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Research Paper

Title: Impacts of Foreign Aid, Terrorism and Military Expenditure upon Innovation

Capacity: Evidence from a Terrorism-prone Emerging Economy

Muhammad Athar Nadeem

School of Management - University of Science and Technology of China, Hefei

Email: [email protected], [email protected]

XU Yi*

Assistant Professor


Email: [email protected]

LIU Zhiying

Professor



Amna Younis

School of Public Affairs - University of Science and Technology of China, Hefei


Faisal Asghar



*Corresponding author*Corresponding author’s Phone No. (0086)13515601888

Impacts of Foreign Aid, Terrorism and Military Expenditure upon Innovation Capacity:

Evidence from a Terrorism-prone Emerging Economy

Abstract

Innovation is crucial to sustaining growth. Effective use of foreign aid helps to sustain growth

and foster innovation in the recipient countries. Terrorism hampers economic growth and deters

foreign direct investment, a major source of technology transfer in emerging economies,

especially in terrorism affected ones. Military expenditure helps to maintain law and order in

terrorism prone countries. This study investigates the impacts of foreign aid, terrorism, and

military expenditure upon innovation capacity of a terrorism prone country, i.e. Pakistan, with

the annual time series data from the years 1986-2016. Using the ARDL bound testing

cointegration approach, we confirm the long-run relationships among variables. Notably, our

empirical findings show that there is a significant and negative relationship between foreign aid

and innovation capacity in the long run. Terrorism has a negative but statistically insignificant

impact on innovation while military expenditure and GDP per capita have a positive relationship

with innovation capacity over the long run. Foreign aid has a more deteriorated impact than

terrorism. Furthermore, Johansen cointegration test confirms the long-run association among

variables. Pairwise Granger causality also confirms the bidirectional relationship between

foreign aid and innovation capacity. Through ordinary least square (OLS) method, regression

equation is also estimated. These results provide practical implementation perspectives to

policymakers and law enforcement agencies in terrorism prone emerging economies.

Key Words: Terrorism, foreign aid, foreign direct investment, military expenditure, Pakistan,

innovation capacity, ARDL, Johansen cointegration, granger causality, OLS

1. Introduction

The relationship between foreign aid, terrorism, military expenditure and economic growth has

received much attention in recent years. Over the past decade, terrorism has become a malignant

disease in the world, especially in such a terrorism prone country as Pakistan, which is

committed to nipping the evil of terrorism from the bud. The 911 terrorist attacks in the United

States changed the socioeconomic and geopolitical situation worldwide, and consequently,

terrorism has become a global challenge. Terrorist attacks have also been significantly increasing

in Pakistan after the 911 attack. Pakistan, like many other terrorism prone countries, has

encountered a brutal terrorist wave and intense terrorism episode resulting in not only losses of

precious human lives but also economic damages. According to Okafor & Piesse (2017)

terrorism is linked with socio-economic factors, economic deprivation, religious and ethnic

fractionalization, demographic tension, political transformation and political order. Varied

definitions of terrorism have been used by various academic communities and government

agencies. Sometimes, due to ideological and political conflicts, “One man’s terrorist is another

man freedom fighter” (Europol, 2008; Silke, 2004). Terrorism events create insecurity,

vulnerability, uncertainty, fear and panic in the environment (Aslam & Kang, 2015; Keeney &

Winterfeldt, 2010). Terrorism activities negatively affect the economy of a country by damaging

physical capital, human capital and infrastructure, causing financial instability, decreasing

confidence of investors and increasing counterterrorism cost (Johnston & Nedelescu, 2006).

Terrorists have well-established groups, target the military, police, private properties, tourists,

and use the tactics like bombing, suicide attacks, kidnapping, hijacking, armed attacks (Abadie,

2006), resulting in fatalities and high economic costs (Ali, 2010). Pakistan, as a typical terrorism

prone economy, has been severely affected by such dynamics of terrorism (Aisha & Shehla,

2014).

Terrorist actions lead to grave human right violations, destructing the infrastructure and

economic prospects. Study on terrorism in terrorism prone economies is essential. According to

Eckstein & Tsiddon (2004), most studies exclusively focused on the developed countries

regarding the impact of terrorism. Less attention has been paid to developing countries.

Moreover, analyses of terrorism impact on economic growth might be country-specific and be

prone to heterogeneity bias. Blomberg, Hess, & Orphanides (2004) pointed out that controlling

various country-specific factors through dummy variables in cross-country growth regressions to

study the impact of terrorism on economy is “crude estimation” at best. Similarly, Enders &

Sandler (2006) noted that different levels of terrorism and institutional structures make cross-

country analyses of terrorism ambiguous.

Pakistan faces massive collateral damages in terms of physical and human capital losses and

destructions of infrastructures. From 1970 to 2016 according to Global Terrorism Database

(GTD), there were 1833, 1569, 1225 terrorist attacks on police, military and government

generals & diplomats which resulted in 2920, 3929, 2012 fatalities respectively. According to

GTD, 13,721 terrorist attacks taking place from 1970 to 2016 resulted in 22,962 fatalities.

Notably, 13,681 attacks happened during the sample period of the study (1986-2016), resulting

in 22,902 fatalities. Military expenditures are an integral part of the national budget to protect the

country. Due to terrorism, military expenditures also increased in Pakistan, like other terrorism-

prone countries, to counter terrorism impact. The majority of studies focused on the military

expenditure and economic growth. Some studies found a positive correlation (Farzanegan, 2014;

Kollias & Paleologou, Growth, investment and military expenditure in the European Union-15,

2010) and some studies empirically proved a negative one (Malizard, 2016; Manamperi, 2016)

and some found no correlation (Kollias & Paleologou, 2016; Nikolaidou, 2016) between the

military expenditure and the economic growth of the country. Moreover, terrorism also crashes

the FDI in the country, which is a crucial source to transfer technology from developed countries

to developing countries and consequently increases the innovation capacity of the developing

countries. Pakistan also experienced the low amount of FDI inflows due terrorist attacks.

Pakistan lost the pace to attract the FDI inflows due to its role as frontline ally against terror ism

(Shah, Ahmad, & Ahmed, 2016). Moreover, FDI inflows in Pakistan result from political

reasons, due to its role as frontline ally against terrorism with the US, rather than economic

factors (Mehmood, 2014). Military expenditures are budgeted to curtail unrest and conflicts,

counter the impact of terrorism, and give confidence to both local and foreign investors.

According to Deger & Sen (1983), increasing military expenditure not only protects both

(internal and external) conflicts but also leads to the commercial and economic spin-off, such as

secure investor returns. In the presence of war conflict, higher military expenditures give

confidence to investors through securing and safeguarding the investments and interests of

investors. Military expenditure may cause some positive externalities such as technological spin-

off and human capital formation (Üçler, 2016). Military technology developed by scientists and

engineers may result in many new technologies (Nadaroğlu, 1985).

Foreign aid is essential to enhance economic growth in developing countries. It influences

growth processes, increases productivity and transfers modern technologies (Khan & Ahmed,

2007). Results of foreign aid could be positive or negative, short-run or long-run, depending

upon the policies and absorptive capacity of the respective country. Pakistan, like many other

emerging economies, heavily depends on foreign aid to finance its economic activities. Despite

of an enormous amount of aid, Pakistan is abysmally subject to development indicators.

According to Anwar & Michaelowa (2006), Pakistan received US$ 73.14 billion foreign aid

from 1960 to 2002, but its trickle-down effects on the whole society did not stretch, indicating

that foreign aid failed to enhance the economic growth in Pakistan. According to the World

Bank, the literacy rate was 56.97%, and various other economic indicators, like health,

education, and employment，did not depict an encouraging picture. Foreign aid in Pakistan was

not utilized to boost the economic growth but to serve personal stakes of influential people.

Developing economies used foreign aid to decrease resources gap, encourage industrial

development, and foster domestic investment which could help the developing economies to

“takeoff” into self-reliance growth through generating new domestic investment (Rostow, 1990;

Waterson, 1965). Economists agree that foreign aid is essential for developing economies to

increase their economic growth and claim a positive correlation between aid and economic

growth as it balances the domestic resources and serves as a bridge between saving-investment

gaps. Moreover, foreign aid helps to close the gap of foreign exchange, enhance the managerial

skills, provide access to modern technology and finally allow easy excess to the international

market (Roemer, 1989; Thirlwall, 1999).

We use the time series data of Pakistan from 1986-2016 to conduct a study on the impacts of

foreign aid, terrorism, and military expenditure upon innovation capacity of Pakistan, a typical

victim of brutal terrorism wave. Our ARDL approach to cointegration empirically proves long

and short-term significant negative relationship between foreign aid and innovation capacity in

Pakistan. However, terrorism has a negative but statistically insignificant relationship with

innovation capacity. Military expenditure and GDP per capita have a significant and positive

relationship with innovation capacity both in the long and short-run. Furthermore, results of

Johansen cointegration test also confirm the long-run association among variables.

This paper is hereafter organized as follows: Before Section 2, terrorism, drone attacks,

terroriism cost and National Action Plan (NAP) in Pakistan are describes. Section 2 summarises

the review of literature. Section 3 presents data and empirical framework. Section 4 includes the

results and discussion. Finally, this study provides conclusion, recommendations and policy

implications in Section 5.

