IS “IDENTITY-BASED CONFLICT” A VALID OR BANAL CONCEPT? EVENT HISTORY ANALYSIS OF CIVIL WAR ONSET, 1960-2000
DISSERTATION
Presented in Partial Fulfillment of the Requirements for
the Degree Doctor of Philosophy in the Graduate
School of The Ohio State University
By
Rumi Morishima Tosaka, M.A.
*****
The Ohio State University 2008
Dissertation Committee: Approved by Professor Edward M. Crenshaw, Advisor
Professor J. Craig Jenkins
Professor Pamela M. Paxton ______Adviser Professor Janet M. Box-Steffensmeier Graduate Program in Sociology
Copyright by
Rumi Morishima Tosaka
2008
ABSTRACT
One assumption that is often implicit yet widely held in the conflict literature is the existence of “identity-based (ethnic)” conflicts. While this type of conflict is presumed to be conceptually and empirically distinct from “non-identity” conflicts, few close examinations have been undertaken regarding the validity of this assumption. By using the conditional risk model, a Cox proportional hazard model that allows for multiple failures, this dissertation investigates whether or not the two war “types” evince different causal explanations in ways that can justify the oft-mentioned distinction. Results suggest that while the different “types” of war share many causes, economic exclusion seems more applicable to non-identity civil war (e.g., class-based warfare) while political exclusion better explains identity-based civil war overall, suggesting that there may be some truth to the argument that political recognition plays an important role in identity-based war. First, socioeconomic development and international economic integration seem generally important for war prevention, yet other aspects of modernization show different patterns across the “types” of civil war.
Population growth increases the risk of identity-based war. Economic differentials encourage non-identity wars, whereas political differentials seem to pose a greater
ii danger of identity-based warfare. Second, as for political environments, inclusive political systems exhibit generally beneficial effects, while exclusive ones are the most dangerous, particularly regarding identity-based conflicts. Also, systematic denial of political opportunities, whether through discriminatory policies or deprivation of autonomy, increases the risk of identity war. In contrast, economic discrimination increases the risk of non-identity warfare. Third, the group size/numbers and identity attributes in combination differently affect the risk of the two war “types.” Religious diversity reduces the risk of non-identity wars, whereas linguistic fractionalization and ethnic polarization significantly increase the risk of identity-based civil war. Finally, the results also suggest that “conflict trap” exists regardless of the war “type,” at least up to the second event. It seems that either type of first conflict experience should be recognized as a serious precursory to continued conflict. Despite some scholars’ call for complete abandonment of identity-based conflict studies, it seems the classification merits further debate and continued empirical investigation.
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To my best, truest friend of life Yuji
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ACKNOWLEDGMENT
I owe a great debt of gratitude to people whose support and encouragement have helped me finish what at first had seemed an impossible task. First and foremost, I am thankful to my dissertation committee. Professor Edward M. Crenshaw has provided help and advice in the process as the dissertation committee chair. Professor J. Craig
Jenkins has had me deepen my sociological understanding of collective action and political violence. Special thanks also go to Professor Pamela Paxton for giving me valuable opportunities to develop analytic skills. I am deeply indebted to Professor
Janet M. Box-Steffensmeier for her generous and always responsive comments and advice to my methodology questions.
I also want to thank John Marcotte and Edward Mansfield at the University of
Pennsylvania for their patience and understanding while I was working to complete this project.
Last but certainly not the least, I owe the greatest thanks of all to my best, truest friend and partner of life, Yuji Tosaka, for giving me enduring, unquestioning support at every step through editorial assistance, thought-provoking comments and discussions, and unwavering confidence in my ability to complete the dissertation even when I felt like giving it all up. This dissertation is dedicated to him with my greatest appreciation of his always being “here” for me even from a long distance away.
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VITA
1967…………………………………… Born – Minakuchi, Shiga, Japan
1990…………………………………… Bachelor of Arts, English Literature, Doshisha University, Kyoto, Japan
June, 2001…………………………….. Master of Arts, Sociology, The Ohio State University, Columbus, Ohio.
2001 – 2005…………………………… Graduate Research Associate, Sociology The Ohio State University, Columbus, Ohio.
June, 2003…………………………….. Admitted to Candidacy, Sociology The Ohio State University, Columbus, Ohio.
September, 2005 – Present……………. Data Analyst/Statistical Programmer, Research Data Services, The University of Pennsylvania.
PUBLICATIONS
1. Bollen, Kenneth, Pamela Paxton, and Rumi Morishima. 2005. “Assessing International Evaluations: An Example From USAID’s Democracy and Governance Program.” American Journal of Evaluation, 26: 189-203.
FIELDS OF STUDY
Major Field: Sociology
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TABLE OF CONTENTS
Page Abstract ………………………………………………………………………………………. ii Dedication ……………………………………………………………………………………. iv Acknowledgments …………………………………………………………………………… v Vita …………………………………………………………………………………………… vi List of Tables ………………………………………………………………………………… xi List of Figures ………………………………………………………………………………... xii
Chapters:
1. Introduction …………………………………………………………………………… 1
Significance: why civil war matters …………………………………………………... 1 Problematic: popular taxonomy and under-examined problems ……………………... 5 Present study: what is ahead ………………………………………………………….. 12
2. Theories of civil war ………………………………………………………………….. 16
Conceptualizing civil war …………………………………………………………….. 16 Conceptualizing identity civil war ……………………………………………………. 18 Theories of civil war onset ……………………………………………………………. 23 Socioeconomic structures, domestic …………………………………………….. 23 Structural modernization theory: overview ……………………...... 23 How has structural modernization theory explained the two “types”? …….. 29 Summary and hypotheses …………………………………………………... 33 Socioeconomic structures, external ……………………………………………… 35 World systems theory: overview …………………………………………… 35 How has the world systems theory explained the two “types”? …………… 40 Summary and hypotheses ………………………………………………….. 41 Political Environment, external ………………………………………………….. 42
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World polity/neo-institutionalist theory: overview ………………………… 42 How has the world polity theory explained the two “types”? ……………… 44 Summary and hypothesis …………………………………………………... 45 Political environment, domestic …………………………………………………. 45 Political opportunity/state-centered thesis: overview ……………………… 45 How has political opportunity/state-centered thesis explained the two
“types”? …………………………………………………………………….. 52 Summary and hypotheses ………………………………………………….. 55 Ethnicity, religion, and language: configurations and attributes ………………… 57 Fractionalization and attributes …………………………………………….. 60 Dominance and attributes ………………………………………………….. 64 Polarization and attributes ………………………………………………….. 66 Summary and hypothesis …………………………………………………... 69
3. Method and data ………………………………………………………………………. 73
Modeling strategy ……………………………………………………………………... 73 Conditional risk model …………………………………………………………… 73 Data and variables …………………………………………………………………….. 82 Dependent variable: civil war onset ……………………………………………… 82 Independent variables and controls ………………………………………………. 89 Measuring structural modernization ……………………………………….. 89 Economic development ……………………………………………… 89 Social development ………………………………………………….. 91 Demographic pressure ……………………………………………….. 92 Inequality …………………………………………………………….. 94 Measuring world systems ………………………………………………….. 97 Structural location in world systems ………………………………… 97 Exposure to global capitalism ……………………………………….. 98 Measuring world polity …………………………………………………….. 99 Embedment in world polity ………………………………………….. 99 Measuring level, institutional structures, and change of political
environment …………………………………………………………………100 Level of democracy ………………………………………………….. 100 Institutional structures of polity and discriminatory policies ………...101 Changing political environment ……………………………………... 105
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Measuring ethnic, religious and linguistic configurations ………………..... 108 Fractionalization and attributes ……………………………………… 108 Dominance and attributes …………………………………………….109 Polarization and attributes …………………………………………… 110 Control variables …………………………………………………………… 110 Regression diagnostics ………………………………………………………………...112
4. Results …………………………………………………………………………………113
Descriptive statistics ………………………………………………………………….. 113 Regression analysis by conditional risk model ……………………………………….. 114 The effects of socioeconomic structures ………………………………………….115 Domestic: structural modernization ………………………………………... 115 External: world systems …………………………………………………..... 121 Summary on the effects of socioeconomic structures ………………………123 The effects of political environment ……………………………………………...125 External: world polity ……………………………………………………… 125 Domestic: political opportunity/state-centered thesis ……………………… 125 Summary on the effects of political environment ………………………….. 131 The effects of ethnic, religious, and linguistic configurations and attributes ……. 133 Fractionalization and attributes ……………………………………………..133 Dominance and attributes ………………………………………………….. 135 Polarization and attributes …………………………………………………..136 Summary for the Effects of Group Configurations and Attributes ………… 136 Estimated baseline cumulative hazard function ……………………………………….138
5. Conclusion …………………………………………………………………………..... 140
Appendices …………………………………………………………………………….150
Appendix A: Number of civil war onsets, 1946-2000 ………………………………... 151 Appendix B: Frequency of civil war onsets by even ordering strata, 1946-2000 …...... 152 Appendix C: List of civil war onsets, 1946-2000 …………………………………….. 154 Appendix D: Cumulated geographic distribution of civil wars ………………………. 165 Appendix E: Kaplan-Meier survival estimates of civil war onsets ……………………168
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Appendix F: Descriptive statistics …………………………………………………..... 171 Appendix G: Correlation matrix ……………………………………………………… 173 Appendix H: Conditional risk model of civil war onsets …………………………….. 177 Appendix I: Estimated baseline cumulative hazard for civil war onsets ……………... 188
Works cited …………………………………………………………………………… 191
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LIST OF TABLES
Table Page
B.1 Frequency of intense armed conflict onsets, aggregated, by event ordering strata …... 152
B.2 Frequency of identity civil war onsets by event ordering strata …………………..…..152
B.3 Frequency of non-identity civil war onsets by event ordering strata ……………..…...153
C.1 List of civil war onsets, 1946-2000 ………………………………………………..…. 154
F.1 Descriptive statistics of the variables in analysis ………………………………….…. 171
G.1 Correlation matrix of the variables in analysis ………………………………..……… 173
H.1 Conditional risk model of civil war onset by the war “types” ………………….……. 177
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LIST OF FIGURES
Figure Page
A.1 Number of aggregated civil war onsets 1946-2000 …………………………….……..151
D.1 Cumulated geographical distribution of civil war onsets, aggregated, 1946-2000 …... 165
Figure D.2: Cumulated geographical distribution of civil war onsets, identity, 1946- D.2 2000 ………………………………………………………………………………..…. 166
D.3 Cumulated geographical distribution of civil war onsets, non-identity, 1946-2000 …. 167
Kaplan-Meier survival estimates of aggregated civil war onsets by event ordering E.1 strata …………………………………………………………………………….……. 168
Kaplan-Meier survival estimates of identity civil war onsets by event ordering E.2 strata …………………………………………………………………………………...169
Kaplan-Meier survival estimates of non-identity civil war onsets by event ordering E.3 strata ……………………………………………………………………………….…. 170
Estimated baseline cumulative hazard of aggregated civil war onsets by event I.1 ordering strata, based on Model 11 ……………………………………………………188
Estimated baseline cumulative hazard of identity civil war by event ordering strata, I.2 based on Model 11 …………………………………………………………..………...189
Estimated baseline cumulative hazard of non-identity civil war by event ordering I.3 strata, based on Model 11 …………………………………………………….……… 190
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CHAPTER 1
INTRODUCTION
Ethnic war exists (Sambanis 2006).
The problem… is determining whether a thing called “ethnic war” even exists (King 2001).
SIGNIFICANCE: WHY CIVIL WAR MATTERS
Since the collapse of the Communist bloc, civil war has quickly generated widespread attention among the media, policy makers, and social scientists. A simple search of the English-language literature in Sociological Abstracts shows, for example, that the average number of articles published on civil war increased over tenfold from the 1980s to the 1990s, whereas a new specialized scholarly journal entitled Civil Wars
(Routledge) was launched in 1998 to promote understanding among diverse audience about the phenomenon. These facts attest to the quick ascendancy of the issue among social scientists in the post-Cold War period. This sudden, sharp resurgence of scholarly and public interest is due in part to the relative increase in the frequency of internal war outbreaks.1 Major armed conflicts today are almost exclusively within the boundaries of
1 Before the current rise in interest in civil war, there was a brief period during the 1960s when attention was paid to the increasing prominence of civil wars in international politics, particularly as a factor helping to shape the post World War II 1 existing nation-states. Indeed, while over thirty intense intrastate armed conflicts broke out during the post-Cold War decade, there were only two international disputes in the same time period, i.e., the Iraq invasion of Kuwait in 1991 and the Eritrea-Ethiopia dispute in 1998 (Eriksson and Wallensteen 2004; Gleditsch et al. 2002; Strand et al.
2005).
More importantly, civil war has raised growing public and academic awareness because potential wide-scale intrastate conflicts are rightly seen as a tragic sociopolitical phenomenon whose emergences, trajectories, and outcomes bring enormous, devastating consequences on human welfare and security (Collier et al.
2005; Hoeffler and Reynal-Querol 2003). Civil war inflicts direct and immediate sufferings upon the civilian population. According to recently published data, the number of battle-related casualties from internal armed conflict between 1945 and 2000 is estimated to exceed 7 million (Lacina and Gleditsch 2005), and approximately 30 to
60 percent are believed to have been non-combatant civilians (Human Security Centre
2006).2 In 2005, according to UNHCR (The Office of the United Nations High
Commissioner for Refugees 2006), the conflict-induced population displacement, including refugees and internally displaced persons, amounted to approximately 15 million, and even to a staggering number of 23.7 million if we accept an estimate from
politics of the major powers. For example, see George Modelski (“Internal Settlement of Internal War” in Rosenau [1964]), Eckstein (1964), and Huntington (1962).
2 The most frequently cited figure in the literature is approximately 90 percent of the conflict casualties are civilians by the 1990s (Cairns 1997), but some researchers question this figure as unsubstantiated (Human Security Centre 2005).
2 the Internal Displacement Monitoring Centre at Geneva, Switzerland (Eschembacher
2006). Forced migration not only plunges people into poverty and threatens them with destitution and disease, but also destroys the whole fabric of their social support system, such as family and community ties.
Damages done at the institutional level is also as catastrophic. Civil war diverts crucial resources away from productive activities to destructive enterprises instantly, slows down economic growth, and takes a high toll on the quality of basic public facilities, services, and installations, such as communication and transportation, basic utilities, and public health and educational institutions. The legacy effects of violent conflicts will persist in the long run. A confluence of subsequent political instability, the fear of corruption and residual violence, and the destruction of the entire socioeconomic infrastructures results in the cross-border flight of wealth and human capital and leads to an immense loss of resources and “development in reverse” that will haunt war-torn societies long after the end of internal conflicts (Collier et al. 2005; Hoeffler and
Reynal-Querol 2003). Meanwhile, according to an estimated figure, the number of children under 18 years old recruited and used in battlefields at any one time amounts to
300,000 across the globe as of 2003 (Peters 2005). Not to mention their traumatic experiences and even deaths, postwar reintegration of the youth population that could have otherwise been a foundation for post-civil war rebuilding poses a serious problem to social order and stability.
Further, the impact of intrastate war can also have dangerous regional and global ramifications. Social and military spillovers of armed conflict across the regions
3 destabilize neighboring countries (e.g., spillover of actual violence, massive inflow of refugees, and spread of endemic diseases such as HIV/AIDS) and cause serious security concerns, as abundantly witnessed in former Yugoslavia and the African Great Lakes region during the 1990s. Also, conflict regions, especially with the absence of the effective control of a recognized government, may serve as a hotbed of rebel groups of other countries and international terrorist organizations, as shown in Al Qaeda’s operations in Afghanistan and Somalia.
Disturbingly, the risk of conflict is persistent. On the one hand, the positive trend since the mid-1990s has been an overall decline in the absolute number and magnitude of internal conflicts. (Eriksson and Wallensteen 2004; Human Security
Centre 2005; Marshall and Gurr 2005; see APPENDIX A for the onset numbers between 1946 and 2000). On the other hand, this may not necessarily mean that the world today is more secure from the danger of a renewed civil strife. Even after conflict settlements through regional and international efforts, a number of ceasefires and peace agreements, such as those in Democratic Republic of the Congo and Georgia, have stood on extremely fragile grounds. Meanwhile, the peace process has been on the brink of complete failure in Sudan, whereas Sri Lanka has slipped back in fighting again.
How to escape a conflict trap today is among the greatest challenges still faced by war- torn societies (e.g., International Crisis Group [2006]). Given these serious consequences and the persistent danger of a dramatic spiral of violence, the problem of intrastate conflict clearly warrants increased efforts for systematic investigation by the social science community.
4 PROBLEMATIC: POPULAR TAXONOMY AND UNDER-EXAMINED PROBLEMS
There is one question that has been left relatively under-examined amidst this current boom of conflict research: “identity (or ethnic/religious) conflict.” Despite its frequent appearance in public and scholarly discourse, it is potentially a very problematic concept. The act of using the term itself implies that it is considered to be something distinct from “non-identity conflict” and that the distinctions reflect fundamental differences. In actuality, this assumption is nothing new in the sociological literature. Since the earlier days of the discipline, sociologists have argued that particular parameters around which individuals in a society divide from and associate with each other play a significant role in shaping the nature and characteristics of political conflict. In Marx’s theory, for example, social class serves as the central driver of organizational behavior and political action (Tucker 1978). Weber, on the other hand, considers a multi-dimensional stratification system, where status groups are differentiated according to the distribution of honor and social esteem, and ethnic and religious groups are considered as parameters that can intensify the status stratification most strongly (Weber 1968). Yet, despite its conceptual centrality in the sociological tradition, the argument linking different types of sociopolitical cleavage with significantly different characteristics of political conflict has never really been supported in a systematic manner (Zuckerman 1975). Given that the term “ethnic
(identity) conflict” is often used without much examination today, it would be reasonable to ask what the conditions are under which collective actors come into being as an “ethnic (identity)” or “non-ethnic (non-identity)” groups in conflict. Are the
5 conditions substantively different in any way between the two “types” of conflict? And above all, is “ethnic (identity) war” an empirically valid, fundamentally distinct subcategory of internal war in the first place?
The recent attention to intrastate conflict has been accompanied by an emerging perception that the nature and pattern of conflict have changed since the end of the international system that defined the Cold War era. As many of the new conflicts occurred between different ethnic and religious groups in high-profile regions, such as the former Soviet Union, Yugoslavia, and the Middle East, scholars began to argue that the ideological or economic conflicts shaped by the Cold War have now given way to the “clash of civilizations” (Huntington 1993, 1996). In his 1993 provocative Foreign
Affairs article, for example, Samuel Huntington states that the end of the Cold War marked a new phase of modern history. People increasingly identify and group themselves around cultural identities, he argues, which are defined in terms of shared language, history, religion, customs, and institutions, rather than around competing principles that aim to specify and realize ideal distributive systems. As a result, international politics now revolves around interactions among multiple groupings of different “civilizations,” and social differentiation in terms of economic resources, writes Huntington, is a less relevant cause of conflict today.3
3 Interestingly, parallel arguments have been made in the collective behavior literature that primarily focuses on Western democracies as well. Some social movement researchers distinguish “old” social movements and “new” social movements both conceptually and empirically, the former referring to industrial class conflict over material interests associated with occupational structure and the latter denoting social movements based on so-called “cultural politics” or “identity politics,” including gender, race, ethnicity, and religion. These scholars tend to see the emergence 6 The term “clash of civilizations” has become a catchword in media and scholarly debate about international relations since then. Huntington’s original essay focuses primarily on international conflicts among only seven or eight civilizations that are also regionally divided, and some of his specific hypotheses have been seriously challenged (Ellingsen 2000; Gurr 1994; Russett, Oneal, and Cox 2000). His thesis has broader, rather interesting implications for the study of civil war, however, because it can be understood as a variant of the traditional argument about the linkage of different types of grouping parameters and conflict characteristics. Huntington’s argument suggests that even if political cleavages cut across each other in any society to some degree or other, changes and variations in structural environments may give prominence to particular types of sociopolitical boundaries in fundamentally different manners.
Potentially, the salience of such identities might determine the nature and dynamics of mobilization and violent organized conflict, whether international or domestic.
Indeed, Huntington’s “clash of civilization” thesis has had significant impact on scholarly and public discourse about so-called “ethnic civil war” as well. On the policy design research side, for example, the State Failure Task Force was launched in 1994 at the request of the U.S. government as a data compilation project that examined “ethnic war,” among others, as a distinct form of political instability (Bates et al. 2003; Gurr
of such new movements as a process of social reintegration (rather than deviation) in postindustrial societies that evolve around “new” cultural symbols and institutions. See, for example, Bell (1975), Dalton (1988), Kriesi (1995), Melucci, Keane, and Mier (1989), Melucci (1996), and Wallace and Jenkins (1995). Such distinction, on the other hand, has been criticized as empirically non-existent. See Pichardo (1997).
7 1993, 2000; Gurr and Moore 1997).4 In academic research, many civil war scholars also have echoed Huntington in emphasizing that “civil wars are not all alike” (Doyle and
Sambanis 2000; Gurr 1993, 2000; Gurr and Moore 1997; Horowitz 1985, 2001; Kaldor
1999; Kaufmann 1996; Licklider 1995; Reynal-Querol 2002; Sambanis 2000, 2001;
Snow 1996). Different conflicts may show some important causal heterogeneity, they argue, and armed conflict, therefore, should not be analyzed through the prism of unit homogeneity. In their opinion, intrastate conflict is too complex as a sociopolitical phenomenon to lend itself to a parsimonious explanatory model. Rather than attempt to find overarching rules that dictate mechanisms of all conflict outbreaks, researchers should instead identify key observable heterogeneities, disaggregate conflicts into different types according to their distinct, and theoretically and empirically important characteristics, and place much greater emphasis on developing “contingent generalizations” (David 1997).
For those scholars, collective action organized around ethnic and religious identity is indeed one of the most important “breaks” that make it inappropriate to pool armed conflict information. Journal of Peace Research followed this line of approach by devoting a special issue to the question of resolution of “identity-based disputes,” which is viewed there as distinct from “interest-based” conflicts. Meanwhile, Horowitz
(Horowitz 1985, 2001) asserts that symbolic/political motives underlying ethnic identity,
4 It was commissioned and funded by the Central Intelligence Agency upon the request of former Vice President Al Gore. It was initially hosted by the University of Maryland. The project moved its home to George Mason University in 2005 and is currently called the Political Instability Task Force.
8 rather than material interests, play a central role in the emergence of violent, mass-scale ethnic conflict. “The ethnic group,” he writes, “is not just a trade union” for that very reason. Likewise, scholars like Stuart Kaufman (2001, 2006a, 2006b), Chaim
Kaufmann (1996), Rothman (1997) and Emminghaus, Kimmel, and Stewart (1998) argue that identity civil wars should be distinguished from ideological or non-identity civil wars, both theoretically and empirically, because the two types of civil war emerge with tremendous differences in the organizational constellations, flexibility of individual loyalties, motives for collective action, and goals sought by their participants.
Unfortunately, however, many of these arguments have been limited to the conceptual and descriptive stages to this day. Overall, they have yet to be quantitatively examined with methodological rigor to see if “contingent generalizations” that David
(David 1997) calls for can actually be established. Some quantitative researchers start from a priori assumptions, either implicit or explicit, that such sociopolitical phenomena as identity and non-identity conflicts do exist and that fundamental causal differences exist between them. Yet, the vast majority of such studies simply limit their sample to “ethnic war” or “ethnic conflict” and thereby effectively reify the concept without exploring much empirical justification for doing so (e.g., Gurr 1993, 2000; Gurr and Harff 1994; Gurr and Moore 1997; Olzak and Tsutsui 1998; Reynal-Querol 2002;
Vanhanen 1999).
On the other hand, with a notable exception of Sambanis’s (2001, 2006) large N study, Besançon’s (2005) recent attempt to examine the “Economic Inequality –
Political Conflict (EI-PC) nexus” (Lichbach 1989) across different war types, and
9 Esteban and Ray’s (2007) game-theoretic equilibrium solution, most of the cross- national empirical studies in academic and policy-formation circles during the past decade assume that civil wars can be treated safely as an aggregate category and seek to develop generalizations about the causes of domestic conflict outbreak in general. The predominant tone of the existing quantitative literature has been dictated by a classic rational model of economic utility or “rent seeking” theory, where any conflict is considered as a result of the benefit maximizing behavior of rational individuals who at times engage in civil war on the basis of a cost-benefit calculus about the perceived gains of rebellion (Fearon and Laitin 2003; Skaperdas 1998, 2002). Fearon and Laitin
(2003), for example, conclude that ethnic or religious characteristics are of little relevance to the risk of conflict outbreaks. Collier and Hoeffler’s influential “greed and grievances” thesis, first published in 2001 as a World Bank working paper, considers rebels to be “greedy,” and it is a country’s economic opportunity structure that generally determines the balance of insurgency supply and demand across cases (see also Collier
1999, 2000, 2003; Collier and Hoeffler 1998, 1999, 2005; Hegre et al. 2002).5 Some of these scholars even more explicitly take issue with the “ethnic (identity) conflict” label, arguing that the fluid, manufactured nature of “primordial” identities and substantial heterogeneity across cases that are considered to be ethnic/identity-based make the
5 Collier and Hoeffler’s work has been central in special programs on civil war launched by the World Bank during the post-Cold War period. It has applied economic models to their analysis of general civil war outbreak, duration, prevalence, and termination. The programs are “The Economics of Conflict Program” in 1998, which consists of “The Economics of Civil War, Crime and Violence project” (1998-2005) and “Post-Conflict Transitions research project” (2005-).
10 category conceptually and analytically useless (King 2001; also see Mueller 2000). For this reason, they have even advocated complete abandonment of ethnic (identity) conflict and ethnic/identity-based conflict studies as a distinct concept and subfield of research (Brubaker and Laitin 1998; Gilley 2004).
Between those two extremes of uncritical reification and outright rejection, it is still far from conclusive whether the popular taxonomy between identity and non- identity conflict (or its frequently used variants, ethnic/communal/sectarian conflict and ideological or “people’s” conflict) can point to distinctive causal explanations in a way that is beyond being simply descriptively interesting. During the past decade, the burgeoning literature on internal conflict has reached considerable consensus in identifying factors that put some countries at a higher risk of internal conflict onsets, such as the effect of poverty, economic development, and state strength (Gates 2002;
Lacina 2004). Yet, it is not known whether those factors apply equally to all war episodes, and if not, whether this oft-mentioned taxonomy of identity versus non- identity conflicts represents a key heterogeneity that can explain different mechanisms behind violent conflict events. Meanwhile, the quantitative literature has also presented rather mixed findings in terms of oft-cited political and social factors in this debate, such as democracy (or democratization), inequality, and ethnic/religious identity. And yet, few efforts have been made to explore the possibility that pooled conflict data might be one reason for such inconsistent results, aside from methodological differences.
11 PRESENT STUDY: WHAT IS AHEAD
Investigation into the identity/non-identity taxonomy validity matters because it has not only sociological but also direct polity-design implications for the civil war literature. On the one hand, if causes of civil war are heterogeneous between the categories, that is, if groups form and mobilize around different social bases under different conditions, then treating them as a homogeneous unit leads to a model omitting some key observable variables that can help to differentiate conflict types and formulate well-adapted responses for each (Kaufman 2006a; Sambanis 2001, 2004). On the other hand, it is crucial to see if such concepts as “ethnic/identity war” and “non- identity war” are products of simply “banal” or erroneous characterizations (e.g.
Brubaker and Laitin 1998; Gilley 2004; Kalyvas and Kocher 2007; Mueller 2000) because this taxonomy has too often framed public and scholarly debates about conflict prevention and resolution.6 Clearly, understanding the empirical validity of the taxonomy will help us comprehend its dynamics and create a social and political system that can identify potential internal conflicts, prevent and respond to unfolding tragedies before they occur, provide assistance to peacekeeping operations, and protect the civilian population from their dire consequences.
6 See, for example, Stephen Biddle’s recent opinion piece “Seeing Baghdad, Thinking Saigon” in Foreign Affairs (Biddle 2006). In this controversial paper, he warns against drawing historical lessons from the experience of Vietnam as an attempt to find a solution to the Iraq War. In his view, the former is a class-based “people’s war” whereas the latter is ethnic in nature. Even though his opinion piece is specifically about how to end the war, the explicit assumption is the existence of inherently different war “types.”
12 This dissertation aims to address one of the gaps in the civil war literature by examining the existing theoretical frameworks against the common conflict taxonomy.
It is by no means an attempt to lay out and test new theories. Rather, it is an effort to see if the widely used “identity-based (ethnic) conflict” label can be validated by exploring the oft-mentioned linkage between social and political cleavages and political conflict, i.e., the idea that different conditions make people group and mobilize themselves in conflict around different parameters. Defining identity civil war as the most intense form of armed conflict where at least one contender is organized in terms of primordial identity base, the study assesses the conditions that might lead people to identify and organize themselves into violent collective action, and whether such conditions differ between “identity” and “non-identity” wars. By so doing, it considers the appropriateness of using the prevailing taxonomy as a conceptual and analytic tool. It is intended, ultimately, that the study will add research-derived knowledge, if modest one at best, that can contribute to the sociological literature on collective action and at the same time will provide a better empirical foundation for making public policies that are better suited to reducing the risk of intense armed conflict.
With these goals in mind, this study starts by reviewing how the cumulated literature on social change and collective action have theorized (or implied) causal differences between identity and non-identity conflicts and draws a set of hypothesis
(Chapter 2). The premise is that social system is multidimensional in terms of the different forms of resources (Weber 1968), that each dimension operates as an organizing principle of social differentiation and aggregation, and that those dimensions
13 may vary in their importance and salience under different structural environments
(Lenski 1984 [1966]; also see Blau 1977). Just like social aggregation in terms of economic class, aggregation along ethnic/religious lines is in effect a rule, rather than an exception, as one dimension of social system (Weber 1968) in the vast majority of societies on the globe (Fearon 2004; Williams 1994). However, it is clear that aggregates on each of these dimensions do not always turn into politicized collectivities that are engaged in violent conflict. So, the question is whether or not they emerge at under different conditions.
Chapter 3 explains the statistical method and data used for the analysis. To study multiple civil war onsets, this study employs the conditional risk model (Box-
Steffensmeier, De Boef, and Sweeney 2005; Box-Steffensmeier and De Boef 2006;
Box-Steffensmeier and Zorn 2002; Kelly and Lim 2000; Therneau and Hamilton 1997).
This statistical method is an extension of the Cox proportional hazards model that allows researchers to take into account unobserved unit-level heterogeneity and event dependence and forgo the issue of specifying the shape of the baseline hazard. Thus, the model makes it possible to capture the recurrent nature of the event while controlling for correlated event times and estimating the effect of covariates of interest without making strong distributional assumptions. In this chapter, I also describe the data and data coding rules that are employed as dependent and independent variables. The results of the analysis are presented and discussed in Chapter 4 by comparing how widely tested conflict theories about socioeconomic, political, and social configurations explain the risk of “identity” and “non-identity” civil wars differently. The final chapter
14 (Chapter 5) summarizes the study, makes suggestions for future studies while evaluating strengths and weaknesses of the present research, and reflects on the scholarly and policy implications of the findings.
15
CHAPTER 2
THEORIES OF CIVIL WAR
This chapter starts with defining the social facts in question, i.e., civil war, internal armed conflict, and identity war. It does so by drawing on recent scholarly efforts to disentangle and refine the conceptualizations of those theoretically and empirically complex social phenomena. Then, I will review the literature on collective action and armed conflict, summarize major theories, and draw hypotheses that might account for civil war and possible differences in the causes of different war “types.”
CONCEPTUALIZING CIVIL WAR
“What is a civil war?” is a question that is frequently asked these days. In fact, this question, a prerequisite for any serious analytic engagement itself, has been intensely debated in various fields including the media (e.g., Keegan and Bull 2006;
Rodgers 2006; Wong 2006), policy-oriented research institutes (e.g., Beehner 2006), and social science communities (Sambanis 2004; Sarkees 2000; Singer and Small 1972;
Small and Singer 1982). Drawing on the recent work in social science, this study first defines an “internal armed conflict” as a combat within the boundaries of a recognized sovereign entity that involves (1) the use of armed force between two parties, of which
16 at least one is the government at the onset of the hostilities, and (2) violence and damage that exceed a certain degree of intensity (Gleditsch et al. 2002; Kalyvas 2006;
Strand et al. 2005). Further, following Gleditsch et al. (2002), this study sees “civil war” as a subset of internal armed conflict that involves a substantially high intensity of violence and damage. There has been a terminological multivalence in the literature about violent political conflict (e.g., “revolution” [Goldstone 2001; Goodwin 2001;
Skocpol 1979], “rebellion” [Boswell and Dixon 1990, 1993], “insurgency” [Fearon and
Laitin 2003; McAdam 1982; Wickham-Crowley 1991]), but even when different terms are used, they are considered to be civil wars as long as a given organized act of violence matches the above definition.
A number of points should be elaborated here. First, armed conflict is defined in a way that is agnostic about goals and motivations of actors involved (Kalyvas 2006) to safeguard against the trap of conceptual endogeneity as occasionally seen in the civil war literature (e.g., Gurr 1993a, 1993b, 2000; Gurr and Moore 1997). Thus, the definitional focus here is on the phenomenon of interactive organized violence itself, with the sovereign nation state as its central locus. Second, at least one party in conflict must be the government of an internationally recognized sovereign state at the onset of hostilities. In his extensive discussion about the problems of conceptual and operational definition in the literature, Sambanis (2004) raises this important question about the degree of organization that is required of the parties in conflict and so-called “failed states.” The fall of the functional government does not disqualify a given case from
17 being an internal armed conflict when a state failure is a result of conflict outbreak. The above definition of civil war will be operationalized in Chapter 3 with the help of the
PRIO/Uppsala armed conflict project.
CONCEPTUALIZING IDENTITY CIVIL WAR
The term “identity conflict” has become an increasingly popular term for denoting a presumably distinct class of conflict (Brown 2000; Brubaker and Cooper
2000). Social psychologists often understand “social identity” as a category that individuals belong to and as a widely shared reference point that they rely on to make sense of the place of their group within the social world relative to other groups (Brown
2000; Erikson 1980 [1959]; Hale 2004; Tajfel 1982; Tajfel and Turner 1986). In conflict studies, the term “identity” is generally used to refer to group membership that is determined by common descent attributes (or members’ belief in them), such as ethnicity and religion, which can become a salient political cleavage and serve as a focal point for coordinated action. The term “identity-based conflict” (e.g., Gurr 2000;
Rothman 1997; Rothman and Olson 2001) is therefore often used as interchangeable with or inclusive of a wide variety of ethnic conflict terminologies, e.g., “ethnopolitical conflict,” “religious war,” “sectarian violence,” “cultural conflict,” and “communal violence.” Yet, a number of questions need to be clarified because ethnic identity is a subset of social identity according to the definition above. What is ethnicity (and ethnic
18 identity)? Why is it often considered to be so distinctive as to occupy a prominent position in conflict studies? And why is it that “identity conflict” may be a more appropriate term than “ethnic conflict”?
