An Overview Of Military Expenditure Economics Essay

Military expenditure and foreign direct investment have interrelationship between each other. Military expenditure has both positive and negative effect to the foreign investment as it depend on countries cases. One of the consequences of military expenditure to the economy of the countries is investment effect. The tradeoff between military expenditure and investment is an increase in the defense spending make higher government borrowing and thus absorb the fund that government used to investment would reduce. This is called an opportunity cost that countries need to accept in order for them to allocate more budgets to the military sector. While the positive effect of military expenditure are the introduction of modern skills, strengthen economic infrastructure and reduce the unemployment rate.

How military expenditure can reduce the FDI? This is through the less attractiveness of the country because high military expenditure indicates the country spending less on growth and development. In that case, foreign investors will not invest their money in that country. The consequences are it will affect economic growth as FDI is a main engine for the developing countries to finance their investment for the purpose of expanding their economy.

There is no consensus among analyst to date the interrelation of military expenditure and foreign direct investment as it yield mixed result. Muhammad Khan, et.al (2011), public spending which defense spending can retard the effect of FDI on economic growth if the percentage of defense spending is beyond 6% due to the higher involvement of government role in economy activity.

1.2 An Overview of Military Expenditure

Military expenditure can be define as all cost that the countries used for military activities such as armed forces, equipped for military operation, military space activities, procurement, military research and development, and military aid (SIPRI). All countries need to measure their military expenditure in order to know the burden of military spending in the economy. Moreover, the government can gives more priorities to the sectors that should be focus more such as education and health by compare the military spending with the other sector. Military spending can measure the level of peaceful for the countries and also can indicate the power of the countries. For the researchers, they can study the effect of military spending on economy such as the impact on growth and economic variables, the tradeoff and the determinant of military expenditure.

In 2010, world military expenditure is increasing 1.3% in a real term over 2008. on average, for year 2011 about sixty two countries increase in their military spending while sixty two countries are decreasing. For the period 2002 until 2011, the trend for military spending shows a rapid growth in North America which increasing almost 109% of the real term. From this figure, 75% of it is accounted by Algeria. From year 2000 until 2011, the increasing of military spending for East Asia is 69% while Central and South Asia is just 62%.

In the United State, the military expenditure show a decreasing trend because of the withdrawal US arms force from the Iraq and Afghanistan. In addition, US government wants to reduce the burden of debt. So US have to cut the budget for military sector. While in Europe, because of their economic crisis, majority of countries have to cut down their defense spending. In Western Europe, most of the country start to cut that budget since 2010 while most of the countries in central and Eastern Europe, they start cutting that budget since 2009 as they are the weaker economies in Europe.

Figure 1.0 Biggest decrease in Europe military spending .Sources from SIPRI Press Material, 2011

Russia is the third largest of world military spenders and the budget for military spending is increasing 9.3% in 2011. Russia also plans to increase this defense spending about $749 billion on military equipments, R&D and arms force. Furthermore, Russia plans to replace 70% of Russia’s mostly Soviet-era military equipment with modern weaponry by 2020 (SIPRI Press Material, 2011).

Military spending in Asian countries saw an increasing trend especially for China which likely to overtake Europe. The increasing of military spending in Asian countries is more than 3% in real term for year 2011. According to John Chipmant who is director general of the International Institutes for Strategic Studies (IISS) this is due to rapid economic growth and strategic uncertainty. China shows the rapid increase in military spending by 170% in real term since 2002. This spending at least gives some good impact to China such s increase in salary and also provide a better condition for the defense sector in term of equipment and quality of arms force.

This condition contributes to the increasing India’s military spending by 66% in real term since 2002. Taiwan also increase their military spending but in small scale while Japan shows a decline trend. In average, the other Asian countries like Philippines, Vietnam, Thailand and Cambodia are likely had a similar upward trend of military expenditure (SIPRI Press Material, 2011)

Figure 1.1 Share of World Military Spending for the Top 10 Spenders, 2011. Sources from SIPRI Press Material, 2011

The figure above shows the shares of world military spending for the top 10 spenders in year 2011. 41% of world military spending is accounted for USA while China is the second largest which is 8.2% then followed by Russia (4.1%), UK (3.6%), France (3.6%) and others.

Malaysia was ranked moderate to low in the defense budget transparency compared to others developing countries that scored moderate and low like Indonesia, China and Pakistan. Ministry of Defence in Malaysia is a forth largest that financed by government after Ministry of Education, Ministry of Higher Education and Ministry of Health. In Malaysia budget 2012, the budget spending for military expenditure will reduce by RM100 billion if compared to year 2011 amounted RM13.823 billion.

Figure 1.2 Malaysia’s Military Expenditure (% of GDP)

The figure above shows the military expenditure in percentage of GDP for Malaysia from year 1988 until year 2010. Every year, the government tries to cut the spending on this sector. In year 2009, Malaysia cut the military expenditure almost 50% due to the economic downturn. Navy is affected from the cutting budget where the budget for arm procurement decreased from RM811.12 million to RM100 000 and followed by Army where the amount of decreasing was RM1167.77 million. Meanwhile, Air Force having an increase budget because of purchasing of new type of utility helicopters.

There have a link between military expenditure and military capability but there can be other factor that intervene the link between both of them. High military expenditure can contribute to high military capability for the countries. Even though the country has high level of military expenditure but they use that for maintaining excessively large army, this would not contribute to the high level of capability. If the country try to reduce the size of arm force but they give better training and have equipment that suitable in the modern conflict, the country can develop high military capability. This shows how well the mix between personnel and equipment expenditure in military. Beside that the military capability also depends on efficiency on handling the military expenditure. If the country has poor planning on that expenditure or has corruption in the management, that spending will not generate military capability.

1.3 An Overview of Foreign Direct Investment

Countries need investment in order to develop their own countries as well as economy which can be obtained through public and private funding. Unfortunately, this amount is not enough and need to find the other financial sources outside the country’s boundaries. Therefore, foreign direct Investment becomes important for the development of the countries. Many empirical studies have proved that to those countries that opened to FDI has higher growth rate and give positive impact to economic development. To make this happen, government need to attract foreign investors by made changes in their policies. There have two type of FDI which are:

Direct Investment: investment made by the foreign countries in order to have influence on the development of a firm’s long term strategy.

