Military Expenditure and Economic Growth

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Military Expenditure and Economic Growth

An instrumental variable approach

Malin Dahlberg Løvereide

Thesis submitted for the degree of Master of Philosophy in Economics

30 Credits

Department of Economics Faculty of Social Science

University of Oslo, November 2020

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Abstract

The relevance of the defence-growth nexus is increasing as global military expenditures are continuing to stay on an upward trend. Defence spending now constitutes a significant share of global resources and its economic impact has recently gained increasingly interest in economic literature. The potential effects of military expenditure on economic growth has policy implications. If the net effect is positive, increasing military expenditure can benefit other parts of the economy, e.g. increasing aggregate demand and higher

employment. On the other hand, if the relationship is of a negative nature, then the trade-off coming from increasing military expenditures must be evaluated before allocating resources to military budgets.

This thesis aims to understand the net causal effect of military expenditure on economic growth. Using data on military expenditure from the Stockholm International Peace Research Institute (SIPRI) and data on economic growth from the Penn World Table, I analyze the causal effect of military expenditures on economic growth in a sample of 112 countries between 1993 and 2017. The method used is instrumental variable analysis, where a measure of conflicts in neighboring countries is used as an instrument for military

expenditure. Conflicts in neighboring countries increases military spending in the country itself as a response to the increasing instability and risk of conflict. The Uppsala Conflict Data Program (UCDP) and Peace Research Institute Oslo (PRIO) provides the dataset on armed conflict, which is used to calculate this measure.

The regression include country and time fixed effects and the analysis is conducted on both a full sample and grouped sample of countries. The motivation for grouping the sample is based on empirical evidence from previous research indicating that the level of development and income might affect the relationship between military expenditure and economic growth. I find no significant evidence that military expenditure have an effect on economic growth, neither for the full nor for the grouped samples.

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Acknowledgements

This thesis marks the end of my time at the University of Oslo. I am grateful for the experiences and knowledge the past five years as a student has given me. Writing this thesis has been both challenging and inspiring, and I would like to thank my supervisor Andreas Moxnes for valuable guidance and advice throughout the process.

I am also grateful to my family, friends and fellow students for their support and

encouraging words. Any remaining mistakes, inaccuracies or omissions are solely my own.

Malin Dahlberg Løvereide Oslo, November 2020

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List of figures

Figure 1: Global military expenditure 1988-2019.

Figure 2: Military spending as a share of gross domestic product, by country in 2019.

Figure 3: Mean annual percent military burden in country sample, 1993-2017.

Figure 4: Mean annual percent economic growth in country sample, 1993-2017.

Figure 5: Percent share of countries experiencing conflicts in neighboring countries and countries not experiencing conflicts in neighboring countries.

List of tables

Table 1: Military expenditure, by region and sub-region, 2019.

Table 2: Countries experiencing conflicts in neighboring countries sub-groups.

Table 3: The first-stage effect of neighboring conflict on military burden

Table 4: The effect of an increase in the defence expenditure on annual GDP growth (OLS) Table 5: The effect of an increase in the defence expenditure on annual GDP growth (IV) Table 6: The first-stage effect of neighboring conflict on military burden for developed and developing countries

Table 7: The effect of an increase in the defence expenditure on annual GDP growth for developed and developing countries (OLS)

Table 8: The effect of an increase in the defence expenditure on annual GDP growth for developed and developing countries (IV)

Table 9: The first-stage effect of neighboring conflict on military burden for high and low income countries

Table 10: Table 5: The effect of an increase in the defence expenditure on annual GDP growth for high and low income countries (OLS)

Table 11: The effect of an increase in the defence expenditure on annual GDP growth for high and low income countries (IV)

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1 Introduction

Although defence spending constitutes a significant share of global resources, its economic impact has only recently been an issue of analysis in economic literature (Dunne

& Nikolaidou, 2012). As global military expenditures are continuing to increase, the relevance of this issue is only getting increasingly relevant. In 2019, global military and defence budgets reached the highest level since 1988, estimated to have been $1917 billion worldwide. This is 3,6 percent higher in real terms than in 2018 and 7,2 percent higher than in 2010 (Da Silva et al., 2020).

The potential effects of military expenditure on economic growth has major policy implications. If the nature of the relationship is positive, allocating funds to military budgets can be rationalized by governments in several manners. The most commonly cited positive effect of military expenditure is that it increases aggregate demand in the economy.

Increased aggregate demand would, in turn, reduce unemployment and increase resource utilization, production and economic growth. Others also point to the security enhancing effects of military expenditure. Greater national security is likely to increase incentives to invest and therefore also contribute to accumulation of capital. Additionally, it is possible that military establishments have a modernizing role and can contribute to a change in structure (Deger, 1986). This type of institutional dynamics could stimulate growth to a certain extent, especially in developing economies.

One the other hand, if military expenditure has a net negative effect on the rest of the economy, one must evaluate the trade-offs when allocating funds to military budgets. The negative effects of military burden on economic growth is believed to be indirect and visible through reductions in savings, investments or increasing debt. Most opponents to large military expenditures make the argument that defence expenditures crowd out more productive private and public investments. According to this view, military expenditure has a high opportunity cost and investments in this sector therefore crowd out civilian human and capital investment that would be more profitable. On the supply side, factors of production used by the military are not available for civilian use and may cause a negative effect on economic growth. Moreover, defence is often thought to siphon off research and development resources that can be more fruitfully applied to civilian applications directly (Sandler & Hartley, 1995). Deger (1986) also states that military spending significantly depresses the savings-income ratio, which ultimately harms growth and development.

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2 This thesis aims to estimate the causal relationship of military expenditure on economic growth, using a sample of 112 countries in the period 1993-2017. However, as it's likely that military burden interrelates with economic, geographic and political variables, it is difficult to determine if an effect of higher military expenditure on economic growth is a causal effect or not. Military expenditure is likely determined by more variables than we can control for and these potential omitted relevant explanatory variables could cause the estimation to be inconsistent and biased. Further, several of the previous studies rely on the assumption that the causality between defence expenditure and economic growth goes from the first to the latter, rather than vice versa. If, in fact, the observed relationship between military burden and economic growth is a case of reverse causality, the effect goes from economic growth to military burden. The existence of reverse causality can create unreliable estimation results as well. It is also possible that there are factors that affect a country’s military burden and economic growth simultaneously. The presence of such simultaneous causality makes military burden correlated with the error term in the regression of interest and could make the estimate unreliable.

