CIVILIZATION AND THE MODERN MILITARY: DOES INCREASED MILITARY SPENDING LEAD TO HIGHER LEVELS OF

INNOVATION IN SOCIETY

A Thesis submitted to the Faculty of the

Graduate School of Arts and Sciences of Georgetown University

in partial fulfillment of the requirements for the degree of

Master of Public Policy

By

Xixiang Jia, B.Com.

April 17, 2014 Washington, D.C.

ii

Copyright 2014 by Xixiang Jia All Rights Reserved

iii

The research and writing of this thesis is dedicated to everyone who helped along the way.

Many thanks,

ANDREAS T. KERN

iv

CIVILIZATION AND THE MODERN MILITARY:

DOES INCREASED MILITARY SPENDING LEAD TO HIGHER LEVELS OF INNOVATION IN SOCIETY

Xixiang Jia, B.Com.

Thesis Advisor: Andreas T. Kern, Ph.D.

ABSTRACT

This paper used a series of two way fixed effect models to test the hypothesis that higher

military expenditure will boost the quantity and quality of innovation. The dataset in this paper is

a merged dataset from the World Development Indicators (WDI) dataset and the Quality of

Government (QOG) dataset. It covers 159 countries between 1989 and 2009. Unlike the existing

studies, this paper used multiple indexes to measure innovation, including GNI, Industrial Output,

Service Output, High-Tech Exports, the number of Patents, and the number of Scientific Articles.

These six independent variables would jointly capture the full essence of innovation in terms of

both quality and quantity. Moreover, Military Expenditure per Soldier and its lags are used as the

dependent variable of interest. A series of other control variables are used to capture different

countries’ government quality, educational level, financial market conditions, and infrastructure.

Four of the innovation indicators (GNI, Industrial Output, Service Output, High-Tech Exports)

have shown unambiguously positive correlation with military innovation. Only two indicators

(the number of Patents and the number of Scientific Articles) show relationships with military

expenditure that are insignificant. Considering the fact that military-related Patents and Scientific

Articles are underreported due to the military secrecy policy, I am confident that, if all the

military innovations are disclosed, all six proxies for innovation would show evidence that

v

military expenditure can boost innovation. Finally, this paper provides reasonable grounds for

policymakers to reevaluate the use of their defense budget.

vi

TABLE OF CONTENTS

Introduction................................................................................................................1

Literature Review.....................................................................................................4

Empirical Model and Hypothesis ............................................................................8

Methodology............................................................................................................12

Data Descriptions......................................................................................................18

Empirical Results ....................................................................................................21

Limitation and Challenge.........................................................................................27

Policy Implication....................................................................................................29

Appendix..................................................................................................................36

Bibliography ............................................................................................................45

1

INTRODUCTION

“Every gun that is made, every warship launched, every rocket fired signifies, in the final sense, a theft from those who hunger and are not fed, those who are cold and are not clothed. The world in arms is not spending money alone. It is spending the sweat of its laborers, the genius of its scientists, the hopes of its children... This is not a way of life at all, in any true sense. Under the cloud of threatening war, it is humanity hanging from a cross of iron.”

------Dwight D. Eisenhower, Former U.S. President, April 16, 1953

Above is a famous citation of former U.S President Dwight Eisenhower. It tells us that

every dollar spent by the military comes from a dollar cut in the budget for other projects. There

are many different issues needed to be resolved in the government’s agenda and all those issues

are competing for funding. In 2012, the US defense spending equals 37% of the federal

government budget (Friends Committee, 2013). This is the largest pile of the federal budget.

Under the context of massive government deficit, many politicians, interest groups, lobbyists are

targeting at the defense cut so there will be more money available to other public sectors.

However, cutting defense budgets may result in a series of problems for many countries. For

example, countries may have to reduce the sizes of their armed forces due to the budget

reduction, which would in turn lead to insufficient forces to protect the national security. Also,

lower defense budgets may cause huge unemployment because soldiers in the armies, workers in

the military manufacturing industry and researchers in military research entities may face the risk

of being laid off. Except for the two negative effects I mentioned above, a defense cut would also

reduce innovation in the society. So the aim of this paper is to study how lower defense budget

leads to less innovation.

2

In recent centuries, there are many great innovations generated from the military sector. For

example, submarines, rockets, satellites, nuclear energy, jet aircraft, computers, and the Internet

originate from military laboratories. As the economy becomes more developed and the financial

market become more efficient, those military inventions are being quickly adapted for civilian

use and become the new sources for economic growth.

Several factors explain why the military sector is responsible for so many revolutionary

innovations. First, almost all governments have very strong desires to master the latest

technologies and arm their soldiers with the best equipment, so that they can maintain internal

stability and protect homeland security. Second, the defense department has sufficient resources

and capacities (funds, human capital, knowledge reserving, and consistent policy backup) to

explore the most advanced theories and technologies in the world. Third, like all other public

goods, innovation has long been underinvested in the private sector because firms concerns about

the high fixed cost and free riding problem (Jha, 2010). The thesis tests the hypothesis that

increased military expenditures will lead to higher levels of innovation in the whole society. The

dependent variable is innovation. Historically, researchers have used many different proxies for

innovation: the number of patents, the growth in real GDP, and the growth of multifactor

productivity (Bronwyn, 2011). In my thesis, I use multiple variables to represent innovation. My

independent variables will be military spending per soldier, which measures a country’s

investment in military without biasing countries with huge populations like India and China. My

main model in this thesis is a two way fixed effect model including a series of different controls

as discussed above. Also, the model would include lags of the log (Military expenditure per

soldier). The number of lags is dependent on my trail and error results: I would replicate the

3

model with different number of lags, and I choose the model that has the biggest explanation

power (i.e. largest Square).

Eventually, four of the six proxies for innovation (GNI, Industrial Output, Service Output,

High-Tech Exports) have shown significantly positive correlation with military expenditure.

Only two proxies (the number of Patents and the number of Scientific Articles) show ambiguous

relationship with military expenditure. Due to the secrecy policy in the military sector, military

innovations are underreported in Patents and Scientific Articles. So if we can get a full data

collection about Patents and Scientific Articles (covering all military innovations), I believe that

all proxies for innovation would show evidence that military expenditure can boost innovations.

Given the updated understanding of military expenditure, policymakers should reevaluate

their countries’ defense policy and be more careful with the defense cut. Deng Xiaoping, the

leader of the Chinese Economic Reform in 1978, had a famous theory: “Science and technology

are primary productive forces” (Deng, 1988). Innovation and technology are the leading forces to

improve productivity, expand market, and provide labor opportunities. Thus, if cutting military

spending would slow down the development of civilization, maybe the government should be

more cautious in cutting defense budget. Also, governments should make policies that assist the

military sector to be more efficient and effective in generating new innovations. Finally,

policymakers should help to expand the application of those military innovations in the civilian

sector so that the economy would have more opportunity for growth.

4

LITERATURE REVIEW

November 9th 1989 is a special day in human history, because that is the day when the

Berlin Wall collapsed. To the German people, it meant the reunification of the country; but to the

world, it meant the end of the Cold War and the success of the democratic movements in Europe.

In the following 5 years, the world witnessed a massive (20%) reduction in global military

spending. Thus, more public money that was formerly spent in the military sector could be used

for private consumption and investment (Mintz and Chan, 1991). As a result, many countries

experienced an economic boom. Ke-young and Peter (1993) called this the “peace dividend”.

They found that an increase in a nation’s military spending has a significant negative externality

on its neighbors’ economic growth, because it increases the risk of conducting economic

activities in neighboring countries. As a result, the neighboring countries have to set aside more

resources to achieve relative military balance, which further hinders economic growth. A

simultaneous cut in defense spending would improve overall security and foster economic

growth in all countries.

Beginning in 1996, global military spending gradually increased again and this trend has

been maintained for nearly two decades. Moreover, the increasing concentration of military

spending appeared as a new feature of global military spending—a smaller number of countries

possess a much higher weight of world military spending (Anup, 2013). Such trend is mainly

5

brought by the belief that a larger military expenditure can lead to economic growth as well as

innovation in the society.

Proponents of increasing military spending like Chowdhury (1991) and Benoit (1973)

argue that military spending is delicately planned public spending which addresses idle capacity,

unemployment, and under-consumption problems resulting from low domestic demand. Others

made similar arguments on how military expenditure can help the economy. It is claimed that the

military has been long perceived as “the employer of last resort” (Jeffrey et al, 2011:514), in

which the development of the military creates massive demand for labor, and the military

personnel receive education, training, medical care, housing, and food. The investment in

training and education further help build up human capital because many of the soldiers and

engineers will participate in the production in private sector in the future (Inkeles and

Smith,1974). Stockwell and Laidlow (1981) contend that the military serves as an important

modernizing institution in undeveloped countries, where the military effectively educates

modernized attitudes and attracts foreign investment and donations. By adopting new disciplines,

achieving motivation and pride in national citizenship, the military helps break down the old

value system, and traditional means of production (Weber, 1921; Andreski, 1968). For example,

Cheung (1988) shows that the Chinese People's Liberation Army (PLA) has been responsible for

building up farms and factories, construct railways and roads, developing communication lines,

training 1.5 million technicians, and intensifying the connection between the outback China and

eastern China.

