dependent variables, I added some control variables to deal with the
potential endogeneity. That is why I took in account the GDP per capita in US$, the
unemployment rate in percentage of the total labour force and the short term interest. To these
control variables I decided to create fixed effects for years in using them as dummy variables
with the 2015 one as reference year.
I analysed the relationships between variables on terrorism and the dependent variables in using
a linear regression model with the ordinary least squares (OLS) method. To express the impact
of my main explanatory variables and the dependent variables, I decided to create 6 models.
The first one enables to analyse the impact of the frequency of attacks variable, alone, on the

10
explained variables. The second model shows us the impact of all our variables on terrorism on
our dependent ones. The third model studies the impact of our main independent variable on
terrorism, the frequency of attacks, in adding control variables to deal with the potential
endogeneity. The fourth model analyses the impact of all explanatory variables related to
terrorism with the control variables on dependent variables. In the fifth and the sixth models I
add year dummies to analyse the impact of fixed effect to deal with macroeconomic factors,
events which can impact the results of our variables each year. The fifth model analyses the
impact of the frequency of attacks variable with control variables and the year dummies. The
sixth one analyses the impact of all our independent variables on terrorism added with controls
variables and the year dummies.
Model 1:
Yi = ß1 Frequency of attacks + ui
Model 2:
Yi = ß1 Frequency of attacks + ß2 Number of fatalities + ß3 Number of injuries + ui
Model 3:
Yi = ß1 Frequency of attacks + Control variables + ui
Model 4:
Yi = ß1 Frequency of attacks + ß2 Number of fatalities + ß3 Number of injuries + Control
variables + ui
Model 5:
Yi = ß1 Frequency of attacks + Control variables + Year dummies + ui
Model 6:
Yi = ß1 Frequency of attacks + ß2 Number of fatalities + ß3 Number of injuries + Control
variables + Year dummies + ui
Considering the impact of the military expenditures on the consumer confidence, the number
of tourist arrivals and departures, I used a log-linear model to obtain a significant model.
All estimations were made with Stata.

11
EMPIRICAL RESULTS
0. Descriptive statistics
Analysing the impact of terrorism through three independent variables directly linked to the
terrorism activity, it would be interesting to observe their evolution between 2001 and 2015.
As we can expect when we observe the variable frequency of attacks per year on our period,
we can notice approximatively the same evolution between countries, with some peaks
sometimes. At the beginning of 2000’s years there are a high frequency of terrorist attacks then
a slowing down from 2007 to 2011, to have once again an acceleration from 2012 to 2015.
161
3004
57191
0
500
1000
1500
2000
2500
3000
3500
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Figure 2
Number of fatalities per year
France
USA
UK
Spain
Belgium
0
10
20
30
40
50
60
70
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Figure 1
Frequency of terrorist attacks per year
France
USA
UK
Spain
Belgium

12
When we compare the evolution of the number of dead people in the figure above, take a census
of the number of fatalities per year, we see the extreme heterogeneity between countries. The
heavy toll of the attacks of 2001 makes these attacks the most deadly ones. All other attacks, as
these which took place in Madrid in 2004, in London in 2005, or those of Paris in 2015 seem
to have a little impact compared to those of 2001, while they were also particularly murderous.
This difference demonstrates the high heterogeneity of human tolls caused by terrorist attacks.
When we observe the evolution of the number of injuries per year due to terrorist attacks, we
observe the same heterogeneity than before. Some attacks made much more injured people than
others as those of Madrid, London and Boston in 2013. What is surprising is the low number
of people injured for the United-States in 2001 compared to the number of deaths the same year.
After, having looked at the evolution of our variables on terrorism and before beginning in the
next parts the results, it is interesting to observe if our variables are correlated or not and if yes
in which direction.
As we can notice the contribution of the tourism industry on the GDP and the arrivals of
international tourists are positively correlated with the frequency of the attacks at the 5% level
of significance. The contribution of the tourism industry on the GDP is also correlated
Frequency of
attacks
Number of
fatilities
Number of
injuries
Household
spending
Consumer
confidence
Tourism contribution
to GDP
International
tourist arrivals
Resident tourist
departures
Military
expenditures
Frequency of attacks 1.0000
Number of fatilities 0.2175 1.0000
Number of injuries 0.1942 0.1037 1.0000
Household spending 0.1895 0.0847 0.2120 1.0000
Consumer confidence 0.0495 0.0654 0.1065 0.6584* 1.0000
Tourism contribution to GDP 0.2917* -0.0431 0.2364* 0.0542 0.0102 1.0000
International tourist arrivals 0.3481* 0.0493 0.0746 0.0232 -0.3127* 0.5092* 1.0000
Resident tourist departures 0.1193 0.1156 -0.1308 0.1203 -0.1315 -0.1692 0.3461* 1.0000
Military expenditures 0.1517 0.0865 -0.0512 0.1594 -0.2801* -0.2035 0.5159* 0.7816* 1.0000
legend : * p<.05
Correlation between terrorism variables and the dependant variables
Figure 4
156128
836
1810
0
200
400
600
800
1000
1200
1400
1600
1800
2000
2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015
Figure 3
Number of injuries per year
France
USA
UK
Spain
Belgium

13
positively with the number of injured people caused by terrorist attacks at the same level of
significance. Less surprising, at 5% level of significance, there is a strong positive correlation
between the consumer confidence index and the annual growth rate of households spending
with around 0.66. We also observe a strong positive correlation of about 0.78 between military
expenditures and the number of resident tourist departures. The number of international tourist
arrivals is also positively correlated with the contribution of the tourism industry on the GDP,
the number of resident tourist departures and the part of military expenditures in terms of GDP
at 5% level of significance. Always at 5% level of significance, we notice the negative
correlation between the consumer confidence index and two variables, the military expenditures
and the international tourist arrivals.

