ETHNIC GROUP REBELLION IN CIVIL WAR

A DISSERTATION SUBMITTED TO THE DEPARTMENT OF POLITICAL SCIENCE AND THE COMMITTEE ON GRADUATE STUDIES OF STANFORD UNIVERSITY IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY

Luke N. Condra August 2010

© 2010 by Luke Nayef Condra. All Rights Reserved. Re-distributed by Stanford University under license with the author.

This work is licensed under a Creative Commons Attribution- Noncommercial 3.0 United States License. http://creativecommons.org/licenses/by-nc/3.0/us/

This dissertation is online at: http://purl.stanford.edu/tg251tn4470

ii I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

James Fearon, Primary Adviser

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Jonathan Rodden

I certify that I have read this dissertation and that, in my opinion, it is fully adequate in scope and quality as a dissertation for the degree of Doctor of Philosophy.

Kenneth Schultz

Approved for the Stanford University Committee on Graduate Studies. Patricia J. Gumport, Vice Provost Graduate Education

This signature page was generated electronically upon submission of this dissertation in electronic format. An original signed hard copy of the signature page is on file in University Archives.

iii Abstract

Why do some ethnic groups involve themselves in civil wars, fighting in rebel groups against the state, while others do not? In particular, what explains variation in ethnic group involvement within the same country? The conventional wisdom is that poverty and political grievances are to blame for this involvement and participation in political violence more generally. Such conclusions tend to be based on studies of single cases or large-N studies that analyze a biased sample of ethnic groups. Countering these hypotheses, I propose a theory of ethnic group rebellion that predicts ethnic group involvement in civil war based on sub-national variation in the ability of the state to exercise a monopoly of control over territory and people, and its administrative strength in an area.

Using original geographic, economic, and political data collected on an exhaustive set of ethnic groups from 48 African countries, I test hypotheses emanating from my theory and those of traditional explanations in the context of African civil wars between 1980 and 2006. I find that the distance between an ethnic group‘s inhabited region of the country and the capital city – an important measure of the state‘s presence and capacity for control – is positively related to the probability of involvement in rebellion. The periphery is a high risk region for involvement in both territorial

(separatist/secessionist) rebellion and rebellion designed to take over the state and oust the government.

The goals and organizational requirements for these two types of rebellion are quite different, and my theory proposes that these differences should attract ethnic groups

iv from opposite ends of the economic spectrum. Territorial rebellions are likely to involve the poorest ethnic groups living in the periphery, as these movements do not require a high degree of organization or funding to continue a low-level insurgency against the state. In contrast, wealthier groups, which are likely to be better organized and better able to act collectively, are more likely to be involved in government takeover rebellions, which require a higher degree of rebel competence and strength to confront the forces of the state directly.

The evidence from statistical analysis and case study examination is strongly supportive of the elements of my theory, suggesting that traditional emphasis on poverty, political representation, and transnational ethnic ties as central factors in this type of political violence is misguided and more focus should be placed on the ability of the state to administer its territory effectively and ethnic group responses to variation in this capacity.

v Acknowledgments

No amount of paper could provide ample space to express adequately my thanks to those who have enabled me to complete this dissertation. I am grateful, particularly, to my committee members. Each has contributed uniquely to the development of the project and

I could not have asked for a more helpful set of advisers. Jonathan Rodden pushed me to think about how the insights from the literature on comparative political economy could be applied to my questions and helped me think more systematically about my general theory of ethnic group rebellion. Conversations with Ken Schultz provided me with both big picture ideas for the framing of the project, as well as detailed and practical suggestions for how to present my empirical results in a way that would contribute most to the advancement of my arguments. Above all, I am profoundly grateful to my chair,

Jim Fearon, whose consistent guidance and measured criticism helped me struggle with each new problem that arose, and whose timely encouragement gave me the confidence to persevere and finish the project. Besides being thankful for making me a better scholar,

Jim‘s enthusiasm for the puzzle at the core of this dissertation, as well as his own work in this field, convinced me that my interest was well-placed and that my effort was worthwhile.

Bertram Ang, Jane Esberg, and Eugene Nho provided expert research assistance and I have their independent thinking and diligent work to thank for not taking even longer to finish the thesis. The advice and suggestions of several other colleagues have been particularly useful over the past several years. I thank David Laitin and Mike Tomz for their patient and foundational instruction, as what they taught me in their classes

vi helped me enormously as I tackled all aspects of the dissertation. Discussions with

Bethany Lacina in the early stages of the dissertation and throughout graduate school have been instrumental in how I think about political violence in general. Many colleagues have read parts of the dissertation at one stage or another, or have heard presentations of the work and have offered invaluable feedback. I thank Rikhil Bhavnani,

Ed Bruera, Dara Cohen, Roy Elis, Laurel Harbridge, Ollie Kaplan, Kimuli Kasara,

Charlotte Lee, Maggie Peters, Connor Raso, Natan Sachs, Jake Shapiro, Jed Stiglitz, Alex

Tahk, and Jessica Weeks for their comments at various stages of the project.

I thank Neil Malhotra and Alex Kuo for their friendship, steady encouragement and wise advice; graduate school would have been decidedly less interesting without them. Chris Baughman, Scott Belzer, Lanny Berg, and Sidney Chang provided a useful perspective on my work. I thank them for sharing their lives with me and for often reminding me who I am.

My parents, Ed and Debi Condra, deserve special thanks and more of it than I can provide here. They raised me to pursue Truth fervently and taught me why I should never be afraid of where such a journey might lead. Their life‘s work continues to inspire me to transform a profession into a vocation.