On the other hand, the positive impact of military spending on the society can also be

seen from the trend of innovation. Comparing to traditional military development, which merely

6

brings more casualties and disasters, modern military development brings more positive

contributions to human civilization. In the past two centuries, the military has initiated many

state of the art innovations, which fundamentally changed modern civilization. Ruttan (2006)

compared the public and private research potential and identified the six major innovations

which could not have be discovered in the private sector: aircraft, nuclear power, the computer,

the semiconductor, the Internet, and space communication and earth observing industries. Apart

from those great innovations in the past, the military sector is actively participating in other

cutting edge technologies. According to a report of the PEW project, the US Department of

Defense (DoD) has begun ambitious R&D programs to solve many sophisticated energy issues,

such as maintaining stable fuel supply in the battlefield, providing safer and non-explosive

energy, and reducing energy production cost (Joshua et al. 2011). DoD’s budget for energy

security initiatives in 2011 was $1.2 billion and this is estimated to grow to over $10 billion per

year in the next two decades (Joshua et al, 2011). Clearly, there is enough empirical evidence

showing innovation could be cultivated from the military backyard, yet the purpose of this paper

is not just to show that the military will generate innovation. Essentially, it is to investigate

whether higher military expenditure will increase overall innovation. Scholars have long been

debating this question but no agreement has been reached.

Looney (1988) and Jeffrey (2008) contend that the impact of military expenditure on

innovation is more positive for countries with lower debt levels than those with higher debt level.

Jeffrey (2008) also concluded that military innovation is becoming more congruent to the private

innovation. More synergies rather than trade-off are revealed between the military and private

innovations, so military and private innovation mutually support each other. Gamota (1985)

7

used research data from 1960 to 1985 to show that defense spending has a critical role to

promote the increasing of non-military technologies. Correspondingly, Saal (2001) demonstrated

that military spending has a significant positive effect on manufacturing and industrial

productivity. Joshua et al. (2011: 6) took a similar position and claimed that the military sector

has a peerless advantage in promoting innovations, because the military has kept a good “R&D

infrastructure, the ability to grow demonstration projects to scale, significant purchasing power

and the necessary culture and management infrastructure”.

However, there are also scholars who challenge the innovation promotion theory of

military expenditure. Knight et al. (1996) claimed that increasing military expenditure would

lead to less innovation. First, the military is not governed by the rules of the market. It has too

much price distortion and faces far less competition than private industry. Therefore, military

sector is less efficient than private market. Secondly, higher defense costs will crowd out the

resources and opportunities that should be available to the more productive and competitive

private sector. Similarly� MacNair et al. (1995) studied defense research and development in

NATO countries and conclude that defense R&D is less productive than private R&D investment

for the purpose of driving economic growth. Jacob (2008) also did research to find out the

impact of military R&D on innovation in 25 developed counties from 1985 to 2005. He used the

number of patent to represent innovation in four two-way fixed-effects models. He found that

public R&D has a small but significant crowding out effect on the private R&D. Also, the

defense R&D budget reduces the productivity of private R&D at a statistically significant level.

Rossman (1931) studied the patent filling in England before and after World War I, and

discovered that the war had caused total number of patent filling dropped down by 40% whereas

8

the number of war-related patent doubled. Somewhat differently, Willen (2013) studies the

impact of war on technical innovation in 34 countries over 97 years and concludes that

engagement in war has no impact on the number of patents filed. His model also suggests that

the nature of war (initiator of the war, number of war casualties and nature of the war) is

uncorrelated with the number of patent filling. In the study, he used the fixed effect model and

also included the lags of war. He used the number of patents as proxy for innovation. As for the

dependent variable of interest, he used dummy of war (if a war happen) and country level war

characteristics such as the number of war casualties.

Unlike the previous studies, which only use one proxy for innovation, my thesis will use

multiple measures to represent innovation. Thus, I can capture a better picture about innovation

in terms of both quality and quantity. By doing regressions against all those proxies and

balancing the final results, my study would show a better understanding about how military

expenditure affect innovation.

EMPIRICAL MODEL AND HYPOTHESIS

This paper will use a demand and supply model to analyze the provision of innovation as

a public good. As a major receiver of the government budget, the military sector has huge

influences both on the demand and supply of innovation.

9

The military related demand for innovation comes from the army or the Defense

Department. The primary purpose of the defense budget is to secure the nation, and maintain

strategic advantage over neighbors and enemies. Traditionally, countries forge its army force by

increasing the number of solders, the storage of resources, and the industrial productivity; in

modern times, countries are also competing in the new arena of technology. Military scientists

and engineers become the pioneers for exploring the new worlds of medicine, biology, geology,

physics and nuclear science. Inevitably, an increasing defense budget will increase the demand

for innovation through more investment in training, development and research. Like all public

goods, military innovation has the potential to cause positive externality to society. This means

that the military innovations are not only useful for military purpose, but also useful for private

or business use. Considering the positive externalities, the social optimal demand curve for

innovation should be lower than the former demand curve.

The military related supply of innovation comes from both the military R&D centers and

the weaponry factories. Since military equipment is critical to the national security, weaponry

manufactures are always firmly controlled and backed up by governments (Cameron, 2006). So

even though some weaponry factories are actually private corporations, their special

characteristics make them more like a member of the public sector (Cameron,2006). Compared

with firms in the private sector, the military companies have significant advantages in capital,

human resources and policy support (Atkinson et al, 2012). If an innovation is created in both the

military sector and the private sector, the military firms are more likely to generate more profits

and be more cost efficient. This is because weaponry manufactures can easily utilize their

advantage of capital and government affiliation to capture the civilian market. For example, the

10

Boeing Company frequently applies its military technologies to its civil airplane (Cezmeci,

2005). In contrast, private firms faces huge barriers (both financial and policy constraints) to

expand their innovation to the capture the weaponry market and compete with the existing giant

weaponry suppliers. Clearly, an innovation from the military sector tends to have more

production and capture more market shares. Therefore, it would be easier and cheaper to

generate more innovation from the military sector (as result of the economy of scale). This

implies that the average cost of innovation in the military sector is less than the private sector. So,

as shown in Figure 1, increased demand of innovation from the military sector would reduce the

overall average cost of innovation and shift down the overall supply curve of innovation.

The Figure 1 illustrates how the supply and demand curves for innovation decide the

level of innovation in a society. It helps us to understand how increasing military spending can

motivate innovation.

11

Assume (Q1, P1) is the original market equilibrium, decided by the market supply curve

and demand curve of innovation. Since military innovation is a public goods that implies a huge

positive externality to society, the social supply curve for innovation should be lower than

market supply curve. Thus the deadweight loss (DWL) is generated due to underinvestment in

innovation. As we discussed above, if the government increases military expenditures, more

orders to the military sector would drag down the average cost of providing innovation and shift

down the supply curve. As a result, the new equilibrium will have higher levels of innovation

(Q*>Q1).

From my empirical model, we know that the increased military expenditure would

increase the supply of innovation toward the social optimal level. Thus, the society can reduce

Figure 1: Demand and Supply for Innovation Price

P1

P*

Q1 Q*

MSC1

MSC2

MDC

DWL

A

B

12

the deadweight loss. In the methodology session, I will further introduce the regression models to

exam how effective can the military expenditure increase the provision of innovation.

METHODOLOGY

The basic model for my thesis is a simple two way fixed effect model. In this model, the

dependent variable !""#$%&'#"!" represents the level of innovation of a country C in year T. In

previous research conducted by Willen (2013) and Ward (2008), the number of patents was the

only proxy for innovation. As discussed above, a patent does not measure the quality of

innovations. Therefore, in this paper, six variables (GNI, Industry Output, High Technology

Exports, Service Output, the number of Patent, and the number of R&D Articles) will be used to

represent Innovation. Although any single one of them could not give a comprehensive picture of

innovation, together they capture both the quantity and quality of the level of innovation.

!"#!"#"$!!"#$%&$'!" is the dependent variable measured by a country’s defense

expenditure per soldier. We do not use the raw military expenditure number because it prejudices

data from countries with small populations and few troops. For countries like Sweden or Israel,

aggregate spending on the military could be low relative to India or China, but this does not

mean the Swedish and Israeli governments do not take military issues seriously and spend little

on military R&D.

!"#$! represents the year specific fix effects and !"#$%&'! represents the country

specific fixed effects. !!" is the error term measuring the external noise and shocks.

13

!""#$%&'#"!" = !! + !!!"#"$%$&!!"#$%&$'!" + !"#$!

+ !"#$%!"! + !!"

(1.1)

Since the basic model only incorporate innovation and military expenditure, it only

gives us a rough idea about how military expenditure affects innovation. Both the sign and

magnitude of the coefficient can be affected by many other factors. In order to get more

convincing evidences about how military expenditures increase innovations, we need to add

controls to our basic model.

