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Research article
First published online February 19, 2019

Strategies and Tactics in Armed Conflict: How Governments and Foreign Interveners Respond to Insurgent Threats


We introduce a new data set on the strategies and tactics employed by belligerents in 197 internal armed conflicts that occurred between 1945 and 2013. The Strategies and Tactics in Armed Conflict (STAC) data set provides scholars with a rich new source of information to facilitate investigations of how regimes and their foreign supporters have responded to insurgent threats and the effects of actors’ force employment choices on a wide variety of intra- and postconflict outcomes. In addition to seventeen novel variables that measure the strategies and tactics employed by governments and intervening states, the STAC data set contains independently coded measures of many variables that overlap with existing data sets—a feature that facilitates the replication of existing studies and robustness checks on the results of new studies. We demonstrate the utility of the STAC data with an analysis of the impact of rebel mobilization on the basis of ethnicity on the propensity of governments to employ forced resettlement, civilian protection, civilian welfare projects, and civilian targeting to counter the insurgent threat.
As armed conflicts in Afghanistan, Yemen, and Syria grind on, policy makers, military leaders, and scholars have all struggled to draw lessons from both current and historical conflicts about the most effective strategies and tactics in internal conflicts. Can insurgents be defeated through the use of brute force alone? Under what conditions can governments undermine support for rebel forces by providing security and public goods to civilians? Does leadership decapitation weaken rebel groups? At the same time, recent conflicts have motivated academic research on the causes of civilian victimization by government and rebel forces. Are combatants who receive external support more likely to target civilians? Are democracies less likely to engage in mass killing during counterinsurgency (COIN) campaigns?
To help address open questions in this research area and facilitate the systematic investigation of new questions, this project provides the first comprehensive data collection on the use of a variety of strategies and tactics by governments and foreign interveners in nearly 200 internal armed conflicts from 1945 to 2013. The Strategies and Tactics in Armed Conflict (STAC) data set builds upon existing data sets and introduces novel variables coded from a wide variety of sources. While STAC codes many variables contained in other widely used data sets, it introduces a series of unique measures of specific COIN tactics—from public welfare projects and civilian protection to strategic bombing and forced resettlement—employed by incumbent governments and external interveners. Other variables, like our measures of troop numbers, fatalities, and conflict outcomes, are similar to those coded by other projects. For these variables, STAC often covers a longer time frame and/or provides more detailed information about sources and coding decisions.
The data set is accompanied by case coding notes for each conflict, a detailed codebook, and a bibliography of the 400+ sources used to code the cases. The data set itself identifies which sources were used to code a particular case. Each case has a unique STAC conflict identifier as well as conflict, actor, and dyad identifiers from the Uppsala Conflict Data Program (UCDP) Armed Conflict data set (Allansson, Melander, and Themnér 2017; Gleditsch et al. 2002) and intrastate war name and number from the Correlates of War project (Sarkees and Wayman 2010) whenever a corresponding observation exists in these data sets. This will allow researchers to easily merge in additional data and to evaluate the robustness of their findings by comparing analyses using similar variables coded in multiple data sets. A researcher may, for example, want to compare results of models estimated with our measure of civilian targeting by government forces to results from a model estimated with the one-sided violence data from UCDP.
As we demonstrate below, these data can provide new insight into questions about the causes and consequences of the strategic approaches adopted by governments and intervening states. In addition, the data set can address more basic questions about the historical record. How have the strategies and tactics governments employ to counter armed opposition movements changed over the past six decades? How common is it for regimes to provide civilian protection or other public goods in an attempt to gain civilian support? Finally, this source of independently coded alternative measures of many important characteristics of civil wars facilitates investigations into the robustness of results from previous and future studies.

Existing Studies and Data Sources

A substantial body of existing work has addressed questions about the strategies and tactics employed in civil wars. First, scholars have explored the use and effectiveness of various military strategies using in-depth case studies. Nagl’s (2002) seminal study analyzes COIN lessons from Malaya and Vietnam, emphasizing the importance of adaptability during COIN campaigns. Kilcullen (2009) highlights, among other things, the centrality of “information warfare” in asymmetric wars. Downes (2007) shows that in the case of the Second Anglo-Boer War at the end of the nineteenth century, indiscriminate violence was a partly effective COIN tool. Kaufmann (1996) suggests that foreign interveners’ most likely strategy for success in halting ethnic civil wars is to separate ethnic groups. Each of these studies, and many others, focuses on different aspects of COIN strategies, with diverging lessons learned about the efficacy of various approaches.
Second, a considerable amount of recent empirical work uses data at the level of local communities and subnational geographic regions to evaluate the impact of different types of COIN strategies. These studies rely mostly on recent conflicts in Iraq (Berman, Shapiro, and Felter 2011; Biddle, Friedman, and Long 2012; Condra and Shapiro 2012), Afghanistan (Condra et al. 2010; Hultman 2012; Sexton 2016), and the Caucasus (Lyall 2009; Toft and Zhukov 2012), although some also make use of microlevel data from the Vietnam War (Kalyvas and Kocher 2009; Kocher, Pepinsky, and Kalyvas 2011). While these studies offer superior approaches to estimating local effects with proper identification, their external validity is limited by design and does not allow researchers to draw conclusions about broader trends.
A third group of studies has focused specifically on the use of different types of force by nonstate actors and, in particular, the use of terrorist tactics and civilian victimization. Wood (2010), Wood, Kathman, and Gent (2012), and Ottmann (2017) all provide insight into the determinants of civilian victimization by rebels. In a similar vein, Stanton (2013) analyzes the circumstances under which rebel groups use terrorist strategies, while Thomas (2014) and Fortna (2015) investigate the effects of adopting terrorist strategies on rebels’ ability to get to the negotiating table, obtain concessions, and win civil wars. Among the most recent studies, Greig, David Mason, and Hamner (2016) examine where and when rebels choose to fight. These studies reveal important insights about rebel strategies and their effects but have limited ability to account for the other side of the strategic interaction—government tactics and strategic approaches to counter the insurgency.
Finally, a small set of studies has examined the effects of specific aspects of government or intervening state COIN strategy on campaign outcomes. Lyall and Wilson (2009) show that the mechanization of counterinsurgent forces is associated with lower odds of winning COIN campaigns. They argue that increasing mechanization changes a military’s approach to COIN, reducing contact with the local population in a way that hinders information collection and makes it more difficult for COIN forces to apply force selectively. Enterline, Stull, and Magagnoli (2013) analyze the impact of broad strategy shifts on the success of foreign powers’ COIN campaigns, finding that switching to a hearts-and-minds (HaM) strategy modestly increases the likelihood of COIN success. Both studies are motivated by an interest in assessing what does and does not lead to successful COIN outcomes. While they advance our understanding of the relationship between strategic approaches and armed conflict outcomes, neither study is able to evaluate the breadth of COIN strategies and tactics used by governments or interveners. Enterline, Stull, and Magagnoli specifically call for more data, noting that their “treatment of strategy shifts is very simple, and future research may evaluate the qualitative differences across various HaM implementations and link these with outcomes” (p. 193).
This nonexhaustive list of studies demonstrates a strong interest in specific information on trends, causes, and effects of strategies and tactics in internal armed conflicts. However, currently available data cannot provide answers to many of the big questions in this research area. Accordingly, Shelton, Stojek, and Sullivan (2013) conclude that “an insufficient number of studies empirically evaluate the connection between civil war COIN strategy and overall conflict outcomes across the universe of civil war cases” (p. 526).

