Macroeconomic consequences of terrorism in Pakistan

Macroeconomic consequences of terrorism in Pakistan

Available online at www.sciencedirect.com ScienceDirect Journal of Policy Modeling 35 (2013) 1103–1123 Macroeconomic consequences of terrorism in Pa...

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Available online at www.sciencedirect.com

ScienceDirect Journal of Policy Modeling 35 (2013) 1103–1123

Macroeconomic consequences of terrorism in Pakistan Zahra Malik, Khalid Zaman ∗ Department of Management Sciences, COMSATS Institute of Information Technology, Abbottabad, Pakistan Received 28 May 2013; received in revised form 17 July 2013; accepted 23 August 2013 Available online 7 September 2013

Abstract The objective of the study examines the macroeconomic consequences of terrorism in Pakistan. The study evaluates the short- and long-run relationship between terrorism and economic factors over a period of 1975–2011. Both objectives have been achieved with the sophisticated econometrics techniques including cointegration theory, Granger causality test and variance decomposition, etc. The result reveals that macroeconomic factors, i.e., population growth, price level, poverty and political instability cause the terrorism incidence in Pakistan. However, income inequality, unemployment and trade openness have no long-run relationship with the terrorism incidence in Pakistan. The study may conclude that, for some how, Pakistan’s macroeconomic indicators have significant long-run equilibrium with terrorism incidence. The result of Granger causality indicates that except unemployment, all other macroeconomic indicators have unidirectional causality with terrorism incidence. Unemployment has a bi-directional causality with the terrorism incidence in Pakistan. The results of variance decomposition indicate that there exists statistically significant cointegration among macroeconomic factors and terrorism incidence in Pakistan. Among macroeconomic factors, changes in price level exert the largest influence on terrorism in Pakistan. Contrary, the influence of poverty seems relatively the least contribution level for changes in terrorism incidence in Pakistan. © 2013 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved. JEL classification: C22; E24 Keywords: Terrorism; Inflation; Poverty; Cointegration; Pakistan

1. Introduction There is neither an academic nor an international legal consensus regarding the proper definition of the word “Terrorism”. “One man’s terrorist is another man’s freedom fighter” (Dittrich, 2005).



Corresponding author. Tel.: +92 334 8982744; fax: +92 992 383441. E-mail addresses: soothing [email protected] (Z. Malik), [email protected] (K. Zaman).

0161-8938/$ – see front matter © 2013 Society for Policy Modeling. Published by Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.jpolmod.2013.08.002

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According to FBI (2012, p. 1), Terrorism is the unlawful use of force or violence against persons or property to intimidate or coerce a government, the civilian population, or any segment thereof, in furtherance of political or social objectives. Today, the world axiom a new face of humans, i.e., the face of suicide bombers. The question arises, how a human can destroy him into a thousand pieces? How a human can destroy innocent people? Where these suicide bombers are trained? What literature is provided to them? Why the investigation agencies are failing to know the actual roots of these suicide attacks? What are the factors operating behind these suicide attacks? These are the questions which are unsolved and they need to be answered. Nine-eleven of the tragic events brought up international terrorism on American soil. Symbol of National Defence and terrorists killed U.S. capitalism and its impact on the national conscience was too much. The government closed the borders by tight security at airports and also on airplanes, and then with a big budget to create a new Homeland Security Department Fratianni and Kang (2004). Terrorism is the premeditated use or threat of use of violence by individuals or sub-national groups to obtain a political or social objective through the intimidation of a large audience, beyond that of the immediate victim (Brandt & Sandler, 2009). Although the motives of terrorists may differ, their actions follow a standard pattern with terrorist incidents assuming a variety of forms: airplane hijackings, kidnappings, assassinations, threats, bombings, and suicide attacks. Terrorist attacks are intended to apply sufficient pressures to a government so that it grants political concessions (Sandler & Enders, 2004). Terrorism remains a constant and real risk. Although more effective counter-terrorism measures have frustrated al-Qaeda’s ability to plan large-scale attacks since the events of September 11, 2001, global recorded terror incidents are at historic highs. The emergence of individuals or autonomous groups aligned to the aims of al-Qaeda continues to threaten the developed world, especially as insurgents are able to operate in unstable countries such as Pakistan and Yemen (Kroll, 2010). Terrorism is becoming a weapon of ever increasing importance to reach certain ends, given the potential of mass destruction available to leading international powers and the rise of one superpower dominating the international system. In most cases, terrorism is driven by an ideology comprising a world view with supreme values. Since these values are absolutely true to believers, they have to be preferred to everything, so that terrorists are required to sacrifice not only the lives of others but also their own (Bernholz, 2006). Terror requires education and sophisticated training. Politically unstable countries offer propitious conditions. It has been very sharply noted that terrorist groups operate human resources policies which favor better educated or economically better-off individuals (Krueger & Maleckova, 2003). Terrorism is the most burning issue in today’s world. Terrorism is destroying the social, political and economic setup in worldwide. The annual death rate due to terrorism incidence in Pakistan is increasing at an alarming rate. In year 2003, 164 people died which later reached to 3318 in 2009. Till 2010, 35,000 Pakistani people died in these terrorist activities. These events shows that how much havoc terrorism have played for Pakistan. Terrorism is now a global term. War against terrorism is continuous in tribal areas of Pakistan, so it is greatly influencing Pakistan’s economic situations. Pakistan has played a crucial role in the combat operations conducted by the coalition forces against Taliban and Al-Qaeda elements in Afghanistan and the tribal belt along its border. Equally important is the configuration of economic, social and political forces in Pakistan to determine whether it can effectively provide the logistical and political support to the war against terrorism in the future (Hussain, 2003). In recent year’s number of cases, terrorists have emerged

