In this modern world, most of the nations are commonly facing the threat of terrorism and these nations work in collaboration, in the form of treaties and international organisations, to counter terrorism. The basic purpose of doing so is to lower the economic, social as well as political costs associated with conducting massive operations against terrorists. Nations which are politically integrated generally assume that their collective efforts might significantly reduce transnational terrorism however it is not necessarily the case. Since some countries have their own hidden objectives, so there is game theory involved in such settings and it cannot be simply concluded that political globalisation impedes terrorism. In this study, we aim to analyse theoretically and test empirically that how political globalisation affects terrorism. This research question is not yet scientifically addressed from a regional perspective. The region of South Asia has a strategic location from regional connectivity perspective and it is also important in terms of political integration in relation to terrorism. In this study, we attempt to answer our research question using regional perspectives in terms of regional connectivity, political integration and terrorism.

JEL Classification: F60, D74, H56

Keywords: Political Globalisation, Terrorism, Regional Connectivity, Game Theory, South Asia

1. INTRODUCTION

Globalisation is the most contentious and multifaceted phenomena. On the one hand, many link it to trade, FDI, freedom and economic growth and consider these perceived outcomes as benefits of globalisation. On the other hand many believe that globalisation is causing adverse effects on domestic social values and stable economies [see Fischer (2003)].

Assessing consequences of globalisation can help to resolve contentious policy issues of this kind. In effect, a large body of the literature has explored economic consequences of globalisation. However, research is very limited on how globalisation influences cultural attitudes and terrorism. Terrorism is a complex and multifaceted phenomenon. It is a global challenge and its threat has increased since the tragic incident of 9/11. Since then many nations have become severe victims of conflict and violence. In most cases the roots of incidents of terrorism have been traced in developing economies. The literature generally points out that widespread socio-economic deprivations, high inequality and severe poverty are the main causes of the terrorism [see, for example, Abadie (2004); Piazza (2006); Lee (2011)].

Surprisingly, little attention has been paid to external polit.cal causes of terrorism. In recent decades, the nations are becoming increasingly politically integrated. The advantage of joining hands in terms of international treaties and world organisations is having collective efforts to combat terrorism. However, international political integration causes adverse impact on domestic politics and national sovereignty. In this regard, Rodrik (2002) highlights the concept of political trilemma. According to him 'the nationstate system, democratic politics, and full economic integration are mutually incompatible.' The international political integration squeezes the margin for domestic politics and it becomes difficult to control violent organisations.

Foreign policy in terms of political proximity to the West and alliance structures may also matter to terrorism. Traditionalist or disenfranchised segments of a society may use violence to counter foreign dominance (i.e., Western supremacy) and global modernisation [see for details, Bergesen and Lizardo (2004)].

Existing global political orders may be perceived as unfair from the perspectives of perpetrators. In such situation terrorist organisations find it easy to seek support by buildino on related grievances in the society. In this regard, Addison and Murshed (2005) point out that a conflict between a government and an opposing organisation can be exported to foreign ally of the government.

Some studies such as Blomberg and Hess (2008) show that international cooperation in terms of participation in international organisation help to reduce transnational attacks. However, some studies such as Lai (2007) and Piazza (2008) argue that international political factors contribute positively in terrorism products. La. (2007) and Piazza (2008) find that involvement in interstate war increases terrorism. Plumper and Neumayer (2010) argue that membership of international alliances increases terrorist activities between nations, in particular when there is a major power differential between nations. These studies in general predict that international political factors contribute positively in the production of transnational terrorism.

It is also evident from the recent incidents of terrorism that global political efforts are not successful in fighting against the terrorism. The terrorist activities not only persist in developing world, but also spreading in the peaceful regions of the developed world (for example, recent attacks in France, Germany, and Turkey). These recent developments raise a policy question: Does political integration across countries enhance their capability to combat against terrorism? Surprising such an important question is virtually ignored in scientific research on terrorism. In this paper, we contributed to existing literature on terrorism by answering this question from the perspective of South Asian region.

South Asia is highly economically integrated region and sustainable development of the region as well as world is highly dependent on the peaceful environment of the region South Asia is important to the world in many aspects. The region has world s biggest democratic nation which comprises around 20 percent population of the world. South Asia is highly economically integrated with the rest of the world having trade as percentage of GDP almost 51.30. Recent on going projects like China Pakistan Economic Corridor (CPEC) and India-Iran's Chabahar port projects will enhance inter-regional as well as intra-regional connectivity of the region. However, potential economic benefit from these projects can only be extracted with better law and order conditions in the region.

