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date: 22 November 2017

To Arms, To Arms: What Do We Know About Arms Races?

Summary and Keywords

Arms races are important phenomenon They can involve the commitment of vast amounts of resources that might otherwise be used to help societies. And arms races can be a cause of war, although there is a debate on how this happens. With the arrival of the behavioral revolution in international relations, the number of quantitative studies of arms races exploded. But with the end of the Cold War, interest in studying arms races declined sharply. This is unfortunate because it is fair to say that most of the important questions involving arms races were not resolved in the empirical work that was done during the period of the Cold War.

Keywords: arms races, military expenditures, spiral model, deterrence model, arms competitions, arms race onset, arms races and war, empirical international relations theory

Introduction

This article will briefly review some of the salient aspects of the early studies of arms races through the end of the Cold War then recent (published from 2005 through 2016) quantitative studies of arms races will be considered.1 Finally suggestions will be offered for where this area of study should go.

A (Very) Brief Review of the Early Study of Arms Races

There is a long history of quantitative and systematic scholarship on arms races in the field of international relations. Two of the most important early scholars were Lewis Richardson, who published a number of pieces between the World Wars (see, e.g., Richardson, 1939), and Samuel Huntington who produced a systematic examination of arms races from the 1940s through the onset of the Cold War (Huntington, 1958). As noted above when the behavioral revolution came to the study of international relations, a large number of studies on arms races were undertaken. In what follows, a series of “signature studies” from the beginning of the quantitative study of arms races are discussed. There were two key questions addressed in these studies:

What drives arms races?

Do arms races lead to war?

What Drives Arms Races?

Much of the early work on arms races centered on trying to understand what drives the acquisition of armaments. A great deal of this literature was inspired by the Richardson equations (Richardson, 1939).2 Richardson was interested in understanding what would have an impact on the “defenses” of a pair of states in competition with one another. Note that his writing referred to defenses, not military expenditures. Figure 1 displays the Richardson equations.

To Arms, To Arms: What Do We Know About Arms Races?

Figure 1. Lewis Richardson’s “Linear Theory of Two Nations”

Where:

x, y are the defenses of x and y

k, l are the defense coefficients of x and y

α‎, β‎ are the fatigue coefficients of x and y

g, h are the grievance coefficients of x and y

Richardson assumed that for two states in an arms competition the changes in their defenses would be a function of three things:

  1. 1. The defenses of the other state. He assumed that each state would react positively to the previous defenses of the other state (i.e., increases by one state would lead to increases in the other state). In the Richardson equations the coefficients for the defense of the other side were referred to as defense coefficients.

  2. 2. The state’s own defenses. He assumed that each state would react negatively to its own previous defenses. The coefficients for the state’s own defenses were referred to as fatigue coefficients.

  3. 3. The overall relationship of each state towards the other. He called the coefficients for the overall relationship grievance coefficients and assumed that these coefficients would be positive. That is, for a pair of states engaged in an arms race net of independent of the impact of the defenses of the other side and its own defenses, the grievance of each state towards the other would have a positive impact on its own defenses (i.e., the defenses of each state would increase). Given Richardson’s focus on arms races, he did not consider whether the grievance coefficients could be negative.

The form of the Richardson equations suggested that the coefficients could be estimated by statistical analysis. And there were numerous efforts to do so. Typically researchers used military expenditures as their indicator for defenses.

This is only a brief review of Richardson’s work on arms races (see Leeds & Morgan, 2012, for a good review of arms race studies over the entire time period). This article only references a few additional key studies and discuss the issues and main problems of this research. It begins by noting that through time the methodologies used to explore arms races became more sophisticated. As well, the quantity and quality of data improved. But findings were inconsistent.

A significant focus of this research was to evaluate whether arms expenditures were externally driven or internally driven. If the changes in state A’s defenses were driven by State B’s defenses and vice versa, this suggests the two states were in an arms race against one another. But it was possible that a state’s defenses were driven by internal factors. In Richardson’s formulation, high levels of previous defense would lead to a decrease in current defense due to the costs of building and maintaining a large military. But a number of quantitative researchers embraced—or at least allowed for—a different interpretation. They argued that the military, the defense industry, and portions of the government would press a state to have higher and higher levels of expenditure. That is, while Richardson believed the coefficients for α‎ and β‎ would be negative, other researchers argued they would be positive.

Of course, one could argue that the signs of α‎ and β‎ could be treated as an empirical question: Just estimate the coefficients for a particular pair of states and see what happens. But generally studies were not this atheoretical. Most work involved a great deal of thought about the underlying causal mechanisms that drive changes in defense. Nevertheless, there was a sense that the results of the statistical analysis would serve to clarify the relationships being studied.

Unfortunately the empirical studies did not all point in the same direction for a particular (potential) arms race. Looking at the United States and the Soviet Union, Majeski and Jones (1981, p. 273) found “no causal relations exist between the military expenditure series” of those two countries. On the other hand, at roughly the same time the results of Wallace’s (1980, p. 271) study of the military expenditures of the United States and the Soviet Union “offer[ed] no support whatever to those who are convinced that the arms race results from the ineluctable nature of the political or economic system on either side. Rather, it would appear that both superpowers are reacting to external [emphasis in original] rather than internal stimuli.” There were a number of differences between these two studies. But the fact that these two well-done studies come to completely opposite conclusions (and there are other examples as well) is—to say the least—disheartening. As Moll and Luebbert note in their review of arms races models (1980, p. 166): “The common failure of statistical tests to discriminate among various models indicates that there are fundamental problems with either the explanatory power of the models or the discriminating ability of the tests. Which is the real difficulty remains a subject for future research.”

