Terrorist economic impact evaluation (TEIE) model: the case of Pakistan

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1 Qual Quant DOI /s z Terrorist economic impact evaluation (TEIE) model: the case of Pakistan Alam Khan 1 Zarinah Yusof 1 Springer Science+Business Media Dordrecht 2016 Abstract The terrorist economic impact evaluation (TEIE) model is a new model to evaluate the intensity of terrorism to measure the economic cost in a state (province) of any country within a specific period of time. The TEIE-model examines the measurement of terrorist attacks action with the help of terrorist attacks intensity rate, terrorist attacks death rate, terrorist attacks injuries rate and terrorist attacks property damage. This TEIE-model is the comprehensive indicator to quantify the terrorist attacks action to measure the human and capital loss of a state of a country. This model also applies to the states (provinces) of Pakistan economy. This model helps the policy makers to gauge the intensity of terrorism to measure at state level. The TEIE-model uses dynamic approach to examine economic cost at state level of any economy. Keywords Terrorism Terrorist economic impact evaluation (TEIE) model Pakistan JEL Classification D60 H56 K42 R11 R12 1 Introduction Terrorism is a term which has got more attention in the academic circle after 9/11 catastrophic terrorist attack in USA. So for various attempts have been made to quantify the terrorism score. The literature has used different approaches for the measurement of terrorism like some scholars have used the number of terrorism incidents to measure terrorism, while other scholars have applied the data of killed and injured persons as an & Alam Khan alameconomist85@gmail.com Zarinah Yusof zarinahy@um.edu.my 1 Department of Economics, Faculty of Economics and Administration, University of Malaya, Kuala Lumpur, Malaysia

2 A. Khan, Z. Yusof indicator to measure terrorism (Gries et al. 2011). But all these approaches have shortcomings. Like, when the scholars employ terrorism incidents as benchmark, a serious question raises that it fails to differentiate large and small terrorism incident on the basis of intensity and considers same (Enders and Sandler 2002). For example, the 9/11 World Trade Centre terrorism incident will be considered as same as other terrorism incidents where one person has killed or injured. Therefore, it does not make sense to give common scale to all the incidents. Such traditional terrorism incidents approaches are useful only when the structure of terrorist events remain unchanged. These approaches use the number of killed and injured persons in a terrorism event to measure the terrorism (Sandler and Enders 2002). The main problem in this approach is how the number of killed persons will be equal to the number of injured persons, where in one case the person has lost his/her life, while on the other case the injured person has some existence and survival possibility so he/she will go back to work and will become a productive person of a society (Frey and Luechinger 2003). However in the literature, the comprehensive work related to terrorism measurement has been done by the global terrorism database (GTD), 1 constructed and maintained by the Institute for Economics and Peace. The GTD is recognized to be one of the complete dataset on terrorism related information and data across the globe. So for from year 1970 to 2014, the GTD has codified over 140,000 terrorist attacks. This GTD has introduced global terrorism index (GTI) which evaluates the terrorism intensity and economic cost of a country at a global level (GTD 2015; GTI 2015). The GTD also ranks countries terrorism point of view on the basis of the GTI scores. This GTI terrorism index is good at macro level but unable to provide comprehensive information regarding particular state of a country. For example, within one country, some state may be more affected by terrorism economically than other state of the same country. This study is going to fill this research gap by introducing a new model called terrorism economic impact evaluation (TEIE) model. This is a comprehensive model which measures the terrorism intensity and economic cost of any country at state level and provides true picture of terrorism within a country in different geographical location. This research work is arranged as the second section of study provides the theoretical framework of study, while third part of this research work includes the methodology of the model terrorist economic impact evaluation model (TEIE)-Model. Section 4 of study relates to the application of TEIE-model to the economy of Pakistan. Section 5 of this research work consists of economic cost of terrorism in Pakistan economy. The second last and last part of the research work includes the econometric analysis of study and conclusion, respectively. 2 Theoretical framework of study The relationship between terrorism activities and economic performance is complex. Terrorism may be affected the economic growth rate negatively on one side of the story, but on the other side, strong economic performance may improve the living of standard of the masses and generate less terrorism. Terrorism is considered as a threat to economic performance in general perspective. Among other non-economic factors, terrorism is one of the main aspect which generates destabilization, human capital loss, capital loss, 1 It is recorded by the National Consortium for the Study of Terrorism and Responses to Terrorism (START), based at the University of Maryland. Web Link:

