Using violence,seeking votes

Abstract

In many cases, including some of the most destructive civil conflicts and some of the newest emerging democracies, militant and ex-militant groups form political parties to participate in elections. Despite the prevalence of such electoral participation, it has rarely been studied, and scholars have not explored its influence on outcomes such as conflict or democratization. A lack of comprehensive data has impeded this research. The dataset introduced in this article provides annual data on militant and ex-militant group participation in legislative elections between 1970 and 2010. The Militant Group Electoral Participation (MGEP) dataset allows for further empirical study of the patterns, causes, and consequences of this behavior. Moreover, in combination with other datasets, MGEP stands to provide additional insights on conflict, peace, democratization, and electoral politics more broadly. In this article, I describe MGEP, provide summary statistics on the data, and show its applications, including through a replication study on post-conflict elections.

Keywords

conflict democratization elections militant group electoral participation peace

Introduction

Armed actors often conduct electoral campaigns. Militant groups sometimes compete at the ballot box while also fighting on the battlefield. In 1981, a prisoner from a Northern Irish militant group, the Provisional Irish Republican Army (PIRA), ran for a seat in the House of Commons. He died just after winning the election, but the PIRA then began a coordinated electoral campaign through its political wing, Sinn Fein, while waging war on the government. The simultaneous armed and electoral campaigns continued for over a decade.

Ex-militant groups also compete electorally against incumbent governments after conflicts end, typically enabled by provisions in peace agreements. In 2006, a Maoist militant group and the government of Nepal signed a settlement centered on provisions for combatants to transition to candidates. Both sides participated in 2008 and 2013 in post-conflict elections.

Even victorious ex-militant groups sometimes establish elections after civil conflicts. The Nicaraguan Sandinistas held elections in 1984, after taking control of the state. New states formed from armed campaigns, such as Namibia, also sometimes hold elections. Some militant and ex-militant groups, however, never participate in wartime or post-conflict elections.

When do militant and ex-militant groups participate in elections? Research on cases (e.g. De Zeeuw, 2007; Irvin, 1999), and a cross-national study of armed actors that target civilians (Weinberg, Pedahzur & Perliger, 2009), indicate significant variation in militant and ex-militant group electoral participation. Yet a lack of comprehensive data has impeded systematic research on this topic: what are the causes and consequences of such electoral participation? Answering these questions can help scholars address broader questions on conflict, including why combatants choose the strategies that they do beyond their attacks, how settlements are designed, and whether violence recurs after conflict. This research also speaks to enduring questions in the study of democratization and electoral politics, such as what factors produce new political parties, whether and how voting and violence substitute for each other, how elections with militant and ex-militant group participation affect democratization, and under what conditions particular parties are banned from electoral systems.

This article introduces a new dataset on militant and ex-militant group electoral participation from 1970 to 2010.¹ The Militant Group Electoral Participation (MGEP) dataset, which I compiled from existing armed actor databases using standards described in the next section, comprises annual data on all militant and ex-militant groups that identify their participation in each national legislative election in the states in which they operate. The data capture three distinct types of militant and ex-militant group electoral participation that may be explained by different factors and have different effects: (1) ‘violent’ participation – when an active militant group competes against the government – as in Northern Ireland; (2) ‘peaceful’ participation – when an ex-militant group competes against the government typically with a peace agreement in place – as in Nepal; and (3) ‘won’ participation – when an ex-militant group holds elections after defeating the previous incumbent government on the battlefield – as in Nicaragua. The dataset demonstrates that more than 100 militant and ex-militant groups have participated in elections, and that the most common type is peaceful participation.

This article proceeds as follows. First, it describes how MGEP was constructed, including definitions, data sources, and coding criteria. Next, it provides descriptive statistics of the dataset. Finally, it elaborates on possible applications of these data, including replicating an existing study of post-conflict elections, which demonstrate the dataset’s importance.

MGEP data: Definitions, sources, and criteria

In MGEP, I compile data on militant and ex-militant groups operating in any country in the world for the period 1970–2010 (group-year data). In these data, I identify electoral participation.

Universe of cases

To compile group-year data, I define militant groups as nongovernmental entities using extra-legal violence to achieve political aims. The groups are nongovernmental in that they are not paid by the state to use arms to achieve their political aims, though their aims may be aligned with an elected party’s agenda. The groups have professed political aims, such as policy or regime change, that are stated by the group or the government. Professed intent is a crucial consideration because the groups’ attacks are otherwise common crime. Most fundamentally, finally, the groups use violence that seeks to destroy property and/or cause casualties. I treat militant groups as unitary actors until their own declarations or governmental statements indicate that they have split, although they may contain factions with different policy preferences.

