Autocratic Institutions and Civil Conflict Contagion∗
Jessica Maves
The Pennsylvania State University
Department of Political Science
[email protected]
Alex Braithwaite
University College London
Department of Political Science
[email protected]
February 28, 2012
Abstract
The events of the Arab Spring demonstrate the potential for civil conflict to spread
across borders. This paper offers an account of conflict contagion of this kind. Specifically, we argue that in peaceful neighborhoods those autocracies that provide an opportunity for legal participation in the politics of the state—by way of elected legislatures—
are able to offset violent demands for change from domestic opposition groups. We add,
however, an expectation that when states in the neighborhood experience civil conflict,
such marginal openings in the political institutions of the state may prove insufficient
to appease renewed opposition demands inspired by violent examples set overseas; thus
conflict becomes more likely because of the influence of neighboring conflict. We test
this claim via a multivariate probit analysis in which conflict onset is a function of
institutional design at the state level and conflict within the neighborhood. These
analyses offer support for our test hypotheses.
∗
We would like to thank Nils Metternich, Jakana Thomas, Julian Wucherpfennig, participants at the
poster session of the 2011 Peace Science Society (International) Annual Meetings in Los Angeles, and participants at the UCL SPP research brownbag for their helpful comments and suggestions on an earlier draft
of this paper.
1
Introduction
A considerable literature details the dramatic shift from inter state to intrastate violence in
the post-Cold War era (Gleditsch et al. 2002). Within this, an emerging literature addresses
the potential for the contagion of civil conflict between countries—as highlighted by the
dramatic events of the Arab Spring, during which conflict broke out in numerous states and
quickly spread across international borders. At their core, these events were driven by a desire
to undermine autocratic regimes and catalyze regime change. In each instance subsequent
to the outbreak of large-scale protests in Tunisia in December 2010, this desire appears to
have been enhanced by the precedent set elsewhere in the neighborhood. Motivated by the
apparently contagious processes observed in the Arab Spring, we ask simply: do the very
institutions that are installed by autocrats to offset violent challenges to the state—namely,
elected legislatures—actually have the perverse effect of facilitating those challenges when
they are inspired by conflicts that have recently broken out in neighboring states?1
Beyond the transnational channels or mechanisms through which conflict spreads, we
argue that the domestic political context of a state has an important bearing upon contagion.
The joint influence of neighborhood conflict and openings in domestic autocratic regimes
upon conflict contagion was evident in the mid-to-late 1990s in the Great Lakes region of
Central Africa. Long running civil violence in Burundi and genocide in Rwanda triggered
wide-spread violence in eastern Zaire in 1996 targeted against the Mobutu regime. The gross
instability of these deadly conflicts—which resulted in large numbers of arms, rebels, and
refugees flowing freely across borders—threatened to escalate and spread further afield. Yet,
not all neighboring countries subsequently experienced the onset of new and related conflicts.
Far from being a lottery—with all neighboring countries equally likely to become embroiled
in violence as a consequence of events in Zaire and its eastern neighbors—we argue that the
characteristics of local political institutions acted as one of the most important determinants
1
We consider in the category of “autocracies” all those states that do not meet the common criteria to
be considered fully democratic, which include having a competitively- and popularly-elected legislature and
executive, as well as a peaceful alternation of power between parties.
1
of the subsequent contagion of civil violence.
In the first few years after civil war broke out in Zaire, ultimately resulting in the ousting of Mobutu and the establishment of the Democratic Republic of Congo (DRC), violent
conflict occurred in nearby Chad, Sudan, and Uganda—each of which had newly established
elected legislatures within a context of otherwise autocratic regimes. By contrast, neighboring states with similar levels of poverty and ethnic diversity yet more stable institutions—
such as the Central African Republic, Kenya, and Tanzania—did not experience any new
outbreaks of conflict.
We contend that the apparently contagious processes witnessed in the late 1990s in
Central Africa, as well as those of the Arab Spring, suggest that whether or not conflict
occurs domestically is clearly conditioned by neighborhood events (c.f., Lake and Rothchild
1998, Buhaug and Gleditsch 2008, Gleditsch 2007). The majority of extant accounts explain contagion as a consequence of the spill-over of conflict externalities and/or patterns of
emulation between kin groups across borders. We suggest that these accounts are incomplete insofar as they neglect details of the domestic political context. In response, we argue
that cross-national demonstration effects are most notable in states in which governments
have attempted to appease and accommodate oppositions but in which those governments
have no room remaining to satisfy increased opposition demands. We suggest that increased
opposition demands are, themselves, more likely when potential rebels are inspired by the
employment of violent strategies by challengers to governments in neighboring states. Thus,
we offer an account of civil conflict contagion in which domestic institutions and conflict in
the neighborhood combine to play a central determining role.
The paper proceeds as follows. First, we detail the dominant themes of the extant
literature in order to demonstrate that whilst geographic proximity and neighborhood effects
are well detailed, institutional characteristics of regimes play a surprisingly minor role in
explanations of conflict contagion. Second, we begin to detail our logic by establishing the
baseline expectation that popularly-elected legislatures are institutions that can be installed
2
by autocrats to forestall violent challenges to their authority. Third, we explain why this
initial logic does not hold in cases in which neighborhoods experience civil conflict outbreaks.
Fourth, we identify and specify an appropriate research design to test the implications of
this bipartite theoretical logic. Fifth, we discuss the results of multivariate probit analyses
and a variety of checks employed to confirm their robustness. Finally, we offer discussion of
the implications of the study and possible avenues for future research on the topic of conflict
contagion.