Terrorism in Pakistan

Pakistan has never experienced a good relationship with some neighboring countries due to some

domestic and global issues. In 1979, foreign invasion in Afghanistan had an impact on the

terrorism in Pakistan. Pakistan participated in this war to fight against the foreign invasion in

Afghanistan (Cooley, 2002). According to him, Pakistan provided moral, strategic, logistical

supports to defeat the foreign troops in Afghanistan. To counter the threat of foreign invasion in

the region, US injected around $ 6 billion to fight against foreign troops in Afghanistan (Weiner,

1998). In 1989 after defeating foreign troops from Afghanistan, US left this region with

thousands of armed militants that afterward became a threat for Pakistan. America’s intervention

in Afghanistan affected the security situation in Pakistan as Pakistan is a major ally of the United

States against the terrorists and the US declared Pakistan as its non-NATO ally in this war

against terrorism. Being a frontline ally against terrorism, Pakistan has suffered a massive loss in

terms of physical and human capitals and destructions of infrastructures, and political

instabilities. According to Mehmood (2014), over the past decade, Pakistan has the highest

terrorism-related death records and the number is even greater than the total terrorism-related

deaths in the North America and European continents. Initially these terrorist activities were

limited to the war rotten areas of Federally Administrative Tribal Areas (FATA), but later on,

these terrorist activities spread across the country. Terrorism wave severely hit Pakistan. On

December 27, 2008, the former Prime Minister of Pakistan (Benazir Bhutto) was assassinated in

Rawalpindi, Pakistan (BBC, 2018). Shahzad, Zakaria, Rehman, Ahmed, & Fida (2016) stated

that both foreign and domestic elements were involved in terrorist activities in Pakistan.

Considering the background of terrorism in Pakistan, it is of both uniqueness and importance to

study the impact of terrorism related indicators upon its innovation capacity for the following

two reasons. First, Pakistan has an intense history of terrorism after the 911 attacks and serves as

a major ally of the USA against terrorism. Second, according to Frey, Luechinger, & Stutzer

(2007) terrorism impact on growth is hypothesized to be more evident in developing countries

rather than developed countries. From 1986, casualties have increased in Pakistan due to terrorist

activities. According to Syed, Saeed, & Martin (2015) the 1980s was considered as watershed

years in the Pakistan’s history. Figure 1 describes the number of causalities resulting from

terrorism in Pakistan. Figure 2 shows the geographical spread of terrorist activities in Pakistan

from 1974-2007.

19861988

19901992

19941996

19982000

20022004

20062008

20102012

20142016

0

500

1000

1500

2000

2500

3000

3500

Yaers

Num

ber o

f Cau

saliti

es

Figure 1: Terrorism related causalities in Pakistan

Source: Global Terrorism Database (GTD)

Figure 2: Geographical Spread of Terrorist activities in Pakistan (1974-2007)

Source (Hussain, 2010)

Drone attacks in Pakistan

US government adopted drone attack policy to target terrorists in Pakistan. The Pakistani

government is against this policy. Figure 3 shows the history of drone attacks in Pakistan. A

common argument against the drone attack policy is that it generates more extremists and

terrorists as it gives room to the militant group to take advantage of civilian’s deaths to gain

sympathies of their families and recruit more people in their militant wings.

2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 20170

102030405060708090

Years

No.

of d

rone

atta

cks

Figure 3: No. of drone attacks in Pakistan Source: South Asia Terrorism Portal (SATP)

War against terrorism: cost in Pakistan

In recent years, Pakistan’s economy has been under enormous pressure and its economic growth

becomes stagnant due to the high cost of the war against terrorism. Terrorists expand their

activities across the country, and Pakistan faces internal problems. Pakistani army launched

operations in FATA and Khyber Pakhtunkhwa (KPK). Since 2007, Pakistani army has launched

11 military operations1 to counter the activities of terrorist groups. More recently, Pakistani army

launched a grand military operation “Operation Radd-ul-Fasaad 2017” across the country.

Millions of people displaced from their homes and hosting the Internally Displaced Person

(IDP’s) added to the heavy financial burden, which disrupted economic growth in Pakistan and

targets of economic growth became even more hopeless. According to Pakistan Economic

Survey (2016), Pakistan faces worse socio-economic, political, and security consequences due to

war against terrorism and the cost of terrorism in Pakistan totaled US$ 123.13 billion or Rs.

10373.93 billion since the turn of the century. It is an enormous amount for an emerging economy

like Pakistan. Table 1 reports the cost of the wars on terror borne by Pakistan. In sum, terrorist

1. https://www.dawn.com/news/1316332

activities impose a substantial economic sanction on the economy and adversely affect its

development in all the sectors. However, active military combat restricts terrorist activities and

generates reduction in the cost of war on terror. Because of these military combats and

operations, loss continues to decrease from US$ 23.77 billion in 2010-11 to US$ 3.88 billion in

2016-17. According to Annex- IV of Pakistan Economic Survey (2016), the chapter “Impact of

War in Afghanistan and Ensuing Terrorism on Pakistan’s Economy” acknowledged the vital role

to counter the terrorism. Moreover, economic growth has started across different segments of the

economy due to the counter-terrorism success.

Table 1

Cost of wars on terror (2001 to 2016)

Years Billion US$ Billion Rs.2001-2002 2.67 163.902002-2003 2.75 160.802003-2004 2.93 168.802004-2005 3.41 202.402005-2006 3.99 238.602006-2007 4.67 283.202007-2008 6.94 434.102008-2009 9.18 720.602009-2010 13.56 1136.402010-2011 23.77 2037.332011-2012 11.98 1052.772012-2013 9.97 964.242013-2014 7.7 791.522014-2015 9.24 936.302015-2016 6.49 675.762016-2017 3.88 407.21Total 123.13 10373.93

Source: Pakistan Economic Survey

National Action Plan

Pakistan’s political and military leadership learned much from such deadly terrorist attacks as in

Army Public School Peshawar in December 2014 resulting in 141 causalities of which 132 were

children. In January 2015 National Action Plan (NAP) was implemented across the country,

gaining unanimous support from all Pakistani provisional and federal governments. It consists of

20 major points to reinforce the legitimacy of the government in the country.2

2. Review of literature

Terrorism

Terrorism affects economic growth

Terrorism and violent criminal activities are designed to create fear and panic in the community

(Buckelew, 1984). Terrorism can be defined as “… the planned use or threat of extra-normal

violence by sub-national groups to obtain a political, religious, or ideological objective through

threats of a large audience, usually not directly involved with the decision making” (Enders &

Sandler, 2002).

Several studies empirically examined the relationship between terrorist activities and economic

growth and concluded terrorism adversely affects the country at macroeconomic level (Abadie &

Gardeazabal, 2008; Abadie & Gardeazabal, 2003; Buesa, Valiño, Heijs, Baumert, & Gómez,

2007; Eckstein & Tsiddon, 2004). Different authors studied and discussed the theoretical

framework about the channels through which terrorism impedes economic growth of a country

(Eckstein & Tsiddon, 2004; Frey, Luechinger, & Stutzer, 2007; Mirza & Verdier, 2008; Sandler

& Enders, 2008). Channels through which terrorism may damage the economic activities include

FDI inflows (Enders & Sandler, 1996), international trade (Nitsch & Schumacher, 2004) and

other sectors of the economy like tourism (Enders, Sandler, & Parise, 1992). These adverse

effects may result in constraining the economic growth in the country (Abadie & Gardeazabal,

2003; Crain & Crain, 2006; Gaibulloev & Sandler, 2008). Possible costs of terrorism could be

divided into direct and indirect costs. According to Collier (1999) most possible and direct threat

of civil wars, of which terrorism in a country can be considered a related fact, destruct the

physical capital and public infrastructure and lead to loss of human capital.

2. Further detailed discussion of the Nation Action Plan (NAP) and its features, please see http://nacta.gov.pk/NAPPoints20.htm

Simultaneously, transition costs augmented, as a result, reduce the security and effectiveness of

government enforcement. Blomberg, Hess, & Orphanides (2004) and Gaibulloev & Sandler

(2008) pointed out that terrorism diverts the economic activities away from investment spending

to non-productive spending like defense mechanism against counter-terrorism activities.

Terrorism affects FDI

Over the last decades, there has been extraordinary research concern about the role of scientific

and technological advancement to drive the economic progress (Araújo & Salerno, 2015; Temiz

& Gökmen, 2014). Despite the substantial consensus that technological innovation plays a vital

role in long-term economic growth, there is a debate about underlying drivers of innovation

process (Furman, Porter, & Stern, 2002). Previous studies focused on the national investment

and its relationship with innovation and argued that national investment is essential to ensure

long-term economic growth (Abramovitz, 1956; Romer, 1990; Solow, 1956). There was an

ongoing debate that national innovation capacity is a closed or open system. According to

Furman & Hayes (2004) and Porter & Stern (2002), a country’s ability to innovate depends on its

financial resources and such factors as national investment, human capital, accumulated

technological sophistication, innovative environment, national industrial cluster, and linkage

strength between common innovation infrastructure and its industrial cluster. Other scholars

argued that it is an open system as a country embraces the international trade and FDI, which

allows the country to benefit from advance foreign technologies (Eaton & Kortum, 2002; Gong

& Keller, 2003). Due to repaid increase in economic integration like frequent international trade,

openness to FDI as well as information and communication technologies (ICT) makes a

traditional closed approach to national innovation capacity less relevant, as openness to FDI

promotes international diffusion of technologies (Gong & Keller, 2003). Economists,

policymakers, and international business scholars devoted their attention to the role of

technology which helps the latecomer economies to increase technological progress and wealth

at a higher rate than that of leading innovator economies (Araújo & Salerno, 2015; Ellis, 2010;

Love & Ganotakis, 2013). Emerging economies have recognized the importance of technology to

increase innovative capacity and, therefore, tried hard to reduce technology gap and stimulate

innovation capacity. To pursue the advancement, a country must be interacting globally to

survive and grow, as it cannot grow in isolation. Participation of a country in the international

economic activities results in global innovation and economic growth (Bosworth, 1984; De

Mello, 1999; Eaton & Kortum, 1996). Gong & Keller (2003) suggested that FDI and

international trade facilitate the diffusion of technologies and knowledge across the countries,

which makes it potentially valuable as a vehicle of international technology diffusion. FDI is

vital to fund capital projects, enhancing the technologies in the majority of developing countries.