According to Country Reports on Terrorism (2015) released by U.S State department, around 2259 terrorist attacks took place in three South Asian countries-Bangladesh, India and Pakistan which is around 19 percent of total attacks. The report further documents that so far Nepal is safe from transnational terrorism but since Nepal's borders are open with India, it can become the safe haven for terrorist organisations in near future. The region of South Asia is very important from strategic point of view. United States shares number of interests in this region since Cold War with Russia. The region has seen number of inter-state wars as well as intra-state violence activities. Although the phenomenon of terrorism is not very old for the region but an exponential increase in terrorist attacks can be seen in the region just after 9/11. One can argue that this exposure to terrorism can be a part of so-called Guerilla Wars in the region. India accused, mainly by 1SI, of fuelling ethnic and sectarian violence in Pakistan while Pakistan is believed to be the culprit of 1993 Mumbai bombing, 2008 Mumbai attacks and other related incidents as well as Khalistan Movement. In similar manner, Sri Lanka is in conflict with Tamil forces who are alleged to be supported by India. The similar nature of conflict issues can also be found in Nepal and India. It seems that south Asian region is an important and unique case study of conflict in which countries are facing threats from potential terrorists as well as their neighbouring countries.

In the context of this debate, it is important to answer the important policy question that whether economic globalisation/integration helps the region to escape terrorism or regional cooperation could settle down the things. (1) The study is important in the regional context because it answers whether countries like Pakistan, India and Sri Lanka should seek help from international organisations through political integration or countries should strengthen regional cooperation to eliminate this threat.

Rest of the paper proceeds in following manner, Section 2 provides brief literature review on terrorism and political globalisation. Section 3 explains methodology and Section 4 discusses the data and descriptive statistics. Section 5 comprises of results and discussion while Section 6 provides conclusion and policy recommendations.

2. LITERATURE REVIEW

Terrorism is one of the extensively discussed issue in social science research. One line of the research on terrorism tried to scientifically observe the potential reasons of violence, conflict and terrorism (see, for example, Muller and Seligson (19870; London and Robinson (1989); Blomberg, et al. (2002); Abadie (2004); Piazza (2006); Bravo and Dias (2006) and Lee (2011) while others tried to capture the consequences of massive terrorist attacks on affected country, spillover effects on its neighbourhood, and collectively on the world [see, for example: Huddy, et al. (2002); Silke (2003); Gupta, et al. (2004); DiMaggio (2006); Bird, et al. (2008) and Sandler and Enders (2008)].

Researchers are found to be in consensus on what we call the "consequences" of terrorism but there is wide range of disproportionate conclusions on "what prompt" masses to exercise violence. There are two schools of thought which differ on what causes terrorism. One school of thought links terrorism to economic injustice like poverty and inequality [see, for example, Gurr (1970) while other school of thought links violence and terrorism to political structure [see, for example, Tilly (1978); Lai (2007); Piazza (2008); Blomberg and Hess (2008)].

In the whole debate on causes of terrorism, one important link probably has not took such attention which was needed, that is, link of political globalisation/integration with transnational terrorism. Although some potential research can be found on how terrorism affect global integration process [see, for example: Murphy (2002); Khan and Estrada (2016); Blomberg and Hess (2005)] but a little empirical evidence has been found on other way around i.e. how globalisation/global integration affect terrorism. In particular, the role of political integration is virtually ignored in the case of South Asia region. In this paper, our focus is on whether process of economic, political and social globalisation increases or decreases transnational terrorist incidents with particular focus on political globalisation.

Out of very little research on consequences of globalisation on terrorism, Li and Schaub (2004) can be regarded as one of the comprehensive study both in terms of theoretical arguments as well as empirical analysis. They argued that since different components of economic globalisation differently alter the costs/incentives associated to conduct terrorist attacks, so these components differently affect transnational terrorism. Their empirical findings suggest that international trade, investment portfolio, and FDI of a country has no direct role in accelerating transnational terrorist incidents while economic development and an increase in trade of a country with its top trading partner impedes transnational terrorist attacked with in the country. Cronin, et al. (2006) also provide the theoretical foundations of how globalisation can affect international terrorism. They concluded that one benefit of globalisation is that terrorism can be halted through global cooperation of law enforcement, intelligence sharing, and primarily through global controls on financial activities while Zimmermann (2011) believes that globalisation substantially decreases the opportunity cost of terrorism.

