1. Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=cjgr20
Download by: [College Of the Holy Cross] Date: 05 August 2016, At: 07:32
Journal of Genocide Research
ISSN: 1462-3528 (Print) 1469-9494 (Online) Journal homepage: http://www.tandfonline.com/loi/cjgr20
Habituation to atrocity: low-level violence against
civilians as a predictor of high-level attacks
Charles H. Anderton & Edward V. Ryan
To cite this article: Charles H. Anderton & Edward V. Ryan (2016): Habituation to atrocity:
low-level violence against civilians as a predictor of high-level attacks, Journal of Genocide
Research, DOI: 10.1080/14623528.2016.1216109
To link to this article: http://dx.doi.org/10.1080/14623528.2016.1216109
View supplementary material
Published online: 05 Aug 2016.
Submit your article to this journal
View related articles
View Crossmark data
3. generates a ‘demand’ for civilian attacks. Additional insights from the rational addiction
literature point to a simple extension of the model into atrocity habituation in which an
actor’s foray into civilian killing generates its own impetus (demand) for even more civilian
killing.
The survey of VAC motives, literature summary and theoretical model guide our con-
struction of hypotheses about risks for relatively large civilian attacks, i.e. those involving
100 or more fatalities and those involving twenty-five to ninety-nine fatalities. We empiri-
cally test our hypotheses using a pooled sample of forty-nine African countries from 1997
to 2014 based on VAC data from the Armed Conflict Location and Event Dataset (ACLED).
To preview our main result, we find that the number of previous low-level VAC attacks sig-
nificantly affects the number of high-level attacks. To our surprise, some tests show that
prior low-level civilian attacks better predict high-level attacks than civil conflict. Our
results are robust over alternative estimators including negative binomial, logit, zero-
inflated negative binomial, rare events logit and fixed effects; over alternative measures
of low- and high-level civilian attacks; and over alternative measures of control variables.
Given the strength of our results and the emergence of easily accessible data on low-level
VAC, we conclude that low-level civilian attacks (including those with zero fatalities) can be
a valuable explanatory variable and early warning indicator of severe atrocities for scho-
lars, policymakers and activists working on genocide risk and prevention.
Intentional violence against civilians
By whom?
As distinct from non-political mass murders such as most mall shootings, civilians are
intentionally attacked in political contexts by governments and non-state actors including
rebels and militia groups. Figure 1 shows the number of intentional civilian attacks by fatal-
ity levels conducted in African nations by governments, rebels and militia groups from
Figure 1. Intentional violence against civilians in Africa by governments, rebels and militia groups,
1997–2014.
2 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
4. 1997 to 2014. The data source is ACLED, which defines VAC as ‘deliberate violent acts per-
petrated by an organized political group such as a rebel, militia or government force
against unarmed non-combatants’.2
The dark columns show the number of attacks by
governments across the fatality categories. The grey and white columns show the same
for rebel and militia groups, respectively.
The figure shows 28,633 intentional civilian attacks across the three groups in African
states over the period. Most attacks (17,120 or 59.8 per cent) involved zero fatalities;
they should not be excluded from empirical work on VAC because, even when nobody
is killed, they can involve such harms as kidnapping and/or rape. Moreover, as we will
show, low-level attacks can be precursors to later high-level attacks. The figure also
shows that militia attacks make up more than half (15,124 or 52.8 per cent) of all
attacks across the three groups. In ACLED, such militia groups can be aligned with govern-
ments or rebel groups or they can be independent. Note also that attacks involving com-
paratively large fatalities are relatively rare; attacks with twenty-five to ninety-nine fatalities
across the groups numbered 551 (1.9 per cent of the total), while those with 100 or more
fatalities numbered 228 (0.8 per cent of the total).3
Why?
The reasons for VAC can be as numerous as the various motivations of governments,
rebels and militias and the particular and changing circumstances in which they
operate. Nevertheless, scholars generally conclude that such attacks often occur during
wars or other crises involving control of territory and, by extension, the polity.4
During
civil wars, for example, governments and rebel and militia groups generally view civilians
as a critical resource. Controlling populations allows a group to control resources, includ-
ing financing, safe havens, information and new recruits.5
Contesting groups will use inti-
midation and violence to compel civilians to support them or even destroy civilians in an
effort to deny such resources to the enemy. During non-war crises (e.g. severely contested
elections, coups), perceptions of existential threat can lead to drastic choices by state
leaders, including repressive VAC.6
Rebel movements and other non-state groups also
resort to VAC when facing threats and seeking support.7
Although war is considered a critical risk factor in the VAC literature, it is not necessary
for VAC to occur. In societies where the government is dictatorial or weak, government
actors may face few checks and balances to their power. Meanwhile, non-government
actors may feel little loyalty towards government. Under such conditions, predation of civi-
lians can occur through looting, forced relocation, rape and kidnapping.8
These too are
obviously civilian attacks, even when fatalities are zero.
Ethnic and religious differences between groups can also foster (or be manipulated to
promote) VAC during crises.9
Classification of people into different groups by ethnicity,
race, religion, etc. is the first of Stanton’s eight stages of genocide (recently expanded
to ten stages).10
During war or other crisis, government, rebel and militia leaders can
find it beneficial to accentuate group characteristics such as ethnicity or religion. Coalesc-
ence along group lines can generate ‘group formation economies’ including unity of
purpose within the group, ability to root out informants, low-cost recruitment of personnel
and resource support from local and overseas ethnic kin. During crises, people can ‘find it
easy to exaggerate differences between our group and others, enhancing in-group
JOURNAL OF GENOCIDE RESEARCH 3
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
5. cooperation and effectiveness, and—frequently—intensifying antagonism toward other
groups’.11
The lack of democratic checks and balances on power within states can also lower
resistance to VAC among contesting groups.12
Regarding governments, for example,
the ‘more repressive and dictatorial a government, the more will fear inhibit opposition
[to harming civilians]. Opposition to early steps along a continuum of destruction also
decreases when free expression is inhibited … ’.13
The lack of checks and balances on con-
testing actors within states can also be due to weak external constraints. Some scholars
note that greater integration by states into the world economy through trade and/or par-
ticipation in international organizations provides avenues by which governments (and
perhaps other actors who aspire to government power) can be constrained from perpe-
trating civilian atrocities.14
The civil war literature covers implications of ‘lootable resources’, such as oil, minerals
and diamonds, on intrastate violence.15
There is substantial debate on how resources
affect civil strife, for example whether resources are a key element over which the comba-
tants fight (an end), a source of financing for wars driven by other factors (a means) or a
source of grievances from perceived distributional injustices. The means/ends/grievances
distinction does not require an either/or perspective on the roles of lootable resources in
intrastate crises since all three can operate. Regarding VAC, lootable resources can provide
financial rewards from participating in atrocities, means by which civilians can be attacked
and an opportunity to wreak vengeance against an out-group. Similarly, external sources
of resources (e.g. aid) can allow organizations to carry out more VAC attacks than
otherwise.16
There are also aspects of behaviour that can habituate actors to VAC. While con-
tests over political and territorial control can be brutal and cross into VAC, inter-
national laws and norms restrain such atrocities. Once such laws and norms are
broken, it becomes easier for actors to carry out additional and more extreme VAC.
One explanation for escalating aggression against civilians is ‘habituation to atrocity’,
in which initial low-level VAC incidents lower inhibitions to more numerous and
severe attacks.17
When restraints to VAC are challenged, some people from an in-
group will first passively tolerate civilian abuse, later participate in relatively minor
VAC incidents and later move to more severe VAC.18
VAC can escalate from the in-
group owing to rewards offered by political leaders and via the ‘foot-in-the-door
phenomenon’ in which people who have become habituated to small requests will
tend to comply with later larger requests.19
Moreover, vested interests can form
around VAC programmes because they allow people to enrich themselves, achieve
status in a social hierarchy and coerce others to go along with them to reinforce jus-
tification of their actions.20
As resistance to VAC within the in-group diminishes, per-
petrators can come to justify and even laud their actions by claiming that VAC is
essential to their survival, cleanses the territory of a persistent problem group or
makes up for past injustices.
Empirical literature on ‘low-level’ intentional violence against civilians
Supplementary Table S1 summarizes key variables and data sources for twenty pub-
lished large-sample empirical studies on risks for low-level VAC.21
Most of these
4 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
6. studies consider the impact of violent conflict on VAC risk, with many finding a positive
and significant effect.22
Many also find that non-democratic regimes are associated with
a significant risk of government VAC,23
but a few find that democracy is correlated with
a greater risk of rebel VAC.24
Some also include per capita GDP and/or trade openness.