Researchers argued that FDI inflows in the country stimulate the technological change through

the adoption of foreign technologies, skills, necessary capitals to enhance the productivity level

(Bekhet & Mugableh, 2013; Fedderke & Romm, 2006; Singhania & Gupta, 2011). Policymakers

believed that FDI is an essential tool to boost the economic development in the developing

economies. Empirical results show that international economic activities like international export

and FDI enhance the national innovation capacity of the country (Wu, Ma, & Zhuo, 2017).

OECD states that innovation increases with strong FDI, as for knowledge diffusion inward FDI

is an important channel (OCED, 2005). Coe & Helpman (1995), identified the role of FDI to

transfer technologies, which as a resultant increasing the total factor productivity (TFP). One of

the primary motives of the developing economies to attract FDI is to obtain advanced

technologies from developed countries to enhance their domestic innovation capacity (Cheung &

Ping, 2004). FDI enhances national innovation capacity in developing countries and to some

extent answers the question that how latecomers countries close the gap in their national

innovation capacity with the more developed countries (Wu, Ma, & Zhuo, 2017).

Among other factors, peace and stability are critical to attracting capital inflow in the country.

The behavior of foreign investors is difficult to predict, as it includes various factors such as

prior experience, wisdom, perception to take the risk, and, tolerance about political and

economic risks. Terrorism creates fear, panic and economic uncertainty in the country and thus

increases the country-specific risk at a certain level, which may induce the investor perception to

invest in lower risk country as compared to high-risk country (Shahzad, Zakaria, Rehman,

Ahmed, & Fida, 2016). These diversified options divert the attention of foreign investors to

invest less in highly risky countries and thus find refuge through investment in low terrorism

prone countries (Enders, Sachsida, & Sandler, 2006; Enders & Sandler, 1996). Terrorist

activities discourage the FDI in the host country due to its potential risk. According to Shahbaz,

Shabbir, Malik, & Wolters (2013) terrorist threats not only decrease the public investment but

also lower the FDI in the host country. Abadie & Gardeazabal (2008) concluded that there is a

significant decrease in FDI in a country due to terrorist risk. Terrorism is negatively associated

with financial markets, decreasing the flow of investments (Abadie & Gardeazabal, 2003).

Enders & Sandler (1996) examined terrorism impact on net FDI in Spain and Greece by using

VAR analysis, and they found that net FDI decreased by 13.5% and 11.9% respectively in these

countries due to terrorism. Lutz & Lutz (2006) studied the impact of terrorism on FDI in 23 Latin

American countries and found the significant and negative impact of terrorism on FDI. Terrorism

generates uncertainty and risks in the society that adversely affects both domestic and foreign

investment and, thereafter-economic activities (Wagner, 2006). Further, empirical results show

that terrorism decreases the FDI when potential investors have diversified options for their

investment (Agrawal, 2011; Blomberg & Mody, 2005).

Foreign Aid

Over the past decades, emerging economies received an enormous amount of foreign aid from

developed economies to accelerate their economic growth, but the link between aid and

economic growth is controversial in the literature. Countries with adequate policies utilize

foreign aid and resultantly achieve economic growth. After the war, South Korea received an

ample amount of foreign aid from western countries. The commitment of the South Korean

government to effectively utilize foreign aid for their economic development is one of the

excellent success stories. According to Kim (2011), the South Korean government showed a high

commitment and utilized foreign aid efficiently by investing in mega projects to cope with

national challenges. Taiwan is a regional success story of effectively utilizing foreign aid in the

1960s to boost their economic development (Bräutigam & Knack, 2004). Similarly, many other

studies also confirm this positive association between foreign aid and economic development.

For example, Lloyd, Morrissey, & Osei (2001) and Gounder (2001) found a positive association

between aid and economic growth in Ghana and Fiji respectively. During a study of five African

countries, Irandoust & Ericsson (2005) found a positive association between domestic saving,

economic growth and foreign aid in all the sample countries. Later on, Feeny (2007) found a

positive link between aid and economic growth in Melanesia and Sharma & Bhattarai (2013)

found a positive link between aid and growth in Nepal, one of the highest recipient countries of

foreign aid in the developing economies. In their recent work in Sierra Leone, Kargbo & Sen

(2014) reported the evidence that foreign aid contributed positively to the economic growth.

Dalgaard, Hansen, & Tarp (2004) studied the association between aid and productivity both

theoretically and empirically and found that foreign aid stimulated the growth. In a sample of 40

African Union member states, Loxley & Sackey (2008) supported statistically significant and

positive impact of foreign aid on economic growth in the sampled countries. Although foreign

aid contributes positively in sampled countries, they emphasized to formulate strategies to reduce

the future dependence on foreign aid. In five south Asian economies, Asteriou (2009) studied the

long-run association between foreign and GDP growth and found a positive relationship between

them. Later on, Mekasha & Tarp (2013) conducted a meta-analysis from the year 1970 to 2004

by using 68 published and unpublished studies related to foreign aid and growth, and found

statistically significant and positive relationship. In a sample of 36 sub-Saharan African countries

from the mid-1960s to 2007, Juselius, Møller, & Tarp (2014) investigated the relationship

between foreign aid and key macroeconomic variables and found the positive long-run impact of

aid on macroeconomic variables. Nwaogu & Ryan (2015) investigated the relationship between

aid, FDI, and remittance with economic growth in 34 Latin American & Caribbean and 53

African countries and concluded that foreign aid positively linked with growth in the regions.

Empirical evidence also supports the fact that foreign aid can be useful in the recipient country

only based on some fundamental conditions, including financial liberalization and sound

macroeconomic policies, institutional quality, governance, and democracy. According to the

WorldBank (1998), an assessment study on foreign aid reported that it contributes positively to

economic growth in recipient economies that pursued the sound monetary, fiscal and trade

policies. Likewise, many researchers argued that foreign aid could stimulate the economic

growth only in those countries that have sound policy environment (Bhattarai, 2009; Burnside &

Dollar, 2000; Collier & Dollar, 2002). While others reported that foreign aid positively

contributes to growth with the condition of financial liberalization (Ang, 2010; Nkusu & Sayek,

2004) and level of democracy (Islam, 2003; Svensson, 1999).

On the other hand, empirical literature shows that many other countries failed to utilize foreign

aid efficiently and did not achieved great success. For example, Ali & Isse (2005) reported a

negative association between aid and economic growth, but a positive link could be identified

with economic growth after the interaction of foreign aid with policy. They further suggested

that properly structured policies stimulated the effectiveness of aid on economic growth. Rajan &

Subramanian (2008) found a negative link between aid and economic growth; they suggested

that foreign aid cannot boost economic growth even with the proper policy-oriented

environment. In Southeast Asian Countries, Burke & Ahmadi-Esfahani (2006) found that aid had

no connection with growth in these countries. By using ARDL bound testing approach in case of

Pakistan, Khan & Ahmed (2007) found a negative link between aid and growth. Several other

studies also reported a negative link between aid and economic growth (Ang, 2010; Mallik,

2008; Ovaska, 2003; Svensson, Aid, growth and democracy, 1999). In a sample of 32 sub-

Saharan African countries, Bräutigam & Knack (2004) investigated the link between foreign aid,

governance and institutions and reported that foreign aid worsened the governance situation.

Further, they reported a negative relationship between foreign aid and tax share to GDP and

dependency on foreign aid for a long run weakened the national institutions. More recently,

Sarwar, Hassan, & Mahmood (2015) investigated the link between foreign aid and governance in

Pakistan and found a negative relationship; they further reported that foreign aid had a

deterioration effect on economic and political institutions. Young & Sheehan (2014) reported the

detrimental impact of foreign aid on openness to international trade, property rights, and legal

system in the recipient country. Other studies reported foreign aid increased rent-seeking

activities and size of government that benefited only a few (Boone, 1996; Svensson, 2000).

Military Expenditure

Military expenditure is a vital component of a country’s budget, but its relationship with growth

is still controversial. Several channels define the impacts of military expenditure upon the

economic growth of the country. According to Alptekin & Levine (2012), results of different

channels vary, and therefore the net effect is ambiguous. Empirical findings have not produced

conclusive results and conclusions about positive, negative or insignificant impacts. It is not easy

to conclude a single framework that alone gives definitive answers. Benoit (1973; 1978) was the

first who concluded that military expenditures enhance the growth rate in the developing

countries. Before 1995, economists had not analyzed military expenditures in developing

countries as an important economic phenomenon. After that researchers concluded different

results but the consensus was not reached. According to the Deger & Sen (1995), military

expenditures were provoked in the developing countries by the need of security (both external

and internal) and therefore demands of military expenditures eventually came from perceived

security risks. They further stated that military expenditure arised due to various types of threats

(internal and external) and directed losses due to these threats. Military expenditures are often a

direct response to various types of security challenges and threats (both internal and external)

because these threats pose a direct challenge to the authority of government. It is necessary to

defend the legitimacy of the country and government against the threats posed by internal

insurrections and external conflicts. The government(s) may have to decide how much military

expenditure budget they have to allot in response to these threats. Security allocations take place

within strict economic, financial and budgetary constraints Deger & Sen (1995). Attempts to

give too much to the military expenditures will retard the development of a country while too

little allocation may allow threats (internal and external) to grow and produce instability and

conflicts debilitating the growth of a country.