An ordinary least square (OLS) estimate could create an inconsistent and biased result, because it fails to recognize the potential effects from omitted variables, reverse- and simultaneous causality. To be able to identify a possible causal effect, this thesis utilizes the instrumental variable (IV) method with a measure of conflicts in neighboring countries as the instrument. It is likely that the emergence of conflicts in neighboring countries is exogenous with respect to the country itself. Additionally, the variation in exposure to conflicts in neighboring countries provides a good opportunity to compare countries

exposed to conflicts in neighboring countries and those that are not or to a lesser degree. By using the IV approach, I am able to minimize the potential issues from omitted variables, reverse- and simultaneous causality and more accurately estimate the relationship of interest.

In previous research, there is little consensus on the net effect of military expenditure on economic growth, often referred to as the defence-growth nexus. Some studies indicate a net positive effect of military expenditures on economic growth. In Benoit’s seminal paper from 1978, he found that least developed countries (LDCs) with a heavy defense burden generally had the most rapid rate of growth, and those with the lowest defense burdens tended to show the lowest growth rates. Benoit attributed this positive net effect to high military expenditure providing jobs and increasing employment, involving in infrastructure, training and research and development (Alptekin & Levine, 2012). The same type of

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3 arguments have been used to explain the net positive effects found in Middle Eastern

countries (Yildirim† et al., 2005), Jordan (Dimitraki & Win, 2010) and wealthy capitalist countries (Baran & Sweezy, 1966). A study of Indonesia emphasized the development of human capital as the main reason why military expenditure had a net positive effect (Chairil et al., 2012).

On the other hand, several studies indicate a net negative effect. Cappelen et al. (1984) found that military spending has a positive impact on manufacturing output, but a negative effect on investment. These two effects have an opposite impact on economic growth.

According to their analysis of 17 OECD countries, the latter effect dominates and there is a net negative effect of military spending on economic growth. Investigating LDCs, Deger (1986) also found a net negative relation between economic growth and defense

expenditure. Deger also emphasized that there are two contradictory effects of military expenditure. In LDCs, there is one structural effect (the role of modernization) and one resource based effect (lack of domestic savings). The military may have stimulating effects on the former but depresses the latter. The evidence of his analysis suggests that the

negative effect is dominant. Most literature that finds a net negative relationship, attributes the result to the indirect effects of military expenditure on economic growth. The indirect effects are visible through reductions in savings, investments or increasing debt. Such arguments are made by researchers finding net negative effects in Greece (Antonakis, 1997), Peru (Klein, 2004) and the EU15 (Dunne & Nikolaidou, 2012).

A third group of previous literature find no significant effect of military expenditure on economic growth. Among others, the results of Kollias et al. (2017) and Abdel-Khalek et al.

(2020) indicate the absence of a strong and robust nexus and a weak causal relationship.

Their studies examine the economic effects of military spending in Latin American countries and India, respectively. The lack of significant effects could be the result of several estimation implications. First, the regression analysis on both variables could prove to not produce a statistically significant coefficient of correlation. Second, the nature and the amount of defence spending may vary over time. Third, defence spending may not be large enough to have a statistically meaningful effect on economic growth.

The results of this thesis supports the claim of the third group of literature. The analysis find no significant net effect of military expenditure on economic growth. The results remain the same when grouping the sample into different levels of development and income. This thesis contributes to the existing literature in two ways. First, it includes a broad sample of countries to take into account the heterogeneity of countries. A limited part

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4 of the existing research uses larger samples of countries. Most papers uses samples of rather homogenous countries, e.g. the EU15 (Dunne & Nikolaidou, 2012), OECD (Cappelen et al., 1984), Latin American (Kollias et al., 2017) or Middle Eastern (Yildirim† et al., 2005) countries. Second, the instrumental variable method has not, to my knowledge, been used to analyze the defence-growth nexus previously.

This thesis has the following structure. Section 2 consists of two main parts. Firstly, I will elaborate on the current level of global military expenditures and the historical trends.

Then, I will give an overview of previous research. In section 3, a brief discussion of the possible theoretical channels through which military expenditure might affect economic growth is given. Section 4 presents the data, explains the rationale behind data selection and provides descriptive statistics. In section 5, I will present the instrumental variable method and discuss the necessary assumptions, in addition to the presentation of the technical set up of the analysis. Section 6 presents the results and evaluates whether the assumptions hold.

The section ends with robustness tests to confirm the results of the regression. In the final section, the results are discussed before a I evaluate the limitations of the analysis. Finally, the section ends with some indications of future lookouts of military expenditure. The statistical software used to analyze the research question is STATA 16.

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2 Background

2.1 Global military spending

Global military expenditures are continuing to increase. In 2019, military and defence budgets reached the highest level since 1988, estimated to have been $1917 billion

worldwide. This is 3,6 percent higher in real terms than in 2018 and 7,2 percent higher than in 2010 (Da Silva et al., 2020). As seen in figure 1, world military spending has been rising from 2015 to 2019, after having decreased steadily from 2011 until 2014 following the global financial and economic crisis. In per capita terms, military spending is also on an upward trend. Military spending per capita rose from $243 in 2018 to $249 in 2019, as the 1,1 percent growth in the world population was surpassed by the growth in military spending (Da Silva et al., 2020). In 2019, the five largest spenders were the United States, China, India, Russia and Saudi Arabia, which together accounted for 62 per cent of global military spending. Military expenditure increased in Europe, Asia and Oceania, the Americas and Africa. The total military expenditure in the Middle East, for the countries which data is available, decreased.

Figure 1: Global military expenditure 1988-2019.

Source: Da Silva et. al. (2020).

Notes: The absence of data for the Soviet Union in 1991 means that no total can be calculated for that year.

Rough estimates for the Middle East are included in the world totals for 2015–19.

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2.2 Military Burden

A country’s military expenditure as a share of GDP, also known by the name “military burden”, is a measure of the relative economic burden the military places on that country (Da Silva et al., 2020). In 2019, the world military burden was 2,2 percent, an increase from 2,1 in 2018. There are great variations in the military burden of each of the world regions.

In 2019, on average, countries in the Americas had the lowest military burden, at 1,4 percent of GDP. For African countries the average was slightly higher, at 1,6 percent. In both Asia and Oceania and Europe, it was 1,7 percent. The highest average, 4,5 percent, was for states in the Middle East for which data is available. As seen in the text box in Figure 2, six of the 10 countries with the highest military burden are in the Middle East. On the other hand, Costa Rica, Iceland and Panama do not have a military and therefore have no military burden at all (Da Silva et al., 2020).