Finally, I thank my best friend and most ardent supporter, my wife, Morgan. No one could hope to find a more loving partner. For most of us, our profession is a fickle companion, alternately praising and condemning us on a whim. Morgan‘s unconditional love for me has made the frustrations tolerable and any accomplishments sweeter. I hope to be as supportive to her as she has been to me.

vii Table of Contents

Abstract iv

Acknowledgments vi

1. Introduction 1

2. A Theory of Ethnic Group Rebellion 18

3. Ethnic Group Data Sources and Collection Methods 45

4. Effect of Peripheral Location and Wealth on Group Rebellion 57

5. Role of Political Representation and Cultural Difference on Rebellion 96

6. Transnational Ethnic Ties as Encouragement for Rebellion 126

7. Evaluating Theory‘s Predictions in Senegal, Niger, Guinea, and Chad 143

8. Conclusion 188

References 193

viii List of Tables

4.1 Summary Statistics of Geographic Variables 79

4.2 Cross-Tabulation of Dichotomous Dependent Variables 79

4.3 Estimates of Relationship between Rebellion and Distance from Capital City 80

4.4 Correlation Coefficients: Group Economic Proxies And Distance from Capital City 81

4.5 Estimates of Relationship between Rebellion and Group-Level Wealth Index 82

4.6 Estimates of Interactive Effect of Distance and Wealth on Rebellion 83

4.7 Cross-Tabulation of Distance and Wealth for Ethnic Groups in Countries Experiencing Territorial Rebellion 84

4.8 Cross-Tabulation of Distance and Wealth for Ethnic Groups in Countries Experiencing Government Takeover Rebellion 85

Appendix 4.1a Estimates of the Relationship between Rebellion (All Types) and Wealth 87

Appendix 4.1b Estimates of the Relationship between Rebellion (All Types) and Wealth 88

Appendix 4.2a Estimates of Relationship between Rebellion (No Coups) and Wealth 89

Appendix 4.2b Estimates of Relationship between Rebellion (No Coups) and Wealth 90

Appendix 4.3a Estimates of Relationship between Territorial Rebellion and Wealth 91

Appendix 4.3b Estimates of Relationship between Territorial Rebellion and Wealth 92

Appendix 4.4a Estimates of Relationship between Wealth and Government Takeover Bids 93

ix Appendix 4.4b Estimates of Relationship between Wealth and Government Takeover Bids 94

Appendix 4.4c Estimates of Relationship between Wealth and Government Takeover Bids 95

5.1 Effect of Co-ethnic Representation and Cultural Similarity on Rebellion 120

5.2a Effect of Co-ethnicity on Rebellion 121

5.2b Effect of Cultural Similarity on Rebellion 122

5.3 Effect of Natural Resources on Rebellion 123

5.4 Interactive Effect of Natural Resources and Co-ethnic Representaion on Rebellion 124

5.5 Changes in Marginal Effect of Political Representation on Rebellion 125

6.1 Effect of Cross-Border Ethnic Ties on All Rebellion 139

6.2 Effect of Cross-Border Ethnic Ties on Government Takeover Rebellion 140

6.3 Effect of Cross-Border Ethnic Ties on Territorial Rebellion 141

6.4 Relationship between Transnational Ethnic Kin and All Rebellion 142

6.5 Relationship between Transnational Ethnic Kin and Government Takeover Rebellion 142

6.6 Relationship between Transnational Ethnic Kin and Territorial Rebellion 142

x List of Figures

3.1 Geographical Data in Central African Republic 55

3.2 Benin Ethnic Groups‘ Educational Achievement over Time 56

7.1 Senegal‘s Casamance Rebellion 180

7.2 Niger‘s Separatist Rebellions 181

7.3 Guinea and the RFDG Insurgency 182

7.4 Chadian Rebellion 183

7.5 Relative Poverty of Senegalese Ethnic Groups 184

7.6 Relative Poverty of Nigerien Ethnic Groups 185

7.7 Relative Poverty of Guinean Ethnic Groups 186

7.8 Relative Poverty of Chadian Ethnic Groups 187

xi Chapter 1

Introduction

1.1 Theory and Contributions

What explains why some ethnic groups in a country are involved in civil wars but others are not? In this dissertation I advance a theory of ethnic group rebellion that argues the primary factor determining whether an ethnic group engages in civil war against the state is the degree to which the state can exercise effective control over sub-national territory and population. I argue that the state‘s capacity to quell and discourage violence of this kind is weakest in areas that are far from the capital city, the state‘s center of economic, political, and military power. While arguing that peripheral location is the overriding and most important factor for understanding ethnic group rebellion, particularly in Africa, my theory develops an argument about the different goals and natures of two main types of civil conflict to explain how state capacity and ethnic groups‘ economic well-being interact to predict different risk profiles for ethnic groups involved in each type of civil war.

Poorer ethnic groups in the periphery are more likely to be involved in civil wars that involve disputes over territory and regional political autonomy because this is the only violent option available to them. These groups live too far from the capital and too poor to mount an insurgency designed to take over the state. Their peripheral location affords them the opportunity to mount weak, sparsely populated and poorly organized rebellions because the state has difficulty combating it in the hinterlands. In contrast, insurgencies designed to oust the government and capture the state require more organized movements that can confront the state, so peripheral location should not be as

1 important and the movements should be launched by ethnic groups with large populations, which provide a recruitment base and sanctuary for insurgents in the same way that the periphery provides safety for ethnic groups engaged in territorial rebellions.

In recognizing differences across these types of rebellion, the theory shows how each has a different risk profile, both connected to the ability of the state to control its territory.

In testing elements of this theory across the experience of an exhaustive set of 359 ethnic groups across 48 African countries over a period of 25 years, the dissertation makes significant theoretical and methodological contributions to the study of civil war.

The analysis that follows provides tests not only of the theory of rebellion just described, but also of three other prominent competing theories of civil war involvement.

The first theory is that poorer ethnic groups are more likely to rebel because their relative poverty provides greater motivation and capacity to mobilize. Contrary to conventional wisdom, my analysis shows that this story is only partly true: poverty is associated only with involvement in territorial rebellion, not government takeover rebellion. In fact, there is evidence suggesting that relatively wealthier ethnic groups are more likely to be involved in bids to take over the state, a finding that supports my theory that such movements require involvement by groups that are better organized and can fund an insurgency that must conquer state forces. These findings controvert conventional wisdom about how poverty influences rebellion risk and provides a theoretical foundation for interpreting the result. The empirical basis for the findings is also arguably stronger than that of previous studies of the question, which make generalizations based on a single case (e.g., Stewart 2000; Murshed and Gates 2005), or

2 examine country-level onset instead of variation in rebellion among ethnic groups (e.g.,

Østby 2008; Besançon 2005).