14
1. Impact of terrorism on consumption
A. Household expenditures
H1 H2 H3 H4 H5 H6
coef/se coef/se coef/se coef/se coef/se coef/se
Frequency of attacks 0,083*** 0,075*** 0,035** 0,029* 0,031** 0,029**
(0,016) (0,017) (0,016) (0,017) (0,013) (0,014)
Number of fatalities 0,000 -0,000 -0,000
(0,001) (0,001) (0,000)
Number of injuries 0,002 0,002* 0,001
(0,001) (0,001) (0,001)
GDP per capita in US$ 0,000*** 0,000*** 0,000** 0,000**
(0,000) (0,000) (0,000) (0,000)
Unemployment in total % of
labour force
-0,125*** -0,131*** -0,114*** -0,112***
(0,036) (0,036) (0,033) (0,033)
ST interest rates per annum 0,369*** 0,352*** 0,293* 0,273*
(0,099) (0,098) (0,156) (0,157)
year==2002 0,576 0,396
(0,716) (0,765)
year==2003 0,614 0,410
(0,743) (0,795)
year==2004 1,376* 0,894
(0,760) (0,852)
year==2005 0,822 0,481
(0,754) (0,822)
year==2006 0,222 0,007
(0,774) (0,829)
year==2007 -0,043 -0,252
(0,804) (0,856)
year==2008 -2,488*** -2,711***
(0,799) (0,859)
year==2009 -2,689*** -2,984***
(0,957) (1,020)
year==2010 0,359 0,059
(1,012) (1,075)
year==2011 -0,854 -1,160
(1,003) (1,065)
year==2012 -1,291 -1,579
(1,065) (1,137)
year==2013 -0,692 -1,060
(1,116) (1,190)
year==2014 0,278 -0,045
(1,116) (1,187)
o._Iyear_2015 Ref Ref
Number of observations 74 74 70 70 70 70
Adjusted R2 0,261 0,268 0,553 0,564 0,755 0,754
note: *** p<0.01, ** p<0.05, * p<0.1
Table 4 Household expenditures in terms of annual growth rates

15
When we compare the results of our six models on the household spending we observe the
biggest significance comes from the fifth and sixth models through the adjusted coefficient of
determination of around 0.75. This means the fifth and the sixth models explain each one around
75% of the variation of the dependent variable, the annual growth of household expenditure.
The model six explains also around 75% of the variance of the household spending but in
considering one less significant variable, the year 2004, which makes it more interesting for us.
When we observe the main independent variable on terrorism, the frequency of attacks, is
positive and significant in all the models. However, its impact decreases with the addition of
control variables, and some year dummies as 2008 and 2009. This means an increase of the
frequency of attacks has for result the rise of annual household expenditures. In the sixth model,
the rise of the frequency of attacks per one unit increases the annual growth of household
spending by around 0.03 at the 5% level of significance. This result is not very surprising,
although the uncertainty should push the consumers to save their money rather spending it.
After the attacks of 2001, the United States have experienced an increase of the household
consumption, more recently France has known the same effect with an increase of the
consumption in January, after the Charlie Hebdo attacks. Our result can be explained by
national revivals rather than psychosis scenarios.
The variable number of injuries is non-significant in our model, except in the fourth one at 10
% level of significance. The variable number of fatalities has also no significant impact on
annual household spending.
The impact of control variables on our model is very important. What is interesting is to look
at the significant effects and their stability in all our models. As we can observe the adjusted R
squared increased from 0.26 to 0.55 in adding the control variables. The difference in the
accuracy of our estimation comes from the addition of our control variables namely the GDP
per capita, the unemployment rate, the short term interest. In the third model 55% of the
variation of the annual growth rate of household expenditures is explained by the frequency of
attacks and the control variables. The two main control variables significant in this model are
the unemployment and the short term interest rate at 1% level of significance. In the fifth model
the control variables are also significant. The unemployment rate is significant at 5% level of
significance which means that the rise of the unemployment rate by 1% will decrease the annual
growth household spending by 0.11. The reduction of household spending can be explained by
the decrease of households’ incomes due to the unemployment. When we observe the
correlation between the household spending and the unemployment rate, we note a strong
negative correlation with around -0.57 at 5% level of significance (Figure 5, Appendix). The
significance of the short term interest rate at the 10% level of significance can be explained by
the fear of household to have higher interest rates later, pushing them to consume now rather
than saving their money. Households can think that terrorist attacks will happen causing in the
future a rise of interest rates. This can explain that an increase of the short term interest rate by
1% will increase the annual growth of household spending by 0.30.
The impact of some year dummies is also very significant. We notice it by the rise of the
accuracy of our estimation in adding year dummies, the adjusted R squared which increases at
0.75. The years related to the global financial crisis, 2008 and 2009, have a significant impact
on the household spending at the 5% level of significance. Their high coefficients can be
explained by multiple factors which compose these years and that affect our dependent variable.
Compared to the year 2015, we can observe smaller household expenditures during these years.