In model 1.2, we added the eight controls to the basic model. Democracy is a dummy

variable indicating whether a country has a democratic system. This dummy is included to

consider the possible impacts of government type on innovation. Tertiary is a country’s

enrollment rate for tertiary education. It is included because countries with higher education level

tend to be more productive (Thorpe, 2012). M_freedom is monetary freedom, measuring the

level of price control. We are assuming that a market with less government intervention would

encourage firms to do more R&D projects because their profits from innovation will have more

certainty and less risk. Life_ex is the expected years of living for males. We use males’ life

expectancy to represent a country’s level of stability and conflicts. In countries with a lot of

conflicts, men are more likely to involved into trouble; many of them will be employed by the

government to kill other men and it is really easy for them to kill or be killed. So that country’s

male life expectancy tends to be low. It is also possible that a higher level of conflicts will take

away a nation’s scarce resources that could be useful for doing R&D. This variable is included to

14

separate the effect of conflicts on level of innovation. Credit is the domestic credit to the private

sector. This variable is used to measure the market efficiency or market barriers for companies

trying to raise money. In country with higher market efficiency, it is easier for firms to raise

money to do research. So we need credit to control for the impact of market efficiency on

Innovation; R_spread is the interest rate spread, this variable is used to measure the economic

risk. In an economy with lower risk or uncertainty, people are more confident to invest thus there

should be a higher level of innovation. Therefore, we need to control R-spread. The last variable

we would like to control is the number of internet users per 100 people. This represents the level

of infrastructure. We control infrastructure because infrastructure is a good stimulus for market

expansion that encourage firms to invest in conducting new research (Joshua et al. 2011).

!""#$%&'#"!" = !! + !!!"#"$%$&!!"#$%&$'!"

+ !!!"#$%&'(&)!"! + !!!"#$%&#'!"

+ !!!_!"##$%&!" + !!!"#$_!"!"

+ !!!"#$%&!" + !!!_!"#$%&!"!"#$!

+ !!!_!"#$%&!" + !!!"#$%"$#!" + !"#$!

+ !"#$%&'! + !!"

(1.2)

It is possible that the impact of military expenditure on innovation is dependent on some

other variable, so we added two interaction terms in model 1.3. !"#"$%$&!!"#$%!"#!" ∗

!!!"#$%&#'!" is controlled because I believe the level of education affects the marginal effect

15

of military spending on innovation. Given the same level of R&D funding, the country that has

higher education level tend to do more productive of their research.

!"#"$%$&!!"#$%&$'!" ∗ !"#$%&!" is controlled because market efficiency also tends

to affect a country’s efficiency in generating innovation from military R&D. However, the effect

of this interaction term seems to be controversial: on one hand, in countries where market

efficiency is low (high capital threshold, high set up cost, redundant policy restriction), the

military provides the only possible means to develop big technology. So in these countries,

higher military expenditure tends to develop more innovation; one the other hand, for countries

with fewer barriers to the weaponry market and more credit to the private sector, innovation from

military industries is more easily to be adapted for business use and the market value is more

likely to be realized. Thus, I would expect to see that if we use GNI, Industry Output, Service

Output, High-tech Exports to represent innovation, the sign of the interaction terms tend to be

positive; and if we use the number of Patents or the number of Scientific Journals to represent

innovation, the sign of the interaction terms should be negative.

!"#"$%$&!!"#$%&$'!" ∗ !!!"#$%&#'!" is controlled because I believe the level of

education affects the marginal effect of military spending on innovation. Given the same level of

R&D funding, the country that has higher education level tend to do more productive of their

research.

!"#"$%$&!!"#$%&$'!" ∗ !"#$%&!" is controlled because market efficiency also tends to

affect a country’s efficiency in generating innovation from military R&D. However, the effect of

this interaction term seems to be controversial: on one hand, in countries where market

efficiency is low (high capital threshold, high set up cost, redundant policy restriction), the

16

military provides the only possible means to develop big technology. So in these countries,

higher military expenditure tends to develop more innovation; one the other hand, for countries

with fewer barriers to the weaponry market and more credit to the private sector, innovation from

military industries is more easily to be adapted for business use and the market value is more

likely to be realized. Thus, I would expect to see that if we use GNI, Industry Output, Service

Output, High-tech Exports to represent innovation, the sign of the interaction terms tend to be

positive; and if we use the number of Patents or the number of Scientific Journals to represent

innovation, the sign of the interaction terms should be negative.

!""#$%&!!"!" = !! + !!!"#"$%$&!!"#$%&$'!"

+ !!!"#$%&'(&)!"! + !!!"#$%&#'!"

+ !!!_!"##$%&!" + !!!"#$_!"!" + !!!"#$%&!"

+ !!!_!"#$%&!"!"#$! + !!!_!"#$%&!"

+ !!!"#$%"$#!" + !!"!"#"$%$&!!"#$%&$'!"

∗ !"#$%&#'!" + !!!!"#"$%$&!!"#$%&$'!"

∗ !"#$%&!" + !"#$! + !"#$%&'! + !!"

�1.3�

It is also possible that the marginal effect of military spending is dependent on the level of

military spending itself. So we include the quadratic term of !!!"#"$%$&!!"#$%&$'!"! in Model

2.1. If the quadratic term is significant, there will be an optimal level of military spending. If the

increase military expenditure is still lower than the optimal level, more military expenditure

17

could increase innovation. If the increased military spending exceeds optimal level, more

investment in military sector would actually reduce innovation.

!""#$%&'#!!" = !! + !!!"#"$%$&!!"#$%&$'!"

+ B!!"#"$%$&!!"#$%&$'!"! + !"#$! + !"#$%&'!

+ !ℎ!"#! + !!"

�2.1�

In Model 2.2, I incorporate all controls in model 1.3. So the new model tends to capture a

more precise impact of military spending on innovation. However, I may d encounter the

problem of over controlling and loose efficiency in the new model.

!""!"#$%!&!" = !! + !!!"#"$%$&!!"#$%&$'!"

+ B!!"#"$%$&!!"#$%!!"!"!

+ !!(!ℎ!"#!!"#$%"&')+ !"#$! + !"#$%&'!

+ !!"

�2.2�

In addition to the basic model 1.1, I include lags of military spending in the model 3.1. This

is following the rationale that many innovations take many years to develop. Therefore, the

current innovation might be the result of some military expenditure incurred several years ago.

The exact number of lags is dependent on whether this number of lags can generate the largest

adjusted R square for the model.

18

!""#$%&'#"!" = !! + !!!"#"$%$&!!"#$%&$'!"

+ !!!"#"$%$&!!"#$%!"#!�!!!+. . .…

+ !!!"#"$%$&!!"#$%&$'!�!!! + !"#$!

+ !"#$%&'! + !!"

�3.1�

DATA DESCRIPTION

A. Dataset

The dataset for this paper is drawn from the World Development Indicator (WDI) dataset

compiled by the World Bank and the Quality of Government (QOG) dataset compiled by

University of Gothenburg. The WDI data set contains country-level panel data on 214

different economies from 1960 to 2009. In order to match WDI data with the QOG data,

country groups, such as East Asia, Middle East and OECD, were removed. The WDI dataset

contains variables for up to 19 topics from Economic Policy to Social Development. The

Quality of Government dataset contains country-level data on 214 countries that existed from

1946 to 2012. Countries that collapsed were removed because their information was not

reported in the WDI dataset. The merged dataset include country-level data for159 countries

from 1989 to 2009.

B. Variables

19

The Appendix table A-1 provides variable descriptions. It explains the sources and the

meaning of the variables. The Appendix table A-2 has information about all of my descriptive

statistics.

As we can see from the table, there are six proxies for the Innovation: High-Tech Exports,

GNI, Industry output, Service output, the number of Patents, and the number of Scientific

Articles. High-Tech Exports, GNI, Industry Output and Service Output are used to approximate

the value or quality of innovation; the number of Patents and the number of Scientific Articles

are used to approximate the quantity of innovations. Since all six variables have large ranges and

variations, I use their the logged values in my model.

The independent variable of interest in this paper is military spending per soldier. We can

see that there is wide range between the highest and lowest military expenditure per soldier. The

maximum value is $1,292,440, which is about 15,368 times of the lowest value. Actually, the

$1,292,440 record belongs to Kuwait in 1990, it is no wonder that it is this high, because that

country was using all available resources in 1990 to fight against the Iraq invasion. Since there

are only 2,753 observations for the military expenditure per soldier, I have dropped all other

variables that have so few observations to make the panel dataset more balanced. Moreover,

taking out those variables will make the correlations of variables more reliable.

Moreover, the thesis also uses other important controls, including Democracy, Monetary

Freedom, Conflicts, Credit to Private Sector, Internet Users, Interest Rate Spread, Life

Expectancy for male, Internal Conflicts, Regime Quality and Tertiary Enrollment. Since the

variation and ranges for all other controls are not so large, I do not need to do log

transformations on those variables.

20

C. Correlations

The correlation table in Appendix B is the first part of my correlation table. Correlations

with an absolute value larger than 0.75 are displayed in bold italics. We can see the correlations

among the six proxies for innovation are all bigger than 0.75. This is reasonable because all these

variables are strongly related to innovation, so the correlations among them should also be strong

and positive.

We should notice that military expenditure per soldier has mild positive correlations

(around 0.4) with all innovation proxies. This suggests that an increases in military spending are

positively associated with an increases in the level of innovation.

Moreover, Tertiary (tertiary education enrollment), Credit to Private Sector, Life

Expectancy for Male, and Government Quality all have a mild and positive correlation with the

control proxies.

As for the other important controls, we can see that some have relatively strong correlations,

for example, Internet Users has a 0.6424 correlation with the Tertiary Enrollment because

countries with better infrastructure always tend to be wealthier and they can provide more

tertiary education. This should also alert us the fact that we may have multicolinearity problem,

and we should be cautious about this in the analysis stage.