So far no study provides comprehensive discussion on possible effects of political globalisation on terrorism. In particular, the empirical evidence on political globalisation and its effects on terrorism in the case of South Asia region are missing. In this study, we fill the research gap by providing an empirical analysis on how terrorism is affected by global political integration in South Asian region.

3. METHODOLOGY

In the given section, we develop a design for empirical analysis of impact of political integration of nations on terrorist activities. Firstly, we have to define our focused variables and then to develop a theory for empirical analysis. Our dependent variable, terrorism is a dynamic concept as Schmid and Jongman (1988) find out 109 operational definitions of terrorism covering 22 different elements. The U.S. Department of Defense defines terrorism as, "The unlawful use of violence or threat of violence to instill fear and coerce governments or societies. Terrorism is often motivated by religious, political, or other ideological beliefs and committed in the pursuit of goals that are usually political. "Our main variable of concern is political globalisation. According to Moghadam (2005) "Political Globalisation refers to an increasing trend toward multilateralism (in which the United Nations plays a key role), toward an emerging 'transnational state apparatus,' and toward the emergence of national and international nongovernmental organisations that act as watchdogs over governments and have increased their activities and influence". (2)

From the discussion in the first two sections, it is evident that political globalisation has an impact on terrorism but the direction of impact is arguable. In order to check our hypothesis empirically, we proceed following Li and Schaub (2004) who tried to unleash the possible link of economic globalisation with terrorism. Li and Schaub (2004) argued that economic globalisation decreases the relative cost of terrorist activities, hence increasing such activities. In our case, incentives associated with terrorist activities will be increased due to political globalisation. Since countries are collectively working to halt terrorism, so terrorists might increase their activities to show the world its collective failure. It can also be argued that most of the countries might have some hidden objectives which make such integration ineffective. Moreover, such integration increases the state of anger on the part of terrorist organisations. For example, if Pakistan join hands with U.S to halt terrorism, terrorist organisations like Tehrik-e-Taliban Pakistan (TTP) might increase its activities in Pakistan as a result of anger. On the other hand, it can be argued that political integration can increase the strength of nations which is important to impede terrorism. Lastly, many countries have strategic interests in this region so the outcomes of political integration are not simple and straight-forward but they are rather game theoretic. Since political integration can affect terrorism in either way, we set the following hypothesis for empirical analysis:

Hypothesis: Whether global political integration fosters regional terrorism in the case of South Asia.

The Model

In the light of above discussion on possible links between terrorism and political globalisation, we can write our model in general form as,

Terrorism = f(Political Globalisation)

This relationship can be written in specific form as,

[Terrorism.sub.it] = [[alpha].sub.0] + [[alpha].sub.1] Political [Globalisation.sub.it] + [[mu].sub.it]

Since there are number of determinants of terrorism which have been discussed in literature review and if those determinants are not included in the model, the results will be highly biased. Thus, in order to account for this biasness, we have included number of control variables consistent with Li and Schaub (2004). Similarly, terrorism is defined in three different specifications for empirical analysis i.e. number of attacks, number of kills, and number of wounds.

In the given study, we used the following three econometric models to get empirical results.

[mathematical expression not reproducible] (1)

[mathematical expression not reproducible] (2)

[mathematical expression not reproducible] (3)

where, [v.sub.i] refers to time-invariant country specific effects and [[mu].sub.it] refers to error term in all three models.

In order to get empirical results, we used fixed effect model to estimate the above three equations. The reason of using fixed effect instead of OLS method of estimation is that fixed effect model controls the country specific and time invariant variables such as climate, geography and religion to minimise omitted variable bias Wooldridge (2015).

In addition, we also used counts of number of attacks, kills and wounds as dependent variable to obtain robust results. Since conventional estimation techniques cannot be used when dependent variable is based on count outcomes, we use conditional fixed effect negative binomial regression model. Lastly, we use fixed effect instrumental variable regression model by taking "number of Troops in UN peacekeeping mission" as instrument to check robustness of results as well as to counter the argument of reverse causality.

4. DATA

The empirical analysis is based on five South Asian countries: Bangladesh, India, Nepal, Pakistan, and Sri Lanka. We started our analysis from 2000 for two reasons. Firstly, political globalisation index does not contain data before 2000 for Sri Lanka. Secondly, all of the countries included for analysis experienced exponential rise in terrorism since 2001. To avoid selection bias, we included only that period which is important regarding terrorism. Likewise, we have restricted our analysis till 2013 as the data on political globalisation index is available till 2013.

In order to proceed further, we have discussed below the variables which are being used in the study. We mainly followed Li and Schaub (2004) in selection of most of the variables. Since Li and Schaub (2004) argued that different indicators of economic globalisation can have different impact on terrorism, we used each indicator separately as control variable instead of KOF index of economic globalisation.

