Embed Size (px)
Citation preview
The Impact of terrorism on Turkey's economic performance From 1990 to 2016
First-AuthorMario 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-AuthorDonghyun 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-AuthorAlam Khan,
Department of Economics,Faculty of Economics and Administration,
University of Malaya,Kuala Lumpur 50603,
[H/P] (+60) 114391931[E-mail] [email protected]
1
AbstractThis research work applies the terrorist attack vulnerability evaluation model (TAVE-
Model) 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.
KeywordsTerrorism, economic modeling, economic desgrowth, policy modeling, Turkey
JEL CodeR11, R12
2
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
3
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.
4
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
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 TurkeySource: 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
5
southeast Turkey, the traditional heartland of the Kurdish minority; and sporadic strikes by
radical Islamists and liberal student (Rodoplu, Arnold and Ersoy, 2003).
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.
6
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
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 (Φ i) 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 (Φ i) 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 (ζ j) need
7
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 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+/- (1)
αV+/- = ƒ (βΦi:ζj) (2)
Figure 2 The Mega-Dynamic Disks Coordinate Space in Vertical Position
8
Source: Ruiz Estrada (2014)
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);
9
strategic army locations (7:3); society anti-terrorist support (7:2); military knowhow (6:2); (viii)
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 (Ω t) 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
(1990-2016) 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).
10
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
11
(2016). Since we use long and 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
(GV:SV:MV:JIV)-1+…+βL∞(GV:SV:MV:JIV)Xtp/∞
(GV:SV:MV:JIV)-n+utk(GV:SV:MV:JIV)
(3) Therefore,
E /Utk/ = Ko (4.1)Var (Utk) = σi
(GV:SV:MV:JIV) (4.2)Cov(Utk, Utk
s) = 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:∞:∞)-
n+utk(0.01:∞:∞)… (5)
SV0.01 ® … ® 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:∞:∞)-n+utk(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:∞:∞)-n+utk
(0.05:∞:∞) (7)
.
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)
12
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
13
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
14
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.
15
In the case of Turkey, both types of groups are highly active and suspected of playing major
roles 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
16
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 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 TAVE-Model can offer fresh, valuable insights into the economic causes and
consequences of terrorism.
References
Abadie, A. (2005). Poverty, Political Freedom, and the Roots of Terrorism. AmericanEconomic Review 95:50-56.
Abadie, Alberto & Javier Gardeazabal (2008) Terrorism and the world economy. EuropeanEconomic Review 52(1): 1–27.
Araz-Takay, B., K. P. Arin, and T. Omay (2009). The Endogenous and Non-linear Relationshipbetween Terrorism and Economic Performance: Turkish Evidence. Defence and PeaceEconomics 20 (1): 1-10.
Bilgel, F. and Karahasan, B. C. (2015). The Economic Costs of Separatist Terrorism in Turkey.Journal of Conflict Resolution, 1-23.
Blomberg, S. B., Hess, G. D., & Orphanides, A. (2004). The macroeconomic consequences ofterrorism. Journal of monetary economics, 51(5), 1007-1032.
17
Collier, P. (1999). Doing Well Out of War. Paper prepared for conference on EconomicAgendas in Civil Wars, London.
Collier, P., Elliott, V., Hegre, H., Hoeffler, A., Reynal-Querol, M., & Sambanis, N. (2003).Breaking the Conflict Trap: Civil War and Development Policy. (Washington, DC:World Bank): Oxford University Press.
Derin-Gure, P. (2011) Separatist Terrorism and the Economic Conditions in South-EasternTurkey. Defence and Peace Economics 22 (4): 393-407.
Drakos, K. (2004). Terrorism-induced structural shifts in financial risk: airline stocks in theaftermath of the September 11th terror attacks. European Journal of Political Economy,20(2), 435-446.
Eckstein, Z., & Tsiddon, D. (2004). Macroeconomic consequences of terror: theory and the case of Israel. Journal of monetary economics, 51(5), 971-1002.
Enders, W. and Sandler, T. (2006). The Political Economy of Terrorism (Cambridge University Press, Cambridge. U.K.).
European Union Bank. (2016). Annual Economic Data Report (Statistics). Retrieved from: https://www.ecb.europa.eu/stats/html/index.en.html.
Global Terrorism Database (2016). http://www.start.umd.edu/gtd/.
Granger, C. W. (1987).Some recent development in a concept of causality. Journal of econometrics, 39(1), 199-211.