2
Civil Conflict Contagion
Contagion is a process in which the likelihood of an event occurring in the state is directly
influenced by the earlier occurrence of similar events outside of the state. Such processes are
most commonly hypothesized and depicted in cases of the diffusion of policy adoptions between states, with successful changes being mimicked by actors overseas (see, e.g., O’Loughlin
et al 1998, Elkins and Simmons 2005, Shipan and Volden 2008, Beissinger 2007). In most
instances, it is suggested that emulation is encouraged by examples that increase either the
incentives of or information of value to possible adopters (Kuran 1998, Elkins and Simmons
2005, Danneman and Ritter 2012). In this section we review extant studies that identify
geographic proximity and access to information and information flows as possible channels
through which incentives and information pass, facilitating conflict contagion. Both of these
component parts of the contagion literature places a distinct focus upon the attributes of
sub-state actors, the activities of actors external to the state, and the contexts within which
transnational transactions occur. Accordingly, this review is offered as a means of prompting further examination of the role of state structure and domestic political institutions in
facilitating conflict contagion.
3
2.1
Geographic Proximity
Perhaps the most fundamental premise of the relevant literature on civil conflict is that
contagion is most likely between pairs of states that are geographically proximate (Lake
and Rothchild 1998, Buhaug and Gleditsch 2008, Braithwaite 2010). These mainstream
geographic accounts rest upon the observation that states proximate to ongoing conflicts
have both exposure to externalities and a great opportunity for emulation. Many negative
externalities of conflict (including conflict-affected refugees and weapons) flow abundantly
from conflict zones to neighboring states (e.g., Salehyan and Gleditsch 2006). Moreover,
the underlying correlates of civil conflict (including political regimes, poverty, and valuable
natural resources) cluster geographically (e.g., Buhaug and Gleditsch 2008).
Despite the simplicity of their core assumptions, geographic proximity arguments vary
significantly in regards to the geographic scale across which they claim contagion processes
operate. A number of studies have identified a significant impact of contagion across considerable geographic distances. Successful protests in the US in the 1960s and 1970s were
mimicked by similar movements in other western states (Hill, Rothchild, and Cameron 1998,
McAdam and Rucht 1993). Similarly, terrorist groups in Western Europe in the 1970s and
1980s are shown to have emulated the approaches employed by movements in Latin America
up to a decade earlier (Midlarsky, Crenshaw, and Yoshida 1980). Shifting to a national and
sub-national level, recent studies suggest that it is crucial to pay attention to the direct geography of violence; that it is important to identify the precise locations of conflict in order
to see whether contagion affects those states that are actually proximate to violence. Both
Buhaug and Gleditsch (2008) and Braithwaite (2010) demonstrate, for instance, that the
strongest contagion effects are observed in cases in which states’ borders abut conflict zones
in neighboring states. These studies note respectively, however, that this strong contagion
effect is enhanced by the existence of cross-border ethnic kin groups and diminished by state
capacity.
4
2.2
Information Access and Exchange
Claims about exposure to externalities as a cause of contagion relate most keenly to cases
within smaller geographic range. By contrast, emulation-based arguments resting upon flows
of information as the source of contagion are not similarly restricted and can potentially
extend across more significant geographic scales. Hill and Rothchild (1986) explored the
influence of access to media in the contagion of collective dissent. They demonstrated that
contagion appeared to influence those countries i in which media is available and another
country j typically (though not exclusively) in the same region had recently experienced similar collective action. In other words, they demonstrate that proximity influences contagion
when receiver countries are sufficiently open to information and have a domestic demographic
profile that suggests a willingness to rebel. This intuitive conclusion—that potential rebels
must be willing and able to witness rebellion elsewhere—informs much of the subsequent
discussion of demonstration effects in the literature on conflict contagion. The declining
costs of cross-border communication (flow of information and transportation of goods and
peoples) are thus identified as facilitating an increasing tendency towards contagion of ethnic
conflict (Kuran 1998).
Beiser (2011) offers an intuitive assessment of the plausibly interactive relationship between shared identity and access to and flows of information between countries i and j. She
argues that, above and beyond the influence of geographic proximity, we ought to anticipate contagion within pairs of states that have discriminated ethnic groups or are ethnically
polarized and that the effect should be enhanced in cases in which information is able to
flow in and out of the countries in question (via access to open media, for instance). Via
an innovative research design—employing combinations of non-geographic and geographic
spatial weights matrices as covariates in country-year level models of conflict onset—Beiser
demonstrates significant support for the claim that countries with a small number of discriminated ethnic groups are more likely to experience civil conflict onset in years after which
similarly structured (in terms of having discriminated ethnic groups) countries experience
5
civil conflict. This effect is enhanced where citizens have access to increasingly free media
(measured via per capita counts of radios, TVs, and internet usage).
In determining which non-geographic channels are most likely to facilitate contagion
effects based upon emulation, Beiser’s work follows a long tradition of studies that claim that
ethnic ties are central. Most of these claims center upon the notion that even if information
about conflict is flowing, there needs to be a motivation to receive it. Gurr’s (1993) study of
minority mobilization employed data on 227 communal groups across 90 countries—what has
subsequently formed the basis of the Minorities at Risk database—in an effort to forecast
episodes of protest and rebellion. He shows that economic indicators of deprivation aid
leaders’ attempts to mobilize, that prior mobilizations strongly predict future mobilizations,
and that democracies typically host more protest than they do rebellion with the opposite
true of non-democracies. Above and beyond these central findings, his empirical investigation
also suggests that tactics of conflict—including both protest and rebellion—diffuse via shared
ethnic ties; that is to say that he observed contagion of violent strategies between ethnic kin
groups in different countries but within the same neighborhood. Comprehensive critiques
of Gurr’s analyses notwithstanding (see, e.g., Lindstrom and Moore 1995), the contagion
finding is both consistent and robust.
Lake and Rothchild’s (1998) impressive edited volume points towards three broad possible mechanisms or channels through which ethnic violence may spread across international
boundaries, each of which points toward the idea that potential rebels learn from their observation of conflict in neighboring studies; a premise that will subsequently form an important
part of our own logic. They begin by noting that ethnic conflict overseas may alter the local
balance of power between competing ethnic groups. It could be, for instance, that the flow of
refugees from neighboring conflicts directly alters the demographic and ethnic balance within
the home country (see, e.g., Newland 1993, Keller 1998, Buhaug and Gleditsch 2008). Second, ethnic groups may mobilize as a result of a demonstration effect, taking up arms when
they observe this action reaping rewards elsewhere (see, e.g., Kuran 1998). In particular,
6
events unfolding overseas might alter the local ethnic group’s perception of the efficacy of
safeguards contained in extant agreements with the government (Lake and Rothchild 1998:
26). Third, violence overseas may encourage local ethnic rebels to adjust their calculus of
the costs of rebellion and their probability of success. Hill, Rothchild, and Cameron (1998)
suggest, for instance, that this kind of logic underpinned the spread of violence between
nascent states in the Former Yugoslavia, triggered by Slovenia’s relatively straightforward
break away.