3 Terrorist economic impact evaluation (TEIE) model uncertainty, and so these affected factors are the inputs of the economic growth rate. The terrorism problem may be solved by increasing the opportunity costs of terrorism. When there is economic growth rate, then the prospective terrorist groups will have extra economic alternative such as jobs, employment opportunities, and decrease in poverty and relative deprivation and this will further rise the opportunity costs of terrorism (Frey and Luechinger 2003). The economic theory postulates that terrorist activities lead to poor economic performance. The justification of terrorists to destabilize the economy can be explained from the rational choice theory. A government that is attacked by terrorists may take on a rational perspective, where it compare the cost of accepting the terrorist demand against the cost of prolonged the terrorists campaign that results from continuous resistance by the government (Sandler and Enders 2008). Thus, destabilization is a main objective of terrorists. Poor economic performance due to terrorism means that accepting terrorists demand has become relatively less costly from the government perspective. Terrorism is anticipated to destabilize the economy by imposing direct cost and by doing actions that result in poor economic performance. The direct cost includes loss of human capital and the loss of property and infrastructure. The indirect cost comes from the response of economic actors to terrorism. Due to terrorism, security measures may be taken, which increases the transportation cost and obstacles in international trade (Khan and Yusof 2015). The resources are shifted from productive sector to non-developmental expenditure like defence, etc., as a result the economic performance of the economy further deteriorates. The additional economic theory immiserizing modernization theory explains that economic performance and terrorism have two way linking, if the benefits of economic prosperity could not be moved through trickle-down outcome to poor persons of the economy. The poor economic performance boosts income inequality, and therefore poverty is triumphed in the economy. The surge in the level of poverty reduces the opportunity cost of terrorism which favours the poor common people to go for terrorism incidents and other types of terrorism activities. This theory defines the economic performance and vicious circle of terrorism (Shahbaz 2013). The relative deprivation which is also one of the main cause of terrorism is explained by Gurr (1970). When there is relative deprivation in the individuals, the inequality is created, which forces the people for doing suicides and other types of terrorist activities. 3 Introduction to terrorist economic impact evaluation (TEIE) model The intensity of terrorism is based on the number of terrorism incidents, number of deaths, number of injuries and property damage in particular state in a specific period of time. So mainly terrorism is based on these four indicators of any terrorism attack. The three stages are involved in the measurement of TEIE-model. There are two main hypotheses to test in this research study. H1 Terrorism intensity to measure the economic cost is uniform among all the states of a country. H2 Terrorism intensity to measure the economic cost is not uniform among all the states of a country. The TEIE model is based on Omnia Mobilis assumption. This assumption explains that all relevant variables are important and should be accounted for in policy modeling. The

4 A. Khan, Z. Yusof logic of using the Omnia Mobilis assumption is to cover all possible relevant variables in the study and include any relevant variable in the research study (Ruiz Estrada 2011). The dynamic imbalance state (DIS) is built on the application of Omnia Mobilis assumption (Ruiz Estrada 2011). The DIS is not chaos; it is an unconditional and unexpected complex sensitive reaction of all possible uncertain events that is generated by controlled and uncontrolled variables simultaneously under irregular scenarios (Ruiz Estrada and Yap 2013). 3.1 Stage 1: measurement of terrorist attack intensity The intensity of any terrorist action in any area can be measured on the basis number of terrorism incidents, number of people killed, number of injuries and property damage in a particular locality. The intensity of terrorist action can be written in functional form as below <¼fðe; j; v; lþ; ð1þ where (<) represents the terrorist attack action, (e) is used for number of terrorist incident, (j) is used for number of killed people in terrorist action, (v) is representing the number of injured person in the terrorist attack and l explains the property damage of a terrorist attack. At this stage 1, weights are assigned to all these four dimensions of terrorist attack action. The GTI methodology approach is used for the weights to the number of incidents, number of deaths, number of injuries and property damage in particular state. These four factors of terrorist action have different intensity and magnitude. g< ¼f ð e þ hj þ pw þ rlþ; ð2þ where = 1, h = 3, p = 0.5, r = 2. The above weights are assigned on the basis of individual intensity of the four factors of terrorist attack action. These weights are taken from the GTI. It can be represented in matrix form as below Dð<Þ ¼ o<=oe o<=oj o<=ow o<=ol : ð3þ The final value of the terrorist attack magnitude can be calculated as below. 1 h<i ¼ 1 1 6¼ IR: ð4þ o<=oe o<=oj o<=ow o<=ol 3.2 Stage 2: terrorist attacks intensity growth rate At this stage, the terrorist attacks intensity growth rate of the four dimensions of terrorist attacks are measured. There are four sub-stages in the terrorist attacks intensity growth rate Terrorist attacks incidents growth rate The procedure for the terrorist attacks incidents growth rate is as below: D e 0 e min 0: e max e min ð5þ