We defined our dependent variable in three ways which represent terrorism i.e. no. of attacks, no. of kills, and no. of wounds. Since dependent variable is based on count outcomes, ordinary least square (OLS) method of estimation is invalid in this case. Keeping this in mind, firstly we developed three variables by taking log of no. of attacks, no. of kills, and no. of wounds (3) and used them as a dependent variables. In our analysis we ignored isolate domestic terrorism from transnational terrorism by identifying the starting and the ending locations of terrorism events. The data on terrorism has been collected from Global Terrorism Database (GTD) which is a reliable source of data on terrorism.

Our focused independent variable, political integration, is an index of Political Globalisation. Basically, KOF is a composite index comprises of three areas, Economic Globalisation, Political Globalisation and Social Globalisation. The index take value from 0 to 100 where higher value means higher level of globalisation. KOF political globalisation index yearly rate the countries on the basis of following areas: Embassies in Country, Membership in International Organisations, Participation in U.N. Security Council Missions, and International Treaties.

In the scope of our study, it can be argued that there is reverse causality between terrorism and political globalisation i.e. countries integrate politically when they face the risk of terrorism. In order to counter this bias, we use "number of troops on UN peacekeeping mission" as an instrument to get causal impact. The instrument is theoretically valid since it is directly related to global political integration but it does not directly links terrorism.

As it has been argued in previous section that the result will be biased if we do not control our regression for potential determinants of terrorism. In this regard, some of the important variables for which we controlled our analysis are: country's economic situation (measured by growth in per capita income), incentives to inhabitants inform of physical investment (measured by gross fixed investment as percentage of GDP), and some indicators of economic globalisation i.e. foreign direct investment, Income from migrants, and trade openness. Social Globalisation is taken as control variable taken from KOF index. In addition, we controlled our regressions for political freedom i.e. political rights and social liberality. The detail of variables and their sources is given in the Table 4.1.

Table 4.2 shows descriptive statistics of our variables while Table 4.3 shows country-wise statistics on terrorist attacks that is 'number of kills' and 'number of wounds'. It can be seen that Pakistan is highly conflict-affected country of the region with mean attacks around 684 and standard deviation of 190. On average 1144 people have been killed yearly in these attacks while average 1968 people have been wounded in these attacks. Similarly, India experienced on average 431 attacks yearly from 2000 till 2015. Other three countries in the study experienced less violence (i.e. average attacks are in double-digits) as compared to Pakistan and India.

The following figures show the relation between terrorism and political globalisation. Figure 1 shows association between political globalisation and log (attacks), Figure 2 shows association between political globalisation and log (kills), and lastly, Figure 3 shows association between political globalisation and log (wounds). Since trend line has positive slope, it means that there is positive relationship between terrorism and political globalisation.

5. RESULTS AND DISCUSSION

This section reports empirical results and their interpretations. We have estimated three models using three different measures of terrorism and three econometric techniques. Models 1, 2, and 3 provide baseline regression results which are estimated using fixed effects model. In the next step, empirical estimates are drawn (in Models 4, 5 and 6) using conditional fixed effects negative binomial regression models. Finally, we address the issue of reverse causality (in Models 7, 8 and 9) by employing fixed effects instrumental variables method of estimation.

Baseline Results

The results of first three regression models shown in Table 5.1 are our baseline results estimated using fixed effect model. The reason of using fixed effect instead of OLS method of estimation is that it controls country specific, time-invariant fixed effect which reduces the chances of suspected omitted variable bias. Since dependent variable is in log form, our first three models are in log-linear form.

The results of Model 1 show that coefficient of our focused independent variable political globalisation has positive sign which indicates that increase in political globalisation index increases terrorism rather than decreasing it. The coefficient of our focused variable is statistically significant at 1 percent level of significance. In addition, per capita income, investment, FDI, remittances, social globalisation and political rights have negative impact on terrorism. These results are logically valid. Since increase in per capita income and investment means less poverty and inequality, so it will decrease terrorism. In order to check robustness of the results, we used different specifications of different variables, i.e. log of number of people killed in attacks, in Model 2. Estimated results of Model 2 are similar to previous results. The coefficient of our focused independent variable has positive and significant impact on terrorism. While coefficients of all other variables, except FDI, provide similar results. Model 3, having dependent variable, log of number of wounds has similar results to that of two previous models. The coefficient of our focused variable is statistically significant and consistent with previous two estimated models.

