All is Not Fair in Love and War: Ethnic Inequality,
Social Capital and Violence in Sub-Saharan Africa
Mai Nguyen
∗
†
September 2013
Abstract
Popular sentiment and intuition would suggest that inequality is a primary determinant of violence; yet, most empirical studies do not find a robust relationship between
economic inequality and civil conflict. The failure of past studies to find an empirical
connection between inequality and violence may be due to improper measurement and
inadequate theorizing about the role of inequality rather than the lack of any substantial relationship. This paper argues that because internal conflicts tend to be group
events, economic differences between groups should be examined as a determinant of
violence. My analysis examines the impact of ethnic inequality, or economic differences
between ethnic groups, on political violence in the context of Sub-Saharan Africa. Additionally, I propose a mechanism linking ethnic inequality to violence through the
reduction of social capital. Using survey data from the DHS and Afrobarometer, I find
a robust positive association between ethnic inequality and political violence at the
country and region levels. Higher levels of ethnic inequality increase the probability of
conflict as well as the total number of violent events and fatalities. However, causal mediation analysis shows social capital is not a statistically significant mediating variable,
highlighting the need to examine other possible mechanisms in future research.
∗
I would like to thank Oeindrila Dube and Nathaniel Beck for valuable comments and suggestions. I also
thank Hannah Simpson, Saad Gulzar, Kelsey Liddy and Christine Vu for helpful feedback.
†
New York University, Wilf Family Department of Politics, [email protected].
1
Introduction
Intuition suggests that economic inequality should contribute to higher levels of conflict. The
empirical support providing a link between inequality and conflict is mixed at best (Lichbach
1989). While some studies find a positive association (see Barrow 1967; Alesina and Perotti
1996) others find a negligible effect of inequality on civil war onset (see Collier and Hoeffler
2004; Fearon and Laitin 2003). These divergent results have resulted in a shift away from
examining inequality as a determinant of violence. However, the lack of significant findings
connecting inequality to conflict may be an artifact of inadequate theorizing or measurement of inequality rather than the absence of any meaningful relationship. A plethora of
case studies show that relational or horizontal inequalities are more important in determining violence. Because most internal conflicts tend to be group phenomena, it is economic
differences between groups, not individuals, that matter most (Stewart et al. 2008b). The
purpose of this paper is to empirically examine the relationship between ethnic inequality,
that is economic differences between ethnic groups, and political violence.
Using survey data from the DHS and Afrobarometer, I create a measure of between-group
inequality that measures economic differences between ethnic groups. Applying my measure
in a cross-country analysis in Sub-Saharan Africa I find that ethnic inequality affects the
onset of conflict, total number of violent events as well as total number of fatalities. Higher
levels of ethnic inequality are associated with increased probability of conflict as well as
increased total number of violent events and fatalities. Holding all other variables constant,
a one standard deviation increase in ethnic inequality increases the probability of conflict by
nine percent, the total number of violent events by four, and the total number of fatalities by
approximately thirty. Additionally, in a subnational analysis I find that economic differences
between ethnic groups measured at the local level also contributes to the total number of
1
violent events and number of fatalities within a region (or first administrative level): a one
standard deviation increase in ethnic inequality increases the total number of violent events
and fatalities by approximately 26 percent and 30 percent, respectively. My results imply
there is a strong relationship between ethnic inequality and political violence.
My analysis delves further by identifying a possible mechanism in which ethnic inequality
contributes to violent conflict. I posit that economic differences between ethnic groups
decrease the level of social capital within a society and therefore provide favorable conditions
for violence to occur. Following insights from Varshney (2002), I propose that a reduction
in social capital, or civic engagement, due to high levels of ethnic inequality impairs a
community’s ability to peacefully resolve disputes between groups. When trust and norms
of cooperation are diminished, opportunities arise for politicians or leaders to polarize and
institutionalize violence. Using a measure of generalized trust, I find the introduction of
social capital decreases the magnitude of ethnic inequality, suggesting that ethnic inequality
may impact violence through lowering social capital. However, mediation analysis shows
other mechanisms exist connecting ethnic inequality to violent conflict.
The paper is organized as follows. Section 2 provides a brief background of inequality in the
civil war literature, stipulates why ethnic inequality should be emphasized as a determinant
of violence and specifies one possible mechanism by which ethnic inequality affects conflict.
Section 3 describes the data and measurements used as well as the estimation framework
employed. In section 4 I report the results of my analysis. Section 5 concludes.
2
2
Background
There exists myriad theories connecting inequality to conflict. Many instrumental arguments
posit that the unequal distribution of resources can generate material incentives for conflict
to the extent that seizure of the state brings material gains and opportunity for individuals to
share the whole income of the economy (Blattman and Miguel 2010; Fearon 2007; Acemoglu
and Robinson 2005). Other explanations linking economic inequality to conflict stem from
psychological arguments concerning relative deprivation. According to this theory, collective
violence is a reaction to frustrations and widespread discontent resulting from a gap between
an individual’s value expectations and value capabilities, that is what an individual believes
she deserves versus her reality (Gurr 1970). Relative deprivation does not derive from absolute poverty, but rather from comparisons with others in the same society. Inequality
contributes to a perceived discrepancy between individual aspirations and actual economic
status, which in turn may trigger radical action and conflict (Davies 1962).
Individual feelings of self-worth are inextricably tied to group identity. Self-esteem is largely
a function of the esteem accorded to groups of which one is a member: positive or favorable
comparisons between the in-group and out-group provide members with high subjective
status or prestige while unfavorable comparisons result in low prestige and negative social
identity (Turner 1982). Group comparison and claims of worth and legitimacy are key drivers
of conflict. Focusing primarily on ethnic group identities, Horowitz (1985) argues: “conflict
arises from the common evaluative significance accorded by the groups to acknowledged
group differences and then played out in public rituals of affirmation and contradiction”
(222). For Horowitz, the source of ethnic conflict in the modern state is the struggle for
relative group wealth.
Although there is an abundance of arguments linking inequality theoretically to conflict, em3
pirical evidence finding a robust relationship is mixed. Numerous studies find that economic
inequality has a positive impact on violence while others show negative or negligible relationships (Lichbach 1989). Barrows (1967) and Alesina and Perotti (1996) find that inequality
is highly correlated with political instability, as measured by incidences of elite instability,
communal instability, turmoil or number of annual political murders. In contrast, many recent studies using cross-national data find no relationship between inequality (as measured
by the Gini coefficient) and civil conflict onset (e.g. Collier and Hoeffler 2004, Fearon and
Laitin 2003).
