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The Impact of terrorism on Turkey's
economic performance From 1990 to 2016
First-Author
Mario Arturo RUIZ ESTRADA,
Department of Economics,
Faculty of Economics and Administration,
University of Malaya,
Kuala Lumpur 50603,
[Tel] (+60) 37967-3728
[H/P] (+60) 126850293
[E-mail] [email protected]
Second Co-Author
Donghyun PARK,
Principal Economist,
Asian Development Bank (ADB)
6 ADB Avenue, Mandaluyong City, Metro Manila,
Philippines 1550.
[Tel] (+63) 26325825
[E-mail] [email protected]
Third Co-Author
Alam Khan,
Department of Economics,
Faculty of Economics and Administration,
University of Malaya,
Kuala Lumpur 50603,
[H/P] (+60) 114391931
[E-mail] [email protected]
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Abstract
This research work applies the terrorist attack vulnerability evaluation model (TAVEModel) to evaluate the effect of terrorism on the economic performance of Turkey. We examine
both the short run and long run economic impact of terrorist attacks in Turkey. The TAVE-Model
applies a number of indicators to evaluate the economic impact. The indicators are economic
desgrowth (-δ), intensity of terrorist activities (αi), terrorist attack losses (-π), economic wear (Π),
level of terrorist attack tension (ζ), level of terrorist attacks monitoring (η), and total economic
leaking (Ωt) under a terrorist attack. The idea of TAVE- Model is that the economic impact of a
terrorist attack depends on a country’s vulnerability to attacks from domestic and international
terrorist groups. The application of model to Turkey is highly topical in light of the spate of terrorist
attacks the country suffered recently. The results of TAVE- Model confirms that economic leaking,
economic desgrowth, and economic wear has increased from 1990s to 2016. The issue of terrorism
in Turkey is a multidimensional, which requires an effective social assistance programs as well as
a stronger and impartial justice system, that will render poorer Turks and will increase the
opportunity cost of terrorism.
Keywords
Terrorism, economic modeling, economic desgrowth, policy modeling, Turkey
JEL Code
R11, R12
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1. Introduction
Terrorism, which has plagued many countries since the 9/11 attack at the beginning of the
new millennium, is a global challenge. Terrorism issue has affected both advanced economies and
developing economies. Terrorists can strike anywhere any time in the world (e.g., terrorist attacks
took place in France, Denmark, and Turkey in November 2015 and in Indonesia in January 2016).
The underlying drivers of terrorism are multidimensional, extending from religious fanaticism to
a feeling of estrangement from society to outrage at perceived geopolitical injustice. However,
economic factors contribute to the rise of terrorism. Poor economic performance limits
employment and other economic opportunities, and worsens income inequality and poverty. The
surge in poverty and inequality increases the number of potential recruits for terrorism. Lack of
economic opportunities can be a powerful driver of terrorism, in conjunction with social and
political dynamics.
Terrorist strikes can harm business and consumer confidence, which reduces investment
and consumption and hence overall economic growth. Terrorist attacks that target vital
infrastructure such as power plants or roads paralyze transportation, communication, and the entire
economy. Terrorist attacks have four major economic consequences. These are (1) destruction of
human and physical capital, (2) increase in vulnerability, (3) expansion of defense expenditures,
and (4) re-allocation of scare resources from developmental to non-developmental purposes. Some
sectors such as the tourism or hotel industry (Abadie & Gardeazabal, 2008) are especially
vulnerable. Most studies of terrorism look at either developed economies or developing economies.
So far four studies – Araz-Takay et al. (2009), Ocal and Yildirim (2010), Derin-Gure (2011) and
Bilgel and Kaashan (2015) - examined the impact of terrorism on Turkey’s economic performance.
All the above studies applied traditional methodology to investigate the relationship between
terrorism and economic performance.
The central objective of our paper is to assess the extent to which terrorism affects the
economic performance of Turkey by applying TAVE model (Ruiz Estrada, Park, Kim & Khan,
2015). This paper contributes to the literature and differs from previous studies by moving on
from classical economic models such as the linear and non-linear models to a new economic
mathematical modelling and mapping of terrorist attacks. The new analytical framework is based
on high-resolution multidimensional graphs and a new mathematical framework. Another
advantage of the TAVE model is that it explicitly distinguishes between the pre-attack phase and
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post-attack phase. The rest of the paper is organized as follows. Section 2 presents the theoretical
framework of terrorism. Section 3 gives a short overview of terrorism in Turkey. Section 4 presents
an introduction to the mega dynamic disks coordination space in vertical position. Section 5
discusses the results of the application of the TAVE model to Turkey. Section 6 reports and
discusses the main findings of our econometric analysis of the determinants of terrorism in Turkey.
Section 7 concludes the paper.
2.