These inclusion criteria are similar to many definitions of militancy, but more lower-level militant groups are allowed into MGEP than into most civil war and civil conflict datasets. For example, MGEP includes 312 ‘armed actors’ or ‘rebel’² groups in the UCDP/PRIO Armed Conflict Dataset (Gleditsch et al., 2002; Pettersson & Wallensteen, 2015),³ but also 420 additional groups that do not achieve sufficient battle-death thresholds to qualify them for UCDP/PRIO.⁴ All of these groups conduct attacks and employ a range of other strategies to achieve their aims.⁵ The data include the years in which these groups are actively attacking, as well as ten years afterward.^6,7 These data allow the examination of the universe of strategic decisions that groups make and a comprehensive identification of variation in electoral participation. Different types of groups, including militant and ex-militant groups, are clearly differentiated in the data, so that users can chose the categories appropriate for their studies, including subsetting to only UCDP/PRIO groups.⁸

To identify the militant groups that comprise the dataset, I combined different armed actor datasets and used systematic standards to determine when organizations met the definition of a militant group. I first compiled the groups in the UCDP/PRIO Armed Conflict Dataset, as well as the Terrorist Organization Profiles (TOPs), the Terrorists, Insurgencies, and Guerrillas in Education and Research’s Terrorist Groups Worldwide (TIGER), and the Minorities at Risk (MAR) qualitative notes (for more source information, see Online appendix 1.1). I chose these datasets because they capture both the standard civil conflict actors and armed actors that operate at lower levels of violence. Each is also a global dataset that covers most of the period that I examine. In order to ensure that each group had stated political aims and conducted attacks, as described, my research team then consulted the source articles in the Global Terrorism Database and searched news using LexisNexis and OpenSource (which translates local news sources into English) (for more source information, see Online appendix 1.1). We coded the group as active for each year in which it used violence. A coder and a checker examined each case. A more detailed description of the variables and coding procedures, including reliability statistics, is in Online appendix 1.

Variables of interest

To identify electoral participation within the militant group-year data, this study focuses on one particular component of political participation. Electoral participation is defined here as presenting candidates eligible under the established rules to contest legislative elections in the state(s) in which the group operates. The militant or ex-militant group, or its clearly designated political wing, must present at least one candidate eligible under the electoral rules to contest that election. Independent candidates therefore do not count. I focus on official national legislative elections because these represent the most overt strategies of participation.⁹ Militant and ex-militant groups sometimes engage in lower-level electoral participation and in providing potentially covert support for political parties, but these may serve different functions for the group.¹⁰ I return to the possibilities for other types of political participation in the final section.

For each active year and for ten years afterward (see footnote 7), militant and ex-militant group participation is coded in the official national legislative elections in the state(s) in which the group seeks to achieve its political agenda.¹¹ My research team gathered and checked the election records from these countries,¹² using the main election aggregators, including Election World and the PARLINE database,¹³ and searching national links and academic case studies for complete election results if any candidates were left unidentified by these sources. We sought to identify whether the group – or an alias identified by the militant group datasets – participated in each election by running candidates through its own political party or through a political party with which it had an announced alliance. In most cases, the country in which the militant group operates is clear, and it usually targets the same country. We checked target countries for electoral participation when they differed, however, and we did not identify any additional instances of electoral participation.

Based on existing case studies, as well as the data coding process, three distinct categories emerge within electoral participation:

‘Violent’ participation, which occurs when the militant group competes against the government without a peace agreement in place.¹⁴ Indeed, in violent participation, the militant group typically actively attacks during the electoral process. Besides the PIRA in Northern Ireland (described in the introduction), cases of violent participation include the Mohajir Qami Movement in Pakistan and Hezbollah in Lebanon.

‘Peaceful’ participation, which occurs when the ex-militant group competes against the government after a peace agreement.¹⁵ The peace agreement typically provides for ex-militant political parties and post-conflict elections. Besides the Maoists in Nepal (see introduction), militant groups that participated peacefully in elections include the Farabundo Martí National Liberation Front in El Salvador and Renamo in Mozambique.