At the center of the definitional debate about ethnicity/ethnic identity lies the theoretical divide between two broad strands of analytical frameworks, which are often called primordialism and constructivism (or circumstantialism) (Bell 1975).7 The major disagreement is about the degree to which ethnic identity is static (or even fixed) or fluid (and even arbitrary). Primodrialists (Geertz 1963b, 1973; Shils 1957) generally assume that people’s ethnic and religious identities have deep historical, or perhaps even genetic, foundations that set clear-cut, enduring boundaries and differentiate a
Gemeinschaft-like social group from another.8 In this view, such distinctive, (quasi-) primary groups are bound together by particular immutable characteristics that are evenly and consistently distributed within each group and that qualify people for group membership, such as common culture and common history. Ethnic identity is given at birth, not chosen at will, and in this regard ethnic groups can be seen as analogous to an extended family or kinship (Horowitz 1985).
In contrast, constructivists’ (Barth 1969; Chandra 2006) central assumption is the socially constructed or “imagined” (Anderson 1991) nature of ethnicity and ethnic
7 Glazer and Moynihan’s actual term is “circumstantial.”
8 A more radical primordialism is proposed by sociobiologists, who regard genetic reproductive capacity as the basis, not only of families and clans, but of wider kinship-based groupings like ethnic groups. Van den Berghe (1987) argues that these groups are biologically bound by mechanisms of nepotism and inclusive fitness. This argument has been attacked for reducing cultural and social behavior to biological drives, and for failing to account for the binding of large ethnicities and nations. 19 identity. In this view, symbolic contents of ethnic identity are malleable and fluid, and the immutable tie of blood as imaged by primordialists is but a product of changing social, political, and historical circumstances or even manipulation. Moreover, ethnic identity categories are shot through with a whole web of possible subsets, of which only some become articulated at a time as definitional elements of ethnicity, and they easily overlap with other types of social identity that allow people to assume various identities in different situations—a point that is implicit in a plethora of attributes and characteristics listed in the primordialist definition (Chandra and Laitin 2002; Chandra
2006). Therefore, according to constructivism, any definition of ethnicity based on those attributes and characteristics that can be arbitrarily chosen is overall useless for analytical purposes.
Despite the long debate between the two ideal types of ethnicity and ethnic identity, however, the divide between primordialism and constructivism is actually less striking than it appears (McKay 1982; Scott 1990). Primordialists mostly agree that ethnicity and ethnic identity is socially constructed at some point in history. Horowitz
(1985) notes that ethnicity is a subjective belief in common descent “whether or not an objective blood relationship exists” and represents the “putatively ascriptive characters,” whereas Geertz (1963a) also states clearly that primordial attachment is “the assumed givens” of social existence. Constructivists, on the other hand, agree that ethnic group identities are generally stable and durable once they are constructed. Chandra (2006), for example, considers “stickiness” (i.e., constrained change) and “visibility” as two intrinsic properties of the attributes of ethnic identity categories (all my emphasis).
20 Laitin (1998) adds that important identity dimensions can persist and endure because they serve in some way to maintain social equilibrium (e.g., few would start learning a new language if nobody else around them understands it and starts learning it too).
Thus, while it serves as a point of reference for understanding selfhood and group membership, social identity is necessarily situational and ever-changing because external environmental changes lead to a re-evaluation of one’s social location. Yet, ethnicity may be “special” in that it serves as a particularly robust point of reference, that is, a readily accessible mental map that fits the observed reality of society and thereby helps people to make sense of the world due to easy and effortless communication and simple, visible differences (Hale 2004). As Scott (1990) puts it, it does not seem far-fetched to assume that ethnic identities are effectively primordial because ethnicity is hardly mutable and relatively persistent in many cases while its relative importance is socially constructed over time and place.
As for the constructivists’ argument that ethnic identity is analytically useless due to the arbitrary nature of its chosen attributes, anthropologist Thomas Eriksen
(2001) offers an important insight. After examining three conflict cases in detail (the breakup of Yugoslavia in 1991, the Fijian coup, and the Hindutva movement in India) as well as other African examples (the Sudanese civil war, Hutus and Tutsis in the Great
Lake region, and Somalia), he shows that the term “ethnic” and its conventional definitions do not describe all those conflicts appropriately. What is common, he argues, is:
21 “collective identities perceived locally as imperative and primordial, identities associated with a deep moral commitment, whether ethnic (based on notions of kinship and descent), regional (based on place), or religious (based on beliefs and forms of worship).”
Eriksen notes that such identities are, though mutable, not a mere invention.
They are broadly based on real or putative kinship. In many cases, he points out, what passes for ethnicity at the local level is kinship and it works as an important organizing principle in many places today. As such, these groups are largely self-recruiting (also see Horowitz 1985; Sambanis 2006). For this reason, Eriksen proposes that “identity politics” should be used as a more general term to refer to inter-group conflicts, rather than emphasize the “arbitrary” choice of specific ethnic attributes. This view of primordial identities seems useful in classifying some ambiguous cases as “identity- based” conflicts. Somalia, for example, is widely viewed as largely homogeneous in ethnicity, religion, and language, a fact that makes some scholars question a classification of its domestic conflict as an ethnic one (Brubaker and Laitin 1998). Yet, one could reasonably see it as identity-based on the ground that the country has failed to replace local clan-based loyalties with allegiance to a Somali nation as it dissolved into protracted fighting among sub-national groups.
Informed by these insights, this study focuses on ascriptive categories of membership regardless of specific ethnic attributes and characteristics for possible membership qualification. “Identity conflict,” as a subset of internal armed conflict defined above, is understood as an armed combat within the boundaries of a sovereign state with at least one of the fighting parties organized around the parameter of a primordial collective identity, real or perceived. The central definitional point here is
22 the line of demarcation with which populations are differentiated as major organizational and mobilization bases, rather than their specific agendas, grievances, or goals (Ellingsen 2001).
THEORIES OF CIVIL WAR ONSET
Socioeconomic Structures, Domestic
Structural Modernization Theory: Overview
The major general assumption underlying structural modernization and functionalist theories is that all societies are integrated, balanced systems under the state of normality and that such systems progress in a fairly predictable, evolutionary sequence. Traditional societies are characterized by a lower degree of differentiation and reliance on rudimentary, non-industrial technology. Their development path follows the social and economic patterns that marked the historical experiences of developed societies. Such developing societies eventually become more alike in many aspects of modern social life, such as the use of complex, advanced technology, a highly differentiated production and market system, sustained economic growth, and mass affluence (Lenski 1984 [1966]; Levy 1967; Rostow 1960).
The universal pattern of developmental process is driven primarily by the force of technological development. Technology is considered to have a systems logic that dictates other aspects of social life and that eventually dissolves and overrides older forms of social organization (Kerr 1964; Lenski 1984 [1966]). As countries undergo the transition process toward a modern social order, the “logic of industrialization” unfolds
23 and generates considerable changes in the nature of social order (Durkheim [1893]
1960; Kerr 1964) economic organization (Huntington 1968; Kerr 1964; Olson 1963), demographic patterns (Davis 1945; Kirk 1996; Notestein 1945; Thompson 1929), cultural value (Almond and Verba 1963; Inkeles 1969; Inkeles and Smith 1974), and the form and role of government (Lipset 1959, 1981).
In this theory, the transition process is a period of major social turmoil and disorder because different parts of the social system fall into disarray once rapid and massive changes set in. The risk of domestic conflict is considerably low both before and after this period. Though at substantively different stages, both traditional and modern societies are structurally balanced, institutionally stable, and socially and politically integrated (Durkheim 1960 [1893]; Inkeles 1969; Inkeles and Smith 1974;
Kahl 1968). Traditional societies are structurally in equilibrium because their distributive system is primarily based on “needs” (Lenski 1984 [1966]). Large-scale struggles for scarce resources are less likely to occur when societies are limited to inefficient means of subsistence, lower levels of specialization both at community and individual levels, and a relatively small economic surplus. Such societies are also
“morally” stable due to their communal tendency characterized by the shared values of the “conscience collective” (Durkheim 1960 [1893]). Social order in advanced societies is made possible by differentiation that is facilitated by advanced technology. Economic surplus made possible by technological development is now distributed based on
“power,” but unlike traditional societies, individuals and social organizations become organically interlocked and functionally interdependent on one another for their
24 different specialties (Lenski 1984 [1966]; Olsen 1978). Further, affluence discourages conflict outbreaks by making distribution a positive-sum game (Kerr et al. 1964), whereas the high level of functional diversity and complexity creates cross-cutting cleavages, people identify themselves with multiple groups, and as a result the intersection of groups is created on multiple dimensions of social and political life
(Dahl 1971; Lijphart 1977; Lipset 1959, 1981; Roccas and Brewer 2002). This, as well as other aspects of broad social development such as expansion of education enrollment and working-class population with negotiation power, helps reduce the intensity of political emotions and generate tolerance for out-group members by assuring people that while they may find themselves in the minority on one issue, they may constitute the majority on others and can seek to advance reform by more moderate means
(Almond and Verba 1963; Inkeles 1969; McAdam 1982).
In contrast, countries in the midst of transition to modern society are in the greatest danger of conflict outbreak because the old and new systems, as well as previously isolated groups, come into contact with one another and cause a temporary state of disequilibrium (Alonso 1980; Huntington 1968; Olson 1963; Olzak 1992;
Turner 1993). The reasons for such high conflict risks are twofold, each at a different level. At the macro-structural level, some parts of the system always lead and others lag in the early-to-intermediate stages of modernization. Development does not spread evenly and generates social and economic differentials (Alonso 1980; Ogburn 1964;
Rogers 2003). Such structural imbalances inevitably lead to a critical, if temporary, mismatch with the existing capacity of society because effective social infrastructures
25 and political institutions as social control mechanisms lag behind significantly amidst rapid socioeconomic changes (Crenshaw and Oakey 1998; Huntington and Nelson
1976; Kasarda and Crenshaw 1991).
As a result, this structural imbalance causes strains at the macro and micro levels as well. While rapid modernization weakens existing social stabilizers and safety nets, it also provides individuals with both new means and motivations to mobilize for political conflict (Goldstone and Useem 1999; Useem 1998). Increase in social density and geographic mobility increases inter-group contact, which facilitates group comparison and makes distributive gaps at the macro level increasingly salient as a root cause of their hardship. Also, economic development makes individuals “socially mobilized” at the same time by providing new resources and opportunities to organize collective action (Deutsch 1961; Lipset 1981; Tarrow 1998; Tilly 1978). In this situation, individuals and groups whose interests depend on the dissolving old institutions feel threatened by the rise of new systems associated with economic development and resist its impacts, while individuals who benefit from the emerging social order often attempt to repress resistance caused by such “traditional mindsets” (Kerr et al. 1964). When economic interaction is a zero-sum-game with no institutionalized means to address the increasing grievances, aggrieved groups may find no alternatives other than to use extra-institutional action (Olzak 1992; Paige 1975; Tilly 1978).
Empirical research has focused on specific, intrinsic elements associated with this modernization process and their impact on highly intense conflict: population expansion and inequality. Rapid population growth, especially in youth cohorts, has
26 recently gained increasing attention as a major risk factor (Goldstone 2001a;
Huntington 1996; Urdal 2005). In the transitional period toward modern social order, population expansion and high population density occur as improvements in living standards produce a rapid decline in mortality rate while fertility does not decline as much due to the slower change in cultural norms and values (Davis 1945; Kirk 1996;
Notestein 1945; Thompson 1929). What is particularly notable is that the youth population typically grows faster than the aged population and generates a sizable
“surplus” that cannot be functionally integrated in the social system.
The problem with youth bulges is grounded in the absence of conformity- inducing constraints and the lack of social, cultural, and emotional support in a transitory society. In general, the youth population has much discretionary time at hand with fewer social responsibilities and obligations (e.g., job, family) that would help tie them to society and keep them from committing themselves to high-risk, time- consuming activism (Goldstone 2001a; McCarthy and Zald 1977). This poses a greater danger when the development of the institutionalized regulating/integrating mechanism, such as the labor market and public services, chronically lags behind the population growth and fails to absorb the increasing youth population into the system. Youth idealism, impulsiveness, and admiration of anti-authority thinking further make the expanding young adult population “the protagonists of protests, instability, reform, and revolution.” A large pool of those disaffected young people is typically available and
27 indeed is more susceptible to recruitment into rebel groups with relative ease
(Goldstone 2001; Huntington 1996) because the opportunity cost is low for the youth in modernizing society with weak integration power (Collier 1999).
Geographic concentration (rapid urbanization) is another specific demographic phenomenon that is often considered to increase social inequality and the risk of civil war outbreak (Olson 1963). In the modernization processes, rapid demographic expansion spurs massive rural-to-urban migration because of uneven rural social development and expanding economic opportunities driven by investment concentration in the urban sector that occurs in search of larger markets and better socioeconomic infrastructure (Crenshaw 1992; Kerr 1964; Kuznets 1963; Simpson 1993). Resulting rapid urban growth often becomes super-heated and outpaces the carrying capacity of the yet inflexible, nascent urban infrastructure to accommodate the increasing needs for public services. This discrepancy stirs discontent with uneven distribution among the population (Goldstone 1991). At the same time, high population density tends to generate a social network and organization (Hawley 1986) as a mobilization base for collective action (McAdam 1982).
Destabilizing impact of expanding inequality amidst explosion of surplus population and regional/sectoral concentration has also been debated (Alonso 1980;
Kuznets 1963; Lenski and Nolan 1984). On the one hand, because distributive gaps are inherently relational, the sense of deprivation caused by comparison or power relations between groups with different economic resources may play a major role in civil war onsets. Perhaps the most dominant argument to this day is that income and wealth (i.e.,
28 asset) inequality has a positive impact on political conflicts. The central idea is that discontent and frustration about widening distributive gaps may inspire low-income individuals, who have nothing much to lose, to participate in collective action and resort to extra-institutional means (i.e., political violence) to address their distributive claims
(Alesina and Perotti 1996; Muller 1985; Muller and Seligson 1987; Nafziger, Stewart, and Väyrynen 2000). However, recent civil war studies focusing on the “greed and grievance” question largely reject the effects of inequality as a grievance indicator on armed conflicts as well (Collier 1999; Collier and Hoeffler 2004; Fearon and Laitin
2003). This state of the literature warrants an additional test.
How Has Structural Modernization Theory Explained The Two “Types”?
While some scholars argue that modernization theory, due to its emphasis on economic variables, does not adequately explain the risk of identity-based civil war which in their view is driven by political reasons (Sambanis 2001; 2006), many sociologists have long suggested that the broad development process temporarily increases but eventually reduces the risks of both identity (Dahl 1971; Olzak 1992) and non-identity (Kerr 1964) conflicts. Yet, some believe that specific aspects of modernization process may increase a certain “type” of civil wars. First, although the effects of demography on different “types” of conflict onset in modernizing societies have not been explicitly specified, contemporary observers, policy advocates, and many social scientists have been vocal in their concern that the youth bulge is related to political conflict via such identity issues as ethnicity, religion, and regions (e.g.,
29 Huntington 1996; Urdal 2005; Dugger 2007; Fuller 2004; Helgerson 2002; Kaplan
1994; Sciolino 2001; Zakaria 2001). In modifying his original thesis (Hungtington
1993), Huntington (1996) links the expansion of the youth cohort with what he sees as the upsurge of the inter-civilizational conflicts since the late twentieth century. He suspects, for example, that the growth in the youth population in major Islamic countries is somehow related to the Islamic Resurgence in the 1970s and 1980s (e.g., the Iranian Revolution of 1979). Esty et al.’s (1998) analysis using the State Failure data adds some empirical evidence and suggests that the presence of a youth bulge as measured by the ratio of 15 to 29 year-age bracket to that of 30 to 54 year-olds significantly increases the risk of “ethnic war” as opposed to “revolutionary war” in the dataset. Though any result from these observations should be interpreted with caution unless the youth bulge information is disaggregated, it would be useful to see if the variable behaves differently between the two “war types” samples. Then we may obtain some clue as to whether those arguments are simply newly ethnicized in the context of public debate today or whether there might be something to it with regard to the youth bulge and the different war “types.”
Second, past arguments suggest in a relatively explicit manner that inequality in modernization matters significantly more for the risk of non-identity conflicts, especially class-based revolutionary type. On the other hand, others have stated that more equality might increase the risk of identity war outbreak, and horizontal (group- specific) equality may be the most dangerous one (e.g., Besançon 2005; Horowitz 1985;
Robinson 2001; Sambanis 2001). Rebellion by non-identity groups is often believed to
30 erupt due to dramatic changes in the structure of work and life and widening inter-class inequality in industrialization. As a result, the early to intermediate stages of industrialization tend to produce greater degrees of class-based rebellion (Kerr 1964).
Although his empirical investigation does not exactly tap the question of different conflict “types,” Midlarsky’s (1988) use of a land inequality variable explicitly assumes that the patterns of class grouping exist when he emphasizes the environment in which actors (peasants) are most keenly aware of their holdings relative to their rulers (land owners).
In contrast, other scholars have argued that more equality in modernizing societies poses a danger of identity conflict because of the different ways that identity groups are motivated for collective action (McAdam 1982; Horowitz 1971, 1985) and because of the new competition that identity groups are getting into over niche space
(Olzak 1992; Olzak and Tsutsui 1998). Either way, what is inherently dangerous in terms of identity war risks is “horizontal ordering,” in which identity groups in a similar, rather equal position exist, each with its own internal stratification structure (Robinson
2001). Social psychological and cultural hostility explanations suggest that under such settings, the parallel groups coming in contact during modernization are incessantly feeling uncertain about their place in the world, are constantly comparing themselves with each other, and act to claim the superior status of their world view and group worth, often by excluding other groups. Competition theory argues that relatively equal identity groups become serious competitors with one another when flowing into the
31 burgeoning industrial labor market, and slightly stronger groups attempt to drive slightly weaker ones out of the niche space to maintain their advantage (Beck and
Tolnay 1990; Olzak 1992; Olzak and Tsutsui 1998).
Yet, the traditional “relative deprivation” and related theories claim otherwise in terms of the relationships between modernization, equality, and identity conflict.
Proponents of this argument state that inequality between identity groups increases the probability of identity conflicts, thereby making effectively the same argument about the inequality and non-identity conflict nexus. The main idea is that greater inter-group differentials generate a strong collective sense of grievance and cohesion among members of disadvantaged identity groups, and in the end facilitate mobilization for identity-based conflicts (Gurr 1970; Østby 2005). Thus, although absolute poverty may simply lead to apathy and no action and hence comparison is still an important factor,
“vertical (or hierarchical) ordering” where stratification coincides with ethnic lines is viewed as the more conflict-prone structure. The argument here is that grievances and group cohesion soar and consequently conflict risks increase as people with a shared identity see themselves as suffering from greater inequality as opposed to other mainstream groups. Thus, a critical test should be possible not just between the two arguments about equality and identity conflict but also between differences (or absence thereof) of the two “types” of conflict.
32 Summary and Hypotheses
Modernization theory asserts that social turmoil and conflict are increased in modernizing societies. The theory offers a wide array of hypotheses on the effects of modernization on armed civil conflict. Uneven development of different parts of society and newly emerging social associations in modernization processes generate grievances, the temporal inability of social institutions to accommodate them, and increasing resources that can be mobilized by potential rebels. However, this effect if any should be temporary and modernization eventually reduces the risk of intense, violent conflict.
H1: The effect of economic development on the risk of armed conflict onset
should exhibit an inverted U-shaped curve. That is, the risk of civil war onset is
the highest at the intermediate level of economic development, whereas it
should be the lowest in societies at lower and higher levels of development.
I will test industrial labor force both in the linear and quadratic term, because the size of working-class may not always increase concomitantly with economic development and hence the functional form is not entirely clear.
H2: Increase in industrial labor force should (eventually) decrease the risk of
civil war onset.
Social development associated with economic modernization should nurture tolerant, moderate attitudes among people. Expansion of education is one of the key elements modernization theorists have emphasized.
H3: Expanded education enrollment should reduce the risk of civil war onset.
33 Demographic hypotheses involve the effects of rapid population expansion, youth bulge, and geographic concentration. The basic logic underlying them is rising demands beyond society’s carrying capacity and intensified competition over limited resources that do not keep up with the expanding population when social density increases. Thus, possible impacts of density are also examined here along with that of growth, concentration, and the presence of large youth population. Recent public and scholarly discourse considers a rapid expansion in the youth population to be a particularly dangerous risk factor for identity-based rebellion.
H4: Countries with high population density are at the higher risk of civil war
onset than countries with low population density.
H5: Countries with high population growth are at the higher risk of civil war
outbreak than those with low population growth.
H6: Rapid urban population growth increases the risk of civil war onset.
H7: Countries with the “youth bulge,” that is, with the larger proportion of
youth population, are at the higher risk of civil war, especially identity-based
civil war, than countries without.
Modernization theory sees widening inequality in the transition time as another major destabilizer of social systems. Many scholars assume that economic inequality increases the risk of non-identity war, such as peasant rebellion, by making the economic-class line salient. On the other hand, some scholars argue that more equality among identity groups increases the risk of identity conflict by intensifying competition
34 and a sense of fear of elimination, whereas others think inequality is a risk factor for identity-based war in a similar way as non-identity conflict. Still others believe that it is political, not economic, differentials that matter to identity-based civil war.
H8: Countries with higher levels of income inequality should be at the greater
risk of civil war onset. This may especially be true in terms of non-identity civil
war.
H9: Countries with higher levels of land inequality should be at the greater
risk of civil war onset. This may especially be true in terms of non-identity civil
war.
H10: Countries with lower levels of inter-group economic/political inequality
are at the lower OR higher risk of identity civil war.
Socioeconomic Structures, External
World Systems Theory: Overview
Unlike structural modernization theory, some sociological theories emphasize international forces in accounting for the probability of civil war. Particularly interested in the ways in which each society is embedded within the structural context of the international community, these theories attempt to explain how each country’s connections with other countries may shape internal social, economic, and political interactions, including armed conflict. One such approach is world systems and dependency theories, which examine the effects of economic interactions between different global processes on domestic societies.
35 World systems theory emerged as a counter-argument against dominant analytic approaches to understanding development and social change, especially structural modernization theory (Wallerstein 1974). It finds fault with the view that the nation state is the sole unit of analysis and rejects “developmentalism,” the notion presented most prominently by Rostow (1960) that each society evolves along a similar, universal set of stages toward modernity. The capitalist world system is understood instead as the unit of analysis for studying change in the modern world. In other words, it is the inclusive “totality” that, by its own systemic logic, generates recurrent and identifiable structures and processes serving as the prime engine of social change and development in each nation state. Thus, socioeconomic development and social structures in any national unit are determined not by internal exchanges but predominantly by external forces that are emergent from the structure and processes of global capitalism
(Goldfrank 2000; McMichael 2000; Timberlake 1987; Wallerstein 1974). The world systems school, therefore, sees the sources of domestic conflict in the disruptive effect of capitalist incorporation and the class polarization driven by dependent status in the world system.
According to world systems theory, the capitalist world-economy is a relatively stable hierarchical system comprised of three interrelated yet distinct economic zones: core, periphery, and semi-periphery (the latter two being developing nations). The three economic zones are demarcated by the extent and intensity of economic production and are integrated by the web of market mechanisms that are distorted in favor of the world bourgeois class by the economically, geopolitically and militarily stronger core
36 economies. The fundamental structural relationships driving the capitalist world system are those between the core and periphery. These relationships are created as core nations penetrate and reorganize production systems on the periphery. The core zone has strong states and specializes in high-technology, high-wage, most value-added production and services, whereas the peripheral zone has weak states, specializes in labor-intensive and low-wage production and extractive industries, and lacks technology and capital for self-generating development. The unequal exchange and international divisions of labor is an intrinsic, integral part of the capitalist world system that enables the core to extract economic surplus and capital accumulation from the periphery. The semi-periphery is the other structural element that plays an essential part in the capitalist world system. It serves as intermediaries in core-periphery trade and as the buffer zone that absorbs some of the peripheral opposition to the core through sub- imperial practices. The semi-periphery also serves as the intermediate zone that introduces some degree of mobility in the world system, particularly according to the systemic cycles of economic expansion and contraction. Nevertheless, upward mobility is rather difficult and often comes at the expense of downward mobility of other societies.
Notably, this core-periphery relationship does not necessarily mean the absolute immiserization of peripheral countries, according to some world systems theorists (see, however, earlier work by Wallerstein). The interests of foreign corporations are not totally incompatible with the development of small economic sectors on the periphery.
For example, multinational corporations may seek to reap gains from industrial capital
37 investment into peripheral economies by manufacturing and selling low-cost goods in the home countries. Yet, their development is structurally distorted, or “disarticulated”
(Amin 1976) due to their external market orientation and dependence on foreign economic interests (Cardoso 1973; Cardoso and Faletto 1979; Evans 1979; Lipton
1977; O'Donnell 1979). Increase in direct investment from multinational corporations results in the concentration of labor, capital, technology, and production infrastructure in export sectors. Those small export-oriented sectors, or “export enclaves,” utilize modern capital-intensive techniques and experience significant development, while traditional subsistence economic sectors are excluded from the accumulation process with only labor-intensive technology and little capital available. As a result, different economic sectors and regions in peripheral countries develop unevenly, if at all
(Boswell and Dixon 1990, 1993; Dixon and Boswell 1996; Moaddel 1994) and the displacement of rural, agricultural populations and their massive inflow into urban centers far exceed their capacity to utilize extensive inputs from the traditional sector
(e.g., labor force). Many of those surplus people are denied entry to small, affluent export enclaves and are forced into dead-end, low-wage employment in informal, tertiary urban sectors (Bradshaw 1987; Evans and Timberlake 1980).
The process of peripheralization increases the risk of intense violent conflict, or
“anti-systemic movements” in Wallerstein’s terms, in less developed countries (Jackson et al. 1978; Wallerstein 1983; see also Chase-Dunn and Rubinson 1977; see Chase-
Dunn 1998 on the conflict potentials in semi-peripheral countries). The populations left behind in disarticulated, dependent development become large reservoirs of strong
38 discontent and grievance and are prone to be driven toward rebellious actions against the exploitative system dominated by the core economic influence. Conflict potentials on the periphery are further increased by the absence of political routes open to these aggrieved people as they try to address their problems via institutionalized means.
Dependent development is often characterized by the exclusive triple alliance of transnational capital, local capital, and the entrepreneurial fraction of the state (Cardoso
1973; Cardoso and Faletto 1979; Evans 1979; Lipton 1977; O'Donnell 1979), an alliance that effectively excludes the masses from both political decision making and consumption. Drawing on the tradition of world systems theory but focusing more on political elites, urban bias theory describes the grim realities of rural life in the developing world (Lipton 1977). In this view, developmental authoritarian states make most macro-and microeconomic policy initiatives to favor the overdevelopment of urban areas heavily at the expense of the rural sectors because the elites and most powerful interest groups in LDCs, such as government officials, large industrialists and traders, intellectuals and professionals, rely primarily on urban resource bases.
Meanwhile, the states expand their repressive functions to stabilize and manage domestic investment environments, capital accumulation, and economic growth by employing coercion (e.g., security/police forces and military) and by dismantling the populist sectors (such as labor union and other political organization) that resist capital accumulation.
39 How Has World Systems Theory Explained the Two “Types”?
World systems theory, shaped by its Marxist lineage, has focused on global and domestic class configurations and has never been very genuinely interested in distinguishing identity and non-identity conflicts in their analysis. The identity issue seems to be viewed as a form of the superstructure (i.e., an ideology that helps to establish and consolidate the capitalist control) and/or as a requisite factor in the operation of the capitalist world system itself (i.e., nationalism and nation states). For
Wallerstein, the notion of ethnicity (or other identity) is essentially a function of each country’s position within the world system (Balibar and Wallerstein 1991).
Many world systems proponents have attempted to theorize ethnic/identity conflicts in terms of their effects on the capitalist world system only through the dichotomy of articulation (functional) and resistance (socialist/anti-capitalist) (Balibar and Wallerstein 1991; Dunaway 2003; also see Friedman 1989, 1990, 2004). Some state that the incidence of ethnic conflict is the highest at the time of the declining hegemonic world order (Friedman 2004), whereas others claim that the highest incidence is during the peak of hegemons’ ascendancy (Dunaway 2003). But this theoretical school apparently lacks a coherent explanation or a clear consensus on the question and does not seem to have made much progress as Chase-Dunn (1998) observed that “it is a difficult one [distinction] to make” in many instances. Thus, the hypothesized effects of each country’s structural location on the risk of identity and non-identity conflict should show, if it does, in indistinguishable manners.
40 Summary and Hypotheses
In summary, for world systems theory, all social phenomena manifested at the domestic level are shaped by the forces that are external in origin. Conflict potential is ultimately determined by the structural location in the capitalist world system (i.e., the way that each nation is incorporated into the webs of international production and capital/commodity exchange). Peripheralization increases the risks of war onset, regardless of the “types,” due to internal development structures that are characterized by severe social, economic and regional/sectoral differentials and accompanying repressive political environments that exclude victims of disarticulated development.
H11: Peripheral countries are at the greater risk of civil war onset than core or
semi-peripheral countries.
H12: Higher trade dependence leads to the greater risk of civil war onset.
H13: Higher foreign direct investment penetration leads to the greater risk of
civil war onset.
H14: Higher primary commodity export dependence leads to the greater risk of
civil war onset.
From the framework of world systems and dependency theories, however, the hypothesized effects should exhibit no real difference between the risks of the two conflict “types.” Conflicts, either identity or non-identity based, occur as a result of peripheralization and disarticulation driven by the operation of the capitalist world system.
41 Political Environment, External
World Polity/Neo-Institutionalist Theory: Overview
While emphasizing the global hierarchies in the world system as a major cause of conflict, world systems theory undertheorizes other aspects of international relations than economic exchanges. Scholars from neo-institutionalist/world polity perspectives have proposed alternative approaches to understand global development and examine external effects on intrastate conflict. This theory aims to shed light on evolving and expanding cultural and institutional frames of global society and their impacts on national societies and the nature and behavior of embedded actors, including states and social organizations, especially those operating at the global level.
According to world polity/neo-institutionalist theory, world culture has crystallized since the middle of the nineteenth century originating in European tradition, and has become pervasive and persistent since the end of the last world war. It refers to a world institutional and cultural order that is constituted by a “set of scripts to be followed” (i.e., universally applicable and valid models) and that generates values through the collective referral of authority. World polity/neo-institutionalist theory emphasizes the ways in which international cultural principles and institutions, such as state sovereignty, human, women’s, and individual rights, justice, rational progress, and formal public education, have gained strong authority and legitimacy, provide a common frame of understanding across the globe regarding the legitimacy of various actors and their goals and actions, and thereby shape the behavior of nation states, organizations, and individual actors (e.g., Ramirez, Soysal, and Shanahan 1997). As a
42 result, nation states have exhibited considerable institutional similarities regardless of their levels of socioeconomic development (Boli and Thomas 1997; Meyer et al. 1997).
World polity/neo-institutionalist theory does not consider that cultural isomorphism is a result of a simple and smooth convergence process. Yet, while some proponents do recognize the existence of competing models in less integrated countries within a world cultural system (Meyer et al. 1997) and argue that such competitions indeed can lead to serious conflict (Hironaka 2005), the theory overall suggests that the greater integration of countries into the world cultural and institutional order may ultimately help stabilize the country and prevent violent conflict (Boli and Thomas
1997; Hegre, Gissinger, and Gleditsch 2003; Huntington 1991c; Meyer et al. 1997;
Olzak and Tsutsui 1998). International non-governmental organizations in various fields and governmental actors and their global networks (international governmental organizations, IGO) are two key players in global politics that may prevent violent armed conflict in different ways. INGOs tend to create, elaborate, transmit and carry out global principles that underlie international cultural and institutional order, whereas governmental actors tend to enforce such frames of world culture that have become widely accepted and, by so doing, preserve its legitimacy. Since the authority of nation states is rooted in world culture, membership in a large number of international governmental organizations increases the authority of state, expands its power over its citizens, and thereby raises the cost of challenging state authority (Olzak and Tsutsui
1998; Tsutsui 2004). In contrast, international non-governmental organizations (e.g., professional associations, social movements, subculture groups, or labor unions) are
43 “built on world-cultural principles of universalism, individualism, rational voluntaristic authority, progress, and world citizenship” (Boli and Thomas 1997). Transnational voluntary organizations nurtures a global public space by representing, carrying out, and disseminating global principles (e.g., human rights) and promotes the citizenry that is aware of the outputs of the administrative machinery but is ready to pressure those in power in the political machinery. International civil society networks, therefore, increase political mobilization by providing cultural framing (Benford and Snow 2000;
Tsutsui 2004) and communication resources, and thereby place constraints on action that the government may take against non-governmental actors. But it may be a conventional, non-violent protest or participation in civic activities (Almond and Verba
1963), rather than by extra-institutional, violent means.
How Has the World Polity Theory Explained the Two “Types”?
It is not entirely clear if the world polity/neo-institutionalist approach predicts the risk of two “types” of armed conflict differently. Olzak and Tsutsui (1998) and
Tsutsui (2004), for example, limit their focus on ethnic mobilization and find a conflict prevention effect of participation in international governmental organizations. Yet, their study does not preclude the possibility that their model may apply to non-identity conflict as well. Indeed, since the universal frame of world culture should be politically appealing for a wide range of non-governmental actors working on various issues related to human rights and other global principles (e.g., women’s rights, worker’s
44 rights, and minority’s rights), it is expected that mobilization from global civil society is increased across various types. However, the way that various social groups are mobilized and the way that the state responds are both shaped by the same world culture as well which hardly legitimizes extra-institutional action on either side. Thus, it seems hard to predict a higher risk of intense, violent conflict from the framework of world polity theory itself.
Summary and Hypothesis
From the above argument laid out by world polity/neo-institutional theory, the following hypothesis will be tested.
H15: The more exposed and isomorphic a society is to world cultural
institutions, the less likely it experiences either identity or non-identity civil war
outbreak.