Portfolio Investment: investment made through bond, share in order to obtain short term profitability.

As mentioned before, FDI can brings many benefits to the economy such as can stable the capital flow, become more productive as there have many competitors, generate employment, transfer of technologies and can fill the saving gap between the required funds for growth and the internal saving capacity of a country. Moreover, FDI is crucial factor of the country’s degree of economic globalization and integration into world economy (Marial et al., 2009). Demekas et al. (2005) states that FDI can be the vehicle for the country to finance their external current deficit as FDI are a non debt creating financial commitments.

The amount of increases of FDI inflow for three sub regions including East Asia, South-East Asia and South Asia was 24% in 2010. Figure 1.3 below shows FDI inflow to ASEAN countries for year 2010 where Malaysia was the third largest recipient after Singapore and Indonesia. FDI inflow into Singapore was increased from US$15.3 billion to US$38.6 billion in 2010 while for Indonesia, the country also had a strong growth from US$4.9 billion in 2009 to US$13.3 billion in 2010. An increasing trend for all ASEAN countries in their FDI inflow was due to the growth in domestic demand and strong expansion in private sector activities (MalaysiaInvestment Performance, 2011)

Figure 1.3 FDI Inflows to ASEAN Countries in 2011. Sources: MIDA

Foreign direct investment (FDI) is part of Malaysia engine of economic growth. In 1980s, Malaysia is one of the countries in the world that produce the primary product such as tin, rubber and palm oil. Then, year by year Malaysia starts to expend the market in heavy industries. According to Prempeh (2003), Malaysia becomes more attractive in term of the flow of FDI because of several factors such as the undervalued of Malaysia’s Ringgit, low cost of labor supply and low inflation rate.

Malaysia inflow of FDI has an increasing trend start from 1970 until 1994. After that, the amount of FDI inflow shows a decreasing rate because of the great recession in 1994 and it start to recovery in late 1978. In 1983, Malaysia FDI decline due to the world recession and electronic crisis and the amount of reduction was recorded about RM500 million compared to the year 1982. After Government of Malaysia implement Industrial Master Plan (IMP) in 1986- 1995, FDI inflow has increased by RM126 000 million in 1993. Malaysia faces a drop in FDI again in 1994, 1997 and 2001 due to the Asian financial crisis and incident of September 11, 2001 respectively.

According to Minister of International Trade and Industry, Datuk Seri Mustapa Mohamed, the FDI for last year (2011) is the highest where the amount is RM32.9 billion has been recorded. This is due to the Economic Transformation Programme (ETP) which attracted many foreign investors to invest in Malaysia. The major foreign investment is come from Japan, Singapore, Netherlands and Taiwan. Even in year 2010, there has been a wave of FDI flows into Malaysia amounted RM20.3 billion where most of this figures is go to the manufacturing sectors. There have measure to increase FDI and investment that government already done. Part of them are empowering Malaysia Investment Development Authority (MIDA) to attract investment, benchmarking Malaysia’s attractiveness, reducing corporate and personal income taxes to selected industries, easing regulator burden and providing industry tailored incentives.

All these shows that investors’ confident toward Malaysia’s opportunity business and Malaysia is on track to achieve the investment target by year 2020 where the goal is RM1.2 trillion under ETP. From this, the employment opportunities can be create where about 149 496 jobs can be offered to the citizen. Out of the total investment, 55.4% is come from domestic investment and the rest (44.3%) is from FDI. The figure below shows the Malaysia’s FDI inflow from year 1980 until 2009. In year 2009, the FDI in Malaysia fell sharply due to the United State financial crisis that happened in 2008 which have negative impact to the inflows and outflows of FDI. But it recovery in 2010 after government announced the liberalization measures aimed at luring investment.

Figure 1.2 Malaysia FDI Inflows

Figure 1.3 below shows total investment approved in Malaysian economy for the year 2011. The total investment approved was amounted RM148.6 billion and about 4964 projects was approved in Malaysia due to the Economic Transformation Programme (ETP) and also New Economic Model (NEM). This is expected to create 149496 job opportunities in Malaysia including all investment that approved in service, manufacturing and primary sectors. For service sector, there has an increasing rate up to 75.5% and the amount was RM64.4 billion. This can generate about 43,784 jobs from this sector. For the manufacturing sector, the amount of investment increase to RM56.11 billion compared to 47.2 billion in last year (2010) and for the primary sector, it contributes 18.9% of total investment in Malaysia.

Figure 1.4 Total Investments Approved in the Malaysian Economy in 2011.

Sources: MIDA

Figure 1.5 below represent the investment and employment in project approved for year 2006 until 2011. From year to year, Malaysia was record higher foreign investment compared to the domestic investment. This showed that Malaysia was a competitive investment location for foreign investors. From 100% of total investment approved in Malaysia, 61% was foreign investment. There have decline in total investment in 2009 but yet still foreign investment greater than domestic investment.

Figure 1.5 Investment and Employment in Project Approved, 2006-2011.

Sources: MIDA

The major sourced of foreign investment were come from Japan, Korea, USA, Singapore and Saudi Arabia. Japan was the largest investor with RM 10.1 billion and mainly in E&E products. Korea was the second largest investors with RM5.2 billion and mainly in manufacture of lithium ion batteries. Unites State was the third largest investors with RM2.5 billion and mainly in diversification project for manufacture of electronics, bio and chemical. Singapore was focus in the food manufacturing and E&E industries while Saudi Arabia was focus in manufacturing of polycrystalline silicon.

1.4 Problem Statement

Questions have been raised about the effect of military expenditure on economic growth. The opportunity cost for military expenditure can be an important thing that countries must be note in order to know the effect of that spending to the growth of the countries. There must be a tradeoff or an opportunity cost for the countries if they focus more on military expenditure as it is an unproductive expenditure. The reason for this argument is government will take some portion of resources away from the other economic activities like investment, health and education which those are all the productive expenditure. But from the other perspective, military expenditure also has their own role to the economic such can strengthen economic infrastructure, reduce unemployment and can encourage fully utilization of the existing production facilities.

