Fiscal Response to Terrorism in Pakistan: The Role of Institutions

ISSN(E):2522-2260 ISSN(P):2522-2252 Journal DOI: https://doi.org/10.29145/jqm Indexing/Abstracting Fiscal Response to Terrorism in Pakistan: The Role of Institutions Author(s) Tahir Mukhtar1, Zainab Jehan2 Affiliations 1&2 Fatima Jinnah Women University, Rawalpindi, Pakistan Email: [email protected] Manuscript Information Submission Date: July 24, 2020 Publication Date: February 28, 2021 Conflict of Interest: None Supplementary Material: No supplementary material is associated with the article Funding: This research received no external funding Acknowledgment: No additional support is provided Citation in APA Style: Mukhtar, T., & Jehan, Z. (2021). Fiscal Response to Terrorism in Pakistan: The Role of Institutions. Journal of Quantitative Methods, 5(1), 154-192. The online version of this manuscript can be found at https://ojs.umt.edu.pk/index.php/jqm/article/view/492 DOI: https://doi.org/10.29145/2021/jqm/050107 Published by Department of Quantitative Methods University of Management and Technology, Lahore, Pakistan This manuscript has been published under the terms of Creative Commons Attribution 4.0 International License (CC-BY). JQM under this license lets others distribute, remix, tweak, and build upon the work it publishes, even commercially, as long as the authors of the original work are credited for the original creation and the contributions are distributed under the same license as original. Additional Information Subscriptions and email alerts: [email protected] For further information, please visit https://ojs.umt.edu.pk/index.php/jqm 154 | Fiscal Response to Terrorism in Pakistan Journal of Quantitative Methods 5(1) 154-192 https://doi.org/10.29145/2021/jqm/050107 Fiscal Response to Terrorism in Pakistan: The Role of Institutions Tahir Mukhtar1, Zainab Jehan2 Fatima Jinnah Women University, Rawalpindi, Pakistan Email: [email protected] Received:July 24, 2020, Last Revised: Aug 31, 2020, Accepted: Dec 1, 2020 Abstract This study empirically estimates the fiscal consequences of terrorism in Pakistan by using annual time series data from 1984 to 2016. By employing the autoregressive distributed lag (ARDL) technique, the study has gauged the impact of terrorist incidents on two important facets of fiscal policy, namely, tax revenue and defense spending. The results reveal that terrorism has detrimental ramifications for fiscal policy in Pakistan. Specifically, on the one hand, an increase in terrorist incidents tends to bring a fall in tax revenue while on the other hand, they induce a rise in defense outlays, thus deteriorating both fronts of the fiscal position. Notably, the moderating role of institutional quality appears significant and indicates that institutional quality has not only a significant direct impact on fiscal policy, but it also helps in completely mitigating (reducing) the harmful impact of terrorism on defense spending (tax revenue) in Pakistan. These findings suggest that there is a need to take appropriate steps for strengthening institutional setup to control the fallouts of terrorism on fiscal behavior of the government of Pakistan. Keywords: terrorism; tax revenue; institutional quality; ARDL 1&2 JEL Classification: E62; H2; E02; H5; F35 Copyright © 2021 The Authors. Production and hosting by School of Business and Economics, University of Management and Technology, Lahore, Pakistan. This is an open access article and is licensed under a Creative Commons Attribution 4.0 International License. 1. Introduction The conceptual and operational definitions of terrorism have been rehabilitated since the 9/11 incident due to the victimization of world economic leader, massive destructions, and Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |155 long lasting socio economic and political implications. The incident has led to change the geopolitical relations of world economic and political powers as it has compelled many countries to become allies directly or indirectly in eradicating the roots of terrorist activities at national and international levels. Theoretically, terrorism is an act of violence to achieve various economic, political and ideological goals by means of threat and fear (Humphreys, 2006).1 Hence, any non-state actor creating threat, using illegal forces (intimidation or oppression) to achieve economic and/or a non-economic gains such as political, social or religious is termed as terrorism (LaFree & Dugan, 2017). The major purpose of terrorist activities is to obtain several benefits by pressurizing governments, and creating political disruptions which further create uncertainty in political regimes and interruption in policies. This leads to reallocation of resources from productive to non-productive activities (Michael, 2007). Over the course of time, the world has witnessed not only an increase in terrorist incidents but also an elevated severity attached to these episodes (Zakaria et al., 2019). Terrorism has various direct and indirect ramifications for victim countries. The direct consequences, for instance, include demolition of infrastructure, loss of human lives, and direct spending on armaments and security by the governments. The indirect implications, on the other hand, are hard to measure as terrorism hits economic activities by instigating uncertainty which then influences consumption and investment decisions, diverts government expenditures from development to non-development fronts such as maintaining law and order situation, improving security measures internally and externally. Empirically, economic consequences of terrorism have widely been discussed widely. The evidence proclaims that it deteriorates economic growth by increasing uncertainty, shattering The Global Terrorism Database (LaFree and Dugan, 2017) defines terrorism as “the threat or actual uses of illegal force and violence by a nonstate actor to accomplish an economic, political, social or religious goal through oppression, fear and/or intimidation”. 1 Journal of Quantitative Methods Volume 5(1): 2021 156 | Fiscal Response to Terrorism in Pakistan investors’ confidence, deferring investment decision, reducing foreign direct investment and trembling stock market performance (Gaibulloev & Sandler, 2009; Gries et al., 2011; Freytag et al., 2011; Raza & Jawaid, 2013; Gaibulloev et al., 2013; Hyder et al., 2015; Estrada et al., 2015; Shahzad et al., 2016; Shah et al., 2016). The recent surge in empirical literature is to examine not only the direct but also the indirect economic, social and political implications of terrorism through changing the composition of government expenditure. As terrorism forces governments to shift resources towards improving law and order enforcement and reconstruction of affected areas, it not only hinders the process of economic growth but also reduces governments’ ability to generate revenue. In developing countries, this increase in nondevelopmental expenditures takes place at the expense of developmental expenditures (Michael, 2007). Gupta et al. (2004) conclude that persistent acts of terrorism lead to increase the share of military expenditures in total government outlays in low and middle income countries while the impact on tax revenue is reported as insignificant. Similarly, Gaibulloev and Sandler (2008) report that countries with a higher than median level of terrorism experience larger increase in military spending as compared to the countries with a lower than median level of terrorist incidents. They also show an insignificant impact of terrorism on tax revenue. Moreover, Drakos and Konstantinou (2014) contend that terrorism leads to higher government spending on defence while it reduces the expenditure on social safety net. On the other hand, it distorts the tax base and contracts government revenues thus putting pressure on fiscal management. Cevik and