These divergent conclusions have led many researchers to focus primarily on the opportunity
structures that make internal conflict possible while minimizing the role of inequality and
grievances generally. However, the abandonment of inequality as a causal factor appears
premature. The lack of significant findings connecting inequality to conflict may be an
artifact of inadequate theorizing or measurement of inequality rather than the absence of
any meaningful relationship. Cramer (2003) argues: “the rationalization of a given causal
relationship involving inequality and conflict seems to be driven by an apparently arbitrary
selection of assumptions...this does not give much confidence in the treatment in mainstream
economics of the role of inequality in the origins of conflict” (400). Inequality itself is not as
important as the established social relationships that lie behind the observable manifestation
of inequality. Consequently, Cramer advocates for the examination of ‘relational inequality’–
that is differences between men versus women, agricultural laborers versus landowners, or
different ethnic groups–to understand the consequences of inequality (404).
Similarly, Stewart (2008a) emphasizes the role of horizontal inequalities (economic, social
or political inequalities between culturally defined groups) in explaining conflict (3). Most
internal conflicts are organized group conflicts rather than simply a matter of individuals
committing violence against others (Stewart 2008a, 12; see also Robinson 2001). As such,
4
group motives and inter-group dynamics are vital to examine as a determinant of conflict.
Using information from case studies of eight different countries in three separate regions,
Stewart et. al. (2008b) find that the probability of conflict rises where socioeconomic
horizontal inequalities are higher. Inequalities between groups–whether they are political,
social or economic–determines conflict; however, standard measures of inequality such as the
Gini coefficient do not capture these group asymmetries (Acemoglu and Robinson 2005).
This distinction explains the failure of past studies to find a relationship between inequality
and conflict.
Groups are composed of individuals who internalize the same social category membership
as a component of their self-concept (Turner 1982). Group formation can occur along a
variety of social categories including class, religion, race, ethnicity, occupation, etc. The
primary groups that will be examined in this analysis are ethnic groups. In many SubSaharan African countries, ethnic identity is the most prominent social cleavage (Horowitz
1985; Scarritt and Mozaffar 1999; Stewart 2002). Although ethnicity is just one of several
possible social identities, ethnic divisions remain salient and an important determinant of
violence. Fearon and Laitin (1997) note:
“the fact that ethnic groups are often characterized by relatively dense social
networks and low-cost access to information about the past history of individuals’ behavior has an important consequence for intra- versus intergroup relations.
Within groups, people who exploit the trust of others can be identified as individuals and sanctioned with relative ease by the response of the ethnic community”
(719).
The presence of ethnic groups, and the social networks inherent within them, assists in
the mobilization of violent collective action. Inequality between different ethnic groups
5
rather than overall inequality then becomes a driving factor of conflict. Alesina et. al.
(2012) note “inequality in income along ethnic lines is likely to increase animosity, impede
institutional development, and lead to state capture and conflict” (1). Using a spatial dataset
of economic performance, Cederman et. al. (2011) find that ethnic groups with wealth levels
far from the country average are more likely to experience civil war. Their results show
that horizontal inequalities between ethnic groups, in terms of both power and economic
inequality, contributes to violence.
Although evidence suggests a connection between relational inequalities and violence, arguments linking ethnic inequality to violence may still be susceptible to the same critiques
levied against grievance-based arguments of conflict. Grievances tend to be universal, yet
large-scale violence does not occur everywhere. The mere existence of grievances, such as
inequality, does not necessitate conflict. The question then becomes how does ethnic inequality come to affect the onset of violence? In order to address this concern, it is necessary to
explicate a mechanism by which ethnic inequality can produce violence. Below I present one
possible pathway in which inequality between ethnic groups could lead to violent conflict.
2.1
Ethnic Inequality, Social Capital and Violence
I now turn to my own account of how ethnic inequality may lead to violence by decreasing
social capital. Social capital, also referred to as civic engagement, is commonly conceptualized to represent the features of social organization, such as trust, norms and networks,
that can improve the efficiency of society by facilitating coordinated action (Putnam 1993).
Social capital has a collective as well as individual aspect. Individuals will pursue interactions with others for their own benefit, whether it is to fulfill an economic interest or even
to satisfy a psychological need. The social relations that individuals pursue can also have
6
broader impacts for the community as a whole (Putnam 2000). As more and more people
pursue interactions, the community becomes connected through a dense network of social
interactions.
These social connections, although themselves important, are crucial because of the rules
of conduct they create and sustain. Fukuyama (2001) recognizes social capital as an instantiated informal norm that promotes cooperation between two or more individuals (7).
For Fukuyama (2001), it is the norm created that is important in defining social capital
and all the other components such as trust, civil society and the like all arise from those
created norms. Social networks naturally involve mutual obligation and responsibility for
action, which in turn creates social norms. One important social norm created is generalized reciprocity, which is the “continuing relationship of exchange that is at any given time
unrequited or imbalance, but that involves mutual expectations that a benefit granted now
should be repaid in the future” (Putnam 1993). The community becomes bounded together
by horizontal relations of reciprocity and cooperation.
When economic or political dealings are embedded in dense social networks, the community
is able to decrease opportunism and discourage malfeasance or defection (Putnam 2000).
Because social capital promotes high levels of cooperation, trust, reciprocity and collective
well-being, societies with high levels of social capital tend to be characterized by more
effective institutions (Putnam 1993), higher economic growth (Knack and Keefer 1997; Zak
and Knack 2001; Guiso et al. 2004), and stronger democracy (Fukuyama 2001).
Manifold individual and contextual factors contribute to social capital. Individual characteristics of age, gender and education are found to be consistent indicators of participation
and trust (Putnam 2000; Alesina and La Ferrara 2000; Ulsaner 2002; Bjornskov 2006). The
most important contextual factor that explains variation in levels of social capital across dif-
7
ferent localities is heterogeneity in terms of ethnic or racial diversity and economic inequality
(Alesina and La Ferrara 2002).
Ethnic inequality, or economic differences between ethnic groups, can have adverse consequences for social capital. This idea comes from Allport’s conditions for reducing intergroup
prejudice: “prejudice...may be reduced by equal status contact between majority and minority groups in the pursuit of common goals. The effect is greatly enhanced if this contact is
sanctioned by institutional supports, and provided it is of a sort that leads to the perception
of common interests and common humanity between members of the two groups” (1954).
The key point is equal status between majority and minority groups will aid in reducing
prejudice; alternatively, when there is unequal status between groups prejudice will increase.
Ethnic inequality increases negative stereotypes and prejudices and consequently reduces social capital. Empirical analysis supports this notion: examining United States Metropolitan
sampling areas Tesei (2011) finds racial income inequality reduces the average level of trust,
group membership and happiness within a community.