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from relatively affluent countries. Yet it is noteworthy that the Taliban who were integrally linked with Al-Qaeda predominantly came from poverty stricken Afghanistan. Those Taliban terrorists who were recruited from religious seminaries (madrassas) in Pakistan also came from the poorest sections of Pakistan’s society. Therefore, poverty can be as fertile a breeding ground for terrorism as a sense of political injustice (Hussain, 2003). Terrorism is expected to influence the GDP. Actual attacks as well as the threat of such attacks create instability in the economic, political and personal sphere, negatively affecting growth rates and the GDP (Haj-Yehia, 2006). In general, GDP has proven to be sensitive to the number of terrorist casualties. The affected GDP in turn may inflict political instability in case governmental counterterrorism measures turn out to be insufficient (Abel & Bernanke, 1998). The rapidly growing debt servicing burden together with a slowdown of GDP growth and government revenues that had occurred at the end of the Bhutto period in 1977 would have placed crippling fiscal and political pressures on the Zia regime (Hussain, 2003). A rising economic crisis is adding to the political instability in the country, with GDP growth stagnating at 2.4 percent in fiscal year 2010–11, barely offsetting population growth, as compared to 3.8 percent in the preceding year, and the population in poverty burgeoning to an estimated 90 million out of a total population of about 177 million (Pakistan Assessment, 2012). The political rights seem more closely associated with terrorism than civil liberties and the finding depicts that freedom of press does not seem to play a major role as other political considerations (Campos & Gassebner, 2009). Political instability, bad law and order situation, army’s interference, bomb blasts, terrorism, inconsistent economic policies etc. are the factors which are disturbing domestic and foreign investment. Pakistan investors are taking away their money to Dubai and other countries of the world.9/11 incident, Gulf war and the baseless allegations of terrorism hamper the image of Pakistan which has been affected very badly at the international level. So, in the current scenario, Pakistani’s have limited job opportunities in other countries of the world (Khan, 2011). Abadie (2006, p. 7) finds that political rights are a crucial factor. The terrorist risk is not significantly higher for poorer countries, once the effects of other country-specific characteristics, such as the level of political freedom, are taken into account. In contrast with the results for civil wars, lack of political freedom is shown to explain terrorism, and it does so in a non-monotonic way. Countries with intermediate levels of political freedom are shown to be more prone to terrorism than countries with high levels of political freedom or countries with highly authoritarian regimes. An important investigated reason is country size, which is most often calculated as total population and/or as percentage of the total population living in specific urban areas. The most obvious thing is that whenever there is any insurgency and any terror activity occurs, it results in a much bigger loss. The justification is that larger fractions of population in urban centers make terror attacks more deadly (Gassebner, Keck, & Teh, 2005). A country like Pakistan which is being targeted by terrorist for more than a decade and which urban population has sacrificed almost 15 thousand lives needs a much better strategy to overcome these problems and to get rid of these attacks immediately (Tellis, 2008). There also exists a close association between poverty and income inequality because both concepts indirectly imply the similar ideology. According to Akram, Wajid, Mahmood, and Sarwar (2011), when an individual is stated to be poor then there is another individual who is rich. And when we say income distribution is unequal we imply that there is another form of distribution that is equal. Also, it implies that as a result of income distribution, some individuals are getting more or less (better or worse off) than other individuals. Although, terrorism is not new to Pakistani society, but its effect is greatly felt after second

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Afghan War. It is mainly because Pakistan is a neighbor country of Afghanistan. According to Government of Pakistan (2011), during last ten years after US invaded Afghanistan, Pakistan economy has suffered around 68 billion US dollars loss. This war although have repercussions for all economic sectors, this study will analyze its impact on emigration emanating from Pakistan (Hyder & Hussain, 2011). It seems quite clear and obvious that poverty and terrorism are closely interrelated (Conelly, 2008). Poverty can surely lead to a sense of social problems, destroying the setup of the society, which could make easily convince the middle class of the society to join some terrorist groups. By doing this, they believe that they could generate some income and can bring some happiness to their families. There are certain factors in Pakistan which tries to convince people for terrorist activities and motivate them to indulge in the terrorist acts against their own people at the cost of money and wealth. In terms of the calorific norm, the percentage of the population below the poverty line increased from 17% in 1987–88 to 32% in 1999–2000. While one third of the population was hungry the majority of the population was deprived of access over basic services such as education, health, sanitation and safe drinking water (Hussain, 2003). Unemployment is one of the biggest problems of Pakistan. In the current situation, more than 3 million people are unemployed in Pakistan and unemployment ratio is more than 12% (Khan, 2011). Due to 9/11 incident, Gulf war and the baseless allegations of terrorism the image of Pakistan has been affected very badly at international level. In the current scenario, Pakistan has limited job opportunities in other countries of the world (Khan, 2011). The economic impact of terrorism can be estimated from a series of perspectives. There are direct costs to property and immediate effects on productivity; the living standard of people is badly affected as well as longer term indirect costs of responding to terrorism. In recent time the inflation in Pakistan is increasing at an alarming rate (Center for Research and Security, 2009). The prices in market jumps whenever a suicide attacks occur in any part of the country. This shows that terrorism is the main cause of rapid inflation in Pakistan. In these circumstances when there are threats of terrorist acts, some alternative roots are chosen for trades which proved to be extremely expensive and pose great threat on inflation and push it ever higher. Terrorist assaults can negatively affect the volume of trade in direct and indirect ways. Direct impact is illustrated by the direct destruction of traded goods or infrastructure necessary for trade, e.g. ships, ports, et cetera. The destruction can have a significant negative effect for individuals and companies. However, the impact on the macro economy will be limited, especially in comparison to the indirect effects (Brück, Schneider, & Karaisl, 2007). The merchandize trade deficit improved by $240 million and declined from $12.3 billion in July–April 2009–10 to $12.1 billion in July–April 2010–11. The substantial increase of 14.7 percent in imports is more than neutralized by 27.8 percent growth in exports which caused the trade deficit to improve. Pakistan’s continuing engagement with the production and export of Islamist extremism and terrorism continued to produce a bloody blowback at home, with a total of at least 6142 persons, including of 2797 militants, 2580 civilians and 765 Security Forces (SFs) personnel killed in 2011. However, even this worrying total constituted an improvement of 17.75 per cent over the preceding year. 7435 persons, including 5170 militants, 1796 civilians and 469 SF personnel, had been killed in 2010. While civilian and SF fatalities increased by whopping 43.65 and 63.11 percent, respectively, the steep decline (45.89 percent) in fatalities among the militants, primarily due to Islamabad’s approach of going soft on terror, was the sole reason for the decrease in overall fatalities through 2011 (Pakistan Assessment, 2011). In Pakistan, particularly, macroeconomic consequences has been appeared due to terrorists attacks, therefore, the objective of this study is to empirically investigate the macroeconomic