Ricco (2015) document that the adverse impact of terrorism on tax revenue is only marginal while a significant increase in military spending has been observed in response to the increasing terrorist activities. Yogo (2015) also reveals that terrorist activities create uncertainty in the conduct of fiscal policy in developing countries. Pakistan constitutes a good case to be considered for examining fiscal response to terrorism as the country has remained a victim of extensive terrorist activities. It is an undeniable fact that Pakistan has been among those countries which are always at the Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |157 forefront of terrorist incidents due to various factors such as religious, geographical, ethnic, political, and economic- mainly unemployment and income inequality(Ismail & Amjad, 2014; Khan, et al., 2016; Syed et al., 2015). The incident of 9/11 has played a vital role in engorging terrorism in Pakistan. Terroris has trapped the country in social destruction, economic deterioration and political instability which is exerting huge pressure on government to manage this menace. For instance, studies such as (Hyder et al., 2015; Khan et al., 2016; Khan & Yusof, 2017; Mehmood, 2014; Shahbaz et al., 2013; Shahzad et al., 2016, Zakaria et al., 2019) conclude that the economic growth process is immensely deteriorated in Pakistan due to terrorism. In view of Farooq (2014) the cost of war against terrorism is much higher than its benefits to Pakistan. Moreover, the study shows that macroeconomic performance measured through various indicators has deteriorated due to terrorism while terrorism has increased government expenditure. Nasir and Shahbaz (2015) also report that terrorism Granger causes military expenditures in Pakistan. Shahzad et al. (2016) substantiate the claim that terrorism forces governments to redirect expenditure from developmental to nondevelopmental projects such as increasing security standards for improving law and order situation. More recently, Zakaria et al. (2019) have identified the impact of terrorism, internal and external conflict on economic growth, fiscal spending, FDI, and domestic investment. The study concludes that terrorism adversely affects macroeconomic performance and puts pressure on fiscal budget by increasing government expenditures on managing security affairs of the country. Other studies examine the impact of terrorism on tourism (Rauf et al., 2016) environmental pollution (Bildiricia & Gokmenoglu, 2020); education policy (Iqbal, 2019) and financial market performance (Gul et al., 2010) of Pakistan. These studies have unanimously concluded that terrorism has adverse effects on macroeconomic performance no matter what indicator is used to measure it. To eradicate the menace of terrorism, an effective antiterrorism campaign has been launched through Nation Action Plan, Zarb-e-Azb, and Operation Radd-ul- Fasaad in Pakistan. These initiatives have produced desired outcomes in reducing the number of terrorist attacks and severity of terrorism in Pakistan but at Journal of Quantitative Methods Volume 5(1): 2021 158 | Fiscal Response to Terrorism in Pakistan massive financial and administrative costs (Government of Pakistan, 2017). Mubashra and Shafi (2018) also report that counter terrorism activities have short- and long-run effects on Pakistan’s economy. On the one hand, the counter terrorism actions make fiscal position of government vulnerable due to higher defence and security spending, rehabilitation, and reconstruction. On the other hand, terrorism hampers economic activity, delays investment and consumption plans, discourages foreign direct investment, and reduces international trade; therefore, restricts government’s ability to collect revenues through tax and non-tax sources. Hence, terrorist activities are like a double edged sword for a developing country like Pakistan. Historically, defence spending has a major share in total outlays of the government of Pakistan because of various internal or external, and actual or perceived security concerns. It is observed that there are extreme fluctuations with an increasing trend in the defence spending (Government of Pakistan, 2017). Blomberg et al. (2004) describe that terrorism induces higher government spending to improve law and order, and security situation. On the contrary, the revenue side of the government of Pakistan does not exhibit an encouraging picture. For example, the tax revenue as percent of GDP has declined from a peak of 13.7 in 1997 to 9.2 in 2016 (Government of Pakistan, 2017). This is alarming as over the years defence spending is mounting due to increasing security pressures; however, the resources to meet these expenditures are not supportive. As explained by earlier literature, an increase in defence expenditure crowds out not only public development expenditure but also private investment (Blomberg et al., 2004; Gaibulloev & Sandler, 2008), hence, the revenue generating capacity of a government is certain to get squeezed. Despite voluminous literature on examining the consequences of terrorism, the literature is relatively scant on investigating the fiscal consequences of terrorism, particularly, in countries which are not only major victims of terrorism but are also on the frontline in the war on terror. In this perspective, Pakistan makes a good case to be investigated, particularly, in the wake of the 9/11 incident as Pakistan has played significant role in Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |159 the national and global war on terror. The existing literature mostly reports the economic consequences of terrorism, however, terrorism related research lacks the fiscal dimension in Pakistan. There are two empirical studies which have examined the fiscal consequences of terrorism in Pakistan, namely (Nasir & Shahbaz, 2015; Zakaria et al., 2019). The former, by taking two variables model, concludes that terrorism Ganger causes military spending in Pakistan, while the later reports the impact of terrorism on overall public expenditures. These studies, thus, do not provide a detailed insight to the fiscal consequences of terrorism. Therefore, to abridge this gap in the existing literature, the purpose of the present study is to estimate fiscal response to terrorism in Pakistan. To this end, we have taken two important aspects of fiscal policy; tax revenue as percent of GDP, and defence spending as percent of GDP. The first measure shows the strength of fiscal accounts while the latter presents the proportion of government revenues allocated for the security and law and order maintenance. Hence, both measures portray important facets of fiscal position of Pakistan and the impact of terrorism on these indicators will help us draw important findings for researchers as well as for policy-makers. Furthermore, as mentioned above, few empirical studies have shown that terrorism creates disruption in fiscal policy by redirecting government expenditures towards defence spending and by reducing tax revenue, nonetheless, we have not come across any study which has gauged the role of factors which can mitigate/reduce the adverse impact of terrorism on fiscal position, specifically for Pakistan. Therefore, this study also aims to quantify the fiscal response to terrorism in Pakistan by incorporating the role of one such factor, namely, institutions. Institutions, as documented by North (1990) define the rules, explain the functioning of various sectors, and most importantly facilitate the transmission mechanism of stabilisation policies. In addition, by defining the rules and setting the parameters, institutions help in mitigating the menace of various shocks including terrorism. In the similar vein, Acemoglu and Robinson (2010) emphasize the significance of institutions for better economic and political performance. They argue that good