Recent innovations have resulted in a proliferation in hypothesized mechanisms and measurements of ethnic similarity. Ethnic ties between countries have been shown to result in
contagion most notably when ethnic polarization is also observed (Forsberg 2008). Elsewhere, it is claimed that ethno-linguistic similarity between members of populations across
borders is what matters to the process of contagion (De Groot 2011). For others still it is
specifically religious ties between states that determine whether or not violent movements
and activities will prove contagious (Fox 2004). For Buhaug and Gleditsch (2008) whether
or not a state is likely to experience a new onset depends upon whether or not they have
ethnic linkages to a population in a neighboring conflict area. For Cederman, Girardin, and
Gleditsch (2009), it is especially important that the links be to neighboring ethnic kin that
are especially populous.
2.3
Regime Types
It is our assessment that surprisingly little attention has been played to the role of domestic
political institutions and regime types in explaining processes of conflict contagion. We
suggest that this is surprising when you note that regime types vary in terms of openness
and when you consider that more open regimes are, presumably, both more vulnerable to
spillover and more welcoming of information from overseas. Nonetheless, it remains the case
that relatively little is known about the domestic political context within which contagion
processes operate.
7
As noted earlier, Gurr’s (1993) study concluded that (nonviolent) protest and (violent)
rebellion occurred at differential rates in states with different institutional structures, with
democracies experiencing the less violent variant. Gurr claimed that this finding supported
the more general theoretical argument that, ‘in long-established democracies the utility of
nonviolent communal activism is high, whereas the process of democratization provides opportunities that spur the mobilization of communal groups for both protest and rebellion’
(1993: 189). As Hill, Rothchild, and Cameron (1998) note, however, political opportunities for protest are only slowly dynamic and are shrouded in uncertainty. Accordingly,
under the same local context, any two individuals may reach divergent conclusions regarding the prospects for mobilization against the state. Accordingly, they argue that political
opportunity structures conducive to protest require the addition of a crucial catalyst for
mobilization—namely, the demonstration of utility elsewhere.
The omission of assessment of political institutions in the contagion of conflict is surprising, moreover, given that there is a well-established, and growing, literature on the diffusion
of democracy, and how the spread of democracy affects the likelihood of conflict. Huntington (1991) hypothesized a “demonstration effect” as an implicit part of his Third Wave of
democracy, and Starr (1991) built substantially on this idea by arguing that geographic proximity is key to enabling the dissemination of knowledge regarding the value of the innovation
that is political democracy. O’Loughlin et al. (1998) made an important empirical contribution to this literature through employing a variety of global and local indicators of spatial
association to show that prior episodes of democratization have a significant bearing upon
the location of future transitions. More recently, Brinks and Coppedge (2006) demonstrate
that states often change their regime to match the average level of (non-)democracy with
their contiguous neighbors; Gleditsch and Ward (2006) concur that states are more likely to
democratize if their neighbors are democratic. They also demonstrate that democratization
often results from a change in the relative power of domestic actors and their evaluation of
political institutions, and that this changed position and perception is primarily influenced
8
by forces that are external, rather than internal, to the state. Finally, agent-based modeling
has been used to yield some fascinating results with regard to revolution and conflict. Elkink
(2011) uses a mass-based modeling approach to demonstrate that the diffusion of attitudes
and revolutions leads to spatial clusters of democracies. Cederman and Gleditsch (2004)
show that “zones of peace” are possible in hostile, often non-democratic, international environments, through a situation of collective security facilitated by the existence of just a few
fellow democracies in the neighborhood.
3
Autocratic Regimes
We follow Lake and Rothchild (1998) in assuming that civil violence is most likely to spread
to those states that have a sufficient underlying likelihood of experiencing conflict and least
likely to spread to those states that have discovered domestic solutions to discontent. Herein,
we suggest that such solutions are most likely to have been designed and implemented in
states with democratic institutions of government. Accordingly, our theoretical perspective
focuses upon identifying those institutional aspects of autocratic regimes that may increase
the underlying threat of civil violence and, thus, make the state a potential candidate for
conflict contagion. It is worth noting, however, that we also focus upon specifying those
factors of institutional design that might successfully reduce the likelihood of conflict noting,
as do Lake and Rothchild, that institutions may either facilitate or hinder the spread of
conflict across borders.
3.1
Why Legislatures?
Legislatures, namely those that are popularly elected and that represent a variety of social
preferences (often by way of multiple political parties), serve to forestall the onset of armed
conflict in most democracies. Because democratic legislatures are such that opponents are
largely able to not only address contentious issues in a diplomatic forum, but also enact
9
policy changes as a result of these discussions, these institutions serve as an important
domestic solution to popular discontent—even though debates among politicians and their
constituents can be quite rancorous, rarely do they ever escalate to the level of violent revolt.