Gries, T., T. Krieger, and D. Meierrieks. (2011). Causal Linkages between DomesticTerrorism and Economic Growth. Defence and Peace Economics 22 (5): 493-508.
Johansen, S. (1988). Statistical analysis of co-integration vectors. Journal of economic dynamics and control, 12(2), 231-254.
Keynes, J. M. (1919). The Economic Consequences of the Peace. Macmillan, London, UK.
Landes, William M. (1978), “An Economic Study of Aircraft Hijackings, 1961-1976,” Journal of Law and Economics, 21(1), 1-31.
Interior Ministry of Turkey. (2016). Information about Anti-Terrorism Plan. http://www.mia.gov.tr/
Ito, H., and Lee, D. (2005). Assessing the impact of the September 11 terrorist attacks on US airline demand. Journal of Economics and Business, 57(1), 75-95.
Lorenz, E. (1993). The Essence of Chaos, Washington: University of Washington Press.
18
Mirza, D. and Verdier, T. (2008). International trade, security and transnational terrorism:Theory and a survey of empirics. Journal of Comparative Economics 36, 179-194.
Naor, Z. (2006). Untimely Death, the Value of Certain Lifetime and Macroeconomic Dynamics. Defence and Peace Economics 17(4), 343-359.
Öcal, N., & Yildirim, J. (2010). Regional effects of terrorism on economic growth in Turkey: Ageographically weighted regression approach. Journal of Peace Research, 47(4).
Republic of Turkey Ministry of Defense (2016). General Information. http://www.msb.gov.tr/en-US/Press/Tumu
Robbins, L. (1942). The Economic Causes of War. Jonathan Cape, London, UK.
Rodoplu, Ulkumen, Arnold, J., & Ersoy, G. (2003) Terrorism in Turkey: Implications foremergency management. Prehosp Disast Med 18(2): 152–160.
Ruiz Estrada, M.A. (2011). Policy Modeling: Definition, Classification, and Evaluation. Journal of Policy Modeling, 33(4), 523-536.
Ruiz Estrada, M.A. (2012). A New Multidimensional Graphical Approach for Mathematics and Physics. Malaysian Journal of Sciences, 31(2), 175-198.
Ruiz Estrada, M.A. 2014. An Introduction to the Mega- Dynamic Disks Coordinate Space in Vertical and Horizontal Position. Malaysian Journal of Sciences, 33(2): 105-109.
Ruiz Estrada, M.A., Park, D., Kim, J. S., and Khan, A. (2015). The Economic Impact ofTerrorism: A New Model and Its Application to Pakistan. Journal of PolicyModeling, 37(6), 1065-1080.
Sandler, Todd, John T. Tschirhart, and Jon Cauley (1983). A Theoretical Analysis of Transnational Terrorism,” American Political Science Review, 77(1), 36-54.
Sandler, T., & Enders, W. (2008). Economic consequences of terrorism in developed anddeveloping countries. Terrorism, economic development, and political openness, 17.
Tavares, J. (2004). The open society assesses its enemies: shocks, disasters and terrorist attacks. Journal of monetary economics, 51(5), 1039-1070.
Turkish Statistical Institute (2016). Available athttp://www.turkstat.gov.tr/Start.do;jsessionid=DVWvXJGf8QlQbkDvHJB141m2pZVhnTkvWpRQLr2ZShcf8B2kVnn!274137585.
World Bank (2016). Household final consumption expenditure per capita (constant 2005 US$). In World Bank (Ed.). Washington, D.C.: World Bank.
19
Annex1. 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 (P i; 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) (3) ∆ΐ→0 ∆έ→0 ∆Λ→0 ∆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.)
∂ζ/∂ΐ ∂ζ/∂έ | J’ | = ∂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)
20
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 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 TAVE-Model 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 (P2). 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 anti-terrorist 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 AttackThe terrorist attack consists of two stages – preparatory stage and the attack itself.
Terrorist Attack Preparation StageIn 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)
21
The Terrorist AttackThe 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 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 post-terrorist attack stage.
∂αi/∂α1 ∂αi/∂α2 ∂αi/∂α3 | J’ (αi) | = ∂αi/∂Λ ∂αi/∂α5 ∂αi/∂α6 (12) ∂αi/∂Λ ∂αi/∂α8 ∂αi/∂α9
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.
22
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 (18)
Π” = ∂2Πt/∂Π2t+1 (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 EffectIn 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 DesgrowthIn 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
23
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 are shocks that cannot be predicted and monitored easily by traditional methods of linear and non-liner 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 multi-dimensional 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
