Violence: from the Political to the Personal Examining Links Between

VIOLENCE: FROM THE POLITICAL TO THE PERSONAL

EXAMINING LINKS BETWEEN CONFLICT AND INTERPERSONAL

VIOLENCE IN LIBERIA

by Jocelyn Kelly

A dissertation submitted to the Johns Hopkins Bloomberg School of Public Health in conformity

with the requirements for the degree of Doctor of Philosophy

Baltimore, Maryland

March 17, 2017

Abstract

Background: Each year, war and interpersonal violence account for a significant burden on morbidity and mortality worldwide. Roughly one third of violence-related deaths are attributed to interpersonal violence and one-fifth are attributed war. New scholarship has shown how violence can spread across populations temporally and spatially. Yet the link between armed conflict and postconflict interpersonal violence is poorly documented.

Methods: This dissertation will use multilevel modeling to assess the link between levels of armed conflict at the district level and postconflict individual-level interpersonal violence in a single, conflict-affected nation (Liberia). Armed Conflict Location and Event Data ACLED data will be used to provide a measure of the extent to which a community has been affected by conflict at the district-level during the country’s civil war from 1999-2003. The primary predictor of conflict is whether a district experienced any versus no conflict-related fatalities during war. Demographic and Health Survey (DHS) data from 2007 will provide information about health and social characteristics at the individual level, including the project outcomes of past-year non-partner physical violence (NPPV) and intimate partner violence (IPV).

Results: In the bivariate model, conflict as measured by a district experiencing any versus no fatalities, was associated with NPPV (OR 2.62, p<0.001). However, as individual-level demographic characteristics were added during the stepwise model fitting procedure, this association became attenuated and no longer reached significance (aOR 1.43, p=0.197). For IPV, there was a strong association with conflict in the bivariate analysis (aOR 2.10, p<0.001). This ii

association remained significant, although attenuated, after individual-level characteristics were added to the final multilevel model (aOR 1.55, p<0.001).

Discussion: The impact of political conflict on future interpersonal violence has implications for a country’s ability to achieve lasting peace and prosperity. This research suggests that living in a district that experienced fatalities during war can increase the risk of experiencing interpersonal violence in the postconflict period. These results were more pronounced for IPV than for NPPV.

Even after adjusting for known individual-level correlates of IPV, residence in a fatality-affected district was significantly associated with a 50% increase in risk of abuse.

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Committee of Final Thesis Readers

Committee Members Department Michele R. Decker, PhD Population, Family and Thesis Advisor Reproductive Health International Health and Courtland Robinson, PhD Population Elizabeth Colantuoni, PhD Biostatistics

Judith Bass, PhD Mental Health

Alternate Committee Members Renee Johnson Mental Health Population, Family and Kristin Mmari Reproductive Health

Acknowledgements

This dissertation is dedicated to those who have helped me learn throughout my life.

To Dr. Justin Kabanga, whose kindness and tireless dedication to the women of Congo has served as an inspiration to me and to all those around him.

To Dr. Michael VanRooyen, who created the Harvard Humanitarian Initiative. A space so unique and bold that it changed how I thought about humanity, public health, and the intersection between science and compassion .

To my thesis committee for their patience, support and kindness is making this project possible, and to Michele Decker whose tireless dedication to this work made it immeasurably better.

To my father, who saw the beauty and symmetry in the world and all the shades between black and white. To my mother - her wisdom, sense of adventure and audacity opened the world for me. To my grandmother, Catherine Noble Deverall, and my Great Aunt, Jean Nielson - and the long line of fearless women who came before them - for serving as role models. To my brother, Stephen, who journeyed with me through all of the early adventures. To my sister, Kelly, without whom I could not have achieved any of this.

Table of Contents Abstract ...... ii Committee of Final Thesis Readers ...... iv Acknowledgements ...... v List of Tables ...... viii List of Figures ...... ix . Introduction ...... 1 Background and Significance ...... 2 Specific Aims and Hypotheses ...... 4 Dissertation Overview ...... 6 Chapter One References ...... 7 . Background and Conceptual Framework ...... 9 Overview ...... 10 Quantifying the Impact of Conflict ...... 10 Beyond Battle Deaths: The Ripple Effects of War ...... 13 Violence: from the Political to the Personal ...... 14 Social Cognitive Theory and Conceptual Framework ...... 20 Liberia Country Profile ...... 28 Chapter Two References ...... 33 . Methods ...... 42 Overview ...... 43 Data sources ...... 44 Study Sample ...... 48 Dependent Variables ...... 50 Primary Exposure: Defining Conflict at the District-Level ...... 53 Independent Variables ...... 54 Data Analysis ...... 57 Ethical Considerations ...... 60 Chapter Three References ...... 62 . Defining the Primary Exposure ...... 64 Quantification of Events and Fatalities ...... 65 The Liberian Conflict Across Time and Space ...... 67 Ways to Measure Conflict Exposure ...... 70 Discussion ...... 73 Chapter Four References ...... 78 . Non-Partner Physical Violence ...... 79 vi

Results ...... 80 Discussion ...... 91 Chapter Five References ...... 99 . Intimate Partner Violence ...... 101 Results ...... 102 Discussion ...... 111 Chapter Six References ...... 119 . Conclusions ...... 121 Summary of Findings ...... 122 Strengths and Limitations ...... 125 Implications ...... 129 Chapter Seven References ...... 131 Appendix 1. Eligibility for inclusion analytic sample ...... 133 Appendix 2. Liberian Conflict across Space and Time ...... 134 Appendix 3. District-level summary measures of key demographic variables ...... 136 Appendix 4. Non-Partner Physical Violence Tables ...... 138 Appendix 5. Intimate Partner Violence Tables ...... 142

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List of Tables

Table 2.1 Conceptual Framework: Linkages between conflict and interpersonal violence ...... 27 Table 3.1 DHS questions assessing partner physical and sexual violence ...... 52 Table 3.2 Independent Variables by Ecological Model Level ...... 57 Table 4.1 Distribution of Dichotomous Fatalities and Events at Individual and District Level ...... 70 Table 4.2 Distribution of Three Levels of Fatalities and Events at Individual and District Level .. 70 Table 4.3 Distribution of Quartile and Quintiles of Events at Individual and District Level ...... 72 Table 4.4 Distribution of Cumulative Years of Events at Individual and District Level ...... 73 Table 4.5 Distribution of Combined Cumulative Years of Events at Individual and District Level73 Table 4.6 Final Measures of Conflict Exposure to be Included in Analysis ...... 75 Table 5.1 Individual-Level Prevalence and District-Level Summaries of Past-Year NPPV by District Conflict Status ...... 80 Table 5.2 Bivariate Model Associations of Factors Associated with Past-Year NPPV ...... 83 Table 5.3 Association of Past-Year NPPV with Dichotomous District-Level Fatalities: Stepwise Model Fitting ...... 85 Table 5.4 Association of Past-Year NPPV with Three Measures of District-Level Events ...... 87 Table 5.5 Distribution of NPPV Across Non-Fatal Event Years ...... 88 Table 5.6 Association of Past-Year NPPV with District-Level Non-Fatal Cumulative Event Years ...... 88 Table 5.7 Association of Past-Year NPPV with Dichotomous District-Level Fatalities Among Non-Migrants ...... 90 Table 6.1 Individual-Level Prevalence and District-Level Summaries of Past-Year IPV by District Conflict Status ...... 102 Table 6.2 Bivariate Model Associations of Factors Associated with Past-Year IPV ...... 104 Table 6.3 Association of Past-Year IPV with Dichotomous District-Level Fatalities: Stepwise Model Fitting ...... 107 Table 6.4 Association of Past-Year IPV with Three Measures of District-Level Events ...... 108 Table 6.5 Distribution of IPV across non-fatal event years ...... 109 Table 6.6 Association of Past-Year IPV with District-Level Non-fatal Cumulative Event-Years 109 Table 6.7 Association of Past-Year IPV with Dichotomous District-Level Fatalities Among Non- Migrants ...... 111

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List of Figures

Figure 3.1 Timeline of Conflict Relative to Collection of Individual-Level Data ...... 43 Figure 4.1 Number of Fatalities and Events by District from 1999 to 2003 ...... 66 Figure 4.2 Graph of Conflict Events and Fatalities in Liberia from 199 to 2003 ...... 67 Figure 4.3 Graph of Conflict Fatalities in Liberia from 1999-2003 Disaggregated by Fatality- Affected District ...... 69 Figure 4.4 Histogram of Conflict Events from 1999 to 2003 ...... 71 Figure 5.1 Within Versus Between Variation in NPPV Across Districts ...... 81 Figure 6.1 Within Versus Between Variation in IPV Across Districts ...... 103

. Introduction

Background and Significance

The 40 most unstable countries in the world are home to 38% of the globe’s population

(Burnham and Robinson, 2007). Recent conflicts in the Middle East have led to the highest number of refugees since World War II and the economic impact of violence due to death and displacement is estimated to cost 13.4% of the world’s GDP annually (Institute for Economics and Peace, 2015). The most current Global Peace Index report reveals widening divisions between the most and the least peaceful countries such as Syria, Iraq and South Sudan, which are spiraling further into violence (Institute for Economics and Peace, 2015). Current conflicts are often characterized by chronic poverty; a vacuum of state and civil institutions; poor access to healthcare and education; low levels of state accountability; and the risk of continuing instability.

There exists an understandable assumption that the worst consequences of conflict result from violence-related deaths that affect combatants more than civilians and are relatively restricted to theaters of violence. However, new lines of scholarship document the wide-ranging effects of political instability on a myriad of health outcomes, and on populations that may not seem directly involved in hostilities. In particular, the impact of political conflict on future interpersonal violence has implications for a country’s ability to achieve lasting peace and prosperity. This study will seek to explore this association using a multilevel modeling approach that links political violence at the district level with data on violence between individuals in the postconflict phase.

The connection between public and private violence is not well established in the literature.

However, a limited number of survey studies have shown that exposure to conflict-related violence at the societal level can be associated with an increase the risk of other types of

violence, including non-partner physical violence (NPPV) and intimate partner violence (IPV) at the individual level (Saile et al., 2013; Falb et al., 2013; Gupta et al., 2012; Vinck and Pham

2013). In its seminal report on all forms of violence, the World Health Organization (WHO,

2002) estimated that globally 1.6 million people died from violence in 2000–roughly one third of these deaths were a result of interpersonal violence and one fifth were a result of war. Each year, violence (political, interpersonal and self-inflicted) is one of the leading causes of death for people aged 15 to 44 (WHO, 2002). Women are particularly vulnerable to certain forms of interpersonal violence such as sexual assault, physical battery and violence from partners

(Garcia-Moreno, 2013). Globally, 35% of women have been physically or sexually abused during their lifetime and 30% of women who have been in a relationship have experienced physical or sexual violence from a partner (WHO, 2013). Data on the global prevalence of physical violence from a non-partner are relatively limited. However, findings from a WHO survey of 11 countries show high rates of physical battery against women from a range of abusers beyond intimate partners, including teachers, employers and family members (WHO,

2013). Sequelae of these abuses include injury, death, poor reproductive health outcomes and increased risk of mental health issues for both intimate partner violence (Campbell, 2002;

Garcia-Moreno et al., 2006; Stöckl et al., 2013) and non-partner violence (WHO, 2013). Because women bear a disproportionate burden of many forms of violence, and better data exist on women’s experiences compared to men, this project will focus on women’s experiences of two forms of violence: NPPV and IPV.

Specific Aims and Hypotheses

This project proposes to further explore the link between war and different forms of interpersonal violence by combining data on conflict events at the district level with individual health data on interpersonal violence. Results of this work will contribute to understanding the effects of conflict on different types of interpersonal violence post conflict. This research will focus on

Liberia, a country that experienced violent conflict from 1986 to 2003. Demographic and Health

Survey (DHS) data will provide information about health and social outcomes, specifically non- partner physical violence and IPV. Armed Conflict Location and Event Data Project (ACLED) data will be used to provide a measure of the extent to which a community has been affected by conflict, i.e., the conflict-affectedness at the sub-national district level. Within countries experiencing conflict, there is often a great deal of heterogeneity among areas and regions that endure different levels of violence and insecurity. This conflict-affectedness information at the district level can be combined with DHS data at the individual level to examine the links between interpersonal violence and conflict. A multi-level modeling approach will account for the hierarchical structure of the data (Kreft and Leeuw, 1998). In this case, women’s experiences will be nested in districts, which will be classified according to whether or not the district has experienced conflict-related events.

Specific Aims:

1a. Quantify the association between past-year NPPV and district-level conflict-affectedness.

This research will assess whether individuals in geographic units that experienced conflict in the five years before the DHS survey was conducted are more likely to report experiencing past-year

NPPV as measured by the DHS. The hypothesis is that individuals living in conflict-affected districts will have higher proportions of non-partner physical violence than those living in non- conflict affected districts in Liberia, after adjusting for relevant individual-level characteristics.

1b. Conduct sensitivity analyses to determine how the association between NPPV and conflict is affected by difference characterization of conflict. The hypothesis is that conflict fatalities, which are highly visible and traumatic manifestations of conflict, will have the strongest association with non-partner physical violence compared to other measures of conflict, including all conflict events and cumulative years of conflict events.

2a. Quantify the association between past-year IPV and district-level conflict-affectedness.

This research will assess whether individuals living in geographic units that experienced conflict in the five years before the DHS survey was conducted are more likely to experience past-year

IPV as measured by the DHS. The hypothesis is that individuals living in conflict-affected districts will have higher proportions of past-year IPV than those living in non-conflict affected districts in Liberia, after adjusting for relevant individual-level characteristics.

2b. Conduct sensitivity analyses to determine how the association between intimate partner violence and conflict is affected by difference characterization of conflict. The hypothesis is that conflict fatalities, which are highly visible and traumatic manifestations of conflict, will have the strongest association with intimate partner violence compared to other measures of conflict, including all conflict events and cumulative years of conflict events.

Dissertation Overview

This introductory chapter outlines the issue being examined for the dissertation and the associated research aims. Chapter Two provides an overview of efforts to quantify the impact of conflict and the literature documenting the association between conflict and interpersonal violence. Chapter Two also outlines the theoretical framework being used to structure the research question. The datasets and study methods are described in Chapter Three. Chapter Four provides a background on the Liberian conflict and summarizes the different ways to measure conflict for this analysis. Chapters Five and Six present results of the data analysis related to

NPPV and IPV, respectively. Finally, Chapter Seven provides the discussion of the findings, strengths, limitations, research implications and possibilities for future research.

Chapter One References

Burnham, G., & Robinson, C. (2007). Protecting the rights of those in conflict. The Lancet, 370(9586), 463–464.

Campbell, J. C. (2002). Health consequences of intimate partner violence. The Lancet, 359(9314), 1331–1336.

Falb, K. L., McCormick, M. C., Hemenway, D., Anfinson, K., & Silverman, J. G. (2013). Violence against refugee women along the Thai–Burma border. International Journal of Gynecology & Obstetrics, 120(3), 279–283.

Garcia-Moreno, C., Jansen, H. A., Ellsberg, M., Heise, L., & Watts, C. H. (2006). Prevalence of intimate partner violence: Findings from the WHO multi-country study on women's health and domestic violence. The Lancet, 368(9543), 1260–1269.

García-Moreno, C. (2013). Global and regional estimates of violence against women: Prevalence and health effects of intimate partner violence and non-partner sexual violence. Geneva, Switzerland: World Health Organization.

Gupta, J., Reed, E., Kelly, J., Stein, D. J., & Williams, D. R. (2012). Men's exposure to human rights violations and relations with perpetration of intimate partner violence in South Africa. Journal of epidemiology and community health, 66(6), e2–e2.

Institute for Economics and Peace (2015). New York: Global Peace Index. Accessed at: http://economicsandpeace.org/wp-content/uploads/2015/06/Global-Peace-Index-Report- 2015_0.pdf

Kreft, I. G., & De Leeuw, J. (1998). Introducing multilevel modeling. London, England: Sage.

Saile, R., Neuner, F., Ertl, V., & Catani, C. (2013). Prevalence and predictors of partner violence against women in the aftermath of war: A survey among couples in northern Uganda. Social Science & Medicine, 86,17–25.

Stöckl, H., Devries, K., Rotstein, A., Abrahams, N., Campbell, J., Watts, C., & Moreno, C. G. (2013). The global prevalence of intimate partner homicide: A systematic review. The Lancet, 382(9895), 859–865.

Vinck, P., & Pham, P. N. (2013). Association of exposure to intimate-partner physical violence and potentially traumatic war-related events with mental health in Liberia. Social Science & Medicine, 77, 41–49.

World Health Organization. (2002). WHO report on violence and health. Geneva, Switzerland: World Health Organization.

World Health Organization. (2013). Global and regional estimates of violence against women: Prevalence and health effects of intimate partner violence and non-partner sexual violence. Geneva, Switzerland: World Health Organization.

. Background and Conceptual Framework

Overview

This chapter begins with an overview of scholarship related to the pattern, scope and impact of conflict and goes on to trace the evolution of modern conflict studies from simply quantifying battle deaths to recognizing the myriad non-fatal impacts of war. In the past decade, population- based studies began to reveal that deaths from malnutrition, disease and the breakdown of healthcare systems were far more widespread than deaths from direct violence in war. The latest evolution in this research examines how violence in conflict may normalize violence in the post conflict phase, and may impact levels of interpersonal violence, specifically NPPV and IPV.

Quantifying the Impact of Conflict

Armed conflict has been a cause of mortality and morbidity throughout human history, but calculating its burden has been difficult. Attempts to look at the characteristics of political instability began in earnest after World Wars I and II—conflicts that highlighted the ability of local disputes to take on worldwide significance. One of the most comprehensive of these efforts is the Uppsala Conflict Data Program (UCDP), which has tracked profiles of conflict for over half a century. The database has recorded 254 armed conflicts since 1946. In 2015 (the last year of available data) there were 70 armed conflicts active worldwide, the highest number since the

Cold War. While inter-state conflicts have decreased, intra-state conflicts have steadily increased since the end of World War II (Pettersson et al., 2015).

The post-World War II era saw renewed attempts to understand deadly engagements and to quantify their impact (Richardson, 1960; Gleditsch et al., 2014). In these efforts, the number of conflict-related deaths was used to determine the difference between war and peace. There was less emphasis placed on measuring other indicators, such as morbidity, trauma and economic impacts, partly due to lack of awareness of the multidimensional effects of war and partly due to measurement challenges. As a result, violence-related deaths became a defining metric of conflict. Even in modern definitions, mortality delimits the border between conflict and peace.

This is due in some measure to the fact that lethal violence is taken seriously in societies across the globe and tends to be recorded more systematically—in vital registration systems, legal records, media accounts and clinical records—than other forms of harm. Thus, while the number of deaths is clearly not the only impact of conflict, it lends itself to measurement and is therefore used as a proxy for other types of harm (Global Burden of Armed Violence, 2011).

1 Images from Themner & Wallensteen, 2014

The Peace Research Institute of Oslo (PRIO) and the UCDP track global hostilities over time and provide a launching point for a great deal of current conflict-related work. These organizations define conflict as “a contested incompatibility that concerns government or territory or both, where the use of armed force between two parties results in at least 25 battle-related deaths in a calendar year.” “War” is defined as “conflicts with at least 1,000 battle-related deaths in a calendar year” (Themner & Wallensteen, 2014). However, even as mortality remains a definitional part of conflict research, new efforts seek to paint a more nuanced and detailed picture. This is particularly important since the nature of conflict is changing. Current conflicts, such as those in South Sudan, Afghanistan and the Democratic Republic of the Congo illustrate a broader trend—an evolution away from the “old wars” of the 19th and 20th centuries where regular armed forces fight each other in decisive encounters to defend well-defined state interests. Current conflicts, in contrast, (sometimes called “new wars”) are characterized by protracted transnational hostilities that combine conflict, organized crime and massive human rights violations perpetrated by an array of state and non-state actors (Kaldor, 1999).

Classic assumptions from “old” wars dictate that combatants rather than civilians are most affected by conflict, and that men are at greater risk of conflict-related harms than women and children. Yet, new analyses highlight the fact that children, adolescents and women shoulder a significant proportion of the burden of war (Murray, 2002; Plümper, 2006). Murray (2002), using statistics from the Global Burden of Disease Study, found that civilians and combatants are equally at risk for direct conflict-related mortality. Having systematic estimation of deaths in armed conflict is a relatively recent advance. However, mortality is far from the only effect.

Beyond Battle Deaths: The Ripple Effects of War

Increasingly, studies have documented the detrimental impacts of conflict looking not only at excess mortality but also at a range of other health outcomes; including injury, trauma and disease (Toole, 1993; Sapir & Gomez, 2006; Coghlan et al., 2006). A central conundrum, however, is that the chaotic nature of war has made the systematic examination of its human impact difficult or impossible. Murray et al. (2002) discuss how examining the health impact of conflict has fallen into a limbo between the fields of public health and political science and receives inadequate attention from both. However, new techniques to measure physical and mental health are changing existing understandings of the impact of war. A number of efforts— by the WHO, the Global Burden of Disease Project, the Global Burden of Armed Violence

Project, UCDP, as well as academics and non-governmental organizations—have taken the first steps to estimate direct and indirect effects of political instability in new ways.

Population-based surveys that draw on family health history and verbal autopsy have proven effective in documenting the impact of conflict in a number of countries, as have nesting mortality assessments within other planned assessments (Doocy et al., 2007). Despite these advances, differing methodological approaches can still lead to widely varying estimates of mortality even in the same conflict-affected region (Spiegel & Robinson, 2010). Among the most notable efforts were mortality studies done in Iraq and the DRC. In a national cross-sectional cluster sample survey in post-invasion Iraq, Burnham et al. (2006) found that between the start of hostilities in 2003 and 2006, the excess deaths corresponded to 2.5% of the population in the study area. Violent death during the invasion accounted for a majority of the excess mortality and predominantly affected men 15 to 44 years old. A national mortality study done in 2004 in

the DRC found that, in contrast to Iraq, most of the excess deaths in this context were due to preventable conditions such as malnutrition and infectious diseases rather than violence

(Coghlan et al., 2006). The study found that the highly insecure provinces in the east of the country had a crude mortality rate 93% above the norm in sub-Saharan Africa. Very young children were the most disproportionately affected by excess mortality. An analysis of over 90 mortality and nutritional surveys in Angola from 1999 to 2006 had a similar finding (Sapir &

Gomez, 2006). This study found that children under five were the most affected by malnutrition and excess death during the conflict. The same study found that excess civilian deaths due to conflict accounted for 70% of all recorded deaths during the study period, with internally displaced persons (IDPs) having a substantially higher rate of excess deaths than those who were not displaced. In other conflict settings, indirect causes of death such as inadequate access to water, sanitation, and health services have accounted for most civilian deaths, and have had a differentially large impact on children and early teens (Toole, 1993; Sapir & Gomez, 2006).

Violence: from the Political to the Personal

One of the newest frontiers in understanding conflict involves quantifying how political violence may impact human aggression even after formal peace is declared. An increasingly rich body of literature documents the “contagion” of violence. Like diseases and many complex social phenomena, violence can be transmitted across individuals, groups, generations and different levels of social organization. In a 2012 report titled Contagion of Violence, the Institute of

Medicine (IOM) noted that the spread of abusive behaviours can be traced temporally and spatially as well as across the population and individual levels. Violence can persist and morph into other kinds of violence. For instance, studies have documented how experiencing or

witnessing IPV in childhood can be associated with an increase in the chances of being both a perpetrator (Barnett et al., 2010; Stith et al., 2000) and a victim later in life (Kitzmann, 2003).

However, there are far fewer studies looking at how experiences of conflict-related violence at the community level impact interpersonal violence and very few attempts to quantify this phenomenon.

Yet, war and interpersonal violence, including NPPV and IPV, both account for a significant burden on morbidity and mortality worldwide. Roughly one third of violence-related deaths are attributed to interpersonal violence and one fifth are attributed to war. Each year, all forms of violence are a leading cause of death for people aged 15 to 44 (WHO, 2002). The next section will outline the literature on IPV and NPPV—the two primary outcomes for this research—and will examine how war-related violence at the community level is associated with each.

Intimate Partner Violence and Conflict-Related Violence

Thirty-five percent of women globally have been victims of physical or sexual abuse during their lifetime and one third of women who have been in a relationship have experienced physical or sexual violence from a partner (WHO, 2013). Globally, intimate partner violence is the most common form of violence against women (Devries et al., 2013), and is the leading cause of homicide against women (Stöckl et al., 2013). Sequelae of this violence include poorer mental and reproductive health outcomes (Campbell, 2002; Decker et al., 2014; Devries et al., 2013), including unintended pregnancy; physical harm; increased vulnerability to sexually transmitted diseases, and HIV (Decker et al., 2009; Jewkes et al., 2010; Kouyoumdjian et al., 2013; Miller et

al., 2010; Mitchell et al., 2016; Pallito et al., 2013; Stockman et al., 2013); difficulty seeking healthcare; increased vulnerability to substance abuse (Decker et al., 2014; Heise et al., 2002;

Stockman et al., 2013); and limited personal agency (Campbell, 2002; WHO, 2013).

Intimate partner violence can also be a major factor affecting the physical and mental health of individuals before, during and after conflict (Stark et al., 2010). Because it occurs in homes rather than in theaters of war and may be condoned or overlooked because of cultural norms,

IPV is often far less visible than conflict-related sexual violence (Stark & Ager, 2011; Hynes et al., 2004; Peterman et al., 2011; Parmar et al., 2012). This form of abuse, however, may be more common than sexual violence perpetrated by armed actors during war (Stark & Ager, 2011;

Peterman et al., 2011; Parmar et al., 2012). A survey of displaced women in northern Uganda found rates of past-year rape by partners was 41% compared to 5% by non-partners (Stark et al.,

2010) and an analysis of DHS data from the DRC found that the number of women reporting

IPV was 1.8 times higher than women reporting non-partner rape (Peterman et al., 2011).

Exposure to political violence and to human rights abuses at the individual level has been linked to higher rates of IPV perpetration among men in conflict and postconflict settings (Clark et al.,

2010; Gupta et al., 2009; Gupta et al., 2012; Vinck & Pham, 2013). Research from Uganda,

Thailand, Cote D’Ivoire and Liberia has found that women who have higher levels of conflict- related abuses also report higher levels of IPV victimization during and after conflict (Saile et al.,

2013; Falb et al., 2013a; Gupta et al., 2012; Vinck & Pham 2013). In a cross-sectional survey of refugees affected by the Burmese conflict, women who experienced conflict victimization were

5.9 times more likely to report past-year IPV than women who did not report conflict victimization (Falb et al., 2013a).

Findings from conflict settings throughout the world suggest that intense daily stressors, including IPV, may help account for high levels of psychological distress that previously were attributed solely to the stress of war (Miller et al., 2010). A survey among refugee women on the

Thai-Burma border found that levels of past-month suicide ideation were significantly higher among women experiencing IPV (26.7%) than women who experienced conflict victimization

(5.2%), but ideation was highest among women experiencing both kinds of trauma (50.0%) (Falb et al., 2013b).