The second competing theory of rebellion tested here is that which links political grievances and cultural difference to rebellion. The analysis again shows this claim to have little, if any, supporting evidence across cases. Ethnic groups that have enjoyed co- ethnic representation in the country‘s presidency are not significantly less likely to rebel, all else equal. Neither are linguistically distinct ethnic groups more likely to rebel, as the standard theory of political grievances would predict.

The third competing theory revolves around a posited heightened expectation of foreign support for insurgency held by ethnic groups with strong ethnic ties to groups in neighboring countries. These ethnic groups should be more likely to rebel, so goes the argument, because they expect material support from a sympathetic neighboring government. In fact, there is little to no evidence that this argument holds true across cases. While African conflicts are famous for their international character, it is not the case that ethnic groups with cross-border ethnic ties are more likely to rebel, which further supports my argument that the most important factor governing rebellion is a group‘s ability to avoid state control.

Methodologically, the project represents one of the first studies to present results that are informed by a research design largely free from common sources of bias associated with more traditional studies of ethnic group rebellion, and that are based on data collection at the appropriate level of analysis for purposes of answering the research question. While scholarship on civil war has focused on the comparative political economy of development, political representation, cultural difference, or geographic

3 conditions favoring insurgency, this dissertation represents one of the first efforts to include each of these aspects in a single analysis of ethnic group and state behavior over such a wide span of space and time. Not only does the study employ a unique research design and introduce original data that are superior to other efforts to answer similar questions, it joins a small but growing set of studies that consider how aspects of space affect political outcomes of interest in the field of international security and political violence. The theory of ethnic rebellion presented makes use of the spatial attributes of politics to outline how different types of geography affect both the opportunity and motivation to engage in civil conflict and provides a profile of ethnic groups that are at greatest risk of rebelling against the state.

Moreover, the study represents an advance in its choice of unit of analysis and level of aggregation, as both are more suited to the question under study: the ethnic group instead of artificially created grids (e.g., Buhaug and Rod 2006) or administrative regions

(e.g., Ostby 2008) as the unit of analysis, and group involvement in a rebellion movement instead of conflict events/zones (e.g., Ostby et al. 2006; Hegre and Raleigh 2006) or country-level onset (e.g., Ostby 2008) as the dependent variable.

Finally, the project improves in important respects upon studies using a case study approach to analysis of this research question. While the scope of this analysis makes detailed exploration of the specifics of individual cases prohibitive, the comprehensive nature of the analysis provides valuable context with which the conclusions drawn from analysis of individual cases can be understood and checked.

4 In the remaining portion of the chapter, the research question is discussed more specifically in the context of Africa, and the remaining chapters of the dissertation are outlined.

1.2 The Puzzle of Ethnic Group Rebellion

To begin further discussion of the research question, let me be clear about the term

―ethnic rebellion‖ and what I mean to imply by its use. I do not mean it to be a type of rebellion, in contrast to a communist insurgency, for example. Scholars have devised many typologies to categorize conflicts into meaningful typologies, and ―ethnic conflict‖ is one that is used quite often. While this categorization is not what is meant here, explaining ―ethnic‖ rebellion is an important effort, given the frequency of its appearance in observed conflicts.1 Instead, I simply mean that we are interested in which ethnic group(s) within a country are primarily involved in insurgent groups fighting against the state. This point is important because it means that we are not trying to classify the aims as being particularly exclusive to one or more ethnic groups, as often is the case in separatist movements. We are interested in broad participation in insurgent groups. To motivate this further, consider a few examples of ethnic group involvement in civil war across the African continent since 1980.

Angola has endured two main civil conflicts since gaining its independence from

Portugal in 1975. Three independence movements – begun by the MPLA, UNITA, and

FNLA – emerged to struggle against Portuguese control prior to 1975. The inability of these groups to share power post-independence led to civil war between the MPLA-led

1 Data from Sambanis (2001) puts the worldwide percentage of civil conflicts since WWII that have been fought with aims and along lines that can be considered ―ethnic‖ at around 70%.

5 government and UNITA, drawing in international support for both sides. Beginning in

1991 and continuing today, the secessionist group FLEC fought against the Angolan government for the independence of the oil-rich Cabinda region. Among other ethnic groups, members of the Bakongo – who live in Angola, Congo, and the Democratic

Republic of Congo (DRC) – were significantly involved in all of these rebel organizations.

Contrast the Bakongo‘s involvement in the Angolan conflicts to their behavior in neighboring DRC. Depending on how one divides them, the DRC has endured up to eight different civil conflicts since its independence in 1960, two of which began since the mid-1990s. With the support of Rwanda, Uganda, and Angola, the Kabila-led AFDL began a rebellion against Mobutu‘s regime in eastern Congo in October 1996 and captured Kinshasa in the spring of the following year. Kabila‘s new government almost immediately faced a new rebellion that began in 1998 and still simmers in the eastern part of the country. Despite the enormous confluence of domestic groups and international governments to these latest DRC conflicts, and in contrast to the Angola case, the

Bakongo people were not significantly involved in these two conflicts‘ rebel groups.