Results Using Conditional F.E Negative Binomial Regression Model

In Models 5.1, 5.2, and 5.3 we used number of attacks, kills, and wound instead of log of number of attacks, kills and wound as dependent variable. The conventional estimation techniques fail to provide better results when dependent variable is count outcomes so following Li and Schaub (2004), we used conditional F.E negative binomial regression modelto get empirical results. Model 4 shows that coefficient of our focused independent variable is positive and statistically significant indicating that political integration tends to increase rather than decrease terrorism in South Asian Nations. Similarly, Model 5 and Model 6 show similar results. All these results are highly statistically significant.

Reverse Causality and Results of F.E Instrumental Variable Regression Model

It can be argued that there is suspected reverse causality between terrorism and political globalisation. Countries enter to agreements when they face risk of terrorism. If this is the case, then results of our above estimated models are biased and misleading. In order to get unbiased and consistent results, we used fixed effects instrumental variable technique to estimate our results using "number of troops in UN peacekeeping missions" as an instrumental variable. Our instrument is theoretically valid and qualifies exclusion restriction. The estimated results are provided in Table 5.3.

Although coefficient of political globalisation is statistically insignificant in model 7 and model 9 but the estimated results of our fixed effect instrumental variable are consistent with baseline results. On the basis of these results, we can claim that political globalisation do not impede terrorism because these results are robust to use of six different specifications of dependent variable and three different econometric techniques.

6. CONCLUSION

The regional connectivity is of critical importance in the case of South Asia region which is fast growing region and where many infrastructure development projects such as CPEC and Chabahar Port Project are in progress. Timely and successful completion of these projects is largely based upon law and order conditions of the region. Since 9/11 the region has been exposed to number of terrorist attacks. According to Country Reports on Terrorism (2015), around 2259 terrorist attacks took place in three South Asian countriesBangladesh, India and Pakistan which is around 19 percent of total attacks. It is noteworthy that the numbers of attacks in these three countries were around 250 in the year of 2000. An increase of 800 percent in total number of attacks poses a serious regional threat.

The region is integrating with the rest of world in terms of socioeconomic and political interactions. In particular, region is participating in international organisations and treaties to combat the terrorism. These parallels developments suggest links between international political integration and regional disintegration in terms of violence and conflict. It is important to explore how political integration influences regional peace.

The objective of this study has been to empirically test how global political integration is linked with terrorism in the case of South Asia region. The analysis is based on five South Asian countries: Pakistan, India, Bangladesh, Nepal and Sri Lanka. We have employed six different measures of terrorism and empirical results are drawn using fixed effects, conditional negative binomial method, and instrumental variables approach.

The empirical findings of the study reveal that global political integration accelerates rather impedes terrorism in the case of South Asia region. This finding remains robust across different methods of estimation, different specifications and alternative measures of terrorism. In particular, this finding is not plagued with the problem of endogeneity.

This finding is not surprising as there is asymmetry involved in political integration due to local interests of countries. After 9/11, it can be seen on media that analysts question so-called War on Terrorism as "If it is Pakistan's War?" Pakistan struggled much in after entering America's War on Terrorism. This was the consequence of political globalisation for Pakistan. So our study concludes that global cooperation has more cost than return for the region.

In the dimensions of this study, the policy recommendation is that countries of South Asian region need to promote regional instead of global political integration. In this regard, SAARC can play its role to promote political cooperation and intelligence sharing among the nations. Since timely and successful completion of many infrastructure projects and connectivity projects is based upon law and order conditions in the countries, regional cooperation would be helpful to melt the ice among nations.

Noman Ahmad <[email protected]> is MPhil Student, School of Economics Quaid-iAzam University, Islamabad. Muhammad Tariq Majeed <[email protected]> is Assistant Professor, School of Economics, Quaid-i-Azam University, Islamabad.

REFERENCES

Abadie, A. (2004) Poverty, Political Freedom, and the Roots of Terrorism. National Bureau of Economic Research. (No. w10859).

Addison, T. and S. M. Murshed (2005) Transnational Terrorism as a Spillover of Domestic Disputes in other Countries. Defence and Peace Economics 16:2, 69-82.

Bergesen, A. J. and O. Lizardo (2004) International Terrorism and the World-System. Sociological Theory 22:1, 38-52.

Bird, G., S. B. Blomberg, and G. D. Hess (2008) International Terrorism: Causes, Consequences and Cures. The World Economy 31:2, 255-274.

Blomberg, S. B. and G. D. Hess (2005) The Lexus and the Olive Branch: Globalisation, Democratisation and Terrorism. Democratisation and Terrorism (November 2005).