16
B. Consumer confidence
When we study the impact of our variables on the consumer confidence, we observe the
significance of the frequency of attacks and the control variables. When we take the model two,
we notice that it explains around 39.8% of the variation of the consumer confidence index
against 98.7% in the model three. This difference in the adjusted R squared comes from the
C1 C2 C3 C4 C5 C6
Frequency of attacks 4,411*** 4,363*** 0,311*** 0,300** 0,268*** 0,273***
(0,604) (0,656) (0,116) (0,122) (0,097) (0,103)
Number of fatalities -0,009 -0,002 0,001
(0,027) (0,004) (0,003)
Number of injuries 0,021 0,005 -0,002
(0,040) (0,006) (0,005)
GDP per capita in US$ 0,002*** 0,002*** 0,001*** 0,001***
(0,000) (0,000) (0,000) (0,000)
labour force
2,071*** 2,041*** 1,525*** 1,517***
(0,257) (0,261) (0,246) (0,252)
ST interest rates per annum 5,871*** 5,829*** 8,828*** 8,889***
(0,708) (0,719) (1,164) (1,188)
year==2002 17,082*** 17,774***
(5,333) (5,774)
year==2003 22,512*** 23,281***
(5,536) (6,000)
year==2004 21,228*** 22,788***
(5,658) (6,434)
year==2005 14,024** 15,196**
(5,616) (6,210)
year==2006 4,866 5,675
(5,766) (6,261)
year==2007 -4,573 -3,780
(5,985) (6,460)
year==2008 -6,792 -5,947
(5,954) (6,486)
year==2009 20,319*** 21,389***
(7,128) (7,699)
year==2010 23,101*** 24,193***
(7,541) (8,121)
year==2011 19,064** 20,170**
(7,470) (8,045)
year==2012 16,880** 17,966**
(7,929) (8,588)
year==2013 19,698** 21,015**
(8,309) (8,987)
year==2014 21,142** 22,327**
(8,311) (8,965)
o._Iyear_2015 (dropped) (dropped)
Adjusted R2 0,411 0,398 0,987 0,987 0,993 0,992
note: *** p<0.01, ** p<0.05, * p<0.1
Table 5 Consumer confidence, in long-term average with 100 as basis and the amplitude adjusted

17
addition of control variables. Although the model three has an adjusted R squared of 0.987
against 0.993 in the model five, it takes in account less variables as the year dummies which
contribute faintly to the accuracy of our estimation. This is why I consider as more interesting
the third model.
In all our models, our variables the number of fatalities and injuries per year are not significant.
Regarding the variable frequency of attacks, it is significant but its coefficient is not stable, it
decreases with the addition of other variables. In the third model, the variable frequency of
attacks is significant at the level of significance 5%, which means one more terrorist attack
increases the consumer confidence index by 0.31. This positive relation can be explained by
the indirect effect of the rise of the frequency of attacks. The repetition of terrorist attacks can
have for consequences to improve the quality of institutions, the functioning of countries. The
terrorism can push the populations to interest themselves to problems constituting their society
and being able to be a breeding-ground for terrorism, and encourage political leaders to take
measures to deal with them. Also, after a terrorist attack, the security which is reinforced can
reassure consumer on capacities of the State to guarantee their safety and to protect their way
of life. The national unity, we generally find after a tragedy, is another factor which can make
the society stronger and increase the consumer confidence.
Concerning the control variable, we notice the significance of the GDP per capita on the
consumer confidence at the 1% level of significance. One unit more of GDP per capita increases
the consumer confidence index by around 0.002. Richer are consumers bigger is their
confidence about their current economic situations and the future one. The short term interest
rate and the unemployment rate are also significant at 1% level of significance. The rise of the
short term interest rate by 1% will increase the consumer confidence index by 5.87. When we
observe the correlation between the consumer confidence index and the short term interest rate,
we note a moderate positive correlation with around 0.27 at 5% level of significance (Figure 5,
Appendix). Although, the rise of short term interest rate decreases the investment capacity of
consumers, the positive relation we have can be explained by a higher remuneration of savings
which improves the current economic situation of consumers. Result more surprising and that
could be the topic of studies, the rise of the unemployment by 1% increases the consumer
confidence index by 2.07.
2. Impact of terrorism on tourism industry
A. Tourism state

