The Reciprocal Relationship Between
Military Conflict and Democracy
1 August 2005
Abstract:
Does democracy cause peace, or is democracy a consequence of peace? The burgeoning
democratic peace literature has provided strong empirical evidence for the claim that democracies
are a cause of peace. However, several skeptics of the democratic peace have suggested that the
statistical findings are spurious. They contend that democracy is a consequence of peace. That
is, democratic institutions and norms grow more rapidly in a peaceful environment. In this case,
some other factor (e.g., geographic isolation) is the root cause of peace among democratic sates.
We test these competing claims using a simultaneous equation model. Using a unique data set of
all international disputes from 1960-1988, we find strong support for reciprocal causation. As the
democratic peace theorists claim, democracy causes peace even after controlling for military
conflict in the system and region. Conversely, peace in the region appears to encourage the
development of democratic polities.
David L. Rousseau
Department of Political Science
Rockefeller College of Public Affairs and Policy
University at Albany (SUNY)
135 Western Avenue
Albany, NY 12222
and
Hyung Min Kim
Department of Political Science
University of North Carolina at Chapel Hill
361 Hamilton Hall, CB #3265
Chapel Hill, NC 27599-3265
Paper prepared for the annual meeting of the American Political Science Association, September
1st – 4th, 2005, Washington, DC. Please send comments to the first author.
INTRODUCTION
Does democracy cause peace, or is democracy a consequence of peace? The enormous
democratic peace literature claims democracy is a cause of peace. Over the last decade a strong
consensus has emerged supporting the claim that democracies do not use large-scale violence
against other democracies (i.e., the existence of a dyadic democratic peace). More recently, a
number of scholars have demonstrated that democracies are often more peaceful in general (i.e.,
the existence of the monadic democratic peace). From the perspective of this literature,
democratic institutions, norms or identities reduce the probably that democratic polities resort to
military force to resolve international disputes.
But several skeptics of the democratic peace argue that democracy is a consequence of
peace. That is, democratic institutions and norms grow more rapidly in a peaceful environment.
In this case, some other factor (e.g., geographic isolation) is the root cause of peace among
democratic sates. Thus, skeptics such as Thompson (1996) and Layne (1994) argue that the
statistical correlation between democracy and peace in the democratic peace literature is spurious.
The purpose of this paper is to sort out these completing claims. Using a simultaneous
equation model, we test both arguments in the same statistical model. We find strong support for
reciprocal causation. As the democratic peace theorists claim, democracy causes peace even after
controlling for military conflict in the system and region. Conversely, the skeptics are correct in
their narrow claim that peace in the region encourages the development of democratic polities.
However, the broader skeptical claim that the democratic peace will evaporate after controlling
for conflict in the environment is not supported by our simultaneous equation model.
Our model contributes to the literature in five ways. First, it is one of only a handful of
models that directly tests for reciprocal causation. Second, it employs a data set of international
disputes that is broader in scope than analyses that are restricted to wars or militarized crises.
Third, unlike previous simultaneous equation models, the data set employs a directed dyad
structure that allows us to test monadic and dyadic arguments simultaneously. Fourth, the model
directly compares explanations from the systemic, regional, dyadic, and monadic levels. Fifth,
the model employs a unique conflict initiation variable that allows a more theoretically precise
test of the democratic peace side of the equation.
The remainder of the paper is divided into six sections. The following section explores
the causal mechanisms (or micro-foundations) of both halves the reciprocal relationship. The
second section explains the procedure and rationale for our simultaneous equation model. The
third section presents our hypotheses and the fourth section describes the data set. The fifth
section describes the results of the model and the sixth section summarizes our findings.
CAUSAL MECHANISMS IN THE LITERATURE
Part I: Military Conflict Decreases (or Increases) Democracy
Reflecting on the formation of national states in Western Europe, Tilly (1975) argues that
war (and the tax systems created to pay for war) played a vital role in the development of strong
states. “War made the state and the state made war” (Tilly 1975, 42). Beginning with almost 500
independent political entities in 1500, Europe gradually consolidated into approximately twenty
sovereign states by the start of the 20th century. Many alternatives to the sovereign state, ranging
from empires and city states to trading leagues and federations, fell by the wayside during the
consolidation process (Spruyt 1994).
Tilly argues that the costs of war increased rapidly due to three revolutionary factors:
technical innovations in weapons (e.g., cannons and firearms), tactical changes in warfare (e.g.,
the rise of infantry), and an expansion in the size of armies. Prior to these changes, rulers in the
largely non-monetized feudal economic and military system had relatively few expenses and
could typically manage affairs using the revenue produced from crown lands. In the feudal
system, land was exchanged for military service and the vassals were responsible for equipping
2
and supplying their own troops. After the technical and tactical innovations, the larger mercenary
armies and their advanced weapons became horrifically expensive. Desperate for additional
revenue, rulers in Europe expanded the tax base and institutionalized the collection process. The
first bureaucratic structures were created for revenue collection. The need for ever larger armies
led to the extensive use of mercenaries from the 15th to the 18th century, and then to the use of
conscripts during the Napoleonic Wars and the total wars of the 20th century. Tilly also argues
that the mobilization of fiscal and human resources triggered resistance. He contends that states
responded to this threat by increasing the coercive apparatus (e.g., police) to minimize
disruptions. For the region as a whole, the process was autocatalytic. As rulers fielded even
larger armies or equipped their artillery with even more accurate cannons, neighbors were forced
to raise revenues in order to respond in kind. The upward spiral continued in Europe for at least
600 years.
If war did in fact contribute to the state building process, a second question emerges: just
what sort of sovereign state emerged from this violent environment? Hintze (1906), like Tilly,
argues that state development is inextricably linked to military development. While domestic
factors such as the distribution of power between interest groups played an important role in state
development, the level of external threat together with the state’s position in the international
political hierarchy could be decisive. "All state organization was military organization,
organization for war" (1906, 181). Hintze also argues that external threats can lead to the
development of autocratic institutions.1
England, with her insular security, was not directly exposed to the danger of
these wars. She needed no standing army, at least not one of Continental
proportions, but only a navy which served commercial interests as much as war
aims. In consequence, she developed no absolutism. Absolutism and militarism
go together on the Continent just as self government and militia in England. The
main explanation for the difference in the way political and military
organizations developed between England and the Continent -- one which
became more and more distinct after the middle of the seventeenth century -- lies
in the difference in the foreign situations (Hinze 1906, 199).
England did not become democratic because it was either Protestant (Weber 1958), or
industrialized (Smith 1965; Dahrendorf 1967), or agriculturally commercialized (Moore 1966).
Rather, the lack of a persistent external threat allowed interest groups to maintain and expand
political and economic privileges. For Hintze, this path was simply impossible for a state such as
Prussia, which had to centralize power and eliminate domestic opposition in order to efficiently
mobilize the resources required to maintain independence in an anarchic world.