Although legislatures in autocracies are often maintained for a similar purpose of dealing
with domestic discontent, they are not always as effective in preventing civil conflict as
their democratic counterparts appear to be. Gandhi and Przeworski (2006) argue that
dictators use legislatures to encourage cooperation and prevent rebellion when threatened
by an organized opposition. This type of venue allows a dictator to entertain demands by
political groups, often only those approved by the dictator for participation in the system,
rather than relegating the voicing of those demands to the streets in very public displays of
opposition to the regime. Gandhi and Przeworski (2007) then demonstrate that dictatorships
with a legislature survive longer than those regimes that lack this type of institution. Related
to the notion of dictatorial survival, the probability of intrastate conflict in these regimes
generally appears to be diminished in the presence of a legislature. Maves (2012) finds
that non-democracies with a popularly-elected legislature (even those which include multiple
political parties) are less likely to experience civil war onset than those states without these
institutions, which follows nicely the finding by Fjelde (2010) that the authoritarian regime
type most likely to have a legislature, single-party civilian dictatorships, is least likely to
experience intrastate conflict. By affording the opposition an opportunity to express its
discontent in the controlled environment of a legislature, the dictator is often able to coopt,
and even appease, the opposition such that engaging in civil conflict is no longer in its best
interest (Gandhi 2008, Lust-Okar 2005)
This logic leads to our first hypothesis, which tests the conventional wisdom that nondemocratic legislatures facilitate the peaceful resolution of demands by the opposition:
Hypothesis 1 Autocracies with an elected legislature are less likely to experience the onset
of civil conflict than are autocracies that lack an elected legislature.
10
3.2
When Things Go Awry
While recent work finds that legislatures can be important tools for dictators to facilitate
their own job security, these institutions—particularly those legislatures and political systems
in which multiple political groups are allowed to organize and participate—may also serve
to cultivate unintentionally a dangerous population of latent violent groups in the form of
political parties and movements. Importantly, the majority of autocracies tolerate the existence of multiple political parties: according to a widely-used dataset on political institutions
in dictatorships and democracies, multiple parties exist in autocracies with a legislature in
over 60 percent of country-years (Cheibub, Gandhi, and Vreeland 2010). While these parties
are (almost) always organized with the express purpose of nonviolent participation in the
country’s political institutions, in order to be able to do this successfully they have had to
overcome collective action problems (see, e.g., Aldrich (1994) for a demonstration of this
with regard to political parties in the United States).
Such collective action problems are commonly viewed in the civil wars literature as one
of the biggest impediments to the formation of viable rebel groups (c.f., Lichbach 1995,
Weinstein 2005). With regard to other forms of violence, Aksoy, Carter, and Wright (2012)
demonstrate that, in autocratic regimes, terrorist organizations are more likely to emerge
and engage in terrorism when political parties are allowed but no elected legislature exists
to channel the opposition’s mobilized energies into support for the regime via cooptation.
As a result, we can expect that political parties have the potential to be a violent threat to
regimes in which they are not able to be appeased (or coopted) through existing institutional
channels.
While the complete lack of political institutions through which opposition forces can work
to exact change may compel these groups to engage in violence, this is not the only condition
under which we might expect to observe armed challenges against the regime. Gandhi and
Przeworski (2006) contend that the existence of a legislature in an autocracy indicates that
opposition forces in the state are well-organized and cannot be bought off with rents. Thus,
11
a considerable concession has already been made by the regime to allow this institution
to exist, and if the opposition continues to gain in strength relative to the government, it
is difficult to imagine what more a dictator could provide to appease his challengers while
still maintaining office. If stepping down is not an option (which it is often not for most
autocrats), a regime that has already established a legislature has little left with which to
bargain and buy off the emboldened opposition, making conflict more likely.
A common theme in literature on civil war is the notion of relative strength: namely,
that intrastate conflict becomes more likely as rebels increase in their capabilities relative to
the government. This could involve an opposition group obtaining more material or human
resources, or the regime being weakened due to economic crisis, internal squabbles among
ruling elite, and so on. Another factor that is likely to affect both sides of this equation—
would-be rebel groups as well as the regime—is conflict in the neighborhood.
3.3
How Institutions Affect Contagion
Like the changed perceptions of ethnic groups as discussed by Lake and Rothchild (1998)
and Kuran (1998), political oppositions in general that are organized to contest legislative
elections or simply to engage in dialogue with the regime may adjust their calculations of
the costs and benefits of participation in existing institutions once they observe groups in
other countries challenging their governments for additional authority. This increases the
local opposition’s resolve.
Observing challenges elsewhere may compel moderates and undecided segments of the
population to support the opposition in a struggle against the government (see, e.g., Tarrow
(1998) for more on the development and expansion of social movements). An alreadyorganized opposition group like a political party should be particularly effective in attracting
these newly motivated individuals unhappy with the regime, as the vast majority of parties
arise from efforts to coalesce popular support around a set of ideals fashioned into a political
platform for participation in a legislature. This increase in the number of supporters provides
12
the opposition with heightened human capital in proportion to the regime, thereby increasing
the opposition’s relative strength. Since, by forming a legislature, the regime has already
signaled its inability to provide much more in the way of concessions, the opposition may be
particularly inclined and able to pursue radical methods to have its demands met—violence,
in particular.
Simultaneously, the regime is likely to be weakened by spill-over from conflict in the
neighborhood. This can take the form of impaired economic activity, increased flows of arms
and refugees, and so forth (Buhaug and Gleditsch 2008). Several studies find that civil war is
more likely to break out in weak or temporarily weakened states than in strong ones (Hegre
et al. 2001, Fearon and Laitin 2003, and Hegre and Sambanis 2006) and that contagion of
civil conflict is a greater threat the less capable the state (Braithwaite 2010). Furthermore,
this weakening of the regime only serves to facilitate an additional increase in the opposition
group’s strength relative to the government.
In order to improve its prospects for the future, the opposition is motivated to demand
a better deal from the regime. However, because the regime is likely to be only temporarily
weakened by a neighboring civil war, the opposition may anticipate that the regime will
renege on any promises made at this time once it regains its strength (following the termination of the neighboring civil war). The continued existence, or the introduction, of
an autocratic legislature is likely to be insufficient in appeasing an emboldened opposition,
because that opposition does not trust the regime to maintain the institution or to respect
any policy concessions granted in a time of desperation. As a result, the opposition will use
its improved position of strength relative to the regime to seek more dramatic changes by
way of armed conflict (Acemoglu and Robinson 2006, Boix 2008). Those opposition groups
with a history of participation in a legislature should be already organized and particularly
well-placed and well-prepared to rally support behind their cause and their calls for more
dramatic change.