Political Environment, Domestic
Political Opportunity/State-Centered Thesis: Overview
The civil war literature during the last decade has been marked by the micro- economic, utilitarian school of thought. In that school, political variables are typically treated as a proxy for political grievance and rejected as unimportant in explaining civil war (Collier and Hoeffler 2004; Fearon and Laitin 2003). Such economic arguments are criticized, however, for underestimating the important role that the political environments within national boundaries may play in constraining and determining the
45 embedded behavior of political actors. Critics argue that to the extent that the political realm is structured around constant interaction for power and control over regimes between “members” of the polity who enjoy easy, routine access to government power and “challengers” who are excluded (Tarrow 1998; Tilly 1978), the relationships between conflict and government attitudes toward potential rebels—under what condition such inherently conflicting interaction could be a real zero-sum game— should naturally be one of the central inquiries in conflict studies.
Political arguments assume two major points. First, political grievance does matter as a key to form an “action frame” (Klandermans 1997; Simon and Klandermans
2001). Conflict researchers emphasizing this socio-psychological point argue that politics, namely political exclusion, can generate and strengthen a sense of injustice regarding the lack of recognition and make the identification of a “them” (i.e., target) as opposed to a “us” easier, and it serves as one primary source of grievance and compelling organizing and mobilizing principle (e.g., Sambanis 2001, 2006; Gurr 1993a,
1993b, 2000). Second, whether and how such action frames—their extent, intensity, and form—may translate into actual collective action depends largely on the opportunities and constraints structured by the nature of and change in the broader political system in which actors are embedded (Kitschelt 1986; Kriesi et al. 1992; Shorter and Tilly 1974;
Tarrow 1998; Tilly 1978). The lack of access to institutionalized political means not just helps generate an action frame but structurally leaves actors with nothing other than extra-institutional means, and slight cracks of the exclusive political system can lead to violent mobilization as they seek to gain access to the polity (Goodwin 2001).
46 Such conflict-inducing political structures are often distinguished in two different aspects in terms of elite cohesion, governments’ capacity, and formal structures/informal strategies of the state. One analytic focus is on static opportunity structure, i.e., the relatively stable level of institutionalized civil and political rights
(Boswell and Dixon 1993; Ellingsen 2000; Hegre et al. 2001; Muller 1985; Muller and
Seligson 1987; Muller and Weede 1990) or structural characteristics of regimes
(Bratton and Van de Walle 1997; Goodwin 2001; Huntington 1991c; Kriesi 1995; Linz and Stepan 1996; Montalvo and Reynal-Querol 2003a; Reynal-Querol 2002a, 2002b,
2005). The other approach emphasizes the changing aspect of political opportunities, i.e., shifting institutional structures of the political system such as regime change and instability (McAdam 1982; Skocpol 1979; Tarrow 1998; Tilly 1978).
Past quantitative studies about collective action and conflict have typically used the static level of democracy as measured on a continuous scale and have found some good evidence for an inverted U-shaped curve hypothesis. That is, semi-democratic
(often called “anocratic”) countries are most politically unstable and thus most susceptible to rebellion (Boswell and Dixon 1993; Ellingsen 2000; Hegre et al. 2001;
Muller 1985; Muller and Weede 1990; Weede 1987). Non-institutionalized action is discouraged under democracy because it possesses an institutionalized arrangement for managing and channeling conflicts and extensive opportunities not only eliminate the need for unruly action but also inflate the opportunity cost for rebellion. Under severely repressive, autocratic regimes, the balance of power between challengers and their target are less likely to be so even as to allow for room for the rebels to organize and
47 mount fighting. When a country has a mixture of democratic and autocratic elements in a single political system, however, actors lack formal, institutionalized access to the political decision making process but obtain some safer space outside the polity to organize and mobilize independent of government’s repressive controls. This combination of restricted political access and moderate level of opportunities defines the political opportunity structure that increases the potential for civil war to break out.
Despite its theoretical soundness and the decent support it has received, past test results for the U-shaped curve argument may still be open to further investigation because the meaning is not entirely clear when it is argued that intermediate levels of democracy is at the highest risk of rebellion (Lacina 2004). First, the static state of the polity can be specified in more substantive manners than the simple level of democracy.
There may be some substantive variations even across countries with similar levels of democracy in terms of regimes’ formal institutional arrangements and informal strategies. Such variations in specific political system types may offer a more substantive explanation for the risks of armed conflict than simple levels of democracy
(Kriesi 1995; Reynal-Querol 2002a, 2002b). Second, while some nations under this broad middle-range category may have a real structural combination of democracy and autocracy features in a single stable polity, others may fall under this group simply because they are in the middle of transition between the two. Those mix and transitory cases may be different in their institutional strengths against potential rebellion.
Social movement scholars have argued that the combination of regime’s institutional structure (i.e., the degree of its strength and centralization) and its
48 prevailing strategies toward challengers (i.e., repressive/inclusive) may shape the intensity and strategy of collective action. Past research suggests that collective action tends to be most radical and least formally organized when the state is structured to impose itself on the society in an exclusive manner (Kitschelt 1986; Kriesi et al. 1992).
On this point, many civil war researchers have shown that some particular institutional structures of democratic regimes, i.e., the type and selection-procedure of political leadership and decision making, may determine states’ capacity for conflict prevention and hence the level of the opportunity cost of rebellion. Lijphart (1977), Cohen (1997),
Reynal-Querol (2002a, 2002b) and Reynal-Querol and Montalvo (2003a) argue that the proportional representation/parliamentary systems lower the probability of rebellion because their integrative nature increases the opportunity cost of extra-institutional action, whereas the majoritarian system (e.g., presidential system) is more susceptible to the risk of conflict for the opposite reason as its political closedness lowers the opportunity cost of extra-institutional action.
A similar idea is frequently implied (if not always explicitly discussed) in the democratization literature as well. This literature, however, focuses on the importance of institutional structures of autocratic regimes in explaining political turmoil in democratization processes (Bratton and Van de Walle 1997; Geddes 1999; Goodwin
2001; Huntington 1991b, 1991c; Linz and Stepan 1996). Scholars argue that personalistic dictatorship is most conflict-prone on the ground that different institutional structures of autocratic regimes shape domestic balances of power and modes of interaction among key social and political actors (Geddes 1999; Huntington 1991a,
49 1991c; Linz and Stepan 1996). The strong patrimonial nature of personalistic dictatorship blurs the distinction between the private (ruler’s dynasty) and the public
(the state) through arbitrary use of power and leaves no room for social and political society, rule of law, and the effective state apparatus to develop. As a result, key players for political negotiation are absent, state officials and military elites are alienated, and violent challenge intended to replace the ruling elite by “revolutionary movements” erupts (Goodwin 2001). Such opposition movements cannot be defeated easily under personalistic dictatorship because the states are typically institutionally weak and cannot mobilize broad and strong support.
In some cases, exclusive systems may take more direct expression of political exclusion by means of discriminatory policies that deny some segments of society access to political and economic resources or even by means of deprivation of autonomous status (Gurr 1993a, 2000). In this vein, Cohen, Brown, and Organski
(1981) argue that conflict potentials increase when traditional subnational populations are deprived of their autonomous social structure by expansionist states seeking to extend its control over subnational territories and populations.
We may also be able to decompose the oft-mentioned inverted U-shaped curvilinear relationship between democracy level and conflict by looking into the changing aspects of political opportunity (Piven and Cloward 1979; Tarrow 1998; Tilly
1978). The political process thesis argues that the expansion and contraction of political opportunities are associated with political realignment and power reconfiguration and shows that the trajectory of such opportunity structures corresponds to the rise and fall
50 of collective action (Jenkins 1983; McAdam 1982). Similarly, past revolution and conflict studies based on the state-centered theory suggest that the instability of existing social and political institutions and the loss of their legitimacy create a systemic crisis that renders the state vulnerable, opens new opportunities for challengers, and thereby spurs mobilization for rebellions (Kimmel 1990; Skocpol 1979).
Regime transition is a major systematic change in political environment that may play a particularly important role in facilitating violent collective challenges.
Indeed, especially after the major political transformation in many of the former communist countries, regime transition is often mentioned in the conflict literature and public discourse as a highly destabilizing phenomenon (Huntington 1997; Linz and
Stepan 1996; Snow 1996). Some of the intermediate regimes highlighted in the
“inverted U-shaped curve” argument may be such transitory countries rather than stable hybrids (Hegre et al. 2001; Lacina 2004). Those countries holding nation building elections may especially be at the high risk of conflict (Geertz 1963b; Horowitz 1985;
Linz and Stepan 1996). For one thing, the state building process shapes the framework for collective action by creating a target point for political competition and also by providing both tangible and intangible resource bases for mobilization (Tarrow 1998).
At the same time, the absence of a history of lawful, routinized transfer of power and fluid political conditions may put competing political actors in the state of uncertainty about their future chance in the emerging political system when the old regime is losing its monopoly over coercion but the new one has yet to establish its rule (Geertz 1963b;
51 Linz and Stepan 1996; Przeworski 2000). Less institutionalized political mobilization in transitory countries can be a serious threat to maintenance of social order (Huntington
1968).
Despite the number of such theoretical arguments and in-depth descriptions, however, there have been surprisingly few, if any, systematic cross-national studies in the civil war literature about the effects of political change on civil war risk.9 Hegre et al.’s (2001) study is a notable exception that attempts to elucidate this relationship.
Their study suggests that regime transitions of any kind are destabilizing and dangerous, but that the dangerous level effect of intermediate regimes persists even long after the transition period. Given this state of the literature, the present study attempts to add to their study by testing to see if regime transition is a changing political opportunity that significantly affects conflict risk.
How Has Political Opportunity/State-Centered Thesis Explained the Two “Types”?
In what ways might these theoretical arguments apply to the two “types” of armed conflict? Past civil war studies have argued (or at least have implicitly assumed) that politics matters more for identity-based war because of its association with the
“status” stratification system (Weber 1968) and because of the specific way that identity
9 Quantitative, cross-national studies about the regime transition effects on the risks of armed conflict conducted in the past are mostly by international relations scholars, and as such, previous research testing this relationship concentrates heavily on international armed conflict (e.g., Russett, Oneal, and Cox 2000). Even with an impressive body of cumulated studies in the IR literature, the hypothesized relationship is far from robust and conclusive, with some results supportive of the democratic peace thesis (Russett and Oneal 2001; Ward and Gleditsch 1998) and others of system inconsistency (Mansfield and Snyder 2005). 52 groups’ behavior may be shaped (e.g., Ellingsen 2000; Sambanis 2001, 2006). When he states that ethnic (or identity) groups are not trade unions, Horowitz (1985, 2001) assumes that collective action of those two types of social groupings is driven by considerably different motivations. The pursuit of economic interests and economic antagonism that often results as its by-product may well account for non-identity conflicts (e.g., class-based), he states, whereas political factors better explain identity conflicts because the maintenance of relative group worth against competing groups and the fear of exclusion or even extinction are prime driving forces for identity groups’ political action. Lack of democracy and loss of voice make this status parameter salient and threatening to the core of their group identity.
Different political systems have often been associated specifically with the risks of identity conflict as well, for reasons similar to those used to explain its relationships with the level of democracy (Cohen 1997; Goodwin 2001; Lijphart 1977; Montalvo and
Reynal-Querol 2003a; Reynal-Querol 2002a). Although autocratic systems are generally considered to be more conflict-prone than democratic systems, theories about the relationships between autocratic regime types and specific types of conflict are ambiguous and demand empirical exploration. As for democratic institutions, however, not all types of social groups and organizations in civil society are equally conducive to democracy, and exclusive, non cross-cutting groups may not help democratic development (Paxton 2002). In this light, identity groups may indeed be particularly problematic because their less fluid and less mobile identity can quickly turn electoral contestation into a zero-sum game. Thus, democratic systems that are designed to
53 disperse, rather than institutionally reinforce, identity division, should better contain identity conflict. Some argues that the majoritarian system (Horowitz 1985) is better suited for this purpose because it promotes the formation of coalitions among groups. In contrast, others argue the proportional representative system may be more effective
(Cohen 1997; Lijphart 1977; Montalvo and Reynal-Querol 2003a; Reynal-Querol 2002a,
2002b, 2005) as a system of identity conflict management because it does not artificially force groups to establish larger parties due to its decentralization of political representation and inclusiveness in terms of power-holding. These competing explanations form a critical test.
Regime transition, especially democratic transition, is often viewed as a dangerous political opportunity structure that increases the risk of identity-based civil war. Identity groups may have a sense of ambivalence about their fate during nation building. Aside from high hopes for their modern nation, there is also a strong desire for public acknowledgment of their own identity in the new society, which also reflects the fear of exclusion from the emerging political center (Geertz 1963b). Thus, the unleashing of ethnic, religious, and other communal-based identities during nation building (specifically democratization) opens new opportunities for groups to mobilize their support bases in such identity terms (Huntington 1997; Linz and Stepan 1996;
Horowitz 1985; Snow 1996). As a result, political parties in plural societies merely reflect identity-based cleavages, political assertiveness hits its peak along that line during periods of transition toward democracy, and “ethnodemocracy” is forged during the process (Snyder 2000). In such a situation of partial or failed democratic transitions,
54 the risk of identity armed conflict is expected to increase substantially (Gurr 2000). The political freedom of expression in multinational contexts may only facilitate extremist calls to national self-determination by violent means (Snow 1996; Snyder 2000) because democratic elections in divided societies are often at the risk of taking on zero- sum nature (Linz and Stepan 1996).
Summary and Hypotheses
Countries at the intermediate level of democracy are considered to be at the highest risk of armed conflict because the mixture of democratic and autocratic regime characteristics gives moderate opportunities to potential rebels with grievances and interests. However, this relationship may be more relevant for the risk of identity war than non-identity war because identity groups are more aggrieved about the lack of recognition of their identity in the political system.
H16: Countries at intermediate levels of democracy are at the highest risk of
civil war outbreak, whereas democratic or autocratic countries are at lower risk.
This relationship may be stronger for the risk of identity-based civil war.
Different institutional structures, even at the same level of democracy/autocracy, vary in their degrees of political inclusiveness and power decentralization, and thus differently shape the environments in which collective actors find it easier or tougher to mount rebellion. This may be more relevant to identity-based war because their irreconcilable nature may make political contestation into a zero-sum game.
55 H17: Countries under the parliamentary system are at the lowest risk of civil
war outbreak than any other institutional arrangement, OR countries under the
presidential system are at the lowest risk of civil war outbreak than any other
institutional arrangement. The effect may be more prominent for the risk of
identity-based civil war.
H18: Countries under personalistic regimes are at the highest risk of civil war
outbreak than any other institutional arrangement. The effect may be more
prominent for the risk of identity-based civil war.
Likewise, countries that systematically exclude identity groups from political and economic processes by means of severely discriminatory policies should radicalize those groups and drive them to violent challenge against the state. The most extreme form of exclusion would be deprivation of social groups’ autonomous status.
H19: Countries where more groups are politically and economically
discriminated against in systematic manners are at the higher risk of identity-
based conflict.
H20: Countries where more groups are deprived of their autonomous status are
at the higher risk of identity-based conflict.
Another political opportunity approach is to consider changing opportunity structures as triggering collective challenge based on the calculation or perception about their chance of success or the need to defend their status. Democratic transition and
56 nation-building elections might be dangerous, especially for identity-based conflict, because they unleash communal-based identities when a democratic nation is not consolidated yet.
H21: Transitional regimes, especially democratic transition, are more likely to
increase the risk of civil war onset, especially identity-based civil war.
Ethnicity, Religion, and Language: Configurations and Attributes
The presence of primordial communities has often been associated with political strife throughout history. Since identity civil war is, by definition, an intense armed combat that is fought by identity groups, including ethnic, religious, and linguistic ones in many instances, it is not surprising if ethnicity and ethnic identity should play an important role in increasing the potential risk of identity-based civil war. Obviously, it is impossible by definition for an ethnically, religiously, and linguistically homogeneous society to experience identity armed conflict involving ethnic groups.
As many scholars have pointed out, however, there is no necessary link between those primordial parameters and armed conflict (Fearon and Laitin 1999; Hutchinson and Smith 1996). Indeed, the presence of multiple ethnic groups in a given society alone would not account for the risk of “identity” war as opposed to “non-identity” war because ethnic heterogeneity tends to be a common, everyday human experience in the modern world. The vast majority of national societies today are ethnically heterogeneous (Williams 1994) when one takes a close look at the characteristics that have conventionally been considered to be definitional components of ethnicity. For
57 example, according to the World Christian Encyclopedia (Barrett 1982), there are only about twenty national cases that are effectively homogeneous in terms of religion.
Approximately 95% of the countries covered by the encyclopedia have three or more cultural groups cohabitating, whereas minorities are less than 1% of the entire population in only four cases (South and North Koreas, Portugal, and San Marino).
Likewise, although its coding system has some apparent problems in terms of precision and consistency, the World Factbook (Central Intelligence Agency 2006) suggests that only a handful of countries are effectively homogeneous with the biggest ethnic group clearly above 98-99% of the population (e.g., North and South Koreas, Japan, Lesotho,
Bangladesh, Egypt). In terms of language, in approximately 95% of the countries three or more languages are used as first languages (Gordon and Grimes 2005).
It seems safe to say, therefore, that ethnic, religious, and linguistic diversity is simply a fact of life in most societies around the globe, just as socioeconomic differentiation is the rule, not the exception. Indeed, it is almost the universal fact that people tend to categorize themselves into kinship-like groups and derive social identity primarily from their group membership (Eriksen 2001). And in all those heterogeneous countries, ethnicity (and religion and language) is “socially relevant” but not usually
“politicized” (Fearon 2004). As social identity theory (Brown 1988, 2000; Tajfel 1982;
Tajfel and Turner 1979, 1986) and self-categorization theory (Turner et al. 1987) suggest, however, the potential problem with this identity parameter is that the formation of self-identity inherently promotes in-group favoritism and inter-group competition because people seek to see where they stand in relation to “others” and try
58 to achieve and maintain the superior position by making comparisons against the
“others,” i.e., similar or proximal out-groups that are sufficiently relevant and comparable. Thus, the negative identification of the “others” that mobilization is called against is an indispensable factor for collective action, along with the positive sense of
“us” characterized by common traits and solidarity (Coser 1956; Gamson 1992; Simmel
1955), and the urge for pre-eminence vis-à-vis out-groups may in some cases create more conflictive dynamics that help to mobilize people beyond non-violent inter-group interactions of everyday life. These inter-group dynamics are particularly problematic with regard to primordial groups because of the essential impermeability of group boundaries (Hale 2004; Horowitz 1985).
The question is what conditions turn this existing diversity, rather than any other parameter of social differentiation, into a major, salient centripetal force to mobilize people. In other words, when and how might ethnic, religious, and linguistic differentiation be politicized enough to cause competing identities to erupt in contentious collective action? There seem to be two major keys to this question. One is the form of diversity, i.e., how a given society is divided internally in terms of its inter- group composition. Ever since the days of Simmel (1964), many social scientists (e.g.,
Blau 1977) have suggested that there are inter- and intra-organizational dynamics shaped solely by different numeric aspects of social groups. In conflict studies, such numeric characteristics of identity groups have often been examined by focusing on fractionalization, domination, and polarization. The other key is specific attributes that may serve as the centerpiece of people’s identity. Different attributes such as ethnicity,
59 religion, and language may differ in salience if they are associated with different types of symbolism and different degrees of exclusiveness regarding the definition of group membership (Alesina et al. 2003; Horowitz 1985; Reynal-Querol 2002a). Taken together, different combinations of forms and attributes might facilitate or inhibit the
“us versus them” dichotomy by generating a particular inter-group interaction context.
Fractionalization and Attributes
One of the conventional ideas is that a fractionalized society is more conflict- prone. The reasons often raised in the literature on ethnicity and political violence are largely two-fold. First, “subcultural pluralism” can cause violent, non-negotiable ethnic conflicts because fractionalized configurations of society is more susceptible to identity- based grievances, primordial sentiments, fear, security dilemma, and desire for self- preservation (Dahl 1989; Gurr 1993a, 1994; Linz and Stepan 1996). Such emotions are even attributed to a human behavioral disposition to “ethnic nepotism” and the drive for the survival of their genes (Vanhanen 1999). Multiple small groups that are divided along powerful ethnic, religious, or linguistic lines may also mean that the unity and cohesion of such groups are more likely to be maintained in ways that help trigger conflict situations (Sambanis 2001, 2006; also see Simmel 1964).
Second, fragmentation may also raise the practical question of sociopolitical coordination, cooperation, and integration (or lack thereof). The problem is what democracy scholars see as an inherent contradiction between polis and demos, i.e., between subcultural pluralism and the legitimacy and unifying power of the state (Dahl
60 1989; Linz and Stepan 1996). The legitimacy of a state can be seriously questioned if groups with nationalist aspirations refuse to recognize the multinational character of the state, reject any compromise with other groups, or try to exclude them from citizenship.
Thus, significant subcultural pluralism may hinder the support for and understanding of functional political institutions and create difficulties in building up trust across the inter-group cleavages, overcoming potential communication barriers, and finding moderate common ground for cooperative solutions. The failure to maintain subcultural pluralism at “tolerable levels” may make a society susceptible to grave political instability (Dahl 1989). Some relevant implications are offered by economics research as well, which suggests that fractionalization leads to low-quality government and dysfunctional institutions that result in poorly formulated public/economic policies, low public participation is social activities, low economic growth, and persistent poverty
(Easterly and Levine 1997; La Porta et al. 1999; Mauro 1995).
The danger of subcultural pluralism may be even more serious in the process of democratic nation-state building (Linz and Stepan 1996; Snyder 2000). Linz and Stepan
(1996) assert that “the more the population of the territory of the state is composed of plurinational, lingual, religious, or cultural societies, the more complex politics becomes” due to the fundamental difficulty in constructing a power sharing system.
Echoing Linz and Stepan, Emminghaus et al. (1998; also see Horowitz 1971) call attention to the danger of unbalanced development of dual identities and observe that
“the formation of cultural identities about primordial sentiments without the parallel or subsequent development of civil identities has led to primordial violence.”
61 This conventional idea that fractionalization has such dissolving and conflictual effects has been challenged, however. Blau (1977) argues that increasing fractionalization structurally enhances the probability of inter-group contacts and hence associations, reduces the parameter’s salience and exclusive tendencies, and thereby makes society more inclusive. His theory of social structuralization states that social differences become less conspicuous paradoxically as they become more pervasive and that hence such differences do not create barriers against communication. Meanwhile, economists argue that the coordination/integration problem arises for the rebel side rather than for the state (Bates 1999; Collier 2001; Collier and Hoeffler 1998, 2000,
2004; Grossman 1991). In this view of utility maximization and profit optimization, the risk of conflict onset is a function of the budget constraints and transaction costs of rebellion. That is, armed combat is simply high-risk activism for the rebels and they are less likely, as risk-averse rational calculators, to engage in civil war if the costs are too high relative to the perceived gains. In this light, the effects of fractionalization should reduce the risk of civil war outbreak because key tangible and intangible resources are mutually incompatible in such societies. On the one hand, especially for newly formed rebels, ascribed statuses as an intangible resource can help overcome the classic
“collective action problem” (Olson 1971) because they promote efficient organization and effective social control by providing a primary basis for social cohesion, such as
“visible” and “sticky” (Chandra 2006) boundary markers, collective social and cultural beliefs, and densely interactive networks for successful recruitment and sustained commitment (Hechter 1987; Sambanis 2001, 2006; Simmel 1964). On the other hand,
62 fractionalization keeps the rebel from becoming a well-staffed, militarily and financially viable force for large-scale combat action by limiting the size of the socially cohesive recruitment pool (Collier and Hoeffler 2000, 2004). Any attempt to reach out across the ethnic lines and overcome this obstacle of scarce tangible resources would require far greater efforts in coordinating necessary labor for collective action in societies with higher degrees of fractionalization. For the very reason that primordial identity serves as an effective social cohesion promoter, it is costly and difficult to coordinate and maintain organizational solidarity for rebellion across multiple distinct groups.
Therefore, economic analysts conclude that rebels cannot use their intangible and tangible resources to their advantage at the same time in such fractionalized societies.10
Fractionalization with different attributes has different effects on the two types of civil war in different theories. The “subcultural pluralism” argument assumes that any type of primordial diversity, whether it is ethnic, religious, or linguistic, can trigger identity-based war. The non-identity type of civil war is effectively out of its theoretical and analytic scope. On the other hand, in the utility maximization thesis, fractionalization reduces the risk of both “types” of civil war. First, the “limited resource availability” argument implies that fractionalization can inhibit mobilization for identity conflicts (Fearon and Laitin 1999; also see Montalvo and Reynal-Querol
2002; Reynal-Querol 2002a). Although a strong sense of solidarity can perhaps lead to
10 Although some past studies find a negative quadratic effect on conflict, the dependent variable in those studies is almost always conflict duration (e.g., Collier and Hoeffler 1998). Similarly, Ellingsen (2000) and Elbadawi and Sambanis (2002) find a negative quadratic relationship between fractionalization measured by the number of groups and armed conflict incidence. 63 identity-based protest, the probability of highly intense identity-based violence should be low due to the absolute quantitative limitation in the size of its recruitment pool and military/financial resource base (Bates 1999). Second, the “coordination costs/communication barrier” argument can be understood as implying the deterrence effect of fractionalization on the risk of non-identity wars. It can be even exploited by those in power (the state) to impede non-identity mobilization because, as Bonacich
(1972, 1979) shows, ethnic heterogeneity tends to hamper working-class cohesion that might have helped to challenge the ruling class. The utility maximization thesis is not quite explicit about different effects of attributes, but past civil war research from that angle has in large part focused on ethnic/ethnolinguistic fractionalization. The linguistic aspect seems to particularly make sense since it is directly relevant to communication/coordination difficulties. Yet, Alesina et al.’s (2003) finding about a positive relationship between religious fractionalization and government quality and broad social conditions implies the possible importance of that attribute as well. Like
Blau (1977), they conclude that the existence of diverse ideas (i.e., religion) may make society inclusive and tolerant and bring about a positive impact on overall human development. If their argument is valid, it might also mean that religious fractionalization can be effective in reducing the risk of both types of civil war.
Dominance and Attributes
Compared to fractionalization, the effects of ethnic, religious and language dominance on the risk of conflict are considerably under-studied and hence still far
64 from conclusive with some opposite views that both lack much cumulated empirical support in the literature. One argument is that the chance of political instability and conflict is highest when one single group constitutes a permanent majority in society
(e.g., Bates 1999; Gurr 1993a; Jackman 1978). The problem that scholars with this view see in this configuration is not that of cooperation or coordination (or lack thereof) among different identity groups, but the presence of dynamics that are similar to what
Tocqueville (1954) and Dahl (1956) would call the tyranny of the majority. As Collier
(2001) and Collier and Hoeffler (2001, 2004) conclude in their empirical studies, the dominant majority in power may have both the ability and the incentive to perpetuate their superior position and exploit and repress the minorities. Some social psychologists suspect that it is even a universal human predisposition to form “group-based social hierarchies” in which dominant groups oppress subordinate groups (Sidanius et al.
2004). Politics most likely becomes volatile and conflictive when the majority has a sufficient size to “privatize the state” and thereby permanently exclude the others from the polity (Bates 1999) because such exclusion and denial of access to privileges the dominant group enjoys reinforce the psychological bases of group identities by provoking their ethnic grievances, hatreds, and violent challenges (Gurr 1993a;
Sambanis 2001, 2006).
Past research has found conflicting evidence indicating that ethnic dominance is a stabilizing force for society, however. Ellingsen (2000), for example, defines dominance by a threshold of 80% of the total population and finds that it has a deterrence effect on armed conflict. This may be due to a logic similar to the transaction
65 cost argument: a substantial gap between the dominant and minority groups in terms of their power and resources makes mobilization costly. Under the condition of greater social homogeneity where the dominant groups is close to 100%, the minorities are inevitably minuscule and their resources are reduced in ways that discourage their military challenge against the state and lead to lower risks of civil war (Ellingsen 2000;
Jenkins and Kposowa 1990, 1992; Kposowa and Jenkins 1993). Further, minority groups coexisting with the dominant group might not be in such strong solidarity as often believed. As Blau (1977) suggests, given the way that inter-group relations are structurally shaped by size and number, small groups are more likely to get involved in inter-group associations with a large group.
Although those past arguments implicitly assume that dominance should be more relevant to identity-based conflict, they are not very explicit about how different attributes work under this configuration (simply “ethnic dominance” in most studies).
Dominant language might help reduce the risk of conflict by promoting inter-group communication if its practical aspect as a communication tool takes precedence over its symbolic aspect. This awaits empirical examination.
Polarization and Attributes
A closer look at some of the major “dominance” arguments would call into question how dominant “dominance” is. In fact, this concept has been operationalized in various ways and thus it is not even clear if the authors are actually investigating the
66 same thing.11 Perhaps the most problematic is the specification by Collier (2001) and
Collier and Hoeffler (1998, 2001, 2004). After experimenting with various specifications of dominance, they eventually settle with an operationalization that covers a considerably wide spectrum, i.e., with a dummy variable to indicate the presence of the largest group that constitutes 45–90% of the total population on the ground that its “level of significance and the size of the coefficient reach a maximum” in their experiments. Yet, not only is this specification too broad to ensure meaningful interpretation, but it is also unclear if calling such configurations “dominance” quite matches their theoretical argument. Drawing upon the theory of rent-seeking and rebel financial viability, the authors state that “the incentive to exploit the minority increases the larger is the minority, since there is more to extract.” “A minority may be most vulnerable,” they conclude, “if the largest ethnic group constitutes a small majority.” At the same time, they argue that rebel forces must be sufficiently large to defend themselves from the state (Collier 2001; also see Collier 2000; Collier and Hoeffler
1999, 2001, 2004).
Theoretically, Collier and Hoeffler’s argument seems much closer to the concept of polarization (competition), where society is divided into two groups of exactly the same size at the conceptual and numerical extremes.12 Polarization is considered to be
11 For example, Jackman (1978) and Jenkins and Kposowa (1990) specify their variables as linear, whereas Ellingsen (2000) chooses a threshold of 80% of the total population. Collier and Hoeffler code “dominance,” as discussed in the text, when the largest group comprises 45–90% of the total population.
12 In fact, Gates (2002) notes that Collier and Hoeffler’s dominance measure empirically would be almost indistinguishable from Reynal-Querol’s (Montalvo and 67 highly dangerous for two reasons. First, with such demographic configurations, the two groups are often nearly comparable in resources and therefore consider each other to be their serious rival while seeing a realistic chance for tangible gain and/or military victory (Ellingsen 2000; Jenkins and Kposowa 1990, 1992; Kposowa and Jenkins 1993;
Montalvo and Reynal-Querol 2002, 2003a, 2003b, 2005a, 2005b; Reynal-Querol
2002b). Second, group identity may become most salient in the state of polarization because the existence of a few large, similar, and proximal groups decreases inter-group interaction and strengthens in-group cohesion (Blau 1977). Taken together, this configuration may generate the “fear of extinction” and urge for its domination by making the demands of each group mutually exclusive and making the possibility of permanent defeat—the flip side of real chance for victory—real as well (Horowitz
1985). Therefore, polarized societies should more likely be headed for intense conflict, specifically (and by definition) identity-based conflict (Reynal-Querol 2002a).
Many of the past studies are investigating conflict incidence, however, and their empirical implications for conflict onset are still ambiguous. Schneider and
Wiesehomeier (2006) reassess Reynal-Querol and Montalvo’s (2005a, 2005b) studies and conclude that polarization only explains civil war incidence but does not increase the risk of onset. On the other hand, Esteban and Ray (2008) construct a theoretical
Reynal-Querol 2000, 2003a, 2003b, 2005a, 2005b; Reynal-Querol 2002a) or Esteban and Ray’s (1994, 2008) indices, popular polarization indicators in the literature, if the dominance is specified as the range of 45–80%. Yet, Collier and Hoeffler (2004) examine both their specification and the polarization index and claim that the 45–90 dominance better explains the risk of civil war onset. 68 model suggesting that polarization is indeed a strong predictor of civil war onset. The state of the literature thus demands an additional test for the detrimental effect of polarization on identity-based civil war.
The differential impact of polarization of different attributes has not been much studied. Echoing Huntington’s (1993, 1996) “clash of civilization” thesis, however,
Reynal-Querol (2002a) and Montalvo and Raynal-Querol (2002, 2003a, 2003b, 2005a,
2005b) have argued that polarization and the immutable and irreconcilable nature of religion (rather than ethnicity) are the most detrimental combination for the risk of identity-based conflict. Because their findings are, as reviewed above, based on analyses of conflict incidence, their thesis would merit further examination.
Summary and Hypotheses
Most societies in the world today are heterogeneous, but primordial identity is seldom politicized even though people are aware of the symbolic boundaries and may condition their action accordingly on a daily basis. To find out how conditions of diversity may drive societies into civil war, this subsection considered two possible keys in combination: (1) the form of diversity, i.e., how society is divided internally, and (2) specific attributes that may serve as a core of people’s identity. First, as for fractionalization, the “subcultural pluralism” thesis argues that any type of primordial diversity (ethnic, religious, or linguistic) can increase the risk of identity-based war because of the primordial sentiments of small cohesive groups and difficulties in sociopolitical coordination and cooperation among them. Hence a positive linear
69 relationship would be expected between fractionalization and the risk of identity-based war. The utility maximization thesis argues, on the other hand, that fractionalization should decrease the risk of both “types” of civil war because the size of recruitment pool as a viable military and financial resource and the level of social cohesion as an intangible resource are mutually incompatible conditions for launching large-scale fighting in fractionalized societies. The social structuralization thesis sets forth the same proposition about the effect of fractionalization from a different theoretical angle.
Increasing fractionalization increases the chance of inter-group associations, reduces the parameter’s salience, and thereby makes society more inclusive. This implies a critical test between the “subcultural pluralism” argument versus the utility maximization thesis or the social structuralization thesis.
H22: The higher level of fractionalization should increase the risk of identity
civil war, OR the higher level of fractionalization should decrease the risk of
civil war outbreak regardless of the civil war “types.”
Proponents of the “subcultural pluralism” argument are also concerned that democratic state building poses a great challenge in fractionalized societies because of the difficulty in creating a power sharing system among multiple groups.
H23: The effect of fractionalization is the greatest during the process of
democratic state building.
Past research not only disagrees about the effect of dominance but also is not clear about attribute effects under this configuration. Societies with a dominant group may be at the higher risk of identity war because the majority is able to exclude
70 minority groups, thereby increasing their grievance/identity salience. On the other hand, dominance may make society safer from the risk of civil war outbreak because the dominant group literally dominates while the inevitably minuscule minorities do not control sufficient resources to launch a military challenge against the majority. Further, due to the higher rate of inter-group association vis-à-vis the majority, small groups under dominance may not be so highly cohesive. This forms another critical test.
