Dimensions of Hard Power. Lemke D

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Dimensions of Hard Power:

Regional Leadership and Material Capabilities

Douglas Lemke

Pennsylvania State University

[email protected]

DraftPlease, do not quote or cite without prior permission

Abstract:

In this paper I focus on the hard power capabilities of regional powers, characterizing them in

terms of their endowments of economic, demographic, and military capabilities. I compare them

to their regional neighbors in terms of disparities between the states across these differentcomponents of hard power. I accomplish this by calculating regional shares of these capabilities,and then describing each region in terms of how dominant the regional power is. Power politics

theories like power transition theory anticipate that peace is more likely the more dominant the

premier state is. To test such arguments, I investigate whether regional variation in how

relatively powerful the regional powers are influences how peaceful the region is. Similarly,

related theories of hegemonic stability anticipate that collective goods like international

organizations are more easily provided when a clearly hegemonic actor exists to pay the costs of

constructing and maintaining IOs. To test such arguments I investigate whether the distribution

of relative power within regions influences the density of international organizations amongregional members.

Keywords: Power transition theory, Hegemonic stability theory, statistical analysis

Paper prepared for the first Regional Powers Network (RPN) conference at the GIGA German

Institute of Global and Area Studies in Hamburg, Germany, 15-16 September 2008.


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1. Introduction

A great deal of international political activity can be distinguished from global politics by thefact that while it involves more than two international system members, the behaviors are often

relevant only to a subset of the globe, namely to the region within which they occur. Scholarly

research increasingly reflects this reality, as evidenced both by this conference, and by a growing

number of research monographs specifically about regional international relations (inter alia,

Gleditsch 2002; Lemke 2002; Buzan/Waever 2003; Miller 2007).

I add to this growing literature by investigating the role of hard power capabilities in

identifying which states dominate in their regions, and in distinguishing between conflictual

versus peaceful regions. I also use information about capability distributions to anticipate which

regions enjoy higher levels of cooperative interaction. I adapt well-established IR theories tomotivate hypotheses about regional variation in conflict and cooperation. I find that knowing

something about how concentrated power is within a region helps to anticipate whether that

region will be peaceful and whether it will enjoy many or few cooperative organizations. The

strong evidence presented in the pages to follow is based on the theories motivating my

analyses, but it is uncommon to find statistical analyses of these phenomena aggregated at the

regional level. Consequently the findings below may well be the first of their kind.

I begin with brief summaries of the theories motivating my statistical analyses, and draw

out regional implications of those theories. I then provide specific details about my research

design so that interested readers can evaluate the steps I have taken to generate the results central

to this paper. After that I present my statistical analyses, describe the results, and then conclude

with a discussion of how my findings might be of use to the wider research community interested

in the study of Regional Powers.

2. IR Theories and Regional Politics

Of all the power politics theories hypothesizing links between the distribution of power and the

occurrence of interstate conflict, power transition theory enjoys the strongest empirical support

(Organski/Kugler 1980; Lemke 2002). Power transition theory predicts that conflicts such as

wars are more common the more evenly distributed power is between potential belligerents. A

roughly equal distribution of power is the most likely to coincide with war, because when the

two sides are roughly equal, neither is sure it will lose any war that might be fought, and thus it is

possible for both sides simultaneously to believe each might prevail. Power transition theory

thus hypothesizes that when power is evenly distributed war is likely, but when there is an


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imbalance suggesting one side appears certain to win should war occur, that will deter the

weaker side from resisting and war will be very unlikely.1

In previous work I tested power transition theory within regional settings (Lemke 2002).In that study I undertook statistical analysis of the influence of the distribution of power on the

probability of interstate conflict among dyads within regional subsets of the international system.

I found that pairs of roughly equal states located in the same region were significantly more

likely to experience interstate conflict than were pairs of unequal states similarly proximately

located. Here I differ from that earlier work by studying regional groups of states combined,

rather than individual dyadic pairs within regions. I hypothesize that the more unequal the

distribution of power within a region, and specifically the greater the share of regional power

held by the regionally strongest state (hereafter designated the Regional Power), the less likely

will be interstate conflict within that region, and specifically the lower the incidence of wars and

disputes.

This regional hypothesis differs from the dyadic focus in past evaluations of the theory,

but is nevertheless consistent with it. Again, power transition theory anticipates that parity

increases the risk of interstate conflict because potential belligerents are simultaneously more

likely to believe they both might win if they fight. Aggregated to the regional level, the greater

the share of capabilities held by the Regional Power, the surer all region members are that they

would lose in any conflict against the Regional Power. They will thus be less likely to challenge

the Regional Power. Reflexively, the greater the share of capabilities held by the Regional

Power, the less likely it will need to use force to extract concessions from other members of its

region. When preponderant, the Regional Powers disproportionate capabilities will deter other

states from resisting it. Finally, it is reasonable to expect that conflicts between non-Regional

Power states will be less likely the greater the relative capabilities of the Regional Power.

Specifically, the more preponderant the Regional Power is, the less likely are other region

members to be belligerent because to be so could indicate to the Regional Power that they are

threats to regional peace and stability. If identified as such a threat, they risk being disciplined

by the preponderant Regional Power. In all these regional expectations about parity and war,

preponderance and peace, the clarifying nature of preponderance dampens the probability and

incidence of conflict parallel to the same dyadic expectation in traditional power transition

theory analyses.

1Power transition theory refines the hypothesis by stipulating that rough equality, or parity, of power increases the

probability of war given disparate evaluations of the status quo between the potential war fighters. That is, the equal

states must have a serious disagreement between them. For the purposes of this paper I relax this stipulation andfocus solely on the distribution of power and the incidence of interstate conflict (as did early power transition

analyses such as those in Organski/Kugler 1980).