Lasswell (1941), writing at a time in which the world (but not yet the United States) had
plunged into the Second World War, takes a similar position. "The purpose of this article is to
consider the possibility that we are moving toward a world of "garrison states" -- a world in
which the specialists of violence are the most powerful group in society" (1941, 455). While not
arguing the outcome was inevitable, Lasswell feared that mobilization for total war encouraged
the authoritative allocation of resources at the expense of market allocation and the emphasis of
the collective or the public at the expense of the individual or the private. During war "decisions
will be more dictatorial than democratic, and institutional practices long connected with modern
democracy will disappear (1941, 461)." Throughout his essay, Lasswell emphasizes the role of
propaganda in mobilizing resources, increasing morale, repressing dissent, socializing the young,
and ritualizing democracy. In sum, Lasswell echoes Hintze's contention that a hostile external
environment leads to autocratic institutions but he focuses on the demise of existing modern
democracies rather than the rise of embryonic autocratic states.
External threats can undermine pluralist institutions and norms in a number of ways.
First, the existence of a severe external threat can lead to the elimination of free and fair elections
(e.g., the U.K. suspends national elections during both World Wars). Second, during war
3
decision-making power constitutionally vested in the legislature is often shifted to the executive
branch (e.g., Germany during the First World War). Third, the hostile external environment can
lead to a suspension of the rule of law in the hope of efficiently mobilizing resources (e.g.,
Lincoln's suspension of habeas corpus during the American Civil War (Rehnquist 1998)). Fourth,
threats often lead to the expansion of the power of the military over economic and political
decision making (e.g., Ludendorff's military dominated government in Germany during the First
World War). Fifth, threats often lead to the barring (or removal) of individuals of suspect
loyalties from holding public office (e.g., the removal of Jews from the provincial government in
Vichy France in 1940). Sixth, in the face of an intense threat, even short of war, states often
restrict the right of its citizens to form political parties and compete in the political arena (e.g.,
Communists in the United States during the early Cold War era). Seventh, severe threats often
lead to restriction in the right to vote in national and local elections (e.g., Japanese-Americans
during internment in the Second World War (Daniels 1981)). Finally, in the hope of minimizing
opposition to extraction policies, governments often repress free speech (e.g., Espionage Act of
1917 in the United States).
While threats and wars often result in a restriction of political rights, Downing (1992)
argues that under certain conditions war may expand rather than contract political rights.
Downing focuses on the medieval origins of constitutional government in Europe. Medieval
customs and institutions (e.g., reciprocal rights between peasants and lords; the balance of power
between the crown, the Catholic Church, nobles and burghers; prototype parliaments; and the rule
of law) laid the foundation for participatory government. As with Tilly, the military revolution
plays a decisive role in Downing's story. States that are forced to extract resources domestically
are apt to develop autocratic structures capable of squeezing every last cent out of reluctant lords,
merchants, and peasants. In contrast, states that are capable of raising capital in markets or
fighting on the territory of other states (where they can extract resources from the victims of
aggression) are less likely to develop autocratic institutions. In sum, the lack of a medieval
legacy, the presence of severe security threats, and the reliance on domestic resources to finance
military campaigns all increase the probability of an autocratic state.
Downing emphasizes that external threats and the need for wartime revenue often
triggered an intense battle between the crown and its subjects. New taxes were not dictated by
rulers from above; they were negotiated with interest groups from below. Feudal customs and
institutions limited the crown’s ability to raise revenue. When the crown "asked" for
extraordinary revenue, it was often forced to reward interest groups for compliance.
[E]states were essential to finance and consensus building: they debated matters
of war, foreign policy, trade, and justice. … [E]states often took advantage of any
upper hand they might have had by enhancing their privileges and liberties, and
by expanding their role in the machinery of government. In exchange for
financial support, more often than not in time of war, estates assumed increasing
control of law making (Downing 1992, 31).
Moreover, this expansion of rights related to the extraction of human resources as well.
As the size of armies grew, the royal treasury simply could not afford to pay the mercenaries for
their services. The solution was to enlist volunteers and conscripts. But why would a young
peasant from Languedoc or Hanover volunteer for the hardship of military service or honor a
conscription notice?2 They complied because they were citizens -- members of a community that
provided rights and demanded obligations. The Napoleonic Wars are considered a historical
turning point because large “citizen” armies began to take the place of “mercenary” armies that
had dominated in Europe since the end of the Middle Ages (Dupuy 1984, 156). Although
Napoleon's Grand Armée assembled for the invasion of Russia in 1812 was dominated by foreign
troops, it contained some 300,000 French conscripts and volunteers (Finer 1975, 146). "Citizen”
armies would play a central role in the next global wars: World War I and World War II.
4
In sum, the Extraction School posits that raising revenues and armies requires political
compromises and triggers social change.3 Mobilization for large-scale war can, therefore, have
the unintended consequence of expanding political rights in the long run.4
Part II: Democracy Decreases Military Conflict
The enormous democratic peace literature focuses on whether more inclusive and
competitive polities are less likely to use military force to resolve international conflicts (Kant
1795). At least three distinct causal mechanisms have been proposed in the literature: 1) a
structural explanation, 2) a political norms explanation, and 3) and an identity explanation.
The institutional structures school focuses on the relationship between political structures
and the domestic political costs of using force (Morgan and Campbell 1991; Morgan and
Schwebach 1992). According to this school, decisions to use military force are choices made by
political leaders based on domestic and international cost/benefit calculations. Foreign policy
decisions can have costly domestic political repercussions. The expenditure of resources and loss
of human life can mobilize opposition groups or fracture a ruling coalition. Relative to other
political systems, democratic decision makers must be more sensitive to these potential domestic
costs. This constrains their behavior in comparison with leaders of non-democratic states.
Immanuel Kant, the first proponent of the democratic peace, uses this argument to support his
claim that oligarchies are more likely to initiate war than republics (Kant 1795).5
The political norms school emphasizes the socialization of political leaders within their
domestic political environment (Maoz and Russett 1993; Russett 1993; Dixon 1993, 1994; Huth
and Allee 2002). This argument has two parts. First, democratic political leaders are socialized
within a system that emphasizes compromise and non-violence. Leaders typically resolve
political conflicts in democracies through negotiation and log-rolling. Losing a political battle
does not result in the loss of political rights or exclusion from future battles. Moreover, coercion
and violence are not seen as legitimate means for resolving a dispute. Conversely, nondemocratic political leaders are socialized in an environment in which politics is more akin to a
zero sum game in which rivals and losers are regularly removed from the game. Coercion and
violence are more widely accepted as legitimate means for resolving political conflicts. In
general, political leaders are more likely to impose decisions rather than compromise when
dealing with the opposition. Second, the norms school assumes that domestic political norms are
naturally externalized by decision makers when they confront international disputes. Thus,
democratic polities are expected to be less likely to use military force to resolve disputes.
The shared identity school argues that peace between democracies is a function of a
common social identity (Hopf 1998; Kahl 1998/99). Social identities are bundles of shared
values, beliefs, attitudes, norms, and roles that are used to draw a boundary between the "ingroup" and the "out-group" (Rousseau, forthcoming). Members of the in-group are viewed as
less threatening than members of the out-group. If democratic polities use democratic norms or
values to define the in-group, then they will view the actions and capabilities of the other
democracies as less threatening. The shared identity will reduce the likelihood that either party
quickly resorts to violence to resolve a political dispute.