These results suggest that interactions between conflict-related community violence and personal violence deserve further analysis. Yet few studies have examined district-level conflict and its impact on IPV. A study in Peru found that women who had greater exposure to conflict violence at the provincial level had an increased risk of IPV (Gallegos and Gutierrez, 2011). Similar to the project being proposed here, Gallegos and Gutierrez combined DHS data with conflict event data from the Peruvian Truth and Reconciliation Commission (ACLED data is not available for Peru).

The Truth and Reconciliation Commission data provide information on the year, location, severity, victim and perpetrator of violent events such as kidnapping, assassination, and sexual assault. Each province in Peru was given a conflict intensity score. Through multilevel modeling, the project found that women exposed to conflict violence, especially in late childhood and early teen years, had a greater probability of being a victim of IPV. One standard deviation increase in exposure to conflict at the age of 16, for instance, was associated with an increase in the

probability of experiencing IPV by 2 percent. This finding was statistically significant, and represents a notable increase when one considers that the average incidence of past-year IPV in

Peru is 15%. A similar study set in Rwanda and presented at the Population Association of

America Annual Meeting in 2014 found that exposure to violent conflict significantly increased an individual’s risk of IPV. Experiencing one violent conflict per year within a 50 km radius corresponded with a 1.1% increase in IPV (Janko et al., 2014).

Non-Partner Physical Violence and Conflict-Related Violence

Data on non-partner physical violence are relatively limited compared to IPV, and methodologies for collecting this information vary widely. Physical abuse from non-partners does not garner as much attention from the public health community as IPV, possibly because rates of this abuse vary drastically from country to country (Devries et al., 2013) and because

IPV is more prevalent than NPPV in a number of contexts (WHO, 2005; Devries et al., 2013).

IPV is often assessed as part of research on women’s health and gender-based violence, while

NPPV is not (Abrahams et al., 2014). Additionally, the concept, nature, and perpetrators of non- partner physical violence are extremely diffuse across different contexts. NPPV encompasses corporal punishment by teachers or employers, violence from police or other authority figures, and beating within the household by parents, siblings or extended family. In many instances, non-partner physical violence is aggregated with reports of non-partner rape, making it difficult to know the level of physical abuse occurring in the absence of a sexual assault (Fulu et al.,

2013; Garcia-Moreno, 2013; Hossain et al., 2016). In their overview of violence against women,

Watts and Zimmerman (2002) describe violence by family members in their typologies of

gender-based violence. However, other types of non-partner violence—for instance violence by employers or teachers—are not described.

Available data do, however, highlight the widespread nature of non-partner abuse in many settings although the nature of violence varies by country. The WHO multicountry study revealed notable variation in levels of physical violence across the globe, ranging from almost two thirds of women reporting physical abuse in Samoa to only 5% in Ethiopia and Japan

(WHO, 2013). Women experienced aggression from a range of actors, including teachers, employers and acquaintances, with fathers and other male family members cited as the most common abusers. Generally, women living in urban centers were more vulnerable to non-partner abuse in most settings, and in some countries, nearly 20% of respondents stated they faced violence from two or more perpetrators (WHO, 2013). A multicountry study of perpetration of violence by men in Asia and the Pacific found that lifetime rates of perpetration of physical violence ranged from 12% in Indonesia to 62% in Papua New Guinea (Fulu et al., 2013).

Within the limited scholarship on NPPV, some settings appear comparably burdened by IPV and

NPPV. In Liberia, for instance, national data estimate that half of all women have at some time experienced physical violence, and half of ever-partnered women report experiencing some form of IPV (Liberia Institute of Statistics, 2008).

Distinct patterns of interpersonal violence before, during and after a conflict have been documented in Cote d’Ivoire, a country adjacent to Liberia. In this cross-sectional study, male and female respondents were asked to recall their experiences with different forms of violence

before, during and after the five-year period of political unrest from 2002 to 2007 (Hossain et al.,

2016). One quarter of women (24%) and 27% of men reported lifetime physical violence. Rates of physical violence during the conflict were significantly higher for men (12.6%) than for women (8.9%), but levels dropped to notably low and comparable levels for both sexes in the 12 months directly after conflict (3.0% for women and 3.6% for men). Patterns of who perpetrated this violence were distinct for each group. For women, the most common perpetrators of violence before, during and after conflict were family members. For men, family members were the most common perpetrators of violence before and after the conflict, but combatants were the most common perpetrators of physical violence during the conflict. While the levels of non- partner violence were relatively low after the crisis for both women and men, it is interesting to note that IPV increased sharply for women, but not for men, in the year after conflict. Another study among rural women in Cote D’Ivoire found that women who reported victimization of their family during the conflict had 1.7 greater odds of reporting past-year in-law abuse compared to women whose families were not victimized (Falb et al., 2013c).

Social Cognitive Theory and Conceptual Framework

A central thesis of this project is that violence can be transmitted through pathways that involve social learning and social network influences. However, to date, no theoretical framework has been suggested to explain the factors and pathways that underpin this link. This project draws on

Social Cognitive Theory (SCT) and the Heise Gender-Based Violence Framework to postulate how collective violence may be correlated with violence at the personal level. This section first reviews each of these theories and then proposes a framework for understanding the “contagion”

of violence from the political level to the personal level using the explanatory power of SCT to explain interactions between behavior, environment and social norms.

The Heise Gender-Based Violence Framework

Heise (1998) proposes an ecological framework that draws on diverse disciplines to outline the causes of IPV at multiple levels, from the individual to the societal. In her seminal article on this framework, Heise notes that previous efforts to understand the complex phenomenon of gender- based violence (GBV) were hindered by disciplinary barriers that tended to focus narrowly on either purely individual factors or purely societal factors. The ecological framework she developed has the advantage of acknowledging different levels of influence at the micro-, meso- and macro-levels. Heise’s framework resulted from her comprehensive review of the literature to identify root causes of IPV across diverse disciplines. As she notes, however, the model is not intended to be exhaustive, since there may be untested factors that contribute to the problem that have not been studied in the literature. As described previously, models for NPPV are less well- developed than those for IPV, partly because the definition of non-partner violence is much more diffuse and it is seen as less of a public health burden globally than IPV. Thus, Heise’s model provides an important foundation for building the framework for the current project since it represents one of the most comprehensive attempts to understand IPV, one of the more well- studied forms of interpersonal violence. Heise’s model examines factors contributing to IPV at the personal level, at the level of the “microsystem” (the immediate circumstances surrounding abuse), at the “mesosystem” (the interaction between the individual and the social environment), and finally at “exosystem” (the level of institutions and social structures). The factors that are

identified in Heise’s model for non-conflict interpersonal violence are used to postulate contributing factors for the current model, described at the end of this chapter.

The Heise framework reviewed above provides an important launching point for understanding violence, yet it cannot be translated wholesale to the current research question. In addition to understanding the factors related to interpersonal violence, the current effort requires an understanding of which pathways are at play in translating conflict-related influences into personal violence.

Social Cognitive Theory

SCT provides a uniquely fitting approach for understanding how conflict-related experiences influence personal violence. It posits that social, environmental and personal factors interact to shape complex human behavior (Bandura, 1977; Glanz & Rimer, 1997), through a molding process of attention to, or witnessing, a given behavior (Bandura, 1973; Bandura, 1977; Bandura,

1986; Glanz & Rimer, 1997). In particular, SCT looks at how personal agency is influenced by the environment and by observed social behaviours in the environment. Indeed, one of the founding scholars of SCT drew on human aggression as a case study for this theory (Bandura,

1977). Retention of the behaviour depends on an individual’s exposure to it and to their ability to observe the consequences (whether these are positive or negative). Finally, an individual may reproduce behavior based on whether it is perceived as appropriate in a given context (Bandura,

1973; Bandura, 1977). An individual doesn’t mimic every element of observed behavior, but engages in a cycle of observation, action and adaptation based on observed and experienced feedback. This dynamic interplay between personal attributes, structural factors, and social

norms is known as reciprocal determinism in SCT, and is reflected in the two-way arrows between the main elements of the conceptual model below (Table 2.1). While this project will not assess reciprocal determinism, SCT provides a valuable framework for understanding how social and environmental context interplay with individual factors to influence behaviour. This is particularly important since this model was explicitly created to better understand how social learning, and the spread of human behavior, occurs through populations and across time.

The concept of contagion (or social diffusion) has proven a powerful explanatory framework for forms of violence as diffuse as handgun violence, political violence, and terrorism (Bandura,

1973; Bohstedt, 1994; Fox, 2004; Sedgwick, 2007; Fagan et al., 2007; Nacos, 2009; Kathman,

2011). A number of researchers have noted that broad political-level violence can “trickle down” to community, family and individual violence (Cummings et al., 2010; Cummings et al., 2011;

Dubow et al., 2010; Mullins et al., 2004). In their examination of transmission of violence within war-affected families in northern Uganda, Saile et al. (2014) conclude that individual factors alone cannot account for the perpetuation of personal violence after war, and allude to SCT as a potentially valuable framework for exploring mediating processes. Here, SCT is used to explain the pathways through which violence may diffuse from political violence to personal aggression.

Below is a review of factors that have been associated with interpersonal violence, drawing on both Heise and SCT models, as well as the wider literature. These factors are used to postulate the current theoretical model.

Conflict-Related Environment

The influence of the conflict environment on individual behaviour is a central focus of this project. The conflict environment is the primary predictive exposure in the model. While conflict is directly measured through the events recorded in the ACLED dataset, this measure serves as an indicator for the underlying and complex environment of conflict.

At the structural level, conflict often triggers deterioration of important services, including public health infrastructure and legal and justice mechanisms (Annan & Brier, 2010). These systems often serve to deter interpersonal conflict and address its adverse effects. Economic systems often go into crisis leading to decreased employment and higher levels of poverty, which can be drivers of interpersonal stress and violence (Baranyi et al., 2011).

Social customs and practices can also lead to the normalization of violence (Galtung, 1969;

Galtung, 1990). War in particular can legitimize violence; aggression may become not only relatively more acceptable, but an adaptive behavior (Annan & Brier, 2010; Clark et al., 2010,

Ember & Ember, 1994; Saile et al., 2014; Vinck et al., 2007). A number of changes occur at the societal level due to conflict, including the dissolution of social networks and the weakening of social ties and support structures (Annan & Brier, 2010); the blurring of established gender roles and norms (Jewkes, 2002); and the precipitation of a crisis in traditional family roles (Jewkes,

2002).

Heise’s model identifies factors in the exosystem independent of conflict that also influence violence. These include women’s isolation from social networks, high residential mobility and low peer support (Abramsky et al., 2011; Jewkes et al., 2013). In addition, cultures that promote

male dominance through rigid gender roles, permissive state-level norms toward violence, and social acceptance of interpersonal violence and physical punishment are linked with interpersonal violence (Kishor & Johnson, 2004; Uthman et al., 2009; Jewkes, 2013; Linos et al.,

2013; WHO, 2014).

Relational factors

Understanding the characteristics of a woman’s partner is particularly important for a conceptual model that includes IPV. A partner’s alcohol use, education level, control of wealth, his own experience with abuse in childhood, his own transaction of sex, multiple sex partners, and relationship discord are correlates of IPV (Hindin et al., 2008; Abramsky et al., 2011; Jewkes,

2013). A study of DHS data across multiple regions found that IPV was also higher among couples that did not make joint decisions (Kishor & Johnson, 2004; Linos et al., 2013). In other settings, child marriage has been associated with IPV; however, this has not been established as a risk factor in Liberia and so was not included in this analysis (Speizer & Pearson, 2011).

Individual-level factors

At the individual level, established correlates of interpersonal violence globally include younger age, education level, wealth, experience ofd or witnessing of violence previously, a history of mental health problems, and alcohol or substance abuse (Abramsky et al., 2011; Hindin et al.,

2008; Jewkes, 2013, Linos et al., 2013; WHO, 2013; WHO, 2014). For IPV specifically, having more children under five in the household and attitudes condoning domestic violence by victim and perpetrator are also risk factors (Jewkes, 2002; WHO, 2013). In Liberia specifically, women’s educational level, marital status and employment status have also been shown to be

associated with IPV (Liberia Institute of Statistics, 2008). Finally, religion is not shown to be consistently associated with interpersonal violence globally or in Liberia. However, it is included in the analysis since it is a key demographic variable and may provide insight into the research question.

Table 2.1 Conceptual Framework: Linkages between conflict and interpersonal violence

Conflict-Related Environment (Primary Exposure) Social Factors Individual Factors

Normalization of violence Age and acceptance of aggressive behaviours Number of children under 5 in household Dissolution of social networks and taboos Education

Weakening of social ties Religion and support structures Civil status Disruption of peaceful interpersonal conflict Age married resolution mechanisms Wealth Crisis in family roles and responsibilities Employment

Desensitization to Witnessing or experiencing violence violence in childhood

High residential mobility Attitudes condoning violence

Rigid gender roles that Mental health promote male dominance

Structural Factors Relational Factors *

Weakened legal and Partner’s drug and alcohol Interpersonal justice mechanisms consumption Violence

(Outcome) Deteriorated public Partner’s education level

health infrastructure Partner’s control of wealth

Low employment and Partner’s history with abuse decreased economic opportunity Partner’s mental health

Vacuum of state and civil Partner’s history transacting institutions to resolve sex/having multiple partners conflict and discourage violence High levels of discord in relationship

Bolded variables are those measured in the DHS dataset *Relevant to IPV specifically

Liberia Country Profile

Liberated American slaves began settling in what is now Liberia in 1822. Situated in West

Africa, Liberia is one of the continent’s smaller countries, with a population of 4.3 million. A number of scholars have argued that Liberia has experienced high levels of violence and human exploitation since the country’s inception, with the civil wars serving as the most public and definitive expressions of this fraught history (Ellis, 2006; Nmoma, 1997). Liberia’s roots in the slave trade and the colonization of indigenous Liberian tribes by freed American slaves created a two-tiered society where violence toward the underprivileged classes was commonplace. A small ruling elite largely controlled politics and wealth in the country while indigenous peoples were forced into hard labor in rubber and lumber plantations (Christy, 1931). In her article on the civil war in Liberia, Nmoma (1997) describes how the oppressive rule of Americo-Liberian elites set the stage for the subsequent civil wars. Indeed, the slave-like conditions imposed on many of

Liberia’s ethnic tribes sparked a series of unsuccessful rebellions throughout the late 19th and early 20th centuries (Kieh, 2008).

Conditions improved under President William Tubman’s administration from 1944 to 1971. His

26-year rule saw a period of unprecedented economic growth, efforts to decrease ethnic tensions, and stimulation of new investment (New World Encyclopedia, 2016). From 1950 to 1960, largely as a result of the Administration’s “open door” policy to investment, the country enjoyed the second largest rate of economic growth in the world. However, Tubman’s death was followed by a descent again into authoritarianism and repression overseen by Tubman’s long- term vice president, William Tolbert (International Crisis Group, 2003).

In 1979, riots broke out in the country in reaction to rising prices of rice, economic stagnation and increasing tension between the Americo-Liberian population and indigenous groups (Insight on Conflict, 2014). During the continuing unrest in 1980, a young army sergeant, Samuel Doe, and 16 other army soldiers assassinated the sitting president and took control of the country. Ten years of authoritarian rule followed until Charles Taylor launched a rebellion that resulted in the assassination of Doe and other government officials in 1989. This action launched the first

Liberian war, which lasted from 1985 until 1996. Taylor was elected president during the period of relative peace that ensued. In 1999, however, anti-government fighting once again broke out.

Rebel groups entered Liberia from neighboring countries and fighting became widespread in the following year (BBC, 2014; CIA World Factbook, 2015).

In 2003, after international intervention, a peace agreement was signed and rebel troops were demobilized. Over 150,000 people died and 850,000 people were displaced into bordering nations (United Nations, 2013). In both wars, combatants were both victims and perpetrators of human rights abuses, including rape, torture, and murder (Johnson et al., 2008). Disarmament,

Demobilization, and Reintegration (DDR) programming was widely seen as ineffective, and many former combatants dropped out or re-joined armed groups in neighboring countries

(Johnson et al., 2008).

Liberia’s 13 years of unrest heralded the greatest economic collapse of a sub-Saharan African nation recorded in modern history. After reaching its peak in 1979, GDP fell 90% from 1987 to

1995. With the end of the civil conflict in 2003, the extent of the devastation to the country’s infrastructure, economy and human capital became apparent. When the first formal elections

were held in 2005, the average income for a Liberian was 25% of what it had been before the instability (Radelet, 2007). Personal economic ruin was mirrored in the government, which expended less on its citizens between 2000-2005 than any other country in the world (Radelet,

2007). Violence and the downfall of community and state institutions during the unrest resulted in the ruin of health and educational services, disintegration of economic markets as roads became impassible, and an absence of state-provided electricity and piped water until 2006

(International Crisis Group, 2003).

Statistics about levels of interpersonal violence in the country before the civil wars are almost entirely absent. The DHS surveys – one of the best sources of population-based data about health and social outcomes – only provide information on interpersonal violence starting in 2007. While a 1986 DHS survey was conducted, no information on violence was collected. However, some information pointing to deep structural inequality for women does exist. A 2001 UNESCO report, written during the height of Liberia’s second civil war, found that Liberia had one of the lowest gender parity scores globally, and that girls were disproportionately represented in the number of out-of-school children in Liberia, a fact that could possible have lifelong consequences for this cohort of women (Kirk, 2003).

One indication of Liberia’s development pre, during, and postconflict is the Human

Development Index (HDI), which aggregates information about lifespan, education and gross domestic product (GDP) to rank countries globally (Anand, 1994). The first year the HDI was published was 1970, Liberia had an HDI score of 0.295, putting it in the lowest quartile of development. However, the country’s HDI scores climbed steadily in the following decade

before plummeting with the onset of war in 1989 to 0.125 (Klugman, 2010). A clue to the level of gender inequality in Liberia comes from HDI’s 2010 report; Liberia was one of only eight countries where women had fewer than half of the years of education as men (Klugman, 2010).

While this statistic was published almost seven years after conflict, it indicates a persistent and possibly worsening gap between opportunities afforded to men and women.

Violence and tolerance of abuse were pervasive in post-conflict Liberia, as reported in the DHS conducted five years after the war. These data demonstrate the high burden of violence in the country. In the 2007 survey, over one-third of women in Liberia reported ever having experienced physical violence by a partner, and 11% reported partner sexual violence (Liberia

Institute of Statistics, 2008). Just under half of all women (49%) reported having experienced some kind of violence (physical, sexual or emotional) from a partner. Sixty percent of women believed that a husband is justified in beating his wife for at least one reason. Almost half of women surveyed (45%) indicated that they experienced some kind of physical violence from the age of 15, and one third of women experienced this abuse in the past 12 months.

The gender inequality index (GII) was introduced in 2010. While the measure is a relatively crude guage of inequality, it provides some insight into the status of women in Liberia. The first

GII measures are available for Liberia only seven years after the end of the conflict. However, they still indicate a highly inequitable nation. Liberia is consistently among the bottom ten countries for gender equality. And yet, Liberia also elected the first female head of state in Africa in the first election held after the end of conflict, Ellen Johnson Sirleaf. During her administration, Sirleaf has promote free and compulsory for all elementary school aged children

and established a Truth and Reconciliation Commission to help come to terms with the decades of conflict and ethnic tension in the country.

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. Methods

Overview

The goal of this project is to contribute to understanding the effects of conflict on different types of interpersonal violence. In a single, conflict-affected nation (Liberia), the Demographic and

Health Survey (DHS) data from 2007 will provide information about health and social characteristics, including the outcomes of NPPV and IPV. Armed Conflict Location and Event

Data Project (ACLED) will be used to provide a measure of the extent to which a community has been affected by conflict at the sub-national district level during the Liberian conflict from 1999 to 2003 (Figure 3.1). This conflict-affectedness information at the district level can be combined with DHS data at the individual level to examine the links between interpersonal violence and conflict. This chapter will begin with a review of the study aims, the data sources and key definitions. In the final section, the procedures for fitting the multilevel model will be described.

Figure 3.1 Timeline of Conflict Relative to Collection of Individual-Level Data

2007 1999 to 2003 2004 to 2006 DHS data Active conflict Postconflict collected

Study Aims

Aim Summary Dependent Variable Hypothesis 1a. Quantify the association Using DHS and ACLED data, Past-year non-partner Individuals living in conflict- between past-year non-partner this research will assess physical violence affected districts will have physical violence and conflict- whether individuals in higher proportions of past year affectedness geographic units that non-partner physical violence experienced conflict are more than those living in non- likely to experience past-year conflict affected districts in non-partner physical violence Liberia, after adjusting for as measured by the DHS. relevant individual-level characteristics. 1b. Conduct sensitivity analyses Examine different ways to None Conflict fatalities, which are to determine how the association define conflict in the NPPV highly visible and traumatic 43

between non-partner physical model by looking at fatal manifestations of conflict, are violence and conflict is affected events, non-fatal events and the the most impactful measures by difference characterization of accumulation of conflict over of conflict. conflict. time by district.

2a. Quantify the association Using DHS and ACLED data, Past-year intimate partner Individuals living in conflict- between past-year intimate this research will assess violence affected districts will have partner violence and conflict- whether individuals in higher proportions of past year affectedness geographic units that IPV than those living in non- experience conflict are more conflict affected districts in likely to experience past-year Liberia. IPV as measured by the DHS.

2b. Conduct sensitivity analyses Examine different ways to None Conflict fatalities, which are to determine how the association define conflict in the IPV highly visible and traumatic between intimate partner model by looking at fatal manifestations of conflict, are violence and conflict is affected events, non-fatal events and the the most impactful measures by difference characterization of accumulation of conflict over of conflict. conflict. time by district.

Data sources

Demographic and Health Surveys (DHS): Individual-Level Data

The DHS Program has been collecting data since 1984 in over 90 countries. The program, funded by the U.S. Agency for International Development (USAID), examines fertility, family planning, maternal and child health, gender dynamics, HIV/AIDS, malaria, and nutrition. A notable strength of DHS data is that it brings standard procedures and methods across a large range of surveyed countries. A core standard questionnaire is administered in all countries, with some variation to ensure that questions are culturally appropriate and relevant.

The DHS Women’s Questionnaire collects data on women aged 15 to 49 years, including demographic information, contraceptive use, employment, empowerment, geographic information system (GIS) information about the cluster where the woman were sampled, as well as information about the women’s husband. Data on interpersonal and partner violence was first collected as part of the DHS in 1990 in Colombia, and a standardized module was developed in

2000 based on WHO ethical guidelines for research on domestic violence (WHO, 2016). The

Domestic Violence (DV) Module is now applied in conjunction with the Women’s Individual

Questionnaire during the administration of the DHS in over 25 countries, including Liberia.

The number of women pre-selected to take the domestic violence module in each household is established using a matrix known as the Kish grid technique that matches the number of eligible women with a random number generated as part of the household identifier (Kish, 1965). When the interviewer arrives at the first question of the domestic violence module, he or she establishes whether the woman has been pre-selected; if so, the module is administered. Only one woman per household is eligible to take the domestic violence module in order to ensure that others in the house do not know what types of questions were asked. The module is administered only to individuals in a private setting, and an additional consent script is read to the respondent. If privacy cannot be ensured, the domestic violence module is not administered.

In the DHS data, Global Positioning System (GPS) data for clusters are randomly offset in order to safeguard respondent privacy and confidentiality (Burgert et al., 2013). In urban areas, clusters are displaced by 0 to 2 kilometers, and in rural areas locations are displaced between 0 to 5 kilometers (Perez-Heydrich et al., 2013). The displacement is checked to ensure that displaced clusters do not leave national or administrative boundaries (thus, a cluster in Liberia would not be displaced in a way that it would move to Sierra Leone, for example). Since this displacement does not move clusters across administrative boundaries, it does not impact the current analysis.

Armed Conflict Location and Event Data Project (ACLED): Conflict-Affectedness Data

ACLED data provide the dates and locations of all political events related to conflict and unrest in over 50 countries. It serves as the most comprehensive source of conflict data globally. Data exist for all African countries and for Haiti, Laos, Cambodia, Nepal, and Myanmar since 1997.

Other countries have been included in the dataset more recently (for instance, data for

Afghanistan and Pakistan have been available since 2006). ACLED data provide the date, location, and implicated actors of political events that may occur in the course of civil and communal conflicts, violence against civilians, rioting and protesting. Armed actors may include governments, rebels, militia, organized political groups, ethnic groups, and civilians. Detailed geographic information for each event is coded, including the name of the location as stated in media reports, GIS coordinates of that location, and the georeferenced spatial precision scale of information.

The database draws on three different types of sources in order to achieve comprehensive reporting of events: local, regional, national and continental media are reviewed daily; NGO reports are used to ensure reporting occurs in remote or hard-to-access locations; and Africa- focused news reports and analyses are used to supplement previous sources. ACLED states that this methodology achieves the most comprehensive source material currently available for digital conflict event coding. Every event is indexed with its source(s) so users can refer to the original report (Raleigh et al., 2010). These data are used to create monthly conflict trend reports on the state of political violence in Africa. In addition, ACLED data have also been used in peer- reviewed analyses of the patterns and correlates of civil war, including topics such as population concentration and civil war, and climate variability and civil war.

The ACLED data allow for the creation of a conflict-affectedness variable using data on the location and nature of conflict-related events. ACLED data contain information on conflict- related events, including battles, riots and violence against civilians. Geocoding is a process of providing detailed location data about a given event or variable. ACLED geocodes event data at the first and second administrative boundary levels and provides latitude and longitude coordinates for each event. For this project, the ACLED Version 5 database was used to determine the numbers of fatal and non-fatal conflict events. This project will focus specifically on the 1999–2003 hostilities, since conflict-related data are available only for this time period.

All events recorded after 2003, the official end of the second Liberian war, were removed from the dataset.

Linking DHS and ACLED Data

A number of steps were undertaken to merge information from both the ACLED and DHS databases. From the ACLED database, fatalities and non-fatal conflict events that occurred during the course of the second Liberian conflict (1999–2003) were selected. GPS coordinates were provided in the ACLED database for each encounter. These events were placed on a map of

Liberia that outlined boundaries at the primary and secondary district levels using QGIS

Software (QGIS Development Team, 2015). By merging ACLED data with administrative boundary data, it was possible to place each fatal event within a given district. This information was exported into an excel file containing fatality counts by secondary district.

A similar process was undertaken with the DHS database. A GIS file is available by request from the DHS program. The GIS file contains GPS coordinates for each sampled cluster in the DHS

data. DHS clusters were mapped using QGIS to determine the secondary administrative boundary in which DHS clusters were located. This information was then exported into an excel file, which contained information about which DHS clusters were within which secondary districts.

DHS cluster numbers and fatality counts were merged with secondary administrative boundaries.

This file was merged with the individual recode DHS dataset for Liberia in 2007 with the secondary administrative district number as the unique identifier.