Theoretical Framework of Terrorism
Terrorism, which is a form of conflict, has harmful effects on the economic and social well-
being of society. Collier (1999) presents the different ways through which civil war affects
economic performance. Collier’s insights can be applied to terrorism, which is considered another
type of violence. The effects of conflict on the economy are the loss human of human life and
destruction of capital, high transaction costs, reduced savings, high risk and uncertainty, capital
flight and brain drain, increased insecurity, and shift of resources from developmental purposes to
non-developmental purposes. Eckstein and Tsiddon (2004), Naor (2006), and Mirza and Verdier
(2008) provide a theoretical basis for how terrorism negatively affects economic performance. On
the other hand, Tavares (2004), Abadie (2005) and Gries, Krieger, and Meierrieks (2011) found
no effect of terrorism on economic performance.
Terrorism affects the targeted economy via various channels. Sanders and Enders (2008)
emphasizes the adverse effect on FDI, which is often a major engine of growth for developing
countries. Such capital flight from terrorism-affected economies is equivalent to the effect of civil
war (Collier et al., 2003; Sandler & Enders, 2008). Drakos (2004) and Ito and Lee (2005) argue
that terrorism affects the whole economy and, in some cases, specific sectors. For example, airline
profits and tourist numbers fell after the 9/11 attacks. Another major economic cost of terrorism is
the increase in security expenditure. For example, after 9/11, the US spent a lot of resources on the
Department of Homeland Security. In 2015, the US allocated around US$ 598 billion to military
expenditures (Enders & Sandler, 2006). Terrorism also increases the cost of doing business and
trade because of the increase in insurance premium, expenditure on the purchase of security
equipment, and increase in the salaries of employees who are exposed to risks.
The economic impact of terrorism is also related to the size of the GDP and the economic
diversification. For example, the US Department of State Fact Sheet (2002) reports that the
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shipping sector of Yemen was seriously affected by the terrorist attacks on the USS Cole and MV
Limburg. The competitive advantage shifted to Djibouti and Oman because of these terrorist
attacks. Insurance premium rose by 300% in Yemen. The average monthly loss to the shipping
industry of Yemen was amounted to $3.8 million. This type of economic cost is substantial for a
small economy like Yemen.
3.
An Overview of Terrorism in Turkey
Turkey is an upper middle-income economy, which has been suffering from terrorism since
1960s. For more than three decades, Turkey has suffered terrorist attacks, which have taken away
almost 35,000 lives so far (see Figure 1).
Figure 1: Terrorism Incidents in Turkey
Source: GTD (2016)
The Partiya Karekeren Kurdistan (PKK), a Kurdish terrorist group (called KADEK in
2002), was responsible for most terrorist attacks during this period. Some factors contributed to
instability and terrorism in Turkey during the 1960s and 1970s. Examples include rapid
urbanization; high unemployment due to growth of urban population; mounting conflict in
southeast Turkey, the traditional heartland of the Kurdish minority; and sporadic strikes by radical
Islamists and liberal student (Rodoplu, Arnold and Ersoy, 2003).
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During 1990s, three main groups of terrorists emerged in Turkey - Kurdish separatist
groups, radical Islamic terrorists, and leftist terrorists. All these three groups of terrorists have their
own objectives. For example, the main objective of Kurdish separatist terrorist groups is to
establish an independent state across the ethnically Kurdish areas of southeastern Turkey, northern
Syria and Iraq, and western Iran. The ultimate objective of radical Islamic terrorist groups in
Turkey has been to topple the secular Turkish state and set up a Sharia-based Islamic state. The
third terrorist group, leftist terrorist groups, plans to set up a communist state in Turkey under a
Marxist philosophy and ideology.
Ocal and Yildirim (2010) argue that terrorism activities in Turkey can be arranged into four
major events. The first was the late 1960s when conflicts erupted between left-wingers and
conservatives. Political unrest centered on educational institutions. According, to a Ministry of
Foreign Affairs report, Turkey expected 43,000 terrorist attacks between 1978 and 1982. The
economic downturn of the 1980s further worsened the situation. Turkey returned to civilian rule
in 1983 under a new constitution. The second event of terrorism in Turkey was radical religious
terrorism which aimed to impose Islamic law, and to overthrow the secular and democratic
government.
The third and most violent kind of terrorism in Turkey is separatist and ethnic terrorism
carried out by ethnic Kurds. The fundamental objective of Kurdish terrorism is the creation of an
independent Kurdish state. There are sizable economic disparities between the underdeveloped
Kurdish area of southeastern Turkey and the more advanced western regions of the country.
Economic gap between different regions within the same country breeds resentment and discontent
in the underdeveloped areas, which may lead to insurgency and terrorism. The fourth event of
terrorism in Turkey is the rise of extremist global religious terrorist organizations. The most visible
and powerful example is ISIS, the world’s most dangerous terrorist group which is based largely
in Iraq and Syria. In fact, ISIS controls and rules large swathes of the two countries, which both
share long borders with Turkey. ISIS-controlled areas in Iraq and Syria form a self-styled caliphate.
Turkey is a key member of the anti-ISIS coalition, along with NATO and a number of Arab
countries. In addition to posing a threat to Turkey from Iraq and Syria, ISIS has staged some
terrorist attacks on Turkish soil since 2015.