H24: Societies characterized by dominance are at the higher risk of identity civil
war onset, OR societies characterized by dominance are at the lower risk of
identity war onset.
Polarization (competition) is considered to be dangerous particularly as a cause of identity-based civil war because the two groups of the same size are often comparable in resources and therefore consider each other as a serious competitor for power and control and see a realistic chance for victory. In addition, group identity may be most salient in this configuration because the existence of a few large groups strengthens in-group cohesion as an inverse function of decreasing inter-group interaction. Taken together, polarization worsens the “fear of extinction” and urge to dominate the rival group because the realistic chance for victory conversely means a real possibility of permanent exclusion. Although there is little empirical research investigating the effects of different attributes under polarized conditions, such configurations may heighten the risk of civil war if countries are polarized along the religion parameter due to its presumed immutability and irreconcilability among competing world views.
71 H25: Countries at the higher level of polarization would have a higher risk of identity-based civil war outbreak. The risk may be higher if society is religiously polarized.
72
CHAPTER 3
METHOD AND DATA
MODELING STRATEGY
Conditional Risk Model
Some countries are caught in recurring cycles of conflict. Countries such as
Ethiopia, Indonesia, Sri Lanka, Democratic Republic of the Congo, and Iraq are well- known examples that have experienced repeated outbreaks of conflict. Indeed, a
“conflict trap,” or the recurrent nature of internal armed conflict, is one of the greatest concerns for researchers and policy makers in this field (Collier and Sambanis 2002;
Collier et al. 2005). Thus, this study brings multiple failures within units into the analytic picture.13 The major issue when analyzing grouped duration data with repeated events is that event times are most likely correlated. Two sources generate this problem:
(1) unobserved unit-level heterogeneity and (2) event dependence. First, just as in the
13 Some studies using the traditional duration modeling often examine the first failures only. This approach makes an implicit but strong assumption that the first event time represents the time to all events. This is most likely a poor assumption because there probably are event dynamics where a war outbreak facilitates or inhibits further outbreaks. The first failure approach results in a considerable loss of such information and hence in a non-random sample, and as such could lead to misleading conclusions (Pandeya et al. 2005). See also Walter (2004) for an example of essentially the same problem.
73 cross-national time-series setting, the assumption that each of the observations is independent does not hold when each unit contributes multiple observations to the data.
Specifically, some countries are more conflict-prone than others for reasons that are unknown or unaccounted for and that are germane only to their cases. Such heterogeneity generates within-unit correlation in terms of the occurrence and timing of the event. Second, the occurrence of one event may increase or decrease the risk of subsequent events. Indeed, conflict studies suggest that earlier wars may shape the conditions that facilitate subsequent conflicts by failing to resolve deep-seated hatreds, breeding new grievances, or further worsening macro-level forces that caused earlier wars in the first place (Doyle and Sambanis 2000; Doyle and Sambanis 2006; Kaufman
2006; Licklider 1995). When the risk of later failures is a function of previous failures, it leads to within-unit event time correlation as well. To account for these two types of correlated event times effectively while forgoing the issue of specifying the shape of the baseline hazard, this study employs the conditional risk model, an extension of the Cox proportional hazard model (Box-Steffensmeier and Zorn 2002; Cox 1972, 1975; Kelly and Lim 2000; Prentice, Williams, and Peterson 1981; also see Box-Steffensmeier et al.
2005; Box-Steffensmeier and De Boef 2006).
The predominant approach that has been used to assess the risk of multiple conflict outbreaks in past civil war studies is to use binary time-series cross-sections data (BTSCS) and apply a binary link model, such as the logit or probit regressions.14
14 One notable exception in the civil war literature is the study by Hegre et al. (2001) that estimates a Cox model. The way that the authors set up the data has some serious drawbacks (Box-Steffensmeier et al. 2005). First, even though they consider 74 One major problem with ordinary binary-link models is that they do not consider the issue of duration dependency. That is, ordinary binary link models are essentially equivalent to a parametric model with the exponential distribution for the event times, and the assumption therefore is a time-invariant, or flat, hazard rate. In this setting, the risk of conflict onset does not depend on how long each country has “survived.”
Showing that BTSCS data are equivalent to grouped duration data, Beck, Katz, and Tucker (1998) propose a method (“the BKT model”) for dealing with this issue by including duration dependency parameters in the logit, such as natural cubic spline functions of “peace years.” Inclusion of such smoothing functions is a major improvement over the ordinary binary-link models because it obviates the issue of their exponential equivalence in an easily applicable manner. Further, because such smoothing functions are empirically estimated, few assumptions are needed in its application, which gives an advantage to the BKT model over traditional parametric models.
While this modeling has become popular in civil war research, however, there are some drawbacks with this dominant approach. First, it still requires researchers to figure out and specify the number and placement of the knots to break the time interval and fit cubic equations. Unfortunately, many past conflict studies ignore this
multiple failures in the analysis, they discard considerable information by taking what they call “a snapshot approach.” Second, they do not control for the lack of event time independence properly. Finally, they define the risk set unconditionally, which may not be an appropriate approach.
75 requirement and hence fail to ensure that the splines are correctly fit.15 Second, it is not clear how one can account for multiple failures in the BKT modeling. Event independence is assumed in this model; that is, the second and subsequent events are identical to the first event and hence their occurrences are independent of the number and timing of previous events. As discussed above, this assumption is most likely inappropriate; countries with previous conflict experiences are probably at greater risk for another conflict onset because second and subsequent events are more than likely to be affected by the previous events (Collier and Hoeffler 2004; Doyle and Sambanis
2000, 2006; Licklider 1995; Richardson 1960). As a solution to this event dependence issue, Beck, Katz, and Tucker suggest the use of an event counter variable to take into account the number of previous events that each unit has experienced. Yet, as Box-
Steffensmeier and Zohn (2002) demonstrate, the use of an event counter variable for this purpose is not entirely satisfactory because the effects of repeated events cannot be fully captured by simply allowing the baseline hazard to change in a monotonic manner for each additional event.
Conditional risk model is a variant of the Cox proportional hazard model that accounts for these statistical problems. First, unlike traditional parametric regression models and the BKT approaches, the Cox proportional hazard regression leaves the baseline hazard unspecified and examines the hazard rate as a function of covariate parameters only. This allows researchers to estimate the effect of covariates of interest
15 Jones and Branton (2005) also note that given the often highly discretized nature of political data, the inclusion of smoothing functions as duration dependency parameters can create a false sense of precision about the shape of the baseline hazard.
76 without making assumptions about the distributional form of the duration times, and therefore to forgo the issue of temporal dependency. Secondly, the conditional risk model resolves the violations of the standard Cox model’s assumptions of event time independence (Box-Steffensmeier and De Boef 2006; Box-Steffensmeier and Zorn
2002; Kelly and Lim 2000; Prentice et al. 1981). In this study, the variance of the parameter estimates is adjusted by clustering on unit in order to account for unit-level heterogeneity.16 To incorporate event time dependence, the model is stratified by the event number so that the baseline hazard varies across strata and yet the covariate parameters are restricted to be the same across the ordered events. The hazard rate for the ith cluster and the kth civil war onset in this model is modeled as follows:
hik (t) = hok (t)exp(X ik β ) [formula (1)] where hik denotes the hazard for the ith country cluster and the risk of the kth civil war outbreak is specified as a function of h0k. β are regression parameters and X is a vector of covariates that can be either time-invariant or time-variant.
The frequency of events for each event order strata can affect the stability of estimates. Specifically, the number of event observations is small in the higher order strata, as indicated in the left side of Table B.1 to Table B.3 of APPENDIX B. To deal
16 Box-Steffensmeier and De Boef (2006) and Box-Steffensmeier, De Boef, and Joyce (2007) show that the incorporation of a frailty term into the estimates best accounts for various conditions of unobserved unit-specific effects. This study nonetheless uses the variance-corrected approach to account for the repeated, hence correlated, nature of the conflict observations because the model estimates do not exhibit any difference with or without a frailty term (as specified as following a gamma distribution) included, the frailty terms are nonsignificant, and it thus seems safe to
77 with this problem in a way that can achieve stable estimates while at the same time retaining the information available, I follow Therneau and Hamilton’s (1997) recommendation and collapse the higher order strata into one level, as presented in the right side of the same table.
Other than the issue of correlated event times, another key modeling question is the definition of the risk set, i.e., how to define a set of units that are at risk for the kth event at a given point in time. In this study, the risk set is defined by using the “gap time” formulation. In this design, it is conditionally defined, that is, a country unit cannot be at risk for the kth armed conflict onset until the end of the (k – 1)th conflict onset. In other words, the country risk of conflict onsets is conditioned on previous onsets. Incorporating the gap time formulation into the formula (1) above leads to:
hik (t) = hok (t − tk−1 )exp(X ik β ) [formula (2)] where (t – tk-1) denotes a gap time data structure to estimate the hazard that indicates the risk for conflict onset k since the previous (k – 1) civil war broke out. The partial likelihood for the formula (2) is expressed as follows:
δ ⎛ ⎞ ik ⎜ ⎟ n K e X ik β PL(β ) = ⎜ ⎟ [formula (3)] ∏∏⎜ K ⎟ i==k k 1 X ik β ⎜ ∑Yik e ⎟ ⎝ k =1 ⎠
conclude the frailty term is not significantly different from zero and therefore is not needed.
78 where k denotes the event order strata, δ is a censoring variable (1 = event observed, 0
= censored), and Y refers to an at-risk indicator (1 = the unit is at risk for the current kth event; 0 is out of the current kth risk set). Efron’s approximation is used to deal with tied data.
This study assumes that risks of civil war start for country units at the point of emergence of the post-World War II international system or at the point of their independence after World War II. For the practical reason that most of the international political and economic data collections started only in the 1960s and the 1970s, however, most of the actual hypothesis testing is conducted from 1960 to 2000. This essentially means that country units are coming under analysis in a delayed manner despite its exposure to the risk prior to that point. This could cause an issue analogous to left truncation, where the sample under observation may be skewed because some at- risk cases are absent simply due to their earlier experience of the event prior to their entry into study. In the conditional risk model where multiple failures are considered, however, this should not pose a serious problem. Because this study models repeated events, country units stay in the risk set for the next possible kth event after experiencing the (k – 1)th one. Therefore, there is no period of real ignorance about the event. Further, specifically in this study, information about any previous conflict experience during the at-risk period prior to observation is taken into account. For example, if country A had one previous conflict experience four years before its entry
79 into study, the ordering of the next possible event is known to be the second and the time elapsed since the last event is known to be four and counting (Andersen and Gill
1982; Box-Steffensmeier and Jones 2004; Kelly and Lim 2000).17
Similarly, this analysis treats the “war incidence (i.e., presence)” country years as something analogous to interval truncations. Existing civil war studies have not yet reached a consensus on how war incidence years should be treated. Because civil war is defined as an armed combat between the central government and a rebel group and because different warring party configurations define different conflicts separately, it is possible that another war occurs while the previous one is still ongoing (in Sri Lanka, for example, Janatha Vimukthi Peramuna, a Marxist political organization that had aimed to become part of a democratic framework, revolted in the late 1980s, while
Tamil secessionist groups such as the Liberation Tigers of Tamil Eelam had been in a long-term conflict with the government). However, those non-onset years that are coded as 0’s should be substantively different depending on war’s presence and absence, and ongoing war can affect each country’s macro-level structures that are relevant to the independent variables to be tested. In the face of this issue, observations are dropped by
Collier and Hoeffler (2004) and Sambanis (2001) while a given conflict is ongoing even if another civil war erupts during those years (they thus intend to differentiate “onset” and “duration” both conceptually and empirically). When the data are set up that way, however, the prevalence years are essentially treated as no-risk time periods. Fearon and
17 During the time period between 1946 and 1960, there were 25 civil war onsets, of which 10 cases are identity-based and 15 cases are class-based.
80 Laitin (2003) and Fearon (2005) question this, arguing that such data treatment potentially introduces bias by dropping civil war observations that occurred while another one is ongoing. In this study, the country-years are treated still at risk even when a civil war is ongoing, but at the same time they are considered as not being under observation (or analysis) and re-entering the study once the previous war is over.18 Like the left truncation treatment discussed above, conflict onset information during war presence is considered in the analysis. Whether a given country experiences a war onset or not while the previous one is ongoing, it re-enters the analysis regardless.
Information used to define an at-risk population (i.e., country-unit) is derived from updated system membership data originally coded in Gleditsch and Ward (1999), which is the data that the PRIO/Uppsala Armed Conflict Data relies on for their conflict coding. However, a number of modifications are added to the original coding based on different interpretations about changes in the state membership system. Gleditsch and
Ward treat as a single continuous polity each pair of (1) the Socialist Federal Republic of Yugoslavia and Serbia and Montenegro before and after the former’s dissolution, (2) the Soviet Union and current Russia before and after the former’s dissolution, (3) West
Germany and present Germany before and after the former’s unification with the East, and (4) North Yemen and present Yemen before and after the former’s unification with
South Yemen, respectively. In this study, each of those paired cases is considered to be separate polities.
18 This data decision results in removal of 10 onsets from the analysis of aggregate civil war, and 7 onsets from the analysis of identity-based civil war, for the
81 DATA AND VARIABLES
Dependent Variable: Civil War Onset
Scholars have long debated the best way to define and measure civil wars. As illustrated in Sambanis’s extensive review (2004), identifying and measuring civil war is an extremely difficult task due to the complex nature of the phenomenon, resulting conceptual disagreement, and general lack of reliable sources of information. In order to identify civil war onsets, this study relies primarily on two sources complied by the
Uppsala Conflict Data Project (UCDP)—(1) the “Main Conflict Table” from
PRIO/Uppsala Armed Conflicts Dataset, 1946–2004 (version 3, 2005) (Gleditsch et al.
2002; Strand et al. 2005), and (2) Battle Deaths Data version 1.0, (Lacina and Gleditsch
2005) —with the former being my primary information base and the latter being a supplementary information source for coding modification decision making.
The PRIO/Uppsala database has some notable strength as a result of their efforts to overcome data difficulty. Specifically, it follows a methodologically rigorous, transparent operational procedure. The data collection method is explained in detail in the codebook and project members’ articles published in Journal of Peace Research, allowing users to have a clear idea about the measure constructed (Gleditsch et al. 2002).
The data also help researchers to understand the trajectories of armed conflict because they provide information about the changing conflict intensity of each conflict year.
Such information is useful for obtaining a more accurate picture of conflict onset and
time period from1960 through 2000. There is no case removed from the analysis of class-based civil war due to this data treatment.
82 recurrence. Meanwhile, the Battle Deaths Data is currently the best information source available for battle-related casualties. Its goal is precision on an annualized basis, unlike some major conflict casualty data sets publicly available to researchers today.19
The Armed Conflict Data project at the University of Uppsala defines an armed conflict as “a contested incompatibility that concerns government and/or territory where the use of armed force between two parties, of which at least one is the government of a state, results in at least 25 battle-related deaths” (Strand et al. 2005).20 This closely matches the conceptualization of armed conflict discussed in the previous chapter. Civil war, a subset of armed conflict, is conceptualized in terms of its substantially high intensity in terms of violence and damage. The PRIO/Uppsala data (ver.3) provide the following three thresholds to measure the level of intensity for each conflict year:
(1) Minor: At least 25 battle-related deaths per year for every year in the period.
(2) Intermediate: More than 25 battle-related deaths per year and a total conflict history of more than 1000 battle-related deaths, but fewer than 1,000 per year.
(3) War: At least 1,000 battle-related deaths per year.
19 For example, the Correlates of War project has the variables “StDeaths” (total battle deaths of state participants) and “ToDeaths” (total battle deaths of all participants), both of which measure total deaths counts throughout the whole course of a given conflict. Likewise, the Political Instability Task Force at George Mason University has a variable that indicates the annual number of fatalities related to fighting, but it is scaled to an ordinal variable with the highest of the eleven categories simply top-coded.
20 In line with the dominant position in the literature, the “battle-related death” concept in the PRIO/Uppsala Armed Conflict Data includes both military and civilian casualties (Nils Petter Gleditsch, personal communication, October 25, 2005). This is reasonable because civilians are often targeted in civil wars by combatants who seek to bring a population and its territory under control (Sambanis 2004).
83 In this study, an armed conflict is coded as a civil war basically on the threshold of the highest intensity above, i.e., at least 1,000 annual battle-related deaths. Aside from the consistency between the concept and the measurement, the use of this operational threshold also helps to maintain good continuity and comparability with a number of previous studies based on the Correlates of War data that also use 1,000 annual battle-related deaths as the intensity threshold. Because the COW dataset has been the main conflict data supplier during the past three decades, it is a major plus that the PRIO/Uppsala Armed Conflict Database allows for conceptual and operational continuity with the COW Project.21
However, because war in the PRIO/Uppsala is simply about “conflict years” when armed conflict reaches the high intensity threshold, it does not automatically translate into conflict onset and recurrence. To identify the onset and recurrence of a distinct war, I examine spells and the intermittence of highly intense conflict years in terms of warring parties’ configurations and duration of interval (“peace”) years. After selecting conflict years with the “war” cases from the PRIO/Uppsala’s Conflict Main
Table, its supplemental file Armed Conflict List 1946-2004, and Battle Death Data ver.1, each single spell is checked regarding whether it is a single conflict case.22 A
21 The recently published definition of “war” in the COW data (Sarkees 2000), i.e., “at least 1,000 battle deaths resulted during the civil war” leaves their operationalization ambiguous. According to Meredith Sarkees, however, the COW data have always been based on the “more than 1,000 annual battle deaths” criterion originally set forth by Singer and Small (1982) (personal communication, September 15, 2005).
22 The Armed Conflict List 1946-2004 includes information about possible high- intense armed conflicts that are at this point included as minor or intermediate conflicts 84 different conflict (i.e., state versus a different identity or non-identity collectivity) might erupt immediately after the end of the previous one. Such cases involving changes in warring configurations are differentiated by using the conflict parties’ information in the Main Table and the Conflict List. 23
Next, conflict start year, continuity, and renewal are slightly fine-tuned. This is because violence intensity during the same armed conflict typically fluctuates and therefore simple reliance on spells of high intensity conflict years to identify an onset can be misleading. For this modification, I rely on the “best” and “high” estimates of the number of battle-related causalities from Battle Death Data ver.1 (Lacina and
Gleditsch 2005).24 Some conflict start year information is slightly modified primarily because the PRIO/Uppsala’s coding system, based on the calendar year, might produce possibly misleading evaluation about intensity. One good example is a conflict that
in the Main Table. I consider such cases in the analysis as well. Also, the Battle Death Data are used when it disagrees with the Armed Conflict Data in a way that the former codes a given case as high intensity whereas the latter codes it as lower intensity. Such cases are included into the analysis.
23 Configuration changes here also include cases where an opposition group came into power and became part of the state. First, the organizational basis of parties in conflict is one essential part of the war type argument, and second, such cases by definition also mean regime changes, and their removal from the analysis might bias the effect of the regime-related variables.
24 The Battle Deaths Data have the variable named “bdeadlow” (their low estimate of total battle deaths in the conflict for the population identified). This variable is adjusted to agree with the intensity coding of the PRIO/Uppsala Armed Conflict Dataset. However, although the PRIO/Uppsala and the Battle Death Data agree on their intensity rating most of the time, the project authors do not consider this low estimate as their best estimate (personal communication with Bethany Lacina, December 6, 2006).
85 erupted sometime in the latter half of a calendar year (e.g., August) resulting in 900 deaths by the end of that calendar year and that continued well into the next calendar year cumulating over annual 1,000 deaths by the end of that year. In such cases, the
PRIO/Uppsala codes the first year as an “intermediate” conflict and the second year as a “war,” although the fighting might have been extremely intense from the beginning but the time might have been just “too short” to reach the 1,000 threshold until the end of the first calendar year.25 After all, in some cases, automatic application of a certain threshold may be rather arbitrary (Sambanis 2004), and in this regard, the
PRIO/Uppsala’s “intermediate” category can be particularly problematic. Whereas it may be as intense as the “minor” category, this category also can involve a highly intense combat episode that forms part of a full-fledged “war” (this is due to the operationalization of the “intermediate” category as shown above). Given this potential problem, conflict spells in this study include cases where 900 or more deaths (but less than 1,000) were inflicted directly before the PRIO/Uppsala’s “war” onset years. Thus, in such cases, the start year for that spell is pushed one year forward. Also, cases where
500 or more deaths are counted in the “best” estimate and 1000 or more deaths are counted in the “high” estimate continuously before and after conflict spells are included as well.
25 One example (and rather extreme one) would be the first Russia-Chechnya war. In the PRIO/Uppsala, the conflict is coded as starting in 1995. Yet, Russia launched its full-scale attack toward Grozny on December 11, 1994. While the Battle Death data count more than 900 battle-related deaths, the PRIO/Uppsala codes this episode as intermediate due to its coding scheme.
86 As for duration, I follow Doyle and Sambanis (2000, 2006) and code the intermittence of the same conflict (i.e., conflict with the same group configuration) when at least two years of peace with less than 100 annual battle-related deaths is maintained. In such cases, two war onsets are coded, with the latter one being considered as a recurrence. Otherwise, the second spell is treated as a continuation of the first one and only one onset (the first year of the first spell) is coded. Also, once an armed conflict breaks out, conflict years are considered to be a conflict duration when it is characterized by sustained violence and when there is no 3-year period during which the conflict causes fewer than 1,000 battle-related deaths (Doyle and Sambanis 2000,
2006). Otherwise, the conflict is considered to be subdued below the “war” level. As discussed above, these ongoing conflict years are used to determine the time periods analogous to interval truncation.
Those cases are further classified into the two “types” of armed conflict onsets, based primarily on the coding schemes by the Political Instability Task Force (Gurr et al. 2001) and Ellingsen (2001). Since the Task Force has been a major supplier of war type data and conflict scholars often rely on its information (e.g., Besançon 2005;
Reynal-Querol 2002a; Sambanis 2001), the use of its classification scheme should be helpful in evaluating existing arguments about different war “types.” More importantly, the Task Force’s and Ellingsen’s conceptualizations are mostly similar in key elements to the definition that was presented in Chapter 2. The Task Force and Ellingsen state, respectively:
87 Ethnic wars are episodes of violent conflict between governments and national, ethnic, religious, or other communal minorities.
Cultural conflicts are defined as internal armed conflicts with a cultural aspect to them. By cultural aspect, I refer to the presence of linguistic, religious and ethnic differences (whether one or all three of them) between the warring parties.
Armed conflict cases identified above are evaluated against their coding and coding criteria. Conflict cases that are coded differently in this study are done so by examining types of the major cleavage. That is, the focus is upon the “presence of linguistic, religious and ethnic differences … between the warring parties” and upon their self-recruiting character (Sambanis 2006). Non-identity war, though practically the residual category, includes intense armed conflict that is based on social class and mostly conforms to the Task Force’s definition of “revolutionary war”:
Revolutionary wars are episodes of violent conflict between governments and politically organized groups (political challengers) …. “Politically organized groups” may include revolutionary and reform movements, political parties, student and labor organizations, and elements of the armed forces and the regime itself.
The conflict cases are classified by two coders to check reliability, and there is a reasonable level of agreement between the coders and among the data sets
(approximately 90%). The conflict cases selected and classified this way are presented in APPENDIX C. Using this information, this study estimates and compares three separate sets of models, i.e., an aggregated conflict model, identity war model, and non- identity war model, by considering the independent variables described below. The
88 resulting data covering the time period from 1960 to 2000 include 79 events for 177 countries for civil war in general, 52 events for 178 countries for identity-based civil war, and 32 events for 177 countries for non-identity conflict.26
Independent Variables and Controls
Measuring Structural Modernization
Economic Development
The previous chapter discussed the process of structural modernization by focusing on four major aspects: economic development, social development, demographic pressure, and inequality. First, real GDP per capita serves as a direct measure of economic development level. Real GDP per capita used in this study is standardized in constant international dollars (with the base year 2000), is adjusted for inflation, and is calculated by using the chain index in order to reflect the actual local purchasing power of each currency. The data are taken primarily from Penn World
Table ver. 6.2 (Heston, Summers, and Aten 2006). To estimate missing values and expand the coverage, I follow the method used by Fearon and Laitin (2003) and obtain a logged term of real GDP per capita. To test its curvilinear effect, its squared term is entered in the models.27
26 Due to the removal of those country years where conflict is ongoing (i.e., incidence years except for the first year) and a set of simultaneous onsets of identity and non-identity wars, the sum of identity and non-identity war onsets is not equal to aggregated civil war.
27 The original real GDP per capita data Penn World Table ver. 6.2 are extended backward by using the GDP per capita % annual growth data from World Development 89 Second, the level of industrialization also serves as an indicator of structurally modernized economy and is operationalized in one commonly used way, as the percentage share of the industrial labor force out of the total economically active population. The information about the percent of the economically active population in industry is taken from the data originally estimated by the International Labour
Organisation’s (ILO) Economically Active Population Estimates and Projections,
1950–2025, which is the most comprehensive, detailed and comparable information currently available (International Labour Office 1986). The data file is retrieved from the United Nations Common Database (UNCDB) (Department of Economic and Social
Affairs, The United Nations 2005). The ILO examines existing data of country-reported labor force information to select observations that are sufficiently comparable and then uses econometric models to estimate those cases where no country-reported, cross-
Indicators (World Bank 2005). Predicted values are obtained from the regression model of logged real GDP per capita on year and logged energy consumption per capita for those countries where at least eleven observations are complete in both development variables. The energy consumption variable comes from National Material Capabilities ver. 3.02 (Singer, Bremer, and Stuckey 1972; Singer 1987), and the population variable used to calculate per capita figures is a combination of World Development Indicators (World Bank 2005), Penn World Table ver. 6.2, Cross-Sectional and Time-Series Data Archive (Banks 2005), and National Material Capabilities ver. 3.02 (Singer et al. 1972; Singer 1987). Then, for cases that are still missing, predicted values are obtained from the regression model of logged real GDP per capita on an interaction term between logged energy consumption per capita and region dummies, between year and region dummies and an oil exporter dummy variable. The information about geographical regions is primarily from the Minorities at Risk Project (Center for International Development and Conflict Management, University of Maryland 2005) and the United Nation’s country/region code system (The United Nations Statistics Division 2005). The oil exporter variable is defined as oil export being 33% or more of total merchandise exports (Fearon and Laitin 2003; Sambanis 2004) and created from World Development Indicators (World Bank 2005).
90 country comparable data currently exist. Because the ILO currently estimates this variable every ten years, I interpolate the in-between years. Also, results should be interpreted by noting that the currently available ILO estimates cover the time period of
1950 to 1990 only.
Social Development
The level of social development is measured by gross secondary school enrollment. The gross ratio is the ratio of total enrollment, regardless of age, to the population of the age group that officially corresponds to the level of education shown.
Although it may be a less ideal indicator as such than the net ratio (where the number of children of official school age defined in a national education system is the numerator), the gross ratio provides a considerably greater country/year data coverage. Also, the available net and gross ratio data are correlated at .95, suggesting that the use of the gross figures is reasonably safe. Further, substantively, the gross ratio better reflects the reality of older people attending school in developing countries where official school age is often in name only and hence may actually be better suited for cross-national studies like this one. I rely on the estimates published in World Development Indicators
(CD-ROM) 1999 and 2004 and World Tables of Economic and Social Indicators 1950-
1992 (World Bank, International Economics Department 1993 [1997]). The overlapping portions of those two issues of World Development Indicators are almost perfectly correlated (r = .998), allowing me to safely combine them for better time coverage. I
91 interpolate the combined variable to fill in cases that are still missing and create data from 1960 to 2000. I rely on World Tables to fill in values the former Yugoslavia and
Czechoslovakia as well as for the pre-1960 years.
Demographic Pressure
A country’s population size is one of the covariates that has received the most robust and significant support in past civil war studies. The consistent positive effect of this variable might partly be because the armed conflict onset is operationalized typically by relying on the 1,000 battle-related deaths threshold (Sambanis 2004); with this operationalization, it would be less likely that civil war onsets are coded for small countries. Given the past results and the war variable of the present study, the total population of a country is included in the model as a control variable to take into consideration of the country size. For good coverage, I create a total population variable by combining series, in order of priority, from Sambanis (2004), Fearon and Laitin
(2003), National Material Capability ver. 3.02 (Singer et al. 1972), World Development
Indicators (World Bank 2005), Penn World Table ver. 6.2 (Heston et al. 2006) and
Cross-National Time-Series Data Archive (Banks 2005). These data are nearly perfectly correlated (r > 0.999), assuring overall reliability in combining them.
This study tests four aspects of the demographic pressure, i.e., population density, population growth, urban growth, and youth bulge. Population density is measured in the conventional manner (i.e., the total population divided by per square kilometers) will be included in the model (Hauge and Ellingsen 1998). The data come
92 from World Development Indicators (World Bank 2005), and the variable is logged to correct for skewness. Annual population growth rate in percent is calculated from the total population variable created above and lagged one year. An urban growth variable is created by using World Development Indicators (World Bank 2005). The percent of annual growth in urban population is calculated and lagged one year after obtaining the raw number of urban population from the urban population data (% of total) and the total population data.
The idea of youth bulges is measured in two ways. First, it is operationalized as the size of youth cohorts relative to the total adult population. The total adult population as the denominator seems to be a better choice both theoretically and empirically than the conventional measure that uses the total population. Theoretically, the expansion of youth population is believed to be detrimental because of their competition with older cohorts; youth cohorts are an at-risk segment of population because they are disadvantaged due to the mismatch of their size and existing institutional capacities of the society. Empirically, the use of the total population would lead to an underestimation of the youth bulge in countries with rapidly growing population because the size of the non-adult population tend to be large and can inflate the denominator (Urdal 2005). Following Urdal, I use youth cohorts as the population of 15 to 24 years old relative to the total adult population as 15 years old and above. I also dummy-code the youth bulge so that the concept taps a critical threshold of youth cohorts expansion as suggested by Huntington (1996). Since Huntington argues that the
93 critical threshold that increases the risk of conflict outbreak is youth cohorts that are
20% out of the total population, I follow Urdal (2005) again to code cases as 1 where the youth population exceeds 35% out of the total adult population.
The information about the age-composition of the population in each country is collected from the World Population Prospects, the 2004 Revision (Population Division of the Department of Economic and Social Affairs of the United Nations Secretariat
2004). The strength of this source is its good country and temporal coverage (195 independent countries, 1950 onwards). Also, because it is based on the most recent demographic data available for each and every country of the world, its estimate is considered to be the most reliable and comparable information currently available regarding the age composition of the total population in each country. To calculate the variables, I use the middle-variant estimate by the United Nations. Since the data are estimated for every five year period, I interpolate this variable for better coverage and calculate the youth bulge variables.
Inequality
Inequality is measured by aggregated individual and the (implicit) group income information. The Gini index of income inequality is originally from Collier and
Hoeffler (2004) and Feng, Kugler, and Zak (2005). These authors derive their information from Deininger and Squire’s (1996) income inequality data. The difference is that while Collier and Hoeffler use a five-year panel, Feng, Kugler, and Zak expand the data to cover some countries that are not covered by Deininger and Squire. The
94 latter dataset also predicts the missing values with an OLS model that includes GDP per capita, literacy rate, and relative political extraction with regions controlled. Because the two studies both examine a different set of countries, I interpolate Collier and
Hoeffler and then combine the two sources for better coverage. The data have information for the period from 1960 to 1995, and some new countries after the end of the Cold War are not covered. This caveat should be kept in mind when interpreting the results.
Group-level inequality measures have not received as much attention in the literature as individual level inequality indicators (Fossett and South 1983). When group-level inequality has been discussed at all, analytic results are too often interpreted by deducing between-group differentials from socioeconomic inequality as conventionally measured (i.e., between-individual differentials) or by committing the ecological fallacy of drawing conclusions about groups when countries are studied
(Besançon 2005; also see Robinson 2001 for good discussion about the issue).28 The present analysis uses the “political differentials index” and the “economic differentials index” from the Minorities at Risk Project (Center for International Development and
Conflict Management, University of Maryland 2005; Gurr 1993a) as one of the best existing attempts to approximate the extent and intensity of political and socioeconomic inequality between groups. These “inter-group differentials” variables are presumably
28 One notable exception is Østby’s (2005) recent compilation and analysis of group inequality data. Unfortunately, the number of countries covered is quite limited due to the constraint of data availability (44 countries with no temporal variation). Indeed, the lack of reliable data sources with wide temporal and spatial coverage makes quantitative studies even more difficult when it comes to measuring group inequality.
95 to capture objective political and economic inequalities between groups by coding the status of the group “with respect to the dominant group(s).” Intended simply to measure the objective political and economic state of each group, these variables are not meant conceptually to capture discriminative policies. Coded as an ordinal scaling ranging from -2 (advantaged) to 4 (extreme differentials) with 0 indicating no differentials with regard to the dominant group(s), the variables are supposed to provide a good idea about the depth (i.e., the intensity of the differentials) and width (i.e., the proportion of groups out of the total population that suffers from the differentials) of inequalities at the group level.29 The data are supposed to provide a broad picture of group differentials
(i.e., which minorities are disadvantaged) in a way that does not contradict various case reports. Thus, to measure the degree of severity of group-level differentials, I use the percentage of those groups that are explicitly coded as more than substantially disadvantaged. No at-risk group is present when a country is not covered by the
Minorities at Risk. Such countries are safely coded as zero.
Another implicit group level differential measure is land (asset) inequality. As already discussed, inequality in land ownership may be an indication of another form of political cleavage (Midlarsky 1988). The data on inequality in land ownership are taken from Collier and Hoeffler (2004), the original source of which is provided as a Gini coefficient in Deininger and Squire’s (1996, 1998) inequality data. Because the actual
29 Each code number is defined as follows: -2 = advantaged, -1 = some advantages, 0 = no differentials, 1 = slight differentials, 2 = substantial differentials, 3 = major differentials, 4 = extreme differentials.
96 data are constructed on individual level information, however, the result should be interpreted with caution. It should also be noted that the coverage is limited to the period from 1960 to 1995.
Measuring World Systems
Structural Location in World Systems
World systems theory sees the structural location of each nation state within the global capitalist system as the root cause of intrastate development (Chase-Dunn 1998;
Wallerstein 1974, 1983). To measure world systems locations, the present analysis will use Bollen’s correction (1987; also see Bollen and Appold 1993) of Snyder and Kick’s
(1979) world systems measure and Kick and Davis’s (2001) new world systems coding.