‘Won’ participation, which occurs when the ex-militant group holds elections after seizing power through military victory or a peace agreement that splits a state. The ex-militant groups in these cases then hold elections in which the former governments typically do not compete. Besides the Nicaraguan and Namibian cases (see introduction), militant groups that similarly participated in elections include the Uganda National Liberation Army and the Revolutionary Front for an Independent East Timor.

Most militant and ex-militant groups engage in just one type of electoral participation, but a few fall into multiple categories over time. An additional variable included in the dataset is ‘prior’ participation, which indicates whether a group participated in elections in the five years before fighting.

These constitute three distinct types of electoral participation, each coded in a separate column in MGEP. The incentives underlying each type of militant and ex-militant group electoral participation – and thus the theory explaining these groups’ behavior – likely differ, as the descriptive statistics suggest.

MGEP descriptive statistics

This section presents descriptive statistics of militant and ex-militant group electoral participation from MGEP. While different types of participation may have different causes and consequences, as I will discuss, the data as a whole show the importance of studying militant and ex-militant group electoral participation. Overall, the dataset consists of 16,029 group-years.¹⁶ While conflict is dispersed across 109 different states, electoral participation is most common in a few subregions, including Central America, sub-Saharan Africa, and Southeast Asia (see map in Online appendix 2), where conflict is also especially prevalent. Turning from the state to the group level, the resulting dataset contains 752 militant groups.

Figure 1.

New instances of militant and ex-militant group electoral participation over time

Of these, either during or after conflict, 102 unique groups – or 14% of all militant and ex-militant groups – have engaged in at least one type of electoral participation between 1970 and 2010. These percentages are especially striking among election-years with any active militant or ex-militant groups, as about one-quarter of these groups participate in some of these election-years (see figures in Online appendix 2).

The data show that each of the three categories of militant group electoral participation occurs regularly, and peaceful participation is the most frequent. There are 24 instances in which a militant group newly begins violent participation, carried out by 21 groups. There are 54 such instances of peaceful participation, carried out by 53 groups. And there are 31 such instances of won participation by groups.

These data present interesting patterns that merit further study: for example, peaceful participation increases after the Cold War. Figure 1 shows all new instances of participation, separated by type. Until 1989, no instances of peaceful participation occurred, while multiple instances of both other types did. In other work, I argue that systemic factors had an important effect on peaceful participation over time and across states (Matanock, 2016b, forthcoming). As I will discuss next, the existing studies of militant and ex-militant group electoral participation have just started to explore the causes and consequences of each category.

MGEP applications

In this section, I highlight how these data can be productive by discussing MGEP’s compatibility with existing datasets and providing ideas on where our understanding of conflict and democratization could be augmented with MGEP. I then demonstrate one such use by replicating a study on post-conflict elections. Finally, I discuss MGEP’s limitations and future extensions.

MGEP can be joined with existing datasets to answer a host of democratization and electoral politics questions. Much of this work is at the state level or election level, which means that indicators of militant and ex-militant group electoral participation can easily be added to empirical analyses, using the standard election and country codes included in MGEP.¹⁷ Using these variables, the dataset is compatible with the National Elections across Democracy and Autocracy dataset (Hyde & Marinov, 2012) and the Democracy and Dictatorship data (Cheibub, Gandhi & Vreeland, 2010), for example, so studies using these datasets could simply add MGEP variables aggregated to the election level or state level to their analyses.

Including militant and ex-militant group electoral participation in studies of democratization and electoral politics may improve existing understandings of these processes. Studies about whether elections have positive effects on democratization (e.g. Lindberg, 2009), for example, could include MGEP indicators to test whether militant or ex-militant group participation is a mediating factor. Studies on electoral integrity and electoral contentiousness (e.g. Norris, 2015) could similarly benefit from including MGEP variables. The case of Colombia, for instance, shows that negotiating ex-militant group electoral participation as part of a peace agreement changed the constitution, making elections more broadly representative (Shugart, 1992). The dataset could ultimately also contribute to our thinking about whether violence and voting are potential substitutes (e.g. Przeworski, 1991) and whether groups see them as such (e.g. Staniland, 2015). It could develop our understanding of how democratization affects conflict (e.g. Cederman, Hug & Krebs, 2010; Mansfield & Snyder, 2005) and conflict affects democratization (e.g. Fortna & Huang, 2012). In general, emerging literature linking elections and conflict can benefit from consideration of militant group electoral participation (e.g. Dunning, 2011; Steele, 2011).