Figure 2: Military spending as a share of gross domestic product, by country in 2019.

Notes: Countries with military burden over 4% are listed. Disputed territories are not market on the map.

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2.3 Regional trends

Military expenditure varies greatly between countries and world regions. Table 3 summarizes the 2019 military expenditures as well as the trend of the past decade. In at least four of the world’s five regions, military expenditure increased in 2019. The highest increase was in Europe, followed by Asia and Oceania, the Americas and Africa. Due to lacking and uncertain data, there are great difficulties estimating total military spending in the Middle East (Da Silva et al., 2020).

Table 1: Military expenditure, by region and sub-region, 2019.

Region and sub-region Spending ($ b.), 2019

Change (%) World share

(%), 2019 2018-19 2010-19

World 1 917 3,6 7,2 100

Africa North Africa Sub-Sahara Africa

41,2 23,5 17,7

1,5 4,6 -2,2

17 67 -15

2,1 1,2 0,9 Americas

Central America and the Caribbean North America

South America

815 8,7 754 52,8

4,7 8,1 5,1 0,2

-13 49 -15 8,9

43 0,5 39 2,8 Asia and Oceania

Central Asia East Asia Oceania South Asia South East Asia

523 2,2 363 29 88,1 40,5

4,8 16 4,6 3,5 6,4 4,2

51 63 58 25 41 34

27 0,1 19 1,5 4,6 2,1 Europe

Central Europe Eastern Europe Western Europe

356 31,5 74 251

5 14 4,9 3,9

8,8 61 35 -0,6

19 1,6 3,9 13

Middle East 147 -7,5 … 8,9

Notes: Spending figures are in US$, at current prices and exchange rates. Changes are in real terms, based on constant (2018) US$. Percentages below 10 are rounded to 1 decimal place; those over 10 are rounded to whole numbers. Figures and percentage shares may not add up to stated totals or subtotals due to the

conventions of rounding. Uncertain data is marked by red and numbers unable to estimate is marked by “…”.

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2.3.1 Europe

European military spending in 2019 was $356 billion, 5,0 percent higher than in 2018 and 8,8 percent higher than in 2010 (Da Silva et al., 2020). In 2019, Europe was the third- largest spending region and five of the world’s 15 largest military spenders were in Europe:

Russia, France, Germany, the UK and Italy.

Military spending in Western Europe in 2019 was $251 billion, increasing 3,9 percent from 2018 to 2019, but the number has decreased 0,6 percent since 2010 (Da Silva et al., 2020). Historically, Western European military spending increased consistently during the cold war before falling sharply between 1990 and 1995. Since then, military spending has gradually increased, partly because of the wars in Iraq and Afghanistan, before falling sharply in 2009 due to the economic crisis (Perlo-Freeman, 2016).

In Eastern Europe, military expenditure totaled $74,0 billion in 2019. This was 4,9 percent higher than in 2018 and 35 percent higher than in 2010 (Da Silva et al., 2020). All seven countries in Eastern Europe increased their military spending in 2019. Before 1992,

‘Eastern Europe’ consisted only of the Soviet Union, for which there is no data. However, there was likely a collapse in spending by Russia and other former Soviet States from 1992, compared to the previous level of Soviet spending, with the declines continuing until 1998.

From 1999 onwards, since Vladimir Putin became President of Russia, military spending in the region has increased substantially, led by Russia but followed by most other countries in the region. The total is still far lower, however, than spending levels by the former USSR (Perlo-Freeman, 2016).

Central European spending in 2019 was $31,5 billion, 14 percent higher than in 2018 and 61 percent higher than in 2010 (Da Silva et al., 2020). Pieter Wezeman,a researcher at SIPRI, attributes the increase in Central and Eastern Europe to growing perceptions of a threat from Russia (Stockholm International Peace Research Institute, 2019), as Russian military expenditure has grown significantly over the past two decades. Central European military spending saw a generally increasing trend during the cold war years, followed by a massive fall in 1990 with the reunification of Germany and thus the end of a separate East Germany as part of the total. After another fall in 1991 as the cold war ended, the total remained relatively flat over the 1990s, before increasing as most countries in the region joined or prepared to join NATO. This ended in 2007 after which the beginnings of the global economic crisis led to sharp falls up to 2013. Since then, the region has been on an upwards trend, with Poland leading on (Perlo-Freeman, 2016).

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2.3.2 Asia and Oceania

Military spending in Asia and Oceania was $523 billion in 2019 and accounted for 27 per cent of the global total. Five of the top 15 global spenders in 2019 are in Asia and Oceania: China, India, Japan, South Korea and Australia. The 4,8 percent rise in the region’s military spending in 2019 continued an uninterrupted upward trend dating back to at least 1989. Asia and Oceania is the only region with continuous growth since 1989 and the growth of 51 percent over the last decade was by far the largest of any region (Da Silva et al., 2020). The increase was due primarily to the rise in Chinese military spending, which in 2019 accounted for 50 percent of total spending in the region, up from 36 percent in 2010. There were substantial increases in all of Asia and Oceania’s sub regions between 2018 and 2019 and over the past decade. Strong economic growth throughout the region is a major driver, although periodic regional conflicts and tensions, especially between India and Pakistan and between North and South Korea, also played a role (Perlo-Freeman, 2016).

The highest level of increase is found in Central Asian countries. At $71,1 billion, India had the highest military spending in South Asia in 2019. It was 6,8 percent higher in 2019 than in 2018 (Da Silva et al., 2020). India’s military expenditure has risen significantly over the past few decades. As mentioned, India’s tensions and rivalry with China and Pakistan are among the major drivers for its increased military spending. Pakistan’s own military expenditure rose by 70 percent over the last decade, to reach $10,3 billion.

In addition to China, Japan and South Korea are the largest military spenders in East Asia. Military spending by Japan was $47,6 billion in 2019, 0,1 percent lower than in 2018.

Its spending increased by 2,0 percent between 2010 and 2019. In South Korea the upward trend in military spending has been increasing since 2000. In 2019 its military spending reached $43,9 billion, an increase of 7,5 percent in 2018 and of 36 percent in 2010 (Da Silva et al., 2020). South Korea attributes the increasing military spending to an uncertain security environment. In particular, the tension between North and South Korea is likely to have contributed to both sides putting large resources into modernizing their militaries (Smith, 2019).