Recently, the government of Turkey has taken steps to tackle terrorism. In July 2014, the
government has implemented anti-terrorism policy after ceasefire with the Kurdish Communities
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Union (KCK) broke down. A large segment of the Turkish population, exhausted from PKK
terrorist attacks, shows strong support for the government’s tough anti-terrorist operations.
Nevertheless, the government too will need to communicate better with Islamist and Kurdish
citizens, and to build deeper trust with both communities. Sound economic development policies,
combined with socially enlightened environment, will disconnect terrorists from the general
Turkish population, including ethnic Kurds. Good relationship and trust between the Turkish
government and all segments of the Turkish population, as well as inclusive economic growth and
social progress that benefits all, is essential for defeating terrorism in Turkey.
4. An Introduction to the Mega-Dynamic Disks Coordinate Space in Vertical Position
Initially, the mega-dynamic disks coordinate space in vertical position (Ruiz Estrada, 2014)
proposes a new graphical modeling to visualize a large amount of data. Firstly, this specific
coordinate space shows one single vertical straight axis that is pending among all endogenous
variables. Hence, we are available to plotting our endogenous variable on this single vertical
straight axis that is represented by αV+/-. Secondly, each exogenous variable in analysis is
represented by its specific coordinate system such as βΦi:ζj. Where “Φi” represents the sub-space
level in analysis, in this case either from sub-space level zero (SS0°) to sub-space level infinite
(SS360°); “ζj” represents the disk level in analysis at the same quadrant of exogenous variables (in
our case, from disk level j=1, disk level j=2, disk level j=3,…, to disk level j=∞…). In fact, we
assume that all exogenous variables are using only real positive numbers (R+). In order to plot
different exogenous variables in the mega-dynamic disks coordinate space in vertical position,
each value need to be plotted directly on its radial subspace in analysis (Φi) and disk level in
analysis (ζj) respectively. Each “i” is a radius that emanates from the origin and in defined by the
angle which can range from 0 to just before 360°, a theoretical infinite range. Each disk is a
concentric circle that starts from the origin outwards towards a theoretical infinite value. At the
same time, all these values plotted in different axis levels in analysis (Φi) and disk levels in analysis
(ζj) need to be joined with its endogenous variable “αV+/-” until we build a series of coordinates.
All these coordinates need to be joined by straight lines until yields an asymmetric spiral-shaped
geometrical figure with n-faces (see Figure 1) and disk levels in analysis (ζj) need to be joined
together by straight lines directly to the endogenous variable αV+/- until a cone-shaped figure with
n-faces is built (see Figure 2). It is important to mention at this juncture that the endogenous
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variables “αV+/-” is fixed according to any change associated with its corresponding exogenous
variables in βΦi:ζj, where i = {0°, 1°, 2°,…,360°} and j = {0, 1, 2,…,∞…} , αV+/-. Hence, we can
imagine a large number of exogenous variables moving all the time in different positions within
its radius in real time continuously. At the same time, we can visualize how all these exogenous
variables directly effect on the behavior the endogenous variable (αV+/-) simultaneously. αV+/- is
fixed according to any change can be occurred among the infinite exogenous variables in βΦi:ζj,
where i = {0°, 1°, 2°,…,360°} and j = {0, 1, 2,…,∞…}, YV+/-. Hence, we can imagine a large number
of infinite exogenous variables moving all the time in different positions within its radius in real
time continuously (Ruiz Estrada, 2011b). At the same time, we can visualize how all these
exogenous variables are affecting directly on the behavior of the endogenous variable (αV+/-)
simultaneously. Moreover, the endogenous variable (αV+/-) can fluctuate freely (see Figure 2). In
our case, the endogenous variables (αV+/-) can show positive/negative properties according to our
multidimensional coordinate space. In the case of exogenous variables, these can only experience
non-negative properties. The mega-dynamic disks multivariable random coordinate space in
vertical position is represented by:
(βΦi:ζj, αV+/-) where βΦi:ζj ≥ 0; i = θ° ; j =R+ ≥ 0; αV+/-= R+/αV+/- = ƒ (βΦi:ζj)
(1)
(2)
Figure 2
The Mega-Dynamic Disks Coordinate Space in Vertical Position
Source: Ruiz Estrada (2014)
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5. Application of TAVE-Model to Turkey
In this section, we apply the TAVE-Model to a possible terrorist conflict between the
Turkish government (P1) and domestic and international terrorist groups in Turkey (P2). The
TAVE-Model is using four different types of players. The first group of players is the Turkish
government that we identified as the first player (P11) and the second group is the domestic terrorist
groups in Turkey is the second player in the first group (P12). The second group of players is
external players are following by the European Union that is the first player in the second group
(P21) and the international terrorist group in our case is identified as Islamic Stare –IS- group that
in our case is player the second player in the second group (P22) (see an annex for methodology of
TAVE-Model).