The former proxies a state’s level of economic development and/or income earning
opportunities in the regular economy, while the latter captures how a state’s economy
(and more broadly its society) is integrated with the rest of the world. Some studies
find that greater per capita GDP can reduce VAC risk.25
Similarly, a small number of
studies incorporate trade openness, with a few finding that it significantly reduces
VAC.26
Some studies also find that ethnic fractionalization is associated with a greater
VAC risk.27
Finally, some studies find that resources (natural or external support) signifi-
cantly increase VAC.28
Several other important themes emerge from the empirical literature on low-level
VAC. First, some studies focus on VAC by governments,29
others on VAC by rebel
groups,30
and some by both governments and rebels.31
None of the studies appear to
include militia group attacks, but ours will. Second, almost all of the studies measure
the dependent variable by the number or presence of civilians killed.32
Empirical work
on civilians killed is vitally important, but there is a scarcity of empirical research on
the number or presence of attacks. Political actors usually choose attacks, not a specific
number of civilians to kill; the latter is conditional on the former and other circumstances
associated with the attacks. Hence, our empirical model focuses on the number of civilian
attacks. Third, almost all of the empirical studies on VAC control for the presence and/or
magnitude of conflict (and we will too), but additionally, most (sixteen of twenty in Table
S1) build their samples around conflict cases (e.g. warring actors, states involved in con-
flict, etc.). But wars or even sub-war violence are not necessary for VAC to occur. Hence,
in our sample of African states, some are at times involved in violent conflicts but some
are not.
Theoretical model: VAC as a rational choice
General background of the model
By rational choice, we mean that actors have objectives (e.g. territorial control) and con-
straints (e.g. resource limits) and they attempt to achieve their objectives as best they
can subject to their constraints. It is well known that human choices are more complex
than depicted by ‘narrow’ rational choice models, so economists, social psychologists
and others incorporate additional complexities into rational choice models to capture
such realities. We do so as well by incorporating perspectives from the economics of iden-
tity and rational addiction literatures.33
We assume the model below operates in the context of war or other crises in which a
government, a potential or actual rebel group or an ethnic/religious community, which
could form a militia, feels threatened. We assume each group (government, rebel organ-
ization and militia group) has solved collective action issues within its organization such
that each group via its key leaders can be treated as a decision maker. Finally, we
assume each group seeks to control territory and consume ‘regular goods’ such as food,
clothing and shelter.
JOURNAL OF GENOCIDE RESEARCH 5
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
7. Cobb-Douglas production function for territorial control
Assume a group’s inputs to achieve territorial control (Q) are to fight or contest groups (F)
inimical to its security and to perpetrate strategic violence against civilians (V). Fighting is a
direct means to control territory, but it is not the only means. By threatening or attacking
civilians, a group can attempt to keep villages ‘loyal’, thus providing sources of funding,
safe havens, support for supply lines and recruits. Moreover, such attacks can potentially
undermine civilian support for other groups.
We assume an actor’s control can be enhanced by ethnic or religious identity (I) among
members of the group owing to identity-based economies.34
As noted earlier, such econ-
omies include unity of purpose within the group, enhanced ability to root out informants,
low-cost recruitment of personnel and resource support from ethnic kin.
For a given amount of fighting of other groups, we assume a group’s production func-
tion for territorial control (Q) is of the Cobb-Douglas (CD) form:35
Q = Fw
Vy
Ii
0 , w, y, i , 1, w + y + i = 1. (1)
Since the productivity parameters (w, y, i) respectively on fighting (F), VAC (V) and iden-
tity formation (I) are positive and less than 1, equation (1) implies positive and diminishing
marginal productivity for each ‘input’. Further, the summation condition (w + y + i = 1)
implies that ‘production’ of territorial control is governed by constant returns to scale
(i.e. raising each input by x per cent will cause the amount of territory controlled to rise
by x per cent).
Stone-Geary utility function over territory and consumption goods
Each group achieves utility from territorial control (Q) and a composite consumption good
(C), which represents items such as food, clothing and shelter. For analytical tractability, we
assume two types of civilian attacks: (1) strategic (V), which aid in the control of territory
via the production function noted above, and (2) gratuitous (˜V), which can generate utility
for the group without necessarily having any strategic value. Total civilian attacks will be
V + ˜V. Furthermore, following insights into habituation from social psychology and the
rational addiction literature, we assume that gratuitous VAC in the previous period (˜V−1)
can lower resistance to such atrocities in the present and become a ‘bad habit’.36
A functional form that is particularly useful for modelling the ‘bad habit’ of atrocity is
the Stone-Geary (SG) utility function:
U = bqln(Q − gq) + bcln(C − gc) + b˜vln(˜V − ˜V−1)
0 ≤ bj ≤ 1, j
bj = 1 (j = q, c, ˜v)
(2)
The β terms reflect the comparative importance of the various ‘goods’ in generating
utility. The gamma terms (gq, gc) show the minimum necessary amount of the respective
good. Specifically, gq is the minimum amount of territory the group must achieve to be
viable and gc is minimum (subsistence) consumption. The final piece of the utility function
is the separate addition to utility from gratuitous civilian attacks, ˜V (recall that strategic
civilian attacks, V, is a means to greater Q via the production function). Following the
SG functional form, ˜V−1 can be thought of as a ‘gamma term’ (minimum necessary ˜V)
6 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
8. for utility to be generated from the ‘bad habit’ or ‘addiction’ of gratuitous civilian attacks.
Including a variable minus its previous period value is the simplest way to introduce
rational addiction into a model.37
Complete statement of the model
An actor facing insecurity has resources (R) available to allocate to fighting (F), strategic
VAC (V ), identity formation (I), composite good (C) and gratuitous attacks (˜V). The
‘prices’ for these inputs are given by Pj, (j=f,v,i,c), with each price representing the
average or unit cost per item. We assume for simplicity that the prices of strategic and gra-
tuitous civilian attacks are the same (=Pv). Plugging equation (1) into (2), an actor’s con-
strained maximization problem is:
max
F,V,I,C,˜V
U = bqln(Fw
Vy
Ii
− gq) + bcln(C − gc) + b˜vln(˜V − ˜V−1)
subject to R = Pf F + PvV + PiI + PcC + Pv
˜V
(3)
There is no savings in the model, so all resources are spent as shown by the constraint
equation. A key theoretical mechanism posited in (3) is the presence of past VAC in the
utility function. This implies that if an organization’s leaders chose atrocities in the past
(˜V−1 . 0), they will have a change in preferences in the present in favour of atrocities,
which is the nature of a ‘bad habit’ in the rational addiction literature.
Key theoretical results related to VAC
The model in (3) leads to demand functions for the choice variables, including demand
functions for strategic and gratuitous VAC (V and ˜V), as functions of prices, resources
and productivity and utility function parameters. A rich theoretical analysis of the
demand functions could follow, but our objective is to focus on theoretical predictions
related to VAC. We refer the interested reader to a supplementary mathematical appendix
in which we derive the most important of these theoretical results. Here we provide an
intuitive summary of the key ideas demonstrated there.
The broader context in which the demand for VAC arises in the model is one of inse-
curity for groups over territorial control in which fighting and/or other forms of contesta-
tion are present. It will, of course, be important to control for this in our empirical inquiry.
The supplementary appendix demonstrates that: (1) prior period civilian attacks can gen-
erate an additional demand for VAC in the present; (2) greater resources promote VAC; and
(3) the ‘law of demand’ holds in which VAC attacks rise when the unit cost of such attacks
goes down.
The theoretical model in (3) represents a critical decision by the leaders of political
organizations: should civilians be attacked? The leaders answer ‘yes’ or ‘no’ and, if yes,
choose the amount and severity of such attacks. Such choices depend on the ‘benefits’
of VAC (e.g. controlling territory, gratuitous utility) relative to the costs (proxied by the
unit cost of VAC, Pv). When conditions are relatively peaceful, strategic and gratuitous
benefits of VAC will be low. When internal political constraints on VAC are rigorous (e.g.
democratic checks and balances), there is a large economic opportunity cost of violence
JOURNAL OF GENOCIDE RESEARCH 7
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
9. (e.g. the economy is doing well) and external linkages to the rest of the world are robust
(e.g. through trade), the cost or price of VAC will be high. If benefits of VAC are sufficiently
low and price sufficiently high, the amount of VAC demanded will be zero. If benefits of
VAC are sufficiently high relative to the costs, however, a positive amount of VAC is
demanded (chosen) in the model. In addition, if an organization is rich in resources, its
demand for VAC would be greater, everything else the same.
Empirical research design
Habituation hypotheses
Guided by previous literature and our theoretical model, we focus on habituation to atro-
city in our empirical analyses. In particular, we hypothesize:
H1: The number of prior period acts of low-level VAC will increase the number of high-level
attacks in the present, everything else the same.