Study Sample

The process of merging two datasets requires considering which records have matched successfully and understanding if, and why, others have not. In the 2007 Liberia DHS, the DV module was administered in all households. For the 2007 Liberia DHS, a total of 7,448 women were sampled, with 7,092 women completing the survey, a 95% response rate. A total of 4,913 women were sampled for the DV module. Of the 4,913 women interviewed for the DV module in the 2007 Liberia DHS, 411 individuals were not given a geographic identifier in the DHS dataset, representing 8.4% of the sample. The DHS Program notes that these clusters are purposefully coded as missing geographic information in cases where geographic coordinates were not collected or could not be verified. Since there is no way to geographically locate the data from these individuals, these data were removed from the analysis, leaving 4,502 individuals who had taken the DV module of the DHS and who were successfully located into districts. The following analysis draws on this dataset of 4,502 individuals.

Of the 4,502 individuals administered the DV module, both partnered and non-partnered women were asked about non-partner violence. Those respondents who reported having experienced violence were asked a series of questions to determine who perpetrated this violence. These women were then asked how often they had experienced physical violence in the past 12 months.

Over 99% of the individuals (4,470) who answered questions about ever-violence also answered the follow-up question about past-year violence. Those respondents answering they experienced violence “often” or “sometimes” (as opposed to not at all) were coded as having experienced non-partner violence. Women who did not respond to the question about past-year violence were dropped from the analysis. This analysis uses the complete case method— cases with missing information that would eventually be dropped from the model are excluded at the start of the analysis. Thirteen cases were dropped from the analysis because of missing information in the independent variables added to the model, leaving 4,457 individuals (99.0% of the original

4,502) in the final analytic sample for Aim 1a. The derivation of each analytic sample is shown in Appendix 1.

Only ever-partnered women were asked questions about intimate partner violence (Aim 2a). Just over 80% of women (n=3,648) were administered questions about intimate partner violence because they reported being currently or formerly in a union. Women were asked a series of questions about whether they had ever experienced different types of abuse. Women responding that they had ever experienced abuse were then asked if they had experienced abuse in the past

12 months. Over 98% of the sample (n=3,596 women) responded to this question and are thus eligible for inclusion in the analysis. When the final model was run, 122 cases (3.4% of the sample) were dropped due to missing data in the predictor variables. Partner education was the

variable that contributed most to missing data—2.8% (99 cases) of the sample were missing this data element. Because of the very low level of missing data, however, no replacement or imputation was used in this analysis. Of the 3,596 original cases with data about physical violence, 3,452 (96.6%) were used in this analysis, providing the final analytic sample for Aim

2a.

The WHO defines interpersonal violence as “the intentional use of physical force or power, threatened or actual, against another person, that either results in or has a high likelihood of resulting in injury, death, psychological harm, maldevelopment or deprivation” (WHO, 1996).

Thus, NPPV and IPV, the primary outcomes, are categories within the umbrella of interpersonal violence. For the purpose of this paper, interpersonal violence will be used when referring to any violence between two people in a non-conflict setting, and could include violence either by a partner or non-partner.

Dependent Variables

Intimate Partner Violence in the Past 12 Months

The WHO defines intimate partner violence as “behavior by an intimate partner or ex-partner that causes physical, sexual or psychological harm, including physical aggression, sexual coercion, psychological abuse and controlling behaviours.” The WHO defines physical violence as “the intentional use of physical force or power, threatened or actual, against oneself, another person, or against a group or community that either results in or has a high likelihood of resulting in injury, death, psychological harm, maldevelopment, or deprivation.” The DHS module and

WHO ethical guidelines use the term domestic violence for partner-perpetrated violence. In all other instances, this project uses the term intimate partner violence (IPV).

The DHS Domestic Violence module uses a modified Conflict Tactics Scale (CTS) to measure

IPV, one of the most widely measured and reliable measurement tools for IPV (Straus et al.,

1990). Strengths of this assessment include the number of opportunities to disclose violent events; detailed information about a range of behaviors; and its widespread use (Hindin et al.,

2008). The latest iteration of the revised CTS produced reliability scores between .79 and .95

(Straus et al., 1990). The CTS assesses a set of specific behaviours and experiences, whether or not they are perceived as abuse in a given setting. Thus, respondents are not asked to report whether they self-identify as “abused,” rather they report concrete experiences. For this reason,

CTS provides comparable estimates of violence across different settings (Hindin, 2008). This approach is in keeping with recommendations from the WHO Ethical and safety recommendations for intervention research on violence against women (WHO, 2016).

Ever-partnered women were asked about a list of eight specific behaviors they may have experienced that would classify as physical or sexual violence (Table 3.1). Women answering

“yes” to any of the items from a to g were classified as having experienced partner physical violence ever by the DHS. Women answering “yes” to items h or i were classified as having experienced sexual partner violence.

For those items where women answered “yes,” they were then asked about the frequency of the act in the 12 months preceding the survey: “not at all,” “often,” “sometimes.” Past-year IPV is

defined as having experienced any physical or sexual violence (items a to h) in the past 12 months (women responding “often” or “sometimes”).

Table 3.1 DHS questions assessing partner physical and sexual violence

Does/Did your (last) husband/partner ever do any of the following things to you:

a) Push you, shake you, or throw something at you? b) Slap you? c) Twist your arm or pull your hair? d) Punch you with his fist or with something that could hurt you? e) Kick you, drag you, or beat you up?

f) Try to choke you or burn you on purpose? g) Threaten or attack you with a knife, gun, or other weapon?

Partner physical physical Partner violence

h) Physically force you to have sexual intercourse with him even when you did not want to? i) Force you to do any sexual acts you did not want to? Partner Partner sexual violence

Non-partner Physical Violence in the Past 12 Months

At the end of the DV module, there is a series of questions about physical abuse as an adult.

Ever-partnered women are asked, “From the time you were 15 years old, has anyone other than your (current/last) partner hit, slapped, kicked, or done anything else to hurt you physically?”

Never-partnered women are asked, “From the time you were 15 years old has anyone ever hit, slapped, kicked, or done anything else to hurt you physically?” The possible answers are “yes,”

“no,” “refuse to answer.” Those respondents who answer “yes” are asked, “Who has hurt you in this way?” A series of possible actors are listed, including: mother/stepmother, father/stepfather, sister/brother, daughter/son, other relative, former husband/partner, current boyfriend, former boyfriend, father in-law, other in-law, teacher, employer/someone at work, police/soldier, other

(specify). Survey enumerators are asked to check all that apply; thus a respondent can report abuse from more than one kind of perpetrator. The respondent is then asked, “In the last 12 months, how often have you been hit, slapped, kicked, or physically hurt by this/these

person(s)?” Possible responses are “not at all,” “sometimes,” or “often.” For the purposes of this project, women responding "sometimes” or “often" to this question were coded as having experienced past-year non-partner violence. It is worth noting that this approach differs from the conflict-tactics scale used for IPV. In contrast to the CTS, this method of assessing physical violence has not been validated cross-culturally. In the follow-up question about perpetrators, women are given a list of possible actors. Particularly, perpetrators of potential criminal violence

(for instance, being mugged by a stranger) are not prompted in this list. While women have the option to choose “other” and to specify the actor, the heavy emphasis on in-home violence (from a parent or sibling, for instance) could bias the results toward measuring only this type of violence. It is also worth noting that the last question assesses past-year non-partner violence but does not specify the past-year perpetrator.

Primary Exposure: Defining Conflict at the District-Level

Political violence is defined by ACLED as “the use of force by a group with a political purpose or motivation” (Raleigh & Dowd, 2015). It defines a politically violent event as “a single altercation where often force is used by one or more groups for a political end, although some instances, including protests and non-violent activity, are included in the dataset to capture the potential pre- cursors or critical junctures of a conflict” (Raleigh & Dowd, 2015). The quantification of politically violent events in Liberia allows us to characterize conflict in the country through fatalities and events—the primary exposures for this analysis.

A central question of this project is how one defines conflict. Within the ACLED dataset, there are two measures of political conflict: fatalities and events. Events are coded in ACLED as any

recorded political incident, including clashes, protests, riots and battles. Thus, an event may be relatively peaceful and may not result in any fatalities, or it may represent a highly violent clash with many deaths. The fatality measure indicates are events that result in one or more fatalities.

Every conflict fatality represents a political event, but not all events result in fatalities. This begs the question of which indicator to use and how to characterize it.

Fatalities are definitive, visible and traumatic manifestations of conflict. In contrast, events are more diffuse and may not be violent in nature. Thus, any exposure to conflict fatalities at the district level is the most relevant measure for this project, and has been chosen as the principal predictor. Because the definition of conflict is central to this research, a full analysis of characterizing conflict is presented in Chapter 4. Box plots were used to visualize the distribution of each outcome across fatalities and events. Visualizations of natural breaks in the data and

Stata’s xtile command were used to parse conflict fatalities and events into categories, and the distribution of each outcome was then assessed to determine what categorization of conflict was most informative for the analysis. The spatial distribution of conflict was examined by plotting events and fatalities on a map of Liberia using the QGIS Software (QGIS Development Team,

2015) for each year of conflict individually, and then for all years of conflict simultaneously.

Independent Variables

The variables that are included in this analysis have been chosen based on theory as well as by those variables that have been found to be significantly associated with interpersonal violence, as described in Chapter Two. Covariates of interest have been summarized in Table 3.2.

Age was included in the model since progressive years of age may be protective against IPV

(Hindin et al., 2008). Children under 5 was measured by the variable in the DHS data that counts children aged 5 and under living with the respondent. Wealth and employment are measured in the DHS by indices designed to allow for cross-country comparisons. The wealth index is constructed by collecting data on the assets owned by each household, including items such as television, construction materials, and access to water, sanitation and electricity. A continuous scale is constructed based on answers to these questions, and then households are categorized into quintiles based on the continuous measure (Rutstein & Staveteig, 2014). Labor-market participation is measured in the DHS by asking a series of questions about current and past employment, including informal jobs such as working on a farm or in a family shop. Those women who state that they are working at the time of the survey, and those who state that they worked in the past 12 months are classified as “employed.”

Women report whether they are never married, married, living together as if married

(cohabitating), divorced/separated, or widowed. Educational attainment is measured through a categorical variable with the following responses: no education, incomplete primary, complete primary, incomplete secondary, complete secondary, and higher.

Exposure to violence and having permissive attitudes toward violence are measured through three separate questions in the DHS. Witnessing domestic violence in childhood was assessed through a single item. Respondents were asked whether their father had ever beaten their mother

(yes/no response). Beating by father was also assessed through a single item. Respondents were asked if they had ever been physically hurt by their father (yes/no). Finally, permissive attitudes

towards IPV were assessed through a five-item scale. Respondents were asked whether wife beating was justified (yes/no) in the follow circumstances: “She goes out without telling the husband”; “She neglects the children”; “She argues with husband”; “She refuses to have sex with husband”; and “She burns the food.” In accordance with other similar studies, the five-item assessment was collapsed into a variable that takes a value of 1 if women think beating is justified for any reason, and 0 if she thinks wife beating is never justified (Gallegos & Gutierrez,

2011). There was significant overlap between respondents with permissive attitudes toward IPV and beating by father and father beating mother. Thus, all three constructs (permissive attitudes towards IPV, witnessing domestic violence or experiencing beating by one’s father) were collapsed into a dichotomous construct of any exposure to violent experiences and attitudes based on a “yes” response to any of these measures.

Women were asked whether their partner drinks alcohol. The response was a simple “yes” or

“no” option. Women stating “yes”’ were coded as having partners who drink alcohol. Women’s report of their partner’s alcohol consumption may not be as accurate as self-report from the partner. However, the relatively low rate of “don’t know” response (0.7%), and the fact that this is a relatively simple question (as opposed to asking how often or how many drinks one’s partner has per week) suggests this might be a fairly reliable, if crude, indication of partner alcohol use.

A study surveying alcohol use questions across all DHS surveys found that the wording of alcohol-related questions varied substantially from survey to survey (Mallick & Shireen, 2015).

This study suggested that a more reliable approach would be to use a validated measure such as

AUDIT-C (Bush et al., 1998). Despite potential limitations, this partner alcohol-use measure is still included given consistent associations of alcohol use with IPV (Heise, 2011).

Table 3.2 Independent Variables by Ecological Model Level

Structural characteristics Conflict fatalities (primary predictor) Partner education level Relationship characteristics Partner alcohol abuse Age Number of children under 5 Education level Individual-level characteristics Religion

Civil status Wealth index Worked in past 12 month Experiences and attitudes towards violence

Data Analysis

All analyses were conducted with Stata/SE 14.0 (StataCorp LP, College Station, TX).

Exploratory data analysis included visualizing the data to assess for outliers using box plots, looking at patterns of missing data, and exploring the distribution of individual variables by constructing histograms and tables for continuous and categorical variables, respectively. For each outcome (NPPV and IPV), box plots visualized levels of each outcome in fatality and non- fatality districts. Separate bivariate logistic regression models were used to assess the relationship between each outcome (NPPV and IPV) and each independent variable at the individual level. A bivariate model was also used to examine the relationship between the main predictor (fatalities) and each outcome. For the final model, a multilevel approach was used to account for the nested structure of the data, with clustering of women within districts.

Independent variables were added in blocks through a stepwise procedure.

The intraclass correlation was calculated for each intercept-only model of NPPV and IPV to explore how much variation is accounted for by the clustering at the district level. The within- 57

versus between-district variation was visualized by plotting the proportion of individuals experiencing the outcome in each district with a 95% confidence interval. The y-axis shows the proportion of women experiencing the outcome at the district level against each district, plotted on the x-axis. These plots were constructed separately for NPPV and IPV.

Model Specification

Multilevel logistic regression models were used to quantify the effect of district level conflict on the odds of NPPV and IPV after sequentially adding blocks of independent variables as described in Table 3.2. The models included a random intercept for district to account for the geographic clustering of the sample.

Multilevel Model - Multiple Levels of Exposure:

Logit ()* = 1 = -0 + 01) + β1I mid level conflict) + β2I(high level conflict)) + β3A)*

Multilevel Model - Dichotomous Exposure:

Logit ()* = 1 = -0 + 01) + β1I conflict) > 0 + β2A)*

In the regression equation above, i indexes the district and j indexes the individual. ()* is the indicator for whether a woman (j) in district (i) has reported experiencing violence in the last 12 months. β_0+b0i defines the district level odds of a woman experiencing violence in district i given no conflict holding the individual-level covariates fixed. In the first equation, the conflict affectedness variable has three categories, defined in by no- medium- and high, with the no- conflict category as the reference. β1 gives the odds ratio of NPPV or IPV if the district experienced mid-levels of conflict to the odds of NPPV or IPV if the district had experienced no

conflict. β2 gives odds ratio of NPPV or IPV if the district experienced high-levels of conflict to the log odds of NPPV or IPV if the district had experienced no conflict. X_ij contains women’s individual-level characteristics summarized in Table 3.2, including: i) age ii) number of living children under 5 iii) religion iv) education level v) wealth index and vi) husband or partners’ education level (for the IPV model only). As noted before, the individual level characteristics are added to the model in blocks of related variables.

The second equation expresses the model in the case of a dichotomous measure of conflict. Here, β1 gives the odds ratio of NPPV or IPV if the district experienced any conflict to the odds of NPPV or IPV if the district had experienced no conflict.

The ICC was calculated for both the intercept-only models of NPPV and IPV. This was done by taking the variance of the random intercept of the final model divided by the variance plus π^2/3

(Koch, 1982). For all analyses, significance was assessed using an alpha of 0.05.

Sample Weights

In order to take into account the complex survey design of the DHS, the survey weights for the

DV module were included in all analyses, using the probability weight or pweight option within the gllamm survey command. The probability weight is defined as the inverse probability of the respondent’s being included in the sample. The pweight command assumes weights are specified at at least two levels in the data. Since the data were not weighted at the district level, the level-1 weight within Stata was specified as 1. The level-2 weights were calculated using the d005 variable for the DV module specified in the DHS. The d005 weight in the DHS was rescaled by dividing it by 100,000 to create a scale between 0 and 1.

Sensitivity Analysis for Migration 59

A core hypothesis of this project is that conflict has a profound and lasting effect on a place.

Thus, a woman moving to a district a week after the official end of the war would still experience these myriad effects, even if she did not spend the war there herself. In the models presented in the following chapters, there is an implicit assumption that those sampled in conflict districts five years after conflict have spent enough time in the district where they were sampled to have absorbed the effects of the place in which they reside. Data collected by the DHS, however, also makes it possible to examine this directly. One survey item asks respondents how long they have lived in their place of residence. For the purposes of this analysis, those who had lived in their residence for less than four years (since the end of the war), or who said they were

“only visiting” were dropped from the analysis. Sensitivity analyses are run for both NPPV and

IPV to test whether restricting the sample to non-migrants changed the association between each outcome and conflict.

Ethical Considerations

The Johns Hopkins Ethical Review Board approved this secondary data analysis. The data used in the secondary analysis was de-identified and therefore poses minimal risk to study participants. The georeferenced DHS data that have been linked to the ACLED dataset are only identified at secondary administrative boundary levels, minimizing the risk that participants could be identified by their place of residence. In addition, the DHS georeferenced data are randomly displaced between 0 to 10 kilometers to ensure respondent confidentiality. The displacement is restricted so points remain in the country and region originally sampled. While this offset is negligible for the scale at which the data are analyzed, it precludes identification of subjects based on their place of residence.

The protocols for data collection for the DHS surveys are described in detail in the Guide to DHS statistics: Demographic and Health Surveys methodology (Rutstein & Rojas, 2006). In addition, the procedures for collecting domestic-violence data are described in detail in the Ethical and safety guidelines for the Demographic and Health Surveys (DHS, no date) and procedures for collection of georeferenced data are described in detail in Incorporating geographic information into Demographic and Health Surveys: A field guide to GPS data collection (Burgert & Zachary,

2013). The DV module for the DHS was developed to comply with the WHO’s Putting women first: Ethical and safety recommendations for research on domestic violence against women

(WHO, 2016).

Chapter Three References

Burgert, C. R., Colston, J., Roy, T., & Zachary, B. (2013). Geographic displacement procedure and georeferenced data release policy for the Demographic and Health Surveys. DHS Spatial Analysis Reports No. 7. Calverton, Maryland, USA: ICF International.

Bush, K., Kivlahan, D. R., McDonell, M. B., Fihn, S. D., & Bradley, K. A. (1998). The AUDIT alcohol consumption questions (AUDIT-C): an effective brief screening test for problem drinking. Archives of Internal Medicine, 158(16), 1789-1795.

Demographic and Health Survey Data. Ethical and Safety Guidelines for Implementing the DHS Domestic Violence Module. Retrieved from: https://www.dhsprogram.com/topics/gender- Corner/upload/DHS_Domestic_Violence_Module_Ethical_Guidelines.pdf

Gallegos J.V, Gutierrez, I. A. (2011). The Effect of Civil Conflict on Domestic Violence: the Case of Peru. Unpublished manuscript. Retrieved from: http://jvgalleg.mysite.syr.edu/default_files/Research_files/Gallegos- Gutierrez%20%20Civil%20Conflict%20and%20Domestic%20Violence%20JDE.pdf

Heise, L. (2011). What works to prevent partner violence? An evidence overview. Retrieved from: http://researchonline.lshtm.ac.uk/21062/

Hindin, M. J., Kishor, S., & Ansara, D. L. (2008). Intimate partner violence among couples in 10 DHS countries: predictors and health outcomes. DHS Analytical Studies No. 18. Calverton, Maryland, USA: Macro International Inc.

Kish, L. (1965). Survey sampling. New York, New York: John Wiley & Sons.

Koch, Gary G. (1982). "Intraclass correlation coefficient". In Samuel Kotz and Norman L. Johnson. Encyclopedia of Statistical Sciences. (pp. 213–217). New York, New York: John Wiley & Sons.

Mallick, L, Shireen A. (2015). An Inventory of Alcohol-Related Questions in the Demographic and Health Surveys and an Analysis of Alcohol Use and Unsafe Sex in Sub-Saharan Africa. DHS Analytical Studies No. 53. Rockville, Maryland, USA: ICF International.

Perez-Heydrich, C., Warren, J. L., Burgert, C. R., & Emch, M. (2013). Guidelines on the Use of DHS GPS Data. DHS Spatial Analysis Reports No. 8. Calverton, Maryland, USA: ICF International.

QGIS Development Team, 2015. QGIS Geographic Information System. Open Source Geospatial Foundation Project.

Raleigh, C., Dowd, C. (2015). Armed conflict location and event data project (ACLED) codebook. Available at: http://www.acleddata.com/wp- content/uploads/2015/01/ACLED_Codebook_2015.pdf

Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing acled: An armed conflict location and event dataset special data feature. Journal of Peace Research, 47(5), 651-660.

Rutstein, S. O., Rojas, G. (2006). Guide to DHS statistics. Calverton, Maryland: ORC Macro.

Rutstein, S. O., Staveteig, S. (2014). Making the Demographic and Health Surveys Wealth Index Comparable. DHS Methodological Reports No. 9. Rockville, Maryland, USA: ICF International.

StataCorp LP. (2011). Stata/SE 14.0 for Windows. College Station, TX: StataCorp LP.

Straus, M. A., Gelles, R. J., & Smith, C. (1990). Physical violence in American families: Risk factors and adaptations to violence in 8,145 families (pp. 49-73). New Brunswick, NJ: Transaction Publishers

World Health Organization. (1996). WHO Global Consultation on Violence and Health. World Health Organization, Geneva, Switzerland. WHO/EHA/SPI. POA. 2). Available at: http://www. who. int/violenceprevention/approach/definition/en.

World Health Organization. (2016). Ethical and safety recommendations for intervention research on violence against women . Department of Gender and Women’s Health, Geneva, Switzerland.

. Defining the Primary Exposure

Results

This section will first give an overview of the Liberian conflict to provide context for the measures of conflict used in this project, including the primary predictor–conflict fatalities, as well as conflict events and cumulative event years. The chapter will then present different ways of characterizing each of the predictors for the analyses presented in Chapters Five and Six.

Quantification of Events and Fatalities

Over the course of the conflict, nine of the 61 districts in the dataset experienced fatalities

(14.7%). There were 182.4 fatalities per district on average, but a notably large range of values

(range 1-327, SD 113.4). Thirty-nine of the 61 districts experienced conflict-related events

(63.9%). Of these districts, there were 41.9 average events per district (range 1-134, SD 54.0).

Seventy-six percent of individuals in the dataset (n=3,734) lived in districts with conflict events, compared to 33.3% (n=1,497) who lived in districts with fatalities. As noted in the methods, all fatalities represent an event but not all events result in fatalities. The histograms in Figure 4.1 visualize the number of fatalities and events by district. As noted above, there is considerable variation in the number of both fatalities and events that districts experience. We can leverage this variation by finding a number of different ways to “slice” the fatalities and events as the primary predictor. The following sections will explore how the civil war varies over space and time and then present different ways to characterize the conflict measure.

Figure 4.1 Number of Fatalities and Events by District from 1999 to 2003

Number of Fatalities by District1999-2003 400 300 200 Fatalities 100 0 1 4 27 35 38 39 40 47 52 District Identifier

Number of Events by District 1999-2003 150 100 Events 50 0 1 4 7 9 10 11 12 13 14 15 17 18 19 23 26 27 29 32 33 34 35 37 38 39 40 42 45 46 47 48 50 51 52 53 54 57 58 59 62 District Identifier

The Liberian Conflict Across Time and Space

The Liberian conflict exhibits variation in the temporal and geographic distribution incidents.

The graphs below trace the number of fatalities and events across the five years of the civil war from 1999 to 2003. Figure 4.2 depicts a sharp peak in conflict fatalities and events in June through September 1999. Figure 4.3 shows fatalities disaggregated by affected districts. The

1999 spike in fatalities occurs in Kolahun District in Lofa County, which lies at the intersections among Liberia, Sierra Leone and Guinea. In 1998, Human Rights Watch reported that internal conflicts within Sierra Leone, Liberia and Guinea continued to fuel violence across the borders of the tri-state region. The Liberian Government was accused of providing troops and material support to the Sierra Leonean rebel group the Revolutionary United Front (RUF). In August

1999, rebels seized control of Kolahun, killing hundreds of Sierra Leonean refugees who had sought shelter from their own civil war in the Liberian border region. The hostilities resulted in the renewed displacement of over 10,000 refugees before the Guinean and Liberian

Governments reached an agreement to ease the growing tensions between the two governments

(Human Rights Watch, 2002). Despite this bilateral agreement, the violence in Lofa County was seen as triggering internal violence in Liberia that launched the second civil war in 1999 (Insight on Conflict, 2014).

Figure 4.2 Graph of Conflict Events and Fatalities in Liberia from 199 to 2003

Conflict Fatalities

After the Kolahun clashes, conflict fatalities in Liberia were relatively restricted from 2000 to

2002. However, Figure 4.3 shows another sharp spike in conflict fatalities in May through July

2003. The 2003 spike in fatalities occurred in District 47, greater Monrovia. These deaths coincided with fighting between the Armed Forces of Liberia and rebel forces in a confrontation that became known as the siege of Monrovia (Robison, 2013). From mid-July to mid-August, the city of Monrovia was shelled, and hundreds of civilians were killed. By the end of the siege,

United Nations Mission in Liberia (UNMIL) peacekeepers had gained access to the city, and

President Taylor had resigned and gone into exile (Human Rights Watch, 2002; Insight on

Conflict, 2014). The graphs illustrate the spike in events and fatalities at the start and at the end of the war. The disaggregation by district also highlights the extent to which some districts experience very high fatalities while others experience little or none.

Figure 4.3 Graph of Conflict Fatalities in Liberia from 1999-2003 Disaggregated by Fatality- Affected District

*Only districts with any conflict fatalities are shown

The maps in Appendix 2 further illustrate how fatalities and events spread across space and time.

Conflict fatalities begin in the northwest of the country near the border with Sierra Leone in

1999. In subsequent years, fewer conflict-related fatalities were recorded but showed a spread of the conflict through the center of the country. In the final year of the conflict, there is a concentration of conflict-related fatalities in the capitol of Monrovia in the southwest of the country. Through all five years of the second civil war, we see fewer conflict fatalities in the east of the country, highlighting the geographic variation in political violence.

Conflict Events

A similar pattern is seen for conflict events. In 1999, conflict events are concentrated in the country’s northern border with Sierra Leone. From 2000 to 2002, largely non-fatal events diffuse through the northern districts, increasing in number each subsequent year. In 2003, events are concentrated around Monrovia and represent the most fatal events of the conflict, with one 2003 event resulting in over 100 fatalities.

Ways to Measure Conflict Exposure

N-tiles of Conflict One of the most straightforward ways of defining fatalities and events is by a dichotomous measure: any versus none. Table 4.1 shows the distribution of districts and individuals across these categories.

Table 4.1 Distribution of Dichotomous Fatalities and Events at Individual and District Level

None Any Fatalities Fatalities 0 fatalities 1-327 fatalities Districts 52 districts 9 districts Individuals (% sample) 3,005 (66.8%) 1,497 (33.2%) Events Events 0 events 1-134 events Districts 22 districts 39 districts Individuals (% sample) 768 (17.1%) 3,734 (82.9%)

However, presence or absence of conflict may mask complexities in the association, for instance a dose response relationship between conflict and each outcome. Therefore, the next approach was to look at three categories of the predictor (Table 4.2). This approach now allows us to see how different levels of conflict (no, medium and high) relate to the outcome.