This logic leads to our second hypothesis, which adds an important caveat to the con-
13
ventional wisdom that legislatures are effective in deterring the onset of civil war. This is a
reasonable, but we expect only partial, truth. Instead, we propose the following:
Hypothesis 2 Autocracies with an elected legislature are more likely to experience the onset
of civil conflict if conflict breaks out elsewhere in the neighborhood.
4
Data
Our test hypotheses require comparison of rates of conflict onset and contagion across autocratic regimes with different types of institutional design. Accordingly, we limit our analyses
to data on autocracies. While a variety of datasets exist to assess dimensions of and differences among autocratic regimes, we use the data collected by Cheibub, Gandhi, and
Vreeland (2010; henceforth CGV) due to its wealth of information about the institutions in
these regimes. As a result, we, like these authors, code autocracies as those regimes in which
at least one of the following conditions is not met: both the legislature and the executive
are elected, multiple political parties outside the influence of the regime exist and compete
in these elections, and executive power has been handed over peacefully between parties as
the result of elections.2 140 countries are represented in this population of autocracies from
1960-2001, with the country-year as our unit of observation.
Our dependent variable indicates whether the autocracy experiences the onset of intrastate conflict in a given year. It is coded 1 for country-years in which a civil war broke
out, and 0 otherwise. The data come from the Uppsala/PRIO Armed Conflict Dataset
(henceforth ACD; Gleditsch et al. 2002, Harbom and Wallensteen 2009), which defines an
intrastate armed conflict as fighting between the government of a state and one or more
internal opposition group(s) resulting in at least 25 deaths per year. We include both internal armed conflicts and internationalized internal armed conflicts, where the latter involves
2
Because of this last rule, countries like Botswana and South Africa, as well as Japan and Mexico for
most of the period in this study, are considered to be non-democratic. Dropping the few countries which do
not experience executive turnover does not affect our results.
14
intervention from other states on one or both sides of the war. Additionally, if a country
experiences onsets of multiple civil wars in a given year, additional observations of that
country-year are created.
One variable from the CGV dataset is of particular interest to us: CLOSED, a threecategory coding of the nature of the country’s legislature. This variable is coded 0 if the
legislature is closed, 1 if the legislature is appointed, and 2 if the legislature is elected. From
this, we generate a dummy variable to indicate whether the autocracy had a legislature on 1
January of the observed year. We try alternate specifications of this, one where we code as a
1 only those states with elected legislatures and all else 0, as well as another where we code
as 1 both autocracies with elected as well as appointed legislatures. Our results are robust
to both codings, and we report the results from the variable for elected legislatures only, as
the process of elections suggests that the dictator has made an additional concession to the
opposition by foregoing his ability to hand-pick members of the institution, and as a result
he may not have much else to give in order to appease the opposition — short of completely
democratizing. This variable, Legislature, serves as our primary independent variable for
tests of Hypothesis 1, and it is one of the constituent terms in our tests of Hypothesis 2
which require an interaction term.
The other constituent term in the interaction is a variable created by Buhaug and Gleditsch (2008). This variable captures the weighted distance between state i and civil wars in
other countries j, by normalizing the inverse of the distance between states i and j by the
sum of the inverse distances between i and all other states j. Coding of civil war onsets
is again based on observations from the ACD. Higher values could mean that most states
in the international system experienced conflict, or—more likely—that a country or set of
countries in close proximity to i were host to a civil war in the previous year. Further detail
on the construction of this variable is available in the original study by Buhaug and Gleditsch. It covers country-years from 1960-2001, and this serves to constrain the time period
of our study accordingly. As was mentioned earlier, the effect of civil conflict at a state’s
15
immediate borders may be greater than if a civil war takes place further afield, so to control
for this possibility we also include a dichotomous variable coded 1 if country i shares a border with another country j which experienced a civil war in the previous year, and 0 if all
countries bordering country i were at peace. Both of these variables are constructed to take
into account what happened in the year prior to the country-year observation of interest,
in an effort to ensure that we are capturing the effect of civil conflict spreading from other
countries j to the country of interest i, not the other way around.
Hypothesis 2, the expectation that autocracies with legislatures are more likely to experience the onset of civil war when a nearby country is embroiled in conflict, necessitates the
inclusion of an interaction term. This variable is the product of our dichotomous variable
Legislature and of the weighted distance to conflict in neighboring states. We also create
an interaction using the binary variable of conflict in a neighboring country in place of the
weighted distance measure.
We include in all model specifications a standard set of control variables commonly used in
studies of civil war onset and conflict contagion. First, we include the natural log of GDP per
capita, lagged one year, using data from Gleditsch (2002). Economic development typically
has a robust negative relationship with the probability of civil conflict onset. Second, we use
the natural log of the population of a country, which is expected to be positively correlated
with the likelihood of civil war onset in keeping with past findings.
Third, we take the percentage of countries within 3000 kilometers that are coded as
democracies in the CGV dataset to account for the level of democracy in the neighborhood,
lagged one year. We use data from the CGV dataset rather than the oft-employed Polity
data because the latter has been shown by Vreeland (2008) to be endogenous to the process
of civil war onset, and thus it cannot produce reliably unbiased results. We expect that more
democratic regions should be less likely to experience civil war onset.
Fourth, we use another variable from Buhaug and Gleditsch (2008) to indicate whether
ethnic ties exist between the populations of the autocracy of interest and a neighboring
16
conflict zone. Buhaug and Gleditsch find that conflict is more likely to break out when
a country hosts an ethnic group that also resides in a warring territory in a nearby state.
Fifth, we include a variable that accounts for the number of years since the autocracy last
experienced a civil war. Last, we construct a binary variable to indicate the end of the Cold
War period, as this has been shown to increase not only the likelihood of states having a
legislature but also the likelihood of civil conflict contagion.3
5
Results
The results of our multivariate analyses are detailed in Table 1. Model 1 omits the interaction
term in order to demonstrate the effect of the constituent terms independent of one another.
Models 2 and 3 present results from the full specification of our model including, respectively,
the weighted distance and binary measures of neighboring conflict. The final model omits the
two control variables capturing neighborhood democracy and ethnicity—this is a ‘domestic’
specification of the model.
