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POWER OR PROFIT? THE UNITED STATES AND THE INTERNATIONAL ARMSTRADE

Jonathan Caverley (Northwestern) & Ethan Kapstein (University of Texas, Austin and the Center for a New American Security)

Abstract

Economic globalization and American preeminence have both profoundly affected theinternational arms trade. This paper explores the circumstances under which the United Statestakes advantage of its defense industrial market power to collect economic rents from theinternational arms trade and those under which it will forgo these rents to advance its

geopolitical ends. As we will show, the United States distributes much of the potential financialgains from arms cooperation to its partners in exchange for their joining an American-dominatedglobal arms network, limiting their ability to act autonomously in the military sphere. We alsotest, for the first time, the many competing theories of defense globalization using statisticalanalysis of a large time series cross-sectional data set, which measures the financial premium paid (or subsidy received) by US arms customers from 1970-2007.

Acknowledgements

Ethan Kapstein wishes to thank the Alfred P. Sloan Foundation for a grant to the Center for a New American Security (CNAS), which made this research possible. The authors wish to thank Deborah Avant, Daniel Drezner, Eugene Gholz, Victoria Greenfield, Lloyd Gruber, GeorgiaKernell, Charles Lipson, Steven Nelson, Keven Ruby, Jason Searight, Duncan Snidal, and SrdjanVucetic for invaluable feedback. They also thank Zackary Hosford and Kristin Lord at CNASfor their assistance and comments, along with seminar participants at Georgetown, Northwestern,and the University of Chicago.



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This paper examines how the United States exploits its dominant position in the

international arms market. As we will show, the U.S. uses its power in this sector to reward some

states with lower prices for armaments for some customers while extracting financial rents from

others—particularly those having an “asymmetric” or “hierarchical” security relationship with

the United States—for these same or similar weapons systems. In making this argument, we

draw on a unique dataset, enabling us to test our hypotheses empirically.

More broadly, we hope to trigger a renewed interest in the defense industry and arms

trade by researchers. In no other sector of economic activity do the traditionally separate

academic disciplines of “security studies” and “international political economy” intersect more

than in the production of armaments. For a great power, the defense industry provides an

essential element of state sovereignty, yet that industry increasingly confronts a “borderless

world” of global supply chains and sales.1 Research on the defense industry promises insights

into such areas as arms racing, alliance formation, the domestic sources of foreign policy, and the

power of multinational firms. More specifically, the topic is the ideal arena to examine the many

theoretical claims about the role of the United States in international politics.

Arms transfers are not just of academic interest, but are central issues in both foreign

policy and the domestic economy, as defense-related industries serve as important generators of

new technology, skilled jobs, and exports. The global arms industry is currently booming, with

sales in 2011 of approximately $60 billion, increasing by a third since 2002.2 With domestic

1 Ohmae 1999.2 SIPRI 2012.



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defense budgets likely to fall in coming years in many arms producing countries, exports can be

expected to become even more critical to the industry’s economic health.

The United States plays a dominant role in this market. Despite the fact that global arms

sales represent only a small percentage of its domestic weapons expenditures, the United States

has been the most significant player in this market by virtue of the massive scale on which it

produces advanced weaponry relative to the world’s other military and industrial powers.

Furthermore, as one observer notes, “more than any other country, the United States has

contributed to escalating market pressures and to lowering the bar on export controls”.3 From

2006-2011 the U.S. exported weapons to 81 countries and in 2011 accounted for 30% of all arms

exports, well ahead of Russia (24%) and Germany, France and the United Kingdom combined

(21%).4

America’s large market share is evidence of the tremendous competitive advantage its

industry enjoys in a globalized arms market. But the United States has not always used this

advantage like a classic monopolist, setting prices at the optimal level that provides rents to

producers on the one hand while deterring new entrants on the other. The United States

frequently sells its equipment at relatively low or subsidized prices, making American taxpayers

support foreign arms sales. Poland, for example, agreed in 2002 to buy $3.5 billion worth of

American-built F-16s (instead of Swedish Gripens or French Mirages) only after the U.S.

Congress provided a low-interest loan and the F-16’s manufacturer, Lockheed-Martin, promised

to invest $6 billion in the Polish economy by way of so-called “offset” agreements, which are

frequently a quid pro quo for doing business in this sector.5

3 Epstein 2006.4 SIPRI 20125 Keller and Nolan 2001.



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Why does the United States sometimes subsidize foreign buyers’ arms purchases,

particularly when it does not really need these sales (unlike most other producers) to acquire

technology or amortize production costs, given its large R&D expenditures and long weapon

runs for domestic needs? Conversely, under what circumstances does the United States use its

market power to collect monopolistic rents?

In an effort to address these questions, this paper seeks to make two contributions. The

first is to examine the unique economics of the global defense industry to show how Washington

can trade economic rents from its arms sales for gains in international influence, providing a

richer, more accurate theorization of the options available to the United States (and possibly

some other states) vis-à-vis the global defense industry. Second, and equally importantly, we

provide one of the first systematic, quantitative analyses of the competing theories of

globalization and the defense industry developed over the past two decades. The data collected

and analyzed here represent a significant update to previous efforts to measure prices in the

defense trade.6 We find strong evidence for the primary and counter-intuitive implication of our

theory; namely, as its market power rises vis-à-vis its client states, the United States tends on

average to reduce the economic rents extracted from its customers. We also find that the United

States rewards states with similar foreign policy preferences, but does not extend this preferential

pricing to states already within its security hierarchy. Combined, our theoretical and empirical

findings advance and synthesize important schools of thought on American foreign policy in a

unipolar, and post-unipolar, world

This paper proceeds in five sections. Following this introduction, the next section reviews

the existing literature on defense industrial globalization and its effect on proliferation and on

6 Smith and Tasiran 2005.



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U.S. grand strategic interests. The third section presents our theory of market power and rent

collection in the global arms industry, and lays out the hypotheses that we test using our arms

trade dataset. The fourth, empirical section briefly reviews the macro-level trends of the defense

industry, and then analyzes newly-collected data estimating the premium paid by US weapons

customers on their arms purchases. Additionally, in pooled time-series, cross-sectional analysis

(TSCS), we show that the U.S. gives preferential pricing to states with similar foreign policy

preferences, while charging more to states that are in an asymmetric or dependent security

relationship. The final section discusses the theory’s implications for theories of hegemony and

unipolar order, as well as the decline in U.S. market share over the past decade.