Table 4.2 Distribution of Three Levels of Fatalities and Events at Individual and District Level 70

No Conflict Low Conflict High Conflict Fatalities 0 fatalities 1-50 fatalities 51 and over Districts 52 districts 5 districts 4 districts Individuals (% sample) 3,005 individuals (66.8%) 359 individuals (8.00%) 1,138 individuals (25.2%) Events 0 events 1-15 events 15-134 events Districts 22 districts 27 districts 12 districts Individuals (% sample) 756 individuals (16.9%) 2,167 individuals (48.5%) 1,547 individuals (34.6%)

A visual inspection of the distribution of events (Figure 4.4) shows a clustering of districts that experienced only between 1 and 14 conflict events, with the remaining districts experiencing more than 15. These cut-points were used to define no-, mid- and high-event categories (0 events, 1–12 events, and 12 events and over).

Figure 4.4 Histogram of Conflict Events from 1999 to 2003 60 40 Percent 20 0 0 10 20 30 40 50 60 70 80 90 100 110 120 130 Events

Because of the greater number of events, it is possible to create more categories of this predictor, while still having an adequate sample in each category. Table 4.3 shows the distribution of the population across the quartiles and quintiles of events by districts. In the case of quartiles and quintiles, however, we see that a relatively small percent of the sample is present in these categories. For instance, only 15% of the sample is represented in the second quartile of events.

Similarly, only 12% of the sample is present in the second and third quintiles of events.

Table 4.3 Distribution of Quartile and Quintiles of Events at Individual and District Level

Ntile 1 Ntile 2 Ntile 3 Ntile 4 Ntile 5 Quartiles of events 1-2 events 3-6 events 7-17 events 18-134 events 10 districts 10 districts 10 districts 9 districts -- 780 individuals 582 individuals 974 individuals 1,398 individuals -- (20.9%) (15.6%) (26.1%) (37.4%) Quintiles of events 1-2 events 3-5 events 6-8 events 9-27 events 27-134 events 10 districts 7 districts 8 districts 7 districts 7 districts 780 individuals 692 individuals 1,129 individuals 697 individuals 859 individuals (20.9%) (12.0%) (12.8%) (21.0%) (33.3%)

Cumulative Years of Impact

Another way to describe conflict exposure is through cumulative years of impact. For each district, one can determine if fatalities or events occurred in no years, one year, or multiple years of the conflict. This measure is used to assess whether the impact of conflict might result from an accumulating burden of war experiences rather than a simple count of experiences. Because of the relatively limited number of districts that experienced fatalities (nine out of 61), we see that no districts experienced fatalities in every year of the conflict. Three districts experienced fatalities in two years of the conflict (Table 4.4), and six districts experienced fatalities in one year of the conflict. A closer analysis shows perfect overlap between the districts that are in the

highest tertile of fatalities, and the districts with two cumulative years of fatalities. This is not surprising given the limited number of districts in this analysis—those with the highest number of fatalities also experienced this violence across more than one year. In contrast, because there are more events, we see that there are two districts that experience events during all five years of conflict, and five districts that experience events during four of the five years (Table 4.4).

Table 4.4 Distribution of Cumulative Years of Events at Individual and District Level

0 Years 1 Year 2 Years 3 Years 4 Years 5 Years Fatalities 52 districts 6 districts 3 districts ------3,005 individuals 443 individuals 1,012 individuals ------(66.8%) (10.7%) (22.5%) Events 22 districts 17 districts 12 districts 3 districts 5 districts 2 districts 768 individuals 982 individuals 1,294 individuals 131 individuals 451 individuals 896 individuals (17.1%) (21.8%) (28.7%) (2.9%) (10.0%) (19.5%)

Because there are a relatively limited number of districts in some of the cumulative-year categories, and correspondingly low numbers of individuals (for instance only 3 districts and 3% of the sample had events in three out of five of the years), the cumulative years of events were aggregated into three categories (Table 4.5).

Table 4.5 Distribution of Combined Cumulative Years of Events at Individual and District Level

0 Years 1 Year 2-3 Years 4-5 Years 22 districts 17 districts 15 districts 7 districts 768 individuals (17.1%) 982 individuals (21.8%) 1,425 individuals (31.7%) 1,327 individuals (29.5%)

Discussion

Consideration of the tempo and distribution of conflict fatalities and events can inform different ways of measuring the district-level predictors in this analysis. The first approach to

characterizing fatalities and events was the most straightforward—a dichotomous measure of any versus none. The advantage of this approach is greater statistical power to detect differences between two groups. The disadvantage is that one might miss nuances in the relationship between levels of political violence and the outcome of interest, for instance, a possible dose- response relationship between the level of conflict and the outcome. The very limited number of districts experiencing fatalities (n=9) and the widely varying number of fatalities in these districts makes it difficult to split the dataset into smaller fatality categories. For instance, we see that in the second tertile of fatalities, we now only have 433 individuals (10.7% of the sample).

Therefore, the dichotomous characterization of fatalities was used for analysis.

In contrast, there were many more events, divided much more widely among districts (n=39 districts with events). Dividing events into three groups still resulted in large groups of individuals in each category. Splitting events into quartiles, however, resulted in only four districts being represented in the highest category. Similarly, once the events were sliced into quintiles; one district (greater Monrovia, the capitol) remained in the highest quintile of events.

Having only the highest category dominated by one relatively unique district detracts from the ability to isolate the effect of the events. This is especially true since Monrovia, as the capital, may be unique from other districts.

Finally, cumulative years of fatalities and events were assessed. Because of the limited number of fatalities, we saw that districts with deaths in two or more conflict years were also the same districts in the highest tertile of fatalities. For this reason, the conflict-year measure was dropped from the fatalities analysis. In contrast, there were many more events than fatalities, and they

were distributed over time in such a way that the cumulative-year measure adds new information to the analysis. The aggregate measure of conflict-years (1 year, 2–3 years, 4–5 years) yields a relatively even distribution of individuals across each group. The measures used to assess conflict in the subsequent sections are summarized in Table 4.6.

Table 4.6 Final Measures of Conflict Exposure to be Included in Analysis

Fatalities Events Dichotomous Dichotomous No-, medium-, and high events Cumulative event years

Few studies have combined ACLED data with data on interpersonal violence. Each of these previous efforts have taken a slightly different approach. In the analysis of IPV in Rwanda, the authors calculated the mean number of conflict days per year within 50 kilometers of each of the

492 DHS clusters in the five years leading up to the 2010 DHS survey data collection (Janko et al., 2014). Conflict included any kind of battle between rebels or violence against civilians. In the analysis of Peruvian conflict, ACLED data were not available. Instead, the authors used data from the Truth and Reconciliation Commission which specified events such as kidnapping, assassination, and sexual assault. Each province was given a conflict severity score, and this was combined with DHS data (Gallegos & Gutierrez, 2011). The current project takes an approach more similar to the latter project, since a central hypothesis of this work is that social boundaries, such as districts and provinces, have a collective experience of conflict which then affects the residents of that unit. No published studies to date have explicitly gone on to examine the relative importance of fatal events versus non-fatal events for the contagion of interpersonal violence. As noted in the introduction, the central thesis of this project is that experiencing fatalities at the district level can influence levels of violence postconflict. For this reason, the 75

dichotomous measure of fatalities (any versus none) remains the central predictor for this project.

However, it is also important to explore whether more nuanced measure of conflict and non-fatal events related to conflict are also associated with the outcome, which we achieve via sensitivity analyses.

Limitations

The ACLED database attempts to record all conflict events through a process of triangulation of different sources of information (Raleigh, 2010). Despite this, a potential limitation of this approach is that ACLED may not accurately capture levels of conflict experienced in Liberia. It is possible that some conflict incidents were not added to the database, resulting in under- reporting of true conflict. In this case, however, the database might still provide relatively accurate information about the relative level of conflict from one district to another, even if absolute levels may be under-reported, as long as the non-reported cases were distributed relatively randomly. The database would be systematically biased if there was a systematic neglect in certain areas or certain types of districts (such as highly rural regions). This is particularly of concern if areas that were coded as conflict-free did in fact experience conflict, disrupting the dichotomous measures of fatalities and events. However, the map of the conflict progression based on ACLED data (Appendix 2) closely matches narrative descriptions of the conflict reported in the media and by actors on the ground. In addition, conflict incidents are reported in very remote areas throughout the course of conflict, suggesting that efforts were made to track the progression of hostilities accurately.

Another notable challenge is that the ACLED database provides a relatively crude measure of the effects of war. The use of fatalities and events (the only indicators available in the dataset) are used here as a proxy for the much broader impacts of conflict on affected populations. ACLED cannot measure infrastructure damage, the impact of looting, or the dissolution of social supports, for instance—all noted impacts of war with detriments for human health (Annan &

Brier, 2010; Baranyi, 2011). It is also possible that certain impacts of conflict, such as displacement, are much more disruptive than others, like the destruction of roads. However, we are not able to explore these dynamics in the current project. Future research could seek district- level data on infrastructure damage, economic changes before and after conflict, within- and between-district displacement and migration, and could attempt to create measures that capture more detail about the nature of conflict in Liberia to add more district-level information to the model.

Conclusions

Policymakers, health practitioners and humanitarians still struggle to define conflict and to understand its myriad impacts. Currently, there are inconsistent definitions of war and peace, making it difficult to determine when a country has slid from one into the other—from counting battle deaths in a calendar year (Themner & Wallensteen, 2014) to counting absolute levels of mortality (Global Burden of Armed Violence, 2011). A goal of this work is to inform the discussion about which aspects of conflict (whether deaths or non-fatal events or any kind of conflict event), are most detrimental and to explore how different characterizations of conflict relate to interpersonal violence in post-war periods.

Chapter Four References

Annan, J., & Brier, M. (2010). The risk of return: Intimate partner violence in Northern Uganda's armed conflict. Social Science & Medicine, 70(1), 152-159.

Baranyi, S., Beaudet, P., & Locher, U. (2011). World development report 2011: Conflict, security, and development. Canadian Journal of Development Studies/Revue Canadienne d'Études du Développement, 32(3), 342-349.

Central Intelligence Agency. (2014). World Factbook Kenya Country Profile. Retrieved from: https://www.cia.gov/library/publications/the-world- factbook/geos/print/country/countrypdf_ke.pdf

Gallegos J.V, & Gutierrez, I. A. (2011). The effect of civil conflict on domestic violence: The case of Peru. Unpublished manuscript. Retrieved from: http://jvgalleg.mysite.syr.edu/default_files/Research_files/Gallegos- Gutierrez%20%20Civil%20Conflict%20and%20Domestic%20Violence%20JDE.pdf

Global Burden of Armed Violence. (2011). Geneva, Switzerland: Geneva Declaration. Accessed August 19, 2014. Retrieved from: http://www.genevadeclaration.org/fileadmin/docs/GBAV2/GBAV2011-Ex-summary-ENG.pdf

Human Rights Watch (2002). Back to the brink: War crimes by Liberian Government and rebels. Retrieved from: https://www.hrw.org/reports/2002/liberia/Liberia0402.pdf

Insight on Conflict (2014). Liberia: Conflict profile. Retrieved from: https://www.insightonconflict.org/conflicts/liberia/conflict-profile/

Janko, M., Bloom, S. & Spencer, J. (2014, May). Community exposure to violent conflict increases the risk of intimate partner violence in Rwanda: Paper presented at the annual meeting of the Population Association of America. Boston, MA. Abstract. Retrieved from: http://paa2014.princeton.edu/abstracts/141125

Raleigh, C., Linke, A., Hegre, H., & Karlsen, J. (2010). Introducing ACLED: An armed conflict location and event dataset special data feature. Journal of Peace Research, 47(5), 651-660.

Robison, R. (2013). A biographical encyclopedia of contemporary genocide: Portraits of evil and good. Reference & User Services Quarterly, 52(3), 260.

Themnér, L., & Wallensteen, P. (2014). Armed conflicts, 1946–2013. Journal of Peace Research, 51(4), 541-554.

. Non-Partner Physical Violence

Results

This chapter examines the association between conflict at the district level and individual

women’s experiences of non-partner physical violence (NPPV) in the 12 months before the 2007

DHS survey. Table 5.1 provides descriptive statistics of NPPV at the individual and district

level. The mean individual-level prevalence of NPPV in fatality districts is 9.39% and just under

8% in non-fatality districts (7.59%). Similar levels are seen when we look at mean levels of

NPPV at the aggregate district level (8.57% in fatality districts, 7.84% in non-fatality districts).

The mean individual level of NPPV is 8.86% for individuals living in event districts, and 5.96%

for individuals living in non-event districts. Similarly, at the aggregate district level, the mean

level of NPPV is 8.86% in event districts and 5.96% in non-event districts.

Table 5.1 Individual-Level Prevalence and District-Level Summaries of Past-Year NPPV by District Conflict Status Individual-Level Prevalence District-level Mean N Adjusted percent* Mean (25, 75 percentile) All districts 344 8.40 7.95 (2.0, 12.0) Fatalities Non-fatality districts 212 7.59 7.84 (1.18, 11.9) Fatality districts 132 9.39 8.57 (2.94, 12.1) Events Non-event districts 46 5.96 5.84 (0.00, 11.1) Event districts 298 8.86 9.14 (2.1, 15.0) *The individual-level percentages are adjusted to account for the DHS complex survey design using the weights for the DV module

Figure 5.1 shows the within versus between district variation in NPPV; the vertical lines show

the amount of variation in the outcome within each district, while the distribution of points along

the y-axis shows how the outcome estimate varies across all districts. Graph A depicts levels of

NPPV across all districts in the dataset. Graph B shows the same information, but distinguishes

fatality-affected districts in red, and non-fatality districts in blue. Graph C displays conflict-

event-affected districts in red and non-event districts in blue. On a purely visual inspection, it is

difficult to discern patterns between fatality-affected and event-affected districts versus non-

affected districts. However, these graphs emphasize clear across-district variation in each

outcome. The intraclass correlation for the intercept-only model of NPPV is 0.233. This means

23% of the total variance of NPPV is accounted for by the clustering, suggesting that a

multilevel modeling approach could be informative.

Figure 5.1 Within Versus Between Variation in NPPV Across Districts

Graph A. Within Versus Between Variation in NPPV by District

Proportion ExperiencingWomenNPPV Districtby 0 1 11 21 31 41 51 61 District

Graph B. Within Versus Between Variation Sorted by Districts with Conflict Fatalities (red=fatality)

Proportion ExperiencingWomenNPPV Districtby 0 1 11 21 31 41 51 61 District

Graph C. Within Versus Between Variation in NPPV Sorted by Districts with Conflict Events (red=events)

Proportion ExperiencingWomenNPPV Districtby 0 1 11 21 31 41 51 61 District

The bivariate relationship between NPPV and each independent variable is given in Table 5.2

Seven percent of women experienced NPPV in no-fatality districts, compared to almost 9% of 82

women in fatality districts (OR 2.62 p<0.001). Older age was protective against NPPV (OR 0.94, p<0.001). Married (OR 0.33, p<0.001), divorced (OR 0.43, p<0.001) and widowed (OR 0.38, p<0.001) women also had lower risk of NPPV than never-partnered women. Risk factors associated with increased odds of NPPV included primary (OR 1.71, p<0.001) and secondary

(1.56, p=0.007) education versus no education; being in the middle (OR 1.60, p=0.030) or richer

(OR 1.53, p=0.022) quintiles compared to the poorest; and having previous experiences with violence (OR 2.01, p<0.001).

Table 5.2 Bivariate Model Associations of Factors Associated with Past-Year NPPV

Total Sample No NPPV NPPV Odds P N (%) N (row %) N (row %) Ratio Value

Conflict No fatalities 2,973 66.7 2,761 92.9 212 7.1 -- -- experience Fatalities 1,484 33.3 1,352 91.1 132 8.9 2.62 <0.001 Age (mean, SE) 30 9.2 30.3 9.12 26.3 8.87 0.94 <0.001 No. children under 5 (mean, SE) 1.29 1.07 1.29 1.07 1.26 1.08 1.03 0.536 Education No education (ref) 2,092 46.9 1,962 93.8 130 6.2 -- -- Primary 1,520 34.1 1,386 91.2 134 8.8 1.71 <0.001 Secondary or above 845 19.0 765 90.5 80 9.5 1.56 0.007 Religion Christian (ref) 3,802 85.3 3,505 92.2 297 7.8 -- -- Muslim 470 10.5 434 92.3 36 7.7 0.81 0.482 Other 185 4.2 174 94.1 11 5.9 0.73 0.234 Civil Status Never married (ref) 843 18.9 723 85.8 120 14.2 -- -- Married 2,061 46.2 1,955 94.9 106 5.1 0.33 <0.001 Living together 1,155 25.9 1,063 92.0 92 8.0 0.43 <0.001 Widow/divorced 398 8.9 372 93.5 26 6.5 0.38 <0.001 Wealth Poorest (ref) 1,015 22.8 967 95.3 48 4.7 -- -- Poorer 998 22.4 920 92.2 78 7.8 1.40 0.070 Middle 928 20.8 843 90.8 85 9.2 1.60 0.030 Richer 877 19.7 793 90.4 84 9.6 1.53 0.022 Richest 639 14.3 590 92.3 49 7.7 1.09 0.753 Employment Didn't work in past 12 1,413 31.7 1,289 91.2 124 8.8 -- -- months Worked in past 12 months 3,044 68.3 2,824 92.8 220 7.2 0.76 0.060 Aggregate No violent experiences 1,254 28.1 1,195 95.3 59 4.7 -- -- Violence Violent experiences 3,203 71.9 2,918 91.1 285 8.9 2.01 <0.001 Measure

Model 1 in Table 5.3 examines the association between past-year NPPV and dichotomous fatalities, the predictor of primary interest. As noted in the methods section, this analysis uses a stepwise modeling approach where blocks of independent variables are added sequentially.

When demographic and marriage variables are added to the model, the bivariate relationship between NPPV and conflict (OR 2.62, p<0.001) attenuates but remains significant (aOR 1.52, p=0.019). This suggests that demographic variables such as education, religion and civil status can account for much of the excess risk of NPPV in fatality-affected districts. In Model 3, economic variables are added to the analysis, slightly attenuating the effect estimate of conflict with NPPV (aOR 1.50, p=0.102) and causing the association to become insignificant. In the final model, the relationship between NPPV and conflict fatalities attenuates still further and does not reach significance after adjusting for other individual-level variables (aOR 1.43, p=0.197).

Other correlates of NPPV in the final model included age; civil status; wealth; and positive score on the aggregate violence measure. For each year a woman aged, she was 4% less likely to experience non-partner violence (aOR 0.96, p<0.001). Being ever-partnered versus being single was also all protective against non-partner violence. Women who were married were 50% less likely to experience NPPV (aOR 0.50, p<0.001) than never-partnered women, women who were cohabiting (aOR 0.55, p<0.005) and divorced or widowed (aOR 0.60, p<0.05) women had similar protective effects. Being in the fourth wealth quintile (richer versus poorest) was the only wealth level to be significantly associated with higher levels of non-partner violence (aOR 1.63, p<0.05) in the final model. Having any past experiences with violence was associated with a two-fold increase in the odds of experiencing NPPV (aOR 1.97, p<0.001).

Table 5.3 Association of Past-Year NPPV with Dichotomous District-Level Fatalities: Stepwise Model Fitting

Model 2: Demographic and Marriage Model 3: Demographic, Marriage and Model 4: Demographic, Marriage, Model 1: No covariate model Variables Economic Variables Economic, and Violence Variables N=4,457 N=4,457 N=4,457 N=4,457 P Low High P Low High P Low High P Low High aOR aOR aOR aOR Value CI CI Value CI CI Value CI CI Value CI CI Districts with fatalities 2.62 <0.001 1.94 3.53 1.52 0.019 1.07 2.16 1.50 0.102 0.92 2.44 1.43 0.197 0.83 2.44

Age 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98

No. children under 5 1.03 0.627 0.92 1.14 1.02 0.716 0.92 1.14 1.03 0.660 0.92 1.15

No education (ref) ------

Education Primary 1.17 0.312 0.86 1.58 1.15 0.376 0.84 1.58 1.15 0.401 0.83 1.57

Secondary and above 1.31 0.121 0.93 1.84 1.34 0.167 0.89 2.01 1.37 0.144 0.90 2.08

Christian (ref) ------Religion Muslim 0.95 0.853 0.54 1.65 0.92 0.789 0.52 1.65 0.89 0.717 0.49 1.64

Other 0.60 0.116 0.32 1.13 0.62 0.155 0.32 1.20 0.66 0.242 0.33 1.32

Never married (ref) ------Married 0.53 0.003 0.35 0.81 0.52 0.002 0.35 0.79 0.50 <0.001 0.33 0.76 Civil Status Living together 0.59 0.009 0.39 0.88 0.57 0.003 0.39 0.83 0.55 0.002 0.37 0.81

Widow/divorced 0.64 0.068 0.39 1.03 0.62 0.046 0.38 0.99 0.60 0.043 0.37 0.98 Poorest (ref) ------

Poorer 1.42 0.100 0.94 2.14 1.42 0.110 0.92 2.17 Wealth Middle 1.50 0.079 0.95 2.34 1.52 0.079 0.95 2.44 Richer 1.60 0.046 1.01 2.54 1.63 0.034 1.04 2.58 Richest 1.13 0.647 0.68 1.87 1.17 0.549 0.70 1.97

Didn’t work in past 12 months ------Employment Worked in past 12 months 1.14 0.501 0.77 1.70 1.14 0.521 0.76 1.72 Aggregate No violent experiences ------Violence

Measure Violent experiences 1.97 <0.001 1.37 2.83

Sensitivity Analysis for Conflict Measures

After specifying the initial model through the stepwise procedure, other models were fit to examine the sensitivity of our model to different characterizations of conflict-affectedness as described in Chapter 4. Table 5.4 compares the results of three models looking at conflict events measured by dichotomous events, three categories of events, and cumulative event-years. There was no significant relationship between NPPV and districts experiencing any versus no events

(aOR 1.39, p=0.126). When the count of events was split into three categories, there was no significant relationship between NPPV and conflict in either the mid- (aOR 1.20, p=0.384) or the high-event category (aOR 1.52, p=0.065) compared to the no-event category. It is worth noting, however, that both adjusted odds ratio point estimates suggest higher conflict is associated with higher NPPV, and this relationship almost approaches significance in the third tertile (aOR 1.52, p=0.065). In the last model, individuals in districts that experienced conflict events in four or five years of the war were almost three times as likely (aOR 2.93, p<0.001) to experience past-year

NPPV compared to individuals living in districts with no events during the war. Full stepwise model-fitting results for each analysis discussed in this section are given in Appendix 3.

Table 5.4 Association of Past-Year NPPV with Three Measures of District-Level Events

Dichotomous Events Three categories of Events Cumulative Event Years No events (ref) 1st tertile (ref) 0 event years (ref) Any events 2nd tertile 1 event year 3rd tertile 2-3 event years 4-5 event years N=4,457 N=4,457 N=4.457 aOR P Value Low CI High CI aOR P Value Low CI High CI aOR P Value Low CI High CI District event measures 1.39 0.126 0.91 2.12 1.20 0.384 0.79 1.82 1.31 0.267 0.82 2.09 1.52 0.065 0.97 2.39 1.18 0.521 0.72 1.94 2.93 <0.001 1.71 5.04

Age 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98 No. children under 5 1.03 0.658 0.91 1.15 1.03 0.651 0.92 1.15 1.04 0.550 0.92 1.16

No education (ref) ------Primary 1.12 0.440 0.84 1.51 1.14 0.419 0.83 1.56 1.14 0.402 0.84 1.55

Education Secondary and above 1.36 0.142 0.9 2.05 1.36 0.148 0.9 2.08 1.32 0.190 0.87 1.99 Christian (ref) ------Muslim 0.92 0.757 0.54 1.57 0.89 0.683 0.51 1.56 0.88 0.686 0.46 1.67

Religion Other 0.67 0.180 0.37 1.21 0.66 0.211 0.34 1.27 0.72 0.244 0.42 1.25 Never married (ref)

------Married 0.49 <0.001 0.33 0.73 0.5 0.001 0.33 0.75 0.52 <0.001 0.35 0.77 Living together 0.55 <0.001 0.38 0.8 0.55 0.002 0.37 0.8 0.56 0.002 0.39 0.81 Civil Status Civil Widow/divorced 0.6 0.042 0.37 0.98 0.6 0.040 0.37 0.98 0.61 0.045 0.37 0.99 Poorest (ref) ------

Poorer 1.4 0.111 0.92 2.13 1.39 0.132 0.9 2.15 1.39 0.134 0.9 2.13 Middle 1.55 0.061 0.98 2.46 1.52 0.078 0.95 2.43 1.49 0.080 0.95 2.35 Wealth Richer 1.73 0.040 1.03 2.91 1.67 0.028 1.06 2.65 1.43 0.131 0.9 2.28 Richest 1.26 0.323 0.8 2 1.22 0.454 0.73 2.04 0.93 0.749 0.58 1.48

- Didn’t work in past 12 months ------

ment Worked in past 12 months

Employ 1.15 0.498 0.77 1.71 1.14 0.524 0.76 1.7 1.15 0.453 0.79 1.68

No violent experiences ------Violent experiences Measure

Violence Violence 2.03 <0.001 1.44 2.87 2.0 <0.001 1.38 2.89 1.98 <0.001 1.36 2.89 Aggregate Aggregate

Sensitivity analysis of the impact of fatal versus non-fatal events

Individuals living in districts that experienced conflict events in four or five years of the conflict were significantly more likely to experience violence. Not surprisingly, however, those districts that experienced the most events over time were also the most likely to experience fatalities.

Thus, the association between NPPV and cumulative years could be driven by fatalities rather than the accrual of events. This question is fundamental to Aim 1b of this project, which is to fully examine how different measures of conflict may relate to violence outcomes. In order to further explore this, the same model was run but excluded fatal incidents and kept only non-fatal clashes. Once fatal events were excluded, there were almost no individuals left in the sample in the highest category (4–5 years), as shown in Table 5.5. Therefore, the 2–3 and 4–5 event year categories were combined in the analysis.

Table 5.5 Distribution of NPPV Across Non-Fatal Event Years 0 event-years 1 event-year 2-3 event-years 4-5 event-years Total N (%) N (%) N (%) N (%) N (%) No NPPV 710 (25.63) 759 (27.4) 1,286 (46.43) 15 (0.54) 2,770 (100) NPPV 46 (21.7) 73 9 (34.4) 91 (42.9) 2 (0.94) 212 (100) Total 756 (25.3) 832 (27.9) 1,377 (46.2) 17 (0.57) 2,982 (100)

When the association between NPPV and only non-fatal events was examined (Table 5.6), there was no significant association between cumulative event-years and NPPV, either at the 1 event- year level (aOR 1.43, p=0.189) or the two- to five-year event level (aOR 1.28, p=0.319).