Military expenditures increase / decrease economic growth

Sufficient evidence shows that military expenditure increases or decreases the economic growth

of a country. Researchers and economists over the time have analyzed the causes and

consequences of military expenditures on economic growth by using time series and cross-

sectional data. After Benoit’s work, many researchers confirmed the positive relationship

between military expenditure and economic growth (Narayan & Singh, 2007; Shieh, Lai, &

Chang, 2002; Tiwari & Shahbaz, 2013; Wijeweera & Webb, 2009; Yildirim, Sezgin, & Öcal,

2005). Contrastingly, some other researchers found negative relationship between military

expenditures and economic growth (Gerace, 2002; Lai, Shieh, & Chang, 2002; Shahbaz, Afza, &

Shabbir, 2013; Shahbaz & Shabbir, 2012).

According to Üçler (2016), there are only a few studies that discussed the relationship between

military expenditure and investment. Military expenditure has a positive impact on economic

activities, e.g. production (Şimşek, 2003) and military expenditure creates demand, which

increases utilization capacity and resultantly maximizes the output level (Üçler, 2016). As a

result, growth and investment increase in the form of capital gain (Looney, 1994). We argue that

the relationship between military expenditure and economic growth varies across regions and

countries. However, the relationship and elasticity between these two depends upon the

economic, cultural, political, historical and institutional structures of a country.

Military expenditures increase governance and law &order

Military expenditures are budgeted as an integral part of a national budget to protect the

sovereignty and legitimacy of the country against internal and external threats. Military

expenditures for the law enforcement are used to counter the terrorism. According to Murray

(2005), the law enforcement agencies serve as the last line of defense in the war against

terrorism. The core responsibility of the law enforcement agencies is to maintain the law & order

situation in the country for political stability of the government that increases the confidence of

business sectors in the country and attracts foreign investor to invest in the country. FDI plays a

critical role to provide accesses to new markets, latest technologies, products, cheaper resources,

skills and financing (Salim, Razavi, & Afshari-Mofrad, 2017). FDI is a vital source to

bring technology in the developing countries and thus help them to increase

their innovation capacity. Meanwhile, empirical results show that international economic

activities like international export and FDI will enhance the national innovation capacity of the

country (Wu, Ma, & Zhuo, 2017). Khan (2007) concluded that in developing

countries the role of FDI had been widely recognized as growth enhancing

factor. While investing abroad, foreign investors are concerned not only

about the essential features of the host country such as infrastructure, factor

price, natural resources, financial capability for payment of debt and

financial development, but also about the assurance of secure return on

foreign investment, which leads their concerns towards terrorism (Enders &

Sandler, 1996), political uncertainty (Li, 2006; Nigh, 1985) and armed

conflicts (Lee, 2016). According to Maizels & Nissanke (1987), foreign

investors think that the host country will have high security through military

expenditure and thus protect their investment and this gives a positive

signal to the investor to invest in the host country. Several others studies

also report that military expenditure boosts the confidence of business

sector in conflicting countries which results in economic growth and

investment (Barro & Sala-i-Martin, 2004; Dunne, Smith, & Willenbockel,

2005; Kennedy, 1974; Whynes, 1979).

3. Data, variables and Methodology

3.1. Material and model

This study examines the impacts of terrorism and foreign aid on innovation capacity in an

emerging economy, i.e. Pakistan. Military expenditure and GDP per capita are also included in

the empirical model. We conduct a study from 1986 to 2016. The variables are selected for two

reasons. First, Pakistan has a long history of terrorism, but there was a sudden hike of terrorism

in Pakistan especially after the 911 attack in the United States. Resultantly, Pakistan has been

suffering massive collateral damages, so it serves as a best laboratory for the study of impacts of

terrorism on innovation capacity. Second, Pakistan has received a substantial amount of foreign

aid, but its social and economic indicators are not encouraging. Third, there was an increase in

military expenditures against terrorism. Data related to terrorism are taken from Global

Terrorism Database (GTD), an open source terrorist events database around the globe from

1970-2016. The GTD is based on open media reports and continues to be updated when new

events occur and new information becomes available. Data related to GDP per capita and

military expenditures as percentages of GDP are taken from the World Bank. Data related to

foreign aid (Net Foreign Development Assistance) obtained from WorldBank development

indicators. The number of patents is our dependent variable, a proxy to measure the innovation

accessed by United States Patent & Trademark Office (USPTO). Patents are widely used in the

literature to measure the innovation capacity and contain rich information about creative

activities, so patents statistics are widely used to measure and analyze the innovation. Patents are

treated as a unit of innovation (Egan, 2012). According to Grupp (1998), patents are considered

as a most important indicator to measure the output of technology-oriented invention/innovation

processes. Similarly, many other authors also consider patents are an indicator of innovation

(Griliches, 1990; Nagaoka, Motohashi, & Goto, 2010). Innovation is measured in terms of

patents granted. The relationship between foreign aid, terrorism, military expenditure, GDP per

capita and patents in the multivariate model can be expressed in the following empirical

Equation 1:

INNOVATION t=∅ 0+∅ 1 LTSM t+∅ 2 LAIDt+∅ 3 MEGDPt+∅ 4 LGDPPC t+εt (1)

Where INNOVATION is the number of patents granted by the United States Patents &

Trademark Office (USPTO); LTSM measures terrorism; LAID is the foreign aid; MEGDP is the

military expenditure as % of GDP; and LGDPPC is GDP per capita.

3.2. Methodology

3.2.1. Unit root test for stationarity

Before the ARDL approach to cointegration, our first step is to examine the unit root properties

of the variables. Macroeconomics time series may include unit roots, and unit roots, i.e. non-

stationarity regressors, violate the standard assumptions in many econometric models. If the

variables are not stationary, the unit root test (to check the stationarity of variables) is the usual

practice today. Therefore, it is imperative to check the stationarity of the variables, i.e. to

establish the integration order of each variable. For the cointegration analysis, all the variables

must integrate on a higher order. Commonly used unit root tests are Augmented Dickey-Fuller

(ADF) test (Dickey & Fuller, 1979), Phillips and Perron (PP) test (Phillips & Perron, 1988) and

DF-GLS (Elliott, Rothenberg, & Stock, 1996). This study uses the Phillips & Perron (1988) to

find the unit roots and check whether stationarity exits at the level or their first difference. The

null hypothesis of the test is non-stationary against the alternative hypothesis of stationary.

3.2.2. ARDL approach to cointegration

Auto-regressive distributive lag (ARDL) approach is to investigate the long-run association

among variables. ARDL bound testing approach to cointegration was developed by Pesaran &

Pesaran (1997), Pesaran, Shin, & Smith (2000), and finally by Pesaran, Shin, & Smith (2001).

This approach of cointegration has several advantages over traditional approaches: Phillips &

Hansen (1990), Johansen & Juselius (1990) and Engle & Granger (1987). For example,

traditional approaches to cointegration require that the variables included in the model be

integrated at a unique order. ARDL approach to cointegration can be applied even if the

variables have integrated at a different order of integration: a mixed order of integration, i.e.

I(1)/I(1) or I(1)/I(0). However, the underlying assumption of such traditional approaches to

cointegration as E.G. cointegration, Johansen cointegration, and fully modified ordinary least

square (FMOLS) is that all variables should integrate in the same order. Another essential

advantage of ARDL bound testing approach to cointegration is that it provides reliable results for

both short-run and long-run relationship at the same time and is suitable for small data.

Laurenceson & Chai (2003) noted that in ARDL approach to cointegration, an unrestricted

model of Error Correction Model (ECM) takes suitable lags that enable it to capture the data

generating process from general to specific framework of the specification. Besides, according to

Banerjee & Newman (1993), by using ARDL model dynamic ECM can derive through simple

linear transformation. This error correction model puts together the short-run dynamics along

with long-run equilibrium and does not lose the information in the long-run. ARDL approach

estimates the long-run association of variables in the form of below mentioned un-restricted error

correction model (UECM) in Equation (2):

∆ INNOVATION t=i0+∑i=1

l

ji ∆ INNOVATION t−i+∑i=1

l

k i ∆ LTSM t−i+∑i=1

l

li ∆ LAIDt−i+∑i=1

l

mi ∆ MEGDPt−i+∑i=1

l

ni ∆ LGDPPC t−i+α 1 INNOVATION t−1+α 2 LTSM t−1+α3 LAIDt−1+α 4 MEGDPt−1+α5 LGDPPC t−1+εt ,

(2)

Where Δ represents the first difference operator and i0 as constant. ji, k i,li, mi, ni respectively

represent the short-run coefficients of INNOVATION, LTSM, LAID, MEGDP, and LGDPPC. ε t

denotes the error term. F-test has been conducted to ascertain the long-run relationship in

Equation (2) Null hypothesis of this test involves no cointegration with a zero-joint restriction on

αs in ECM as H0: α1 = α2 = α3 = α4 = α5 = 0.

ARDL bound testing approach to cointegration Pesaran, Shin, & Smith (2001) and computed F-

statistics considering the null hypothesis (H0: α1 = α2 = α3 = α4 = α5=0) are given by Equation

(2). Asymptotic distributions of test statistics are non-standard. Variables are integrated into the

form of I(0) or I(1). These are also known as assumptions of ARDL approach to cointegration.