5 Terrorist economic impact evaluation (TEIE) model Equation (5) explains the terrorist attacks incidents growth rate, where (e 0 ) represents actual number of terrorist incidents happen in particular state in that particular year. The sambal (e max ) means the maximum number of incidents occur in a particular state in a study period time, while the (e min ) uses for the lowest number of terrorist incidents occur in a state in a study period Terrorist attacks death rate The mathematical formula for the terrorist attacks death rate is as below D j 0 j min 0: j max j min ð6þ Equation (6) shows the terrorist attacks death rate (j), where (j 0 ) represents actual number of deaths in terrorist attacks particular state in that particular year. The sambal (j max ) means the maximum number of deaths in a particular state in a study period time, while the (j min ) explains the lowest number of deaths in a state in a study period Terrorist attacks injury growth rate D W 0 W min 0: W max W min ð7þ Equation (7) clarifies the terrorist attacks injury rate (W), where (W 0 ) sambal means the actual number of injured person in terrorist attack in a particular state in that particular year. The sambal (W max ) means the maximum number of injured people in a particular state in a study period time, while the (W min ) describes the lowest number of people injured in terrorist attack in a state in a study period Terrorist attack property damage rate The terrorist attack property damage rate can be measured as below D l 0 l min 0: l max l min ð8þ Equation (8) describes the terrorist attack property damage rate (l), where (l 0 ) sambal means the actual number of property damage in particular state in that particular year. The sambal (l max ) means the maximum number of property damage in a particular state in a study period time, while the (l min ) explains the lowest number of property damage in a terrorist attack at state level in a study period. 3.3 Stage 3 At this stage, the methodology of principal component analysis (PCA) is used to assign different relative weights to these indices of four dimensions of terrorist attack action. There are mainly three steps are involved in the process of PCA. At the first step, calculate the covariance matrix. At the second step, performing eigenvalue decomposition on the same covariance matrix and in the third step is select the most significant component

6 A. Khan, Z. Yusof among all components. To get the percentage value of contribution of components, divide the eigenvalue by the sum of all eigenvalue. The formula for percentage contribution of component as below k i P M j¼1 k : ð9þ j Once the relative weights assign on the basis of, PCA this will explain the relative intensity among four terrorism dimensions intensity rate in a particular period of time. The final step of the methodology of TE-model is to sum up the weighted indices of the four dimensions of terrorism attack action and will calculate the TEIE score for the particular year. The formula is as below TEIE ¼ Ue þ Xj þ aw þ kl; ð10þ where in Eq. (10) (Ue), (Xj), (aw), ( kl) shows the terrorist attack incidents weight rate in a particular state in a specific period of time, terrorist attack deaths weight rate, terrorist attack injuries weight rate and terrorist attack property weight, respectively. 0 TEIE 1: ð11þ The TEIE value for the particular year will be in the range of 0 and 1, where 0 value will mean the least terrorism vulnerability of a state of country to measure the economic cost, while 1 value will explain the highest vulnerability of terrorism to measure the economic cost of a state of a country. 4 Application of TEIE-model to Pakistan Pakistan is a South Asian economy, which shares its boarders with neighbour s countries, India, China, Afghanistan and Iran. The Afghanistan and Iran is on west boarder, while Indian on east and the Arabian Sea on south. Pakistan has four states (provinces), namely Punjab, Sindh, Khyber Pakhtunkhwa, and Baluchistan. Punjab is the biggest province of the country with highest population number. The capital of the Punjab State is Lahore. The second state of the country is Sindh and famous for its industries and most of the industries are located in the capital of this state Karachi. Khyber Pakhtunkhwa is the third state of the country. Most of the population are belonging to Pathan family and that s why its name has been changed from N.W.F.P. to Khyber Pakhtunkhwa. The capital of the state is Peshawar, which is near to Afghanistan boarder. The fourth province/state of Pakistan is Baluchistan and the biggest province by area. The capital of this state is Quetta. Terrorism is one of the main issue of this Pakistan economy. So for more than 50,000 civilians were killed in various terrorism incidents in Pakistan. According to the GTI (2015) report, Pakistan is ranked fourth on the basis of annual GTI report. But, the government of Pakistan has taken several steps for the counter-terrorism policies after the attack in Peshawar on the Army Public School in KPK State on 16 December The terrorist attacks in Mastung in Baluchistan and Safoora in Sindh has further strengthened national consensus to eliminate terrorism from Pakistan. This national commitment led to the announcement of national action plan (NAP) in January 2015 by the Prime Minister, which led to the formation of military courts and removal of moratorium on death penalty.