Table 5.6 Association of Past-Year NPPV with District-Level Non-Fatal Cumulative Event Years

Model with Only Non-Fatal Events N=2,973

aOR P Value Low CI High CI 0 event-years ------

1 event-year 1.43 0.189 0.84 2.44 2-5 event-years 1.28 0.319 0.79 2.10 Age 0.96 0.001 0.94 0.98 No. children under 5 1.08 0.248 0.95 1.24 No education (ref) ------Education Primary 1.05 0.814 0.70 1.56 Secondary and above 1.00 0.999 0.53 1.90 Christian (ref)

Religion Muslim 0.98 0.920 0.63 1.51 Other 0.81 0.543 0.41 1.59 Never married (ref)

Married 0.49 0.017 0.27 0.88 Civil Status Living together 0.53 0.020 0.31 0.90 Widow/divorced 0.78 0.485 0.40 1.55 Poorest (ref) ------Poorer 1.33 0.236 0.83 2.12 Wealth Middle 1.59 0.091 0.93 2.74 Richer 1.22 0.447 0.74 2.01 Richest 1.15 0.646 0.63 2.09 Didn't work in past 12 ------months Employment Worked in past 12 1.21 0.446 0.74 1.98 months Aggregate No violent experiences ------Violence Measure Violent experiences 2.12 0.005 1.25 3.60

Sensitivity analysis on the impact of migration

A sensitivity analysis restricted to non-migrants was conducted to test whether people with sustained exposure to a conflict districts exhibited a stronger effect. Of the people reporting

NPPV, over two-thirds (77.5%, n= 3,448) were classified as non-migrants, meaning they did not move from one district to another after the conflict. Table 5.7 below gives the results of the analysis restricted to non-migrants and dichotomous fatalities. We see a larger effect estimate

(aOR 1.70, p<0.093) for this model than for the same model with the full population (aOR 1.43, p=0.197, Table 5.3); however, this association does not reach significance at the p<0.05 level.

Table 5.7 Association of Past-Year NPPV with Dichotomous District-Level Fatalities Among Non-Migrants

N=3,448

aOR P Value Low CI High CI Districts with fatalities 1.70 0.093 0.92 3.16 Age 0.96 0.002 0.94 0.99 No. children under 5 1.04 0.543 0.91 1.19 No education (ref) ------Education Primary 1.16 0.435 0.80 1.67 Secondary and above 1.06 0.780 0.69 1.64 Christian (ref) ------Religion Muslim 0.95 0.855 0.55 1.64 Other 0.67 0.331 0.30 1.49 Never married (ref) ------Married 0.44 <0.001 0.29 0.67 Civil Status Living together 0.45 <0.001 0.31 0.67 Widow/divorced 0.62 0.064 0.37 1.03 Poorest (ref) ------Poorer 1.45 0.143 0.88 2.39 Wealth Middle 1.38 0.215 0.83 2.27 Richer 1.82 0.023 1.09 3.05 Richest 1.31 0.373 0.72 2.40 Didn’t work in past 12 Employment months ------Worked in past 12 months 1.28 0.207 0.87 1.88 Aggregate No violent experiences ------Violence Measure Violent experiences 2.24 <0.001 1.50 3.35

Perpetrators of Non-Partner Physical Violence

The DHS survey does not provide information about the perpetrators of the past-year non-partner violence. It does, however, provide information about perpetrators of ever non-partner violence to provide some insight. Respondents for ever-NPPV report mothers or stepmothers are the most common perpetrators of non-partner violence (11.7%), followed by fathers or stepfathers

(8.51%), and sisters or brothers (4.02%). Family members account for 70% of all reports of ever- violence, with parental violence being the most common form of family-violence (Appendix 3).

There was significant overlap between those experiencing family abuse and those reporting abuse by a non-family actor. Only 74 individuals out of the 1,161 individuals reporting ever- violence experienced only non-family violence. All other respondents experienced both non- family and family abuse, making it impossible to assess non-family violence separately due to the very small sample of these individuals.

Discussion

The aim of this analysis was to quantify the association between conflict-affectedness and past- year NPPV. The hypothesis was that individuals living in conflict-affected districts in Liberia would have higher proportions of past-year NPPV than those living in non-conflict affected districts, after adjusting for individual-level characteristics. The primary predictor—dichotomous fatalities—was strongly associated with NPPV in the unadjusted model. However, as individual- level demographic, economic and violence characteristics were added during the stepwise model-fitting procedure, this association became attenuated. In the final model, an individual living in a district with any conflict fatalities was 40% more likely to experience NPPV than individuals living in a district with no fatalities. However, this result did not reach significance at the p<0.05 level, suggesting that including the district-level fatality measure did not add significant explanatory power to the final model that also had individual-level information about demographics, wealth and previous experiences with violence.

The cumulative event-year measure reached significance in the highest category. Individuals living in districts that had experienced events for four or five years of the civil war were three times more likely to experience past-year NPPV than individuals in districts that experienced no

years of conflict events (aOR 2.93, p<0.001). Districts with the greatest number of events were also those that experienced the most fatalities. In order to tease apart whether the effect was due to fatalities or to the accumulating experience of conflict events, fatal events were dropped from the analysis and only non-fatal events were used to aggregate cumulative event years.

Experiencing non-fatal events was not highly associated with conflict. More closely examining the 4 to 5 event-year category shows that districts with the highest number of fatalities cluster in this group. The small sample of districts with fatalities made it impossible to look at a dose- response relationship between NPPV and fatalities. However, these results suggest that districts with the highest number of fatalities may be the most highly associated with NPPV, and may be driving the highly significant result for the cumulative event-year measure. These findings suggest that conflict fatalities, rather than other measures of conflict, are the most highly associated with higher levels of NPPV postconflict and that districts with the highest fatalities might be the most associated with NPPV. There was no significant association between NPPV and dichotomous events or three categories of events.

A sensitivity analysis restricted to non-migrants, who were posited to experience a stronger effect based on sustained exposure to a conflict-affected place, revealed an effect estimate greater than that in the full sample (give both here with p value), though it did not reach significance at the p<0.05 level (aOR 1.70, p<0.10). The sample size for non-migrants was one- third smaller, thus it is possible that the lack of significance could be due to the diminished sample size. It is also possible that residing in a district during conflict is not qualitatively more

impactful than moving to a highly affected district after conflict when the effects are still palpable.

Very few studies have examined the association between non-partner abuse and conflict, and little research has been done looking at family-level abuse (Abrahams et al., 2014; Devries et al.,

2013; Falb et al., 2013; WHO, 2005), the main form of NPPV reported here. This is an important area of inquiry, however, since family structures are one of the primary sources of security and protection both during and after conflict (Horn, 2010; Annan & Briar 2010; Betancourt et al.,

2010). Equally important is the recognition that families may also be sources of instability and maltreatment to those trying to recover from the effects of conflict (Annan & Brier 2010; Raj et al., 2014). This may be particularly true for women who have experienced conflict-related sexual violence and face social stigma from their family and community as a result (Kelly et al., 2011).

Falb et al. (2013) found that certain types of victimization during conflict, including sexual violence and severe injury, were associated with higher levels of in-law abuse postconflict for women living in rural Cote d’Ivoire. However, other adverse experiences during conflict, such as being forced to flee and having a family member experience abuse, were not related to in-law victimization. These studies point to the complex dynamics at play within families. Conflict trauma that directly threatens a woman’s perceived value as a wife and mother has been linked with maltreatment, neglect, ostracization and stigma in a number of war-related contexts in

Africa (Kelly et al., 2011, Kohli et al., 2012; Annan & Brier, 2010). However, other trauma that doesn’t directly relate to women’s gendered productive and reproductive roles within the family may not be associated with abuse. A multi-country study of violence perpetration by men found widely varying levels and patterns of violence across different settings (Fulu et al., 2013; WHO,

2013), emphasizing the need for further research on this topic and the importance of tailoring context-appropriate responses. For this project, an analysis of the perpetrators of ever-NPPV showed that the vast majority of abusers were family members. Seventy-percent of perpetrators were related to the victim, with parental violence being the most common form of abuse. These bivariate model results suggest that individuals living in conflict-affected districts may experience higher levels of NPPV, predominantly characterized by family violence. As previous work suggests, in-home violence, particularly from extended family, may increase after conflict because of trauma, stigma and changing roles wrought by political violence.

Limitations

The current project examined all forms of non-partner violence, including that from family members, acquaintances and strangers. This approach means that it was not possible to tease apart whether different abusers may have had different patterns of aggression postconflict. As noted earlier, NPPV is measured in a less precise way than IPV, through a response to one question rather than through the Conflict Tactics Scale. This is especially unfortunate since

NPPV is a more diffuse phenomenon than IPV, occurring in the home, school, workplace and elsewhere. The data did not allow for the parsing out different kinds NPPV for analysis. Some studies suggest, however, that abuse patterns may vary by perpetrator, even within family abusers (Annan and Brier, 2010; Falb, 2013; Kelly, 2011). The survey in Cote d’Ivoire suggests, for instance, that in-laws may be most focused on preserving the reputation and integrity of the family, and particularly the husband, rather than supporting the wife as an individual. A study in eastern Democratic Republic of the Congo with survivors of conflict-related sexual violence

found that women were treated more kindly and supportively by blood relatives (such as parents and siblings) than by husbands and members of the husband’s family (Kelly et al., 2011).

A potential attenuating effect on the association between NPPV and conflict would be dissolution of family structures due to displacement, migration, or violence during the war.

These destructive effects may also persist long after conflict (Colletta & Cullen, 2000; Annan &

Brier, 2010) and may result in less exposure to family violence. For instance, children and young adults surviving the war might be more likely either to form a partnership to create a new household in the face of family separation, or may be compelled to live with distant relatives rather than nuclear family. Annan and Brier (2010) found that when young women reintegrated with their biological parents after abduction in northern Uganda, the reception was overwhelmingly positive and compassionate, however, returnees describe intense neglect and mistreatment from extended family. Collectively, these findings speak to the highly complex dynamics that may be at play when looking at NPPV, and specifically family violence, postconflict. The respondent’s experience in the war; the family structure in which she currently lives; and the specific perpetrator within the family may all influence the association between

NPPV and conflict.

No scholarly work has documented the changes in family structures in Liberia in the post-war period. However, NGOs, the International Committee of the Red Cross and the United Nations were unanimous in their calls for improved family reunification services, suggesting that separation of family members was a major problem during the war and into the post-war period.

It is possible that respondents living in conflict-affected districts were less likely to live with

family members, and therefore were at less risk of family abuse. Data gathered within the DHS do not allow us to assess this hypothesis. However, future work, including qualitative data on the structure of families after the war, could provide insight into this theory.

This analysis was not able to look at all forms of non-partner violence. Rather, the question used to determine non-partner violence specifically identified “hitting, slapping and kicking.”

Respondents, therefore, might have been more likely to report the specified forms of violence.

Hitting may be most common in the family, while other forms of violence such as restraining someone against their will; strangulation; threatening with weapons; or other abuses might be more common from strangers or other actors. Thus, this analysis focused on a very specific form of violence (such as hitting) that seems to be most common within families.

The DHS survey does not specify the perpetrators of past-year violence, limiting the ability to interpret these findings. It is impossible to know how much past-year NPPV was perpetrated by family versus acquaintances versus strangers. It is possible that family violence is fundamentally different than non-family violence and may have a different relationship with conflict exposure.

A very small number of individuals experienced ever- stranger violence in the absence of family violence, making it impossible to conduct a sensitivity analysis on victims of non-family violence. In addition, this project does not look at the specific type of war-time abuse experienced by individuals. Some forms of war violence may impact NPPV while others may not.

Beyond the challenges with NPPV measurement, this study has inherent limitations associated with the structure of the data. This is a cross-sectional analysis and as such cannot assume causality between fatalities and NPPV. Looking at district-level effects may still be a relatively unsophisticated way of measuring exposure to conflict. The limited number of districts affected by fatalities meant it was not possible to look at a dose-response relationship between the predictor and the outcome. Work in other settings might be able to further explore this association. Finally, the five-year time lag between the end of the war and the data collection may have affected the results. Work in other countries with different time lags may reveal if and how there is a decay in this association over time.

Conclusions

In the bivariate analysis, women living in a fatality-affected district had more than 2.5 times the risk of NPPV than women living in a non-fatality district (p<0.001). As individual-level variables were added to the model, this association attenuated and did not reach significance in the final model. However, the relatively low prevalence of past-year NPPV and the fact that the effect size of the stepwise model was persistent suggests that an association may exist. This work seeks to contribute to the literature by serving as one of the first efforts to use multilevel modeling to examine the association between non-partner physical violence and armed conflict.

Data from the DHS emphasized the extent to which NPPV is driven by family violence. A very limited number of programs in postconflict settings address family violence, and those that do exist tend to be focused on high-risk groups, such as those reintegrating child soldiers or abducted children, or survivors of sexual violence. However, these findings suggest families

residing in a fatality-affected district, not only families that experienced the most traumatic events during conflict, may be at risk of increased violence. Family mediation services have been used in eastern DRC to promote acceptance of survivors of sexual violence into the home (Kelly et al., 2011). A similar approach may prove promising for all families who are in highly war- affected areas. Providing holistic counseling services may help address this issue. For instance, counseling to parents and children separately and then as a group may improve family dynamics

(Catani et al., 2008), and help prevent the increase of violence within the home postconflict.

These approaches might be promising in fatality-affected districts to reduce NPPV within the home.

Chapter Five References

Annan, J., & Brier, M. (2010). The risk of return: Intimate partner violence in Northern Uganda's armed conflict. Social Science & Medicine, 70(1), 152-159.

Betancourt, T. S., Agnew-Blais, J., Gilman, S. E., Williams, D. R., & Ellis, B. H. (2010). Past horrors, present struggles: The role of stigma in the association between war experiences and psychosocial adjustment among former child soldiers in Sierra Leone. Social Science & Medicine, 70(1), 17-26.

Catani, C., Jacob, N., Schauer, E., Kohila, M., & Neuner, F. (2008). Family violence, war, and natural disasters: A study of the effect of extreme stress on children's mental health in Sri Lanka. BMC psychiatry, 8(1), 33.

Colletta, N. J., & Cullen, M. L. (2000). Violent conflict and the transformation of social capital: Lessons from Cambodia, Rwanda, Guatemala, and Somalia (Vol. 795). Washington DC: World Bank Publications.

Falb, K. L., Annan, J., Hossain, M., Topolska, M., Kpebo, D., & Gupta, J. (2013). Recent abuse from in-laws and associations with adverse experiences during the crisis among rural Ivorian women: Extended families as part of the ecological model. Global Public Health, 8(7), 831-844.

Kohli, A., Tosha, M., Ramazani, P., Safari, O., Bachunguye, R. Zahiga, I., Iragi, A. & Glass, N. (2012). A Congolese community-based health program for survivors of sexual violence. Conflict and Health, 6(1), 1.

Horn, R. (2010). Responses to intimate partner violence in Kakuma refugee camp: Refugee interactions with agency systems. Social Science & Medicine, 70(1), 160-168.

Kelly, J. T., Betancourt, T. S., Mukwege, D., Lipton, R., & VanRooyen, M. J. (2011). Experiences of female survivors of sexual violence in eastern Democratic Republic of the Congo: A mixed-methods study. Conflict and Health, 5(1), 25.

Raj, A., Gomez, C. S., & Silverman, J. G. (2014). Multisectorial Afghan perspectives on girl child Marriage: Foundations for change do exist in Afghanistan. Violence against Women, 20(12), 1489-1505.

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Results

This section will explore the association between past-year IPV and conflict at the district level.

The average individual-level prevalence of IPV in fatality-affected districts was 45.1% and

35.5% in non-fatality districts (Table 6.1). IPV prevalence is 39.7% in event districts and 38.4%

in non-event districts. District levels of IPV mirror the individual-level patterns—with a district-

level IPV prevalence of 46.1% in fatality districts and 36.8% in non-fatality districts; and a

prevalence of 39.3% in event districts and 36.3% in non-event districts.

Table 6.1 Individual-Level Prevalence and District-Level Summaries of Past-Year IPV by District Conflict Status

Individual-level District-level mean N Adjusted percent* Mean (25, 75 percentile) All districts 1,418 39.4 38.2 (24.1, 53.3) Fatalities Non-fatality districts 919 35.5 36.8 (23.7, 52.2) Fatality districts 499 45.1 46.1 (38.7, 56.8) Events Non-event districts 261 38.4 36.3 (21.4, 60.7) Event districts 1,157 39.7 39.3 (26.9, 55.6) *The individual-level percentages are adjusted to account for the DHS complex survey design using the weights for the DV module

There is notable between-district variation (a wide dispersal of IPV estimates across districts) as

visualized in Graph A in Figure 6.1 below. Graphs B and C distinguish fatality and event-

affected districts in red. It could be argued that fatality-affected districts seem to have higher IPV

estimates than non-fatality districts; however it is difficult to distinguish a clear pattern. The

intraclass correlation in the intercept-only IPV model is 0.108, meaning 11% of the total variance

of IPV is explained by clustering. This suggests that variation due to clustering is not extreme,

but can still provide valuable information.

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Figure 6.1 Within Versus Between Variation in IPV Across Districts

Graph A. Within Versus Between Variation in IPV by District

.2 Proportion ExperiencingWomenIPV

0 1 11 21 31 41 51 61 District

Graph B. Within Versus Between Variation in IPV Sorted by Districts with Conflict Fatalities (red=fatality)

.2 Proportion IPV by Districts w/ Fatalities Proportion Districts by IPV

0 1 11 21 31 41 51 61 District

Graph C. Within Versus Between Variation in IPV Sorted by Districts with Conflict Events (red=events)

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.2 Proportion IPV by Districts w/ Events Proportion Districts by IPV

0 1 11 21 31 41 51 61 District

The bivariate relationships between IPV and each independent variable of interest were

examined (Table 6.2). In the unadjusted analysis, 45% of women in districts with conflict

fatalities experienced IPV, compared to 37% of women living in districts without conflict

fatalities (OR 2.10, p<0.001). Older age was protective against IPV (OR 0.96, p<0.001). Risk

factors associated with increased odds of IPV included having more children under the age of 5

in the house (OR 1.08, p=0.027), having primary vs no education (OR 1.30, p<0.001), being

widowed or divorced versus being married (OR 1.70, p<0.001), having previous experiences

with violence (OR 1.84, p<0.001), and having a partner that uses alcohol (OR 2.14, p<0.001).

Table 6.2 Bivariate Model Associations of Factors Associated with Past-Year IPV

Total Sample No IPV IPV OR P Value N (row %) N (row %) N (%) Conflict-Related Environment Conflict No fatalities 2,415 69.5 1,526 63.2 889 36.8 -- -- experience Fatalities 1,059 30.5 585 55.2 474 44.8 2.10 <0.001 Relational Factors Partner Education No education (ref) 973 28.0 588 60.4 385 39.6 -- -- Primary 756 21.8 455 60.2 301 39.8 1.05 0.710

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Secondary or above 1,745 50.2 1,068 61.2 677 38.8 0.93 0.760 Partner uses Does not drink alcohol 2,204 63.4 1,489 67.6 715 32.4 -- -- alcohol Drinks alcohol 1,270 36.6 622 49.0 648 51.0 2.14 <0.001 Individual Factors Age (mean, SE) 32.2 8.4 33.1 8.4 30.8 8.2 0.96 <0.001 No. children under 5 (mean, SE) 1.32 1.05 1.30 1.10 1.40 1.00 1.08 0.027 Age married (mean, SE) 18 4.3 18.1 4.4 17.9 4.1 0.99 0.250 Education No education (ref) 1,863 53.6 1,168 62.7 695 37.3 -- -- Primary 1,080 31.1 633 58.6 447 41.4 1.30 <0.001 Secondary or above 531 15.3 310 58.4 221 41.6 1.13 0.250 Religion Christian (ref) 2,927 84.3 1,763 60.2 1,164 39.8 -- -- Muslim 408 11.7 264 64.7 144 35.3 0.76 0.180 Other 139 4.0 84 60.4 55 39.6 1.15 0.500 Civil Status Married (ref) 2,016 58.0 1,285 63.7 731 36.3 -- -- Living together 1,116 32.1 657 58.9 459 41.1 1.14 0.350

Widow/divorced 342 9.8 169 49.4 173 50.6 1.70 <0.001 Wealth Poorest (ref) 877 25.2 559 63.7 318 36.3 -- -- Poorer 837 24.1 507 60.6 330 39.4 1.05 0.790 Middle 727 20.9 443 60.9 284 39.1 1.02 0.880 Richer 622 17.9 354 56.9 268 43.1 1.16 0.390 Richest 411 11.8 248 60.3 163 39.7 0.94 0.750 Employment Didn't work in past 12 850 24.5 489 57.5 361 42.5 -- -- months Worked in past 12 2,624 75.5 1,622 61.8 1,002 38.2 0.91 0.260 months Aggregate No violent experiences 930 26.8 659 70.9 271 29.1 -- -- Violence Measure Violent experiences 2,544 73.2 1,452 57.1 1,092 42.9 1.84 <0.001

The first model of this analysis looks at the association between IPV and dichotomous fatalities using the stepwise model fitting procedure described in Chapter Three (Table 6.3). In the bivariate model, there is a significant relationship between conflict fatalities and IPV (aOR 2.10, p<0.001). In Model 2, basic demographic variables are added, resulting in a small attenuation of the primary effect estimate (aOR 2.04. p<0.001). Adding partner level characteristics (aOR 2.08, p<0.001) and economic characteristics (aOR 2.07, p<0.001) in Models 3 and 4 also resulted in only small changes in the adjusted odds ratio. The largest change on the effect estimate occurred in the final stepwise fitting procedure when previous experiences with violence and partner’s alcohol abuse are added to the model (aOR 1.55, p<0.001). This suggests that individual-level

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factors related to prior violence and alcohol use by a woman’s partner may account for a notable portion of the excess IPV risk in fatality-affected districts. It is possible that those districts that experienced conflict five years prior may also be more likely to have higher levels of childhood violence (father hit mother and father hit respondent), and more likely to have higher levels of alcohol abuse currently. When these variables are added to the model, they partly account for the excess risk in fatality districts. However, these variables do not wholly account for the higher risk, and residence in a fatality district is associated with 55% increase in IPV risk compared to non-fatality districts (aOR 1.55, p<0.001).

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Table 6.3 Association of Past-Year IPV with Dichotomous District-Level Fatalities: Stepwise Model Fitting

Model 4: Demographic, Model 5: Demographic, Model 2: Demographic Model 3: Demographic and Model 1: No covariate model Marriage and Economic Marriage, Economic, Violence Variables Marriage Variables Variables and Alcohol Variables N=3,474 N=3,474 N=3,474 N=3,474 N=3,474 P Low High P Low High P Low High P Low High P Low High aOR aOR aOR aOR aOR Value CI CI Value CI CI Value CI CI Value CI CI Value CI CI Districts with fatalities 2.10 <0.001 1.60 2.76 2.04 <0.001 1.60 2.61 2.08 <0.001 1.69 2.56 2.07 <0.001 1.70 2.53 1.55 <0.001 1.26 1.92 Age 0.97 <0.001 0.96 0.98 0.97 <0.001 0.96 0.98 0.97 <0.001 0.96 0.98 0.97 <0.001 0.95 0.98 No. children under 5 1.07 0.082 0.99 1.15 1.07 0.105 0.99 1.15 1.06 0.114 0.99 1.14 1.05 0.190 0.97 1.14 No education (ref) ------Education Primary 1.14 0.110 0.97 1.34 1.18 0.025 1.02 1.37 1.19 0.024 1.02 1.38 1.21 0.020 1.03 1.42 Secondary and above 1.11 0.330 0.90 1.35 1.18 0.139 0.95 1.46 1.19 0.189 0.92 1.56 1.28 0.068 0.98 1.66 Christian (ref) ------Religion Muslim 0.80 0.258 0.54 1.18 0.79 0.265 0.53 1.19 0.79 0.259 0.52 1.19 1.01 0.948 0.65 1.58 Other 1.11 0.655 0.71 1.72 1.08 0.744 0.69 1.68 1.09 0.689 0.71 1.69 1.02 0.933 0.69 1.50 Married (ref) ------Civil Status Living together 0.98 0.890 0.74 1.30 0.97 0.815 0.74 1.27 1.00 0.989 0.79 1.27 Widow/divorced 1.76 <0.001 1.36 2.26 1.75 <0.001 1.36 2.26 1.88 <0.001 1.44 2.45 No education (ref) ------

Partner Primary 0.94 0.631 0.74 1.20 0.94 0.616 0.74 1.20 0.94 0.597 0.73 1.20 Education Secondary and above 0.83 0.059 0.69 1.01 0.83 0.045 0.69 1.00 0.82 0.039 0.68 0.99 Age married 1.00 0.776 0.97 1.02 1.00 0.802 0.98 1.02 1.00 0.923 0.98 1.02 Poorest (ref) ------Poorer 1.14 0.443 0.81 1.60 1.16 0.370 0.84 1.59 Wealth Middle 1.08 0.573 0.83 1.40 1.10 0.485 0.84 1.43 Richer 1.20 0.160 0.93 1.55 1.24 0.092 0.97 1.58 Richest 1.02 0.908 0.71 1.47 1.10 0.649 0.73 1.67 Didn't work in past 12 months ------Employment Worked in past 12 months 1.02 0.785 0.88 1.19 0.95 0.571 0.81 1.12 Aggregate No violent experiences ------Violence Measure Violent experiences 1.99 <0.001 1.54 2.58 Partner doesn't drink alcohol ------Alcohol Partner drinks alcohol 2.40 <0.001 2.01 2.86

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Sensitivity Analysis for Conflict Measures

After fitting the primary model to examine the relationship between IPV and conflict fatalities, different ways of characterizing conflict were examined (Table 6.4). There was no significant relationship between IPV and dichotomous events (aOR 1.17, p=0.334) nor between IPV and mid-levels (aOR 1.28, p=0.178) or high-levels of events (aOR 1.03, p=0.876) compared to no events. However, individuals in districts experiencing the highest number of event-years were significantly more likely to experience IPV compared to those in districts with no event-years

(aOR 1.88, p<0.001). This relationship did not reach significance at the two- to three-year level

(aOR 1.20, p=0.335) or one-year level (aOR 1.31, p=0.144). As in the previous chapter, a sensitivity analysis was conducted to examine whether these results persisted when only non- fatal events were included in the analysis.