Pesaran, Shin, & Smith (2001) computed two sets of asymptotic critical values (C.V). First set

has the assumption that variables integrate as I(0) and the second set assumes the variables

integrate as I(1). These values are called lower critical bound (LCB) and upper critical bound

(UCB) respectively. The decision about cointegration is made as follows. If calculated value of

the F-statistics is higher than UCB, the null hypothesis with the assumption of no cointegration is

rejected, and we conclude that dependent variable and its regressors cointegrate for a long-run

association. However, if calculated value of F-statistics is less than LCB, the null hypothesis

with the assumption of no cointegration among variables cannot be rejected. If calculated value

of F-statistics falls between UCB and LCB, the decision regarding cointegration among variables

is undecided. Pesaran, Shin, & Smith (2001) generated the critical bounds, which might be

proper for a small sample. After the estimating of the long-run association between variables, the

error correction model (ECM) is valid to calculate the short-run relationship and error correction

term (ECT) for empirical model. Therefore, the ECM can be denoted as follows in Equation (3):

∆ INNOVATION t=Ω0+∑i=1

l

Ω1 i ∆ INNOVATION t−i+∑i=1

l

Ω2 i ∆ LTSM t−i+∑i=1

l

Ω3 i ∆ LAIDt−i+∑i=1

l

Ω4 i ∆ MEGDPt−i+∑i=1

l

Ω5 i ∆ LGDPPC t −i+ϕ ECT t−1+εt ,

(3)

Where Δ represents the first difference operator. Ω2, Ω3, Ω4, and Ω5 indicate the short-run

coefficient of LTSM, LAID, LME, and LGDPPC respectively. Ω0 is the constant. Error

correction term i.e. ECT t−1shows the speed of adjustment for the long-run equilibrium.

3.2.3 Johansen Cointegration

Johansen cointegration test also allows examining the long-run association among variables.

Johansen cointegration developed by Johansen & Juselius (1990) is applied to investigate the

long-run association among variables under consideration. The underlying assumption to apply

the Johansen cointegration is that all the variables under consideration are stationary at first

difference, i.e. I(1). After the affirmation of the cointegration equation through trace test and

maximum Eigenvalue test, a vector error correction model (VECM) is applied to investigate the

long-run association among variables. A negative sign of error correction model (ECM) indicates

the presence of long-run relationship and the value of ECM ranges from 0 to 1 which shows how

much adjustment takes place every year.

4. Empirical results and discussion

Descriptive statistics are reported in Table 2.

Table 2Descriptive statisticsMean 3.774 5.927 21.156 5.040 6.459Median 2.000 5.717 21.083 4.390 6.235Maximum 16.000 7.963 22.044 7.598 7.274Minimum 0.000 4.025 20.375 3.265 5.813Std. dev. 4.716 1.209 0.496 1.558 0.482Skewness 1.534 0.290 0.242 0.367 0.426Kurtosis 4.165 1.738 1.933 1.502 1.683

Before the cointegration, the unit root test is performed to diagnose the nature of dataset. The

unit root properties of such variables as patents (INNOVATION), terrorism (LTSM), foreign aid

(LAID), military expenditure (MEGDP), and GDP per capita (LGDPPC) are investigated by

Philips and Pearson unit root test. Table 3 reported the results of unit root test which show that

all the variables become stationary at their first difference, i.e. I(1). It is, therefore, apt to apply

the ARDL approach to investigate the long and short-run relationship among variables.

Table 3

Unit Root Test

Level 1st differenceVariables Test statistics P-value Test statistics P-value Patents granted (INNOVATION) 0.112 0.961 -6.989 0.000***

Terrorism (LTSM) -1.689 0.427 -6.047 0.000***

Foreign aid (LAID) -2.125 0.237 13.603 0.000***

Military expenditure (MEGDP) -1.273 0.629 -4.895 0.001***

GDP per capita (LGDPPC) 0.347 0.977 -4.938 0.000***

Note: *, ** and *** denotes significant level at 10%, 5% and 1%

In time series analysis, selection of appropriate lag is imperative before the ARDL approach to

cointegration. Statistical analysis is applied to find out the optimal lag length of VAR. The

models are selected through Schwarz information criterion (SC) criteria for the period 1986-

2016.

After selection of appropriate lag length, the ARDL approach is applied to find out the long-run

relationships. Results of calculated F-Statistics for the model are reported in Table 4. Value of

calculated F-statistics (4.048) is higher than the UCB (3.87), significant at 2.5% level for the

period of 1986-2016. Hence, it is evident that there is a long-run association between patents

(INNOVATION) and all the regressors (LAID, LTSM, MEGDP, LGDPPC) in both periods.

Table 4

Results of bound testing for ARDL model

Estimated Model INNOVATION = f(LTSM,LAID,MEGDP, LGDPPC)

Test Statistics Value F-Statistics 4.048

Critical bounds valueLevel of Significance I(0) bound I(1) bound

10% Level 2.2 3.095% Level 2.56 3.49

2.5% Level 2.88 3.871% Level 3.29 4.37

Based on the bound test, LTSM, LAID, MEGDP and LGDPPC appear as long-run variables

impacting upon INNOVATION. The ECT reflects the long-run association of the variables

Table 6. The ECT of period 1986-2016 is ϕ = -0.578248 and the significance is at 1% level. It

shows a stable long-run association between INNOVATION and its regressors (LTSM, LAID,

MEGDP, LGDPPC), meaning that INNOVATION and its regressors are co-moving. The ECT

coefficient (ϕ) implies comparatively quick adjustment process to reinstate the equilibrium in the

model following a disturbance. Likewise, we can say that the magnitude of ECT for Equation (3)

shows 58 % adjustment of deviation is found from the long-run equilibrium to the short-run in

the sample period of the study.

Table 5

Long-run estimates - Impacts of terrorism, foreign aid, military expenditure, GDP per capita on

innovation capacity

Variable name Coefficient Standard Error T - Statistic prob.

LTSM -2.190103 1.336956 -1.638127 0.114LAID -5.264653 2.830072 -1.860254 0.075*

MEGDP 2.877786 1.177663 2.443642 0.022**

LGDPPC 26.436679 7.062527 3.743232 0.001***

C -56.800084 46.099655 -1.232115 0.229Note: *, ** and *** denotes significant level at 10%, 5% and 1%

Table 6Short-run estimates - Impacts of terrorism, aid, military expenditure, GDP per capita on innovation capacity

Variable name Coefficient Standard Error T - Statistic prob.

LTSM -1.266423 0.786795 -1.609598 0.120LAID -3.044276 1.366775 -2.227342 0.035**

MEGDP 1.664075 0.687437 2.420693 0.023**

LGDPPC 15.286964 4.117625 3.712569 0.001***

CointEq(-1) -0.578248 0.159506 -3.625247 0.001***


The long-run and short-run results for the period 1986-2016 are reported in Table 5 and 6

respectively. The foreign aid is negatively associated with the innovation capacity in Pakistan in

both the short and the long run and the statistical significances are at 5% and 10% levels

respectively. The coefficient of foreign aid reveals that 1% increase in the ineffective use of

foreign aid causes the innovation capacity to decrease by 3.04% in the short run and 5.26% in the

long run. Terrorism is found to be negatively associated with the innovation capacity in both the

short and the long run but the associations are statistically insignificant (p > 0.1). The military

expenditures are found to have a positive relationship with the innovation capacity. The

coefficient of military expenditure (p < 0.05) reveals that a 1% increase in military expenditure

causes the innovation capacity to increase by 1.66% in the short run and 2.88% in the long run.

The coefficient of GDP per capita reveals that a 1% increase in GDP per capita causes the

innovation capacity to increase by 15% in the short run and 26% in the long run, implying that

the GDP per capita significantly contributes to the innovation capacity in Pakistan.

Table 7 reports the diagnostic test. The CUSUM test is applied to check that empirical model is

well defined. The stability is examined through the CUSUM test proposed by Brown, Durbin, &

Evans (1975) and the plotted stability is showed in Figure 4. According to Abbas & Awan

(2017), the CUSUM square uses the square of recursive residuals with a similar process as

CUSUM test. Our finding indicates that the line stays within a 5% level of significance, which

assures the stability of the model.

Figure 4: CUSUM

Table 7Diagnostic testsINNOVATION = f(LTSM,LAID,LME,LGDPPC)R-square 0.834Adj. R. squared 0.800DW 2.235F-Statistics 24.284(0.000)JB - normality test 0.554 (0.757)LM test (Breusch–Godfrey serial correlation) 1.196 (0.245)Heteroskedasticity TestsBreusch—Pagan–Godfrey 2.256 ( 0.087)ARCH 0.405 ( 0.512)White 1.111 ( 0.376)CUSUM *

Note: P values reported in parenthesis – (*) Shows that line stays within 5% level of significance

Johansen Cointegration Test – Results

Johansen cointegration test is also applied from 1986-2016 to investigate the long-run impacts of

aid, terrorism, military expenditure, and GDP per capita upon innovation capacity. Table 3

reports the results of stationarity test. It is evident from the table that a unit root exits in the

series at their level, which implies that the null hypothesis with the assumption of non-stationary

is not rejected. After the first difference of the variables, however, the null hypothesis is rejected

in favor of the alternative hypothesis, which implies that all the variables are integrated at their

first difference I (1).

After the analysis of the fundamental characteristics of time series, our next step is to analyze

whether a long-run association exists among variables. Results of the trace test and maximum

Eigenvalue test are reported in Table 8. According to the trace test results, there is one

cointegration equation, which implies that long-run association exists among variables.

Similarly, according to the maximum eigenvalue test, there is one cointegration equation at 5%

level of significance, which means that there exit long-run impacts of terrorism, foreign aid,

military expenditure, and GDP per capita upon innovation.

Table 8Results of Johansen cointegration.