H2: The number of prior period acts of low-level VAC will have a greater impact on the number
of high-level attacks in the present the greater the severity of the prior period acts of low-level
violence, everything else the same.
To test H1 and H2, we use several estimation methodologies and variable measures. We
do not estimate separate equations for attacks by governments, rebels and militia groups
but instead empirically model the aggregate number of such attacks across states and
years. Our objective is not to empirically sort out possible interdependencies of action-
reaction (between governments and rebels, for example) or complementarity or substitut-
ability (between government and government-aligned militias, for example).38
Instead, we
include broad measures of political, economic and social conditions that theory and pre-
vious empirical work suggest are important for fostering environments in which civilians
are at risk of attack from governments, rebel groups and militias.
Dependent variable
High-level VAC attacks
Our dependent variable is the number of high-level civilian attacks in African states per
year by government, rebel and militia groups based on ACLED. VAC events in ACLED
include attacks by the three actors as well as fatality estimates per attack, including
attacks with zero fatalities. This allows us to construct counts of VAC attacks per country
per year by various fatality thresholds. Our dependent variable is operationalized by the
count of high-level attacks (100 or more and twenty-five to ninety-nine fatalities) by the
three perpetrator groups.
Key independent variable
Previous low-level VAC attacks
Our key independent variable is not the lag of the dependent variable, but the count of the
previous year’s low-level VAC attacks. We operationalize prior ‘low-level’ attacks as those
conducted by government, rebels and militia groups involving no fatalities (Civilian
8 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
10. Attacks, Zero Fatalitiest−1), one to four fatalities (Civilian Attacks, 1to4 Fatalitiest−1) and five
to twenty-four fatalities (Civilian Attacks, 5to24 Fatalitiest−1). By hypothesis H1, we expect
the number of prior low-level attacks to be positively associated with higher-level attacks.
By H2, we anticipate that the greater the severity of prior low-level attacks, the larger the
impact on high-level attacks.
Control variables
Conflict Magnitude
Following almost all empirical studies of VAC risks, we control for civil conflict. We turn to
the Uppsala Conflict Data Program/Peace Research Institute Oslo (UCDP/PRIO) Armed
Conflict Dataset v.4-2015 to construct our conflict measure.39
We create an ordinal scale
of civil conflict magnitude by distinguishing states and years in which there was civil
war (code 3), sub-war civil conflict in which the cumulative intensity of violence reached
a war threshold (code 2), sub-war civil conflict in which cumulative intensity did not
reach a war threshold (code 1) and no civil conflict (code 0).
The UCDP/PRIO dataset records civil conflicts in which the government is fighting one
or more rebel groups. In our coding we take this into account by adding the coded values
for each civil conflict within a state. For example, in Angola in 1998 there was civil war with
the National Union for the Total Independence of Angola (UNITA) (code 3) and sub-war
civil conflict only with the Front for the Liberation of the Enclave of Cabinda (FLEC) via
its armed wing, the Forças Armadas de Cabinda (FAC) (code 1), for an overall code of
4. The conflict magnitude measure is lagged one year. Based on our theoretical model
and previous literature, we expect Conflict Magnitude to be positively associated with
high-level civilian attacks.
Adjusted Polity2
Our measure of a state’s political system is created from the Polity IV project.40
We begin
with the 21-point Polity2 measure, which ranges from −10 for full autocracy to +10 for full
democracy. In Polity2 coding, cases of foreign interruption (with standardized authority
score −66) are treated as missing, cases of interregnum or anarchy (−77) are set to a
neutral score of zero and cases of transition (−88) are interpolated when feasible. We
modify the Polity2 protocol by classifying cases of interregnum or anarchy (−77) as
missing rather than a neutral score of zero.
In an important analysis of Polity2 coding, Vreeland shows that two of the five
components underlying a state’s Polity2 score include instances where a government con-
ducts repressive violence and even genocide.41
Among the five component scores of
Polity2, three relate to executive power (XCONST, XRCOMP and XROPEN) and two to political
participation (PARREG and PARCOMP). Vreeland shows that civil war and VAC enter the
coding of PARREG and PARCOMP, implying that Polity2 data may generate a spurious
relationship between Polity2 and political violence, especially if one is testing a nonlinear
relationship. Vreeland’s solution is to create an alternative Polity2 score from the three
executive components only. Following Vreeland, we adjust the Polity2 score by summing
the three executive component scores. The resulting measure, Adjusted Polity2, ranges
from −6 for perfect autocracy to +7 for perfect democracy, which is lagged one year. We
expect Adjusted Polity2 to be negatively associated with high-level civilian attacks.
JOURNAL OF GENOCIDE RESEARCH 9
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
11. Log GDP Per Capita
Our gross domestic product per capita measure comes from the World Bank and is the
one-year lag of the natural logarithm of real GDP per capita in constant 2005 US
dollars.42
We expect Log GDP Per Capita to be negatively associated with high-level civilian
attacks.
Trade Openness
For a state’s trade openness, we use the World Bank’s measure of trade as a per cent of
gross domestic product, which we lag one year.43
We expect Trade Openness to be nega-
tively associated with high-level civilian attacks.
Resource Exports
To construct our measure of resources, we sum (by year and by state) the World Bank’s
measures of fuel exports and ores and metals exports as a per cent of merchandise
exports, which we lag one year.44
We expect Resource Exports to be positively associated
with high-level civilian attacks.
Ethnic Fractionalization
We include a measure of ethnic fractionalization based on country-specific, time-invariant
ethnic group shares provided in Alesina et al.45
Being time-invariant, we do not lag our
ethnicity measure. We expect Ethnic Fractionalization to be positively associated with
high-level civilian attacks.
Population
Our measure for population comes from the World Bank and is the natural logarithm of
population, which we lag one period.46
We make no hypothesis about the relationship
between VAC and Population, but it is a frequent control variable in VAC studies.
Table 1 presents descriptive statistics for the variables summarized in this section.
Empirical analyses and results
Negative binomial
Our dependent variable is measured by the count of high-level civilian attacks, so we
begin with negative binomial (NB) regression to test our hypotheses. Table 2 shows the
coefficient estimates for the NB model. The dependent variable is measured by
the count of civilian attacks involving 100 or more fatalities. Columns 1 and 2 show the
results of Model 1 in which prior low-level civilian attacks are measured by the count of
such attacks involving zero fatalities only (column 2 shows results when the highly insig-
nificant GDP per capita and population variables are removed). Results indicate that prior
low-level attacks have a small and insignificant impact on high-level attacks, while conflict
magnitude, resource exports and ethnic fractionalization have positive and significant
effects and more democratic political systems (Adjusted Polity2) reduce high-level attacks.
Columns 3 and 4 in Table 2 represent a change in the measure of our key explanatory
variable from the number of prior zero-fatality attacks to the number of attacks involving
one to four fatalities. Coefficient estimates for prior civilian attacks are now positive and
10 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
12. significant and their magnitudes have risen sharply from about 0.004 to 0.02. Coefficient
estimates on conflict magnitude, resource exports and ethnic fractionalization remain
positive and significant and that for Adjusted Polity2 remains negative and significant.
Note that the coefficient estimate on conflict magnitude has fallen from about 0.52 to 0.33.
Columns 5 and 6 in Table 2 represent a change in the measure of our key explanatory
variable from the number of attacks involving one to four to those involving five to
twenty-four fatalities. Coefficient estimates for prior civilian attacks are positive and signifi-
cant and their magnitudes have again risen sharply from about 0.02 to 0.06. Coefficient
estimates for resource exports and ethnic fractionalization remain positive and significant
and that for Adjusted Polity2 remains negative and significant, but notice that the coeffi-
cient estimate for conflict magnitude in column 5 is close to zero and far from significant.
Column 6 shows that results remain similar after the highly insignificant variables, conflict
magnitude and population, have been removed.
The results in Table 2 are broadly supportive of our habituation hypotheses. In four of
the six regressions, the coefficient estimate on prior period low-level civilian attacks is posi-
tive and significant, which supports hypothesis H1. Moreover, when our measure of prior
low-level attacks goes from very low fatalities (namely, zero fatalities) to still low but more
severe fatalities (one to four and five to twenty-four), the impact on the number of high-
level attacks becomes greater, which supports hypothesis H2. Finally, and to our surprise,
we find that when our measure of prior period low-level civilian attacks becomes modestly
more severe (one to four and five to twenty-four fatalities), the effect of conflict magnitude
on high-level attacks diminishes and even vanishes (in a statistical sense).