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Like power transition theory, hegemonic stability theory (Kindleberger 1974; Keohane

1980; Snidal 1985) also focuses on power relations and interstate behavior. But whereas power

transition theory focuses on conflictual relations, hegemonic stability theory traditionally offershypotheses about the creation and maintenance of institutions contributing to cooperative

interstate relations (Gilpin 1981 offers one of the earliest connections between power transition

and hegemonic stability logics). Specifically, hegemonic stability theorists argue that institutions

designed to help states cooperate with each other in the achievement of mutual gains are more

likely to be created and maintained when there are disproportionately powerful states among the

potential cooperators. These cooperative institutions benefit all states participating in them, but

are often costly to create and maintain. Consequently, the logic of collective action (Olson 1965)

becomes relevant, as each member of the collective of potential cooperators prefers both that the

institutions be created and that the costs associated with them be borne by other members of the

collective.

Of the three mechanisms Olson identified as enhancing the probability of the collective

good being achieved, that of the privileged actor is most relevant to hegemonic stability theory.

A privileged actor has so many resources at its disposal that its perceived relative costs of paying

to provide the collective good is smaller than its perceived benefit from the collective good being

achieved. Consequently groups fortunate enough to contain a privileged actor are much more

likely to realize their common interests than are groups without a privileged actor.

Hegemonic stability theorists explicitly translate the logic of the privileged actor to IR.

They expect that when there is a disproportionately powerful state in the collective, all eyes turn

to it as the prominent solution to the collective action problem. Possessing a disproportionate

share of resources, that powerful state is able, if willing, to provide the collective good from

which all benefit. In his original formulation of hegemonic stability theory, Kindleberger (1974)

specifically focused on the provision of institutions providing international financial stability.

Such institutions were lacking, or failed, during the Great Depression because there was no

hegemon or international privileged actor to provide them. Great Britain had traditionally

fulfilled this role because its adherence to the gold standard provided an anchor currency for the

international financial system. But by the late 1920s it had declined such that it was no longer a

privileged actor. After World War II the United States emerged with such a disproportionate

share of world power that it was able and willing to use its surplus capabilities to construct new

institutions to provide international financial stability.

A regional hypothesis is easily developed from this discussion of hegemonic stability

theory. Specifically, I hypothesize that the greater the share of capabilities held by a Regional

Power, the more likely that state can function as a privileged actor. Consequently, the greater the

share of power of each regions strongest state, the greater the number of regional international

organizations that region will enjoy.


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3. Research Design

Testing the hypotheses about the distribution of power and the prevalence of conflict andcooperation within regions requires the definition of regions and of hard power capabilities. It

also necessitates a valid measure of regional conflict and of the presence or absence of

cooperative institutions within regions. Happily, previous researchers have addressed these

needs and provided both the definitions and the datasets necessary to test my hypotheses.

3.1 Defining Regions

A first wave of scholarly interest in regional analyses struggled to provide clear and widely

acceptable definitions of regions (summarized in Lemke 2002:Chapter 4). Although consensus

on a definitive list of regions and state members eluded scholars, there was agreement thatregions were characterized by physical proximity and a sense of identity as a region among

member states (Thompson 1973). Recent work emphasizes the importance of self-identification

in the definition and functioning of a region (Hemmer/Katzenstein 2002).

Absent a widely accepted list of regions and state members, I err on the side of caution

and employ three separate designations of regions and their state memberships. The first

designation lists regions as defined by the Correlates of War Project (COW). The COW

definition of regions is based on the projects list of state members of the international system

(Russett, et al. 1968; COW 2008). Once the member states of the system are identified, the

COW project then groups them into six large regions: Western Hemisphere, Europe, sub-Saharan

Africa, the Middle East and North Africa, Asia, and Australia and the Pacific Islands. Although

there are a few questionable designations (Turkey is a Middle Eastern state according to the

COW project, Russia is, and has always been, solely European, etc), the COW regional

designations are largely non-controversial and certainly are widely used. In the analyses below

COW Regions indicates analyses of observations of these six regions. The Appendix at the

end of this paper lists each COW Region and its member states.

My second designation of regions elaborates on a list of regions and member states I

offered in previous work (Lemke 2002). There I defined regions by proximity and the ability to

interact, designating states members of the same region only if they possessed the ability to

interact militarily by moving their military forces to each others capital cities. I determined

which states had the ability to engage each other in this intrusive military fashion by detailed

analysis of the power projection capabilities of states (see Lemke 2002:Chapter 4 for specific

details). Groups of proximate states all sharing the ability to interact militarily with each other

then constitute regions. This explicitly-military definition produces a list of twenty-two regions.

Necessarily these regions are considerably smaller than the six intuitively produced by the COW

Project. The earlier time period studied and broader range of explanatory and control variables

included in my earlier work restricted me to analysis of only 17 of these regions, but I elaborate


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the list to the 22 reported in this papers Appendix so that coverage here is global. Drawing on

the title of my earlier work, these smaller, militarily-defined regions are referred to below as

Regions of War and Peace Regions, abbreviated in the results tables as RoW&P Regions.Within COW and Regions of War and Peace Regions, the strongest state is designated

the Regional Power. This is a default definition that ignores questions of whether that locally

strongest state fulfills any leadership role within the area believed to constitute the region.

Further, this empirical approach to defining regions ignores questions of whether other states

assigned to the region identify with that region. In COW and Regions of War and Peace

Regions, the United States is identified as either a Western Hemisphere or a North American

actor. But surely the United States sees itself primarily as a global actor, and as a Western

Hemisphere or North American actor only secondarily. Thus, while the COW and Regions of

War and Peace definitions of regions are plausible, they do not fully satisfy the conceptual

definition of regions, and thus can be critiqued on grounds of construct validity.