The TAVE-Model assumes that P11 (Turkish government) will get fully support from the
European Union (P21). On the other hand, P12 (domestic terrorist groups from Turkey) will get
fully support from the international terrorist group such as IS (P22). The three main elements that
can precede a deep and longer conflict between P11 (Turkish government) and P12 (domestic
terrorist groups from Turkey) are: (i) Income inequality distribution; (ii) domestic and international
politics polarization that can generate a rapid expansion and infiltration of extremist terrorist
groups into Turkey from Syria, Iraq, Afghanistan. (iii) Rivalry for political control between pro
and opposition political parties in Turkey. These factors have jointly generated a high level of
terrorist tension between P11 and P12.
According to the TAVE-Model, the level of military tension between P11 (Turkish
government) and P12 (domestic terrorist groups from Turkey) rises from 0.45 in 1990 to 0.95 in
2016. The economic leaking (Ωt) from the terrorist war is moving from 0.35 in the 1990s to 0.85
in 2016. In the case of Economic desgrowth (-δ) is located -0.25 in the 1990s and -0.65 in year
2016. The war economic wear (Π) was 0.85 in the 1990s and 0.85 in year 2016. If war intensified
between the two main players (P11 and P12), we need to take into account their relative weighing
for each case in study.
The TAVE-Model indicates that the relative military weighting of Turkish government (P11)
versus domestic terrorists (P12) is as follows (P11:P12): military external support (6:2); war
technological systems (6:1); army size (7:2); strategy, information, and logistic systems (7:2);
strategic army locations (7:3); society anti-terrorist support (7:2); military knowhow (6:2); (viii)
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transportation, communications, and IT systems (5:1). P1 (the Turkish government) enjoys a clear
overall superiority relative to P2 (the domestic terrorist groups in Turkey) (See Figure 3).
Figure 3: The Relative Military Weighting of Turkish Government (P11) versus
Domestic Terrorists Groups (P12)
Source: Interior Ministry of Turkey (2016) and Republic of Turkey Ministry of National (2016)
Defense.
Hence, Turkish government (P11) is likely defeat domestic terrorist groups, especially if it can
generate more favorable economic and social conditions. Economic leaking (Ωt) during the
terrorist conflict from 1990’s to 2016 is equal to 0.85. The terrorism economic desgrowth (-δ) is
estimated from 1990’s to 2016 is equal to -0.65. Finally, the terrorism losses (-π) between 1990’s
and year 2016 is located in -0.70 and the economic wear (Π) in the same period of analysis (19902016) is equal to 0.85 according to TAVE-Model (See Figure 4). The losses of terrorism during
1990’s to 2016 indicate the terrorism cost during the period. If there would be no terrorist attack,
the potential economic growth rate (zero terrorism incidents) during 1990’s to 2016 will be higher
than the actual economic growth rate (in the presence of terrorism).
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Therefore, Turkey will suffer sizable overall economic losses in the event of a terrorist conflict,
and post-conflict reconstruction is bound to be a costly endeavor in the short and long term.
Figure 4: Graphical Representation of Economic Leaking (Ωt), Terrorism Economic Desgrowth (-δ),
Terrorism Losses (-π), and Economic Wear (Π) between the period of 1990 and 2016
Sources: Turkish Statisitucal Institute (2016), European Union Bank (2016) and
World Bank (2016).
6. Empirical Analysis of Terrorism: How Income Inequality Distribution Can
Generate Terrorism?
In this part, using the sample case of Turkey, we empirically analyze how changes in both
income inequality distribution and the GDP regional distribution are correlated with change in
incidence of terrorist attacks. We used ∂ (differentiation in) the income equality distribution (∂YD)
and ∂ (differentiation in) the GDP regional equality distribution (∂GDPRED) as independent
variables, and examined how these two variables affect ∂ (differentiation in) terrorist attacks in
Turkey between 1990 and 2016. The data to analyze the incidents of terrorist attacks are originated
from the Global Terrorism Data Base (GTD, 2016). The data on independent variables have been
taken from various economic reports on the Turkish economy such as the Turkish statistical
institute (2016) and Republic of Turkey Ministry of Defense (2016). Since we use long and
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different time series data, we implemented mega-dynamic disks coordinate space in vertical
position which
can address multi-stationary and other problems related with time series data. As shown in Figure
5, the estimated results tell us that in different multi-distributed lag model in different periods of
time the terrorist incidents are positively correlated to ∂ (differentiation in) the income inequality
distribution and ∂ (differentiation in) the GDP regional distribution in Turkey according to our
results in this model. The coefficient of multi-distributed lag model of changes in different terror
incidents is statistically significant at 0.01 level according to the multi-distributed lag model. In
case of Δ GDP, the coefficient signs of the first, second and second lag of Δ GDP are positive and
statistically significant at (0.01) 10% and (0.001)1%, respectively. The result can be summarized
as:
Yntp(GV:SV:MV:JIV)=α(GV:SV:MV:JIV)+βL0(GV:SV:MV:JIV)Xtp/0(GV:SV:MV:JIV)+βL1(GV:SV:MV:JIV)Xtp/1(
L∞
tp/∞
tk
(3)
GV:SV:MV:JIV)-1+…+β (GV:SV:MV:JIV)X
(GV:SV:MV:JIV)-n+u (GV:SV:MV:JIV)
Therefore,
E /Utk/ = Ko
(4.1)
Var (Utk) = σi(GV:SV:MV:JIV)
(4.2)
Cov(Utk, Utks) = σi(GV:SV:MV:JIV) (4.3)
Thus, General-Space 1 in the Sub-Space 0.001, General-Space 2 is using Sub-space 0.01, and
General-space 3 is fixed by sub-space 0.05 that we represent is follow by equation 5, 6 , and 7.