Decreased levels of social capital in turn have important implications for violence. In Ethnic
Conflict and Civic Life (2002), Varshney attempts to explain the variation in ethnic violence
in different parts of India. He finds the most proximate cause of intercommunal violence
stems from the pre-existing local networks of civic engagement: where such networks of
engagement exist, tensions and conflicts are regulated and managed, whereas, in places
where these networks are absent, communal identities lead to violence (9). Conflict between
groups is natural in plural societies; however, the extent to which these conflicts are resolved
peaceably or devolve into violence is dependent upon the pre-existing networks of civic
engagement present in the society. Sustained interaction and cooperation between groups
allows for communication to moderate tensions and decreases opportunities for leaders or
politicians to polarize and institutionalize violence. However, when trust and cooperation
8
is diminished due to ethnic inequality, networks or other social structures that regulate and
manage conflict will dissolve or fail to form in the first place.
Ethnic inequality adversely affects violence because it erodes a society’s ability to peacefully
resolve disputes between groups. Communities then become more susceptible to exogenous
shocks (e.g. violence in a neighboring area, economic shocks, etc.) or the manipulations
of leaders who may want to foment violence. This argument links grievance-based theories
to opportunity structures: when there is high economic disparity between ethnic groups
individuals become more amenable to the recruitment or mobilization process. Inequality
between ethnic groups decreases trust and increases prejudice, making mobilization along
ethnic lines more likely. Ethnic inequality contributes to violence by creating favorable
conditions for conflict to occur.
3
Research Design
A statistical analysis of violent events at the country and subnational region levels serves
as empirical test of the relationship between ethnic inequality and violence. Additional
specifications and a mediation analysis are performed to examine if social capital contributes
as a mediating variable.
3.1
Data and Measurement
The dependent variable in this analysis is conflict and is derived from the Armed Conflict
Location and Event Dataset (ACLED), which collects political violence data for developing
states from 1997-2012. This dataset contains information on the specific dates and locations
9
of violent events, the types of events, fatalities and actors involved. Event data is gathered
from a variety of sources including local media, humanitarian agencies, reports from developing countries and research publications. For this analysis, the only event types considered
are battles and violence against citizens.1 Conflict is a dichotomous variable coded 1 for
a country or region if at least one violent event took place in the corresponding year that
resulted in at least 25 deaths. Additionally, I include a measure of total violence, coded as
the total number of violent events that occur within a country or region for a given year, and
total fatalities, coded as the total number of fatalities due to violent events for each country
or region in a given year.
One advantage of using event data from ACLED, as opposed to other conflict datasets,
is the high level of disaggregation. Because precise locations are recorded, conflict can be
analyzed at the subnational level. Most studies of civil conflict have been conducted at
the country level; however, many explanations of why and where conflict occurs refer to
phenomena that may vary geographically within countries (Ostby, Nordas and Rod 2009,
303). Disaggregation is especially critical in examining effects of inequality or social capital,
which can vary greatly between different communities. In most civil conflict the intensity of
violence is at close quarters and it is the felt inequalities at the local level that matter most
(Cramer 2003). In addition to country level analysis, I also run specifications examining
violence at the region or first administrative level.
Estimates for between-group inequality were calculated using survey data from the Demographic Health Surveys (DHS) and the Afrobarometer. The survey design for DHS incorporates a two-stage cluster sampling procedure that aims to provide a representative sample of
1
ACLED defines a battle as a “violent interaction between two politically organized armed groups at a
particular time and location.” Violence against citizens is defined as “deliberate violent acts perpetrated by
an organized political group such as a rebel, militia or government force against an unarmed non-combatant.”
See ACLED website for more information on definitions and coding procedures: http://www.acleddata.
com/data/.
10
the target population at the national, regional and residential level. A stratified sample of
enumeration areas (EAs) is selected with probability proportional to size in the first stage;
households are selected from each enumeration area with systematic sampling in the second
stage. The sample for the primary analysis here includes 24 Sub-Saharan African countries
in which DHS data on household assets and ethnicity is available.
Additional data on inequality as well as social capital measures come from the Afrobarometer,
a research project that collects data on the social, political and economic atmosphere of
Africa. The samples in Afrobarometer are designed to generate a representative cross-section
of all citizens of voting age in a given country. The sample design uses random selection
methods at every stage as well as probability proportionate to population size to ensure that
larger geographic units have a greater probability, proportionate to size, of being included
in the sample.
Ethnic inequality is calculated using between-group inequality (BGI), following Baldwin and
Huber (2010), Huber and Mayoral (2012) and Huber and Suryanarayan (2013). BGI is a
component of the Gini index that assigns each group’s mean income to every member of
that group.2 The BGI measure is based on the average income differences between groups
weighted by the group size and can be interpreted as the expected difference in mean income
of the ethnic groups of two randomly selected individuals (Baldwin and Huber 2010). The
formula for BGI is as follows:
1
BGI =
2ȳ
n X
n
X
!
pi pj |ȳj − ȳi |
(1)
i=1 j=1
where i and j index groups, p is the proportion of the population in group i (j ), ȳ is the
mean income of the society as a whole, ȳi is the mean income of group i, ȳj is the mean
2
For a full discussion on the derivations and meanings of the different Gini index components see Huber
and Mayoral (2012).
11
income of group j and there are n groups in the society.
Ethnic group codings were derived from Fearon’s (2003) classification of ethnic categories,
which emphasizes groups that are understood as descent-based and are locally viewed as
socially or politically consequential. Ethnic or tribal categories were aggregated up to fit
Fearon’s classification and observations were dropped when aggregation was not possible.
Accurate income information is not available for many African localities; therefore, I use a
measure of relative wealth from DHS calculated using household asset data. Household assets
used in calculation of the wealth index include: ownership of electricity, radio, television,
refrigerator, bicycle, motorcycle, car; dwelling characteristics such as material of floor, wall
and roof; type of toilet facility; and source of drinking water. Each household asset is assigned
a weight calculated through a principal component analysis and resulting asset scores are
standardized to be normally distributed with a mean of zero. Standardized asset scores are
assigned for each household for each asset and then summed to create an index of factor
scores.3 The index scores are then rescaled to their percentile ranks (ranging from 0 to 100)
to create a final measure of relative wealth.
The Afrobarometer dataset does not contain a wealth measure, so I created an index of economic well-being as a proxy for income following a similar procedure to the DHS. Economic
well-being was calculated from variables that measured how often a respondent (or anyone
in their family) has gone without food, water, medical care, cooking fuel or a cash income.