The structural and political norms arguments imply that democratic polities should be
less likely to use force regardless of the regime type of the opponent. This is a purely monadic
structural argument: the regime type of the state alone determines its behavior in international
disputes. However, the literature has proposed a dyadic explanation: due to the expectation that
other democracies will be similarly constrained (by institutions or norms), democracies are only
peaceful with other democracies. This is referred to as the dyadic democratic peace in the
literature. While empirical evidence for the dyadic democratic peace is extensive, only recently
has strong evidence emerged in support of the monadic democratic peace.6 Finally, the shared
identity argument is generally stated as a dyadic argument: if you are a member of the in-group, I
5
am less likely to view your capabilities and actions as threatening. In general, the construction of
an identity requires an “other.”7
Part II: Reciprocal Causation
Skeptics of the power of the democratic peace often suggest that reciprocal causation
could explain the results.8 Case studies by Layne (1994) and Thompson (1996) suggest that the
peaceful nature of the environment rather than the peaceful disposition of democracies best
explains the democratic peace. Over time, a handful of studies have attempted to explore the
reciprocal relationship using a variety of data sets and estimation methods (see Table 1). In
general, modeling the reciprocal relationship has been slowed by the lack of appropriate
estimation tools that are able to simultaneously address all the problems with the data (e.g., one
dichotomous and one continuous endogenous variable, time dependency, lag structures, weak
instruments). As with any complicated statistical analysis, fixing one problem tends to raise a
host of new and difficult issues.
Mousseau and Shi (1999) argue that the reciprocal relationship can be probed by focusing
on “anterior” regime changes – that is, regime changes that occur as states prepare for war (as
opposed to regime changes during war (concurrent changes) or after a war (posterior changes)).
Using democracy data from Polity III and war data from the COW interstate war data base for the
period from 1816 to 1992, they employ an interrupted time series analysis with a separate
regression for each case. Overall, they find only limited support for the anterior effect of war
preparation decreasing democracy and conclude that the democratic peace is robust. However,
our ability to draw firm conclusions from the analysis is hampered by a number of problems: 1)
only modeling one half of the reciprocal relationship; 2) extensive missing data; 3) short time
series for each separate regression; and 4) a lack of control variables.
Crescenzi and Enterline (1999) examine the reciprocal relationship between democracy,
democratization, and interstate conflict at the systemic level of analysis. Using democracy data
from Polity III and war data from the COW interstate war data base for the period from 1816 to
1992, they employ Granger causality and vector auto-regression techniques. Overall, they find
little support for the triangular relationship across time and space. For example, systemic warfare
does decrease the proportion democratic in the system at particular times (e.g., Europe from
1936-1992), but not at other times (e.g., Europe 1816-75) or in other places (e.g., Asia from
1945-92 or Africa from 1960-92). The results do not conclusively resolve the debate because the
systemic analysis does not shed light on the dyadic or monadic democratic peace and the lack of
control variables raises the issue of omitted variables.
James et al. (1999) attempt to explore the relationship between MID involvement and
democracy using a simultaneous equation model. However, they found the results “implausible”
due to the rarity of conflict involvement and their multiple categorical dependent variable.
Therefore, James et al. employ a standard two-step procedure to estimate one half of the
reciprocal relationship (MID involvement decreases democracy) and multinomial probit model
for the other half (democracy decreases MID involvement). Although James et al. conclude that
democracy is not an important cause of peace, their findings are severely criticized by Oneal and
Russett (2000).9 Problems with the analysis include 1) the failure to model reciprocal causation
directly, 2) the use of an extremely weak instrument for the two-step equation, 3) the use of a
dyadic data structure; 4) the employment of a highly skewed multinomial dependent variable; 5)
the absence of important control variables; and 6) the selection of a flawed level of democracy
variable.
Reuveny and Li (2003) examine the reciprocal relationship using a three equation
simultaneous equation model. Using democracy data from Polity 98 and militarized dispute data
from the COW Militarized Interstate Dispute (MID) data base for the period from 1950 to 1992,
they find support for both sides of the reciprocal relationship: democratic dyads decrease MID
involvement and MID involvement decreases democratic dyads. While the analysis is an
6
important contribution to the field, it cannot resolve the debate due to 1) the use of an
involvement conflict variable, 2) the nondirected-dyad data structure, 3) the employment of
lagged dependent variables (Achen 2000); 4) the use of the imprecise “weak link” assumption, 5)
the neglect of the time series problem, and 6) an extremely complicated three equation design.
Rasler and Thompson (2004) examine the reverse casual arrow from military conflict to
democracy using three separate equations modeling domestic resource inequality, democracy, and
military disputes. The data set consists of nine great powers (Austria-Hungary, Britain, China,
France, Germany, Italy, Japan, Russia, and the United States) from 1816 to 1992. Using data
from Vanhanen (1997), Polity III, and the MID, they find that wars and threats decrease
democracy and that democracy (using the Vanhanen but not Polity III) decreases MID dispute
involvement. As with the previous analyses, the article has a number of important limitations
including 1) the limited number of countries in the data set; 2) the failure to model reciprocal
causation directly; 3) the lack of control variables; 4) the use of lagged dependent variables; 5)
the use of an involvement conflict variable; and 6) the averaging of variables over ten year
periods.
In sum, a handful of authors have attempted to tackle the very difficult problem of
reciprocal causation. While each paper has important strengths and have added to our
understanding of the problem, no paper has been able to resolve the debate. We hope to
contribute to this on-going discussion by presenting a new test which eliminates some (but
certainly not all) of the problems arising in these earlier studies.
THE SIMULTANEOUS EQUATION MODEL
Most studies of military conflict and democracy have restricted their focus to one half of
the reciprocal relationship. This is due both to subfield interests (e.g., comparative politics
specialists are more interested in the cause of democracy and international relations specialists are
more interested in the cause of military conflict) and the technical difficulty associated with
modeling reciprocal causation. Unfortunately, the failure to address simultaneity can lead to
erroneous conclusions. Gujarati (1995: 647) explains that simultaneity problems occur when the
endogenous variable in one equation (e.g., military conflict) appears as an explanatory variable in
another equation of the system (e.g., democracy). As a result, such an endogenous explanatory
variable becomes stochastic and is correlated with the disturbance term of the equation where it
appears as an explanatory variable. In this situation, Gujarati argues that the classical OLS will
produce estimators that are not consistent regardless of the size of the sample (see also Greene,
1997: chap. 16). The rationale for avoiding the classical OLS (ordinary least squares) method in
the presence of the simultaneity problem also applies to the case of the classical logit or probit
method because key assumptions of the OLS method (such as nonstochasticity and the
independent distribution of the explanatory variables) are also incorporated into logit and probit
analyses. Thus, employing a standard probit model in a situation with reciprocal causation will
result in biased coefficients.