21

EMPIRICAL RESULTS

The methodology has led us to six families of regressions. Each family uses a different type

of proxy for innovation: GNI, Industry Output, Service Output, High-Tech Output, the number

of Patent and the number of Scientific Papers. Using multiple dependent variables helps to

comprehensively capture the relationship between military spending and innovation. I use six

fixed effect models for each innovation proxy. Moreover, I use log transformation for variables

with really wide scales.

In table C-1, I choose log�GNI�to be the proxy for innovation. The first regression is a

simple fixed effect regression between log (GNI) and log (Military Spending per soldier). The

predicted coefficient is 0.238 and is significant on the 1% significance level. This indicates that

if we increase military spending per solider by 1%, the GNI will increase by 0.238%. Since I

have controlled for the fixed year and country effect, the R square has the high value of 0.82. In

Model 2, I added 7 more control variables including Democracy, Education level, Money Market

Freedom, Life Expectancy, Market Efficiency and Infrastructure on innovation. As I expected,

the Money Freedom, Market Efficiency and Infrastructure have a positive and significant effect

on the log(GNI). However, the effect of tertiary enrollment and democracy seem to have a weak

effect on GNI. In the third model, I add two interaction terms, but the result is insignificant. The

fourth and fifth models both include the quadratic term of Military expenditure. However, the

insignificant coefficients of the quadratic terms do not provide evidence that military expenditure

has a nonlinear relationship with innovation. In Model 6, I introduce lags to military expenditure

22

for 5 years and only the first lag of military expenditure is significant. However, the coefficient

of the first lag is only about one fifth the coefficient of the current year. This implies that military

expenditure will have a positive effect on GNI for two consecutive years and the effect on first

year is much larger than that of the second year. When we go through from Model 1 to Model 6,

we notice that 5 of the 6 models have predicted a positive (from 0.238 to 0.435) and significant

coefficient of log (military spending per solider). So we have reasonable grounds to believe that

increasing military expenditure is positively associated with GNI. However, we are still unsure

about the causality between military expenditure and GNI. It could also be the case that richer

countries incur higher military expenditures because they can afford expensive military

procurement and research. Benoit (1978) pointed out that there is little evidence to support the

idea that higher GNI leads to higher military expenditure. To the contrary, the military

expenditure is more likely to be determined by the political and military leaders to fulfill the

need for deterrence, threats and combat. If Benoit’s conclusions are valid, we have reasonable

grounds to believe that higher military expenditure would lead to higher GNI.

In Appendix table C-2, I take Industry Output as a proxy for innovation. Similar to the last

case, five of the six models show positive (from 0.239 to 0.666) and significant coefficient on

military expenditure. Therefore, I am confident that an increase in military expenditure is

positively associated with Industry output. In Model 3, I integrate the interaction term. Originally,

I expected that a higher civic education level would lead to higher Industry Output. However, the

term log (Military per soldier)*Tertiary enrollment has a negative and significant coefficient (-

0.00332**). This means that higher military expenditure would actually reduce the positive

effect of education on innovation. The reason might be that more military expenditure would

23

allow military to attract skilled labors from the manufacturing jobs in the private sector toward

non-production jobs such as soldiers, and scientific researchers. Looney (1992) confirmed that

there is a huge impact of military expenditure on human capital. He pointed that many

governments (especially in the Arab world) have set a high priority on attracting skilled laborers

to the military sector and there is a big competition for human capital between the military sector

and private sector. The more human capital is absorbed by the military sector, the higher

opportunity cost incurs to the private sector. Thus, there will be decreasing pressure on Industry

Output. In model 5 where I include quadratic terms and interaction terms, the coefficient on the

term log (Military expenditure per soldier)*Credit to Private Sector is also negative and

significant (-0.000984*). Since credit to the private sector is an indicator of financial market

depth, a negative coefficient means that higher military expenditure would reducing the positive

effect of financial market development on industrial output. This reinforces the expectation that

higher military expenditure would distort resources invested in the private sector and financial

market, this would further cast a downward pressure to the industrial output. In Model 6, the

coefficient of the first year lag is positive and significant. This is similar to the previous GNI

case; it means that higher military expenditure is likely to boost industry for the two following

years. However, the fourth lag has a negative and significant coefficient. This means that the

military expenditure four year ago would have a negative impact on current innovation. Since the

size of this negative is small; it is wholly dominated by the positive impact of the military

expenditure for current year and previous year.

In Appendix Table C-3, I used Service Output to proxy for innovation. The result is similar to

the previous two cases. Five of the six models show positive and significant coefficient

24

log(military expenditure per soldier). When I compare the coefficient of log(military expenditure

per soldier) in Table 2 and Table 3, I find that the coefficients in Table 2 are systematically

bigger. This shows that military based innovation has a bigger impact on second industrial

sectors (i.e. the manufacture industry) than the tertiary industrial sectors (i.e. the service

industry). The interaction terms in Table 3 are all insignificant, so the crowding out effect to

service output is not as obvious as for industry output. In Model 6, which includes lagging

military expenditure terms, I also observe that that the first-year lag has a positive and significant

effect on Service Output.

In Appendix Table C-4, all the dependent variables are Hi-Tech Export. If my hypothesis

that military spending boosts innovation is valid, I expect to see that increasing military

expenditure is associated with more high-tech exports. As I expected, the majority of the six

models (4 out of 6) show positive and significant coefficient on military expenditure. Only one

model shows negative and significant coefficient and one model shows insignificant coefficient.

In Model 5, the two interaction terms, log (Military expenditure per soldier)*Tertiary and

log(Military expenditure per soldier)*Credit to Private Sector, are negative and significant. Like

the analysis for Table 2 and Table 3, this supports the previous judgment that military

expenditure crowds out private investment. Moreover, in Model 5, the coefficient on military

spending is negative and significant; but the coefficient on squared military spending is positive

and significant. This indicates the effect of military spending on high-tech export is not linear. If

we hold other factors fixed and increase military spending fixed, the high-tech exports will

decrease at a decreasing rate; after high-tech export reach its minimum level, it will start to

increase. This suggests that as military spending crowds out investment for the private sector,

25

High-Tech Exports will be reduced, however, if a country constantly invests in the military

sector, its military industry will accumulate knowledge and expertise, and this will start to boost

high-Tech Exports. In Model 6, I include 2 lags of military expenditure. However, only the

current year military expenditure is positive and significant, while the two lags are insignificant.

In Appendix Table C-5, I use the number of Scientific Journals as proxy for innovation.

Surprisingly, none of the six models in this table predicts a significant coefficient for log military

spending. So it seemed that an increase in military spending is not associated with any change in

the number of Patents. It is very possible that the number of military related Scientific Journals is

undervalued because of the secrecy and regulation in military sector. When a country conducts

more military research, there must be more scientific papers produced. However, the government

strongly controls public access to their scientific research. Therefore, the scientific journal

figures here probably do not include military related research papers. Similar to the previous

samples, the interaction terms in Model 3, and Model 5 are negative and significant. This further

confirms that military spending would crowd out the scientific research in the private sector.

In Appendix Table C-6, log (Patent) is chosen the as proxy for innovation. The basic Model

1 indicates a negative and significant coefficient for log(military spending per soldier). The

models presents in column 2-5 all predict negative and insignificant coefficient for log(military

spending per soldier). In Model 3, the two interaction terms of log(military spending per

soldier)* Tertiary and log( military spending per soldier)* Credit to private sector are negative

and significant. Therefore, if we hold all other factors fixed, an increase in either Tertiary

enrollment or Credit to Private market will reduce the number of patent. In Model 5, the

interaction term is negative and significant again. In Model 6, the log(military expenditure per

26

soldier) itself is not significant; but the first and second lag of log(military expenditure per

soldier) is negative and significant, indicating that if we hold all other factors fixed, increasing

military spending in one year will reduce the number of patents in the second or the third year.

To conclude, four of the six proxies for innovation (GNI, Industry Output, Service Output

and High-Tech Exports) are positively related to military expenditure. Since these four variables

can be used to measure the quality and value of innovation, I am confident that military

expenditure leads to innovation, which brings a lot of economic benefits to society. As for the

proxy of Patents and the number of Scientific Journals, they seem not to be associated with any

change of military expenditure. I believe that this is because military data and records are critical

to the national security, so the government intentionally conceals them. If Patents and the

Scientific Articles include the true military related Patents and Scientific Articles, they should

also be positively related to the military expenditure. Therefore, the hypothesis that military

Figure 2 Summaries of Coefficients on Log(military expenditure per soldier) in the Regression Models

Proxies for Innovation Negative and significant

Insignificant Positive and significant

Total

GNI 0 (6) 1 (6) 5 (6) 6 (6)

Industrial Output 0 (6) 1 (6) 5 (6) 6 (6)

Service Output 0 (6) 1 (6) 5 (6) 6 (6)

High-Tech Exports 1 (6) 1 (6) 5 (6) 6 (6)

Patents 0 (6) 6 (6) 0 (6) 6 (6)

Scientific Articles 1 (6) 5 (6) 0 (6) 6 (6)

Total 2 (36) 15(36) 20(36) 36 (36)

* the number in the parenthese means the total number of experiments

27

expenditure can boost innovation should be justified based on all evidences from my regression

analysis.