No. of Coin. Equ (s) Trace test Eigen value test

Trace statistic

0.05 c.v prob.Y Max-eigen statistic

0.05 c.v prob.Y

None X 85.45088 69.81889 0.001 44.26980 33.87687 0.002At most 1 41.18108 47.85613 0.183 26.82788 27.58434 0.062At most 2 14.35320 29.79707 0.820 9.125953 21.13162 0.822At most 3 5.227247 15.49471 0.784 4.665947 14.26460 0.783At most 4 0.561300 3.841466 0.453 0.561300 3.841466 0.453

(i) Trace test (1 cointegrating eqn(s) at the 0.05 level)

(ii) Max-Eigenvalue test (1 cointegrating eqn(s) at the 0.05 level)

(iii) X - Denotes rejection of the null hypothesis at the 0.05 level

(iv) Y- MacKinnon-Haug-Michelis (1999) p-values

(v). C.V. (Critical value)

Vector error correction model (VECM)

Evidence from the trace test and maximum eigenvalue indicates that there are long-run

associations among variables. To ascertain the long-run relationship among variables, the vector

error correction model (VECM) is applied. The results of VECM further confirm the existence of

a long-run relationship among variables. The value of ECM is (-0.025971) with t-value of (-

2.05115) and significance at 5% level, which further confirms the long-run association among

variables.

Pairwise Granger Causality Test

Results of pairwise Granger causality test for the two periods are reported in Table 9.

Table 9Pairwise causality test.Hypothesis F-stat P-value Level of Sig. Direction of Causality1986-2016LTSM ≠ INNOVATION 1.600 0.222 Insignificant

NeutralINNOVATION ≠ LTSM 0.012 0.987 Insignificant

LAID INNOVATION 5.011 0.015 0.01Bidirectional

INNOVATION LAID 5.622 0.009 0.01

MEGDP ≠ INNOVATION 0.939 0.404 InsignificantNeutral

INNOVATION ≠ MEGDP 0.226 0.799 Insignificant

LGDPPC INNOVATION 4.172 0.027 0.05Unidirectional

INNOVATION LGDPPC 1.398 0.266 Insignificant

LAID LTSM 4.192 0.027 0.05Unidirectional

LTSM LAID 1.335 0.282 Insignificant

MEGDP ≠ LTSM 1.014 0.377 InsignificantNo Causality

LTSM ≠ MEGDP 0.118 0.888 Insignificant

LGDPPC LTSM 3.041 0.066 0.10Bidirectional

LTSM LGDPPC 2.975 0.070 0.10

MEGDP LAID 2.691 0.088 0.10Bidirectional

LAID MEGDP 3.303 0.054 0.05

LGDPPC LAID 6.634 0.005 0.01Unidirectional

LAID LGDPPC 1.693 0.205 Insignificant

LGDPPC MEGDP 0.239 0.788 InsignificantUnidirectional

MEGDP LGDPPC 3.580 0.043 0.05

Further, to reaffirm the empirical results that were acquired by the ARDL approach, Johansen

cointegration and Granger causality, OLS method is also applied. Results of OLS regression

model reported in Table 10.

According to ordinary least square results, considering the coefficients with regarding to t-

statistics and significance, it is possible to explain that terrorism and foreign aid have a negative

effect on innovation capacity while military expenditures and GDP per capita have positive

effect on innovation capacity in both the periods.

Table 10OLS estimation results.

1986-2016Variable name Coefficient Standard Error T - Statistic prob.

C -72.12660 28.35173 -2.543993 0.017**

LTSM -1.853639 0.805668 -2.300747 0.029**

LAID -2.894983 1.490402 -1.942417 0.063*

MEGDP 2.333645 0.684165 3.410938 0.002***

LGDPPC 21.11165 3.773188 5.595175 0.000***


5. Conclusion and recommendations

Such emerging economies as Pakistan have received a considerable amount of aid, but the

trickle-down effects have not been found in its economic situation. Our study is to investigate

whether the foreign aid, terrorism, and military expenditure have an impact on the innovation

capacity of such an emerging economy as Pakistan. Our unique effort fills the gap in the

literature and offers new insight for future research and debate. This study uses the available data

for the periods 1986-2016 and incorporates such most critical variables as foreign aid, terrorism,

military expenditure and patents with particular reference to Pakistan. We estimate the empirical

model using a time series data of Pakistan and calculate both the short-run and long-run results.

By using ARDL bound testing cointegration approach and Johansen cointegration test, we find

that the foreign aid and terrorism have negative relationships with the innovation capacity in

Pakistan while the military expenditures and the GDP per capita have significant positive

relations with the innovation capacity in Pakistan in both the long run and the short run. Despite

the fact that terrorism hits more brutally to Pakistan, but its impact is insignificant. The foreign

aid is founded to have a more deteriorated impact on the innovation capacity in Pakistan. This

study calculates the pairwise Granger causality result. According to econometric analysis applied

in this study (ARDL approach to cointegration, Johansen cointegration test, Granger Causality

test and OLS), there is negative relationship between terrorism, foreign aid and innovation.

While military expenditure and GDP per capita positively associated with innovation capacity.

This study has policy implications for the Pakistani government and other similar terrorism

prone countries and their law enforcement agencies. Decreasing the terrorism may be the first

and pivotal step to boost economic activities in all the sectors of the economy in terrorism prone

countries. Better surveillance and intelligence could prompt a sharp decline in the terrorist

activities, which consequently bring peace, stability, improve the security situation, and build

investor confidence.

Moreover, the negative impact of foreign aid on the innovation capacity in Pakistan has several

implications for Pakistan and other developing countries. It is highly recommended that the

government and policymakers should take appropriate steps to formulate the policies for

effective use of foreign aid to nourish growth. Because of the adverse effect of aid on innovation

capacity might be due to weak economic policies and ineffective use of foreign aid. One other

possible reason for the underperformance is its heavy dependency on foreign aid, as a country

might show less commitment to promoting the investment activities when it relies too much on

foreign aid. Similarly, it makes less effort to attract the FDI inflows, a critical source of

technology transfer from developed economies to developing economies to enhance the

innovation capacity. The ineffective use of foreign aid in Pakistan and other developing countries

might also weaken governance system in the country and increase rent seeking activities. To

enhance the growth and innovation capacity in the country over the long run, FDI must be

elevated and appropriate institutional policies for proper utilization of foreign aid and good

governance in the country become a prerequisite. Hence, policymakers in Pakistan and other

emerging economies should develop attractive policies, foster sound macroeconomic

environment and build an appropriate legal structure to stimulate both foreign and domestic

investment to attain better innovation capacity. It is also advised that too much dependency on

foreign aid over an extended period may not be a good solution to boost economic growth.

References

Abadie, A. (2006). Poverty, political freedom, and the roots of terrorism. American Economic Review, 96(2), 50-56.

Abadie, A., & Gardeazabal, J. (2008). Terrorism and the World Economy. European Economic Review, 52, 1-27.

Abadie, A., & Gardeazabal, J. (2003). The Economic Costs of Conflict: A Case Study of the Basque Country. American Economic Review, 93, 113-132.

Abbas, F., & Awan, H. S. (2017). What Determines Health Status of Population in Pakistan? Social Indicators Research, 1-23.

Abramovitz, M. (1956). Resource and output trends in the United States since 1870. The American Economic Review, 46(2), 5–23.

Agrawal, S. (2011). The impact of Terrorism on Foreign Direct Investment: which sectors are more vulnerable?

Aisha, I., & Shehla, A. (2014). Determinants of terrorism in Pakistan: An empirical investigation. Economic Modelling, 37, 320–331.

Ali, A. (2010). Economic cost of terrorism: A case study of Pakistan. Strategic Studies, 30, 1-2.

Ali, A. M., & Isse, H. S. (2005). An empirical analysis of the effect of aid on growth. International Advances in Economic Research, 11(1), 1-11.

Alptekin, A., & Levine, P. (2012). Military expenditure and economic growth: A meta-analysis. European Journal of Political Economy, 28(4), 636-650.

Ang, J. B. (2010). Does foreign aid promote growth? Exploring the role of financial liberalization. Review of Development Economics, 14(2), 197-212.

Anwar, M., & Michaelowa, K. (2006). The political economy of US aid to Pakistan. Review of Development Economics, 10(2), 195-209.

Araújo, B. C., & Salerno, M. S. (2015). Technological strategies and learning-by-exporting: The case of Brazilian manufacturing firms, 2006–2008. International Business Review, 24(5), 725-738.

Aslam, F., & Kang, H.-G. (2015). How Different Terrorist Attacks Affect Stock Markets. Defence and Peace Economics, 26(6), 634-648.

Asteriou, D. (2009). Foreign aid and economic growth: New evidence from a panel data approach for five South Asian countries. Journal of policy modeling, 31(1), 155-161.

Banerjee, A. V., & Newman, A. F. (1993). Occupational choice and the process of development. Journal of political economy, 101(2), 274-298.

Barro, R. J., & Sala-i-Martin, X. (2004). EconomicGrowth. 2nd ed. Cambridge, MA: The MIT Press.

BBC. (2018). Pakistan profile - Timeline. Retrieved May 16, 2018, from https://www.bbc.com/news/world-south-asia-12966786

Bekhet, H. A., & Mugableh, M. I. (2013). Examining the equilibrium relationships between foreign direct investment inflows and employment in manufacturing and services sectors: evidence from Malaysia. Journal of Social and Development Sciences, 4(1), 32-38.

Benoit, E. (1973). Defense and economic growth in developing countries.

Benoit, E. (1978). Growth and defense in developing countries. Economic development and cultural change, 26(2), 271-280.

Bhattarai, B. P. (2009). Foreign aid and growth in Nepal: an empirical analysis. The Journal of Developing Areas, 42(2), 283-302.

Blomberg, S. B., & Mody, A. (2005). How severely does violence deter international investment?

Blomberg, S. B., Hess, G. D., & Orphanides, A. (2004). The macroeconomic consequences of terrorism. Journal of Monetary Economics, 51(5), 1007–1032.

Boone, P. (1996). Politics and the effectiveness of foreign aid. European economic review, 40(2), 289-329.

Bosworth, D. L. (1984). Foreign patent flows to and from the United Kingdom. Research Policy, 13(2), 115–124.