Of course, if one varies statistical techniques, data, and spatial temporal domain, this may have an impact on the results. But for research based on the Richardson equations there was another issue that led to inconsistent findings about whether particular dyads were engaged in an arms competition or were being driven by internal factors. Consider two polar cases: one in which both states in a dyad are driven internally and the other in which both states in a dyad are driven externally. If both states are driven internally and each is growing at a constant rate there will be a solid correlation between the data series of the two states (i.e., it will look like an externally driven arms race).3 And if both states are driven externally by each other’s defenses the relationship within each state to its own previous level of defenses should be strong (i.e., an externally driven arms race may look like a situation in which both states are driven by internal forces). So regardless of whether a dyad is purely driven internally or whether there is a pure arms race in a dyad, there should be a high degree of correlation between (a) the defenses of both sides, and (b) within each state its defenses and its previous defenses. This is only a simplistic depiction of the relationships, but it should convey the essence of the issue (see Stoll, 1982). To illustrate one aspect of this argument consider the simple set of correlations displayed in Table 1.

Table 1. Correlation Among European NATO Members Military Expenditures, 1960–1990

BEL

DEN

FRN

GMY

GRC

ITA

LUX

NTH

NOR

POR

SPN

UKG

BEL

1.00

DEN

.95

1.00

FRN

.88

.91

1.00

GMY

.91

.95

.89

1.00

GRC

.97

.93

.83

.86

1.00

ITA

.80

.86

.95

.89

.73

1.00

LUX

.76

.82

.95

.85

.69

.95

1.00

NTH

.97

.97

.93

.95

.94

.89

.84

1.00

NOR

.88

.93

.97

.92

.83

.96

.92

.94

1.00

POR

−.13

−.03

−.22

.01

−.18

−.01

−.23

−.08

−.08

1.00

SPN

.91

.97

.97

.92

.87

.95

.89

.95

.98

−.09

1.00

UKG

.74

.80

.93

.81

.68

.92

.92

.82

.92

−.22

.91

1.00

Note: N = 31

Country Abbreviations

  • BEL

    Belgium

  • DEN

    Denmark

  • FRN

    France

  • GMY

    Germany

  • GRC

    Greece

  • ITA

    Italy

  • LUX

    Luxembourg

  • NTH

    Netherlands

  • NOR

    Norway

  • POR

    Portugal

  • SPN

    Spain

  • UKG

    United Kingdom

Table 1 shows the correlations of military expenditures among European members of NATO from 1960 through 1990.4 As you can see the correlations are fairly high. In fact, the average correlation is .70 and it rises to .88 if Portugal is removed. At the risk of being painfully obvious, none of the members of the dyads of European NATO countries were engaged in an arms race against each other; they were allied to one another. The point is that simply looking at military expenditure patterns is a poor way to judge whether countries are engaged in an arms race against one another.

So the earlier generation of arms race studies did not come to firm conclusions about whether particular dyads were engaged in an arms race or whether their increases in defenses were driven by something else (in particular by internal factors). While the sophistication of studies increased, as did the quality of the data, consensus was not achieved.

Do Arms Races Lead to War?

The second major area of research was whether states involved in arms races were likely to end in war. The belief that arms races lead to war was one of the main motivations for Richardson’s work; he felt that arms races were a primary cause of World War I. This was also the prime motivation of Huntington’s work (1958). Post World War II, this was a very important question for the obvious reason that an arms competition between the superpowers that ended in war might result in a nuclear exchange and the killing of millions and millions of people.

While a number of theoretical approaches were applied to this question, the two most prominent approaches were the spiral model and the deterrence model (Levy & Thompson, 2010, pp. 30–31). In the spiral model, states take actions against each other (increasing their defenses) for defensive purposes. However, while each state regards its own actions as defensive, each sees the other state’s states actions as offensive. Consequently the defensive actions of each state will be misperceived as offensive and lead to war.

While the spiral model focuses on the unintended consequences of defensive actions, the deterrence model focuses on the failure of states to take sufficient action to convince other states that if a war is fought they will lose. That is, war comes about if a state fails to increase its defenses sufficiently to dissuade the other state from attacking.

Each line of reasoning is plausible. What is less plausible is that it is an either–or situation (Levy & Thompson, 2010, p. 31). It seems more likely that sometimes one model would apply and that in other situations the other model would apply. So, the critical issue is what distinguishes these two situations. Uncovering the factors that differentiate the validity of these two perspectives is a far more difficult and demanding task. And one that was not directly addressed in most of the early literature on arms races.

There is another important issue. How do we select cases to study to evaluate whether arms races lead to war? There were a large number of studies that looked at this relationship. But how did researchers studying this question identify arms races?

One approach was used by Huntington (1958). Huntington (1958) was the first well-known effort to gather information on a series of arms races and draw general conclusions as opposed to a deep dive into a particular arms race. He relied on his study of history to identify thirteen arms races from 1840 through the Cold War.5 And of course, instead of creating one’s own list of arms races one could use the consensus of historians to identify a number of races or even a single race for close study. But it seems likely that any two scholars would not achieve a complete consensus on arms races; that is, which states were involved and the years of the arms race. As well this entire approach is unlikely to be as rigorous as one that relies on quantitative measures. A different approach was used by Wallace (1979). He looked at major power dyads involved in militarized interstate disputes. Militarized interstate disputes occur when a state explicitly threatens, displays, or uses military force (Palmer, d’Orazio, Kenwick, & Lane, 2015). Wallace created an index for the military expenditures of each of the two states in a dispute over the previous ten years. He then multiplied the two indices together. He inferred that the higher the value of this multiplicative index, the more rapid the military growth in the dyad and the greater the chance of the dispute ending in war. His empirical analysis supported this assertion.

There were a number of issues with his research. One was that many of the major power dyads that ended in war were associated with the World Wars (i.e., these cases were not independent of one another). There were also several issues about his multiplicative index. First the multiplicative index could produce a large value even if one side’s value was very small; that is, while one state in the dyad may have rapidly increased its military expenditures, the index could produce a large value even if the other state was showing only small increases in military expenditures. This is not an arms race. A second issue with the index was that if the expenditures of both sides were declining for the first nine years but turned up in the last year the index would have a positive value. Finally, no one was able to replicate the index. As well as noted by Leeds and Morgan (2012, p. 144) a number of follow-up studies (Weede, 1980; Altfeld, 1983; Diehl, 1983) found a much weaker relationship than Wallace.