SV0.001=Y0tp(0.001:∞:∞)=α(0.001:0:0)+βL0(0.001:0:0)Xtp/0(0.001:0:0)+βL1(0.001:1:1)Xtp/1(0.001:1:1)+…+βL∞(0.001:∞:∞)Xtp/∞(0.001:∞:∞)tk
(5)
n+u (0.01:∞:∞)…
SV0.01 ® …
tk
n+u (0.01:∞:∞)...
®
Y0tp(0.01:0:0)=α(0:0:0)+βL0(0.01:0:0)Xtp/0(0.01:0:0)+βL1(0.01:1:1)Xtp/1(0.01:1:1)-1+…+βL∞(0.01:∞:∞)Xtp/∞(0:01:∞:∞)(6)
SV0.05=Y0tp(0.05:0:0)=α(0.05:0:0)+βL0(0.05:0:0)Xtp/0(0.05:0:0)+βL1(0.05:1:1)Xtp/1(0.05:1:1)-1+…+βL∞(0.05:∞:∞)Xtp/∞(0.05:∞:∞)tk
(7)
n+u (0.05:∞:∞)
.
Finally, we find a final general determinant from the general vector in analysis according to
Expression 8.
∆=
GV0.001 = SV0.001
GV0.01 = SV0.01
GV0.05 = SV0.05
.
.
(8)
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Table1: Variables Definition and Data Description
Variables
Symbols
Description
Data Source
Terrorist
Attacks
ΔTA
The threatened or actual use of
illegal force and violence by a
non‐ state actor to attain a
political, economic, religious,
or social goal through fear,
coercion, or intimidation.
GTD
GDP regional
Distribution
ΔGDPRED
Final output of goods and
services from different states
or prefectures from the same
country
World Bank
Income
Inequality
Distribution
ΔYD
Income inequality refers to the
extent to which income is
distributed in an uneven
manner among a population.
World Bank
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According to our research results econometrically. If we increase ∂YD to 1.5% then we can
reduce Δ terror incidents by -0.50%, but in the case if we increase ∂YD to 2.5% can react positively
the reduction of Δ terror incidents in -1.50% (See Figure 5 & 6). We probe that ∂YD and Δ terror
incidents exist a strong and positive correlation in different levels. Hence, we can observe that the
coefficient of the first multi-lag and second multi-lag of Δ terror incidents is statistically significant
at sub-space (0.001) and sub-space 2 (0.05) level. In case of ∂YD, the coefficient signs of the first
multi-lag is negative and non-statistical significant according to our results in 1% and 10%, but
the second multi-lag of ∂YD is positive and statistically significant at 5% respectively.
Figure 5: Calibration of High Risk of Terrorism Expansion
Source: Author’s Analysis
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Figure 6: Calibration of High Risk of Terrorism Expansion
Source: Author’s Analysis
7. Concluding Observations and Policy Implications
Terrorist attacks often wreak sizable damage on economic performance but measuring this
impact with any degree of accuracy is intrinsically difficult. Terrorism is a multidimensional
phenomenon rooted in a wide range of factors. In this paper, we take a closer look at economic
factors. In this context, we set forth a new model for evaluating the economic impact of a terrorist
attack. The terrorist attack vulnerability evaluation model (TAVE-Model) investigates the
formation of terrorist attacks in three different phases: (i) origins of terrorist attack; (ii) terrorist
attack; (v) the post-terrorist attack phase. The TAVE-Model is based on a number of indicators,
including economic desgrowth (-δ), intensity of terrorist attack (αi), terrorist attack losses (-π),
economic wear (Π) due to an attack, level of terrorist attack tension (ζ), level of negotiation (η)
and total economic leaking (Ωt) due to an attack.
The basic intuition behind the model is that the economic impact of a terrorist attack
depends on a country’s vulnerability to attacks from domestic and international terrorist groups.
In the case of Turkey, both types of groups are highly active and suspected of playing major roles
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in the recent outbreak of terrorist attacks. The primary internal threat is the Kurdistan Workers’
Party or PKK, which is a long established terrorist group which recruits its members from Turkey’s
large ethnic Kurdish minority, which accounts for 15 to 30% of Turkey’s population. The primary
external threat is the Islamic State or ISIS, the world’s most dangerous terrorist group which has
gained control of significant swathes of Iraq and Syria, both of which border Turkey. Furthermore,
there are significant numbers of ISIS supporters, sympathizers and operatives within Turkey, and
ISIS may have been responsible for some recent terrorist attacks on Turkish territory.