This contrast between the DRC and Angolan Bakongo people, and other pairs like them elsewhere in Africa, illustrates an interesting type of variation in the identity of rebel actors in civil conflicts. How do we explain the Bakongo‘s involvement in UNITA and FLEC in Angola, and its non-involvement in neighboring DRC? The obvious answer one would offer is that the Angolan Bakongo live in an entirely (or, at least somewhat) different historical, economic, political, and social environment than the Bakongo of the

DRC. Given these differences across countries, we should not find it surprising that the

6 same ethnic group‘s behavior vis-à-vis the state sometimes is different depending on the country in which it lives. Indeed, political scientists have shown that it is precisely because of these cross-country differences that an ethnic group‘s behavior may be at odds with its ethnic kin next door (e.g., Posner 2004). In short, we appeal to country-specific factors to explain the variation illustrated by the Bakongo experiences and an entire research program has emerged since at least the early 1990s, one of whose main contributions to the advancement of conflict studies has been to theorize which of these factors might matter for civil war onset, duration, and termination; collect data to measure those factors; and devise ways to test the hypotheses (e.g., Sambanis 2001;

Fearon and Laitin 2003; Collier and Hoeffler 2004; Walter 2006). Whatever they emphasize, theories emanating from this research program rely on the fact that Angola is, in various and important ways, different from the DRC and as such, the contrast between the Bakongo groups ceases to be much of a puzzle. The traditional puzzle has been, why did Angola suffer a civil war when it did, or why Angola and not neighboring Zambia?

Now, consider another example of a critically important question of popular involvement in civil wars from Africa that illustrates the difficulty this research program faces in theorizing an answer to the question, or in testing the quality of that answer.

In 1983, a group of mainly Diola civilians led by Senegalese army veterans demonstrated in the city of Ziguinchor, calling for the independence of Casamance, a region heavily populated by Diola. Ensuing clashes between the military and the demonstrators left 80 injured and 29 dead. The survivors fled into the region‘s dense forest and mangrove swamps and subsequently set up rebel bases and established the

MFDC‘s military arm. These rebels began a military training program that continued

7 through the 1980s and planned out attacks on government positions. Intermittently, from

1990 to 2003, this Diola rebel group (which split into other rebel groups) fought against the Senegalese government in a guerilla-style conflict over the autonomy of the

Casamance region. None of the other Senegalese ethnic groups were even minimally involved in the rebel side of the conflict.

Next, consider the conflict that erupted in the early 1990s in Congo, as three militias fought for political control on behalf of their ethnically-based political parties: the Ninjas for MCDDI, the Cobras for PCT, and the Ntsiloulous for UPADS (and the

Cocoyes for Frédéric Bigsangou‘s movement, albeit not a political party like the others).

The conflict saw the political parties trading time in office as their militias fared well or suffered setbacks on the battlefield. A peace accord was signed in March 2003 but, as with many such agreements, peace has been fragile due to various actors‘ reneging on terms of the accord, leading to further (though more limited) violence into 2010. The

Ninjas‘ membership was drawn from the MCDDI‘s main ethnic group, the Laari, a subgroup of Congo‘s plurality ethnic group, the Kongo. The Cocoyes, as well, were drawn from the Laari ethnic group (part of the larger Kongo group), whose geographic area of concentration is in the southern part of the country, west of Brazzaville. The

Cobras, on the other hand, were made up of the Mbosi people who live principally in the north-central part of the country. Finally, the Ntsiloulous, who did not emerge until 1998, took their members from both the Ninjas rebel group and Kongo ethnic groups.

What is the explanation for the involvement of the Diola people in Senegal‘s

Casamance conflict and the non-involvement of the Wolof, Fulani, Serer, Mandinka, and

Soninke groups in conflict with the central government? Similarly, what explains the

8 Kongo and Mbosi involvement in rebel groups in Congo‘s civil war, as well as the fact that the Teke, Mbete, and Sanga groups – who make up about 33% of the country‘s population – were not involved significantly in any of the warring factions? We cannot explain within-country variation of the type illustrated by these examples of civil war by offering theories that are based on cross-country models and data.

The important work done already that relies on cross-country variation to explain why, for example, some countries and not others fall into civil war, is not at all irrelevant for understanding sub-national political and social behavior. The lessons learned about the risk factors for onset, duration, termination, or any other civil war-related dependent variable, may transfer down to the sub-national level and explain the variation of the kind illustrated by the Senegalese and Congolese conflicts. But, they may not.

The reason that the cross-national work on civil wars referenced above is so powerful is because it recognized a fatal flaw in the existing scholarship on civil wars, which was one of selection bias, on at least two counts. First, conclusions drawn from studies of civil wars suffered from the fact that they typically focused only on countries that had experienced civil war, selecting on the dependent variable and thus unable to conclude whether their findings were valid in light of peaceful countries‘ experiences.

Second, even when studies examined some conflict-ridden and peaceful countries together, one could have only limited confidence in the findings because the sample was so small. The cross-national, large-n research program addressed both of these problems by measuring country-level theorized risk factors for both peaceful and conflict-ridden countries, and for many countries – sometimes virtually every country in the world.

9 As with those previous studies of civil war, there is a similar problem inherent in the existing scholarship addressing the factors that put sub-national actors at risk for becoming involved in a civil war. There are hundreds of articles and books giving rich,

‗thick‘ histories and explanations for the conflicts arising in Africa and other parts of the world. Yet, almost all of them suffer from the fact that the conclusions they draw for why certain groups, and not others, came to be involved in that conflict are difficult to extend beyond the scope of that specific historical place and time.

To illustrate this point more clearly, consider the prevailing explanations for the case of Senegal. Many sources point to factors that purportedly set apart the Diola people from other ethnic groups in the country. These factors include, but are not limited to, the government‘s inequitable redistribution of revenues garnered from Casamance‘s natural resources; historical under-provision of goods and services to Casamance, relative to other regions; predominance of Christian animism among the Diola, in contrast to the

Muslim majorities in other ethnic groups; Casamance‘s distance from the seat of power and its dense forests and mangrove swamps which made for hospitable terrain for rebels to evade government capture indefinitely; or that Diola are unique in that they do not speak the country‘s lingua franca, Wolof, and are thus at a disadvantage in the economic and political spheres (see discussion and sources in Humphreys and Mohamed 2005).