An alternative approach to defining regions is suggested by the researchers associated

with the Regional Powers Network. They begin with identification of states that play an

important and active role in supervising, or at least attempting to influence, states proximate to

them, designating these active, important states as Regional Powers. Working papers by

network scholars suggest a list of five candidate Regional Powers and the regions within which

they operate: Brazil in South America, South Africa in southern Africa, Iran in the Middle East,

India in South Asia, and China in East and Southeast Asia. In the analyses below RPN

Regions refers to the five regions so designated, a full list of which appears in this papers

Appendix.2

As mentioned above, I evaluate my two hypotheses about regional power distributions

and conflictual or cooperative relations within regions in separate but complementary analyses of

each regional designation. This means that for each type of analysis I have created separate

COW Regions, Regions of War and Peace Regions, and RPN Regions datasets. That there are

substantial differences across the three datasets can be seen by how much the sample size

analyzed varies across analyses. But the differences are apparent from the definitions as well.

COW Regions and RPN Regions tend to be quite large, with vast areas and many member states.

In contrast, Regions of War and Peace Regions are generally much smaller (there are 9 in Africa

alone, for example). Further, there is a potentially important difference introduced by the fact

that the RPN Region list is not globally comprehensive while the other two regional designations

are. In spite of these differences in measurement and conceptualization, the analyses below are

remarkably consistent regardless of how regions are defined and measured. This suggests that

while it is important to generate the best definitions of regions and empirical measures thereof so

2This list of regions is based on my interpretation of RPN-affiliated work. The list is not drawn from any official

RPN designation or dataset, and so if error is introduced by defining RPN Regions as I do, that error is mine.


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that validity is insured, the fundamental patterns anticipated by the hypotheses introduced in the

last section are robust regardless of those definitional issues.

3.2 Defining Hard Power Capabilities

I employ the COW Projects composite capabilities index to gauge the relative capabilities of

Regional Powers compared to the rest of their region. The COW capabilities index is described

in detail in Singer (1987), and is available from COW (2005). It includes consideration of each

states assets along military, demographic, and economic dimensions. The military dimension

incorporates each states number of military personnel and military expenditures. The

demographic dimension involves both national population and that subset of national population

residing in cities. The economic dimension is represented by both iron/steel production and

energy consumption. Since these elements of material capabilities, of hard power, are measured

on different scales, they can only be combined after being transformed into each states share on

each dimension. To make this transformation I sum the total number of military personnel (for

example) in a region, and then divide each states actual number of personnel by the regional

total. Following a similar procedure I generate each states share of regional military power by

combining the shares for both military dimensions, and then dividing by two. A composite

indicator is constructed by summing all six component shares, and dividing by six. The

concentration of power within a region for a given year then is indicated by the strongest states

share of the composite indicator.

Gauging relative power by regional shares is better than consideration of raw power

totals (e.g. regional share of troops is better than raw number of troops) because there is

tremendous variation across regions in how large armies are, how high energy consumption is,

etc. Thus, a Regional Power in Africa might have vastly lower total number of troops than does

a Regional Power in Asia, but yet still have a comparable relative advantage over the rest of the

members of its region. By using regional shares to measure hard power I am able to make

different regions comparable on the main independent variable of interest.

Similarly, employing the COW capabilities index indicators instead of Gross Domestic

Product or some other candidate measure of power/capabilities is advantageous because it

permits me to replicate my analyses alternating economic power for demographic power for

military power. With COWs capabilities data I can determine whether different dimensions of

power contribute more than others to regional conflict and cooperation. Whats more, there is

some evidence that little is lost by foregoing other measures of power like GDP, because

correlations between GDP and COWs composite index of capabilities (i.e., the average of all six

components), is routinely greater than 0.9 across different temporal and spatial domains.

The identity of Regional Powers in the COW and Regions of War and Peace Regions is

determined by the actual distribution of hard power. Whichever member of the region has the

greatest share of capabilities is designated by me as the Regional Power. But the Regional


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Powers within RPN Regions are determined without explicit reference to actual capabilities.

Within the Regional Powers Network, Regional Powers are identified based on their influence

and activity levels. It is likely that Regional Powers so designated enjoy greater relative powerthan the other members of their regions, but it is not necessarily the case. This separation

between data on relative power and designation of a RPN Regional Power allows an interesting

investigation of whether Regional Powers so designated really are more powerful. It turns out

that they very significantly are.

Table 1 reports the mean values across the three dimensions of power and for the

composite/combined index, for all state years of RPN Region members, in the 1960-2000 period.

The table distinguishes the average power share for Region Members (of RPN Regions) from the

averages for the Regional Powers. The differences across all three dimensions, and in the

composite indicator as well, are enormous. RPN Regional Powers are roughly ten times more

powerful than their regional neighbors across all power measures.

Table 1: Average Shares of Capabilities within Regions:

Economic Military Demographic Composite

Region Member 0.04 0.05 0.05 0.05

Regional Power 0.56 0.41 0.48 0.48

Differences within each column are statistically significant at p < 0.01 level.

In Table 2 I present a somewhat different analysis of how hard power capabilities

coincide with whether a state is a Regional Power. Here the analysis is of all states in one or

another RPN region. The dependent variable is a dichotomous indicator that takes on a value of

1 only for those states designated as the Regional Power. The independent variable is each

states Composite Capabilities Share in 1960 (or in the first year the state existed, if it was not

yet independent in 1960). Each case then represents each RPN Region member states existence

from 1960 to 2000. In 1960 it might have been an open question which African state (for

example) might rise to Regional Power status as the post-colonial period unfolded. Reflecting

this, the logistic regression reported in Table 2 uses initial endowment to predict, in a sense,

which states would rise to prominence. As can be seen by the large, positive and significant

coefficient, having a large hard power initial endowment predisposed the Regional Powers to

their eventual status.