Table 1 about here
Model 1, a probit with the full set of control variables but without the interaction term
of an autocratic legislature and neighboring civil conflict, does not indicate support for the
first hypothesis. It appears as if the effect of legislatures on the probability of civil war onset
is not distinguishable from zero, although the coefficient sign is in the expected negative
direction. However, each of the subsequent models suggest that this null finding may be the
result of conflating two conditions under which autocratic legislatures might operate.
Model 2 builds upon Model 1 by adding the interaction term previously discussed. Here,
we observe support for both Hypotheses 1 and 2. The coefficient for the legislature variable
is negative and statistically significant, meaning that autocracies that have this type of
3
We had also hoped to include a variable accounting for the level of information flows and press freedoms
in an autocracy, but the systematically poor coverage and quality of available data for our sample means that
our results would be considerably obscured by including such a variable. Ideally, promising new research in
this field will yield more reliable data and the effects of information flows on civil conflict diffusion can be
better addressed in the future.
17
institution are less likely to experience the onset of civil war than those autocracies that lack
a legislature—but specifically only in the absence of conflict in the neighborhood. When,
however, civil conflict occurs in neighboring countries j and, in particular, in instances in
which that conflict occurs more closely to country i, having a legislature makes country i
more likely to experience the onset of a civil war. Moreover, this finding does not apply only
to cases in which the neighboring conflict variable could hypothetically take into account
conflicts that may be geographically distant from country i; Model 3 uses a dichotomous
indicator of whether any country j which shares a border with country i was host to a
civil war in the previous year, and the results for the effect of autocratic legislatures on the
probability of civil war onset remain the same (although the coefficient is somewhat smaller
in magnitude).
In order to aid inspection of the marginal effect of having a legislature upon the likelihood
of conflict in i across the full range of the neighborhood conflict variable, we have graphed
the effect of our interaction term in Figure 1. The coefficient estimate is represented by the
solid line and its concomitant 95% confidence intervals are shown as dotted lines.
While the confidence intervals include zero for a good portion of the values of conflict
proximity, Figure 1 still robustly supports our expectations. When civil war in the international system is non-existent or occurs a great distance from country i, a legislature will
have a negative effect on the probability of conflict outbreak—this is true for the range of
the variable on the x-axis between 0 and 0.1. However, when country i is located in a region
experiencing a great deal of conflict, or if a neighboring state j has experienced a civil conflict (meaning that the proximity of conflict variable is high and approaching 1), then the
presence of a legislature makes i more vulnerable to the onset of civil war within its own
borders—this appears to be the case for the range of the variable between .65 and 1, which
contains in excess of 10% of all observations on this variable.
Returning to the results detailed in Table 1, it is interesting to note that we also find
that the neighboring civil conflict constituent term is statistically significant and negative
18
Figure 1: Marginal Effect of Legislatures As Proximity to Conflict Changes
in Model 2, and negative (though not statistically significant) in Model 3. How is this
intriguing result best explained? We remind the reader that this study focuses specifically
on autocracies, and suggest that this negative coefficient may result from the presence of
hardline dictators who look to quash rebels preemptively. This coefficient represents the
effect of neighboring conflict on autocracies that do not have legislatures, as otherwise these
observations would be included in the interaction term. Thus, these regimes are likely capable
of dealing with their domestic opposition in ways that do not require cooperation from the
opposition — by engaging in repression or buying cooperation with rents, for example. As a
result, these dictators presumably are less likely to experience the onset of civil war because
they have been so successful at preventing a domestic threat that one simply does not exist
at a meaningful level, or the dictator immediately resorts to repressive tactics upon observing
19
conflict in the neighborhood in order to prevent his opposition from challenging the regime.4
In Model 4, we drop the “neighborhood” control variables to ensure that these variables
are not unduly influencing our results or causing some ordinarily consistent predictors of civil
war onset to return statistically insignificant parameter estimates here. We find that the
effects of our main explanatory variables—autocratic legislature, neighboring civil conflict,
and the interaction of these constituent terms—are robust to a more domestically-oriented
model specification.
With regard to our control variables, it is important to note that they consistently behave
in the expected ways across the models. Both GDP per capita and the number of years
since the last civil war, which are widely seen as two of the most reliable predictors of
civil war onset, are quite close to statistical significance at the 95 percent level in all model
specifications and the sign of their coefficients is in the expected negative direction. The
effect of population size is robustly positive and statistically significant, meaning that civil
wars are more likely to occur in larger states than in smaller states. Neither of the regional
variables commonly associated with an increased likelihood of civil conflict diffusion are
anywhere close to having effects that are statistically differentiable from zero, thus we are
not concerned by the fact that the coefficient for shared ethnicity is negative. The variable
indicating whether an observation exists in the post-Cold War period is robustly positive
and statistically significant, meaning that civil war onset is more likely to occur after 1990.
In sum, we find support for both of our hypotheses. In the absence of neighboring civil
war, elected legislatures have the expected pacifying effect in autocracies. However, when
civil conflict occurs in the neighborhood, autocratic states with a legislature are more likely
to experience the onset of civil war than those autocracies without a legislature.
4
See Danneman & Ritter (2012) for a compelling discussion of this possible mechanism.
20
6
Robustness Checks
Related to the last point, we readily acknowledge the concern that dictators, upon observing
violence in neighboring states, may try to appease their own domestic opposition by offering
a major concession in the form of establishing or maintaining a legislature. Thus, there may
be an issue of endogeneity at play as the presence of autocratic institutions may be related
systematically to the occurrence of civil conflict elsewhere in the neighborhood. If this is the
case, then the use of an ordinary maximum likelihood estimator such as a probit model will
produce biased inferences. We would like to use an instrumental variable to account for the
presence of a legislature, but scholars have been hard-pressed to identify an instrument that
is exogenous yet simultaneously sufficiently predictive of the dictator’s decision to maintain
a legislature. Instead, we opt to use a seemingly unrelated bivariate probit model building
on Model 2 from Table 1.5
The results detailed in the first pair of models in Table 2 do not provide support for the
first hypothesis, although this is not too surprising since these models replicate and extend
the version of the model without the interaction term—thus it is likely that they conflate
the cases in which the neighborhood is conflict prone with the cases in which it is peaceful.