When so many excellent studies reiterate similar factors that purportedly led the

Diola to launch a separatist rebellion, it is probably fair to say that these factors were at least partly responsible and can explain this conflict particularly well.2 Can we interpret

2 Though, it should be noted that the case studies do not agree always on the primary causes of participation, which only underscores my argument that more precise data is needed in order to test standard stories, and it is needed for a much larger sample of ethnic groups than is typically collected. For example, Humphreys and Mohamed (2005) use cross-regional data to argue against the conventional

10 them as examples of patterns that tell us something about conflict participation more generally? Do they extend beyond the Senegal case? Are we to conclude from the

Casamance conflict that groups which (1) reside in natural resource-rich areas; (2) are economically disadvantaged; (3) practice a different religion from their countrymen; (4) reside in the country‘s periphery; (5) inhabit insurgent-friendly terrain; (6) or do not speak the lingua franca are at higher risk to become involved in rebellions? Perhaps each of these hypotheses is true. Or, perhaps these factors do not underlie general patterns but rather, are part of the idiosyncratic circumstances of the Senegal case. The truth is that this type of study has great difficulty convincing us that its conclusions can be considered as general principles because they are not systematically based on other countries‘ experiences.

Consider the behavior of other African ethnic groups that casts some doubt on the suggestion that the Diola in Senegal should serve as the quintessential example for understanding risk factors for rebellion. Are natural resource-rich areas at risk? The Fang,

Eshira, and Baloumbo ethnic groups in Gabon live in oil-rich areas but have not mounted a rebellion against the state; neither have the Sakalava in Madagascar. Are economically disadvantaged groups more likely to rebel against the state? One might measure economic status in a variety of ways, but wages are an obvious choice. In Uganda, one of the two ethnic groups with the highest proportion of members in the bottom quintile of mean national wages – the Kiga – were not involved significantly in the civil conflicts of that country. Are peripheral groups at greater risk, perhaps because this is an obstacle to the state‘s projection of its military power? The Lobi people of Côte d‘Ivoire, who were wisdom that Casamance was underserved by the government in the public infrastructure area. Instead, they see Casamance as not much better or worse than other regions in a country that, as a whole, is underserved in traditional infrastructure.

11 not involved significantly in that country‘s conflict, reside principally in an area that is even more distant from the capital and center of state power than the Mande and Senufo who are linked to rebel organizations in that conflict. Similarly, the Beti and Bamileke groups of Cameroon, involved in rebel groups in that conflict, reside demonstrably closer to the capital than all other groups of the country, who also happen not to have been involved in the conflict. Much has been made about the importance of rough terrain, both in single case studies of civil war and in cross-national studies of onset. But, the dozen or so linguistic groups that are combined in my dataset and constitute the Lagoon Type group in Côte d‘Ivoire reside, on average, in higher elevation areas than the rebellious

Mande and Senufo groups.

These simple descriptive facts illustrate why the general research question is important, because even the most careful or persuasive conclusions, based on one or a few cases, could be erroneous when generalized to other times and places. Indeed, once one examines the variation across groups within countries (and across countries) in this way, a puzzle emerges that may not have been apparent prior to a collection and examination of these types of data.

Those studies which have tried to avoid the problem encountered by single country case studies have enlarged their sample of ethnic groups beyond that of one or two countries at a time. However, these studies are hampered by a slightly different, but perhaps no less damaging, problem of selection bias: almost exclusively they rely on the

Minorities at Risk (MAR) dataset, which measures factors that theoretically put ethnic groups at greater risk of various types of political violence. As the name suggests, however, the dataset‘s use is problematic because it includes only ethnic groups that are

12 pre-judged to be ‗at risk‘; it leaves out any groups that do not fit pre-determined criteria for inclusion, and this decision almost certainly introduces bias into conclusions drawn from certain (though, not all) studies that use these data, in much the same way that those earlier studies of civil war were biased in selecting on the dependent variable.

This study seeks to recognize the methodological problems inherent in existing scholarship on this research question and goes to great lengths to avoid them in a way that permits strong confidence in, and extended applicability of, the conclusions it draws.

First, it recognizes that country-level, cross-national data collection and analysis has difficulty explaining sub-national variation in peaceful and rebellious ethnic groups.

Consequently, the research design and data collection of the project is geared toward identifying and explaining sub-national variation, not variation across countries.

Second, to reduce the potential for omitted variable problems and to improve the parsimony of theory, the project attempts an analysis of all ethnic groups in all of Africa.

Instead of relying on a sample of groups whose selection criteria is correlated with the dependent variable that we are trying to explain, I examine a set of groups that includes the vast majority of a country‘s population and does not distinguish between groups that should or should not – based on a priori theory – be at risk for different types of conflict behavior. Since the set of groups I use is arguably exhaustive, both within countries and across countries, there is less potential for omitted variable bias contaminating results.

1.3 Plan of the Dissertation

In this dissertation I advance a theory of ethnic group rebellion in Africa that uses an original dataset situated at the ethnic group level across 48 African countries designed to

13 study the political, economic, and geographic determinants of civil conflict between 1980 and 2006. The central insight of the argument is that the continent‘s historical pattern of political and economic development increased the risk of rebellion for ethnic groups that are situated farther from the capital city and the center of economic and military power.

As an extension of this general theory of group rebellion, I argue that because incentives and constraints vary across different types of civil conflict, we actually see a stark contrast in the types of ethnic groups that are involved in attempts to take over the government through conflict, as opposed to those who are involved in territorial or secessionist conflicts. The historical patterns of economic and political development, as well as consequences of political geography, make certain ethnic groups more likely to engage in one type of conflict over the other. This empirical pattern has implications for public policy, as governments face very different threats to the regime depending on the ethnic group‘s economic status, political representation, natural resource base, and human and physical geography. Whereas relatively poorer groups are more likely to reside in the state‘s periphery and are more likely to foment territorial and secessionist conflicts, relatively well off groups reside closer to the political and economic center and are more likely than their poorer counterparts to launch conflicts designed to take over the central government.