institutions ensure accountability, transparency, and good governance, therefore, help in effective policy formulation as well Journal of Quantitative Methods Volume 5(1): 2021 160 | Fiscal Response to Terrorism in Pakistan as proper implementation of these policies. Hence, this study aims to introduce the moderating role of institutional quality in terrorism-fiscal policy nexus of Pakistan. The moderating role of institutional quality is captured by introducing an interaction term of terrorism and institutional quality index. In doing so, we quantify not only the direct impact of terrorism on fiscal policy but also its conditional impact through institutional quality. Finally, the empirical analysis is conducted by employing the Autoregressive Distributive Lag (ARDL) technique over the period of 1984 to 2016. This technique enables the study to quantify the short run as well as the long run implications of terrorism for fiscal policy of Pakistan in the presence of institutional quality. The rest of the study is structured as follows: section 2 contains the methodology, data and estimation technique; section 3 presents empirical findings followed by section 4 which concludes the study. 2. Analytical Framework 2.1. Model Specification and Data There is a paucity of research work pertaining to fiscal response to terrorism. In existing body of relevant literature, we find the application of single equation models for executing the desired empirical task. For determining the fiscal behaviour of a government various economic, political and institutional factors play their roles. However, in an empirical investigation only selected macroeconomic determinants of fiscal actions are incorporated. To this end choice of explanatory variables depends upon the objectives of the study keeping in view the nature of fiscal action. As our study aims to estimate fiscal response to terrorism in the presence of institutional quality through gauging effect of terrorist incidents on tax revenue collection and defence spending in Pakistan, therefore, terrorism and institutional quality variables have been plugged into the standard econometric models of tax revenue and defence spending. Considering the relevance with our study, we prefer to use modified versions of regression models presented by (Maizels & Nissanke, 1986; Teera, 2003; Gupta et al., 2004; Cevik & Ricco, 2015; Chuku et al., 2019). Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |161 Thus, to conquer the basic aim of the study we specify the following econometric models: 𝑇𝐴𝑋 = 𝑓(𝑇𝐸𝑅, 𝐺𝐷𝑃𝐺𝑅, 𝐼𝑁𝐹, 𝐺𝐸, 𝐴𝐼𝐷) (1) 𝑇𝐴𝑋 = 𝑓(𝑇𝐸𝑅, 𝐺𝐷𝑃𝐺𝑅, 𝐼𝑁𝐹, 𝐺𝐸, 𝐴𝐼𝐷, 𝐼𝑁𝑆, 𝑇𝐸𝑅 ∗ 𝐼𝑁𝑆) (2) 𝐷𝐹𝑆 = 𝑔(𝑇𝐸𝑅, 𝐺𝐷𝑃𝐺𝑅, 𝑇𝐵, 𝐴𝐼𝐷, 𝐷𝐹𝑆𝐼) (3) 𝐷𝐹𝑆 = 𝑔(𝑇𝐸𝑅, 𝐺𝐷𝑃𝐺𝑅, 𝑇𝐵, 𝐴𝐼𝐷, 𝐷𝐹𝑆𝐼, 𝐼𝑁𝑆, 𝑇𝐸𝑅 ∗ 𝐼𝑁𝑆) (4) The present study uses the annual time series data of Pakistan over the period 1984 to 20162. In above models, TER refers to log of number of terrorist attacks (or incidents) reported in a year, TAX indicates tax revenue as percent of GDP, GDPGR indicates economic growth performance proxied by growth rate of real GDP, INF is inflation rate i.e. annual growth rate of consumer price index (CPI), GE refers to Gross public expenditure(as percent of GDP), AID refers to foreign aid i.e. foreign Loans and foreign Grants as percent of GDP, DFS indicates defence spending as percent of GDP, TB shows trade balance as percent of GDP, DFSI shows defense spending of India as percent of GDP, INS refers to Institutional quality index( composite index of five aspects relating to institutional quality namely bureaucratic quality; corruption; democratic accountability; ethnic tensions and law and order). We have constructed the institutional quality index by means of Principal Component Analysis (PCA) technique. These five indicators are converted into scale of 1-10 respectively for comparability purpose. TER*INS indicates interaction term of number of terrorist attacks and institutional quality index. The interaction term captures the impact of terrorism on each aspect of fiscal policy in presence of institutional quality. The coefficient of the interaction term will identify whether institutional quality in Pakistan is helpful in completely eliminating/minimizing the adverse impact of terrorism. Number of terrorist attacks variable is logarithmic throughout our estimation task. The details of data sources for each of these variables is given in table 1. 2 The selection of time period is based on the availability of data on institutional quality index which is available only from 1984-2016. Journal of Quantitative Methods Volume 5(1): 2021 162 | Fiscal Response to Terrorism in Pakistan Table 1. Variables’ Description and Data Source Variable TAX GE Construction Tax revenue as percent of GDP Gross public expenditure as percent of GDP Data Source Government Financial Statistics published by IMF (2018) GDPGR Economic growth performance proxied by growth rate of GDP Inflation rate i.e. annual growth rate of consumer price index (CPI) Foreign aid i.e. foreign loans and foreign grants as percent of GDP Defence spending as percent of GDP Trade balance as percent of GDP Defence spending of India as percent of GDP Institutional quality index( composite index of five aspects relating to institutional quality) Number of terrorist attacks reported in a year. World Development Indicators published by World Bank (2019) INF AID DFS TB DFSI INS TER TER*INS International Country Risk Guide by PRS group (2013) Global Terrorism Database (GTD) introduced by LaFree and Dugan (2017) and maintained by the University of Maryland. Interaction term of number of terrorist attacks and institutional quality index Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |163 In models (1) and (2) terrorism is expected to bring a decline in tax revenue collection due to its detrimental effect on economic activities and business (Gupta et. al., 2004). Growth rate of GDP has expected positive relation with tax revenue as increase in economic growth performance is likely to promote business activities and expand the size of the economy, leading to raise public revenue through taxes.Inflation is expected to cause a fall in tax revenue because rising trend of general price level means decrease in purchasing power of public which will certainly open the doors for tax evasion on the part of tax payers. Moreover, inflation will also lower the value of tax revenue collected by the government. Rise is government expenditure is expected to raise tax revenue as postulated by (Barro’s, 1974; Peacock & Wiseman’s, 1979) spend-revenue hypothesis. Aid-Taxation relationship may be positive or negative, depending upon the composition of aid, conditionalities attached with aid, existing tax system in a country and government behaviour (Gupta et al., 2004; Benedek et al., 2014). Good quality intuitional set up is more likely to create incentives for investment, technology adoption, and opportunity to accumulate human capital for workers, leading to create a very conducive environment for business and hence for enhancing tax collection (Hussain et al., 2016). The expected link of interaction term of terrorism and institutional quality to tax revenue is ambiguous as it is subject to the extent to which institutional quality succeeds in overcoming ill effects of terrorism on tax revenue. As far as models (3) and (4) are concerned, it is expected that defence spending will increase with the occurrence of terrorist incidents as anti-terrorism actions require more expenditure on security. Economic growth has an expected positive association with defence spending because a growing economy has an enhanced capacity to increase its defence allocations (Dunne et al., 2003). The share of the trade balance in GDP reflects the openness of an economy and its nature of link with defence spending is ambiguous (Rosh, 1988; Dunne et al., 2003). Similarly, whether foreign aid hinders or promotes defence spending is also unclear (Kono & Montinola, 2013). Pakistan’s defence spending is expected to respond India’s defence allocations positively since Journal of Quantitative Methods Volume 5(1): 2021 164 | Fiscal Response to Terrorism in Pakistan Pakistan has serious security threats from India (Chawla, 2001; Sheikh et al., 2013; Aslam et al., 2014). The institutional quality’s relationship is likely to be negative with defence spending because countries with better institutional quality would have fewer chances of using military action to solve external and internal disputes (Desta, 2009). Finally, expected link of interaction term of terrorism and institutional quality with defence spending is ambiguous. 