Contexts of Exit and the Mental Health and Economic Incorporation of Migrants in Canada
by
Marie-Pier Joly
A thesis submitted in conformity with the requirements for the degree of Doctor of Philosophy Department of Sociology University of Toronto
©Copyright by Marie-Pier Joly 2017
Contexts of Exit and the Mental Health and Economic Incorporation of Migrants in Canada
Marie-Pier Joly
Doctor of Philosophy
Department of Sociology University of Toronto
2017 Abstract
My dissertation explores the impact of contexts of exit on the mental health and economic incorporation of migrants living in Canada, with a specific emphasis on the impact of armed conflicts and human rights violations in countries of origin. The first paper in my dissertation explores the impact of armed conflict according to varying defining characteristics such as severity of the conflict and intra- vs. inter-state focus and finds that migrants from countries with severe intrastate conflict have worse mental health than migrants from countries with no to minor armed conflict and the native-born. The impact of armed conflicts differs by gender, with women experiencing more depressive symptoms and men experiencing more anxiety symptoms. The second paper shows that the impact of armed conflicts is similar to, but does not replace, the impact of human rights violations in countries of origin. The impact of human rights violations is not more pronounced in situations of armed conflicts, and on its own, human rights violations have essentially similar long-term impact on the mental health of migrants as armed conflicts.
Each of the first two papers demonstrates that armed conflicts and human rights violations in countries of origin often provoke multiple stressful life events and conditions during the life span that can have cumulative mental health consequences for migrants. The last paper in my dissertation explores the employment and occupational status of migrants from armed conflict ii countries. It finds that in spite of their high levels of education in Canada, migrants from armed conflict countries experience more difficulties in finding employment, particularly in the early years after migration, and in general achieve lower levels of occupational status, given their education, relative to other migrants and the native-born. When migrants come from countries in conflict, there appears to be an additional discount applied to their job market options after migration. Specifically, education completed prior to migration translates less often into employment success in this group.
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Acknowledgments
I am very grateful for the guidance and support I received that assisted me in completing this dissertation.
I thank my supervisors Blair Wheaton and Patricia Landolt for their mentorship and on-going support. Their guidance, expertise, and expectations have been highly influential to both my academic and professional development over the years. I would also like to thank my other committee member, Jeffrey Reitz. His critical insight and expertise have been invaluable to me throughout my doctoral studies.
I would also like to thank my external and internal committee examiner, Jen‟nan Reid and Scott
Schieman for their very helpful comments.
I would like to thank my parents, Sylvie Marion and Mario Joly, and my sister Mélanie Joly. I would not have made it here without their immense help and support that contributed to the person I have become. I thank them in particular for their patience, encouragement, and listening.
Thank you also to my friends, Vivien Carli, Marianne Quirouette, Emily Laxer, Holly Pelvin,
Jeanette Chua, and Mark Van Der Maas for their encouragement, support and friendship during the dissertation process.
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À mes parents.
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Table of Contents
Chapter 1 Introduction ...... 1
Current Literature...... 2
Studies and Data ...... 3
Overview of Dissertation ...... 4
References ...... 7
Chapter 2 The Impact of Armed Conflict in the Country of Origin on the Mental Health after Migration to Canada ...... 10
Introduction ...... 10
Literature Review...... 12
Stress Proliferation ...... 12
Pre-Migration ...... 13
Post-migration ...... 13
Gender ...... 14
Analytical Questions ...... 15
Methods...... 15
Data ...... 15
Measures ...... 16
Analysis ...... 22
Results ...... 22
The Effect of Armed Conflict on the Mental Health of Women and Men ...... 24
The Effects of Traumatic Events, Chronic Stress, and Pre-migration Mental Health ...... 25
Effects on Depression among Women ...... 26
Effects on Anxiety among Men ...... 28
Discussion ...... 29
References ...... 34 vi
Chapter 3 Human Rights Violations and Conflict in Countries of Origin and the Mental Health of Migrants ...... 48
Introduction ...... 48
Literature Review...... 50
Contexts of Exit and Mental Health ...... 50
Contexts of Exit, Cumulative Stress, and Mental Health...... 53
Analytical Questions ...... 55
Methods...... 56
Data ...... 56
Measures ...... 56
Analysis ...... 61
Results ...... 63
Descriptive Results...... 63
Psychological Distress After Migration ...... 65
Discussion ...... 71
References ...... 75
Chapter 4 The Employment and Occupational Status of Migrants from Countries Experiencing Armed Conflict ...... 90
Introduction ...... 90
Literature Review...... 92
Immigration and Employment Outcomes ...... 92
Contexts of Exit and Employment Outcomes ...... 93
Linking Armed Conflict in Countries of Origin to Employment Outcomes ...... 93
Analytical Questions ...... 96
Methods...... 96
Sample ...... 96
Measures ...... 96 vii
Outcomes ...... 96
Contexts of Exit and Reception ...... 97
Mediators and Controls ...... 99
Analysis ...... 101
Results ...... 103
Descriptive Statistics ...... 103
Contexts of Exit and Employment Status ...... 104
Contexts of Exit and Occupational Status ...... 107
Discussion ...... 110
References ...... 115
Chapter 5 Discussion and Conclusion ...... 126
Research Contribution ...... 126
Armed Conflicts and Mental Health ...... 127
Armed Conflicts, Human Rights Violations, and Mental Health ...... 128
Armed Conflicts and Migrant Employment Outcomes ...... 129
Future Research Directions ...... 130
References ...... 133
Appendices ...... 136
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List of Tables
Table 2.1 Descriptive Statistics by Nativity and Conflict Status ...... 43
Table 2.2 Results from Four Regression Equations on Risk Factors Characterizing Armed Conflicts and Mental Health ...... 44
Table 2.3 Ordinary Least Squares Regression of Depression on Conflict Background in Countries of Origin and Stress Histories among Women ...... 45
Table 2.4 Ordinary Least Squares Regression of Anxiety on Conflict Background in Countries of Origin and Stress Histories among Men ...... 46
Table 3.1 Countries of Origin by Levels of Human Rights Violations and Conflict at the Time of Migration ...... 85
Table 3.2 Descriptive Statistics by Nativity and Contexts of Exit ...... 86
Table 3.3 Mean Symptoms of Psychological Distress by Contexts of Exit ...... 87
Table 3.4 Ordinary Least Squares Regression of Psychological Distress on Contexts of Exit and Socio-demographic Characteristics ...... 88
Table 3.5 Ordinary Least Squares Regression of Psychological Distress on Contexts of Exit and Post-Migratory Factors...... 89
Table 4.1 Descriptive Statistics of Variables by Nativity and Conflict Status ...... 122
Table 4.2 Effects of Armed Conflict and Human Rights Violation on Employment Outcomes by Migrant Group and Nativity...... 123
Table 4.3 Linear Probability of OLS Regression of Unemployment on Conflict Background in Countries of Origin ...... 124
Table 4.4 Ordinary Least Squares Regression of Occupational Status on Conflict Background in Countries of Origin ...... 125
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Figure
Figure 2.1 Initial Conditional Coding of Foreign Born Status and Conflict Exposure ...... 47
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List of Appendices
Appendix A Depression and Anxiety Items and Major Traumatic Events (Chapter 1) ...... 136
Appendix B Psychological Distress Items (Chapter 2)...... 138
Appendix C Political Terror Scale (Chapters 3 and 4) ...... 139
Appendix D Life Events Items (Chapter 3) ...... 140
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Chapter 1 Introduction
When armed conflict and gross human rights violations characterize a migrant‟s country of origin, their impact may not be reversed by the simple act of migration. Both armed conflict and human rights violations may lead to stressful life experiences that cumulate and spread both before and after migration and have long-term consequences in the lives of migrants in a new land. Though migrants may choose to leave their countries of origin due to insecurity and violence to seek a better life, their lives may nonetheless be affected by their contexts of exit even after resettlement.
This dissertation advances our understanding of the impact of contexts of exit by examining the effect of armed conflict and human rights violations in the country of origin on the mental health and employment experiences of migrants living in Canada. This examination is motivated by both the migration and the mental health literatures. Though distinct, each literature emphasizes the effect of social contexts on individuals‟ lives, and together these literatures provide the frame for this dissertation.
In the migration literature, it is argued that contexts of exit, i.e.“the conditions under which a particular immigrant group leave its countries of origin,” can have a substantive impact on migrant adaptation and settlement experiences (Portes and Böröcz 1989:615). Scholars have focused on a combination of linkages starting with the social or political context in an origin country, involving subsequent major life events resulting from differences in context, variation in the accumulation of human and economic capital in countries of origin, and migrant adaptation outcomes (e.g. Portes and Rumbaut 2006; Vega and Rumbaut 1991; Rumbaut 1991; Zhou and
Xiong 2005).
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The literature on mental health also helps us to understand the effect of armed conflict and state repression, manifest in the approach used here as human rights violations, by emphasizing how social contexts can have a cross-over effect into individual‟s lives (Aneshsenel and Sucoff 1996;
Wheaton 1999). Viewed from the stress process perspective (Pearlin 1989), armed conflict and human rights violations both can be seen as macro level stressors that can lead to subsequent exposures to stressful experiences at the individual level both before and after migration. Pearlin
(1989) argues that primary stressors, especially if occurring at crucial moments in the life course or under conditions of major life transitions, can initiate a chain of subsequent exposure to stressful situations.
Current Literature
Current understandings of the impact of contexts of exit remain based largely on studies that compare outcomes involving one or a few countries of origin and/or focus mainly on the plight of refugees after migration. In each case, these studies provide an incomplete assessment of the effects of contexts of exit because it is difficult to cumulate findings from studies using different measures on different samples at different points in time, because individual case studies cannot be generalized presumptively to other countries with similar conditions, and because the situation of other broad classes of migrants are in the background in studies of refugees.
Moreover, most research on migrants from traumatic contexts of exit focuses on the individual experience of a context– and retrospective reports of that experience – and not the context per se
(e.g. Marshall et al. 2005; Momartin et al. 2003; Sabin et al. 2003). The distinction is important: in this dissertation, the focus is on the context at the national level, where contexts are understood as social units in which individuals are embedded. Once this distinction is made, it is possible to see that both individual experience and contextual exposure combine to produce
3 long-term outcomes after migrationoccur. Studies that focus on retrospectively self-reported experience may therefore miss the importance of the generalized state of threat, insecurity, instability, uncertainty, and disruption of both lives and institutions accompanying contexts marked by serious armed conflict and/or human rights violations. Because of the climate described by these contexts, they may also miss the important possibility that these contexts affect the mental health of the entire population. Importantly, the few studies which manage to consider both the effect of armed conflict and human rights violations are generally limited by other design or measurement issues such as focusing on a single country of origin, or measuring only individuals‟ experiences with these stressors, thus limiting generalizability (e.g. Ai,
Peterson, and Ubelhor 2002; Momartin et al. 2003; Priebe et al. 2010).
Studies and Data
My dissertation utilizes with both country-level and individual-level data.
Individual-level data come from both the 1995 Toronto Study of Intact Families (TSIF) and the
2011 Neighbourhood Effects on Health and Well-being (NEHW) study (O‟ Campo et al. 2015).
The Toronto Study of Intact Families is a multistage probability sample of 888 married men and women, with 9-16 year old children. The NEWH study is multistage probability sample of 2, 412
English-speaking adults between 25 and 64 years of age living in the Metropolitan Toronto area.
Both surveys collected data on a large proportion of foreign-born respondents from roughly 110 different countries of origin. In each case, the sampling was random: in the TSIF study, randomly selected households in Toronto were screened for family type and presence of children and qualifying families were chosen for interview. In the NEHW study, 2006 Census tracts were first randomly selected; within tracts, household addresses were randomly selected.
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I merged country-level data with the data from each study. Generally, this involved knowing what year migrants left their home country and then matching the relevant country information that applied at that time to each respondent. I use three different online data sources: The UCDP
Conflict Termination dataset (Kreutz 2010), the Political Terror Scale (Gibney and Dalton 1996), and the Penn World Tables (Heston, Summer, and Aten 2002, 2009). The UCDP Conflict
Termination dataset consists of armed conflicts data, collected by the Department of Peace and
Conflict Research at Uppsala University. Data include the type, intensity, location, and the dates for the beginning and the end of each armed conflict that has occurred between 1946 and 2007.
The Political Terror Scale consists of data on the levels of human rights violations of countries between 1976 and 2008, based on Amnesty International annual country reports. The Penn
World Tables are used to derive relevant data on country Gross Domestic Product per capita for the years 1950 to 2007 to allow a more specific assessment of the effect of armed conflict net of economic conditions.
By using these data, I am able to compare migrants according to their contexts of exit defined here by the levels of human rights violations and the presence or absence of armed conflict in countries of origin at the time of migration, in addition to the native-born. And by merging country-level data with survey data both by countries of origin and time of immigration, I am able to consider variations in mental health and employment outcomes according to changing contexts in countries of origin over time.
Overview of Dissertation
This dissertation is composed of three independent publishable papers, in addition to this introductory chapter and the concluding chapter. Together, these papers contribute to our
5 understanding of how contexts of exit may have long-term impact on the mental health and economic incorporation of migrants.
The first paper (chapter 2) argues that the impact of armed conflicts on migrant mental health may depend on defining characteristics of that conflict such as its type (intrastate vs. inter-state), its severity (major vs. minor), and its timing relative to migration (ongoing or terminated), and that the import of armed conflict as a context of exit differs across genders. The focus is on variation in the types of conflict and their long-term mental health consequences after migration, the gender differentiation of these patterns, and the explanation of these patterns through the proliferation of traumatic and chronic stressors both before and after migration.
The second paper (chapter 3) considers the joint effect of human rights violations and armed conflicts in countries of origin on migrant mental health. It argues that human rights violations and armed conflicts may have a generalized impact on a population by promoting fear and a general sense of insecurity and unpredictability. This paper focuses on the combination of patterns of human rights violations and armed conflict that have the strongest long-term impacts on migrant mental health and finds that the effect of each can be detected within levels of the other. Like the first paper, this paper attempts to explain these long-term impacts using a stress proliferation argument, but in this case, because of the study data used, the specification of this argument is considerably broader than in the first paper, including more sufficient measurement of chronic stress, discrimination, and acculturative stress after migration.
The third paper (chapter 4) examines the effects of armed conflict and human rights violations in the country of origin on migrant employment outcomes. It argues that armed conflict may have a more disruptive impact on the educational system and the economic growth of the country of origin, which may negatively affect eventual employment outcomes after migration. To test this,
6 it explores the effect of armed conflict on migrant employment outcomes in Canada net of human rights violations, while considering the role of education, prior work experience, and past traumatic experiences before migration.
Chapter 5 highlights the main contributions of this dissertation, summarizes the common and unique findings across the three papers, and discusses some of the suggested directions for future research.
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References
Ai, Amy L., Christopher Peterson, and David Ubelhor. 2002. “War-Related Trauma and
Symptoms of Posttraumatic Stress Disorder Among Adult Kosovar Refugees.” Journal of
Traumatic Stress 15:157-160.
Aneshensel, Carol S. and Clea A. Sucoff. 1996. “The Neighborhood Context of Adolescent
Mental Health.” Journal of Health and Social Behavior 37:293-310.
Gibney, Mark and Matthew Dalton. 1996. “The Political Terror Scale.” Policy Studies
and Developing Nations 4:73-84.
Heston, Alan, Robert Summers, and Bettina Aten. 2002. “Penn World Table Version 6.1.”
Center for International Comparisons of Production, Income and Prices at the University of
Pennsylvania.
____. 2009. Penn World Table Version 6.3. Center for International Comparison of Production,
Income and Prices at the University of Pennsylvania.
Kreutz, Joakim. 2010. “How and When Armed Conflicts End: Introducing the UCDP Conflict
Termination Dataset.” Journal of Peace Research 47:243-250.
Marshall, Grant N., Terry L. Schell, Marc N. Elliott, S, Megan Berthold, and Chi-Ah Chun.
2005. “Mental Health of Cambodian Refugees 2 Decades After Resettlement in the United
States.” Journal of American Medical Association 294:571-579.
Momartin, Shakeh, Derrick Silove, Vijaya Manicavasagar, and Zachary Steel. 2004.
“Comorbidity of PTSD and Depression: Associations with Trauma Exposure, Symptoms
Severity and Functional Impairment in Bosnian Refugees Resettled in Australia.” Journal of
Affective Disorders 80:231-238.
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O‟ Campo, Patricia J., Blair Wheaton, Rosane Nisenbaum, Richard H. Glazier, James R. Dunn,
and Catharine Chambers. 2015. “The Neighbourhood Effects on Health and Well-being
(NEHW) Study.” Health & Place 31:65-74.
Pearlin, Leonard I. 1989. “The Sociological Study of Stress.” Journal of Health and Social
Behavior 30:241-256.
Portes, Alejandro and József Böröcz. 1989. “Contemporary Immigration: Theoretical
Perspectives on its Determinants and Modes of Incorporation.” International Migration
Review 23:606-630.
Portes, Alejandro and Rubén G. Rumbaut. 2006. Immigrant America: A Portrait. Berkley:
University of California.
Priebe, Stefan, Marija Bojic, Richard Ashcroft, Tanja Franciskovic, Gian Maria Galeazzi,
Abdhulah Kuculakic, Dusica Lecic-Tosevski, Nexhmedin Morina, Mihajlo Popovski,
Michael Roughton, Matthias Schützwohl, and Dean Ajdukovic. 2010. “Experience of
Human Rights Violations and Subsequent Mental Disorders - A Study Following the
War in the Balkans.” Social Science & Medicine 71:2170-2177.
Rumbaut, Rubén G. 1991. “Migration, Adaptation, and Mental Health: The Experience of
Southeast Asian Refugees in the United States.” Pp. 381-424 in Refugee Policy:
Canada and the United States, edited by H. Adelman. Toronto: York Lane Press.
Sabin, Miriam, Barbara Lopes Cardozo, Larry Nackerud, Reinhard Kaiser, and Luis
Varese. 2003. “Factors Associated with Poor Mental Health Among Guatemalan
Refugees Living in Mexico 20 Years After Civil Conflict.” Journal of The
American Medical Association 290:635-642.
Vega, William A. and Rubén G. Rumbaut. 1991. “Ethnic Minority and Mental Health.” Annual
Review of Sociology 17:351-383.
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Wheaton, Blair. 1999. “Social Stress.” Pp. 277-300 in The Handbook of the Sociology of
Mental Health, edited by C.S. Aneshensel and J.C. Phelan. New York: Kluwer
Academic/Plenum.
Zhou, Min and Yang Sao Xiong. 2005. “The Multifaceted American Experiences of the
Children of Asian Immigrants: Lessons for Segmented Assimilation.” Ethnic and Racial
Studies 28:1119-1152.
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Chapter 2 The Impact of Armed Conflict in the Country of Origin on the Mental Health after Migration to Canada Introduction
The mental health of migrant populations affected by armed conflict has received growing attention in the literature since the 1980s (Ingleby 2005). The accumulated evidence suggests that exposure to armed conflict is associated with a range of mental health outcomes, including symptoms of posttraumatic stress disorder (PTSD), depression, and anxiety (Hollifield et al.
2002). Much of this evidence comes from studies that examined the effects of retrospectively reported war-related traumatic events on mental health (Miller and Rasmussen 2010), often among refugees from specific war-torn countries (Kienzler 2008).
In this literature, specific war-related events have been reported to have substantial and often long-lasting impacts (Bhui et al. 2003; Schmidt, Kravic, and Elhert 2008). For example, threats to life (Momartin et al. 2004) witnessing war-related violence (Hauff and Vaglum 1993; Sabin et al. 2003), and being injured (Sabin et al. 2003) have been identified as mental health risks in migrant populations several years following the end of the war. In addition, there is consistent support for the notion that cumulative exposure to war-trauma is associated with more symptoms
(Ai, Peterson, and Ubhelor 2002; Bogic et al. 2012; Marshall et al. 2005; Michultka, Blanchard, and Kalous 1998; Mollica et al.1998).
The common use of retrospective self-reports for measuring armed conflict exposure in the current literature does have limitations (Wessells 1998). The validity of retrospective data may often be compromised by the unreliability of self-reported traumatic experiences, recall biases, or current mental health (Bell et al. 2012; Spinhoven, Bean, and Eurelings-Bontekoe 2006). Our
11 approach in studying the association between armed conflict and mental health begins with a more objective measure of armed conflict. We draw on publicly available armed conflict data at the country level to explore the effect of armed conflict at the time of migration on the mental health of immigrants, including those who had no direct exposure to the conflict. As recently reported, armed conflicts have potentially far-reaching consequences for the mental health of populations beyond those living in conflict-zones (Bell et al. 2012; Londoño, Romero, and Casas
2012; Turnip, Klungsøyr, and Hauff 2010).
However, it is also plausible that long-term mental health consequences may vary depending on the nature of the armed conflict (WHO 2002). For example, not only have mental health outcomes been noted to vary according to the intensity of conflict (Summerfield 1991), but also according to the location of conflict (Desjarlais et al. 1995). Armed conflicts have become increasingly severe historically (Melander, Öberg and Hall 2009; Wessels 1998) and occur more often as internal strife (Kaldor 2013). Civilians are increasingly targeted in intra-state conflicts, becoming a population at risk for mental health problems (Carballo et al. 2004; Levy and Sidel
2008). In comparison to inter-state conflicts, intra-state conflicts also have more long-term effects on distress (Desjarlais et al. 1995).