Table 6.4 Association of Past-Year IPV with Three Measures of District-Level Events

Dichotomous Events Three categories of Events Cumulative Event Years No events (ref) 1st tertile (ref) 0 event-years (ref) Any events 2nd tertile 1 event-year 3rd tertile 2-3 event-years 4-5 event-years N=3,474 N=3,474 N=3,474 P Low High P Low High P Low High aOR aOR aOR Value CI CI Value CI CI Value CI CI 1.17 0.334 0.85 1.59 1.28 0.178 0.89 1.84 1.31 0.144 0.91 1.89

1.03 0.876 0.67 1.59 1.20 0.335 0.83 1.75

1.88 <0.001 1.29 2.75 Age 0.96 <0.001 0.95 0.98 0.96 <0.001 0.95 0.98 0.97 <0.001 0.95 0.98 No. children under 5 1.06 0.137 0.98 1.14 1.05 0.177 0.98 1.14 1.06 0.161 0.98 1.14 No education (ref) ------Education Primary 1.21 0.023 1.03 1.43 1.20 0.032 1.02 1.43 1.22 0.014 1.04 1.43 Secondary and above 1.24 0.108 0.95 1.63 1.26 0.112 0.95 1.67 1.27 0.077 0.97 1.67 Christian (ref) ------Religion Muslim 1.00 0.999 0.63 1.58 1.03 0.909 0.64 1.65 1.00 0.986 0.63 1.57 Other 1.04 0.849 0.69 1.57 1.06 0.783 0.70 1.60 1.03 0.878 0.70 1.52 Married (ref) ------Civil Status Living together 0.94 0.600 0.73 1.20 0.94 0.656 0.72 1.23 0.98 0.854 0.77 1.24 Widow/divorced 1.79 <0.001 1.41 2.27 1.81 <0.001 1.42 2.30 1.83 <0.001 1.43 2.34 No education (ref) ------Partner Primary 0.93 0.564 0.73 1.18 0.93 0.540 0.73 1.18 0.94 0.613 0.73 1.20 Education Secondary and above 0.82 0.024 0.69 0.97 0.82 0.024 0.68 0.97 0.82 0.033 0.68 0.98

Age married 1.00 0.954 0.98 1.02 1.00 0.983 0.98 1.02 1.00 0.965 0.98 1.02 Poorest (ref) ------Poorer 1.11 0.530 0.80 1.55 1.12 0.508 0.79 1.59 1.14 0.426 0.82 1.58 Wealth Middle 1.00 0.981 0.76 1.33 1.04 0.814 0.76 1.43 1.08 0.578 0.82 1.41 Richer 1.04 0.794 0.77 1.41 1.12 0.542 0.77 1.63 1.21 0.148 0.93 1.58 Richest 0.87 0.525 0.55 1.35 0.96 0.894 0.56 1.66 1.08 0.718 0.71 1.65 Didn't work in past 12 months ------Employment Worked in past 12 months 0.95 0.523 0.81 1.11 0.95 0.583 0.80 1.13 0.95 0.558 0.81 1.12 Aggregate No violent experiences ------Violence Measure Violent experiences 1.95 <0.001 1.53 2.49 1.99 <0.001 1.54 2.57 1.98 <0.001 1.54 2.55 Partner doesn't drink alcohol ------Alcohol Partner drinks alcohol 2.40 <0.001 2.01 2.86 2.41 <0.001 2.03 2.88 2.39 <0.001 2.00 2.84

Sensitivity analysis of the impact of fatal versus non-fatal events

The districts with four to five cumulative event years were also those districts with highest fatalities. Once non-fatal events were excluded from the four to five year category, only 14 individuals remained (Table 6.5). As before, therefore, this category was combined with the two to three event year category.

Table 6.5 Distribution of IPV across non-fatal event years

0 event years 1 event year 2-3 event years 4-5 event years Total N (%) N (%) N (%) N (%) N (%) No IPV 406 (26.6) 385 (25.2) 725 (47.5) 10 (0.66) 1526 (100) IPV 253 (28.5) 253 (28.5) 379 (42.6) 4 (0.45) 889 (100) Total 659 (27.3) 638 (26.4) 1104 (45.7) 14 (0.58) 2415 (100)

To examine whether fatalities drove the result in the cumulative years model, only non-fatal events were included in the following analysis (Table 6.6). In this analysis, this association no longer reaches significance in the one event-year category (aOR 1.20, p=0.312) or the 2 to 5 event-year category (aOR 1.14, p=0.404), suggesting that fatalities rather than an accumulation of events are driving the effect.

Table 6.6 Association of Past-Year IPV with District-Level Non-fatal Cumulative Event-Years

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N=2,415

aOR P Value Low CI High CI 1 event-year 1.20 0.312 0.84 1.71 2-5 event-years 1.14 0.404 0.84 1.56 Age 0.96 <0.001 0.95 0.98 No. children under 5 1.00 0.976 0.90 1.10 No education (ref) ------Education Primary 1.18 0.117 0.96 1.44 Secondary and above 1.19 0.395 0.80 1.76 Christian (ref) ------Religion Muslim 0.76 0.067 0.57 1.02 Other 1.20 0.460 0.74 1.93 Married (ref) ------Civil Status Living together 0.93 0.601 0.71 1.22 Widow/divorced 2.35 <0.001 1.60 3.45 No education (ref) ------Partner Primary 0.85 0.257 0.64 1.13 Education Secondary and above 0.73 0.005 0.59 0.91 Age married 1.00 0.704 0.98 1.03 Poorest (ref) ------Poorer 1.32 0.098 0.95 1.83 Wealth Middle 1.14 0.400 0.84 1.56 Richer 1.10 0.517 0.83 1.45 Richest 1.46 0.097 0.93 2.30 Didn't work in past 12 ------Employment months Worked in past 12 months 0.97 0.839 0.76 1.25 Aggregate No violent experiences ------Violence Violent experiences Measure 2.17 <0.001 1.67 2.81 Partner doesn't drink alcohol ------Alcohol Partner drinks alcohol 2.36 <0.001 1.84 3.02

Sensitivity analysis on the impact of migration

Over two thirds of the IPV sample (79.2%, n= 2,752) did not migrate from conflict-affected districts after the war. To measure whether the effect of postconflict migration affected the primary model, a sensitivity analysis was done with only non-migrants (Table 6.7). We see a larger effect estimate (aOR 1.79, p<0.05) for the non-migrant model than for the same model with the full population (aOR 1.55, p<0.001) (Model 5, Table 6.3).

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Table 6.7 Association of Past-Year IPV with Dichotomous District-Level Fatalities Among Non- Migrants

N=2.752

aOR P Value Low CI High CI Districts with fatalities 1.79 0.013 1.13 2.82 Age 0.97 <0.001 0.96 0.99 No. children under 5 1.08 0.109 0.98 1.19 No education (ref) ------Education Primary 1.18 0.096 0.97 1.43 Secondary and above 1.09 0.549 0.82 1.46 Christian (ref) ------Religion Muslim 0.85 0.532 0.51 1.42 Other 1.11 0.661 0.70 1.77 Married (ref) ------Civil Status Living together 1.00 0.989 0.77 1.30 Widow/divorced 1.96 <0.001 1.45 2.67 No education (ref) ------Partner Primary 0.96 0.782 0.71 1.30 Education Secondary and above 0.84 0.074 0.69 1.02 Age married 0.99 0.589 0.97 1.02 Poorest (ref) ------Poorer 1.13 0.532 0.78 1.64 Wealth Middle 1.13 0.461 0.81 1.59 Richer 1.33 0.141 0.91 1.95 Richest 1.16 0.649 0.61 2.23 Didn't work in past 12 ------Employment months Worked in past 12 months 1.02 0.767 0.87 1.21 Aggregate No violent experiences ------Violence Violent experiences Measure 2.14 <0.001 1.70 2.68 Partner doesn't drink ------Alcohol alcohol Partner drinks alcohol 2.37 <0.001 1.97 2.86

Discussion

A woman living in a district with any conflict-related fatalities was 50% more likely to experience IPV than a woman living in a no-fatality district (aOR 1.55, p<0.001). These findings support the hypothesis that political violence during conflict is associated with higher

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levels of IPV after conflict, even after adjusting for relevant partner-level and individual-level characteristics.

In the analysis of event-years, women living in districts with conflict events during four to five years of conflict were almost twice as likely to experience IPV compared to women living in no- event districts (aOR 1.88, p<0.001). This effect estimate is, in fact, higher than in the dichotomous fatality model (aOR 2.10, p<0.001). This may be partially explained by the fact that the districts with the very highest number of fatalities were clustered in the highest event-year category. Districts with lower fatalities were clustered in the lower event-year categories. These results suggest a possible dose-response relationship, with the highest fatality districts entailing more IPV risk. However, for this project, the small sample size of fatality districts makes it impossible to examine this association directly. As before, districts in the highest event-year category were also those experiencing the most fatalities. When fatal events were excluded from the analysis, there was no longer an association. This suggests that fatalities, rather than an accumulation of conflict events, continue to drive the association with the outcome. There was no significant association between dichotomous or three-level measures of events and IPV.

Sensitivity analysis with non-migrants revealed an even stronger effect estimate than the primary analysis. Non-migrant women living in a fatality-affected district were 1.8 times as likely to experience IPV compared to a counterpart living in a non-fatality district (aOR 1.79, p<0.05).

The impact of conflict may be strongest when individuals have a longer exposure to conflict- affected environments.

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It may seem striking that conflict could have such a large effect on human aggression even five years after conflict has ended. However, a cluster randomized survey conducted seven years after the end of the war in Liberia found that intimate partner physical violence (IPPV) was significantly associated with exposure to war events in both men and women (Vinck & Pham,

2013). Women were more likely to be victims of IPV, and men were more likely to perpetrate

IPV, for every war-related trauma they experienced. These strong associations, almost a decade after the war, speak to the lasting impact of collective trauma on violence in the home. While previous work focuses on drawing links between conflict and IPV at the individual level, this analysis demonstrates that conflict at the district level is also associated with higher rates of IPV.

It is not clear whether this effect is driven by the fact that individuals in districts with conflict fatalities are directly affected by war and thus have the risk factors identified by Vinck and

Pham, or whether there are more subtle processes at play. For instance, as hypothesized in the conceptual model in Chapter 2, it is possible that simply residing in a place highly affected by violence, even without direct experience with the war, may lead to higher rates of IPV. This could be a result of factors such as high unemployment, a less effective justice system, and higher rates of secondary trauma and use of alcohol.

Beyond the Liberian context, a number of surveys have begun documenting the link between political violence and IPV in places as diverse as Côte d’Ivoire, Myanmar, Uganda and

Afghanistan (Annan & Brier, 2010; Catani et al., 2008; Clark et al., 2010, Falb et al., 2013a;

Gupta et al., 2012; Hossain et al., 2014; Saile et al., 2013). These studies point to a persistent link between war and IPV, but also highlight important nuances as well. Refugee women on the Thai-

Burmese border reported rates of IPV six times higher than non-refugee women (Falb et al.,

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2013a). This work suggests that perhaps layered vulnerabilities, such as displacement and experiencing personal war trauma may have an interaction effect leading to far higher rates of

IPV than experiencing only one event alone. This project is not able to look at the difference in experiences of women directly affected by war versus those not directly affected. However this would be a valuable area for future inquiry. Future DHS surveys in postconflict contexts might consider adding a small module on war experiences and displacement to add to this literature.

Far fewer studies have examined conflict at the community level and IPV, yet early results suggest significant effects between place and interpersonal violence. Studies in both Rwanda and Peru found that individuals living in places affected by violent conflict were at higher risk for IPV (Gallegos & Gutierrez, 2011; Janko et al., 2014). This project contributes to this literature by showing that district-level violence is associated with increased risk of IPV at the individual level in another context, and even years after the cessation of war.

A troubling implication of the work on IPV and war is that conflict may create cohorts of individuals more likely to both perpetrate and experience violence. Previous work has clearly shown a link between children’s exposure to IPV within the household and future perpetration and victimization (Roberts et al., 2010; Eriksson & Mazerolle, 2015). This means that having a group of individuals exposed to mass violence may set off a ripple effect across generations.

Understanding the link between war, IPV and mental health will be critical for disrupting cycles of violence both immediately following and long after peace is declared. Poor mental health may be both a risk factor for, and a result of, war trauma and IPV. A study on the Thai-Burmese border found suicidality was significantly higher among women experiencing IPV (26.7%) than

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women who experienced conflict victimization (5.2%). However, women experiencing both kinds of trauma had the highest rates of trauma (50.0%) (Falb et al., 2013b). While further work is needed, this study suggests that collective trauma may not be as distressing as sustained daily abuse, at least in some settings. Vick and Pham’s survey also found that experiencing IPPV among women and perpetrating IPPV among men was significantly associated with poorer mental health outcomes, even after adjusting for exposure to conflict, suggesting that partner violence may create trauma beyond that of the war itself. These findings emphasize Miller’s assertion that intense day-to-day stressors like IPV may substantially contribute to the mental health burden that was previously attributed solely to war (Miller et al., 2010).

The evidence of a consistent link between IPV and political violence underscores the need for better response to this issue. This is particularly important because studies in both war- and peace-time contexts have shown that exposure to violence, especially as an adolescent, can make one more likely to perpetrate or experience violence throughout the lifecycle (Jewkes et al.,

2013; Gallegos & Gutierrez, 2011), and to pass on this legacy of behaviours to one’s children

(Roberts et al., 2010; Eriksson & Mazerolle, 2015).

IPV represents a complex problem that requires intervention at multiple levels. Scholarship emphasizes the need for interventions that focus on at-risk populations early in life, both for primary and secondary prevention programs. Interventions that target mental health issues, substance abuse and aggressive behaviour in an integrated way may be most effective (Jewkes,

2002; Jewkes et al., 2008; Porter, 2013).

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Addressing IPV postconflict requires even more specialized approaches. Postconflict peacebuilding and reconciliation efforts should find ways to explicitly incorporate IPV and domestic violence messaging. Trauma-healing programs with former combatants and highly affected communities can target the problem of aggression within the home and should focus on conflict resolution, de-escalation and behavior modification. Combining multiple interventions to address the manifold impact of war is critical for achieving holistic and long-term change. Some of the most effective approaches described in the Democratic Republic of the Congo merge economic interventions; individual and family counseling; and community messaging (Kelly et al., 2011; Kohli et al., 2015).

Limitations

IPV, fundamentally, is about interactions between dyads of people. Because no data were collected on men’s experiences, this work was only able to examine the experiences of women.

While still illuminating, a large component of the IPV puzzle remains missing. It is not clear to what extent living in a conflict-affected district may differentially impact women and men.

Intriguingly, results from the Vinck and Pham study found that war-trauma was associated with men’s perpetration of IPV but not associated with women’s perpetration of IPV against a male partner. These results suggest that war trauma may manifest differently by gender, at least in some contexts. Survey studies have also highlighted the link between experiencing human rights abuse and collective violence and IPV perpetration among men (Clark et al., 2010; Gupta et al.,

2009; Gupta et al., 2012; Vinck & Pham, 2013). A future study that has data on both women and men might further explore this association.

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To date, there have been no published studies that attempt to tease apart how much of the association between war and IPV is due to living in a conflict-affected district versus having personal war experiences. Combining the Vinck and Pham study with ACLED data, for instance, could be informative to tease apart the risk attributable to personal experiences versus contextual effects related to place of residence.

A further limitation of this work is that it is unable to explore the pathways through which spread of violent behavior occurs. This analysis is not able to tease apart the relative effects of war, such as destruction of infrastructure, the breakdown of justice systems, and the dissolution of social structures, in order to understand which of these may play key roles in the spread of aggression.

Qualitative work with perpetrators, victims, and service providers could help illuminate this link.

Additionally, undertaking this type of research in a place with more district-level data, such as availability of health clinics, levels of unemployment, and the condition of infrastructure, could also be informative.

Longitudinal data, which traces the same individuals before, during, and after conflict and links individual- and district-level characteristics with IPV, would be ideal for examining this research question. This was not possible for the current project: DHS data represent a cross-sectional measure of IPV, and therefore do not assess causality. Additionally, the limited number of districts with conflict fatalities made it difficult to examine a dose-response relationship with the predictor. Work in other contexts might allow for further examination of the association between fatalities and IPV. Looking at district-level conflict may also be too broad. Districts were chosen as significant units for this project because they represented heterogeneous units that still had

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adequate sample size. However, data from other countries might provide a look at sub-district effects or at whether individuals within a certain radius of conflict are more at risk for future violence. Finally, the five-year time lag between the civil war and the DHS may have attenuated the association between IPV and the conflict measures.

Conclusions

These results contribute to a growing literature that documents the link between collective violence and IPV, and are among the first efforts to use a multi-level modeling approach to explore this issue. Rather than looking at individual experiences with war and IPV risk, this work suggests that residence in a conflict-affected district serves as a risk factor for IPV victimization.

Future research could tease apart which effects are due to neighborhood effects of violence and which effects are due to personal experience with war trauma.

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Chapter Six References

Annan, J., & Brier, M. (2010). The risk of return: Intimate partner violence in Northern Uganda's armed conflict. Social Science & Medicine, 70(1), 152-159.

Decker, M. R., Miller, E., Illangasekare, S., & Silverman, J. G. (2013). Understanding gender- based violence perpetration to create a safer future for women and girls. The Lancet Global Health, 1(4), e170-e171.

Decker, M. R., Latimore, A. D., Yasutake, S., Haviland, M., Ahmed, S., Blum, R. W., Sonenstein, F., & Astone, N. M. (2015). Gender-based violence against adolescent and young adult women in low-and middle-income countries. Journal of Adolescent Health, 56(2), 188-196.

Eriksson, L., & Mazerolle, P. (2014). A cycle of violence? Examining family-of-origin violence, attitudes, and intimate partner violence perpetration. Journal of interpersonal violence, 30(6), 945-964.

Falb, K. L., McCormick, M. C., Hemenway, D., Anfinson, K., & Silverman, J. G. (2013b). Suicide ideation and victimization among refugee women along the Thai–Burma border. Journal of Traumatic Stress, 26(5), 631-635.

Gallegos, J.V, & Gutierrez, I. A. (2011). The effect of civil conflict on domestic violence: The case of Peru. Unpublished manuscript. Retrieved from: http://jvgalleg.mysite.syr.edu/default_files/Research_files/Gallegos- Gutierrez%20%20Civil%20Conflict%20and%20Domestic%20Violence%20JDE.pdf

Gupta, J., Reed, E., Kelly, J., Stein, D. J., & Williams, D. R. (2012). Men’s exposure to human rights violations and relations with perpetration of intimate partner violence in South Africa. Journal of Epidemiology and Community Health, 66(6), e2-e2.

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Hossain, M., Zimmerman, C., Kiss, L., Kone, D., Bakayoko-Topolska, M., Manan, K.A., Lehmann, H.,& Watts, C. (2014). Men’s and women’s experiences of violence and traumatic events in rural Cote d'Ivoire before, during and after a period of armed conflict. British Medical Journal, 4(2), e003644.

Janko, M., Bloom, S. & Spencer, J. (2014, May). Community exposure to violent conflict increases the risk of intimate partner violence in Rwanda: Paper presented at the annual meeting of the Population Association of America. Boston, MA. Abstract retrieved from: http://paa2014.princeton.edu/abstracts/141125

Jewkes, R. (2002). Intimate partner violence: Causes and prevention. The Lancet, 359(9315), 1423-1429.

Jewkes, R., Nduna, M., Levin, J., Jama, N., Dunkle, K., Puren, A., & Duvvury, N. (2008). Impact of stepping stones on incidence of HIV and HSV-2 and sexual behaviour in rural South Africa: Cluster randomised controlled trial. British Medical Journal, 337, a506.

Jewkes, R., Fulu, E., Roselli, T., & Garcia-Moreno, C. (2013). Prevalence of and factors associated with non-partner rape perpetration: Findings from the UN Multi-country Cross- sectional Study on Men and Violence in Asia and the Pacific. The Lancet Global Health, 1(4), e208-e218.

Miller, E., Decker, M. R., McCauley, H. L., Tancredi, D. J., Levenson, R. R., Waldman, J., Schoenwaldd, P., Silverman, J. G. (2010). Pregnancy coercion, intimate partner violence and unintended pregnancy. Contraception, 81(4), 316-322.

Porter, A. (2013). ‘What is constructed can be transformed’: Masculinities in postconflict societies in Africa. International Peacekeeping, 20(4), 486-506.

Roberts, A. L., Gilman, S. E., Fitzmaurice, G., Decker, M. R., & Koenen, K. C. (2010). Witness of intimate partner violence in childhood and perpetration of intimate partner violence in adulthood. Epidemiology, 21(6), 809.

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. Conclusions

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Summary of Findings

Armed conflict and interpersonal violence together account for a significant burden on the health of human populations. Currently, levels of political violence are among the highest since World

War II, with worldwide conflicts increasing dramatically since 2012 (Pettersson et al., 2015).

Understanding the long-term impact of war is vital for effective and sustained recovery, yet there has been little scholarship on enduring consequences of political violence. Assessing how violence may persist and morph after war is critical, since it may impact a country’s ability to achieve sustained peace.

Chapter Four addresses the different ways that conflict can be characterized. Aims 1 and 2 hypothesize that conflict fatalities are the most definitive and violent measure of armed conflict, and will therefore serve as the primary predictor. Efforts to quantify the severity of war have often relied purely on measures of mortality (Themner & Wallensteen, 2014; Global Burden of

Armed Violence, 2011; Gleditsch, 2014). This is partly due to the fact that lethal violence is taken seriously in societies across the globe and tends to be recorded more systematically than other forms of harm. However, it is also important to explore whether non-fatal expressions of conflict impact human outcomes, perhaps in different ways than conflict fatalities.

The ACLED dataset records conflict events, which can include riots, protests, battles and clashes, as well as fatalities. Since not all conflict events resulted in fatalities, there were significantly more events than deaths. Only nine of the 61 districts in the dataset experienced fatalities. Those districts experienced 14.4 fatalities per district on average, but with a very large range of deaths (range 0-327). Thirty-nine of the 61 districts experienced conflict-related events.

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There were 10.9 average events per district (range 0-134). The chapter visualized the distribution of both events and fatalities across individuals and districts. Based on those results, four final measures of conflict were proposed. Dichotomous fatalities remained the primary predictor for subsequent analyses, and three other measures of conflict were used in follow-up sensitivity analyses. Because of the limited number of districts experiencing fatalities, deaths were not divided into further categories. However, events were quantified as a dichotomous measure, as a three-category measure (no-, medium-, and high), and by cumulative years of events over the course of the conflict (0 years, 1 year, 2-3 years, 4-5 years). These four measures of conflict inform the analyses in the following chapters.

Chapter Five examined the association between past-year non-partner physical violence (NPPV) and armed conflict using a multilevel modeling approach. The bivariate analysis found that an individual living in a district with any conflict fatalities was 40% more likely to experience

NPPV than an individual living in a no-fatality district. This association remained relatively consistent throughout the stepwise model fitting procedure but did not reach significance at the p<0.05 level in the final model (aOR=1.43, p=0.197). Individuals living in districts with the highest number of event-years were three times more likely to experience NPPV compared to those living in districts with no events (aOR 2.93, p<0.001). Those districts were also the most likely not only to have cumulative years of events, but also to experience the highest number of fatalities. When only non-fatal events were included in the model, this effect was no longer significant, suggesting districts with the highest fatalities drove the association. The results from the dichotomous fatalities and event year models suggest that there may be an association

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between conflict fatalities and NPPV, although model results using the dichotomous fatalities predictor were not significant at the p<0.05 level in the final model.

Chapter Six presented the multilevel modeling analysis examining the association between past- year IPV and armed conflict. A woman living in a district with any versus no conflict fatalities has 1.55 times greater odds of experiencing IPV compared to a woman living in a no-fatality district (p<0.001) after adjusting for relevant individual level characteristics. There was no association between IPV and events when the latters was split into two (any versus none) or three

(no, medium and high) categories. However, a woman living in a district that had events in four or five years of conflict was almost twice as likely to experience IPV compared to women living in no-event districts (aOR 1.88, p<0.001). Once fatal events were excluded, however, this association no longer reached significance, suggesting that fatalities were the main driver of this association. When the analysis was restricted to women who had lived in the same district since the war, an even larger effect estimate was seen between IPV and dichotomous fatalities (aOR

1.79, p<0.05).

Overall, this research suggests that living in a district that experienced fatalities during war can increase the risk of experiencing interpersonal violence in the postconflict period. These results were more pronounced for IPV than for NPPV. Even after adjusting for known individual-level correlates of IPV, residence in a fatality-affected district was significantly associated with a 50% increase in risk of abuse. For both NPPV and IPV, the effect estimate of the association with fatalities was stronger for non-migrants than for the entire sample, although these results only

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reached significance with IPV. These results suggest that association between each outcome and living in a conflict-affected district may strengthen with length of exposure.

As hypothesized, fatalities, compared to non-fatal events, during conflict were more highly associated with postconflict interpersonal violence. While death is clearly not the only consequence of conflict, it is a highly visible, traumatic and irreversible trauma that may have unique impacts even years after the end of hostilities. Non-fatal protests, riots or clashes may not serve as adequate proxies of levels of wartime violence. Events can be heterogeneous, varying from non-violent civil disobedience to violent encounters, and therefore their association with interpersonal aggression may be attenuated. The conceptual model in Chapter Two posits that violence is reproduced when it is first perceived as a successful adaptation to the environment.

The individual then goes through an iterative process of mimicking and adapting the behavior to serve his or her own environment. Fatalities signal the occurrence of definitive violence, and may therefore be the most powerful predictors of future violence even after war has ended.

Strengths and Limitations

Combining two datasets that were not originally intended to assess the impact of conflict on health entails a number of inherent limitations. Because these data are cross sectional, we cannot establish causal pathways between conflict and interpersonal violence. Instead this project examines the correlation between conflict experience at the district level and individual experience of violence. Ideally, one survey would ask about health outcomes before, during, and after a conflict and examine each individual’s experience with conflict-related events in order to correlate these with individual experiences related to interpersonal violence. Because this project

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draws on data that have already been collected in a standardized format, it does not include information that would have been pertinent to this analysis, such as personal exposure to conflict events. Despite this challenge, the DHS geo-referenced data allowed for successful matching of the vast majority of the data into districts that had a conflict designation. The fact that the conflict events preceded the measurement of the outcomes helps suggest a causal relationship between conflict events and IPV and NPPV. Further work that examines a dose-response relationship with fatalities and possible pathways for violence contagion can further elucidate this link. It is possible that the effects of conflict on personal behavior begin to decay after a certain amount of time. It is not clear, therefore, whether a five-year lag between conflict and measurement of violence is ideal for this project. Looking at other countries where the time lag is both shorter and longer would help explore this question.

Looking at conflict-affectedness at the secondary administrative boundary is still a relatively crude design. It is possible that there is significant heterogeneity in the distribution of violent events within these boundaries, and that this would not be captured with the current approach.

However, other studies combining ACLED data with survey data have successfully found conflict-related effects with this approach. Future work could explore which boundaries of conflict are most meaningful, whether these are administrative boundaries, Euclidian distance or social groupings. Different conflicts may all have different relationships between boundaries of conflict events and the outcomes of interest, so examining different ways of defining conflict in different countries will be a valuable next step for the research. In particular, it will be interesting to see if fatalities persist as the most meaningful measure of conflict in multiple contexts. If so, this would suggest that, as in this research, the highly traumatic and definitive experience of

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conflict is indeed the aspect of violence most likely to create a contagion effect. An as yet unexplored area is whether high levels of interpersonal violence before conflict can make a country more prone to political violence. This would be a promising area for future research.