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factors which badly affected by terrorism in Pakistan over a period of 1975–2011. The more specific objectives are: I. To estimate whether there is a long-run relationship between macroeconomic factors, economic growth and terrorism in Pakistan. II. To explores the influencing directions between macroeconomic indicators and terrorism. III. To compare the influencing magnitude of macroeconomic factors on terrorism in Pakistan. The study is divided in to following sub-sections: after introduction which is presented in Section 1 above. Section 2 describes the literature review. Analytical framework is shown in Section 3. Data source and methodological framework presented in Section 4. Results are discussed in Section 5. Final section concludes the study. 2. Literature review The nexus between macroeconomic factors and terrorism has been widely discussed in the empirical and theoretical academic literature. The term terrorism could be defined as violence designed to induce fear in the avowed enemy by an individual or group, against non-combatant members of another group within the same State or against non-combatant citizens of other States. Terrorism in many cases is rooted in economic deprivation, a sense of social or political injustice, or a narrowing of the mind induced by ideological indoctrination. The terrorist often claims that their action is an expression of his political or religious belief or retaliation against his sense of injustice (Hussain, 2003). Terrorism can be analyzed two basic categories of terrorist activity. On the one hand, terrorists can act by using episodic single attacks, like the 9/11 attacks. On the other hand, there is terrorism referred to as campaign terrorism, which tends to return on a regular basis (European Vulnerabilities, 2008). Yang, Klyueva, and Taylor (2011) examine the relationship between the effects of increasing anti-terrorism expenditure on economic growth rate and social welfare in the context of Chicago over the period of 1992–2011. The result shows positive relationship between terrorism expenditure and economic growth in Chicago. Roberts (2009) explores the link between the consequences of human-made and natural disasters, taking US country-over the period of 1989–2008. The study evaluates the macroeconomic impacts of the 9/11 attack on U.S. real GDP growth and the unemployment rate by examining how forecasts of these variables were revised after the attack occurred. The effect of terrorism on exchange rates resulting from distrust in terrorism affected currency or effects on consumer sentiment, unemployment levels, inflation, interest rates, GDP, consumption, investment, and trade. Campos and Gassebner (2009) investigated the relative roles of economic and political conditions of 130 countries over the period of 1968–2003. The result shows that domestic political instability is the main reason for international terrorism. Shahrestani and Anaraki (2008) have found the evidence of a cast light on the effects of terrorism on some macroeconomic variables in the context of Iran, using cross-section data of 2005 for a sample of 30 developed and developing countries, using the Generalized Method of Moment (GMM) in different regions, and demonstrate how terrorism incident affect the GDP growth, FDI and total factor productivity. The results suggest that terrorism has adversely and significantly affected economic growth, FDI and total factor productivity. Anderson (2008) analyzes trade and enforcement policies in a setting of rational terrorism on trade in the context of Paris over a period of 2006–2008. The result concludes that trade draws potential terrorists and economic predators

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into productive activity, but trade also draws terrorist attacks. Larger trade reduces the risk of terrorist attack when the wage elasticity is high, associated with low ratios of predators to prey and high wages; but it may increase the risk of terrorist attack when the wage elasticity is low, associated with high ratios of predators to prey. Alberto and Gardeazabal (2008) examine the impact of terrorism on the world economy in the context of 186 countries and territories, over the period of 2003–2007. The main conclusion of this study is that mobility of productive capital in an open economy may account for much of the difference between the direct and the equilibrium impact of terrorism. Piazza (2006) explores the links between poverty and terrorism using a country-year database of 172 countries from 1970 to 2006. Central to this study is the argument that because terrorism is not a mass phenomenon but rather is undertaken by politically marginal actors with often narrow constituencies, the economic status of subnational groups is a crucial potential predictor of attacks. Using data from the Minorities at Risk project, determine that countries featuring minority group economic discrimination are significantly more likely to experience domestic terrorist attacks. Shughart (2006) traces the history of modern terrorism from the end of the Second World War to the beginning of the twenty-first century. This study argues that terrorism is rooted in the artificial nation-states created during the interwar period and suggests solutions grounded in liberal federalist constitutions and new political maps for the Middle East, Central Asia and other contemporary terrorist homelands. Blomberg, Hess, and Weerapana (2004) perform an empirical investigation of the macroeconomic consequences of international terrorism and interactions with alternative forms of collective violence. The analysis is based on a rich unbalanced panel data set with annual observations on 177 countries from 1968 to 2000. The study finds that, on average; the incidence of terrorism may have an economically significant negative effect on growth, albeit one that is considerably smaller and less persistent than that associated with either external wars or internal conflict. Krueger and Maleckova (2003) find the connection between poverty, education and terrorism in the context of Mexico over the period of 1987–2002. Instead of viewing terrorism as a direct response to low market opportunities or ignorance, the result reveals that it is more accurately viewed as a response to political conditions and long-standing feelings of indignity and frustration that have little to do with economics. Matto and Rathindran (2001) measuring services trade liberalization and its impact on economic growth, for a sample of 60 countries for the period 1990–1999. This study has three rationales i.e., first, it explains how the impact of liberalization of service sectors on output growth differs from that of liberalization of trade in goods. Secondly, it suggests a policy-based rather than outcome-based measure of the openness of a country’s services regime. Such openness measures are constructed for two key service sectors, basic telecommunications and financial services. Finally, it provides some econometric evidence relatively strong for the financial sector and less strong, but nevertheless statistically significant, for the telecommunications sector that openness in services influences long run growth performance. The result suggests that countries with fully open telecom and financial services sectors grow up to 1.5 percentage points faster than other countries. Elbakidze and Jin (2000) examine the impacts of socio-economic conditions on the probability and frequency of participation in transnational terrorism events from 1980 to 2000 with the annual terrorism participation counts for 77 countries. In their study, authors empirically examine the impacts of socio-economic conditions on the probability and frequency of participation in transnational terrorism events. The results suggest that extreme poverty may preclude the opportunities to participate in terrorism acts while relative alleviation of poverty levels may provide