1. Defense Market Globalization: What Are the Consequences?

While large conventional weapons platforms are among the most potent expressions of

state power, the defense industry’s production of complex armaments is also under increasing

pressure from economic globalization. With growing specialization, some countries are better

suited than others to produce and export certain components, in turn creating longer supply

chains. As costs rise for high-technology weaponry, so too must production runs in order to

amortize expenses and keep prices down; that, in turn, requires exports. Thus, as with other

aspects of global business, defense can be made subject to greater competition from imports

while exports are used as a way of reducing unit costs for production. Nonetheless, despite the

clear economic gains from liberalization, the defense industry continues to suffer (or enjoy)

tremendous inefficiencies and state interference. National governments subsidize significant

amounts of research and development, while export controls, protectionism, and sanctions are

rampant and accepted practices.



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Many scholars recognized that globalization would influence the arms industry early in

the post-Cold War era, and constructed theories to explore whether that trend would help or

hinder state efforts to increase their own and global security. Some turned their attention to the

largest defense firms. Anne Markusen, for example, argued that defense-industrial globalization

would lead to increasing concentration, with emerging multinational defense firms slipping away

from what were previously monopsonistic relationships with a single national sponsor. This

meant that firms could pursue their own economic interests with greater autonomy, taking less

account of the security interests of any particular state client.7 Related, some identified a “gray

threat,” in which the post-Cold War decline in defense budgets would result in pressure on firms

to export to states with security interests that differed from those of the United States and

Western Europe.8

While focusing less of firms, Stephen Brooks also emphasizes the constraints

globalization can place on states, advancing an ambitious liberal theory in which the complex

production chain of advanced weapons produces a pacifying effect on world politics.9 According

to Brooks, even the United States is unable to “run the tables” due to its ultimate dependence on

the global supply chain for materiel and technology. In essence, as every country becomes more

dependent on the global economy, the costs of going to war increase, resulting in a more

peaceful world.

Where Brooks emphasized the systemic, pacifying effects of defense globalization, others

focused on the diffusion of defense technology to emerging competitor states in the developing

7 Markusen 1999.8 Flamm 1999.9 Brooks 2005.



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world.10 In response, the United States would be forced to pursue a policy of “running faster” in

its weapons development.11 Aaron Friedberg agreed that globalization confronted US policy-

makers with a choice between sustained autonomy and low-levels of government intervention in

which the “US has accepted the process and consequences of globalization, rather than

attempting to resist them.” According to Friedberg, this “ideological” approach to defense-

industrial globalization would undermine the economic competitiveness of the United States in

defense and related high-technology sectors given the important role that the state must play in

supporting them.12

Other approaches privileged the state’s role over that of economic forces. While a simple

realist approach would predict autarky on the part of the United States, the more nuanced

approaches of Michael Mastanduno and Joseph Grieco suggested interdependence as a means of

consolidating American power, while avoiding the spread of technology and military capability

to weaker states.13 Another branch of realist logic would suggest that defense industrial

balancing would take place in an increasingly unipolar world. Seth Jones, for example, finds the

beginnings of balancing in arms production on the part of European states. 14 However, the

rationalization of the European arms industry has proceeded so slowly that this claim has become

increasingly unconvincing.

A final group combined the economics of arms and globalization with theories of state

action. Peter Dombrowksi and Eugene Gholz detail the unique ability of the U.S. government

10 Moran 1990; Vernon and Kapstein 1991; Bitzinger 1994.11 Carter, Lettre et al. 2000.12 Friedberg 1991.13 Grieco 1988; Mastanduno 199814 Jones, 2007.



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and its defense industry to make world-beating, cutting-edge weaponry.15 Ethan Kapstein

specifically linked the massive economies of scale from globalization to the potential for an

American arms trade monopoly. Kapstein predicted (wrongly) that the United States would take

advantage of this monopoly power to sell only finished products to customers.16 Jonathan

Caverley also recognized the importance of U.S. market power, but supposed that the United

States would leverage the sale of weapons for geopolitical influence, giving up some economic

benefits in exchange for its clients’ agreement to refrain from competing with U.S. products or to

sell to such geopolitical rivals as China.17 While Caverley provided little empirical support for

this claim, Stephanie Neuman gives several qualitative examples of the United States using its

market leverage to influence other states’ international behavior.18

We expand on these

arguments, which emphasize the geopolitical influence inherent in market power, theoretically in

the next section.

2. Network Goods and Asymmetric Alliances

We make two, related economic arguments in this section. The first is that, in an industry

with the economic characteristics of the defense sector, globalization is far more likely to lead to

monopoly than to competition. Where there is monopoly one is likely to find the extraction of

rents, and the second part of our argument uses economic theories of alliances to consider what

form these rents take and how they are distributed.

Throughout this section we use the theoretical conceit of states as firms seeking to

produce security as a logical starting point, given its quite literal application to the building of

15 Dombrowski and Gholz 2006.16 Kapstein 1994.17 Caverley 2007.18 Neuman 2010.



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weapons. States can choose to arm, which results in a more reliable improvement in security, or

rely on allies, which improves security more quickly and cheaply but at the cost of reduced

reliability.19 In the case of weapons production one can build them oneself, or buy weapons off-

the-shelf or collaboratively from another state, a cheaper but less reliable source. As in alliances,

states cooperate when the resulting gains from exchanging heterogeneous goods—for example,

better value weapons and foreign policy autonomy--are sufficiently high for all parties.20

2.1 An Arms Program as a Network Good

For modern conventional weapons, every additional customer, especially early on in the

development process, reduces the up front costs associated with the project considerably.

Furthermore, new customers in a weapons program lead to longer production runs, further

reducing costs. In addition to these cost reductions from economies of scale and learning effects,

several aspects of a weapons program further increase the value for all consumers. States

purchasing a weapons system often supply components for it, potentially increasing the pool of

available technology and encouraging gains from specialization. Finally, commonality in

weapons delivers increased interoperability in joint deployments. Combined, these factors lead

to a “network effect” for the finished product. The positive externalities associated with the wide

use of a particular weapons platform by a group of states exert monopolistic pressures on the

market, and an industry standard tends to emerge just as Microsoft Office did in the software

market.21 The network effect produces strong incentives to extend program membership widely

if not indiscriminately.

19 Morrow 1991.20 Lake 1999, 5.21 Midford 2006, 279; Caverley 2007, 603-604.



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These monopolistic tendencies are further enhanced by the likelihood that a single, large

state can provide this product more effectively than a group of smaller states. A thorny

combination of principal-agent dilemmas and collective action problems makes international

arms cooperation difficult.22 The prospects for successful cooperation in a transnational weapons

acquisition are therefore greatly improved by the existence of a state with little need to cooperate.