Although these data reflect the period circa 1960 to 1970, I consider this covariate as effectively time-invariant during the time period under study. Other than the practical reason for data unavailability, this decision seems theoretically acceptable because world systems theory holds that countries are structurally and systematically positioned in the global system and that their mobility is a slow, long-term historical process. I first combine Bollen (1987) and Bollen and Appold (1993) to expand the coverage (the overlapping cases between the two are exactly the same). Then, I use Kick and Davis’s data to fill in cases that are still missing. Kick and Davis offer their measure for 1960 and 1970 on an eleven-point scale, with block 1 = the core, block 2 = the socialist semi-
97 core, block 3 = the capitalist semi-core, block 4 to 6 = semi-periphery, and block 7 to 11
= periphery. I take the average between the two time points and recode it as follows: block 1 = core, block 2 to 6 = semi-periphery, and block 7 to 11 = periphery.
Exposure to Global Capitalism
The degree to which each country is structurally exposed to or penetrated by global capitalism is operationalized in three ways. First, because world systems theory typically considers foreign direct investment (FDI) as an agent of foreign economic penetration, this study examines the effects of foreign direct investment inward stock as a percentage of GDP. GDP serves as a proxy of the total capital stock because it has been reported that the total capital stock and GDP are highly correlated (e.g., de Soysa and Oneal 1999; Firebaugh 1996). The data on FDI inward stock at book value are from
UNCTAD Foreign Direct Investment Database, which includes statistical tables from
World Investment Report 2004 (United Nations Conference on Trade and Development
2004). Some missing values presented as “negative inward accumulation” are replaced by zero. The variable is lagged one year. Also, to correct for skewness it is logged after adding 1 to cases with a zero figure. The result should be interpreted with the temporal coverage of this data in mind (1970 onward).
Furthermore, two trade-related variables are examined: trade openness and oil export. Trade openness is the total trade (import + export) as a percentage of real GDP and serves as an indicator of the degree to which each country is integrated into the international economy. The data comes from Penn World Table ver. 6.2 (Heston et al.
98 2006). Following Fearon and Laitin (2003) and Sambanis (2004), the oil exporter variable is measured as a dummy variable indicating oil export being 33% or more out of total merchandise export and is created from World Development Indicators (World
Bank 2005). Both the variables are lagged one year.
Measuring World Polity
Embedment in World Polity
Membership in international governmental organizations (IGO) and international non-governmental organizations (INGO) is considered to be a proxy of the degree to which each country is enmeshed in world political culture. Thus, this study first uses the percent of full IGO membership out of possible membership (Olzak and
Tsutsui 1998). The data are taken from International Governmental Organization Data ver. 2.1 (Pevehouse, Nordstrom, and Warnke 2004) and the variable is created by dividing the number of each country’s IGO memberships by the number of possible memberships each year and then multiplying it by 100. Missing observations are interpolated. Information about the number of INGO’s in each country comes from the
Union of International Associations’s Yearbook of International Organizations (Union of International Associations, various years). The data covers the time period from 1960 to 2000. Missing years between these two points are estimated by interpolation. Then, the number of INGO’s is standardized as a per 1,000 people figure, so that it captures the levels of exposure to world culture. This figure is logged to adjust for skewness.
99 Measuring Level, Institutional Structures, and Change of Political Environment
Level of Democracy
To measure the level of democracy, this study relies on the Polity IV Project
(Marshall, Jaggers, and Gurr 2003). Despite conceptual and empirical critiques (e.g.,
Gleditsch and Ward 1997), Polity is probably the most widely used data source to study patterns of institutional structures of political regime, their transitions, and their impact upon other social phenomena, including armed conflict. One obvious reason for its wide use is that this dataset has incomparably good temporal and spatial coverage, as well as a wide range of component scores regarding political participation, competition, and election processes that constitute its three major composite indicators, i.e., institutional democracy (DEMOC), autocracy (AUTOC), and polity (POLITY).
The present analysis uses the POLITY2 score. The POLITY variable is a combination of DEMOC and AUTOC, computed by subtracting the AUTOC score from the DEMOC score ranging from -10 to 10. The institutional democracy score taps the presence of institutions and procedures that ensure the competitiveness of political participation, competitiveness and openness of executive recruitment, and constraints on the exercise of power by the executive branch. In contrast, the institutional autocracy score indicates the lack of such institutions and resulting political restriction/suppression and arbitrariness. Thus, the polity score is supposed to show the overall level of a country’s democratic/autocratic institutions. For better data coverage,
I use the modified version of the POLITY score (POLITY2) created and recommended
100 by the Polity Project.30 The variable is converted from the original 21-point scale of -10 to 10 so that the variable ranges from 0 to 20 and lagged one year. To test a curvilinear effect, a quadratic term along with the original term is included in the model.
Institutional Structures of Polity and Discriminatory Policies
The degree to which each country’s politico-institutional structures are inclusive or exclusive is measured in two ways. First, I focus on possible impacts of different types of democratic institutions (Montalvo and Reynal-Querol 2003; Reynal-Querol
2002a, 2002b). This is designed to test whether different structures of political institutions generate different levels of conflict risks under democracy, even though many of them are high-income countries that are generally much less conflict-prone
(Collier and Sambanis 2002). For democratic regime type information, I use Golder’s
(2005) classification of political regimes based on Przeworski et al.’s (2000) scheme, in which democracies are distinguished by three types of the executive branch, i.e., parliamentary democracy, mixed democracy, and presidential democracy.
(1) Presidential system: the government serves at the pleasure of the president who may be directly elected or indirectly elected, i.e., the president selects and determines the survival of the government.
(2) Parliamentary system: the government serves as long as it maintains the confidence of the legislature.
30 The Polity Project converts the nominal regime coding inserted into the POLITY’s ordinal scale in the following ways: (1) Interruption: treated as system missing in POLITY2; (2) Interregnum: converted into 0 in POLITY2; (3) Transition: prorated across the span of the transition in POLITY2.
101 (3) Mixed system (also called semi-presidential, premier-presidential, or president-parliamentary): the president is elected for a fixed term with some executive powers and the government serves at the discretion of the legislature.
Empirically, cases under a mixed system with civil war experience are very rare; there are only three such cases in the data. Thus, this category is collapsed with parliamentary system.31 Two dummy variables are created from the above information, with dictatorship serving as the reference category. Hence, the test is whether different democratic institutions may be significantly less conflict-prone than dictatorship.
Second, I also examine the effect of different institutional structures of dictatorship. What seems particularly important in terms of political exclusiveness is the effect of the neopatrimonial/personalistic (or “sultanistic” in Weber’s definition) regime type (Bratton and Van de Walle 1997; Geddes 1999; Huntington 1991; Linz and Stepan
1996; Peceny, Beer, and Sanchez-Terry 2004). I rely on Geddes’s updated and revised authoritarian regime classification data to code this regime type, and include its regime type information as a dummy variable.32 This data conceptually classifies dictatorship into large three categories, military, personalist, and single-party. Geddes defines a regime as personalistic if “the leader … had consolidated control over policy and recruitment in his own hands, in the process marginalizing other officers’ influence
31 It made no difference whether to collapse it with parliamentary or presidential systems.
32 I would like to thank Barbara Geddes, professor of political science at UCLA, for generously sharing with me her most updated data since her APSA conference presentation in 1999.
102 and/or reducing the influence and functions of the party” (Geddes 2004) and then operationalizes it according to a set of criteria about the characteristics of political leadership, political party, and military influence.33
In the actual operationalization and coding, Geddes allows for hybrid types of two or three ideal types and differentiates six different types of dictatorship. I first include from her original information all the cases that are coded as “personalist” and hybrid personalist and create a dummy variable for this group. The other cases, and the rest of the cases not covered in Geddes, are first classified into “other dictatorship”
(polity score < -6), “anocracy” (polity score from -6 to +6), and “democracy” (polity score > =6) with the help of the Polity IV Project data. Then, for the dictatorship cases that are not covered in the original data due to coverage differences between Geddes and the PRIO/Uppsala data, a few more additional personalists are identified based on her coding criteria and information from the U.S. State Department and the Library of
33 Geddes asks the following questions: (1) Does the leader lack the support of a party? (2) If there is a support party, was it created after the leader's accession to power? (3) If there is a support party, does the leader choose most of the members of the politburo-equivalent? (4) Does the country-specialist literature describe the politburo- equivalent as a rubber stamp for the leader? (5) If there is a support party, is it limited to a few urban areas? (6) Was the successor to the first leader, or is the heir apparent, a member of the same family, clan, tribe, or minority ethnic group as the first leader? (7) Does the leader govern without routine elections? (8) If there are elections, are they essentially plebiscites, i.e., without either internal or external competition? (9) Does access to high office depend on the personal favor of the leader? (10) Has normal military hierarchy been seriously disorganized or overturned? (11) Have dissenting officers or officers from different regions, tribes, religions, or ethnic groups been murdered, imprisoned, or forced into exile? (12) Has the officer corps been marginalized from most decision making? (13) Does the leader personally control the security apparatus?
103 Congress.34 The personalist, other dictatorship, and anocracy categories are included in the analysis with the democracy category as the reference group. Thus, the test is to see if those non-democracies are significantly different from democracy in their propensities for violent conflict.
To measure political exclusion actually embodied in the form of implemented policy, I use Minorities at Risk project to measure economic and political discrimination against minority groups because discrimination is to deny access to resources and opportunities because of people’s group membership. The data are primarily derived from the discrimination data compiled by Asal and Pate (2005) and are cross-checked with the MAR data variable wherever the latter data are available. The variable is created by measuring the percent of minority groups in each country that are formally discriminated against severely in terms of economic and political opportunities.
Countries with no groups listed are assumed to be zero. To correct for skewness, these variables are logged after adding 1 to deal with 0 values.
Finally, impacts of lost autonomy and modern (weak) state expansion are examined to tap Cohen et al.’s (1981) argument that conflict potentials increase when traditional subnational populations are deprived of their autonomous social structure by expansionist states seeking to extend its control over subnational territories and populations. Although it is not an interaction term in a statistical sense, the idea of lost autonomy captures the central idea of the argument. The information is derived from the
Minorities at Risk Project (Center for International Development and Conflict
34 Accessible at www.state.gov/r/pa/ei/bgn/ and
104 Management, University of Maryland 2005; Gurr 1993a), which has a composite index of lost autonomy from 1960 that indicates groups that have lost autonomy or undergone a transfer of control from one country to another. This index is constructed by considering the magnitude of change, group status prior to change, and the year of groups’ loss of autonomy, and ranges from 0 (no historical autonomy) to 6.0. The group level information from this data is aggregated into the country level to create two variables that serve as a proxy of each country’s historical relationships between the central government and subnational groups: (1) the sum of the lost autonomy index for each country year and (2) the sum of the lost autonomy index for each country year weighed by the proportion of the relevant group populations out of the country’s total population. In order to add one more test meant to touch upon some aspect of state expansion, I also include a variable of the percent of military expenditure out of GDP and its interaction term with the political discrimination variable. The military expenditure data come from National Material Capability ver. 3.02 (Singer et al. 1972), and the GDP data are from World Development Indicators (World Bank 2005), both in current U.S. dollars. The variable is logged after adding 1 to deal with zeros and lagged one year.
Changing Political Environment
Despite its centrality in social movement and collective behavior theories (especially the political process thesis), the changing state of the national political environment has
http://lcweb2.loc.gov/frd/cs/cshome.html
105 not been operationalized and studied so much in regard to internal armed conflict. In existing empirical studies, the concept of regime change has been measured in a number of different ways. For example, Mansfield and Snyder’s international conflict research
(Mansfield and Snyder 2005) converts the 21-point scale into four regime change dummy variables. Studying the magnitude of communal protests and rebellion in the
1980s, Gurr (1993b) creates a simple first-difference change score of the Polity’s democracy indicator between 1975 and 1986. The study of civil war outbreak by Hegre et al. (2001) measures democratization by looking at the impact of the “proximity of regime change,” which is based on the time elapsed (measured by day) since the last regime transition.35
The present study constructs a variable for regime change by using the Polity
Project IV and modifying Ward and Gleditsch’s (1998) operationalization. This approach is to create change scores on the polity variable over five years to address processes of regime change. Because the effect of regime change on the risk of civil war may depend on the initial level, I differ from Ward and Gleditsch in including an interaction term between the change score and the initial level. To avoid the sample reduction, all the available and necessary information from the Polity Project prior to
1960 is used, so that the first ten years of the time period under study will not be dropped from the analysis. Ward and Gleditsch’s approach allows for full use of the
35 Hegre et al. measure more than six-point upward mobility of the Polity score as the “large democratization/autocratization,” whereas they define the “small democratization/autocratizatoin” as two to five points change. Their measure assumes that the impact of these shifts on the risk of war onset is decaying over time.
106 information available (the 21-point scale), and by adding a control for the initial polity level, we can address a potential problem with Hegre et al.’s (2001) assumption that the effect of upward or downward mobility on the risk of civil war is independent of the initial polity level.
One weakness of these regime change measures including Ward and Gleditsch
(1998) is that they do not clearly differentiate liberalization and democratization.
However, a more specific argument regarding the danger of democratization is about the effect of democratic nation building elections (Linz and Stepan 1996; Snyder 2000).
The idea is that democratic nation-building elections can only highlight potential conflicts and differences in interests and opinions among groups, and that until democratic “consolidation” is realized, political order rests on a delicate, fragile balance among such diverse social forces. Thus, a possible negative impact of new democratic elections, if any, is supposedly strong at first but it should attenuate as time passes by.
To measure this possible effect, this study uses the information compiled by Golder
(2005). Golder’s data include a dummy variable for the time period between 1946 and
2000 coding observations as 1 if the first legislative election since independence or the first elections since a regime transition to democracy occurred. Using this new democracy variable, I create another variable that indicates the temporal proximity of the first national election. It is intended to capture the temporarily decaying potential impact of the nation-building democratic election on the risk of conflict onset.
Following Hegre et al.’s (2001) idea of “proximity,” I use the formula exp{(-years since the democratic election)/X}, where X is set to 4 in this study. This assumes that the
107 effect of the first nation building election on the risk of armed conflict onset is initially strongest (= 1) and then is halved approximately every three year. This variable is examined with one year lagged.
Measuring Ethnic, Religious and Linguistic Configurations
Fractionalization and Attributes
To measure fractionalization, the present analysis employs the fractionalization index. The data come from Alesina et al.’s ethnic, religious, and language fractionalization indexes (2003). In past empirical studies, Taylor and Hudson’s (1972) ethnolinguistic fractionalization index (ELF) has probably been the most widely and conventionally used measure to capture a society’s ethnic landscape and to examine its implications for other social phenomena, such as development and conflict. However, it has also been pointed out that there are some major problems in Taylor and Hudson’s
ELF (Alesina et al. 2003; Fearon 2003; Posner 2004). The issue most relevant to the present study is that Atlas Narodov Mira (Miklukho-Maklai Ethnological Institute
1964), the ethnographic data on which ELF is constructed, suffers from vagueness and arbitrariness of coding rules. For example, it codes groups mainly based on language, but sometimes on race, and other times on national origin (Fearon 2003). In other cases, ethnographically distinct groups in rivalry are lumped into one single group population, when one of them is catalogued as a subset of the other, larger umbrella group (Fearon
2003; Posner 2004). This would make it very difficult to examine possibly distinct attribute effects.
108 The choice of Alesina et al.’s (2003) fractionalization indexes and the number of groups in a given country helps to overcome this weakness in the traditional ELF.
Alesina et al. compile separate variables for linguistic, religious, and ethnic fractionalization to avoid ELF’s conceptual and operational vagueness and to allow for testing different attributes of ethnicity. Further, the authors systematically perform this data collection and compilation by cross-checking multiple newer data sources that are presumed to be better reflective of the reality of ethnic composition in each country.36
The fractionalization index is based on the Herfindahl concentration index, which is computed as (1 – Herfindahl of group shares), and as such indicates the probability that two randomly selected individuals in a country are from different groups. The variables used in the present analysis are converted to a 0 – 100 scale.
Dominance and Attributes
To measure dominance, this paper uses a dummy variable indicating whether the largest group equals or exceeds the line of the 80 % of the total population (Carment
1993; Ellingsen 2000). Cases where the largest group exceeds the 80% line are coded as
1. Data to create this variable come from Ellingsen (2000, 2002), which is based on a
36 The authors’ major information sources are the Encycropedia Britannica and the CIA World Factbook, with supplement information from other sources for better coverage. For details see Alesina et al. (2003).
109 combination of various annual issue of three sources. 37 While the data coverage does not extend beyond 1994, data from 1995 to 2000 are extrapolated because ethnic composition seldom if ever changes significantly in such a short term.
Polarization and Attributes
Finally, to measure polarization, I rely on Reynal-Querol’s ethnic and religious polarization indexes (Montalvo and Reynal-Querol 2005a; Reynal-Querol 2002a).38 The basic idea of her polarization index (“Q-index”) is to capture how far the distribution of the groups deviates from a bimodal distribution. The variable is designed to reach the maximum when there are two large groups of the same size. Although no linguistic polarization index is available (and hence its effect cannot be examined in this study), the polarization index in terms of ethnic and religious diversity allows us to consider the currently popular argument that religion is more exclusive and less negotiable and is therefore most conflict-prone than ethnicity. The indexes range from 0 to 100.
Control Variables
As already mentioned above, total population serves as one of the control variables in this study. This variable is logged and lagged one year. Also controlled for
37 The authors use the Britannica Books of the Year, Gale Research’s Handbook of the Nations, and the United Nations’ Demographic Yearbook.
38 The author uses Clévenot’s L’état des Religions Dans le Monde and The Statesman’s Yearbook for religious polarization and Encyclopedia Britannica for ethnic polarization.
110 in the model is a regional diffusion effect of armed conflict. As APPENDIX D shows, civil war seems to be disproportionately distributed in terms of geography, suggesting that there might be some regional effect at work. Specifically, even though civil war is by definition a domestic phenomenon, “bad neighbors” where armed conflict is ongoing may destabilize the entire region and may significantly increase the likelihood of conflict in a country around them (Hegre et al. 2001; Lake and Rothchild 1998;
Sambanis 2001). In fact, there have been instances where violent conflict seems to have spilled over the borders into neighboring countries and transformed into
“internationalized intrastate conflict”(Gleditsch et al. 2002; Strand et al. 2005).39 To control for this possible regional contagion effect, I follow Salehyan and Gleditsch
(2006) to create a dummy variable that indicates ongoing conflict in neighboring countries. Neighboring countries are defined as those separated by 150 miles of water or less by using the dyad information from Correlates of War Direct Contiguity Data, version 3.0, 1816–2000 (Correlates of War Project 2002; Stinnett et al. 2002).
Information about ongoing wars in the neighboring countries comes from the same conflict data for the dependent variable, i.e., the PRIO/Uppsala Armed Conflict Dataset ver. 3 (Gleditsch et al. 2002; Strand et al. 2005). The difference between the COW and the PRIO/Uppsala’s coding of the state system membership are filled in by adding dyad information based on the PRIO/Uppsala’s coding of the state system membership. The variable is coded as 1 if any one of the neighboring countries defined as above has an ongoing civil war in a given year, and is included in the model with a one year lag. I
39 One such example is the case of Democratic Republic of the Congo.
111 also include a variable of mountain terrain to control for each country’s ecological given within which actors are physically constrained. It is measured as the proportion of a country’s terrain which is mountainous, and the data come from Fearon and Laitin
(2003) and Collier and Hoeffler (2004), originally from Gerrard (2000) that includes altitude, plateaus, and rugged uplands.
REGRESSION DIAGNOSTICS
One assumption of the Cox model is proportional hazard, that is, the effect of covariates is constant over time. To evaluate this assumption, the scaled Schoenfeld residuals of each theory covariate in the model are regressed on time for the null hypotheses of zero-slope (Grambsch and Therneau 1994). In addition, the residuals are plotted against time for visual check as well. There does not seem to be a serious violation that could lead to a misleading conclusion.
Influence diagnostics are conducted by examining scaled efficient score residuals. To do this, efficient score residuals are first obtained after the initial Cox model estimations and then a matrix of these residuals is created. This matrix of the efficient score residuals is then scaled by multiplying it by the variance-covariance matrix generated by the corresponding Cox models. The resulting influence matrix provides information about the leverage of each observation on the Cox model’s parameter estimates. As a result, some influential observations are identified. Those observations are noted and dropped out of the analysis in the next chapter.
112
CHAPTER 4
RESULTS
DESCRIPTIVE STATISTICS
For descriptive information about the risk of civil war outbreak, Figure E.1 through Figure E.3 in APPENDIX E use the Kaplan-Meier method to provide visualized expression of the estimated survival function of each “type” of civil war outbreak by event ordering.40 Also, APPENDIX F includes a table of descriptive statistics and APPENDIX G presents a correlation matrix for the variables in the analysis. The data are limited to the time period of 1960 to 2000 for consistency with the inference statistics presented in the following section.
As shown in the figures, regardless of the war classification, the gradual estimated survival curves for the first onset suggest that countries tend to stay without war experience for long periods. This is not surprising given that civil war is a rare, anomalous event and that there are quite a few countries that did not experience any war outbreak during the time period under study (cf. APPENDIX D). As for aggregated civil war, the estimated probability that a country will be without the first war outbreak
40 Despite the presence of delayed entry cases, the Kaplan-Meier estimator can safely be utilized in this particular study because the estimator empirically never reaches zero before any delayed case comes under observation/analysis. 113 for twenty years or longer is approximately 0.80. Countries tend to escape the outbreak of identity and non-identity civil war for long periods as well (the estimated survival probability for any time from twenty years and up is approximately 0.85 and 0.93, respectively).
Once a country experiences the first onset, however, the risk of subsequent civil war onsets seems to increase greatly. The estimated probability that a country with the first onset experience does not experience the second onset for twenty years or longer is approximately 0.38. The same statistics by war type is about 0.30 and 0.56 for identity and non-identity war onsets, respectively, suggesting that in identity civil war, countries that have gone through the first conflict may plunge back into another fighting more quickly than they do in non-identity war. The survival curves for the second and subsequent onsets are virtually indistinguishable, however.
REGRESSION ANALYSIS BY CONDITIONAL RISK MODEL
This section discusses the results from regression analyses that utilize the conditional risk model (presented in APPENDIX H). The discussion is organized in the following way: the tested theories/hypotheses set forth in chapter 2 are broadly grouped into result discussion subsections about (1) the effects of socioeconomic structure; (2) the effects of political environment; and (3) the effects of ethnic group composition. In the first section, the test results regarding structural modernization and world systems theory are presented by highlighting similarities and differences in socioeconomic impact on the risk of different war “types.” The second section discusses, by war type,
114 the results obtained by testing the world polity thesis, the political opportunity theory specified in various forms, and the arguments about state centralization and deprived minority groups. Finally, in the third section, the test results about the effects of various forms of ethnic composition are presented and discussed.
The Effects of Socioeconomic Structures
Domestic: Structural Modernization
Table H.1 in APPENDIX H presents the results of the regression analysis.41
Model 1 through Model 11 are to test structural modernization/functionalist theory by examining the effects of macro-level socioeconomic structures at the domestic levels on the three civil war categories. In contrast to some arguments that economic factors are not important for identity-based conflict, Model 1 suggests the general importance of economic development for the risk of civil war regardless of the war “types”
(Hypothesis 1). The quadratic terms of real GDP per capita (logged, one-year lagged) along with its base term is significant in the hypothesized direction at the 0.01 level for the aggregated civil war category and at the 0.05 level for the identity and the non- identity categories (all in a two-tailed test), indicating that the risk of civil war outbreak is the highest at the middle range of development.
A word of elaboration would be needed here; the point of inflection seems to occur at a low stage of “middle range development.” Although, unfortunately, conditional effect plots cannot be provided with accuracy because the estimated
41 The models do not have an intercept term because in the Cox model it is subsumed into the baseline hazard h0(t). 115 baseline hazard function cannot be smoothed out in stratified model, a crude visualization where the varying baseline hazard estimates are “collapsed together” suggests that even though the effect of the real GDP per capita variable on the risk of war does take the hypothesized shape, i.e., a bell-shaped curve where the middle-range is at the highest risk, those countries at the highest risk fall in an approximate range of
$1,000 to $3,000 real GDP per capita. Indeed, over 40% of the aggregated war cases examined in this model erupted within this relatively narrow range, and some of those countries would be even classified today as a “low income country” according to the
World Bank income group classification. Thus, it must be noted that the risk of conflict seems to go up at a fairly early phase of middle-range development.
In Model 2 and Model 3, broad socioeconomic phenomena associated with economic development are examined (Hypothesis 2 and 3). Due to collinearity, the two variables used here (i.e., the percent of secondary school enrollment and the percent of industrial labor force out of total labor force) are included separately from each other and from the real GDP per capita variables. The significantly negative coefficient for the industrial labor force variable in Model 2 offers general support for the thesis of structural modernization as well: industrialization has a preventive effect on the risk of civil war. Yet, as suspected, the quadratic term (not presented in the table) does not show statistical significance. Model 3 indicates that the higher level of secondary school enrollment decreases the risk of civil war outbreak for all the three categories. This is in line with the argument by modernization theorists that higher levels of education nurture tolerant attitudes, reduces radical, violent collective action (Lipset 1959, 1981),
116 and lead to less violent organized protest aiming for reform (McAdam 1982). Because exponentiated coefficients indicate the hazards ratio for a one-unit change of the corresponding covariate, the result shows that a one point increase in industrial labor force out of total labor force decreases the hazard of civil war in general by approximately 4.9% (i.e., [e-0.051 – 1] ×100), the hazard of identity civil war by 4.0%, and the hazard of non-identity war by 3.9%. Likewise, a one point increase in secondary education enrollment decreases the hazard of civil war in general by 1.6%, the hazard of identity civil war by 1.1%, and the hazard of non-identity war by 1.9%.42
Model 4 to Model 8 aim to shed light on the effects of demographic forces with the control variables and real GDP per capita held constant. Although demographic transition is an intrinsic aspect of structural modernization, the war “types” exhibit some different patterns here. Model 4 tests the effect of population density as an aspect of competition over resources (Hypothesis 4), but this variable does not show any relevance to the risk of any “type” of civil war, and its quadratic term (not presented here) is not significant either. As shown in Model 5, however, the annual population growth variable shows statistical significance for the risk of non-identity war
(Hypothesis 5). The hazard of identity civil war goes up by an estimated 9.3% (= [e-0.089
– 1] ×100) for a one-unit increase in this variable. Perhaps, as implied in the discussion about the effect of ethnic, religious and linguistic composition below, population
42 Rounding of the coefficients may produce slight differences between the results of the ([eβ – 1] ×100) and the presented hazard ratio (the latter is more precise). 117 growth may particularly affect relational dynamics, or the balance of power, among identity groups, facilitate inter-group rivalry, competition, and “fear of extinction”
(Horowitz 1985), and thereby increase the conflict risk.
In Model 6, the effect of demographic concentration in the urban sector is examined. The result lends support to Hypothesis 6 for aggregated civil wars and identity-based civil wars. For aggregated civil wars, when urban population grows by one-unit the year before, the risk of war outbreak increases by 6.8% (= [e0.066 – 1] ×100), and for identity-based civil wars, a one-point increase in urban population in the previous year yields a 6.2% (= [e0.060 – 1] ×100) increase in the hazard of war onset.
Meanwhile, this variable does not have any significant effect on the risk of non-identity war. Thus, it seems that rapid urban growth facilitates inter-group contact, comparison, competition and ultimately conflict, yet it does not seem to help group people around other parameters, such as economic class. This may not be such a surprising result since migration decision-making is often based on informal ethnic support/information network and migrants tend to form an intra-group community in cities, whereas rapid urban growth may result in a dual economy where power concentrates on the developmentalist state and working class is divided across sectors with vastly different structures (Lipton 1977).
Model 7 and Model 8 add two youth bulge variables, respectively, to test
Hypothesis 7. First, Model 7 examines the effect of the percent of youth population defined as 15 to 24 year old on conflict risk. Apparently, the larger youth population moves countries toward conflict outbreak more quickly regardless of the war “types.”
118 For each additional point increase in youth population, the hazard of civil war goes up by 6.5% (= [e0.063 – 1] ×100) for the aggregate category, by 10.1% (= [e0.096 – 1] ×100) for the identity war category, and by 8.4% (= [e0.084 – 1] ×100) for the non-identity war category. As indicated in the pseudo R-square, this variable makes the best contribution to the model’s explanatory power among the demographic variables associated with structural functionalism.43
Model 8 tests the youth bulge thesis by using a variable operationalized differently based on Huntington’s (1996) argument about youth population and the increased risk of “clash of civilizations.” This variable should exhibit facilitating effects on identity conflict most prominently if he is correct regarding his warning about the threshold of youth population over the 25% line out of the total population. However, the variable (measured as 35% of total adult population) does not show statistical significance for the identity war category. Exceeding this threshold makes conflict outbreak approximately twice as likely for aggregated and non-identity civil wars (e0.643
= a hazard ratio of 1.90 and e0.770 = a hazard ratio of 2.17, respectively). It might be that
Huntington’s theoretical threshold is wrong for identity war, or other factors might need to be controlled in order for its effect to show up. Yet, this threshold variable consistently fails to support his argument as it never attains statistical significance for identity civil wars in the following models, either.
Model 9 to Model 11 examine the “Economic Inequality – Political Conflict (EI-
PC) nexus” thesis. Typically, structural modernization theorists argue that inequality in
43 The pseudo R-square serves as an indicator of the relative strengths of models. Unlike the ordinary R-square, it cannot be interpreted as the explained variance. 119 modernization processes can serve as a source of grievances that may be conducive to rebellion. Yet, as reviewed in the previous chapter, some scholars believe that potential effects of inequality, depending on parameters that differentiate groups, may exhibit different patterns between the different civil war “types.” Economic inequality, which implies inter-class differentials, is considered to be a significant risk factor for non- identity war (Hypothesis 8 and 9), whereas greater equality among identity groups may facilitate identity civil war by intensifying inter-group competition (Hypothesis 10). The results in Model 9 and Model 10, though they need to be taken with some caveat due to the small sample size, show the relevance of economic inequality to non-identity conflict. The income inequality variable is statistically significant in the hypothesized direction, indicating that a 1-unit income Gini coefficient increase increases the hazard of non-identity conflict by 4.4% (= [e0.043 – 1] ×100). Likewise, the Gini land inequality variable exhibits a significantly positive effect on the risk of non-identity war, with the hazard increased by 2.9% (= [e0.029 – 1] ×100) for an additional one-unit increase in the land Gini coefficient. But economic inequality does not seem to have significant impact on identity war risk.
Model 11 includes the percent of population that suffers from severe economic and political differentials as a proxy of inter-group economic and political inequality in order to examine the inter-group psychological argument that political, rather than economic, inequality is more relevant to identity civil war. The result does suggest that group-level political differentials play a more critical role in driving countries toward identity conflict outbreak than economic inequality. This variable is statistically
120 significant in the aggregated conflict, with an increase in the hazard by 1.8% (= [e0.018 –
1] ×100) for an additional one point increase in the population suffering from political inequality. Its effect is larger on identity conflict with an increase in the hazard by 2.9%
(= [e0.028 – 1] ×100) for an additional one point increase in the population that lacks access to political resources. However, this variable does not have significant impact on non-identity conflict. Inter-group economic differentials do not account for any types of civil war in a statistically significant manner.
External: World Systems
Model 12 through Model 15 continue to examine the impact of macro-level socio-economic forces, but the analyses focus this time on those forces coming from outside a country. Theoretically, they test the relevance of world systems theory to the risk of domestic conflict with the domestic variables with significant effects held constant (the non-identity model thus does not include the political differential variable).
Model 12 is a direct test of the impact of structural locations in the capitalist world system. World systems theory argues that peripheral countries are at the greatest risk of civil war outbreak (Hypothesis 11). The result shows, however, that the hazard of civil war onset when a country is located in the peripheral zone of the world economy is only approximately 32% (i.e., = e-1.152) of the hazard when a country belongs to the other zones. When this variable is replaced by a dummy indicator of the semi-periphery, the result exhibits a significant positive effect on the risk of identity civil war (coefficient =
1.022, robust standard error = 0.397, p<0.05 in a two-tailed test; not presented in the
121 table).44 This result could be taken effectively as a support to modernization theory in the term of world systems theory, and it is hardly explainable from the original scheme of world systems theory. Further investigation would be needed.
Model 13 through Model 15 add specific indicators of the exposure of each country to the global economy and examine their relevance to the risk of intrastate war
(Hypothesis 12 to 14). In world systems theory, incorporation into the capitalist world system through international trade, investment, or dependence on extractive exports is structurally patterned and should increase the danger of domestic conflict. The results here indicate otherwise, however. In Model 13, openness to international trade decreases the risk of civil war onset for the aggregate civil war (p<0.10, two-tailed test) and non-identity civil war (p<0.05, in a two-tailed test). The substantive impact is relatively small, however. A one-unit increase in international trade per GDP translates into a 0.70 % (= [e-0.007 – 1] ×100) and a 1.23% (= [e-0.012 – 1] ×100) decrease in the hazard of aggregate and non-identity civil wars, respectively. The variable is not significantly associated with the risk of identity civil wars. Perhaps the ways in which trade orientation works with regard to identity war risk might differ across cases too widely to exhibit any pattern and may possibly depend, for example, on the actual commodity composition of trade as some scholars (e.g., Collier and Hoeffler 2004) have suggested.
44 The two world systems dummy variables, when included together in the model, apparently compete with the economic development variables and cause a collinearity problem. Because world systems theory is primarily about the impact of the global hierarchical system and is conceptually distinct from modernization theory in that sense, it should be tested with development levels controlled for, and therefore I choose to test those dummy variables one by one. 122 Model 14 adds another piece of evidence that higher levels of integration into international economy help reduce the risk of civil war outbreak. A 1% increase in foreign direct investment penetration yields an approximately 0.19% decrease in the risk of civil war in general, a 0.28% decrease in the risk of identity civil war, and 0.29% decrease in the risk of non-identity civil war.45 In these models, the impact of real GDP per capita weakens, suggesting that domestic development may lead to international economic integration to reduce the risk of conflict. Finally, Model 15 tests the possible impact of a country’s status as an oil exporter on the risk of civil war. This variable is often considered to be an indicator of extractive-resource dependency and resulting institutional fragility in world systems theory, and to be an indicator of lootable resources by economic theorists. The result does not support either of the arguments about the risk of civil war outbreak regardless of the war “types.”