Logit
We also test our hypotheses using logit in which positive counts of the dependent variable
are treated as 1 and zero counts as 0. Columns 1 and 2 of Table 3 show the results of Model
1 in which prior low-level civilian attacks are measured by the count of such attacks invol-
ving zero fatalities only (column 2 shows results when the highly insignificant population
Table 1. Descriptive statistics.
Variable Mean Standard Deviation Minimum Maximum
Civilian Attacks,
100+ fatalities
0.26 1.30 0 16
Civilian Attacks,
25to99 Fatalities
0.64 3.13 0 45
Civilian Attacks,
5to24 Fatalities
3.20 10.15 0 144
Civilian Attacks,
1to4 Fatalities
9.16 29.78 0 382
Civilian Attacks,
Zero Fatalities
19.72 61.81 0 707
Conflict Magnitude 0.50 1.01 0 4
Adjusted Polity2 0.74 3.69 −6 7
Log GDP Per Capita 6.60 1.08 4.29 9.58
Trade Openness (per cent of GDP) 75.95 46.67 17.86 531.74
Resource Exports (per cent of Merchandise Exports) 31.68 31.18 0 99.67
Ethnic Fractionalization 0.66 0.23 0.04 0.93
Log Population 16.05 1.26 13.05 18.99
Note: N=868 except for Conflict Magnitude (916), Ethnic Fractionalization (912), Adjusted Polity2 (876), Log GDP Per Capita
(889), Trade Openness (882), Resource Exports (587) and Log Population (931).
JOURNAL OF GENOCIDE RESEARCH 11
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
13. variable is removed). Results indicate that prior low-level attacks have a small and signifi-
cant impact on high-level attacks, while conflict magnitude, resource exports and ethnic
fractionalization have positive and significant effects. Unlike Table 2, however, we now
find that the negative coefficient estimate on Adjusted Polity2 is insignificant.
Moving to columns 3 and 4 and then to 5 and 6 in Table 3 represents changes in the
measure of our key explanatory variable from the number of attacks involving zero fatal-
ities to attacks involving one to four and five to twenty-four fatalities, respectively. While
some coefficient estimates and significances change across several of the variables, we still
find support for our habituation hypotheses. Coefficient estimates for prior civilian attacks
are positive and significant and their magnitudes rise sharply from about 0.005 to 0.05 to
0.11. Meanwhile, the coefficient estimate for conflict magnitude falls from about 0.5 to 0.3
and then to about zero.
Table 2. Effects of low-level civilian attacks on high-level civilian attacks.
Estimator: Negative Binomial
Dependent Variable: High-level civilian attacks (100+ fatalities per attack)
(1)
Model 1
Initial
(2)
Model 1
Refined
(3)
Model 2
Initial
(4)
Model 2
Refined
(5)
Model 3
Initial
(6)
Model 3
Refined
Constant −4.215
(4.388)
[0.337]
−4.659***
(1.548)
[0.003]
−2.316
(3.911)
[0.554]
−2.276
(2.449)
[0.353]
−3.085
(3.625)
[0.395]
−2.250
(2.778)
[0.418]
Conflict Magnitudet−1 0.518***
(0.189)
[0.006]
0.524***
(0.185)
[0.005]
0.330**
(0.154)
[0.032]
0.331**
(0.141)
[0.019]
0.050
(0.204)
[0.806]
Adjusted Polity2t−1 −0.149*
(0.079)
[0.059]
−0.126*
(0.069)
[0.069]
−0.198**
(0.084)
[0.019]
−0.198**
(0.079)
[0.012]
−0.180**
(0.086)
[0.036]
−0.176**
(0.074)
[0.018]
Log GDP Per
Capitat−1
−0.292
(0.319)
[0.360]
−0.396
(0.324)
[0.222]
−0.396
(0.326)
[0.223]
−0.449
(0.380)
[0.238]
−0.446
(0.379)
[0.239]
Trade
Opennesst−1
−0.025
(0.016)
[0.108]
−0.032**
(0.016)
[0.045]
−0.021
(0.014)
[0.138]
−0.021
(0.014)
[0.136]
−0.018
(0.015)
[0.218]
−0.019
(0.014)
[0.176]
Resource
Exportst−1
0.036***
(0.007)
[0.000]
0.033***
(0.007)
[0.000]
0.038***
(0.007)
[0.000]
0.038***
(0.007)
[0.000]
0.035***
(0.007)
[0.000]
0.035***
(0.007)
[0.000]
Ethnic Fractionalization 3.172**
(1.382)
[0.022]
3.258**
(1.477)
[0.027]
2.246*
(1.213)
[0.064]
2.247*
(1.225)
[0.067]
2.768*
(1.469)
[0.060]
2.764*
(1.503)
[0.066]
Log Populationt−1 0.060
(0.206)
[0.772]
0.002
(0.200)
[0.990]
0.046
(0.179)
[0.796]
Civilian Attacks,
Zero Fatalitiest−1
0.004
(0.003)
[0.159]
0.005
(0.003)
[0.118]
Civilian Attacks,
1to4 Fatalitiest−1
0.023***
(0.009)
[0.007]
0.023***
(0.008)
[0.003]
Civilian Attacks,
5to24 Fatalitiest−1
0.063***
(0.024)
[0.008]
0.065***
(0.025)
[0.008]
Pseudo R2
Log Likelihood
Observations
0.191
−144.248
530
0.184
−145.583
532
0.206
−141.521
530
0.206
−141.522
530
0.226
−137.954
530
0.226
−138.004
530
Notes: Cluster robust standard errors in parentheses; p-values in brackets.
*p≤0.10, **p≤0.05, ***p≤0.01 (two-sided).
12 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
14. Zero-inflated negative binomial
In our dataset, there are 868 country-years in which there could have been one or more
high-level civilian attacks by government, rebels and militias. Among our two count
measures of high-level attacks, there were 799 country-years in which there were zero
100+ fatality attacks and 748 country-years in which there were zero 25 to 99 fatality
attacks. This implies that there could be two distinct processes by which a state might
have zero counts for high-level attacks. Within one population of states, some might
have political, economic and conflict magnitude conditions that correlate to zero high-
level civilian attacks. But within another population of states, some might never have
high-level civilian attacks. These latter states are classified as ‘certain zero’ in zero-inflated
negative binomial (ZINB) methodology. Hence, zero counts can arise from either popu-
lation of states, but positive counts only come from the former.
Table 3. Effects of low-level civilian attacks on high-level civilian attacks.
Estimator: Logit
Dependent Variable: High-level civilian attacks (100+ fatalities per attack)
(1)
Model 1
Initial
(2)
Model 1
Refined
(3)
Model 2
Initial
(4)
Model 2
Refined
(5)
Model 3
Initial
(6)
Model 3
Refined
Constant −6.310
(5.873)
[0.283]
−2.290
(3.348)
[0.494]
−2.131
(4.977)
[0.668]
−0.586
(2.655)
[0.825]
−4.198
(5.810)
[0.470]
−0.364
(3.545)
[0.918]
Conflict Magnitudet−1 0.375**
(0.166)
[0.024]
0.488***
(0.163)
[0.003]
0.225
(0.159)
[0.157]
0.313**
(0.124)
[0.012]
−0.011
(0.223)
[0.620]
Adjusted Polity2t−1 −0.095
(0.084)
[0.254]
−0.070
(0.068)
[0.300]
−0.177*
(0.095)
[0.063]
−0.183**
(0.083)
[0.028]
−0.137
(0.103)
[0.184]
−0.122
(0.076)
[0.107]
Log GDP Per
Capitat−1
−0.536
(0.369)
[0.146]
−0.523
(0.332)
[0.116]
−0.794*
(0.413)
[0.055]
−0.895**
(0.395)
[0.024]
−0.930
(0.584)
[0.111]
−0.995*
(0.533)
[0.062]
Trade
Opennesst−1
−0.024
(0.018)
[0.197]
−0.025
(0.016)
[0.123]
−0.010
(0.015)
[0.488]
−0.008
(0.016)
[0.615]
Resource
Exportst−1
0.031***
(0.007)
[0.000]
0.033***
(0.009)
[0.000]
0.030***
(0.007)
[0.000]
0.031***
(0.008)
[0.000]
0.024***
(0.008)
[0.001]
0.027***
(0.008)
[0.001]
Ethnic Fractionalization 3.215*
(1.778)
[0.071]
3.605*
(1.968)
[0.067]
2.035*
(1.183)
[0.085]
2.174*
(1.186)
[0.067]
2.547
(1.788)
[0.139]
3.114
(1.908)
[0.103]
Log Populationt−1 0.269
(0.268)
[0.317]
0.106
(0.269)
[0.693]
0.266
(0.320)
[0.406]
Civilian Attacks,
Zero Fatalitiest−1
0.005**
(0.002)
[0.026]
0.006***
(0.002)
[0.010]
Civilian Attacks,
1to4 Fatalitiest−1
0.049***
(0.011)
[0.000]
0.054***
(0.010)
[0.000]
Civilian Attacks,
5to24 Fatalitiest−1
0.110***
(0.019)
[0.000]
0.117***
(0.018)
[0.000]
Pseudo R2
Log Likelihood
Observations
0.259
−91.570
530
0.254
−92.227
530
0.325
−83.368
530
0.322
−83.878
531
0.368
−78.059
530
0.362
−78.888
531
Notes: Cluster robust standard errors in parentheses; p-values in brackets.