Given sufficient domestic market size and the industrial skill set to make these weapons, such a

state enjoys the superior ability to commit not only to making the weapon, but to do so

unilaterally.23 Enjoying such leverage over a network good like defense production delivers what

Lloyd Gruber calls “go-it-alone” power.

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2.2 Monopoly Leads to Rent Collection

The United States’ privileged market position in providing this network good delivers

monopolistic power. Monopoly is traditionally associated with economic profits, but we argue

that in the case of the arms industry, rent is fungible. The United States can distribute the rent to

various actors and collect it in more than one form. The United States might use arms sales to

reduce its own per unit costs. Or perhaps American defense firms, newly empowered abroad as

well as entrenched in the iron triangle at home, collect the rent in terms of higher profits. States

do not necessarily place maximizing economic rents from a position of market power as their

preeminent goal.25

We argue that the ability to exclude, akin to having sole control of “the launch

button” within an alliance, is itself the primary political benefit or form of rent for the United

22 DeVore 2011.23 Drezner 2007, 55-56; Simmons 2001.24 Gruber 2000.25 Gowa 1994; Gowa and Mansfield 2004.



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States.26 For example, the United States suspended Israel’s access to its multination Joint Strike

Fighter program until that country ceased exporting unmanned aerial vehicle parts to China. The

United States can be expected to pay for this privilege.

A monopolist with the unique ability to control network membership can be expected to

use this power when the benefits of excluding a state outweigh the opportunity costs of not

having it join the network. Indeed the network effect makes the power to exclude that much

more potent. As the network expands, those that still remain shut out find themselves at an

increasing disadvantage in terms of acquiring high value weapons. Moreover, the marginal cost

to the hegemon of excluding any given state from a large network is low. The United States can

be expected therefore to exclude potential rivals in either production (Russia) or great power

military competition (China). It can also be expected to reward medium sized states for joining

its network, thereby preventing valuable market share (and unit cost reduction) from going to

competitor products. In essence, by denying competitors access to these markets, the U.S. can act

to increase the costs of competing in both arms production and acquisition.

In essence we argue for a joint product model approach to arms purchases, in which one

benefit--a better value weapon--is available to all, while another--the ability to use access to the

weapons to advance its geopolitical ends--is available only to the United States. Our theory

inverts the joint product approach used by Todd Sandler and Keith Hartley to explain why

smaller NATO states did less free-riding than predicted by Mancur Olson and Richard

Zeckhauser’s pure public goods theory of alliances: building conventional forces not only

contributed to the public good of alliance-wide deterrence, but also had private benefits, such as

26 Morrow 1991, Sandler and Murdoch, 2000.



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damage-limitation in the event of a war, for the small state.27 However, if the hegemon rather

than the small state enjoys private benefits, and cooperation is somewhat costly for small states

(due to the potential restrictions on its foreign policy), then hegemons should expend resources

to convince states to bandwagon. When a product results in both a network-wide benefit and a

private one for the exclusive use of the most powerful state, smaller states can be expected to

“free ride” more; joining the network but demanding side payments from the hegemon.

2.3 Implications

Our paper’s central argument is that the United States is willing to exchange economic

rents for geopolitical benefits as it shapes its arms transfer policies. This implies that it will be

most likely to subsidize or sell at preferential prices to those clients who are potentially “on the

fence” in terms of their foreign policy positions on issues of importance to the United States, or

who might even be willing to purchase weapons from competitors. In contrast, countries that are

highly dependent upon the U.S. for their security are less likely to receive preferential pricing.

Our first hypothesis reflects our view that, because keeping states inside its network of

arms clients provides geopolitical benefits, the United States can be expected to pay for it. In

other words, the United States encourages dependency on its defense industry while

discouraging imports of arms from other entrants. Thus,

H 1: States with a higher reliance on US weapons imports pay lower prices for US arms

exports.

This hypothesis stands in stark contrast to the alternative argument that states that import from

multiple suppliers can use competition to lower prices for American weapons. We also argue that

27 Sandler and Hartley 2001, Olson and Zeckhauser 1966.



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the United States will aggressively price its products for states that are liberalizing their arms

purchase policies:

H 2: States with larger, more open arms markets pay lower prices for US arms exports.

We also argue that client states that have a higher potential for an independent foreign policy are

rewarded for siding with the United States on international issues, while those states already in a

hierarchical relationship with the United States (and thus having less capacity for an autonomous

foreign policy) are more vulnerable to financial rent extraction. We use David Lake’s

conception of hierarchy for the following hypothesis:

H 3: States in a more hierarchical security relationship with the United States pay higher

prices for American arms exports.

Finally, having controlled for hierarchical relationships, we theorize that the United States

rewards countries with foreign policies in line with its own preferences:

H 4: States with foreign policies closer to the United States’ pay lower prices for

American arms exports.

3. What Do the Empirics Show?

In testing our hypotheses, we take advantage of the ability of prices to efficiently incorporate

large amounts of information about complex economic transactions. Case studies, the dominant

means of analyzing the defense industry to date, capture the byzantine complexity of political

and economic interactions between multiple actors balancing several different goals, but the

results are often not generalizable. We take the opposite tack by examining the aggregate effects

of these tradeoffs through the construction of a data set of the prices paid by states for a given

basket of American weapons annually. Examining the relative prices of arms purchases also



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allows for a fine-grained analysis of the arms vs. allies tradeoff and the effect of hierarchy.

Finally, there has also been relatively little of this type of analysis of the defense industry in

academic and policy journals.

3.1 Trends in American Arms Exports

Our data set includes every country to which the United States physically transferred weapons

each year for the years 1970 to 2007. To get a sense of the volume of weapons being imported,

we first turn to data collected by the Stockholm International Peace Research Institute.28 SIPRI

draws on a variety of publically available sources to generate an annual report on global arms

transfers using a “Trend Indicator Value” (TIV, in constant 1990 USD), designed to express the

level of military resources being transferred to another country. Thus the same variant of an F-16

being sent to Pakistan and to Poland by the United States would be assigned the same TIV,

regardless of the price these two governments paid for it. TIVs therefore express the volume of

arms rather than their price tag.29

In the remainder of this empirical section, we take a two-pronged approach to the SIPRI

data. We first present a broad overview of trends in the amount, actors, and competitiveness of

the defense industry to assess the United States’ market position. Figure 1 compares trends in

states’ shares of international arms exports and defense spending from 1970 to 2010.