Bräutigam, D. A., & Knack, S. (2004). Foreign aid, institutions, and governance in sub-Saharan Africa. Economic development and cultural change, 52(2), 255-285.

Brown, R. L., Durbin, J., & Evans, J. M. (1975). Techniques for testing the constancy of regression relationships over time. Journal of the Royal Statistical Society. Series B (Methodological), 149-192.

Buckelew, A. H. (1984). Terrorism and the american response. San Rafael, CA: Mira Academic Press.

Buesa, M., Valiño, A., Heijs, J., Baumert, T., & Gómez, J. G. (2007). The Economic Cost of March 11: Measuring the Direct Economic Cost of the Terrorist Attack on March 11, 2004 in Madrid. Terrorism and Political Violence, 19(4), 489-509.

Burke, P. J., & Ahmadi-Esfahani, F. Z. (2006). Aid and growth: A study of South East Asia. Journal of Asian Economics, 17(2), 350-362.

Burnside, C., & Dollar, D. (2000). Aid, policies, and growth. American economic review, 90(4), 847-868.

Cheung, K. Y., & Ping, L. (2004). Spillover effects of FDI on innovation in China: Evidence from the provincial data. China economic review, 15(1), 25-44.

Coe, D. T., & Helpman, E. (1995). International r&d spillovers. European economic review, 39(5), 859-887.

Collier, P. (1999). On the economic consequences of civil war. Oxford economic papers, 51(1), 168-183.

Collier, P., & Dollar, D. (2002). Aid allocation and poverty reduction. European economic review, 46(8), 1475-1500.

Cooley, J. K. (2002). Unholy wars: Afghanistan, America and international terrorism. London: Pluto Press.

Crain, N. V., & Crain, W. M. (2006). Terrorized economies. Public Choice, 128(1-2), 317-349.

Dalgaard, C. J., Hansen, H., & Tarp, F. (2004). On the empirics of foreign aid and growth. The Economic Journal, 114 (496).

De Mello, L. R. (1999). Foreign direct investment-led growth: evidence from time series and panel data. Oxford economic papers, 51(1), 133-151.

Deger, S., & Sen, S. (1995). Military expenditure and developing countries. Handbook of defense economics, 1, 275-307.

Deger, S., & Sen, S. (1983). Military expenditure, spin-off and economic development. Journal of development economics, 13(1-2), 67-83.

Dickey, D. A., & Fuller, W. A. (1979). Distribution of the estimators for autoregressive time series with a unit root. Journal of the American statistical association, 74(366a), 427-431.

Dunne, J. P., Smith, R. P., & Willenbockel, D. (2005). Models of military expenditure and growth: A critical review. Defence and peace economics, 16(6), 449-461.

Eaton, J., & Kortum, S. (2002). Technology, geography, and trade. Econometrica, 70(5), 1741-1779.

Eaton, J., & Kortum, S. (1996). Trade in ideas Patenting and productivity in the OECD. Journal of international Economics, 40(3-4), 251-278.

Eckstein, Z., & Tsiddon, D. (2004). Macroeconomic Consequences of Terror: Theory and the Case of Israel. Journal of Monetary Economics, 51(5), 971-1002.

Egan, E. J. (2012). The economics of patent citations: Startup commercialization strategy, value, and success. University of California, Berkeley.

Elliott, G., Rothenberg, T. J., & Stock, J. H. (1996). Efficient tests for an autoregressive unit root. Econometrica, 64, 813–836.

Ellis, P. D. (2010). International trade intermediaries and the transfer of marketing knowledge in transition economies. International Business Review, 19(1), 16-33.

Enders, W., & Sandler, T. (2002). Patterns of transnational terrorism, 1970–1999: alternative time-series estimates. International Studies Quarterly, 46(2), 145–165.

Enders, W., & Sandler, T. (1996). Terrorism and foreign direct investment in Spain and Greece. Kyklos, 49(3), 331-352.

Enders, W., & Sandler, T. (2006). The Political Economy of Terrorism. New York, NY: Cambridge University Press.

Enders, W., Sachsida, A., & Sandler, T. (2006). The impact of transnational terrorism on US foreign direct investment. Political Research Quarterly, 59(4), 517-531.

Enders, W., Sandler, T., & Parise, G. F. (1992). An econometric analysis of the impact of terrorism on tourism. Kyklos, 45(4), 531-554.

Engle, R. F., & Granger, C. W. (1987). Co-integration and error correction: representation, estimation, and testing. Econometrica: Journal of the Econometric Society, 251-276.

Europol. (2008). EU Terrorism Situation and Trend Report 2008. European Police Office.

Farzanegan, M. R. (2014). Military spending and economic growth: the case of Iran. Defence and Peace Economics, 25(3), 247-269.

Fedderke, J. W., & Romm, A. T. (2006). Growth impact and determinants of foreign direct investment into South Africa, 1956–2003. Economic Modelling, 23(5), 738-760.

Feeny, S. (2007). Impacts of foreign aid to Melanesia. Journal of the Asia Pacific Economy, 12(1), 34-60.

Frey, B. S., Luechinger, S., & Stutzer, A. (2007). Calculating tragedy: Assessing the costs of terrorism. Journal of Economic Surveys, 21(1), 1-24.

Furman, J. L., & Hayes, R. (2004). Catching up or standing still? National innovative productivity among ‘follower’ countries, 1978–1999. Research Policy, 33(9), 1329–1354.

Furman, J. L., Porter, M. E., & Stern, S. (2002). The determinants of national innovative capacity. Research policy, 31(6), 899-933.

Gaibulloev, K., & Sandler, T. (2008). Growth consequences of terrorism in Western Europe. Kyklos, 61(3), 411-424.

Gerace, M. P. (2002). US military expenditures and economic growth: some evidence from spectral methods. Defence and Peace Economics, 13(1), 1-11.

Gong, G., & Keller, W. (2003). Convergence and polarization in global income levels: a review of recent results on the role of international technology diffusion. Research Policy, 32(6), 1055-1079.

Gounder, R. (2001). Aid-growth nexus: empirical evidence from Fiji. Applied Economics, 33(8), 1009-1019.

Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic Literature, 28(4), 1661–1707.

Grupp. (1998). Foundations of the Economics of Innovation—Theory, Measurement and Practice. Cheltenham: EdwardElgar.

Hussain, S. E. (2010). Terrorism in Pakistan: Incident patterns, terrorists’ characteristics, and the impact of terrorist arrests on terrorism.

Irandoust, M., & Ericsson, J. (2005). Foreign aid, domestic savings, and growth in LDCs: An application of likelihood-based panel cointegration. Economic Modelling, 22(4), 616-627.

Islam. (2003). Political regimes and the effects of foreign aid on economic growth. The Journal of Developing Areas, 37(1), 35-53.

Johansen, S., & Juselius, K. (1990). Maximum likelihood estimation and inference on cointegration—with applications to the demand for money. Oxford Bulletin of Economics and statistics, 52(2), 169-210.

Johnston, R. B., & Nedelescu, O. M. (2006). The Impact of Terrorism on Financial Markets. Journal of Financial Crime, 13(1), 7-25.

Juselius, K., Møller, N. F., & Tarp, F. (2014). The Long‐Run Impact of Foreign Aid in 36 African Countries: Insights from Multivariate Time Series Analysis. Oxford Bulletin of Economics and Statistics, 76(2), 153-184.

Kargbo, P. M., & Sen, K. (2014). Aid Categories that Foster Pro‐Poor Growth: The Case of Sierra Leone. African Development Review, 26(2), 416-429.

Keeney, G. L., & Winterfeldt, D. V. (2010). Identifying and Structuring the Objectives of Terrorists. Risk Analysis, 30(12), 1803-1816.

Kennedy, G. (1974). The Military in Third Word. London: Duckworth.

Khan, M. A. (2007). Foreign direct investment and economic growth: the role of domestic financial sector (No. 2007: 18). Pakistan Institute of Development Economics.

Khan, M. A., & Ahmed, A. (2007). Foreign aid—blessing or curse: Evidence from Pakistan. The Pakistan Development Review, 215-240.

Kim, J. (2011). Foreign aid and economic development: The success story of South Korea. Pacific Focus, 26(2), 260-286.

Kollias, C., & Paleologou, S. M. (2010). Growth, investment and military expenditure in the European Union-15. Journal of Economic Studies, 37(2), 228-240.

Kollias, C., & Paleologou, S. M. (2016). Investment, growth, and defense expenditure in the EU15: Revisiting the nexus using SIPRI’s new consistent dataset. The Economics of Peace and Security Journal, 11(2), 28-37.

Lai, C. C., Shieh, J. Y., & Chang, W. Y. (2002). Endogenous growth and defense expenditures: A new explanation of the Benoit hypothesis. Defence and Peace Economics, 13(3), 179-186.

Laurenceson, J., & Chai, J. C. (2003). Financial reform and economic development in China. Edward Elgar Publishing.

Lee, H. (2016). Does armed conflict reduce foreign direct investment in the petroleum sector? Foreign Policy Analysis, 13(1), 188-214.

Li, Q. (2006). Political Violence and Foreign Direct Investment. Research in Global Strategic Management , 12, 231–255.

Lloyd, T., Morrissey, O., & Osei., R. (2001). Aid, Exports andGrowth in Ghana. CREDIT Research Paper No. 01/01 .

Looney, R. E. (1994). The Economics of Third World Defense Expenditures. London: JAI Press.

Love, J. H., & Ganotakis, P. (2013). Learning by exporting: Lessons from high-technology SMEs. International business review, 22(1), 1-17.

Loxley, J., & Sackey, H. A. (2008). Aid effectiveness in Africa. African Development Review, 20(2), 163-199.