*p≤0.10, **p≤0.05, ***p≤0.01 (two-sided).
JOURNAL OF GENOCIDE RESEARCH 13
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
15. Note that regression will not distinguish between the two processes by which an exces-
sive number of zeros can arise, but ZINB can distinguish the two sources of zeros. Specifi-
cally, ZINB combines a binary (logit) model of the predictors of the ‘certain zero’ class and a
count (NB) model of the predictors of the count process for those not in the ‘certain zero’
class. The correlates of the binary and count portions of the model often differ, i.e. factors
that affect whether states are in the ‘certain zero’ class can differ from the correlates of the
counts of attacks for non-certain zero states.
Table 4 shows the coefficient estimates for the ZINB model in which the dependent
variable is measured by 100+ fatality attacks. The bottom of the table shows the binary
(inflated zero) part of the model. At the bottom of column 1, we find a negative and stat-
istically significant effect of prior low-level (zero-fatality) civilian attacks on the likelihood of
being in the ‘certain zero’ class. That is, the coefficient estimate of −0.424 implies that
higher counts of prior zero-level attacks make being a ‘certain zero’ less likely. Hence,
states that avoid prior attacks (even those with zero fatalities) are more likely to be
‘certain zero’. The lower portion of column 1 also shows that conflict magnitude and
ethnic fractionalization have expected negative signs (more conflict and higher fractiona-
lization make ‘certain zero’ less likely) but are insignificant. The top part of the table shows
the count portion of the model in which resource exports and ethnic fractionalization have
positive and significant impacts and Adjusted Polity2 and trade openness have negative
and significant impacts on the count of high-level attacks. Note in the top portion that
prior low-level (zero fatalities only) attacks and conflict magnitude do not have significant
impacts on high-level attacks. Column 2 of Table 4 runs the ZINB model again, but with the
highly insignificant prior low-level attacks, conflict magnitude and GDP per capita vari-
ables removed from the top portion and the highly insignificant ethnic fractionalization
removed from the bottom. Results in the bottom part of the model continue to show a
negative and significant coefficient estimate on prior zero-fatality attacks and insignifi-
cance for conflict magnitude.
Columns 3 and 4 of Table 4 rerun the regressions of columns 1 and 2, but with a more
severe measure of prior low-level attacks (i.e. those with one to four fatalities). In the
bottom portion of the table, coefficient estimates on prior attacks continue to have the
expected negative and significant effects. But note now that the coefficient estimate for
ethnic fractionalization is highly negative and significant and that for conflict magnitude
is also negative and significant. Hence, ethnic fractionalization and conflict magnitude sig-
nificantly and substantially reduce the likelihood of ‘certain zeros’. In the top portion of
Table 4, columns 3 and 4 show that prior attacks and resource exports have positive
and significant impacts on the count of high-level attacks, while Adjusted Polity2 con-
tinues to have a negative and significant impact. We also find that ethnic fractionalization
has a negative and significant effect on the count of high-level attacks. Perhaps most sur-
prising in column 3 is the negative and insignificant coefficient estimate on conflict mag-
nitude, which we remove in column 4.
Columns 5 and 6 of Table 4 rerun the regressions of columns 1 and 2, but with a more
severe measure of prior low-level attacks (i.e. those with five to twenty-four fatalities). In
the bottom portion of the table, coefficient estimates on prior attacks continue to have
the expected negative and significant effects and the same for ethnic fractionalization,
but note that the coefficient estimate on conflict magnitude shrinks in absolute value
and is no longer significant. In the top portion of the table, columns 5 and 6 show that
14 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
17. prior attacks and resource exports have positive and significant impacts on the count of
high-level attacks, while Adjusted Polity2 continues to have a negative and significant
impact. Perhaps most surprising in columns 5 and 6 are the negative coefficient estimates
on conflict magnitude (one of which is significant), suggesting that after controlling for
modestly severe prior attacks and the ‘certain zero’ process in the lower portion of the
table, conflict magnitude does not elevate the count of high-level civilian attacks.
Each column in Table 4 also includes the Vuong z value, which is used to compare ZINB
and NB estimation methods. In each column of Table 4, the z value is statistically signifi-
cant, indicating that ZINB is preferred to NB as an estimation method. This Vuong
test result holds for all ZINB models available for this article (including supplementary
tables).
We find good support for our habituation hypotheses in Table 4. Specifically, the
paucity of prior low-level civilian attacks explains the tendency of states to be ‘certain
zeros’ regarding high-level civilian atrocities. Moreover, the greater the number of prior
low-level attacks, the greater the count of high-level attacks, everything else the same.
These results are consistent with hypothesis H1. We also find that such results can be
reinforced when we use a more severe measure of prior attacks (i.e. those involving
one to four and five to twenty-four fatalities). Specifically, the absolute values on the coef-
ficient estimates on prior civilian attacks are about nine times larger in columns 5 and 6
relative to 3 and 4 in the bottom portion and about double in the top portion of Table 4.
These results are consistent with hypothesis H2. We also note that the results on the theo-
rized positive effect of conflict magnitude on high-level attacks are simply not as compelling
as those for prior civilian attacks. Of the ten coefficient estimates on conflict magnitude in
the top and bottom portions of Table 4, only two have the predicted sign and are significant.
Meanwhile, of the eleven coefficient estimates on prior attacks, ten have the predicted sign
and are significant.
We reran the ZINB models in Table 4 using a less severe proxy of high-level VAC for the
dependent variable, i.e. the count of attacks involving twenty-five to ninety-nine fatalities.
In Table 5, coefficient estimates on prior low-level attacks are not as large in magnitude as
those in Table 4 and fewer are significant (six of eleven vs. ten of eleven). Nevertheless, all
coefficient estimates on prior low-level VAC have the correct sign, and coefficient esti-
mates on conflict magnitude are never significant in the top portion of the table and sig-
nificant in only three of six cases in the bottom portion.
Table 4. Continued.
Estimator: Zero Inflated Negative Binomial
Dependent Variable: High-level civilian attacks (100+ fatalities per attack)
(1)
Model 1
Initial
(2)
Model 1
Refined
(3)
Model 2
Initial
(4)
Model 2
Refined
(5)
Model 3
Initial
(6)
Model 3
Refined
Log pseudolikelihood
Observations
Zero Observations
Vuong z value
−131.645
530
497
2.57***
[0.005]
−132.440
532
499
3.09***
[0.001]
−131.207
530
497
3.05***
[0.001]
−132.255
532
499
3.22***
[0.001]
−123.044
530
497
2.96***
[0.002]
−123.902
531
498
3.69***
[0.000]
Notes: Robust standard errors in parentheses; p-values in brackets. For Vuong test, square bracket shows Pr>z for
regressions without robust standard errors.
*p≤0.10, **p≤0.05, ***p≤0.01 (two-sided).
16 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
19. Robustness
To test for robustness, we ran numerous additional regressions, which are available in Sup-
plementary Tables S2–S10.
Alternative measures of selected independent variables
In Table S2 we replaced the Alesina et al. ethnic fractionalization measure with that from
Fearon and reran the regressions of Table 4.47
The signs on the coefficient estimates of
ethnic fractionalization are similar across the two measures. More important for our
study is that coefficient estimates on prior low-level attacks are similar in magnitude
and significance across Tables 4 and S2.
In Table S3, we added to the regressions of Table 4 a measure of ethnic polarization
from Alesina et al.48
Coefficient estimates for ethnic polarization in the top and bottom
portions of the table are significant in only one of nine cases. Meanwhile, the coefficient
estimates on prior low-level attacks are similar in magnitude and significance across Tables
4 and S3.
We also replaced our conflict magnitude measure with a lagged dummy (1/0) variable
indicating whether civil war was present during a country-year and reran the regressions
in Table 4. The civil war measure is constructed from the same dataset used to construct
our conflict magnitude measure. In Table S4, eight of eleven coefficient estimates on prior
low-level attacks have the predicted sign and are significant. We also find evidence that
the counts of high-level attacks rise when the severity of prior low-level attacks rises
from zero only to one to four and then five to twenty-four fatalities. In addition, the
civil war dummy has the correct sign and is significant in eight of eleven coefficient esti-
mates. We take this as evidence that both low-level prior attacks and high conflict magni-
tude, i.e. civil war, significantly elevate high-level VAC attacks.