Responses for each individual variable were recoded, assigned a weight generated through a
principal component analysis and aggregated to create an index of economic well-being. The
index scores of economic well-being were then rescaled to their percentile ranks to create the
3
Wealth indices for DHS data are calculated by household whereas data on ethnicity is gathered by
individual, so creation of the ethnic inequality estimate the ethnicity of the head of household as the ethnicity
of entire household.
12
final measure of relative wealth.
The identified ethnic groups and measures of relative wealth were then applied to the BGI
formula to create the final measure of ethnic inequality. Due to data limitations, yearly values
of ethnic inequality could not be calculated, so ethnic inequality measures are static for the
entire time period.
4
However, relative inequality tends to be characterized by considerable
inertia (Cederman et al. 2011; Stewart et al. 2008), so it would seem reasonable to use
my measure of ethnic inequality. For the main specification I rely on the DHS data due
to its broader temporal and geographic scope. Afrobarometer measures were used in the
specifications in which I test for a causal pathway involving social capital.
I also include a variety of controls for historical institutions and geographic measures from
Nunn and Puga (2012). These variables include terrain ruggedness, land area, average distance to nearest ice-free coast, diamond extraction, quality of governance and percentage European descent.5 I include controls for logged population and logged total GDP as reported
from the Penn World Table (2012) as well as a measure of ethno-linguistic fractionalization
(ELF) taken from Fearon (2003), which uses data from the Atlas Narodov Mira.6
4
The only exceptions are when there are major reorganizations of regions, provinces or prefectures at
the first administrative level. Where such reorganizations occur, the most immediate survey data after the
reorganization is used to calculate ethnic inequality measures.
5
For data and explanation of measures see http://diegopuga.org/data/rugged/.
6
ELF measures the probability that two randomly chosen individuals will belong to different groups. The
standard formula for ELF is:
ELF = 1 −
n
X
p2i
i=1
where p is the proportion of individuals who belong to group i and n is the number of groups within the
society. The scale ranges from zero to one with one being a completely fractionalized society (Fearon 2003).
13
3.2
Estimation Framework
In order to estimate the impact of ethnic inequality on violence, I use a multivariate regression
analysis. By exploiting the variation in ethnic inequality between countries as well as between
regions within countries, I can measure how economic differences between ethnic groups
affects violent conflict. The basic specification is as follows:
yit = β0 + β1 (EthnicInequalityi ) + β2 Xi + β3 Zit + it
(2)
where y represents the outcome for country i in year t, X is the vector of time-invariant
country controls, and Z is the vector of time-varying control variables. The error term, ,
is clustered at the country level to allow for serial correlation and heteroskedasticity. The
main coefficient of interest is β1 , which measures the impact of ethnic inequality measured
for country i. For the subnational analysis, I examine the impact of ethnic inequality on
violent events at the region (or first administrative) level using a fixed effects model. The
following specification is used:
yrit = β0 + β1 (EthnicInequalityri ) + αi + rit
(3)
where y represents the outcome for region r in country i in year t, α represents country
fixed effects and is the error term. For specifications at both the country and region level
I expect the coefficient for ethnic inequality to be positive, signaling that higher levels of
economic inequality between ethnic groups increase violence. A logistic regression is used
for the dichotomous outcome variable of conflict. Total violent events and total fatalities
are both non-negative count variables; therefore, poisson regression is used for specifications
involving these outcome variables.
14
4
Results
I begin by presenting baseline results looking at the relationship between ethnic inequality
and political violence at the country and region (or first administrative) levels. I then present
results testing for mediation involving social capital. Descriptive statistics for all variables
are shown in the appendix (tables A.3 and A.4).
4.1
Baseline Results
Table 1 presents the baseline results of the cross-country analysis. Because ethnic inequality is derived from DHS survey data collected primarily from 1993 to 2000, the preferred
specification examines violent events after the survey period. The dependent variable in the
first column is conflict, measured as at least one violent event in a country-year with at
least 25 total fatalities. The dependent variable for the second column is the total number
of violent events for a country-year. Finally, the dependent variable for the third column is
total number of fatalities from violent events for each country-year.
In column 1, the coefficient for ethnic inequality is positive and statistically significant at the
p < 0.05 level. This result shows countries that have higher levels of ethnic inequality are
more likely to experience conflict. Holding all other variables at their means, a one standard
deviation increase in ethnic inequality increases the predicted probability of experiencing
conflict by approximately nine percent. The control variables behave in a predicted manner.7
7
Control variables listed are used in all specifications involving country level analysis and omitted from
presentation in remaining tables.
15
Table 1: Effects of Ethnic Inequality on Political Violence, 2000-2012
Conflict
Total Violent
Total Fatalities
Events
(1)
(2)
(3)
Ethnic Inequality
6.416**
2.873**
6.341***
(3.133)
(1.205)
(1.580)
ELF
5.344
4.376*
9.184***
(3.986)
(2.428)
(2.949)
Terrain Ruggedness
0.986
0.456
2.383**
(1.131)
(0.777)
(0.968)
Governance
-0.756
-1.121
0.047
(1.532)
(0.795)
(0.922)
Land Area
-0.004
-0.010*
0.008
(0.011)
(0.005)
(0.007)
Distance to Coast
2.520
1.221**
0.581
(1.679)
(0.602)
(0.742)
Diamond Extraction
0.087
-0.005
-0.008
(0.090)
(0.013)
(0.017)
European Descent
-0.202
0.357
-0.088
(0.412)
(0.264)
(0.349)
Population (Logged)
1.596
1.517**
0.832
(1.072)
(0.619)
(0.830)
Total GDP (Logged)
-0.639
-0.713*
-0.943**
(0.506)
(0.378)
(0.392)
Constant
-16.788*
-9.318**
-3.972
(8.906)
(3.828)
(3.858)
# Observations
312
312
312
The method of estimation is logit for the first column and poisson for the last columns.
Robust standard errors are in parentheses and clustered at the country level.
*** p<0.01, ** p<0.05, * p<0.1
16
For the total number of violent events, column 2, the coefficient for ethnic inequality is
positive and significant at the p < 0.05 level. Higher levels of ethnic inequality are associated
with an increase in the total number of violent events. Keeping all other variables at their
means, a one standard deviation increase in ethnic inequality increases the number of violent
events by four. Again, all other variables behave in the expected manner. The coefficient for
ethnic inequality in regards to total number of fatalities, shown in column 3, is also positive
and statistically significant at the p < 0.01 level. This result implies that higher levels of
ethnic inequality are associated with more fatalities. A one standard deviation increase in
ethnic inequality increases the predicted number of fatalities by approximately 30, or about
22 percent of the mean value, holding all other variables at the means.