Available Data on Strategies and Tactics in Intrastate Conflict

Several quantitative data sets contain some information on the strategies and tactics used in internal armed conflicts, but none of them encompass both (a) the population of internal armed conflicts over several decades and (b) the variety of strategies and tactics that counterinsurgents employ, which range from different types of brute force—aerial bombing, decapitation, and mechanized assaults—to HaM strategies like providing population security.
Balcells and Kalyvas (2014) categorize internal armed conflicts in the UCDP/PRIO Armed Conflict data set as conventional, irregular, or symmetric nonconventional. The annual data on “technologies of rebellion” are available for 128 conflicts between 1946 and 2008. This classification provides some information about the warfighting strategies of governments and insurgents but does not capture variation in the specific strategies and tactics employed.
Valentino, Huth, and Balch-Lindsay (2004) provide original data on mass killings of civilians during 147 interstate and intrastate wars that occurred between 1945 and 2000. Their dichotomous mass killing variable indicates that there were at least 50,000 intentional civilian deaths within five years.
The UCDP one-sided violence data set (Allansson, Melander, and Themnér 2017; Eck and Hultman 2007) codes direct, intentional killing of civilians by governments and formally organized armed groups in noncombat contexts. The annual, actor-level data go back to 1989. They exclude civilian deaths attributed to collateral damage.
Enterline, Stull, and Magagnoli (2013) provide data on broad strategy shifts in sixty six COIN wars fought by foreign powers in the twentieth century. Specifically, they record whether the foreign power’s strategy changed during the course of the war and, if so, whether it shifted toward a HaM emphasis.
A study from the RAND Corporation (Paul, Clarke, and Grill 2010) lists the presence or absence of a wide range of “good” and “bad” tactics employed by counterinsurgents in thirty insurgencies between 1978 and 2008.
For some country-specific cases, scholars have compiled a wealth of information on government-dissident interactions. One example of such data is the “Government Actions in a Terror Environment data” project (Dugan and Chenoweth 2012). This project covers Israeli government responses to terrorist attacks from 1987 to 2004. Like similar projects, the event-specific depth of such data comes at the cost of limited coverage as far as actors (e.g., nonstate actors), types of actions, and time and space are concerned.
The STAC data advance beyond these sources by providing conflict-specific information on a variety of strategies and tactics employed by governments and external interveners across the population of internal armed conflicts that began after 1944 and ended between 1947 and 2013.