18
T1 T2 T3 T4 T5 T6
Frequency of attacks 0,498*** 0,488*** 0,121*** 0,120*** 0,105*** 0,104***
(0,062) (0,066) (0,025) (0,026) (0,024) (0,025)
Number of fatalities -0,002 -0,001 -0,000
(0,003) (0,001) (0,001)
Number of injuries 0,005 0,002* 0,001
(0,004) (0,001) (0,001)
Domestic tourism spending in
billion US$
-0,006 -0,004 -0,007* -0,007
(0,004) (0,004) (0,004) (0,004)
Foreign tourism spending in
billion US$
0,033 0,027 0,062*** 0,060***
(0,021) (0,021) (0,019) (0,020)
GDP per capita in US$ 0,000 0,000 -0,000*** -0,000***
(0,000) (0,000) (0,000) (0,000)
labour force
0,552*** 0,560*** 0,367*** 0,375***
(0,079) (0,077) (0,076) (0,079)
(0,154) (0,153) (0,350) (0,360)
year==2002 4,136*** 3,939***
(1,337) (1,446)
year==2003 5,887*** 5,650***
(1,464) (1,584)
year==2004 6,327*** 5,906***
(1,490) (1,692)
year==2005 4,844*** 4,520***
(1,475) (1,627)
year==2006 3,300** 3,080*
(1,462) (1,581)
year==2007 1,118 0,925
(1,471) (1,580)
year==2008 1,276 1,065
(1,503) (1,625)
year==2009 8,637*** 8,299***
(2,091) (2,240)
year==2010 9,091*** 8,747***
(2,226) (2,376)
year==2011 8,985*** 8,652***
(2,161) (2,307)
year==2012 8,389*** 8,038***
(2,368) (2,534)
year==2013 10,001*** 9,595***
(2,487) (2,661)
year==2014 10,687*** 10,317***
(2,503) (2,667)
Adjusted R2 0,461 0,460 0,952 0,953 0,964 0,963
note: *** p<0.01, ** p<0.05, * p<0.1
Table 6 Tourism contribution to GDP

19
As in the previous relationships, we see the importance of the addition of control variables to
those on terrorism in the accuracy of the regression. Indeed, when we compare the model one
and two and their adjusted R squared of 0.46 to the model three and four with an adjusted R
squared of 0.95, we observe the important role played by the control variables. We also observe
the low contribution of year dummies in the accuracy of our models. Two models are
particularly interesting, the third and the fourth one. The difference between the two models
comes from the significance of the variable number of injuries at 10 % level of significance in
the fourth model. I will consider in my analysis the model three taking in account the non-
significance of the variable the number of injuries in the sixth model, when we add year
dummies.
Contrary to the frequency of attacks variable, significant at 1 % level of significance in all
models, the variables number of injuries is only significant in the model four at 10% level of
significance. We observe stable coefficients for the frequency of attacks variable from the
model three to six. In looking at the model three, we note that one more terrorist attack increases
the tourism industry contribution to GDP by 0.12. This result can be explained by the
investments made by the tourism industry to attract tourists and minimise the impact of
terrorism. The tourism industry can invest in the advertising and make more affordable prices
to be more competitive. Also, public actors can promote the different regions which compose
their country in order to increase the tourist activity.
Surprisingly, we notice that the variables domestic and foreign spending are not significant in
the model three and four. However, we observe that they are significant in the model five. At
10% level of significance, the spending made by residents within the country, represented by
the variable domestic tourism spending, is significant, meaning that an increase of domestic
tourists spending of one billion US dollars decreases the tourism industry contribution to GDP
by around 0.007. The spending made by foreigners in a country, represented by the variable
foreign tourism spending, are significant at 5 % level of significance in the model five and six,
meaning that an increase of foreigner tourists spending by one billion US dollars will increase
the tourism industry contribution to GDP by respectively 0.062 and 0.060. Indeed, more
foreigners spend in a country more tourism activities will tend to grow and contribute to GDP.
Other control variables significant in the third model, the unemployment rate and the short term
interest rate at 1% level of significance. An increase of 1% of the unemployment rate will
increase the tourism industry contribution to GDP by 0.552. When we observe the correlation
between the tourism contribution to GDP and the unemployment rate, we note a strong positive
correlation with around 0.50 at 5% level of significance (Figure 5, Appendix). The rise of the
unemployment can foster people to work in the tourism sector to have an additional income.
The precarious economic situation of unemployed people and the constant demand in low
skilled labour in the tourism sector tend to encourage unemployed people to apply for a job or
to launch their own activity in the tourism industry. The attractiveness of the sector for this
population tend to increase the contribution of tourism to GDP. The impact of the short term
interest rate is more important, its rise of 1% will increase the tourism contribution to GDP by
1.12.