The ‘simultaneous equation’ model used to test our hypotheses is an instrumental
variables, limited information two-stage probit least squares estimation method. The model
requires two steps. First, we regress the endogenous explanatory variables (use of force and
democracy) on all of the predetermined variables in the whole system to eliminate the likely
correlation between those endogenous explanatory variables and the stochastic disturbance terms
in each equation, which violates the assumptions of the classical OLS and probit methods. This
first step provides us a ‘proxy’ for each endogenous explanatory variable—called an instrumental
variable—that is uncorrelated with the disturbance term in each equation. Second, we regress our
two original endogenous variables on these proxies (or instrumental variables) plus the other
independent variables in each equation (Gujarati, 1995: 686-88). This two-stage probit least
squares method gives us an unbiased and efficient estimator of each parameter in the equations
(Amemiya, 1978; Heckman, 1978; Maddala, 1983).
7
In general, the simultaneity problem has been explored with one of two estimation
approaches: 1) simultaneous equation models or 2) distributed lag models. As just discussed, the
simultaneous equation approach involves estimating two (or more) equations with each
endogenous variable appearing as an exogenous variable in the other equation(s). In the
democracy and military conflict literature, Reuveny and Li (2003) employ a simultaneous
equation model using a two-stage least squares method. In the interdependence and war
literature, Keshk, Pollins, & Reuveny (2004), Mansfield (1994), Polachek (1997), and Kim
(1998) use simultaneous equations estimation methods. In contrast, distributed lag and related
estimation models are time series methods that allow the researcher to relax assumptions such as
right hand variable exogeneity and fixed lag structures. In the democracy and military conflict
literature, Crescenzi and Enterline (1999) employ a distributed lag model at the system level. In
the interdependence and war literature, Oneal, Russett, & Berbaum (2003), Reuveny (2001), and
Reuveny & Kang (1996, 1998) use the distributed lags or other related estimation methods.
Although the distributed lag approach has a number of advantages (e.g., permits a richer
modeling of temporal dependence), we have selected the simultaneous approach for three
reasons. First, distributed lag and related models tend to be very sensitive to the length of the lag
selected by the modeler (Geweke, 1984). Second, the conclusion drawn from the bilateral
Granger causality models may depend on the inclusion of a third variable (Granger, 1980). Third,
detrending a series may either change its dynamic properties or lead to different causality
conclusions (Kang, 1985). Thus, in this paper we will restrict our analysis to a simultaneous
equation model.
HYPOTHESES
Figure 1 illustrates the hypotheses tested in this paper according to their level of analysis.
The two endogenous variables are the Level of Democracy and Aggressive Use of Force. The
Level of Democracy variable was constructed by subtracting the Polity IV autocracy index from
the democracy index to produce a variable which ranges from -10 to +10. To ease interpretation
of the statistical results, this variable was rescaled from 0 to 20. The Polity democracy index is
comprised of four components: openness of executive recruitment, competitiveness of executive
recruitment, competitiveness of participation, and legislative constraints on the executive. The
autocracy index contains all the elements in the democracy index plus a fifth component, the
regulation of participation. For example, using the Polity data and this method, the United States
receives a score of 20 for all years in the data set while the Soviet Union receives a score of 6 for
all years after the death of Stalin.
Aggressive Use of Force is defined as the use of military force on the territory of another
sovereign state. The variable codes for both uses of force by a state’s regular armed forces and
through third parties on the territory of another state (e.g., the American support for the
Mujahideen in Afghanistan during the 1980s).10 The Aggressive Use of Force variable differs
from a conflict involvement variable in that the target of aggression is not coded as having used
force. For example, while autocratic Germany used aggressive force in 1914, democratic
Belgium did not. Aggressive Use of Force is also superior to a conflict initiation variable for our
particular data set because the rarity of initiation undermines our ability to derive meaningful
results from the statistical analysis.
Systemic Level Variables
The relationship between democracy and military conflict has been explored at the
systemic level in several articles (McLaughlin 1996; Crescenzi and Enterline 1999). The causal
mechanism in these systemic variables involves demonstration effects. For example, the more
violence there is in the international system, the more likely a state leader is to view violence as a
legitimate means for resolving a dispute. Similarly, the greater the proportion of democracies in
the system, the more likely a state is to view democracy as a legitimate and effective form of
8
government. This demonstration effect should encourage the development of democratic
institutions and norms.11 In our model, Systemic War, measured as the total number of wars in
the international system in a given year, is expected to decrease the Level of Democracy and
increase the Aggressive Use Force. Data on wars are from the MID data set. System Democracy
Average, measured as the average level of democracy using the 0-20 Polity scale in a given year,
is expected to increase the Level of Democracy and decrease the Aggressive Use Force. Finally,
Systemic Trade Openness, measured as the average level of trade interdependence in the
international system in a given year, is expected to increase the Level of Democracy and decrease
the Aggressive Use Force. Interdependence is measured as exports plus imports divided by gross
national product using the Expanded Trade and GDP Data Version 3.0 by Gleditsch (2002a).
Regional Level Variables
Gleditsch (2002b) argues that powerful regional effects have been neglected in the
literature. Therefore, we have created a parallel set of regional variables for our conflict,
democracy, and interdependence variables. Regional coding relied on the Correlates of War
region variable which divides the world into five regions (Europe, Middle East, Africa, Asia, and
North/South America). The operationalization of the regional variables is identical to the
systemic variables.
Dyadic Variables
The dyadic variables capture the interaction of states (e.g., shared alliance) or relative
measures (e.g., the balance of power). In our model, all the dyadic variables are on the right-hand
side of the figure and only impact the state’s propensity to use military force. First, the shared
alliance hypothesis predicts that if two states are members of an alliance against a common foe,
they will be less willing to use military violence to resolve a dispute (Gowa 1999). This dummy
variable takes the value of 1 when the two states in the dispute share a defense pact, neutrality
pact, or an entente. Otherwise the value is 0. The sources for this variable is the Correlates of
War (COW) Formal Alliances data set, Version 3.03 (Gibler & Sarkees, 2004). Second, the
balance of power hypothesis predicts that the stronger state in the dyad will be more likely to
resort to military violence (Huth 1996). Each state’s military capability is the average of three
components—number of troops, military expenditures, and military expenditures per soldier—
from the COW National Material Capabilities data set (ICPSR 9903, Singer & Small, 1993). The
final product ranges from 0 to 1. A value of more than 0.50 indicates that the state’s military
capability is superior to its opponent, while a value of less than 0.50 indicates military inferiority.