2.2. Estimation Technique The selection of an estimation methodology relies on the nature of data set used in an estimation process. As the present study deals with time series data, therefore, the estimation begins with examining the unit root properties of all variables given in models (1) to (4). To this end, the study employs the renowned Augmented Dickey-Fuller (ADF)test3. This test is based on the null hypothesis that a given times series contains a unit root. For estimation purpose of all the four models pertaining to the fiscal response to terrorism,the study has used the Autoregressive Distributed Lag (ARDL) cointegration technique developed by Pesaran et al. (2001).4 This technique is considered quite useful in obtaining consistent parameter estimates whether the underlying regressors are I (0), I (1) or a combination of both. Moreover, it is capable enough to yield efficient and consistent empirical results for the small data size like ours. The ARDL representations of the equations (1) and (4) can be formulated as: 3 For robustness check, we have also employed an alternate unit root test namely PP test (Phillips-Perron) unitroot test. 4 Other time series techniques are GMM-IV technique, VECM, FMOLS and DOLS techniques. However, these all require same order to integration or level of stationarity for all variables. ARDL is the only technique available which provides efficient estimates under different order of integration of regressors and I(1) for the dependent variable. Therefore, this study has used ARDL estimation technique for empirical estimation of all models. Journal of Quantitative Methods Volume 5(1): 2021 |165 Fiscal Response to Terrorism in Pakistan 𝑝 𝛥𝑇𝐴𝑋𝑡 = 𝛼0 + ∑ 𝛼1 𝛥𝑇𝐴𝑋𝑡−𝑖 𝑖=1 𝑝 𝑝 + ∑ 𝛼2 𝛥𝑇𝐸𝑅𝑡−𝑖 + ∑ 𝛼3 𝛥𝐺𝐷𝑃𝐺𝑅𝑡−𝑖 𝑝 𝑖=0 𝑝 𝑖=0 𝑝 𝑖=0 𝑖=0 + ∑ 𝛼4 𝛥𝐼𝑁𝐹𝑡−𝑖 + ∑ 𝛼5 𝛥𝐺𝐸𝑡−𝑖 + ∑ 𝛼6 𝛥𝐴𝐼𝐷𝑡−𝑖 + 𝛼7 𝑇𝐴𝑋𝑡−1 + 𝛼8 𝑇𝐸𝑅𝑡−1 + 𝛼9 𝐺𝐷𝑃𝐺𝑅𝑡−1 𝑖=𝑜 + 𝛼10 𝐼𝑁𝐹𝑡−1 + 𝛼11 𝐺𝐸𝑡−1 + 𝛼12 𝐴𝐼𝐷𝑡−1 + 𝑢1𝑡 ( 5) 𝑝 𝛥𝑇𝐴𝑋𝑡 = 𝛽0 + ∑ 𝛽1 𝛥𝑇𝐴𝑋𝑡−𝑖 𝑖=1 𝑝 𝑝 + ∑ 𝛽2 𝛥𝑇𝐸𝑅𝑡−𝑖 + ∑ 𝛽3 𝛥𝐺𝐷𝑃𝐺𝑅𝑡−𝑖 𝑖=0 𝑝 𝑝 ∑ 𝑖=0 𝑖=0 𝑝 + ∑ 𝛽4 𝛥𝐼𝑁𝐹𝑡−𝑖 + ∑ 𝛽5 𝛥𝐺𝐸𝑡−𝑖 + 𝑖=0 𝑝 𝛽6 𝛥𝐴𝐼𝐷𝑡−𝑖 + ∑ 𝑖=0 𝑖=0 𝑝 𝛽7 𝛥𝐼𝑁𝑆𝑡−𝑖 + ∑ 𝑖=0 𝛽8 𝛥(𝑇𝐸𝑅 ∗ 𝐼𝑁𝑆)𝑡−𝑖 + 𝛽9 𝑇𝐴𝑋𝑡−1 + 𝛽10 𝑇𝐸𝑅𝑡−1 + 𝛽11 𝐺𝐷𝑃𝐺𝑅𝑡−1 + 𝛽12 𝐼𝑁𝐹𝑡−1 + 𝛽13 𝐺𝐸𝑡−1 + 𝛽14 𝐴𝐼𝐷𝑡−1 + 𝛽15 𝐼𝑁𝑆𝑡−1 + 𝛽16 (𝑇𝐸𝑅 ∗ 𝐼𝑁𝑆)𝑡−1 + 𝑢2𝑡 (6) 𝑝 𝛥𝐷𝐹𝑆𝑡 = 𝛾0 + ∑ 𝛾1 𝛥𝐷𝐹𝑆𝑡−𝑖 𝑖=1 𝑝 𝑝 + ∑ 𝛾2 𝛥𝑇𝐸𝑅𝑡−𝑖 + ∑ 𝛾3 𝛥𝐺𝐷𝑃𝐺𝑅𝑡−𝑖 𝑖=0 𝑝 𝑖=0 𝑝 + ∑ 𝛾4 𝛥𝑇𝐵𝑡−𝑖 + ∑ 𝛾5 𝛥𝐴𝐼𝐷𝑡−𝑖 𝑖=0 Journal of Quantitative Methods 𝑖=0 Volume 5(1): 2021 166 | Fiscal Response to Terrorism in Pakistan 𝑝 + ∑ 𝛾6 𝛥𝐷𝐹𝑆𝐼𝑡−𝑖 + 𝛾7 𝐷𝐹𝑆𝑡−1 + 𝛾8 𝑇𝐸𝑅𝑡−1 + 𝛾9 𝐺𝐷𝑃𝐺𝑅𝑡−1 𝑖=𝑜 + 𝛾10 𝑇𝐵𝑡−1 + 𝛾11 𝐴𝐼𝐷𝑡−1 + 𝛾12 𝐷𝐹𝑆𝐼𝑡−1 + 𝑢3𝑡 (7)𝛥𝐷𝐹𝑆𝑡 = 𝛿0 𝑝 + ∑ 𝛿1 𝛥𝐷𝐹𝑆𝑡−𝑖 𝑖=1 𝑝 𝑝 𝑖=0 𝑝 𝑖=0 𝑝 + ∑ 𝛿2 𝛥𝑇𝐸𝑅𝑡−𝑖 + ∑ 𝛿3 𝛥𝐺𝐷𝑃𝐺𝑅𝑡−𝑖 𝑝 ∑ 𝑖=0 + ∑ 𝛿4 𝛥𝑇𝐵𝑡−𝑖 + ∑ 𝛿5 𝛥𝐴𝐼𝐷𝑡−𝑖 + 𝑖=0 𝑝 𝛿6 𝛥𝐷𝐹𝑆𝐼𝑡−𝑖 + ∑ 𝑖=0 𝑖=0 𝑝 𝛿7 𝛥𝐼𝑁𝑆𝑡−𝑖 + ∑ 𝑖=0 𝛿8 𝛥(𝑇𝐸𝑅 ∗ 𝐼𝑁𝑆)𝑡−𝑖 + 𝛿9 𝐷𝐹𝑆𝑡−1 + 𝛿10 𝑇𝐸𝑅𝑡−1 + 𝛿11 𝐺𝐷𝑃𝐺𝑅𝑡−1 + 𝛿12 𝑇𝐵𝑡−1 + 𝛿13 𝐴𝐼𝐷𝑡−1 + 𝛿14 𝐷𝐹𝑆𝐼𝑡−1 + 𝛿15 𝐼𝑁𝑆𝑡−1 + 𝛿16 (𝑇𝐸𝑅 ∗ 𝐼𝑁𝑆)𝑡−1 + 𝑢4𝑡 (8) In equations (5) to (8), the coefficients attached with difference operators measure short run dynamics, whereas, the parameters attached with one period lagged variables capture the long run relationships. Notably, β8 and β16in equation 6 represent, respectively, the direct and conditional impact of terrorism on tax revenue as percent of GDP while δ8 and δ16 in equation 8 capture, respectively, the direct and conditional impact of terrorism on defence spending as percent of GDP. For checking the existence of long run relationship between fiscal variables and all explanatory variables, we formulate four null hypotheses of no cointegration for equations (5) to (8) as follows: 𝛼7 = 𝛼8 = 𝛼9 = 𝛼10 = 𝛼11 = 𝛼12 = 0 𝛽9 = 𝛽10 = 𝛽11 = 𝛽12 = 𝛽13 = 𝛽14 = 𝛽15 = 𝛽16 = 0 Journal of Quantitative Methods Volume 5(1): 2021 |167 Fiscal Response to Terrorism in Pakistan 𝛾7 = 𝛾8 = 𝛾9 = 𝛾10 = 𝛾11 = 𝛾12 = 0 𝛿9 = 𝛿10 = 𝛿11 = 𝛿12 = 𝛿13 = 𝛿14 = 𝛿15 = 𝛿16 = 0 Equations (5) to (8) are representing four error correction models in which the lagged error correction term (ECT) in each case is obtained through the linear combination of lagged level variables. Pesaran et al. (2001) provide new critical values for the standard F test with a view to test null hypothesis of No cointegration. In this regard, an upper bound critical value is used when all regressors in a given model are integrated of order one i.e.I(1). A lower bound critical value is employed when all regressors are stationary at level i.e.I(0). However, theyestablish that the upper bound critical values are still valid in case some regressors are I(0) and some are I(1). 3. Discussion of Results 3.1. Unit Root and Cointegration Tests As we are working with time series data, therefore, it seems essential to commence our empirical task by probing the stationarity properties of all the time series included in models (1) to (4) using ADF unit root test.Results displayed in table 2 show that all time series are integrated of order one i.e., I (1) except terrorism and foreign aid which are integrated of order zero i.e., I (0)5. The mixed order of integration of regressors makes a valid case to use the ARDL technique in the present study. After selecting optimal lag using the Schwartz Bayesian Criteria, value of F-test statistic is estimated to test the null hypotheses of no cointegration in case of all the four models i.e. (5) to (8) as an initial crucial step.In table 3 we can see that acomparison between calculated value of F-test statistic with that of its critical counterpart as provided by Pesaran et. al. (2001) reveals that null hypotheses of no cointegration between fiscal variables and all the regressors are rejected in case of all the four 5 For robustness check, we have also employed an alternate unit root test namely PP test (Phillips-Perron) unitroot test. The estimates reports the mixed order of integration of selected variables and are listed in Appendix 1. Journal of Quantitative Methods Volume 5(1): 2021 168 | Fiscal Response to Terrorism in Pakistan models i.e. (5) to (8). Hence, it turns out that selected fiscal variables forms a long run relationship with the explanatory variables given in models (5) to (8). Table 2. Unit Root Test Results Variable Test Statistic Level 1stDifference TAX TER GDPGR INF GE AID INS TER*INS DFS TB DFSI 1.032 -4.079 -3.423 -2.307 -2.703 -4.003 -1.282 -1.920 -0.829 -2.182 -1.224 -6.678 -7.118 -6.868 -5.947 -4.599 -7.782 -4.942 -6.314 -4.818 Mackinnon Order of Critical Integration Values (5% Level of Significance) -3.558 I(1) -3.558 I(0) -3.558 I(1) -3.558 I(1) -3.558 I(1) -3.558 I(0) -3.558 I(1) -3.558 I(1) -3.558 I(1) -3.558 I(1) -3.558 I(1) Table 3: Cointegration Test Model F-stat Value 1 2 3 4 5.16 22.23 4.84 7.87 F-statistic Critical Value (5% Significance Level) I(0) I(1) 2.62 3.79 2.32 3.50 2.62 3.79 2.32 5.50 Outcome Cointegration Cointegration Cointegration Cointegration 3.2. Descriptive Statistics The descriptive statiscs are presented in table 4. These statistics indicate that the mean value of tax revenue and fiscal spending in Pakistan are higher than the standard deviation hence we may conclude that variation in these variables are not large. Journal of Quantitative Methods Volume 5(1): 2021 |169 Fiscal Response to Terrorism in Pakistan Notably, the mean value of DFS is higher than