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marginal resources to participate in terrorism acts and materialize accumulated hatred. Barros, Faria, and Gil-Alana (2008) analyses terrorism attacks against USA citizens in Africa from 1978 to 2002. The result concludes that attacks to USA citizens in Africa are persistent and perpetuated in countries characterized by poverty and low level of political and economic freedom. Bari (2009) understands and captures the gendered perceptions on the war on terrorism and growing radicalization through using scientific tools of research in the context of Pakistan over the time period 1985–2008. The study shows that all men, women and children suffered from the social, economic and political impact of terrorism/Talibanization; however, women are disproportionately affected due to their dependent socio-economic position in local cultures. Zafar (2008) examine that the control of accidents within the design basis in the context of Pakistan over the period of 2008. This study aims to provide information about the evolution of research reactors, barriers available for their protection, various modes of nuclear terrorism, and physical protection measures required to reduce the probability of terrorist act, happening and to cope with such scenarios. In addition, Pakistan’s response to nuclear terrorism and improvements needed to manage such emergencies (if they happen) are also discussed in this study. The result reveals that the knowledge of science and technology and their accessibility to terrorists has made the threat of nuclear terrorism no longer a fiction but real with their intention to inflict catastrophic damages to man, environment, and property. Prieto-Rodríguez, Rodríguez, Salas, and Suarez-Pandiello (2009) proposes a new methodology to measure social impact of ETA terrorism in Spain over a period of 1993–2004. This new methodology is based upon multidimensional terrorism index that is not only covered deaths but deals other variables such as injuries, bombs and kidnappings. The result opines that national terrorism is usually linked to territorial defined political objectives, independence inmost of the cases. On the contrary, global terrorism is usually focused on factors like religion and the socioeconomic organization of a nation. However, both kinds of terrorism manifest themselves through their impact on the citizen’s state of mind. Sow (2007) examines the political conflict between the different factions of the country in the context of Pakistan over the time period 2002–2007. Since 9/11, Pakistan has been a close ally of the United States. But this close relationship with the U.S. is a huge problem for the Pakistan government. The Islamic political parties, which have a large influence in the country, are against an alliance between Pakistan and the United States of America. Kollias, Messis, Mylonidis, and Paleologou (2009) empirically examines the effectiveness of security spending as a policy option against terrorism in Greece by using annual data from 1974 to 2004 periods. The results suggest that spending on infrastructure, technology and capital equipment can potentially prove to be an effective counterterrorist policy option. Kollias, Mylonidis, and Paleologou (2013) further evaluates the effectiveness of public order spending on recorded crime between 1974 and 2004 in Greece. The result shows that public order outlays do not have any crime reducing impact. Terrorist attacks occur in an apparently random manner. However, as terrorist groups are rational, there must be an underlying pattern behind terrorist attacks (Barros, Passos, & Gil-Alana, 2006). According to Frey (2009, p. 781) Terrorists can be considered rational actors in the sense that they want to reach their goals as efficiently as possible. Thus, rationality does not refer to the goals the terrorists want to achieve. The specific goals of a terrorist group may appear outlandish and difficult to appreciate by outside observers, but terrorists, nevertheless, will endeavor to reach these goals as efficiently as they can. They strive to achieve a maximum effect through the actions chosen.

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Macroeconomic factors and terrorism is a growing concern for developing countries like Pakistan, hence there is a pressing need to evaluate and analyze the macroeconomic indicators of terrorism and to find out the inter relationship. In the subsequent sections an effort has been made to empirically find out the casual relationship between macroeconomic factors and number of incidence in the context of Pakistan.

3. Theoretical framework Terrorism is particularly a reliant variable and it is difficult to measure that terrorist and the organizations that they are affiliated with are not likely to reveal themselves so that academics might count them. The clandestine nature of these groups and their operatives makes a level of terrorism difficult to ascertain. Tragically, only clear indicator of terrorism is when a group or individual succeeds in executing an attack (Ekey, 2003). The effects of terrorism on GDP can be understood by examining the relationship between terrorism, wealth, and consumption. Terrorism is expected to cause incomes to fall, for a variety of reasons. Since terrorism often destroys capital assets, a terrorist attack can have large ramifications for business. Terrorism has been shown to have the smallest effect on GDP growth; investment spending tends to adjust more negatively to terrorism than do other spending components of GDP (Blomberg, Hess, & Weerapana, 2004). Poverty has as a very strong influence on domestic terrorism and a small, but significant, effect on transnational terrorism (Enders & Hoover, 2012). The relationship between terrorism and inflation has been debated in the literature. The expectation is that high inflation rates will be positively related to terrorism, as there is significant evidence that hyperinflation is related to regime change and political instability (Piazza, 2006). However, Shahbaz and Shabbir (2010) conclude that an increase in inflation raises terrorist attacks and rising inflation reduces the purchasing power and increases poverty. Piazza (2006) indicates that there is a positive relationship between terrorism and political instability. Political instability is a main or deep cause of international terrorism because terrorism requires skills (mostly military and organizational) that can be honed in countries that are politically unstable (Campos & Gassebner, 2009). The effects of transnational terrorism on trade through an increase in trade costs. It is quite likely that there is a two-way relationship between trade openness and transnational terrorism. On the one hand as expected, transnational terrorism affects negatively bilateral trade flows. Though the effect on average seems to be quite modest, there are good reasons to believe that it is nonlinear and substantially bigger for countries which are recurrently suffering or committing terrorism. On the other hand, globalization and more specifically trade integration, impacts as well transnational terrorism (Daniel & Verdier, 2006). The effects of transnational terrorism on income inequality are spawned in the very poorest of countries or in the very richest. One explanation is that there is little surplus to support terrorism in the poorest countries and that high income countries have the resources to thwart terrorism. Moreover, the literature on the economics of crime strongly supports the notion that an individual’s economic circumstances have a nonlinear (or ambiguous) effect on participation in crime (Enders & Hoover, 2012). In an early study, show that an increase legal income will generally decrease an individual’s crime related activities because of increased foregone earnings. However, if the increase applies to all agents, the expected return to participation in crime increases so that income and criminal activities can move in the same direction. Undoubtedly, a number of terrorist groups have been able to finance many of their activities by robbery and/or by kidnapping high-income individuals (Block & Heineke, 1975). On the empirical side, find that wealth has a positive effect

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on crime in low income countries but a negative effect on crime in wealthier counties (Muroi & Baumann, 2009). Unemployment or the average national unemployment rate for each country would be expected to bear a significant positive relationship with terrorism, as unemployment precipitates the stress of idle workers who might suffer from unmet economic expectations and therefore turn to political violence (Piazza, 2006). Many Western European countries have seen increasing tension between local communities and immigrant groups, especially Muslims, with mutual grievances. These tensions have been more apparent and stronger since 9/11/2001. Intensified by further terrorist attacks, the war on terror, and the fear of terrorism, negative attitudes toward Muslim populations have been increasing (Staub, 2007). The study transformed the series into natural-log form to avoid sharpness in the data. Another advantage of natural-log transformation is that it directly provides elasticities. The log-linear specification provides reliable empirical evidence which can be helpful to control terrorism by reducing inflation and economic deprivation. Following the above discussion, algebraic equation for empirical purpose is modeled as follows: Log(TI) = α0 + α1 log(GDPPC) + α2 log(POP) + α3 log(GINI) + α4 log(UN) + α5 log(CPI) + α6 log(TOP) + α7 log(POV ) + α8 (PI) + ε

(1)