Third, the preferential trading area (PTA) hypothesis predicts that if both states are members of
the same trading organization they will be less likely to use violence because of the costs
associated with disrupting the economic relationship. The dummy Shared PTA variable is coded
as 1 if both states in a dispute share at least one preferential trading agreement identified by
Pevehouse and Mansfield (2003). Fourth, the joint democracy hypothesis predicts that the more
democratic a state, the less likely it is to use military force to resolve the dispute against other
democracies (Russett and Oneal, 2001). This hypothesis tests the dyadic version of the
democratic peace. In order to isolate the impact of level of democracy when facing a democratic
opponent, we introduce an interactive term which multiplies the state’s Level of Democracy by a
dummy variable indicating whether or not the opponent is a democratic state. If a state’s
opponent scores 17 or greater on the democracy scale, the dummy variable is coded 1. Otherwise
the variable is coded 0. Thus when the opposing state is not democratic, this variable takes on a
value of 0. When the opposing state is a democracy, however, this variable is equal to the state’s
Level of Democracy score. Including this interaction as a separate variable in the analysis will
allow an identification of the additional effect that the Level of Democracy score has on a state’s
behavior because the opposing state is democratic. The final two dyadic variables control for
Contiguity and Distance. In both cases, we predict that geographic proximity increases the
9
probability of using force because it is easer to project power across short distances. If the two
states in a dispute share a boundary on land or are separated by less than 150 miles of water either
directly or through their colonies or other dependencies, the variable Contiguity is coded 1;
otherwise, it is coded 0. The variable Distance is the natural logarithm of the great circle distance
between the two states in a dispute.
Monadic Variables
As Figure 1 illustrates, seven monadic variables influence a state’s level of democracy.
First, membership in the global liberal trade organization known as GATT (General Agreements
on Tariffs and Trade) increases the probability that a state adopts a liberal political structure.12
The open trading system created by GATT aided many states in increasing income very rapidly
during the post-World War II period. Both the rise in income and the growing legitimacy of the
“trading state strategy” (versus the military expansion strategy) facilitated transitions to
democracy (Rosecrance 1985). Second, the per capita income hypothesis predicts that rising
income will increase the level of democracy. The creation of a strong middle class, the expansion
of education, and the desire to protect private property all contribute to demands for a more
inclusive and competitive political system. Third, the greater the percentage of arable land in a
country will be associated with higher levels of democracy. Arable land tends to be positively
correlated with a greater percentage of independent farmers in society; the independent land
owners are often the backbone of a democratic polity. Fourth, the openness hypothesis predicts
that the more a state trades, the more likely it is to have a high level of democracy. Historically,
economic liberalism is highly correlated with political liberalism (Russett and Oneal 2001;
Rousseau, forthcoming). Fifth, the growth hypothesis predicts that positive economic growth in
terms of gross domestic product will be positively associated with the level of democracy
(Reuveny and Li 2003). Conversely, economic decline should decrease democracy. Sixth, the
inflation hypothesis predicts that the economic turmoil caused by high inflation will undermine
democracy (Reuveny and Li 2003). Finally, the model includes a lagged level of democracy
dependent variable and a “years” variable in order to address temporal dependency. The data for
Per Capita Income and Openness are from the Expanded Trade and GDP Data Version 3.0 by
Gleditsch (2002a). The data for Member of GATT are from Reinhardt (1999); the data for Arable
Land are from Central Intelligence Agency (2004); the data for Growth Rate and Inflation are
from Heston, Summers, and Aten (2002).
Finally, Figure 1 indicates that two monadic variables influence a state’s propensity to
use force aggressively. First, the satisfaction hypothesis predicts that states which are satisfied
with the status quo are less likely to use aggressive force. This variable is coded 1 if a state is
satisfied with the status quo regarding the issue at stake in the dispute at the time the crisis begins.
Otherwise it is coded 0. For example, in a territorial dispute, if the defending state is content with
the current distribution of territory, it is coded as satisfied. In several disputes, both states are
dissatisfied (e.g., India versus China in 1962). Second, we predict that major powers will be
more likely to resort to military violence to resolve a dispute. The variable is coded as 1 if a state
is a major power identified by the COW Project: for the entire time period of our analysis, the
U.S., France, Great Britain, the Soviet Union, and China qualify as major power. The data for
Major Power are taken from EUGene (Expected Utility Generation and Data Management
program) Version 2.40 by Bennett & Stam (2000a). The data for Satisfaction are from Rousseau
(2005).
Equations (1) and (2) summarize the model that will be tested below.13
(1) Aggressive Use of Force,t = 0 + 1*Level of Democracy,t + 2*Systemic War,t + 3*System
Democracy Average,t + 4*Systemic Trade Openness,t + 5*Regional War,t +  6*Regional
Democracy Average,t + 7*Regional Trade Openness,t + 8*Shared Alliance Ties,t +
10
9*Balance of Forces,t + 10*Shared PTA,t + 11*Joint Democracy,t + 12*Contiguity,t +
13*Distance,t + 14*Major Power,t + 15*Satisfaction with the Status Quo,t + + 16*Peace
Year,t + 17*Spline1,t + 18*Spline2,t + 19*Spline3,t + e
(2) Level of Democracy,t = 0 + 1*Aggressive Use of Force,t + 2*Systemic War,t + 3*Systemic
Democracy Average,t + 4*Systemic Trade Openness,t + 5*Regional War,t +  6*Regional
Democracy Average,t + 7*Regional Trade Openness,t + 8*Membership in GATT,t + 9*Per
Capita Income,t + 10*Arable Land,t + 11*Openness,t + 12*Growth Rate,t + 13*Inflation,t +
14*Lagged Level of Democracy,t-1 + 15*Years,t + e
Although the model may appear unnecessarily complex, simultaneous equation models
must meet several conditions.14 In practice, each equation must contain one or more strong
predictors of the endogenous variable that do not appear in the other equation. This is often
particularly difficult in international relations (Kim and Rousseau 2005). James et al. (1999), for
example, employ an extremely weak instrument that explains very little of the variance of the
endogenous variable. While they explicitly recognize this in the article, they do not appear to
question whether such a weak instrument can tell us anything at all about the reciprocal
relationship between democracy and war. In contrast, our somewhat lengthy equations provide
very strong instruments for the two-step probit model.
THE DATA SET
We test our hypotheses using a set of international disputes from 1960 to 1988 developed
by Rousseau (2005). The primary source for the identification of each international dispute is a
data set developed by Sherman (1994) that identifies all domestic quarrels and international
disputes from 1945 to 1988. Rousseau has modified Sherman’s data set in a number of ways.
First, he has restricted his data set to the period of 1960-88. Second, he has removed all domestic
quarrels because the main focus of his research is on a state’s decision to use military force
against other states. Third, he has eliminated several categories of dispute cases in order to focus
on political-security conflicts that have some probability of escalating to military conflict between
internationally recognized sovereign states. Fourth, he has aggregated types of disputes (e.g.,
U.S. vs. Cuba territorial dispute in 1962 and U.S. vs. Cuba regime type dispute in 1962) into a
single dyadic dispute because these disputes are not independent events.