For this work, the limited number of fatalities and their clustering in only nine of the 61 districts made it impossible to explore other ways of measuring this predictor. A study in another conflict may be able to divide fatalities in other measures (such as tertiles, or cumulative fatality-years) to examine whether there is a possible dose-response relationship between postconflict violence and war death.

Given the dearth of data on Liberia at the sub-district level, this project does not take into account district-level characteristics other than conflict experience. In this case, the district covariate captures unmeasured district level characteristics that are not explicitly included in the model. However, future analyses in countries with more available data could include district- level covariates that provide information about availability of healthcare, state of infrastructure, and other covariates that may be related to the research question. There may also have been significant variation in the types of conflict events that occurred from district to district.

Although conflict fatalities are a clear and uniform measure, conflict events can vary greatly in their violence and severity. This heterogeneity could not be captured in the current analysis and may have contributed to the lack of significant association between events and the two outcomes of interest.

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The ICC for both outcomes shows a significant effect of clustering in these data, suggesting that people within districts tend to be more like each other than otherwise similar persons from other districts. A strength of the multilevel modeling approach compared to logistic regression is the ability to explicitly take into account the correlation among individuals within districts. The district-level covariate acts as a proxy for other important cluster-level characteristics that are not measured.

Finally, of the two outcomes, NPPV was measured in a less precise way than IPV. While IPV was measured through the CTS scale, NPPV was determined through a response to one question.

Unfortunately, this approach is especially challenging because NPPV is a more diffuse phenomenon that may occur in the home, school, workplace and elsewhere. It can include domestic violence, criminal acts and abuse from authority figures. The data do not allow for parsing out different kinds of NPPV for analysis. This is especially unfortunate since studies in conflict zones emphasize the fact that different profiles of NPPV may have different associations with conflict (Annan & Brier, 2010; Catani et al., 2008; Falb et al., 2013, Hossain et al., 2014;

Kelly et al., 2011). It is possible that for this reason, the results related to NPPV were less robust than for IPV. Past-year prevalence of NPPV was also lower, affecting the statistical power of the analysis.

Despite these limitations, this research represents a contribution to the growing literature on the links between violence during and after armed conflict. While the detrimental effects of violent conflict are widely recognized, a surprisingly small literature quantifies the scope and nature of these impacts. In particular, the ways in which political violence may impact interpersonal

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violence are understudied. This project attempts to contribute to this understanding by combining two large multi-country databases to examine this issue. This is one of the first examinations of the impact of conflict on interpersonal violence using population-based data and comprehensive recording of conflict events.

Implications

Violent conflict may change in scope and nature over time. It continues, however, to be a constant of human existence. Recent work in conflict zones has revealed the hidden and unexpected impact of conflict on human aggression postconflict (Gupta et al., 2009; Gupta et al.,

2012; Saile et al., 2013; Vinck & Pham, 2013). At the same time, new studies are illuminating the links between macro-level factors, such as inequitable gender norms and bias in ownership rights, and interpersonal violence (Heise, 2015). This work contributes to these collective efforts by illuminating district-level effects of conflict on individual interpersonal violence.

Recent efforts have identified a number of ways to address IPV and NPPV, lessons that can be leveraged in postconflict contexts where the need for prevention and treatment may be even more pressing. A multi-sectoral approach is required to address complex human behaviors such as those examined here. A recent overview of IPV intervention in the Lancet recommends a multi-pronged approach that addresses negative social norms, the stigmatization of victims of violence and gender stereotypes (García-Moreno et al., 2015). These messages can be incorporated into peace and reconciliation messaging in postconflict periods to promote gender- transformative messages during what is already a time of deep change. Messages of non-violence from political leaders can be effective in reducing both political and non-political violence,

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especially during times of upheaval. As health systems rebuild, violence screening can be built into services. At the community level, research in violent contexts has highlighted the importance of integrating services: community-level messaging about non-violence and gender equity is most effective when combined with individual counseling and family counseling

(Abramsky et al., 2011; Catani et al., 2008; Falb et al., 2013, Jewkes et al., 2011).

The chronic and often invisible stressor of interpersonal violence after conflict can create a significant burden on societies attempting to heal after conflict. Understanding hidden and long- term impact of conflict will help service providers and practitioners better anticipate and address these issues. Understanding the extent to which interpersonal violence may increase during conflict and after can help local health systems, religious organizations, civil society and non- governmental organizations anticipate increases in interpersonal violence. It is possible that higher rates of postconflict violence are an unrecognized problem that impedes recovery. By acknowledging and addressing these problems, communities can more effectively rebuild.

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Chapter Seven References

Annan, J., & Brier, M. (2010). The risk of return: intimate partner violence in Northern Uganda's armed conflict. Social Science & Medicine, 70(1), 152-159.

García-Moreno, C., Zimmerman, C., Morris-Gehring, A., Heise, L., Amin, A., Abrahams, N., & Watts, C. (2015). Addressing violence against women: a call to action. The Lancet, 385(9978), 1685-1695.

Gleditsch, K., Metternich, N., Ruggeri, A. (2014) Data and progress in peace and conflict research. Journal of Peace Research. 51 (2). 301-314.

Global Burden of Armed Violence. (2011). Geneva, Switzerland: Geneva Declaration. Retrieved August 19, 2014, from http://www.genevadeclaration.org/fileadmin/docs/GBAV2/GBAV2011- Ex-summary-ENG.pdf

IOM (Institute of Medicine) and NRC (National Research Council). 2012. Contagion of violence: Workshop summary. Washington, DC: The National Academies Press.

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Jewkes, R., Sikweyiya, Y., Morrell, R., & Dunkle, K. (2011). Gender inequitable masculinity and sexual entitlement in rape perpetration South Africa: findings of a cross-sectional study. PloS one, 6(12), e29590.

Kelly, J. T., Betancourt, T. S., Mukwege, D., Lipton, R., & VanRooyen, M. J. (2011). Experiences of female survivors of sexual violence in eastern Democratic Republic of the Congo: a mixed-methods study. Conflict and Health, 5(1), 1.

Pettersson, T., & Wallensteen, P. (2015). Armed conflicts, 1946–2014. Journal of Peace Research, 52(4), 536-550.

Themnér, L., & Wallensteen, P. (2014). Armed conflicts, 1946–2013. Journal of Peace Research, 51(4), 541-554.

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Appendix 1. Eligibility for inclusion analytic sample

Women taking the domestic violence module geo-located into districts (n=4,502) Respondents not currently 91.6% match rate Missing response to past- or formerly in a union year NPPV (n=854) (n=32) 20.1% of sample 0.7% of sample

Respondents asked about Cases entered in NPPV IPV final model (n=3,648) (n=4,470) 81.0% of sample 99.3% of sample

Non-response to IPV Cases dropped from model questions due to missing predictors (n=52) (n=13) 1.4% of sample 0.3% of sample

Cases entered in IPV final Analytic Sample model Aim 1 (n=3,596) (n=4,457) 98.6% of sample 99.0% of sample

Missing data on predictor variables (n=122) 3.4% of sample

Analytic Sample Aim 2 (n=3,474) 95.2%

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Appendix 2. Liberian Conflict across Space and Time

Conflict Events in Liberia during the Second Civil War by Year

1999 2000

2001 2002

2003 1999-2003

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Conflict Fatalities in Liberia during the Second Civil War by Year

1999 2000

2001 2002

2003 1999-2003

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Appendix 3. District-level summary measures of key demographic variables

District-level summary measures of key demographic variables

Across Districts n=61 Variables No conflict fatality districts Conflict fatality districts Total 25th pcntl Median 75th pcntl 25th pcntl Median 75th pcntl 25th pcntl Median 75th pcntl Age 29.8 30.9 31.8 29.4 30.4 31.1 29.8 30.9 31.7 Number of children under 5 1.3 1.4 1.5 1.1 1.2 1.3 1.2 1.4 1.5 Age at marriage or cohabitation 17.0 17.8 18.4 17.7 18.0 18.4 17.1 17.8 18.4 Years of marriage or cohabitation 2.7 3.0 3.3 2.7 2.9 3.4 2.7 3.0 3.3 Religion Christian 85.9 86.3 98.6 71.4 80.8 96.1 82.9 85.5 98.4 Muslim 0.0 9.4 6.6 2.0 13.8 25.7 0.0 10.1 11.8 Other 0.0 3.4 4.8 0.0 4.5 4.8 0.0 3.6 4.8 Missing 0.0 0.8 1.3 0.0 1.0 2.0 0.0 0.8 1.5 Education None 43.0 56.9 67.6 54.9 56.3 71.4 43.0 56.8 70.4 Primary 28.3 34.8 45.0 17.8 26.3 33.3 24.0 33.6 44.2 Secondary 2.9 8.0 9.9 9.5 16.5 22.1 3.2 9.2 11.8 Higher 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Missing 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Marriage versus cohabitation Never married 7.3 11.1 17.9 11.9 16.9 19.1 7.6 13.5 17.9 Married 35.8 55.6 76.5 28.9 48.5 64.4 35.8 55.6 76.5 Cohabiting 6.1 23.5 35.3 7.6 22.6 42.1 6.1 23.5 35.3 Widowed/divorce 0 2.7 4.4 2.9 6 7.2 0 2.7 4.4 Partner Edu No 16.1 29.9 40.0 19.1 27.0 37.9 16.1 29.5 38.9 Primary 12.0 21.1 25.1 10.1 15.7 19.0 11.9 20.3 24.6 Secondary 22.2 31.4 39.3 27.5 34.8 42.9 22.9 31.9 41.2 Higher 0.0 1.8 2.8 1.7 3.1 3.2 0.0 1.9 2.9 Missing 0.0 2.7 4.3 0.0 2.7 4.0 0.0 2.7 4.3 Wealth index Poorest 15.0 36.6 58.8 2.4 21.4 28.8 10.6 34.4 55.8 Poorer 19.4 29.1 37.8 13.8 22.6 28.6 19.2 28.2 37.5 Middle 10.0 19.5 30.0 14.3 24.7 33.3 10.9 20.3 30.4 Richer 0.0 9.8 16.1 7.3 21.0 32.5 0.0 11.4 22.2

Richest 0.0 4.8 4.3 1.0 10.3 9.5 0.0 5.7 5.6 Worked in past 12 months Yes 70.2 86.3 93.7 62.3 65.7 72.5 62.3 65.7 72.5 No 6.2 3.7 29.7 27.5 34.3 37.7 6.7 15.4 34.2 Missing 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 Experiences and attitudes with violence Yes 62.1 76.0 89.3 76.2 78.7 88.1 67.4 76.6 89.2 No 10.7 24.0 37.9 11.9 21.3 23.8 10.8 23.4 32.6 Missing 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Partner uses alcohol Yes 24.2 30.6 40.3 26.1 31.4 35.3 24.6 31.3 38.5 No 45.9 54.0 64.3 41.2 49.4 54.9 45.3 53.8 61.3 Missing 6.9 11.1 19.0 11.9 16.9 21.4 7.4 11.8 20.0

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Appendix 4. Non-Partner Physical Violence Tables

Perpetrators of ever-violence N %

Mother or stepmother 526 11.68 Father or stepfather 383 8.51 Sister or brother 181 4.02 Other relative 138 3.07 Other in-law 6 0.13 Mother in-law 5 0.11 Daughter or son 4 0.09 Father in-law 4 0.09 Former boyfriend 149 3.31 Former husband/partner 142 3.15 Current boyfriend 100 2.22 Teacher 62 1.38 Employer/work colleague 5 0.11 Other 49 1.09

Model with Dichotomous Events

aOR P Value Low CI High CI aOR P Value Low CI High CI aOR P Value Low CI High CI aOR P Value Low CI High CI

Districts with events 1.25 0.377 0.76 2.04 1.46 0.147 0.87 2.45 1.36 0.176 0.87 2.10 1.39 0.126 0.91 2.12 Age 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98

No. children under 5 1.03 0.634 0.92 1.14 1.02 0.745 0.91 1.14 1.03 0.658 0.91 1.15

No education (ref) ------

Education Primary 1.15 0.336 0.86 1.54 1.13 0.419 0.84 1.51 1.12 0.440 0.84 1.51

Secondary and above 1.34 0.116 0.93 1.94 1.33 0.164 0.89 1.99 1.36 0.142 0.90 2.05

Christian (ref) ------Religion Muslim 0.97 0.904 0.57 1.64 0.94 0.824 0.56 1.59 0.92 0.757 0.54 1.57

Other 0.63 0.114 0.35 1.12 0.64 0.136 0.36 1.15 0.67 0.180 0.37 1.21

Never married (ref) ------

Married 0.52 0.001 0.35 0.78 0.52 0.001 0.35 0.76 0.49 <0.001 0.33 0.73 Civil Status Living together 0.60 0.011 0.40 0.89 0.58 0.003 0.40 0.83 0.55 0.002 0.38 0.80

Widow/divorced 0.63 0.061 0.39 1.02 0.62 0.044 0.39 0.99 0.60 0.042 0.37 0.98

Poorest (ref) ------Poorer 1.41 0.098 0.94 2.11 1.40 0.111 0.92 2.13

Wealth Middle 1.54 0.055 0.99 2.39 1.55 0.061 0.98 2.46

Richer 1.73 0.036 1.04 2.88 1.73 0.040 1.03 2.91 Richest 1.24 0.393 0.76 2.02 1.26 0.323 0.80 2.00 Didn’t work in past 12 months ------Employment Worked in past 12 months 1.16 0.471 0.78 1.71 1.15 0.498 0.77 1.71

Aggregate No violent experiences ------Violence Measure Violent experiences 2.03 <0.001 1.44 2.87

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Model with Three Categories of Events

aOR P Value Low CI High CI aOR P Value Low CI High CI aOR P Value Low CI High CI aOR P Value Low CI High CI

First tertile of events ------Second tertile of events 1.04 0.879 0.61 1.79 1.13 0.582 0.73 1.76 0.94 0.543 0.76 1.16 1.20 0.384 0.79 1.82

Third tertile of events 2.57 <0.001 1.77 3.73 1.62 0.051 1.00 2.62 1.90 <0.001 1.40 2.57 1.52 0.065 0.97 2.39

Age 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98

No. children under 5 1.03 0.623 0.93 1.14 1.03 0.619 0.92 1.14 1.03 0.651 0.92 1.15

No education (ref) ------

Education Primary 1.17 0.311 0.86 1.58 1.17 0.302 0.87 1.59 1.14 0.419 0.83 1.56 Secondary and above 1.32 0.111 0.94 1.86 1.34 0.148 0.90 1.99 1.36 0.148 0.90 2.08

Christian (ref) ------

Religion Muslim 0.94 0.834 0.54 1.65 0.87 0.587 0.52 1.44 0.89 0.683 0.51 1.56

Other 0.60 0.110 0.32 1.12 0.64 0.144 0.35 1.16 0.66 0.211 0.34 1.27 Never married (ref) ------

Married 0.53 0.003 0.35 0.81 0.52 0.002 0.34 0.79 0.50 0.001 0.33 0.75 Civil Status Living together 0.59 0.009 0.39 0.88 0.58 0.003 0.41 0.83 0.55 0.002 0.37 0.80 Widow/divorced 0.63 0.064 0.39 1.03 0.62 0.042 0.39 0.98 0.60 0.040 0.37 0.98

Poorest (ref) ------

Poorer 1.43 0.097 0.94 2.19 1.39 0.132 0.90 2.15 Wealth Middle 1.53 0.056 0.99 2.37 1.52 0.078 0.95 2.43 Richer 1.61 0.042 1.02 2.53 1.67 0.028 1.06 2.65 Richest 1.08 0.782 0.63 1.84 1.22 0.454 0.73 2.04

Didn’t work in past 12 months ------Employment Worked in past 12 months 1.14 0.469 0.79 1.65 1.14 0.524 0.76 1.70 Aggregate No violent experiences ------Violence Measure Violent experiences 2.00 <0.001 1.38 2.89

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Model with Cumulative Event Years

Model 4: Demographic, Marriage, Model 2: Demographic and Marriage Model 3: Demographic, Marriage and Model 1: No covariate model Economic, Violence and Alcohol Variables Economic Variables Variables N=4.457 N=4.457 N=4.457 N=4.457 P Low High P Low High P Low High P Low High aOR aOR aOR aOR Value CI CI Value CI CI Value CI CI Value CI CI 0 event years ------

1 event year 1.44 0.233 0.79 2.63 1.23 0.436 0.73 2.09 1.25 0.408 0.74 2.12 1.31 0.267 0.82 2.09 2-3 event years 1.30 0.336 0.76 2.20 1.13 0.674 0.63 2.04 0.94 0.901 0.34 2.61 1.18 0.521 0.72 1.94

4-5 event years 3.42 <0.001 2.06 5.68 1.85 0.023 1.09 3.14 1.75 0.059 0.98 3.13 2.93 <0.001 1.71 5.04

Age 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98 0.96 <0.001 0.94 0.98

No. children under 5 1.03 0.599 0.93 1.14 1.03 0.611 0.93 1.14 1.04 0.550 0.92 1.16 No education (ref) ------

Education Primary 1.16 0.335 0.86 1.58 1.16 0.355 0.85 1.58 1.14 0.402 0.84 1.55

Secondary and above 1.28 0.167 0.90 1.80 1.32 0.194 0.87 2.02 1.32 0.190 0.87 1.99 Christian (ref) ------

Religion Muslim 0.93 0.794 0.52 1.64 0.88 0.658 0.51 1.53 0.88 0.686 0.46 1.67

Other 0.60 0.115 0.31 1.13 0.62 0.170 0.32 1.22 0.72 0.244 0.42 1.25 Never married (ref) ------

Married 0.53 0.004 0.35 0.81 0.53 0.002 0.35 0.80 0.52 0.001 0.35 0.77 Civil Status Living together 0.59 0.010 0.39 0.88 0.57 0.003 0.40 0.83 0.56 0.002 0.39 0.81

Widow/divorced 0.64 0.077 0.39 1.05 0.61 0.051 0.37 1.00 0.61 0.045 0.37 0.99 Poorest (ref) ------Poorer 1.41 0.102 0.93 2.14 1.39 0.134 0.90 2.13 Wealth Middle 1.52 0.105 0.92 2.53 1.49 0.080 0.95 2.35 Richer 1.58 0.059 0.98 2.55 1.43 0.131 0.90 2.28 Richest 1.09 0.758 0.63 1.87 0.93 0.749 0.58 1.48

Didn't work in past 12 months ------Employment Worked in past 12 months 1.15 0.520 0.75 1.75 1.15 0.453 0.79 1.68

Aggregate No violent experiences ------Violence

Measure Violent experiences 1.98 <0.001 1.36 2.89

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Appendix 5. Intimate Partner Violence Tables

Dichotomous events Model 5: Demographic, Marriage, Model 3: Demographic and Model 4: Demographic, Marriage Model 1: No covariate model Model 2: Demographic Variables Economic, Violence and Alcohol Marriage Variables and Economic Variables Variables N=3,474 N=3,474 N=3,474 N=3,474 N=3,474 P Low High P Low High P Low High P Low High P Low High aOR aOR aOR aOR aOR Value CI CI Value CI CI Value CI CI Value CI CI Value CI CI Districts with events 1.03 0.880 0.74 1.42 1.01 0.957 0.77 1.32 1.02 0.875 0.78 1.35 2.07 <0.001 1.70 2.53 1.17 0.334 0.85 1.59 Age 0.97 <0.001 0.96 0.98 0.97 <0.001 0.96 0.98 0.97 <0.001 0.96 0.98 0.96 <0.001 0.95 0.98 No. children under 5 1.07 0.092 0.99 1.15 1.06 0.126 0.98 1.15 1.06 0.114 0.99 1.14 1.06 0.137 0.98 1.14 No education (ref) ------Education Primary 1.13 0.155 0.96 1.33 1.17 0.048 1.00 1.37 1.19 0.024 1.02 1.38 1.21 0.023 1.03 1.43 Secondary and above 1.09 0.432 0.87 1.37 1.17 0.183 0.93 1.47 1.19 0.189 0.92 1.56 1.24 0.108 0.95 1.63 Christian (ref) ------Religion Muslim 0.80 0.253 0.55 1.17 0.80 0.267 0.54 1.19 0.79 0.259 0.52 1.19 1.00 0.999 0.63 1.58 Other 1.10 0.669 0.71 1.71 1.07 0.747 0.69 1.67 1.09 0.689 0.71 1.69 1.04 0.849 0.69 1.57 Married (ref) ------Civil Status Living together 0.98 0.874 0.74 1.30 0.97 0.815 0.74 1.27 0.94 0.600 0.73 1.20 Widow/divorced 1.76 <0.001 1.37 2.26 1.75 <0.001 1.36 2.26 1.79 <0.001 1.41 2.27 No education (ref) ------Partner Primary 0.94 0.611 0.74 1.19 0.94 0.616 0.74 1.20 0.93 0.564 0.73 1.18 Education Secondary and above 0.83 0.057 0.69 1.01 0.83 0.045 0.69 1.00 0.82 0.024 0.69 0.97 Age married 1.00 0.781 0.97 1.02 1.00 0.802 0.98 1.02 1.00 0.954 0.98 1.02 Poorest (ref) ------Poorer 1.14 0.443 0.81 1.60 1.11 0.530 0.80 1.55

Wealth Middle 1.08 0.573 0.83 1.40 1.00 0.981 0.76 1.33 Richer 1.20 0.160 0.93 1.55 1.04 0.794 0.77 1.41 Richest 1.02 0.908 0.71 1.47 0.87 0.525 0.55 1.35 Didn't work in past 12 months ------Employment Worked in past 12 months 1.02 0.785 0.88 1.19 0.95 0.523 0.81 1.11 Aggregate No violent experiences ------Violence Measure Violent experiences 1.95 <0.001 1.53 2.49

Partner uses Does not drink alcohol ------

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alcohol Drinks alcohol 2.40 <0.001 2.01 2.86

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Model with Three Categories of Events

Model 5: Demographic, Marriage, Model 3: Demographic and Model 4: Demographic, Marriage Model 1: No covariate model Model 2: Demographic Variables Economic, Violence and Alcohol Marriage Variables and Economic Variables Variables N=3,474 N=3,474 N=3,474 N=3,474 N=3,474 P Low High P Low High P Low High P Low High P Low High aOR aOR aOR aOR aOR Value CI CI Value CI CI Value CI CI Value CI CI Value CI CI No events ------Medium events 0.95 0.938 0.30 3.08 0.95 0.795 0.66 1.37 0.96 0.773 0.70 1.30 1.32 0.138 0.92 1.89 1.28 0.178 0.89 1.84 High events 1.86 0.003 1.24 2.80 1.03 0.834 0.81 1.30 1.06 0.645 0.82 1.37 1.74 <0.001 1.28 2.35 1.03 0.876 0.67 1.59 Age 0.97 <0.001 0.96 0.98 0.97 <0.001 0.96 0.98 0.97 <0.001 0.96 0.98 0.96 <0.001 0.95 0.98 No. children under 5 1.07 0.079 0.99 1.15 1.07 0.104 0.99 1.15 1.06 0.120 0.98 1.14 1.05 0.177 0.98 1.14 No education (ref) ------Education Primary 1.13 0.119 0.97 1.33 1.18 0.032 1.01 1.37 1.16 0.058 0.99 1.36 1.20 0.032 1.02 1.43 Secondary and above 1.09 0.430 0.88 1.34 1.16 0.194 0.93 1.45 1.19 0.210 0.91 1.57 1.26 0.112 0.95 1.67 Christian (ref) ------Religion Muslim 0.79 0.242 0.54 1.17 0.79 0.253 0.52 1.19 0.79 0.272 0.52 1.20 1.03 0.909 0.64 1.65 Other 1.10 0.664 0.71 1.70 1.08 0.743 0.70 1.66 1.09 0.695 0.71 1.67 1.06 0.783 0.70 1.60 Married (ref) ------Civil Status Living together 0.97 0.829 0.73 1.29 1.01 0.912 0.78 1.32 0.94 0.656 0.72 1.23 Widow/divorced 1.76 <0.001 1.36 2.26 1.79 <0.001 1.39 2.30 1.81 <0.001 1.42 2.30 No education (ref) ------Partner Primary 0.95 0.645 0.75 1.20 0.95 0.658 0.75 1.20 0.93 0.540 0.73 1.18 Education Secondary and above 0.83 0.053 0.69 1.00 0.82 0.033 0.68 0.98 0.82 0.024 0.68 0.97 Age married 1.00 0.754 0.97 1.02 1.00 0.780 0.98 1.02 1.00 0.983 0.98 1.02 Poorest (ref) ------

Poorer 1.14 0.424 0.83 1.55 1.12 0.508 0.79 1.59 Wealth Middle 1.12 0.385 0.87 1.43 1.04 0.814 0.76 1.43 Richer 1.30 0.025 1.03 1.63 1.12 0.542 0.77 1.63 Richest 1.14 0.492 0.78 1.66 0.96 0.894 0.56 1.66

Didn't work in past 12 months ------Employment Worked in past 12 months 0.97 0.711 0.83 1.13 0.95 0.583 0.80 1.13 Experiences and attitudes ------with 1.99 <0.001 1.54 2.57 violence Alcohol Partner doesn't drink alcohol ------

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Partner drinks alcohol 2.41 <0.001 2.03 2.88

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Model with Cumulative Event Years

No education (ref) ------Education Primary 1.11 0.191 0.95 1.31 1.17 0.082 0.98 1.40 1.18 0.04 1.01 1.37 1.22 0.014 1.04 1.43 Secondary and above 1.05 0.635 0.85 1.29 1.16 0.204 0.92 1.46 1.19 0.185 0.92 1.55 1.27 0.077 0.97 1.67

Christian (ref) ------Religion Muslim 0.77 0.186 0.52 1.14 0.78 0.251 0.52 1.19 0.77 0.212 0.52 1.16 1.00 0.986 0.63 1.57 Other 1.14 0.531 0.76 1.72 1.07 0.749 0.69 1.66 1.11 0.612 0.73 1.70 1.03 0.878 0.70 1.52

Married (ref) ------Civil Status Living together 0.98 0.882 0.72 1.32 0.97 0.838 0.74 1.28 0.98 0.854 0.77 1.24 Widow/divorced 1.76 0 1.36 2.27 1.76 0 1.36 2.28 1.83 0 1.43 2.34

No education (ref) ------Partner Primary 0.95 0.641 0.75 1.20 0.93 0.577 0.73 1.19 0.94 0.613 0.73 1.20 Education Secondary and above 0.83 0.049 0.69 1.00 0.81 0.026 0.68 0.98 0.82 0.033 0.68 0.98 Age married 1.00 0.758 0.97 1.02 1.00 0.722 0.97 1.02 1.00 0.965 0.98 1.02

Poorest (ref) ------

Poorer 1.14 0.436 0.82 1.60 1.14 0.426 0.82 1.58 Wealth Middle 1.08 0.590 0.82 1.43 1.08 0.578 0.82 1.41 Richer 1.19 0.239 0.89 1.60 1.21 0.148 0.93 1.58