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Table 2: Logistic Regression of Regional Power Status:

Dependent Variable = 1 if state is a Regional Power

Coefficient Standard Error Significance Level

Composite Capability Share 19.56 6.2 0.002Constant -6.27 1.76


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Iranian ascent. Rather, it appears there is something different, something particularly ambitious,

that differentiates Iran as a minority-endowment Regional Power from the leading states of the

other RPN regions.

Iran aside, it is quite clear that even though capability share plays no explicit role in the

designation of Regional Powers within the Regional Powers Network, it is nevertheless the case

that capabilities are an implicit predictor of Regional Power status, and offer a clear distinction

between region members and Regional Powers (as shown by the difference of means in Table 1).

This comparability between RPN Regions Regional Powers and the Regional Powers identified

for the COW and Regions of War and Peace Regions, suggests that it is legitimate to test the two

hypotheses advanced above against regions and Regional Powers regardless of how they wereidentified.

3.3 Measuring Regional Conflict and Regional Cooperation

In testing my two hypotheses Regional Power capability shares are the independent variable

predicting how much conflict each region experiences and how many cooperative institutions are

created. I thus need data on these two important dependent variables. The Correlates of War

Project again conveniently provides the necessary data.

I employ the COW Projects Militarized Interstate Dispute (MID) dataset to measure the

amount of regional conflict (described in detail by Ghosn, et al., 2004). According to the COW

Project, a MID is any instance of militarized conflict between two or more states. A conflict is

militarized whenever threats to use force, displays or demonstrations of force (such as troop

mobilizations, or the dispatch of naval vessels to a foes coasts), or actual uses of force occur.

Use-of-force MIDs that generate more than 1000 battle fatalities also satisfy the COW Projects

criteria for interstate war. In this way the MID dataset combines all wars and disputes within one

general category. Combining low-level MIDs with wars is particularly useful for statistical

analysis, because wars are so rare that some regions experience none over long intervals of time

(think of North or South America in the latter half of the 20 th century). While it is advantageous

to be a resident of a region without wars, it is disadvantageous for statistical analysis because it is

impossible to analyze the influence of a variable such as Regional Powers capabilities shares on

the probability or incidence of war if war never occurs during the period analyzed.


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These distinctions between low-level MIDs and wars, and this digression about the statistical

difficulty of estimation given too little variation, is directly relevant to the analysis of the

regional power transition hypothesis. Power transition theory is traditionally about war onset.

Ideally then my first dependent variable would be the number of wars in each region. But given

that in some regions the number of wars does not vary from year to year (since it is always zero),

that is not possible. But using MIDs introduces considerably more variation in international

conflict from year to year for all regions (while there are fewer than 100 interstate wars in the

entire 1816-on COW time span, there are over 3000 MIDs). But since the theory being tested is

really about wars rather than threats and other low level disputes, the way I measure conflict

introduces measurement error. Measurement error generally produces weak statisticalrelationships. Consequently the strong support uncovered for the regional power transition

hypothesis below is likely particularly robust. Had I the ability to test the power distribution

war relationship with estimable data, the relationship would probably be stronger than that

reported here.

In the regional power transition analyses reported below, the dependent variable is the

number of new MIDs in each region each year. All MIDs are counted equally, even though

some conflicts are clearly more consequential than others. To generate these annual counts of

MIDs, I selected as Region X MIDs all entries in the MID dataset that had originators in the

region of interest exclusively. For example, a dispute featuring Peru and Ecuador as the only

originators would be considered a South American MID (relevant in either the Regions of War

and Peace or RPN Regions analyses). However, a dispute featuring the United States, Peru and

Ecuador as the originators would be considered an internationalized, or cross-regional MID, and

would not be listed as a South American MID. It would, however, be a Western Hemisphere

MID in the COW Regions analysis.

My last measurement issue concerns how to measure regional cooperation/collective

good provision. I use the COW International Governmental Organizations dataset (described in

detail by Pevehouse, et al. 2004) to obtain information about the presence of regional

organizations. I assume that regions characterized by greater numbers of regional IOs are more

cooperative than regions with few or no regional IOs. It is reasonably well established that states

with more IO memberships are more peaceful (Jacobson, et al. 1986), and that pairs of states

with more joint IO memberships enjoy more peaceful dyadic relations (Russett/Oneal 2001).


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Since peace would seem a prerequisite for cooperation, it is plausible to argue that regions with

greater numbers of IOs would similarly be more peaceful (the correlations between the number

of regional IOs and the number of regional MIDs is negative and statistically significant in the

regional datasets analyzed below). Certainly such assumptions appear to appeal to hegemonic

stability researchers, because in most of their empirical evaluations they determine whether

hegemons establish and maintain international organizations in various issue areas.

To create my regional IO variable I looked at the membership of all IOs listed in the

COW IGO dataset for the years 1960 to 2000. I eliminated IOs that were either global or trans-

regional in membership. I coded an IO as regional if its membership was exclusively or at least

overwhelmingly composed of states in COW or RPN regions.3

A final stipulation was that toqualify as a regional IO, the Regional Power of the region in question had to be a member of the

IO. I record for each year how many regional IOs exist for each region because the regional

Hegemonic Stability hypothesis does not distinguish the initial creation of an IO from its

subsequent maintenance.

Using a variety of Correlates of War resources, I have constructed region-year datasets

where each case represents one region year. In the three datasets I built, each observation

records the share of regional power (measured in four ways) the Regional Power of that region

possessed in that year. It also records how many MIDs began during that year. Finally, the

datasets also indicate how many regional IOs were in existence for each year. While it was

tremendously useful to be able to draw on so many existing data compilations to construct my

region-year datasets, the exercise was nevertheless quite time consuming because none of the

existing datasets I worked with are aggregated at the regional level. A great deal of manipulation

was necessary to turn them into region-year datasets. I suspect that the time consuming nature of

this task explains why analyses like those reported in the next section have never before been

reported.