LIMITATIONS AND CHALLENGES

Similar to the exiting studies in this area, this thesis is also subject to some degree of

measurement errors. According to the Oslo Manual, (2005, p46) “an innovation is the

implementation of a new or significantly improved product (good or service), or process, a new

marketing method, or a new organizational method in business practices, workplace organization

or external relations”. Although it is easy to define innovation, it is very hard to measure it. An

increase in innovation can happen in both the qualitative and quantitative dimensions (more

technologies and ideas vs better technologies and ideas).

In Jacob’s (2008) and Willen’s (2013) studies, innovation is only measured by the number

of Patents. Technically, the number of Patents is a good measure of the quantity of new ideas.

But it has many drawbacks: First, different countries may follow different standards and criteria

for deciding whether a new idea be registered with a patent. Second, patents give the same

weight to all new ideas and fail to evaluate their quality. Third, patents fail to account for non-

technology innovations such as new processes, organization systems, or marketing methods.

Clearly, it is difficult to have a precise measure of innovation for research purpose. However, an

even harder topic is to capture the military related innovation.

28

As we know, military innovations are primarily used for military purposes. According to

Eleazar (2010), military innovations happen in five dimensions: “maneuver, doctrine, training,

combined-arms fighting, and defensive technology” Eleazar (2010:11). For example, a new

method of training is a military innovation. It promotes the efficiency and effectiveness of the

army, and reduces casualties and other losses in the future. However, it is difficult to establish a

common standard to measure the value of the improving efficiency and effectiveness. Also, if

this training method reduces the number of casualties, it is hard to estimate its corresponding

economic value because lives are priceless and we cannot give a price to lives. Similarly, it is

also hard to estimate the economic value of better security of the nation. Also, some military

innovation is critical to maintain the security of the country. Unfortunately, all previous studies

fail to account for the value of military innovation for national security purpose.

Furthermore, it is difficult to fully capture the value of military innovation for

commercial purpose. To estimate the dollar value of military innovations, we need to solve two

problems: 1. In an efficient market, the value will be calculated as the net present value of the

increase of future cash flow. If we take the innovation of the Internet or GPS as an example, the

creations of these technologies bring major breakthroughs to human society and create trillion-

dollar markets. However, it is impossible to approximate the value of these technologies in early

stages, because we have no idea about how people in the future utilize the early technologies.

More specifically, if there is no Internet, there will be no Google or Amazon. But people did not

incorporate the value of Google or Amazon into the value evaluation of Internet when it is just

invented. Therefore, it is impossible to discover and value all the benefits from innovation. 2.

The value of military innovation to the market does not merely depend upon the quality of

29

innovation itself, but also upon many other factors such as the market efficiency, policy

constraints, and time. Bronwyn (2011) suggests that productivity growth and GDP are good

proxies for innovation because they constantly reflect the market value of innovation. However,

the same military innovation could have different monetary values simply because different

countries have different capacities to adapt the innovation for business use.

Therefore, due to the measurement error, all proxies for innovation cannot give a full

picture of the military based innovations and we believe the levels of innovation are largely

undervalued. In my thesis, even though I have used multiple proxies to measure innovation, but

the measurement error still exists. Since this bias is a downward bias, we believe the true impact

of military expenditure on innovation should be even larger.

POLICY IMPLICATIONS

In the previous session, I have used six different proxies for innovations and conducted

series of fixed effect models to test the hypothesis that military expenditure can boost innovation.

As I have expected, the regression models have shown concrete evidences supporting the idea

that increased military expenditure can lead to higher levels of innovation. Based on these

findings, policymakers should have a new understanding of their defense budgets and take

measures to adjust the policy accordingly. The new policies should strengthen the investment in

the military innovation, elevate the quality of military innovation, and assist the value realization

30

of the military innovation. Therefore, I would suggest the policy implications from four different

perspectives.

The first implication is that governments should reevaluate the impacts of military

innovation and optimize their military expenditure accordingly. According to the Stockholm

International Peace Research Institute (Bodell, 2013), countries like United States (4.4%), Russia

(4.4%) and Saudi Arabia (8.9%) have very high military spending as percentage of GDP. The

United States and Russia are well known as the top-two winners in the world weaponry market,

and they both have advanced military technologies. Saudi Arabia is not very productive in

military innovation even though it spends too much money on the military sector. This is because

Saudi Arabia buys excessive amounts of weaponry from the United States and uses its military

purchasing policy as tool to strength the bilateral relationship between Saudi Arabia and America.

Had this country tried to replace the weaponry input with domestic research and manufacturing,

we would expect to see more military innovation from Saudi Arabia. Countries like China (2%),

Japan (1%), Germany (1.4%) and Brazil (1.5%), have relatively low military spending as

percentage of GDP (Bodell, 2013). Since Military Spending would boost innovation, these

countries may undervalue the positive impacts of increasing military expenditure. So countries

like China, Japan and Germany should carefully reevaluate their military expenditure and decide

whether to increase their defense budgets and harvest the subsequent military innovation.

However, boosting innovation is not the sole or most important purpose for increasing military

spending. In the real world, there are many reasons for a country to increase its defense budget.

For example, a country may need more defense budget to maintain internal stability, protect the

boarders, and fight natural disasters. So when these countries decide whether to increase military

31

expenditure, they need to balance many different concerns, rather than merely focus on the need

for extra military innovations.

Second, Governments should restructure the military budget and devote more resources

to military research and development. Since most of the military innovation and new

technologies come from the military labs and research centers, boosting effects of military

spending on innovation has to work through military R&D programs. If the government is

concerned about boosting innovation from the military sector, it is better for them to restructure

the defense budget and directly increase investment in military R&D programs. In recent decades,

countries like India and Israel have put a lot of effort into promoting their military

industrialization, which helps them maintain strong regional power and brings huge economic

benefits (Hoyt, 2007)

Third, Countries should increase mutual trust with their neighbors and cooperate in

military R&D. Due to the long time span and high fixed cost, many countries are not able to

design and produce the weaponry on their own. To overcome this problem, countries could

cooperate in the military R&D projects with their neighboring countries. This kind of

cooperation would greatly increase mutual trust and promote regional peace. Moreover,

countries would share the cost and risk of making military R&D investments. There are many

great examples for this kind of international cooperation. For example, the Eurofighter Typhoon

is a advanced fighter jet project developed by UK (33%), Germany(33%), Italy(21%) and

Spain(12%). Each country committed experts and capital into the project and the partnership and

they maintain a long-lasting political and industrial relation” (Benien, 2013). Right now, these

32

four nations not only purchase the Eurofighters for their own air forces, but also export the jets to

countries like Saudi Arabia, Australia, and Canada.

Fourth, Governments should increase the mobility between the military sector and private

sector in terms of human capital, ideas and capital. In many countries, the military has

accumulated advanced innovations from their R&D projects. However, due to the barriers

between the military sector and private sector, a great number of those valuable technologies and

ideas are hidden in the research centers of weaponry suppliers. If governments can build a

channel that allows the military sector and the private sector to share knowledge, capital and all

other resources, the nation will gain substantial benefit from higher productivity and stronger

economic growth.

Technically, the channel should address three different perspectives. The first is the

exchange and communication of technologies and ideas. It means that governments should

encourage communication and cooperation among military manufactures and scientific centers,

encouraging them to work together with the competitive and trustworthy private corporations. If

both the military and the private counterparties are interested in the transfer of certain

technologies and these technologies can truly serve the public interest, government should

facilitate these transfers under careful review and supervision. A good example is the private

Chinese company, Beidou Navigation Service Company (Zheng, 2014), which closely

cooperates with the Chinese military to promote the newly developed Chinese navigation

system- Beidou.

The second dimension is the mobility of human resources. This means that government

should allow some military experts and scientists to join private corporations and research

33

centers. The experts and scientists in the military sector have developed special experience and

expertise from their previous research. If the government can allow some of them to work and

cooperate with private companies, those experts would facilitate the transfer of military

technologies or bring some new innovations.

The third dimension is to encourage private capital to enter the military sector by listing

weaponry manufacturers in the financial markets. The government can choose have the option of

retaining shares in the newly listed firm or fully privatizing the firm. In both cases, the military

manufacturers are able to attract private investment and therefore have more capacity to initiate

new research and projects. In return, the public would demand more disclosure and transparency

from those listed military companies. Their managers would be forced to improve the

performance of the listed weaponry corporation through investing in more productive projects,

optimize the corporation structures, reduce the fraud and cost, and improve efficiency and

productivity. If the managers work hard and earn good profits for the stakeholders, they will be

rewarded with bonus options and promotions; If they shirk the job and generate poor

performance, they might be fired and it would be hard for them to get another job in a well

informed market. In the western world, there are many well-known weaponry companies already

listed in the financial market, for example, the Lockheed Martin Corporation and the Raytheon

Company. These companies are very successful and they provide America with advanced

military equipment. In the last decades, the Chinese government set these successful defense

companies as a good example and has been endeavoring to transfer the national defense

companies to public listing company. The government believed that this procedure would help

34

the defense companies to construct a modern management system, accept social supervision, and

address investments in both the military and civilian sectors (Sun, 2007).

Certainly, to apply military technologies in the private sector would be beneficial to a

country’s economic growth, but it also involves some costs. The biggest concern is that the

valuable and confidential technologies would be leaked to the country’s enemies or competitors.