28 SIPRI 2011.29 The TIV data has several limitations. It conflates used weapons with new weapons (albeit witha 60% handicap) and considers the transfer of a weapon into a country and the licensed production of that same weapon within that country as equivalent “imports.” Since SIPRI relieson open source information, there are no doubt biases involved in the information gathered, asmany transfers will remain secret. For example, the countries that produce official data on their arms exports account for over 90% of the total volume of deliveries of major conventionalweapons, so while there is some bias in the data, it is only likely to matter at the margins.



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The massive American advantage in market share in the 1990s appears due largely to the

disappearance of the Soviet Union. Neither the three principal European exporters (France,

Germany, and Britain) nor the rest of the world’s exporting states filled the post-Cold War gap.

However, the “rise of the rest,” i.e. peripheral states, has continued steadily since the early 1980s.

While there appears to be a correlation between defense spending and exports, one can plausibly

claim that the European states offset declining spending in the 1980s with more exports. Russia

has successfully boosted its exports without large increases in its defense expenditure over the

past decade. American exports, on the other hand, have declined even as post-9/11 defense

budgets have soared, a development to which we return in the conclusion..

The United States does not have an absolute monopoly, and indeed it has declined in

market share since the 1990s. Still, it remains the preeminent exporter of weapons, with Russia

once again in second place. If the United States (and Russia) are losing market share to new

exporting nations, it is a quite nascent trend. The United States throughout this time period has

been a very privileged actor in this industry, allowing us to examine the prices other states pay

for American weapons to determine when the United States collects rent and when it subsidizes

states’ joining its network by buying American weapons.

3.2 Dependent Variable: Import Premium

Our dependent variable for the remainder of the paper, Import Premium, is a ratio of two data

sources on prices associated with the physical transfer of weapons from the United States to

other countries. The denominator is SIPRI’s TIV, described in detail above. The numerator is the

annual financial value in constant 1990 US dollars of all reported commercial and non-



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commercial weapons transfers to another state, contained in the US Government’s Defense and

Security Cooperation Agency’s Historical Data Series.30

The DSCA report lists the value of all weapons transactions for every country and every

year. It reports both Foreign Military Sales (transactions directly involving the US government)

and Commercial Military Sales (transactions in which the importing state negotiates directly with

the arms-producing company). It also reports the values of various programs that offset the client

state’s costs of purchasing weapons. These include grants given directly to purchasers of

American arms through the Foreign Military Financing program, and other, smaller subordinate

programs such as the Excess Defense Articles Program and the now-defunct Military Assistance

Program. It does not include the offset agreements that are normally negotiated by the client state

directly with the American corporation producing the weapon for export. 31 Nor is it likely to

accurately assess the follow-on service agreements that normally accompany these sales. Some

sales are classified, but a recent GAO report states that this amount is small relative to overall

export totals.32 Nonetheless, the data are the best available means to analyze a large cross-section

of countries across several decades. The ratio takes the following, unit-less form:33

30 DSCA 2010.31 A FOIA request for these data has been pending (with an unanswered appeal) since 2010. Ingeneral, the evidence appears overwhelming that the costs of offsets are passed on to the purchaser, Brauer and Dunne 2011, 251-254. As such, offset are best described as a means of animporting state of shifting resources within its own borders, and is less relevant to our purposes.32 United States Government Accountability Office 2010.33 SIPRI cautions against directly comparing TIVs with “the sales values or the financial value of export licenses” in order to “measure the economic burden of arms imports or the economic benefits of exports.” We do not abuse the data in this manner; rather we seek to study the ratioof one cross-sectionally and longitudinally comparable measurement over another one. Thisratio has attractive econometric qualities besides being an accurate measurement of thedependent variable. Previous work using the TIV in this matter includes Smith and Tasiran,2005.



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

!!"

Figure 2 depicts each observation of the premium over time, using a logarithmically

scaled y-axis, as well as each year’s average premium. The graph makes clear the extremely

wide range taken by the premium. The (unlogged) ratio takes on values less than one; many

countries pay less for their annual exports than SIPRI’s estimated production costs. The

premium clearly and consistently rises over time, with the exception, perhaps surprisingly, of the

period of maximum American market share in the 1990s. As in Figure 1, the graph depicts a

relatively stagnant period that coincides with the rising US market share of the 1990s, followed

by a (puzzling) sharp spike in the premium as U.S. market share declines to its historic level in

an increasingly competitive market. The United States enjoys a large market share, but a more

competitive market appears to be merging.

3.3 Independent and Explanatory Variables

Many potential measures of American market power are year-specific, leaving us with few

observations and no cross-sectional variation. We operationalize market power with a measure

that varies across client-states and within them over time (and are thus robust to year fixed

effects and other efforts to address the challenges of analyzing time-series, cross-sectional, or

TSCS, data). Figure 4 shows each country’s Reliance on American weapons as a percentage of

their annual arms imports. The figure shows that countries that do buy US weapons tend to be

quite reliant; the mean for export dependence is 57% (s.d. of 36). Even states that export

weapons but also buy from the United States have an average dependence of 55%. On the other

hand, it appears that, after a period of relative stability in the 1990s, countries are growing less



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reliant on American weapons. One plausible explanation for these trends is that the United

States failed to prevent entry of competitors, weapons manufacturing and technology have

indeed diffused, and the market is much more competitive than before.

Secondly, we also seek to operationalize the foreign policy preferences that might

influence the U.S. decision to redistribute rent. We first use Gartzke’s “ Affinity of Nations”

index, which describes the similarity of a client’s preferences with the United States based on

UN General Assembly voting positions.34 We also employ David Lake’s “Index of Security

Hierarchy”, which contains two relevant components.35 Lake first computes the total number of

American active duty military personnel stationed abroad by country (normalized to 1 for the

highest country value in 1995). He then creates an independent alliance index e index, the

inverse of the number of states that are allied with a given country that is not also an ally of the

United States (ranging from 0 to 1). Lake computes the Hierarchy index by summing the indices

of military personnel and independent alliances, normalized to one for the highest country value

in 1995. The index only extends to the year 2000, however, which limits that range of time we

can analyze.

Finally we operationalize the Liberalization of the client state’s market. The potential

client state’s relative autarky (or liberalization) in terms of weapons purchases is also likely to

influence the level of subsidy from the United States. We therefore include a measure of defense

imports to give a sense of potential market size for American weapons.