20
B. Tourism flows
a. Impact on international tourist arrivals
A1 A2 A3 A4 A5 A6
(0,108) (0,118) (0,019) (0,020) (0,017) (0,018)
(0,005) (0,001) (0,001)
(0,007) (0,001) (0,001)
(0,000) (0,000) (0,000) (0,000)
labour force
0,396*** 0,391*** 0,321*** 0,320***
(0,041) (0,042) (0,042) (0,043)
(0,114) (0,115) (0,199) (0,203)
year==2002 2,690*** 2,780***
(0,913) (0,989)
year==2003 3,507*** 3,609***
(0,947) (1,027)
year==2004 3,368*** 3,600***
(0,968) (1,102)
year==2005 2,178** 2,344**
(0,961) (1,063)
year==2006 1,040 1,147
(0,987) (1,072)
year==2007 -0,244 -0,139
(1,024) (1,106)
year==2008 -0,529 -0,417
(1,019) (1,111)
year==2009 3,077** 3,223**
(1,220) (1,318)
year==2010 3,296** 3,445**
(1,290) (1,391)
year==2011 2,935** 3,086**
(1,278) (1,378)
year==2012 1,971 2,115
(1,357) (1,471)
year==2013 2,496* 2,677*
(1,422) (1,539)
year==2014 2,626* 2,786*
(1,422) (1,535)
Adjusted R2 0,418 0,404 0,989 0,989 0,993 0,993
note: *** p<0.01, ** p<0.05, * p<0.1
Table 7 Number of international tourist arrivals

21
In analysing the impact of our variables on the number of international tourist arrivals, we
observe the importance of the model three with an adjusted R squared of 0.99, meaning that
around 99% of the variation of the number of international tourist arrivals is explained by the
variation of the frequency of terrorist attacks and control variables. Although for some of them
significant, the year dummies do not bring a lot to our estimation. The model three is the most
interesting one.
In all our models we observe the significance of the frequency of attacks and the control
variables at the level of significance 1%. We note particularly stable coefficients for the
frequency of attacks variable from the model three. In the third model, one more unit of the
frequency of attacks, so one more terrorist attack, increases the number of arrivals of
international tourists by around 7.8%. Roman Egger and Christian Maurer (2016) evoke this
positive relationship. It can be explained by the arrival of tourists coming in a country to support
the population touched by the attacks. The rise of the frequency of attacks can foster public
authorities and the tourism industry to make more and more advertising campaigns promoting
the country and attractive prices in order to attract tourists. Here, we have to consider the impact
of tourist arrivals on the whole country and not from a local point of view. Generally, after a
terrorist attack, tourists tend to avoid the place hit by the attack. Nevertheless, they tend to go
in other areas. After the tragedy which struck Paris the 13th
November 2015, we assisted to a
decrease of the number of foreign tourist arrivals, however this was not the case in other regions
in France.
All control variables are significant at 1% level of significance. One more unit of GDP per
capita will increase the number of international tourist arrivals per 0.03%. This positive relation
comes probably from bigger capacities of richest countries to provide a tourism industry of high
quality and to preserve cultural buildings and spaces. The security aspect can also influence the
choice of international tourists. There is a positive relation between the unemployment rate and
the number of international tourist arrivals, one percent more of unemployment increases the
number of international tourist arrivals by around 39.6%. When we observe the correlation
between the number of international tourist arrivals and the unemployment rate, we note a
moderate positive correlation with around 0.27 at 5% level of significance (Figure 5,
Appendix). This can be explained by the rise of tourism services when unemployed people
transform themselves to the tourism industry, where the needs in terms of labour are important
and the qualification requirements low. This increase of services can foster tourism. Concerning
the short term interest rate, we observe it has a positive impact on the number of tourist arrivals,
when the short term interest rate increases by 1%, the number of international tourist arrivals
increases by around 94%.

22
b. Impact on resident tourist departures
D1 D2 D3 D4 D5 D6
(0,107) (0,116) (0,018) (0,019) (0,014) (0,015)
(0,005) (0,001) (0,000)
(0,007) (0,001) (0,001)
(0,000) (0,000) (0,000) (0,000)
labour force
0,259*** 0,257*** 0,165*** 0,163***
(0,039) (0,040) (0,036) (0,037)
(0,109) (0,111) (0,171) (0,172)
year==2002 2,539*** 2,751***
(0,782) (0,835)
year==2003 3,519*** 3,758***
(0,812) (0,867)
year==2004 3,307*** 3,851***
(0,830) (0,930)
year==2005 2,221*** 2,611***
(0,824) (0,898)
year==2006 0,653 0,905
(0,846) (0,905)
year==2007 -0,889 -0,643
(0,878) (0,934)
year==2008 -0,927 -0,666
(0,873) (0,938)
year==2009 3,699*** 4,041***
(1,045) (1,113)
year==2010 4,020*** 4,368***
(1,106) (1,174)
year==2011 3,451*** 3,806***
(1,095) (1,163)
year==2012 3,043*** 3,380***
(1,163) (1,242)
year==2013 3,449*** 3,875***
(1,219) (1,299)
year==2014 3,523*** 3,891***
(1,255) (1,332)
Adjusted R2 0,410 0,395 0,990 0,990 0,995 0,994
note: *** p<0.01, ** p<0.05, * p<0.1
Table 8 Number of resident tourist departures