Data Set Construction

We define internal armed conflict as a conflict between an incumbent government and an armed, organized, domestic opponent that resulted in at least twenty-five deaths directly attributable to one of the actors within one year. Domestic unrest (e.g., demonstrations, protests, rioting) that cannot be linked to an organized group with a political objective does not qualify as an armed conflict for the purposes of this study, even if the unrest results intwenty-five or more deaths.
We began constructing the data set by aggregating conflicts identified in the dyadic version of the UCDP armed conflicts data set (Harbom, Melander, and Wallensteen 2008), Correlates of War Intrastate Wars data (Sarkees and Wayman 2010), the insurgencies data set created by Lyall and Wilson (2009), the Fearon and Laitin (2003) list of violent civil conflicts, and Nonviolent and Violent Campaigns and Outcomes (NAVCO) data set (Chenoweth 2011). Using data available in these data sets, a list of sources to be consulted for all cases, and case-specific sources, coders eliminated duplicate cases and cases that did not fit our definition of internal armed conflict. We also revised start and end dates for conflicts when the dates coded in the extant data sets were not consistent with our definition based on further research into fatalities on each side of the conflict or other information about, for example, when a peace treaty took effect. Finally, we combined some of the conflict dyads identified by UCDP because the rebel groups had substantially similar objectives, claimed to represent the same population, were active during the same time period, and were either formally allied, part of the same umbrella organization, or engaging in significant cooperation on the ground. In these cases, we code variables for the primary rebel group, operationalized as the group that had the greatest number of troops for the majority of the conflict.1 This process, which is documented in a spreadsheet available online, resulted in a final set of 197 cases. Online Appendix A contains a list of these cases.
For each internal conflict, we coded external interventions on behalf of the government based on existing databases and, where applicable, further research. Sources included work by Regan and collaborators (Regan 2002; Regan and Aydin 2006), Pickering and Kisangani (2009), and Sullivan and Koch (2009). A foreign regime maintenance (FRM) intervention was coded if an external state sent at least 500 regular,2 combat-ready troops (ground, air, or naval) to the location of the conflict with the intent to defend or otherwise assist the incumbent government in their fight against the insurgents.
The core of the STAC data is information on strategies and tactics employed by counterinsurgents—governments and, when present, intervening states. This information was coded from systematic searches of a wide range of sources. Coders investigated whether each actor employed specific tactics and the extent to which these tactics were emphasized in the actors’ COIN strategy. These tactics are described in more detail below.
All coding was conducted between 2009 and 2017 by two faculty investigators and a team of undergraduate and graduate research assistants. In addition to identifying the armed conflicts to include as cases, the first eighteen months were spent in an iterative process of defining variables, developing operational definitions, writing coding rules, identifying sources, and coding cases. Research assistants underwent training, including several trial runs of coding cases, and participated in biweekly team meetings with the principle investigators in which ambiguous cases and difficult coding decisions were discussed. Coders were provided with coding procedures, a common list of approved sources, a detailed codebook, and a template for recording case notes. In the beginning, all cases were coded by two coders and discussed in meetings to identify weaknesses in our coding rules and procedures. Once firm coding rules were established, “practice” cases were recoded following the revised codebook.
Each research assistant consulted at least three approved sources for each case—including peer-reviewed articles and academic books, chronologies of international events, newspapers, and reports issued by governmental and nongovernmental organizations. When the common list of sources did not provide sufficient information, research assistants identified case-specific sources for approval by the faculty investigators. The codebook contains a full bibliography of all sources and the data set, and case notes indicate which of the over 400 primary and secondary sources were used to code each case. The written instructions given to research assistants and the codebook are available in the Online Appendix.
To access intercoder reliability, we assigned a second, independent coder to one-third of the cases. The second coder was instructed to recode all of the COIN strategies and tactics variables without access to the first coder’s ratings. We then calculated chance-corrected interrater agreement coefficients for each of these variables. The Krippendorff’s α and Gwet’s AC for each of the COIN strategies and tactics variables are listed with other descriptive statistics in Tables 1 and 2 (Gwet 2014; Krippendorff 2013). There is no consensus in the social sciences about what constitutes an acceptable level of intercoder agreement. Recent scholarship on intercoder reliability for content analysis has suggested that a Krippendorff’s α of .8 or above indicates high reliability, while coefficients between .667 and .8 are appropriate for more tentative conclusions (Krippendorff 2013). However, the Krippendorff’s α is known to produce low coefficients for variables with skewed distributions—even when intercoder agreement is high (Feng 2015; Lacy et al. 2015). It is not surprising, therefore, that the α coefficients for some of our tactical variables are in the more tentative range. Gwet’s AC, an alternative measure recommended for skewed variables, is above .8 for all of the variables. In order to avoid introducing any personal bias into the data, when coders disagreed on the value of a variable, we randomly assigned one of the values (Lacy et al. 2015).3
Table 1. Government COIN Strategies and Tactics.
VariableNMeanMinimumMin (%)MaximumMax (%)αAC
Force model1951.311 (light)692 (heavy)31.90.95
Strategic bombing1951.511 (none)674 (extensive)5.83.90
Civilian projects1931.291 (none)824 (extensive)2.74.95
Civilian protection1931.441 (none)694 (extensive)2.80.93
Forced Resettlement1901.511 (none)744 (extensive)9.68.83
Decapitation1911.831 (none)504 (extensive)8.80.86
Civilian targeting1950.900 (rare)442 (extensive)33.84.90
Mass killing1961.020 (no)881 (yes)12.72.93
Note: Alpha and AC measure intercoder reliability for our assessment with two independent codings of one-third of the data. Both measure interrater agreement correcting for expected agreement due to chance. Alpha is the Krippendorff’s α reliability statistic. AC is Gwet’s (2014) alternative measure for variables with rare categories.
Table 2. Intervening State COIN Strategies and Tactics.
 NMeanMinimumMin (%)MaximumMax (%) αAC
Force model421.471 (light)532 (heavy)47.92.96
Ground combat1950.1690 (no)831 (yes)17.79.93
Ground troops1970.1970 (no)801 (yes)20.86.90
Force type424.081 (display)105 (combat)65.78.88
Civilian projects391.361 (none)794 (extensive)5.70.93
Civilian protection391.511 (none)674 (extensive)5.76.83
Civilian targeting381.501 (rare)713 (extensive)17.80.86
Decapitation391.231 (none)793 (moderate)3.71.83
Strategic bombing411.9251 (none)584 (extensive)18.84.91
Note: Alpha is the Krippendorff’s α reliability statistic. AC is Gwet’s (2014) alternative measure for variables with rare categories. With the exception of ground troops and ground combat, intervention variables are only coded for conflicts in which a third-party state intervened to defend the incumbent regime.
The complete data set was also subjected to a series of intercase consistency reviews. The principal investigators conducted analyses of variable codings within cases that had a relatively low probability of co-occurrence and flagged each of these cases for review by a graduate student or postdoc research assistant. We flagged, for example, conflicts in which the government was coded as engaging in both high levels of civilian targeting and high levels of civilian protection, cases coded as having little to no evidence of civilian targeting and extensive strategic bombing, and conflicts coded as ending in government military victories in which the postconflict government composition was coded as representing the opposition.4

Data Description

Like many existing data sets, STAC codes conflict initiation and termination dates, estimates of troop levels on each side, and estimates of government and rebel troops killed in each conflict. Nominal variables record the leader or party affiliation of the incumbent regime and the name of the primary opposition group. The remainder of this section focuses on variables that are either unique to the STAC data set or coded in a more complete and detailed way than in existing data sets.