Additional independent variables
We also considered how humanitarian aid and peacekeeping affect VAC. We measure
humanitarian aid by the net official development assistance and official aid received by
a country per year as a per cent of its GDP based on World Bank data.49
The resulting
Table 5. Continued.
Estimator: Zero Inflated Negative Binomial
Dependent Variable: High-level civilian attacks (25 to 99 fatalities per attack)
(1)
Model 1
Initial
(2)
Model 1
Refined
(3)
Model 2
Initial
(4)
Model 2
Refined
(5)
Model 3
Initial
(6)
Model 3
Refined
Log pseudolikelihood
Observations
Zero Observations
Vuong z value
−256.077
530
468
3.89***
[0.000]
−257.639
532
470
4.52***
[0.000]
−251.989
530
468
3.43***
[0.000]
−252.631
532
470
3.89***
[0.000]
−245.341
530
468
3.55***
[0.000]
−245.809
532
470
3.97***
[0.000]
Notes: Robust standard errors in parentheses; p-values in brackets. For Vuong test, square bracket shows Pr>z for
regressions without robust standard errors.
*p≤0.10, **p≤0.05, ***p≤0.01 (two-sided).
18 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
20. measure, official development assistance (ODA), is lagged one period. Peacekeeping
data are provided by the Stockholm International Peace Research Institute (SIPRI).50
SIPRI data show the yearly number of military troops, civilian police and observers pro-
vided to various locations in the world by the United Nations, African Union and other
multilateral organizations. We developed two measures of peacekeeping personnel,
total number of personnel (troops, police, observers) and number of troops only, each
lagged one period.
In Table S5 we reran the empirical models of Table 4 with ODA included. Coefficient
estimates for ODA did not achieve statistical significance in any regressions, but those
for prior low-level attacks were similar in magnitude and significance across Tables 4
and S5. In Table S6, we reran the empirical models of columns 3–6 of Table 4 for each
peacekeeping personnel measure (eight new regressions). Peacekeeping personnel had
significant negative effects on the count of high-level VAC in four regressions, but in
others the effects were insignificant. More important for our study is that all sixteen coeffi-
cient estimates on prior low-level attacks had the correct sign and fourteen were
significant.
In Table S7 we included a lagged dependent variable (i.e., Civilian Attacks, 100+
Fatalitiest−1 and Civilian Attacks, 25to99 Fatalitiest−1) and reran the regressions for
columns 3–6 of Tables 4 and 5 (eight new regressions). The coefficient estimates on the
lagged dependent variable in the top portion of Table S7 were significant in only one
of six cases while those in the bottom portion were significant in only three of seven
cases. Meanwhile, all fifteen coefficient estimates on prior low-level attacks had the
correct sign and ten were significant.
Alternative estimators
We reran the logit models in Table 3 using rare events logit, which corrects for possible
underestimation of rare event probabilities in finite samples.51
In Table S8, coefficient esti-
mates, significances and implications from rare events logit are quite similar to those in
Table 3.
Although we have included a relatively large number of control variables in our empiri-
cal models, regression analysis can be prone to exclude relevant variables, which is known
as omitted variable bias. In samples in which dependent variable measures change within
states across time, fixed effects regression can exploit within-state variations to dampen
the effects of omitted variable bias. In our study, however, fixed effects methods come
with a serious drawback. Specifically, fixed effects estimation drops all observations in
which there is no within-state variation for the dependent variable. Hence, in logit
regression, observations for states with no VAC attacks across the sample period (depen-
dent variable = 0) or with attacks in each year (dependent variable = 1) are dropped. Simi-
larly, in NB regression, observations for states with zero counts of VAC across time are
dropped. Owing to the presence of inflated zeros in our dependent variable data, we
lose more than half of our sample when we apply fixed effects methods to our NB and
logit models.
Despite these drawbacks, in Tables S9 and S10 we reran the initial regressions of Tables
2 and 3, respectively, with fixed effects. To increase observations, we also included a lower
threshold of high-level civilian attacks for the dependent variable (i.e., twenty-five to
JOURNAL OF GENOCIDE RESEARCH 19
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
21. ninety-nine fatalities per attack). Despite the large loss of observations, we still find strong
empirical support for our habituation hypotheses. Specifically, all twelve coefficient esti-
mates of the effects of low-level on high-level attacks are positive and significant. More-
over, there is good evidence of an increase in the magnitude of effect for more severe
low-level attacks.
Discussion and conclusions
Our theoretical and empirical analyses provide support for our habituation hypotheses:
(H1) prior period acts of low-level VAC increase the number of high-level attacks in the
present, and (H2) prior period acts of low-level VAC have a greater impact on the
number of high-level attacks in the present the greater the severity of the prior
period acts of low-level violence. Of the 127 coefficient estimates for the effects of
prior low-level attacks on current high-level attacks in Tables 2–5 and Supplementary
Tables S2–S10, 122 have the predicted sign of which 103 are significant. Further, of
the seventy-six regressions across these tables that assessed correlates for the count
or presence of high-level attacks and in which the measure for prior low-level
attacks increased in severity (e.g. from zero only to one to four to five to twenty-
four fatalities), sixty-three show an increase in the magnitude of effect for more
severe low-level attacks.
Our results are generally robust across alternative measures for control variables,
additional control variables and various estimation methods. We also find in many of
our regressions that prior and more severe low-level civilian attacks better predict high-
level attacks than conflict magnitude, although this result did not hold when the conflict
measure was the presence of civil war. In numerous ZINB regressions that did not make it
into our supplementary tables, we found that Adjusted Polity2, GDP per capita, trade
openness and resource exports rarely achieved statistical significance in the inflate
portion of the model.
In addressing the risk of high-level VAC attacks, our results can be helpful to scholars,
policymakers and activists focusing on atrocity prevention. Our major message is that
‘small’ civilian attacks matter, certainly in their own right, but also for the prevention of
more serious attacks later on. Small attacks (perhaps often below the recognition of the
international community) can lead to deteriorations in norms against attacking civilians
among state, rebel and militia actors, which in turn can escalate the number and severity
of VAC events.
Future work on atrocity prevention can use regional and global data on ‘small’ VAC inci-
dents as early warning indicators of more severe atrocities. For Africa (and several Asian
states) ACLED’s dataset is frequently updated and, as already noted, tracks VAC incidents
even when attacks involve zero fatalities. For global VAC, the Political Instability Task
Force’s Worldwide Atrocities Dataset tracks incidents in which as few as five civilians are
killed and their data is updated regularly.52
VAC incidents involving as few as twenty-
five civilians killed are tracked by UCDP’s One-Sided Violence Dataset, which is updated
yearly.53
Finally, UCDP’s Georeferenced Event Dataset provides regularly updated data
on civilian attacks in Africa, the Middle East and Asia in which as few as one civilian is
killed.54
20 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
22. Notes
1. Charles H. Anderton, ‘Datasets and trends of genocides, mass killings, and other civilian atro-
cities’’, in Charles H. Anderton and Jurgen Brauer (eds.), Economic aspects of genocides, other
mass atrocities, and their prevention (New York: Oxford University Press, 2016), pp. 52–101.
2. Clionadh Raleigh and Caitriona Dowd, ‘Armed conflict location and event data project (ACLED)
codebook 2015’, 2015, p. 13, available at: http://www.acleddata.com/wp-content/uploads/
2015/01/ACLED_Codebook_2015.pdf (accessed 4 June 2015).
3. To focus on groups that are likely to support or contest the state, we exclude inter-communal
VAC from our sample.
4. Stathis N. Kalyvas, The logic of violence in civil war (New York: Cambridge University Press,
2006); Benjamin Valentino, Final solutions: mass killing and genocide in the 20th century
(Ithaca, NY: Cornell University Press, 2004).
5. Kalyvas, The logic of violence, ch. 5; Benjamin Valentino, Paul Huth and Dylan Balch-Lindsay,
‘“Draining the sea”: mass killing and guerrilla warfare’, International Organization, Vol. 58,
No. 2, 2004, pp. 375–407; Jeremy M. Weinstein, Inside rebellion: the politics of insurgent violence
(New York: Cambridge University Press, 2007).
6. Christian Davenport, ‘State repression and political order’, Annual Review of Political Science,
Vol. 10, 2007, pp. 1–23.