3 I do not test my hypothesis about Regional Power share and the number of regional IOs for Regions of War and

Peace regions because regions so designated are so small that very few IOs exist within them. Further, the COWIGO dataset requires that IOs possess at least three member states. Since some Regions of War and Peace regions

have only two members, it is impossible for them to have any regional IOs due to the definition COW imposes.


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4. Empirical Analyses

I begin with analysis of the regional power transition theory hypothesis, that the stronger a

regions Regional Power, the fewer international conflicts it will have. Table 3 reports simple

correlations between the capability share of the Regional Power and the number of MIDs begun

for each year in each of the three region-year datasets. The hypothesis is of a negative

correlation between the variables, because stronger Regional Powers are expected to deter

conflicts. As seen in Table 3, the regional Power Transition Theory hypothesis is quite strongly

supported.

Table 3: Correlations between Capability Shares and Militarized Dispute Frequencies:Cell entries correlate Row Variable with MID Frequency

COW Regions RoW&P Regions RPN RegionsComposite Share -0.60*** -0.09*** -0.42***

Demographic Share -0.57*** -0.16*** -0.28***

Military Share -0.54*** -0.16*** -0.27***

Economic Share -0.44*** -0.01 -0.04* = p < 0.10; ** = p < 0.05; *** = p < 0.01

All of the cell entries in Table 3 are in the expected negative direction, and all but two of

them enjoy the highest level of statistical significance. Only the economic share dimension of

hard power, and then only for the Regions of War and Peace and RPN regions, is not

significantly or strongly related to MID onset frequencies, but even then the direction of the

relationship is as expected.

While bivariate correlations such as those in Table 3 are suggestive and important, they

do not allow me to determine whether the relationships uncovered might be spurious. That is,

they do not permit control of the possible confounding effects of other variables. To be taken

seriously, concerns of spuriousness require some expectation, some argument, about why anobserved correlation between two variables might be caused by co-variation with a third

variable. Otherwise introducing control variables into an analysis is the equivalent of a fishing

expedition designed to see if the coincidental inclusion of additional variables washes out

other findings (see Ray 2003 for a discussion of these issues).

There is a very real threat to the validity of the inference that the correlations in Table 3

are causal. Specifically, it could well be that the persistent negative correlation between


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Regional Powers capability share and number of MID onsets is caused by the number of states

in the region in question. Region membership varies substantially within and across my datasets,

from a low of 2 in some of the Regions of War and Peace regions to a high of nearly 50 in one of

the COW and Regions of War and Peace regions. Regions with more state members are likely to

have more MID initiations, other things being equal, simply because with more states there are

more opportunities for conflict. But at the same time, more states in a region means a larger

regional total for each hard power dimension, and thus mathematically it is necessarily the case

that the more states in a region, the lower the Regional Powers power share will be. Thus, it is

plausible to expect that the number of states in a region is negatively related to power share and

positively related to number of MIDs. Since the number of states in a region is logically prior tothe distribution of power or subsequent conflict behavior, it could be argued to cause both of

these logically subsequent variables and thus also to cause the negative correlation between

them. Consequently the number of states in the region must be controlled for in order to support

any claim that the negative correlations in Table 3 are not spurious. Table four reports OLS

regressions of the Number of MIDs regressed on the Composite Capability Share of the Regional

Power, controlling for the number of states in the region.4

Table 4: Ordinary Least Squares Regressions of Regional Militarized Dispute Frequencies:

Dependent Variable = Number of MIDs

COW Regions RoW&P Regions RPN RegionsCoefficient Coefficient Coefficient

Composite Capability Share -7.14*** -0.38* -3.11***

Number of States in Region 0.02* 0.08*** 0.10*

Constant 6.28*** 0.40*** 2.64**N (regions years) 246 850 205R2 0.37 0.19 0.19

F 69.89*** 99.8*** 23.55***

* = p < 0.10; ** = p < 0.05; *** = p < 0.01

4 OLS is not an ideal estimator in this instance because there cannot be a negative number of MIDs (or of regional

IOs as in OLS analyses reported below). But there is nevertheless substantial variation in MID onsets (ranging from

zero to more than a dozen) which OLS can analyze. A maximum likelihood count model might be a better choice

given the range of possible values on the dependent variable, but MLEs requires large sample sizes due to theirefficiency assumptions. I just do not have enough cases to make it unambiguously clear that the deficits of OLS

here would be offset by the advantages of an MLE estimator like a count model.


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Table 4 reports results only when power is measured by the composite version of all three

of the capability dimensions, but substantively identical relationships are estimated in regressions

employing Regional Power share of the individual capability dimensions. Even controlling for

the number of states in the region, the relationship between the Regional Powers share of hard

power resources and the number of MIDs is negative and statistically significant. The number of

states variable is clearly important too, as evidenced by its persistent positive and significant

influence on the number of MIDs, as expected. But that positive relationship in no way suggests

that the correlation between power share and MIDs is spurious. These results provide strong

support for the regional Power Transition hypothesis. The more powerful the Regional Power,

the more peaceful its region.It remains now to see whether the regional Hegemonic Stability Theory hypothesis is

supported as well. Table 5 reports simple correlations between the Regional Power capability

share (reported all four ways) and the number of regional organizations in existence. The

expectation here is of a positive correlation between the two variables, since the greater the

Regional Powers share of capabilities, the more it approximates a privileged actor, and the

greater its ability to provide the regional collective good of constructing and maintaining

regional organizations.