In Eq. (1), TI is dependent variable which shows terrorism incidence (number of incidence), the independent variables are GDPPC is a gross domestic product per capita measured in US$, POP is population growth measured in percentage, GINI is income inequality coefficient, UN is unemployment in percentage, CPI is consumer price index which represents changes in price level in the economy i.e., inflation, TOP is trade openness i.e., exports plus imports as percentage of GDP, POV is headcount ratio which represents poverty and PI is political instability which represents as an index, log is natural logarithm and ε is error term. 4. Data source & methodological framework This study is based on annual data of terrorism incidence (TI), GDP per capita, population growth, income inequality, unemployment, inflation, trade openness, poverty and political instability covering a time period from 1975 to 2011 in Pakistan. Annual data are used in this study to avoid the seasonal biases. The time series data are collected from International Financial Statistics (IFS, 2011), World Bank, World Development Indicators (WDI, 2011) and State Bank of Pakistan (SBP, 2010). The literature suggest that trade imbalance and oil prices are significantly positive in relation such that for the oil importing countries like Pakistan with the increase in oil prices the trade imbalance increases greatly (Le & Chang, 2011). The expectation is that high inflation rates will be positively related to terrorism (Piazza, 2006). The population plays a crucial role in the incidence of terrorism. There is a positive relationship between terrorism and political instability due to the political violence (Piazza, 2006). Thus literatures suggest that the unemployment, per capita income, trade openness and poverty have long run positive and strong relation (see Daniel & Verdier, 2006; Enders & Hoover, 2012; Muroi & Baumann, 2009; Piazza, 2006). Fig. 1 highlights in schematic fashion the methodological approach adopted in the paper. According to this framework, number of incidence has been checked on GDP through macroeconomic indicators.

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GINI

UN GDPPC

CPI

Terrorism (TI)

TOP POV

PI

Fig. 1. Research framework of the study source: self extract.

Following seven equations (Panels A–G) are used to assess terrorism as a result of macroeconomic factors in Pakistan i.e., PanelA : TI, POP, GDPPC

(2)

PanelB : TI, GINI, GDPPC

(3)

PanelC : TI, UN, GDPPC

(4)

PanelD : TI, CPI, GDPPC

(5)

PanelE : TI, TOP, GDPPC

(6)

PanelF : TI, POV, GDPPC

(7)

PanelG : TI, PI, GDPPC

(8)

where, TI represents Terrorism incidence; GDPPC represent GDP per capita (current US $); POP/GDP represent population as percentage of GDP; GINI/GDP represent income inequality as percentage of GDP, UN/GDP represent unemployment as percentage of GDP, CPI/GDP represent inflation as percentage of GDP, TOP/GDP represent trade openness as percentage of GDP. POV/GDP represents poverty as percentage of GDP and PI represent political instability. 4.1. Econometric model of the study The time series data often show the property of non-stationarity in levels and the resulted estimates usually provide spurious results. Thus, the first step in any time series empirical analysis was to test for presence of unit roots to remove the problem of inaccurate estimates. The other important step was to check the order of integration of each variable in a data series in the model to establish whether the data under hand suffer unit root and how many times it needed to be differenced to gain stationarity.

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The test for co-integration consists of two steps: first, the individual series are tested for a common order of integration. If the series are integrated and are of the same order, it implies co-integration.1 Dickey and Fuller (1979, 1981) devised a procedure to formally test for nonstationarity. The Augmented Dickey Fuller (ADF) test is used to test the stationarity of the series. The ADF test is a standard unit root test: it analyzes the order of integration of the data series. These statistics are calculated with a constant, and a constant plus time trend, and these tests have a null hypothesis of non-stationarity against an alternative of stationarity. Johansen’ cointegration tests applied on the series of same order of order of integration i.e., I(1) series which determine the long run relationship between the variables. When series are cointegrated of order 1, trace test (Johansen’s Approach) indicates a unique cointegrating vector of order 1 and hence indicates the long run relationship. In the multivariate case, if the I(1) variables are linked by more than one co-integrating vector, the Engle–Granger (1987) procedure is not applicable. The test for co-integration used here is the likelihood ratio put forward by Johansen and Juselius (1990), indicating that the maximum likelihood method is more appropriate in a multivariate system. Therefore, this study has used this method to identify the number of cointegrated vectors in the model. The Johansen and Juselius method has been developed in part by the literature available in the field and reduced rank regression, and the co-integrating vector ‘r’ is defined by Johansen as the maximum Eigen-value and trace test or static, there is ‘r’ or more co-integrating vectors. Johansen (1988) and Johansen and Juselius (1990) proposed that the multivariate co-integration methodology could be defined as: (TI)t = (MF, GDPPC) where MF represents Macroeconomic factors which is a vector of elements. Considering the following autoregressive representation: TIt = π◦ +

K 

πi (TI)t−1 + μt

T =1

Johansen’s method involves the estimation of the above equation by the maximum likelihood technique, and testing the hypothesis Ho ; (π = Ψ ξ) of “r” co-integrating relationships, where r is the rank or the matrix π(0 ∠ r ∠ P), Ψ is the matrix of weights with which the variable enter co-integrating relationships and ξ is the matrix of co-integrating vectors. The null hypothesis of non-cointegration among variables is rejected when the estimated likelihood test statistic φi {= ˆ ˆ p −n t=r+1 ln(1 − λi } exceeds its critical value. Given estimates of the Eigen-value (λi ) the Eigenvector (ξ i ) and the weights (Ψ i ), we can find out whether or not the variables in the vector (TI) are co-integrated in one or more long-run relationships among (MF, GDPPC). This paper investigates the influence of Pakistan’s macroeconomic indicators on terrorism from two perspectives. One is to conduct the modified Granger causality (Granger, 1988) and Johansen cointegration tests to explore the influencing directions between different macroeconomic indicators and terrorism, respectively; the other is to compare the influencing magnitude of different macroeconomic indicators on terrorism based on the vector error correction model (VECM) and variance decomposition approach.

1 If the series are integrated with the mixture of order of integration i.e., I(0) and I(1), it implies bonds testing approach which was proposed by Pesaran et al (2001).

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In order to undertake the modified version of Granger causality for a VAR model with 3 lags (k = 2 and dmax = 1), we estimate the following system of equations: ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ TI TI TI TI ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎦ ⎦ + A3 ⎣ CPI ⎣ POP ⎦ = A0 + A1 ⎣ GINI ⎦ + A2 ⎣ UN GDPPC GDPPC GDPPC GDPPC ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ⎡ ⎤ ε1t TI TI TI ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎢ ⎥ ⎢ + A4 ⎣ TOP ⎦ + ⎣ ε2t ⎥ ⎦ + A5 ⎣ POV ⎦ + A6 ⎣ PI ⎦ GDPPC GDPPC GDPPC ε 3t

where A1 and A6 are the 3 × 6 matrices of coefficients with A0 being 3 × 1 identity matrix, and εt are the disturbance terms with zero mean and constant variance. From Eq. (6) we can test the hypothesis that Pakistan’s macroeconomic indicators does not Granger cause carbon emissions with the following hypothesis i.e., 1 2 H01 = a12 = a12 =0 i are the coefficients of the macroeconomic variables in the first equation of the syswhere a12 tem presented in Eq. (6). Besides, we can test the opposite causality from Pakistan’s terrorism formation to macroeconomic scale in the following hypothesis: 1 2 H02 = a21 = a21 =0 i are the coefficients of the terrorism variable in the second equation of the system where a21 presented in Eq. (6). It should be noted that we incorporate the variable GDPPC into Eq. (6) to avoid the omitted variable bias when we examine the Granger causality bias when we examine the Granger causality between macroeconomic indicators and terrorism.