Results for the subnational analysis are shown in table 2. The dependent variable for the
first column is conflict for each region (or first administrative level) for each year. The
dependent variable for the second column is total number of violent events for each region
(or first administrative level) for each year. Finally, the dependent variable for column 3 is
total number of fatalities for each region-year. Ethnic inequality is the measure of economic
differences between ethnic groups for that particular region (or first administrative level).
Table 2: Effects of Ethnic Inequality on Political Violence by Region, 2000-2012
Conflict
Total Violent
Total Fatalities
Events
(1)
(2)
(3)
Ethnic Inequality
1.619
4.542***
4.925***
(1.579)
(0.203)
(0.088)
Country Fixed Effects
Yes
Yes
Yes
# Observations
2697
2697
2697
# of Groups
24
24
24
The method of estimation is Logit for first column and Poisson for last two columns.
*** p<0.01, ** p<0.05, * p<0.1
17
The coefficient for ethnic inequality in the first column is positive but is not statistically
significant at standard levels. One reason ethnic inequality is not significant may be due to
disaggregation and the definition of conflict used: isolated occurrences in particular regions
may not reach the minimum threshold (25 deaths) to be considered a civil conflict. Turning
to the second measure of political violence, results show ethnic inequality affects the total
number of violent events. The coefficient for ethnic inequality in column 2 is positive and statistically significant at the p < 0.01 level. Everything else the same, a one standard deviation
increase in ethnic inequality increases the total number of violent events by approximately
26 percent. Column 3 results show ethnic inequality is statistically significant in regards to
total fatalities at the p < 0.01 level. A one standard deviation increase in ethnic inequality
increases the total number of fatalities by approximately 30 percent. These results suggest
local economic differences between ethnic groups impact the occurrence of violent events and
fatalities at the regional level.
4.2
Sensitivity Analysis
Several different tests were carried out to explore the robustness of my results. Because
studies of conflict may be sensitive to different definitions or classifications of events, I provide an alternate specification using intra-state war as coded by Correlates of War (COW).8
Table A.5 (shown in the appendix) presents results of this sensitivity analysis. The dependent variable is conflict, coded as 1 if an intra-state war took place in that country-year and
0 otherwise. Column 1 presents results for the time period explicitly following the survey
period, that is from the year 2000 onward. I also include a specification that extends the
sample period back to 1960. The backward projection allows me to see if the original results
can be generalized beyond the initial time period studied. Column 2 presents results for
8
See http://www.correlatesofwar.org/for data and codebook.
18
the sample including all country years beginning in 1960. The results remain similar to my
original specifications. Ethnic inequality has a positive and statistically significant relationship with conflict when the sample period is extended and when alternate classifications of
conflict are used.
As another robustness check, I run specifications using an alternate method of estimation,
results are presented in the appendix in table 3A. Using an ordinary least squares (OLS)
regression does not alter the results significantly with the exception of total number of
fatalities, which is estimated with considerably more error. Ethnic inequality has a positive
and significant relationship with conflict and total number of violent events at the country
level. At the region level, ethnic inequality is positively associated with total number of
violent events. Further, I repeat estimations of ethnic inequality and violence dropping one
country at a time; results remain unchanged with ethnic inequality still being positively
related violence.9 As a final sensitivity check, I re-run country and region level analysis
including year dummies to account for any possible time trends. Results for this analysis
are shown in the appendix table A.7. Again, results remain similar and ethnic inequality is
positively related to violence.
In addition to several sensitivity checks, I also explore the relationship of within-group inequality and conflict. In an analysis of 89 countries, Huber and Mayoral (2012) find a
robust positive relationship between within-group inequality—that is, income heterogeneity
within groups—and civil conflict. These results support Esteban and Rays (2011) argument that economic diversity within a group should be positively associated with conflict
because both labor and capital are provided. Higher levels of within-group inequality would
mean that an ethnic group has both rich individuals to fund conflict and poor individuals to
serve as combatants. To account for Esteban and Ray’s argument, I examine the impact of
9
Results from this analysis are not shown due to space constraints.
19
between-group inequality (my measure of ethnic inequality) versus within-group inequality
on violence. Results for this analysis are shown in the appendix table A.8.
At the country level, between-group inequality remains positive and significant for conflict and total number of violent events, even with the inclusion of within-group inequality.
Within-group inequality is positive but not statistically significant at standard levels for
conflict and total number of violent events. Furthermore, when between-group inequality is
accounted for, within-group inequality becomes negative in regards to conflict and violent
events. For total number of fatalities both between-group inequality and within-group inequality are both positive and significant when examined individually; however, both become
insignificant when examined jointly and the coefficient on within-group inequality becomes
negative. These results suggest that within-group inequality does not increase violence: in
fact, within-group inequality is inversely related to conflict when accounting for betweengroup inequality. If out-group differences and comparisons are the basis of conflict then
lower levels of within-group inequality would be more important to increase internal group
cohesion. Although labor and capital may be important in carrying out collective violence,
cohesion and coordination would also be necessary to successfully mobilize individuals: the
latter two factors would be accounted for with lower levels of within-group inequality.
This relationship is even more evident in the subnational analysis. Within-group inequality is
consistently negative at the region level, signaling that cohesion may be more important than
having a diversity of people who can fund or fight in a conflict. Both between-group inequality
and within-group inequality are statistically significant for total number of violent events and
total number of fatalities, both individually and when jointly examined. These results imply
that at the local level both internal group cohesion as well as out-group antagonism contribute
to violence. Stated simply, collective violence at the local level requires individuals who are
able to fight together and a reason to fight.
20
4.3
Social Capital as a Mediating Variable
In order to test my argument about the mechanism between ethnic inequality and violence, I
introduce a measure of social capital. Existing studies of social capital use a wide variety of
measures to account for the level of social capital within a society: these include participation
in associational activities, membership in community groups, voting rates, among others.
For my analysis I focus on the level of generalized trust within a society as a measure of
social capital. Generalized trust is regarded as a fundamental dimension of social capital
(Putnam 1993; Fukuyama 2001; Putnam 2000) and one of the most common measures
used in empirical analysis of social capital (Knack and Keefer 1997; Alesina and La Ferrara
2002; Uslaner 2006; Bjornskov 2006, Putnam 2007). Generalized trust is preferred over
other measures of social capital due its stability across countries. Participation is a less
reliable measure because definitions of activities or types of groups included (or excluded)
from surveys can vary across locations. Hooghe et al. (2008) note: “[generalized trust] is a
well-tested scale that seems to be sufficiently cross-culturally equivalent and valid to use in
comparative research” (205). The measure of generalized trust is obtained from the following
survey question in the Afrobarometer: “Generally speaking, would you say that most people
can be trusted or that you must be very careful in dealing with people?” Responses were
coded 0, representing no or low levels of trust, or 1, representing high levels of trust.10
Individual responses were then aggregated to create mean levels of trust at the country and
regional level.