Vulnerability to internal and external terrorist threats jointly determines the leakage from
economic growth (-δ) and hence the impact on growth. We believe that the TAVE-Model can
contribute to a better and deeper understanding of the economic impact of terrorism. More
specifically, the model will contribute to more accurate and meaningful measurement of the
economic damage of terrorist attacks. According to analysis under the model, when a country has
low real GDP growth rate (∆Оr) is small, its economic performance will always be affected due to
total economic leaking (Ωt). Such an economy will also suffer permanent economic desgrowth (δ). In contrast, a country with high real GDP growth rate (∆Оr), total economic leaking (Ωt) will
be limited initially and cause economic desgrowth (-δ) only at a later stage.
In the analytical framework of the TAVE-Model, four variables influence economic wear
(Π) from a terrorist event. More precisely, economic desgrowth (-δ), terrorist attack losses (-π),
total economic leaking (Ωt) due to a terrorist attack, and the intensity of terrorist attack (αi) jointly
determine economic wear (Π). In fact, the model allows for a better understanding and appreciation
of how terrorist attack losses (-π) and economic leaking (Ωt) due to an attack can directly cause
economic wear (Π). Our analysis shows a positive association between large terrorist attack losses
(-π) and a high intensity of terrorist attack (αi) volumes on one hand and large total economic
leaking (Ωt) and economic desgrowth (-δ) on the other.
The magnitude (∆) of the terrorist attack, along with real GDP growth rate (∆Оr), total
economic leaking (Ωt), and magnitude of terrorist attack losses (-π), will collectively determine
the recovery process of player 1 (P1), in terms of its economic desgrowth (-δ). It is our hope that
the TAVE-Model can inform and guide policymakers to better prepare for and cope with the
economic consequences of terrorist attacks. Terrorism also entails significant non-economic costs,
including the loss of human life, physical and emotional pain, and psychological intimidation.
Terrorism is by no means a purely or even largely economic phenomenon. Nevertheless, economic
16
cost should play a role in the calculus of relevant policymakers in allocating scarce, finite
government resources to the fight against terrorism – i.e. how much to invest and in which specific
anti-terrorists’ endeavors to invest, both to prevent terrorism and to reduce the economic loss from
attacks once they occur.
Quantitative estimates of the economic cost generated by the analysis of the TAVE-Model
should provide relevant policymakers with at least broad, first-order guidance about what is at
stake economically in the fight against terrorism. Broadly speaking, the most effective way to fight
terrorism is to implement economic and social policies that promote economic growth and
development, which dampen support for terrorism. Our analysis of Turkey confirms that a strong
economy can be a potent deterrent against terrorism. Another powerful tool is better military and
civilian intelligence. Effective social assistance programs as well as a stronger and impartial justice
system will render poorer Turks, including members of the Kurdish minority, less vulnerable to
the propaganda of radical groups. In conclusion, while terrorism in Turkey is a multidimensional
issue, as it is any country at risk of terrorist attacks, we hope that our analysis based on the TAVEModel can offer fresh, valuable insights into the economic causes and consequences of terrorism.
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Annex
1. An Introduction to the TAVE-Model
The terrorist attack vulnerability evaluation model (TAVE-Model) (Ruiz Estrada, Park,
Kim, Khan, 2015) is divided into three sections: (i) origins of terrorist attack; (ii) terrorist attack;
(iii) post-terrorist effects. Furthermore, the TAVE-Model uses three different groups of players.
The first group of players is the main conflict players (Pi; i= (1,2). The first player (P1) is the
government forces. The second player (P2) can be any domestic terrorist group. A terrorist attack
is defined as the physical or psychological attack of any armed group or gang on the civil society
(Ruiz Estrada and Park, 2008). Therefore, a terrorist attack uses violent and destructive actions
without any mercy or compassion for the civil society. A terrorist attack uses sophisticated
methods, techniques, and systems of violence and violence to intimidate and humiliate the civil
society. In addition, terrorist groups require a strong ideological, political, economic,
technological, and social platform to achieve a longer institutional life.
i. Origins of a Terrorist Attack
The TAVE-Model that any terrorist attack originates from the following four basic factors:
(i) historical issues (ΐ); (ii) economic issues (έ); (iii) ideological and religion differences (Λ); and
(iv) civil society control (μ). These four factors directly affect “the level of terrorist attack tension
(ζ).” in this model. The level of terrorist attack tension (ζ) is in function of these four variables
(Expression 1.)
ζ = ƒ(ΐ, έ, Λ, μ)
(1)
Therefore, the next step is to calculate the minimum and maximum level of terrorist attack
tension (ζ) through the application of the first derivative according to expression 2 and 3.