the mean value of DFSI indicating that, on average, Pakistan defence expenditures(as percent of GDP) are higher than India. Similarly, it is bserved that standard deviations of DFS is higher than DFSI. On average Pakistan has expeienced 411 terrorist attacks with huge variation of 610. The maximum number of attacks experienced by the county is reported as 2214. On average Pakistan’s trade balance emerged as negative with moderate standard deviation oevr the selected period of time. Variables TAX DFS TER GDPGR INF GE AID TB DFSI INS Obs 33 33 33 33 33 33 33 33 33 33 Table 4. Descriptive Statistics Mean S.D Min 11.38 1.60 8.94 5.06 1.58 3.26 411.61 610.52 0 4.52 1.88 1.01 8.18 3.97 2.54 20.56 5.18 13.32 3.94 1.61 1.28 -5.35 3.56 -12.39 2.91 0.401 2.39 2.46 1.45 1 Max 13.71 7.60 2214 7.71 20.28 29.42 7.23 1.03 3.95 4.78 3.3. Tax Revenue and Terrorism: Role of Institutional Quality As a next step, we focus on the outcomes of estimation endeavours of models (5) to (8) one by one. We take start with the interpretation of results of model 5 contained in table 5 which has three sections. In the upper section, long run estimates of tax to GDP ratio are presented. It can be seen that terrorism is significantly associated with tax revenue collection with a negative signin Pakistan. This finding is in accordance with theoretical prediction that terrorism adversely impacts tax collection efforts. The coefficient of terrorism carries the value-0.116 which implies that one percent increase in terrorist attacks leads to bring 0.116 percent decline in tax to GDP ratio in Pakistan. Since 1980s, terrorism has become endemic with recurrent attacks and extremely high fatalities in Pakistan. The incidence of terrorist attacks has significantly increased in wake of the USA attacks on Afghanistan in 2001. Domestic business and investment activities have severely affected from persistently occurring curse in the country which has adverse ramifications for tax collection. Journal of Quantitative Methods Volume 5(1): 2021 170 | Fiscal Response to Terrorism in Pakistan Table 5. Tax Revenue and Terrorism Dependent Variable: Tax Revenue Selected Model: ARDL(1,2,2,1,1,1) Regressor Coefficient t-value TER -0.116** -2.513 GDPGR 0.262*** 3.814 INF -0.077** -2.491 GE 0.497*** 3.356 AID -0.441** -2.232 Error Correction Model: Short Run Estimates Regressor Coefficient t-value Constant 6.011*** 6.415 D(TER) -0.254 -0.952 D(TER)t-1 -0.381 -1.424 D(GDPGR) 0.143* 1.928 D(GDPGR)t-1 0.367 1.133 D(INF) -0.113** -3.074 D(GE) 0.156*** 5.742 D(AID) -0.095** -2.948 ECTt-1 -0.781*** -6.944 Diagnostic Tests 2 𝜒𝑆𝐶 = 0.312(0.656) 𝜒𝐻2 = 0.845(0.414) 2 𝜒𝐹𝐹 = 0.981(0.282) 𝜒𝑁2 = 1.312(0.461) Note:***, **and * indicate significant at 1%, 5% and 10% levels respectively,χ_SC^2,χ_H^2,χ_FF^2and χ_N^2 denote LM test for serial correlation, heteroscedasticity,functional form and normality respectively. The associated p values are in parentheses. This finding substantiates the argument put forward by Gupta et al. (2004) that terrorism results in crumbling tax base through devastation of business firms and hampering the tax administration with net outcome of fall in tax revenue collection. Moreover, this outcome corroborates with what have been documented by (Gupta et al., 2004; Cevik & Ricco, 2015; Chuku et al., 2019). Among other determinants of tax revenue, we find that economic growth performance and public spending are positively while inflation rate and foreign aid are negatively associated with Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |171 tax to GDP ratio in Pakistan. All these findings are in accordance with our expectations. Persistent and strong economic growth performance stimulates business activities and employment level in a country which helps to raise tax to GDP ratio. The positive impact of public spending on tax revenue with statistical significance validates the spend-revenue hypothesis developed by (Barro, 1974; Peacock & Wiseman, 1979). This hypothesis is based on the notion that variations in government expenditure lead to produce changes in tax revenue i.e. at first, a government spends and then it strives for covering the expenditures through taxes. The adverse influence of inflation on tax revenue can be explained through the loss of purchasing power caused by rising price level that may result in tax evasion by the tax payers and value of tax collected. Finally, foreign aid adversely impacts tax revenue implying that the persistent dependence on foreign economic assistance did not let our decision makers to take appropriate measures for expanding the tax base in the country. Furthermore, the successive governments intentionally avoided introducing certain essential reforms in taxation system in order to please business community, industrialists and big land holders in the country due to flows of foreign aid in Pakistan. On fiscal front it can safely be stated that foreign aid is one of the hurdles in the way of increasing tax to GDP ratio in the country. However, in the short run, we find that terrorism has no role in shaping the behaviour of tax to GDP ratio in Pakistan (see middle section of table 5). This finding is justified on the ground that terrorist incidents do not spontaneously influence different sectors of an economy and business activities to a great extent and hence the tax generating capacity of a country like Pakistan is more likely to remain unaffected from the acts of terrorists. Rest of explanatory variables are found significant having expected impact akin to their long run effects. The coefficient of lagged error correction term (ECT) carries a negative sign which signifies stability of long run equilibrium relationship between tax revenue as percent of GDP and all the explanatory variables of model (1). The coefficient value of lagged ECT is -0.781with significance at 1% level. It specifies that if the long run equilibrium between tax to GDP ratio Journal of Quantitative Methods Volume 5(1): 2021 172 | Fiscal Response to Terrorism in Pakistan and all the regressors of model (1) is disturbed, in every short run period, almost 78% correction towards restoring the long run equilibrium will take place. At the lower section of table 4, four diagnostic tests are reported which clearly depict that the estimated model does not suffer from serial correction, heteroscedasticity, functional form and normality issues. These outcomes actually increase our confidence on the overall findings of the estimated model. Finally, CUSUM and CUSUM of Squares tests suggest stability of the parameter estimates of estimated model as their plots stay within 5% level of significance (see figure 1). Figure 1. Plots of CUSUM and CUSUMSQ Tests 15 10 5 0 -5 -10 -15 90 92 94 96 98 00 02 04 CUSUM 06 08 10 12 14 16 10 12 14 16 5% Significance 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 90 92 94 96 98 00 02 04 CUSUM of Squares 06 08 5% Significance In the next step, we incorporatethe role of institutional quality in the analysis. As apparent from table 6, institutional quality is a significant determinant of tax to GDP ratio in the long run. Positive association between institutional quality and tax revenue implies Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |173 that a well-functioning institutional setup helps government for uplifting tax to GDP ratio. Coefficient of terrorist incidents again carries negative sign and its value is -0.151which suggests adverse consequence of terrorist activities for tax revenue efforts. Table 6. Tax Revenue and Terrorism: Role of Institutions Dependent Variable: TAX Selected Model: ARDL(1,1,1,2,1,2,1,1) Regressor Coefficient t-value TER -0.151** -2.795 GDPGR 0.319** 2.593 INF -0.150*** -3.924 E 0.401* 1.833 AID -0.277** -2.035 INS 0.299** 2.324 TER*INS -0.067** -2.523 Error Correction Model: Short Run Estimates Dependent Variable: D(TAX) Regressor Coefficient t-value Constant 3.180*** 7.873 D(TER) -0.043 0.742 D(GDPGR) 0.026 0.314 D(INF) -0.134 -0.412 D(INF)t-1 0.028 0.723 D(GE) 0.121*** 6.893 D(AID) -0.097*** -3.554 D(AID)t-1 -0.036 -1.338 D(INS) 0.146*** 9.244 D(TER*INS) 0.058*** 4.077 ECTt-1 -0.482*** -17.781 Diagnostic Tests 2 𝜒𝑆𝐶 = 0.642(0.420) 𝜒𝐻2 = 0.994(0.242) 2 𝜒𝐹𝐹 = 0.728(0.385) 𝜒𝑁2 = 2.421(0.262) Note:***, **and * indicate significant at 1%, 5% and 10% levels 2 2 respectively.