MGEP is also compatible with many widely used datasets on conflict and militant groups (e.g. Gleditsch et al., 2002). The standard UCDP/PRIO conflicts are a subset of the organizations coded in MGEP (see the definitions above). These groups are clearly marked and can be matched using UCDP/PRIO’s conflict and dyad identifiers, included in MGEP, which allow the use of that research group’s variables collected over time, such as, at the conflict-level, incompatibility and type of termination, and at the group-level, capacity and foreign funding variables. Many existing studies exclusively examine UCDP/PRIO conflicts, so they will be able to add MGEP variables to their analyses.

Examining MGEP variables may improve existing understandings of conflict processes. For example, the indicator of violent participation by militant groups could add a new dimension to discussion of costly nonviolent behavior, beyond providing public or club goods (e.g. Berman & Laitin, 2008). The causes and consequences of violent participation are also understudied in their own right, primarily limited to case studies (e.g. Irvin, 1999). New analyses using MGEP show that militant groups tend to employ violent participation in territorial conflicts (Matanock, 2015; also see Brathwaite, 2013). While case studies mention the effects of militant group electoral participation, there is little systematic analysis across cases (except, recently, Heger, 2015).

The indicators of ‘post-conflict’ participation – peaceful and won – could augment an evolving research agenda on settlement design and peace. These MGEP variables may explain variation in how peace agreements establish power distribution between former combatants (e.g. Harbom, Högbladh & Wallensteen, 2006), when stabilizing constraints from good governance are present (Walter, 2015), or whether dynamics of ethnic conflict produce conflict recurrence after such a settlement.¹⁸ While case studies have previously hypothesized that peaceful participation influences subsequent democratization and peace (e.g. Lyons, 2005), more recent work has started to use systematic cross-national data to examine the causes and consequences of including these electoral provisions in settlements (e.g. Matanock, 2016a, 2016b, forthcoming).¹⁹ There is variation in the subsequent quality of the political parties, too (Manning, 2008; Söderberg Kovacs, 2007). These data can also contribute to the debate over post-conflict elections. Existing studies show that post-conflict elections are characterized by status quo bias (e.g. Reilly & Nordlund, 2008), but the design of such elections may intentionally seek to mitigate uncertainty by ensuring that the balance of power between parties is reflected in the outcome (e.g. Durant & Weintraub, 2014; Hartzell & Hoddie, 2015), which should be especially the case in post-conflict elections with peaceful participation, an insight that can be tested using MGEP.

Ex-militant group participation in post-conflict elections

In order to advance an empirical debate, as well as to illustrate a use of MGEP, I replicate a study of post-conflict elections. The empirical results on post-conflict elections suggest that they may slow economic recovery but also encourage lasting peace, although results on conflict are mixed (Brancati & Snyder, 2013; Collier, Hoeffler & Söderbom, 2008; Flores & Nooruddin, 2012). The mixed results may be due to conflation of different types of militant group electoral participation: post-conflict elections following a settlement that provides for both combatant sides to participate may be fundamentally different from elections orchestrated only by the side that secures control over the state.²⁰

To understand the effects of ex-militant group participation, I introduce both post-conflict electoral categories – elections with peaceful and won participation – to a study on economic recovery and conflict recurrence. I chose Flores & Nooruddin (2012) (hereafter F&N) because it is well-cited, well-done, and, unlike many other studies on post-conflict elections, examines all of the UCDP/PRIO cases.

I am able to replicate F&N’s findings, even in the slightly smaller sample that results from the merge due to temporal and definitional differences,²¹ and support the claims from their baseline model. As F&N argue, longer time until economic recovery is correlated with post-conflict elections – potentially because investors are wary about the uncertainty that surrounds elections in these contexts – but longer time until conflict recurrence is also correlated with them.

After replicating these models, I modify their post-conflict election indicator to identify the peaceful and won participation categories, leaving elections without ex-militant group participation as the third category. The results are striking (Table A3.1 in Online appendix 3). Examining economic recovery, I find that among the estimated coefficients for the post-conflict election indicators, only that for the peaceful participation indicator is statistically significant (positive and large). Examining conflict recurrence, I find that the estimated coefficients for all of the post-conflict elections are statistically significant (positive), but those for the won and peaceful participation indicators are much larger than that for the government-only indicator.