7. Reed M. Wood and Jacob D. Kathman, ‘Competing for the crown: inter-rebel competition and
civilian targeting in civil war’, Political Research Quarterly, Vol. 68, No. 1, 2015, pp. 167–179;
Reed M. Wood, ‘Rebel capability and strategic violence against civilians’, Journal of Peace
Research, Vol. 47, No. 5, 2010, pp. 601–614; Idean Salehyan, David Siroky and Reed
M. Wood, ‘External rebel sponsorship and civilian abuse: a principal-agent analysis of war
time atrocities’, International Organization, Vol. 68, No. 3, 2014, pp. 633–661; Weinstein,
Inside rebellion.
8. Elisa von Joeden-Forgey, ‘Gender and the genocidal economy’, in Anderton and Brauer, Econ-
omic aspects of genocides, pp. 378–395.
9. Hanne Fjelde and Lisa Hultman, ‘Weakening the enemy: a disaggregated study of violence
against civilians in Africa’, Journal of Conflict Resolution, Vol. 58, No. 7, 2014, pp. 1230–1257;
Geoffrey Robinson, ‘State-sponsored violence and secessionist rebellions in Asia’, in Donald
Bloxham and A. Dirk Moses (eds.), The Oxford handbook of genocide studies (New York:
Oxford University Press, 2010), pp. 466–488; Leo Kuper, The pity of it all: polarisation of
racial and ethnic relations (London: Duckworth, 1977).
10. Gregory H. Stanton, ‘The 8 stages of genocide’, Genocide Watch, 1998, available at: http://
www.genocidewatch.org/genocide/8stagesofgenocide.html (accessed 4 June 2015).
11. James E. Waller, Becoming evil: how ordinary people commit genocide and mass killing
(New York: Oxford University Press, 2007), p. 201.
12. Barbara Harff, ‘No lessons learned from the Holocaust? Assessing risks of genocide and politi-
cal mass murder since 1955’, American Political Science Review, Vol. 97, No. 1, 2003, pp. 57–73;
Rudolph J. Rummel, Statistics of democide: genocide and mass murder since 1900 (Piscataway,
NJ: Transactions Publishers, 1998).
13. Ervin Staub, The roots of evil: the origins of genocide and other group violence (New York:
Cambridge University Press, 1989), p. 65.
14. Harff, ‘No lessons learned’.
15. James Ron (ed.), ‘Paradigm in distress? Primary commodities and civil war’, Journal of Conflict
Resolution, Vol. 49, No. 4, 2005, Special Issue, pp. 441–633; Anke Hoeffler, ‘On the causes of civil
war’, in Michelle R. Garfinkel and Stergios Skaperdas (eds.), The Oxford handbook of the econ-
omics of peace and conflict (New York: Oxford University Press, 2012), pp. 179–204.
16. Salehyan et al., ‘External rebel sponsorship’, and Weinstein, Inside rebellion, find that when
rebels have good internal access to natural resources and they receive external support
from non-democracies and multiple supporters, they are more likely to conduct VAC.
17. Another plausible explanation for correlation between past and present atrocities is bureau-
cratic inertia, which is often empirically modelled using a lagged dependent variable. Below
JOURNAL OF GENOCIDE RESEARCH 21
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
23. we find empirical support for our habituation hypotheses even after including a lagged
dependent variable.
18. Staub, The roots of evil, p. 68.
19. Waller, Becoming evil, p. 232.
20. Kalyvas, The logic of violence, p. 58.
21. For surveys of empirical studies of mass atrocity risks, see Anke Hoeffler, ‘Development and the
risk of mass atrocities: an assessment of the empirical literature’, in Anderton and Brauer, Econ-
omic aspects of genocides, pp. 230–250; and Charles R. Butcher and Benjamin E. Goldsmith,
‘Economic risk factors and predictive modeling of genocides and other mass atrocities’, in
Anderton and Brauer, Economic aspects of genocides, pp. 569–590.
22. J. Michael Quinn, ‘Territorial contestation and repressive violence in civil war’, Defence and
Peace Economics, Vol. 26, No. 5, 2015, pp. 536–554; Wood and Kathman, ‘Competing for
the crown’; Reed M. Wood, ‘Opportunities to kill or incentives for restraint? Rebel capabili-
ties, the origins of support, and civilian victimization in civil war’, Conflict Management
and Peace Science, Vol. 31, No. 5, 2014, pp. 461–480; Salehyan et al., ‘External rebel
sponsorship’.
23. Kristine Eck and Lisa Hultman, ‘One-sided violence against civilians in war: insights from new
fatality data’, Journal of Peace Research, Vol. 44, No. 2, 2007, pp. 233–246; Reed M. Wood, Jacob
D. Kathman and Stephen E. Gent, ‘Armed intervention and civilian victimization in intrastate
conflict’, Journal of Peace Research, Vol. 49, No. 5, 2012, pp. 647–660.
24. Lisa Hultman, ‘Attacks on civilians in civil war: targeting the Achilles heel of democratic
governments’’, International Interactions, Vol. 38, No. 2, 2012, pp. 164–181; Wood et al.,
‘Armed intervention’.
25. Quinn, ‘Territorial contestation’; Philip Hultquist, ‘Is collective repression an effective counter-
insurgency technique? Unpacking the cyclical relationship between repression and civil con-
flict’, Conflict Management and Peace Science, 2015, doi:10.1177/0738894215604972; Hultman,
‘Attacks on civilians’; Wood, ‘Rebel capability’.
26. Uih Ran Lee, ‘Hysteresis of targeting civilians in armed conflict’, The Economics of Peace and
Security Journal, Vol. 10, No. 2, 2015, pp. 31–40.
27. Wood, ‘Opportunities to kill’; Hultman, ‘Attacks on civilians’.
28. Wood and Kathman, ‘Competing for the crown’; Hultman, ‘Attacks on civilians’; Salehyan et al.,
‘External rebel sponsorship’.
29. Hultquist, ‘Is collective repression’; Quinn, ‘Territorial contestation’.
30. Martin Ottmann, ‘Rebel constituencies and rebel violence against civilians in civil conflicts’,
Conflict Management and Peace Science, 2015, doi:10.1177/0738894215570428; Wood and
Kathman, ‘Competing for the crown’; Salehyan et al., ‘External rebel sponsorship’.
31. Sebastian Schuttee, ‘Geographic determinants of indiscriminate violence’, Conflict Manage-
ment and Peace Science, 2015, doi:10.1177/0738894215593690; Fjelde and Hultman, ‘Weaken-
ing the enemy’.
32. Exceptions in Table S1 are Yuri Zhukov, ‘On the logistics of violence: evidence from Stalin’s
great terror, Nazi-occupied Belarus, and modern African civil wars’, in Anderton and Brauer,
Economic aspects of genocides, pp. 399–424; Wood and Kathman, ‘Competing for the
crown’; and Reed M. Wood, ‘From loss to looting? Battlefield costs and rebel incentives for vio-
lence’, International Organization, Vol. 68, No. 4, 2014, pp. 979–999.
33. For defences and criticisms of rational choice theory in the study of VAC, see, respectively,
Charles H. Anderton and Jurgen Brauer, ‘Genocide and mass killing risk and prevention: per-
spectives from constrained optimization models’, in Anderton and Brauer, Economic aspects of
genocides, pp. 143–171; and Manus I. Midlarsky, The killing trap: genocide in the twentieth
century (New York: Cambridge University Press, 2005), pp. 64–74.
34. George A. Akerlof and Rachel E. Kranton, ‘Economics and identity’, The Quarterly Journal of
Economics, Vol. 115, No. 3, 2000, pp. 715–753.
35. Cobb-Douglas is the most widely taught specific functional form in economics and is available
in virtually all intermediate microeconomics textbooks.
22 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
24. 36. On social psychology and atrocity habituation, see Waller, Becoming evil, pp. 232–233. A
seminal article on rational addiction is Gary S. Becker and Kevin M. Murphy, ‘A theory of
rational addiction’, The Journal of Political Economy, Vol. 96, No. 4, 1988, pp. 675–700.
37. Walter Nicholson and Christopher Snyder, Microeconomic theory: basic principles and exten-
sions, 11th
edn. (Mason, OH: South-Western, 2012), p. 113.
38. Much more data work is necessary to estimate simultaneous equations for attacks by govern-
ments, government-aligned militias, rebels, rebel-aligned militias and independent militias
because ACLED does not code militia attacks across the various militia categories. None of
the studies in Table S1 that estimate separate equations for VAC by government and rebels
use simultaneous equation methods.
39. Nils Petter Gleditsch, Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg and Håvard
Strand, ‘Armed conflict, 1946–2001’, Journal of Peace Research, Vol. 39, No. 5, 2002, pp. 615–
637; Therése Pettersson and Peter Wallensteen, ‘Armed conflict, 1946–2014’, Journal of
Peace Research, Vol. 52, No. 4, 2015, pp. 536–550.