The broader issue is about the basic research design used by Wallace. He did not examine whether arms races lead to war. He looked at dyads that engaged in militarized interstate disputes and asked whether if prior to the dispute the dyad engaged in rapid military growth. If so, Wallace predicted (and his results—with the caveats of other studies noted above—supported this) that the states were very likely to engage in war.

For the moment let us accept Wallace’s findings. Understanding the conditions under which a dyad that engages in a militarized interstate dispute is more likely to end in war is a contribution to understanding why wars happen. But it does not explain the relationship between arms races and war. Even if we accept Wallace’s index as a valid indicator his research design does not allow for the possibility that there may be many arms races that are not associated with disputes. Including these cases may produce very different conclusions about the linkage between arms races and war.

The End of the Cold War and the Decline of the Study of Arms Races

The early work on arms races did not reach firm conclusions on either of the two main questions. There was little consensus on whether particular dyads engaged in an arms race (as noted earlier, two sophisticated studies of the United States-Soviet dyad reached opposite conclusions). And if there was little consensus on whether particular dyads were engaged in an arms race, it was of course difficult to determine whether arms races led to wars. As noted above Wallace (and those studies that followed up on his research) did not provide a definition of an arms race. They circumvented the problem by studying dyads involved in disputes and asking if their prior military expenditure patterns predicted to the outbreak of war.

One other aspect of the early studies of arms races—regardless of which of the two main themes was being investigated—was the fact that most were monocausal. The early studies that were conducted included few control variables, and their central focus was on arms races.6 This focus was not inappropriate. It made no sense to build and test more complicated models without first exploring simple conceptions. But if we discover that simple models do not predict outcomes then we need to move on and develop more comprehensive models.

And indeed, through time, arms race studies became more sophisticated. But the problem of identifying arms races still remained. And then the Cold War ended. It is fair to say that the Cold War—and the United States-Soviet arms race—stimulated a great deal of work on arms races. This did not mean that most scholars focused on this one arms race dyad, but their research was inspired by this important and potentially deadly competition. Once that competition disappeared so did a great deal of the scholarly interest in arms races.

Research on arms races did not totally disappear after the Cold War, but if only a few pieces of research are conducted on a topic, researchers the field are unlikely to reach a conclusion. Every piece of research involves making choices about things such as indicators, time period, and analysis techniques. All of these decisions—as well as others—are important. Researchers do their best when making these decisions, but no one can be sure they have made the right decisions. So, no one study—no matter how well done—will convince the field that we understand something. We need a series of studies in which researchers made different decisions on critical elements of the research to reach the same conclusion. When there are enough diverse studies that reach the same conclusion, the field can reach a consensus.

To be clear, this is not an indictment of the work that was done after the end of the Cold War and before the studies to be reviewed here. It was of high quality. There simply was not enough work—and enough variability—to reach firm conclusions.

The Current State of Quantitative Arms Race Studies

The focus of this article is on recent quantitative studies, in particular published work between 2005 and 2016. The search was primarily in journals that focus on quantitative research. But journals that are not primarily quantitative were searched as well as books. The studies identified are listed in the “Further Readings” section of this article.

Below the key elements of recent studies are discussed. Studies were put into groups to facilitate comparison. The groups are as follows:

Singleton studies. There are several recent studies that are unique and do not fall into a category that has multiple studies.

Course of arms competitions. Studies in this group try to account for the interaction of armaments behavior between sets of states.

Arms race onset. This was not a topic that was the focus of early research. Studies in this group try to account for the initiation of arms competitions between states.

Arms races and war. These studies seek to determine if (or under what circumstances) arms races lead to war.

As mentioned earlier, the current studies are more sophisticated than the earlier studies. This sophistication encompasses the methodology used (and in some cases the data), but what is to be highlighted here is the theoretical aspects of the recent work. Most importantly, the current work has moved away from the monocausality of earlier studies. The current research on whether arms races lead to war is premised on the belief that arms races are only one factor (albeit an important one) in the relationship between r a pair of states and whether this dyad engages in war.

Recent Arms Race Studies

Within each category listed above (except for the singleton papers), studies will be reviewed in chronological order. A complete description of each study will not be offered. The focus is on the basic approach used in each study, the time period examined, the measure of armaments/arms race, and the primary results.

Singletons

As noted earlier, there were several quantitative studies that about individual topics within the general area of research on arms races. These subjects were not part of the initial set of quantitative studies on arms races.

Nuclear Proliferation. Kroenig (2016) explored whether the size of the United States nuclear arsenal from 1945 to 2011 influenced other countries to explore, pursue, or acquire nuclear weapons. The underlying idea behind the paper is that under Article VI of the Nuclear Proliferation Treaty (NPT) existing nuclear powers pledged to reduce their nuclear arsenals. But for many years the United States and the Soviet Union did not do this; instead they increased their arsenals. And even when they started to reduce their nuclear arsenals, one can argue that nonnuclear states were dissatisfied with the pace of reductions. So it is plausible that other states would begin their own nuclear programs both because the United States was neglecting its obligations under the NPT and/or because they saw the United States as a threat that could only be countered by nuclear weapons. The key dependent variable of the study comes from Singh and Way (2004). In each year they classify states into one of the following (ordinal) categories:

The state does not have any desire to acquire nuclear weapons.

The state seriously considers a nuclear weapons program.

The state makes an active effort to pursue a nuclear weapons program.

The state explodes a nuclear weapon or assembles a weapon.

Kroenig finds that while a state’s nuclear program is influenced by a variety of security, economic, and political variables it is not affected by the United States’ nuclear arsenal. This is not an arms race study per se, but it does relate the arms behavior (in terms of nuclear weapons) of other countries to the United States.

Aid and Arms Races. Collier and Hoeffler (2007) examine whether there is a positive relationship for developing countries between the receipt of aid and military spending during the 1960–1999 time period. The idea is that aid is fungible and as it increases this allows the government of the developing country to shift its own resources to the military.