The final data set consists of 223 international disputes between pairs of countries.15 The
disputes vary in length from one to twenty-nine years. The directed dyad data structure includes
observations for both states in order to isolate the behavior of each party in the dispute. As
Bennett and Stam explain, ‘a directed dyad study easily allows for behavioral choices and dyadic
outcomes to be different in the two directions and hence allows simultaneous testing of varied
theories and hypotheses’ (2000b: 655). In our model, testing monadic and dyadic hypotheses
using a conflict initiation dependent variable requires the use of a directed dyad structure. The
cross sectional pooled time series directed dyad data set contains 5770 observations. 16
The use of this dispute data set differentiates our research from most other scholarly work
on the reciprocal relationship between democracy and military conflict. First, most international
disputes do not escalate into crises in which one or both parties threaten or use military force and,
by the same logic, most international crises do not escalate into wars in which one or both parties
use large military forces to resolve the crisis.17 Therefore, most previous empirical studies that
have focused on crises or wars capture only a small sub-set of the population of international
conflicts. The issue is important with respect to the liberal peace because if democracies choose
not to escalate a political dispute into a militarized crisis or war, then analyses restricted to the
11
subset of crises and wars will underestimate the pacifying impact of each pillar of the liberal
peace.
Second, many other scholarly work on the conflict and democracy relationship use a
state’s conflict involvement rather than a state’s conflict initiation as their dependent variable
(Reuveny and Li 2003). Conflict involvement is an acceptable dependent variable for the war
causes autocratization side of the reciprocal relationship because target states often have to raise
huge armies and restrict political rights. However, conflict involvement is a poor dependent
variable for the democracy causes peace side of the equation because it groups victims of
aggression (e.g., Belgium 1914) with the aggressors (e.g., Germany 1914). Given the absence of
a theoretically ideal variable, we employ a conflict initiation variable in our primary analysis and
a conflict involvement variable in our sensitivity analysis.
Third, much of the work on the liberal peace in general (e.g., Russett & Oneal, 2001) and
the conflict-democracy relationship in particular (e.g., Reuveny and Li 2003) employ nondirected dyad structures. This data structure makes it impossible to test monadic arguments and
encourages the use of conflict involvement variables. In order to distinguish the monadic effect
of democracies from the dyadic effect of two democracies, we employ the directed dyad design
(Bennett & Stam, 2004).
RESULTS
Our simultaneous equation model is an instrumental variables, limited information twostage probit least squares estimation method.18 Table 2 displays the results from three alternative
model specifications: Model 1 employs a traditional single equation OLS model with the Level of
Democracy dependent variable, Model 2 employs a traditional single equation probit model for
the Aggressive Use of Force dependent variable, and Model 3 employs the simultaneous equation
model. The results in Model 3 clearly indicate a reciprocal relationship between conflict and
democracy. The Aggressive Use of Force Instrument in the top half of Model 3 is negative and
statistically significant (B=-0.190, SE=0.07, t-statistic=2.64). As Lasswell and Hintze expected,
using military force can lead to a decrease in a state’s level of democracy. Conversely, the Level
of Democracy Instrument in the lower half of Model 3 is negative and statistically significant
(B=-0.020, SE=0.01, t-statistic=2.61). In addition, the dyadic Joint Democracy variable remains
negative and strongly significant in the lower half of Model 3. The model refutes the skeptics’
claim that controlling for conflict in the environment will cause the democratic peace to
evaporate.
Given our focus on the reciprocal model, we will restrict our discussion to Model 3. The
marginal impacts of the variables in the top half of the table can be interpreted just like any OLS
regression. The marginal impacts for the variable in the Aggressive Use of Force equation can be
found in Table 3. The top half of Model 3 provides strong support for several hypotheses
purporting to explain the level of democracy in a state. As expected, Systemic War decreases the
level of democracy and System Democratic Average and Systemic Trade Openness increase the
level of democracy. At the regional level, the results are more mixed. While the Regional
Democratic Average variable is positive as expected, the Regional War variable is significant in
the wrong direction: at the regional level violence seems to increase democracy. However, the
marginal impact of all these systemic and regional variables are quite small compared to other
levels of analysis. For example, a one standard deviation increase in the amount of regional war
only increases the level of democracy by 0.33 on the 0 to 20 Polity scale. At the monadic level,
trade openness, high per capita income, membership in GATT, and the level of democracy last
year all increase the level of democracy in the current year. Finally, Years and Growth have a
statistically significant negative but substantively unimportant impact on the level of democracy.
While the inclusion of a lagged dependent variable is common in simultaneous equation
models in international relations (e.g., Reuveny and Li 2003), Achen (2000) argues that the
inclusion of a lagged dependent variable can depress the size of the estimated coefficients for the
12
other independent variables. In our model, while the removal of the lagged dependent variable
still produces support for the reciprocal model, the size of the coefficients of many (but not all)
variables increases dramatically (e.g., Systemic War by 2.1 times, monadic Openness by 3.5
times, Membership in GATT by 3.8 times). While we have included the lagged dependent
variable in order to address dependency across time and to significantly increase the power of the
instrument, we recognize that it limits our ability to draw strong conclusions about the marginal
impacts of other variables in the democracy equation.
The lower half Model 3 in Table 2 provides the simultaneous equation results for the
Aggressive Use of Force half of the reciprocal relationship. As expected, the amount of Systemic
War has a positive impact on the use of aggressive force by a state. However, unexpectedly,
Systemic Trade Openness also is positively associated with military violence. At the regional
level, both the Regional Democratic Average and the Regional Trade Openness are negatively
associated with the use of force. At the dyadic level, Joint Democracy and Satisfaction with the
Status Quo decrease the probability of military force. For example, the marginal analysis in
Table 3 indicates that a shift from autocracy (0) to full democracy (20) when facing a democratic
opponent decreases the predicted probability of using force from 6.10 to 1.18 percent. Similarly,
a shift from dissatisfied to satisfied with the status quo decreases the predicted probability of
using force from 5.31 to 1.58 percent. Finally, Balance of Forces, at the dyadic level, increases
the probability of military force. Increasing the level of balance of forces from one standard
deviation below the mean (0.21) to one standard deviation above the mean (0.79) increases the
probability of military force by 1.94 percent (i.e., a shift in the predicted probability of using
aggressive force from 4.41 to 6.35 percent).
We conducted a number of sensitivity analysis in order ensure our findings are robust.
The primary results (i.e., statistical significance in the expected direction for both halves of the
reciprocal relationship) are stable when (1) replacing MIDs for wars in the systemic and regional
conflict measures, (2) dropping the lagged democracy variable from the model, (3) employing
alternative operationalizations for the economic interdependence variables, and (4) dropping the
temporal corrections (i.e., peace years and splines) from the model. When using conflict
involvement in place of aggressive use of force, the Level of Democracy Instrument decreases the
Aggressive Use of Force (B=-0.19, SE=0.005, t-statistic=3.53) but the Aggressive Use of Force
Instrument falls just short of statistical significance. Thus, the monadic and dyadic democratic
peace variables remain robust in all analyses and the conflict decreases democracy hypothesis in
most analyses.
As with any two-step model, a strong instrument is vital. In our model, the instruments
are quite powerful for both the democracy equation (R2 of 0.74) and the aggressive use of force
equation (pseudo R2 of .46). Although the findings are stable using a wide variety of independent
variables, removing a large number of statistically significant variables from the instrument can in
some specifications cause the monadic democracy variable in the use of force equation to drop
below statistical significance. Unfortunately, most researchers employing simultaneous equation
models in international relations fail to report the goodness of fit for the instruments.