We include a battery of control variables that may have a confounding effect on the

relationship between our explanatory and dependent variables. Involvement in a militarized

dispute obviously shapes one’s demand for weapons. We note if a state has experienced a

34 Gartzke 2010.35 Lake 2007.



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militarized conflict over the past five years.36 We also include a measurement of regime type

from the Polity dataset.37 If one is a potential competitor for the United States, one may be more

or less likely to be a customer. We therefore include a dummy variable, arms exporter , if SIPRI

has recorded any transfers of weapons for that year by the state. 38 To distinguish liberalization

from demand and ability to pay, we also include the size of the state’s defense budget (in logged,

constant 1990 dollars), taken from the Correlates of War, to give a sense of potential market size

and demand for American weapons.39 Wealthier countries, operationalized by logged GDP per

capita, are not only more likely to afford large militaries, but are likely to invest in the capital-

intensive platforms in which the United States specializes.

40

3.4 Preliminary Analysis

The time-series, cross-sectional (TSCS) nature of the dataset offers a host of challenges. Since

we are examining prices of imports rather than the decision to import, we do not analyze

country-years in which no deliveries were taken. Because many countries do not take any arms

deliveries from the United States in a given year, but have done so at other times, this makes the

data far from rectangular (and precludes imputing the dependent variable), further complicating

TSCS analysis.41

As this section seeks to show, we take a very conservative approach to the

data’s analysis at the risk of Type II errors.

36 Gleditsch, Wallensteen et al. 2002.37 Gurr, Marshall et al. 2012.38 As an alternative to the dichotomous exporter variable, we also used the logged TIV for allexports by the country-year as well as the number of workers employed in the domestic armsindustry. Neither measure was close to significant.39 Singer, Bremer et al. 1972.40 Sechser and Saunders 2010.41 Beck and Katz 2011.



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Pooling the data requires a set of often-unnoted assumptions about its structure. Before

considering whether pooling is appropriate, we therefore analyze our data through separate

regressions by year and by country.42 Graphically analyzing the results of these regressions not

only suggests whether pooling the data is appropriate, but delivers insights on its own.

We first look at the cross-section regression by year. In order to maximize the number of

years available, we exclude Hierarchy from these regressions (which only goes to 2000). Figure

4 presents each coefficient in a multivariate model run for each year, as well as a representation

of its 90% confidence intervals. Inspecting the graphs, it appears that larger amounts of arms

imports consistently result in lower American prices, while higher defense budgets consistently

translate into higher prices. On the other hand, exporters, GDP per capita, affinity, and conflict

involvement rarely achieve significance, and the direction of the coefficient changes as well.

More interesting are the regression coefficients for Reliance and Polity, which appear to have

some time-dependent effects. The Reliance coefficients are consistently negative (and

sometimes significant), but appear to have shrunk in magnitude in the mid-1990s. More recently,

the coefficient’s magnitude has grown and has been consistently significant at 90% levels.

Whereas Polity seems to have little effect for much of the time period, since 2000, more

democratic states appear to be paying less for their weapons. More generally, it is difficult to

claim that the data contain no time-related trends.

We performed a similar analysis within each country across time. Figure 5 presents the

coefficients for our four explanatory variables by country (from a multivariate regression

including a lagged dependent variable). To aid the eye, instead of plotting confidence intervals as

in Figure 4, we depict coefficients significant at the 90% level as a solid black dot. Figure 5

42 Jennifer Hill and Andrew Gelman, 2007, 159 describe this “secret weapon.”



21

suggests that the theory’s explanatory variables often significantly explain variation in prices

within states. It appears clear that the more reliant a country is on the United States for its

weapons, the lower a price it pays for them. A liberalized arms market leads to lower American

prices, but states with big defense budgets tend to pay more for their weapons. Clearly, Reliance

and Imports are significant predictors of price for many countries, and have negative coefficients

for most countries as predicted by the theory. The coefficients for Affinity also appear to have

the theoretically predicted direction, but the effect is less robust across countries. Hierarchy has

the weakest relationship with some extremely visible outliers. Nonetheless, the coefficients are

often significant and most are positive. These graphs also help us identify outliers such as the

Dominican Republic, Romania, and Uruguay, which appear particularly clearly in the

coefficients for Hierarchy.43

In general, the coefficients for each country cluster around the same value, which

suggests that there may be a common effect across countries. However, F-tests clearly reject the

null hypothesis that country fixed effects are not significant. More importantly, the time trends

suggested in Figure 4 mean that we must interpret the pooled results with caution.

3.5 Pooled Analysis Results and Analysis

We now analyze the pooled data set using a number of different statistical models. Again, we

emphasize the conservative nature of our modeling choices. We use unit fixed effects at the

expense of testing slow-moving variables such a client’s Polity score.44 In particular we control

for potential time-based factors by a number of ways. A lagged dependent variable (LDV) has

43 We exclude these outliers in regressions to check the robustness of our findings, reported inthe appendix, with no effect on the outcome.44 We also analyzed our data without country fixed effects. Our main theoretical variables werelargely unaffected, and thus we report them only in the appendix. However, involvement inconflict did change greatly; the coefficient is positive and significant.



22

become nearly de rigeur in TSCS analysis, but results in approximately 20% of the observations

being thrown out. Moreover, use of an LDV and fixed effects will result in biased coefficients,

although the effect over a long enough time series is small. We therefore analyze all our relevant

models with and without a LDV. In some models we also included year fixed effects, and in

others we incorporated year and year squared terms in an attempt to de-trend the data.45

In addition to these “levels” analyses, we then analyzed the data using a first differences

model, which is the only specification appropriate if the data are neither stationary nor

cointegrated. We find no evidence of non-stationarity, but tests for this in TSCS data exhibit low

power. We include the first difference model not only for the sake of conservatism, but also

because it examines the short-run dynamics of the data in a clear and useful way. The

conservative nature of this approach is apparent in a telling Beck and Katz observation that, “In

practice, first-difference models often perform poorly (at least from the perspective of the

researcher, for whom changes in x appear unrelated to changes in y).”46

Finally, we employ an error correction model (ECM). So far, our models have made

strong assumptions about the effect of independent variables on the dependent over time. Those

without a LDV assume that changes in independent variables (and the errors) instantaneously

affect the dependent variable, while those incorporating an LDV assume that the effect continues

over time, decaying geometrically. Arms imports deals are years in the making and deliveries of

a product take place over a long time period as well. We are therefore interested in the long-term

effects of changes in the explanatory variables, and a great deal of time-dependent bias is likely

to be in the data. We therefore take the advice of to pick the most general model possible, using

45 We analyzed models with a year cubed variable as well, but this was essentially perfectlycollinear with year, and therefore had no effect on estimates or errors.46 Beck and Katz 2011, 343.