To test the possibility of generalized trust, or social capital, as a mediating variable I use
the following specification:
yit = β0 + β1 (EthnicInequalityi ) + β2 (M eanT rust) + β3 Xi + β4 Zit + it
10
Respondents who did not answer or answered “do not know” were dropped from analysis.
21
(4)
where y is the outcome variable for country i for year t. X is the vector of time-invariant
controls, Z is the vector of time-varying control variables and is the error term. If the
hypothesis of a mechanism is correct, the introduction of mean levels of trust should decrease
the effect ethnic inequality has on conflict or total violence.
Results of the analysis testing the presence of a mediating variable are shown in Table
3. Panel A presents estimates at the country level and panel B shows estimates at the
subnational level. Survey data from the Afrobarometer Round 3 was collected in 2005;
therefore, only violent events occurring after that time are included in the analysis.
Similar to the analysis using DHS data, ethnic inequality is associated with an increased
probability of conflict, a higher number of violent events and a higher number of total
fatalities at the country level. For all dependent variables, the coefficient for ethnic inequality
is positive and statistically significant. The regional specifications using the Afrobarometer
data also show that ethnic inequality is statistically significant with regards to all three
measures of violence.
In the even numbered columns mean level of trust is included to test any mediating effects.
In all the specifications, the introduction of trust decreases the magnitude of the coefficient
for ethnic inequality. The results from this analysis imply there is some evidence that social
capital may be a mediating variable; however, social capital is only a partial mediator. Ethnic
inequality remains positive and statistically significant in all specifications even controlling
for social capital, suggesting that other mechanisms may exist.
22
23
Table 3: Effects of Ethnic Inequality and Social Capital on Political Violence, 2005-2012
Conflict
Total Violent Events
Total Fatalities
(1)
(2)
(3)
(4)
(5)
(6)
Panel A: Country Level
Ethnic Inequality
72.176**
62.290***
77.573***
59.445***
85.132***
57.401***
(28.182)
(23.234)
(10.450)
(13.239)
(9.433)
(10.901)
Trust
-11.667**
-7.392***
-7.944***
(5.517)
(2.175)
(2.271)
Country Level Controls
Yes
Yes
Yes
Yes
Yes
Yes
# Observations
120
120
120
120
120
120
Conflict
Total Violent Events
Total Fatalities
(7)
(8)
(9)
(10)
(11)
(12)
Panel B: Region Level
Ethnic Inequality
4.568**
4.212*
2.970***
2.879***
4.390***
4.246***
(2.162)
(2.159)
(0.223)
(0.222)
(0.127)
(0.125)
Trust
-1.665
-0.606***
-1.739***
(1.561)
(0.187)
(0.115)
Country Fixed Effects
Yes
Yes
Yes
Yes
Yes
Yes
# Observations
1224
1224
1224
1224
1224
1224
# of Groups
15
15
15
15
15
15
The method of estimation is logit for columns 1, 2, 7 and 8 and poisson for the rest. Source of data is the
Afrobarometer Round 3. Control variables used in panel A include terrain ruggedness, land area, average
distance to nearest ice-free coast, diamond extraction, quality of governance, percentage European descent,
logged population, logged total GDP and ELF. Robust standard errors clustered by country are shown in
parentheses for panel A. Country fixed effects were included for specifications in panel B.
*** p<0.01, ** p<0.05, * p<0.1
An alternate method of estimating a causal pathway is to use a linear structural equation
model framework for mediation analysis proposed by Baron and Kenny (1986). Their framework is based on a system of linear equations taking the following form:11
Mi = α1 + β1 Ti + i1
(5)
Yi = α2 + β2 Ti + γMi + i2
(6)
where M represents the mediating variable (in this case social capital), T is the independent
variable, ethnic inequality, and Y is the outcome variable. After fitting each equation via a
least squares, the coefficients β1 (representing the effect of ethnic inequality on social capital)
and γ (representing the effect of social capital on political violence) are multiplied to get
the mediation effect (MacKinnon et al. 2002). The estimated mediation effect is equal to
βˆ1 γ̂. Standard errors and significance for the mediation effect are then calculated via Sobel’s
(1982) multivariate delta method.
12
Because the framework requires models to be linearly
fitted, OLS estimations will be used in the following specifications.
Results from the mediation analysis following Baron and Kenny’s (1986) framework are
presented in table 4. Panel A shows the estimation from equation 5 looking at the impact of
ethnic inequality on generalized trust. Ethnic inequality is negatively associated with trust,
meaning higher levels of between-group inequality lead to lower levels of trust.
11
These equations are taken from Imai et al. (2010) and are simplified from Baron and Kenny (1986).
Sobel’s multivariate delta method of testing the significance of the indirect effect of the independent
variable on the dependent variable via the mediator follows:
q
sab = b2 s2a + a2 s2b + s2a s2b
12
where a represents the path from the independent variable to the mediator with standard error sa and b
is the path from the mediator to the dependent variable with standard error sb .
24
Table 4: Social Capital as Mediating Variable
Panel A: Effect of Ethnic Inequality on Trust
Trust
(1)
-0.840**
(0.398)
Yes
120
0.394
Ethnic Inequality
Country Level Controls
# Observations
R-Squared
Panel B: Effect of Ethnic Inequality and Trust on Political Violence
Conflict
Total Violent
Total Fatalities
Events
(2)
(3)
(4)
Ethnic Inequality
6.675**
1883.545**
5214.847**
(2.382)
(646.249)
(2332.755)
Trust
-1.287
-357.164*
-1043.864
(0.769)
(191.685)
(687.259)
Country Level Controls
Yes
Yes
Yes
# Observations
120
120
120
R-Squared
0.417
0.368
0.251
Panel C: Estimated Mediation Effects
Dependent Variable
βˆ1 γ̂
Standard Error
p-value
Conflict
1.081
0.824
0.190
Total Violent Events
300.018
214.800
0.163
Total Fatalities
876.846
578.578
0.130
The method of estimation is OLS for all columns. Source of data is Afrobarometer Round 3.