Summary on the Effects of Socioeconomic Structures
Overall, structural modernization theory seems to explain the risk of civil war well but world systems theory does a poor job. In contrast to some arguments about its irrelevance to identity-based conflict, broad socioeconomic developments can increase the risk of conflict temporarily regardless of the “types” but such an effect eventually attenuates. Some aspects of modernization, however, show different patterns across the different “types” of civil war as the theory would expect. Although the youth bulge
45 Because the hazard and the covariate are both logged in the model, the coefficient is interpreted as the percent change in the hazard for a 1% change in the covariate in its original metric. 123 seems to facilitate intense armed conflicts across all the different types, population growth shows a significantly positive effect on the risk of identity conflict only.
Because this is an aggregate measure, how population growth may contribute to inter- group competition cannot be clearly understood. Detailed group-by-group annual population data, if available some day, would help with further investigation. The results of the inequality effect tests suggest that economic differentials matter to non- identity wars, whereas political differentials are detrimental to identity wars. Income inequality, as an indicator of general levels of economic inequality, and land inequality, as a proxy of economic strata, show a significant positive effect only on non-identity wars. Meanwhile, identity-group political differentials significantly increase the risk of aggregated wars and identity wars, but economic differentials do not.
Second, as expected, possible differences between the two “types” cannot be well identified by the world systems theory, and in addition, the hypotheses for world systems theory are not supported in any war “type” models. The most conflict-prone stratum is not the periphery, but the semi-periphery, in the world system—a result that could rather lend support to structural modernization theory. Further, higher levels of integration into the international economy seem to decrease, not increase, the risk of intense armed conflict. This finding might rather be in line with Barbieri and Reuveny’s
(2005) study, which suggests that economic globalization reduces conflict risks
(although their conclusion is drawn from analysis of civil war presence). Further investigation is needed to confirm the beneficial effect of economic globalization.
124 The Effects of Political Environment
External: World Polity
Model 16 to Model 26 add variables that measure political environments in various ways. While the previous four models focus on economic aspects of external effects, Model 16 and Model 17 investigate how political involvement in the global political arena may have a beneficial impact as a conflict preventer, as world polity theory argues (Hypothesis 15). The results, however, do not provide such evidence for either of the war “types,” in contrast to what some research implicitly assumes (e.g.,
Olzak and Tsutsui 1998); neither countries’ IGO membership nor greater prevalence of
INGO’s among people shows any significant effect in either direction. This suggests that “world culture” does not really matter; while IGO’s may have symbolic importance, their actual enforcement power over their member countries’ behavior oftentimes has been quite limited. As for INGO’s, one possibility is that they may not be a homogeneous category in terms of their direct or indirect impact on conflict prevention and management. While some INGO’s may be contributing to reducing the conflict risk, perhaps others may have the opposite effect, intended or not, by helping to create opportunities to recruit and organize for collective action. Thus, the result may well be rather inconclusive and more fine-grained investigation may be necessary.
Domestic: Political Opportunity/State-Centered Thesis
The next nine models direct attention to domestic political conditions. The tests start with the oft-mentioned curvilinear effect of levels of democracy in Model 18
125 (Hypothesis 16). Although the coefficients all indicate the hypothesized direction, this variable is only significant for the aggregate civil war model.46 The weakness and non- consistency of this variable continue to show in most of the models after Model 27 when it is included as an indicator of the standard arguments about curvilinear democracy effects. It might be that relatively static levels of democracy are not very critical, or as Lacina (2004) points out, that different things are lumped together in the
“middle level of democracy” category.
Thus, Model 19 through Model 21 seek to throw light on specific institutional aspects of political environments. The results for the identity war models overall suggest that specific institutional structures and policies emerging from them may be more relevant to the risk of identity conflict, as some scholars argue. First, Model 19 focuses on specific institutional designs of democratic regimes in terms of their political inclusiveness and exclusiveness (Hypothesis 17). The test uses a dummy variable for the parliamentary/mixed democratic system and the presidential system, with autocratic regime as the reference group. The result supports the argument for aggregated civil and identity-based civil war that the parliamentary system is more inclusive and hence more effective in conflict prevention than the presidential system. The hazard of aggregated civil war and identity-based war in countries under a parliamentary/mixed system is nearly half the hazard in authoritarian countries (e-0.663 = a hazard ratio of 0.515 and e-
0.641 = a hazard ratio of 0.527, respectively), whereas there is no significant difference in
46 An accurate conditional effect plot cannot be generated for the same reason as discussed for the quadratic effect of real GDP per capita (i.e., baseline hazard estimates cannot be smoothed out in stratified models). A rough visualization shows the hypothesized inverted U-shaped curve, however. 126 the hazard of civil war between the presidential system and authoritarian regime. In contrast, those dummy variables are not relevant for non-identity civil wars. Overall, the result seems to conform to what past studies have suggested: democratic systems that are designed to disperse identity division, rather than institutionally reinforce it, should better contain conflict.
Model 20 explores different institutional structures of autocratic regimes and tests the argument that “personalistic dictatorship” is the most conflict-prone because of its strong patrimonial nature and arbitrary use of power and political exclusiveness
(Hypothesis 18). The result shows that personalistic regimes indeed matter. The hazard of civil war in general in countries ruled by a sultanistic leader is approximately three times higher than in countries under democratic rule (e1.118 = a hazard ratio of 3.057).
The result also shows that anocratic regimes are detrimental as well but to a lesser extent, with a hazard of civil war two times greater than democratic regimes (e0.726 = a hazard ratio of 2.067, p<0.10 in a two-tailed test). The result for dictatorship other than personalistic rule is statistically non-significant, however, indicating that it is safer than personalistic and anocratic regimes from the risk of plunging into civil war. A similar pattern holds for identity and non-identity civil wars. The hazard of identity-based and non-identity war is about three times larger than in a democratic regime (e1.051 = a hazard ratio of 2.861 and e1.238 = a hazard ratio of 3.449, respectively), although non- identity conflict passes a stronger test (p<0.05 in a two-tailed test for identity-based conflict, p<0.10 in a two-tailed test for non-identity conflict). Meanwhile, dictatorship other than personalistic regimes is not statistically significant for either “type” of civil
127 war. Taken together, the results from Model 19 and Model 20 suggest that the degree of political inclusiveness/exclusiveness, rather than the level of democracy, is a more important risk factor in civil war outbreak, and more consistently so in identity civil war.
Model 21 examines the effect of exclusiveness from a different angle, i.e., discriminatory political and economic policies that the state actually takes against some groups in society (Hypothesis 19). Here, the result shows a clearly different pattern across the different war “types.” As for civil war in general and identity-based war, the larger population exposed to severe group-based political discrimination increases the hazard of conflict outbreak. A 1% change in the population politically discriminated against creates an approximately 0.529% increase in the risk of aggregated civil war and a slightly larger 0.733% increase in the risk of identity civil war. In both cases, this variable passes a strong test, i.e., p< 0.001 in a two-tailed test and increases the models’ explanatory power relative to the previous models (pseudo R square is 0.169 for civil war in general and 0.225 for identity-based conflict). The variable of discriminatory economic policies is not statistically significant in these two war models.
On the other hand, the political discrimination variable is not statistically significant for the non-identity type of war. Instead, the variable of discriminatory policies in terms of economic opportunities shows a significant effect on the risk of war onset at the p<0.05 level in a two-tailed test. For each 1% increase in the population economically discriminated against, the hazard of non-identity conflict goes up by an estimated 0.276%. The result from Model 21 suggests that there may be some truth to
128 the oft-made argument that identity-based conflict are driven partly by a sense of grievance about identity groups’ status recognition, whereas class-based conflict is due to the systematic denial of access to economic opportunities.
Model 22 through Model 24 add some more tests for the effect of political exclusion on the risk of identity-based conflict that happens when traditional subnational populations are deprived of their autonomous status by expansionist states that seek to extend its control over subnational territories and populations (Hypothesis
20). Since it is an extreme form of identity-based political exclusion and the argument is conceptually little relevant to non-identity conflict, the test here is done for civil war in general and identity-based civil war only. First, Model 22 and Model 23 enter the lost autonomy variables, each differently specified. In the former model, the sum of the lost autonomy index for each country year is included. For civil war in general, this variable is significant in the hypothesized direction but in a weak test (at p< 0.10 in a two-tailed test). As expected, lost autonomy has a stronger effect on the risk of identity civil war with a stricter statistical test. For one unit increase in lost autonomy score, the hazard of identity-based conflict goes up by 8.3% (= [e0.080 – 1] ×100) at p<0.05 in a two-tailed test. The detrimental effect of the presence of peoples that have lost their autonomy reveals itself in stronger statistical tests when it is measured by the sum of the lost autonomy index weighed by the proportion of the relevant group populations (at p<
0.05 in a two-tailed test for civil war in general, and at p< 0.001 in a two-tailed test for identity-based civil war).
129 Model 24 adds another test by throwing light on the expansionist state and marginalization of minority groups. The main effect terms, i.e., political discrimination against minorities and military expenditure per GDP as a proxy of state expansion, are both positively significant. The interaction effect is not statistically significant in either the aggregated or identity civil war model, however. Thus, the effects of group-based political exclusion and state expansion are constant (i.e., do not change across each other’s value).
In Model 25 and Model 26, changing political environments, as opposed to relatively static ones, are examined (Hypothesis 21). Model 25 includes a five-year first difference score of polity, the initial level of polity to control for the floor, and their interaction term. Statistical significance shows in the identity civil war model only, for the main effect of the first difference score and the interaction term. However, the result is in the direction opposite to the hypothesis. A one point increase in five-year polity change score decreases the hazard of identity war by an estimated 13.2% (= [e-0.141 – 1]
×100) if a country was most autocratic five years earlier as defined by the Polity Project.
The significantly positive effect of the interaction term indicates that the beneficial effect of polity change increases if a country is more democratic at the initial point of t-
5. Expanding democracy is conducive to preventing identity civil war, perhaps due to the recognition of identity groups’ status. Model 26 uses the variable of “proximity of democratization” to test the argument that democratic nation-building through elections
130 may increase the risk of civil war onset by opening opportunities for political actors that are not ready for mass political participation and thereby destabilizing society. The result, however, does not support this thesis for either category of civil war.
Summary for the Effects of Political Environment
The results in this sub-section exhibit some broad but discernable patterns. First, despite the argument often made in the collective behavior/social movement literature, it may not be the mere level of democracy that plays a key role in preventing or facilitating conflict outbreak. A more important and specific explanation seems to be inclusiveness/exclusiveness of political systems. In countries where political institutions and policies are designed to systematically exclude polity outsiders, those excluded may launch a challenge against the insiders in a more violent manner, but under more integrative systems, the risk of conflict decreases due to more moderate repertoires that challengers may use. Second, although it is also relevant to the non-identity “types,” political inclusion/exclusion seems to play an important role more consistently in identity-based war outbreak. As for political institutions, parliamentary/mixed democracy is at a significantly lower risk of civil war in general and identity war, but the democratic institutions dummy variables are not relevant to non-identity war.
Countries under personalistic autocracy are at a high risk of war outbreak regardless of the “types,” but this variable passes a stronger statistical test both in aggregated civil war and identity war models. This result seems to be in line with past studies that suggest the importance of power-sharing systems as a measure of identity conflict
131 prevention. This may be because under a parliamentary system power tends to be more divided due to its collegial executive branch and therefore keeps primordial identities from becoming salient. Conversely, under personalistic rule where power is concentrated and exercised in an exclusive, arbitrary manner by a patrimonial leader
(often from a dominant identity group), challengers may resort to violent means. As for policies, political discrimination matters to the risk of civil war in general and identity war, whereas the risk of non-identity war is worsened by economic discrimination policies. The result exhibits the differences between the identity and non-identity civil war models that many scholars have noted in the literature (e.g., Horowitz 1985;
Kaufman 2006a, 2006b; Kaufmann 1996; Sambanis 2004).
The indicators of identity groups’ loss of autonomous status to the power of the expanding state—an extreme form of political exclusion and deprivation—show results that seem to add a confirmation to the above argument. Historical heritage of lost autonomy makes a country more conflict-prone, and it shows a stronger effect for the risk of identity war than aggregated civil wars. Finally, the result shows some evidence that positive changes in political environment have a preventive effect against identity- based civil war outbreak. This result is opposite to the expectation that democratization may have a detrimental impact on the risk of identity conflict. After all, the movement toward inclusion of various social groups in the political processes seems to serve as effective conflict deterrence.
132 The Effects of Ethnic, Religious, and Linguistic Configurations and Attributes
Fractionalization and Attributes
This sub-section examines the effect of different ethnic composition—the size and numbers of identity groups and the combination of different identity attributes—on the different war “types.” First, Model 27 and Model 28 examine how various aspects of social fractionalization may affect the risk of civil war onset (Hypothesis 22). In
Model 27, the impact of the degree of ethnic and religious fractionalization is tested.
Ethnic fractionalization does not show any statistical significance in any of the three war-type models. Thus, an ethnically diverse society may be no less dangerous than a homogeneous one, contrary to the argument made by past research that a heterogeneous society is safer (Collier 2001; Collier and Hoeffler 2004; Montalvo and Reynal-Querol
2002; Reynal-Querol 2002). The coefficient of religious fractionalization, however, is negative and significant for the non-identity war model only, contrary to the hypothesis.
A one point increase in religious fractionalization yields a 1.7% decrease (= [e-0.017 – 1]
×100) in the hazard of class-based civil war. Empirically, this result seems to be partly in line with Alesina et al.’s (2003) findings that religiously fractionalized societies see a broad positive effect on various political and social indicators, such as the quality and performance of government, including corruption control, tax compliance, good infrastructure quality, lower infant mortality rate, lower illiteracy rate, and higher education attainment. And the reason that this variable shows only for the non-identity type might indeed be due to the combination of the configuration and the attribute. The lack of religious domination and challenges against religious establishments may reduce
133 the salience of competing world views, and religious ideas themselves may further make people less centered on materialistic interests (or perhaps, religion may indeed be opium of the people, as Marx put it) and more focused on common goods.47 Resulting beneficial impacts on broad social conditions along with the lack of religious salience may be especially effective at containing a non-identity type of conflict.
In Model 28, possible effects of linguistic fractionalization are tested. As hypothesized, the result shows that higher levels of linguistic fractionalization significantly increase the risk of civil war, especially identity civil war, although the test is rather weak (for one point increase in linguistic fractionalization, a 0.9% increase in the hazard of war outbreak increases by 0.9% for the aggregate category and by 1.2% for the identity-based category, both at p<0.10 in a two-tailed test). In addition to its symbolic significance for identity groups’ status (Horowitz 1985), language plays an obvious practical function. It may be that the existence of multiple languages in one nation-state may hinder inter-group communication in a more direct way than fractionalization in other dimensions, and that such societies lack structural opportunities that can nurture mutual understanding and development of broad social networks. On the contrary, it may rather facilitate intra-group cohesiveness, in combination with lower degrees of the collective action problem. This obstacle may be especially difficult to overcome when none of the languages used is widespread and predominant enough to motivate speakers of another one to learn it.
47 I tried including dummy variables for Western Europe, Eastern Europe, North America, Latin America, and the Middle East as rough measures of the monotheistic religions that might deter the idea of class distinction from becoming prominent. Those dummy variables did not show statistical significance or affect the result. 134 Model 29 explores Hypothesis 23 that the effect of ethnic diversity may be conditional on processes of democratic nation building (Lintz and Stepan 1998; Snyder
2000). Their argument is that the desire for status attainment that multiple nations within a single state each possess may be intensified by democratization and may lead to identity conflict outbreak as a result. The result does not support the thesis, however.
There might be another force at work that is worth considering. For example, in today’s world, nation-building elections are often overseen by international observers, and such supervision might keep a country from quickly plunging into violence.
Dominance and Attributes
Model 30 and Model 31 examine the effects of dominance on the risk of civil war (Hypothesis 24). As discussed in Chapter 2, there are competing theories that predict the effect of dominance in opposite directions. Model 30 suggests that dominance has the effect that is mostly a mirror image of that shown in Model 27, that the conflict risk of countries where a single religious group is predominant is significantly high, and that it is especially exhibited in the non-identity war model.
Meanwhile, neither of the dominance variables is statistically significant in the identity war model as hypothesized. For aggregated and identity-based civil war, societies under religious dominance are approximately twice as susceptible as more diverse societies
(e0.637 = a hazard ratio of 1.891 and e0.857 = a hazard ratio of 2.357, respectively). Model
31 tests possible impacts of language dominance. This variable has no effects on the risk of civil war, however.
135 Polarization and Attributes
Model 32 examines the effects of the competition/polarization aspect of ethnic composition in society (Hypothesis 25). The idea, as discussed in Chapter 2, is that groups of similar size (and of similar resources, by extension) should have a more intense sense of rivalry and competition and that such rivalry may lead to violent conflict outbreak because primordial identity is irreconcilable. To test the argument, ethnic and religious polarization indexes are entered in Model 32. The results show that a country is at a significantly higher risk of civil war in general and identity-based civil war when two ethnic groups of similar size coexist in society (for both types of civil war, the hazard increases by approximately 1.8% for one point increase in ethnic polarization). This is not the case when a society is ethnically fractionalized (Model 28) or dominated by a single powerful ethnic group (Model 29). Ethnicity may be most salient when it takes the form of polarization and may set itself up as a predominant parameter of social division. If that is the case, it is not very surprising that ethnic polarization measures are not statistically significant in the non-identity war model.
Religious polarization shows statistical significance in none of the war type models.
Summary for the Effects of Group Configurations and Attributes
Recently, an increasing number of civil war studies argue that more fractionalized societies are safer from the danger of civil war outbreak than less fractionalized ones, and that polarized societies are more dangerous than less polarized ones. The logic behind this argument is that less group identity salience results in more
136 inter-group associations and lower levels of group resource bases in fractionalized societies as opposed to polarized societies. Yet, this argument is not made in any definitive manner when some studies examine aggregated civil wars while others limit their sample to “ethnic conflict” cases with different attributes incorporated across different studies.
This subsection has sought to re-examine these studies to see how the past results might compare if the sample is explicitly classified into the two war “types” rather than simply limiting the sample to “ethnic conflicts.” The results suggest that the relationships between ethnic composition and the risk of war may be fairly complex ones. The effects seems to be different across the war “types,” not only by size and numbers of identity groups but also by different identity attributes that delineate society into groups. First, religious fractionalization and dominance matter to non-identity conflict in the opposite directions. That is, religiously fractionalized society seems to have a built-in buffer against the risk of non-identity war, but religious dominance works as a war facilitator. It might be that the effects of size/numbers (i.e., the lack of religious salience, more tolerance for coexistence) and of characteristics of attributes
(i.e., less focus on pursuing materialistic interests) simultaneously work to prevent non- identity war from erupting. As for religious dominance, about 46% of the religiously dominant countries with (an) aggregated war outbreak experience(s) are Islamist (e.g.,
Indonesia, Iran, Morocco), suggesting the need for more fine-grained investigation of religious dominance. Meanwhile, linguistic fractionalization and ethnic polarization significantly increase the risk of identity-based civil war. Fractionalization in terms of
137 size/numbers may deter conflict in some cases generally (e.g., Collier and Hoeffler
2004). But when groups are delineated around the axis of language, then perhaps the existence of multiple languages may be a direct and practical barrier against inter-group communication and associations. Conversely, it may imply the existence of internally highly cohesive groups that are difficult to reconcile. Ethnicity, on the other hand, may become most salient when society is polarized on this axis (i.e., when two equally powerful groups coexist in a society), and when this forms the major parameter shaping the nature of conflict. Opportunities for members of each group to make comparison with the other group and the similar amount of mobilizable resources may make the society more conflict-prone.
ESTIMATED BASELINE CUMULATIVE HAZARD FUNCTION
Finally, Figure I.1 through Figure I.3 in APPENDIX I present the estimated cumulative baseline hazard functions by event ordering number for the models of the different war “types.” Although the model used to generate those figures is Model 11
(for the relatively strong explanatory power of the model), the other models indicate essentially the same pattern. As shown, the hazard of the second civil war outbreak is overall higher than that of the first outbreak regardless of the “types.” This suggests that there is some truth to the idea of “conflict trap”; once a country experiences whichever
“type” of intense armed conflict, the risk of the next outbreak increases. The first ten years after the first one seems to be an especially dangerous time period, as the rather steep slopes for the second onsets before t = 10 exhibit. The slopes for the war onsets
138 higher-ordered than the second ones (Figure I.1 and Figure I.2) do not show any clear difference from those for the first onset. This might be simply due to the still smaller number of higher-ordered events, but the possibility of war attrition and wariness after experiencing the first two wars cannot be ruled out, as Walter (2004) suggests. This demands further investigation in future studies, given that the amount of existing civil war research directly incorporating varying hazards in the analytic picture is substantially limited to this day.
139
CHAPTER 5
CONCLUSION
As evidenced by the level of interest in civil war and its grave consequences for aggrieved warring countries and their populations since the end of the Cold War,
“ethnic conflict,” or “identity-based conflict,” is clearly recognized as a major focal point in both public discourse and academic research. Witnessing countries such as former Yugoslavia, Rwanda, Somalia, and Afghanistan suffering by one atrocity after another as their subnational groups were apparently bound by the parameters of ethnicity and religion, people tend to make an implicit assumption that there is a category called “ethnic/identity-based” conflict that is conceptually and empirically distinct from that which is not ethnic or identity-based. In the academia as well, many scholars make an a priori assumption that reifies “ethnic conflict” and conduct ethnic conflict research accordingly. Yet it is not entirely clear if “ethnic/identity-based conflict” really exists as a conceptually and analytically distinct category. Some have started to question the justification for the research focusing on “ethnic/identity conflict” and have advocated complete abandonment of the concept and the field of ethnic conflict studies.
140 In this intellectual context, this dissertation attempted to compare causes of these two oft-mentioned conflict types, “identity-based war” and “non-identity war.” in order to see if there are any notable causal differences between them that can justify the conceptual distinction so widely and commonly made. It did so by drawing on an oft- used conflict classification that focuses on major organizing parameters of warring parties and by employing the conditional risk model to analyze civil war data from 1960 to 2000.
The results show that while the different “types” of war appear to share many causes in common, variables that are relevant to political exclusion or competition along identity parameters seem to better explain identity-based civil war overall and other variables relevant to economic exclusion seem more applicable to non-identity civil war. First, as for the effects of socioeconomic structures, a higher economic development level as well as broad social development reduce the risk of civil war outbreak regardless of the “types,” although the risk goes up at early phases of the modernization process. Unlike some existing arguments, such as Sambanis’s (2001) claim that the modernization thesis (and its emphasis on economic variables) does not explain identity civil war well, the results in this study suggest the general importance of socioeconomic development for conflict prevention overall. Similar implications are seen in the test results for the external economic forces, though not very strongly and consistently. Overall, the conflict risk seems to be the same or reduced with more international economic exchanges, a result that suggests the importance of economic integration as a conflict reducer.
141 Nonetheless, some engines/by-products of modernization processes, such as demographic change and inequality, show different patterns across the different “types” of civil war. Particularly noteworthy is the impact of economic and political inequalities upon the risk of non-identity and identity civil wars, respectively. Economic differentials (income and land inequalities) encourage non-identity wars, whereas political differentials seem to pose a greater danger of identity war outbreak. Although further research with better, aggregate-specific inequality data is needed, the result seems to suggest that differentials along a particular status parameter may make that parameter more salient as an organizing principle.
As for the effect of political environment, inclusive institutional structures exhibit generally beneficial effects. This result is consistent particularly for identity- based conflict; the negative effect of parliamentary systems and the positive effect of personalistic regimes are statistically significant in stronger tests. The importance of political integration as a way of identity conflict prevention is further implied in the tests for the effect of actual policies of political discrimination and exclusion. Where there is systematic denial of political opportunities to some groups, whether through implementation of discriminatory policies or through deprivation of political autonomy, the risk of identity conflict increases significantly. In contrast, the risk of non-identity war goes up when there exists systematic economic discrimination. Thus, as a whole, there seems to be some truth to the argument that the presence or absence of political
142 recognition and the sense of group worth and empowerment play a more important role in identity-based conflict than in non-identity conflict (Horowitz 1985; Sambanis 2001,
2006).
Finally, this study reveals rather complex relationships between identity attribute composition and the risk of war; both the size and numbers of identity groups and the combination of different identity attributes seem to differently affect the risk of the two
“types” of conflict. Apparently, some enhance certain parameter salience whereas others may erase it. Interestingly, for example, religiously diverse society seems to reduce the risk of non-identity war. It might be due to the combination of the effect of size/numbers (reduction of religious grouping salience and increased tolerance) and characteristics of attributes (lesser degree of materialism). On the other hand, linguistic fractionalization and ethnic polarization significantly increase the risk of identity-based civil war. It may be that language diversity is a direct hindrance against increase in inter-group association and as a result highlights social division, whereas the existence of equally strong two ethnic groups is the most dangerous configuration that intensifies a sense of rivalry between the identity groups.
Other than examining the theoretical covariate effects, the use of the conditional risk model in this study allowed for varying baseline hazards for multiple events and thereby provided a better picture of a “conflict trap” than many of the previous civil war studies that do not consider event dependence. The results suggest that such conflict dynamics do exist regardless of the different war “types” at least up to the second event.
Perhaps due to the increasing attention given to conflicts involving ethnicity and
143 religion, some scholars seem to emphasize that the persistent nature of political violence is particularly characteristic of identity-based political violence. Yet, no matter what parameter divides a society into warring groups, it seems that any first conflict experience should be taken as a serious alarm in that it sets conditions that can quickly trigger the second one. Meanwhile, the results are unclear about higher-ordered events than second ones. A country tends to experience a greater number of identity-based conflicts than non-identity conflicts, and in that sense it might be the case that identity- based conflicts have an inclination to persist. However, their hazards seem virtually the same as the first onset in both aggregated civil war and identity-based civil war. While the limited number of higher order events generally makes it statistically difficult to be confident about any further escalation process, there are actually some countries that are plagued with multiple (i.e., more than two) eruptions of conflict. Therefore, this is probably one of the areas where case studies focusing on chronically conflict-prone societies would be greatly helpful in enhancing our understanding of heterogeneity that may set conflict trap processes in motion (Sambanis 2004). The picture of highly conflict-laden society lost by further collapsing higher-ordered events could be obtained through detailed qualitative approaches.
There are a number of important tasks that the present research could not achieve but that future studies should attempt to overcome in order to enhance our understanding of civil war and their “types.” First and most critically, conflict classification data should be improved based on clearer conceptualization and operationalization in order to further investigate any possible substantive difference
144 between conflict types, as currently existing war type information is not as fine-grained as it ideally should be. It would be an understandably difficult task. Although social scientists have long sought to extract some patterned essence from the complex reality of society that is delineated along some major grouping parameters, such as economic class, social and political status, and cultural signifiers (e.g., ethnicity, gender), such parameters usually cut across one another, the salience of such parameters changes across time and place due to a variety of causes, and those parameters often interact and/or co-vary with one another. This is one of the major reasons why some scholars believe that the “identity-based (ethnic) war” label is not very much useful. Yet after all, the vast majority of empirical studies are conducted by highlighting one or two parameters dividing the complex social system (e.g., labor movement, women’s political participation, and so on), and an improved coding scheme that can determine whether a primordial identity is the predominant line defining groups in conflict would help further examine the usefulness (or lack thereof) of the concept in an empirical manner. One such effort has been in progress in Sambanis’s multi-year data project, where he codes war types by focusing on recruitment practices and alliance patterns for all rebels groups in all civil wars (e.g., to be qualified as “ethnic war,” the majority of the parties involved must be recruiting from within their own groups and not across
145 groups).48 Constructing such data would be highly labor-intensive and time-consuming, but when available, it should help promote further investigation into the conceptual and empirical validity of war-type classification.
Second, improvement of international data quality and coverage and new ideas about creating variables better tapping theories would help increase our confidence in theory tests in the future. As is often the case with cross-national large N studies, the present study suffered from unavailability and unreliability of data that would have allowed better tests of a number of interesting sociological theories. What is especially problematic with civil war research is that the quality and availability of data itself may have been strongly influenced by the phenomenon itself (e.g., inability of government agencies in warring countries to collect reliable statistical data), yet it is also due to the chronic lack and/or poor quality of key information, as seen in inequality data.
Particularly, the only available data directly measuring the concept of “horizontal inequality” (i.e., group-specific inequality) could not be used in this analysis due to its limited time/space coverage, despite its theoretical importance for identity-based conflict. Further, even when available, data often do not seem to carry the full nuances and implications of tested theories. For example, despite its rich implications for cross- cutting cleavages and their impact on grouping parameters, structural modernization theory’s focus on increasing social complexity, differentiation, and interdependence cannot be fully captured by a GDP variable or an energy consumption variable (even
48 Nicholas Sambanis, personal communication, October 6 and 29, 2006. I thank Nicholas Sambanis, professor of political science at Yale, for generously explaining his ongoing project upon my inquiry and sharing his paper in progress.
146 though these are commonly used variables to test the modernization thesis). Hopefully, continuous efforts by international institutions, research centers, and scholars to compile reliable data would lead to improved quality of conflict studies, including the present one.
Third (and related to the first and second points), it would be highly beneficial for civil war research to combine quantitative approaches as used in this study with qualitative research. The value of triangulation is always evident, but the importance of a multiple method approach cannot be emphasized enough in the field of civil war research given the extremely heterogeneous nature of the events in question. As noted above, the current state of available quantitative data does not allow researchers to test some key theories of civil war fully, and some of the important concepts are difficult or almost impossible to measure quantitatively. For example, one of the key arguments in the collective action literature is the framing process for mobilization (Benford and
Snow 2000; Snow, Zurcher, and Ekland-Olson 1980). This dynamics might be very important in determining which grouping parameter may play a dominant role in mobilizing people, but it is very difficult to be measured quantitatively. There are other factors that are difficult for quantitative approach to capture adequately, such as each country’s historical legacies. Qualitative studies would make a significant contribution to filling this gap in the empirical literature.
Fourth, it would be important to figure out reasons for the apparent absence of unobserved heterogeneity in the data used in this study. I chose to use the variance component correction approach to address the issue of within-unit correlation over
147 incorporation of the frailty in the model because I did not find clear evidence of its presence. This, of course, does not reject the possibility that there exists heterogeneity in the data, and in fact its non-presence is even implausible for the reason stated above
(e.g., immeasurable concepts omitted from the model, poor data quality). One explanation would be the rarity of the events under study. The small number of civil war events in the presence of the considerable within- and across- unit heterogeneity might have made it difficult for the random effect to show statistical significance, because it can blur the patterns of unit-level shared frailty relative to across-unit variation (Box-Steffensmeier, De Boef, and Sweeney 2005).49 This statistical explanation needs to be confirmed because it is particularly important in statistical analysis of civil war to control for what is quantitatively unaccountable and immeasurable in order to achieve generalizability.
Finally, another aspect of conflict escalation processes could also be examined.
This study used the conventional threshold of annual 1,000 battle-related deaths to identify civil war onsets, and addressed one aspect of conflict escalation, i.e., the dynamics of conflict traps, by allowing the baseline hazards to vary across multiple outbreaks. This approach has rarely if ever been used in the past conflict literature and should be taken by future studies of multiple onsets. Yet, another important aspect of conflict escalation is the way in which an apparently minor skirmish can lead to a full- scale violence, and there might be some notable variation in such processes across
49 Aggregated civil war occurrences from the end of World War II to the year 2000 are only about 1.6% of the total country years, for example.
148 conflicts fought by groups delineated along different axes. While conflict data that track fluctuating violence intensity in a reliable, fine-grained manner is currently scarce, systematic efforts for data collection have begun, as seen in Battle Deaths Data (Lacina and Gleditsch 2005). It would be worth starting future analysis built on such efforts in order to launch the process of knowledge accumulation.
Despite the recent overall decline in the number of civil war around the globe, its threat to international security and devastating consequences on human welfare is the harsh reality we are still faced with today, and any responses to achieve effective conflict management and resolution should be based on research-based knowledge. This dissertation sought to contribute to such research-based knowledge by investigating the commonly used taxonomy that has been framing public, policy and academic discourse about civil war, yet it is just one of the early efforts in this research field. Even though some of the results seem to suggest that there might be war-type differences, the usefulness of the concept of “identity/non-identity war” (or lack thereof) needs further confirmation. In the meantime, debate about the validity of “identity conflict” as a distinct conflict category is still continuing. Given its implications for both sociological theories and effective public policy formation, it is hoped that further empirical research will be conducted to determine if this widely used conflict classification represents major heterogeneity in terms of causes of civil war.
149
APPENDICES
APPENDIX A
NUMBER OF CIVIL WAR ONSETS, 1946-2000
8
7
6
5
4
3
2
1
0 1946 1950 1960 1970 1980 1990 2000 Year
Figure A.1: Number of aggregated civil war onsets 1946-2000
151
APPENDIX B
FREQUENCY OF CIVIL WAR ONSETS BY EVENT ORDERING STRATA, 1946-2000
Strata As In Conflict Data Strata Collapsed Number Number Event Order Strata of Onsets Percent Cum. of Onsets Percent Cum. 1 61 54.0 54.0 61 54.0 54.0 2 26 23.0 77.0 26 23.0 77.0 3 12 10.6 87.6 12 10.6 87.6 4 8 7.1 94.7 14 12.4 100.0 5 4 3.5 98.2 6 2 1.8 100.0 Total 113 100.0 113 100.0
Table B.1: Frequency of intense armed conflict onsets, aggregated, by event ordering strata
Strata As In Conflict Data Strata Collapsed Number Number Event Order Strata of Onsets Percent Cum. of Onsets Percent Cum. 1 34 49.3 49.3 34 49.3 49.3 2 18 26.1 75.4 18 26.1 75.4 3 9 13.0 88.4 17 24.6 100.0 4 4 5.8 94.2 5 3 4.3 98.6 6 1 1.4 100.0 Total 69 100.0 69 100.0
Table B.2: Frequency of identity civil war onsets by event ordering strata
152
Strata As In Conflict Data Strata Collapsed Number Number Event Order Strata of Onsets Percent Cum. of Onsets Percent Cum. 1 36 76.6 76.6 36 76.6 76.6 2 9 19.1 95.7 11 23.4 100.0 3 1 2.1 97.9 4 1 2.1 100.0 Total 47 100.0 47 100.0
Table B.3: Frequency of non-identity civil war onsets by event ordering strata
153
APPENDIX C
LIST OF CIVIL WAR ONSETS, 1946-2000
Country Start End Identity State Non-State Opposition Note Afghanistan 1978 1978 No National Revolutionary People’s Democratic Party of Afghanistan The 1978 coup and following insurgency. PDPA Party (Sardar Mohammad (PDPA) launched a coup known as the Saur Revolution Daoud Kahn) and took over the government from Sardar Mohammad Daoud Kahn. Afghanistan 1979 1991 Yes People’s Democratic Party Mujahideen, Hezb-i-Islami Internationalized up to 1988, back to intrastate in of Afghanistan (PDPA) + 1989. USSR Afghanistan 1992 1995 Yes Afghan government Hezb-i-Islami, Taliban Configuration change. 154 (Burhanuddin Rabbani) Afghanistan 1996 2001 Yes Taliban UIFSA Configuration change. Algeria 1993 2001 No National Liberation Front The militant Islamic Salvation Front (FIS), the A post-election dispute that turned to a civil war. (FLN) Armed Islamic Group (GIA), the Salafist Group for Preaching and Combat (GSPC), Armed Islamic Movement (AIS). Angola 1975 1995 Yes Popular Movement for the National Union for the Total Independence of US-USSR proxy war with ethnic based Liberation of Angola Angola (UNITA) + US, Zaire, S Africa organization. Internationalized up to 89. Intra- (MPLA) + Cuba, USSR state 1990-1995. Angola 1998 2001 Yes The The Angola National Union for the Total Independence of The 1994 peace accord (Lusaka protocol) government (MPLA) + Angola (UNITA) collapsed. 1998 is intrastate, 1999- are Namibia (from 1999) internationalized. Azerbaijan 1992 1994 Yes Turkic Azeris Christian Armenians + Armenia Massacre of Azerbaijanis at Khojali in Nagorno- Karabakh. Internationalized 1992 - 1993.