When we examine the second pair of models (the replication of Model 2 from Table 1 and the
bivariate probit corrolary) including the interaction term, we find once again that when we
take account of the potentially endogenous role of institutional choice, neighboring conflict
still has the effect of reversing the dampening effect of legislatures.
Table 2 about here
The correlation coefficient ρ does not come close to approaching statistical significance,
however, which suggests that these processes (civil war contagion and the dictator’s decision
to maintain a legislature) are unrelated processes in the presence of conflict in the neighborhood. As a result, we refer back to the findings of Table 1 as the most appropriate test of our
5
See Greene (1997, p. 906-911) and Maddala (1983, p. 122) for more information about the mechanics
of the bivariate probit.
21
hypotheses. We maintain that autocracies with a legislature are less likely to experience the
onset of civil war when there is no conflict in the neighborhood, but once a nearby country
becomes embroiled in conflict, this type of institution leaves a dictatorship vulnerable and
more prone to armed challenges from its own domestic opposition.6
To assess whether the composition of the legislature matters with regard to the likelihood
of civil war onset, we replace the dichotomous variable Legislature with a measure of the size
of the largest party in legislature, from Hadenius and Teorell (2007)7 . We use this variable
to replicate the core model of our study, Model 2 from Table 1. The results are presented in
Table 3.
Table 3 about here
Because the dataset by Hadenius and Teorell begins its coverage in 1972, first we re-run
Model 2 using only those observations that are included in both our dataset and that of
Hadenius and Teorell. Even with the limited sample, the results are much in line with the
previous findings in Table 1. Although Hypothesis 1 is not strongly supported because the
coefficient for Legislature is not statistically significant at conventional levels, it is still in the
expected negative direction. However, we do still find support for Hypothesis 2, in that the
presence of a legislature in country i, in combination with civil conflict in the neighborhood,
increases the likelihood of civil war onset in country i.
With regard to the way in which the nature of representation in autocratic legislatures
affects the propensity for intrastate conflict to occur, the results from Table 3 are much
6
In order to provide an additional tough test of our hypotheses, we also replicate our single equation
models from Table 1 including country fixed effects. The results of this specification (not reported here for
the sake of brevity) provide some additional support for our second hypothesis, that autocracies with an
elected legislature are more likely to experience civil conflict when there is civil war in their neighborhood as
compared to those autocracies that do not have this type of institution. The coefficient for the interaction
term is statistically significant at the 92 percent level, and although this is just below the rigorous standard
for hypothesis rejection employed in this paper, it is impressive given the inclusion of country fixed effects.
We are not too concerned by the finding that the constituent term for the presence of an elected legislature
is not statistically significant because this variable is largely time-invariant, and the effects of variables with
this trait tend to wash out when country fixed effects are taken into account when modeling.
7
According to the codebook, this variable “counts the largest parties’ number of seats divided with
the legislative assemblies’ total number of seats expressed in fractions. In countries with a two-chamber
parliament the lower house is counted” (p.9 of their codebook).
22
in line with the general expectations of our hypotheses. Namely, as the seat share of the
largest party in the legislature increases, the probability of civil war onset decreases as long
as the autocracy is in a peaceful neighborhood. This is in keeping with the expectation of
Hypothesis 1, that autocratic legislatures make civil war onset less likely. However, and in
keeping with Hypothesis 2, the positive sign on the interaction term indicates that as the
seat share of the largest party in legislature increases, civil war becomes more likely when
there is another civil war nearby. When the opposition can participate, but is increasingly
marginalized in political institutions, the impetus to challenge the regime in a violent manner becomes increasingly attractive only when opposition forces in other neighboring states
engage in such behavior. Thus, even when we take into account the more nuanced details
concerning representation in autocratic legislatures, we find that these institutions are only
peace-inducing when there is no civil war in neighboring countries.
7
Conclusion and Future Directions
This paper began with the ambition of examining the role of institutional design within
autocratic states upon the likelihood of civil conflict onset and contagion. Our analyses have
demonstrated robustly that the presence of a legislature reduces the likelihood of conflict
onset within the state but that this effect is reversed and the risk of contagion increased in instances in which neighboring states experience civil conflict. This has quite clear implications
for the stability of the process of democratization in autocratic states.
In looking to build upon this finding it will be especially important to account for the
role of the state and of external actors in securing against the threat of contagion. In doing
so, three recent studies act as exemplars. First, Danneman and Ritter (2012) argue that
governments resort to repression in order to preempt and offset the likelihood of civil conflict
breaking out within the state. The trigger for this preemption, they clearly demonstrate, is
the onset of conflict elsewhere in the state’s neighborhood.
23
Second, Kathman (2010) shows that neighboring third party interventions (i.e., those in
which conflict-free state i intervenes in j ’s conflict) are designed to counter the threat of
spillover and to protect the state against harm. Focused upon a different set of actors but
reaching ultimately a similar conclusion, Beardsley (2011), third, finds that the expected risk
of civil conflict in state i increases by up to 70% when a conflict breaks out in neighboring
state j ; yet, if that neighboring conflict j attracts UN peacekeepers (whether many or few
in number), there is a 0% change in the likelihood of conflict in i. Beardsley persuasively
argues that this is likely because peacekeepers are employed to directly manage the very
artefacts of conflict (arms, refugees, rebels) that typically cross borders and facilitate contagion. It would seem prudent to look to integrate these transnational considerations within
the institutional design framework introduced in the current paper, especially insofar as this
highlights plausible strategies for successfully managing the process of democratization.