The dependent variable in the study is the log of military expenditures as a proportion of GDP (this is a common measure of defense effort or defense burden). Collier and Hoeffler (2007, p. 4) average their data over five-year periods.7 They also note that the dependent variable is “problematic because data on military expenditure are unreliable” (Collier & Hoeffler, 2007, p. 4).

They have a number of independent variables. I will only note those that are relevant to their primary hypothesis and to arms races. They include aid as a proportion of the state’s GDP.8 They also measure what they term threat from neighbors (sum of military expenditures of neighbors divided by the country’s GDP) and emulation of neighbors (sum of military expenditures of neighbors divided by the sum of neighbors’ GDP).

Their key finding is that aid is positively related to a state’s military expenditures as a proportion of GDP. The impact of aid can be quite substantial. The authors estimate that African military spending is “almost double its level in the absence of aid” (Collier & Hoeffler, 2007, p. 4). As well, there is a positive relationship between the emulation variable and the state’s defense effort. As was true of the previous piece of work reviewed, this is not a “classic” arms race study. But it does relate the military expenditures of a state to the defense efforts of its neighbors.

Regional Aspects of Arms Races. Goldsmith (2007) studies military spending using spatial econometrics techniques. This facilitates the estimation of the impact of military spending in a region on the individual states within the region. His9 measure of spending is defense burden: the military expenditures of the state divided by its GDP. This variable is logged. He asks whether arms races occur among nearby states. He analyzes the year of 1991 and also the time period 1980–1991.

Goldsmith finds that there is clustering around low defense burdens in the Americas and Africa, and there is clustering around high defense burdens in Europe and the Middle East. While most people would have anticipated clustering around high defense burdens in the Middle East on the surface, the same finding about Europe seems puzzling. But recall the time frames that Goldsmith studies: 1991 and 1980–1991. These periods included portions of time that involved the violent breakup of Yugoslavia.

Although Collier and Hoeffler and Goldsmith do not have the same focus, their conclusions have something in common. They both find that there can be regional aspects to arms competitions. This needs additional study, and the arms race dynamics study discussed next does precisely that (albeit for only one arms completion).

Arms Race Dynamics. Abu-Qarn and Abu-Bader (2009) studied the Israeli–Arab arms race. The Arab states that were part of the study were Egypt, Jordan, and Syria. The time period they studied was 1960–2004. They examined a four-actor system and also a two-actor system in which Egypt, Jordan, and Syria were considered to be a single actor. They also included several structural breaks: the 1973 Arab–Israeli War, the initiation of peace talks in the late 1970s, and the 1979 peace agreement between Israel and Egypt.

They used two measures of defense. One was the military expenditures of the countries. The other was the most widely used measure of military burden (or effort): military expenditures divided by GDP. These two variables were calculated for all four countries in the study. As noted earlier, they also considered a single Arab actor. When analyzing a single Arab actor their measures combined the data for all three Arab countries.

Their results indicate that the Arab states respond to Israeli changes in both military expenditures and the military burden of Israel. They found evidence of bidirectional causality between Israel and Syria. However, Jordan was usually found to be uninvolved in an arms competition with Israel. The same results apply when the structural breaks are included in the analysis.

This concludes the analysis of the “singleton” studies. All the studies but Kroenig’s find that arms competitions do occur between states. In addition the studies of Collier and Hoeffler (2007), Goldsmith (2007), and Abu-Qarn and Abu-Bader (2009) all find—albeit in very different ways—that there can be arms competitions at a regional level.

Arms Race Onset

During the time period examined there were a series of studies on the onset of arms races. This was not as popular question in the early arms race literature because of the focus—both direct and indirect—on the arms race between the United States and the Soviet Union. Since that arms race was already underway, the emphasis was on the two issues noted above: what drives arms races (internal or external forces) and whether arms races lead to war. But in the current era without a signature arms race, the question of arms race onset has attracted more attention.

Rider (2009) uses the rivalry dyad-year as the unit of analysis. His dyads are taken from Thompson’s (2001) rivalry data set. Most rivalry data sets identify cases in terms of the amount of conflict they experience. But Thompson uses a very different procedure to create his data set. He identifies rivalries not though their conflict behavior but through a study of history. His question is whether government officials viewed particular states as rivals.10 To identify arms races Rider uses the same procedure as Gibler, Rider, and Hutchison (2005); this is discussed in the review of that piece in this article. The time frame for his study is 1816–2000. Rider finds that if a rivalry dyad experiences at least one militarized interstate dispute in the past five years this is positively associated with the onset of an arms race. Furthermore, an arms race is more likely if the dispute is about territory.

Rider, Findley, and Diehl (2011) cover several of the major questions about arms races. They compare dyads in rivalry with dyads that are not involved in rivalry. They use Diehl’s (1983) arms race index. He takes the average of the percentage increase between t-3 and t-2, and the percentage increase between t-2 and t-1 for each state in the dyad and then adds the two figures together. The time period of their study is 1816–2000. They found no “automatic” relationship between being involved in a rivalry and an arms race (about 75% of rivalries never experience an arms race). But nevertheless, arms races are more likely to occur within a rivalry than between two states that are not engaged in a rivalry. Furthermore, an arms race is more likely to occur after the rivalry “locks in” (i.e., after the rivalry experiences its ninth or tenth militarized interstate dispute).

Another study by Rider (2013) also deals with the outbreak of conventional arms races. He relies on a variant of Diehl’s arms race index. An arms race exists if over a three-year period the states have at least an 8% increase in military expenditures or personnel. They do not have to be involved in a dispute to be coded as being involved in an arms race. He studies the time period 1919–1995. He finds that arms races are most likely when the dyad has both a territorial issue and the states in the dyad are involved in a rivalry.

A final study of arms race onset is by Lee and Rider (forthcoming). They predict the likelihood of a pair of states engaging in a militarized interstate dispute or an arms race. Using two data sets about territorial claims they study the time periods 1870–2001 and (separately with a different data set on territorial claims) 1919–1995. They use the same measure of arms race as Gibler, Rider, and Hutchison (2005). The key concepts that they add to the onset of arms race literature (as one would expect from the title of the article) are trade interdependence and trade dependence. They find that these two factors are negatively correlated with the onset of arms races.