Control variables used in panel A and panel B include include terrain ruggedness, land area,
average distance to nearest ice-free coast, diamond extraction, quality of governance, percentage
European descent, logged population, logged total GDP and ELF. Estimated mediation effects
follow Barron and Kenny’s (1986) linear structural equation model and standard errors are
derived using Sobel’s (1982) multivariate delta method.
*** p<0.01, ** p<0.05, * p<0.1
Estimation results following equation 2 are presented in panel B and examine the effect
of ethnic inequality and trust on the three outcome variables measuring violence. Finally,
panel C shows the calculated mediation effects (βˆ1 γ̂) as well as the derived standard errors
and p-values. The p-values from panel C show that the estimated mediation effects are not
statistically significant at standard levels. The indirect effect of ethnic inequality on violence
25
via social capital is not significantly different from zero. Similar results hold for a region level
analysis: estimated mediation effects are not significantly different from zero. This result
shows that most of the effect of ethnic inequality on political violence is not mediated by
generalized trust.
Several explanations can be levied to account for the null result in mediation analysis. First,
imprecise measurement of social capital can lead to spurious results. Although generalized
trust is a commonly used measure of social capital, it may not actually capture the existence of communal structures that can resolve or spark conflict. Additionally, Imai et al.
(2010) argue that Baron and Kenny’s (1986) framework will only identify the average causal
mediation effect if the strong assumption of sequential ignorability holds, that is there is no
confounding between the independent variable and the mediator. Because the measure used
in this analysis is trust, there could be confoundedness even accounting for covariates used as
controls. Other variables may exist that affect both ethnic inequality and trust; furthermore,
low levels of trust between ethnic groups can impact behavior that results in higher levels
of ethnic inequality. Therefore, social capital could still be a mechanism by which ethnic
inequality affects violence, but trust is not the appropriate measure to use. An alternate
explanation would be that social capital does not contribute as a causal pathway and other
mechanisms exist connecting economic difference between ethnic groups to violence.
26
5
Conclusion
Previous studies fail to find a robust relationship between inequality and conflict; however,
the findings presented here suggest that it is incorrect conceptualizing and measurement of
inequality that has led to these null results. Inequality does affect violence when examined as
a group phenomenon, that is, economic differences between groups and not just individuals.
I find that economic differences between ethnic groups affect the onset of conflict, total
number of violent events and total number of fatalities at the country level. Furthermore,
ethnic inequality is associated with a higher number of violent events and fatalities at the
subnational region level. These results show ethnic inequality is an important determinant
of violence in the context of Sub-Saharan Africa.
The country-level and subnational level analyses provide empirical support for theories regarding horizontal or relational inequalities (see Stewart 2008a): where horizontal economic
inequalities are higher, the probability of violence increases. My results also lend support to
theories of relative deprivation in terms of group rather than individual comparisons. Further, my analysis affirms Horowitz’ argument concerning conflict in the modern state as a
struggle for relative group wealth (1985).
The empirical examination of a mechanism linking economic differences between ethnic
groups to violent conflict is less conclusive. There is some evidence to show that social
capital may be a mediator between ethnic inequality and violence: introduction of generalized trust, a measure of social capital, slightly decreases the magnitude of ethnic inequality.
However, ethnic inequality remains positive and statistically significant, implying that other
pathways may exist to explain how economic differences between groups lead to violence.
Further analysis using a linear structural equation model proposed by Baron and Kenny
(1986) shows that the estimated indirect effect of ethnic inequality on violence mediated
27
via social capital is not statistically significant. This result highlights the need to find better measures of social capital, or alternatively, emphasizes the necessity of examining other
mechanisms.
There are many ways to improve this analysis for future research, the first being more refined
data on wealth in developing countries. Although asset indices are valuable for providing a
general sense of relative wealth for households, it is often imprecise in measuring economic
output or activity. Furthermore, caution must always be taken when using survey data due
to response bias: results could be affected if people systematically under or over report their
possession of assets. It is important to note that the identification strategy employed here
is not sufficient to infer causality. It is difficult to claim truly exogenous variation in ethnic
inequality and social capital, as many cultural and historical factors exist affecting both that
may not have been accounted for. My results do not imply that ethnic inequality is the only,
or even the most important, determinant of conflict; rather, my results show that holding
all else equal, higher levels of economic inequality between ethnic groups contributes to the
onset of violent events.
Despite its empirical limitations, the findings of this analysis have important implications.
First, my results suggest grievance-based arguments should not be rejected in analysis of conflict. Instead there needs to be better theorizing about the contexts in which grievances such
as inequality are most important. Second, my research highlights the need to re-evaluate
standard measures of inequality. Economic inequality is often a significant factor in development and economic growth. Results from this analysis show how relational inequalities
between groups rather that individual measures should be emphasized for future research.
28
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34
A
Appendix
Table A.1: DHS Countries Used in Analysis
Country
Survey Year Number of Regions
Benin
1996, 2006 6,12
Burkina Fasso
1993, 2003 5, 13
Cameroon
1998, 2011 4,12
Central African Republic
1994
16
Chad*
1996
15 (including N’djamena)
Democratic Republic of Congo
2007
10
Ethiopia
2000
11
Gabon
2000
4
Ghana
1993
10
Guinea
1999
8
Ivory Coast*
1994
10
Kenya
1993
8
Liberia*
2007
6
Mali
1996
8
Mozambique
1997
10
Namibia
2000
13
Niger
1992
8 (including Niamey)
Republic of Congo*
2005
4
Rwanda
1992
5
Senegal*
1999
11
Sierra Leonne
2008
4
Togo
1998
6 (including Lome)
Uganda
1995
4
Zambia
1992
9
* Countries with rearranged first administrative level units but there exists
no updated DHS data.
35
Table A.2: Afrobarometer Countries Used in Analysis
Country
Survey Year Number of Regions
Benin
2005
12
Botswana
2005
15
Ghana
2005
10
Kenya
2005
8
Madagascar
2005
6
Malawi
2005
3
Mali
2005
9 (including Bamako)
Mozambique
2005
10
Namibia
2005
13
Nigeria
2005
36
Senegal
2005
11
South Africa
2005
9
Tanzania
2005
26
Uganda
2005
4
Zambia
2005
9
Table A.3: Summary Statistics (DHS)
Variable
Mean Std. Dev.