24
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30
Table 1: The Effect of Non-Democratic Legislatures and Neighboring Civil Conflict on the
Likelihood of Civil War Onset, 1960—2001
Model 1
Model 2
Model 3
Model 4
(No interaction) (Weighted) (Binary) (Domestic)
Legislature
-0.064
-0.303∗∗
-0.263∗
-0.307∗∗
Neighboring Civil Conflict
(weighted distance)
(0.087)
(0.104)
-0.101
-0.824∗∗
-0.838∗∗
(0.170)
(0.271)
(0.273)
Neighboring Civil Conflict
(dichotomous)
(0.115)
(0.102)
-0.147
(0.128)
Legislature*Neighbor CC
(weighted distance)
1.058∗∗∗
1.070∗∗∗
(0.332)
(0.329)
0.350∗
Legislature*Neighbor CC
(dichotomous)
(0.174)
ln(GDP)t − 1
-0.112
-0.120
-0.115
-0.105
(0.073)
(0.073)
(0.071)
(0.057)
ln(Population)
0.102∗∗
0.106∗∗∗
0.094∗∗
0.108∗∗∗
(0.033)
(0.033)
(0.033)
(0.034)
-0.100
0.032
-0.056
(0.222)
(0.223)
(0.222)
-0.206
-0.140
-0.220
(0.295)
(0.295)
(0.295)
-0.006
-0.006
-0.006
-0.006
(0.004)
(0.004)
(0.004)
(0.004)
0.424∗∗∗
0.407∗∗∗
0.398∗∗∗
0.399∗∗∗
(0.111)
(0.112)
(0.110)
(0.103)
∗
-1.528
∗
(0.736)
(0.740)
(0.717)
(0.497)
-664.484
3,898
-660.049
3,898
-661.543
3,898
-660.213
3,898
% Neighbors Democratic
Neighbors’ Ethnicity
Peace Years
Post-Cold War
Constant
-1.681
Log likelihood
No. of observations
∗∗
-1.539
Notes: Significance levels (two-tailed): ∗: 5% ∗∗: 1% ∗ ∗ ∗: 0.1%.
Coefficients with robust standard errors in parentheses.
31
-1.750∗∗∗
Table 2: Bivariate Probit Analysis as Robustness Check of Model 2 from Table 1
Civil war onset
Legislature
Neighboring Civil Conflict
(weighted distance)
Probit
(No interaction)
Bivariate Probit
(No interaction)
Probit
(Interaction)
Bivariate Probit
(Interaction)
-0.042
(0.086)
-0.219
(0.188)
-0.299∗∗
(0.101)
-0.445∗
(0.182)
-0.072
(0.174)
-0.112
(0.184)
-0.836∗∗
(0.272)
-0.854∗∗
(0.273)
1.136∗∗∗
(0.331)
1.111∗∗∗
(0.342)
Legislature*Neighbor CC
(weighted distance)
ln(GDP)t − 1
-0.116
(0.072)
-0.109
(0.068)
-0.123
(0.072)
-0.117
(0.068)
ln(Population)
0.100∗∗
(0.033)
0.101∗∗
(0.032)
0.105∗∗
(0.034)
0.106∗∗∗
(0.033)
% Neighbors Democratic
0.042
(0.246)
0.040
(0.248)
0.102
(0.247)
0.099
(0.248)
Neighbors’ Ethnicity
-0.175
(0.301)
-0.141
(0.293)
-0.099
(0.299)
-0.070
(0.291)
Peace Years
-0.008
(0.004)
-0.008∗
(0.004)
-0.007
(0.004)
-0.007
(0.004)
Post-Cold War
0.420∗∗∗
(0.113)
0.432∗∗∗
(0.114)
0.404∗∗∗
(0.113)
0.416∗∗∗
(0.115)
Constant
-1.675∗
(0.742)
-1.619∗
(0.753)
-1.552∗
(0.745)
-1.513∗
(0.756)
Legislature
Neighboring Civil Conflict
(weighted distance)
-0.305
(0.274)
-0.308
(0.273)
Military
-1.111∗∗∗
(0.173)
-1.110∗∗∗
(0.173)
Monarchy
-1.207∗∗∗
(0.318)
-1.207∗∗∗
(0.318)
Inherited Parties
0.108
(0.095)
0.108
(0.095)
Past Transitions
-0.345∗∗∗
(0.093)
-0.346∗∗∗
(0.092)
Post-Cold War
0.146
(0.119)
0.146
(0.119)
Constant
1.160∗∗∗
(0.145)
1.161∗∗∗
(0.145)
Log likelihood
-649.805
-2578.129
-644.808
-2573.341
Correlation ρ
0.130 (0.116)
0.111 (0.124)
No. of observations
3,755
3,755
3,755
3,755
Notes: Significance levels (two-tailed): ∗: 5% ∗∗: 1% ∗ ∗ ∗: 0.1%.
Coefficients with robust standard errors in parentheses.
32
Table 3: The Effect of Seat Share in Non-Democratic Legislatures and Neighboring Civil
Conflict on the Likelihood of Civil War Onset, 1972—2001
Elected Legislature Size of Largest Party
(H&T sample)
(H&T sample)
Legislature
-0.202
(0.143)
-0.321#
Legislative Seat Share
(0.177)
Neighboring Civil Conflict
Legislature*Neighbor CC
-0.606∗
-0.399
(0.297)
(0.318)
0.921
∗
(0.386)
0.832#
Legislative SS*Neighbor CC
(0.386)
ln(GDP)t − 1
-0.098
-0.099
(0.072)
(0.070)
ln(Population)
0.098∗
0.104∗∗
(0.039)
(0.040)
-0.205
-0.209
(0.269)
(0.269)
-0.275
-0.261
(0.315)
(0.317)
-0.006
-0.004
(0.004)
(0.004)
0.452∗∗∗
0.449∗∗∗
(0.122)
(0.116)
∗
-1.677∗
(0.785)
(0.759)
-506.821
2,806
-507.222
2,806
% Neighbors Democratic
Neighbors’ Ethnicity
Peace Years
Post-Cold War
Constant
Log likelihood
No. of observations
-1.648
Notes: Significance levels (two-tailed): #: 10% ∗: 5% ∗∗: 1% ∗ ∗ ∗: 0.1%.
Coefficients with robust standard errors in parentheses.
33