The theory of ethnic group rebellion and civil war that I advance here is powerful because it is both parsimonious and provocative. The ethnic group‘s distance from the capital city may be proxying for underlying, non-geographic, risk factors, as I explain in a later chapter. I argue, however, that geography is not only a proxy for variables we would like to measure but cannot. Geography itself has an impact on the likelihood that

14 an ethnic group will rebel, evidenced by how the effects of various risk factors are conditional on geography. My theory and empirical findings themselves raise new questions about how geography is historically influential in affecting economic development, inequality, and political representation across ethnic groups, questions that animate the work not only of scholars of international security and political violence, but also the scholarship of comparative political economy and electoral politics.

The rest of the dissertation is organized as follows. In chapter 2, I outline in more detail my theory of ethnic rebellion, one that is based primarily on an understanding of variation in state capacity across sub-national space to predict ethnic group rebellion. The project involved considerable effort in original data collection, and this collection process is described in chapter 3.

Chapter 4 tests the hypotheses derived from the theory on ethnic rebellion outlined in chapter 2, along with competing claims that emphasize poverty and inequality as decisive. The core result from this chapter is that an ethnic group‘s location relative to the capital city is consistently the best predictor of whether or not that ethnic group rebelled during the time period under study. The evidence indicates that the role of poverty as a risk factor for rebellion is limited and conditional on the group‘s distance from the capital. At high levels of distance, the effect of wealth is negative and significant, producing the expected relationship between poverty and rebellion. However, this result only holds for territorial rebellion; in fact, wealth is positively related to rebellion in the full sample, as well as government takeover rebellions, though the result is not significant in the latter case.

15 In chapter 5 the core claims that political marginalization and cultural difference should positively impact rebellion risk are tested, with little evidence that either relationship consistently holds across the data. In this chapter, I propose a specific mechanism through which political representation is more likely to be consequential for rebellion: through its impact on the distribution of revenue derived from the export of oil and diamonds. While a number of mechanisms have been proposed in the literature linking natural resources to country-level civil war onset, systematic study of natural resource wealth‘s impact on sub-national violence is much rarer. I argue that political representation in the central government should impact rebellion risk through a particular channel, through the chief executive‘s role in establishing the rules determining revenue distribution among the center and regional governments. In Africa, where politics is often conducted for the benefit of co-ethnics, chief executives should be more likely to establish favorable arrangements for revenue sharing among co-ethnics than other ethnic groups. This prediction is borne out in the empirical analysis, which shows that while oil wealth in a group‘s region does have a positive impact on risk of territorial rebellion, this risk is dramatically mitigated if the ethnic group has had co-ethnic representation in the presidency.

Chapter 6 tests the competing explanation of ethnic group rebellion implied by the dominant theory explaining foreign intervention in civil wars. This theory predicts foreign involvement in neighbors‘ civil wars when the foreign country‘s key domestic ethnic constituency pressures its leadership to act on behalf of ethnic kin abroad. The implication from this theory for explaining ethnic group rebellion is that groups with co- ethnics in neighboring states ought to anticipate future foreign support for their

16 movement and be more likely to rebel. In fact, the data provide very little evidence in support of this prediction, suggesting that while foreign intervention in these conflicts is rampant, it does not encourage rebellion beforehand.

Chapter 7 complements the statistical analysis in previous chapters by providing a more detailed examination of the particular causes of conflict involvement in two territorial conflicts and two government takeover rebellions across Africa. The first is the

Casamance separatist movement in Senegal. The second case covers the dual conflicts over regional autonomy in Niger, first in the Air and Azawad Tuareg regions of the north, and then the Toubou and Kanuri insurgency in the east. The third case examines the brief two year insurgency in Guinea that featured a melding of that conflict with the one already underway in neighboring Sierra Leone and Liberia. The fourth case involves the long-running civil war in Chad that dates to 1965 and continues to this day. In each case, the particular attributes of the conflict are explored in an attempt not only to provide valuable context to the statistical results, but to determine whether events from these cases can help refine the statistical models to better capture determinants of behavior.

Chapter 8 offers a brief conclusion with a review of the main results and implications for future research.

17 Chapter 2

A Theory of Ethnic Group Rebellion

2.1 Introduction

In this chapter I present the basic arguments underlying the theory of the dissertation, explaining why certain ethnic groups are more likely to be involved in rebellion than others within the same country. At its core, my theory posits that the most important factor in determining whether an ethnic group rebels is whether an ethnic group, with sufficient motivation for rebellion, is afforded ample opportunity to do so by virtue of the state‘s inability to control and monitor certain sub-national regions.

The remainder of the chapter proceeds as follows. In the next section I explain the historical conditions that led to African states developing into weak institutions of coercive capacity and the implications this has for ethnic group rebellion across the continent. This insight, that peripherally located ethnic groups are more likely to rebel than others, and that this factor is the best predictor of rebellion for so many ethnic groups across 25 years of conflict forms the basis of my theory on ethnic group rebellion.

In the third section, I review several literatures that propose different theories to explain rebellion, all related to predictions about how poverty impacts the motivation to rebel. These theories have been offered up to explain variation in civil war onset at the level of countries, as well as participation in rebel organizations by sub-national groups and individuals. A review of these literatures reveals that while the majority of studies link relative poverty to rebellion through various mechanisms, there are a non-trivial number of studies that argue for the opposite relationship and propose mechanisms by which relatively wealthy groups in society might be more likely to rebel.

18 The fourth section explains how my theory of ethnic group rebellion provides a way to account for these competing predictions by arguing that the two main types of popular rebellion observed in Africa, those fought over issues related to territorial autonomy and those fought over control of the state, are more likely to be pursued by poorer and wealthier groups, respectively. Due to the pattern of sub-national economic development in most African states, poorer ethnic groups tend to be located in the periphery, while wealthier groups are closer to the center. This variation affects both motivation and opportunity to rebel, in that ethnic groups far from the center are too poor and disorganized to mount serious challenges to the state but can engage in low-level conflicts over territorial autonomy that do not require confrontation with the state; and wealthier groups are better able to launch insurgencies designed to take control of the state and its resources for the benefit of their co-ethnics. This theory accounts for conflicting results in the extant literature by disaggregating rebellion according to its goals and organizational requirements to be successful, and then developing arguments about how different levels of economic well being are more or less suited to those types of insurgency.