5. Results and discussion 5.1. Cointegration among macroeconomic indicators and terrorism incidence The present study conducts the augmented Dickey-Fuller (ADF) unit root tests for all variables with regard to their stationary properties. The detailed results are shown in Table 1. The results reveal that all variables in this study are non-stationary at their level, however, stationary at their first differences, therefore, we say that all variables are I(1) series at 1% level over a period of 1975–2010. Fig. 2 shows the plots of macroeconomic indicators and terrorism incidence i.e., TI; GDPPC; POP; GINI; CPI; UN; TOP; POV and PI in their first difference forms, which sets the analytical framework as regarding the long-term relationship between the variables. 5.2. Long-run relationship between macroeconomic indicators and terrorism incidence After that, we take terrorism incidence (TI) as the dependent variable and each Macroeconomic indicators and GDP per capita (GDPPC) together as the independent variables, and then the Johansen cointegration among them is tested according to Johansen (1988). From the results in Table 2, we find that except income inequality (GINI), unemployment (UN) and trade openness (TOP), all other macroeconomic indicators have at least one cointegration relationship with

Z. Malik, K. Zaman / Journal of Policy Modeling 35 (2013) 1103–1123

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Table 1 Augmented Dickey-Fuller (ADF) test on the levels and on the first difference of the variables (1975–2011). Variables

Level

First difference

Decision

Constant

Constant & trend

Constant

Constant & trend

−0.812 (0)

−1.883 (0)

−6.983(0)

−7.197(0)

GDP

2.119(0)

0.387 (0)

−4.311(0)

−4.834(0)

POP

−0.839 (8)

−1.887(9)

−3.005 (2)

−3.672 (1)

GINI

−1.637 (1)

−1.988 (1)

−4.255(2)

−4.197(2)

UN

1.403 (7)

−2.249 (4)

−8.493*(0)

−2.689(7)

CPI

−2.164 (4)

−1.900 (1)

−9.011 *(0)

−8.913*(0)

TOP

−2.425(0)

−2.602(0)

−5.870*(0)

−5.819*(0)

POV

−2.782(1)

−2.585(1)

−2.244(0)

−2.465(0)

PI

−1.471(1)

−2.163(3)

−4.303*(3)

−15.036(3)

TI

Non-stationary at level but stationary at first difference i.e., I(0) Non-stationary at level but stationary at first difference i.e., I(1) Non-stationary at level and first difference i.e., I(9) Non-stationary at level but stationary at first difference i.e., I(2) Non-stationary at level but stationary at first difference i.e., I(0) Non-stationary at level but stationary at first difference i.e., I(0) Non-stationary at level but stationary at first difference i.e., I(0) Non-stationary at level & at first difference i.e., I(0) Non-stationary at level but stationary at first difference i.e., I(3)

Note: The null hypothesis is that the series is non-stationary, or contains a unit root. The rejection of the null hypothesis is based on MacKinnon (1996) critical values i.e., at constant: −3.639, −2.951 and −2.614 are significant at 1%, 5% and 10% level respectively. While at constant and trend: −4.252, −3.548 and −3.207 are significant at 1%, 5% and 10% level respectively. The lag length are selected based on SIC criteria, this ranges from lag zero to lag eight.

terrorism incidence at 5% level. Therefore, we may say that, for the most part, Pakistan’s macroeconomic indicators have significant long-term equilibrium with terrorism incidence. 5.3. Causality among macroeconomic indicators and terrorism incidence Subsequently, we conduct the modified Granger causality tests by Toda and Yamamoto (1995) for macroeconomic variables and terrorism incidence. The variable GDPPC is incorporated as an explanatory variable to avoid the omitted variable bias. Results are shown in Table 3. The POP granger cause the incidence of terrorism as it is significant at 1% level. However, TI does not granger cause the POP, therefore we conclude that POP and TI has unidirectional casualty runs between them. The rate of population has a positive effect on terrorism such as population, ethno-religious diversity, increased state repression and, most significantly, the structure of party politics are found to be significant predictors of terrorism (Piazza, 2006). The GINI granger cause the incidence of terrorism as it is significant at 1% level. However, TI does not granger cause the GINI, therefore we conclude that GINI and TI has unidirectional casualty runs between them.

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Table 2 Results of Johansen cointegration tests.

Panel A: Series: TI, POP, GDPPC

Panel B: Series: TI, GINI, GDPPC

Panel C: Series: TI, UN, GDPPC

Panel D: Series: TI, CPI, GDPPC

Panel D: Series: TI, TOP, GDPPC

Panel D: Series: TI, POV, GDPPC

Panel D: Series: TI, PI, GDPPC

Hypothesized no. of CE (s)

Eigenvalue

Trace statistic

5% Critical value

Prob.

Nonea At most 1a At most 2 None At most 1 At most 2 None At most 1 At most 2 Nonea At most 1 At most 2 None At most 1 At most 2 Nonea At most 1a At most 2 Nonea At most 1a At most 2

0.415608 0.369539 0.016975 0.377349 0.201886 0.000801 0.507390 0.121710 0.017920 0.527198 0.197173 0.013046 0.414503 0.193868 0.017766 0.481996 0.381980 0.021791 0.880427 0.696522 0.195085

34.53076 16.26651 0.582139 23.80251 7.694359 0.027229 26.53288 4.583691 0.560545 33.38209 7.913444 0.446494 26.13675 7.936754 0.609473 39.47534 17.11108 0.749086 45.93280 18.32306 2.821249

29.79707 15.49471 3.841466 29.79707 15.49471 3.841466 29.79707 15.49471 3.841466 29.79707 15.49471 3.841466 29.79707 15.49471 3.841466 29.79707 15.49471 3.841466 29.79707 15.49471 3.841466

0.0132 0.0382 0.4455 0.2089 0.4986 0.8689 0.1136 0.8513 0.4540 0.0185 0.4747 0.5040 0.1247 0.4722 0.4350 0.0028 0.0283 0.3868 0.0003 0.0182 0.0930

Note: Dependent variable in each Johansen cointegration test is TI. a Denotes rejection of the hypothesis at the 5% level.