Military spending in South East Asia increased by 4,2 percent in 2019 to reach $40,5 billion, after a 4,1 percent fall in 2018. Over the last decade, spending increased by 34 percent. Seven of the eight states in the sub-region for which data is available increased their military spending between 2010 and 2019. The largest spenders in the sub-region in

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10 2019 were Singapore, Indonesia and Thailand (Da Silva et al., 2020). For several countries, the increases in the past decade are partly to pay for expansion of the capabilities of their armed forces as a reaction to Chinese claims and activities in the South China Sea.

The country that spends most on military in Oceania is Australia and its military expenditure in 2019 was $25,9 billion. This was 2,1 percent higher than in 2018 and 23 percent higher than in 2010. This increase comes as a response to Australia perceiving military threats in its neighborhood, including from China, and globally as heightened (Da Silva et al., 2020).

2.3.3 Africa

Up to 1995, African military spending held a relatively stable level or fell slightly. The main reasons being that the region struggled under debt burdens, the end of the cold war caused numerous proxy conflicts to end and the end of Apartheid in South Africa caused their spending to fall significantly. Since then, however, African military spending has surged as the continent’s economies have improved, while increasing oil resources have frequently been squandered on high military spending (Perlo-Freeman, 2016).

At an estimated $41,2 billion, military expenditure in Africa accounted for 2,1 percent of the global total in 2019. The marginal growth in spending in 2019 was the first increase in African military expenditure for five years (Da Silva et al., 2020). Despite annual decreases from 2015 to 2018, increases in other years meant that total African military spending grew by 17 percent over the past decade.

Military spending by countries in North Africa is estimated to have totaled $23,5 billion in 2019, representing 57 percent of the total for Africa. Due to long-standing tensions between Algeria and Morocco, domestic insurgencies and continuing civil war in Libya, military spending in the sub-region was 4,6 percent higher than in 2018 and 67 percent higher than in 2010. Algeria’s military expenditure of $10,3 billion in 2019 was the highest in North Africa, and Africa as a whole, and accounted for 44 percent of the sub-regional total. At 6,0 percent of its GDP, Algeria’s military burden was the highest in Africa in 2019 as well (Da Silva et al., 2020).

Military spending in sub-Saharan Africa decreased by 2,2 percent in 2019 and reached

$17,7 billion, which was 15 percent lower than in 2010. At $3,5 billion, South Africa’s military spending was the highest in sub-Saharan Africa in 2019 (Da Silva et al., 2020). Its

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11 spending fell by 1,5 percent in 2019, the fourth consecutive year of decrease. The second largest spender in the sub-region in 2019 was Nigeria. In recent years, military spending by sub-Saharan African countries has been volatile. Of the 19 countries that increased military spending in 2019, 8 decreased spending in 2018. Similarly, 13 of the 23 countries that lowered spending in 2019 had raised spending in 2018. This means that the trend changed for 21 of the 42 countries in the sub-region for which relevant data is available in 2019.

Armed conflict is a major driver for the volatile nature of military spending in sub-Saharan Africa (Stockholm International Peace Research Institute, 2020).

2.3.4 Americas

Military expenditure in the Americas reached $815 billion in 2019 and accounted for 43 percent of the global total (Da Silva et al., 2020). Three countries from the region were among the top 15 global spenders in 2019: the USA, Brazil and Canada. Despite the 4,7 percent overall increase in 2019, military spending by states in the region was in 2019 13 percent lower than in 2010.

Spending by the two North American countries, the US and Canada, accounted for 92 percent of the total for the Americas in 2019. Military spending by the United States grew by 5,3 percent to a total of $732 billion in 2019 and accounted for 38 percent of global military spending. The increase in US spending in 2019 alone was the same size as the whole of Germany’s military expenditure for that year. Pieter D. Wezeman, researcher at SIPRI, elaborates on this point and states that “the recent growth in US military spending is largely based on a perceived return to competition between the great powers”, (Stockholm International Peace Research Institute, 2020).

In South America, total regional spending generally increased slightly up to 1989. The dramatic falls up to 1992 are almost entirely due to a near halving of military spending in Brazil, plagued by hyperinflation and a debt crisis, although there were also major cuts by Argentina and Colombia. Since then, the trend has been generally upwards as the region’s economies have tended to strengthen (Perlo-Freeman, 2016). From 2010 to 2019, South America’s military expenditure increased by 8,9 percent, following the upward trend the past decade. However, the spending changed relatively little from 2018 to 2019, only up 0,2 percent to $52,8 billion in 2019. In 2019, the three main contributors to South American military spending were Brazil, Colombia and Chile. Together, they accounted for 80 percent

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The Central America and Caribbean military expenditure is dominated by Mexico, although civil wars in Central America in the 1980s and 1990s also had an impact. The region’s spending increased significantly up to 1984, before remaining flat up to 2004.

Thereafter, the drug wars have seen a sharp increase in military spending (Perlo-Freeman, 2016). Total military expenditure by states in Central America and the Caribbean was $8,7 billion in 2019. Military spending in the region increased by 8,1 percent in 2019 and by 49 percent over the last decade (Da Silva et al., 2020). Mexico’s military spending accounted for 75 per cent of the sub-regional total. At $6,5 billion, it was 7,9 percent higher than in 2018.

The growth was largely due to the costs associated with the government’s strategy of using the military to combat drug cartels.

2.3.5 The Middle East

The Middle East region is heavily influenced by the difficultness of estimating and collecting reliable data on military expenditure. However, based on available data, SIPRI estimates the combined military expenditure to $147 billion for states in the Middle East (Da Silva et al., 2020). Of the countries in the Middle East for which data is available, the combined military expenditure fell by 7,5 percent from 2018 to 2019.

Two of the top 15 global spenders in 2019 are in the Middle East: Saudi Arabia and Israel. Saudi Arabia is by far the largest military spender in the region, with an estimated total of $61,9 billion in 2019. Military spending by Israel was $20,5 billion in 2019, a slight increase of 1,7 percent compared with 2018. Between 2010 and 2019, Israeli military spending increased steadily, and in 2019 it was 30 per cent higher than in 2010 (Da Silva et al., 2020).

One can trace some trends in the area's military expenditure, despite the issue with collecting reliable data. Firstly, the first Gulf War in 1990-91 showed a very sharp increase in military spending. E.g. in 1991 Kuwait’s military spending was more than 100% of its GDP, after which the total fell back. Second, the region's strong oil revenues and continuing regional conflicts and tensions has led to a strongly increasing trend in military spending since then (Perlo-Freeman, 2016).