𝜒𝑆𝐶 ,𝜒𝐻2 ,𝜒𝐹𝐹 and 𝜒𝑁2 denote LM test for serial correlation,heteroscedasticity, functional form and normality respectively. The associated p values are in parentheses. The coefficient of interaction term (β16), capturing the conditional/indirect impact of terrorism through institutional quality, appears negative with statistical significance at Journal of Quantitative Methods Volume 5(1): 2021 174 | Fiscal Response to Terrorism in Pakistan conventional level. This implies that even in the presence of institutional quality, terrorism imparts adverse impact on tax revenue. However, it is pertinent to mention that the size of the adverse impact reduces from -0.151 to -0.067 in presence of institutional quality. This finding reflects that the present structure of intuitional quality in Pakistan reduces the adverse impact of terrorism on tax revenue to some extent but not completely eliminating it. This directs our attention towards improving the current state of institutional quality in a manner that it not only directly improves the state of tax collection but also helps in eliminating the adversity of terrorism for revenue collection. Badshah et al. (2012) also explain that the existing set of institutions are not capable to play significant role in combating the pitfalls of terrorism in terms of socio political destructions. Therefore, a new set of institutions would be required to combat this menace. It is widely documented that institutions significantly contribute in the growth process both directly and indirectly. Good quality institutions help in channelizing the resources towards productive activities and results in higher investment levels, increase in social capital stock, and effective management of ethnic diversity and conflicts (Kemal, 2003). Rest of explanatory variables, namely, economic growth performance, inflation rate, government spending and foreign aid are all found significant and their impacts are similar to the first case (see table 5). In short run, terrorism emerges as insignificant factor in determining tax to GDP ratio (see middle section of table 6). Coefficient of institutional quality is significant with expected positive sign. Moreover, coefficient of interaction term of terrorism and institutional quality is significant with positivesign. This implies that institutional quality will not let tax revenue collection to fall despite the occurrence of terrorist attacks in the short run. Economic growth performance and inflation rate do not impact significantly tax to GDP ratio in the short run. Nonetheless, government spending and foreign aid exert positive and negative impact on tax to GDP ratio, respectively. From the outcomes of four diagnostic tests, we see that estimated model does not suffer from serial correction, heteroscedasticity, functional form and normality problems (see Journal of Quantitative Methods Volume 5(1): 2021 |175 Fiscal Response to Terrorism in Pakistan lower section of table 6). Finally, the estimates of model (6) are found to be stable based on the outcomes of CUSUM and CUSUM of Squares tests (see figure 2). Figure 2. Plots of CUSUM and CUSUMSQ Tests 16 12 8 4 0 -4 -8 -12 -16 90 92 94 96 98 00 02 04 CUSUM 06 08 10 12 14 16 5% Significance (a) 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 90 92 94 96 98 00 02 04 CUSUM of Squares 06 08 10 12 14 16 5% Significance (b) 3.4. Defence Spending and Terrorism: Role of Institutional Quality The regression results for defence spending response to terrorism are reported in table 7. The upper section of table 7 displays results for the long run relationship. The regression coefficient of number of terrorist incidents is positive and Journal of Quantitative Methods Volume 5(1): 2021 176 | Fiscal Response to Terrorism in Pakistan significant, reflecting that the curse of terrorism matters for increase in military expenditure in Pakistan. Table 7. Defence Spending and Terrorism Dependent Variable: DFS Selected Model:ARDL(1,2,1,1,2,1) Regressor Coefficient t-value TER 0.234** 2.341 GDPGR 0.080*** 6.023 TB 0.225** 2.216 AID 0.341* 1.814 DFSI 0.438** 2.143 Error Correction Model: Short Run Estimates Dependent Variable: D(DFS) Regressor Coefficient t-value Constant 2.542*** 5.136 D(TER) 0.084 1.671 D(TER)t-1 0.013 0.758 D(GDPGR) -0.053 -0.642 D(TB) 0.086 0.169 D(AID) 0.060 2.274 D(AID)t-1 -0.03 -0.48 D(DFSI) 0.162*** 6.581 ECTt-1 -0.344*** -5.829 Diagnostic Tests 2 𝜒𝑆𝐶 = 0.332(0.551) 𝜒𝐻2 = 0.820(0.366) 2 𝜒𝐹𝐹 = 0.818(0.362) 𝜒𝑁2 = 2.015(0.312) Note:***, **and * indicate significant at 1%, 5% and 10% levels respectively. 2 2 𝜒𝑆𝐶 ,𝜒𝐻2 ,𝜒𝐹𝐹 and 𝜒𝑁2 denote LM test for serial correlation, heteroscedasticity, functional form and normality respectively. The associated p values are in parentheses. This finding is consistent with our prior expectation that terrorism forces a government to strengthen its national security and terrorism combating ability which demands for increasing public spending on these heads. The armed forces of Pakistan are actively involved in eradicating the evil of terrorism from the country. For the last fifteen years the strict actions have been taken on war footing to secure the homeland from terrorist attacks.Under these circumstances defence spending hasincreased which is more likely to divert the resources of the government from public sector Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |177 development spending and social sector development programs to military expenditures in Pakistan.The positive relationship between defence spending and terrorism is also documented by (Nasir & Shahbaz, 2015; Gupta et al., 2004; Cevik & Ricco, 2015; Chuku et al., 2019; Zakaria et al., 2019) report that terrorism leads to an increase in overall fiscal spending. The coefficients of economic growth, trade balance, foreign aid and defence spending of India are all positive and significant indicating the vital contribution of these factors in defence spending of Pakistan in the long run. It is an undeniable fact that accelerating economic performance of a country ensures availability of resources to a government for increasing defence capability. The defence is categorised a public good and conventional theory of public finance postulates a positiverelationship between economic growth and defence spending. Trade balance (as percent of GDP) reflects trade openness which is also one of the indicators of defence expenditure. With the increase in the degree of a country’s integration with the world economy, it becomes easier for the country to access finance for the purchases of military equipment, leading to higher defence spending (Rosh, 1988). Since 1980s Pakistan has been practicing trade liberalization policies, therefore, positive impact of trade balance on defence spending in Pakistan is justified on the ground presented by Rosh (1988). Foreign aid tends to raise defence spending in Pakistan seems reasonably just as since 1950s the country has been found allied with the USA and its partner Western Powers against the socialist block during the Cold War era and the international terrorist organizations like AlQaeda. The donors provided substantial amount of foreign aid which has been used by the successive governments in the country for meeting development and non-development targets including defence expenditures. Finally, we find that Pakistan’s defence spending positively responds to that of its arch rival India. This finding is in accordance with political and economic logic. The