Continued
Table C.1: List of civil war onsets, 1946-2000 Table C.1 continued
Country Start End Identity State Non-State Opposition Note Argentina 1975 1977 No Argentina government Revolutionary Army of the People (ERP), “Dirty War (Guerra Sucia)” (Jorge Rafael Videla Montoneros. Redondo) Bolivia 1946 1946 No Revolutionary Nationalist Movement (MNR) “Sexenio,” before the years of the 1952 revolution.
Bosnia- 1992 1995 Yes Bosnia-Herzegovina Serbian republic of Bosnia and Herzegovina, Conflict that subsequently occurred after the Herzegovina Serbian irregulars + Yugoslavia Bosnian parliament’s declaration of the republic’s independence in 1992, until the 1995 Dayton Accord. Bosnia- 1993 1994 Yes Bosnia-Herzegovina Croatian Republic of Bosnia and Herzegovina + Herzegovina Croatia, Croatian irregulars Dominican Rep 1965 1965 No Dominican military Constitutionalists (Pro-Bosch) Military coup by pro-Bosch PRD members government (Emilio de (Constitutionalists) led to violence between those los Santos, Ramon Tapia, favoring the return to government by Bosch Manuel Tavares) (president ousted by a right-wing military coup in 1963) and those who proposed a military junta 155 committed to early general elections. Myanmar 1948 1956 Yes Burmese government Burmese Communist Party (BCP), The Red Flag (1) Identity war (KNU), (2) non-identity war (BCP (Thaik/Nu) Communists, White Flag Communists, the PYA - et al). Both 1948 - 1956. White Band (Socialists). Arakanese Muslims, and the Karens (Karen National Union (KNU)). Myanmar 1961 1975 Yes Burmese government Kachin Independence Organization (KIO) Separatist uprising. (Thaik/Nu to Win) Myanmar 1964 1970 Yes Burmese military Shan State Army (SSA), Shan State Independence Separatist uprising. government (Win) Army (SSIA). Myanmar 1968 1978 No Burmese military Burmese Communist Party (BCP) Recurrence of the 1948 conflict that involved the government (Win) communist/leftist organizations. Myanmar 1992 1992 Yes Burmese military Karen National Union (KNU), God’s Army. Separatist uprising. Recurrence of the 1948 government (Maung) conflict that involved the Karen.
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Myanmar 1994 1994 Yes Burmese military Mong Thai Army (MTA) Separatist uprising. government (Shwe) Burundi 1998 2002 Yes Brundi government (Tutsi Party for the Liberation of the Hutu People and its Hutu uprising. dominant) split factions Cambodia 1967 1969 No Cambodian government Communist Party of Kampuchea (CPK, aka (Sihanouk) Khmer Rouge) Cambodia 1970 1975 No Cambodian government CPK, Sihanoukists, NVA/VC + North Viet Nam (Lon Nol) + USA Cambodia 1978 1978 No Democratic Kampuchea Kampuchean United Front for National Salvation (CPK) (KUFNS, Heng Samrin) + Viet Nam Cambodia 1979 1989 No People’s Republic of Khmer Rouge (Pol Pot), United National Front for Kampuchea (Heng an Independent, Neutral, Peaceful and Samrin) + Viet Nam Cooperative Cambodia (Sihanouk), Khmer people’s National Liberation Front (Son Sann) Sri Lanka 1971 1971 No Sri Lanka government Janatha Vimukthi Peramuna (JVP, People’s 156 (Sri Lanka Freedom Liberation Front) Party, SLFP) Sri Lanka 1985 2001 Yes Sri Lanka government LTTE (Liberation Tigers of Tamil Eelaqm), (Sri Lanka Freedom TELO (Tamil Eelam Liberation Organization), Party, SLFP) PLOTE (People’s Liberation Organization of Tamil Eelam) Sri Lanka 1989 1989 No Sri Lanka government Janatha Vimukthi Peramuna (JVP) (Sri Lanka Freedom Party, SLFP) Thailand 1972 1972 No Thai government Communist Party of Thailand (CPT) Thailand 1977 1981 No Thai government Communist Party of Thailand (CPT)
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Chad 1965 1988 Yes Chad government National Liberation Front of Chad (FROLINAT), (Chadian Progressive Chadian National Front (FNT), Chadian Armed Party, CPP, Tombalbaye) Forces (FAT), Afmed Forces of the North (FAN), People’s Armed Forces (FAP), etc. + Lybia Chad 1989 1990 Yes Chad government (Habre) Patriotic Salvation Movement (MPS, Idriss Deby), MOSANAT + Libya China 1946 1949 No People’s Liberation Army Nationalists China 1947 1947 Yes Nationalists Taiwanese aborigines Separatist insurgency. Unrelated to later Mainland- Taiwan conflict. China 1950 1950 Yes Chinese government Tibet Separatist insurgency. China 1956 1956 Yes Chinese government Tibet Separatist insurgency. China 1959 1959 Yes Chinese government Tibet Recurrence of the 1956 outbreak. Colombia 1948 1958 No Colombian Conservative Colombian Liberal Party, Colombian Communist “La Violenica” Generally considered as starting in Party Party the year of Jorge Eliécer Gaitán assassination
157 (Livingstone 2004, p.42). Colombia 1989 2004 No Colombian government Revolutionary Armed Forces of Colombia (Liberal Party) (FARC), Democratic Alliance/M-19 (M-19), National Liberation Army (ELN) Congo, Rep. 1997 1999 No Democratic and Patriotic Cobra (Sassou) + Angola Supporters of the current and recent governments Forces (FDP, Lissouba, have included people from a broad range of ethnic Kolelas) Cocoyes, Ninjas, and regional backgrounds. Ntsiloulous. Dem Rep of the 1964 1965 Yes Congolese government National Liberation Council (CNL) Congo (Adula) Dem Rep of the 1996 1997 Yes Zaire government (MPR, AFDL (Kabila) + Rwanda “First Congo War” Congo Mobutu) + Angola
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Dem Rep of the 1998 2001 Yes Congolese government Congolese Democratic Rally (RCD), Congolese “Second Congo War” Congo (Kabila, AFDL) + democratic Rally-Liberation Movement (RCD- Zimbabwe, Angola, ML), Congolese Liberation Movement (MLC) + Namibia, Chad Uganda, Rwanda Costa Rica 1948 1948 No Costa Rica government National Liberation Army (Jose Figueres Ferres) “Costa Rica Civil War.” Armed uprising led by (National Republican José Figueres against a disputed presidential Party) election result. Cuba 1958 1958 No Fulgencio Batista Fidel Castro “26th of July Movement” government El Salvador 1980 1991 No El Salvador government People’s Revolutionary Army (ERP), Armed (Majano) Liberation Forces (FAL), Armed Forces of National Resistance (FARN), Popular Liberation Forces (FPL), Revolutionary Party of Central American Workers (PRTC), Farabundo Marti Front for National Liberation (FMLN)
158 Ethiopia 1974 1991 Yes Ethiopian government Eritrean Liberation Front (ELF), Eritrean People’s “Eritrean War” Liberation Front (EPLF) Ethiopia 1976 1991 Yes Ethiopian government Ethiopian People’s Revolutionary Party (EPRP), “White Terror” to “Red Terror.” (the Derg, Mengistu) Tigrean People’s Liberation Front (TPLF), Ethiopian People’s Democratic Movement (EPDM), Ethiopian People’s Revolutionary Democratic Front (EPRDF). Ethiopia 1977 1978 Yes Ethiopian government + Western Somali Liberation Front (WSLF) + “Ogaden War.” Cuba Somalia, Ogaden National Liberation Front (ONLF). France 1961 1962 No French government Secret Army Organization (Organisation armée Over France’s colonial policy over Algeria. secrète, OAS) Georgia 1992 1993 Yes Georgian government Republic of Abkhazia “Abkhazia Crisis.” Separatist insurgency. The same year (1992) Rep of South Ossetia had a highly intense violence.
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Greece 1946 1949 No Greek government Democratic Army of Greece (DSE) Guatemala 1968 1994 No Guatemala government 13 November Revolutionary Movement, Rebel The end day, according to the peace accord Armed Forces, Guerilla Army of the Poor, between the government and the rebels, is 1996. Guatemalan Worker’s Party, Organization of The last three years before the official settlement Armed People, Guatemalan National seem to have been relatively low-intensity. Revolutionary Unity India 1948 1951 No India government Communist Party of India (CPI) India 1984 1993 Yes India government Sikh insurgents “Operation Bluestar” India 1990 2004 Yes India government Kashmir insurgents Separatist uprising. India 1991 1991 Yes India government ULFA (United Liberation Front of Asom) Separatist uprising (Assam). Indonesia 1950 1950 Yes Indonesia government Republic of South Moluccas Separatist uprising. (Sukarno) Indonesia 1953 1953 Yes Indonesia government Darul Islam Darul Islam Movement (Aceh and South Sulawesi) (Sukarno)
159 Indonesia 1958 1961 Yes Indonesia government PRRI, Permesta c.f., Herbert Feith, Daniel S. Lev. 1963. “The End (Sukarno) of the Indonesian Rebellion” Pacific Affairs, Vol. 36. Indonesia 1975 1980 Yes Indonesia government Revolutionary Front for an Independent East Separatist uprising. (Suharto) Timor (FRETILIN) Indonesia 1976 1978 Yes Indonesia government Organization for a Free Papua (OPM) Separatist uprising. (Suharto) Indonesia 1990 1991 Yes Indonesia government Gerakan Aceh merdeka: Free Aceh Movement Separatist uprising. (Suharto) (GAM) Iran 1979 1985 Yes Iran government Kurdish Democratic Party of Iran (KDPI); (1) Identity (KDPI) 1979-1985; (2) Non-identity (Khomeini) Mujahideen e Khalq (PMOI) (PMOI) 1979-1982. Iran 1988 1988 Yes Iran government Mujahideen e Khalq (PMOI); Kurdish Democratic (1) Identity (KDPI); (2) Non-identity (PMOI). (Khomeini) Party of Iran (KDPI) Iraq 1959 1959 Yes Iraqi government Nationalists (Shammar) (Qassim)
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Iraq 1961 1970 Yes Iraqi government Patriotic Union of Kurdistan (PUK) Separatist uprising. Iraq 1974 1975 Yes Iraqi government Kurdish Democratic Party of Iraq (KDP), Patriotic Recurrence of the 1961 outbreak. Union of Kurdistan (PUK) Iraq 1987 1988 Yes Iraqi government (Ba’ath Kurdish Democratic Party of Iraq (KDP), Patriotic The Barzanis to The “al-Anfal Campaign” by the Party; Hussein) Union of Kurdistan (PUK) Iraqi government against the Kurds. Recurrence of the 1961 outbreak. Iraq 1991 1992 Yes Iraqi government (Ba’ath Kurdish Democratic Party of Iraq (KDP), Patriotic Two identity conflicts onset. Recurrence of the Party; Hussein) Union of Kurdistan (PUK) for one, Supreme 1959 (SCIRI) and 1961 (PUK) outbreak. Council for the Islamic Revolution in Iraq (SCIRI) for the other. Korea, Rep of 1948 1950 No South Korean government Yosu Rebellion, People’s Army, Military faction Leftist insurgencies + USA Laos 1959 1973 No Lao Royal government + Pathet Lao, Neutralist Party (Lao Pen Kang) Internationalized from 1960. Thailand, South Viet Nam, USA 160 Lebanon 1958 1958 No Lebanon government Independent Nasserite Movement, Mourabitoun (Camille Chamoun) militia Lebanon 1975 1990 Yes Lebanon government Progressive Socialist Party/Lebanese National (Maronite dominant), Movement, Phalangist militia (Lebanese Forces), state failure immediately PLO, Hezbollah + Syria after. Liberia 1990 1996 No Liberian government National Patriotic Front of Liberia (NPFL, Charles (National Democratic Taylor) Party, Doe) Morocco 1975 1989 Yes Moroccan government Polisario (Popular Front for the Liberation of Separatist uprising. Western Sahara (Sahrawi Arab Saguia al Hamra and Rio de Oro) + Mauretania Democratic Republic (SADR)). Internationalized 1975 - 1979. Mozambique 1981 1992 No Mozambique government Mozambican National Resistance (MNR, Internationalized 1985-90 (Zimbabwe). (Front for Liberation of RENAMO) Mozambique, Frelimo) + Zimbabwe
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Nicaragua 1978 1979 No Nicaraguan government FSLN (Sandinista) (Somoza) Nicaragua 1983 1988 No Nicaraguan government Contra (mainly Nicaraguan Democratic Forces, (Sandinistas) FDN) Nigeria 1967 1970 Yes Nigerian government Republic of Biafra (The Lgbo) Separatist uprising. Pakistan 1971 1971 Yes West Pakistan Mukti Bahini “Bangladesh Liberalization War” Pakistan 1974 1977 Yes Pakistani government Baluchi (western region of Pakistan, mostly Sunni Separatist uprising. (Pakistan People’s Party) Muslim, speaking a north western Iranian language) separatists. Pakistan 1995 1996 Yes Pakistani government Mohajir Qaumi Movement (MQM) MQM lobbying for greater autonomy for Karachi (Pakistan People’s Party) and more say in running Sindh province than native Sindhis have. Pakistani government rejected. Paraguay 1947 1947 No Paraguay government Febreristas, Liberals, Communists (Morínigo, pro-Axis) 161 Peru 1981 1995 No Peru government Sendero Luminoso, Tupac Amaru Revolutionary 1992 the major leaders of Sendero arrested. Movement Philippines 1946 1954 No Philippine government People’s Liberation Army (HUK) Philippines 1972 1993 Yes Philippine military Mindanao Independence Movement (MIM), Moro Separatist uprising. C.f., Abubakar,Carmen A. government (Marcos) National Liberation Front (MNLF). 2004. “Review of the Mindanao Peace Processes” Inter-Asia Cultural Studies: 5, 3, 450-464. Philippines 1981 1994 No Philippine government Communist Party of the Philippines (CPP) Recurrence of the 1946 outbreak. Philippines 2000 2000 Yes Philippine government Moro National Liberation Front (MNLF), Moro Separatist uprising. Recurrence of the 1972 Islamic Liberation Front (MILF), Abu Sayyaf outbreak. Group (ASG, an Islamic terrorist group), MNLF faction. Guinea-Bissau 1998 1999 No Guinea-Bissau Military Junta for the Consolidation of Democracy, “Guinea-Bissau Civil War.” government (Vieria) + Peace and Justice (Mane) Senegal, Guinea
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Russian 1994 1996 Yes Russian government Republic of Chechnya. Separatist uprising. Russia sent troops in 1994. c.f., Federation Gall, Carlotta; Thomas de Waal. 1998. Chechnya: Calamity in the Caucasus. New York University Press.; Faurby, Ib; Märta-Lisa Magnusson. 1999. “The Battle(s) of Grozny”. Baltic Defense Review 2: 75-87. Russian 1999 2001 Yes Russian government Republic of Chechnya. Separatist uprising. Recurrence of the 1994 Federation outbreak. Ceasefire of 1996 broken, Chechnya invasion into Republic of Dagestan. Rwanda 1990 1994 Yes Rwanda government Rwandan Patriotic Front (RPF, Tutsis) (Hutu) Rwanda 1998 2001 Yes Rwanda government Rwandan Patriotic Front (RPF, Tutsis) Recurrence of the 1990 outbreak. (Hutu) Sierra Leone 1994 1999 No Sierra Leone government Revolutionary United Front, Armed Forces “Sierra Leone Civil War.” It is generally Revolutionary Council, Kamajors. considered as started in 1991. Abidjan peace
162 settlement in 1996. The Lome’ Peace Accord in 1999. Somalia 1988 1991 Yes Somali government Somali Salvation Democratic Front (SSDF), Somali National Movement (SNM), Somali Patriotic Movement (SPM), United Somali Congress (USC). Somalia 1992 1996 Yes State failure; the Barre United Somali Congress (USC) factions. Somali State failure, tribal conflict. (Darod clan) regime National Movement (SNM) declared as collapsed independent Somaliland. South Africa 1978 1988 Yes South Africa government South West Africa People’s Organization Separatist uprising. (SWAPO, Namibia) Zimbabwe 1976 1979 Yes Rhodesia government Zimbabwe African National Union (ZANU), White-dominated Rhodesian government versus Zimbabwe African People’s Union (ZAPU) black nationalist/Marxist ZANU and ZAPU.
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Yemen, S. 1986 1986 No Yemen government (Ali Faction of Yemenite Socialist Party (Abdul Fattah Intra-party conflict. Nasir Muhammad) Ismail) Sudan 1963 1972 Yes Sudan government Anya Nya, Southern Sudanese Liberation Separatist uprising. Movement (SSLM) Sudan 1983 2002 Yes Sudan government Sudanese People’s Liberation Movement (SPLM), Separatist uprising. National Democratic Alliance, Sudan Alliance Forces Syrian Arab 1982 1982 Yes pan-Arab Baath (Alawite- Muslim Brotherhood Republic controlled) Tajikistan 1992 1996 Yes Tajikistan government United Tajik Opposition (UTO, a loosely Internationalized 1993 - 1996. (Emomali Rahmonov) + organized group of disenfranchised regions, Russia, Uzbekistan democratic liberal reformists, and Islamists) Turkey 1992 2000 Yes Turkish government Kurdistan Workers Party (PKK) Secessionist insurgency. Uganda 1978 1979 No Ugandan government Uganda People’s Army (UPA), Uganda National “Uganda-Tanzanian War.” (Amin) + Libya Liberation Army (UNLA) + Tanzania. 163 Uganda 1981 1991 Yes Uganda government National Resistance Movement (Yoweri Anti-government uprising (coup), Acholi vs. (Obote, Uganda National Museveni), Okello (UNLA brigadier), Uganda Lango. Liberation Army) Democratic Christian Army (UDCA, later Lord’s Resistance Army, LRA) USSR 1946 1948 Yes USSR United Democratic Resistance Movement of Two identity conflicts onset. (1) vs. Lithuania; (2) Lithuania, Ukraine Partisan Army of Ukraine vs. Ukraine. Vietnam, Rep of 1955 1975 No South Vietnam + USA, FNL (National Liberation Front). Intrastate 1955 - 1956, internationalized 1962 - Australia, New Zealand, 1964. Philippines, South Korea, Thailand Yemen, N. 1948 1948 No Imam Yahya Opposition reformist Anti-government uprising (coup). Yemen, N. 1962 1970 No Yemen Arab Republic Royalists (the Crown Prince Muhammad al-Badr) The end of the process marked by the republican’s (Sanaa) + Egypt victory.
Continued Table C.1 continued
Country Start End Identity State Non-State Opposition Note Yemen, Rep. 1994 1994 No Yemen government South Yemen. Although secessionist insurgency, it is not along (General People’s with ethnic line. Congress, Salih) Yugoslavia SFR 1991 1991 Yes Yugoslavia government Republic of Croatia (Croatian Democratic Union, Separatist movement (within the border of fmr (Serbs) HDZ. Franjo Tudjman) Yugoslavia, rather than independent Croatia). Serbia and 1998 1999 Yes Serbian government Kosovo Liberation Army (Kosovo Albanian) + Separatist movement. Internationalized 1999. Montenegro (Milosevic) NATO
164
APPENDIX D
CUMULATED GEOGRAPHICAL DISTRIBUTION OF CIVIL WARS
165
0 1 2 3 4 5 6
Figure D.1: Cumulated geographical distribution of civil war onsets, aggregated, 1946-2000
166 0 1 2 3 4 5 6
Figure D.2: Cumulated geographical distribution of civil war onsets, identity, 1946-2000
167
0 1 2 4
Figure D.3: Cumulated geographical distribution of civil war onsets, non-identity, 1946-2000
APPENDIX E
KAPLAN-MEIER SURVIVAL ESTIMATES OF CIVIL WAR ONSETS
1.00
0.75
0.50
0.25
0.00
0 20 40 60 Analysis time
First war onset Second war onset Third war onset Forth war onset
Figure E.1: Kaplan-Meier survival estimates of aggregated civil war onsets by event ordering strata
168
1.00
0.75
0.50
0.25
0.00
0 20 40 60 Analysis time
First war onset Second war onset Third war onset
Figure E.2: Kaplan-Meier survival estimates of identity civil war onsets by event ordering strata
169
1.00
0.75
0.50
0.25
0.00
0 20 40 60 Analysis time
First war onset Second war onset
Figure E.3: Kaplan-Meier survival estimates of non-identity civil war onsets by event ordering strata
170
APPENDIX F
DESCRIPTIVE STATISTICS
Variable N Mean Std dv. Min Max Aggregated civil war 6037 0.015 0.121 0 1 Identity civil war 6037 0.010 0.098 0 1 Non-identity civil war 6037 0.005 0.073 0 1 Population (logged) 6037 15.730 1.671 11.599 20.954 War in the neighborhood 5834 0.368 0.482 0 1 Mountain terrain 5947 16.967 21.024 0 94.300 Real GDP per capita (logged) 6025 8.204 1.131 5.139 11.478 Industrial labor force % of total 4058 19.595 12.351 0.240 50.190 Secondary school enrollment & gross 5658 45.353 32.660 0 160.7 Population density (logged) 5606 3.630 1.509 -0.465 8.699 Population growth annual 5991 2.045 2.370 -51.390 42.232 Urban growth annual 5414 3.739 2.828 -35.778 38.820 Youth bulge (15-24) % 5863 29.940 6.568 13.078 42.483 Youth bulge (1 = 35%+ of adult pop) 5863 0.244 0.430 0 1 Gini income inequality 4364 40.096 9.473 17.830 63.180 Gini land inequality 3284 63.513 16.640 22.747 97.973 % of pop experiencing econ inequality 5751 9.340 16.747 0 85 % of pop experiencing pol inequality 5776 11.665 20.137 0 90 Periphery 5343 0.634 0.482 0 1 Openness 5291 67.869 52.953 2.004 986.452 FDI stock/GDP (% logged) 4618 1.458 1.416 0 7.493 Oil export 5808 0.142 0.349 0 1 IGO membership 6011 17.383 7.191 0.312 53.107 INGO per K population (logged) 5798 -3.246 1.516 -10.009 1.352 Polity 5691 9.487 7.528 0 20 Parliamentary/mixed democracy 5983 0.266 0.442 0 1 Presidential democracy 5983 0.118 0.322 0 1
Continued
Table F.1: Descriptive statistics of the variables in analysis
171 Table F.1 continued
Variable N Mean Std dv. Min Max Anocratic regime 5616 0.209 0.407 0 1 Personalistic regime 5616 0.235 0.424 0 1 Dictatorship 5616 0.244 0.430 0 1 Political discrimination % pop (logged) 5930 0.764 1.307 0 4.500 Economic discrimination % pop (logged) 5930 0.481 1.081 0 4.454 Autonomy lost 6037 1.915 3.149 0 19.700 Weighed autonomy lost 6021 0.201 0.319 0 1.610 Military expenditure/GDP (% logged) 4938 0.640 1.606 -6.908 4.982 Polity change (t) – (t-5) 5286 0.415 4.021 -18 18 Polity 5-year lag 5305 9.076 7.548 0 20 Proximity of democratization 5984 0.031 0.141 0 1 Ethnic fractionalization 5792 45.900 26.447 0 93.020 Religion fractionalization 5794 42.178 24.168 0.230 86.030 Linguistic fractionalization 5606 39.750 29.352 0.210 92.270 Ethnic dominance 5923 0.548 0.498 0 1 Religious dominance 5885 0.611 0.488 0 1 Language dominance 5797 0.598 0.490 0 1 Ethnic polarization 4985 51.917 24.024 1.700 98.200 Religious polarization 5026 48.548 35.324 0.100 100.000
172
APPENDIX G
CORRELATION MATRIX
Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 2 Identity civil war 0.778 3 Non-identity civil war 0.688 0.153 4 Population (logged) 0.091 0.109 0.023 5 War in the neighborhood 0.042 0.058 0.016 0.124 6 Mountain terrain 0.001 -0.011 0.032 0.137 0.107 7 Real GDP per capita (logged) -0.094 -0.097 -0.027 0.018 -0.318 0.013 8 Industrial labor force % of total -0.071 -0.072 -0.016 0.056 -0.334 -0.023 0.864 9 Secondary school enrollment (gross) -0.057 -0.060 -0.018 0.077 -0.336 -0.035 0.834 0.805 173 10 Population density (logged) 0.030 0.029 0.006 0.255 -0.151 0.042 0.125 0.283 0.322 11 Population growth annual 0.037 0.042 0.017 -0.080 0.252 0.027 -0.408 -0.497 -0.524 -0.258 12 Urban growth annual 0.023 0.032 0.005 -0.137 0.367 0.041 -0.621 -0.707 -0.726 -0.263 0.584 13 Youth bulge (15-24) % 0.080 0.063 0.054 -0.119 0.360 0.064 -0.747 -0.749 -0.797 -0.226 0.600 0.702 14 Youth bulge (1 = 35%+ of adult pop) 0.036 0.020 0.019 -0.149 0.146 0.007 -0.416 -0.372 -0.454 -0.077 0.323 0.375 0.601 15 Gini income inequality 0.046 -0.001 0.072 -0.068 0.176 0.070 -0.421 -0.402 -0.489 -0.177 0.297 0.261 0.572 0.380 16 Gini land inequality 0.001 -0.036 0.039 -0.115 0.045 0.135 0.109 0.124 -0.114 -0.193 0.101 -0.017 0.163 0.073 0.210 17 % of pop experiencing econ inequality 0.049 0.065 0.005 0.109 0.242 0.141 -0.266 -0.236 -0.292 -0.202 0.194 0.253 0.303 0.064 0.128 18 % of pop experiencing pol inequality 0.051 0.059 0.013 -0.019 0.301 0.043 -0.303 -0.275 -0.341 -0.279 0.244 0.265 0.348 0.160 0.094 19 Periphery 0.013 0.020 -0.020 -0.237 0.229 -0.092 -0.579 -0.612 -0.684 -0.254 0.426 0.500 0.666 0.371 0.333 20 Openness -0.019 -0.006 -0.018 -0.516 -0.021 -0.179 -0.093 -0.179 -0.072 -0.055 0.222 0.248 0.233 0.071 0.092 21 FDI stock/GDP (logged) -0.021 -0.026 -0.007 -0.104 0.088 -0.033 0.131 0.045 0.190 0.020 -0.035 -0.114 -0.028 -0.011 0.074 22 Oil export 0.048 0.087 0.012 0.044 0.169 0.039 0.066 -0.013 -0.065 -0.114 0.191 0.147 0.197 0.008 -0.059 23 IGO membership -0.047 -0.044 -0.031 0.357 -0.275 -0.037 0.600 0.584 0.577 0.164 -0.426 -0.607 -0.660 -0.348 -0.247 1 = Civil war, aggregated.
Continued
Table G.1: Correlation matrix of the variables in analysis Table G.1 continued
Variables 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 24 INGO per K population (logged) -0.118 -0.135 -0.035 -0.767 -0.286 -0.091 0.488 0.453 0.471 -0.030 -0.248 -0.369 -0.379 -0.120 -0.168 25 Polity -0.047 -0.055 -0.015 0.098 -0.190 0.135 0.589 0.567 0.657 0.277 -0.433 -0.583 -0.571 -0.306 -0.214 26 Parliamentary/mixed democracy -0.041 -0.039 -0.027 0.118 -0.235 -0.111 0.527 0.502 0.615 0.274 -0.427 -0.544 -0.635 -0.395 -0.371 27 Presidential democracy -0.006 -0.031 0.019 -0.035 0.018 0.319 0.102 0.098 0.017 -0.026 0.000 -0.046 0.095 0.120 0.156 28 Anocratic regime 0.049 0.033 0.070 0.051 0.152 0.221 -0.177 -0.119 -0.193 0.078 0.155 0.090 0.276 0.203 0.385 29 Personalistic regime 0.064 0.066 0.009 -0.082 0.158 -0.266 -0.509 -0.482 -0.440 -0.212 0.220 0.335 0.372 0.254 0.105 30 Dictatorship -0.041 -0.024 -0.039 -0.100 -0.045 -0.037 -0.066 -0.085 -0.182 -0.110 0.160 0.273 0.105 0.011 -0.080 31 Political discrimination % pop (logged) 0.134 0.143 0.055 0.006 0.227 0.133 -0.265 -0.283 -0.325 -0.038 0.218 0.292 0.257 0.149 0.063 32 Economic discrimination % pop (logged) 0.079 0.054 0.056 -0.156 0.143 -0.015 -0.216 -0.197 -0.246 -0.046 0.112 0.173 0.220 0.036 0.123 33 Autonomy lost 0.132 0.162 0.055 0.518 0.328 -0.007 -0.320 -0.246 -0.222 0.102 0.121 0.111 0.171 0.028 -0.002 34 Weighed autonomy lost 0.053 0.064 0.053 0.047 0.352 -0.002 -0.346 -0.298 -0.291 -0.101 0.211 0.235 0.267 0.095 0.071 35 Military expenditure/GDP (logged) 0.060 0.069 0.028 0.019 0.119 -0.142 0.096 0.100 0.105 -0.022 0.051 0.011 -0.088 -0.106 -0.254 36 Polity change (t) – (t-5) 0.036 -0.004 0.073 0.038 0.038 0.053 0.082 0.104 0.097 0.015 -0.017 -0.070 -0.057 -0.066 -0.020 37 Polity 5-year lag -0.067 -0.053 -0.054 0.077 -0.210 0.106 0.543 0.508 0.602 0.268 -0.422 -0.543 -0.538 -0.269 -0.202 38 Proximity of democratization 0.061 0.004 0.081 0.019 0.074 0.105 -0.036 -0.010 -0.010 -0.022 0.002 0.010 0.021 -0.023 0.086 39 Ethnic fractionalization 0.072 0.070 0.042 -0.059 0.271 0.130 -0.595 -0.645 -0.588 -0.349 0.407 0.467 0.580 0.244 0.353 40 Religion fractionalization -0.016 -0.022 -0.016 0.146 -0.146 -0.237 0.138 0.136 0.236 0.181 -0.105 -0.161 -0.171 -0.090 -0.071
174 41 Linguistic fractionalization 0.094 0.113 0.032 0.072 0.203 -0.014 -0.500 -0.546 -0.369 -0.072 0.239 0.349 0.305 0.105 0.007 42 Ethnic dominance -0.069 -0.063 -0.045 0.029 -0.217 -0.152 0.330 0.344 0.338 0.110 -0.244 -0.310 -0.426 -0.077 -0.247 43 Religious dominance -0.016 0.000 -0.013 0.166 0.001 0.283 0.312 0.279 0.144 -0.149 -0.051 -0.145 -0.170 -0.029 -0.142 44 Language dominance -0.069 -0.076 -0.035 -0.042 -0.130 -0.118 0.356 0.350 0.277 0.006 -0.159 -0.229 -0.273 -0.008 -0.079 45 Ethnic polarization 0.048 0.022 0.051 -0.116 0.198 0.211 -0.252 -0.247 -0.271 -0.268 0.264 0.182 0.436 0.182 0.394 46 Religious polarization 0.078 0.077 0.025 0.070 0.152 -0.064 -0.497 -0.469 -0.382 0.072 0.306 0.366 0.501 0.176 0.236 1 = Civil war, aggregated.