Deger and Sen (1995), Devarajan et al. (1996), Glomm and Ravikumar (1997), Shieh et al.(2002), Luca Pieroni (2004) claims that high level of military expenditure can retard the development as the resources can not be allocated to the better use. According to Collier (2001) in the World Bank Policy Research Working Paper, found that on average the country growth rate will reduce which lead to decreasing about 20% level of income when the country double the military expenditure. According to Hamid Davoodi et.al (2001), reduction in international tension will lead to reduce in military spending and thus increase the nonmilitary spending in total spending.

However, the world still continues to spend more from the total budget on military sector. As increase in military expenditure will reduce the saving and reduce the investment because higher defense spending make higher government borrowing thus absorb funds that would otherwise have gone partly to investment. Degar (1986) found that military expenditure and investment have negative effect as increase in military spending may cause higher inflation and taxation. In the long run, it can impede the growth of Malaysia economy. By reducing excessive defense spending, the country can use it to provide short term economic stimulus where it can be as a precautions when the country facing recession. In addition, reducing military expenditure will not cause job losses (in term of army).

All these factors above can affect the Foreign Direct Investment (FDI) come to countries. One of the main reasons the reduction of FDI inflow is the country is less attractive and not offer better return to them. If the countries spend more on military, the foreign investors feel that the country has less attraction as we spending less on economic development. Moreover, less resources that will contribute to the development of human capital in which this factor has a favorable impact on inflow of FDI. Less human capital development shows that the country has less supply of skilled labor even though they have abundant of labor force. This view is supported by Sharma and Gani (2004) who state, “…increase in FDI are associated with slight improvement in human development.” Thus, it may lead to other consequences because FDI inflow played as one of the main engines of economic growth. Most of developing countries face shortage of capital to finance their investment. So, to solve that problem they need to attract as much as FDI to their countries. This is called crowding out effect or opportunity cost in order to increase military expenditure.

In Malaysia, military expenditure is decreasing over year while FDI is fluctuate over year, but starting 2009 FDI is plumped and already exceed the investment target. All these can give positive impact to the Malaysia economic growth theoretically. The relationship between military expenditure, FDI and economic growth has attracted interest for the economist to investigate since it yielded mixed result on different cases of countries and there is no specific prediction about the relationship between them.

As such, in the case of Malaysia, this study wants to know whether there have long run relationships between military expenditure, FDI and economic growth and causal effect between them.

1.5 Research Objective

The research study should have objective because it is important thing to researcher in guiding her in order to achieve the reason why and purpose of doing the research. The purpose of this study is dividing into two categories:

The general objective is to study the long run relationship between military expenditure, FDI and economic growth.

The specific objective is to determine the causal effect between military expenditure and FDI.

1.6 Research Question

There are several research questions that has been developed regarding the problem statement occurred. The research questions can be:

What is the long run relationship between military expenditure and FDI on economic growth?

What is the causal effect between military expenditure and FDI?

1.7 Significance of the Study

The impact of military expenditure on economic growth is an arguable issue that is increasing in military spending may absorb fund that would otherwise have gone partly to investment which in turn lead to inefficient resources allocation. The correlation between military expenditure, FDI and economic growth is a crucial issue since military is important to protect the security of country and FDI also important for developing countries like Malaysia.

This study makes a contribution to the existing studies on military expenditure and economic growth. In spite of the importance of causal relationship between military expenditure and growth, defense spending also has an impact on the investment of the country. Therefore, analyzing the interwoven between military expenditure, FDI and growth is the contribution in this study.

1.8 Organization of the Study

The paper is organized as follow. In section 2, there provide the literature review of the effect military expenditure on FDI and growth. Section 3 will explain the methodology used in this studies. Section 4 will explain about the findings and last section (section 5) will explain about conclusion and recommendation for this study.

CHAPTER 2: LITERATURE REVIEW

There are various empirical studies on the military expenditure and FDI on economic growth.

2.1 Military Expenditure

The first serious discussion and analyses of the relationship between military expenditure and GDP growth emerged during the 1970s by Emile Benoit (1973). His study was focus on forty four less developed countries (LDCs) and found that military expenditure has positive relationship with economic growth. However, Nicole Ball (1983) argues that military expenditure can not contribute to higher growth. After that, Benoit (1978) revise back and found two effect of military expenditure on growth which is positively and negatively. The negative effect is military expenditure absorb the resources from productive activities while the positive effect is it can reduce the unemployment, improve infrastructure and involve in research and development (R&D).

Deger (1981) points out that increase in military expenditure reduce the fund that should be used for the formation of human capital. This view is supported by Muhammad Khan, et.al (2011) who studies a time series analysis of public spending, FDI and economic growth for Pakistan from 1975- 2008. He state that public spending give negative impact to economic growth due to large proportion of budget is use for defense spending, thus it can neglected the human capital. As the result, it can impede the growth.

Deger and Smith (1983) traces that the higher military spending can retard the economic growth of that countries. This is because the military spending will take a portion of capital equipment that will use for consumption and investment as these can slow the growth of economy. In less developed countries, Deger (1986) claims that there is negative relationship between both variables as military spending can give bad impact to investment and also can not generate saving for the countries. Moreover, Dunne et al (2002) investigated the relationship between military spending, investment and economic growth in small industrializing economies. He used panel data method and found that military spending can harm growth by give negative impact to investment.

According to Dunne (2010), who investigates about military expenditure spending and economic growth in Sub Saharan, he found that military spending give negative impact to economic growth in both short run and long run. For the case of Egypt and Israel, reducing the military spending can improve the economic growth. While in the case of Syiria, the shifting resources from military to civilian spending will not enhance growth unless the civilian can produce productive activities (Suleiman Abu-Bader, 2005).