CONCLUSIONS
Skeptics of the democratic peace have argued that the empirical literature has discovered
a spurious correlation. Democratic polities are not less likely to use force (against other
democracies or in general). Rather, the skeptics claim that democratic states tend to be nurtured
in peaceful environments. While this causal story may be true for some democratic states, the
simultaneous equation model presented in this paper refutes the skeptics’ argument. Even after
controlling for the amount of violence in the area, democracies were less likely to use force
against other democracies (i.e., support for the dyadic democratic peace) and less likely to use
force against other states in general (i.e., support for the monadic democratic peace).
13
Future analysis of the reciprocal relationship should focus in three areas. First, the
robustness of the results needs to be tested with a distributed lag model. Both simultaneous
equation models and distributed lag models have strengths and weaknesses. If the results are
robust using either method, we can be much more confident of our findings. Second, although
the use of a dispute data set is an important step forward for this literature, the time frame of the
dispute data set remains relatively short at 29 years. Future analysis should extend this data set,
particularly after the Cold War in order to ensure the reciprocal relationship is stable across time.
Third, more work needs to done comparing liberal and realist explanations across levels of
analysis. While we have shown that regional and systemic variables cannot wipe out the monadic
and dyadic democratic peace, we did discover some anomalous findings that must be explored in
future analysis. For example, why might regional war increase transitions to democracy and why
might systemic openness increase the probability of the use of force? Will future qualitative (or
quantitative) analysis support Downing’s causal story for the first finding and Waltz’s causal
story for the second finding?
14
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18
(-)
Proportion Democratic
(+)
Trade Interdependence
(+)
(-)
(-)
(+)
Level of
Democracy
(-)
Dyadic
(-) or (+)
Monadic
(+)
(+)
Member of GATT
Per capita Income
Arable Land
Openness
Growth Rate
Inflation
Last Year’s Democracy
Aggressive
Use of Force
Military Conflict
Proportion Democratic
Trade Interdependence
(+) Military Conflict
(-) Proportion Democratic
(-) Trade Interdependence
(-)
(+) Shared Alliance
(-) Balance of Power
Shared PTA
(-)
Joint Democracy
(+)
Contiguity
(-)
Distance
(+)
(+)
(+)
(-)
(+)
Major Power
(-)
Satisfaction
Systemic
Military Conflict
(+)
Regional
(+)
(-)
Dyadic
Military Conflict
Proportion Democratic
Trade Interdependence
Monadic
Regional
Systemic
Figure 1: Hypotheses Ordered by Level of Analysis.
(+)
19
Table 1: Empirical Analysis of the Reciprocal Relationship Between War and Military Conflict
Author
-------Mousseau & Shi
Journal
-------JPR 1999
Method
--------Single Equation
Finding
--------War Does Not Decrease Democracy
Crescenzi & Enterline
JPR 1999
Distributed Lag Model Great Regional and Temporal Variance;
No Clear Pattern in Either Direction
James et al.
DPE 1999
Hybrid Approach
War Decreases Democratization
Reuveny & Li
ISQ 2003
Simultaneous Model
Joint Democracy Reduces Conflict;
Conflict Reduces Joint Democracy
Rasler & Thompson
CPS 2004
3 Single Equations
Threat Decreases Democracy;
Democracy Decreases Conflict
Notes: Journal of Peace Research (JPR), Defense and Peace Economics (DPE), International Studies
Quarterly (ISQ), and Comparative Political Studies (CPS).
20
Table 2: Statistical Results Comparing OLS, Probit, and Simultaneous Equation Models
Endogenous Variable
Aggressive Use of Force
Systemic War
Systemic Democratic Average
Systemic Trade Openness
Regional War
Regional Democratic Average
Regional Trade Openness
Openness
Per Capital Income
Arable Land
Member of GATT
Lagged Level of Democracy
Year
Growth
Inflation
Constant
Model 1: OLS
Beta
SE
Level of Democracy
-0.102
-0.092
0.595
0.454
0.181
0.092
-0.032
0.032
0.0003
0.771
1.392
0.738
-0.036
-0.016
-0.005
65.021
Endogenous Variable
***
***
***
***
*
*
*
SE
0.17
0.02
0.15
0.25
0.04
0.03
0.03
0.004
0.00003
0.51
0.17
0.01
0.02
0.01
0.01
34.28
-0.014
0.030
0.041
0.509
0.073
-0.014
-0.011
-0.039
0.357
-0.050
-0.637
-0.147
0.028
-0.292
-1.260
-0.120
0.027
-0.001
-1.109
4069
0.73
Model 3: Simultaneous Equation
Beta
SE
Level of Democracy
-0.190
-0.090
0.616
0.488
0.184
0.075
-0.036
0.032
0.0003
0.766
1.347
0.733
-0.034
-0.019
-0.006
60.473
Aggressive Use of Force
Level of Democracy
Systemic War
System Democratic Average
Systemic Trade Openness
Regional War
Regional Democratic Average
Regional Trade Openness
Joint Democray
Balance of Forces
Shared Alliance
Satisfaction w/ Status Quo
Contiguity
Distance
Major Power
Peace Years
Spline 1
Spline 2
Spline 3
Constant
Number of observations
R-squared
Log Likelihood
***
***
*
***
***
Model 2: Probit
Beta
5593
1073
**
**
***
***
***
**
***
*
**
***
***
***
*
0.005
0.010
0.070
0.068
0.017
0.012
0.014
0.011
0.122
0.080
0.079
0.077
0.028
0.113
0.079
0.012
0.003
0.001
0.793
**
***
***
*
***
**
***
***
***
***
*
*
*
0.07
0.02
0.14
0.24
0.04
0.03
0.03
0.01
0.00003
0.52
0.15
0.01
0.02
0.01
0.01
34.80
Aggressive Use of Force
-0.020
0.049
0.076
0.662
0.034
-0.030
-0.030
-0.036
0.306
0.115
-0.533
-0.026
0.052
0.031
-1.146
-0.109
0.024
-0.001
-1.995
**
***
***
*
*
***
*
***
***
***
***
*
*
0.01
0.01
0.09
0.08
0.03
0.02
0.02
0.01
0.16
0.10
0.09
0.11
0.03
0.14
0.09
0.01
0.004
0.001
0.98
4056
0.73
713
Notes: All significance tests are one-tailed: *p<=0.05, **p<=0.01, ***p<=0.001. In Model 3, the Aggressive Use of
Force variable in the top half of the model is an instrument. Similarly, the Level of Democracy variable in the bottom
half of the model is an instrument.