Table 5: Correlations between Capability Shares and Regional Organizations:

Cell entries correlate Row Variable with Number of Regional Organizations

COW Regions RPN RegionsComposite Share 0.26*** 0.21***

Demographic Share -0.08 0.17**

Military Share 0.42*** 0.03

Economic Share 0.21*** -0.02

* = p < 0.10; ** = p < 0.05; *** = p < 0.01

The correlations reported in Table 5 are generally supportive of the hypothesis tested. In

the COW regions three of four power measures are statistically significantly, positively related to

the number of IOs in the region. The Demographic Share variable is negatively related to the

number of IOs (in COW regions), but that contrary correlation is not statistically significant.

Turning to the RPN regions, the Composite and Demographic shares are positively and

significantly correlated with the number of regional IOs, but the Military and Economic shares


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are not. On balance the entries in Table 5 tend to support the regional Hegemonic Stability

Theory hypothesis, because five of eight strongly conform with expectations, and while the other

three are not as expected, none of them are significant. The Hegemonic Stability hypothesis is

supported, but not as strongly as the Power Transition hypothesis.

As in the analysis of power shares and MIDs, the number of states in the region must be

controlled for here. As the number of states in a region increases the Regional Powers share of

power must decline. But at the same time there is a strong theoretical reason to expect that as the

number of states in the region increases, the number of IOs created and maintained in that region

will also decrease. Olson (1965) writes at length about how group size complicates the

collective action problem. The more members of the collective, the stronger the incentive to freeride on the collective-good-providing efforts of other group members. The harder it is to

coordinate across larger groups, and thus the greater the cost of providing the collective good at

all. Since Hegemonic Stability Theory explicitly builds on Olsons collective good work, there

is a theoretical as well as statistical justification to control for the number of states and thus to

ensure that the positive correlations in Table 5 are not caused by the negative relationship

logically and theoretically expected between them and the number of states in the region. Table

6 reports results of OLS regressions controlling for region size.

Table 6: Ordinary Least Squares Regressions of Regional Organizations:

Dependent Variable = Number of Regional IOs

COW Regions RPN RegionsCoefficient Coefficient

Composite Capability Share 9.75*** 2.31*

Number of States in Region 0.19*** -0.03

Constant -5.63*** 0.95N (regions years) 246 205

R2

0.45 0.05

F 101.16*** 4.91*** = p < 0.10; ** = p < 0.05; *** = p < 0.01

As can be seen in Table 6, in both COW and RPN regions, even controlling for the

number of states in the region, the stronger the Regional Power the greater the number of

regional IOs. This relationship is particularly strong in the COW regions, but is significantly

present in the RPN regions as well. I report results with only the composite version of capability


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share here in order to save space, but regressions with Regional Powers share of the other three

dimensions of hard power produce results consistent with those reported in Table 6. Thus, it is

safe to conclude that the regional Hegemonic Stability hypothesis is supported. The greater the

relative capabilities of the Regional Power, the greater the number of regional IOs.5

5. Discussion and Conclusions

In the pages above I have developed region-level hypotheses from prominent IR theories

originally pitched at other levels of aggregation. Having done so I then probed the importance of

the distribution of hard power resources on the characteristics of regional groups of states. I

found that the regional distribution of power, and specifically how preponderant the Regional

Power within each region is, strongly influences the amount of conflict the region will

experience, and also helps predict how many regional IOs exist in the region. I demonstrated

that these supportive findings of my regional hypotheses are robust across different designations

of regions, across different measures of capabilities, and across different types of statistical

estimation. It is fair to conclude from this that Power Transition Theorys and Hegemonic

Stability Theorys regional hypotheses are strongly supported.

That does not mean, however, that research about regional conflict or cooperation has

reached some logical stopping point. Rather, the analyses reported here suggest that much more

work remains to be done. Specifically, the statistical results generated in this paper provide

evidence only that the distribution of power and the amount of conflict or the number of regional

international organizations covary significantly. That is an important first step in establishing

that a causal relationship exists between these variables. But the statistical relationships

uncovered here cannot indicate why the relationships exist, nor can they provide any detailed

5 Note that the number of states in the region is not consistently negatively related to the number of regional IOs. In

fact, it is positively and significantly related to the number of IOs in COW regions. This is exactly contrary to

Olsons expectations. Happily, this does not call into doubt the positive relationship between Regional Powers

share and the number of regional IOs, but it is nevertheless interesting to ask why Olsons expectations are not

supported here. One interesting possibility is pointed out by Miles Kahler (1992) in his article about the political

attractiveness of security multilateral memberships in IOs. While not related to regional IOs, Kahler develops aclear argument about exceptions to Olsons logic about group size caused by factors he did not consider, such as

norms of sovereign equality of states.


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information of how the causal relationships might function. Additional work is necessary to

flesh out both of these subsidiary, related questions.

With respect to these questions, it would be interesting to investigate why the presence of

powerful states is associated with fewer conflicts and more IOs. Is there evidence that member

states in regions with strong Regional Powers restrain themselves from entering into conflicts

with other member states out of fear of reprisals by the preponderant Regional Power?

Similarly, do particularly powerful Regional Powers identify themselves as responsible for the

peace and stability of their regions? If a member state in such a region started MIDs regardless

of the preponderance of the Regional Power, would we be likely to observe subsequent MIDs in

which the preponderant Regional Power punished the state threatening the regional peace andstability?

A related question asks who creates the IOs in regions characterized by preponderant

Regional Powers? Hegemonic Stability Theory predicts the preponderant Regional Power will

pay for the construction and maintenance of the IOs that serve the regional collective good. Has

Brazil paid these construction and maintenance costs for the many regional IOs operating in

South America, or has India similarly been the provider of regional IOs in South Asia? Further,

are the regional IOs providing collective goods? Do they benefit member states as anticipated by

the theory, or do they serve the self-interests of the Regional Power?