Many studies about this issue were found that the higher military expenditure led to lower economic growth (Deger and Sen, 1995; Devarajan et al., 1996; Glomm and Ravikumar, 1997; and Shieh et al., 2002). This is supported by Luca Pieroni (2004) that the economic growth will down if the public expenditure is getting increase because there are potential that government using the resources for unproductive public sector such defense spending. In average, increase in military expenditure will reduce the growth of economy especially for poor countries. This is because they import the military equipment so when they increased the spending, the opportunity cost that they may bear is less development for their countries (Paul Collier, 2006).

Kalakech et al. (2009) claims that defense expenditure and growth has negative correlation in the long run and insignificant in the short run. According to Shieh et al (2002), who studied about the impact of military burden on long run growth and welfare, there have three channels that military can affect the private investment. First is through budgetary crowding out effect. Increase in military spending can cause the fund that will use for having better infrastructures become less. As the quality of infrastructure decrease, it can lower the production of private output and decrease the resources of investment. Skabic and Orlic (2007) and Botric and Skuflic (2006) found that the development of infrastructure is important to attract more inflow of FDI to the host country but it contrary in the case of Africa. Nnadozie and Osili (2004) study on United State’s outflow of FDI to Africa, they point out that the roles of infrastructures have less important to attract FDI. Second is through spin off effect. Higher military expenditure can increase the home weapon stock. Thus, it increases the private production and also can promote investment. Third is through resources mobilization effect. It can increase private investment by reducing consumer current consumption in order to have higher future utility.

High military spending significantly retard the economic growth and if the country reduces the spending, it would not affect the security of the country (Khilji, 2005). Beside that, Henderson (1988) claims that high level of military spending can lead to poverty through unemployment and inequality. This view is support by Tang et al. (2009), who find that the unemployment rate in low and middle income countries increasing when the military spending increase. Numerous studies have attempted to explain that increase in military spending can cause income inequality due to different income payment to workers in military and civilian sector (Abell, 1994; Melman, 1974; Carson, 1987). This is because workers in hi-tech industries can get high income and spending on hi-tech weaponry generates relatively fewer employment gains, especially for minorities and other disadvantaged individuals who do not possess the requisite education or skills to work in the defense industry or related research activities (Carson, 1987).

It can not be conclude that increase in military expenditure can harm the economic growth. As analyzed by Deger and Sen (1983), military expenditure have positively and negatively correlation with growth. For positive correlation, military expenditure can increase aggregate demand and creating new technical progress while for the negative correlation, military expenditure can affect investment, saving and balance of payment. This is in line with Hassan et al. (2003). He investigates the effect of military spending on growth and FDI in five South Asian Regional Cooperation Council (SAARC) countries from year 1980 to 1999. He found positive relationship between military spending and economic growth.

Baker (2007) investigated the impact of the Iraq war and higher military spending on US economy. The result shows that military spending can enhance the economy in the short run by boosting demand, providing more employment and increasing in output. The bad impact for high military spending can be seen in the long run which after 10 years the country imposed higher defense spending. High inflation can lead to increasing the long term interest rate and these can cause lower in investment. Moreover, high real interest rate helps to push up the dollar currency which can lead to lower export and higher import as the imported goods become relatively cheaper than their own goods. In this case, the government will face larger current account deficit and lead to larger foreign debt.

In the case of USA, the government increase their military spending after the 11 September 2011 attack and give impact to the economy. High level of military expenditure increasing the US debt and reduce the productive capital. Beside that, the raising of interest rate can lower the private investment. Even though the increasing in military expenditure can contribute some good impact such as the stimulate effect of government purchase and high foreign saving through high interest rate, but all these cant recover the loss from impose high level of military expenditure (Edward, 2011).

Military spending did not directly harm the economic such as disturb the government to provide better health and education but it can induced economic stimulation. This is because military spending not only as a source for increasing an aggregate demand but as a protection for foreign investment and give profits for defense contractors (Baran and Sweezy, 1996). Military spending can enhance the security of the countries, thus it can promote more foreign investment come to that countries. In the other cases Gold (2005) reported that military spending can improve the demand stimulation but in a small percentage and it happen by coincidence rather than by volition.

Higher level of military expenditure can retard the human development. Nabe (1983) study about the effect of military expenditure on social development (in term of education and health) for twenty-six African countries from 1967 to 1976, he claims that military expenditure give negative impact to social development. However, Verner (1983) carried out an investigation on the effect of military expenditure on education in 18 Latin America and the result showed that only one country has negative tradeoff, ten countries have positive tradeoff and the other seven countries have no significant tradeoff. It means that not all countries have the same cases and it’s depending on how the spending of military sector has been used. Military expenditure can builds human capital if parts of the spending are used for education and training for army, discipline and so on. Furthermore, it can improve welfare and productivity if this spending is used for revamping the economy during crisis such as floods, tsunami, attacks from terrorist and so on.

Sharma and Gani (2004) study about The Effect of FDI on Human Development for middle and low income countries for the period from 1975 to 1999. They predict that military expenditure has negative relationship with human development that contributes to less FDI. This is because of high level of military expenditure indicates less activity for enhancing human welfare and productivity. Thus, it will cause reduction in FDI inflow as foreign investor less interested to that country. However, the result shows that the positive relationship between both variable. The reason is defense sector provide major employment and the income effect raise human development. Thus, attract the FDI inflow to that country.

Labor resources are one of the importance factors to generate more FDI. Zenegnaw (2010) study the determinant of FDI from the demand side, he claims that the more African countries spend on human capital, the more FDI they can attract to flow to their countries. Li and Liu (2005) pin points that the interaction of strong human capital with FDI can give strong impact to economic growth for the country.

Borensztein et al (1998) shows that a country can get fully benefits from FDI when the country achieves certain level of human capital and FDI are a complementary with human capital. They also claim that FDI generates more growth compared to domestic investment. This is because human capital plays an important role as an absorptive capacity to take advantages from FDI. Nunnenkamp and Spatz (2003) note that better institutions and educated workforce are important factors for a country to attract FDI but they claim that the benefit from FDI is quite skeptical, so the contribution of FDI on growth is quite weak.