To this date, the effects of armed conflict on mental health have been primarily assessed among migrant populations from a single country -- generally ones that have been recently affected by intra-state armed conflict (Ingleby 2005). It is thus difficult to assess, first, the impact of conflict relative to those without this experience, and second, the effect of armed conflict net of the social circumstances in which it occurs.
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This study aims to address these limitations in two ways. First, to better isolate the effects of conflict, we compare the mental health of migrants who emigrated during an armed conflict to migrants from non-conflict countries – thus considering a wide range of country backgrounds simultaneously, as well as the native-born, using data from Toronto Canada. Our analytical approach includes two comparison groups in a multi-step design: the comparison of the foreign- born without conflict to the native-born tests the effect of nativity, without the added layer of exposure to conflict and the comparison between the foreign-born with vs. without conflict backgrounds tests the effect of conflict, holding nativity constant.
Second, we specify the effects of armed conflict according to its intensity and location. To our knowledge, these contextual factors have been used to further define the consequences of conflicts on adult mortality (Li and Wen 2005), and general health (Iqbal 2006; Openshaw 2012) at the country-level, but have not been used to examine mental health disparities among immigrants.
After the assessment of mental health differences among these groups, we assess whether differences due to migration from armed conflict can be attributed in part to greater exposure to pre-migratory conflict-related stress or subsequent stress experienced following migration. We consider the gender specificity of these patterns throughout, given the plausibility of gendered experiences of conflict in the country of origin.
Literature Review Stress Proliferation
We use the stress proliferation argument derived from the stress process framework (Pearlin et al. 1981; Pearlin 1999) to suggest mechanisms for the long-term impact of armed conflict in
13 countries of origin on mental health after migration. The underlying assumption of the stress proliferation argument (Pearlin1999; Pearlin, Aneshensel, and Leblanc 1997) is that a stressor rarely occurs in isolation: individuals exposed to a particular stressor are often exposed to multiple subsequent (“secondary”) stressors, often resulting from the initial (“primary”) stressor.
Pre-Migration
Armed conflict may have a long-term effect on mental health through its associated life consequences, including the spread of further stressors. Prior to migration, this may involve higher rates of war-related stress such as witnessing killing, or experiencing the unnatural death of a family member (Steel et al. 1999), but also greater risks of living in poverty, experiencing spouse abuse (Miller, Kulkarni and Kushner 2006) or parental mental health problems (Catani et al. 2008; Mghir et al. 1995). Our study of the association between exposure to armed conflict and the experience of traumatic events includes Canadians, so that we can make comparisons with a baseline profile of the rate of exposure to the same events in the host country population.
Post-migration
In recent years, research has accumulated suggesting that post-migratory chronic stressors can add to the trauma experienced prior to migration (Fenta, Hyman, and Noh 2004; Hauff and
Vaglum 1995; Li 2002; Miller et al. 2002; Schweitzer et al. 2006). Post-migration, conflict exposure may particularly affect employment sustainability, as well as marital and family relations. For example, there is evidence that war-related trauma affects labor market outcomes.
Hauff and Vaglum (1993a) found that war trauma experienced by refugees impacts both the type of jobs they find and their participation in the labor market. In their study, refugees usually worked in low-skilled and temporary jobs and experienced high levels of unemployment. The recollection of war has also been found to lead to social isolation and inability to work (August
14 and Gianola 1987). In addition, war trauma has been shown to have a long-term effect on family conflict (Rousseau, Drapeau and Corin 1997).Whether post-migratory stressors actually mediate the effect of armed conflict exposure on mental health is, however, a question that requires further investigation. Thus far, studies have been inconsistent in their results. Although one study did not find that post-migratory stress had a mediating role (Bogic et al. 2012), another revealed that fleeing from conflict was indirectly associated with post-traumatic stress symptoms through post-migration stressors (Steel et al. 1999).
Gender
The mental health consequences of armed conflict may be gendered. As noted by Kastrup
(2006), men and women experience the effects of armed conflict differently -- leading to potentially distinctive mental health problems. Recent findings reveal that, in a context of conflict, men and women often have different exposure to stressors – both in terms of the type and number of stressors encountered (Scholte et al. 2004). Bhui and colleagues (2002) show that men tend to have greater exposure to being close to death and suffering from serious injury while women have greater exposure to sexual abuse. Tang and Fox (2001) also report that men report overall higher levels of traumatic events. Miller and colleagues (2008) on the other hand find no gender difference in war-related stress, but a difference in daily stressors, suggesting that women experience more daily stress in the context of war than men. Women may also be more isolated from community involvements and experience the loss of relatives (Martin 2004).
Besides the issue of differential exposure, evidence also suggests that men and women have differential vulnerability to the effects of armed conflict. Although some studies show no gender differences in mental health outcomes (Beiser, Turner, and Ganesan 1989; Marshall et al., 2005;
Schweitzer et al. 2011), other studies show a greater association between female gender and
15 psychological distress (Hauff and Vaglum 1993), depression (Sabin et al. 2003; Scholte et al.
2004), anxiety (Scholte et al. 2004), and PTSD (Ai et al. 2002). Because gender is used as a control in many studies, differential pathways to mental health are not typically explored. Only a few studies have explored the specific question of gender difference in the impact of stress exposure on mental health among conflict affected populations (Chung et al. 1998; Miller et al.
2008). These studies, however, either do not consider gender differences among migrants or they focus on migrants from one country at a time. The different types or levels of exposure to stressors between men and women across countries of origin, both prior to and after migration, may or may not translate into different mental health outcomes, but nonetheless suggest the importance of separating men and women in our analysis.
Analytical Questions
Our study adds to previous research by exploring the effects of armed conflict at the country level, using objective sources of data, and by further incorporating gender differences. We ask three basic research questions: 1) What kinds of conflict in the country of origin, parsed in terms of severity and location, have long-term impacts on mental health? 2) How are these impacts explained by stress proliferation both before and after migration? 3) Are the pathways leading to long-term mental health adjustment gender-specific?
Methods Data
We use data from the Toronto Study of Intact Families and the UCDP Conflict Termination
Dataset v. 2.1 (Kreutz 2008). The Toronto Study of Intact Families is a study of husband-wife families with children in the Metropolitan Toronto area. The data were collected between 1993
16 and 1996 in three contiguous cities in the metropolitan area of Toronto: Toronto, Mississauga, and Brampton. Families were screened in randomly selected Census tracts to target husband-wife families with one child between 9 and 16.In each family, face-to-face interviews were conducted separately with the mother, father, and the selected child. The final response rate was 70 percent.
Excluded from our study are cases for which data were missing on the country of origin. After this exclusion, our study sample consists of 881 women and 850 men, among whom slightly more than 50 percent are migrants.
We merged data from the UCDP Conflict Termination dataset (Kreutz 2010) with the family data in Toronto. This dataset is a project undertaken by the Uppsala Conflict Data Program at the
Uppsala University. It contains global data on armed conflicts that have occurred between 1946 and 2007. For each episode of armed conflict, the start and end dates are reported. Building on the UCDP/Prio Armed Conflict dataset (Gleditsch et al. 2002), the conflict termination dataset also provides information on the location and intensity of armed conflicts.
Measures
Mental Health. We use a 46-item Composite Distress Scale (CDS) to measure two mental health outcomes: symptoms of depression and anxiety. The CDS includes items that are commonly employed in prominent scales to assess depression and anxiety (Beck et al. 1961; Hamilton 1960;
Langner 1962; Radloff 1977; Spielberger, Gorsuch, and Lushene 1970). In this study, symptoms of depression and anxiety are respectively assessed by a 20- and 11-items subscale, for both women and men. The depression scale includes well-known items such as feeling sad, blue, or depressed; having trouble concentrating; feeling like nothing seems worthwhile in life; and losing interest in doing anything. The anxiety subscale includes items such as feeling bothered by tense, sore, or aching muscles; feeling short of breath or being smothered; and feeling discomfort
17 or having pain in the stomach. For each items, respondents were asked to report on a 4-point scale how often they experienced these symptoms over the past month, ranging from (1) „not at all‟ to (4) „most of the time‟. The reliability of the depression and anxiety subscales is respectively .93 and .81 for women, and .93 and .77 for men.
Exposure to Armed Conflict at time of Migration. To create this variable, we merged our
Toronto family data and the UCDP Conflict Termination dataset (Kreutz 2010) by country of origin and date of migration. We considered the start and end year of armed conflict episodes as well as the year of immigration to Canada to determine whether migration occurred during a period of armed conflict. This allows us to classify migrants into three distinct groups, each represented by a dummy variable: migrants exposed to armed conflict (i.e. migrants who emigrated after the start date and before the end date of an episode of armed conflict); migrants with a history of armed conflict exposure but no conflict at the time of migration (i.e. migrants who emigrated two or more years following the termination date of an earlier conflict episode in their country or origin)1; and migrants who had no armed conflict exposure (i.e. migrants who came from a country where there has been no armed conflict during their lifetime). One of the principal aims is to assess the impact of the armed conflict in country of origin at time of exit.
Exposure to prior conflict, although not ongoing at time of emigration, could still have long-term mental health consequences. Thus this group is separated from the reference group -- migrants with no past conflict exposure in the country of origin.
In order to specify the effects of foreign-born status with and without the experience of armed conflict, we used conditional coding (Ross and Mirowsky 1992). Our approach is represented in
Figure 2.1. All foreign-born respondents are first coded 1 and Canadian-born respondents are coded 0 on a “foreign-born” dummy variable. Then we divided the foreign-born into three
18 groups: those exposed to conflict at the time of migration, those exposed to a history of conflict that had ended at time of migration, and those who have not been exposed to any conflict over their lifetime. We first coded a dummy variable =1 if migration occurred during a period of conflict, and =0 if not. We then coded a dummy variable to contrast those with a history of conflict (=1) vs. those with no history of conflict (=0). The net result of this approach was to make the reference group among the foreign-born those who had not been exposed to conflict in their lifetime. Canadian-born respondents have to be coded 0 on all of these variables. Using this coding system, our variable for armed conflict exposure contrasts the foreign-born who experienced conflict at the time of migration with those who experienced no conflict in their lifetime, and our variable for history of armed conflict contrasts those who have experienced conflict in their lifetime, but had ended, with those who had experienced no conflict. This coding leads to an interpretation of “foreign-born” as the effect of foreign-born without exposure to conflict vs. native-born Canadians, thus testing the effect of nativity per se. This two-step coding means that we can calculate the total difference between those from countries with ongoing conflict at time of migration and the Canadian-born, using the sum of differences captured by the foreign-born and the conflict exposure dummy variables.
Using the UCDP data, we are able to further consider potential armed-conflict related-risk factors, including, the intensity of armed conflicts (major vs. minor refers to over 1,000 vs. fewer than 1,000 conflict casualties) and the location of the conflict (intra-state vs. inter-state). These distinctions allowed us to further sub-divide the “conflict exposure” group in Figure 2.1 into sub- groups, i.e., major vs. minor, and inter-state vs. intra-state. The number of respondents who experienced different types of conflict varied considerably: some are quite rare (e.g., inter-state conflict, n=9 and 13 for women and men respectively), while other categories are larger. There
19 are 122 female and 134 male respondents who have experienced intra-state conflict and 74 and
85 who have experienced major conflict. These sub-samples are not large, but they are large enough to detect strong effects. For example, we used the results we report below to calculate equivalent effect sizes using Cohen‟s d (Cohen 1988) for the difference of means in two groups – exposed to conflict vs. not. The effect sizes of observed significant differences on depression and anxiety in our sample approximate .3 in both cases --- standing for an effect size in the small to moderate range.
Self-reported Stressors. We include two types of stressors: past major traumatic events, and current chronic stress. Self-reported major traumatic events are measured in four distinct time frames, using the age information given for each event for first and last occurrence: before the respondent‟s first exposure to armed conflict, after the first armed conflict exposure but before the onset of the armed conflict episode that occurred prior to migration, during the ongoing episode of armed conflict at time of migration, and after migration. The ages of first and last occurrence of each traumatic stressor were compared to ages at first and last conflict to determine boundaries defining the cumulation of stress in each time period. We consider the impacts of traumatic events occurring before the conflict at time of migration as a control, but the impact of conflict during the current conflict at the time of migration is considered as a mediator
– a test of early stress proliferation.
Each of the four indices for traumatic events we construct is based on a count of events experienced by the respondents over specific time frames. We utilize11-items from a 27-items checklist (Turner, Wheaton, and Lloyd 1995) to assess those that occurred prior to migration and the whole checklist for those experienced following migration (see Appendix B for the complete list of items). We restrict the count of pre-migratory events because some questions on this list
20 are broad in connotation and are likely to occur for many reasons beyond exposure to armed conflict (e.g., parental divorce). Instead, during this period we focused on items that were the most comparable to items utilized to assess war-related stress (Mollica et al. 1992). These consist of items such as being hospitalized for more than two weeks, having witnessed or seen someone killed, having been in combat in a war or lived near a warzone, the death of parent or other relatives, and having suffered from a life-threatening accident or injury.
In order to compare the impact of pre-migratory stress among those from conflict countries both to migrants from non-conflict countries and to Canadians, we went beyond the prevalent usage of assigning the mean time of exposure among those actually exposed to conflicts at each stage to the other two groups. Instead, we assigned a random time of exposure to other groups, based on the actual distribution of exposure among the exposed group. This approach was both necessary and advantageous. First, we found that using either median or mean exposure times in comparison groups resulted in some cases in assigned ages that exceeded actual age, and/or were out of order. Second, our approach improves on approaches using the average time of exposure in non-exposed groups by replicating the whole distribution of exposures in the conflict group in the non-exposed groups. This approach is often used in epidemiology in assigning exposure times to control groups in case-control studies. While we stop short of individual-pair matching, our approach recognizes the importance of matching not only on the mean but on the variation in exposure times across groups as well (Thomas 2009; Wacholder et al. 1992). Third, by using this approach, we can compare rates of exposure to traumatic events over comparable life periods, using the Canadian profile as a baseline rate – since these events do occur without exposure to armed conflict – and migrants from non-conflict countries as a comparison rate in other countries of origin not experiencing conflict.
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Finally, our measure of chronic stress is a count of stressful life conditions such as living in poverty, marital and family conflict, unemployment, as well as repetitive and noxious work. The questions we utilized to create our measure only provide information about current exposure, and thus this study can only address the effect of chronic stress following migration.
Pre-migration Mental Health. Two retrospective mental health measures are examined prior to migration: episodes of depression and of anxiety. Our measure of depression is derived from two questions asking respondents if they felt sad, blue, or depressed; and if they lost interest in most things for a period of two weeks or more, and when this first occurred. Anxiety is measured by a single question asking respondents whether they felt worried or anxious most of the time over the period of six months or more, followed by the age of occurrence. We have constructed our past mental health variables in two-steps. Among migrants, we considered the reported age at first onset of depressive and anxiety symptoms and the age at migration to designate pre-migration mental health problems. We then designated the earliest episode among the Canadian-born up to the median age of migration among the foreign-born (24)2. Respondents who gave at least one affirmative answer on the questions measuring depression were coded „1‟ if their onset was prior to migration and, among native-born, prior to the age of 24, „0‟ otherwise.
Other Variables. We also examine the effects of economic development, length of residence, and other controls including age at last conflict, ethnicity, and education. Economic development of countries of origin was measured by GDP per capita extracted from the Penn World Tables
(Heston, Summer, and Aten 2002). Preliminary analyses revealed, however, that this variable had no linear or non-linear effect on outcomes. As a result, this variable was removed from the results reported in this paper. Length of residence is a continuous variable created only for migrants, using conditional coding (Ross and Mirowsky 1992). Canadian-born respondents are
22 coded „0‟ on this variable. The squared term for this variable is also included in the analyses, to detect nonlinear trajectories of adjustment. Age at last conflict is a continuous variable. To construct this variable in all groups, we assigned the mean age immigrants reported at last conflict to immigrants who had a history or no exposure to conflict, and the Canadian-born (21 and 23 respectively for women and men). There are five dummy variables for ethnicity: Southern
European, Middle Eastern, South Asian, Western European and other ethnicities. The first three are retained in our analyses, as they showed significantly different mental health results from the reference group: Northern/Western European. Ethnic groups for which we found no significant differences are combined into a single group: other ethnicities. Education is measured in years.
Analysis
We begin our analysis with a series of bivariate OLS regressions to better specify the mental health effect of armed conflicts by considering the intensity and location of conflicts. We then continue with multiple regressions for the focal effect of exposure to armed conflict. After controls are included, we consider progressively pre-migratory and then post-migratory stressors to assess the degree to which the effect of armed conflict on mental health is explained by the proliferation of secondary stressors. For each gender, separate analyses are performed for depression and anxiety.
Results
Table 2.1 presents descriptive statistics, by foreign-birth and conflict status for both men and women. We observe that the large majority of migrants who emigrated during an ongoing conflict did so during an intra-state conflict. More than half of them emigrated during a major conflict. Migrants who had a history of conflict exposure represent a smaller but notable share of
23 those who came from countries not in conflict at time of exit. The proportion of migrants who came from countries with a conflict background (current + history of conflict) is thus considerable – representing just over one third of the total foreign-born populations in our sample.
We also note significant differences for specific groups on some variables. Migrants who came from countries with ongoing conflict have been living in Canada on average for 13 years, in comparison to 20 years for migrants from non-conflict countries. They are also more likely to be
Southeast Asian, East Asian and Middle Eastern, but in the last case only among men. Men who came from conflict countries have on average a higher level of education than Canadian-born and other migrant men.
Given the random timing assigned in non-exposed groups, we find higher numbers of traumatic events among both women and men from conflict countries. Women also show a higher rates of previous reported depression in both conflict and non-conflict countries compared to Canadians.
We also observe significant differences with regards to exposure to traumatic stress post- migration among women. Women from conflict countries have fewer traumatic exposures in
Canada than migrants from non-conflict countries and Canadian-born – a finding that simply reflects the fact that more of these events occurred earlier in life in this group. A similar pattern is found for men, but there is no significant difference between conflict-affected migrants and
Canadian-born. For chronic stress, women from conflict countries had a significantly higher mean than Canadian-born. Mean differences for men are non-significant on this stress measure.
There are two important clues in these findings: first, we see that migrants from conflict countries do report apparently higher levels of traumatic events relative to Canadians and
24 migrants from non-conflict countries, and we also see differences in current chronic stress between women from conflict countries vs. Canadians.
The Effect of Armed Conflict on the Mental Health of Women and Men
Table 2.2 shows results from four regressions that were conducted to specify the effect of armed conflict at time of migration on the mental health of migrant women and men. To clarify the estimated effects, we also include migrants who have experienced a history of exposure to armed conflict but no conflict at the time of migration.
Model 1 considers all episodes of armed conflict at the time of migration. Results demonstrate that men who emigrated during an armed conflict have more symptoms of anxiety than migrant men who emigrated from no-conflict contexts (0.468, p<0.05). In contrast, men who emigrated after a conflict has been resolved have lower anxiety symptoms (-0.713, p<0.05). This measure does not predict any long-term mental health differences among women. Thus, the only apparent effect of current armed conflict occurs among foreign-born men, and only on current anxiety, while earlier conflicts that have ended may result in a decrease in anxiety specifically. These findings concur with the literature which suggests that ongoing armed conflicts are associated with lower level of mental health than resolved ones (Porter and Haslam 2005). Model 2 shows the estimated effect of the severity of armed conflict on mental health. Women and men who emigrated during a major armed conflict report more symptoms of depression and anxiety respectively than migrants who had no exposure to armed conflict (2.633, p<0.05, 0.689, p<0.05). Emigrating during a minor armed conflict appears to have no detrimental effect of mental health. Model 3 examines the potential effect of the location of armed conflicts. As indicated, intra-state armed conflicts represent a risk factor on the anxiety among men (0.627,
25 p<0.05). Its effect is less significant among women (1.346, p<0.10).3 The effect of inter-state conflicts here is suspect, due to the very small sample size in this group (9 women and 13 men).
If we had used all armed conflict as our indicator of „current conflict‟ at time of exit, we note that we would have missed significant findings among women especially.
Considering the overlap in the armed-conflict related risk factors, we also consider their combined effect in model 4. Our intention is to specify whether exposure to major and intra-state armed conflict has a stronger combined effect on mental health after migration relative to no or minor conflict exposure. We deleted migrants who came from countries affected by inter-state conflicts here and in all later analyses due to the small N in this group. As results in model 4 demonstrate, the estimated effect of major and intra-state armed conflict exposure on depression among women is significant (3.279, p<0.01) and on anxiety among men (0.810, p<0.05). Thus, what emerges from these findings is that major intra-state armed conflict has a long-term effect on women‟s and men‟s mental health, and is the most efficient representation of which features of location and intensity of conflict that matter. This finding is consistent with what the literature suggests is important about exposure to armed conflict, but it is also important because studying armed conflict in general would either underestimate or miss its impact on mental health.