2.2 Perils of the Periphery: State Capacity and Ethnic Group Rebellion

To explain ethnic group rebellion, I argue that within country variation in this behavior is best understood in light of African states‘ inability to monitor and control their territory and population. My theory emphasizes the different elements of natural and human geography that make this particularly difficult for African states.

19 To understand how African governments are historically weak when it comes to exercising a monopoly of control over their territory, it is necessary to study how modern states in Africa were established and what preexisting cultural elements put governments at a disadvantage, relative to Europe, for example, in terms of consolidating control over their population and exercising a monopoly of violence over their territory. Africa‘s ethnic landscape had a profound influence on the building of the nation-state, an experience that contrasts sharply with state formation and the rise of nationalism elsewhere in the modern era, explained most famously in the work of Gellner (1983;

1997) and Anderson (1983).

Gellner argued against two theories of nationalism that were opposed to each other. On the one side were those who thought nationalism and its sentiments are both universal and necessary. On the other side was Kedourie (1960), who argued that nationalism was quite accidental in history. In explaining the rise of nationalism, Gellner

(1997, 10) rejected the view that it arises accidentally and argued that, instead, nationalism was ―neither universal and necessary nor contingent and accidental‖, and instead was a byproduct of particular social forces that are not universal nor omnipresent.

Though nationalism was technically possible in pre-industrial agrarian societies, it did not arise until the industrial age. Faced with an upper limit on possible output, but not on population growth, agrarian society is in a zero-sum game competing for resources, and in this society, fate is tied to one‘s rank and status. Society is organized hierarchically, with boundaries between strata determinedly guarded by their members.

Rank and status are the culture of the society and pains are taken to reinforce common knowledge of their existence. The main function and organizing principle of culture in

20 agrarian society ―is to reinforce, underwrite, and render visible and authoritative, the hierarchical status system of that social order‖ (Gellner 1997, 20). This is why, in

Gellner‘s view, nationalism – ―the view that the legitimate political unit is made up of anonymous members of the same culture‖ – does not arise in agrarian society. Status, distinction, and hierarchy are reinforced in that society‘s culture; unity and standardization are not.

Industrialization, because of its ability to weaken the limits of productivity, brought with it social mobility and equality.3 With a new foundation of economic growth, this new society demands of its members an ability to adapt to changing productive roles.

Meritocracy, not heredity, is now the method of wealth and status transference, and instead of cultural heterogeneity, cultural homogeneity develops as the need for communication is attenuated in the marketplace. The standardization of education and above all, literacy and the written vernacular available via mass printing (Anderson

1983), hastens the emergence of culture common to all in the polity and people are able to identify themselves with an imagined community, as occurs in Weber‘s (1976) account of peasants becoming Frenchmen.

These imagined communities, these sentiments of shared identity and cultural unity, came with the modern industrial age. Traditional agrarian societies, which dominate Africa, did not develop into entities where the national and political units were congruent, partly because the continent did not industrialize when Europe did and partly because of colonialism. Consequently, a plethora of ethnic and ethno-linguistic identities

3 See Deutsch (1953) for a similar theory of nationalism that emphasizes, similarly to Gellner, the shift from subsistence agriculture to an exchange economy and resulting social mobility.

21 are salient and ripe for political use, as they have not been replaced or subsumed under a national identity in many states.

In the case of Africa, these problems are compounded by the particular way in which pre- and post-colonial African states evolved. In addressing the problem of persistent state weakness in Africa, a trait similar to but distinct from the lack of nationalism there, Herbst (2000) asks why the continent did not evolve politically in the same way as Europe did, where rulers had incentive to acquire more land and extend their domain and states were created through these wars of conquest (Tilly 1990). In contrast to Europe, African states‘ low population densities and large territories created financial disincentives for leaders to consolidate power and project it to the periphery.

Governments claimed sovereignty over their territory but in fact, the hinterlands were often left largely to their own devices. ―How power was actually expressed was often similar to the precolonial model of concentric circles of authority. States had to control their political cores but often had highly differentiated control over the outlying areas‖

(Herbst 2000, 134).

African states‘ political development and diverse set of ethnic groups have several implications for understanding why state weakness is so critical for understanding political violence in general and ethnic group rebellion in particular. First, states failed to develop national unifying cultures, meaning that ethnic identity could be used more easily to mobilize against the state. Second, and reinforced by Africa‘s delay in moving to a culture of industrialization, concentrating political power in the center heightened the benefits received from one‘s own group holding power. As Gellner (1997, 18) writes: ―In north Africa, the local name for the state is or was Makhzen, a word with the same root as

22 store, magazine. The term is highly suggestive: government is by control of the store; government is the control of the store‖ (emphasis in original). Control of the machinery of the state becomes highly lucrative, and there is no shortage of academic work describing the resulting clientelism that exists in these states. Exclusion from political power is devastating because it can deliver a sentence of poor public goods provision, political persecution, or even official annihilation. Third, as Herbst and others have pointed out, regions farthest from the center of power usually exhibited the slowest rate of economic development. Schools, clinics, and roads were simply not built. Human capital was not developed as well.4 These areas tend to be poorer today and this poverty and inequality can translate into a greater motivation to change the political system.

Fourth, these peripheral under-developed regions are, by definition, near the borders of other countries, borders which are porous and largely unguarded. The lack of state control over territory makes rebellion in these areas more enticing because the risk of state capture and punishment is reduced.