23
Studying this time, the impact of our variables on the number of resident tourist departures, we
observe the significant impact of the frequency of attacks in all our models. We also note the
importance of the control variables in the accuracy of our estimations in looking the adjusted
coefficient of determination of the model one at 0.41 and this of the model three at 0.99 in
adding the control variables. As in the previous subpart on the number of international tourist
arrivals, I consider the model three as the most interesting one regarding the low accuracy
provided by the addition of year dummies.
The coefficient of the frequency of attacks variable is stable from the model three to the sixth
one. In the model three, the frequency of attacks is significant at 5% level of significance which
means one more terrorist attack per year will increase the resident tourist departures by around
5.8%. This can be explained by the traumatism caused, the fear and unsafety feelings
encouraging resident tourists to leave their country and going abroad. We can note the absence
of significance for the two other variables on terrorism, the number of fatalities and injuries.
Concerning the control variables, the GDP per capita variable is significant at 1% level of
significance, one more unit of the GDP per capita, so one US dollar more, will increase the
resident tourist departures by 0.03%. The raise of people wealth can foster people to travel
abroad rather than staying in their country. The unemployment is also significant at 1% level
of significance, the raise of the unemployment rate by one percent will increase the resident
tourist departures by 25.9%. When we observe the correlation between the number of resident
tourist departures and the unemployment rate, we note a strong negative correlation with around
-0.49 at 5% level of significance (Figure 5, Appendix). The hardening economic situation and
the decrease of incomes related to the rise of the unemployment rate can explain it. People can
travel abroad, particularly in developing country, to have cheaper holidays. The short term
interest rate is another variable significant at 1% level of significance. The increase of one
percent of the short term interest rate will raise the resident tourist departures by 87.7%. The
rise of the short term interest rate is associated with a high currency value which increases the
purchasing power of residents abroad. In this way, residents can travel abroad to benefit of the
advantages of a strong currency rather than staying in their country and not benefiting of it.

24
3. Military expenditures in response to attacks
A. Impact of terrorism on military expenditures
M1 M2 M3 M4 M5 M6
Frequency of attacks 0,105*** 0,104*** 0,013 0,013 0,014* 0,016*
(0,015) (0,016) (0,008) (0,009) (0,008) (0,009)
Number of fatalities -0,000 0,000 -0,000
(0,001) (0,000) (0,000)
Number of injuries 0,000 -0,000 -0,000
(0,001) (0,000) (0,000)
(0,000) (0,000) (0,000) (0,000)
labour force
-0,055*** -0,054*** -0,014 -0,011
(0,018) (0,018) (0,021) (0,021)
ST interest rates per annum -0,005 -0,005 -0,261*** -0,265***
(0,049) (0,050) (0,100) (0,101)
year==2002 -0,556 -0,735
(0,457) (0,490)
year==2003 -0,728 -0,913*
(0,474) (0,510)
year==2004 -0,793 -0,934*
(0,485) (0,546)
year==2005 -0,975** -1,156**
(0,481) (0,527)
year==2006 -0,972** -1,168**
(0,494) (0,532)
year==2007 -0,866* -1,060*
(0,513) (0,549)
year==2008 -0,966* -1,174**
(0,510) (0,551)
year==2009 -1,536** -1,757***
(0,611) (0,654)
year==2010 -1,746*** -1,975***
(0,646) (0,690)
year==2011 -1,782*** -2,007***
(0,640) (0,683)
year==2012 -2,285*** -2,549***
(0,679) (0,729)
year==2013 -2,561*** -2,815***
(0,712) (0,763)
year==2014 -2,762*** -3,022***
(0,712) (0,761)
Adjusted R2 0,397 0,380 0,898 0,895 0,908 0,907
note: *** p<0.01, ** p<0.05, * p<0.1
Table 9 Military expenditures in percentage of GDP

25
Analyzing the impact of our variables on the raise of military expenditures, we observe,
contrary to our previous relationships, that the frequency of attacks is not significant in all our
models. We note when we add control variables to our terrorism variables the frequency of
attacks is not significant anymore. However, when we add the year dummies in the models five
and six the frequency of attacks is once again significant. Considering the adjusted R squared
of 0.908, I will analyze particularly the model five. This means 90.8% of the variation of the
military expenditure in percentage of GDP is explained by our independent variables. In
previous relationships, I often chose to analyze the model three regarding its accuracy compared
to the model two, the low difference with the models five and six and the significance of the
frequency of attacks. Considering the fact the variable on the frequency of attacks is not
significant in the model three but is significant in the model five, I think the model five is more
interesting to analyze.
In the model five, we note the significance of the frequency of attacks at 10% level of
significance. One more unit of the frequency of attack will increase the military expenditures
expressed in percentage of GDP by 0.014. The strengthening of the security by the raise of
military expenditures expresses the need of governments to reassure their citizens and the world
about the capacity of their country to assure the safety and the stability of their institutions.
Among the control variables, we note the significance of the short term interest rate at 5% level
of significance. The raise of the short term interest rate by one percent will decrease the military
expenditures in percentage of the GDP by around 0.261. The increase of the short term interest
rate affects the public deficits of countries and so the public debt. To deal with that countries
tend to reduce their spending in the defense sector. Generally, in developed countries, the
department of defense constitutes an adjusting variable to manage the public deficit.
The impact of the addition of year dummies does not bring a lot to the accuracy of the
estimation. However, the negative coefficient we find for some year dummies is explained by
multiple factors which compose these years and that affect our dependent variable.
B. Impact on consumer confidence and tourism flows