7 Terrorist economic impact evaluation (TEIE) model The Parliament passed the 21st Amendment on 7 January 2015 to provide legal cover to the NAP (Interior Ministry of Pakistan 2015). The other objective of NAP is to prevent hate speeches and terrorism related literature. For this purpose, the use of loudspeakers use have been restricted. This NAP has also work on the freezing the fund of terrorist group for financing. The funds of many terrorist groups like TTP, Al Rashid Trust, Jaish-e-Mohammad and Harkat ul Jihad Islami have been banned and these terrorist groups finances have been frozen in Pakistan. They are not allowed to open new office on the other name of organization. All the internal NGOs were directed to register with the Interior Ministry of Pakistan for the purpose to check and balance on the activities of foreign funded NGOs (Crisis Group 2015). The following part of study explains the results of TEIE-model of the four states of Pakistan (Table 1). TEIE-model analyses the data of terrorism of the four provinces of Pakistan from the year 2002 to According to the statistics of terrorism events, terrorism has got the momentum after the US attack on Afghanistan in the year At the initial year of study 2002, the TEIE-model results of all the provinces show that the terrorism is very limited in the year 2002 and the economic cost is very few. The value of TEIE-model of the Punjab for the year 2002 is 0.083, while the value of TE-model for the state of Sindh, KPK, and Baluchistan are 0.063, and 0.030, respectively. These statistics explain that in the year 2002, the state of terrorism index for the Punjab is 8.3 %, for Sindh is 6.3 %, for KPK 0.4 % and for Baluchistan it is only 0.3 %. The TEIE-model results for the four states of Pakistan for the year 2014 are 0.61, 0.85, 0.74 and 0.76 for the states of Punjab, Sindh, KPK and Baluchistan, respectively. The results of TEIE-model explain that in the year 2014, the terrorism intensity score for Punjab is 61 %, for Sindh it is 85 %, for KPK it is 74 % and for Baluchistan it is 76 %. The estimated value of the TEIE-model for the last year of study is too high and confirms that the terrorism activities were too high among all the states of the Pakistan economy and also confirm the economic cost is high. Although, all the four states are affected by terrorism, but overall from the year 2002 to the year 2014, the Baluchistan and KPK states are the most affected states of Pakistan economy followed by Sindh and Punjab (see Figs. 1, 2). These results of study also confirm that the terrorism activities to measure economic cost are not uniform among all the states of Table 1 The results of TEIEmodel Years KPK Punjab Sindh Baluchistan

8 A. Khan, Z. Yusof Fig. 1 Spatial distribution of terrorist attacks in Pakistan: Source author Fig. 2 Province wise TEIE-model distribution. Source author same country. Within same country of Pakistan, some states (provinces) are more affected than other state at the same time (Figs. 3, 4, 5, 6). 5 The impact of terrorism on economic performance of Pakistan economy To measure the economic impact of terrorism; we use the terrorist attack vulnerability evaluation (TAVE) model work (Ruiz Estrada et al. 2015) for this part of the research (Fig. 7).