ƒ’(ζ) = (∂ζ/∂ΐ) + (∂ζ/∂έ)+ (∂ζ/∂Λ) + (∂ζ/∂μ) (2)
ƒ (ζ) = ∑(lim ∆ζ/∆ΐ )+ (lim ∆ζ/∆έ )+ (lim ∆ζ/∆Λ)+ (lim ∆ζ/∆μ)
’
∆ΐ→0
∆έ→0
∆Λ→0
(3)
∆μ→0
Moreover, the level of terrorist attack tension (ζ) applies a second derivative to find the
inflection point according to expression 4.
ƒ”(ΐ, έ, Λ, μ, ρ)= (∂2ζ/∂ΐ2) + (∂2ζ/∂έ2) + (∂2ζ/∂Λ2)+ (∂2ζ/∂μ2) (4)
To probe the level of terrorist attack tension (ζ) is necessary to apply the Jacobian
determinants under the first-order derivatives (see Expression 5.)
| J’ | =
∂ζ/∂ΐ ∂ζ/∂έ
∂ζ/∂Λ ∂ζ/∂μ
(5)
On the other hand, the application of the Jacobian determinants under the second-order
derivatives can help to find the inflection point in the level of terrorist attack tension (ζ) between
the two players
(see Expression 6.)
∂2ζ/∂ΐ2 ∂2ζ/∂έ2
| J’’ | =
∂2ζ/∂Λ2 ∂2ζ/∂μ2
(6)
Consequently, in the initial stage of any terrorist attack, we need to assume that the level
of terrorist attack tension (ζ) (endogenous variable) is going to determine the level of terrorist
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attacks monitoring (η) (exogenous variable) via cooperation between external intelligence
agencies [Rb; b = (1, 2,…,∞)] and domestic intelligence agencies (U). In this part of the TAVEModel we are able to show that if the level of terrorist attack tension (ζ) is rising then the level of
terrorist attack monitoring (η) is going to be more intensive, to the point of exhausting all
possibilities to get more information of potential terrorist attacks from player 2 (P 2). Hence, the
level of terrorist attack monitoring (η) directly depends on the level of terrorist attack tension (ζ)
in the short run. In addition, the level of terrorist attacks monitoring (η) also involves the antiterrorist contingency actions plans in case of a potential terrorist attack anytime and anywhere.
It is possible to observe the relationship between the level of terrorist attack tension (ζ) and
the level of terrorist attack monitoring (η), evident in a logarithmic curve in the 2-dimensional
Cartesian plane (see Expression 7). External intelligence agencies (R) and domestic intelligence
agencies (U) may play a crucial role in the level of terrorist attack monitoring (η). According to
this research, if the level of terrorist attack tension (ζ) reaches its maximum then the level of
terrorist attack monitoring (η) will play an important role in reducing potential terrorist attacks on
player 1 (P1).
ζ = xlog2(η) => { η / η : R ∩ U }
(7)
ii. The Terrorist Attack
The terrorist attack consists of two stages – preparatory stage and the attack itself.
Terrorist Attack Preparation Stage
In the pre-terrorist attack stage, it is necessary to assume that both players
have different strategy (ω) levels. (See 8).
P1(ω1) ≠ P2(ω2)
(8)
Thus, the levels of total economic linking (Ωt) for player 1 (P1) changes by a different
amount (∆), as in (9).
P1(∆Ωt)
(9)
In the period of the terrorist attack, the player 1 (P1) is exposed to risk of heavy or light
terrorist attack from player 2 (P2). This means that if the level of terrorist attack tension (ζ) reaches
its maximum limit then the level of terrorist attack monitoring (η) almost fail (see Expression 10.)
ζmax = ƒ’(η) = ∂xlog2(ζ)/∂η > 0
(10)
Accordingly, this part of the TAVE-Model requests the application of a second derivative
to observe the curve inflection point.
ζmax = ƒ”(η) = ∂2xlog2(ζ)/∂η2 > 0
(11)
The Terrorist Attack
The TAVE-Model assumes that if a terrorist attack starts now from player 2 (P2) on player
1 (P1), economic desgrowth (-δ) can be large but in different magnitudes P1 (∆-δ). The intensity of
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terrorist attack (αi) is going to affect total economic leaking (Ωt). At the same time, economic
desgrowth (-δ) and terrorist attack losses (-π) will show the same trend. We used nine main
variables to measure the intensity of terrorist attack (αi). These nine variables include (i) military
external support (α1); (ii) anti-terrorist attack technological systems (α2); (iii) army size (α3); (iv)
strategy, information, and logistic systems (α4); (v) favorable natural and geographical conditions
(α5); (vi) civil society support (α6); (vii) the terrorist group knowhow (α7); (viii) transportation,
communications, and IT systems (α8); and (ix) industrial structures (α9). The TAVE-Model also
assume that in the long run economic desgrowth (-δ) and terrorist attack losses (-π) can pose
significant difficulties to the recovery of player one (P1), in different magnitudes, in the postterrorist attack stage.