The Effects of Traumatic Events, Chronic Stress, and Pre- migration Mental Health
The following tables present the results of a series of models that carry forward the best specification of the impact of armed conflict among men and women, adding controls, pre- migratory stressors and mental health, and post-migratory stressors, in turn. Using the conditionally coded variables, we show four estimated differences across groups: 1) the effect of foreign-born status experiencing little or no conflict vs. native-born Canadians, 2) the effect of
26 major intra-state conflict among the foreign-born vs. minor or no conflict among the foreign- born; 3) the sum of those two effects, to show the overall difference between the foreign-born who migrated during a major intra-state conflict and Canadians (with a post-hoc test of the significance of this difference); and 4) the effect a history of armed conflict only, as a control which clarifies the reference group. There are 69 women and 78 men in these regressions in the major intra-state conflict group4.
Effects on Depression among Women
Model 1 in table 2.3 shows that women who migrated during an episode of major intra-state armed conflict experience more symptoms of depression compared to women who emigrated from minor to no-conflict countries (3.278, p<0.01) as well as women born in Canada (4.057, p<0.001). The difference in depression between women who emigrated after earlier conflict and those who emigrated during a minor a no conflict is not significant. In model 2, there is a slight increase in the coefficient for armed conflict exposure once we control for age at last conflict, ethnicity and education, indicating that at least one variable here is acting as a minor suppressor.
In supplementary analyses conducted, we noted the suppressing effect of ethnicity especially.
We note as well that there is a smaller difference between women from major intra-state conflict backgrounds and the Canadian born (3.346, p<0.01). The coefficient for history of armed conflict is negative, but only borderline significant (p<0.10).
In models 3 and 4, exposure to traumatic events that occurred prior to migration, whether prior to first conflict, or between the first and last conflict episode, have no effect on the estimated effect of exposure to major, intra-state conflict. In Model 5, we add the effect of traumatic events that occurred during the armed conflict at the time of migration. This tests the initial stage of stress proliferation. There is a small decrease in the estimated effect of major-intra-state conflict, but
27 the effects of the traumatic events measured during this period is only borderline significant
(0.761, p<.10).
In model 6, we assess the effect of pre-migration mental health. The effect of major, intra-state conflict is essentially unchanged, suggesting that earlier mental health episodes closer to the timing of the conflict do not account for its long-term effect on the present. Still, pre-migration depression seems to have an independent impact on current depression. In model 7, we see that length of residence in Canada has a minor suppression effect on the estimated effect of major intra-state conflict on women's current depression. Traumatic events experienced after migration
(model 8) do predict current depression, but they too result in a small suppression effect. This is due to the timing issue discussed earlier: women exposed to major, intra-state conflict have already experienced more of these events earlier. The change in the net estimated impact of armed conflict from model 8 to model 9 indicates that the effect of armed conflict exposure is mediated to some degree by chronic stressors post-migration, thus giving some support to this version of a stress proliferation argument. Although the change in coefficient estimating the difference in depression among women from countries affected by major and intra-state conflict and women from low or non-conflict countries is relatively modest, (3.091 to 2.681), the coefficient estimating the difference with Canadian-born women is reduced considerably (4.372 to 3.471) This is also consistent with the descriptive statistics reported in Table 2. These findings suggest that women from major, intra-sate conflict backgrounds in countries of origin suffer from greater chronic stress compared to others. In analyses not shown, we find that women who emigrated during an ongoing conflict have especially higher levels of unemployment and are more likely to be exposed to noxious job conditions than migrants with no conflict exposure and the Canadian-born.
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Effects on Anxiety among Men
Model 1 in table 2.4 demonstrates that men who emigrated from a country affected by major, intra-state armed conflicts report more symptoms of anxiety than men who came from minor/no conflict countries (0.750, p<0.05) as well as those born in Canada (0.838, p<0.05). Conversely, men who had only past exposure to conflict in their country of origin experience less anxiety symptoms compared to those who emigrated from countries with minor or no conflict at time of exit (-0.727, p<0.05). Model 2 shows that the effect of armed conflict remains almost the same when controlling for the effect of age at last conflict, ethnicity, and education. The difference with Canadian-born men however is considerably reduced (from 0.838, p<0.05 to 0.565, p<0.10), primarily because the net difference between the foreign-born from minor or no conflict backgrounds and Canadians is now negative. When we add major traumatic events that occurred prior to any armed conflict episode in model 3 and events that occurred between the first exposure to conflict and the onset of the current conflict in model 4, the coefficients for the difference between migrants from conflict countries vs. migrants from minor or no conflict and the Canadian-born remain relatively unchanged. This shows that traumas experienced prior to the conflict episode at time of migration do not explain the effect of armed conflict.
The effect of major traumatic events that have occurred during the ongoing armed conflict episode at time of migration in model 5 is however notable. In this model, the net impact of armed conflict decreases from .766 (p<.05) to .589 (p<.10). Thus, traumatic events occurring during the conflict period do help explain the difference in anxiety between men who came from armed conflict countries and those who came from minor/no conflict countries, as well as
Canadian-born men (from 0.584, p<0.10 to 0.434, p>0.10). In comparison to women, this earlier stage of stress proliferation predicts higher anxiety in the long-term among men. We know from
29
Table 2 as well that men from major, intra-state conflict backgrounds report more of these events relative to both men from minor/no conflict backgrounds and Canadians. In models 6 to 9, however, it appears that pre-migration mental health, length of residence in Canada and post- migratory stressors have little further influence on the effect of armed conflict on anxiety, even though these variables have predictable independent effects on current anxiety. The exception is the length of residence, which has no significant independent effect.
Discussion
In this study, we examined the effect of emigrating during an armed conflict on the mental health of migrants in a destination country, in this case, Canada. We attempted to specify and then explain its long-term impact, first by isolating the location and intensity of conflicts as the primary issue, second by combining these contextual factors to fully specify the kind of conflict that mattered most, and third by exploring the contributing effect of stress exposure both prior to and after migration. Results suggested that there is a long-term impact of major, intra-state armed conflicts on current depression among women and anxiety among men. This effect includes differences with women who came from minor or non-conflict countries born in Canada.
Our study is one of the few to demonstrate this kind of long-term impact on mental health, specifically through the use of independent event data on armed conflict. In fact, our results seem to contrast with some previous research. For example, Silove et al. (2007) report that 11 years after resettlement Vietnamese refugees had better mental health than native-born Australians.
Conversely, we observed that migrants who came from armed conflict countries had worst mental health than native-born, despite having lived in Canada, on average, for over 10 years.
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Our findings suggest the importance of specifying the nature of conflict in assessing the long- term consequences among migrants. It is clear that ongoing and severe intra-state conflicts at the time of exit have long-lasting impacts on mental health after migration. Past conflicts that have ended have no detrimental impact in our data. This is important because in many studies only conflict per se is measured. In our data, this would dilute the long-term impacts we observe.
Our results support the stress proliferation argument to a point, but the pathways through which the contexts of exit affected the mental health of migrants were found to be gender-specific.
Among women who left their country during a major intra-state conflict, higher levels of post- migratory chronic stress contributed to their higher levels of depression. These women were particularly affected by higher levels of work-related stress. The mediating effect of post- migratory chronic stress, although modest, deserves particular attention and should be investigated further, especially with broader but also more salient measures of chronic stress: it is very possible that our data do not contain sufficient measurement of stressors which focus on acculturative stress and living conditions, for example. Conversely, among migrant men, major traumatic events that occurred during the ongoing armed conflict episode at time of migration help explain post-migratory differences in anxiety. When we look at which events are most implicated in this effect, we note, in analyses not shown, that men who emigrated from a country affected by major intra-state conflicts have had higher rates of a major illness or accident (11%), witnessed violence (12%), and been in a combat or lived near a warzone (17%). These rates were not only distinguishable from those found for Canadian born and migrants who came from non- conflict countries, but also from women who migrated during conflict. With the exception of living near a warzone, for which comparable prevalence was found, women reported lower direct exposure to trauma. Through the identification of different pathways through which the context
31 of exit exerts its impact on mental health among men and women, this study adds to the current literature by providing new understanding about gender differences in mental health among migrants from armed conflict countries of origin.
The gender-specific findings we report do not exactly follow the standard template in the mental health literature (Aneshensel, Rutter, and Lachenbruch1991). Although it could be seen as
“typical” that it is women‟s depression that is affected by past exposures to conflict, it is not as typical that it is men‟s anxiety that is affected. This finding may be a clue however that the differential nature of trauma men and women experience during conflict may also affect their mental health differently. We speculate that men may be more directly involved in the conflict experience, thereby exposing themselves to more severe type of traumatic events. Women, on the other hand, may be more isolated from their usual roles and activities, and in a situation of unusual vulnerability. We contend this could explain, in part, the gender differences in mental health outcomes observed here.
This study has important limitations. First, the cross-sectional design of this study may be problematic in terms of retrospective reports of past traumatic stressors, and may be susceptible to present state bias (Bell et al. 2012; Spinhoven, Bean, and Eurelings-Bontekoe 2006). That limitation is however less of an issue with the conflict variables, as they are derived from independent data. Second, the relatively small sample size prevented us from further distinguishing the nature of these past conflicts. Third, our findings would also benefit from more specific measurement of war-associated stressors and post-migratory stressors, including measures that would allow for the assessment of the stressfulness of the trauma experienced. The effects of pre-migration and post-migration stress must be stated as the best possible given the data, but not as ideal as a specific stress history focused on armed conflict life histories and the
32 experience of immigrants post-migration. Fourth, our analyses would have benefited from a measure of social support. This would have allowed us to examine, for example, whether the effects of armed conflict and individually-reported stressors are influenced by the existence of social support. Finally, we study an intact family sample, which may potentially restrict the generalizability of the effects we observe.
Given these limitations, however, our findings clearly point to the presence of a long-term effect of exposures to major internal armed conflicts prior to migration on key mental health outcomes years after exiting the area of the armed conflict and also years after their entry into Canada.
Significantly, it is not any form of armed conflict exposure that matters, but specifically the more virulent and proximal forms. Given the adjunct stressors assessed in our data, the effect of more serious and ongoing exposure to armed conflict operates like a dominant, “master” stressor that sufficiently marks those exposed that it operates independently of many other life experiences in the interim, and lasts years after arrival in a new country and the exposure to conflict prior to migration.
______
NOTES
1. The range of two years beyond previous conflicts imprecision in the estimation of age of leaving the country of origin. Weallow for a one year minus (start date) to one year plus (end date) range in specifying a conflict as current, in part to allow for mid-year migrations and some imprecision in reporting year of migration.
2. We note that our effort to match comparison groups was simpler in this case, because this is a control in our analysis.
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3. The significance for the coefficient estimates for the effect of emigrating during inter-state conflict should be interpreted with caution, considering the especially small sample size among women (n=9).
4. The Ns here are slightly different than in Table 2 due to listwise deletions in the regressions when controls and mediators are included.
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Table 2.1. Descriptive Statistics by Nativity and Conflict Status
Women Men Canadian-born No Current Current P<0.05 Canadian-born No Current Current P<0.05 Conflict Conflict Conflict Conflict (n= 338) (n=411) (n=131) (n=300) (n=395) (n=147) Major armed conflict(%) ------56.34 ------58.27 Intra-state armed conflict(%) ------93.39 ------91.31 Major-intra armed conflict (%) ------53.43 ------54.14 History of armed conflict (%) --- 17.97 ------13.59 --- Age at first conflict --- 10.81 (0.23) 12.42 (0.41) * 2 --- 13.00 (0.29) 13.15 (0.41) Age at last conflict ------21.34 (0.31) ------23.86 (0.31) Length of residence --- 19.42 (0.40) 12.25 (0.71) * 2 --- 21.94 (0.43) 13.92 (0.70) * 2 Age 40.33 (0.27) 40.89 (0.25) 40.88 (0.44) 41.75 (0.34) 44.02 (0.30) 45.06 (0.49) * 3,4 Southern European (%) 8.95 21.14 6.75 * 2,4 6.39 28.56 9.97 * 2,4 Middle Eastern (%) 3.63 6.75 8.32 6.00 4.51 12.16 * 2,3 East Asian (%) 1.43 10.39 36.31 * 1 1.59 9.57 26.95 * 1 South Asian (%) 0.00 15.30 29.35 * 1 0.00 12.02 39.41 * 1 Western European (%) 74.62 22.95 10.96 * 1 78.24 24.50 5.52 * 1 Other ethnicities (%) 11.37 23.47 8.32 * 2,4 7.79 20.89 5.99 * 2,4 Education 14.31 (0.16) 14.11 (0.15) 14.32 (0.26) 15.36 (0.25) 14.00 (0.21) 15.92 (0.35) * 2,4 Pre-migration Events (pre-conflict) 0.58 (0.05) 0.45 (0.04) 0.64 (0.08) 0.45 (0.04) 0.29 (0.04) 0.48 (0.06) * 2,4 Events (b/n conflicts) 0.35 (0.04) 0.30 (0.04) 0.40 (0.06) 0.37 (0.05) 0.35(0.04) 0.51 (0.06) Events (last conflict) 0.41 (0.04) 0.33 (0.04) 0.52 (0.06) * 2 0.34 (0.04) 0.21 (0.04) 0.64 (0.06) * 2,3 Depression (%) 17.48 9.02 20.19 * 2,4 12.42 9.92 9.64 Anxiety (%) 5.76 4.98 4.59 0.92 3.57 4.23 Post-migration Traumatic events 1.11 (0.08) 1.43 (0.08) 0.72 (0.13) * 1 0.87 (0.07) 1.13 (0.06) 0.63 (0.10) * 2,4 Chronic stress 1.57 (0.06) 1.75 (0.06) 1.88 (0.10) * 3 2.11 (0.07) 2.21 (0.06) 2.20 (0.10) Notes: Significance between means is assessed through pairwise multiple comparisons; standard errors in parenthesis; "no last conflict" includes immigrants who emigrated from countries that had a history of armed conflict or countries that had no past conflict 1 differences across all three groups; 2 differences between immigrants from conflict and non-conflict countries;3differences between immigrants from conflict countries and Canadian-born; 4 differences between immigrants from non-conflict countries and Canadian-born
44
Table 2.2. Results from Four Regression Equations on Risk Factors Characterizing Armed Conflicts on Mental Health
Women Men
Model N Depression Anxiety N Depression Anxiety 1 Foreign-born no conflict vs. Canadian-born 338 0.800 0.311 341 -0.490 0.036 Armed conflict vs. no conflict 131 0.804 -0.239 147 -0.065 0.468* History of armed vs. no conflict 74 -1.446 -0.632 54 -0.591 -0.713*
2 Foreign-born no conflict vs. Canadian-born 338 0.800 0.311 341 -0.490 0.036 Major armed conflict vs. no conflict 74 2.633* 0.314 85 0.144 0.689* Minor armed conflict vs. no conflict 57 -1.556 -0.952* 62 -0.363 0.164 History of armed conflict vs. no conflict 74 -1.446 -0.632 54 -0.591 -0.713*
3 Foreign-born no conflict vs. Canadian-born 338 0.800 0.311 341 -0.490 0.036 Intra-state conflict vs. no conflict 122 1.346† -0.086 134 0.137 0.627* Inter-state conflict vs. no conflict 9 -6.680* -2.405* 13 -2.161 -1.185 History of armed conflict vs. no conflict 74 -1.446 -0.632 54 -0.591 -0.713*
Foreign-born minor/ no conflict vs. Canadian- 4 born 390 0.660 0.204 398 -0.471 0.079 Major/intra armed conflict vs. minor/ no conflict 70 3.279** 0.550 80 0.116 0.810* History of armed conflict vs. minor/ no conflict 74 -1.306 -0.525 54 -0.610 -0.755*
P<0.001=***, P<0.01=**, P<0.05=*, P<0.10=†
45
Table 2.3. Ordinary Least Squares Regression of Depression on Conflict Background in Countries of Origin and Stress Histories among Women (N=862). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 For. born Minor/ no conflictvs. Can. born 0.779 -0.143 0.112 0.129 0.152 0.349 0.517 1.281 0.790 Major, intra conflict vs. minor/ no conflict 3.278** 3.489** 2.982** 3.039** 2.732* 2.585* 2.811* 3.091** 2.681* Major, intra conflict vs. Can. born 4.057*** 3.346** 3.094* 3.168* 2.884* 2.934* 3.328* 4.372* 3.471* History of conflict vs. minor/ no conflict -1.425 -1.967† -1.845† -1.847† -2.091† -2.131* -1.892† -1.811† -2.455* Age at last conflict -0.032 -0.111 -0.122 -0.104 -0.080 -0.106 -0.106 -0.181 Southern European 3.057** 2.889** 2.889** 3.082** 2.879** 3.040** 3.518** 3.079** Middle Eastern 3.227* 3.128* 3.007* 2.981* 2.552 2.979* 3.023* 2.559 South Asian 2.420 2.355 2.329 2.531* 2.589* 3.699** 3.913** 3.446** Other ethnicities 0.474 0.367 0.332 0.406 0.393 1.077 1.222 0.723 Education -0.038 -0.032 -0.032 -0.037 -0.023 -0.011 -0.013 0.206 Pre-migration Traumatic events (pre- conflict) 1.605*** 1.609*** 1.548*** 1.490*** 1.551*** 1.480*** 1.207*** Events (b/n first & last conflict) 0.314 0.388 0.245 0.277 0.411 0.531 Events (during last conflict) 0.761† 0.536 0.665 0.744 0.648 Depression 2.881** 3.069** 2.713** 2.297 Anxiety 0.942 1.038 1.041 1.918 Post-migration Length of residence -0.201 -0.254 -0.212 Length of residence (sq.) 0.007* 0.006* 0.006* Traumatic events 1.072*** 0.885*** Chronic stress 2.586*** R2 0.013 0.028 0.049 0.049 0.053 0.064 0.075 0.100 0.182
P<0.001=***, P<0.01=**, P<0.05=*, P<0.10=†
46
Table 2.4. Ordinary Least Squares Regression of Anxiety on Conflict Background in Countries of Origin and Stress Histories among Men (N=820). Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 For. born Minor/ no conflictvs. Can. born 0.088 -0.142 -0.148 -0.182 -0.155 -0.174 -0.007 0.068 -0.133 Major, intra conflict vs. minor/ no conflict 0.750* 0.707* 0.723* 0.766* 0.589† 0.646* 0.605† 0.617† 0.625† Major, intra conflict vs. Can. born 0.838* 0.565† 0.575† 0.584† 0.434 0.472 0.598 0.685 0.492 History of conflict vs. minor/ no conflict -0.727* -0.759* -0.753* -0.747* -0.804* -0.910* -0.929* -0.934* -1.022* Age at last conflict 0.034 0.035 0.018 0.201 0.008 0.005 0.003 -0.001 Southern European 0.201 0.200 0.243 0.256 0.295 0.310 0.392 0.231 Middle Eastern 0.551 0.551 0.481 0.434 0.423 0.410 0.459 0.484 South Asian 0.535 0.529 0.532 0.531 0.500 0.513 0.612 0.499 Other ethnicities 0.191 0.189 0.206 0.196 0.168 0.168 0.252 0.308 Education -0.064** -0.064** -0.068** -0.067** -0.069** -0.070** -0.070** -0.041† Pre-migration Traumatic events (pre- conflict) -0.044 -0.049 -0.078 -0.099 -0.101 -0.090 -0.110 Events (b/n first & last conflict) 0.352** 0.347** 0.322* 0.324* 0.312* 0.244† Events (during last conflict) 0.303* 0.213† 0.207† 0.197† 0.249† Depression 0.621† 0.609† 0.636† 0.574† Anxiety 1.509* 1.480* 1.438* 1.633* Post-migration Length of residence -0.017 -0.025 -0.011 Length of residence (sq.) 0.000 0.000 0.000 Traumatic events 0.162† 0.153† 0.546*** Chronic stress R2 0.011 0.025 0.025 0.033 0.038 0.051 0.052 0.056 0.101
P<0.001=***, P<0.01=**, P<0.05=*, P<0.10=†
47
Figure 2.1 Initial Conditional Coding of Foreign-born Status on Conflict Exposure
Foreign-Born Status Conflict Exposure at Migration History of Conflict
Yes = 1 Yes = 1 Foreign-Born =1
No = 0
No= 0
Native-Born=0 Native-Born=0 Native-Born=0
48
Chapter 3 Human Rights Violations and Conflict in Countries of Origin and the Mental Health of Migrants Introduction
The armed conflict and the violations of physical integrity often accompanying fragile or repressive state regimes have resulted in the displacement of large numbers of migrants
(Apodeca 1998; Castles 2003; Davenport, Moore, and Poe 2003; Moore and Shellman 2004;
Schmeidl 1997; Zolberg, Suhrke, Aguayo 1989). Because of the social and political contexts described by these conditions, these migrants may experience later mental health problems after resettlement. The cumulation of stressful life experiences that could follow from these “contexts of exit” (Rumbaut 1991; Vega and Rumbaut 1991) may have additional adverse effects on mental health. The question in this paper is whether armed conflicts and human rights violations leading up to and at time of exit from a home country have long-term deleterious consequences on the mental health of migrants despite the promise of escape from difficulty.