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2.4 The causal effect of military expenditure on economic growth

Ever since the work of Benoit (1978), which found that military burden had a positive effect on economic growth for 44 less developed countries, several approaches and models have been used to investigate the relationship between military burden and economic growth. However, researchers have agreed neither on a theory nor an empirical method that explains the true causal economic effects of military expenditure. Overall, empirical results regarding the effects of defense burden on economic growth tend to fall into three broad categories. The first group of literature finds net positive effects (Benoit 1978; Yildirim† et al. 2005; Farzanegan 2014; Baran and Sweezy 1966), while a second group (Szymanski, 1973; Cappelen et al. 1984; Deger 1986; Dunne & Tian 2015) find a net negative impact.

Naturally, the third group of studies reveal no significant effect on economic growth at all (Kollias et al., 2017; Abdel-Khalek et al. 2020).

Benoit is by many considered the first researcher to directly investigate the relationship between military expenditure and economic growth. In his seminal paper from 1978, he found that countries with a heavy defense burden generally had the most rapid rate of growth, and those with the lowest defense burdens tended to show the lowest growth rates (Benoit, 1978). The study covered the growth rates, investment rates, foreign aid receipts, and certain other variables of 44 least developed countries (LDCs) between 1950 and 1965.

Other researchers have also used samples of developing and low-income countries to investigate the defence-growth nexus. One example is Yildirim† et al. (2005), which empirically examined the effect of military expenditures on economic growth for Middle Eastern countries and Turkey, from 1989 to 1999. The relationship between military

expenditure and economic growth is investigated by using cross‐section and dynamic panel estimation techniques. Their results indicated that military expenditure enhances economic growth in the Middle Eastern countries and Turkey as a whole (Yildirim† et al., 2005).

Other studies have also found positive effects when analyzing one single country. For instance, Farzanegan (2014) found a positive and statistically significant effect of increasing shocks in the military budget to income growth in Iran. He analyzed the response of the Iranian economy to shocks in its military budget from 1959 to 2007, using impulse response functions and variance decomposition analysis.

The argument that military expenditure enhances economic growth in developed countries has also been made. Baran and Sweezy (1966) argue that World War II was important in causing or accelerating the recovery from the Great Depression in the United

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14 States. They state that the principal reason for post-World War II prosperity instead of a return to the conditions of the Great Depression was military spending. Their analysis of the role of military spending in preventing economic stagnation in monopoly capitalist

countries is tested with data from the 18 wealthiest capitalist countries. They define monopoly capitalist countries as countries with large monopolist corporations that control markets but have problems recovering their surplus because of underinvestment. Hence, Baran and Sweezy concluded that the monopoly capitalist system had a powerful tendency toward stagnation, rooted in long-term underinvestment. Their argument is that military expenditures largely compensate for this tendency (Foster, 2008). According to the Baran- Sweezy theory, the greater the role of military spending in an economy, the lower should be the level of unemployment and the more rapid the rate of growth (Baran & Sweezy, 1966).

However, later Szymanski (1973) tested the hypothesis made by Baran and Sweezy by controlling for the factors of GNP, GNP/capita, and the level of either military or non- military spending. While the level of unemployment was found to be associated with the level of military spending as predicted, the rate of growth was found to be negatively associated. Non-military expenditure was found to play a much more significant role in economies than does military spending. Overall, he claimed that the Baran-Sweezy theory of the role of military expenditure on economic stagnation had serious deficiencies

(Szymanski, 1973).

A study of developed countries was done in 1984 by Cappelen et al. They used data from 17 OECD countries in the period 1960 to 1980. Their analysis aimed to investigate the interrelationships between economic growth, manufacturing output, investment, and

military spending, both for the whole sample and for three relatively homogeneous

subgroups of countries. Military spending was generally found to have a positive impact on manufacturing output, but a negative effect on investment. These two effects have an opposite impact on economic growth. The net effect is that military spending has an overall negative effect on economic growth for the whole sample of countries and for the

subgroups, except for the Mediterranean countries (Cappelen et al., 1984).

In another seminal paper, Deger (1986) found a negative relation between economic growth and defense expenditure in less developed economies as well. Deger states that the weakness of previous research is that it ignores the simultaneous effects of the relationship that define the defense growth structure. Further, he claim that Benoit was not wrong in finding a positive growth impact for military spending, but he fails to incorporate all simultaneous effects in his analysis. Taking all evidence together, Deger’s empirical

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15 estimates support the view that military spending does not increase growth rates in LDCs.

Rather, there is a negative relationship between these two variables. According to Deger, there are two constraints on the growth process in LDCs: one structural (the role of

"modernization") and one resource based (lack of domestic savings). The military may have stimulating effects on the former but certainly depresses the latter. The cross-sectional evidence provided by Deger in 1986 suggests that the latter effect is dominant in LDCs.

The last decade, an increasing number of researchers investigating the growth-defence nexus have a particular focus on parameter heterogeneity and using larger samples of countries. Additionally, the post-cold war era has gained increasing focus as more data become available that do not reflect the particular geopolitical environment of the cold war.

In turn, this means that there is more information in the data, which should help researchers in identifying any relation with economic factors (Dunne & Tian, 2015). Among others, Dunne and Tian (2015) examines the impact of military expenditure on economic growth in a large sample of countries. They use an exogenous growth model and dynamic panel data methods for 106 countries over the period 1988 to 2010. Having estimated the model for all of the countries in the panel and finding that military burden has a negative effect on growth in the short and long run, the panel is broken down into various groupings based upon a range of potentially relevant factors, and the robustness of the results is evaluated. The factors considered are different levels of income, conflict experience, natural resources abundance, openness and aid. The estimates for the different groups are remarkably consistent with those for the whole panel, providing strong support for the argument that military spending has adverse effects on growth (Dunne & Tian, 2015).