The onset of arms races was not one of the original major questions in the arms race literature. But it is obviously an important question. Unfortunately, there are only a handful of recent studies on this topic. As you can see, one particular scholar is the driving force in recent work on this topic, but it is fair to say that our understanding of the onset of arms races has been advanced by this recent work. We have learned that arms races are most likely to start if the dyad of states engages in a militarized interstate dispute that is territorial. Also, arms races are more likely if the states in the dyad are engaged in a rivalry. Finally, arms races are less likely to start if the states in a dyad have a high level of trade interdependence or if a state in the dyad is dependent on trade with the other state. Of course, one can say that these findings are “obvious” and “we knew this all the time.” But that is not same thing as having well-done empirical studies that lead to these findings.

Arms Races and War

The final question about arms races that has been explored in recent studies is one of the original focuses of this literature: Do arms races lead to war? This is obviously the most important question in the arms race literature. And as we look at the current world with the interactions between the United States and Russia and between the United States and China, this topic has significant relevance.

Gibler, Rider, and Hutchison (2005) study conventional arms races and war. They define an arms race as involving two rival states, as given in Thompson (2001). In addition, both rivals must have increased the military spending and/or personnel by 8% in every year over the preceding three years. The time period of their study is 1815–1992. They determined that only a few of these situations led to war. Only 13 of 79 wars identified by the Correlates of War Project from 1816 through 1992 were preceded by an arms race. As well, only 25 of the 174 strategic rivals identified by Thompson (2001) had an arms race before a war.

Colaresi and Thompson (2005) built their work on Senese and Vasquez’s steps-to-war model (see below; Senese and Vasquez published several articles before their book was published).11 The time period of the study was 1919–1995. Their research found support for many of the relationships in the steps-to-war model. They code mutual military buildups using Horn’s (1987) definition. For each state in a dyad, the average increase in military expenditure through time is calculated. If the expenditures of both sides were above average in the current year and increasing over the past six years, then a mutual arms buildup is coded. Following the steps-to-war model they do not simply look at military expenditures by the dyad as a causal factor for war; they include additional variables. They find that the greater the number of previous crises, the greater the risk of war. The presence of a mutual arms buildup and a rivalry also increases the chances of war. In fact if the states are involved in a mutual arms buildup, this increases the risk of war by about 180%.

Senese and Vasquez (2008) published a book on their steps-to-war model. Like Colaresi and Thompson, Senese and Vasquez uses Horn’s (1987) measure of mutual arms buildups that was just discussed. But this is only one component of their model of the steps-to-war. Fundamentally, their model is—for lack of a better term—anti-realist. That is, they believe that if states follow the prescription of realism this increases the chances of war.12 They analyze dyadic militarized interstate disputes. They include a set of variables that tap the essence of realism. They conduct their analyses for three time periods: 1816–1945, 1946–1992, and the post-Cold War time period (their data end in 2001).

For the 1816–1945 time period disputes that are about territory are more likely to lead to war.13 The more steps (i.e., actions advocated by realists) that are taken the higher the chances of war. In this time period the presence of an arms buildup increases the chances of war. For the Cold War period, mutual arms buildups do not increase the chances of war. But in the post-Cold War period mutual arms buildups have a positive impact on the chances of war; that is, the post-Cold War period looks a lot like the pre-Cold War period.

Rider, Findley, and Diehl (2011)—discussed earlier in this article—also study the relationship between rivalries, arms races, and war. The time period of their study is 1816–2000. They use Diehl’s operationalization of an arms race. They find that taking rivalries into account is important to understanding that relationship. In particular, locked-in rivalries (those rivalries that have experienced a large number of disputes) that experience an arms race are more likely to experience a war.

Sample (2012) reinforces the results of Senese and Vasquez. She uses Horn’s (1987) measure of mutual arms buildup. She finds—as they do—that a mutual arms buildup in the pre-Cold War era is likely to lead to an escalation of a militarized interstate dispute (or any dispute occurring over the following five years) to war. But this relationship does not occur during the Cold War.

Recent studies of arms races and war embed the presence of an arms race as part of a more extensive model. A significant portion of this work builds on Senese and Vasquez’s steps-to-war model or other conceptions that views the chances of war as a result of multiple factors. That is, while the presence of an arms race in a dyad contributes to the chances of a war breaking out, there are other factors (such as the presence of an ongoing rivalry between the states in the dyad) that also play a role. As with the other later studies of arms races these current studies are more sophisticated. Of course this means methods and data, but more importantly there is increased theoretical sophistication.

The Future of Arms Race Studies

Currently there is a paucity of arms races studies. But with recent events involving the United States, China, Russia, Iran, and North Korea there will be renewed interest in arms races. There is a good base of knowledge to build upon, but there is not consensus on the answer to the most important questions in this literature.14 Clearly it is necessary to move forward both with general inquiries about arms races and studies that offer insight into current situations.

So where do we go? What should we do differently from previous work? This discussion is not about using newer analysis techniques and/or better data. Of course, we should take advantage of these advances. But what should we do beyond this?

Move Away From Using Total Military Expenditures

Most arms race studies—regardless of when they were published—rely on total military expenditures. Some use this as the main variable of interest. There are two alternative variants. One involves what is usually viewed as defense effort (or burden); this is military expenditures divided by the GDP of the country. The other involves an over-time measure involving increases in total military expenditures and/or military personnel; in addition, some studies use defense effort (or burden) over time. On the surface these measures seem like a good approach. And these measures avoid the issues which arise if a researcher tries to construct indicators based on elements such as military hardware or military personnel, as well as attempting to consider elements of quality. It can be difficult to construct good measures of these alternative measures. But there are substantial drawbacks to using total military expenditures.