Strategies and Tactics in COIN

Eight core variables—force model, strategic bombing, civilian projects, civilian protection, forced resettlement, decapitation, civilian targeting, and mass killing—provide information about the government’s COIN tactics and strategy. Table 1 displays descriptive statistics for these variables. Our coding rules consider the extent to which the government emphasized each tactic relative to other tactics in its overall strategic approach to counter the threat posed by the rebel group rather than relative to the prevalence of that tactic in other armed conflicts. While the precise coding rules are dependent on the particular attributes of each tactic, most variables are coded on a four-point scale, where 1 indicates that there is no evidence the tactic was employed and 4 indicates that the government engaged in use of the tactic frequently, deliberately, and as one of its primary tactics to counter the threat posed by the rebel group. For the intermediate categories, the coding rules stipulate that 2 indicates minimal, infrequent government efforts and/or weak evidence of government efforts to employ a tactic; 3 is defined as moderate use of a tactic and is coded for cases that fall between minimal and extensive.5
Force model codes the predominant type of force used by government troops as either light force, defined as primary reliance on ground combat units that do not include large military equipment, or heavy force, defined as significant reliance on large, concentrated-firepower combat with mechanized units. A second variable (strategic bombing) categorizes the role of strategic bombing in the government’s military strategy from 1, indicating no evidence of strategic bombing by government forces, to 4, indicating that the government engaged in strategic bombing often, throughout the conflict, or relied on bombing as a primary tactic for at least one-third of the duration of the conflict. Strategic bombing is defined as centrally coordinated aerial bombardment of military and civilian targets designed to destroy the opposition’s will and ability to fight. A light force model is employed in 69 percent of conflicts. In the majority of conflicts (67 percent), there is no strategic bombing and extensive strategic bombing occurs in only 5 percent of conflicts.
Two variables, civilian projects and civilian protection, code what we consider to be government tactics to win the HaM of the civilian population. Civilian projects codes the role of “projects intended to improve the material well-being of civilians” in the government’s COIN strategy. Coders were instructed to consider activities such as building schools, roads, or hospitals; providing medical care, clean water, or electricity; and creating jobs for local residents. A code of “extensive” required evidence that the government engaged in these activities “often and/or heavily” and “as a deliberate strategy.” Civilian protection captures the role in the government’s military strategy of “attempts to protect noncombatants from harm caused by the insurgents.” A code of “extensive” required “evidence that the government consistently tried to protect civilians from harm and/or deliberately prioritized civilian protection.” The government engaged in efforts to improve the material well-being of civilians in only 18 percent of the internal armed conflicts in our data set. Efforts to protect civilians were more common, playing at least a minor role in government COIN efforts in 31 percent of conflicts.
A more forceful approach is captured by the variables forced resettlement and decapitation. Resettlement is defined as the forced relocation of civilian populations to deny an armed group access to resources, recruits, sanctuary, and other types of support and/or to separate combatants from noncombatants. Governments forcibly relocated civilian populations in just over a quarter of armed conflicts. In 9 percent of the conflicts, forcible resettlement was extensive. The decapitation variable measures the degree to which the government focused on capturing and killing top insurgent leaders. Coders found weak evidence and/or infrequent, unsuccessful attempts at decapitation (coded as 2, minor/rare) in 25 percent of conflicts. Decapitation played a moderate to extensive role in 24 percent of the conflicts in our data set.
The final government COIN variables, civilian targeting, and mass killing code whether government armed forces intentionally selected civilians (noncombatants) as direct targets of attack or regularly conducted military operations without attempting to discriminate between combatants and noncombatants. Mass killing is simply a dummy variable that adopts the Valentino, Huth, and Balch-Lindsay’s (2004) definition of mass killing as more than 50,000 intentional civilian deaths in a campaign within five years. Our operational definition of civilian targeting differs from other measures of civilian victimization in civil wars in that it attempts to identify the role the use of force against noncombatants played in the government’s COIN strategy rather than the number of civilians killed by government forces. The variable is coded on a three-point scale indicating the extent to which the government targeted civilians from rarely or not at all (1) to extensively (3). This operationalization is consistent with the other STAC variables, which focus on actors’ strategic and tactical choices. Although this approach is more subjective than quantitative estimates of the number of noncombatants killed, it has several key advantages. First, considering evidence of the extent to which civilian deaths were systematic, deliberate, and intended as a tactic to combat the insurgency creates a measure particularly well-suited for analyses of the conditions under which governments choose to employ particular tactics. Outcome-focused operational definitions like the number of civilians killed conflate tactical and strategic emphasis with government capacity (e.g., troop strength and firepower). At the same time, coding the relative role of a particular tactic in a government’s overall approach is one way to address the difficulty of making comparisons across armed conflicts with an almost unlimited number of distinguishing characteristics (e.g., conflict duration, geographic spread, population density, size of rebel forces, terrain) that could impact observable outcomes. Finally, our coding considers civilian deaths attributed to the government as evidence the government failed to discriminate between combatants and noncombatants but is not dependent on finding precise numerical data. Consequently, our measure is available for a much longer time span than the UCDP one-sided violence data set.
The government engages in civilian targeting to a moderate extent or greater in just over half of the armed conflicts in the STAC data set. While civilian targeting is rare in about 43 percent of the armed conflicts, moderate to extensive civilian targeting occurred in a slightly higher percentage of cases (44 percent) and mass killing occurred in just over 12 percent.
Considered together, the tactical variables can provide insight into the government’s overall strategic approach to countering an insurgency—even when the government did not consciously employ a coherent strategy throughout the conflict. The Chinese government’s campaign against Tibetan insurgents from 1956 to 1959, for example, is coded as consisting of a heavy force model with minor employment of civilian projects, no evidence of decapitation or civilian protection, moderate strategic bombing, and extensive civilian targeting. In the coding notes, the coder states that the Chinese government “implemented many civilian projects, such as education and infrastructure projects, but the majority of these occurred before the outbreak of organized resistance in 1965…[T]hese programs ended due to extreme Tibetan resistance as the conflict came to a head in 1957 and did not resume until after the fall of the resistance movement in April 1957.” As evidence for moderate strategic bombing, the coder states that “Throughout the conflict, the Chinese army shelled Tibetan villages and monasteries using artillery and bomber aircraft to support troops on the ground conducting raids. Because the air attacks were quite frequent, but the emphasis of the COIN was based on ground raids and killing suspected insurgency sympathizers, strategicair has been coded 3.” Extensive civilian targeting is coded because “The Chinese government operated with great brutality against civilians in Tibetan villages, executing hundreds. It also attacked civilians and monasteries throughout the conflict who were suspected of helping the insurgents. Additionally, when insurgents and civilians gathered in Lhasa to protect the Dalai Lama in March 1959, the Chinese army opened fire on the crowd using mortars and machine guns, killing thousands of insurgents and civilians alike.”
In contrast, the 1963 to 1964 Sudanese campaign against the Anya-Nya rebels in southern Sudan is characterized as employing a light force model, moderate civilian targeting, and no evidence of civilian projects, civilian protection, or decapitation tactics. The coder notes that “Against the Anya-Nya rebels in southern Sudan, the use of small-arms ground fighting was the only tactic used by the government. No other COIN strategies were employed, leading all variables to be coded as 1. However, for [civilian targeting], although there were not orders to attack civilians, troops often ‘took out their frustrations on the civilian population’ and attacked them in their struggle to differentiate combatants” (Ciment 2015).
Each of the COIN strategies and tactics variables is also coded for the primary intervening state if a third-party state deployed at least 500 military troops in an attempt to counter the insurgent threat and maintain the governing authority of the central government. There are forty-two FRM interventions in the data set. In addition to the strategies and tactics variables coded for the government, several additional variables provide a more complete picture of the nature of each FRM intervention. Variables indicate, for example, the number of foreign troops, the number of intervening state fatalities, and the intervention’s start and end dates. Table 2 provides descriptive statistics for the intervention variables. The highest category of force employed by the intervening state is coded from (1) display to (5) ground combat operations with more than 2,000 intervening state troops. Foreign ground troops intervene on the government’s side in fewer than 20 percent of armed conflicts, and foreign troops engage in large-scale, direct ground combat against rebel forces in just thirty-three cases. External interveners were more likely than local governments to employ a heavy force model and engage in strategic bombing, but they were also more likely to provide civilian protection and focus on civilian projects. Civilian targeting was nonexistent or rare in 71 percent, and extensive in only 17 percent, of the foreign interventions for regime maintenance in the data set.