Current understanding of the contextual effect of armed conflicts and human rights violations on migrants‟ mental health is largely based on studies that compare outcomes between refugees and immigrants or migrants more broadly from different countries of origin (see Guarnaccia 1997;
Portes and Rumbaut 2006 for reviews). These studies often exclude the effects of armed conflicts and human rights violations on the mental health of those who are not recognized as refugees, or those who are not the direct targets of violence. Moreover, most studies include only a small number of countries of origin – often one, two, or three – and usually do not account for variation in the level of conflicts and human rights violations over time.
49
The literature on the mental health of migrants from countries affected by armed conflict and high human rights violations focuses attention primarily on the impact of self-reported experiences of trauma rather than the broader social contexts in which they occur (e.g. Marshall et al. 2005; Momartin et al. 2003; Sabin et al. 2003). In the literature on self-reported trauma and victimization, the attention is predominantly on a single country of origin and on the experiences of migrants formally defined as refugees. Because these studies start from a question about direct exposure to armed conflict or human rights violations, they may miss the larger contextual effect of these conditions, i.e., the effect of the national-level social context -- threat, instability, disruption of lives, uncertainty – on the mental health of the entire population. Importantly, studies which jointly consider the effect of armed conflict and human rights violations are generally limited by the same design or measurement issues, such as focusing on a single country of origin or measuring only individuals‟ experiences with these stressors, thus limiting generalizability (e.g. Ai, Peterson, and Ubelhor 2002; Momartin et al. 2003; Priebe et al. 2010).
The link between contexts of exit, stressful experiences in the resettlement country, and mental health is also unclear. A number of studies show that exposure to stressors after migration can have a significant effect on the mental health of migrants who flee from armed conflicts or gross human rights violations situations (e.g. Hauff and Vaglum 1993; Miller et al. 2002; Miller and
Rasmussen 2016; Steel et al. 1999; Schweitzer et al. 2006). However, these studies focus on samples all experiencing the same or similar background context of exit, and so little is known about the actual effect of armed conflicts and/or human rights violations postmigration. These studies would have to compare migrants from conflict vs. non-conflict situations, and higher vs. lower human rights violations to address this question.
50
This paper advances our understanding of the long-term effect of armed conflicts and human rights violations in countries of origin by considering the effect of all combinations of armed conflict and human rights violations on migrants‟ mental health after migration. These contexts of exit are interpreted broadly – as true contextual effects – by seeing that each is a form of chronic stress occurring at the country level (Wheaton et al. 2013). Following Aneshensel
(2013), these kinds of macro-stressors may have important cross-over effects on exposure to related stressors in individual lives. In this paper, the separate (and possible interdependent) effects of conflicts and human rights violations are studied in a broad sample of countries of origin. This approach allows a clearer separation of the effects of these contexts from the particulars of culture, region, or history that naturally are confounded in studies of one, two, or a few countries.
Using country level data from the UCDP Armed Conflict Termination Dataset (Kreutz 2008) and the Political Terror Scale (Gibney and Dalton 1996) and individual level data from a representative probability sample of adults in a large metropolitan area of Canada, this study examines the effect of human rights violations and armed conflict in over one hundred countries of origin on the mental health of migrants. It considers the effects of armed conflicts and human rights violations in all of the combinations they naturally occur at the time of migration, while considering the explanatory role of pre- and post-migratory stressors that follow from these contexts of exit.
Literature Review Contexts of Exit and Mental Health
The literature on social stress argues that social contexts vary in their level of collective threat,
51 instability, uncertainty, and insecurity and thus have direct consequences for mental health
(Aneshensel 1992; Aneshensel et al. 2007). Social contexts that are threatening or perceived as dangerous have also been empirically linked with worse mental health outcomes (e.g.
Aneshensel and Sucoff 1996; O‟ Campo et al. 2015; Ross and Mirowsky 2009; Turner et al.
2013), notably through their influence on subsequent stressors and a cumulative sense of powerlessness, mistrust, victimization, and threat to self. Interpreted as distinct but related forms of contextual threat, armed conflicts and state repression may result in especially long-term consequences on mental health (see de Jong 2002; Pedersen 2002).
Armed conflicts and human rights violations may have generalized impacts on a population, even when individuals are not directly exposed to individual stressors. Contextual stressors exert their impact through their influence on a generalized sense of threat, a continuous sense of uncertainty and unpredictability, the widespread disruption of essential institutions, and thus to the fundamental disruption of lives. In repressive countries, torture, extrajudicial killings, political detention, and forced disappearance lead to fear and insecurity that become part of the social fabric of everyday life (Corradi and Fagen 1992; Desjarlais et al. 1995; Zwi and Ugalde
1991). Furthermore, terror can destroy social relations, amplify a sense of mistrust, and lower a person‟s sense of personal control (Martín-Baró 1989). In conflict-affected countries, bombardment, fighting, deaths and displacement can have adverse impact on mental health that may extend beyond actual conflict zones. Studies from El Salvador and Colombia, for example, found a high prevalence of mental disorders in communities not directly affected by conflict
(Londoño, Romero, and Casas 2012; Ugalde et al. 2000). Furthermore, a study of young adults in
Indonesia reported that forty percent of those who lived in areas not directly affected by the conflict in Mollucas felt their lives were in danger as compared to seventy percent of those in
52 areas most affected by the conflict, and this feeling of threat was associated with an increased risk of psychological distress (Turnip et al. 2010). The effect of armed conflicts on mental health may also extend through time. Joly and Wheaton (2015), using a representative sample of adults living in the city of Toronto, found that migrants who came from countries involved in a major intra-state conflict at the time of migration had worse mental health compared both to those from countries with minor or no conflict, and the native-born population – on average, up to 12 years later.
The relationship between armed conflicts and human rights violations varies across countries, which may result in different mental health consequences. Although armed conflicts and human rights violations often co-occur, in some cases one occurs in the absence of the other. There are cases of human rights violations without conflict, and conflict with some government supported protection of human rights (Thoms and Ron 2007). But it is plausible to expect the impact of human rights violations to be more pronounced in the context of conflict. As de Jong writes:
“Conflicts add to insecurity, oppression, dehumanization, ethnic cleansing, and other human rights violations (...)” (2002:27). This thus suggests that the impact of human rights violations on mental health of migrants cannot be studied on its own, because it may vary depending on whether it is accompanied by armed conflict.
Research on the joint impact of human rights violations and armed conflict on the mental health of migrants in the country of resettlement is limited. Studies mainly focus on a single country of origin in which the impact of human rights violations and armed conflict is primarily examined at the individual-level through retrospective self-reports of traumatic experiences, and often without comparisons to those who are not escaping violence (e.g. Ai, Peterson, Ubelhor 2002;
Marshall et al. 1995; Momartin et al. 2003; Priebe et al. 2010; Sabin et al. 2002). Other studies
53 suggest that armed conflict and human rights violations in countries of origin can negatively affect mental health after migration, but they have not considered the effect of each separated from the other. For example, Cervantes et al. (1989), in a community sample of immigrants from
Mexico and Central America and Mexican and Anglo Americans, found higher levels of posttraumatic stress disorder among immigrants from countries in Central America that had a higher prevalence of conflict and human rights violations at the time. And Steel et al. (2009), using the Political Terror Scale to examine the impact of human rights violation on displaced and refugee populations from conflict-affected countries in a meta-analysis of 161 studies, found a higher rate of PTSD among migrants from countries with high levels of human rights violations
(39 percent) than those from countries with low to moderate levels of human rights violations (28 percent).
Contexts of Exit, Cumulative Stress, and Mental Health
According to the stress proliferation argument, an initial stressor, such as exposure to a threatening social context, can produce a chain of secondary exposure to other stressors (Pearlin
1999; Pearlin, Aneshensel, and Leblanc 1997; Pearlin et al. 1981). The migration literature makes a similar argument, in arguing that contexts of exit may have a significant influence on lived experiences in countries of origin and subsequent adaptation in countries of reception
(Portes and Börözc 1989; Vega and Rumbaut 1991).
Situations of armed conflict and/or severe human rights violations can expose individuals to multiple kinds of traumatic events (Joly and Wheaton 2015; Karam et al. 2008; Kirmayer et al.
2010; Scholte et al. 2004) prior to migration that may undermine mental health. Traumatic events such as being tortured, imprisoned, or injured, experiencing the unnatural death of a family member, and witnessing the disappearances of others or violence, for example, have all been
54 associated with mental health outcomes after migration (Bhui et al. 2003; Hauff and Vaglum
1993; Mollica et al. 1987; Sabin et al. 2003; Silove et al. 2002; Steel et al. 1999; Willard, Rabin, and Lawless 2014).
Research shows that situations of armed conflict and/or severe human rights violations can also expose individuals to more stress after migration. In Canada, armed conflict in the country of origin has been associated with more post-migratory stress (especially work-related stress, including unemployment) among migrant women, which contributed to more depression (Joly and Wheaton 2015). Higher levels of political repression have also been linked with lower levels of employment and economic activity after migration to different countries of destination (Van
Tubergen, Maas, and Flap 2004) and lower earnings among immigrants in the United States upon arrival (Borjas 1987). Trauma associated with war has been reported to negatively impact the sustainability of employment (August and Gianola 1987; Hauff and Vaglum 1993b). Besides labor market outcomes, armed conflict, state repression, and pre-migration trauma have been argued to lead to more acculturative stress (Hovey 1999; Messer and Rasmussen 1986;
Nicholson and Walters 1998). For example, Steel et al. (1999), in a study of Tamil migrants in
Australia, found that fleeing from conflict was associated with more post-migration stressors, including acculturative stress and, in turn, higher levels of post-traumatic stress disorder.
There is also evidence that the accumulation of stressors experienced both prior to and after migration can have significant impact on mental health (Bogic et al. 2012; Hauff and Vaglum
1995; Marshall et al. 1995; Miller et al. 2002; Rumbaut 1991; Schweitzer al. 2006). For example,
Fenta et al. (2004), in a study of Ethiopian refugees and immigrants in Toronto, found that both pre-migratory trauma and post-migratory stressful life events were associated with higher rates of depression. Likewise, Beiser et al. (2011), in a study of Sri Lankan Tamil refugees, found that
55 pre-migratory stressors, as well as poverty and perceived prejudice after migration, were associated with increased risks for posttraumatic stress disorder.
In sum, the life course pathways set in motion by exposure to conflict and /or human rights violation contexts suggest both the generality and stability over time of the mental health consequences. But because of the design of prior studies, we still do not know whether human rights violations or conflict dominate, whether they have multiplicative effects together, whether there are ceiling effects, where one or the other is sufficient for long-term effects, or whether chains of stress experience are set off by either context or only by both together.
Analytical Questions
This study considers the joint impact of human rights violations and armed conflicts in countries of origin on the mental health of migrants up to 41 years later. Because human rights violations and armed conflicts are measured separately, it is possible to ask questions that cannot be asked in the prior literature. For example, is the effect of high levels of human rights violations in countries of origin the same in countries in conflict as in countries not in conflict? Or: what threshold of human rights violations is sufficient to observe mental health impacts, controlling for the co-existence of conflict? These examples suggest a general sequence of analytic questions.
Specifically, this study addresses the following three general questions: 1) does armed conflict or high levels of human rights violation have a more fundamental and definitive long-term effect on mental health after migration?; 2) does the co-occurrence of both armed conflict and human rights violations have uniquely detrimental effects on long-term mental health?; and 3) what are the implications of both pre- and post-migratory stressors in these differences?
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Methods Data
The analysis is based on individual-level data from the Neighbourhood Effects on Health and
Well-being (NEHW) Study in Toronto that has been merged with country-level data from the
UCDP Conflict Termination Dataset (Kreutz 2008) and the Political Terror Scale (PTS) (Gibney and Dalton 1996). The NEHW study is a multistage area probability sample of English-speaking adults aged between 25 and 64 living in the City of Toronto, Canada (O‟ Campo et al. 2015). In- person interviews were done between 2009 and 2011 with 2,412 individuals, representing a response rate of 72 percent. The final sample includes 1,496 Canadian-born and 916 foreign-born respondents from more than 110 different countries of origin.
The UCDP Conflict Termination dataset (Kreutz 2010) consists of global data on armed conflicts collected by the Department of Peace and Conflict Research at Uppsala University for the years
1946 to 2007. The data include the start and end dates of each conflict episode as well as its location, intensity, and type. The Political Terror Scale (Gibney and Dalton 1996) provides annual data on human rights violations from more than 180 countries for the years 1976 through
2008. The data are based on reports published by Amnesty International.
Measures
Psychological distress is measured with 10 items from both the original and revised version of the CES-D scale (Eaton et al. 2004; Radloff 1977). The selected items are similar to those in the
Kessler Psychological Distress Scale (K10) (Kessler et al. 2002) but differ in terms of timeframe1. Respondents were asked how often in the past two weeks they experienced symptoms of anxiety and depression such as feeling “nervous,” “sad,” and “everything was an
57 effort” (the complete list of items are included in Appendix B). Responses are coded from none of the time (1) to all of the time (5) and, for the one positively-worded item, reverse-coded, so that high scores reflect more psychological distress. The scale ranges from 10 to 50 (=0.83).
The context of exit is defined by the occurrence of an intrastate armed conflict and the level of human rights violations in the country of origin at the time of migration. For the measure of armed conflict, I first considered the start and end year of armed conflict episodes as well as the year of immigration to Canada to determine whether migration took place during an armed conflict episode. I then considered information on the type of armed conflicts to determine if migration occurred during an intrastate armed conflict (because interstate conflicts are much rarer and differ in meaning).2 For the measure of human rights violations, I used the country‟s scores on the Political Terror Scale (PTS) associated with the year of immigration. Scores could range from 1 to 5, with higher scores indicating greater repression of human rights (see appendix
C for a description of PTS scores). I identified countries of origin as experiencing low, moderate, and high levels of human rights violations if their score on the PT scale was lower than, equal to, or greater than 3. I also used data collected on politicide and genocide (Harff and Gurr 1988;
Harff 2003) to identify countries experiencing high levels of human rights violations between the years 1970-75, and supplemented these with PTS scores for the year of 1976 to classify countries of origin as either low or moderate level of human rights violations. Migrants arriving before
1970 cannot therefore be included in this analysis.
Results use a conditional coding scheme (Ross and Mirowsky 1992) to separate the effects of foreign-born status per se from differences due to context of exit among the foreign-born.
Foreign-born status is indicated by a dummy variable with foreign-born coded 1 and native-born coded 0. The foreign-born are then sub-divided into the most prevalent combinations of human
58 rights violations (HRV) and armed conflict in countries of origin. This resulted in six dummy variables: 1) high HRV/conflict (N =129) standing for high levels of human rights violations and intrastate armed conflict in countries of origin at the time of migration; 2) moderate
HRV/conflict (N=34); 3) low HRV/conflict (N=13); 4) high HRV/no conflict (N=60); 5) moderate HRV/no conflict (N=99); 6) and low HRV/no conflict (N=288). For comparisons among the foreign-born, I used low HRV/no conflict as the reference group. Because the low
HRV/conflict group was so small, this group was excluded from the analysis. This is preferable to collapsing this very distinct group with others.
In this conditional coding system, the dummy variable for migrant status contrasts migrants from non-conflict countries with low levels of human rights violations with Canadians. The difference between the other migrant groups and the reference group of low HRV/no conflict is estimated by four dummy variables describing combinations of human rights violations and/or conflict, and differences with native-born Canadians are then estimated by the sum of the coefficients for each coded context of exit and for migrant status. The statistical significance of differences is assessed with post-hoc tests.
To account for the chronicity of the conflict and its impact on mental health, I also estimated the duration of the conflict by calculating the number of years between the onset of the ongoing armed conflict and the time of immigration. The native-born and migrants from countries not in conflict at the time of migration are coded 0 on this variable.
Pre-migration trauma is measured with 9 items from an 18-item checklist (Turner, Wheaton, and
Lloyd 1995) that have been shown to increase risk for mental health problems after migration
(Fazel et al. 2012; Ornelas and Perreira 2011; Moussaoui and Agoub 2011). These include:
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“have any of your parents died;” “has a spouse of other loved one, including other children you have had, died;” “have you ever been in a major fire, flood, earthquake, or other natural disaster;” “did you ever have a major illness or accident that required you to spend two weeks or more in the hospital;” “have you ever had a serious accident, injury or illness that was life- threatening or caused long-term disability;” “did either of your parents drink or use drugs so often or so regularly that it caused problems for the family;” “did your parents often have violent arguments;” “did you parent(s) have so little money that you lived much of childhood in poor housing or not being able to pay bills, or buy food and clothes;” and “did either of your parents have such problem with nerves or depressions that they were unable to work or had to have treatment.” To determine whether these events occurred prior to migration, I used the age reported for each event compared to the age at immigration. The final scale consists of a count of traumatic events the foreign-born respondents experienced prior to immigration, on which the native-born are assigned the mean value.
Post-migration stressors include acculturative stress, perceived discrimination, recent stressful life events, financial strain, work-related stress, and non-employment. Acculturative stress is measured with a 10-item scale adapted from the Acculturative Stress Index (Noh and Avison
1996). Foreign-born respondents were asked to indicate how often, on a 4-point scale from never
(1) to very often (4), they experience difficulties because of feelings or circumstances such as
“missing their country of origin,” “having difficulties with the English language,”“being disappointed with their standard of living,” and “having a job that is below their experience and qualifications.” Responses are averaged to form a scale which is standardized to a mean of 0.
Native-born respondents are given a value of 0 using conditional coding (=0.99).
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Perceived discrimination is measured with a six-item version of the Everyday Discrimination
Scale (Williams et al. 1997). Respondents were asked how often in their day-to-day life they are
“treated with less courtesy or respect than others,” “received poorer service than others in restaurants or stores,” “are threatened or harassed,” “are called names or insulted,” or “people think they are better than them,” and “act as if they are afraid of them,” because of their race, ethnicity or culture. Responses for each item range from almost every day (1) to never (6). All items are reverse-coded and averaged (=0.87).
The measure of recent life events is a simple count of affirmative responses to twenty items about stressful events experienced in the past year, similar to many other lists of life events
(Turner et al. 1995). Sample items include being “fired or laid off,” “robbed,” “in trouble with the law,” “accused or arrested for a crime,” having “major financial problems,” “a serious accident or injury,” and experiencing “a change of job for a worse one” (the complete list of items is included in Appendix D).
Financial strain is measured with three items asking respondents the extent to which they “don‟t have enough money to buy the things you (or your kids) need,” “don‟t have enough money to take vacations,” and their “rent or mortgage is too much.” Responses are coded from not true (1) to very true (3) and are averaged (=0.77).
Work-related stress consists of six items. Sample items include “work is boring and repetitive,”
“have no control over the pace of work,” “don‟t get paid enough,” and “have more work to do than most people.” Responses are coded from not true (1) to very true (3) and averaged to form a scale which is standardized to a mean of 0 (=0.65). Respondents who do not work are given a
61 value of 0 on this variable. The financial and work items are both taken from Wheaton‟s larger chronic stress scale (1991).
Non-employment is a dummy variable coded 1 for not employed and 0 for currently employed.
The analysis also includes socio-demographic factors that may be associated with psychological distress or contexts of exit and act as confounders.These include refugee status, gender, education, race, marital status, age, and length of stay. Refugee status is coded 1 for respondents who came to Canada as refugees and 0 for all others. Gender is coded 1 for females and 0 for males. Education is measured with two continuous variables centered at their mean: years of schooling for the foreign-born, with the native-born coded 0, and years of schooling for the native-born, with the foreign-born coded 0. This allows for specific estimation of the distinct effects of education among the foreign-born vs. the native-born. Race/ethnicity is coded into ten dummy variables following a modified version of the classification used by Statistics Canada in the Census of 2006: Black, Caribbean, Latin American, East/Southeast Asian, South Asian, West
Asian/Arab, Jewish, other racial/ethnic group, multiple groups, and White (the reference group).3
Marital status is coded 1 for married and 0 for unmarried. Age is measured in years. Length of stay is a continuous variable, centered to a mean of 0, with native-born coded 0 using conditional coding.
Analysis
The analysis is based on ordinary least squares (OLS) regression. It begins with a set of models that estimate the effect of combinations of human rights violations and armed conflict on psychological distress with adjustment for a battery of socio-demographic characteristics. It then follows with a second set of models that examine the role of pre-migration trauma, length of
62 stay, and post-migratory stressors in mediating the effects of human rights violations and armed conflict on psychological distress. Each model shows differences in distress two ways: first, between those from non-conflict and conflict countries with moderate to higher levels of human rights violations vs. migrants from non-conflict countries with low levels of human rights violations; and second, between all migrant groups vs. the native-born (with post-hoc tests).
The analysis concludes with a series of post-hoc tests that estimate the overall difference between armed conflict and human rights violations on distress. These include comparisons between migrants from countries with conflict vs. no conflict overall and collapsed across levels of human rights violations; comparisons between migrants from countries with high levels of human rights violations vs. lower levels of human rights violations (moderate and low), collapsed across countries differing in the presence of conflict; and comparisons between migrants from countries with both conflict and high levels of human rights violations vs. countries with no conflict and high levels of human rights violations. Because each test was performed while adjusting for control variables, results indicate the statistical difference in distress between groups net of controls.