two neighbours have been engaged in rivalry and confrontation since their inception in 1947. Security threats occur not only from active warfare but also from an increasing military power of potential enemies. Hence, Pakistan’s defence budget positively responds to rise in India’s military purchases. Similar findings are Journal of Quantitative Methods Volume 5(1): 2021 178 | Fiscal Response to Terrorism in Pakistan reported by (Chawla, 2001; Sheikh et al., 2013; Aslam et al., 2014). These studies highlight that both countries keep an eye on each other defence expenditures. In the short run, however, number of terrorist incidents, economic growth performance and trade balance are found to be insignificant determinants of defence expenditure in Pakistan (see middle section of table 7). But the coefficients of foreign aid and defence spending of India are positive and significant, hence exhibit their due role in determining defence spending of the country even in the short run. The coefficient of lagged ECT is negative with statistical significance at 1% level, indicating that the equilibrium relationship of defence spending with all the explanatory variables is stable. The value of coefficient of lagged ECT is -0.344 which indicates that any deviation from the long run equilibrium between defence spending and its determinants will be corrected by 34% in each short run period i.e. a year. The robustness of the model has been confirmed by diagnostic tests reported as the lower section of table 7. The CUSUM and the CUSUMSQ graphical representations refer to the absence of any instability of the estimated parameters as the plots of these statistics remain within the critical bound of the 5% significancelevel (see figure 3). Figure 3. Plots of CUSUM and CUSUMSQ Tests 15 10 5 0 -5 -10 -15 96 98 00 02 04 06 CUSUM 08 10 12 14 16 5% Significance (a) Journal of Quantitative Methods Volume 5(1): 2021 |179 Fiscal Response to Terrorism in Pakistan 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 96 98 00 02 04 06 CUSUM of Squares 08 10 12 14 16 5% Significance (b) We now move to table 8 which contains short run and long run parameter estimates of model (8). From top section of table 8, it is apparent that number of terrorist incidents, economic growth performance, trade balance, foreign aid and defence spending of India affectdefence spending of Pakistan positively and significantly in the long run. Here our main interest is identifying the impact of institutional quality and interaction term of terrorism and institutional quality on defence spending. The regression coefficient of institutional quality is negative and significant, implying that well-working institutions tend to decline the defence spending in Pakistan. This outcome is in line with our prior expectation. The existence of better working institutions is less prone to violence. Good qualities institutions are conducive in strengthening capabilities of a government to well manage domestic and foreign conflicts in a peaceful manner without going for a military solution (Desta, 2009). Pakistan does believe in settling its internal and external conflicts in a diplomatic manner so that military options can be avoided. Additionally, the quality of legal and political institutions also affects defence spending from other vital aspects. Firstly, a sound institutional set up is conducive to determining right priorities in defence allocations. It is quite certain that in total military expenditure there are some essential and relatively more wanted elements of spending which cannot be compromised while other elements are less important and wasteful Journal of Quantitative Methods Volume 5(1): 2021 180 | Fiscal Response to Terrorism in Pakistan that ought to be minimised so that overall defence spending may remain within limits. High quality institutions may promote the former type of spending while weeding out the latter. Table 8. Defence Spending and Terrorism: Role of Institutions Dependent Variable: DFS Selected Model:ARDL(1,1,1,2,2,1,2,1) Regressor Coefficient t-value TER 0.272** 2.517 GDPGR 0.117** 2.121 TB 0.188*** 3.875 AID 0.332* 1.889 DFSI 0.457*** 6.841 INS -0.095** -2.572 TER*INS -0.102*** -3.585 Error Correction Model: Short Run Estimates Dependent Variable: D(DFS) Regressor Coefficient t-value Constant 1.533*** 7.643 D(TER) 0.020 0.441 D(GDPGR) 0.067 1.573 D(TB) 0.134 0.727 D(TB)t-1 0.116 0.482 D(AID) 0.044 0.884 D(AID)t-1 0.142 1.286 D(DFSI) 0.218*** 4.244 D(INS) -0.014 -0.339 D(INS)t-1 -0.052 -1.262 D(TER*INS) 0.086 0.984 ECTt-1 -0.524*** -9.313 Diagnostic Tests 2 𝜒𝑆𝐶 = 0.401(0.531) 𝜒𝐻2 = 0.794(0.374) 2 𝜒𝐹𝐹 = 0.844(0.353) 𝜒𝑁2 = 2.213(0.292) Note:***, **and * indicate significant at 1%, 5% and 10% levels respectively. 2 2 𝜒𝑆𝐶 ,𝜒𝐻2 ,𝜒𝐹𝐹 and 𝜒𝑁2 denote LM test for serial correlation, heteroscedasticity, Functional form and normality respectively. The associated p values are in parentheses. Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |181 In contrast, poor institutions may be unable to distinguish between good and bad military spending or may even create incentives for the latter (Compton & Paterson, 2016). Hence, quality of institutions does matter for reducing defence spending in a country like Pakistan. Secondly, prevalence of corruption in defence purchases cannot be overlooked which is likely to push up military outlays. However, effective and dynamic institutions ensure rule of law and accountability in a country, leading to significantly control corruption. Thus, establishment of a quality institutional structure is more likely to reduce the elements of corruption in military expenditure such that total defence outlays are reduced. Thirdly, an efficient working of institutions ensures correct priorities and appropriate policies and their effective implementation results in creating a conducive environment that encourages domestic and foreign investors to invest in productive activities. So, overall economic performance of a country gets improved and size of the economy widens. Under such a situation, defence spending as percent of GDP is likely to decline. Notably,the coefficient of interaction term of terrorism and institutional framework appears negative and statistically significant. The negative coefficient of the interaction term implies that good institutions will help in reducing uncertainty attached with terrorist attacks, therefore, a decline in defence spending to GDP ratio is experienced in wake of terrorist attacks in the presence of good quality institutional setup in Pakistan. Notably, when we compare the direct and conditional impact of terrorism on defence spending, it is observed that the size of direct impact (0.272) is higher than the conditional impact (-0.102), leaving the overall impact of terrorism on defence spending to remain positive [0.272 +(-0.102) = + 0.170]. Hence, once again we conclude that the current quality of institutions in Pakistan is not strong enough to completely eliminate the adversities of terrorism for fiscal policy.This finding is supported by a descriptive analysis of Badshah et al. (2012) who explains that the existing set of institutions are not capable to play significant role in combating the pitfalls of terrorism in terms of socio political destructions. Therefore, a new set of institutions would be required to combat this menace. Journal of Quantitative Methods Volume 5(1): 2021 182 | Fiscal Response to Terrorism in Pakistan In short run, we find that only defence spending of India is a significant driver of the defence spending of Pakistan (see middle section of table 8). It implies that the decision- makers always keep an eye on military expenditure of India and they retaliate to any change in India’s defence budget. Number of terrorist incidents, institutional quality, their interaction and other explanatory variables fail to play any role in defence spending