First, when dealing with arms races involving the United States in the post-World War II era, what about United States war involvements (in particular, Korea and Vietnam)? If you are conducting an arms race study involving the United States during the time periods of those wars and use a measure based on total military expenditures, then all of the resources that the United States was devoting to the wars will be counted towards the arms race. This makes absolutely no sense. To be sure, this may be an extreme case, but nevertheless it illustrates one way that total aggregate military expenditures figures can be misleading. Arms race studies cannot use total military expenditures when one or both states are engaged in a war with countries that are not involved in the arms race. Of course, one could use a measure of military expenditures that removed the amount being spent on the war(s) involving the state. But determining this amount may not be a simple task.

There are additional issues with military expenditures that are not so simple to resolve. During the Cold War there were extensive efforts both in and out of government to determine the level of military expenditures of the Soviet Union. While there were differences among scholars about the actual level of expenditures of the Soviet Union, there was general agreement on one thing: The official figure for defense that was released by the Soviet government was too low. Among those who were trying to provide more accurate estimates of Soviet military expenditures there were two general approaches.

The first was the residual approach (Becker, 1964). The premise of this approach was that the total Soviet budget contained all the military expenditures of the Soviet Union, but that significant portions of military expenditures were in other budget lines. With an economy based on central planning, everything had to be accounted for. So, if there was spending for defense that was not in the defense budget it had to be located in other parts of the Soviet budget. The residual approach can be illustrated using defense spending in the United States. If the United States builds nuclear weapons, the expenditure for this is not in the defense budget; it is in the budget of the Department of Energy. Furthermore, one can argue that when it comes to outer space, some of United States defense capability has been funded by NASA. But there was a significant difference between the United States and the Soviet Union during the Cold War. First, for the Soviet Union there were more budget lines outside the defense budget that contained spending for the military. Second, the Soviets were trying to hide their total military expenditures, while the “extra” expenditures outside the United States defense budget were (and still are) widely known and fairly easy to include in a budget figure for defense.

So the issue of obtaining an accurate figure for the Soviet budget involved significant effort. It involved estimating the proportion of other budget lines that were part of military expenditures and adding these figures to the Soviet military budget. Since the government of the Soviet Union was trying to keep this secret, there was considerable uncertainty about which additional categories of their budget—and in some cases how much of a budget category—should be added to the “official” Soviet military expenditure budget. This was not an arbitrary process, but different researchers could reach different conclusions about how much of other lines in the Soviet budget should be part of the Soviet military budget.

The second method to create a figure for the total military budget of the Soviet Union was the direct costing method (GlobalSecurity.org., 2016). This involved calculating the cost of individual items (e.g., a specific type of Soviet tank) and then aggregating by the total number of such items. In particular, United States firms were asked to estimate how they would build Soviet military equipment and how much this would cost. Their expenditure estimates were used to compute the total Soviet military budget. But there was a significant problem with this approach. In the United States, labor was (and is) very expensive, so it was (is) efficient to use as much capital as possible. But in the Soviet Union the opposite was true; capital was expensive and labor was cheap. Thus using estimates of the way the United States would build military equipment and then converting this into Soviet military expenditures produced much higher estimates for military items for the Soviet Union than building items the way the Soviets did.

This brief description makes one thing clear: The link between total military capability and total military expenditures may not be simple or direct, and reasonable people can reach very different conclusions. There is no better illustration of this disconnect than the results of the Team B exercise in the 1970s (Cahn, 1993). In the 1970s a great deal of pressure from conservatives in the United States was directed at the CIA’s evaluation of the threat from the Soviet Union. As a result “Team B” was created. A group of conservative critics of the CIA received security clearances and were given access to the same information the CIA used to create its estimates of a variety of things about the Soviet military. Team B concluded that the CIA had significantly underestimated Soviet military capabilities in a number of areas.

Following this exercise (although there was no official acknowledgement that Team B had anything to do with it), the CIA doubled its estimate of the proportion of the Soviet economy that was devoted to the military. This also meant that the estimate of Soviet military expenditures was doubled. But the CIA did not increase its estimate of any element of the Soviet military. To be absolutely clear, the revision of the Soviet military expenditures was not accompanied by a revision of the amount of hardware, personnel, or any other element of the actual military forces of the Soviet Union. The CIA decided that, while the Soviet military was no larger than they initially calculated, the Soviets were far less efficient then the CIA had thought. Consequently, they doubled their estimate of the proportion of the Soviet economy that was devoted to their military.

One can take different positions on the CIA revision. One point of view is that if the Soviet Union was devoting this much effort to the military, they were incredibly dangerous and likely to engage the United States in military conflict (i.e., why devote so much to the military if they didn’t intend to use it?). Alternatively, one could argue that any country that needed to devote so much of their economy to produce a military of that size was bound to encounter significant economic difficulties. But from the point of view of using military expenditures as a gauge of military capability, the fact that the CIA could revise its estimates and double the expenditure figure for the Soviet Union should raise questions for those who feel that military expenditures are the best way to measure military capability. I note as well that the estimation of Soviet military expenditures was considered to be—quite rightly—a very important question. The United States government spent a great deal of effort and resources to calculate this figure. The level of these resources dwarfs the effort that scholars (whether individuals or groups) could do. But even with a large amount of resources, it was not simple or straightforward to generate estimates.

Some may feel that the machinations to create a figure for the military expenditures of the Soviet Union—while perhaps interesting—are a historical footnote. But currently, those interested in military expenditures face a new challenge: How much does China spend on its military? As with the Soviet Union during the Cold War, there is a consensus that the Chinese are not transparent about their military budget. Of course, no one disputes that the United States is spending considerably more on its military than China is spending. But what are the Chinese spending? The recent study of Cordesman with Kendall (2016) offers the following data for the Chinese military expenditure budget in 2015 from a variety of sources (note: all figures are in billions of U.S. dollars):

Chinese official figure:

141.1

US Department of Defense:

180.0

SIPRI15

214.5

IISS16

314.0

As you can see, there is a wide range of estimates. Lack of transparency—the same problem that existed for Soviet military expenditures—makes it difficult to produce a reliable estimate for Chinese military expenditures. Of course, estimating military expenditures for most countries is not as difficult as producing a figure for China. But the Chinese example reinforces the point that using expenditures as a measure of military power is not always simple and straightforward.