23

the ECM to simultaneously estimate both short- and long-run relationships: ∆ = ! + !!!! +

!∆ ! + ! !!! + !.47 In this equation, ! can be straightforwardly interpreted as the short-run

effects of a change in an explanatory variable. In terms of long run effects, ! is the “error

correction rate,” describing how rapidly the dependent variable returns to an equilibrium after a

shock. The long-run effect of an explanatory variable is !/!. Incorporating lagged dependent

and independent variables uses degrees of freedom while simultaneously eliminating

observations. Thus any positive findings are unlikely to be spurious.

Table 1 reports the results of the levels analysis of data running from 1970-2007. Table 2

reports the results of models that include Lake’s Hierarchy index, which cuts off observations

from 2001 onward. Table 3 reports the results of the first differences (Models 13 and 14) and

ECMs (Models 15 and 16). As we analyze the results, we should note that, due to our

conservative approach, failing to reject the null of no effect should be taken with a grain of salt.

On the other hand, a consistently significant finding should be taken seriously. Because the

dependent variable is logged, interpretation of coefficients presented in the tables is relatively

straightforward. For dichotomous (exporter) and other untransformed variables (reliance, and

regime type), the coefficient represents the percent change in the premium for a one-unit increase.

A one percent increase in logged independent variables (imports, military spending, and GDP

per capita) produces a 1.01β

increase in the premium.

In nearly all of the models reported in Tables 1-3, Reliance is negative and significant as

predicted by the theory. Coupled with the cross-sectional and longitudinal regressions depicted

in Figures 4 and 5, we can be confident that those more dependent on the United States for their

weapons exports are not subject to rent collection in the form of increased prices.

47 Suzanna De Boef and Luke Keele 2008.



24

A state buying more weapons from abroad gets much more favorable treatment by the

United States. While the lower premium may simply reflect a discount for larger orders, this

explanation is undercut by the fact that wealthy states with big defense budgets pay more.

Liberalization, rather than defense spending (with its positive coefficients), appears to lead to

lower prices, which conforms to our theory that large, liberal arms markets’ potential for keeping

other non-American competitors economically viable merits extra subsidization.

While not as robust in significance over the course of the many models, the political

variables take on expected directions. States that vote more closely with the United States in the

UN are awarded with lower premiums. The opposing effects of Reliance and Security Hierarchy

are striking indeed. The more hierarchical the security relationship, the US appears to be less

willing to sell cheap weapons. While not dispositive, this supports our theory that the United

States targets its subsidies to influence states with which it is not already in a strongly

asymmetrical relationship.

Regime type’s effect is not robust to the conservative approaches we take here, but that is

not terribly surprising given its slow, often unvarying nature. Interestingly, in the first

differences models of Table 3, we see evidence that democratizing states are more likely to pay

lower prices on its weapons imports. Similarly, it appears that the United States has treated more

democratic states differently in the last decade compared to earlier years. The United States

continues to sell weapons to non-democracies since the Cold War’s end, but the data suggest that

it rewards more liberal states with lower prices for them.

So far, we have shown which of our theoretical variables have the predicted effect across

a multitude of different specifications. To explore the substantive effects in both the short and

long terms (whose coefficient, !/!, is reported with standard errors from the Bewley



25

transformation), we use the error correction model 16 in Table 3 to produce the results in Table

4.48 Increasing US imports as a percentage of all imports (Reliance) by one point results in an

immediate price reduction by 2.2% with an additional 1.3% reduction over subsequent years, a

very large effect. A 10% increase in arms imports results in more than a 10% decrease in the US

premium combined. While a 10% increase in a state’s defense spending produces higher prices,

the same level of liberalization of its arms market swamps this effect. Affinity and Hierarchy are

indexes in which a one point increase represents an enormous change, so instead we choose to

analyze the effect of a one standard deviation increase. In both case the effect is large, but only

significant in the long term. Such an increase in hierarchy results in the US premium rising by

almost 134%, a massive result.49

All of this paper’s analyses taken together, we are confident in claiming that states with

large, liberal arms markets and those heavily reliant on the US for weapons will pay lower prices

for them. Given the difficulties inherent in TSCS data and our conservative approach, we treat

as suggestive our finding that states that appear to have similar foreign policy preferences (at

least as expressed in United Nations voting patterns) will pay less for their weapons, while states

already in a hierarchical security relationship with the United States will pay more. These

findings support a theory suggesting that the United States is trading rents for a larger reliance on

its weapons. On the other hand, the theory that the United States (or its defense industry) is

using its market power to collect financial rents is unlikely to be true. Geopolitical factors

appear to drive pricing for American weapons exports.

48 See De Boef and Keele 2008, 192 for a full explanation of the Bewley transformation’s use asa computational convenience, and can be replicated using the code supplied at WEBSITE.49 Figure 5 shows that there are several outlier countries when it comes to the effect of Hierarchyon Premium. Surprisingly, we ran a similar set of statistical models with the outlier statesexcluded and found little change in the coefficients for Hierarchy. See the appendix for theresults.



26

4. Conclusion: Order Without A Constitution?

In this paper we argue that not only does the United States enjoy tremendous market power in

the international arms market, it also enjoys the privilege of using this market power to advance

its strategic interests. In particular, the United States can forgo its monopolistic economic rents

in order to provide preferential pricing on its exports to countries that could undermine its market

and international influence in the future. A new data set on the prices paid by client states for

American weapons provides support for this claim. US market power appears to correlate with

preferential prices extended to liberalizing arms markets and states more reliant on American

weapons.

That U.S.-dominated trade liberalization makes states more dependent on the United

States than vice versa is not a new argument. We focus on what the United States does with this

asymmetric interdependence. IR theory currently suggests two stark options in a world of

extreme power asymmetry. In one version, “hegemons make a commitment to enjoy short-run

political benefits by reshaping the international system to fit their view of the system and pay the

price of long-term relative decline in power and security”.50 Ikenberry argues that liberal and

forward-thinking hegemons should do the opposite, making short-term sacrifices in order to lock

in long-term benefits through the development of institutionally-based constitutional order. We

propose a solution to this dilemma by pointing out how network effects and economies of scale

that the United States possesses in at least one important area of international politics--

production and sale of major conventional weaponry platforms—provides a surplus it can use for

both short- and long-term gains in influence.