26
Consumer
confidence
Number of international
tourist arrivals
Number of resident
tourist departures
coef/se coef/se coef/se
Military expenditures in % of
GDP
-5,307*** 0,205 0,098
(1,487) (0,284) (0,247)
Frequency of attacks 0,358*** 0,075*** 0,052***
(0,096) (0,018) (0,015)
Number of fatalities -0,000 0,000 0,000
(0,003) (0,001) (0,000)
Number of injuries -0,003 -0,000 -0,001
(0,005) (0,001) (0,001)
GDP per capita in US$ 0,002*** 0,000*** 0,000***
(0,000) (0,000) (0,000)
labour force
1,457*** 0,323*** 0,164***
(0,228) (0,044) (0,037)
ST interest rates per annum 7,483*** 1,317*** 1,470***
(1,141) (0,218) (0,185)
year==2002 13,871*** 2,931*** 2,823***
(5,319) (1,015) (0,861)
year==2003 18,437*** 3,796*** 3,847***
(5,576) (1,064) (0,903)
year==2004 17,829*** 3,792*** 3,944***
(5,964) (1,138) (0,966)
year==2005 9,061 2,581** 2,724***
(5,857) (1,118) (0,949)
year==2006 -0,522 1,386 1,020
(5,905) (1,127) (0,958)
year==2007 -9,406 0,078 -0,538
(6,034) (1,151) (0,978)
year==2008 -12,179** -0,176 -0,550
(6,103) (1,165) (0,989)
year==2009 12,065 3,583** 4,212***
(7,416) (1,415) (1,202)
year==2010 13,709* 3,850** 4,561***
(7,888) (1,505) (1,279)
year==2011 9,520 3,497** 4,002***
(7,843) (1,497) (1,273)
year==2012 4,438 2,637 3,626***
(8,620) (1,645) (1,398)
year==2013 6,074 3,254* 4,149***
(9,119) (1,740) (1,481)
year==2014 6,287 3,406* 4,158***
(9,247) (1,765) (1,502)
o._Iyear_2015 (dropped) (dropped) (dropped)
Number of observations 70 70 69
Adjusted R2 0,994 0,993 0,994
note: *** p<0.01, ** p<0.05, * p<0.1
Table 10 Evolution of consumer confidence and tourism flows with the rise of military
expenditures

27
After having studied the impact of our variables on the consumer confidence, the number of
international tourist arrivals and the number of resident tourist departures, let us add another
independent variable, the military expenditures and observe its impact on the dependent
variables. To study that, I used the sixth models of our relationships and I added the military
expenditures variable. We can notice that military expenditures have no effect on the number
of international tourist arrivals and the number of resident tourist departures. However, this
variable increases the accuracy of the regression on the consumer confidence. In the part one,
we saw that the model sixth explained 99.24% of the variation of the consumer confidence
index. When we add the military expenditures in percentage of GDP as independent variable
the same model explains 99.38% of the variation of the consumer confidence index.
The military expenditures variable has a significant impact on the consumer confidence at the
1% level of significance. Indeed, one percent more of military expenditures decreases the
consumer confidence index by 5.307. When we observe the correlation between the consumer
confidence and the military expenditures, we note a moderate negative correlation with around
-0.28 at 5% level of significance (Figure 5, Appendix). This result can be explained by the fear
that can inspired the raise of military spending. Although, the army permits to defend the
citizens, it can remind people a kind of unsafety. The increase of military spending can make
people think to the terrorist threat, the state of war in which they are, the particularity of the
situation.
CONCLUSION
When we analyse the impact of terrorism on the economy through the consumption, the tourism
and the military expenditures, we observe that the frequency of attacks seems to have a positive
effect on the consumption, the tourism and to a lesser extent on the military expenditures.
The psychological aspect plays an important role to understand our results. People seem more
concerned by the repetition of attacks than by their toll. Regarding the damages caused by
terrorism, the positive coefficients found about the frequency of attacks variable in our
relationships could not be positive without. Between dead or injured people, physically or
mentally, with the post-traumatic stress disorder for instance, the consequences for the human
capital is huge. To this impact we can add damages caused to the physical capital. This is why
decisions, measures taken by governments, political leaders and all other actors in the society
are very important. They permit to limit the consequences of terrorism. In our study they are
probably at the origin of positive relationships we found between the frequency of attacks and
our dependant variables. If no measures were taken after terrorist attacks, the impact on our
dependant variables would probably be negative.
Some results, as the impact of military expenditures on consumer confidence, raise questions
about the efficiency of State policies deal with the terrorism and reassure their citizens.