Richest 1.01 0.973 0.68 1.49 1.08 0.718 0.71 1.65 Didn't work in past 12 months ------Employment Worked in past 12 months 1.02 0.795 0.87 1.19 0.95 0.558 0.81 1.12 Experiences and attitudes ------with 1.98 <0.001 1.54 2.55 violence

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Partner doesn't drink alcohol ------Alcohol Partner drinks alcohol 2.39 <0.001 2.00 2.84

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Jocelyn Kelly

1021 N Garfield St #1014, Arlington VA 22201 jkelly@ hsph.harvard.edu 703.577.9511

Education: Johns Hopkins University Baltimore, MD Doctoral Student September, 2011 - Present Department of Population, Family and Reproductive Health

Harvard School of Public Health Boston, MA Master of Science – Concentration in Population and International Health June 2008 Certificate in Humanitarian Studies and Field Practice at the Harvard School of Public Health

Johns Hopkins University Baltimore, MD Bachelor of Arts in Cognitive Science May 2002 Awards: Phi Beta Kappa, Dean’s List 1998-2002, Full and Departmental Honors

Areas of Experience: Addressing sexual violence in conflict; promoting women’s rights in fragile states; countering human trafficking; public health; evaluation science; statistical analysis of large datasets

Employment: Director, Women in War Program January 2010 - Present Harvard Humanitarian Initiative Boston, MA - Lead and provide strategic direction for the Women in War program - Design, conduct and report on large-scale research programs in conflict and post-conflict environments - Build strong partnerships with hospitals, NGOs and multilateral organizations - Train and supervise research staff in US and DRC - Present research findings to local partners, multi-lateral institutions and national governments - Write peer-reviewed articles and reports on research findings to inform programming and policy - Manage grants and write proposals to fund future research initiatives

Expert trainer September, 2015 – Present UN Women - Serve as on-call gender expert for the Roster for Rapid Response (ExpRes) for the UNDP Bureau for Crisis Prevention and Recovery - Principle Investigator, USAID Investigation of Human Trafficking in DR Congo January 2014 – June 2014 USAID/Social Impact - Supervised project to investigate human trafficking in artisanal mining towns in eastern Democratic Republic of the Congo (DRC) - Supervised training of local partners and digital data collection of over 1500 surveys throughout eastern DRC - First authored final report detailing the scope of risk factors for human trafficking in artisanal mining towns in eastern DRC, and provided recommendations to USAID for future programming efforts to address this issue

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Research Coordinator, Women in War Program June 2008 – January 2010 Harvard Humanitarian Initiative Boston, MA - Designed, conducted and reported on large-scale research programs in conflict and post-conflict environments - Built strong partnerships with hospitals, NGOs and multilateral organizations - Trained and supervised research staff in US and DRC - Presented research findings to local partners, United Nations and US government - Managed grants and write proposals to fund future research initiatives

Consultant June 2009-August 2009 United Nations Boston, MA - Authored report on application of qualitative research techniques in participatory mapping projects for United Nations Threat and Risk Mapping teams in Sudan

Research Associate September 2007 Harvard Program on Policy and Conflict Research Boston, MA - Contributed to design and moderation of a simulation exercise for officials from the UN, international governments, and the NGO community on the right to protect vulnerable populations during humanitarian crises

Researcher June 2007 –August 2007 International Medical Corps Democratic Republic of the Congo - Designed and implemented a survey-based research project on sexual violence and vulnerable women in eastern Democratic Republic of the Congo - Supervised and trained a six-person nursing team to administer surveys and conduct focus groups - Surveyed healthcare providers in Kivu Province - Collected and assessed data to inform program decisions and healthcare provision to survivors of sexual violence

Research Intern January 2007 Uganda Human Rights Commission (UHRC) Kampala, Uganda - Reviewed the proposal for the Right to Health Unit and recommended improvements - Determined the indicators to assess program success - Authored a chapter on the UHRC Annual Report on the human rights impact of the ADF rebel group

Disaster Management Consultant November 2005 – August 2006 SRA International New Orleans/ Washington D.C. - Created reports and analyzed field data for the Director of FEMA Public Assistance in New Orleans - Collected data from field offices; analyzed and summarized data for daily and weekly reports for policy-makers - Coordinated projects in the transitional housing unit of FEMA’s Emergency operating center

Daily Newspaper Editor April 2003 – July 2004 The Herald Mexico Mexico City, Mexico - Authored features on politics and local news 149

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- Managed team of writers and layout staff - Prepared two pages of news daily on subjects ranging from science, to social and political issues - Edited and proofread articles from contributors - Coordinated with co-workers in English and Spanish to meet daily deadlines

Research Assistant 2000-2001 Johns Hopkins Hospital, Neurology/Oncology Unit Baltimore, Maryland - Collaborated with medical researchers to collect data and write articles for peer-reviewed publications

Consultations and Advisory Roles:

Gender Expert, Roster for Rapid Response March, 2014 – Present United Nations Development Program - Serve as on-call gender expert for the Roster for Rapid Response (ExpRes) for the UNDP Bureau for Crisis Prevention and Recovery

Lecturer and Advisor November, 2014 – Present US Army Peacekeeping and Stability Operations Institute (PKSOI) - Served as lecturer at US Army Peacekeeping and Stability Operations Institute during a conference for training peacekeepers - Advise United States Africa Command and PKSOI on the creation of their Handbook “Preparing to Prevent: Conflict-related sexual violence mitigation” a resource for US military personnel to better identify and address sexual violence in the context of peacekeeping missions.

Advisory Panel, State of the World's Girls 2013 August 2012 - Present Plan International London, UK - Serve in on the advisory panel for Plan International’s 2013 Report Advisory Panel for the ‘State of the World’s Girls’ report

Technical Advisory Group, IMAGES Study February 2012 - Present Plan International International - Member of the Technical Advisory Group for multi-site International Men and Gender-Equality Study (IMAGES) study

Member, Expert Advisory Working Group April 2011 - Present Geneva Call Geneva, Switzerland - Serve in on the advisory board for Geneva Call’s initiative to engage armed non-state actors in improving protection for vulnerable groups in conflict

Consultant, Child Soldiers Initiative June 2011 - July 2011 150

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- Helped facilitate a training for the Congolese National Army on the use of child soldiers in conflict - Ran sessions using body mapping methodology to examine the effects of conflict and soldiering on individuals, ran sessions examining how conflict changes one’s behavior and attitudes

Steering Committee Member January 2010 - Present UNICEF Boston, MA - Advise for project aimed at strengthening the prevention of sexual violence in conflict with members of non-state armed groups

Community Service: • Communications Director, African Health Forum, HSPH September 2007 – June 2008 • Director of International Relations, Public Health in Politics January 2008 – June 2008 • EMT with Sterling Volunteer Fire and Rescue, Sterling, VA March 2005 – August 2006 • Disaster Action Volunteer, Red Cross, Arlington VA December 2005 – August 2006 • Mexico City Pediatric Hospital, Mexico City January 2004 – April 2004

Achievements: • Delta Omega Public Health Honor Society Scholarship recipient for “Policy and Practice” category, 2015 • Sommer Scholarship for Excellence in Public Health for PhD studies at Johns Hopkins University, 2013 • Selected as United States Institute of Peace Young Scholars Network member, 2012 • Best Presentation by Junior Researcher, Sexual Violence Research Initiative, 2009 • Gareth Green Award for Excellence in Public Health Practice, Harvard University, 2008 • New Investigator in Global Health, Global Health Council, 2007 • Corbin-Gwaltney Fellowship in magazine journalism from Johns Hopkins University, 2002 • Robert Sibley Award for excellence in writing, 1998

Reviewer: • American Journal of Public Health, British Medical Journal, American Journal of Men’s Health, Journal of Nervous and Mental Disease, Conflict and Health

Professional Membership • Member, American Public Health Association • Member, International Studies Association

Peer-Review Publications: 1. Kelly, J., Branham, L., Decker, M. R. (2016). Abducted children and youth in Lord’s Resistance Army in Northeastern Democratic Republic of the Congo (DRC): mechanisms of indoctrination and control. Conflict and health, 10(1). 2. Albutt, K., Kelly, J., Kabanga, J., & VanRooyen, M. (2016). Stigmatisation and rejection of survivors of sexual violence in eastern Democratic Republic of the Congo. Disasters.

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3. Kelly, J TD “This mine has become our farmland": Critical perspectives on the coevolution of artisanal mining and conflict in the Democratic Republic of the Congo. Resources Policy, 2014; 40: 100-108. 4. Kelly J TD, King-Close A, Perks R. Resources and resourcefulness: Roles, opportunities and risks for women working at artisanal mines in South Kivu, Democratic Republic of the Congo. Futures. 2014;62: 95-105. 5. Bartels S, Kelly J, Scott J, Leaning J, Mukwege D, Joyce N, VanRooyen M. Militarized Sexual Violence in South Kivu, Democratic Republic of Congo. Journal of Interpersonal Violence. 2013; 28(2): 340-358. 6. Bartels SA, Scott JA, Leaning J, Kelly JT, Mukwege D, Joyce NR, VanRooyen MJ. Sexual violence trends between 2004 and 2008 in South Kivu, Democratic Republic of Congo. Prehosp Disaster Med 2012;26(6): 408-413. 7. Kelly J, Betancourt T, Mukwege D, Lipton R, VanRooyen M. Experiences of Female Survivors of Sexual Violence in Eastern Democratic Republic of the Congo: A mixed-methods study. Conflict and Health, November 2011. 8. Kelly J, Kabanga J, Cragin W, Alcayna-Stevens L, Haider S, Vanrooyen M. “If your husband doesn't humiliate you, other people won't”: Gendered attitudes towards sexual violence in eastern Democratic Republic of Congo. Global Public Health: An International Journal for Research, Policy and Practice, 9 June 2011. 9. Bartels S, Scott J, Leaning J, Kelly J, Joyce N, Mukwege D, VanRooyen M. Psychosocial Consequences of Sexual Violence in South Kivu Province, Democratic Republic of Congo. Journal of Peace, Gender and Development (1), March 2011. 10. Kelly J, VanRooyen M. Militia in DRC Speak about Sexual Violence. Forced Migration Review, Issue on Armed Non-State Actors and Displacement (37), March 2011. 11. Gupta J, Reed E, Kelly J, Stein DJ, Williams DR. Men's Exposure to Human Rights Violations and Relations with Perpetration of Intimate Partner Violence in South Africa. Journal of Epidemiology and Community Health. 2012;66(6). 12. Kelly J. Rape in War: Motives of Militia in DRC. United States Institute of Peace Special Report. June 2010. 13. Kelly, J. When NGOs Beget NGOs: Practicing Responsible Proliferation. Journal of Humanitarian Assistance. April 29, 2009. 14. Kelly J. Allied Democratic Forces and Induced Displacement. Chapter in Annual Report, Uganda Human Rights Commission. January, 2007. 15. Krauss G, Abou-Khalil B, Sheth G, Kelly J, Bergey GK, Lesser RP, Coleman D. Efficacy of Levetiracetam for Treatment of Drug-Resistant Generalized Epilepsy. Epilepsia 42 (Suppl 7):181, 2001.

Book Chapters: 1. Scott, J., Molina, R., Kelly, J. Gender-based Violence in Humanitarian Crises. In The Oxford Handbook of Humanitarian Medicine. (forthcoming). 2. Azuero A and Kelly J. A Tale of Two Conflicts: An Unexpected Reading of Sexual Violence in Conflict through the Cases of Colombia and Democratic Republic of the Congo. In Bergsmo M, Butenschon, Wood E (Eds.), Understanding and Proving International Sex Crimes, 2012

Blogs: 1. Kelly, J. Women Around the World Series: The ICC’s New Precedent for Sexual Violence as a War Crime. Blog for Council on Foreign Relations. April 4, 2016

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2. Kelly, J. and Branham, L. On the Ground: Engaging Africa Voices on Kony. Blog for New York Times. March 21, 2012 3. Kelly, J. Ask the Experts: Preventing Sexual Violence. Blog for Council on Foreign Relations. Micah Zenko (eds) June 11, 2012

Other Publications: 1. Women in War Program, Harvard Humanitarian Initiative and Eastern Congo Initiative. We Came Back with Empty Hands: Understanding the Disarmament, Demobilization and Reintegration of Children Formerly Associated with Armed Groups in the Democratic Republic of the Congo. 2013. 2. Kelly J and Branham L. We Suffer From War and More War": An Assessment of the Impact of the Lord's Resistance Army on Formerly Abducted Children and their Communities in northeastern Democratic Republic of the Congo. Harvard Humanitarian Initiative and Oxfam America. April 2012. 3. Kelly J, VanRooyen M , Maclin B, Mullen C. Hope for the future again Harvard Humanitarian Initiative. April 2011. 4. Bartels S, VanRooyen M, Leaning J, Scott J, Kelly J. Now, The World Is Without Me: An Investigation of Sexual Violence in Eastern Democratic Republic of Congo. Harvard Humanitarian Initiative and Oxfam America. April 2010. 5. Kelly J, VanRooyen M, Leaning J, Cragin C. Characterizing Sexual Violence in the Democratic Republic of the Congo: Profiles of Violence, Community Responses, and Implications for the Protection of Women. Harvard Humanitarian Initiative. August 2009.

Presentations Peer-reviewed abstracts: 1. Kelly J. (2016). “Accounting for Sexual Violence in Conflict: Documentation, Explanation, and Justice.” Presentation on Panel at the International Studies Association, Atlanta, March 18. 2. Kelly, J. (2015). “Inside the Lord's Resistance Army: Voices from combatants.” Presentation at the Sexual Violence Research Initiative Conference, Cape Town, South Africa, September 16. 3. Kelly, J. (2015). “Sexual exploitation and discrimination in artisanal mining towns in eastern Democratic Republic of the Congo.” Presentation at the Sexual Violence Research Initiative Conference, Cape Town, South Africa, September 15. 4. Kelly, J. (2013). “Abducted children and youth in Lord's Resistance Army in north eastern Democratic Republic of the Congo: Gendered mechanisms of indoctrination and control.” Presentation at the Sexual Violence Research Initiative Conference, Bangkok, Thailand, October 15-17. 5. Kelly J. (2009). "Experiences of Female Survivors of Sexual Violence in Eastern Democratic Republic of the Congo: A mixed-methods study." Presentation at the Sexual Violence Research Initiative Conference, Johannesburg, South Africa, July 8. 6. Kelly J. (2009). “Experiences and attitudes of Militia members towards the conflict and sexual violence in Eastern Democratic Republic of the Congo: A qualitative study.” Presentation at the Sexual Violence Research Initiative Conference, Johannesburg, South Africa, July 8.

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7. Kelly J. (2008). “Sexual Violence Experiences and Attitudes in Post-Conflict Eastern DRC.” Poster Presentation at Global Health Council's Annual International Conference on Global Health, Washington, D.C, May 27- 31. 8. Kelly J. (2008). “Sexual Violence as a Weapon of War.” Poster presentation at Harvard Medical School 2008 Annual Meeting in Minority Health Policy, Boston, Massachusetts, May 26.

Invited talks: 1. Kelly J. (2016). Expert presenter at USAID Learning Event, “Measuring Human Trafficking: Gauging Awareness and Prevalence through Research” event, Washington DC, April 14. 2. Kelly J. (2016). Advisor for Global Fund meeting, “Rights-based and gender-sensitive programming and implementation in Challenging Operational Environments,” Geneva, February 26. 3. Kelly J. (2016). Invitee the Africa Peacebuilding Roundtable Series at Council of Foreign Relations, “Sources of Instability and Conflict in Africa,” Washington DC, February 10. 4. Kelly J. (2016). Introduction at State Department event “Women and Foreign Policy: Is Everything We Know About Wartime Rape Wrong?” Washington DC, February 19. 5. Kelly, J. (2015). “Participatory Action Research” Invited lecture at “Experts Group Workshop: Conducting population-based surveys on Gender-based violence in conflict and humanitarian settings” World Health Organization and George Washington University, Washington DC, February 3-4. 6. Kelly, J. (2014). Rapporteur at “Baltimore Inter-Generational Consultation on the United Nations Post 2015 Global Development Agenda” Johns Hopkins University, Baltimore MD, November 11. 7. Kelly, J. (2014). Presenter on Health and Human Rights at Harvard School of Public Health Leadership Council, Harvard School of Public Health, Boston, MA, October 16. 8. Kelly, J. (2014). Speaker on Panel addressing Sexual Violence in Conflict. AFRICOM Women, Peace and Security Conference, US Army Peacekeeping and Stability Operations Institute, Carlisle, PA, September 9- 11. 9. Kelly, J. (2014) “Assessment of Human Trafficking and Sexual Exploitation in Artisanal Mining Towns in Eastern Democratic Republic of the Congo.” Workshop on Sexual Violence and Armed Conflict, Harvard Kennedy School, Cambridge MA, September 2-3. 10. Kelly, J (2014). Invited Panelist with the Special Representative to the Secretary General (SRSG) Bangura on the panel “Help End Sexual Violence in Conflict Through What You Buy” at the “The Global Summit to End Sexual Violence in Conflict.” London, UK, June 9-12. 11. Kelly, J. (2014). Participant in Missing Peace Young Scholars Network Workshop. United States Institute of Peace, Washington DC, May 22-23. 12. Kelly, J. (2013). “Human Rights Assessments in Artisanal Mining Towns in Eastern DRC. Presentation at Workshop on Artisanal Mining in DRC. University of Zurich, Zurich, Switzerland. December 13. 13. Kelly, J. (2013). “How Do Warriors Unlearn Violence? Psychosocial Dimensions” Invited lecture at United States Institute of Peace, October 28-30. 14. Kelly, J. (2013). “Human Security and SSR in the DR Congo: New Research and Approaches” Panelist at Great Lakes Policy Forum, Johns Hopkins School of Advanced International Studues, Washington DC, October 23.

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15. Kelly, J. (2013). Speaker on “Caught in Conflict: The Journeys of Child Soldiers” at the Harvard Kennedy School Kennedy Forum, Cambridge MA, October 21. 16. Kelly, J. (2013). Panelist at event Innovative Participatory Research with Former Child Soldiers in Eastern Congo.” Hosted by the Washington Network for Children in Armed Conflict, Washington DC, July 23. 17. Kelly, J. (2013). Participant in Young Scholars Workshop at Missing Peace Young Scholars Network Workshop. United States Institute of Peace, Washington DC, February 14-16. 18. Kelly, J. (2012). Discussant at report Launch of “Peacebuilding 2.0: Mapping the Boundaries of an Expanding Field.” United States Institute of Peace, Washington DC, October 19. 19. Kelly, J. (2012). “Considerations for Methods and Ethics.” Conference on Surviving Violence: Comparative Perspectives. Dalhousie University, Halifax, Canada. September 29. 20. Kelly, J. (2012). Discussant at “Human Rights Impact Assessments and Children’s Rights.” Joint World Bank – UN Seminar on Human Rights Impact Assessments and Other Forms of Analysis in Development Policy and Operations. Washington DC, September 19. 21. Kelly, J. (2012). “Children Born of Sexual Violence in DRC.” presentation at Workshop on Children Born of War, Cambridge College, UK, April 13-14. 22. Kelly, J. (2012). “Addressing Sexual and Gender-based Violence in the Democratic Republic of the Congo.” Panel presentation at “Addressing Sexual and Gender-based Violence in Post-Conflict Settings: National and International Strategies. American University Washington College of Law. Washington DC, February 1. 23. Kelly J. (2011). “Kelly Inheriting Shame: Children born of rape in the Democratic Republic of the Congo” Presentation at Workshop “The legacy of war time rape: Mapping key concepts and issues.” Peace Research Institute of Oslo, Oslo, Norway, October 27-28. 24. Kelly J. (2011). Personal briefing on sexual violence in conflict for the United Nations Special Representative on Sexual Violence in Conflict Margot Wahlström, New York, New York, March 31. 25. Kelly J. (2011). “Rape in War: Motives of Militia in DRC” Invited Presenter at the United Nations, Office for the Coordination of Humanitarian Affairs. New York, New York, March 31. 26. Kelly J. (2011). “Non-state armed actor attitudes towards displaced and other women.” Presenter at Armed non-State actors and the Protection of Internally Displaced Persons, Geneva, Switzerland, March 23 -24. 27. Kelly J. (2011). “Sexual and Gender-based Violence as a Security Issue.” Presenter at the Gender and Security Seminar Series at the John F. Kennedy School of Government at Harvard University Boston, Massachusetts, March 21. 28. Kelly J. (2011). “Young Humanitarians and the Challenge of Professionalization.” Presenter at the Humanitarian Action Summit at Harvard University, Boston, Massachusetts, March 5. 29. Kelly J. (2010). Panel presenter at the Peace Caucus War and Social Injustice Session. “Community responses to conflict in eastern DRC: Comparing men and women's perspectives.” American Public Health Association Conference, Denver, Co, November 6. 30. Kelly J. (2010). Panel presenter on “UN Integrated Mission Challenges in Sub-Saharan Africa.” US Army Peacekeeping and Stability Operations Institute, Sterling, Virginia, October 26. 31. Kelly J. (2010). The Role of Research in Sex Crimes Prosecution. Proving International Sex Crimes, Forum for International Criminal and Humanitarian Law hosted by Yale University and the University of Cape Town, New Haven, Connecticut, October 16. 32. Kelly J. (2010). Panel presenter on “Drawing A Distinction Between Civilians and Combatants,” An "Our World At War" Exhibit Event, Cambridge, Massachusetts, September 24.

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33. Kelly J. (2010). Panel presenter on Sexual Violence in Eastern Congo. Instability Warning and Genocide Prevention Symposium, Vanderbilt University, Nashville, Tennessee, March 17. 34. Kelly J. (2010). Intersections between Sexual Violence and Economics at “Understanding Wartime Rape, Bonn International Center for Conversion, Bonn, Germany, March 11-12. 35. Kelly J. (2010). The Other Side of Gender: Masculinity Issues in Violence Conflict. United States Institute of Peace, Washington D.C., February 23. 36. Kelly J. (2010). Presenter at Congo Day, St. Michael’s College, Colchester, Vermont, February 10. 37. Kelly J. (2009). Panelist for Congo/Women Portraits of War: The Democratic Republic of Congo, New Haven, Connecticut, November 19. 38. Kelly J. (2009). Panelist for the Sexual Violence in Armed Conflict Session for Gender, Peace and Security Conference, Columbia University, New York, New York, October 23. 39. Kelly J. (2009). "Militia Experiences and Attitudes towards Conflict and Sexual and Gender-based Violence in DRC." Invited presentation at United Nations Security Council Briefing, Austrian Mission, New York, NY, June 9. 40. Kelly J. (2009). Guest speaker at Johns Hopkins University "Stop Rape in the Congo Now" activism week, Baltimore, Maryland, April 20-30. 41. Kelly J. (2009). "Rape as a Weapon of War: Violence against Women in Eastern Congo." Invited presentation at St Michaels University, Burlington, Vermont, March 4. 42. Kelly J. (2009). "Dynamics Of Sexual Violence In The Eastern Democratic Republic Of Congo: Perpetrators, Community Response, and Policy Implications." Invited lecture presentation at the Wilson Center, Washington D.C., January 13. 43. Kelly J. (2009). "Research findings and policy recommendations for protection strategies related to Sexual Violence in DRC." Invited presentation at United Nations Security Council Briefing, Austrian Mission, New York, NY, January 12. 44. Kelly J. (2006) “A Human Rights-Based Approach to Emergency Response Moving beyond Hurricane Katrina’s Legacy of Inequality,” Boston, Massachusetts, November 5.

Guest Lectures: 1. Kelly J. (2016). Instructor for World Bank expert learning event, “Artisanal and Small Scale Mining,” Washington DC, March 3. 2. Kelly J. (2016). Lecturer for Global Public Health Course at Stanford University, Palo Alto, California, February 16. 3. Kelly J. (2015). Lecturer for Global Public Health Course at Stanford University, Palo Alto, California, February 25. 4. Kelly, J. (2015). Lecturer at “Health and Security” seminar undergraduate course at Stanford University. Palo Alto, California, February 24. 5. Kelly J. (2015). Lecturer for Gender and Peacebuilding course at United States Institute of Peace, Washington DC, November 20. 6. Kelly, J. (2014). Lecturer at “Gender and Peacebuilding: A Course for Practitioners and Policymakers.” United States Institute of Peace, Washington DC, December 3. 7. Kelly J. (2014). Lecturer for Global Public Health Course at Stanford University, Palo Alto, California, March 11.

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8. Kelly, J. (2013). Lecturer at “Gender and Peacebuilding: A Course for Practitioners and Policymakers.” United States Institute of Peace, Washington DC, April 18. 9. Kelly J. (2013). Lecturer for Global Public Health Course at Stanford University, Palo Alto, California, February 28.

10. Kelly, J. (2012). “Gender: The Impact on Exposure to Violence and Stigma.” Guest lecture at Children, Youth and International Human Rights class at the Harvard Kennedy School of Government. Cambridge, Massachusetts, September 27. 11. Kelly J. (2012). Lecturer for Global Public Health Course at Stanford University, Palo Alto, California, February 21. 12. Kelly J. (2011). Lecturer for Global Public Health Course at Stanford University, Palo Alto, California, February 15.

Conference Attendance: 1. Fragility, Conflict and Violence Forum 2015, World Bank, Washington DC, February 11-13, 2015 2. Male-Directed Sexual Violence Conference, United Nations, New York, New York, 25-26 July 2013. 3. Armed non-State actors and the Protection of Internally Displaced Persons, Geneva, Switzerland, March 23 - 24, 2011. 4. Humanitarian Action Summit, Harvard University, Boston Massachusetts, March 4-6, 2011. 5. Preventing Genocide and Mass Atrocities: Goals and Challenges of International Cooperation, United States Holocaust Memorial Museum, November 15, 2010. 6. American Public Health Association Annual Meeting & Exposition, Denver, Colorado, November 6-10, 2010. 7. Gender, Peace and Security Conference, Columbia University, New York, New York, October 23, 2009. 8. Sexual Violence Research Initiative Conference, Johannesburg, South Africa, July 6 - 9, 2009. 9. “Engaging Men in Preventing and Mitigating Gender-Based Violence in Post-Conflict Settings in Sub- Saharan Africa.” A one-day seminar organized by the World Bank, in collaboration with the MenEngage Alliance and the International Center for Research on Women, Washington D.C., June 9-10, 2009. 10. “Geospatial Science & Technology for Sustainable Development in Africa: Partnerships and Applications." Belfer Center for Science and International Affairs, Harvard Kennedy School, May 10, 2009. 11. Global Health Council's Annual International Conference on Global Health, Washington, D.C., May 27- 31, 2008. 12. American Public Health Association’s Annual Conference, Boston, Massachusetts, November 4-8, 2006.

Computer Skills: Microsoft Excel, Microsoft Word, Microsoft Power Point, STATA, SAS, ArcGIS, GeoDa, SatScan

Languages: Spanish (Intermediate), French (Intermediate), German (Beginner), Kiswahili (Beginner)

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