50 Morrow 1991, 930-931.



27

A globalized but American-dominated defense industry is a form of order that provides

many of the services of an institution but little resembles Ikenberry’s “constitutional order.” The

arrangement lowers transaction costs, reduces uncertainty and solves collective action problems

inherent in weapons cooperation due to fewer, indeed a minimum, potential veto players and a

greater certainty that the weapon will be produced. A large weapons program exhibits large

initial startup costs. The learning effects from a collaborative project are likely to be

considerable. Finally weapons cooperation creates “relations and commitments with other actors

and institutions that serve to embed the institution and raise the costs of change”.51 The

combined effect makes developing a rival, much less a replacement, increasingly daunting. The

wider the network, the less likely a competing good can ever be produced. It is therefore likely

to be a stable arrangement if “no state believes it is profitable to attempt to change the system,”

including perhaps the ones shut out.52

Such a mechanism of hegemonic influence need not be limited to the arms trade,

although this industry’s potential for network effects may make it uniquely prone to it. The

recent use of American logistics infrastructure to allow European states to intervene in Libya and

Mali may be another example of this behavior. The arrangement allows the central state to

enable more interventions it approves of, while maintaining veto power over ones it does not.

On the non-military side, the United States continues to accrue financial and geopolitical

advantages from the continued centrality of the U.S. dollar to the global economy, in part due to

the dollar’s network effects.

That said, while our theory shows the potential for a robust equilibrium of American

long-term influence, and our statistical analysis of American pricing show strong support for the

51 Ikenberry 2001, 70.52 Gilpin 1981, 50.



28

subsidization predicted by this theory, we cannot ignore the broader trends in the arms trade and

their implications for the future of American market power upon which our theory is based.

First, the international arms market appears to be growing increasingly competitive. Second, the

premium for American weapons is rising steadily even as, perhaps not coincidentally, states

grow less reliant on the US for arms imports. This paper has shown that, while American power

in the arms market undeniably provides it with important benefits, its continued domination

appears far from assured.



29

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31

0

. 2

. 4

. 6

0

. 2

. 4

. 6

1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

USA USSR/Russia

France, Germany, and UK Rest of World

Exports Defense Spending

P r o p o r t i o n o f G l o b a l E x p o r t s / S p e n d i n g

Year

Figure 1: Comparing Exports and Defense Spending as Percentage of World Totals, 1970-2010



34

- .

0 6 - .

0 4 - .

0 2

0

. 0 2

- 1 .

5

- 1

- .

5

0

. 5

- 5

0

5

1 0

- 2

0

2

4

- 1

0

1

2

3

- 1

0

1

2

- .

4

- .

2

0

. 2

- 4

- 2

0

2

4

- 5

0

5

1970 1980 1990 2000 2010 1970 1980 1990 2000 2010 1970 1980 1990 2000 2010

Reliance Imports Affinity

Exporter GDPpc Def. Spending

Polity Conflict Hierarchy*

Year

Figure 4: Plotted OLS Coefficients (with 90% c.i.) for Defense Premium by Year

*Other coefficients are from a model excluding hierarchy to maximize time range.



35

-.2 -.1 0 .1 .2

Venezuela, RBUruguay

United Arab EmiratesUK

TurkeyTunisia

ThailandSweden

SpainSouth Korea

SingaporeSaudi Arabia

RomaniaPortugal

PhilippinesPeru

PanamaPakistan

OmanNorwayNigeria

New ZealandNetherlands

MoroccoMexico

MalaysiaJordanJapan

ItalyIsrael

IndonesiaHonduras

GuatemalaGreeceFranceFinland

El SalvadorEgypt

EcuadorDominican Republic

DenmarkColombia

ChileCanada

BrazilBolivia

BelgiumBahrainAustralia

Argentina

Reliance

-5 -4 -3 -2 -1 0



TurkeyTunisia

ThailandSweden

SpainSouth Korea


RomaniaPortugal

PhilippinesPeru

PanamaPakistan

OmanNorwayNigeria


MoroccoMexico

MalaysiaJordanJapan

ItalyIsrael

IndonesiaHonduras


El SalvadorEgypt


DenmarkColombia

ChileCanada

BrazilBolivia

BelgiumBahrainAustralia

Argentina

Imports

-30 -20 -10 0 10 20



TurkeyTunisia

ThailandSweden

SpainSouth Korea


RomaniaPortugal

PhilippinesPeru

PanamaPakistan

OmanNorwayNigeria


MoroccoMexico

MalaysiaJordanJapan

ItalyIsrael

IndonesiaHonduras


El SalvadorEgypt


DenmarkColombia

ChileCanadaBrazil

BoliviaBelgiumBahrain

AustraliaArgentina

Affinity

0 20,000 40,000


UKTurkeyTunisia

ThailandSweden

SpainSingapore

Saudi ArabiaPortugal

PhilippinesPeru

PakistanNorwayNigeria


MoroccoMexico

MalaysiaJordanJapan

ItalyIsrael

IndonesiaHonduras

GuatemalaGreeceFrance

El SalvadorEgypt

EcuadorDenmarkColombia

ChileCanada

BrazilBolivia

BelgiumBahrain

AustraliaArgentina

Hierarchy*

Figure 5: Within-Country Regression Coefficient by Country

(black dots indicate significance at p<0.1)



36

Table 1: Influences on Premium Paid on US Arms Exports, 1970-2007

(1) (2) (3) (4) (5) (6)

ln(Imports) -0.588*** -0.588*** -0.618*** -0.606*** -0.615*** -0.612***

(0.0363) (0.0391) (0.0315) (0.0314) (0.0327) (0.0323)

ln(Defense) 0.371*** 0.277** 0.312*** 0.282*** 0.368*** 0.303***

(0.0894) (0.0962) (0.0808) (0.0816) (0.0811) (0.0809)

Affinity -0.920*** -0.656*** -0.296 -0.150 -0.945*** -0.775***

(0.213) (0.178) (0.222) (0.216) (0.134) (0.128)

Reliance -0.0191*** -0.0178*** -0.0188*** -0.0176*** -0.0192*** -0.0177***

(0.00112) (0.00112) (0.00109) (0.00106) (0.00113) (0.00109)

ln(GDPpc) 0.223 -0.0820 0.609** 0.312 0.682** 0.306

(0.233) (0.201) (0.204) (0.203) (0.213) (0.210)

Exporter -0.0344 0.00402 -0.0698 -0.00364 -0.0507 0.00618

(0.106) (0.0962) (0.0954) (0.0885) (0.0992) (0.0911)

Polity -0.00158 -0.0116 0.00770 -0.000442 0.00360 -0.00525

(0.0108) (0.00963) (0.00884) (0.00857) (0.00925) (0.00888)

Conflict 0.117 0.0888 0.0724 0.0778 0.200* 0.133

(0.0825) (0.0781) (0.0927) (0.0885) (0.0943) (0.0890)

D.V. t-1 0.225*** 0.201*** 0.226***

(0.0403) (0.0218) (0.0215)

Intercept -2.849 1.468 -7.047*** -2.787 -3429.5* -569.0

(2.680) (2.231) (1.785) (1.971) (1399.5) (1393.7)

N 1700 1388 1700 1388 1700 1388

R 2 0.559 0.572 0.423 0.493 0.346 0.431

Standard errors in parentheses (panel corrected in Models 1 and 2), + p<0.10 * p<0.05 ** p<0.01,

*** p<0.001. Not reported: Country fixed effects in all models, Year fixed effects in Models 3 and 4,Year and Year 2 in Models 5 and 6.