Application: Ethnicity and Government Strategies in Armed Conflict

This section demonstrates the utility of the STAC data for answering a research question that scholars have primarily explored in case studies or with quantitative data that were limited to specific conflicts: what determines the strategic approach a government takes to counter an internal armed threat to the regime? When do governments adopt the “population-centric,” HaM approach advocated by current US military doctrine,6 and when do they adopt a more forceful approach? Are the two approaches truly mutually exclusive? While there are lively debates in the academic and practitioner literatures on the effectiveness of various COIN strategies and tactics, to date scholars have not even had sufficient data on the extent to which various strategies and tactics are employed across space and time.
Here, we develop an argument about the conditions under which governments are likely to use violence against noncombatants and forcibly resettle civilian populations and the conditions under which they are likely to employ tactics consistent with a HaM COIN strategy—providing security and other public goods to win the allegiance of the civilian population.
While there is little systematic research on the determinants of COIN strategy and tactics, a growing literature explores the conditions under which governments use one particular tactic—indiscriminate violence against civilians. Mason and Krane (1989) were among the first to tackle the question of government treatment of civilians in civil war in a systematic way. Their theoretical model predicts that carefully targeted repressive violence can temporarily reduce active support for an opposition movement. In contrast, more indiscriminate violence can drive civilians to actively support the rebels because, if the government will target them regardless of their participation status, joining the opposition could increase their security by providing some measure of protection. They argue that state weakness, rather than strategic utility, drives governments to resort to indiscriminate violence.
The model Mason and Krane present is consistent with current US military doctrine on COIN operations, which maintains that harming civilians is counterproductive (US Department of the Army 2006; US Marine Corps 2006). It is also consistent with the work of scholars like Kalyvas (2006) who argue that indiscriminate violence increases grievances, alienates the civilian population, and provides noncombatants with incentives to join the insurgency. Other scholars, however, maintain that there is a strategic logic to targeting civilians and that, sometimes, “barbarism works” (Arreguín-Toft 2001, 41). Or even that using overwhelming force against civilians is necessary for COIN success (Hazelton 2017; Luttwak 2007; Trinquier 1964). As Valentino (2014) notes, “scholars have increasingly come to recognize that large-scale violence against civilians during interstate and civil wars is neither arbitrary, unintended, nor distinct from the central logic of war itself” (p. 94).
Proceeding from the assumption that violence against civilians is strategic, multiple studies have found evidence that governments are more likely to engage in mass killing to counter guerilla insurgents, as opposed to conventionally structured opposition forces (Valentino 2014). Because the support of the population is so critical to irregular forces, and guerilla forces themselves can be difficult to target, governments may target civilians in terror campaigns designed to deter them from providing materials, protection, and intelligence to the rebels (Balcells and Kalyvas 2014; Downes 2008; Valentino, Huth, and Balch-Lindsay 2004). Krcmaric (2018), however, argues that governments should be less likely to victimize civilians in guerilla wars precisely because these wars are a contest between the government and the rebels for the “HaM” of the population. In support of his argument, he finds that mass killing is more likely in civil wars fought with conventional military strategies than in armed conflicts against guerilla forces.
There is also mixed evidence about the impact of ethnic identity on civilian targeting. Articles by Fjelde and Hultman (2014) and Valentino (2014) note the conspicuous lack of evidence that ethnicity drives violence again civilians in armed conflicts. Although Montalvo and Reynal-Querol (2008) find that ethnic polarization is positively correlated with the incidence of genocide in a country, most cross-national studies have found little connection between ethnic diversity and the likelihood of mass killing (Azam and Hoeffler 2002; Harff 2003; Kim 2010; Rummel 1995; Valentino 2004; Valentino, Huth, and Balch-Lindsay 2004; Wood 2010). As Valentino (2004) notes, “Some of the bloodiest mass killing in history have occurred in relatively homogeneous societies, between groups of the same or closely related ethnicity, nationality, religion, or class” (p. 2).
Departing from an analysis of genocide specifically, Fjelde and Hultman (2014) attempt to resolve the dissonance between case studies that emphasize the role of ethnic identity in explaining violence against civilians (Horowitz 1985; Kaldor 2001; Kaufmann 1996; Posen 1993; Sullivan 2012) and quantitative analyses that have failed to find a significant relationship. They argue that cross-country comparisons may not capture the association between ethnic divisions and civilian victimization because the relevant variation takes place at a lower level of analysis. Using disaggregated, georeferenced data on one-sided violence against civilians in sub-Saharan Africa between 1989 and 2009, they find that both governments and rebels engage in higher levels of civilian targeting in areas of the country inhabited predominately by co-ethnics of the opposing side.
In the following analysis, we broaden the focus beyond violence against civilians to explore the correlates of four COIN tactics: civilian protection, civilian welfare projects, forcible resettlement of civilian populations, and civilian targeting. We argue that the HaM approach to COIN—improving material conditions, providing population security, and avoiding harm to civilians—has more strategic utility if the government and the rebels are seeking the support of the same constituency. This is likely to be the case when the opposition is mobilized on the basis of ideology rather than ethnicity. If mobilization is based on class or ideology, the government may be able to discredit the rebels’ grievance narrative by providing public goods (Berman, Shapiro, and Felter 2011; Galula and Nagl 2006; Nagl 2002; US Department of the Army 2006). Moreover, victimizing the civilian population runs the risk of driving uncommitted civilians to support the rebels (Kalyvas 2006; Lichbach 1987; Mason and Krane 1989; Petersen 2001).
In contrast, when a rebel group is mobilized along ethnic lines with the aim of overturning the ethnic balance of power in the country, the government may decide that it can brutally suppress the ethnic population from which the rebels draw their support without alienating their own base of support (Downes 2007; Fjelde and Hultman 2014; Kaufmann 1996). In fact, scapegoating a marginalized ethnic group and responding forcefully could rally support for the government (Bowen 1996; Gagnon 1994; Gurr 2000; Horowitz 1985; Tir and Jasinski 2008). Moreover, if a rebellion draws its support from a marginalized ethnic community, the government may have little access to intelligence that would allow its forces to selectively target combatants (Kalyvas 2006). Because ethnic populations frequently live in concentrated geographic areas or are associated through visible ascriptive characteristics, ethnicity becomes a convenient criterion for collective targeting of the opposition’s support base (Fjelde and Hultman 2014).
We expect, therefore, that forcible resettlement and civilian targeting will be more likely in ethnic conflicts. The government’s COIN strategy will be more likely to include efforts to protect civilians and improve the material welfare of the population when the rebels draw support across ethnic lines.
The STAC data allow for testing this argument across a broad spatial and temporal domain. Ethnic mobilization is coded following a conventional definition, whereby ethnic conflicts involve “groups that identify with a distinct ethnic or cultural heritage” (Regan 1996, 338) “…who are in conflict over the power relationship that exists between those communities and the state” (Sambanis 2001, 261). Based on this definition, almost 40 percent of conflicts in the STAC data set were coded as ethnic conflicts, drawing on the primary and secondary sources described above and identified in the codebook and data set.