These findings suggest that both peaceful participation and won participation may prolong peace, but that peaceful participation, in particular, may also prolong lower economic performance. The explanation for the results on economic recovery could be a refinement of F&N’s theory about investors described above: elections in which former militant groups and governments compete against each other may be more uncertain for investors than those run just by the militant group or the government.²² But it could also point to something besides investor concerns: for instance, peaceful participation may occur after particularly destructive conflicts to civil society on dimensions uncaptured by these models but that impede economic recovery. These findings underscore the importance of further work to see how ex-militant group participation changes our understanding of post-conflict elections. Such studies could also change policy implications in these contexts.

Limitations and future extensions

MGEP allows researchers to revisit long-standing questions regarding conflict and democratization, for example, now accounting for militant and ex-militant group electoral participation.

Like any dataset, MGEP has limitations, some of which can be worked around and some of which can be overcome in future extensions. MGEP is verified and coded from news reports and election records. Vetting all militant groups with consistent rules in news sources should reduce any bias in the initial compilations. But news sources potentially have their own biases. Although the coding included OpenSource, which provides local language articles translated into English, its coverage is not complete. Militant groups in states that receive less news coverage may fail to enter the dataset because their goals or attacks are not properly covered. They may also be less likely to participate in national elections, given the capabilities required, which may mitigate some concerns. To capture more of these cases, I included UCDP/PRIO’s armed actors even when I could not confirm attacks because they have similar coding rules but more resources to search additional sources. Election records may miss minor parties, as they occasionally include only political parties that won seats. For each case, however, we searched multiple sources in order to minimize missing cases.²³

Beyond the sources, electoral participation currently focuses on the most direct and public form of political action: political parties that run candidates eligible for top-level legislative elections. Peripheral or weak groups may be coded as not participating, even if they participate in local elections or place their support behind a particular party. MGEP users should not state that particular groups do not participate at all on the basis of these data. Coding local elections and/or less direct forms of electoral participation, such as backing candidates, would be instructive extensions, especially because the dynamics at work may be quite different from this most direct and public form of participation. Such coding for the entire dataset would require an extensive effort, but MGEP provides a starting place.

Finally, while the data are compatible with many existing conflict and elections datasets, as described, additional extensions would also benefit MGEP. Currently the dataset includes variables that are similar to those in the baseline conflict datasets, such as whether the conflict is territorial or center-seeking. MGEP adds 420 lower-level militant groups to UCDP/PRIO, as described. Therefore, if users want to explore a fuller universe of cases to see, for example, which militant groups can force victory, they could use this dataset as a starting point, adding independent variables or extending those coded in auxiliary UCDP/PRIO datasets.

Studies of conflict and democratization would also benefit from other variables that could be coded. Users wanting to explore whether political parties that emerge from militant or ex-militant group participation fare better than other new parties, or whether having such a political party increases democratization, could use the information in MGEP to research these questions. Such researchers may want to add variables on group aims, which they could categorize from the statements of goals we collected, or vote shares for these parties, which they could identify from the election files that we archived.

Conclusion

The cases mentioned in this article – Northern Ireland, Pakistan, Lebanon, Nepal, El Salvador, Mozambique, Nicaragua, Uganda, and East Timor – include many of the most destructive recent civil conflicts. These states also constitute many of the world’s emerging democracies. Questions of militant and ex-militant group electoral participation are currently understudied, previously impeded by the lack of comprehensive data. The dataset described in this article, by providing annual data on militant and ex-militant group participation in legislative elections between 1970 and 2010, allows for new empirical work on the causes and consequences of this behavior. Moreover, in combination with other datasets, MGEP stands to provide additional insights on conflict, peace, democratization, and electoral politics more broadly.

Footnotes

Replication data

The dataset, codebook, coding notes, and do-files for the empirical analyses in this article, as well as the Online appendices, can be found at http://www.prio.org/jpr/datasets and .

Acknowledgements

A previous version of this article was presented at the 2014 annual meeting of the Peace Science Society. The author thanks Ben Allen, Jessica Maves Braithwaite, Dara Cohen, Nisha Fazal, Page Fortna, Adam Lichtenheld, Katerina Linos, Alison Post, Paul Staniland, and Abbey Steele for comments. She is especially grateful to Natalia Garbiras Díaz for research assistance and to Dan Bacon, Katie Beall, Dorian Bertsch, Priyal Bhatt, Caroline Brandt, Jantsan Damdinsuren, Jake Delaney, Jessie Hao, Chelsea Johnson, Dipin Kaur, Emily Petree, and Mena Tajrishi for work on the dataset. Any errors or omissions are hers alone.

Funding

Generous funding came from the National Science Foundation (SES-1022912).

Notes

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