∂αi/∂α1 ∂αi/∂α2 ∂αi/∂α3
∂αi/∂Λ ∂αi/∂α5 ∂αi/∂α6
∂αi/∂Λ ∂αi/∂α8 ∂αi/∂α9
| J’ (αi) | =
(12)
The final calculation is showing in Expression 13.
αi
=1
/ | J’ (αi) |
(13)
Therefore, the economic wear (Π) due to a terrorist attack depends on changes in economic
desgrowth (-δ) and terrorist attack losses (-π), according to expression 14.
Π = ƒ(-δ,-π)
(14)
The final step is to calculate the total economic wear (Π) due to a terrorist attack, according
to expression 15.
The next step is to specify the limits of each variable involved in the calculation of the
economic wear (Π) due to a terrorist attack, between 0 and 1.
To find the present value of the economic wear (Π) due to a terrorist attack, we assume a
uniform rate of intensity of terrorist attack (αi) and terrorist attack losses (-π) per year, and a
continuous rate of discount of –n. Since, in evaluating an improper integral, we simply take the
limit of a proper integral, the final result is shown in expression 17.
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In the process of calculating the marginal economic wear (Π) due to a terrorist attack, we
apply first-derivative orders (see Expression 18). At the same time, applying the second-derivative
order on economic wear (Π) due to an attack helps us to find the inflection point (see Expression
19)
Π‘ = ∂Πt/∂Πt+1
Π” = ∂2Πt/∂Π2t+1
(18)
(19)
Hence, the boundary conditions for economic wear (Π) due to a terrorist attack are equal
to Expression 20.
Π' = ∂Π’0/∂T│t=0 = 0, ∂Π’1/∂T│t=1 = 1, ∂Π’2/∂T│ t=2 = 2, …, ∂Π’∞/∂T│ t=∞ = ∞
(20)
iii.
Post-Terrorist Attack Effect
In the post-terrorist attack stage, the player 1 (P1) is the final loser, which suffers large
amounts of economic leaking (ΩT), losses (-π), and economic desgrowth (-δ) in the same period
of the terrorist attack, according to expression 21.
P1(-π, -δ, ΩT) < P2(-π, -δ, ΩT) (21)
The TAVE-Model also assumes that the loser (P1) is going to have a hard time to recover
from the terrorist attack. Economic wear (Π) due to a terrorist attack creates huge economic
imbalances, which impede recovery. Intuitively, improving economic desgrowth (-δ) and
minimizing terrorist attack losses (-π) in the loser player (P1) requires a new strategic security plan,
international aid, and institutional and society re-organization to adapt to the new political, social,
technological, and economic post-attack environment.
P1(∂-δo/∂-δf)
(22)
In the long term, the loser players (P1) can show different magnitudes (∆) and trends of
economic desgrowth (-δ) and terrorist attack losses (-π). Furthermore, the recovery of player P1
depends on the cooperative efforts of workers, government, and private sector to reduce terrorist
attack losses (-π) until they are equal or close to zero.
iv.
Economic Desgrowth
In this section, we discuss the concept of economic desgrowth (-δ) (Ruiz Estrada, Yap, and
Park, 2014), which plays an important role in the construction of the TAVE-Model. The main
objective of economic desgrowth (-δ) is to create an economic indicator that can help us to analyze
how controlled and non-controlled shocks can adversely affect GDP in the short run. Economic
desgrowth (-δ) is defined “as an indicator that can show different leakages that is originated from
controlled and non-controlled events that can affect the performance of the final GDP formation
into a period of one year”. The TAVE-Model assumes that the world economy is constantly in a
state of permanent chaos and subject to different levels of vulnerability according to different
magnitudes of irregularities. Economic desgrowth (-δ) applies random intervals, which makes it
possible to analyze unexpected shocks from different controlled and non-controlled events. These
23
are shocks that cannot be predicted and monitored easily by traditional methods of linear and nonliner model. This is because we assume at the outset that the world economy is in permanent chaos
(Gleick, 1988).
At the same time, the TAVE-Model includes the Lorenz transformation assumptions
(Lorenz, 1993) to facilitate the analysis of economic desgrowth (-δ). In addition, the TAVE-Model
assumes that economic desgrowth (-δ) has a strong connection to total economic leaking (Ωt). The
final measurement of total economic leaking (Ωt) is derived by applying a large number of multidimensional partial derivatives on each variable (16 variables) to evaluate the changes of each
variable (16 variables) between the present time (this year) and the past time (last year). Finally,
The calculation of economic desgrowth (-δ) is based on the final real GDP (Or) and total economic
leaking (Ωt). This section of the TAVE-Model reminds us that total economic leaking (Ωt) always
affects economic desgrowth (-δ) behavior. Finally, the modeling of economic desgrowth (-δ) is
based on the application of the Omnia Mobilis assumption by Ruiz Estrada (2011) to generate the
relaxation of the total economic leaking (Ωt) calculation (non-controlled and controlled events)
and the full potential GDP (OP).
24