The third group of literature finds no significant effect of military expenditure on

economic growth. This could either be a result of the relationship lacking altogether, or that military burden has both positive and negative effects on economic growth and that these effects outweigh each other. In this case it could prove difficult to capture an net effect empirically. One of the papers within this group of literature is the research done by Kollias et al. in 2017. They examine the economic effects of military spending in the case of the 13 Latin American countries. The nexus between defence spending, economic growth, and investment is investigated for the period 1961 to 2014, employing both linear and nonlinear tests. Their findings are not uniform across all countries included in the sample. However, the results point to the absence of a strong and robust nexus between the variables examined and a weak causal relationship. In most cases, no nexus could be statistically traced and it is

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16 concluded that this is not a universally applicable inference. Additionally, one of the latest papers on this topic examines the relationship between military expenditure and economic growth in India (Abdel-Khalek et al., 2020). The study uses a time series approach to examine and analyze the relationship between military expenditure and economic growth in India, during the period 1980 to 2016. The study concludes that there is an absence of causal relationship between military expenditure and economic growth in India, during the indicated period.

The impressively large and growing literature on the effects of military spending on economic growth reflects a continuing lack of consensus (Dunne & Tian, 2015). Previous studies on the growth-defence nexus provide a broad range of different results. As several authors have pointed to, the contradictory results could be attributed to various reasons. One of the main causes of diversity of empirical results is related to the empirical research itself.

In particular, the theoretical framework of the empirical analysis, the involved countries in the sample, the employed time period and empirical method may be significant research- related issues responsible for the diversification of the empirical results (Emmanouilidis &

Karpetis, 2020). As it is difficult to focus on all these issues simultaneously, this thesis is particularly concerned with including a large sample of countries and utilizing a different method, the instrumental variable method, to investigate the relationship.

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3 Economic Mechanisms

The relationship between military expenditure and economic growth can be explained using economic theory and mechanisms. Although determining which channel is most relevant for the growth-defence nexus is not the aim of this thesis, a brief overview over the potential channels will provide an understanding of the different ways military spending is believed to influence economic growth.

There are several channels defined through which military expenditure may affect economic growth, both negatively and positively. Each channel may lead to different conclusions and thus the net effect is ambiguous (Alptekin & Levine, 2012). Deger (1986) believes that it is exactly this simultaneous nature of the relationship that determine the link between military expenditure and growth. In general, the literature suggests that there is a trade-off between productive (e.g. private investments and education) and unproductive (defence spending) government spending. Further, Sandler and Hartley (1995) states that the theoretical arguments stem from the comparison between the direct and indirect costs of military activities and its indirect benefits. In their opinion, the effect of military burden also depends on its size compared to the whole economy. When the share of the military burden is small with respect to the whole economy, it is possible to have benefits greater than costs and to obtain a positive impact on the growth rate (Sandler and Hartley, 1995).

Military expenditure could affect economic growth positively through demand, security and spillover effects. On the demand side, an increase in military expenditure would have a positive effect on aggregate demand, and the Keynesian multiplier effect would then affect the economy by increasing resource utilization and reducing unemployment. According to Keynesian theory, the level of economic activity is driven, at least in the short run, by changes in aggregate demand. An increase in government expenditures boost demand for goods and services in the market and this change in aggregate demand creates positive effects throughout the economy. In the case of the government increasing military expenditure, one assumes that this increases demand for goods and services used by the military. In turn, firms offering such goods and services will buy more from suppliers, employ more people and increase their income. The firms pay workers and suppliers, workers and suppliers buy goods from other firms, those firms pay their workers and suppliers, and so on. In this way, the original change in aggregate demand is spread throughout the economy. In turn, all of these effects increase production and economic growth in the economy as a whole.

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18 Military expenditure could also promote economic growth by increasing security, which is essential to incentivize investment and drive innovation. These security-related positive effects on economic factors could therefore also contribute to accumulation of capital, new technology, and increase growth. In economic theory, a widely accepted theory is that well- enforced property rights provide incentives for individuals to participate in economic activities, such as investment, innovation and trade, which lead to a more efficient market.

It is likely that the effects of security works through the same mechanism as property rights.

In case of poor property rights or high risk of war, firms and individuals are likely to be less willing to invest and engage in economic activity. This is due to e.g. uncertainty about whether the profits of the investment will benefit the investor himself. Having well- enforced property rights and national security might increase the likelihood of investment, accumulation of capital and, in turn, increase economic growth.

The impact of military expenditure on growth could also be positive through externalities and spillover effects, such as transfers from military training, technology, research and development. Military training can contribute to improving human capital in an economy, which is assumed to contribute to economic growth. It is also possible that research and development in the military sector produces improved or new technology, which in turn can benefit the civilian part of the economy as well. E.g. the Global

Positioning System (GPS), which was originally started as a project by the U.S. Department of Defense in 1973. The limitation to the United States military was later changed to allow for civilian usage from the 1980s (Roulo, 2018). Additionally, Deger (1986) suggested that military establishments have a modernizing role in developing economies and therefore can contribute to a change in structure. This type of institutional dynamics can stimulate growth to a certain extent.

The potential negative effects of military burden on economic growth is mainly believed to be indirect and visible through reductions in savings, investments and increasing debt levels. Most opponents to high military expenditures make the argument that defence expenditures crowd out private and public investments. According to this view, military expenditure has a high opportunity cost and investments in this sector therefore crowd out civilian human and capital investment that would be more profitable. Additionally, defence is often thought to siphon off research and development resources that can be more

productive if applied to civilian sectors directly (Sandler & Hartley, 1995). Whether or not this is the case comes down to if military research and development can be used by civilian society. One case is the development of GPS, which obviously has had a great positive

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19 effect on civilian society. However, some would argue that a major part of military research and development is aimed at a narrow area of usage that is not applicable to civilian

society.

On the supply side, factors of production used by the military are not available for civilian use and may cause a negative effect on economic growth. In his seminal paper, Deger (1986) also states that defense expenditure allocates scarce resources away from productive civilian investment and fails to mobilize or create any additional savings. More importantly, it significantly depresses the savings-income ratio, which ultimately harms growth and development (Deger, 1986). Further, in countries that are still developing economically, a focus on military spending often means foregoing other important priorities (Beattie, 2018). Many nations have a standing military but an unreliable public

infrastructure, like infrastructure and schools. An extreme example is North Korea, where a major focus on military spending has severely affected the standard of living for the general population.

Additionally, opponents of high military budgets also points to the flaw of ignoring how governments finance their spending. Any government spending that exceeds revenues results in a deficit, adding to the national debt. Military budgets financed by taking on more debt might affect the economy negatively through the increasing debt burden on the

country. An increasing national debt has an economic impact on the whole economy. As the debt grows, the interest expense of the debt grows and the cost of borrowing increases due to the risk that increased debt represents. In theory, the increased debt will eventually drag on economic growth and drive taxes higher (Beattie, 2018).