21
Table 3. Marginal Impact Analysis for the Aggressive Use of Force Dependent Variable
Variables
Probit Method
Predicted
Percentage
Probability
Point Change
6.60
Baseline
Two-Stage Probit Method
Predicted
Percentage
Probability
Point Change
5.31
Level of Democracy
0
10
20
8.20
6.25
4.69
Total
-1.95
-1.57
-3.51
7.33
4.90
3.16
-2.43
-1.74
-4.17
Systemic War
Below 1Standard Deviation (0.5710)
Mean (4.5959)
Above 1Standard Deviation (8.6208)
Total
5.16
6.60
8.32
1.43
1.72
3.16
3.49
5.31
7.82
1.82
2.51
4.34
Systemic Openness
Below 1Standard Deviation (0.8706)
Mean (1.6726)
Above 1Standard Deviation (2.4746)
Total
2.78
6.60
13.60
3.82
7.00
10.82
1.59
5.31
13.91
3.72
8.60
12.32
Regional War
Minimum (0)
Mean (0.9874)
Above 1Standard Deviation (2.7923)
Total
5.72
6.60
8.47
0.88
1.87
2.75
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
Regional Democracy
0
10
20
n.s.
n.s.
n.s.
Total
n.s.
n.s.
n.s.
8.92
4.99
2.58
-3.94
-2.41
-6.35
Regional Openness
Minimum (0)
Mean (2.2951)
Above 1Standard Deviation (5.0170)
Total
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
6.09
5.31
4.49
-0.78
-0.82
-1.60
Joint Democracy*
0
10
20
7.61
3.44
1.36
-4.17
-2.08
6.10
2.84
1.18
-3.26
-1.66
22
Total
-6.24
-4.92
Balance of Forces
Below 1Standard Deviation (0.2085)
Mean (0.5000)
Above 1Standard Deviation (0.7915)
Total
5.36
6.60
8.04
1.23
1.44
2.68
4.41
5.31
6.35
0.90
1.04
1.94
Satisfaction with Status Quo
No
Yes
6.60
1.60
-4.99
5.31
1.58
-3.73
8.70
6.60
-2.10
n.s.
n.s.
n.s.
6.60
3.60
-2.99
n.s.
n.s.
n.s.
Contiguity
No
Yes
Major Power
No
Yes
Note: The table displays the substantive significance of the variables. The marginal impact displays the change in the
predicted probability of using force caused by a shift in the independent variable of interest from X1 to X2. All other
variables are held at their means or modes.
23
ENDNOTES
1
Tilly's later works, such as Coercion, Capital, and European States, AD 990-1992 (1990), probe
this second issue in greater detail.
2
Levi (1997) points out that many men did not honor their conscription notices. Fleeing to the
cities was always a possibility for those without property. For the wealthy, substitution and
commutation were often possible. During the American Civil War men could purchases their
way out of the draft at a fixed rate of $300 (Levi 1997, 99). Resistance to the draft was often so
great, men maimed themselves to make themselves unfit for combat (Levi 1997, 47).
3
For a discussion of mobilization for war in the United States and the emergence of opposition,
see Stein (1980). For a discussion of the impact of war on racial relations in American history,
see Klinkner and Smith (1999).
4
For a statistical analysis of these arguments see Rousseau and Newsome (2004).
5
It should be noted that Kant's explanation combines both normative and structural elements.
6
The dyadic democratic peace argument has received empirical support in Bennett & Stam
(2004), Dixon & Senese (2002), Doyle (1986), Huth & Allee (2002), Maoz & Russett (1993),
Rasler & Thompson (2001), Rousseau et al. (1996), and Russett & Oneal (2001). Authors
finding support for the monadic democratic peace argument include Bennett & Stam (2000b,
2004), Bremer (1992), Huth & Allee (2002), Ireland & Gardner (2001), Morgan & Schwebach
(1992), Oneal & Russett (1997), Rousseau (2005), Russett & Oneal (2001), and Schultz (1999,
2001).
7
The shared identity argument has not been tested extensively. See Risse-Kappen (1995) for
qualitative analysis.
8
Critics have attacked a number of aspects of the democratic peace. Gowa (1999) argues that a
shared alliance among democracies during the Cold War explains the peace among democracies.
Spiro (1994) claims the peace is a function of the rarity of war and proximate democracies. For a
critique of these arguments, see Rousseau (2005). In this paper, we focus on the reciprocal
causality critique.
9
Also see the James et al. (2000) reply to Oneal and Russett (2000).
10
This operationalization addresses, in part, Cohen's (1995) critique of the empirical tests of the
democratic peace that failed to incorporate the use of surrogate forces against one's adversaries.
11
Ramirez et al. (1997) argue for both demonstration effects and external assistance effects in
their study of the diffusion of women’s suffrage. For example, in our study external actors can
assist the adoption war-like strategies by sending military advisors or the development of
democratic institutions by mobilizing interests through non-governmental organizations.
12
The GATT variable is monadic because we are simply coding whether or not a state joined the
organization. A dyadic version of the variable would code whether both parties were members of
the organization. A systemic version would code the proportion of states in the world that are
members of GATT in a given year. The causal mechanisms obviously differ at each level.
13
We estimate the simultaneous equations of current year’s Level of Democracy causing current
year’s Aggressive Use of Force and of current year’s Aggressive Use of Force causing current
year’s Level of Democracy. In other words, we do not use a lagged Level of Democracy variable
for this Equation (1). The peace years variable and cubic splines are included in Equation (1) to
control for temporal dependence (Beck, Katz, & Tucker, 1998). The results remain robust even
when these variables are removed from the model. The lagged level of democracy variable and
the years variable are included in Equation (2) to control for temporal dependence. Once again,
the results remain robust even when these variables are removed from the model.
14
Not only do the two equations to be estimated in this article satisfy the rank condition (the
sufficient condition for identification of a simultaneous equation model), but also the order
condition based on the exclusion restrictions reveals that our two equations are overidentified,
which make it unable to recover unique structural parameters using indirect estimation method.
24
15
For a list of the cases in the data set, see Rousseau (2005).
In order to illustrate the directed dyad structure, consider one of the 223 disputes in the data set:
the Tunisian/French dispute from 1960 until 1963. The time series data includes four years for
each state. This allows the dependent variable (described below) to be coded on a state-by-state
basis. For example, Tunisia used aggressive force three times (1960-62) and France used
aggressive force only once (1961). A non-directed dyadic analysis would not capture this detail
and would provide an inferior test of the theoretical arguments. Finally, the on-going crisis years
were included in the analysis because state officials made a conscious decision to use aggressive
force to resolve the dispute during each year. Deleting on-going crisis years (e.g., 1961-1962 in
the Tunisia-France case) would undermine our ability to explore the reciprocal relationship
between democracy and conflict.
17
War is typically defined as a conflict resulting in more than 1,000 battle deaths among all
participants (see Small & Singer (1982) for further details).
18
We employ the CDSIMEQ procedure in STATA to estimate the simultaneous equations
(http://www.stata-journal.com/software/sj3-2/st0038/). The procedure implements all the
necessary procedures for obtaining consistent estimates for the coefficients, as well as their
corrected standard errors (Keshk 2003; also see Keshk, Pollins, & Reuveny, 2004, for the
application of the procedure for the trade-conflict relationship).
16
25