40. Monty G. Marshall, Ted Robert Gurr and Keith Jaggers, ‘Polity IV project, political regime
characteristics and transitions, 1800–2014’, 2014, available at: http://www.systemicpeace.
org/inscrdata.html (accessed 4 June 2015).
41. James Raymond Vreeland, ‘The effect of political regime on civil war: unpacking anocracy’,
Journal of Conflict Resolution, Vol. 52, No. 3, 2008, pp. 401–425.
42. World Bank, World Development Indicators, available at: http://data.worldbank.org/indicator
(accessed 4 June 2015).
43. World Bank, World Development Indicators.
44. World Bank, World Development Indicators.
45. Alberto Alesina, Arnaud Devleeschauwer, William Easterly, Sergio Kurlat and Romain Wacziarg,
‘Fractionalization’, Journal of Economic Growth, Vol. 8, No. 2, 2003, pp. 155–194. Let pi represent
the population share of group i such that pi = 1. Then Ethnic Fractionalization = 1 − p2
i .
Later, we also consider Alesina et al.’s Ethnic Polarization, which is measured as 4 p2
i (1 − pi)
(Jose G. Montalvo and Marta Reynal-Querol, ‘Discrete polarisation with an application to the
determinants of genocides’, The Economic Journal, Vol. 118, November 2008, pp. 1835–
1865). When using these measures, we normalize group shares to sum to 1 and then apply
the preceding formulas.
46. World Bank, World Development Indicators.
47. Alesina et al., ‘Fractionalization’; James D. Fearon, ‘Ethnic and cultural diversity by country’,
Journal of Economic Growth, Vol. 8, No. 2, 2003, pp. 195–222, available at: https://web.
stanford.edu/group/fearon-research/cgi-bin/wordpress/paperspublished/journal-articles-2/
(accessed 6 June 2015).
48. Alesina et al., ‘Fractionalization’.
49. World Bank, World Development Indicators, available at: http://databank.worldbank.org/data/
(accessed 18 May 2016).
50. At the time of this writing, SIPRI’s peacekeeping dataset (https://www.sipri.org/databases/pko)
was unavailable. Data for 1996–2012 were generously provided by SIPRI. Peacekeeping data
for 2013 and 2014 came from the 2014 and 2015 volumes of SIPRI yearbook: armaments, dis-
armament and international security (New York: Oxford University Press).
51. Gary King and Langche Zeng, ‘Explaining rare events in international relations’, International
Organization, Vol. 55, No. 3, 2001, pp. 693–715; Michael Tomz, Gary King and Langche
Zeng, ‘RELOGIT: rare events logistic regression’, Version 1.1, Harvard University, Cambridge,
MA, 1999, available at: http://gking.harvard.edu/relogit (accessed 21 October 2011).
52. Political Instability Task Force Worldwide Atrocities Dataset. Data available at: http://eventdata.
parusanalytics.com/data.dir/atrocities.html (accessed 25 February 2016).
53. Eck and Hultman, ‘One-sided violence’. Data available at: http://www.pcr.uu.se/research/ucdp/
datasets/ucdp_one-sided_violence_dataset/ (accessed 25 February 2016).
54. Mihai Croicu and Ralph Sundberg, ‘UCDP GED codebook version 4.0’, Department of Peace
and Conflict Research, Uppsala University, 2015, available at: http://ucdp.uu.se/downloads/
ged/ucdp-ged-40-codebook.pdf (accessed 24 July 2016).
JOURNAL OF GENOCIDE RESEARCH 23
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
25. Acknowledgements
We are grateful to Robert Baumann, Bryan Engelhardt, Katherine Kiel, Jens Meierhenrich, A. Dirk
Moses and two anonymous referees for helpful insights on earlier drafts. We also gratefully acknowl-
edge support from the Holy Cross College Summer Research Program. We alone are responsible for
any errors and omissions.
Disclosure statement
No potential conflict of interest was reported by the authors.
Notes on contributors
Charles H. Anderton is Professor of Economics and W. Arthur Garrity, Sr. Professor of Human Nature,
Ethics and Society at the College of the Holy Cross (Worcester, MA, USA). His research interests
include economic aspects of genocides, the bargaining theory of war and rational choice aspects
of violent behaviour. His teaching interests include the following courses: ‘Economics of war and
peace’ and ‘Genocide and mass killing: perspectives from the social sciences’. He is co-editor, with
Jurgen Brauer, of Economic Aspects of Genocides, Other Mass Atrocities, and Their Prevention
(Oxford University Press, 2016).
Edward V. Ryan is an investment analyst at Ballentine Partners, a multi-family office in the Boston
area where he focuses on portfolio strategy and implementation. He conducted research in conflict
economics as a summer research assistant and as an honours student at the College of the Holy
Cross (Worcester, MA). His honours thesis, ‘The Risk Correlates of Violence against Civilians in
Africa’, earned the College’s Freeman M. Saltus award for the best undergraduate paper in econ-
omics for the 2014/2015 academic year.
24 C. H. ANDERTON AND E. V. RYAN
Downloadedby[CollegeOftheHolyCross]at07:3205August2016
![Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=cjgr20
Download by: [College Of the Holy Cross] Date: 05 August 2016, At: 07:32
Journal of Genocide Research
ISSN: 1462-3528 (Print) 1469-9494 (Online) Journal homepage: http://www.tandfonline.com/loi/cjgr20
Habituation to atrocity: low-level violence against
civilians as a predictor of high-level attacks
Charles H. Anderton & Edward V. Ryan
To cite this article: Charles H. Anderton & Edward V. Ryan (2016): Habituation to atrocity:
low-level violence against civilians as a predictor of high-level attacks, Journal of Genocide
Research, DOI: 10.1080/14623528.2016.1216109
To link to this article: http://dx.doi.org/10.1080/14623528.2016.1216109
View supplementary material
Published online: 05 Aug 2016.
Submit your article to this journal
View related articles
View Crossmark data](https://image.slidesharecdn.com/44d72b58-d3e4-4bc2-b3bf-1104b9b285af-161207211539/85/Anderton-Ryan-Publication-JGR-1-320.jpg)
![Habituation to atrocity: low-level violence against civilians as a
predictor of high-level attacks
Charles H. Anderton and Edward V. Ryan
ABSTRACT
‘Habituation to atrocity’ is characterized as an actor’s increased
willingness to carry out high-level violence against civilians (VAC)
owing to the choice of low-level attacks in an earlier period. We
theoretically analyse habituation to atrocity using a rational choice
model in which a government, rebel organization or militia group
allocates resources to fighting, attacking civilians and identity
formation to achieve territorial control. Based upon concepts
available in the rational addiction literature, the model generates
a demand function for VAC in which substantial additional
demand arises owing to the ‘bad habit’ generated by previous
atrocities. The model guides our empirical inquiry into VAC for a
sample of forty-nine African countries over the period 1997 to
2014. We find that the number of past low-level civilian attacks
(even sometimes those involving zero fatalities) significantly
affects the number of high-level attacks in the present. We also
find that previous low-level civilian attacks sometimes better
predict high-level attacks than civil conflict. Our work suggests
that regional and global datasets on ‘small’ VAC incidents can
serve as valuable early warning indicators of more severe atrocities.
Introduction
Since the turn of the twentieth century, the world has endured more than 200 mass atro-
cities in which at least 1,000 civilians were purposely killed. In this time, there have also
been thousands of acts of ‘low-level’ intentional violence against civilians (VAC) in
which at least five civilians were killed.1
There exists substantial literature concerning con-
ditions that enable VAC, including about three dozen published empirical studies of risks
for mass atrocities and about twenty such studies for low-level VAC. Nevertheless, there is
little empirical work on ‘habituation to atrocity’, which we characterize as an actor’s
increased willingness to carry out high-level civilian attacks owing to earlier choices of
low-level civilian attacks.
This article begins with a brief survey of who intentionally attacks civilians and why, fol-
lowed by a summary of empirical literature on risks for low-level VAC. The survey and lit-
erature summary inform our development of a rational choice model designed to identify
conditions in which attacking civilians is an ‘optimal’ choice by a government, rebel organ-
ization or militia group. The model shows how an actor’s desire to control territory
© 2016 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Charles H. Anderton canderto@holycross.edu
Supplementary material for this article is available online at http://dx.doi.org/10.1080/14623528.2016.1216109.
JOURNAL OF GENOCIDE RESEARCH, 2016
http://dx.doi.org/10.1080/14623528.2016.1216109
Downloadedby[CollegeOftheHolyCross]at07:3205August2016](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)