There is a second issue that makes using total military expenditures problematic even if one is confident that there are no issues with the expenditure figures themselves. If the dynamics of an arms race (let us assume it is between just two states) are measured by total military expenditures, this implies that the competition involves all the armed services of both states. But this is—to say the least—implausible. For example, Huntington (1958) lists thirteen arms races from 1840 to the Cold War. For each of these races Huntington lists only a single service; for example, he classifies the Cold War arms race between the United States and the Soviet Union as only involving nuclear weapons.

Of course, one could argue that Huntington was not correct; that is, arms races typically involve more than one military service. But consider the Military Balance 1989–1990 (International Institute for Strategic Studies, 1989); in 1989 (the year many people would argue is the last year of the Cold War), it reported the active duty army of United States was 766,500. The active duty army of the Soviet Union was 1,596,000. Given that the United States army was a bit less than half the size of the Soviet army, it is hard to argue that there was arms race between the two armies. And the classic naval arms race between Great Britain and Germany around the turn of the 20th century was clearly not matched by one between the armies of these two countries. Of course, it is possible that two states can be racing each other in more than one service. But even if one wants to use military expenditures to tap military power researchers should treat arms races as between individual services (although if one assumes that a pair of states are engaged in arms races across multiple services, these individual races may need to be modelled as linked to one another).

Scholars need to seriously consider whether the appropriate measure of arms races is not expenditures at all but is something more directly related the armaments. For example Stoll (2006) identified a number of naval competitions in which states established goals in terms of the number of capital ships:

  • From about 1860 through the early 1900s the British government (in presentations to Parliament) explicitly used the two-power standard. That is, the government argued that the British navy should be as large (or perhaps a bit larger) than the next two largest navies.

  • In 1904 the British government convened a special admiralty committee to study the two-power standard. The conclusions of this committee were expressed in terms of numbers of battleships (Marder, 1961, p. 124).

  • On November 22, 1890, the French Superior Council (the high command of their navy) approved the French naval budget with the statement that “the combatant units of the French fleet must be equal in number to those of the combined fleets of the Triple Alliance” (Ropp, 1987, p. 197).17

  • Between 1907 and 1922 the Japanese navy advocated the so-called 8-8 program. This specified a fleet of eight modern battleships and eight modern cruisers. The Japanese wanted to have a fleet (at least in terms of major units) that was 70% of the size of the United States Navy. This ratio was based on studies by the Japanese navy which concluded that if a smaller fleet was at least 70% the size of a larger fleet, the larger fleet could not win (Evans & Peattie, 1997, pp. 143, 150).

There are other examples of states having goals for their navies tied to ship totals, particularly the number of capital ships. While the operationalization of capital ship changed through time (see Modelski & Thompson, 1988, pp. 50–96), it always meant that capital ship was the type of ship would be part of the battle line in a major naval engagement. Governments commonly used ship counts—not expenditures—to estimate naval capability.

There were also a number of efforts during the Cold War to construct indicators of the nuclear balance between the United States and the Soviet Union (see, e.g., Barash, 1986; Eden, 1989; Pry, 1990a, 1990b; Speed, 1980). Some of these measures were quite complicated, and the scholars did not always reach the same conclusion. But all focus on weapons (their number and characteristics), not military expenditures.

Finally, there were a number of efforts to estimate the balance of conventional forces between NATO and the Warsaw Pact during the Cold War. That is, a number of analysts believed that just using expenditure data was not the best way to evaluate the force balance. Two examples are Kaufman (1983) the Congressional Budget Office (1988) and Epstein (1988).

I am not suggesting that using measures of the appropriate military power will always be simple. In fact in most instances it will be difficult. But given the major problems with using total military expenditures I believe the study of arms races should move in this direction.

The Start of an Arms Race Is Not Always a Dyadic-Level Decision

Virtually all work on arms races treats the onset as a dyadic-level decision, which of course it is. But the empirical work assumes both states decide in the same year to compete against one another. Think about this for a moment. Does this make sense? It is not as if there is a meeting between the two states and they reach an agreement to engage in an arms competition. Of course, if two states see each other as rivals, it is reasonable to expect that the two states may engage in an arms competition, but they need not reach that decision in the same year.

Several examples were listed earlier of simple rules that are well documented among naval historians, but note that they all are about single states. They do not always reference a specific target state. Additionally, consider the situation concerning major power navies after the Russo-Japanese War of 1904–1905. The consensus among naval historians is that Japan was sizing its navy to the United States Navy, and the United States was sizing its navy to that of Great Britain. So the size of each of these navies was tied to another navy but not to each other.

As far as the British are concerned, virtually all scholars of naval competitions would assert that Germany and Great Britain engaged in a naval arms race from 1898 to either 1912 or 1914. There is also significant evidence that the leadership of the German navy began to size their navy to Great Britain’s in 1898, but when justifying its budget request in the British Parliament the Royal Navy did not mention the German navy until 1900.18 These examples—admittedly all about navies—suggest that while there are arms race dyads we are better off thinking in terms of armaments targeting. That is, state A begins to size one of its military services to that of state B. Shortly afterwards state B decides that state A is doing this and begins to size its military service to that of state B. This would probably happen quickly, but there may be a gap of a few years before it occurs. Our research on arms races should acknowledge this and not automatically assume that two states pick the same year to start sizing their military forces to the other state.

Conclusion

In recent years there has been little work on the quantitative study of arms races, whether trying to understand why the start, how they progress, or whether they end in war. These are all important questions. And previous research on arms races did not provide clear answers for any of these questions before scholars turned away from these issues.

As noted earlier, it is likely that we will see a resurgence of interest in arms race studies. And we should definitely build on the foundation of earlier work. While the amount of interest in this topic declined after the Cold War, the sophistication and quality of recent work is to be admired and emulated. Arms races are an important topic worthy of study. Current events also suggest that there will be arms competitions in the near future. So, it is vital that scholarly community re-engage in this field of study.