Table 3 Causality test results among macroeconomic indicators and terrorism incidence. Null Hypothesis

Chi-square statistic

Prob.

TI does not Grange cause the changes in POP POP does not Granger cause the changes in TI TI does not Grange cause the changes in GINI GINI does not Granger cause the changes in TI TI does not Grange cause the changes in UN UN does Granger cause the changes in TI TI does not Grange cause the changes in CPI CPI does Granger cause the changes in TI TI does not Grange cause the changes in TOP TOP does Granger cause the changes in TI TI does Grange cause the changes in POV POV does not Granger cause the changes in TI TI does not Grange cause the changes in PI PI does Granger cause the changes in TI

2.569617 10.67856 2.746985 13.50883 11.19773 18.87084 4.746150 17.61855 0.988782 10.54573 0.708064 12.61764 1.909508 8.995572

0.2767 0.0048 0.2532 0.0012 0.0037 0.0001 0.0932 0.0001 0.6099 0.0051 0.7019 0.0018 0.3849 0.0111

Note: The modified Granger causality test approach used in the table is provided by Toda and Yamamoto’s (1995). And the causality tests between macroeconomic indicators and terrorism incidence are based on the significance of Chi-square statistics for Wald tests of VAR models.

Z. Malik, K. Zaman / Journal of Policy Modeling 35 (2013) 1103–1123 1200

800 700

3.6

1000

3.2

600 800

500 400

2.8

600

300

2.4

400

200

2.0

200

100 0 1975

1117

1980

1985

1990

1995

2000

2005

0 1975

2010

1980

1985

1990

TI

1995

2000

2005

2010

1.6 1975

1980

1985

1990

44 42 40

1995

2000

2005

2010

POP

GDPPC 24

8

20

7

16

6

12

5

8

4

4

3

38 36 34 1975

1980

1985

1990

1995

2000

2005

0 1975

2010

1980

1985

1990

GINI

1995

2000

2005

1980

1985

1990

CPI

.48

40

.44

36

1995

2000

2005

2010

UN 35

30 32

.40

28 .36

25

24 20

.32 .28 1975

2 1975

2010

20

1980

1985

1990

1995 TOP

2000

2005

2010

16 1975

1980

1985

1990

1995

2000

2005

2010

15 1975

1980

POV

1985

1990

1995

2000

2005

2010

PS

Fig. 2. Nonlinear data TRENDS at their first difference.

The result implies that the higher the income inequity, the higher the likelihood of participation in terrorism events and the greater the frequency of participation in terrorism attacks. Th higher levels of inequality are robustly associated with more terrorist activity, implying a link from economic deprivation to terrorist activity (Elbakidze & Jin, 2000). The ‘UN’ granger cause the incidence of terrorism as it is significant at 1% level. However, TI granger cause the UN as it is significant at 1% level; therefore we conclude that UN and TI have bidirectional casualty runs between them. The rate of unemployment has a positive effect on the probability and the frequencies of participation in terrorism events (Benmelech, Berrebi, & Klor, 2009). The CPI granger cause the incidence of terrorism as it is significant at 1% level. However, TI does not granger cause the CPI, therefore we conclude that CPI and TI has unidirectional casualty runs between them. The result implies that the rise in inflation raises terrorist attacks and economic growth also contributes to increase the terrorism (Shahbaz & Shabbir, 2010). The TOP granger cause the incidence of terrorism as it is significant at 1% level. However, TI does not granger cause the TOP, therefore we conclude that TOP and TI has unidirectional casualty runs between them. Openness to trade has a negative effect on the probability and the frequency of participation in terrorism acts. Thus, as a country becomes more globally integrated, as measured by the ratio of the volume of its trade and GDP, the likelihood and frequency of its citizen’s participation in transnational terrorism decreases (Blomberg & Rosendorff, 2006). The POV granger cause the incidence of terrorism as it is significant at 1% level. However, TI does

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Z. Malik, K. Zaman / Journal of Policy Modeling 35 (2013) 1103–1123 Variance Decomposition of D(TI)

Variance Decomposition of D(CPI)

Variance Decomposition of D(GDPPC)

100

100

100

80

80

80

60

60

60

40

40

40

20

20

20

0

0 1

2

3

4

5

D(TI) D(CPI) D(GDPPC)

6

7

8

D(GINI) D(POP) D(POV)

9

10

0 1

2

D(PS) D(TOP) D(UN)

3

4

5

D(TI) D(CPI) D(GDPPC)

Variance Decomposition of D(GINI)

6

7

8

D(GINI) D(POP) D(POV)

9

10

1

6

7

8

D(GINI) D(POP) D(POV)

9

10

D(PS) D(TOP) D(UN)

50

40

40

30

50

5

60

80

60

4

Variance Decomposition of D(POV)

50

70

3

D(TI) D(CPI) D(GDPPC)

Variance Decomposition of D(POP)

90

2

D(PS) D(TOP) D(UN)

30

40

20

30 20

20

10

10

10 0

0 1

2

3

4

5

D(TI) D(CPI) D(GDPPC)

6

7

8

D(GINI) D(POP) D(POV)

9

10

0 1

2

D(PS) D(TOP) D(UN)

3

4

5

D(TI) D(CPI) D(GDPPC)

Variance Decomposition of D(PS)

6

7

8

D(GINI) D(POP) D(POV)

9

10

1

2

D(PS) D(TOP) D(UN)

Variance Decomposition of D(TOP)

70

60

60

50

50

4

5

6

7

8

D(GINI) D(POP) D(POV)

9

10

D(PS) D(TOP) D(UN)

Variance Decomposition of D(UN) 50 40

40

40

3

D(TI) D(CPI) D(GDPPC)

30

30

30

20

20

20

10

10

10 0

0

0 1

2

3

4

D(TI) D(CPI) D(GDPPC)

5

6

7

D(GINI) D(POP) D(POV)

8

9 D(PS) D(TOP) D(UN)

10

1

2

3

4

D(TI) D(CPI) D(GDPPC)

5

6

7

D(GINI) D(POP) D(POV)

8

9 D(PS) D(TOP) D(UN)

10

1

2

3

4

D(TI) D(CPI) D(GDPPC)

5

6

7

D(GINI) D(POP) D(POV)

8

9

10

D(PS) D(TOP) D(UN)

Fig. 3. Variance decomposition.

not granger cause the POV, therefore we conclude that POV and TI has unidirectional casualty runs between them. The fact that most terrorist attacks are staged in low income countries seems to support the notion that poverty causes terrorism (Enders & Hoover, 2012). The ‘PI’ granger cause the incidence of terrorism as it is significant at 1% level. However, TI does not granger cause the PI, therefore we conclude that PI and TI has unidirectional casualty runs between them. The result shows that the occurrence of civil wars increases the number of international terrorist acts originating from that country, attendant fatalities and other indicators of domestic political instability (Campos & Gassebner, 2009). The results reflect that macroeconomic indicators are closely associated with terrorism incidence. In reality the development of macroeconomic indicators are closely related to terrorism incidence development in Pakistan.