Finding information about specific conflict and cooperative behaviors would answer the

why and how questions my research sidesteps. Another question is also sidestepped here,

specifically that asking what else matters? All of the analyses reported above, and all those

undertaken to investigate how sensitive the findings are to variation in research design but not

reported here, support the hypotheses tested. But in no case does that support suggest that all of

the variation has been explained. None of the correlations reported in Tables 3 or 5 have

coefficients of 1. The highest R2 for regressions in Tables 4 or 6 is 0.46. Thus, even in the

strongest result reported above, less than half the variation in the number of regional IOs is

accounted for by knowing how strong the Regional Power is. What else matters? There are

hints perhaps of where to look hidden within the datasets. For example, what makes Iran assert

itself even though it is not the strongest state in its region? Why does Pakistan persist in resisting

Indian hegemony in South Asia? Indias share of the available power in South Asia should deter

Pakistan from ever resisting, much less provoking India, if power transition theory expectations


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are valid. So why does Pakistan persist in asserting itself? Answers to such questions are not to

be found in statistical analyses like mine (although candidate answers could be tested in such

analyses). Rather, they must be proposed by scholars offering better theories about regions and

Regional Powers, and almost certainly by scholars possessing deeper knowledge of the details of

relations and governance within regions. A fruitful collaboration is possible.

References

Buzan, Barry, and Ole Waever. 2003.Regions and Powers. Cambridge, UK: Cambridge

University Press.Correlates of War Project. 2005. National Material Capabilities Dataset, version 3.02.

Available online at: http://correlatesofwar.org.

Correlates of War Project. 2008. State System Membership List, v2008.1. Available online at:

http://correlatesofwar.org.

Ghosn, Faten, Glenn Palmer, and Stuart Bremer. 2004. The MID3 Dataset, 1993-2001:

Procedures, Coding Rules, and Description. Conflict Management and Peace Science

21:133-154.

Gilpin, Robert. 1981. War and Change in World Politics. Princeton, NJ: Princeton University

Press.Gleditsch, Kristian Skrede. 2002.All International Politics Is Local. Ann Arbor, MI: University

of Michigan Press.

Hemmer, Christopher, and Peter J. Katzenstein. 2002. Why is There No NATO in Asia?:

Collective Identity, Regionalism, and the Origins of Multilateralism.International

Organization 56(3):575-607.

Jacobson, Harold, William Reisinger, and Todd Mathers. 1986. National Entanglements in

International Governmental Organizations.American Political Science Review

80(1):141-159.Kahler, Miles. 1992. Multilateralism with Small and Large Numbers.International

Organization 46(3):681-708.

Keohane, Robert. 1980. The Theory of Hegemonic Stability and Changes in InternationalEconomic Regimes. In Change in the International System edited by Ole Holsti,

Randolph Siverson, and Alexander George, 131-162, Boulder, CO: Westview Press.

Kindleberger, Charles. 1974. The World in Depression, 1929-1939. Berkeley, CA: University ofCalifornia Press.

Lemke, Douglas. 2002.Regions of War and Peace. Cambridge, UK: Cambridge UniversityPress.

Miller, Benjamin. 2007. States, Nations, and the Great Powers. Cambridge, UK: Cambridge

University Press.

Olson, Mancur. 1965. The Logic of Collective Action: Public Goods and the Theory of Groups .


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Cambridge, MA: Harvard University Press.

Organski, A. F. K., and Jacek Kugler. 1980. The War Ledger. Chicago: University of Chicago

Press.

Pevehouse, Jon, Timothy Nordstrom, and Kevin Warnke. 2004. The Correlates of War 2

International Governmental Organizations Data version 2.0. Conflict Management and

Peace Science 21:101-119.

Ray, James Lee. 2003. Explaining Interstate Conflict and War: What Should Be Controlled

For? Conflict Management and Peace Science 20(2):1-31.

Russett, Bruce, and John Oneal. 2001. Triangulating Peace. New York: W. W. Norton and

Company.Russett, Bruce, J. David Singer and Melvin Small. 1968. National Political Units in the 20

th

Century.American Political Science Review 62(3):932-951.

Singer, J. David. 1987. Reconstructing the Correlates of War Dataset on Material Capabilities

of States, 1816-1985.International Interactions 14:115-132.

Snidal, Duncan. 1985. The Limits of Hegemonic Stability Theory.International Organization39(4):579-614.

Thompson, William R. 1973. The Regional Subsystem.International Studies Quarterly

17(1):89-118.


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7.Appendix of Regional Membership

I. Regions as Defined by the Correlates of War Project:

Western Hemisphere

United States Antigua & Barbuda Guyana CanadaSt Kitts & Nevis Suriname Bahamas Mexico

Ecuador Cuba Belize Peru

Haiti Guatemala Brazil Dominican Republic

Honduras Bolivia Jamaica El Salvador

Paraguay Trinidad & Tobago Nicaragua ChileBarbados Costa Rica Argentina Dominica

Panama Uruguay Saint Lucia Colombia

St Vincent & Grenadines Venezuela

EuropeGreat Britain Hungary Romania Ireland

Czechoslovakia USSR/Russia Netherlands Czech Republic

Estonia Belgium Slovakia Latvia

Luxembourg Italy Lithuania France

San Marino Ukraine Monaco Malta

Belarus Liechtenstein Albania Armenia

Switzerland Macedonia Georgia SpainCroatia Azerbaijan Andorra Yugoslavia/Serbia

Finland Portugal Bosnia Herzegovina Sweden

Germany Slovenia Norway West Germany

Greece Denmark East Germany CyprusIceland Poland Bulgaria Austria

Moldova

Sub-Saharan AfricaCape Verde Togo Eritrea Sao Tome y Principe

Cameroon Angola Guinea-Bissau Nigeria

Mozambique Equatorial Guinea Gabon Zambia

Gambia Cent. African Rep. Zimbabwe Mali

Chad Malawi Senegal Rep. of the Congo

South Africa Benin Uganda Namibia

Mauritania Kenya Lesotho NigerTanzania Botswana Ivory Coast BurundiSwaziland Guinea Rwanda Madagascar