In all analyses, the sample was weighted by age, gender, nativity status, total household income, and household size (see O‟ Campo et al. 2015). The analytical sample includes 1,461 native-born
Canadians and 610 migrants. Not included are migrants who came to Canada prior to 1970 and could not be coded on measures of human rights violations (n=237), migrants from countries with both intrastate armed conflict and low levels of human rights violations (n=13), and migrants from countries involved in an interstate or external conflict (n=15). Cases with missing information on other variables used in the analysis are also excluded (n=74).
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Results Descriptive Results
Table 3.1 shows countries of origin according to the level of human rights violations and conflict at the time of migration. As can be seen, there are important variations both across and within countries. Just over 20 percent of migrants in the sample come from conflict countries with high levels of human rights violations and 10 percent come from non-conflict countries with high levels of human rights violations. Another 6 percent of the migrants come from conflict countries with moderate levels of human rights violations and 16 percent come from non-conflict countries with moderate levels of human rights violations. Roughly 47 percent of the migrants come from non-conflict countries with low levels of human rights violations. Looking at the number of countries in Table 1 suggests an opportunity to focus on human rights violations and armed conflict broadly as contextual elements, across cultures, periods, levels of development, and political histories. This is not precisely controlled in our analyses, but the variation among countries in the same categories of Table 3.1 is clear.
Table 3.2 presents descriptive statistics for the sample by nativity and contexts of exit. It shows both statistically significant differences for migrants from non-conflict countries with low levels of human rights violations vs. all other migrant groups, as well as for each of the migrant groups and the native-born. In terms of sociodemographic characteristics, migrants from non-conflict countries with low levels of human rights violations are more likely to be female and have on average less education compared to the other migrant groups and the native-born in Canada.
They also are less likely to be married and have been in Canada for a greater number of years than migrants from non-conflict and conflict countries with more severe violations. Migrants from non-conflict countries with low levels of human rights violations and the native-born are
64 more likely to be White, and migrants from conflict countries are more likely to be South Asian.
The proportion of migrants from non-conflict countries with low levels of human rights violations who arrived in Canada as refugees is also significantly lower than among the other migrant groups, with the exception of those from conflict countries with moderate levels of human rights violations.
In terms of stress exposure, there are also significant differences across groups. Prior to migration, migrants from countries with high human rights violations and/or conflict experience more traumatic life events in comparison to those from non-conflict countries with low levels of human rights violations. After migration, migrants from both conflict and non-conflict countries with moderate and high levels of human rights violations experience higher levels of acculturative stress in Canada than migrants from the low/non-conflict reference group.
Migrants from conflict countries with moderate or high levels of human rights violations report more discrimination compared to the native-born; and migrants from conflict countries with high levels of human rights violations are more likely to experience discrimination compared to both migrants from non-conflict countries with low levels of human rights violations and the native- born. This migrant group is also less likely to be exposed to negative life events after arrival than the native-born. All migrant groups experience greater economic hardship than the native-born; and migrants from conflict countries also differ from migrants from non-conflict countries with low human rights violations. Migrants from conflict countries with moderate and high levels of human rights violations also tend to encounter more work-related stress than migrants in the low/non-conflict reference group and the native-born. Taken together, these results suggest that traumatic life events experienced prior to migration may increase the risk of poor mental health for migrants from non-conflict and conflict countries with high levels of human rights violations.
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Acculturative stress may also be an important mental health risk factor for migrants from non- conflict and conflict countries of origin with moderate to high levels of human rights violations and, perceived discrimination, work-related stress, and financial stress may be particular risk factors for migrants from conflict countries.
Table 3.3 presents mean symptoms of psychological distress for migrants by the two contexts of exit overall – before they are combined. The data shows that migrants from conflict countries experience more distress than those from non-conflict countries, and migrants from countries with high levels of human rights violations experience more distress than those from countries with lower levels of human rights violations overall. Migrants from countries with moderate levels of human rights violations have, on average, a similar level of symptoms as those from countries with either lower or higher levels of human rights violations. These results confirm prior results studying each element of contexts of exit separately: each is related to higher distress years after migration. However, we do not yet know how these factors combine, and whether the presence of both adds to the presence of either.
Psychological Distress After Migration
Table 3.4 presents the results of regression models for the effects of human rights violations
(HRV) combined with the presence/absence of conflict in countries of origin on psychological distress. Model 1 is the baseline model. It shows differences in distress between 1) migrants from both non-conflict and conflict countries with moderate to high HRV vs. migrants from non- conflict countries with low levels of HRV (the migrant reference group); and 2) all five migrant groups vs. the native-born, without controls. Models 2 through 8 add controls for the duration of conflict and socio-demographic characteristics, and model 9 introduces all control variables.
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Model 1 shows that migrants from non-conflict and conflict countries with high levels of human rights violations have significantly more symptoms of distress than both migrants from non- conflict countries with low levels of human rights violations and the native-born. Migrants from conflict countries with moderate levels of human rights violations have marginally more distress than those from non-conflict countries with low levels of human rights violations. After controlling for the duration of armed conflict in model 2 and for refugee status in model 3, the coefficients for the effect of high human rights violations in both non-conflict and conflict countries of origin remain virtually the same. From model 3 forward, the effect of moderate
HRV in the presence of conflict begins to be significant.
In model 4, the addition of gender leads to a modest change in the coefficients for high human rights violations in countries of origin (non-conflict and conflict) and increases the coefficient for moderate human rights violations in non-conflict countries. It also modestly increases the coefficient in moderate HRV countries with conflict, signaling that gender is acting as a modest suppressor in these categories. This finding implies that fewer females arrive especially in the non-conflict, moderate HRV category, as suggested in Table 3.2, where we see that this category has the lowest percentage of females (37.87) compared to any other category. Similarly, results in model 5 indicate that marital status suppresses the effect of both moderate and high levels of human rights violations in all countries of origin (non-conflict and conflict), indicating a higher percentage of married migrants in these categories. In model 6, educational attainment has a negative (protective) effect on distress, which is larger in magnitude for the native-born than for migrants. There is, however, very little change in the estimated coefficients for the combined effects of conflict and HRV. In model 7, age also has little influence on the effect of human rights violations in non-conflict and conflict countries of origin. In model 8, controlling for race-
67 ethnicity reduces the effect of high HRV without conflict by 29 percent to marginal significance
(from 1.365, p<.05 to 0.969, p<.07) and reduces the differences in distress between migrants with high levels of human rights violations and the native-born to insignificance. This decrease in the effect of this category appears to be explained in large part by the high levels of distress among West Asians and Arabs. By contrast, the difference between migrants from non-conflict countries with low levels of human rights violations and the native-born is increased and statistically significant at the .05 level, which is due to the lower levels of distress and higher percentage of Blacks in this migrant group.
When all control variables are added in model 9, we see (again) that migrants from non-conflict and conflict countries with moderate to high levels of human rights violations experience more symptoms of psychological distress than migrants from non-conflict countries with low levels of human rights violations. Migrants from conflict countries with high levels of human rights violations also experience more distress compared to the native-born. While those from non- conflict countries with high human rights violations and from conflict countries with moderate human rights violations experience more distress than the native-born as well, the differences are not quite statistically significant (p=0.051 and p=0.053). In effect, the overall influence of the controls, as a group, somewhat cancels the effects of individual controls after all are considered; while female and marital status act as suppressors, race/ethnicity acts as a confounder.
Nonetheless, in the aggregate, we observe the same general differences as in Model 1, except that in the case of two groups --- those coming from the most advantaged low HRV/nonconflict group and from the conflict/moderate HRV group -- net differences are notably stronger.
Table 3.5 presents the results of regression models showing the role of pre-migration stressors and post-migratory factors in explaining differences in psychological distress. Comparing model
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1 (model 9 of Table 2) to model 2, we see that the higher level of distress among migrants from both non-conflict and conflict countries of origin with moderate to high levels of human rights violations, relative to the reference migrant group and the native-born, is partly due to more traumatic events experienced prior to migration. The estimated coefficients for the effect of moderate human rights violations in non-conflict and conflict countries of origin are reduced to marginal significance; and the estimated coefficients for the effect of high human rights violations too are reduced but still statistically significant. This reflects the higher rate of pre- migration stressors relative to the reference group already noted in Table 3.2. In model 3, these differences also diminish slightly once length of stay is controlled --- but more so for the no conflict/some HRV group than others. However, the significance of each effect is not affected.
Models 4 through 8 add post-migratory stressors to model 3 individually before all are added in
Model 9. With the addition of acculturative stress in model 4, which has an understandably positive effect on distress, the coefficients for high levels of human rights violation in non- conflict countries is reduced by 17 percent to marginal significance and, in conflict countries, by
30 percent but remains significant at the .05 level. This finding suggests that acculturative stress helps especially to explain the elevated symptoms of distress among migrants from countries with higher levels of human rights violations.
Model 5 shows that recent life event stress has little to do with explaining the effects of conflict/HRV. Coefficients here either are stable or increase slightly compared to model 3. This means, in general, that migrants from high human rights violation countries do not experience more eventful stressors after migration.
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In model 6, the addition of perceived discrimination substantially reduces the effects of high levels of human rights violations in conflict countries. This effect seems specific to the explanation of differences involving the most stressful context of exit: high human rights violations co-occurring with internal conflict. However, the level of distress among this group of migrants remains significantly higher than migrants from non-conflict countries with low human rights violations (1.051, p =0.02) and the native-born (1.051, p =0.02). Given that perceived discrimination is less implicated in the explanation of differences in non-conflict countries, this finding suggests that perceived discrimination specifically is more prevalent among migrants from conflict countries. This is generally supported by results in Table 3.2.
In model 7, the coefficient for the effect of high levels of human rights violations in non-conflict and conflict countries is reduced with the addition of financial hardship. The combined effect of high human rights violations and armed conflict in countries of origin on distress levels among migrants, although reduced, remains significant at the .01 level. These results suggest that financial challenges are involved in the explanation of the effects of high human rights violations as a context of exit. With non-employment and work-related stress added in model 8, the effects of high human rights violations in countries of origin are also reduced, although somewhat less than in model 7.
When all post-migratory factors are included in model 9, the differences in distress between migrant groups are all reduced to nonsignificance except for the most extreme case: only the difference between migrants from conflict countries with high human rights violations and those from non-conflict countries with low human rights violations remains significant (0.853, p
=0.03). Using Canadians as the reference, we see much less of a role of post-migration stressors in explaining the effects of high human rights violations accompanied by internal conflict: the
70 difference between migrants from conflict countries with high levels of human rights violations and the native-born remains relatively unchanged.
Returning to model 2, we can see some suggestion that human rights violations and conflict may have approximately equal roles in eventual mental health after migration. Collapsed mean differences due to HRV are similar to collapsed differences due to conflict (results not shown).
However, we can assess this issue with more formal post-hoc tests. Specific post-hoc tests were employed to estimate statistical differences between: conflict vs. non-conflict countries overall; high levels of human rights violations vs. lower levels of human rights violations overall; conflict and high levels of human rights violations vs. non-conflict and high levels of human rights violations; conflict and moderate levels of human rights violations vs. non-conflict and moderate levels of human rights violations; and high levels of human rights violations vs. moderate levels of human rights violations in both conflict and non-conflict countries.
The results of these analyses (not shown) generally show that migrants from conflict countries experience more distress than migrants from non-conflict countries (p=0.02), migrants from countries with high levels of human rights violation experience more distress than those from countries with low levels of human rights violations (p=0.00), and migrants from non-conflict and conflict countries with high levels of human rights violations are not significantly different from each other (p>0.05). Migrants from either conflict or non-conflict countries with high and moderate levels of human rights violations do no significantly differ. Results thus show that increasing conflict under high HRV conditions has no significant impact. These results suggest that the effects of “both” do not exceed the effects of “either”, and that the presence of both does not multiply the consequences. But, importantly, results do suggest that we should study the
71 effects of both, in order to show that increases in either conflict or HRV matter, and about equally.
In sum, the results confirm that armed conflicts in countries of origin can have a long-term mental health impact. But, they also show that an increase in human rights violations can also have long-term impacts on mental health. Thus, these results support the conclusion that both conflict and state repression create a social context that can have negative consequences for mental health.
Discussion
This study used country and individual data to examine the effects of human rights violations and armed conflicts in countries of origin at the time of exit on the mental health of migrants.
Findings indicate that, even in the absence of armed conflict, human rights violations can have important repercussions for mental health after migration. Migrants from both non-conflict and conflict countries with moderate to high levels of human rights violations reported more symptoms of psychological distress than migrants from non-conflict countries with low levels of human rights violations. Those from countries with high levels of human rights violations, whether in conflict or not, also reported more symptoms than the native-born.
Socio-demographic factors account for little in these differences. Findings are complex because some factors act as suppressors (marital status and gender), while others act as confounders
(race/ethnicity, length of residence in Canada). Thus, the net effect of these controls is mixed and results in a similar profile of differences relative to models without controls.
The analysis indicates that pre-migration stressors reduce to marginal significance the effect of
“moderate” human rights violations (level 3), but not that of high human rights violations (4 and
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5). Post-migratory stressors, instead, seem to play a larger part of the explanation for the higher levels of distress of migrants from non-conflict and conflict countries with high levels of human rights violations compared to those from non-conflict countries with low levels of human rights violations and the native-born. Economic hardship in particular helps explain the higher levels of distress among migrants from non-conflict countries with high levels of human rights violations; whereas acculturative stress, perceived discrimination, and economic hardship (to a lesser degree) help explain the higher levels of distress among migrants from conflict countries with high levels of human rights violations. It is possible that economic hardship results from more negative labour market outcomes associated with migration from repressive countries of origin
(Borjas et al. 1987; Van Tubergen et al. 2004) and perceived discrimination from more negative attitudes towards migration from conflict countries. It is also possible that acculturative stress is related to and attributable to conflict-related trauma (Messer and Rasmussen 1986) or alternatively to lower levels of preparation leading to migration. Migration that is not planned often leads to more acculturative stress (Gong et al. 2001) partly due to less familiarity with the host country (see Berry et al. 1987; Hovey 2009).
Contrary to what might be expected, the effect of human rights violations is not more pronounced in the presence of conflict. Migrants from conflict and non-conflict countries with moderate to high human rights violations all had similar levels of distress. Post-hoc tests suggested that both conflict and human rights violations increase distress after migration, but the presence of both has no further effect.
On the whole, the results of this study provide new evidence that social contexts that are stressful and threatening at the time of exit can have negative impact on mental health, up to 20 years or more after migration. In results not shown, an interaction between length of stay and the four
73 migrant categories was tested, but was not significant. The results also provide further support to the stress proliferation argument and, consistent with the migration literature, show that contexts of exit can have an impact on pre-migratory experiences and can be consequential for the adaptation in the country of reception as well.
Some important limitations, however, must be considered in interpreting the results. First, the list of items used to measure traumatic events that occurred prior to migration may not capture some of the traumatic events experienced under situations of severe human rights violations and/or armed conflict. Second, mental health was measured at only one point in time, that is, the time of the interview. A measure of mental health at other time points, such as prior to an armed conflict and/or a situations of severe human rights violations, would have allowed to better assess the impact of armed conflict and human rights violation on mental health after migration. Result may differ if broader measures of current mental health were used. Lastly, the sample size of migrants from armed conflict countries with low to moderate levels of human rights violations was small and not sufficiently large to separate the overall effects of human rights violations from armed conflict per se.
Despite these limitations, this study advances our understanding of the impact of contexts of exit on mental health. Taking all results together, it seems that migrating from high human rights violations situations or from an armed conflict situation each result in lasting impacts on mental health. It also seems that migration from conflict and high human rights violations situations does not result in further mental health problems. But the role of stress trajectories both before and after migration does differ to some degree depending on whether the issue is armed conflict or high human rights violations. Because the effect of race/ethnicity is controlled, the differing roles of acculturative, discriminatory, and financial and work stress suggest that coming from a
74 conflict background results in somewhat different problems of adjustment than coming from a human rights violations background. In turn, this implies that overly specific or insufficient measures of the challenges presented by stress after migration will misrepresent the life course consequences of migration from conflict and human rights violations conditions. The quite generalized deployment of stressors differing in content and phenomenology allowed the explanation of differences in distress between and across a diverse set of migrant groups from a very large and diverse set of country origins.
______
NOTES
1. The timeframe is two weeks instead of 30 days.
2. For example, from 1946 to 2001, there have been 42 interstate conflicts and 163 intrastate conflicts (Gleditsch et al. 2002).
3. I used Statistics Canada‟s population group classification to code the race/ethnicity variables, which include: White, Chinese, South Asian, Black, Filipino, Latin American, South Asian,
Arab, West Asian, Japanese, Korean, Aboriginal, Jewish, Caribbean, and „other.‟ I coded
Aboriginal into the „other‟ group and combined West Asian and Arab together as well as
Southeast Asian, Korean, Japanese, and Filipino into the 'East/Southeast Asian' group because of their small sample size.
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Table 3.1. Countries of Origin by Levels of Human Rights Violations and Conflict at the Time of Migration (n=610) Non-Armed Conflict Armed Conflict N =447 N =163 Levels of human rights violations Australia Haiti Romania Bahamas, the Hungary St-Vincent & the Grenadine Low Barbados Iceland Singapore N =288 Czech Republic Italy Switzerland DR Congo Jamaica Taiwan, China Dominican Japan Tanzania Republic Kenya Trinidad and Tobago Ecuador Kuwait Ukraine Fiji Lavtia United Kingdom France Malaysia United States Gambia, the Mauritius Uruguay Germany New Zealand Venezuela Ghana Poland Vietnam Greece Portugal Yugoslavia Grenada Rep. of Ireland Zimbabwe Guyana Rep. of Macedonia Albania Israel South Africa India Argentina Jamaica South Korea Iran Moderate Bangladesh Lesotho Soviet Union Lebanon N =133 Bosnia Malaysia Sri Lanka Philippines Brazil Mexico Taiwan, China South Africa Bulgaria Moldova, Rep. of Thailand Sri Lanka Chile Nicaragua Trinidad and Tobago Turkey Cuba Nigeria Ukraine Ecuador Panama United Arab Emirates Estonia Peru United States Ethiopia Poland Venezuela Greece Romania Vietnam Guyana Saudi Arabia Yemen India Serbia Yugoslavia Iran Sierra Leone Afghanistan Liberia Bangladesh India Serbia Angola Mexico High Bosnia Iraq South Africa Bangladesh Morocco N =189 Brazil Israel South Korea Bosnia Nepal Chile Jamaica Syrian, Arab Rep. Burundi Nicaragua China Mexico Tanzania Chile Pakistan El Salvador Montenegro Venezuela Colombia Peru Ghana Pakistan Egypt Philippines El Salvador Russia Ethiopia Somalia Guatemala South Africa India Sri Lanka Indonesia Turkey Iraq Uganda Lebanon Zimbabwe Note: countries that do not change in classification are in regular type; countries that do change in classification are in italic type.