9 Terrorist economic impact evaluation (TEIE) model Fig. 3 KPK terrorist attacks distribution: Source author Fig. 4 Punjab terrorist attacks distribution: Source author The use of economic desgrowth helps to investigate potential economic cost that is generated due to terrorism. According to our estimated results of TAVE-model application, the economic desgrowth caused by terrorism in Pakistan has an impact of % in the year This economic desgrowth value for the year 2002 explains that the economic cost of terrorism in this particular year is negligible. The economic desgrowth created by terrorism is bigger in 2014 at %. For the year 2014, the value of economic desgrowth portrays that economic cost of terrorism is high in this particular period of time and significantly affected the Pakistan economy in the year It is concluded that the difference between these two time periods explains % according to our results estimates. All the results of economic desgrowth confirm that economic cost has increased from the year 2002 to Pakistan economy is suffering more from terrorism from the year The government of Pakistan has allocated a lot of budget for the overcome of the terrorism issue. But unfortunately all these policies are not successful so for and terrorism is increasing day by day. It has cost the economic performance of the economy

10 A. Khan, Z. Yusof Fig. 5 Sindh terrorist attacks distribution: Source author Fig. 6 Baluchistan terrorist attacks distribution: Source author very badly as mentioned in the literature like Mehmood (2014) and Shahbaz (2013), Shahbaz et al. (2013). Our results are also supported from the literature such as Gries et al. (2011) and Blomberg et al. (2004). All these literature confirm that terrorism has negative and significant impact on economic performance. The results of study are in line with the rational choice theory, which explains that terrorist s main objective is to destabilize the economic performance (Sandler and Enders 2008). 6 Econometric analysis: Does terrorism related to economic performance? To test the relationship between terrorism and economic performance, the econometric analysis has been performed to support the hypothesis that terrorism can affect the economic performance. We examine how poverty growth rate and real GDP per capita are

11 Terrorist economic impact evaluation (TEIE) model Fig. 7 Terrorism impact on economic performance on Pakistan. Source author associated to the terrorist attacks vulnerability in Pakistan. The independent variables data have been taken from the various economic issues of Pakistan economy (Government of Pakistan, GOP 2014). The terrorist attacks vulnerability (TAV) data has been collected from GTD. For the time series analysis, we implemented the econometric techniques, vector auto regression (VAR), granger causality and Johnson co-integration. The estimated results tell us that the coefficient of the first lag of terrorist attacks is statistically significant at 1 % level. In case of real GDP per capita, the coefficient signs of both first and second lag of real GDP per capita are negative and statistically significant at 10 and 1 %, respectively (see Table 2). Prior to estimation, we used the lag selectionorder criteria process to infer how many lags should be used for the VAR model. Upon applying the selection-order criteria test, such as Akaike information criterion, Hannan Quinn information criterion and Schwarz criterion, we selected lag 2. For VAR model estimation, it needs to confirm that all the variables are stationary and there should be no unit root issue. To confirm this, we run Granger causality (1987) Wald Table 2 VAR results t-values are shown in parenthesis *p\ 0.10 %, ** p \ 0.5 % Variables TAV RGDPPC PGR TAV t * (3.5) (0.6) -0.3 (-1.4) TAV t (1.02) 0.54 (0.2) 0.42** (2.3) RGDPPC t ** (-1.94) 0.15 (-0.43) 0.03 (0.25) RGDPPC t * (-3.16) 0.04 (0.20) (1.25) PGR t (0.100) 0.68 (-0.40) (-0.50) PGR t (-0.65) 0.26 (0.20) 1.168** (2.4) Constant -0.8 (-0.3) 7.10 (1.06) 4.34 (2.5) R RMSE