Recent research has embedded arms races in a broader set of relations between countries. This line of research should be continued, but it is also vital that arms race studies move away from using total military expenditures, whether by itself or as part of a composite measure. It is critical that we do the hard work of developing more direct measures of military capability. We should also consider whether armaments decisions are also influenced by neighbors and/or regional states. Finally, we should not assume that both states that engage in an arms race make this decision in the same year.

Further Readings: Quantitative Arms Race Studies, 2005 to 2016

Abu-Qarn, A. S., & Abu-Bader, S. (2009). On the dynamics of the Israeli–Arab arms race. The Quarterly Review of Economics and Finance, 49(3), 931–943.Find this resource:

Colaresi, M. P., & Thompson, W. R. (2005). Alliances, arms buildups and recurrent conflict: Testing a steps-to-war model. The Journal of Politics, 67(2), 345–364.Find this resource:

Collier, P., & Hoeffler, A. (2007). Unintended consequences: Does aid promote arms races? Oxford Bulletin of Economics and Statistics, 69(1), 1–27.Find this resource:

Gibler, D. M., Rider, T. J., & Hutchison, M. L. (2005). Taking arms against a sea of troubles: Conventional arms races during periods of rivalry. Journal of Peace Research, 42(2), 131–147.Find this resource:

Goldsmith, B. E. (2007). Arms racing in “space”: Spatial modelling of military spending around the world. Australian Journal of Political Science, 42(3), 419–440.Find this resource:

Kroenig, M. (2016). US nuclear weapons and non-proliferation: Is there a link? Journal of Peace Research, 53(2), 166–179.Find this resource:

Lee, H., & Rider, T. J. (2016). Evaluating the effects of trade on militarized behavior in the context of territorial threat. Foreign Policy Analysis, orw009.Find this resource:

Lee, H., & Rider, T. (forthcoming). Trade, arms races, and territorial disputes. Foreign Policy Analysis.Find this resource:

Rider, T. J. (2009). Understanding arms race onset: Rivalry, threat, and territorial competition. The Journal of Politics, 71(2), 693–703.Find this resource:

Rider, T. J. (2013). Uncertainty, salient stakes, and the causes of conventional arms races. International Studies Quarterly, 57(3), 580–591.Find this resource:

Rider, T. J., Findley, M. G., & Diehl, P. F. (2011). Just part of the game? Arms races, rivalry, and war. Journal of Peace Research, 48(1), 85–100.Find this resource:

Sample, S. G. (2012). Arms races: A cause or a symptom? In J. A. Vasquez (Ed.), What do we know about war? (2d ed., pp. 111–138). Lanham, MD: Rowman & Littlefield.Find this resource:

Senese, P. D., & Vasquez, J. A. (2008). The steps to war: An empirical study. Princeton, NJ: Princeton University Press.Find this resource:

References

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Colaresi, M. P., Rasler, K., & Thompson, W. R. (2008). Strategic rivalries in world politics: Position, space and conflict escalation (1st ed.). Cambridge, U.K.: Cambridge University Press.Find this resource:

Colaresi, M. P., & Thompson, W. (2005). Alliances, arms buildups and recurrent conflict: Testing a steps-to-war model. The Journal of Politics, 67(2), 345–364.Find this resource:

Collier, P., & Hoeffler, A. (2007). Unintended consequences: Does aid promote arms races? Oxford Bulletin of Economics and Statistics, 69(1), 1–27.Find this resource:

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Rider, T. (2013). Uncertainty, salient stakes, and the causes of conventional arms races. International Studies Quarterly, 57(3), 580–591.Find this resource:

Rider, T. J., Findley, M. G., & Diehl, P. F. 2011. Just part of the game? Arms races, rivalry, and war. Journal of Peace Research, 48(1), 85–100.Find this resource:

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Notes:

(1.) Of course, there was some work done after the end of the Cold War and the last few years, but arms races were not a major focus of research. And to be honest, arms races are not a major focus of recent research.

(2.) Richardson’s work was relatively unknown until the September 1957 issue of the Journal of Conflict Resolution (Vol. 1, issue 3). All three articles in that issue were devoted to his work. This was followed by a volume that compiled Richardson’s work on arms races and the causes of war (Richardson, 1960).

(3.) Note: The two states need not be growing at the same rate for this to happen.

(4.) The source for military expenditures is SIPRI (2017). Expenditures are in constant (2014) U.S. dollars. I included all countries that (a) SIPRI classified as Western European and (b) were members of NATO during the entire time period studied.

(5.) Huntington spends no time in his article explaining how he created his list of arms races; he basically just lists the cases in a footnote.

(6.) In later years there was a tendency to include a large number of control variables. But current practice—rightly so—has been to discourage the unfettered use of large numbers of control variables in favor of a more nuanced (and theoretically justified) approach that avoids using a kitchen sink of control variables.

(7.) They do not explain why they average across five-year blocks. They note, “Although this approach has been conventional in much of the growth literature, it has only just begun to be applied to the phenomenon of military spending” (Collier & Hoeffler, 2007, p. 4).

(8.) They create the variable through a process involving instrument variables. See Collier & Hoeffler (2007, pp. 11–12).

(9.) The paper only lists the first two initials of the author. I did not assume the author is male; I verified it.

(10.) This single sentence cannot possibly convey the magnitude of Thompson’s efforts. The reader is invited to read the original reference to these data (noted in the text).

(11.) A chapter in Colaresi, Rasler, and Thompson (2008) provides an updated version of this analysis.

(12.) Of course, realism—in its many variants—is not about preventing war but about the actions states should take to do as well as they can in a hostile world. This might involve fighting wars.

(13.) They look at whether the current dispute or any dispute involving the same states over the next five years ends in war.

(14.) Even if there was a consensus on these questions, one has to consider whether things may be different in the current era.

(15.) Stockholm International Peace Research Institute.

(16.) International Institute for Strategic Studies. Note the figure is for 2014 and is in terms of purchasing power parity (PPP).

(17.) The Triple Alliance involved Germany, Austria-Hungary, and Italy.

(18.) As noted earlier in presentations to Parliament, the British Navy justified its budgetary request in terms of the two-power standard: The British navy should be at least as large as the next two largest navies in the world.