As the military department has devoted massive resources to the military technologies, their

primary goal is to keep strategically advantage over enemies and competitors. Leaking key

technologies would make the country lose its previous strategic advantage. Therefore, the

country should carefully evaluate the potential gain from the transfer of that technology, and the

potential risk of leaking that technology to enemies. If the net benefit is significant and the

country is confident about protecting its core secrets, then it should be a good idea to support the

technology transfer.

Technically, if a country can establish an effective system to manage and minimize the

risk from connecting the military sector and the private sector, that country will derive the

greatest value from its innovation. Therefore, devising and constructing such a risk management

system should be an import topic for future policy research.

Referring to the previous quotation from Eisenhower, all investments in the military

sector come from savings of other sectors. Therefore, policymakers have responsibilities to

spend the taxpayers’ money wisely. Since investments in the military sectors are always billion-

dollar projects, both overinvestment and underinvestment could cause huge losses to the society

at large. So before we decide to make a change to our defense budget, it is very important to

understand the true impacts of such changes. However, due to the lack of data and military

35

privacy policy, most of the existing research might have a biased estimation about impacts of

military expenditure. Therefore, Governments should utilize their internal resources to conduct

more advanced research to discover the true impacts of military expenditure. Only in this way

can the governments implement good defense policies and discharge their responsibilities to

society.

!

!

36

!

Tab

le A

-1:

Var

iab

le D

escr

ipti

on

Var

iabl

eS

hort

form

S

ourc

e D

escr

iptio

nP

roxy

for

Hig

h-te

chno

logy

exp

orts

Te

ch_E

xW

DI

Cur

rent

US

$ v

alue

of t

he h

igh-

tech

exp

orts

Inno

vatio

n

GN

IG

NI

WD

IC

urre

nt U

S $

val

ue o

f the

gro

ss n

atio

nal i

ncom

eIn

nova

tion

Ser

vice

, etc

., va

lue

adde

dS

ervi

ces

WD

IC

urre

nt U

S $

val

ue o

f tot

al o

utpu

t in

serv

ice

indu

stry

Inno

vatio

n

Indu

stry

, val

ue a

dded

Indu

stry

WD

IC

urre

nt U

S $

val

ue o

f the

out

put i

n In

dust

ryIn

nova

tion

Pat

ent a

pplic

atio

ns, r

esid

ents

Pat

ent

WD

IN

umbe

r of

dom

estic

pat

ent a

pplic

atio

nIn

nova

tion

Sci

entifi

c an

d te

chni

cal A

rtic

les

Sci

_Art

icle

sW

DI

Num

ber

of s

cien

tific

and

tech

nica

l jou

rnal

s In

nova

tion

Mili

tary

exp

endi

ture

Mili

tary

_Spe

ndin

gW

DI

Cur

rent

US

dol

lar

valu

e of

the

mili

tary

exp

endi

ture

Mili

tary

Spe

ndin

g

Mili

rary

exp

endi

ture

per

sod

ier

Mill

itary

_Spe

ndin

g_p

erW

DI

Cur

rent

US

dol

lar

valu

e of

the

mili

tary

exp

endi

ture

re

lativ

e to

the

num

ber

of m

ilita

ry p

erso

nnel

sM

ilita

ry S

pend

ing

Mili

tary

exp

endi

ture

(%

of G

DP

)M

illita

ry_P

erc_

GD

PW

DI

The

per

cent

age

of m

illita

ry e

xpen

ditu

re r

elat

ive

to

GD

PM

ilita

ry S

pend

ing

Dem

ocra

cyD

emoc

racy

Q

OG

Cod

ed 1

if d

emoc

racy

, 0 o

ther

wis

eG

over

nmen

t typ

e

Tert

iary

sch

ool e

nrol

lmen

tTe

rtia

ryW

DI

Tota

l enr

ollm

ent i

n te

rtia

ry e

duca

tion

Edu

catio

n

Mon

etar

y F

reed

omM

onet

ary_

Fre

edom

QO

GTo

wha

t ext

ent g

over

nmen

t suc

cess

fully

con

tain

s co

rrup

tion.

G

over

nmen

t ef

ficie

ncy

Life

exp

ecta

ncy

at b

irth

(mal

e)Li

fe_e

xW

DI

The

num

ber

of y

ears

a n

ew b

orn

baby

is e

xpec

ted

to

live

Soc

ial s

tabi

lity

Dom

estic

cre

dit t

o pr

ivat

e se

ctor

(%

of G

DP

)C

redi

t_2_

PS

ecW

DI

Fin

anci

al r

esou

rces

pro

vide

d to

priv

ate

sect

or s

uch

as

loan

s an

d cr

edits

Mar

ket E

ffici

ency

Inte

rest

Rat

e S

prea

dR

_spr

ead

WD

IIn

tere

st c

harg

ed o

n cu

stom

er m

inus

inte

rest

pai

d by

th

e co

mm

erci

al b

anks

Eco

nom

ic R

isk

Inte

rnet

use

rs (

per

100

peop

le)

Intr

_Use

rs

QO

GIn

tern

et u

sers

per

100

peo

ple

Infr

astr

uctu

re

APP

END

IX

!

! 37

!

Table A-2: Descriptive Statistics

Dependent variable- Proxies for Innovation

Variable Obs Mean Std. Dev. Min Max

High-tech exports  2052 9.87E+09 2.96E+10 0 3.81E+11

GNI 2717 2.62E+11 1.02E+12 1.16E+08 1.43E+13

Services Output 2599 1.61E+11 6.79E+11 2.73E+07 1.04E+13

Industry 2603 7.38E+10 2.53E+11 2.49E+07 2.85E+12

Patent 1509 10696 46340.23 1 384201

Scientific journals 2487 4507.964 18677.6 0 209694.7

log (High-tech exports )

2035 18.65336 4.1082 1.386294 26.66697

log (GNI) 2717 23.90225 2.202365 18.56678 30.29372

log (Services Output)

2599 23.17481 2.346655 17.12255 29.96919

log (Industry Output)

2603 22.59817 2.349672 17.0287 28.67823

log (Patent) 1509 5.888899 2.622842 0 12.85892

log (Scientific journals)

2465 5.145697 2.820095 -1.609438 12.25341

Independent variable of Interest 

Variable Obs Mean Std. Dev. Min Max

Military expenditure

2753 6.55E+09 3.53E+10 1653344 6.61E+11

Military expenditure per soldier

2753 32303.8 65437.61 84.28226 1292440

Military expenditure (%GDP)

2742 2.672686 3.582294 0.0465605 117.3877

Independent variables

Variable Obs Mean Std. Dev. Min Max

Democracy 2637 0.5635192 0.496043 0 1

Monetary Freedom 2011 72.24828 17.24671 0 95.4

Conflicts 2089 0.3537578 0.8163713 0 8

Credit to Private Sector (%GDP)

2651 45.62078 44.50411 0 269.667

Internet Users ( per 100 ppl)

2300 12.80863 20.56847 0 94.51987

Interest rate spread

2116 12.00264 62.21803 -165.0617 2334.963

Life expectancy for male 

2725 64.22298 10.1645 24.575 79.8

Internal Conflicts 2089 0.3317377 0.8103455 0 3

Government quality

2288 0.5786218 0.2171992 0.0555556 1

Regime type 2753 49.64039 47.41449 1 100

Tertiary enrollment

1885 28.17327 23.49076 0 121.5065

!

38

!

Table

B-1:

Corre

lation

amon

g Vari

ables

log (H

igh-te

ch exp

orts )

log (G

NI)log

(Serv

ices

Outpu

t)log

(Indu

stry

Outpu

t)log

(Pate

nt)log

(Scie

ntific

Article

s)Mil

itary

expen

diture

Militar

y exp

enditu

re pe

r sold

ierTer

tiary

enrol

lment

Demo

cracy

Mone

tary

Freed

omCo

nflicts

Credit

to Pri

vate S

ector

(%GD

P)Int

ernet

Users

( p

er 10

0 ppl)

Intere

st rate

spr

ead

Life

expect

ancy

for m

ale 

Intern

al Co

nflicts

Gover

nmen

t qu

ality

Regim

e type

log (H

igh-te

ch exp

orts )

1.000

0

log (G

NI)0.8

310

1.000

0

log (S

ervice

s Ou

tput)

0.845

50.9

946

1.000

0

log (In

dustr

y Ou

tput)

0.837

30.9

880

0.976

91.0

000

log (P

atent)

0.763

80.8

035

0.790

10.7

981

1.000

0

log (S

cientifi

c Art

icles)

0.813

00.9

075

0.905

00.8

855

0.874

01.0

000

Militar

y exp

enditu

re0.2

862

0.380

90.3

856

0.356

50.4

007

0.355

61.0

000

Militar

y exp

enditu

re pe

r sold

ier0.3

856

0.469

90.4

723

0.447

30.4

912

0.435

90.3

866

1

Tertiar

y en

rollme

nt0.6

288

0.623

30.6

389

0.600

50.5

262

0.693

60.2

691

0.441

51

Demo

cracy

0.330

40.3

379

0.366

30.3

143

0.204

50.3

668

0.114

00.1

850.4

841

Mone

tary

Freed

om0.2

317

0.230

50.2

641

0.212

90.1

436

0.170

10.0

939

0.286

90.1

989

0.155

51

Confli

cts0.0

695

0.145

60.1

254

0.121

20.0

665

0.105

50.0

830

0.004

2-0.

0315

-0.00

51-0.