Continued Table G.1 continued
Variables 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 17 % of pop experiencing econ inequality 0.182 18 % of pop experiencing pol inequality 0.062 0.768 19 Periphery 0.110 0.197 0.196 20 Openness 0.008 -0.083 -0.011 0.214 21 FDI stock/GDP (logged) 0.043 -0.082 -0.048 -0.007 0.165 22 Oil export 0.103 0.105 0.027 0.202 0.208 0.072 23 IGO membership -0.184 -0.155 -0.228 -0.505 -0.224 0.032 -0.107 24 INGO per K population (logged) 0.102 -0.229 -0.192 -0.209 0.339 0.317 -0.086 0.147 25 Polity -0.017 -0.145 -0.259 -0.533 -0.218 0.189 -0.140 0.556 0.363 26 Parliamentary/mixed democracy -0.245 -0.235 -0.215 -0.531 -0.163 0.055 -0.165 0.592 0.290 0.697 27 Presidential democracy 0.364 0.185 0.036 0.064 -0.154 0.142 0.033 -0.026 0.090 0.333 -0.333 28 Anocratic regime 0.070 0.115 0.057 0.150 0.035 0.026 -0.003 -0.163 -0.114 -0.089 -0.276 0.109 29 Personalistic regime -0.115 0.078 0.139 0.471 0.135 -0.095 -0.007 -0.308 -0.241 -0.546 -0.364 -0.217 -0.245 30 Dictatorship 0.114 -0.021 0.091 0.031 0.136 -0.130 0.115 -0.252 -0.072 -0.520 -0.295 -0.199 -0.193 -0.236 31 Political discrimination % pop (logged) 0.002 0.472 0.387 0.111 0.029 -0.092 0.135 -0.216 -0.168 -0.219 -0.159 -0.004 0.106 0.160 0.019 32 Economic discrimination % pop (logged) 0.200 0.455 0.283 0.247 0.088 -0.042 0.030 -0.181 0.004 -0.088 -0.113 0.123 0.150 0.173 -0.153 33 Autonomy lost -0.071 0.258 0.213 -0.089 -0.241 -0.091 0.030 -0.022 -0.551 -0.077 0.018 -0.115 -0.004 0.151 -0.114
175 34 Weighed autonomy lost -0.057 0.469 0.509 0.052 -0.147 -0.023 -0.008 -0.241 -0.197 -0.197 -0.090 -0.109 0.017 0.229 -0.054 35 Military expenditure/GDP (logged) -0.009 0.212 0.219 -0.206 0.118 -0.018 0.140 0.009 -0.002 -0.155 -0.033 -0.140 -0.049 -0.004 0.216 36 Polity change (t) – (t-5) 0.109 0.011 -0.002 -0.009 -0.060 0.144 0.036 0.056 0.066 0.267 0.056 0.264 0.102 -0.151 -0.176 37 Polity 5-year lag -0.076 -0.151 -0.257 -0.526 -0.185 0.110 -0.159 0.524 0.326 0.852 0.664 0.188 -0.144 -0.462 -0.422 38 Proximity of democratization 0.102 0.091 0.055 0.000 -0.053 0.059 -0.019 -0.028 -0.001 0.109 -0.010 0.253 0.136 -0.078 -0.077 39 Ethnic fractionalization 0.103 0.334 0.343 0.460 0.102 -0.001 0.091 -0.451 -0.294 -0.416 -0.483 0.078 0.107 0.317 0.068 40 Religion fractionalization 0.035 0.017 -0.098 -0.153 -0.026 0.011 -0.219 0.124 -0.027 0.135 0.123 -0.014 -0.056 -0.079 0.006 41 Linguistic fractionalization -0.151 0.156 0.157 0.134 0.146 -0.005 -0.074 -0.257 -0.267 -0.297 -0.198 -0.180 0.004 0.336 -0.008 42 Ethnic dominance -0.203 -0.401 -0.331 -0.323 -0.131 -0.008 -0.179 0.254 0.156 0.140 0.279 -0.157 -0.213 -0.045 0.011 43 Religious dominance 0.119 0.080 0.111 -0.044 -0.173 -0.037 0.143 0.250 -0.020 0.123 0.090 0.132 -0.037 -0.087 -0.082 44 Language dominance 0.009 -0.140 -0.193 -0.075 -0.064 -0.049 0.031 0.220 0.166 0.165 0.142 0.091 -0.100 -0.074 -0.071 45 Ethnic polarization 0.372 0.355 0.424 0.282 0.052 0.024 0.084 -0.298 -0.094 -0.150 -0.422 0.356 0.194 0.078 -0.024 46 Religious polarization 0.077 0.437 0.367 0.351 0.115 -0.033 0.084 -0.417 -0.336 -0.316 -0.390 0.124 0.144 0.226 0.084
Continued Table G.1 continued
Variables 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 32 Economic discrimination % pop (logged) 0.556 33 Autonomy lost 0.239 0.124 34 Weighed autonomy lost 0.407 0.368 0.580 35 Military expenditure/GDP (logged) 0.221 0.085 0.093 0.167 36 Polity change (t) – (t-5) -0.095 0.002 -0.061 -0.052 0.017 37 Polity 5-year lag -0.166 -0.089 -0.043 -0.168 -0.163 -0.278 38 Proximity of democratization 0.000 0.050 -0.017 0.027 0.031 0.437 -0.129 39 Ethnic fractionalization 0.268 0.216 0.318 0.430 -0.100 -0.049 -0.388 0.006 40 Religion fractionalization -0.063 -0.110 0.131 0.096 -0.079 -0.068 0.172 -0.045 0.144 41 Linguistic fractionalization 0.278 0.154 0.506 0.493 -0.077 -0.087 -0.249 -0.049 0.604 0.334 42 Ethnic dominance -0.247 -0.277 -0.183 -0.296 0.173 -0.021 0.151 0.006 -0.634 -0.104 -0.387 43 Religious dominance -0.039 -0.003 -0.061 -0.161 0.101 0.103 0.067 0.002 -0.284 -0.557 -0.405 0.103 44 Language dominance -0.160 -0.106 -0.295 -0.418 0.063 0.007 0.161 0.004 -0.398 -0.094 -0.643 0.571 0.181 45 Ethnic polarization 0.124 0.251 0.053 0.240 -0.054 0.018 -0.159 0.033 0.651 0.097 0.209 -0.670 0.003 -0.313 46 Religious polarization 0.214 0.256 0.270 0.371 -0.042 -0.045 -0.290 0.036 0.560 0.379 0.454 -0.481 -0.446 -0.294 0.400
176
APPENDIX H
CONDITIONAL RISK MODEL OF CIVIL WAR ONSETS
Model 1 Model 2 Model 3 Variables All Identity Non-Id All Identity Non-Id All Identity Non-Id Control Variables Population (logged, 1 year lag) 0.218* 0.333* -0.035 0.195+ 0.328* -0.008 0.249** 0.335* 0.050 (0.093) (0.136) (0.111) (0.109) (0.153) (0.131) (0.092) (0.148) (0.112) War in the neighborhood (1 year lag) 0.329 0.092 0.941* 0.406 0.124 1.002** 0.417 0.070 0.939* (0.307) (0.402) (0.368) (0.350) (0.568) (0.383) (0.299) (0.422) (0.370) Mountain terrain 0.010+ 0.020*** -0.006 0.003 0.011 -0.007 0.010+ 0.021*** -0.009 (0.005) (0.006) (0.008) (0.007) (0.008) (0.009) (0.005) (0.006) (0.008)
177 Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 6.069** 6.968* 4.840* (2.013) (2.861) (2.315) Real GDP per capita, squared -0.410** -0.468* -0.324* (0.128) (0.185) (0.143) Industrial labor force % of total -0.051** -0.041* -0.039+ (0.016) (0.021) (0.022) Secondary school enrollment % gross -0.016** -0.011* -0.019* (0.006) (0.006) (0.008) Pseudo log likelihood -249.822 -151.848 -120.713 -149.439 -89.920 -82.439 -239.294 -150.058 -110.423 Pseudo R-square 0.065 0.084 0.053 0.062 0.063 0.068 0.062 0.073 0.062 Event 77 50 32 49 29 25 75 50 30 Unit 168 169 168 132 132 133 163 163 163 N 5186 5353 5464 3507 3606 3697 4863 5009 5139 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test
Continued
Table H.1: Conditional risk model of civil war onset by the war “types” Table H.1 Continued
Model 4 Model 5 Model 6 Variables All Identity Non-Id All Identity Non-Id All Identity Non-Id Control Variables Population (logged, 1 year lag) 0.282** 0.406** 0.006 0.215* 0.330* -0.042 0.264* 0.386** -0.028 (0.106) (0.143) (0.140) (0.099) (0.138) (0.112) (0.104) (0.144) (0.123) War in the neighbourhood (1 year lag) 0.339 0.126 0.944* 0.314 0.023 0.971** 0.317 0.096 0.988* (0.318) (0.401) (0.409) (0.310) (0.398) (0.371) (0.314) (0.405) (0.401) Mountain terrain 0.010+ 0.022*** -0.007 0.007 0.017* -0.006 0.006 0.017** -0.007 (0.006) (0.006) (0.008) (0.006) (0.007) (0.008) (0.006) (0.006) (0.008) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 6.476** 8.133** 5.442* 6.152** 7.433* 5.311* 6.239* 7.189* 6.080* (2.311) (2.980) (2.729) (2.069) (3.426) (2.072) (2.496) (3.624) (2.906) Real GDP per capita, squared -0.433** -0.536** -0.361* -0.421** -0.507* -0.356** -0.424** -0.484* -0.406* (0.145) (0.192) (0.167) (0.133) (0.226) (0.130) (0.158) (0.236) (0.179) Population density (logged) -0.089 -0.167 0.061 (0.112) (0.148) (0.115) Population growth (annual, 1 year lag) 0.042 0.089* -0.088 (0.054) (0.045) (0.061) 178 Urban growth (annual, 1 year lag) 0.066** 0.060* 0.011 (0.029) (0.026) (0.077) Pseudo log likelihood -228.843 -140.506 -110.778 -236.743 -139.325 -120.314 -206.139 -125.595 -105.171 Pseudo R square 0.071 0.091 0.059 0.069 0.088 0.056 0.083 0.099 0.067 Event 73 48 30 74 47 32 67 43 29 Unit 160 160 160 168 169 168 160 160 160 N 4849 4991 5107 5145 5312 5422 4706 4847 4974 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test
Continued Table H.1 Continued
Model 7 Model 8 Model 9 Variables All Identitya Non-Id All Identity Non-Id All Identity Non-Idb Control Variables Population (logged, 1 year lag) 0.291** 0.338* 0.072 0.306** 0.385** 0.076 0.402** 0.419* 0.349* (0.099) (0.133) (0.124) (0.109) (0.137) (0.133) (0.136) (0.169) (0.138) War in the neighborhood (1 year lag) 0.229 0.056 0.792+ 0.235 0.061 0.826* 0.235 0.171 0.386 (0.317) (0.447) (0.415) (0.323) (0.423) (0.413) (0.410) (0.571) (0.498) Mountain terrain 0.011+ 0.023*** -0.008 0.012* 0.022*** -0.005 0.008 0.012 0.004 (0.006) (0.006) (0.008) (0.006) (0.006) (0.009) (0.007) (0.008) (0.010) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 4.817* 5.068+ 3.760 6.457** 6.920* 5.243* 8.557** 12.480* 10.449** (2.051) (2.912) (2.332) (2.071) (2.841) (2.559) (3.112) (5.750) (3.717) Real GDP per capita, squared -0.318* -0.335+ -0.241+ -0.429** -0.460* -0.340* -0.566** -0.842* -0.628** (0.131) (0.191) (0.145) (0.131) (0.182) (0.158) (0.195) (0.377) (0.227) Youth bulge (15-24, %) 0.063+ 0.096* 0.084+ (0.033) (0.041) (0.050) Youth bulge (1 = 35%+ of adult pop) 0.643* 0.462 0.770+ 0.517 0.414 0.932+ (0.264) (0.374) (0.404) (0.374) (0.509) (0.493) 179 Gini income inequality 0.019 0.023 0.043* (0.016) (0.022) (0.022) Pseudo log likelihood -237.498 -136.388 -110.255 -236.615 -150.120 -109.832 -138.859 -84.308 -65.676 Pseudo R square 0.076 0.103 0.070 0.079 0.090 0.073 0.098 0.119 0.116 Event 75 47 30 75 50 30 46 30 20 Unit 163 163 163 163 163 163 136 136 139 N 5037 5177 5315 5037 5180 5315 3732 3835 3919 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test a. Russia (1994), Former Yugoslavia (1991), and Serbia and Montenegro (1998) are removed as influential (low youth bulge values for their early failures). b. Sierra Leone (1994) is removed as an influential (relatively low income inequality).
Continued Table H.1 Continued
Model 10 Model 11 Model 12 Variables All Identity Non-Id All Identity Non-Id All Identity Non-Id Control Variables Population (logged, 1 year lag) 0.371*** 0.406** 0.256+ 0.348** 0.484*** 0.120 0.243* 0.336*** -0.006 (0.112) (0.148) (0.146) (0.117) (0.114) (0.138) (0.117) (0.097) (0.134) War in the neighborhood (1 year lag) -0.142 -0.344 0.415 0.379 0.329 0.776+ 0.447 0.176 0.953* (0.391) (0.575) (0.462) (0.315) (0.403) (0.470) (0.344) (0.418) (0.443) Mountain terrain 0.013+ 0.011 0.010 0.000 0.010 -0.005 -0.002 0.000 -0.004 (0.008) (0.008) (0.012) (0.006) (0.006) (0.009) (0.006) (0.006) (0.009) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 7.919** 9.252+ 7.024* 6.608** 7.135* 5.433* 6.714** 5.820+ 7.016* (2.443) (4.913) (3.128) (2.223) (3.611) (2.214) (2.171) (3.476) (3.344) Real GDP per capita, squared -0.536*** -0.653* -0.438* -0.439** -0.472* -0.356** -0.454*** -0.422+ -0.450* (0.154) (0.316) (0.189) (0.140) (0.231) (0.137) (0.137) (0.220) (0.205) Youth bulge (1 = 35%+ of adult pop) 0.745* 0.323 1.325* 0.685* 0.303 0.795+ 0.763** 0.549 0.820* (0.362) (0.647) (0.590) (0.267) (0.367) (0.440) (0.285) (0.360) (0.398) Gini land inequality 0.007 -0.013 0.029* (0.011) (0.014) (0.014) 180 % of pop experiencing econ inequality 0.002 0.001 -0.012 (0.007) (0.006) (0.015) % of pop experiencing pol inequality 0.018** 0.028*** -0.007 0.020** 0.032*** (0.007) (0.006) (0.009) (0.007) (0.007) Socioeconomic Structures, External World Systems Periphery (=1) -0.447 -1.152** -0.440 (0.300) (0.412) (0.404) Pseudo log likelihood -96.600 -55.357 -55.776 -191.452 -110.470 -98.082 -170.779 -97.726 -98.845 Pseudo R square 0.118 0.158 0.123 0.126 0.163 0.084 0.130 0.188 0.082 Event 39 26 18 66 43 28 61 40 28 Unit 97 97 99 147 147 147 124 124 129 N 2891 2967 3029 4643 4784 4896 4408 4548 4852 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test
Continued Table H.1 Continued
Model 13 Model 14 Model 15 Variables Allc Identity Non-Idd Alle Identityf Non-Idg Allh Identityi Non-Id Control Variables Population (logged, 1 year lag) 0.321* 0.628*** -0.008 0.399*** 0.498*** 0.038 0.359** 0.505*** 0.069 (0.129) (0.157) (0.162) (0.115) (0.109) (0.162) (0.114) (0.116) (0.129) War in the neighbourhood (1 year lag) 0.075 0.369 0.657 0.035 -0.145 0.990* 0.319 0.181 0.814* (0.340) (0.455) (0.425) (0.338) (0.391) (0.491) (0.307) (0.388) (0.415) Mountain terrain -0.006 0.004 -0.003 0.007 0.017** -0.006 0.003 0.012+ -0.005 (0.006) (0.007) (0.008) (0.005) (0.006) (0.009) (0.006) (0.007) (0.009) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 6.881** 5.575 5.680 4.423 5.266 3.415 5.756** 8.156** 5.214* (2.589) (3.666) (3.679) (2.693) (3.207) (3.035) (2.081) (3.119) (2.504) Real GDP per capita, squared -0.456** -0.362 -0.356 -0.296+ -0.348+ -0.225 -0.382** -0.535** -0.339* (0.162) (0.230) (0.226) (0.166) (0.199) (0.187) (0.130) (0.200) (0.155) Youth bulge (1 = 35%+ of adult pop) 0.974** 0.638 1.170** 0.777** 0.504 0.739+ 0.773** 0.341 0.774+ (0.317) (0.400) (0.437) (0.291) (0.340) (0.440) (0.261) (0.394) (0.400) % of population of political differential 0.018** 0.032*** 0.017*** 0.024*** 0.017** 0.029*** (0.006) (0.008) (0.005) (0.006) (0.005) (0.006) 181 Socioeconomic Structures, External World Systems Openness (1 year lag) -0.007+ 0.000 -0.012* (0.004) (0.004) (0.006) FDI stock/GDP (% logged, 1 year lag) -0.188+ -0.280* -0.290+ (0.111) (0.122) (0.169) Oil export (1 = 33%+ GDP, 1 year lag) 0.382 0.443 0.053 (0.278) (0.354) (0.417) Pseudo log likelihood -147.658 -85.727 -91.079 -151.694 -98.766 -77.667 -202.198 -114.638 -109.666 Pseudo R square 0.147 0.185 0.098 0.121 0.163 0.083 0.112 0.165 0.072 Event 53 34 26 57 39 23 67 43 30 Unit 150 150 156 154 154 160 157 157 163 N 4327 4454 4717 3641 3766 4023 4777 4917 5246 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test c. Iran (1979) and Russia (1994) are removed as influential (Iran: relatively open economy before conflict onset; Russia: relatively open economy for its very early failure). d. Sri Lanka (1971) is removed as influential (high level of openness). e. Liberia (1990) is removed as influential (high FDI stock). f. Indonesia (1990) is removed as influential (high FDI stock). g. Liberia (1990) is removed as influential (high FD stock). h. Azerbaijan (1992) and Iraq (1961) are removed as influential (possibly due to extremely early failures). i. Iran (1979), Dem Rep Congo (1998), Azerbaijan (1992) are removed as influential (Iran: high oil dependence; Dem Rep Congo: low oil dependence with early failure; Azerbaijan: modest oil dependence with early failure).
Continued Table H.1 Continued
Model 16 Model 17 Model 18 Variables All Identity Non-Id All Identity Non-Id All Identity Non-Id Control Variables Population (logged, 1 year lag) 0.371** 0.578*** 0.032 0.437* 0.491* -0.011 0.280* 0.362* 0.047 (0.123) (0.131) (0.120) (0.214) (0.204) (0.242) (0.109) (0.146) (0.128) War in the neighborhood (1 year lag) 0.291 0.199 0.873* 0.187 0.240 0.575 0.264 0.037 0.832* (0.315) (0.375) (0.393) (0.296) (0.381) (0.397) (0.334) (0.427) (0.394) Mountain terrain 0.002 0.009 -0.004 0.004 0.010+ -0.000 0.010+ 0.020** -0.005 (0.006) (0.006) (0.010) (0.006) (0.006) (0.008) (0.006) (0.006) (0.009) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 5.813** 6.178* 5.466+ 6.922** 5.969* 8.040** 5.648** 6.006* 4.779* (2.072) (3.106) (2.847) (2.169) (3.000) (3.016) (2.122) (2.988) (2.381) Real GDP per capita, squared -0.382** -0.398* -0.359* -0.448*** -0.389* -0.498** -0.373** -0.400* -0.306* (0.130) (0.196) (0.177) (0.136) (0.187) (0.186) (0.136) (0.192) (0.149) Youth bulge (1 = 35%+ of adult pop) 0.743** 0.409 0.783+ 0.809** 0.420 1.062* 0.549* 0.341 0.750* (0.257) (0.345) (0.414) (0.284) (0.359) (0.437) (0.255) (0.408) (0.367) % of population of political differential 0.019*** 0.029*** 0.021*** 0.028*** (0.005) (0.005) (0.005) (0.005) 182 Political Environment, External World Polity IGO membership 0.000 -0.046 0.033 (0.029) (0.033) (0.054) INGO per K population (logged) -0.013 -0.028 -0.235 (0.243) (0.236) (0.270) Political Environment, Domestic Political Opportunity/State-Centered Thesis Polity (0-20, 1 year lag) 0.188+ 0.231 0.011 (0.108) (0.158) (0.178) Polity squared (I year lag) -0.010* -0.011 -0.002 (0.005) (0.007) (0.008) Pseudo log likelihood -209.585 -126.444 -109.471 -195.340 -126.211 -97.809 -233.638 -147.524 -109.086 Pseudo R square 0.109 0.150 0.076 0.127 0.145 0.082 0.082 0.095 0.072 Event 69 46 30 65 46 26 75 50 30 Unit 157 157 163 157 157 163 156 156 157 N 4838 4979 5305 4757 4889 5219 4830 4964 5108 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test
Continued Table H.1 Continued
Model 19 Model 20 Model 21 Variables All Identityj Non-Id All Identity Non-Idk All Identity Non-Id Control Variables Population (logged, 1 year lag) 0.325** 0.380** 0.088 0.358** 0.435** -0.037 0.384*** 0.547*** 0.112 (0.112) (0.138) (0.131) (0.113) (0.165) (0.138) (0.115) (0.142) (0.134) War in the neighbourhood (1 year lag) 0.227 -0.033 0.844* 0.208 0.047 1.273* 0.307 -0.033 0.805* (0.325) (0.429) (0.401) (0.343) (0.433) (0.528) (0.307) (0.371) (0.399) Mountain terrain 0.012* 0.022*** -0.007 0.015** 0.024*** 0.000 0.007 0.016** -0.007 (0.006) (0.006) (0.009) (0.006) (0.006) (0.011) (0.006) (0.006) (0.009) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 5.906** 7.735* 4.786+ 5.539* 5.619+ 7.716** 4.626* 4.055 4.539+ (2.152) (3.141) (2.633) (2.245) (2.940) (2.988) (1.870) (2.523) (2.591) Real GDP per capita, squared -0.391** -0.512* -0.310+ -0.361* -0.369* -0.492** -0.311** -0.278+ -0.295+ (0.136) (0.202) (0.165) (0.142) (0.186) (0.190) (0.120) (0.162) (0.161) Youth bulge (1 = 35%+ of adult pop) 0.598* 0.474 0.689 0.510+ 0.292 0.772+ 0.661** 0.368 0.892* (0.271) (0.375) (0.430) (0.295) (0.448) (0.441) (0.244) (0.315) (0.375) Political Environment, Domestic Political Opportunity/State-Centered Thesis 183 Parliamentary/mixed democracy (=1) -0.663* -0.641* -0.564 (0.321) (0.321) (0.727) Presidential democracy (=1) -0.152 -0.652 0.127 (0.438) (0.603) (0.651) Anocratic regime (=1) 0.726+ 0.669 0.478 (0.415) (0.465) (0.776) Personalistic regime (=1) 1.118** 1.051* 1.238+ (0.397) (0.482) (0.692) Dictatorship (=1) 0.364 0.088 0.640 (0.424) (0.524) (0.823) Political discrimination % pop (logged) 0.529*** 0.733*** 0.133 (0.103) (0.140) (0.124) Economic discrimination % pop (logged) 0.095 -0.004 0.276* (0.105) (0.123) (0.126) Pseudo log likelihood -235.127 -142.952 -109.288 -218.855 -139.644 -83.543 -213.364 -127.698 -106.184 Pseudo R square 0.085 0.097 0.077 0.096 0.104 0.133 0.169 0.225 0.103 Event 75 48 30 71 48 25 75 50 30 Unit 163 163 163 156 156 156 163 163 163 N 5021 5162 5299 4808 4921 5054 5009 5152 5287 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test j. Myanmar (1961) and Russia (1994) are removed as influential (Myanmar: under a short-lived parliamentarism at war onset; Russia: possibly due to a very early failure). k. Argentina (1975), Sri Lanka (1971), and France (1961) are removed as influential (apparently due to their then non-autocratic status at the time of war onset).
Continued Table H.1 Continued
Model 22 Model 23 Model 24 Variables All Identity All Identity All Identity Control Variables Population (logged, 1 year lag) 0.206* 0.260* 0.270* 0.370* 0.393** 0.558** (0.093) (0.115) (0.113) (0.151) (0.138) (0.173) War in the neighbourhood (1 year lag) 0.196 0.033 0.212 -0.010 0.208 0.098 (0.349) (0.434) (0.338) (0.425) (0.305) (0.362) Mountain terrain 0.010+ 0.020** 0.010+ 0.020** 0.001 0.008 (0.006) (0.007) (0.006) (0.006) (0.006) (0.008) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 5.428* 5.773+ 5.529* 5.995+ 7.875** 6.398+ (2.163) (3.075) (2.152) (3.068) (2.983) (3.774) Real GDP per capita, squared -0.359** -0.387+ -0.362** -0.396* -0.517** -0.431+ (0.139) (0.197) (0.138) (0.197) (0.185) (0.234) Youth bulge (1 = 35%+ of adult pop) 0.638* 0.471 0.651* 0.455 0.458 0.211 (0.285) (0.453) (0.270) (0.442) (0.346) (0.400) Political Environment, Domestic Political Opportunity/State-Centered Thesis 184 Polity (0-20, 1 year lag) 0.186+ 0.215 0.181+ 0.200 (0.109) (0.161) (0.108) (0.165) Polity squared (I year lag) -0.010+ -0.010 -0.009+ -0.009 (0.005) (0.007) (0.005) (0.008) Political discrimination % pop (logged) 0.433* 0.466* (0.176) (0.232) Autonomy lost 0.062+ 0.080* (0.035) (0.036) Weighed autonomy lost 0.809* 1.119*** (0.319) (0.327) Military exp/gdp (% logged, 1 yr lag) 0.461* 0.408 (0.204) (0.300) Military exp*political discrimination 0.089 0.154 (0.087) (0.106) Pseudo log likelihood -231.878 -145.008 -230.956 -143.873 -166.916 -92.175 Pseudo R square 0.089 0.110 0.092 0.117 0.208 0.258 Event 75 50 75 50 62 39 Unit 156 156 156 156 157 157 N 4830 4964 4830 4964 4255 4370 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test
Continued Table H.1 Continued
Model 25 Model 26 Variables Alll Identitym Non-Idn Allo Identity Non-Id Control Variables Population (logged, 1 year lag) 0.237* 0.214 -0.010 0.275** 0.387** 0.085 (0.105) (0.142) (0.139) (0.101) (0.138) (0.138) War in the neighbourhood (1 year lag) 0.237 -0.030 1.118* 0.246 0.062 0.815+ (0.331) (0.452) (0.477) (0.328) (0.418) (0.422) Mountain terrain 0.008 0.019** -0.010 0.011+ 0.022*** -0.005 (0.006) (0.007) (0.011) (0.006) (0.006) (0.010) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 8.342** 11.067* 7.587** 6.555** 6.785* 4.946+ (2.539) (4.888) (2.920) (2.129) (2.897) (2.598) Real GDP per capita, squared -0.561*** -0.756* -0.487** -0.440** -0.451* -0.322* (0.164) (0.319) (0.185) (0.135) (0.185) (0.160) Youth bulge (1 = 35%+ of adult pop) 0.556+ 0.447 0.548 0.635* 0.485 0.763+ (0.293) (0.380) (0.450) (0.262) (0.387) (0.404) Political Environment, Domestic Political Opportunity/State-Centered Thesis 185 Polity change (t) - (t-5) 0.050 -0.141* 0.061 (0.038) (0.067) (0.053) Polity (0-20), 5-year lag -0.013 0.037 -0.056 (0.023) (0.027) (0.048) Polity change * Polity 5-year lag -0.005 0.010+ -0.007 (0.004) (0.006) (0.005) Proximity of democratization 0.455 0.831 0.684 (0.573) (0.769) (1.024) Pseudo log likelihood -198.956 -113.204 -86.421 -228.074 -149.402 -109.472 Pseudo R square 0.088 0.108 0.110 0.081 0.094 0.076 Event 66 42 25 73 50 30 Unit 156 156 156 163 163 163 N 4539 4670 4799 5017 5162 5297 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test l. Argentina (1975) and Peru (1981) are removed as influential (Argentina: conflict onset amidst short-lived political improvement; Peru: increased intensity of the conflict between the Peru government and Sendero Luminoso while institutionalized political conditions were improving). m. Ethiopia (1974), Iran (1979), and Uganda (1981) are removed as influential (Ethiopia and Iran: apparently due to failure of Polity Project’s conversion recommendation to capture their polity level appropriately; Uganda: conflict onset amidst a short-lived relative political improvement). n. Argentina (1975), Dominican Rep (1965), Peru (1981) are removed as influential (Argentina and Peru: the same as above; Dominican Rep: apparently due to failure of Polity Project’s conversion recommendation to capture their polity level appropriately). o. Russia (1994) and Peru (1981) are removed as influential (contrary to the theory, conflict eruption associated with democratic nation-building seems to be exceptions).
Continued Table H.1 Continued
Model 27 Model 28 Model 29 Variables All Identity Non-Id All Identity Non-Id All Identity Control Variables Population (logged, 1 year lag) 0.304** 0.409** 0.030 0.299** 0.394** 0.066 0.334** 0.454** (0.111) (0.151) (0.132) (0.110) (0.146) (0.128) (0.113) (0.145) War in the neighbourhood (1 year lag) 0.222 -0.003 0.768+ 0.098 -0.118 0.726+ 0.154 -0.054 (0.351) (0.435) (0.438) (0.346) (0.436) (0.423) (0.332) (0.410) Mountain terrain 0.011+ 0.021** -0.008 0.011+ 0.021** -0.005 0.014* 0.025*** (0.006) (0.007) (0.008) (0.007) (0.007) (0.011) (0.006) (0.006) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 4.295+ 4.746 4.124+ 5.143** 5.642* 4.680* 5.997** 6.410* (2.263) (3.082) (2.341) (1.986) (2.805) (2.358) (2.053) (2.732) Real GDP per capita, squared -0.284* -0.314 -0.273+ -0.331** -0.363* -0.305* -0.394** -0.419* (0.143) (0.197) (0.146) (0.125) (0.177) (0.145) (0.128) (0.174) Youth bulge (1 = 35%+ of adult pop) 0.585* 0.441 0.670+ 0.571* 0.368 0.664+ 0.641* 0.535 (0.267) (0.421) (0.355) (0.277) (0.465) (0.394) (0.270) (0.397) Political Environment, Domestic Political Opportunity/State-Centered Thesis 186 Polity (0-20, 1 year lag) 0.175 0.208 0.024 0.185 0.214 -0.014 (0.109) (0.162) (0.160) (0.113) (0.171) (0.187) Polity squared (I year lag) -0.009+ -0.010 -0.002 -0.010+ -0.011 -0.000 (0.005) (0.007) (0.007) (0.005) (0.008) (0.008) Proximity of democratization 1.296 1.750 (1.086) (2.144) Group Configurations and Attributes Ethnic fractionalization 0.009 0.009 -0.003 0.009 0.011 (0.007) (0.008) (0.010) (0.008) (0.008) Religious fractionalization -0.007 -0.005 -0.017* (0.006) (0.008) (0.007) Linguistic fractionalization 0.009+ 0.012+ -0.002 (0.005) (0.007) (0.008) Ethnic fractionalization * Dem proximity -0.005 -0.016 (0.016) (0.038) Pseudo log likelihood -221.994 -140.512 -102.662 -214.589 -133.800 -104.122 -227.829 -146.068 Pseudo R square 0.091 0.105 0.087 0.087 0.104 0.065 0.092 0.106 Event 73 49 29 71 47 29 74 50 Unit 152 152 153 149 149 150 160 160 N 4748 4882 5025 4614 4737 4885 4944 5087 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test
Continued Table H.1 Continued
Model 30 Model 31 Model 32 Variables Allp Identity Non-Id All Identity Non-Id All Identity Non-Id Control Variables Population (logged, 1 year lag) 0.244* 0.347** 0.019 0.238* 0.294* 0.039 0.341** 0.318* 0.112 (0.101) (0.114) (0.131) (0.105) (0.140) (0.131) (0.118) (0.149) (0.147) War in the neighborhood (1 year lag) 0.224 0.000 0.852* 0.260 0.030 0.874* 0.160 0.104 0.556 (0.333) (0.426) (0.392) (0.346) (0.456) (0.409) (0.345) (0.463) (0.419) Mountain terrain 0.011* 0.023*** -0.007 0.010+ 0.020** -0.005 0.009 0.016* -0.000 (0.006) (0.006) (0.008) (0.006) (0.007) (0.009) (0.006) (0.007) (0.008) Socioeconomic Structures, Domestic Structural Modernization Real GDP per capita (logged, 1 year lag) 5.217* 6.785+ 3.710 5.527** 6.147+ 4.715* 6.890* 9.429* 6.231* (2.363) (3.821) (2.293) (2.130) (3.321) (2.135) (2.739) (4.249) (3.074) Real GDP per capita, squared -0.361* -0.464+ -0.251+ -0.369** -0.417+ -0.301* -0.464** -0.645* -0.384* (0.153) (0.247) (0.145) (0.137) (0.217) (0.134) (0.175) (0.279) (0.191) Youth bulge (1 = 35%+ of adult pop) 0.652* 0.618+ 0.608 0.585* 0.389 0.690+ 0.524+ 0.289 0.805* (0.272) (0.375) (0.437) (0.260) (0.379) (0.418) (0.282) (0.387) (0.392) Political Environment, Domestic Political Opportunity/State-Centered Thesis 187 Polity (0-20, 1 year lag) 0.135 0.212 0.052 0.151 0.165 0.017 0.152 0.115 0.164 (0.107) (0.165) (0.162) (0.108) (0.169) (0.176) (0.111) (0.186) (0.139) Polity squared (I year lag) -0.008 -0.010 -0.004 -0.008 -0.008 -0.002 -0.009 -0.006 -0.009 (0.005) (0.007) (0.007) (0.005) (0.008) (0.008) (0.005) (0.009) (0.006) Group Configurations and Attributes Ethnic dominance (1 = 80%+) -0.199 -0.358 0.022 (0.265) (0.352) (0.355) Religious dominance (1 = 80%+) 0.637* 0.413 0.857* (0.309) (0.392) (0.431) Linguistic dominance (1 = 80%+) -0.247 -0.388 0.086 (0.261) (0.369) (0.396) Ethnic polarization 0.018** 0.018* 0.009 (0.006) (0.007) (0.009) Religious polarization 0.003 0.001 -0.001 (0.005) (0.006) (0.005) Pseudo log likelihood -194.071 -117.936 -106.125 -220.800 -136.052 -108.392 -185.645 -117.189 -93.664 Pseudo R square 0.099 0.124 0.090 0.082 0.095 0.072 0.114 0.112 0.080 Event 65 42 30 72 47 30 65 44 26 Unit 143 143 144 142 142 143 117 117 117 N 4647 4783 4894 4664 4798 4935 4067 4179 4317 + p<0.10, * p<0.05, ** p<0.01, *** p<0.001 two-tailed test p. South Africa (1978) and Zimbabwe (1976) are removed as influential (religiously not dominant but racially divided, with war onset).
APPENDIX I
ESTIMATED BASELINE CUMULATIVE HAZARD FOR CIVIL WAR ONSETS
4.00e-14
3.00e-14
2.00e-14
1.00e-14
0 0 20 40 60 Time
First war onset Second war onset Third war onset Forth war onset
Figure I.1: Estimated baseline cumulative hazard of aggregated civil war onsets by event ordering strata, based on Model 11
188
5.00e-16
4.00e-16
3.00e-16
2.00e-16
1.00e-16
0 0 20 40 60 Time
First war onset Second war onset Third war onset
Figure I.2: Estimated baseline cumulative hazard of identity civil war by event ordering strata, based on Model 11
189
8.00e-11
6.00e-11
4.00e-11
2.00e-11
0 0 20 40 60 Time
First war onset Second war onset
Figure I.3: Estimated baseline cumulative hazard of non-identity civil war by event ordering strata, based on Model 11
190
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