Table 3 displays a series of models estimating the effects of ethnic conflict on the odds that the government’s COIN strategy will include efforts to protect civilians, material welfare projects, civilian targeting, or forced population resettlement. The dependent variables in models 1 through 3 are our ordinal measures of the extent to which the government employed civilian protection, material welfare projects, or population resettlement in its COIN campaign. Each dependent variable has four categories: none, minor/rare, moderate, and extensive. The dependent variable in model 4, civilian targeting, has three ordered categories indicating that government forces rarely, moderately, or extensively engaged in civilian targeting in their campaign to combat the insurgency. The first four models are estimated with ordered logit equations. Model 5 fits a logit model for a binary indicator of government mass killing, defined as conflicts with at least 50,000 intentional civilian deaths in a five-year period.
Table 3. Ethnic Conflict and COIN Tactics.
 Model 1Model 2Model 3Model 4Model 5
 Civilian ProtectionCivilian Welfare ProjectsForced ResettlementCivilian TargetingMass Killing
Ethnic conflict0.83 (0.29)0.88 (0.36)3.53 (1.23)**2.80 (0.95)**4.34 (1.89)**
 Cut 1 constant0.74 (0.22)1.45 (0.24)1.64 (0.27)0.11 (0.18)0.06 (0.02)
 Cut 2 constant1.98 (0.29)2.28 (0.29)2.37 (0.31)1.15 (0.19) 
 Cut 3 constant3.78 (0.51)3.78 (39.08)3.00 (0.37)  
Note: Models 1 to 4 are estimated with ordered logistic regression. Model 5 is estimated with logistic regression. Coefficients reported as odds ratios. Robust standard errors clustering on conflict country in parentheses.
*p < .05.
**p < .01.
The results suggest that there is no difference between ethnic and nonethnic conflicts in the extent to which material welfare projects and civilian protection efforts are employed. The estimated odds that either tactic is employed are lower in ethnic conflicts, but the differences are not statistically significant. In contrast, forcible resettlement and civilian targeting play a significantly greater role in ethnic conflicts. Most notably, the odds of state-sponsored mass killing are 4.3 times greater when insurgents are mobilized along ethnic lines.
In Table 4, we fit ordered logit models for forced resettlement and civilian targeting with additional independent variables to control for potential confounding factors. The dependent variable in models 3a and b is our four-category measure of the role of forced resettlement in the government’s COIN strategy. The dependent variable in models 4a through 4c use is four-category measure of civilian targeting in which the highest category is mass killing. Not all control variables are included in every model because missing observations on some variables result in the loss of a significant number of cases when we include these variables.
Table 4. Ordered Logit Models Predicting Forced Resettlement and Civilian Targeting.
 Model 3aModel 3bModel 4aModel 4bModel 4c
 Forced ResettlementForced ResettlementCivilian TargetingCivilian TargetingCivilian Targeting
Ethnic conflict5.00 (3.55)*2.75 (1.34)*9.04 (4.66)**6.90 (3.12)**6.17 (2.43)**
Government troops
 3–10k0.10 (0.12)0.26 (0.23)1.73 (1.66)1.44 (1.18)1.44 (1.05)
 10–30k0.78 (0.70)0.54 (0.46)0.98 (1.13)0.45 (0.34)0.54 (0.36)
 >30k0.14 (0.15)0.28 (0.24)1.02 (1.16)0.78 (0.55)0.96 (0.67)
Rebel troops
 3–10k0.24 (0.21)0.32 (0.21)*1.16 (1.00)1.42 (0.83)1.34 (0.74)
 10–30k0.22 (0.22)0.72 (0.46)2.52 (2.07)4.08 (2.47)*3.06 (1.76)
 >30k0.63 (0.64)0.83 (0.58)5.58 (4.36)*4.94 (2.90)**3.19 (1.67)*
Territorial spread
 25–49% of country1.13 (0.99)0.84 (0.43)1.04 (0.54)0.91 (0.39)1.11 (0.45)
 50–75% of country1.45 (1.51)1.18 (1.12)3.57 (2.66)2.13 (1.42)2.29 (1.38)
 >75% of country0.96 (0.84)0.82 (0.52)7.04 (3.56)**2.27 (1.11)3.43 (1.57)**
Conflict duration2.02 (0.50)**1.89 (0.33)**1.12 (0.17)1.29 (0.15)*1.18 (0.14)
Secessionist aims1.76 (1.18)1.61 (0.91)1.13 (0.57)0.58 (0.27)0.87 (0.37)
Conventional war0.60 (0.42) 0.71 (0.31)  
Reb civ targeting   0.99 (0.32) 
Cut 1 constant4.45 (1.91)4.43 (1.36)2.62 (1.31)2.61 (1.08)2.29 (0.90)
Cut 2 constant5.51 (1.92)5.29 (1.37)3.92 (1.30)3.94 (1.09)3.58 (0.91)
Cut 3 constant6.19 (2.01)6.15 (1.39)5.92 (1.34)5.56 (1.15)5.11 (0.97)
Note: Coefficients reported as odds ratios. Robust standard errors clustered on the conflict country in parentheses.
*p < .05.
**p < .01.
One of the most likely alternative explanations for the use of COIN tactics that target noncombatants is desperation; governments use force against civilians when a conflict is exacting a high price and they are desperate to bring an end to it (Downes 2008; Harff 2003; Kalyvas 2006; Valentino 2004; Valentino, Huth, and Balch-Lindsay 2004). To control for factors likely to increase the costs of conflict for the government, we include categorical measures of government and rebel troop strength and the proportion of the geographic area of the country that experienced significant conflict-related violence. In addition, we add a continuous measure of conflict duration—the natural log of conflict duration in days. In one of the models predicting civilian targeting by the government, we control for civilian targeting by rebel forces with a dichotomous variable. All variables are from the STAC data set and are described in more detail in the codebook.
To control for the possibility that civilian victimization and forced population resettlement are more likely in ethnic conflicts because rebels in these conflicts are more likely to employ guerilla warfare strategies, as opposed to conventional warfighting strategies, we import a measure of the “technology of rebellion” from Balcells and Kalyvas (2014). Their measure codes civil wars in the UCDP/PRIO armed conflict database (Gleditsch et al. 2002) as irregular, conventional, or symmetrical nonconventional on an annual basis.7 In our models, a dummy variable indicates whether each armed conflict was fought predominately as a conventional war. We also test a model with a dummy variable indicating that the conflict began after 1989 because some scholars, including Kalyvas and Balcells, maintain that post–Cold War civil wars are distinct from those that occurred previously (Fortna 2013; Kaldor 2001; Kalyvas 2001; Kalyvas and Balcells 2010).
A dummy variable for secessionist conflicts is included because many ethnic conflicts have secessionist aims and several scholars find lower levels of violence and civilian victimization in wars over territorial control than wars fought for control of the central government (Eck and Hultman 2007; Heger and Salehyan 2007). The inclusion of the secessionist conflict indicator allows us to determine whether the ethnic identity of the rebels, or their war aims, drives the increase in civilian victimization by counterinsurgent forces.
Finally, some prior studies suggest that democratic institutions can constrain governments from victimizing civilians in armed conflicts (Davenport and Armstrong 2004; Rummel 1995), although others find democratic governments just as likely to target civilians when warfighting becomes costly (Downes 2008). We use a continuous measure of the conflict country’s level of democracy in the year the armed conflict began from the Polity IV data set (Marshall and Jaggers 2002).
In models with the control variables, we continue to find that ethnic conflict has a statistically significant effect on levels of forcible resettlement and civilian targeting. We use model 3b to calculate the marginal effects of ethnic conflict on the role of population resettlement and model 4c to calculate the effects of ethnic conflict on the level of civilian targeting, holding all other independent variables at their means. The probability that forcibly resettling segments of the civilian population will play at least a minor role in the government’s COIN strategy is more than twice as high in ethnic conflicts—increasing from just 15 percent in nonethnic conflicts to 33 percent in ethnic conflicts. The only other variable that is consistently statistically significant is conflict duration, which is also positively correlated with forced resettlement. A one standard deviation increase in conflict duration, centered on the mean, increases the probability population resettlement will play a minor or moderate role in the government’s strategy from 10 percent to 24 percent. The probability that deliberate, forcible relocation of civilians will be extensively employed increases from 2 percent to 8 percent with a one standard deviation increase in conflict duration.
Ethnic conflict is also positively correlated with civilian targeting by the government in every model specification. While the predicted probability of at least moderate levels of civilian targeting is approximately 43 percent when the opposition is not mobilized on the basis of ethnicity (all other variables set to their means), the probability of at least moderate levels of civilian targeting rises to 82 percent in ethnic conflicts. The marginal effect of ethnic conflict on the probability of each level of civilian targeting is plotted in Figure 1. Compared to conflicts in which the opposition is not mobilized along ethnic lines, civilian targeting is forty percentage points less likely to be rare (or nonexistent) and twenty-two percentage points more likely to be extensive in ethnic conflicts. Ethnic conflicts are no more likely to involve moderate levels of civilian targeting than nonethnic conflicts, but the probability of mass killing is seventeen percentage points higher in ethnic conflicts. When all other variables are held constant at their mean values, the predicted probability of mass killing by the government is just 4 percent for nonethnic conflicts. The probability of mass killing rises to 21 percent for ethnic conflicts.
Figure 1. Marginal effects of ethnic conflict on extent of civilian targeting. The y-axis shows the difference in the predicted probability of each level of civilian targeting in ethnic versus nonethnic conflicts. Negative numbers mean that a particular level of civilian targeting is less likely in ethnic conflicts; positive numbers indicate that that level of targeting is more likely in ethnic conflicts. Capped bars show the 95 percent confidence interval around each point prediction. Predictions are generated from model 4c holding all covariates constant at their sample means.
There is also evidence that desperation drives governments to target civilians. Civilian targeting plays a more prominent role in campaigns against rebels with greater troop strength and in conflicts that have spread to more than 75 percent of the geographic area of the country. Governments rarely engage in extensive civilian targeting when conflicts are confined to less than a quarter of the country’s territory. When significant conflict-related violence affects more than three-quarters of the country, the likelihood of extensive civilian targeting or mass killing approaches 50 percent. Similarly, the predicted probability of extensive civilian targeting or mass killing is over 40 percent if opposition forces have more than 10,000 troops. In contrast, the probability the government will engage in more than moderate levels of civilian targeting is less than 23 percent when rebel troop strength is under 10,000 troops. Of course, we are not able to determine the direction of causality with these data. Governments may become more likely to victimize civilians as opposition forces gain strength, or civilian victimization could boost rebel recruitment.
Conflict duration has a statistically significant effect on civilian victimization levels in model 4b, but the relationship is not statistically significant in models 4a and c. All other control variables are uncorrelated with civilian targeting, including government troop strength, civilian victimization by the rebels, the technology of rebellion, a post–Cold War indicator, and the regime’s level of democracy.8 Secessionist aims also have no effect on the likelihood the government will target civilians. Although many secessionist conflicts are also ethnic conflicts, civilian victimization by the government is no more likely in conflicts in which the rebels seek to create a separate state outside the current regime’s authority.
More research is clearly warranted, but this difference in COIN approaches may be explained by an internal security dilemma; government actors do not view reconciliation, disarmament, and reintegration as a possibility when ethnicity has been used to mobilize citizens to take up arms against the regime. Although civilian targeting will not win the HaM of the targeted population, in ethnic civil wars, rebels and the government may be trying to win the support of two distinct populations.