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4 Data and descriptive statistics

The aim of this section is to describe and present the data and the sample that is used in this thesis. The thesis uses a balanced panel with data collected from several sources.

Firstly, the data and sources are present, before arguments for data selection follows. The section ends with descriptive statistics of the sample’s military burden, economic growth and conflicts in neighbouring countries.

4.1 Data and data selection

Firstly, data on military expenditure is collected from the Stockholm International Peace Research Institute (SIPRI) database. The data is from the SIPRI Military Expenditure Database updated in April of 2020, which provides military expenditure data by country for the years 1949–2019. The dataset contains several measures of military expenditure,

however I have decided to use the measure of military expenditure as defined by the percentage of defence and military expenditure as a share of gross domestic product. This measure is, as mentioned, also known as a country’s military burden and measure of the relative economic burden the military places on that country. The motivation for using this measure is that it is used by most other literature on the topic, which makes it easier to compare results. The SIPRI database is widely used in research on military expenditure and economic growth. Among others, the database is used by Dunne and Tian (2015), Abdel- Khalek et al. (2020) and Kollias et al. (2017).

Annual gross domestic product (GDP) growth is calculated using data from the Penn World Table version 9.1. The logic behind taking the GDP parameter in this form is to capture the effect of defense expenses on the economy as a whole.

The Uppsala Conflict Data Program (UCDP) and Peace Research Institute Oslo (PRIO) provides the dataset on armed conflict. In this thesis, I use the UCDP/PRIO Armed Conflict Dataset version 20.1. The dataset contains data on armed conflicts, both internal and

external, from 1946 to present day. The main unit in this dataset is a “State-based Armed Conflict”. A state-based armed conflict is defined as “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths in one calendar year” (Department of Peace and Conflict Research, 2020). Additionally, to

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21 calculate the measure conflict in neighbouring countries, a list of all country borders is

collected from the GeoDataSource website (GeoDataSource, 2019).

The availability of data dictates the selection of countries to the sample used in this thesis. All countries with available data on military expenditure, economic growth and conflicts in neighbouring countries between 1993 and 2017 are included in the sample. A full list of the countries included in the sample can be found in Appendix A.

4.2 Descriptive statistics

In total, this thesis includes data from 112 countries over the period 1993-2017. The summary statistics of the sample of countries are presented in figure 3, 4 and 5. The statistics presented in the section are military burden, economic growth and conflicts in neighbouring countries. Additional details are included in Appendix B.

4.2.1 Military burden

In the country sample used in this thesis, the mean military burden have had a downward trend between 1993 and 2017. As figure 3 illustrates, the mean military burden decreased from 2,826 percent in 1993, to 1,853 percent in 2017.

Figure 3: Mean annual percent military burden in country sample, 1993-2017.

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4.2.2 Economic Growth

In the country sample used in this thesis, the mean economic growth has varied significantly between 1993 and 2017. In 1993, the mean economic growth was

1,803 percent, while in 2017 this number had increased to 3,440 percent. Over the period, the highest annual GDP growth was in 2007, with 6,109393 percent. Following the global financial crisis, the lowest annual GDP growth was in 2009 with -0,615 percent.

Figure 4: Mean annual percent economic growth in country sample, 1993-2017.

4.2.3 Conflicts in neighbouring countries

During the time period investigated in this thesis, the number of countries exposed to conflicts in neighbouring countries varies from year to year. Figure 6 illustrates two groups of countries, those which experienced at least one conflict in neighbouring countries (light green) and those that did not experience any conflicts in neighbouring countries (dark green), for each year respectively. The group of countries experiencing no conflicts in neighbouring countries is larger than the other group in 23 of the total 25 years. Only in 1994 and 2016 are the group of countries experiencing at least one conflict in neighbouring countries larger. Further details on the two groups and their composition are included in Appendix B.

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23 Figure 5: Percent share of countries experiencing conflicts in neighboring countries and countries not experiencing conflicts in neighboring countries.

By far the largest sub-group of countries experiencing conflicts in neighbouring countries are those that experience between 1 and 5 conflicts in neighbouring countries, as seen in table 2. There are some countries that experience between 6 to 10 conflicts in neighbouring, while a few countries experience between 11 and 15 conflicts in neighbouring countries.

The maximum amount of conflicts in neighbouring countries experienced by any country for a given year is 15.

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24 Table 2: Countries experiencing conflicts in neighboring countries sub-groups.

Year

Number of countries experiencing 1-5 neighbouring conflicts

1993 48 5 0

1994 53 3 1

1995 50 2 1

1996 46 3 1

1997 35 3 1

1998 36 3 1

1999 42 3 1

2000 39 3 1

2001 42 3 1

2002 40 3 1

2003 39 3 1

2004 44 3 1

2005 42 3 1

2006 41 3 1

2007 49 2 1

2008 48 4 1

2009 49 2 1

2010 48 3 0

2011 50 1 0

2012 48 2 0

2013 49 2 1

2014 51 2 1

2015 48 5 1

2016 52 5 1

2017 47 4 1

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5 Empirical Method

As described in section 2, the relationship between military burden and economic growth has been subject to several studies the past decades. These studies have utilized different empirical methods and economic theories to determine the nature of the relationship.

However, as it's likely that military burden interrelates with several economic, geographic and political variables as well, it is difficult to determine if an effect of higher military expenditure on economic growth is a causal effect or not. For instance, Yesilyurt & Elhorst (2017) found that military spending measured as a ratio of GDP in a country depends primarily on the spending of other countries, but it also depends on several other variables, among which are the level of GDP, the occurrence of international wars, and the political regime. Because of the complex nature of military spending, it is possible that it is determined by more variables than we can control for. In turn, this means that we are potentially omitting relevant explanatory variables and face the issue of omitted variable bias.

Further, several of the previous studies rely on the assumption that the causality between defence expenditure and economic growth goes from the first to the latter, rather than vice versa. Identifying reverse causality is sometimes a matter of common sense, but in the case of the defence-growth nexus this might not be so simple. If, in fact, the observed

relationship between military burden and economic growth is a case of reverse causality, the effect goes from economic growth to military burden. Benoit elaborated on this point and suggested that countries with rapid growth might feel better able to indulge themselves in the luxury of elaborate defense programs, just as rich families are usually more

adequately insured than poor ones. Moreover, rapidly rising national incomes might generate an even more rapidly rising level of tax revenues of which a powerful defense lobby might be try to secure a proportional, or rising, share (Benoit, 1978). One can also expect that countries with high growth are incentivized to invest in more defence and military to protect their rising economy.