12 A. Khan, Z. Yusof Table 3 Results of Granger causality Wald tests Models Excluded F Terror attacks vulnerability RGDPPC 6.90* Terrorist attacks vulnerability Poverty growth rate 1.60 Terrorist attacks vulnerability All 3.84* RGDPPC Terrorist attacks vulnerability 0.93 RGDPPC Poverty growth rate 0.60 RGDPPC All 0.59 Poverty growth rate Terrorist attacks vulnerability 2.80 Poverty growth rate RGDPPC Poverty growth rate All 1.93 * 5 % Significance level Table 4 Results of Johansen cointegration test a One co-integrated vector at 5% Maximum ranks Eigenvalues Max statistic Critical values a tests. For the terrorist attacks vulnerability model, the result claims that the null hypothesis is rejected at 5 % significance level. Thus, variables do Granger-cause the others at 5 % significance level and we can run the VAR for terrorist attacks model (Table 3). We also apply the Johansen co-integration test (1988) to confirm the long run relationship among the variables. Johansen co-integration test uses when all the variables are assumed to stationary at same level. The result of Johansen co-integration test shows that there is one co-integration vector. This means that terrorist attacks vulnerability model has long-run relationship. This long run association confirms that terrorism and economic performance has negative and significant relationship in the case of Pakistan and support our hypothesis (Table 4). 7 Conclusion This research work is related to develop a model to examine the state terrorism activities of any economy to measure the economic cost. The main idea of this work to quantify the terrorism score at the state level of a country to gauge the terrorism intensity of that particular state to measure the economic cost. This research work provides a new concept to compare different states terrorist attacks intensity and economic cost within one country

13 Terrorist economic impact evaluation (TEIE) model on the logic that terrorism economic cost is not uniform within a country. Some areas may be more affected than other areas of the same territory of country. The results of TEIE-model application to Pakistan states confirm that terrorism intensity to measure economic cost is not uniform in the four states of Pakistan economy. The terrorism intensity and economic cost fluctuates across the provinces of Pakistan. The results of economic desgrowth for Pakistan economy explains that the impact of terrorism on economic performance is lesser in years 2002 (-0.02 %) and gradually increased with the highest value of % in It implies that terrorism has caused 2.94 % desgrowth in the Pakistani economy. Keeping in view Pakistan economy, these economic losses are substantial and have offset the economic performance of Pakistan. In a nutshell, results of the study conclude that terrorism has generated economic disaster in Pakistan. It has hampered economic growth by devastating human and physical capital in the form of economic leaking. These economic leaking from economic growth consequently the cause of poor economic performance. The econometric analysis has been applied to support the economic desgrowth results. The econometric analysis of study also confirms that terrorism and economic performance has negative and significant relationship in the case of Pakistan and support the economic desgrowth results. The measurement of terrorism intensity to evaluate economic cost with accuracy is important because if the terrorism intensity is underestimated, the less resources will be allocated for defence to curb terrorism. While on the other hand, if the terrorism intensity is calculated with overestimated, then more budget will be allocated for defence and less resources will be available for the rest of the sectors of the economy. This research study will not only help to the policy makers for the overall defence expenditure of the country, but will also fruitful for the defence measures for the specific state of a country. So the scare resources will be efficiently use in the case of anti-terrorism measures. This research work provides a new platform for the policy makers to devise the anti-terrorism policies for each state according to their intensity of terrorism. This research work can be extended to the district level of the economy and will give us more thorough results of terrorism vulnerability. The causes of terrorism in the states of Pakistan will be also the future and limitations of this research study. References Blomberg, S.B., Hess, G.D., Orphanides, A.: The macroeconomic consequences of terrorism. J. Monet. Econ. 51(5), (2004) Blomberg, S.B., Hess, G.D., Weerapana, A.: Economic conditions and terrorism. Eur. J. Polit. Econ. 20(2), (2004) Crisis Group: Revisiting Counter-Terrorism Strategies in Pakistan: Opportunities and Pitfalls. Asia Report No (2015). Accessed 22 Jan 2016 Enders, W., Sandler, T.: Patterns of transnational terrorism, : alternative time series estimates. Int. Stud. Q. 46, (2002) Frey, B.S., Luechinger, S.: Measuring Terrorism. Working Paper No. 171 (2003) Global Terrorism Database. (2015). Accessed 12 Jan 2016 Global Terrorism Index. (2015). Accessed 12 Jan 2016 Government of Pakistan (Various Issues): Economic Survey of Pakistan. Ministry of Finance, Islamabad (2014) Granger, C.W.: Some recent development in a concept of causality. J. Econom. 39(1), (1987) Gries, T., Krieger, T., Meierrieks, D.: Causal linkages between domestic terrorism and economic growth. Def. Peace Econ. 22(5), (2011) Gurr, T.: Why Men Rebel. Princeton University Press, Princeton (1970)

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