86
Table 3.2. Descriptive Statistics by Nativity and Contexts of Exit Migrants from Non-Conflict Countries Migrants from Conflict Countries Low human rights Moderate human High human rights Moderate human High human Native-born violations rights violations violations rights violations rights violations Canadians N =288 N =99 N =60 N =34 N =129 N =1,461 Length of conflict ------12.69 (1.27) 9.29 (0.61) --- Refugees (%) 5.14 12.41 a 7.66 0.00 18.77 a --- Length of stay 23.34 (0.63) 16.68 (1.05) a 13.92 (1.41) a 11.34 (1.38) a 14.22 (0.84) a --- Age 43.52 (0.60) 42.69 (1.07) 42.49 (1.34) 43.98 (1.50) 42.50 (0.78) 42.77 (0.28) Female gender (%) 59.75 b 37.87 a, b 46.58 a 48.73 49.48 a 49.80 Married (%) 46.16 64.87 a, b 69.54 a, b 81.59 a, b 76.47 a, b 43.03 Education (years) 15.95 (0.24) b 16.93 (0.34) a 16.99 (0.41) a 17.30 (0.54) a 16.83 (0.32) a 16.58 (0.09) Black (%) 9.52 b 6.11 b 1.84 a, b --- 11.07 b 0.27 Caribbean (%) 14.01 b 6.26 a, b ------0.58 East/Southeast Asian (%) 6.19 b 8.37 b 26.65 a, b 25.11 a, b 11.36 a, b 1.76 South Asian (%) 3.31 b 10.20 a, b 15.74 a, b 52.41 a, b 45.63 a, b 1.10 Latin American (%) 3.56 b 20.20 a, b 11.52 a, b --- 6.56 b 0.40 West Asian/Arab (%) --- 8.07 a 7.51 a 5.10 a 7.05 a 0.22 Jewish (%) 2.35 b 5.11 4.28 2.31 1.05 b 6.71 Other (%) 1.01 --- 3.75 a, b --- 0.36 1.12 Multi (%) 25.17 5.84 a, b 18.90 b 8.09 a 12.85 a, b 8.37 White 34.90 b 29.85 9.82 a, b 6.93 a, b 4.07 a, b 79.46 Pre-migration stress 0.83 (0.06) 1.07 (0.13) 1.35 (0.06) a 1.54 (0.06) a 1.10 (0.09) a --- Recent life events 0.53 (0.05) 0.73 (0.14) 0.51 (0.11) 0.43 (0.13) 0.36 (0.06) a, b 0.59 (0.03) Acculturative stress 1.01 (0.04) 1.33 (0.07) a 1.47 (0.09) a 1.70 (0.09) a 1.54 (0.05) a --- Discrimination 1.45 (0.04) 1.41 (0.06) 1.50 (0.07) 1.56 (0.15) b 1.62 (0.06) a, b 1.38 (0.02) Financial strains 0.65 (0.04) b 0.67 (0.06) b 0.78 (0.09) b 0.92 (0.12) a, b 0.78 (0.06) a, b 0.50 (0.02) Work-related stress 0.54(0.03) 0.62 (0.05) 0.60 (0.06) 0.97 (0.13) a, b 0.65 (0.04) a, b 0.56 (0.01) Non-employment 31.16 28.41 31.81 31.54 32.01 29.70 Note: Means and standard errors (in parentheses) for continuous variables and percentages for categorical variables. Statistical differences at the .05 level between migrants from non-conflict countries with low levels of human rights violations and the other migrant groups as well as between the five migrant groups and the native-born are tested with t-tests (for continuous variables) and chi-squared tests (for categorical variables). a= significantly different from migrants from non-conflict countries with low levels of human rights violations b= significantly different from the native-born
87 Table 3.3. Mean Symptoms of Psychological Distress by Contexts of Exit (N =610)
Human Rights Violations Low Moderate High No Conflict Conflict P<0.05 Mean 17.53 18.25 18.82 17.85 18.78 * a, b, c S.D. 7.03 7.30 7.52 7.07 7.71 N= 288 133 189 447 163
Notes: means range from 10 to 50; S.D.= standard deviation; exponentials signify significant differences between migrants groups: a= between migrants from conflict vs. migrants from non-conflict countries b= between migrants from countries with high levels of human rights violations vs. migrants from countries with lower levels of human rights violations overall c= between migrants from countries with high levels of human rights violations vs. migrants from countries with low levels of human rights violations
88 Table 3.4. Ordinary Least Squares Regression of Psychological Distress on Contexts of Exit and Socio-demographic Characteristics (N =2,071) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Context of Exit (vs. no conflict/low h.r.v.) No conflict/some h.r.v. 0.541 0.541 0.489 0.826* 0.935* 0.646† 0.459 0.445 0.960* No conflict/high h.r.v. 1.365* 1.365* 1.347* 1.537** 1.858*** 1.478** 1.338* 0.969† 1.433** Conflict/some h.r.v. 1.240† 1.125† 1.276* 1.383* 1.986** 1.385* 1.335* 1.140† 1.730* Conflict/high h.r.v. 1.250** 1.238** 1.154** 1.383*** 1.888*** 1.344*** 1.221** 1.261* 1.691*** Context of Exit (vs. native-born) No conflict/low h.r.v. -0.390 -0.390 -0.426 -0.520† -0.324 -0.389 -0.307 -0.661* -0.411 No conflict/some h.r.v. 0.151 0.151 0.063 0.306 0.611† 0.257 0.152 -0.216 0.549 No conflict/high h.r.v. 0.975* 0.975* 0.921* 1.017* 1.534** 1.089* 1.031* 0.308 1.002† Conflict/some h.r.v. 0.850 0.735 0.850 0.863 1.662* 0.966 1.028† 0.479 1.319† Conflict/high h.r.v. 0.860* 0.848* 0.728* 0.863* 1.564*** 0.955** 0.914* 0.600 1.280** Controls Duration of conflict 0.030 0.029 Refugee status 0.704 0.969 Female gender 1.306*** 1.138*** Marital status -2.107*** -1.600*** Education - migrants -0.107* -0.099* Education - native-born -0.280*** -0.273*** Age -0.250* -0.117 Age squared 0.002 0.001 Black -1.439* -2.241*** Caribbean 0.994 0.177 East, Southeast Asia 0.828 0.776 South Asian 0.131 0.098 Latin American 0.673 -0.142 West Asian/Arab 3.203*** 2.669** Jewish 0.641 1.346* Other 0.862 0.530 Multi 1.463*** 1.125** *** p<0.001, **p <0.01, * p<0.05, † p<0.10 (one-tailed test) Note: The statistical significance of the differences in psychological distress between migrant groups and the native-born is estimated using post-hoc test.
89 Table 3.5. Ordinary Least Squares Regression of Psychological Distress on Contexts of Exit and Post-Migratory Factors (N =2,071) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Context of Exit (vs. no-conflict/low h.r.v.) No conflict/some h.r.v. 0.960* 0.819† 0.664† 0.472 0.379 0.711† 0.438 0.441 0.189 No conflict/high h.r.v. 1.433** 1.208* 1.104* 0.923† 1.065* 1.030* 0.776† 1.010* 0.666 Conflict/some h.r.v. 1.730* 1.308† 1.106† 0.699 1.015 0.806 0.843 0.191 -0.054 Conflict/high h.r.v. 1.691*** 1.598*** 1.429** 1.008* 1.477** 1.051* 1.235** 1.323** 0.853* Context of Exit (vs. native-born) No conflict/low h.r.v. -0.411 -0.372 -0.109 0.216 0.099 0.000 -0.206 0.144 0.234 No conflict/some h.r.v. 0.549 0.447 0.555 0.688 0.478 0.711† 0.232 0.585 0.423 No conflict/high h.r.v. 1.002† 0.836† 0.995† 1.139* 1.164* 1.030† 0.570 1.154* 0.900† Conflict/some h.r.v. 1.319† 0.936 0.997 0.915 1.114 0.806 0.637 0.335 0.180 Conflict/high h.r.v. 1.280** 1.226* 1.320* 1.224* 1.576** 1.051* 1.029* 1.467** 1.062* Pre-migration trauma 0.958*** 0.886*** 0.740* 0.722** 0.718** 0.314 0.687** 0.187 Post-Migration Length of Stay -0.038† 0.065** -0.044* -0.039* -0.017 -0.033† 0.025 Length of Stay sq. -0.002 -0.002 -0.002† -0.002 -0.004** -0.003* -0.003* Acculturative Stress 2.494*** ------1.163*** Recent Events 1.127*** ------0.496*** Discrimination 2.182*** ------1.273*** Financial Stress 3.909*** --- 2.885*** Work-related Stress 1.803*** 0.618* Not employed 4.024*** 2.174*** *** p<0.001, **p <0.01, * p<0.05, † p<0.10 (one-tailed test) Notes: All models are adjusted for duration of conflict, refugee status, gender, marital status, education, age, and race/ethnicity. The statistical significance of the differences in psychological distress between the migrant groups and native-born is estimated using post-hoc test.
90
Chapter 4 The Employment and Occupational Status of Migrants from Countries Experiencing Armed Conflict Introduction
Studies indicate that the degree of political stability and freedom in the country of origin can impact migrant labour market outcomes in the country of reception. Research shows that migrants from politically less stable and more repressive countries participate less in the labour force, are more likely to be unemployed, and have lower earnings overall than migrants from other countries (Borjas 1987; Fleischmann and Dronkers 2010; Van Tubergen, Maas and Flap
2004).
Scholars argue that the differences in labour market outcomes in the country of reception for migrants from repressive and less stable countries reflect a compositional or a selection effect.
For example, Van Tubergen et al. (2004) find that the higher levels of unemployment and nonemployment of migrants from more repressive countries is explained by lower human capital.
Similarly, Fleischmann and Dronkers (2010) argue that the higher levels of unemployment among migrants from less stable and/or more repressive countries of origin is explained by the higher proportion of refugees, but they do not directly test this interpretation.
Except for these studies, very little research has examined systematically the effect of political contexts in countries of origin on migrant employment outcomes. The present study fills this gap and revisits these hypotheses by considering both the effect of armed conflict in countries of origin and human rights violations on migrant employment outcomes while considering refugee status as a control. It considers also the effect of education completed in the country of origin vs. in the country of reception, a topic that has not yet been studied in relation to the political
91 contexts of exit. It is possible that armed conflict and political instability in countries of origin affect not only education, but also the quality of education, potentially having important consequences for eventual employment outcomes for migrants.
Using country level data from the UCDP Conflict Termination Dataset (Kreutz 2010) and individual level data from a representative sample of adults living in Canada, this study assesses the effect of armed conflict and human rights violations in countries of origin on two labour market outcomes: employment and occupational status. The effect of armed conflict is examined in conjunction with the effects of refugee status, other socio-demographic characteristics, human capital, and three other variables that have been found to influence labour market outcomes: pre- migration traumatic events (Wright et al. 2016); the level of economic development in the country of origin (Fleishmann and Dronkers 2010; Spörlein and Van Tubergen 2014; Van
Tubergen et al. 2014); and the unemployment rate in the country of reception at the time of arrival (McDonald and Worswick 1997).
This study has four specific points of departure from the existing literature. First, it examines the effect of armed conflict in countries of origin in addition to repression and instability, allowing a comparison of the relative labour market consequences after migration. Armed conflicts may have a more direct and disruptive influence on institutional operations, and this could be especially the case with educational institutions that depend on regular attendance and resource support. Second, it considers the effect of education completed in the country of origin and destination separately rather than only the effect of educational attainment overall. Third, it compares employment outcomes among migrants and also between migrants and the native-born in contrast to migrants only, which helps separate the effect of armed conflict and human rights violations from the effect of migration status per se. And fourth, it examines the effect of the
92 political context on employment outcomes in Canada, not in European countries, and thus tests the transferability of previous research findings to a different country context.
Literature Review Immigration and Employment Outcomes
Immigrants in Canada experience considerable disadvantages in the labour market in terms of employment and occupation. Recent immigrants, in particular, are more likely to be unemployed than the native-born (Yssaad 2012). These differences are especially pronounced during periods of employment difficulties (McDonald and Worswick 1997) but diminish with length of stay, with long-term immigrants experiencing comparable rates of unemployment as the native-born
(Yssaad 2012). Immigrants are also more likely to be underemployed and have lower occupational status than the native-born (Boyd 1984; Gilmore 2009; Zietsma 2010). Unlike the evidence for employment status, these differences in occupational status do not change as much as length of stay increases (Galarneau and Morissette 2008; Reitz, Curtis, and Elrick 2014).
Several studies demonstrate that immigrant employment outcomes vary also by race/ethnicity, gender, and entry status. Research shows that racialized immigrants are more likely to be unemployed, work in low-skilled jobs, and experience more difficulties in finding employment in their intended occupation than non-racialized immigrants (Frank 2013; Nakhaie and
Kazemipur 2013). Studies also show that immigrant women have lower occupational attainment and employment probability than men and native-born women (Boyd 1984; Hudon 2015).
Further, studies that compare refugees to other groups of migrants or the native-born often find that refugees are more likely to be unemployed, underemployed, or have lower occupational
93 status, especially in the first years after arrival (Beiser and Hou 2001; Bevelandur and Pendakur
2014; Khran et al. 2000; Phythian et al. 2009).
Contexts of Exit and Employment Outcomes
As the introduction to this paper argues, relatively little is known about differences in migrant employment outcomes across political contexts of exit. In Canada, studies that compare refugees and other classes of migrants usually do not account for the effect of context in countries of origin on employment outcomes. The research that does exist focuses on Europe, and shows that political instability and repression in countries of origin negatively impact migrant employment probability (Fleischmann and Dronker 2010; Van Tubergen et al. 2004) but state repression is apparently not related to achieved occupational status when employed (Spörlein and Van
Tubergen 2014). While the focus in these studies is on repression and political instability, armed conflicts may have important impacts on eventual employment outcomes as well, given their interaction, especially through its influence on educational institutions. Relative to conditions accompanying state repression, which may be more stable and where expected institutional operations are stable, armed conflicts may particularly affect access to, and the quality and continuity of, education, given the inherent instability and unpredictability accompanying armed conflict. Educational institutions may also experience a significant loss in teaching resources and financial support in these conditions (Lai and Thyne 2007; Sinclair 2001).
Linking Armed Conflict in Countries of Origin to Employment Outcomes
Migrants from armed conflict countries may experience some of the same barriers and difficulties in finding employment and employment in higher status occupations as those experienced by migrants from non-conflict countries, but in higher proportion. Overall, research
94 in Canada demonstrates that migrants experience a number of barriers and difficulties in finding employment and employment in higher status occupations. These include the devaluation of foreign education and work experiences (Adamuti-Trache, Anisef, and Sweet 2013; Galarneau and Morissette 2008; Li 2001; Reitz 2001; Wanner 1998), discrimination and exclusion because of race, ethnicity, and national origins (Reitz 2001; Teeluckising and Galabuzi 2005), the demand for “Canadian experience” (Sakamoto, Chin, and Young 2010), and the lack of familiarity with the labour market and of proficiency in English (see Reitz 2007)
Migrants from armed conflict countries may be even less able to transfer their foreign credentials after migration. Armed conflicts usually lead to reduced government spending on education (Lai and Thyme 2007), and can negatively affect the quality of education (Sinclair 2001)which, according to Sweetman (2004), may contribute to differences in returns to education in Canada.
These countries also tend to be less economically developed, a factor also linked with the devaluation of foreign credentials (Bratsberg and Ragan 2012) and associated negatively with employment and occupational status (Spörlein and Van Tubergen 2014; Van Tubergen et al.
2004).
Migrants from armed conflict countries also experience more traumatic events prior to migration than migrants from non-conflict countries (Joly and Wheaton 2015), which may negatively impact the sustainability of employment upon resettlement. For example, Wright et al. (2016), in a sample of Iraqi refugees in the US, find that refugees who were exposed to more traumatic events both prior to and after migration were more likely to be unemployed (Wright et al. 2016).
Hauff and Vaglum (1993), in a study on Vietnamese refugees in Norway, also find that those who were wounded in war were more often unemployed, although they find that those who witnessed others being wounded or killed were not. August and Gianola (1987), in a study on
95
Southeast Asian refugees in the United States, show that the recollection of war can adversely affect the ability to work.
It is also possible that migrants from armed conflicts may experience higher levels of unemployment and lower occupational status in part because they are also more likely to come as refugees than migrants from non-conflict countries (see Fleischmann and Dronkers 2010).
Refugees are not selected on employability like economic immigrants and as a result may experience more difficulties in the labour market. Studies show that human capital can affect refugee employment outcomes. For example, Beiser and Hou (2001) find that English ability is an important predictor of employment among established refugees. Phythian et al. (2009) also find that differences in language ability and education help explain the higher levels of unemployment of refugees in the year of arrival.
On the other hand, differences in employment outcomes between migrants from conflict and non-conflict countries may have little to do with differences between entry statuses. The distinction among migrants by entry status falls short of the complexity of migratory experiences. The migration that results from armed conflict is often structured through „mixed flows‟, composed of both refugees and more economically motivated migrants, and others that blur the boundary between these categories (Van Hear, Brubaker, and Bessa 2009).
Consequently, migrants from armed conflict countries may be more similar to migrants from non-conflict countries in terms of human capital than expected. Some existing research challenges also the human capital argument as an explanation for the employment and occupational status of refugees, finding that refugee employment outcomes have more to do with the devaluation of foreign credentials (Krahn et al. 2009; Lamba 2003). These finding point toward the effects of origins or origins‟ contexts, and not entry status.
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Analytical Questions
This study examines the effect of armed conflict in countries of origin on employment and occupational status among migrants in Canada. In particular, it asks: 1) does armed conflict in country of origin have an effect on eventual employment outcomes in Canada? 2) does the effect of armed conflict have an effect on employment outcomes over and above any effects due to human rights violations?3) do patterns of origin and destination education explain differences due to armed conflict? 4) and, are the effects of armed conflict on employment outcomes explained by traumatic events experienced in the country of origin before migration?
Methods Sample
This study uses data from the Neighbourhood Effects on Health and Well-being (NEHW) Study, a multistage probability sample of English-speaking adults aged 25 to 64, living in the City of
Toronto, Canada (O‟ Campo et al. 2015). The data were collected on 2,412 adults interviewed between March 2009 and June 2011, with a response rate of 72 percent. Because this study focuses on employment and occupational status, the sample for the analysis excludes participants who were out of the labour force (n= 423). It also excludes those with missing data on either the dependent or independent variables (n=143). The final analytical sample consists of 1,846 respondents: 671 foreign-born and 1,175 native-born.
Measures
Outcomes
Labour market incorporation is measured with two outcome variables: unemployment status and occupational status. Unemployment status is a dichotomous variable, coded 1 for unemployed
97 and 0 for employed. In results not shown, initial assessments of a more detailed employment status outcome, distinguishing full-time from part-time in addition, showed no significant differences in the effects of core predictors on full-time vs. part-time. As a result, these categories were combined in this analysis. As noted above, individuals not in the labour force are excluded. These include respondents answering in these categories: retired/disabled, homemaker, student, and volunteering.
Occupational status is a continuous variable, measured using Boyd‟s (2008) occupational status scale. Scale scores were matched to the National Occupation Classification for Statistics (NOC-
S) codes associated with the occupation and main duties reported by the respondents employed at the time of the survey. Scores within the study sample range from 4 to 100, with higher scores indicating higher occupational status.
Contexts of Exit and Reception
Armed conflict in countries of origin is measured with data from the UCDP Conflict Termination dataset (Kreutz 2010) merged with the survey data according to country names. Data used include the type, the start and end dates of each conflict episode for the years 1946-2007, and the year of immigration. I first created a dummy variable for migration status coded 1 for foreign- born and 0 for native-born. Using native-born as the reference group, I then divided the foreign- born into two groups based on the presence or absence of an intrastate armed conflicts in countries of origin at the time of migration and created a dummy variable for conflict status coded 1 for foreign-born from conflict countries and 0 otherwise. Because of this two-stage coding, in all analyses, the dummy variable for migrant status contrasts foreign-born from non- conflict countries with the native-born, and the dummy variable for conflict status contrasts the foreign-born from conflict vs. non-conflict countries. The difference between the foreign-born
98 from conflict countries and the native-born is the sum of the coefficients for migrant and conflict status.
The level of human rights violations in countries of origin is measured with data from the
Political Terror Scale (PTS) (Gibney and Dalton 1996). The data were merged with the survey data by year of immigration and countries of origin. The PTS is a 5-point scale ranging from 1 to
5 that assesses the level of human rights violations of countries for the years 1976 to 2008 (see appendix C and Wood and Gibney 2010:373 for a description of PTS scores). Scale scores were used to code three dummy variables that identify countries of origin with low (< 3), moderate (=
3), and high (>3) human rights violations at the time of migration. I also used data collected on politicide and genocide (Harff and Gurr 1988; Harff 2003) to code countries of origin with high level of human rights violations from 1970 to 1975, and used the PTS scores for the year of 1976 to code countries of origin with low and moderate level of human rights violations for the previous five years. On all three dummy variables, the native-born are coded 0 using the same coding-scheme as for the measure of armed conflict.
Economic development in country of origin is used as a control because of the overlap with the presence of conflict. This variable is measured as a continuous variable constructed by merging data on gross domestic product (GDP) per capita for the years 1950 to 2007 from Penn World
Tables 6.3 (Heston, Summer, and Aten 2009) with data on countries of origin and years of immigration from the survey sample. Because a country‟s level of economic development may be one of the background causes of the armed conflict, this variable is lagged to the year in which the conflict started in the country of origin for migrants from conflict countries. For migrants from non-conflict countries, GDP was measured at the time of migration. The variable is centered at its mean, with native-born conditionally coded 0.
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The unemployment rate at the time of immigration is a control measured as a continuous variable using data from the Canadian Labor Force Survey on the national unemployment rate for the years 1946 to 2010 (Statistics Canada 2017) and data on immigration years from the survey sample. The variable is mean centered with the native-born coded 0.
Mediators and Controls
The measure of education was created in four steps. I first created two variables for education, each measured as continuous variables centered at their means: total years of education for the foreign-born with the native-born coded 0, and total years of education for the native-born with the foreign-born coded 0. I then constructed a variable for the proportion of education completed in countries of origin to address the possibility that foreign educational credentials have lower returns in the destination context. This variable is based on self-reported information on both the number of years of education completed prior to migration and the number of years of education attained overall. Using this information, I created three dummy variables for low (i.e. less than
10 percent), mid (between 10 percent and 90 percent), and high (i.e. more than 90 percent) proportion of education completed in countries of origin. On all three variables, the native-born are coded 0.
To assess whether the effect of education among the foreign-born was sensitive to the proportion of that education in the country of origin, I estimated an interaction between total years of education and the proportion dummy variables for both employment outcomes. This interaction was highly significant in both cases, indicating that the proportion of education in the country of origin does change the impact of education on work outcomes. However, in the case of employment status, the main difference in the effect of education, according to post-hoc tests, occurred only for those with a high proportion of foreign education vs. others. For this analysis,
100 therefore, mid and low were combined. For occupational status, the mid and high categories both showed significant differences relative to the low, and so mid and high were combined. The interactions are presented as separate variables, showing the effect of education among the foreign-born distinguished by low, mid, or high proportion educated in the country of origin as necessary.
Foreign work experience is measured as a continuous variable for the number of years the foreign-born respondents worked for pay prior to immigrating to Canada. The variable is mean centered with native-born conditionally coded 0.
English proficiency is measured with three items asking respondents to indicate how well they are able to “speak in English,”“read a book in English,” and “write a letter in English” on a 5- pointscale from poor (1) to excellent (5). The measure consists of the average responses of foreign-born, centered at their mean, with native-born coded 0(.