It is also possible that there are factors that affect a country’s military burden and economic growth simultaneously. This could for instance be national attitudes or culture, historical factors or other individual characteristics of the country, such as natural resources, population size and access to technology. All of these factors could make countries

more/less likely to allocate funds to defence and military as well as having low/high economic growth. When, in addition to the causal link of interest from military burden to

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26 economic growth, there is a causal link from economic growth to military burden, there are issues with simultaneous causality. The presence of simultaneous causality also makes military burden correlated with the error term in the regression of interest.

An ordinary least square (OLS) estimate would create an inconsistent and biased result, because it fails to recognize the potential effects from omitted variables, reverse- and simultaneous causality. This could potentially lead to the OLS-estimate of the effect of military burden on economic growth being inconsistent and biased. An alternative method to estimate the relationship is using an instrumental variable (IV) approach. By using this method, I am able to minimize the issues discussed above and more accurately estimate the relationship of interest.

“Instrumental variables (IV) regression is a general way to obtain a consistent estimator of the unknown coefficient of the population regression function when the regressor X, is correlated with the error term, u.” (Stock & Watson, 2014, 470). To explain how IV regression works, Stock & Watson (2014) looks at the variation in X as consisting of two parts. The first part is correlated with the error term and causes estimation problems, while the second part is uncorrelated with the error term. “If you had information that allowed you to isolate the second part, you could focus on those variations in X that are uncorrelated with the error term and disregard the variations in X that bias the OLS estimates.” (Stock &

Watson, 2014, 470). An IV regression works exactly like this. The information about movements in X that are uncorrelated with the error term is separated from one or more additional variables, called instrumental variables. Instrumental variables regression uses these additional variables as tools to isolate the movements in X that are uncorrelated with the error term, which in turn produces consistent estimation of the estimation coefficient (Stock & Watson, 2014).

Further, this analysis utilizes the two-stages least-squares (2SLS) method to estimate the effect of military burden on economic growth. The first-stage regression is a regression of equation (1) and the second-stage is a regression of equation (2). Since the assumption is that the instrumental variable is uncorrelated with the error term and have no direct effect on the dependent variable, the effect of external conflicts on economic growth will only be visible through the effect of military share of GDP. This would be the true causal effect of military burden on economic growth.

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27 The empirical approach is summarized by the following two equations:

(1) Milex

it

= ϒ

0

+ ϒ

1

NeighbourConflict

it

+ ν

it

(2) GDPgrowth

it

= β

0

+ β

1

Milex

it

+ ɛ

it

In equation (2), GDPgrowth denotes the annual GDP growth in percentage for each country. Milex denotes annual military expenditure by country as percentage of gross domestic product. NeighbourConflict denotes the number of armed conflicts in neighboring countries. The error terms are denoted by ν and ɛ. Since we are interested in the causal effect of military burden on economic growth, our parameter of interest is β1. The method of 2SLS addresses the problems that an OLS-regression of equation (2) would suffer from.

I also include country and time fixed effects in the regression. Country fixed effects remove between-countries differences in the outcome variables and concentrate on the within-country effects. Additionally, controlling for variables that are constant across entities but vary over time are done by including time fixed effects. Fixed-effects models may over-protect against omitted-variable bias (Gates et al., 2012), and therefore produce conservative estimates. This might be a potential drawback of the method, but it ensures that only the causal effect is captured.

Further, there are two assumptions that must hold when using an instrumental variable successfully in a linear model. First, the instrument must be relevant to produce an accurate estimator. The more relevant the instrument is, the more the variation in X is explained by the instruments. In turn, this means that there is more information available for use in the IV regression. On the other hand, instruments that explain little of the variation in X are called weak instruments. When you have a weak instrument, the 2SLS estimate is no longer reliable (Stock & Watson, 2014). Instrument relevance can be tested by using the rule of thumb and looking at the first-stage F-statistic. The rule of thumb states that a first-stage F- statistic less than 10 indicates that the instrument is weak, in which case the 2SLS estimator (even in large samples) is biased, and 2SLS t-statistic and confidence intervals are

unreliable.

Second, the variable used as instrument must not be correlated with the error term of the explanatory equation. This requirement is instrument exogenity. This requirement states that the instrumental variable cannot pose the same issue as the original variable for which

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28 it is attempting to resolve (Stock & Watson, 2014). If the instruments are not exogenous, then the 2SLS estimates are inconsistent. The idea behind doing an instrumental variables regression is that the instrument constraints information about variation in X, which is uncorrelated with the error term. If the instrument is correlated with the error term and therefore not exogenous, it cannot pinpoint this exogenous variation in X, and it stands to reason that IV regression fails to provide a consistent estimator.

To find a suitable instrument, one must find a variable that affects military burden. The instrument must not have a direct effect on economic growth, only an indirect effect on economic growth through the effect on military burden. Naturally, one variable that is likely to affect a country's military burden is the exposure to war and conflict. War and conflict in one's own country is likely correlated with economic growth directly, and can therefore not be used. However, the emergence of conflicts in neighbouring countries are likely

exogenous with respect to the country itself. Conflict and uncertainty in a neighbouring country is likely to increase one’s own military burden as a response to defend borders and prevent the conflict from spreading into its own territories. Additionally, conflicts in neighboring countries create incentives to upgrade and invest in military equipment. This also means to re-allocate labour force and capital from the civilian sector to the military sector.

In this analysis, I will therefore use conflicts in neighbouring countries as an instrument.

The instrument is calculated by simply counting the number of conflicts in neighbouring countries. The count is done annually for all countries in the sample. The exogenous given number of armed conflicts in neighbouring countries provides a good opportunity to estimate the causal effect by using IV. Using conflict as an instrument when estimating the causal effect of defence expenditure on economic growth makes sense for several reasons.

The design of the IV-analysis makes it possible to compare countries exposed to conflicts in neighbouring countries and those that are not or to a lesser degree, and thereby potentially discovering a causal effect of defence expenditure and economic growth. Since the level of exposure to conflicts in neighbouring countries varies between countries, I am able to include country and time fixed effects in the analysis. Unobserved variables across countries and time most likely had an effect on both economic growth and defence

expenditure and including fixed effects in the regression cancels out these potential effects.

Military Expenditure and Economic Growth