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5.4. Variance decomposition analysis In order to compare the contribution extents of macroeconomic factors over terrorism incidence, the variance decomposition approach is adopted over the sample period. First, we take the terrorism incidence as the dependent variable and macroeconomic factors as independent variables, and conduct the Johansen cointegration test among these variables over a period of 36 years. The results indicate that there exists statistically significant cointegration among macroeconomic factors and terrorism incidence in Pakistan during 1975–2011. Next, we apply the variance decomposition approach based on the vector error correction model (VECM) to explore the influence of different macroeconomic factors on terrorism incidence, and compare their contribution difference. Fig. 3 shows the variance decomposition analysis for macroeconomic indicators and terrorism incidence in Pakistan. The result shows that, among all macroeconomic factors, CPI exerts the largest influence, whose steady contribution level for terrorism incidence changes approaches to 31.11%; while the influence of GDPPC, GINI, POP, POV, PS and TOP follows with steady contribution level of 12.31%, 10.68%, 3.33%, 0.355%, 21.39% and 1.923% respectively. It should be noted that the influence of unemployment (UN) seems relatively the least; only about 0.048%. 6. Summary and policy recommendations Terrorism is the premeditated use or threat of use of violence by individuals or sub-national groups to obtain a political or social objective through the intimidation of a large audience, beyond that of the immediate victim (Rosendorff & Sandler, 2005). The objective of the study is to estimate the long-run relationship between macroeconomic factors, economic growth and terrorism incidence in Pakistan. The study further explores the influencing directions and magnitude between macroeconomic factors and terrorism incidence in Pakistan over a period of 1975–2010. Data is analyzed by cointegration theory, Granger causality test and variance decomposition, etc. The results reveal that macroeconomic indicators act as an important driver for increase in terrorism in Pakistan. The results indicates that macroeconomic factors i.e., population growth, price level, poverty and political instability have a long-run relationship with terrorism incidence in Pakistan. However, income inequality, unemployment and trade openness have no long-run relationship with the terrorism incidence in Pakistan. The results of Modified version of Granger causality indicates that causality runs from macroeconomic indicators to terrorism incidence i.e., income inequality, trade openness, population growth, price level, poverty, inflation and political instability have unidirectional causality with terrorism incidence except unemployment. Unemployment has bidirectional causality with the terrorism incidence in Pakistan. Variance decomposition analysis shows that there exists statistically significant cointegration among macroeconomic factors and terrorism incidence in Pakistan during 1975–2010. Among macroeconomic factors, CPI exerts the largest influence on terrorism incidence in Pakistan, while the poverty seems relatively the least contributor with terrorism incidence. Our empirical results suggest that counter terrorism policymakers would be advised to use more specifically targeted measures to attack the socio-economic roots of terrorism, that improve fiscal responsibility, control inflation and all policy components will help to reduce the threat of terrorism. Instead expenditures across social groups or strengthening and universalizing the rule of law, which more directly improve the economic status of minority and/or socially excluded and vulnerable groups – groups that if aggrieved are more likely to engage in terrorism (Piazza, 2011).

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The controls around various nuclear installations and radiation facilities in Pakistan are enough to deter and delay a terrorist attack and any malicious diversion would be detected in early stages and to provide information about the research reactors, barriers available for their protection, various modes of nuclear terrorism, measures that can be taken to reduce their probability of happening and to cope with such scenarios (Zafar, 2008). Nowadays, government anti-terrorism expenditure is not only a crucial policy but also a direct way to increase the well-being of the public. The propose is that to achieve the goal of an economic growth rate, it may be better to decrease anti-terrorism expenditure to as little as possible and to achieve the maximum social welfare, the government is recommended to reallocate its budget from core infrastructure expenditure to anti-terrorism expenditure (Yang et al., 2011). However the macroeconomic consequences of terrorism are potentially quite significant, confirming the need for a redoubling of public policy efforts toward examining how to best mitigate the associated risk (Blomberg et al., 2004). Terrorism at a psychic level involves a divorce from the wellsprings of reason and humanity that give to the individual his sense of wholeness and relatedness with others. If Pakistan is to avoid becoming a breeding ground of terrorism in the future, then poverty and illiteracy must be overcome. This is necessary to enable its people to love and reason rather than hate and kill (Hussain, 2003). The promotion of public information technologies such as internet and cable to reach population particularly in areas where the writ of the government is minimal and where people have sympathies for terrorists such as tribal areas of Pakistan (Vira & Cordesman, 2011). The policies should be made and extended on the global level to increase the cooperation and coordination among the major stake holders in the global financial markets. Policies regarding benign relationships among the central banks of international importance shall also be encouraged so as to extend financial help during the crucial financial crunch after any enormous act of terrorism (Gul, Hussain, Bangesh, & Khattak, 2010). The frontier technological investment that aim at yielding innovations that facilitate international coordination and are able to radically minimize the probability of further and more sophisticated transnational terror attacks (Campos & Gassebner, 2009). Violence in Pakistan has been on the rise as more militant groups target the state. The government has to prioritize the ideological front over the military front. There should be a public awareness campaign regarding the problem, especially the stance of Islam on jihad should be made clear to all (O’Connor, 2012). The Madrassa system requires drastic reformation. Science and other subjects should be introduced into the curriculum for the time being, while in the long run the government should strive for a uniform education system across the country (Sial & Anjum, 2010). Primary education up to 12th grade, or at least 10th grade, should be made compulsory before children move on to religious education. After this pre-requisite is met, every student should have the right to opt for whichever avenue of education he/she wants (OBERVER, 2012). The religious scholars created through this system will be immune to extremist tendencies. This system, however, cannot be implemented without the necessary monetary support (Liebman, 1983). Most countries provide Pakistan with substantial foreign aid packages, which could be better used if funneled to educate the rural populations of Pakistan from which most extremists are born (Cohen, 2011). Ahmed (2007) suggests three foremost policy recommendations for tribal areas of Pakistan i.e., Pakistan’s government should: a. segregate the peaceful tribesmen from the militants; b. reinvigorate the political structure in the tribal agencies; and c. start development activities there.

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