of Pakistan. The coefficient of lagged ECT is negative and significant, showing the stability of the long run association between defence spending and all the explanatory variables. From the coefficient value of the lagged ECT, it is clear that almost 52% deviation from the equilibrium will be corrected every year. Finally, on the basis of four diagnostic tests we see that the model (8) does not suffer from serial correlation, heteroscedasticity, functional form and normality problems (see lower section of table 8). Moreover, results of CUSUM and CUSUMSQ tests also reveal stability of estimated model (see figure 4). Figure 4. Plots of CUSUM and CUSUMSQ Tests 16 12 8 4 0 -4 -8 -12 -16 90 92 94 96 98 00 02 CUSUM 04 06 08 10 12 14 16 5% Significance (a) Journal of Quantitative Methods Volume 5(1): 2021 |183 Fiscal Response to Terrorism in Pakistan 1.4 1.2 1.0 0.8 0.6 0.4 0.2 0.0 -0.2 -0.4 90 92 94 96 98 00 02 04 CUSUM of Squares 06 08 10 12 14 16 5% Significance (b) 4. Conclusion and Policy Implications Terrorism has serious socio-economic and political implications specifically for developing countries. Pakistan has a long history of being on the hit list of terrorist attacks due to several reasons such as religious, ethnic, political, and external. In particular, the incident of 9/11 has brought Pakistan to the front line of global terrorism and to-date the country is facing the consequences of the incident. The present study is conducted to quantify the fiscal consequences of terrorism in Pakistan. The country has devised several strategies to combat the adversities of terrorism.Notably, the fiscal burden of these actions is huge and is becoming unmanageable with the passage of time. By looking at the trend of defence spending, it is evident that the share of defence spending is increasing over time as the security requirements are increasing due to actual or perceived security threats. In order to assess the fiscal response to terrorism in Pakistan, time series data from 1984 to 2016 are employed. By usingthe ARDL estimation technique, the study finds that terrorism has significantly contributed in fiscal difficulties ofthe country. Particularly, the results indicate that an increase in the number of terrorist incidents not only increases the defence spending but it also reduces the tax revenues. Hence, Journal of Quantitative Methods Volume 5(1): 2021 184 | Fiscal Response to Terrorism in Pakistan terrorism has detrimental impact on fiscal behaviour of Pakistan by deteriorating both sides of fiscal position. The study has contributed in an important dimension in the existing literature by taking the moderating role of institutional qualityin terrorism-defence spending and terrorism–revenue collection relationship. The findings reveal that institutional quality helps in mitigating the adverse impact of terrorism on fiscal spending. However, it only reduces the hazards of terrorism for revenue collection but not completely alleviates it. Hence, it necessitates the idea of improving the quality of institution to substantially tone down the adverse impact of terrorism on fiscal account of Pakistan. On the basis of the above findings, it is concluded that terrorism is detrimental for both aspects of the fiscal policy of Pakistan. It is evident that that government is taking various actions to control the terrorist activities at one hand, and to provide rehabilitation and reconstruction packages on the other hand. To effectively counter and control the terrorist incidents, the root cause of terrorism needs to be better identified so that appropriate antiterrorism strategy can be formulated and implemented.Just bringing an increase in defence and military spending will not sufficeto significantly eradicate the menace of terrorism completely. To this end,efforts ought to be made to improvethe quality of institutions for offsetting adverse implications of terrorism for fiscal actions of the government of Pakistan. Conflict of Interest Supplementary Martial None No supplementary material is associated with the article Funding This research received no external funding Acknowledgment No additional support is provided ORCID of Corresponding Author Nill Journal of Quantitative Methods Volume 5(1): 2021 Fiscal Response to Terrorism in Pakistan |185 References Acemoglu, D., & Robinson, J. (2010). The role of institutions in growth and development. Review of Economics and Institutions, 1(2), 1-33. Aslam, A., Sheikh, M. R., Abbas, A., & Masood, S. (2014). The demand for defense expenditures in Pakistan: An empirical analysis. International Journal of Management Research and Emerging Sciences, 4(1), 69-86. Badshah, I., Rehman, H., Khan, S., & Faiz, F. A. (2012). War on terrorism and its impacts on the socio-political structure of pakhtun society of Pakistan. Middle-East Journal of Scientific Research, 12(6), 826-832. Barro, R. J. (1974). Are government bonds net wealth? 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World Bank. https://datacatalog.worldbank.org/dataset/worlddevelopment-indicators Yogo, U. T. (2015). Terrorism and Fiscal Policy Volatility in Developing Countries: Evidence from cross-country and Panel Data. https://halshs.archives-ouvertes.fr/halshs01161601/ Zakaria, M., Jun, W., & Ahmed, H. (2019). Effect of terrorism on economic growth in Pakistan: an empirical analysis. Economic research-Ekonomska istraživanja, 32(1), 1794-1812. https://doi.org/10.1080/1331677X.2019.1638290 Citation: Mukhtar, T., & Jehan, Z. (2021). Fiscal Response to Terrorism in Pakistan: The Role of Institutions. Journal of Quantitative Methods, 5(1), 154-192. https://doi.org/10.29145/2021/jqm/050107 Journal of Quantitative Methods Volume 5(1): 2021 |191 Fiscal Response to Terrorism in Pakistan Annexure Table A1. Unit Root Test Results (Phillips and Perron ) Variable Test Statistic Critical Order of Integration Level 1stDifference Values (5% Level of Significance) TAX -1.830 -6.925 -2.960 I(1) TER -2.276 -5.270 2.960 I(1) GDPGR -3.312 2.960 I(0) INF -2.406 -6.853 2.960 I(1) GE -0.717 -6.590 2.960 I(1) AID -2.046 -6.910 2.960 I(1) INS -1.519 -4.612 2.960 I(1) TER*INS -1.803 -7.860 -2.960 I(1) DFS -0.998 -4.814 2.960 I(1) TB -2.314 -6.128 2.960 I(1) DFSI -1.488 -4.844 2.960 I(1) Table A2a: Chow Break Test Results (TAX) Test Test statistics P-Value F-statistics 0.192 F(1,31) 0.664 Log likelihood ratio 0.204 Chi-Square(1) 0.651 Wald Statistic 0.192 Chi-Square(1) 0.661 Null Hypothesis: No breaks at specified breakpoints Table A2b: Chow Break Test Results (DFS) Test F-statistics P-Value F(1,31) 0.225 Log likelihood ratio 2.202 Chi-Square(1) 0.209 Wald Statistic 2.184 Chi-Square(1) 0.212 Null Hypothesis: No breaks at specified breakpoints Journal of Quantitative Methods Test statistics 2.158 Volume 5(1): 2021 192| Fiscal Response to Terrorism in Pakistan Table A3. PCA Estimates Principal Components Analysis Date: 11/10/18 Time: 19:36 Sample: 1984 2016 Included observations: 33 Computed using: Ordinary correlations Extracting 5 of 5 possible components Eigenvalues: (Sum = 5, Average = 1) Cumulative Cumulative Number Value Difference Proportion Value Proportion 1 2 3 4 5 1.682421 1.320112 1.106013 0.795213 0.096241 0.362309 0.214099 0.310800 0.698972 --- 0.4278 0.2852 0.1832 0.1009 0.0029 1.682421 3.002533 4.108546 4.903759 5.000000 0.4278 0.7130 0.8962 0.9971 1.0000 Eigenvectors (loadings): Variable COR DEA LOR BRQ ETT PC 1 -0.340527 0.554431 0.588286 0.152592 0.455285 PC 2 PC 3 PC 4 PC 5 0.495022 0.074891 0.168775 0.821525 -0.214372 0.614248 -0.307963 0.175276 -0.201767 0.675594 0.332557 0.725624 -0.574247 -0.110916 0.144267 0.388716 0.256176 0.514736 -0.498798 -0.519154 DEA LOR BRQ ETT 1.000000 0.658343 0.437493 0.311009 1.000000 0.487972 -0.606668 1.000000 -0.152035 1.000000 Ordinary correlations: COR DEA LOR BRQ ETT COR 1.000000 -0.677873 -0.642540 -0.415237 0.075826 Journal of Quantitative Methods Volume 5(1): 2021