37

Table 2: Influences on Premium Paid on US Arms Exports with Hierarchy, 1970-2000

(7) (8) (9) (10) (11) (12)

Reliance -0.0186*** -0.0175*** -0.0186*** -0.0175*** -0.0189*** -0.0178***

(0.00126) (0.00128) (0.00122) (0.00120) (0.00122) (0.00121)

ln(Imports) -0.599*** -0.592*** -0.610*** -0.598*** -0.621*** -0.610***

(0.0427) (0.0458) (0.0355) (0.0353) (0.0356) (0.0356)

Affinity -0.908*** -0.679*** -0.121 -0.0425 -0.182 -0.330*

(0.209) (0.169) (0.242) (0.238) (0.152) (0.150)

Hierarchy 1.492** 2.224*** 0.804 1.522* 0.881 1.875**

(0.497) (0.374) (0.649) (0.655) (0.648) (0.663)

ln(Defense) 0.325** 0.258* 0.242** 0.250** 0.171+ 0.198*

(0.101) (0.108) (0.0914) (0.0934) (0.0886) (0.0906)

ln(GDPpc) 0.0716 -0.342 0.431+ 0.0317 0.406+ 0.0330

(0.250) (0.241) (0.240) (0.244) (0.242) (0.247)

Exporter 0.131 0.0115 0.114 0.0534 0.0682 -0.00995

(0.117) (0.117) (0.111) (0.105) (0.112) (0.106)

Polity 0.0107 -0.000852 0.0179+ 0.00799 0.0161+ 0.00396

(0.0121) (0.0108) (0.00968) (0.00951) (0.00975) (0.00963)

Conflict 0.0863 0.107 0.0713 0.102 0.0952 0.127

(0.101) (0.0989) (0.114) (0.110) (0.111) (0.108)

D.V. t-1 0.197*** 0.179*** 0.170***

(0.0476) (0.0247) (0.0246)

Intercept -1.641 3.105 -5.125* -0.767 -22137.4*** -13071.2***

(2.726) (2.448) (2.076) (2.291) (2537.9) (2722.9)

N 1384 1129 1384 1129 1384 1129

R 2 0.545 0.555 0.391 0.454 0.357 0.413

Standard errors in parentheses (panel corrected in Models 7 and 8), + p<0.10 * p<0.05 ** p<0.01, *** p<0.001.

Not reported: Country fixed effects in all models, Year fixed effects in Models 9 and 10, Year and Year 2 in Models

11 and 12. .



38

Table 3: First Differences and Error Correction Models

(13) (14) (15) (16)

Δ Reliance t -0.0248*** -0.0250*** -0.0224*** -0.0221***

(0.00110) (0.00126) (0.00109) (0.00122)

Reliance t-1 -0.00861*** -0.00799***

(0.00129) (0.00145)

Δ ln(Imports) t -0.833*** -0.828*** -0.744*** -0.731***

(0.0313) (0.0358) (0.0315) (0.0352)

ln(Imports) t-1 -0.306*** -0.301***

(0.0366) (0.0418)

Δ Affinity t -0.223 -0.125 -0.332* -0.189

(0.185) (0.192) (0.163) (0.167)

Affinity t-1 -0.516*** -0.528***

(0.107) (0.112)

Δ Hierarchy t -0.385 1.064

(1.233) (1.099)Hierarchy t-1 1.954**

(0.693)

Δ ln(Defense) t 0.0540 -0.0198 0.0413 -0.00920

(0.130) (0.153) (0.116) (0.132)

ln(Defense) t-1 0.170* 0.166+

(0.0805) (0.0897)

Δ Exporter t -0.0378 -0.0857 -0.0430 -0.0535

(0.0838) (0.0972) (0.0869) (0.101)

Exporter t-1 0.0411 0.121

(0.107) (0.129)

Δ ln(GDPpc) t -0.170 -0.530 -0.417 -1.148

(0.901) (0.979) (0.758) (0.812)

ln(GDPpc) t-1 -0.0617 -0.457*

(0.153) (0.189)

Δ Polity -0.0433* -0.0426* -0.0320* -0.0222

(0.0186) (0.0196) (0.0161) (0.0166)

Polity t-1 -0.00600 -0.000188

(0.00819) (0.00906)

Δ Conflict t 0.0694 0.0739 0.125 0.119

(0.0992) (0.115) (0.0930) (0.111)

Conflict t-1 0.108 0.0443

(0.0930) (0.121)

D.V. t-1 -0.587*** -0.622***

(0.0237) (0.0266)

N 1378 1119 1378 1119

R 2 0.450 0.443 0.631 0.644

Standard errors in parentheses (panel corrected in Models 7 and 8), + p<0.10 * p<0.05 ** p<0.01, *** p<0.001.



Table 4: Substantive Effects in the Long and Short Term (Coefficients from Model 16)

Variable Coefficient Change in IV Effect

Short-Run Long-Run Short-Run Long-Run

Reliance -0.0221*** -0.0129*** 1 percentage point -2.19% -1.28%

(0.00114) (0.00127)

ln(Imports) -0.731*** -0.485*** 10% -6.73% -4.52%

(0.0374) (0.0470)

Affinity -0.189 -0.849*** 1 s.d. (0.47) -17.22% -32.90%

(0.207) (0.152)

Hierarchy 1.064 3.144*** 1 s.d. (0.27) 33.28% 133.70%

(0.731) (0.498)

ln(Defense) -0.00920 0.268* 10% -0.09% 2.59%

(0.138) (0.104)

ln(GDPpc) -1.148 -0.735** 10% -10.36% -6.77%(0.988) (0.232)

Exporter -0.0535 0.194 0 to 1 -5.21% 21.41%

(0.105) (0.132)

Polity -0.0222 -0.000302 1 point -2.20% -0.03%

(0.0169) (0.0105)

Conflict 0.119 0.0713 0 to 1 12.64% 7.39%

Standard errors in parentheses (Bewley corrected in long term), + p<0.10 * p<0.05 ** p<0.01, *** p<0.001.

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