0503

1

Credit

to Pri

vate S

ector

(%GD

P)0.6

201

0.595

20.6

219

0.572

20.5

196

0.580

60.3

402

0.535

30.5

196

0.297

10.3

999

-0.07

271

Intern

et Us

ers

( per

100 p

pl)0.4

638

0.447

10.4

671

0.426

20.3

088

0.426

90.2

144

0.569

30.6

424

0.263

10.3

056

0.039

0.595

11

Intere

st rate

spr

ead

-0.06

88-0.

0435

-0.04

21-0.

0391

-0.03

45-0.

0337

-0.03

63-0.

0529

-0.02

28-0.

0307

-0.46

440.0

727

-0.07

71-0.

0597

1.000

0

Life

expect

ancy

for

male 

0.595

00.6

080

0.645

70.6

223

0.510

70.5

793

0.163

70.4

222

0.677

0.444

80.3

222

-0.14

250.5

881

0.5114

-0.07

281.0

000

Intern

al Co

nflicts

-0.06

910.0

213

-0.00

53-0.

0010

-0.116

8-0.

0436

-0.03

45-0.

1227

-0.15

82-0.

0783

-0.13

910.6

167

-0.20

39-0.

1529

0.109

6-0.

2038

1.000

0

Gover

nmen

t qu

ality

0.610

70.5

261

0.559

50.5

143

0.522

00.6

291

0.200

70.4

728

0.533

90.3

315

0.366

1-0.

1245

0.660

10.4

253

-0.09

180.6

418

-0.27

691.0

000

Regim

e type

0.419

00.3

790

0.404

10.3

561

0.305

10.4

050

0.140

60.2

920.5

133

0.676

70.2

861

-0.06

170.4

437

0.360

4-0.

0572

0.467

6-0.

2041

0.521

81.0

000

39

Table C-1 Regression models on ln_GNI-1 -2 -3 -4 -5 -6

VARIABLES ln_GNI ln_GNI ln_GNI ln_GNI ln_GNI ln_GNI

ln_Millatary_per 0.238*** 0.288*** 0.332*** 0.435** 0.193 0.260***

-0.0288 -0.0428 -0.045 -0.175 -0.276 -0.0293

Democracy 0.00876 0.0148

-0.0798 -0.0785

Tertiary 0.00076 0.0108 0.0274***

-0.00179 -0.0105 -0.00986

Monetary_Freedom 0.00197* 0.00145

-0.00107 -0.0011

Life_ex -0.000944 -0.0005

-0.00545 -0.00522

Credit_2_PSec 0.00149** 0.00614 0.00854*

-0.000681 -0.00491 -0.00474

r_spread 0.00018 4.2200000E-05

-0.000921 -0.000973

Internet -0.00248** -0.00141

-0.00109 -0.00121

c.ln_Millatary_per#c.Tertiary -0.00102 -0.00253***

-0.00095 -0.00093

c.ln_Millatary_per#c.Credit_2_PSec -0.000427 -0.000648

-0.000485 -0.000455

c.ln_Millatary_per#c.ln_Millatary_per -0.0114 0.00781

-0.00947 -0.0165

L.ln_Millatary_per 0.0540***

-0.0206

L2.ln_Millatary_per 0.00928

-0.0145

L3.ln_Millatary_per -0.00224

-0.012

L4.ln_Millatary_per -0.0159

-0.0139

L5.ln_Millatary_per -0.0154

-0.0154

Constant 21.19*** 21.12*** 20.70*** 20.36*** 21.11*** 21.11***

-0.262 -0.475 -0.499 -0.809 -1.142 -0.397

Observations 2,717 1,006 1,006 2,717 1,800 1,834

R-squared 0.82 0.873 0.875 0.821 0.859 0.857

Number of country1 157 122 122 157 144 146

*** p<0.01, ** p<0.05, * p<0.1

Note: author's calculations: country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

40

Table C-2 Regression models on ln_Industry-1 -2 -3 -4 -5 -6

VARIABLES ln_Industry ln_Industry ln_Industry ln_Industry ln_Industry ln_Industry

ln_Millatary_per

0.239*** 0.305*** 0.369*** 0.666** 0.311 0.275***

-0.0397 -0.0516 -0.06 -0.292 -0.403 -0.0318

Democracy 0.0168 0.0308

-0.112 -0.109

Tertiary -0.00116 0.0171 0.0271**

-0.00245 -0.0111 -0.0125

Monetary_Freedom

0.000811 0.000213

-0.00131 -0.00136

Life_ex 0.0118 0.0112

-0.0119 -0.0113

Credit_2_PSec

0.00117 0.00454 0.0120*

-0.00086 -0.00607 -0.00615

r_spread 0.000172 4.28000000E-06

-0.00102 -0.00106

Internet -0.00486*** -0.00332**

-0.00154 -0.0016

c.ln_Millatary_per#c.Tertiary

-0.00181* -0.00288**

-0.00103 -0.00118

c.ln_Millatary_per#c.Credit_2_PSec

-0.000296 -0.000984*

-0.000591 -0.000589

c.ln_Millatary_per#c.ln_Millatary_per

-0.0248 0.00254

-0.0155 -0.0234

L.ln_Millatary_per

0.0442**

-0.0222

L2.ln_Millatary_per

0.00721

-0.0179

L3.ln_Millatary_per

0.0256

-0.0165

L4.ln_Millatary_per

-0.0484**

-0.0238

Constant 19.96*** 18.97*** 18.42*** 18.16*** 19.32*** 19.66***

-0.355 -0.81 -0.762 -1.368 -1.71 -0.468

Observations 2,603 986 986 2,603 1,754 1,918

R-squared 0.715 0.802 0.805 0.722 0.765 0.775

Number of country1

153 121 121 153 142 148

*** p<0.01, ** p<0.05, * p<0.1

Note: author's calculations

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

41

Table C-3 Regression models on ln_Services-1 -2 -3 -4 -5 -6

VARIABLES ln_Services ln_Services ln_Services ln_Services ln_Services ln_Services

ln_Millatary_per

0.257*** 0.296*** 0.332*** 0.381* 0.0614 0.265***

-0.0346 -0.0447 -0.0451 -0.204 -0.301 -0.0298

Democracy -0.00466 -0.00178

-0.0734 -0.0732

Tertiary 0.00125 0.00817 0.0294***

-0.00194 -0.0112 -0.0112

Monetary_Freedom

0.00350*** 0.00300**

-0.0012 -0.00123

Life_ex -0.00688 -0.00606

-0.00691 -0.00701

Credit_2_PSec

0.00233*** 0.00773 0.0123**

-0.000711 -0.00498 -0.00517

r_spread 0.000568 0.000432

-0.00114 -0.0012

Internet -0.00339*** -0.00248**

-0.00108 -0.00115

c.ln_Millatary_per#c.Tertiary

-0.000714 -0.00271**

-0.000979 -0.00107

c.ln_Millatary_per#c.Credit_2_PSec

-0.000504 -0.000968*

-0.000492 -0.000496

c.ln_Millatary_per#c.ln_Millatary_per

-0.00717 0.016

-0.0108 -0.0184

L.ln_Millatary_per

0.0694***

-0.023

L2.ln_Millatary_per

0.0254

-0.0157

L3.ln_Millatary_per

-0.0116

-0.0165

L4.ln_Millatary_per

-0.0151

-0.0176

L5.ln_Millatary_per

-0.0179

-0.0209

Constant 20.25*** 20.56*** 20.19*** 19.73*** 20.83*** 20.16***

-0.314 -0.542 -0.561 -0.96 -1.214 -0.493

Observations 2,599 986 986 2,599 1,753 1,766

R-squared 0.8 0.872 0.873 0.801 0.846 0.848

Number of country1

153 121 121 153 142 144

*** p<0.01, ** p<0.05, * p<0.1

Note: author's calculations

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

42

Table C-4 Regression models on ln_Hi-tech Export-1 -2 -3 -4 -5 -6 -7

VARIABLES

ln_Tech_Ex ln_Tech_Ex ln_Tech_Ex ln_Tech_Ex ln_Tech_Ex ln_Tech_Ex ln_Tech_Ex

ln_Millatary_per

0.178 0.208** 0.225 1.021* -1.171** 0.213* 0.213*

-0.118 -0.102 -0.173 -0.608 -0.58 -0.11 -0.11

Democracy -0.208 -0.217

-0.185 -0.182

Tertiary 0.0111* 0.0082 0.0616*

-0.00655 -0.0393 -0.0352

Monetary_Freedom

0.00228 0.0017

-0.0054 -0.0057

Life_ex 0.0966** 0.0998**

-0.0476 -0.0489

Credit_2_PSec

0.000521 0.00872 0.0409**

-0.00212 -0.015 -0.0167

r_spread 0.00467* 0.00453*

-0.00262 -0.00271

Internet -0.00676 -0.00632

-0.00455 -0.00452

c.ln_Millatary_per#c.Tertiary

0.000226 -0.00613*

-0.00361 -0.00317

c.ln_Millatary_per#c.Credit_2_PSec

-0.00078 -0.00404***

-0.00139 -0.00152

c.ln_Millatary_per#c.ln_Millatary_per

-0.0457 0.0900***

-0.0327 -0.0288

L.ln_Millatary_per

0.0134 0.0134

-0.102 -0.102

L2.ln_Millatary_per

-0.00802 -0.00802

-0.114 -0.114

Constant 15.59*** 9.563*** 9.244*** 11.74*** 20.51*** 15.40*** 15.40***

-1.047 -3.086 -3.164 -2.921 -2.969 -1.282 -1.282

Observations

2,035 906 906 2,035 1,459 1,866 1,866

R-squared 0.325 0.365 0.366 0.328 0.43 0.3 0.3

Number of country1

141 111 111 141 131 138 138

*** p<0.01, ** p<0.05, * p<0.1

Note: author's calculations

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

!