Blomberg, S. B., G. D. Hess, and A. Weerapana (2002) Terrorism from Within: An Economic Model of Terrorism. In DIW Workshop "The Economic Consequences of Global Terrorism", Berlin.

Bravo, A. B. S. and C. M. M. Dias (2006) An Empirical Analysis of Terrorism: Deprivation, Islamism and Geopolitical Factors. Defence and Peace Economics 17:4, 329-341.

Cronin, A. K. (2006) Behind the Curve: Globalisation and International Terrorism.

DiMaggio, C. and S. Galea (2006) The Behavioral Consequences of Terrorism: A Meta-Analysis. Academic Emergency Medicine 13:5, 559-566.

Dreher, A., N. Gaston, and P. Martens (2008) Measuring Globalisation: Gauging its Consequences. Springer Science and Business Media.

Fischer, S. (2003) Globalisation and its Challenges. The American Economic Review 93:2, 1-30.

Gupta, S., B. Clements, R. Bhattacharya, and S. Chakravarti (2004) Fiscal Consequences of Armed Conflict and Terrorism in Low-and Middle-Income Countries. European Journal of Political Economy 20:2, 403-421.

Gurr, T. R. (1970) Why Men Rebel. Princeton, NJ: Princeton University Press.

Huddy, L., S. Feldman, T. Capelos, and C. Provost (2002) The Consequences of Terrorism: Disentangling the Effects of Personal and National Threat. Political Psychology 23:3, 485-509.

Khan, A. and M. A. R. Estrada (2016) Globalisation and Terrorism: An Overview. Quality and Quantity 1-9.

LaFree, G. and L. Dugan (2016) Global Terrorism and the Deadliest Groups since 2001. Peace and Conflict.

Lai, B. (2007) Draining the Swamp: An Empirical Examination of the Production of International Terrorism, 1968-1998. Conflict Management and Peace Science 24:4, 297-310.

Lee, A. (2011) Who Becomes a Terrorist?: Poverty, Education, and the Origins of Political Violence. World Politics 63:02, 203-245.

Li, Q. and D. Schaub (2004) Economic Globalisation and Transnational Terrorism a Pooled Time-series Analysis. Journal of Conflict Resolution 48:2, 230-258.

London, B. and T. D. Robinson (1989) The Effect of International Dependence on Income Inequality and Political Violence. American Sociological Review 305-308.

Moghadam, V. M. (2005) Globalising Women: Transnational Feminist Networks. Baltimore MD: The Johns Hopkins University Press.

Muller, E. N. and M. A. Seligson (1987) Inequality and Insurgency .American Political Science Review 81:02, 425-451.

Murphy, J. F. (2002) The Impact of Terrorism on Globalisation and Vice-Versa. The International Lawyer 77-89.

Piazza, J. A. (2006) Rooted in Poverty: Terrorism, Poor Economic Development, and Social Cleavages. Terrorism and Political Violence 18:1, 159-177.

Piazza, J. A. (2008) Incubators of Terror: Do Failed and Failing States Promote Transnational Terrorism? International Studies Quarterly 52:3, 469-488.

Plumper, T. and E. Neumayer (2010) The Friend of my Enemy is my Enemy: International Alliances and International Terrorism. European Journal of Political Research 49:1, 75-96.

Rodrik, D. (2002) Feasible Globalisations. National Bureau of Economic Research. (Working Paper No. 9129, 1-27).

Sandler, T. and W. Enders (2008) Economic Consequences of Terrorism in Developed and Developing Countries. Terrorism, Economic Development, and Political Openness 17.

Schmid, A. and A. Jongman (1988) Political Terrorism: A Research Guide to Concepts, Theories, Databases, and Literature. New Brunswick.

Silke, A. (ed.) (2003) Terrorists, Victims and Society: Psychological Perspectives on Terrorism and its Consequences. John Wiley and Sons.

Tilly, C. (1978) From Mobilisation to Revolution. Reading. MA Addison-Wesley

United States (2015) Country Reports on Terrorism. Department of State. Publication Bureau of Counterterrorism and Countering Violent Extremism (Released June 2 2016).

Wooldridge, J. M. (2015) Introductory Econometrics: A Modern Approach. Nelson Education.

World Bank (2015) World Development Indicators. World Bank, Washington, DC.

Zimmermann, E. (2011) Globalization and Terrorism. European Journal of Political Economy 21, S152-S161.

(1) It is worth mentioning that we have used terms political integration and political globalisation interchangeably in our paper in broader context, both refers to same concept.