Pre-migration traumatic events are measured with 9 items from an 18-item checklist (Turner,
Wheaton, and Lloyd 1995): “have any of your parents died;” “has a spouse or other loved one, including other children you have had died;” “have you ever been in a major fire, flood, earthquake, or other natural disaster;” “did you ever have a major illness or accident that required you to spend two weeks or more in the hospital;” “have you ever had a serious accident, injury or illness that was life-threatening or caused long-term disability;” “did either of your parents drink or use drugs so often or so regularly that it causes problems for the family;” “did your parents often have violent argument;” “did your parent(s) have so little money that you lived much of your childhood in poor housing or being unable to pay bills, or buy food and clothes;” and “did either of your parents have such problems with nerves or depressions that they were unable to
101 work or had to have treatment.”For each item, respondents were asked to indicate whether the event occurred in their lifetime and to report the age they were when the event occurred. I used this information and the age at immigration to create the measure, which consists of the number of events the foreign-born experienced prior to migration, with the native-born given the mean value.
Other controls in the analysis include gender, coded 1 for women and 0 for men, marital status, coded 1 for married and 0 for non-married, race/ethnicity, coded into eight groups using a modified version of Statistics Canada groups classification in the 2006 Census: Black and
Caribbean, Latin American, East/Southeast Asian, South Asian, West Asian/Arab, Jewish, multiple groups, and White (the reference group), age, coded as a continuous variable, refugee status, coded 1 for foreign-born respondents who came to Canada as refugees and 0 otherwise, and length of stay in years, coded as a continuous variable, centered at its mean, with native-born assigned a value of 0.
Analysis
I conduct three sets of analyses.
The first analysis compares the simple bivariate and then simultaneous effects of armed conflict and human rights violations, using logistic regression to model the probability of unemployment, and OLS regression to model occupational status.
The next set of analyses examines the effect of significant contexts of exit on unemployment using the linear probability model (LPM). This is a specific application of OLS regression for studying the probability of a binary outcome. Although this formally breaks one of the fundamental assumptions of OLS – heteroscedastic errors – there are direct benefits in cases
102 where you want to compare the effects of a focal variable – armed conflict – on the probability of unemployment across models with different independent variables. The problem with logistic regression here is that the unobserved heterogeneity in the model changes as you add variables, and as a result coefficients are not comparable (Mood 2010). Wooldridge (2002) shows that the estimates from the linear probability model are in fact unbiased and consistent estimates of the probability of the outcome. In order to compare the effects of the context of exit across models with controls and mediators, I use the LPM in this paper.
The analyses follow a prescribed sequence of models. For example, given that I emphasize the effects of armed conflict at this point, the first two models of these analyses estimate the difference in the probability of being unemployed between migrants from conflict and non- conflict countries, and each of the two migrants group vs. the native-born, first without and then with controls. The next models introduce variables for national unemployment rates in the host country and GDP per capita in the country of origin, education, work experience, language proficiency, and pre-migration events to assess the role of each variable on the effect of armed conflict on unemployment. The second set of analyses examines the effect of armed conflict on occupational status using OLS regression, using the same sequence of models but applied to the sub-sample of respondents employed at the time of the survey (n=1,586).
All analyses are conducted using case weights (see O‟ Campo et al. 2015), adjusted for foreign- born status, gender, total household income and household size at the population level in the city of Toronto.
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Results Descriptive Statistics
Table 4.1 presents descriptive statistics on all variables used in the analysis by nativity and conflict status. As shown, migrants from conflict and non-conflict countries differ from the native-born on most variables. Migrants from armed conflict countries tend to be younger and are more likely to be married than migrants from non-conflict countries and, like migrants from non-conflict countries, are older and more likely to be married than the native-born. They are also more likely to be men, to be refugees, to come from countries with lower levels of GDP per capita and higher levels of human rights violations, and have arrived in Canada more recently than migrants from non-conflict countries. A higher proportion of migrants from armed conflict countries are South Asian, whereas a higher proportion of migrants from non-conflict countries are White, Black, Caribbean or identify with more than one racial/ethnic group.
Migrants from conflict and non-conflict countries differ also on human capital and the number of past traumatic experiences. Migrants from armed conflict countries tend to have higher education overall and have acquired more years of schooling in their countries of origin than migrants from non-conflict countries. They also have had more years of work experience in their countries of origin, are less proficient in English, and have experienced more traumatic events prior to migration.
In terms of employment outcomes, migrants from conflict countries have higher unemployment rates than migrants from non-conflict countries and the native-born, although the differences between migrants groups are not statistically significant. Migrants from conflict countries also
104 have, on average, lower occupational status than both migrants from non-conflict countries and the native-born.
Contexts of Exit and Employment Status
Table 4.2 presents results from bivariate analyses for the effects of armed conflicts and human rights violations on unemployment status and occupational status. Results show that migrants from armed conflict countries have a marginally higher probability of unemployment than migrants from non-conflict countries and a significantly higher probability of unemployment than the native-born. Migrants from countries with moderate or high levels of human rights violations do not have a significantly higher probability of unemployment than migrants from countries with low levels of human rights violations - which contradict findings of Fleischmann and Dronkers (2010) and Van Tubergen et al. (2004).Results also show that migrants from armed conflict countries have significantly lower occupational status than both migrants from non- conflict countries and the native-born. Migrants from countries with moderate to high levels human rights violations do not significantly differ from migrants from countries with low levels of human rights violations or the native-born, with the exception of those from countries with high levels of human rights violations have lower occupational status than the native-born.
One cannot test for the net effects of armed conflict vs. human rights violations just by entering both into the same model. With both variables in the model, the reference group changes to respondents with no conflict and low human rights violations, rather than one or the other in general. Thus, results in this model are not comparable to the two bivariate results. Instead, I constructed post-hoc tests which evaluated the net average effect of each factor within levels of the other. For example, to test the effects of human rights violations, the test averaged the effect of mid/high vs. low human rights violations across no conflict and conflict categories. The same
105 was done for differences due to armed conflict within levels of human rights violations. These tests showed that armed conflict had a net significant average effect on occupational status, but human rights violations did not. Due to this result, the remainder of the analyses focus on the effects of armed conflict only.
Table 4.3 presents results from linear probability regressions for the effects of armed conflicts on the probability of unemployment. Because of the significant interaction between conflict and length of stay (analyses not shown), each model shows effects of conflict on the probability of being unemployed for recent migrants (i.e. migrants who have been in Canada for less than 20 years) and long-term migrants (i.e. migrants who have been in Canada for 20 years or more) separately, with both non-conflict background and the native-born as reference groups.
Model 1 is the baseline model, and shows that recent migrants from armed conflict countries have a higher probability of being unemployed than both migrants from non-conflict countries and the native-born. In contrast, long-term migrants from conflict countries have a lower probability of being unemployed than migrants from non-conflict countries and have a similar probability of unemployment as the native-born. This pattern reflects the interaction: the effects of armed conflict on unemployment occur in the early years, up to 19 years, but after that, eventually, the relationship switches and migrants from conflict countries have a marginally lower probability of unemployment. This could happen for many reasons. One obvious possibility is that migrants may experience fewer barriers in finding employment over time; for example, they may be more proficient in English and familiar with the labour market, and have gained work experience in Canada (see Hou and Beiser 2001). There may also be a higher rate of dual employment in these households over time, perhaps due to necessity in an expensive living environment.
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With adjustment for gender, age, race/ethnicity, marital and refugee status in model 2, the difference between long-term migrants from conflict countries and migrants from non-conflict countries is no longer significant at the .05 level. This is consistent with the interpretation above, if, for example, more migrant women go to work over time. The difference between recent migrants from conflict countries and both migrants from non-conflict and the native-born remains significant and actually increases (from 0.084 to 0.142, and from 0.108 to 0.163). This increase is explained by the suppressing effect of two variables: marital status and refugee status.
The results in model 2 show that being married or being a refugee is associated with a lower probability of unemployment and, as we know from Table 1, migrants from armed conflicts are more likely than any other groups to be married and to have come to Canada as refugees. Thus controlling for marital and refugee status reveals a stronger association between armed conflict in countries of origin and unemployment in the host country among recent migrants, an association that would be missed without these controls.
The addition of the variables for the unemployment rate at the time of arrival and the economic development of the country of origin at the time of migration in models 3 and 4 have no significant effect on the probability of being unemployed.
Model 5 and model 6 add the variables for education and work experience. Model 5 shows that higher levels of education among the native-born is associated with a lower probability of unemployment and that higher levels of education among the foreign-born is associated with a higher probability of unemployment if education occurred primarily in the country of origin.
There is no effect if only a low to mid proportion of education occurred in the home country.
Thus, the employment penalty is conditional, not general, and it only occurs if most of the education occurred in the home country.
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Results also show that the addition of the variable for the interaction between years of education and proportion of education completed in countries of origin reduces the probability differences for recent migrants from armed conflict countries relative to both migrants from non-conflict conflict countries and the native-born by 16 percent and 14 percent, respectively (from 0.142 to
0.119 and from 0.163 to 0.140). This is a modest change, but it does suggest that to some degree for recent migrants from armed conflict countries, a higher proportion of education completed in countries of origin increases the likelihood of being unemployed. Similarly, results from model 6 show that higher years of foreign work experience is associated with a higher probability of being unemployed but the effect on explaining differences due to armed conflict is even smaller.
Results in model 7 and model 8 show that language proficiency in English and pre-migration trauma have no significant effect on the probability of being unemployed. As a result, they also have little effect on estimates of the effect of armed conflict.
Lastly, with all variables added together in model 9, the probability of being unemployed remains significantly higher for recent migrants from armed conflict countries compared to migrants from non-conflict countries and the native-born. In sum, these results suggest that among recent migrants, armed conflicts in countries of origin remains a significant predictor of unemployment in the host country even after accounting for the effects of human capital and other sociodemographic variables.
Contexts of Exit and Occupational Status
Table 4.4 presents the results of regression models for the effects of armed conflict in countries of origin on occupational status. In contrast to Table 2, the result for the interaction between
108 conflict and length of stay was not significant. The analysis thus proceeded with armed conflict as the focal independent variable.
Model 1is the baseline model; it shows the difference in occupational status between migrants from armed conflict countries and migrants from non-conflict countries and between the two migrants groups and the native-born. Models 2 to 8 introduce the variables for sociodemographic controls, unemployment rate in the host country and GDP per capital in the country of origin, human capital, and pre-migration events. Model 9 is the full model and shows the effect of armed conflict on occupational status net of all independent variables.
Results in model 1 of Table 3 show that migrants from armed conflict countries have significantly lower occupational status compared to both migrants from non-conflict countries and the native-born, and migrants from non-conflict countries have marginally lower occupational status compared to the native-born. With the addition of controls for gender, age, race/ethnicity, marital and refugee status in model 2, the coefficient for the difference between migrants from armed conflict countries and migrants from non-conflict countries remains significant and is almost exactly the same. This means that controls do not affect this relationship. By contrast, the coefficients for the difference between migrants from both conflict and non-conflict countries and the native-born are significantly reduced and no longer significant. Analyses not shown, controlling for race/ethnicity separately from the other variables, explain the reduction of these differences with the native-born to nonsignificance. This decrease in the difference between the two groups seems to be largely explained by the lower occupational status of South Asians who emigrate at times when their home country is in conflict.
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In model 3, unemployment rate at the time of immigration is marginally negatively associated with occupational status but has little impact on the effect of armed conflict on occupational status. Results in model 4 show that GDP per capita in the country of origin is positively and significantly associated with occupational status in the host country. The difference between migrants from conflict and non-conflict countries is also reduced from -3.889 to -2.980, and is now only marginally significant at the .10 level. This finding indicates that the impact of armed conflict on occupational status is partially explained by GDP. As shown in Table 4.1, migrants from armed conflict countries more often come from countries with lower GDP per capita compared to migrants from non-conflict countries.
In model 5, educational attainment is positively and significantly associated with occupational status for both native-born and foreign-born. This association appears, however, stronger for native-born and the foreign-born who completed fewer years of education in their countries of origin than for the foreign-born who completed more years in the country of origin – reflecting the significant interaction here (the interaction verifies that the differences are significant). When a migrant has been educated even to a minor degree in the country of origin, there is a penalty to their achieved occupational status, controlling for level of education (2.262, p< .001 for mid/high proportion educated in country of origin vs. 2.863, p< .001 for low). There is no difference between the group with low levels of education in the country of origin and the native-born
(post-hoc test not shown). In this model, we see also that the coefficient for conflict vs. non- conflict is only slightly reduced compared to model 2, suggesting that the higher educational attainment of migrants from conflict backgrounds may not translate into higher occupational status. The reason seems to be the fact that they are also more proportionately educated in their countries of origin, compared to non-conflict migrants.
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Results from models 6 to 8 show that work experience, proficiency in English, and pre-migration trauma have little to do with explaining the effect of armed conflict on occupational status. The coefficients for the effect of conflict on occupational status are about the same compared to model 2 and, except for the variable for language proficiency, which is associated positively and significantly with occupational status, the association between the other two variables and occupational status is not significant.
In model 9, results show that the difference in occupational status between migrants from armed conflict countries and migrants from non-conflict countries is reduced by about 25 percent (from
-3.889 to -2.882) after all variables are introduced together. This difference remains also significant at the .05 level. In contrast, the difference in occupational status between migrants from both conflict and non-conflict countries and the native-born remain non-significant.
Discussion
This study considered first whether armed conflicts have a net effect controlling for co-existing human rights violations in countries of origin. In these analyses, the level of human rights violations in the countries of origin had no net influence on employment outcomes net of armed conflicts, while armed conflicts did have an effect net of human rights violations. These results contradict previous research findings that show that repression in countries of origin is positively and significantly associated with unemployment in the host country (Fleischmann and Dronkers
2010; Van Tubergen et al. 2004), but correspond with findings suggesting that human rights violations are not related to occupational status outcomes (Spörlein and Van Tubergen 2014).
Because of this result, this study mainly considered and sought to explain the effects of armed conflicts on unemployment and occupational status. The analysis of unemployment indicates that
111 the effect of armed conflict essentially depends on the length of stay in the Canada. It shows that recent migrants from conflict countries who have been in Canada for less than 20 years are less likely to be employed than both migrants from non-conflict countries and the native-born; whereas long-term migrants are marginally less likely to be unemployed than migrants from non- conflict country and as likely as the native-born. In contrast, the effect of armed conflict on occupational status does not differ for recent and long-term migrants. Migrants from armed conflict countries, regardless of their length of stay in Canada, tend to have significantly lower occupational status than both migrants from non-conflict countries and the native-born.
The devaluation of foreign credentials seems to, in part, explain the higher unemployment and lower occupational status among recent migrants from conflict countries. Compared to the native-born and migrants from non-conflict countries, higher educational attainment translates less often into a higher probability of employment among migrants who arrived more recently to
Canada from conflict countries. Although recent migrants from conflict countries in fact have high levels of education, most of their education was completed prior to migration, which decreases their chances of employment. Similarly, their higher years of work experience acquired before migrating also does not translate into a higher probability of employment as it might be expected.
Results for occupational status are similar to results for unemployment in that higher educational attainment is associated with reduced returns to occupational status among those migrants with higher levels of foreign education. They differ, however, in some ways. First, GDP per capita in countries of origin helps explain the lower occupational status of migrants from conflict countries but not unemployment. Second, controlling for race/ethnicity accounts for the
112 difference in occupational status between migrants from conflict country and the native-born but not for the difference in unemployment status.
Results from this study contradict some of the arguments in the literature offered to explain the disadvantages migrants from less stable and more repressive countries experience in the labour market of the host country (see Fleischmann and Dronkers 2010; Van Tubergen et al. 2004;
Wright et al. 2016). The higher unemployment and lower occupational status among migrants from conflict countries have less to do with the loss of or low human capital and have more to do with the devaluation of foreign credentials. Further, although migrants from armed conflict countries are more likely to have come to Canada as refugees compared to migrants from non- conflict countries, this compositional difference between the two groups does not explain differences in labour market outcomes. Refugees in this study are less likely to be unemployed and do not have significantly lower occupational status than other classes of migrants as a group.
Traumatic events experienced in countries of origin are also not associated with employment outcomes.
Overall, this study contributes to the growing body of literature that focuses on the devaluation of foreign credentials and its impact on migrants labour market outcomes (e.g. Adamuti-Trache et al. 2013; Buzdugan and Halli 2001; Friedberg 2000; Kanas and Van Tubergen 2009; Li 2001;
Reitz 2001). Findings here indicate that higher proportions of education in the home country result in more negative labour market outcomes and we know from Table 4.1 that migrants from conflict countries have the highest proportion of foreign education. These differences lead to important differences in employment outcomes both compared to other migrants and vs. the native-born. Results (not shown) further indicate that migrants from conflict countries experience
113 especially lower occupational status than the native-born despite their high levels of education, in part due to the devaluation of foreign education from less economically developed countries.1
This study has some limitations. First, the analysis is conducted on a sample of English-speaking adults, who, on average, have relatively high proficiency in English. Therefore, the „true‟ effect of language proficiency on labour market outcomes among migrants may not have been fully captured and, more importantly, this may affect variation among migrants on focal study variables as well. Second, the sample size of refugees is too small (n=38) to conduct meaningful comparisons between refugees and other migrant groups from armed conflict countries. The small N here results in little power to detect differences due to refugee status. Such comparisons would have allowed a further examination of the potential selection effect involved in labour market outcomes. Refugees from armed conflict countries tend to have lower proficiency in
English and lower levels of education overall and, more broadly, tend to be less favorably selected compared to other migrants, who more likely entered through the Canadian point system. Despite this problem, results in Table 4.3 showed that refugees in fact have a lower probability of unemployment. Finally, the survey data used for the analysis do not allow assessment of the potential effect of work experience in Canada, nor the effect of accumulated social capital.
Despite these limitations, this study adds to the literature by examining the effects of armed conflict on long-term employment outcomes. These findings have not been documented before in the literature on this scale in an analysis explicitly separating armed conflict from the effects of human rights violations per se. Even though it is commonly understood that foreign credentials are devalued for migrants in the labour market, this study goes one step further and shows that migrants from conflict countries may experience this problem even more than other migrants.
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Employment discrimination on the basis of race/ethnicity and nationality, and the presumed lower quality of education in the country of origin are some of the factors that may explain the lower return of education among this group. In conclusion, this study makes clear that the difficulties migrants from conflict countries experience in finding employment that corresponds to their levels of education upon arrival can have a long-tem impact on their occupational trajectory.
______
NOTE
1. Following previous research which finds that the devaluation of foreign education is greater for migrants from less developed countries (Bratsberg and Ragan 2002), I have examined whether the devaluation of foreign education was greater among those who come from less economically developed countries by including the variables for education and GDP into a single model. Results showed that the effect of GDP on occupational status was mediated by the variables for foreign education.
115
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Table 4.1. Descriptive Statistics of Variables by Nativity and Conflict Status Conflict No Conflict Native-born n=170 n=501 n=1,175 p<0.05 Age 43.07(12.28) 44.39(12.96) 41.41(8.25) a, b, c Female gender 43.77 50.75 46.39 a Married 76.26 53.47 42.34 a, b, c Refugee 14.64 5.84 --- a Black/Caribbean 7.48 16.58 1.00 a, b, c Latin American 6.30 6.76 0.48 b, c South Asian 46.11 6.11 1.31 a, b, c East/Southeast Asian 12.93 11.98 1.92 b, c West Asian/Arab 8.42 1.78 0.23 a, b, c Jewish 1.30 2.57 6.75 b, c Multi 12.07 19.25 8.54 a, c, White 5.39 34.96 79.76 a, b, c Length of stay 14.86(14.33) 24.44(17.86) --- a Language ability 4.01(1.18) 4.25(1.08) --- a Pre-migration stress 0.35(1.01) 0.21(0.74) --- a Education (yrs) 17.05(5.83) 16.33(4.93) 16.65(2.87) a - -Proportion in origin (0-1) 0.82(0.41) 0.61(0.50) --- a Work prior migration (yrs) 6.75(9.40) 4.01(8.22) --- a Unemployment rate (Canada) 7.99(2.29) 7.59(2.62) --- n.s. GDP per capita (origin) 2,108.66(2,513.03) 6,481.23(9,107.08) --- a Low human rights v. 4.08 64.86 --- a Moderate human rights v. 21.61 22.24 --- a High human rights v. 74.31 12.90 --- a Unemployed 19.67 16.22 13.86 b Occupational status 61.37(30.08) 65.23(27.33) 66.97(18.17) a, b Note: Means and standard errors (in parentheses) for continuous variables and percentages for categorical variables. a= significant difference between migrants from conflict and non-conflict countries b= significant difference between migrants from conflict countries and the native-born c= significant difference between migrants from non-conflict countries and the native-born
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Table 4.2. Effects of Armed Conflict and Human Rights Violations on Employment Outcomes by Migrant Group and Nativity
Context of Exit: Unemployment Occupational Status
Bivariate Effects
Armed Conflict vs. no-conflict 0.041† -3.099*
Armed Conflict vs. native-born 0.063** -5.645**
Mid HRV vs. low HRV -0.022 2.542
High HRV vs. low HRV 0.001 0.998
Mid HRV vs. native-born 0.018 -1.842
High HRV vs. native-born 0.041† -3.386* *** p<0.001, **p <0.01, * p<0.05, † p<0.10 (one-tailed test) Note: All analyses are conducted among a reduced sample given that data on human rights violations are from 1970 onward. For unemployment status, N=1,657 and for occupational status, N=1,466.