Military expenditure can be associated with corruption. In 2001, Gupta et al. investigated the relationship between corruption and military expenditure for up to 120 countries from year 1985 to 1998 and she build two hypothesis. First, the corruption has positive relationship with the share of defense outlay in both GDP and total government spending. Second, corruption has positive relationship with military procurement in relation to both GDP and total government spending. Her hypothesis was truth when the result showed that the corruption was correlated with high military expenditure. The reasons for this argument ware less transparency in the defense sector and high competition between arms producer make them resort to bribes in order to get a contract.

Beside that, the relationship between corruption and government spending has been carried out by Mauro (1997) and he claims that military expenditure contribute to high corruption. According to Hines (1995), mostly corruption was recorded in military aircraft trading.

The impact of low human development and high corruption because of high level of military expenditure is distracting the FDI come to countries. Sharma and Gani (2004) found that human development is an important tool to attract more FDI come especially for low income countries. This is because a better human development means a better quality of education, health and skills that can enhance the growth and development of economy. Meanwhile, Wei (1997) claims that corruption give negative impact to the inflow of FDI as corruption indicates how well the quality of government itself.

This is supported by Habib et al.(2002), Zhao and Du (2003), Skabic and Orlic (2007). They claims that the existences of corruption and low transparency have negative relationship with the inflow of FDI and thus hampering economic development.

Most researcher claims that FDI have positive effect to economic growth through many factors. Lee and Tcha (2004) shows that as the FDI inflow of the countries increase, the GDP and total factor productivity of the countries also increase and the contribution of FDI to economic growth is greater than the domestic investment. This is in line with Borensztein et al. (1998) that claim, FDI is important for transferring technology and contribute more to growth rather than domestic investment does. In a related work, Bashier and Bataineh (2007) found that domestic saving is increase as FDI increase which is another ways for the local investment to increase.

In OECD country, military expenditure and FDI has positive relationship while in non OECD country both two variables have negative correlation (Gilady et al 2002). Li (2008) found that FDI and military conflict have negative relationship due to high military conflict increase the military spending. Thus, it disattract FDI come to that country.

2.2 Government expenditure

Government expenditure can be defined as the spending that make by government to consume final product or services, transfer payment and the other expenditures. According to Vilde (2001), government expenditure is expected to have negative relationship with FDI. This is because increase in government expenditure may crowd out the private consumption expenditure, thus negatively affect output. Beside that, for the case of Philippine economy, the government expenditure can crowd out private investment including foreign investment and thus reduce the economic growth.

Le and Sugra (2005) state that the overused of public spending can lower the role of FDI to improve economic growth for the countries. Hasen (2007) found negative relationship between government expenditure and FDI inflow in his study by using panel data in AMU countries. His reasons is large portion of government expenditure may lead to misuse of that fund by government officials and it also may crowding out investment in critical sector of economy. Moreover, Filipovic (2005) concludes that high government expenditure can cause a complex bureaucratic structure that make that country becomes less attractiveness to be a location for foreign investment. Beside that, it also can increase the level of corruption and bureaucratic red tape.

In contrast, Tanzi (2000) claims that in order to attract capital mobility or foreign investment, government have to spend towards productive public input such as education, health, research and development, training and infrastructures. This view is in line with Zenegwa (2007) that investigate factors affecting FDI from the demand side in Africa. He found that government final consumption give positive effect to attract more FDI come to that country. This is because as more government invests money for infrastructure, education and other things that can expand the development of country, it will attract more FDI inflow.

2.3 Trade openness

Trade openness can be defined as the openness of the economy in order to engage in trading activities with others economies. It is the sum of export and import to GDP which can be one of the important variables to induce FDI inflow. Yasmin et al (2003) points out that trade openness positively significant affecting FDI in the lower and upper middle income group as increase in openness will increase the opportunities to trade compared to the countries with restricted trade policies, thus it attract foreign investors to invest to that countries.

The impact of trade openness to FDI is depends on the types of FDI itself. Increase in trade openness may reduce FDI inflow if the purpose of investment is to serve the local market. This is due to the tariff jumping where the foreign firms feel difficult to export their products to the host country, thus they set up subsidiaries in that country. On the other hand, increase in trade openness may contribute good impact to the FDI inflow if the foreign firms engage in the export oriented activities since increase in openness will reduce the transaction cost (Jordaan, 2004).

There have many researchers found that trade openness give positive impact to the inflow of FDI as the countries more engage in international trade (Velde D.W, 2001; Aizenman and Noy, 2006; Noorbakhsh et al., 2001; Imran et al., 2012; Nonnemberg et al.,2004; sahoo, 2006; Botric et al. 2006; Durham, 2002; Eicher, 2002; Ismail and Yussof, 2003). Kamaruzaman et al.,(2011) claims that trade openness is a important determinant of FDI in Malaysia as they found two way causality both in short run and long run.

For the case of Sub-Saharan countries, one of the macroeconomic factors that can attract FDI inflow come to the countries is trade openness (Asiedu, 2002). Yih Yun et al, (2000) found that trade openness has positively significant for the FDI inflow in Australia’s economy. Other than that, trade openness can be view from the perspective of economic integration among countries. Rahman et al,(2008) points out that in the case of Canada, free trade agreement has significant effect for inflow and outflow of FDI of the country.

Reduction in tariff or restriction in trade will lead attraction of FDI inflow as the countries have favorable condition as the cost of trading is low. Banga et al, (2007) study the impact of trade liberalization on FDI in Indian industries by using panel data for 78 industries from 1991 to 1998. They found that trade liberalization promote the FDI flow in India as the greater extent of international trade can attract a big amount of FDI. In most of emerging economies, trade and FDI are complementary, thus the result show that trade openness has positive effect to FDI (Martens, 2008). Beside that, the complementary between them also can grow the importance of MNE production network and intra-industry and intra-firm trade (Globerman, 2002)

This view is in line with Rose (2002). She claims that the countries that have high trade openness will encourage foreign investor to invest in those countries. This is because of the countries having less potential to default on their international debt. Moreover, Rasekhi (2011) state that trade liberalization has a positive effect on FDI as removal in tariff barriers can reduces cost in multinational firms and trade liberalization can expands the market thus absorb FDI inflow to host country.