Burkina Faso Somalia Comoros LiberiaDjibouti Mauritius Sierra Leone Ethiopia

Seychelles Ghana Dem. Rep. of Congo

Middle East and North Africa

Morocco Egypt Yemen Peoples Rep. Algeria

Syria Kuwait Tunisia Lebanon


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Bahrain Libya Jordan Qatar

Sudan Israel U. Arab Emirates Iran

Saudi Arabia Oman Turkey Yemen Arab Rep.

Iraq Yemen

Asia (Central, East, South, and Southeast)

Afghanistan Japan Laos Turkmenistan

India Vietnam Tajikistan Bhutan

South Vietnam Kyrgyzstan Pakistan Malaysia

Uzbekistan Bangladesh Singapore KazakhstanMyanmar/Burma Brunei China Sri Lanka

Philippines Mongolia Maldives Indonesia

Taiwan Nepal East Timor North Korea

Thailand South Korea Cambodia

Australia/Pacific Islands

Australia Kiribati Marshall Islands Papua New Guinea

Tuvalu Palau New Zealand Fiji

Samoa Vanuatu Tonga Nauru

Solomon Islands Federated States of Micronesia

II. Regions as Defined inRegions of War and PeaceNorth America and the Caribbean

Antigua & Barbuda Bahamas Barbados Canada

Cuba Dominica Dominican Republic GrenadaHaiti Jamaica Mexico St Kitts & NevisSt Lucia St Vincent & Grenadines Trinidad

United States of America

Central America

Belize Costa Rica El Salvador GuatemalaHonduras Nicaragua Panama

South AmericaArgentina Bolivia Brazil Chile

Colombia Ecuador Guyana ParaguayPeru Suriname Uruguay Venezuela

Europe

Albania Andorra Armenia Austria

Azerbaijan Belarus Belgium Bosnia Herzegovina

Bulgaria Croatia Czechoslovakia Cyprus

Czech Republic Denmark East Germany Estonia

Finland France Georgia GermanyGreat Britain Greece Hungary Iceland


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Ireland Italy Latvia Liechtenstein

Lithuania Luxembourg Macedonia Malta

Moldova Monaco Netherlands Norway

Poland Portugal Romania San Marino

Slovakia Slovenia Spain SwedenSwitzerland Ukraine USSR/Russia West Germany

Yugoslavia/Serbia

Africa I: West Africa

Cape Verde Equatorial Guinea Gambia GuineaGuinea-Bissau Mali Mauritania Sao Tome y Principe

Senegal Sierra Leone

Africa II: Gulf of Guinea

Benin Burkina Faso Cameroon GhanaIvory Coast Liberia Niger Nigeria

Togo

Africa III: Central Lowlands

Central African Republic Chad

Africa IV: South Atlantic CoastAngola Congo Gabon Dem. Rep. of Congo

Africa V: Indian Ocean

Kenya Tanzania Uganda

Africa VI: Central Highlands

Burundi Rwanda

Africa VII: Horn of Africa

Djibouti Eritrea Ethiopia Somalia

Sudan

Africa VIII: Southern Africa

Botswana Comoros Lesotho Madagascar

Malawi Mauritius Mozambique NamibiaSeychelles South Africa Swaziland ZambiaZimbabwe

Africa IX: Maghreb

Algeria Libya Morocco Tunisia

Middle East I: Northern Rim

Iran Iraq Turkey


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Middle East II: Arab-Israeli

Egypt Israel Jordan Lebanon

Syria

Middle East III: Arabian PeninsulaBahrain Kuwait Oman Qatar

Saudi Arabia U. Arab Emirates Yemen Yemen Dem. Rep.

Yemen Peoples Republic

Central AsiaAfghanistan Kazakhstan Kyrgyzstan Tajikistan

Turkmenistan Uzbekistan

East Asia

China Japan Mongolia North KoreaSouth Korea Taiwan

South Asia

Bangladesh Bhutan Burma India

Maldives Nepal Pakistan Sri Lanka

Southeast AsiaCambodia Laos South Vietnam Thailand

Vietnam (former North Vietnam)

Asian ArchipelagoBrunei Indonesia Malaysia Philippines

Singapore

OceaniaAustralia Fiji Kiribati New Zealand

Papua New Guinea Solomon Islands Tuvalu Vanuatu

III. Regions as Identified by the Presence of Regional Powers:

Brazils Region

Argentina Bolivia Brazil ChileColombia Ecuador Guyana ParaguayPeru Suriname Uruguay Venezuela

South Africas Region

Angola Botswana Comoros Lesotho

Madagascar Malawi Mauritius Mozambique

Namibia Seychelles South Africa Swaziland

Zambia Zimbabwe


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Irans Region

Bahrain Egypt Iran Iraq

Israel Jordan Kuwait Lebanon

Oman Qatar Saudi Arabia Syria

United Arab Emirates Yemen Yemen Arab Rep. Yemen P. Rep.

Chinas Region

Brunei Cambodia China Indonesia

Japan Laos Malaysia Mongolia

North Korea Philippines Singapore South KoreaSouth Vietnam Taiwan Thailand (North) Vietnam

Indias Region

Bangladesh Bhutan India Maldives

Myanmar/Burma Nepal Pakistan Sri Lanka

Documents

Dimensions of Hard Power. Lemke D