(2) Moghadam, V. M. (2005) Globalising Women: Transnational Feminist Networks. Baltimore, MD: The Johns Hopkins University Press.

(3) Since log of zero in infinity, so we took 1 for the year if there are no. attacks, kills or wounds. This can be seen as caveat of proxy variable we defined for analysis.

Caption: Figure 1: The Impact of Political Globalization on Terrorism

Caption: Figure 2: The Impact of Political Globalization on Terrorism

Caption: Figure 3: The Impact of Political Globalization on Terrorism

Table. 4.2
Descriptive Statistics

Variable                  Obs.    Mean       S.D

Attacks                    80    256.16     443.33
Kills                      79    409.85     624.12
Wounds                     79    730.47    1062.74
Per Capita Income          80    1209.97    793.54
Investment                 78     26.66      6.93
FDI                        80     1.06       0.81
Foreign Remittances        79     8.41       6.73
Trade Openness             79     44.91     13.99
Political Globalisation    70     77.91      9.36
Social Globalisation       70     30.07      7.82
Political Rights           80     3.74       1.27
Civil Liberty              80       4        0.71
Peace                      80    5434.85   3555.654

Variable                  Minimum   Maximum

Attacks                      0       2213
Kills                        0       2871
Wounds                       0       5767
Per Capita Income         459.13    3637.54
Investment                 14.12     39.58
FDI                       -0.098     3.67
Foreign Remittances        1.45      32.23
Trade Openness             25.55     88.64
Political Globalisation    60.54     92.00
Social Globalisation       15.63     43.54
Political Rights             2         6
Civil Liberty                3         5
Peace                       16       11135

Source: Authors' own calculations.

Table 4.3
Country-wise Statistics of Terrorist Attacks

             Variable     Mean      S.D

Bangladesh   Attacks       59      28.92
              Kills       23.5     41.29
              Wounds     158.31    49.81
India        Attacks     431.25    70.93
              Kills      561.25    41.29
              Wounds    1024.75    99.70
Nepal        Attacks       55       7.91
              Kills      118.75    46.26
              Wounds    120.4375   28.59
Pakistan     Attacks     684.47    190.13
              Kills     1144.69    248.93
              Wounds    1968.31    423.59
Sri Lanka    Attacks      53.8     16.62
              Kills      187.13    71.97
              Wounds     357.2     112.78

Source: Authors' own calculations using the data
from Global Terrorism Database [GTD (2015)].

Table 5.1
Terrorism and Political Globalisation: Results of Fixed Effects Model

                     Results for Fixed Effect Model

                          Model 01             Model 02
                     Dependent Variable   Dependent Variable
Variable                In (attacks)          In (kills)

Constant                  -29.842              -41.874
                        (7.1034) **           (6.6844) *
Per Capita Growth         -0.0019              -0.0050
                        (0.0009) ***          (0.0006) *
INV                       -0.1326              -0.1619
                        (0.0365) **          (0.00789) *
TRD                        0.0712               0.0610
                         (0.0115) *          (0.0142) **
FDI                       -0.0007               0.0773
                          (0.1750)             (0.1154)
REM                       -0.0564              -0.2762
                          (0.0472)            (0.0370) *
globsocial                -0.2476              -0.2758
                        (0.0662) **           (0.0468) *
glob_political             0.5912               0.8062
                         (0.1041) *           (0.1041) *
PR                        -0.5461              -0.6020
                        (0.1287) **           (0.0745) *
CL                         0.0919               0.8870
                          (0.1149)           (0.2226) **
Adjusted R-Square          0.3974               0.3042
Observation                  70                   70

                     Results for Fixed
                        Effect Model

                          Model 03
                     Dependent Variable
Variable                In (wounds)

Constant                  -30.440
                        (11.389) ***
Per Capita Growth         -0.0040
                         (0.0008) *
INV                       -0.0398
                        (0.0109) **
TRD                        0.0207
                          (0.0224)
FDI                       -0.0083
                          (0.1440)
REM                       -0.18891
                        (0.0580) **
globsocial                -0.3313
                        (0.1018) **
glob_political             0.6666
                        (0.1814) **
PR                        -0.6495
                         (0.0653) *
CL                         0.6315
                        (0.1128) **
Adjusted R-Square          0.2733
Observation                  70

Robust Standard Errors are given in parenthesis.

Where; *, **, *** represent that parameter is significant
at the 1 percent, 5 percent and 10 percent level of
significance respectively.