Blomstrom and Kokko (1997) conclude that Regional Economic Integration (REI) may stimulate FDI inflow as REI lead to reduction in trade restriction (greater trade openness) and reduce tariff hoping FDI. In additional, REI may increase the growth rate and efficiency of economic thus attracts both foreign and domestic investment. Chakrabarti (2002) on his review about determinant of FDI for cross country regression state that trade openness is the crucial factor of attractiveness of a location for FDI.

Portes and Rey (2005) concludes that there have strong effect of trade openness on FDI as they found the causality between them runs significantly in both ways. For the similar study, Aizenman and Noy (2006) found the causality effect between trade openness to FDI about 31 percent. Singh and Jun (1995) states that there have complementary relationship between trade and FDI inflow as export orientation is very important to attract the FDI go to the country. Resmini (2000) most of vertical FDI is benefit from increasing in trade openness for the case of manufacturing investment in Central and Eastern Europe.

Export promotion can be significant factor to attract more FDI inflow rather than promoting the import substitution. From this it can enhance the trade relation between home and host country, thus this can be positive determinant for FDI. Liu et al.(2001) states that strong trade relation between host and home countries can induce the FDI to the host countries. Singh and Jun (1995) found that export orientation can attract more FDI come to that country. This view is in line with Akinkugbe (2003). He believes that trade liberalization and export orientation policy can be the important factors why foreign investors invest to the host countries.

Onyeiwu (2003) conclude that limitation of trade openness is a part of the reasons less FDI inflow in the MENA region. Grossman and Helpman (1990), Rivera-Batiz and Romer (1991), Barro and Sala-i-Martin (1997) points out that trade openness may contribute to economic growth through spillover effect of FDI. This is because, trade openness may exposed and improve knowledge for high tech products by importing it from others countries.

There have late study about the determinant of FDI and found that trade openness has non effect to the FDI inflow (Schmitz and Bieri, 1972; Wheeler and Mody, 1992).

CHAPTER 3

METHODOLOGY

3.1 Introduction

The purpose of this chapter is to discuss the theoretical framework, procedure and methods that are used to conduct this study. The time series data and econometric methodology is employed in order to analyze the relationship between military expenditure and foreign direct investment. The tests are Augmented Dickey Fuller (ADF) to test the stationarity of the data and ARLD bound test to test the co-integration of both military and FDI variables. Beside that, this chapter also provides description of the data that is used in this study.

3.2 Theoretical Framework

There are several ways in which military expenditure may influence economy and there are categorized into demand effects, supply effect and security effects.

3.2.1 Demand Effects

From the Keynesian point of view, increase in military expenditure can increase demand as well as reduce the unemployment, boost the income and increase the purchasing power. Under consumption theories, military expenditure has crowding out effect in which it can absorb fund or resources from the other productive expenditure such as investment. This budget constraint cause by increasing in military expenditure will be finance through increase taxation, increase borrowing either internal or external and also increase the supply of money. This will lead to further effect such as increase in interest rate and if government wants to finance the budget constraint by increase borrowing, it will increase the government debt. All these may affect the inflow of foreign direct investment to the country as well as reduce the growth.

3.2.2 Supply Effects

Military expenditure may affect the supply side of economy through the factor of production such as labor, resources and technology. The factor of production that will use by the military sector is similar to the factor of production that will use by the civilian to produce potential output. Broadly speaking, increasing in military expenditure automatically increase the usage of factor of production for military sector while reduce the resources for the ability of economy to produce new products. Beside that, military priorities may have qualitative impact on civilian innovation.

3.2.3 Security Effects

Increase in security may protect the civilian and country from internal and external threats. This is important for the market to operate, as an incentive to invest and generate output. For example, the less developed countries the major obstacle to strengthen their economy is war and lack of security. However, military expenditure is not only for the purpose of security so it will not bring positive security effect.

3.3 Model Specification

The model specification to examine the impact of independent variables to dependent variables can be represented as follow:

FDI = f (MILEX, GE, TO)

Where,

FDI = foreign direct investment, GE = government expenditure,

MILEX = military expenditure, TO = trade openness

Broadly speaking, this study want to investigate the link between FDI and MILEX and the other independent variables (GE and TO) which are the control variables that can give some impact to the dependent variables.

3.4 Stationarity

It is important to test for the stationarity of the data in time series analysis. A stationary time series means the data fluctuate around its constant value, while non-stationary means the series having parameters of the cycle change over time. This trend must have to remove in order to avoid that trend to influence or dominate the other features of data. Beside that, non-stationary time series can spurious regressions by having a high R-squared even a set of variables are unrelated or causal relationship. If the time series data is non-stationary, it is important to transform it to stationary by doing first difference. It means it must be differenced d times to convert it become stationary.

Yt = Yt-1 + μt ————————- (1)

The equation above is a first order. If the coefficient of Yt-1 is equal to zero or I(0), it means the series is a stationary and if the coefficient of Yt-1 is equal to 1 or I(1), it means the series contain one unit root. The series have two unit roots or I(2) when the coefficient of Yt-1 is equal two. In that case, it need to differencing twice to transform into stationary but most of the series just have a single unit root.

3.5 Unit Root Test

The non-stationarity can turn the result into unreliable findings. Thus, there is a need to remove the non- stationary data. This can be done by performing the Augmented Dickey-Fuller (ADF) tests. Augmented Dickey-Fuller (ADF) test is an extension from the Dickey- Fuller (DF) test. The DF test is applicable if the єt is white noise but if the error term is autocorrelated, the ADF test is more suitable as it already takes into account the autocorrelation in є. The error tem is said to be autocorrelated when there is autocorrelation in the dependent variables. The simplest form of ADF test is:

ΔYt = αYt-1 + + єt

H0: α = 0 (there is a unit root or time series is non-stationary)

H1: α < 0 (time series is stationary)

This ADF test using m lags of dependent variable. If the result able to reject H0, it proved that, the data is stationary and the result produced will be reliable. However if H0 is accepted, thus it proved that the data is non-stationary (have unit roots) which make the result unreliable to be hold.