Lutz, J. M., & Lutz, B. J. (2006). International terrorism in Latin America: Effects on foreign investment and tourism. The Journal of Social, Political, and Economic Studies, 31(3), 321.

Maizels, A., & Nissanke, M. K. (1987). The causes of military expenditure in developing countries. In S. Deger, & R. West, Defense, security and development (pp. 129-139). London: Frances Pinter.

Malizard, J. (2016). Military expenditure and economic growth in the European Union: Evidence from SIPRI’s extended dataset. The Economics of Peace and Security Journal, 11(2), 38-44.

Mallik, G. (2008). Foreign aid and economic growth: A cointegration analysis of the six poorest African countries. Economic Analysis and Policy, 38(2), 251-260.

Manamperi, N. (2016). Does military expenditure hinder economic growth? Evidence from Greece and Turkey. Journal of Policy Modeling, 38(6), 1171-1193.

Mehmood, S. (2014). Terrorism and the macroeconomy: Evidence from Pakistan. Defence and Peace Economics, 25(5), 509-534.

Mekasha, T. J., & Tarp, F. (2013). Aid and growth: What meta-analysis reveals. The journal of development studies, 49(4), 564-583.

Mirza, D., & Verdier, T. (2008). International trade, security and transnational terrorism: Theory and a survey of empirics. Journal of Comparative Economics, 36(2), 179-194.

Murray, J. (2005). Policing terrorism: A threat to community policing or just a shift in priorities? Police Practice and Research, 6(4), 347-361.

Nadaroğlu, H. (1985). Kamu Maliyesi Teorisi. Istanbul: Beta Publishing Distribution.

Nagaoka, S., Motohashi, K., & Goto, A. (2010). Patent statistics as an innovation indicator. In B. H. Hall, & N. R. (Eds.), Handbook of the Economics of Innovation (pp. 1083–1128). Academic Press.

Narayan, P. K., & Singh, B. (2007). Modelling the relationship between defense spending and economic growth for the Fiji Islands. Defence and peace economics, 18(4), 391-401.

Nigh, D. (1985). The effect of political events on United States direct foreign investment: A pooled time-series cross-sectional analysis. Journal of International Business Studies, 16(1), 1-17.

Nikolaidou, E. (2016). Greece, Portugal, Spain: New evidence on the economic effects of military expenditure using the new SIPRI data. The Economics of Peace and Security Journal, 11(2), 20-27.

Nitsch, V., & Schumacher, D. (2004). Terrorism and International Trade: An Empirical Investigation. European Journal of Political Economy, 20(2), 423-433.

Nkusu, M. M., & Sayek, S. (2004). Local FinancialDevelopment and theAid-Growth Relationship. IMF Working Paper No. 4-238.

Nwaogu, U. G., & Ryan, M. J. (2015). FDI, foreign aid, remittance and economic growth in developing countries. Review of Development Economics, 19(1), 100-115.

OCED. (2005). Innovation policy and performance: a cross-country comparison. OCED paris.

Okafor, G., & Piesse, J. (2017). Empirical Investigation into the Determinants of Terrorism: Evidence from Fragile States. Defence and Peace Economics, 1-15.

Ovaska, T. (2003). The Failure of Development Aid. Cato Journal, 23(2), 175–188.

Pakistan Economic Survey. (2016). Annex-IV: Impact of War in Afghanistan and Ensuing Terrorism on Pakistan's Economy. Retrieved May 28, 2018, from http://www.finance.gov.pk/survey_1617.html

Pesaran, M. H., & Pesaran, B. 1. (1997). Working With Microfit 4.0: Interactive Econometric Analysis. Oxford: Oxford University Press.

Pesaran, M. H., Shin, Y., & Smith, R. J. (2001). Bounds testing approaches to the analysis of level relationships. Journal of applied econometrics, 16(3), 289-326.

Pesaran, M. H., Shin, Y., & Smith, R. J. (2000). Structural analysis of vector error correction models with exogenous I (1) variables. Journal of Econometrics, 97(2), 293-343.

Phillips, P. C., & Hansen, B. E. (1990). Statistical inference in instrumental variables regression with I (1) processes. The Review of Economic Studies, 57(1), 99-125.

Phillips, P. C., & Perron, P. (1988). Testing for a unit root in time series regression. Biometrika, 75(2), 335-346.

Porter, M. E., & Stern, S. (2002). National innovative capacity. The Global Competitiveness Report 102–118.

Rajan, R. G., & Subramanian, A. (2008). Aid and growth: What does the cross-country evidence really show? The Review of economics and Statistics, 90(4), 643-665.

Roemer, M. (1989). The macroeconomics of counterpart funds revisited. World Development, 17(6), 795-807.

Romer, P. M. (1990). Endogenous technological change. Journal of political Economy, 98(5, Part 2), S71-S102.

Rostow, W. W. (1990). The stages of economic growth: A non-communist manifesto. Cambridge university press.

Salim, A., Razavi, M. R., & Afshari-Mofrad, M. (2017). Foreign direct investment and technology spillover in Iran: The role of technological capabilities of subsidiaries. Technological Forecasting and Social Change, 122, 207-214.

Sandler, T., & Enders, W. (2008). Economic consequences of terrorism in developed and developing countries. In P. Keefer, & N. Loyaza, Terrorism, economic development, and political openness (pp. 17-47). New York: Cambridge University Press.

Sarwar, A., Hassan, M., & Mahmood, T. (2015). Foreign aid and governance in Pakistan. Pakistan Economic and Social Review, 53(2), 149-173.

Shah, S. H., Ahmad, M. H., & Ahmed, Q. M. (2016). The nexus between sectoral FDI and institutional quality: Empirical evidence from Pakistan. Applied Economics, 48(17), 1591-1601.

Shahbaz, M., & Shabbir, M. S. (2012). Military spending and economic growth in Pakistan: New evidence from rolling window approach. Economic research, 25(1), 119-131.

Shahbaz, M., Afza, T., & Shabbir, M. S. (2013). Does defence spending impede economic growth? Cointegration and causality analysis for Pakistan. Defence and Peace Economics, 24(2), 105-120.

Shahbaz, M., Shabbir, M. S., Malik, M. N., & Wolters, M. E. (2013). An analysis of a causal relationship between economic growth and terrorism in Pakistan. Economic Modelling, 35, 21-29.

Shahzad, S. J., Zakaria, M., Rehman, M. U., Ahmed, T., & Fida, B. A. (2016). Relationship between FDI, terrorism and economic growth in Pakistan: pre and post 9/11 analysis. Social

Indicators Research, 127(1), 179-194.

Sharma, K., & Bhattarai, B. (2013). Aid, policy, and growth: The case of Nepal. Journal of Economic Issues, 47(4), 895-910.

Shieh, J. Y., Lai, C. C., & Chang, W. Y. (2002). The impact of military burden on long-run growth and welfare. Journal of Development Economics, 68(2), 443-454.

Silke, A. E. (2004). Research on Terrorism: Trends, Achievements and Failures. Londen: Frank Cass.

Şimşek, M. (2003). Kamu Harcamalarının Özel Yatırımlara Etkileri, 1970-2001. Journal of Economics and Administrative Sciences, 4(2), 1-20.

Singhania, M., & Gupta, A. (2011). Determinants of foreign direct investment in India. Journal of international trade law and policy, 10(1), 64-82.

Solow, R. M. (1956). A contribution to the theory of economic growth. The Quarterly Journal of Economics, 70(1), 65–94.

Svensson, J. (1999). Aid, growth and democracy. Economics & politics, 11(3), 275-297.

Svensson, J. (2000). When is foreign aid policy credible? Aid dependence and conditionality. Journal of development economics, 61(1), 61-84.

Syed, S. H., Saeed, L., & Martin, R. P. (2015). Causes and incentives for terrorism in Pakistan. Journal of Applied Security Research, 10(2), 181-206.

Temiz, D., & Gökmen, A. (2014). FDI inflow as an international business operation by MNCs and economic growth: An empirical study on Turkey. International Business Review, 23(1), 145-154.

Thirlwall, A. P. (1999). Growth and Development, With Special Reference to Developing Countries. London: Macmillan Press.

Tiwari, A. K., & Shahbaz, M. (2013). Does defence spending stimulate economic growth in India? A revisit. Defence and Peace Economics, 24(4), 371-395.

Üçler, G. (2016). Testing the relationship between military spending and private investments: Evidence from Turkey. Theoretical & Applied Economics, 23(3), 307-318.

Wagner, D. (2006). The impact of terrorism on foreign direct investment. International Risk Management Institute.

Waterson, A. (1965). Development Planning: Lessons of Experience. Baltimore, MD: John Hopkin University Press.

Weiner, T. (1998). Afghan Taliban Camps Were Built by NATO. Washington, DC: The New York Times.

Whynes, D. K. (1979). The economics of Third World military expenditure. Springer.

Wijeweera, A., & Webb, M. J. (2009). Military spending and economic growth in Sri Lanka: A time series analysis. Defence and Peace Economics, 20(6), 499-508.

WorldBank. (1998). Assessing Aid: WhatWorks, What Doesn’t, and Why. Washington, DC: World Bank.

Wu, J., Ma, Z., & Zhuo, S. (2017). Enhancing national innovative capacity: The impact of high-tech international trade and inward foreign direct investment. International Business Review, 26(3), 502-514.

Yildirim, J., Sezgin, S., & Öcal, N. (2005). Military expenditure and economic growth in Middle Eastern countries: A dynamic panel data analysis. Defence and Peace Economics, 16(4), 283-295.

Young, A. T., & Sheehan, K. M. (2014). Foreign aid, institutional quality, and growth. European Journal of Political Economy, 36, 195-208.

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