The STAC data set introduces seventeen new measures of the strategies and tactics employed by belligerents in 197 intrastate conflicts between 1945 and 2013, providing scholars with a rich new source of information to facilitate investigations into how regimes and their foreign supporters have responded to insurgent threats. We demonstrate the utility of the STAC data with an analysis of the impact of rebel mobilization on the basis of ethnicity on government tactics. Our analysis demonstrates that governments are much more likely to both forcibly resettle and use indiscriminate violence against civilians in ethnic conflicts. Although some of the most brutal mass killing campaigns in history have taken place during civil wars in which ideology rather than ethnicity was the predominant division, governments are almost twice as likely to victimize civilians, and three times as likely to engage in mass killing, when the armed conflict is “among communities (ethnicities) who are in conflict over the power relationship that exists between those communities and the state” (Sambanis 2001, 261).
Future research can use the STAC data to address a wide range of questions of interest to both academics and policy makers. In addition to the research questions raised in this article, the STAC data offer insight into broader trends in the use of strategies and tactics over time, as well as variation in the COIN approaches of different types of states. The STAC data can also be used to investigate the effects of actors’ tactical choices or strategic approach. Examples of such questions include whether attempts to court civilians prolong conflicts, whether mixed strategies are more successful than a pure brute force approach, and whether the effectiveness of strategic approaches varies across time.
One obvious limitation of the STAC data is the use of the conflict as the level of analysis, rather than a more fine-grained focus on temporal or spatial subunits, such as the conflict-year or geographic regions within countries. This limitation reflects the trade-off between breadth and depth common to all data collection efforts. While more disaggregated data sets exist, we do not know of any other data set that provides multiple unique measures of the warfighting behavior of belligerents in such a large number of intrastate wars. One approach for researchers interested in time-varying effects, or concerned about identifying causal mechanisms at a lower level of analysis, would be to take a mixed-methods approach. Just as one might integrate quantitative and qualitative analyses, scholars can employ the STAC data to test hypotheses about macrolevel outcomes (e.g., strategic approach, armed conflict duration, or termination type) and test hypotheses about the microlevel causal mechanisms underlying those macrolevel outcomes with disaggregated data on a smaller number of conflicts. We hope that extensions of this project, including information at a more granular level for a more limited set of cases, will be facilitated by the coding notes and source material citations released with the data set.