3.6 ARDL Bound Testing Approach

In this study, the ARDL bound test is need to employed to test the co-integration between military expenditure, FDI and growth. According to Pesaran (2001), the ARDL test have several advantages such as small sample size is reliable for this test and no restriction imposed on the order of integration of each variable. Furthermore, it applicable when the explanatory variables are endogenous, and is sufficient to simultaneously correct for residual serial correlation. The ARDL bound co-integration test model is:

FDI = f (MILEX, GE, TO)

FDI = inflow of foreign direct investment, % of GDP

MILEX = military expenditure, % of GDP

GE = government expenditure, % of GDP

TO = trade openness

C = constant

There need several steps to done a complete test for the co-integration relationship. First, Breusch-Godfrey LM test is applied in order to make sure the time series is free from serial correlation. The maximum lag order is (p) = 2, in order to avoid the over parameter problem. Different lag order can be use if the estimation indicates serial correlation and the last solution is to use Hendry’s General to Specific model if the result still indicate serial correlation problem.

After the time series is free from serial correlation, the Wald Test is applying to indicate whether all the variables have long run co-integration to each other by imposing restriction on the estimated long run coefficients of MILEX, FDI and GDP. The hypotheses are as follow:

H0: β1 = β2 = β3 = 0 (no long run relationship)

H1: β1 ¹ β2 ¹ β3 ¹ 0 (a long run relationship exists)

The computed F-statistic value will be compared with critical values. According to Pesaran et al (2001), if the coefficients among the lag 1 variables are jointly fall above the upper bound critical value, this implies that here is a long run relationship among the variables. If it below the lower bound, it implies no long run relationship and if it falls in between of upper and lower bound, it implies inconclusive.

3.8 Causality Test

To test the causality test, the ARDL bound test is employ in order to know either it’s FDI and the other control variables can affect MILEX or not. This causality test is similar with the previous ARDL bound test, but the dependent variable is change to MILEX and the independent variables are FDI, GE and TO where the relationship is as follow:

MILEX = f (FDI, GE, TO)

H0: β1 = β2 = β3 = 0 (no long run relationship)

H1: β1 ¹ β2 ¹ β3 ¹ 0 (a long run relationship exists)

Again, based on the result of the F-test, if the computed F-test is higher than the upper bound, the null hypothesis can be rejected thus need to accept alternative hypothesis. If the F-test is lower than the lower bound then the null hypothesis cannot be rejected thus indicates that there have no long run relationship between those variables. However, if the F-test falls between the lower and the upper bounds, a conclusive inference cannot be made.

3.9 Sources of Data

This study is focus on the interrelationship between military expenditure, Foreign Direct Investment (FDI) in Malaysia. This study used yearly data which undertaken from 1970 until 2010 (41 years). This study is based on secondary data derived from several sources.

The data of military expenditure is collected form Stockholm International Peace Research Institute (SIPRI). Military expenditure can be define as all spending that relates to military operation include current and capital expenditure such as arms force, expenditure on defense ministries, paramilitary forces, military space activities, military aid and others. But it is not includes the expenditure on civil defense and current expenditure for previous military activities. This data is obtained from year 1970 until 2010 and it is measure as a share (%) of gross domestic product (GDP).

In this study, FDI is used to take the net FDI inflow. Net FDI inflow is measure as a share (%) of gross domestic product (GDP) and it collected from World Bank from year 1970 until 2010. Data for government expenditure is obtained from World Bank and the trade openness is compute as a ratio of export and import of goods and services to gross domestic product.

CHAPTER 4: DATA ANALYSIS AND FINDING

4.1 Unit Root Test

Augmented Dickey Fuller is applying in this paper in order to test the level of stationary. Based on Pesaran (2001) to use ARDL approaches to are no need to test for unit root because these approach allows the mixture of I(0) and I(1). In order to check the problem of stationary so this test is employed to know whether the time series is stationary or not.

From the table 4.1, the FDI and MILEX series shows that they are significant at 5% of level. Thus the test of unit root could reject the null hypothesis because the p-value is less than critical value. Therefore, both FDI and MILEX series were stationary at levels were integrated of order zero I(0). To ensure both variables are free from non-stationary, this test is extend up to first difference and the result shows that the null hypothesis of the unit root both FDI and MILEX were rejected as they were significant at 1% level of significance thus confirming that they are all stationary.

For the case of GE and TO, the null hypothesis for unit root test could not be rejected as the p-value is greater than the 5% significant level. Therefore, it indicates that both variables are not stationary. Applying the same test up to first different, the p-value are less than 5% significant level thus the null hypothesis of the unit root test for both variables can be rejected. This indicates that both GE and TO variables are stationary after first difference.

Table 4.1 Augmented Dickey Fuller Unit Root Test

Augmented Dickey Fuller (ADF)

Level

Variable

Constant Without Trend

Constant With Trend

LFDI

-3.917224***

(0)

-3.864854**

(0)

LMILEX

-3.051264**

(1)

-3.656734**

(1)

LGE

-1.940502

(0)

-3.021945

(0)

LTO

-0.545775

(0)

-1.858127

(0)

First Difference

LFDI

-8.551413***

(0)

-8.448181***

(0)

LMILEX

-5.027701***

(2)

-4.943434***

(2)

LGE

-7.603343***

(0)

-4.435261**

(2)

LTO

-5.672100***

(0)

-5.615053***

(0)

Note: *** ,** and * denotes significant at 1%, 5% and 10% significance level, respectively. The

figure in parenthesis (…) represents optimum lag length selected based on Akaike Info Critirion

4.2 Diagnostic Test

It is important to check for the diagnostic test in order to make sure the model is accepted. Firstly, the serial correlation test was carried out to ensure there is no autocorrelation in the residual. For this purpose, the Breusch Godfrey (BG) serial correlation LM test is used. Based on LM test if all autoregressive coefficients are simultaneously zero, there is no autocorrelation of any order. Thus, the hypothesis of BG test is:

H0: ρ1= ρ2= … = ρp= 0 (no autocorrelation)

HA: ρ1≠ ρ2 ≠ … ≠ ρp ≠ 0 (autocorrelation)

The result showed that none of the statistics are significant at any level of significant as the p-value is 0.580 thus it indicated that the null hypothesis of no autocorrelation could not be rejected. This indicates that, the model is free from serial correlation problems.