28
REFERENCES
Abadie, Alberto and Gardeazabal, Javier (2001), “The Economic Costs of Conflict: A Case
Study of the Basque Country”, NBER Working Paper, N° 8478, NBER Publishing.
Abadie, Alberto and Gardeazabal, Javier (2008), “Terrorism and the World Economy”,
European Economic Review, vol. 52, pages 1-27.
Barro, Robert and Lee, Jong-Wha (2000), “International Data on Educational Attainment
Updates and Implications”, NBER Working paper, N° 7911, NBER Publishing.
Becker, Gary and Rubinstein, Yona (2011), “Fear and the Response to Terrorism: An
Economic Analysis”, CEP Discussion Paper, N° 1079, Centre for Economic Performance and
London School of Economics and Political Science Publishing.
Bloom, Nicholas (2009), “The Impact of Uncertainty Shocks”, NBER Working Paper, N°
13385, NBER Publishing.
Chernick, Howard, (2005), Resilient City: The Economic Impact of 9/11, Russel Sage
Foundation.
Clark, Andrew and Stancanelli, Elena (2016), “Individual Well-Being and the Allocation of
Time Before and After the Boston Marathon Terrorist Bombing”, PSE Working paper, N°
2016 – 07, Paris School of Economics Publishing.
Egger, Roman and Maurer, Christian (2016), ISCONTOUR 2016 Tourism Research
Perspectives, Books on Demand.
Hines, James and Jaramillo, Christian (2004), “The Impact of Large Natural Disasters on
National Economies”, Mimeo, The University of Michigan Publishing.
Horwich, George (2000), “Economic Lessons of the Kobe Earthquake”, Economic
Development and Cultural Change, vol. 48, pages 521 – 42, The University of Chicago Press.
Karolyi, Georges Andrew and Martell, Rodolfo (2005), “Terrorism and the Stock Market”,
Charles A. Dice Center for Research in Financial Economics Working Paper, N° 2005 – 19,
Charles A. Dice Center for Research in Financial Economics Publishing.
Krueger, Alan, (2008), What Makes a Terrorist: Economic and the Root of Terrorism,
Princeton University Press.
Lenain, Patrick; Bonturi, Marcos and Koen, Vincent (2001), “The economic consequences of
terrorism”, Economics Department Working Paper, N° 334, OECD Publishing.

29
APPENDIX
Table 1 Data sources
VARIABLES ON
TERRORISM
Global Terrorism
Database (GTD)
Organisation for
Economic Co-operation
and Development
(OECD)
World Development
Indicator (WDI) - World
Bank
World Travel &
Tourism Council
(WTTC)
LEVELS OF MEASUREMENT DEPENDENT VARIABLES
Household spending (annual growth rate) – OECD
Consumer confidence index [amplitude adjusted –
long term average] – OECD
Total contribution of travel and tourism on GDP –
WTTC
International tourism, number of arrivals – WDI
International tourism, number of departures – WDI
Military expenditures (% of GDP) – WDI
Consumer confidence index [amplitude adjusted –
long term average] – OECD
International tourism, number of arrivals – WDI
data
International tourism, number of departures – WDI
Table 2 The dependent variables by level of measurement
The consumption expenditures
The tourism industry
The military expenditures

30
Household
spending
Consumer
confidence
Tourismcontribution
toGDP
Domestictourism
spending
Foreigntourism
spending
International
touristarrivals
Residenttourist
departures
Military
expenditures
GDPper
capita
Unemployment
rate
Shortterm
interestrate
Householdspending 1.0000
Consumerconfidence 0.6584* 1.0000
TourismcontributiontoGDP 0.0542 0.0102 1.0000
Domestictourismspending 0.2079 -0.1562 -0.2145 1.0000
Foreigntourismspending 0.1267 -0.2582* -0.0485 0.9376* 1.0000
Internationaltouristarrivals -0.0252 -0.3194* 0.3526* 0.2818* 0.4928* 1.0000
Residenttouristdepartures 0.1949 -0.0627 -0.1401 0.7401* 0.5723* 0.0545 1.0000
Militaryexpenditures 0.1594 -0.2801* -0.2035 0.9066* 0.8519* 0.4346* 0.7774* 1.0000
GDPpercapita -0.0908 -0.2392* -0.5507* 0.7082* 0.6656* 0.0412 0.5799* 0.5882* 1.0000
Unemploymentrate -0.5732* -0.3498* 0.5014* -0.3215* -0.0932 0.2720* -0.4873* -0.3518* -0.2561* 1.0000
Shortterminterestrate 0.3376* 0.2745* 0.1302 -0.0585 -0.1767 -0.1520 0.0846 -0.0242 -0.3791* -0.4148* 1.0000
legend:*p<.05
Correlationbetweencontrolanddependantvariables
Figure5
VARIABLES ON
TERRORISM
CONTROL VARIABLES YEAR DUMMIES
The frequency of attacks per
year - GTD
GDP per capita (current
US$) – WDI
The number of fatalities per
year - GTD
Unemployment, total (% of
total labour force) (national
estimate) – WDI
The number of injuries per
year - GTD
The short term interest rate -
OECD
The year 2015 as the
reference year
Table 3 The explanatory variables by category

MASTER THESIS - THE ECONOMIC CONSEQUENCES OF TERRORISM - Thibaut GRANCHER

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