Research for this project was made possible by all the talented undergraduate and graduate research assistants at the University of Georgia and the University of North Carolina at Chapel Hill who collected and coded data for this project. We are especially grateful to Frances Duffy, Menevis Cilizoglu, and Ghazal Dezfuli for exceptional research assistance.

Authors’ Note

A full list of contributors to this project is available at This data feature article was greatly improved by exceptionally helpful feedback from the journal editor and anonymous reviewers. Any opinions, findings, conclusions, or recommendations expressed are solely the responsibility of the authors and do not necessarily reflect the views of the funding agencies, reviewers, or any other contributors.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This study has been supported by grants from the Office of Naval Research, US Department of the Navy [N00014-09-1-0557], and the Carnegie Corporation of New York [D 15126].



If this could not be determined, we considered how often each group was referenced in our sources and the territorial range of each group’s attacks.
Our definition of “regular” troops includes special forces but excludes covert operatives from, for example, a state’s intelligence service, pro-government militias, and proxy forces from another state or nonstate actor.
Cross-tabulations of the first and second coding of each of the strategy and tactics variables reveal that most of the disagreement between coders occurs in the intermediate categories, indicating that researchers could increase the reliability of the least reliable variables by collapsing the intermediate categories or by creating dichotomous variables. The raw data with the first and second coding of each variable, the cross-tabulations, and a suite of interrater reliability statistics are available at
A list of the cases that underwent additional review, RA notes, final coding decisions, and sources consulted in the review are also available at
During the initial iterative process of writing coding rules and coding practice cases, we found that intercoder agreement was higher, and fewer of the variables were left with missing values, if we used four categories rather than three and the “moderate” category was only defined as falling between “minor/rare” and “extensive.”
COIN manual FM 3–24 advocates a population-centric approach that emphasizes providing security for the civilian population and avoiding noncombatant deaths—even at the expense of short-term military objectives (US Department of the Army 2006; US Marine Corps 2006).
Unfortunately, this variable from Balcells and Kalyvas is only available for 68 percent of the cases in our data set.
Results for models that include control variables not shown in the text are available in the Online Appendix.


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Article first published online: February 19, 2019
Issue published: October 2019


  1. civil wars
  2. internal armed conflict
  3. military intervention
  4. war outcomes
  5. rebellion
  6. civilian casualties
  7. insurgency
  8. military strategy

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Published online: February 19, 2019
Issue published: October 2019



Patricia Lynne Sullivan
Department of Public Policy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Johannes Karreth
Department of Politics and International Relations, Ursinus College, Collegeville, PA, USA


Patricia Lynne Sullivan, Department of Public Policy, University of North Carolina at Chapel Hill, 117 Abernethy Hall, Chapel Hill, NC 27599, USA. Email: [email protected]

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