Next, to know whether the model is well specified, the Ramsey RESET test is performed. Evidently, the result showed that the p-value was 0.515 which is more that 5% level of significance and thus it can be confirm that the functional form was free from misspecification. For the adjusted R-squared, the value 0.52790 means 52.79% of variation in FDI can be explain by all independent variables (MILEX, GE and TO).

The stability test can be represented by CUSUM and CUSUMSQ in figure 4.2.1 and figure 4.2.2 below and it shows that the blue line does not exceed the lower and upper bound, thus it indicates that there is no structural instability exist in this study.

Table 4.2 Diagnostic Checking

Diagnostic Test

R-bar-squared

0.52790

S.E. of Regression

0.45022

LM test

030595***

(0.580)

RESET

0.42394***

(0.515)

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

level, respectively. The figure in parenthesis (…) represents the p-value

Figure 4.2.1 CUSUM

Figure 4.2.2 CUSUMQ

4.3 ARDL Bound Test

The result of bound test to test for cointegration under Narayan (2005) is given in the table 4.3. The computed F-test (4.92281) is greater than the critical upper value (4.803) as tested at 5% level of significance. Therefore, it can be concluded that there is a long-run relationship between FDI and the explanatory variables which are MILEX, GE, and TO as the null hypothesis of no cointegration is be rejected.

Table 4.3 ARDL Bound Test

Level of

Critical value

Computed F-statistic for country

significant

Lower

Upper

Malaysia

1% Significance

5.018

6.610

5% Significance

3.548

4.803

4.92281**

10% Significance

2.933

4.020

Note: The critical values are taken from Narayan (2005), Table Case II, restricted intercept and no trend.

** denote significant at 5% level of significance.

4.4 Long-run Relationship

The results in table 4.3 show long-run relationship for foreign direct investment (FDI) and the independent variables which is MILEX, GE and TO. In this table, we used the determined lag length according to the appropriate lag length criteria based on ‘Specific the order of ARDL model yourself’. In this case the restricted model is been used and the optimum lag length is (1,0,1,1) is chosen.

The result shows that MILEX significantly has a long run relationship with FDI at 1% significant level. The positive coefficient of MILEX implies that the increase in military expenditure (MILEX) by 1% in the long run will increase the foreign direct investment (FDI) in Malaysia by 1.1279%. This result is different from the expected sign where MILEX supposedly has negative relationship with FDI. This is because for the case of Malaysia, the expenditure of military were not used for the war purposed and this spending were focus more on protect our country’s territory and also improve the security of the country. Beside that, the annual budgets for military expenditure was used for the purpose of salaries, allowance and operation cost. The ministry of defense will used part of the portion of total budget for military to give training in order to improved the skilled of arm forces and also to employed more arms. This view is in line with Sharma and Ghani (2004) as their point out that there have positive relationships between military expenditure and FDI because the defense sector provides major employment. Beside that, the income effect could increase human development for those countries thus attract the FDI flow to the countries.

Furthermore, to be a develop countries in year 2020 Malaysia need to diversifying the defense doctrine, strengthen its defense and military system, expend military development, and have a modern and sophisticated defense equipment. So Malaysia is ready enough to face any threat from aggressors and this will make Malaysia as a competitive location for the foreign investor to make investment. This is due to the efficiency in economic and also in term of the safety of the country.

For GE and TO, the result shows that both control variables are insignificant with FDI as the p-value is greater than 5% significant level. This indicate that GE and TO are not contribute to FDI inflow in Malaysia and foreign investors don’t take these factors as an important things to invest in Malaysia.

Table 4.4 ARDL Long-run Result

Regressor

(1,0,1,1)

LMILEX

1.1279***

(2.8290)

LGE

-1.0170

(-0.70544)

LTO

-0.66355

(-1.0924)

INPT

3.2342***

(0.83056)

Notes: ***, ** and * denote significance at 1%, 5% and 10% levels, respectively

4.5 Short run relationship and speed of adjustment

This study is further estimating short-run relationship and the error correction model. The result are presented in table 4.4 where it showed that military expenditure (MILEX) has a positive relationship with foreign direct investment (FDI) in the short run at 5% level of significance. For both control variables (GE and TO), the result show that they are insignificant as the p-value is greater than critical value. Thus, it can be concludes that only MILEX has positive relationship with FDI in the short run and it indicates that increase MILEX by 1% in the short run will increase FDI by 0.87118% in Malaysia.

As indicated in table 4.4, the error correction model (ecm-1) measure the speed of adjustment to restore equilibrium in the dynamic model and the negative sign in the error correction model (ECM) model is statistically significant at 1% significant level and thus confirming a long run relationship exist among variables. The speed of adjustment implied by the ecm coefficient is 77%. The short run model provides information on how the dependent variable which is FDI adjusts to restore long run equilibrium in response to the disturbance. Approximately, 77% of the disequilibrium from the previous year’s shock converge back to the long run equilibrium in the current year.

Table 4.5 Error Correction Representation of ARDL model

Regressor

(1,0,1,1)

dLMILEX

0.87118**

(2.5397)

dLGE

-1.1882

(-1.0113)

dLTO

3.0659*

(1.9322)

INPT

2.4980

(0.79014)

Ecm(-1)

-0.77239***

(-5.2315)

Notes: ***, ** and * denote significance at 1%, 5% and 10% levels, respectively

4.6 Causality Test

This test is apply in order to verify whether FDI, GE and TO have causal effect with MILEX. For this purpose, ARDL bound test is used in order to check the cointegration between those variables.

Table 4.5 shows that computed F-test (2.1578) is lower than the upper bound for any significant level. Therefore it indicate that independent variables (FDI, GE and TO) have no cointegration with MILEX and it can be concludes that the causality effects runs one way which is from MILEX, GE, TO to FDI and not the other way.

Table 4.6 ARDL Bound Test

Level of

Critical value

Computed F-statistic for country

significant