Table 5.2
Terrorism and Political Globalisation: Results of
Conditional F.E Negative Binomial Regression

                                Results for Conditional F.
                              E Negative Binomial Regression

                               Model 04             Model 05

Variables                 Dependent Variable   Dependent Variable
                           (no. of attacks)      (no. of kills)
Constant                        -8.020               -2.692
                              (1.9619) *            (1.782)
Per Capita Growth              0.00073              -0.0004
                             (0.0003) **            (0.0003)
INV                            -0.0890              -0.0381
                              (0.0254) *          (0.0228) ***
TRD                             0.0401              -0.0057
                              (0.0095) *            (0.0107)
FDI                             0.0747               0.2577
                               (0.0927)            (0.0764) *
REM                             0.0895              -0.0337
glob_social                   (0.0244) *            (0.0232)
                               -0.1444              -0.0423
                              (0.0311) *           (0.0340) *
glob_political                  0.1644               0.1062
PR                            (0.0239) *           (0.0210) *
                               -0.5287              -0.7219
CL                            (0.1297) *           (0.1202) *
                                0.3200               0.1936
                               (0.3438)             (0.3552)
Log Likelihood                 -338.27              -364.16
Wald Test([chi square])         113.75               109.76
Prob > ([chi square])         (0.0000) *           (0.0000) *

                          Results for Conditional
                          F. E Negative Binomial
                                Regression

                                 Model 06

Variables                   Dependent Variable
                              (no. of wounds)
Constant                          -4.157
                               (2.1606) ***
Per Capita Growth                 -0.0009
                                (0.0004) **
INV                               0.0133
                                 (0.0276)
TRD                               -0.0169
                                 (0.0113)
FDI                               0.16079
                                 (0.1151)
REM                               -0.0174
glob_social                      (0.0277)
                                  0.0532
                                 (0.0394)
glob_political                    0.0713
PR                              (0.0244) *
                                  -0.5798
CL                              (0.1460) *
                                  0.2217
                                 (0.4049)
Log Likelihood                    -432.93
Wald Test([chi square])            45.92
Prob > ([chi square])           (0.0000) *

Robust Standard Errors are given in parenthesis.

Where; *, **, *** represent that parameter is significant
at the 1 percent, 5 percent and 10 percent level of
significance respectively.

Table 5.3
Terrorism and Political Globalisation: Results of F.E
Instrumental Variable Regression Model

                                 Results for Fixed Effect
                                    IV Regression Model

                               Model 07             Model 08
                          Dependent Variable   Dependent Variable
Variables                    In(attacks)           ln(kills)

Constant                       -10.988              -38.982
                               (14.332)           (17.428) **
Per Capita Growth               0.0002              -0.0047
                               (0.0016)           (0.0020) **
INV                            -0.1299              -0.1615
                              (0.0413) *           (0.0502) *
TRD                             0.0585              0.05903
                              (0.0177) *           (0.0215) *
FDI                             0.2022               0.1084
                               (0.2337)             (0.2841)
REM                             0.0486              -0.2600
                               (0.0855)           (0.1040) **
glob_social                    -0.1706              -0.2640
                             (0.0889) **          (0.1081) **
glob_political                  0.2779               0.7581
                               (0.2343)            (0.2849) *
PR                             -0.5929              -0.6091
                              (0.1827) *           (0.2222) *
CL                              0.2331               0.9087
                               (0.4283)            (0.5209)**
  Adjusted R-Square             0.4561               0.3059
Wald Test([chi square])        1938.00              1556.41
Prob > ([chi square])         (0.0000) *           (0.0000) *

                          Results for Fixed Effect
                            IV Regression Model

                                  Model 09
                             Dependent Variable
Variables                        In(wounds)

Constant                          -14.387
                                  (23.751)
Per Capita Growth                 -0.0023
                                  (0.0027)
INV                               -0.0375
                                  (0.0684)
TRD                                0.0099
                                  (0.0294)
FDI                                0.1645
                                  (0.3872)
REM                               -0.0995
                                  (0.1417)
glob_social                       -0.2658
                                (0.1473) **
glob_political                     0.3999
                                  (0.3883)
PR                                -0.6893
                                (0.3028) **
CL                                 0.7517
                                  (0.7098)
  Adjusted R-Square                0.2434
Wald Test([chi square])           1141.13
Prob > ([chi square])            (0.0000) *

Where, *, **, *** represent that parameter is significant
at the 1 percent, 5 percent and 10 percent level of significance
respectively. At first stage, Political Globalisation has been
regressed on Peace Variable then estimated value of Political
Globalisation is used to obtain the results.