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Table 4.3.Linear Probability OLS Regression of Unemployment on Conflict Background in Countries of Origin (n=1,795) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Conflict (recent) vs. no conflict 0.084** 0.142*** 0.143*** 0.142*** 0.119*** 0.126*** 0.142*** 0.145*** 0.106*** Conflict (longterm) vs. no conflict -0.074* -0.061† -0.061† -0.061† -0.059† -0.056† -0.061 -0.062† -0.062† Conflict (recent) vs. native-born 0.108*** 0.163*** 0.344*** 0.163*** 0.140*** 0.153*** 0.162*** 0.166*** 0.136*** Conflict (longterm) vs. native-born -0.050 -0.040 0.140 -0.040 -0.038 -0.029 -0.041 -0.041 -0.032 No conflict vs. native-born 0.024 0.021 0.201 0.021 0.021 0.027 0.020 0.021 0.030 Women -0.031† -0.030† -0.031† -0.023 -0.025 -0.031† -0.032† -0.018 Age -0.019** -0.019** -0.019** -0.020** -0.019** -0.019** -0.019** -0.020** Age (squared) 0.000** 0.000** 0.000* 0.000** 0.000* 0.000** 0.000** 0.000** Marital status -0.085*** -0.085*** -0.085*** -0.080*** -0.090*** -0.084*** -0.084*** -0.084*** Refugee -0.099* -0.096* -0.099* -0.088* -0.091* -0.097 -0.099*** -0.081 Black/Caribbean 0.025 0.027 0.025 0.034 0.023 0.024 0.256 0.027 Latin American -0.034 -0.033 -0.034 -0.031 -0.039 -0.032 -0.034 -0.039 East/ Southeast Asian 0.018 0.020 0.018 0.018 0.012 0.021 0.019 0.012 South Asian -0.003 -0.002 -0.003 -0.012 -0.015 -0.003 -0.002 -0.027 West Asian/Arab 0.011 0.014 0.011 0.017 0.017 0.013 0.010 0.019 Jewish -0.050 -0.049 -0.050 -0.040 -0.052 -0.050 -0.049 -0.040 Multi 0.153*** 0.155*** 0.153*** 0.157*** 0.150*** 0.153*** 0.153*** 0.154*** National unemployment rate -0.004 -0.004 GDP 1.533 -0.000 Educ- can -0.009** -0.010* Educ- low/mid proportion foreign -0.002 -0.001 Educ- high proportion foreign 0.019*** 0.017*** Worked prior to migration (in years) 0.004* 0.005*** Proficiency in English 0.005 -0.002 Pre-migration trauma -0.011 -0.019 R2 0.010 0.058 0.058 0.058 0.072 0.061 0.058 0.058 0.075 *** p<0.001, **p <0.01, * p<0.05, † p<0.10 (one-tailed test) Note: The differences between migrant from armed conflict countries and the native-born is calculated by summing the coefficients for no conflict vs. native and for conflict vs. no conflict and the statistical significance of these differences are estimated using post-hoc test.
Table 4.4Ordinary Least Squares Regression of Occupational Status on Conflict Background in Countries of Origin (n=1,586) Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9 Conflict vs. no conflict -3.855* -3.889* -3.727* -2.980† -3.445* -3.972* -3.973* -3.771* -2.882* Conflict vs. native-born -5.605*** -2.772 -2.703 -2.620 -2.730† -2.755 -4.924* -2.671 3.384 No conflict vs. native-born -1.750† 1.117 1.024 0.360 0.715 1.217 -0.321 1.040 -0.502 Women -0.300 -0.167 -0.266 -0.212 -0.202 -0.653 -0.402 -0.383 Age 1.079* 1.094* 1.079* 1.078* 1.081* 1.104* 1.085* 1.111* Age (squared) -0.012* -0.123* -0.012* -0.012* -0.012* -0.012* -0.012* -0.012* Marital status 6.538*** 6.567*** 6.419** 5.007*** 6.464*** 7.628*** 6.643*** 5.657*** Refugee -0.958 -0.441 -0.821 1.764 -0.874 3.159 -0.998 4.559† Black/Caribbean -12.126*** -11.924*** -11.140*** -9.085*** -12.148*** -13.839*** -12.107*** -9.979*** Latin American -6.711* -6.322* -6.001* -4.635† -6.755* -2.076 -6.759* -1.530 East/ Southeast Asian -1.657 -1.429 -1.442 -2.027 -1.786 3.318 -1.614 1.216 South Asian -5.633* -5.392* -4.740* -5.422* -5.910* -5.723* -5.368* -5.156* West Asian/Arab -3.826 -3.263 -3.118 -0.079 -3.779 -0.786 -3.927 2.178 Jewish 9.506*** 9.720*** 9.369*** 5.512* 9.456*** 9.380*** 9.520*** 5.415* Multi -3.824* -3.457† -3.202† -4.030* -3.851* -3.192† -3.751* -3.024† National unemployment rate -0.711† -0.286 GDP 0.0003* 0.000 Educ- can 2.765*** 2.746*** Educ- low proportion foreign 2.863*** 2.543*** Educ- mid/high proportion foreign 2.262*** 1.892*** Worked prior to migration (in years) 0.081 0.183 Proficiency in English 8.275*** 5.389*** Pre-migration trauma -1.481 -1.815 R2 0.007 0.067 0.069 0.070 0.232 0.067 0.115 0.068 0.254 *** p<0.001, **p <0.01, * p<0.05, † p<0.10 (one-tailed test) Note: The differences between migrant from armed conflict countries and the native-born is calculated by summing the coefficients for no conflict vs. native and for conflict vs. no conflict and the statistical significance of these differences are estimated using post-hoc test.
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Chapter 5 Discussion and Conclusion Research Contribution
This dissertation advances our knowledge of how armed conflicts and human rights violations can impact the mental health and employment experiences of migrants. Relative to the current literature, it has several specific points of departure. It uses country-level data on both human rights violations and armed conflict rather than retrospective self-reports to examine individual outcomes and focuses on all migrants rather than subgroups, such as refugees. The distinction between individual reports and the specification of the national socio-political context is not just a methodological shift; it is also a shift in conceptual claims. In this dissertation, I emphasize, in each paper, that the national-level context at time of migration can affect individual future outcomes whether or not there is personal experience with or exposure to the threat at the individual level.
Unlike the predominant tendency in the literature on migration, in this dissertation, the effect of human rights violations and armed conflict in countries of origin on migrant mental health and economic outcomes is examined in a global sample of more than 100 different countries of origin rather than just a few. This surely increases the generalizability of the results, but it also makes an important point. Taking a broad comparative approach, in effect, forces a specification of the structural characteristics at the country level that may impact emotional health and economic incorporation after migration, thus rising above the specificities of each country considered one by one.
Finally, I include comparisons in each paper to the native-born, over and above migrants with no conflict or human rights violations in their background. Thus, the design of the analysis adds
126 127 important information to the full profile of differences, often left implicit in prior research. It is this approach that allows us to separate the effects of contexts of exit from the effect of migrating per se.
Each paper in this dissertation incorporates the thinking of the stress process perspective in testing the “spillover” effect of contextual conditions. This approach posits that one way in which threatening social contexts work is through the spillover into increased threat and insecurity in individual lives (Aneshensel and Sucoff 1996), thus starting a process of stress proliferation (Pearlin 1989). Because of differences in the extensiveness of how stress was measured in the surveys used in this dissertation, it is best to consider findings here as a first step in a more complete specification of stress proliferation. Notably, this concept has more relevance in explaining differences in mental health than differences in economic status after migration.
Armed Conflicts and Mental Health
The first paper shows that armed conflicts in countries of origin can have a long-term impact on the mental health of migrant men and women, but that the impact of armed conflict essentially depends on its type, severity, and whether it is ongoing at the time of migration. Analyzing data from the Toronto Study of Intact Families and the UCDP Conflict Termination (Kreutz 2010) dataset, we find that migrants from countries experiencing major intra-state armed conflicts at the time of migration have more mental health symptoms than both migrants from countries with minor or no armed conflicts and the native-born. We also find these differences to be gendered, with women having more depression and men having more anxiety symptoms.
Consistent with the stress proliferation argument (Pearlin 1989), our analysis shows that chronic stress and traumatic life experiences help explain the association between major intra-state armed conflict in countries of origin and migrant mental health. Among women, greater exposure to
128 chronic stress after migration, and among men, greater exposure to traumatic life events experienced during conflicts, help explain the long-term effect of armed conflict on mental health. Our results show also that men from countries experiencing conflict are more likely than women to have witnessed violence or atrocities, to have been injured, or to have been exposed to any traumatic life experience during conflict. These results are in line with previous research documenting gender differences in conflict-related trauma exposure (Scholte et al. 2004; Tang and Fox 2001), and could help explain the higher levels of anxiety symptoms among men than among women after migration. On the other hand, the higher level of consequent chronic stress among women after migration also may reflect the specific difficult circumstances women are in during the acculturation process as spouses and mothers who are also more often in situations of nonemployment and home work.
Armed Conflicts, Human Rights Violations, and Mental Health
The second paper extends the first by examining the joint impact of intra-state armed conflicts and human rights violations in countries of origin on psychological distress among migrant men and women. Analyzing data from the Neighbourhood Effect on Health and Wellbeing Study
(O‟Campo et al. 2015), the UCDP Conflict Termination dataset (Kreutz 2010), and the Political
Terror have Scale (Gibney and Dalton 1996), I find that migrants from both conflict and non- conflict countries with high levels of human rights violations at the time of migration have more psychological distress than migrants from non-conflict countries with low levels of human rights violations and the native-born. I also find that the impact of high human rights violations on migrant mental health is independent of the impact of armed conflict. Even without the explicit presence of armed conflict, high human rights violations can have long-term impact on the mental health of migrants.
129
The impact of high human rights violations and armed conflict in countries of origin on migrant mental health is mostly explained by the combined effect of traumatic and more chronic stressors both before and after migration. Using a different and more comprehensive measure of chronic stress than in the first paper, I find that acculturative stress, discrimination, and economic hardships after migration contribute to higher levels of psychological distress in migrants from conflict countries with high human rights violations. Economic hardships contribute to higher levels of psychological distress in migrants from non-conflict countries with high human rights violations. As in the first paper, these results are consistent with the concept of stress proliferation (Pearlin 1989) and the argument that contexts of exit may lead to more stress both before and after migration (Vega and Rumbaut 1991; Torres and Wallace 2013). These results also add to those of others showing that stressful life experiences after migration help explain the effects of armed conflict exposure in the country of origin on migrant mental health (Steel et al.
1999). Overall, this paper shows that armed conflicts and human rights violations in countries of origin are both associated with migrant mental health, but through somewhat different pathways, and thus emphasizes the importance of considering the effect of each separate of the other.
Armed Conflicts and Migrant Employment Outcomes
Research shows that political instability and repression in countries of origin can affect migrant employment outcomes (Fleischmann and Dronkers 2010; Van Tubergen et al. 2004). However, we know relatively little about the effect of armed conflict. Using the same data sources as in the second paper, the third paper examines whether armed conflict in countries of origin affect migrant employment outcomes, over and above the effect of human rights violations. I find that armed conflict has a net effect on employment and occupational status. The analysis of employment status reveals that recent migrants from a conflict country have a higher probability
130 of being unemployed than migrants from non-conflict countries and the native-born whereas long-term migrants from conflict countries do not. In contrast, the analysis of occupational status shows that both recent and long-term migrants from conflict countries have, on average, lower occupational status than migrants from non-conflict countries and the native-born.
Consistent with literature that documents the devaluation of foreign credentials in the Canadian labour market (e.g. Bauder2003; Reitz 2001), I find that migrants mainly educated in a foreign country receive a penalty in terms of employment and occupational status, given the same level of education as the native-born. This does help explain the effect of armed conflict because a higher proportion of those migrants have been educated mainly in their home country. Racial discrimination and lower quality of and access to education in these countries of origin are some of the factors that could explain the devaluation of foreign credentials among this group.
Future Research Directions
Rather than list the limitations inherent in the three papers in this dissertation – already discussed in each paper --- I consider future research questions that broaden, resolve, or address issues in these papers, and thus, by implication, address some of these limitations.
First, this dissertation focuses on the impact of two related contexts of exit on the mental health and employment experiences of migrants in Canada: armed conflicts and human rights violations in countries of origin. This of course does not exhaust the range of possibilities of specifying contexts of exit. Other research could focus on economic conditions (Montazer and Wheaton
2017), controlled here but not a focus of this research, or on structural conditions that may promote well-being after migration, such as government investment in education, religious or ethnic heterogeneity, or gender equality in the country of origin.
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Second, future research should include a more fully specified comparative approach and thus consider how both contexts of exit and contexts of reception shape the lived experiences of migrants. The contexts of reception (e.g. labour market conditions, national policies, and public discourse) may add to, counteract, or moderate the effect of past exposure to armed conflict or human rights violations in countries of origin on the mental health and employment experiences of migrants. Third, while the third paper focused on the effect of armed conflicts in countries of origin on migrant employment outcomes, research should extend this concern and continue to explore the impact of employment experiences on the mental health of migrants from armed conflict countries. Unemployment and employment in lower occupational status jobs, both more prevalent among these migrants despite on average high levels of education, may affect their mental health. Previous research in Canada shows that unemployment and underemployment can have deleterious effects on the emotional wellbeing and life satisfaction of migrants (Aycan and
Berry 1996; Dean and Wilson 2009; George et al. 2012). In a study of South Asian refugees and resident Canadians, Beiser, Johnson and Turner (1993), also find that unemployment is associated with depression, through the loss of self-esteem, economic hardships, and greater social isolation.
Fourth, it is important as well to broaden the mental health outcomes in future research, following the point of Aneshensel et al. (1991) that the real consequences of stressful life histories can only be assessed when we observe a full range of mental health outcomes. This allows more specifically for gender, age, cultural, and religious differences in the expression of mental health problems, for example.
Fifth, future research should also incorporate refugee status as a focal independent variable, while maintaining comparisons to other migrants. Although I was able to include refugee status
132 in the analyses for the second and third paper, it was included only as a control. Because of the small sample size of refugees, I was unable to explore whether the context of exit had a different effect on the mental health and employment experiences of refugees versus other groups of migrants. Some scholars find that refugees experience more traumatic life experiences than migrants from the same the country of origin (Silove et al. 1998). Others find that refugees are more likely to be unemployed and underemployed because they are less favourably selected or are less able to transfer their foreign credentials in the Canadian labour market (Krahn et al.
2010). A comparison between refugees and other migrants would thus allow an exploration of how pathways from contexts of exit to mental health or employment outcomes are different for each group.
Finally, although this dissertation shows that traumatic life events experienced prior to migration help explain the effect of armed conflict and human rights violations in countries of origin on the mental health of migrants, its effect should be interpreted as the best possible given the data.
Future research should include a measure that better assesses exposure to conflict-related trauma.
This implies a measure of traumatic experience in these social contexts that is maximally sensitive to the problems migrants face in these situations. Current measures probably under- sample the range of problems involved.
Each of these directions promotes a generalization of the approach taken in this dissertation.
Because of the unique features in the approach taken here, the development of this agenda will be able to address broader and different questions about the combined effects of contexts of exit and reception that cannot be addressed in single case or paired comparison approaches.
133
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Appendices Appendix A - Measures for Depression and Anxiety (Chapter 2)
Depression
Including only experiences that could not be caused by illness, medications, or drugs or alcohol, how often in the past month have you… 1. Felt worried or anxious 2. Felt sad, blue, or depressed 3. Felt like you worried a lot about little things 4. Felt like you lost interest in things you usually like to do 5. Felt very restless and unable to relax 6. Felt something terrible was going to happen 7. Felt guilty about things 8. Felt you could not get interested in doing anything 9. Had trouble concentrating on what you were doing 10. Felt like nothing seemed worthwhile in your life 11. Thought about things over and over that have happened to you in the past 12. Felt irritable, tense, or “on edge” 13. Felt like you couldn‟t sit still or paced up and down 14. Felt like you were worthless 15. Felt lonely 16. Worried too much about things 17. Had your feelings hurt 18. Felt hopeless about the future 19. Felt everything is an effort 20. Felt like you lost the ability to enjoy having good thing happen to you
Anxiety
Including only experiences that could not be caused by illness, medications, or drugs or alcohol, how often in the past month have you… 1. Felt your heart beating hard even though you were not exercising 2. Had a spell when you felt faint or dizzy 3. Felt trembly or shaky 4. Felt bothered by tense, sore, or aching muscles 5. Felt short of breath or felt like you were smothering 6. Had dry mouth 7. Had hot flashed or chills 8. Felt discomfort or had a pain in the stomach 9. Felt faint or unreal 10. Felt like you were sweating a lot 11. Felt you lost your appetite
136 137
Major Traumatic Events
1. Did you ever have a major illness or accident that required you to spend two weeks or more in the hospital?* 2. Did you have to do a year school over again? 3. Did your father or mother not have a job for a long time when they wanted to be working? 4. Did something happen that scared you so much you thought about it for years after?* 5. Were you ever sent away from home because you did something wrong? 6. Did either of your parents drink or use drugs so often or so regularly that is caused problems for the family? 7. Were you regularly physically abused by one of your parents? 8. Were you ever suspended or expelled from school? 9. Did your parents often have violent arguments with each others? 10. As a teenager, did you have a child without or before getting married? 11. Did your parent(s) have so little money that you lived much of your childhood in poor housing, or not being able to pay bills, or buy food and clothes* 12. Did either of your parents have such a problem with nerves or depression that they were unable to work or had to have treatment?* 13. Besides getting divorced, did either your father or mother spend more than six months away from home while you were growing up?* 14. Have you ever been divorced or ended a relationship with someone you were in love with? 15. Has someone of you parents died?* 16. Did your parents ever get divorced? 17. Have you ever had a child who died at or near birth or had to be given up shortly after birth?* 18. Has a spouse or other loved one, including other children you have had, died?* 19. Have you ever seen something violent happen to someone or seen someone killed?* 20. Have you ever been in a major fire, flood, earthquake, or other natural disaster? 21. Have you ever had a serious accident, injury, or illness that was life threatening or caused long-term disability?* 22. Has one of your children ever had a near fatal accident or life-threatening illness? 23. Have you ever been in combat in a war, lived near a warzone or been present during a political uprising?* 24. Have you ever discovered your spouse or partner in a close relationship was unfaithful? 25. Have you ever been sexually abused or sexually assaulted? 26. Have you ever been abused by a previous spouse or partner? 27. Have you ever gone through periods of serious verbal abuse, involving things like constant criticism, name-calling, or threats?
*Items included in our pre-migration stress measures.
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Appendix B - Psychological Distress Items (Chapter 3)
In the past two weeks, how often…
1. did you feel that you were just as good as other people?
2. did you feel like everything you did was an effort?
3. did you think that your life had been a failure?
4. did you feel over-excited?
5. were you nervous?
6. were you so nervous that nothing could calm you down?
7. did you feel depressed?
8. did you feel sad?
9. did you feel fidgety and couldn‟t sit in a chair?
10. were you tired all the time?
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Appendix C - Human Rights Violations Coding (Chapter 3 and 4)
From Wood, Reed M. and Mark Gibney. 2010. “The Political Terror Scale (PTS): A Re-
introduction and a Comparison to CIRI.” Human Rights Quarterly 32:367-400.
Level 1. “Countries... under a secure rule of law, people are not imprisoned for their
views, and torture is rare or exceptional.... Political murders are extremely rare...”
Level 2. “There is a limited amount of imprisonment for nonviolent political activity.
However, a few persons are affected; torture and beating are exceptional...
Political murders are rare....”
Level 3. “There is extensive political imprisonment... Execution or other political murders
and brutality may be common. Unlimited detention, with or without trial, for
political views is accepted...”
Level 4. “The Practices of Level 3 are expanded to a larger population. Civil and political rights
have expanded to large numbers of the population. Murders, disappearances, and torture
are part of life...In spite of its generality, on this level, terror affects those who interest
themselves in politics or ideas.”
Level 5. “The terrors of Level 4 have been extended to the whole population... The leaders of
these societies place no limits on the means or thoroughness with which they pursue
personal or ideological goals.”
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Appendix D- Life Events Items (Chapter 3)
In the past twelve months… 1. was there an unwanted pregnancy?
2. was there an abortion or miscarriage?
3. did a close friend die?
4. was fired or laid off?
5. had a major financial crisis?
6. was accused or arrested for a crime?
7. failed school or training programme?
8. dropped out of school?
9. experienced a change of job for a worse one?
10. was there a serious accident of injury?
11. was there a serious illness?
12. did a child die?
13. did a spouse/partner die?
14. was trouble with the law?
15. did anyone have something taken by force (robbed)?
16. did anyone beaten up or physically attacked?
17. went on strike?
18. found out partner was having an affair?
19. a romantic relationship ended?
20. a close relationship ended?