43

!

Table C-5 Regression models on ln_Scientific Articles-1 -2 -3 -4 -5 -6

VARIABLES ln_Sci_Articlesln_Sci_Articlesln_Sci_Articlesln_Sci_Articlesln_Sci_Articlesln_Sci_Articles

ln_Millatary_per

0.0583 -0.00544 0.0446 -0.086 -0.412 -0.0133

-0.0583 -0.0445 -0.0567 -0.291 -0.335 -0.0464

Democracy -0.0305 0.000249

-0.0864 -0.0822

Tertiary 0.00519** 0.0347** 0.0706***

-0.00229 -0.015 -0.0192

Monetary_Freedom

-0.00254* -0.00241*

-0.00144 -0.00133

Life_ex 0.0312* 0.0263*

-0.0162 -0.0152

Credit_2_PSec

-0.000675 -0.0128** 0.00514

-0.00105 -0.00572 -0.00818

r_spread 0.00362*** 0.00371***

-0.00116 -0.0012

Internet -0.00563*** -0.00464**

-0.00176 -0.00178

c.ln_Millatary_per#c.Tertiary

-0.00280** -0.00617***

-0.00141 -0.00168

c.ln_Millatary_per#c.Credit_2_PSec

0.00119** -0.000343

-0.000527 -0.000745

c.ln_Millatary_per#c.ln_Millatary_per

0.00841 0.03

-0.015 -0.0184

L.ln_Millatary_per

-0.0227

-0.0255

L2.ln_Millatary_per

0.0577

-0.0406

Constant 4.229*** 3.684*** 3.487*** 4.831*** 6.217*** 4.738***

-0.522 -1.09 -1.03 -1.409 -1.556 -0.651

Observations 2,465 960 960 2,465 1,692 2,102

R-squared 0.222 0.378 0.395 0.223 0.348 0.23

Number of country1

157 122 122 157 144 152

*** p<0.01, ** p<0.05, * p<0.1

Note: author's calculations

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

44

Table C-6 Regression models on ln_Patent-1 -2 -3 -4 -5 -6

VARIABLES ln_Patent ln_Patent ln_Patent ln_Patent ln_Patent ln_Patent

ln_Millatary_per

-0.159* -0.0469 0.0486 0.197 -0.133 -0.0615

-0.0823 -0.0998 -0.147 -0.291 -0.351 -0.0737

Democracy 0.0342 0.123

-0.218 -0.196

Tertiary -0.00461 0.053 0.0679**

-0.00363 -0.0322 -0.0331

Monetary_Freedom

-0.00266 -0.00154

-0.00236 -0.00189

Life_ex 0.0331 0.0292

-0.0503 -0.0449

Credit_2_PSec

-0.00187 -0.0289** -0.0147

-0.00176 -0.0124 -0.014

r_spread 0.0015 0.00159

-0.00147 -0.00146

Internet -0.00317 -0.000875

-0.0045 -0.00325

c.ln_Millatary_per#c.Tertiary

-0.00536* -0.00673**

-0.00307 -0.00304

c.ln_Millatary_per#c.Credit_2_PSec

0.00258** 0.00147

-0.00115 -0.00127

c.ln_Millatary_per#c.ln_Millatary_per

-0.0202 0.00608

-0.016 -0.0199

L.ln_Millatary_per

-0.108***

-0.0374

L2.ln_Millatary_per

-0.0328

-0.0473

Constant 6.954*** 4.543 3.691 5.417*** 6.381*** 7.504***

-0.784 -3.512 -3.344 -1.442 -1.816 -1.048

Observations 1,509 693 693 1,509 1,171 1,340

R-squared 0.156 0.129 0.18 0.159 0.186 0.16

Number of country1

114 86 86 114 98 109

*** p<0.01, ** p<0.05, * p<0.1

Note: author's calculations

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

country-year fixed effect

45

BIBLIOGRAPHY

Akinson R et al (Jan 2012), “the competitiveness and innovative capacity of the United

States”, U.S. department of commerce & national economic council

Benoit, E. (1973). “Defense Spending and Economic Growth in Developing Countries”,

Boston, D.C. Health& Cp.

Benoit, E. (1978), “Growth and Defense in Developing Countries”, Economic Development

and Cultural Change Vol.26, No.2(Jan., 1978),pp271-280. Chicago: The University

of Chicago Press

Bodell N, (2013), “Stockholm International Peace Research Institute Yearbook 2013”,

SIPRI, Sweden from http://milexdata.sipri.org/

Brownwyn H. (2011). “Using productivity growth as an innovation indicator”, University of

Maastricht and UC Berkeley, Report for the High Level Panel on Measuring

Innovation, DG Research, European Commission

Benien, T. et el (eds).(2013). “The Secret Behind a good weapon fighter system”,

Hallbergmous, Eurofighter Jagflugzeug GmbH, PR & Communications

Cheung, T. (1988). “Disarmament and Development in China: The Relationship Between

National Defense and Economic Development.” Asian Survey 28:757-774

Gamota, G. (1985). Impact of Defense R&D Investments on the Civilian Sector. The

Federal Role in R&D by the National Academy of Sciences, National Academy of

Engineering, Institute of Medicine [Conference], 243–260.

46

Cameron L (2006), “Private military companies: their status under international

humanitarian law and its impact on their regulation”, international review of the red

cross , volume 88 Number863 September 2006

Cezmeci, D (2005), “An Examination of Boeing’s Supply Chain Management Practices

within the Context of the Global Aerospace Industry”. Massachusetts Institute of

Technology, MA

Friends Committee on National Legislation, �April 2013� “World Military Spending”,

from http://www.globalissues.org

Humphreys, M. (2002) “War and Violent Conflict”. Harvard University Program on

Humanitarian Policy and Conflict Research, from

http://www.unglobalcompact.org/docs/issues_doc/Peace_and_Business/Economics_

and_Violent_Conflict.pdf (accessed March 21, 2013).

Hoyt, D. (2007). “Military Industry and Regional Defense Policy( India, Iraq and Israel)”.

New York: Routledge.

Joshua, R. et al, (2011). “From Barrack to the battlefield: Clean Energy Innovation And

America’s Armed Forces”. Washington D.C. : The Pew Charitable Trusts.

Jha S K, (2010). “Linking innovation process to the provisioning of public goods: The case

of neglected disease”, The Innovation Journal, 15 (2) (2010), article 7

Kosuke, Imai &Jeremy Weinstein, (2000). "Measuring the Economic Impact of Civil War."

CID Working Paper, no. 51.

Knight, M. et al. (1996). “The Peace Dividend: Military Spending Cuts and Economic

Growth.” IMF Staff Papers 43(1):1-37.

MacNair, E. S.et al. (1995). “Growth and Defense: Pooled Estimates for the NATO Alliance,

1951-1988”. Southern Economic Journal 61(3), 846–860.

47

OECD/OCDE (2005). “Oslo Manual: Guidelines for collecting and interpreting innovation

data”, third edition, Paris: OECD

Pye, L. 1962. “Armies in the Process of Political Modernization.” Pp. 69-89 in The Role of

the Military in Underdeveloped Countries, edited by J.J. John. Princeton, NJ:

Greenwood Press.

Rossman, J. (1931) "War and Invention." The American Journal of Sociology, volume 36,

no. 4 (1931): 325-333.

Ruttan, V. (2006). “Is War Necessary For Economic Growth? Military Procurement and

Technology Development” (Oxford: Oxford University Press, 2006).

Saal, D. S. (2001). “The Impact of Procurement-Driven Technological Change on U.S.

Manufacturing Productivity Growth”. Aston Business School Research Papers

RP0106.

Tamin, B & Daniel, H. & Jerald S, (1993), “Economic Consequences of Lower Military

Spending: Some Simulation Results”. International Monetary Fund Working Paper,

WP/93/17

Thorpe J, �2012�, “Improving productivity through education”, Pricewaterhousecopper,

from http://www.pwc.se/sv_SE/se/offentlig-sektor/assets/improving-productivity-

through-education.pdf

Weber, M. (1968). “Economy and Society”. Totowa, N.J.: Bedminster

Willén, A.(2013). “WAR AND ITS IMPACT ON INNOVATION: A CROSS-NATIONAL

STUDY”. (Georgetown University, 2013)

Ram, R (1995), “Defense Expenditure and Economic Growth”, in Hartley and Sandler,

“Handbook of Defense Economics”. Pp251-274,

48

Zheng, L. (eds). (2013, July 23), “ Beidou navigation public platform uses satellite

technology” retrieved on Mar 20, 2014 from

http://english.cntv.cn/program/newsupdate/20130723/104604.shtml

Zou, L (eds) (2007, Nov 27), “Propelling the trend of restructuring national defense

companies (in Chinese)” , retrieved on Mar 20, 2014 from

http://news.xinhuanet.com/mil/2007-11/27/content_7152633.htm