Ethnic Inequality (Country)
0.09
0.06
Ethnic Inequality (Region)
0.06
0.05
Within-Group Inequality (Country)
0.1
0.07
Within-Group Inequality (Region)
0.17
0.1
Ethnolinguistic Fractionalization
0.73
0.15
Terrain Ruggedness
0.57
0.66
Governance
-0.9
0.54
Land Area (thousands)
55.98
52.72
Distance to Nearest Ice-Free Coast
0.55
0.37
Diamond Extraction (thousands)
8.62
20.08
Percentage European Descent
0.63
2.17
Population (logged)
9.25
0.98
Total GDP (logged)
9.26
0.93
Conflict (Country)
0.42
0.49
Conflict (Region)
0.11
0.32
Total Violent Events (Country)
50.24
100.43
Total Violent Events (Region)
5.78
25.45
Total Fatalities (Country)
432.59
2127.93
Total Fatalities (Region)
49.74
605.26
36
Min. Max.
0.02
0.33
0
0.31
0.03
0.33
0.01
0.5
0.13
0.9
0.14
3.31
-2.11
0.29
2.47 226.71
0.1
1.25
0
87.15
0
10.85
7.03
11.39
6.39
11.18
0
1
0
1
0
722
0
542
0
28636
0
25986
Table A.4: Summary Statistics (Afrobarometer)
Variable
Mean Std. Dev. Min. Max.
Ethnic Inequality (Country)
0.06
0.04
0.02
0.17
Ethnic Inequality (Region)
0.05
0.05
0
0.34
Generalized Trust (Country)
0.18
0.08
0.06
0.33
Generalized Trust (Region)
0.17
0.12
0.02
0.52
Ethnolinguistic Fractionalization
0.70
0.21
0.06
0.93
Terrain Ruggedness
0.64
0.45
0.14
1.76
Governance
-0.42
0.49
-1.29
0.58
Land Area (thousands)
60.82
36.76
9.41 122.02
Distance to Nearest Ice-Free Coast
0.48
0.28
0.09
1.01
Diamond Extraction (thousands)
28.77
61.06
0
208.69
Percentage European Descent
2.22
5.01
0
18
Population (logged)
9.75
1.09
7.52
11.93
Total GDP (logged)
10.32
1.07
8.77
12.99
Conflict (Country)
0.32
0.47
0
1
Conflict (Region)
0.07
0.26
0
1
Total Violent Events (Country)
43.99
86.47
0
601
Total Violent Events (Region)
3.87
13.94
0
232
Total Fatalities (Country)
117.79
314.55
0
2501
Total Fatalities (Region)
10.39
55.53
0
1010
37
Table A.5: Effects of Ethnic Inequality using COW Coding
Conflict
2000-2010
1960-2010
(1)
(2)
Ethnic Inequality
13.738**
16.958***
(6.890)
(6.495)
Country Level Controls
Yes
Yes
# Observations
264
1224
The method of estimation is Logit for both columns.
Source of data is Correlates of War. Robust standard
errors are shown in parentheses and clustered by country.
*** p<0.01, ** p<0.05, * p<0.1
Table A.6: Sensitivity Check Using OLS
Conflict
Total Violent Events Total Fatalities
(1)
(2)
(3)
Panel A: Country Level
Ethnic Inequality
Country Level Controls
# Observations
1.357**
(0.610)
Yes
312
Conflict
(4)
134.943*
482.177
(67.314)
(514.395)
Yes
Yes
312
312
Total Violent Events Total Fatalities
(5)
(6)
Panel B: Region Level
Ethnic Inequality
0.115
20.732*
121.587
(0.118)
(11.022)
(75.021)
Country Fixed Effects
Yes
Yes
Yes
# Observations
2697
2697
2697
# of Groups
24
24
24
The method of estimation is OLS for all columns. Source of data is the DHS.
Control variables used in panel A include include terrain ruggedness, land area,
average distance to nearest ice-free coast, diamond extraction, quality of governance, percentage European descent,logged population, logged total GDP and ELF.
Robust standard errors clustered by country are shown in parentheses for panel A.
*** p<0.01, ** p<0.05, * p<0.1
38
Table A.7: Effects of Ethnic Inequality on Violence, Including Year Dummies
Conflict
Total Violent Events Total Fatalities
(1)
(2)
(3)
Panel A: Country Level
Ethnic Inequality
6.303*
2.914**
5.663***
(3.359)
(1.361)
(1.727)
Country Level Controls
Yes
Yes
Yes
Year Dummies
Yes
Yes
Yes
# Observations
312
312
312
Conflict
Total Violent Events Total Fatalities
(4)
(5)
(6)
Panel B: Region Level
Ethnic Inequality
1.608
4.544***
4.913***
(1.591)
(0.203)
(0.088)
Country Fixed Effects
Yes
Yes
Yes
Year Dummies
Yes
Yes
Yes
# Observations
2697
2697
2697
# of Groups
24
24
24
The method of estimation is logit for columns 1 and 4, poisson for columns 2,3,5 and 6.
Control variables used in panel A include include terrain ruggedness, land area,
average distance to nearest ice-free coast, diamond extraction, quality of governance, percentage European descent,logged population, logged total GDP and ELF.
*** p<0.01, ** p<0.05, * p<0.1
39
40
Table A.8: Impact of Between-Group Inequality and Within-Group Inequality on Violence
Conflict
Total Violent Events
Total Fatalities
(1)
(2)
(3)
(4)
(5)
(6)
(7)
(8)
(9)
Panel A: Country Level
BGI
6.416**
16.255*
2.873**
8.921**
6.341***
8.421
(3.133)
(9.773)
(1.205)
(3.884)
(1.580)
(8.028)
WGI
1.607
-9.335
1.069
-5.160*
4.087*
-1.734
(3.754)
(6.921)
(1.366)
(2.941)
(1.659)
(6.157)
Country Controls
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
# Observations
312
312
312
312
312
312
312
312
312
Conflict
Total Violent Events
Total Fatalities
(10)
(11)
(12)
(13)
(14)
(15)
(16)
(17)
(18)
Panel B: Region Level
BGI
1.619
1.501
4.542***
4.346*** 4.925***
4.541***
(1.579)
(1.588)
(0.203)
(0.203)
(0.088)
(0.088)
WGI
-0.133
-0.492
-1.027*** -1.062***
-0.984*** -1.166***
(0.796)
(0.839)
(0.092)
(0.096)
(0.038)
(0.040)
# Observations
2697
2697
2697
2697
2697
2697
2697
2697
2697
# of Groups
24
24
24
24
24
24
24
24
24
The method of estimation is logit for columns 1, 2, 3, 10, 11, and 12, poisson for the rest. Source of data is DHS.
Control variables used in panel A include include terrain ruggedness, land area, average distance to nearest ice-free coast,
diamond extraction, quality of governance, percentage European descent,logged population, logged total GDP and ELF.
*** p<0.01, ** p<0.05, * p<0.1