I argue that the history of political and economic development in Africa puts a certain type of ethnic group at greater risk of rebelling against the state, whether in a secessionist bid or in an attempt to seize control of the state. This history makes ethnic groups that are situated farthest from the state‘s center of military and economic power at greatest risk. Their activity is not as well monitored by the state and this creates greater opportunity to foment rebellion. These peripheral groups suffer from lower economic

4 This feature does not, of course, account for all variation in rates of growth across ethnic groups. For example, colonial powers had a tendency to elevate certain groups over others in their administration of territories (e.g. British vis-à-vis Baganda in Uganda [Kiyaga-Nsubuga 1999]) and the effects of this type of policy can be long lasting.

23 development, which provides impetus to change the system through territorial rebellion because they find themselves on the bad end of economic inequality.

My argument that peripheral regions are at higher risk of falling into conflict is not altogether new. Indeed, other studies have suggested similar relationships between peripheral location and rebellion. For example, some have found that simple distance from the core predicts group violence. Tong (1991) studies collective violence in China during the Ming Dynasty period and finds that violence and rebellion occurred with much greater frequency in peripheral regions, which he ties to a lack of central administrative control of these areas.5 Brustein and Levi (1987) examine determinants of the location of anti-state rebellions in 16th and 17th century Europe. Similar to Tong‘s reasoning, they argue that ―the more distant a region is from the ruler‘s seat of power the greater the difficulty for the ruler in mounting an effective deterrent to anti-state collective action.

The likelihood that rulers may have tremendous difficulty in crushing collective action in the outlying provinces should have the effect of augmenting the prospects of successful anti-state rebellions within outlying provinces‖ (p. 484). In a recent study of the interaction between the Indian central government and minority language groups vying for statehood, Lacina (2010) finds that political negotiations are more likely to fail and lead to violence the farther the region is from New Delhi. The common thread running through these studies and others like them (though sometimes it is not emphasized) is that where the state is not present to fend off challenges to its power, populations take advantage of the situation and resort to violence as a way of achieving their political aims. As we will see, while this begs the question of why such groups should engage in

5 He concludes: ―collective violence in the Ming was motivated by the need to survive during times of hardship and when the incapacitation of the state‘s coercive power presented the opportunity to rebel with low risk‖ (p. 8).

24 violence if the state is so weak in these regions, such areas are historically underdeveloped economically and politically, so there is little recourse left to ethnic groups living in these regions to change their situation other than political violence.

In short, and through no real fault of their own but through the pattern of state development in Africa, geography has made some ethnic groups in countries much worse off, and in a variety of ways; so much so, that rebellion becomes a highly attractive option.

2.3 Poverty, Rebellion, and Competing Predictions

2.3.1 Grievances versus Opportunity in Cross-National Results

The conventional wisdom is that poverty is positively associated with most dependent variables falling under the category of civil conflict. Poorer countries are more likely to experience civil war onset (Fearon and Laitin 2003; Collier and Hoeffler 2004), poorer groups are at greater risk of being involved in various kinds of political violence (Gurr

2000), and poorer individuals are more likely to voluntarily join insurgent movements

(Humphreys and Weinstein 2008). The theories explaining the posited relationship between poverty and conflict or violent behavior are several. We might observe poorer countries being at higher risk of civil war onset because these states tend to be administratively and bureaucratically weak, and such states find it more difficult to exercise effective control over their population and territory. Poorer individuals might be more likely to join rebel organizations because it is worth their while: they can move from under- or unemployment to high stakes employment by joining.

25 Gurr and Moore‘s (1997) theory of ethnic-based rebellion is based heavily on the argument that group grievances/relative deprivation and state-sponsored repression is largely responsible for increasing a group‘s capacity to mobilize, which then enables ethnic rebellion against the state. This contention fits into a long tradition of literature that argues for causal link between deprivation/repression and mobilization, and then between mobilization and violence (see Gurr 1993a, Gurr 1993b, Gurr 1996, Korpi 1974,

Lindstrom and Moore 1995, Moore and Jaggers 1990). In case studies of specific civil wars and cross-national statistical studies that focus on ethnic groups, an emphasis is placed on the injustice or inequality perceived by the rebelling group. As Gurr (1970, 9) wrote: ―the greater the intensity of deprivation, the greater the magnitude of [political] violence…the proportion of a population that participates in violence ought to vary with the average intensity of perceived deprivation. Mild deprivation will motivate few to violence, moderate violence will push more across the threshold, very intense deprivation is likely to galvanize large segments of a political community into action.‖ The theoretical tradition linking material deprivation to rebellion is a strong one. Let us examine this argument in more detail.

At first glance, a theory predicting that poverty leads to psychological grievance, which then leads to collective action, seems plausible. Poorer people tend to be the most disadvantaged in any society. They are not only poor, but they are politically and culturally marginalized. If anyone should be willing to act to change their status in society through violent means, it should be the poor.

A classic theory of popular rebellion argues that such behavior is best understood as a reaction to grievances on one or more dimensions, usually political and economic

26 (e.g., Davies 1962; Scott 1976; Muller and Seligson 1987). As Gurr (1970, 12-13) explains, ―The primary causal sequence in political violence is first the development of discontent, second the politicization of that discontent, and finally its actualization in violent action against political objects and actors. Discontent arising from the perception of relative deprivation is the basic, instigating condition for participants in collective violence.‖ Where a discrepancy exists in individual and group consciousness between what ―ought‖ to be (termed ―value expectations‖) and what ―is‖ (termed ―value capabilities‖), we are more likely to see rebellion with the intent to destroy or change the existing system that fosters this discrepancy (Gurr 1970, ch. 2). ―By contrast, if discontented people have or get constructive means to attain their social and material goals, few will resort to violence‖ (Gurr 1970, 317).

Gurr and others argue for a causal link between deprivation and/or repression and mobilization, and then between mobilization and violence (see Gurr 1993a, Gurr 1993b,

Gurr 1996, Korpi 1974, Lindstrom and Moore 1995, Moore and Jaggers 1990). In case studies of specific civil wars and cross-national statistical studies that focus on ethnic groups, an emphasis is placed on how perceived injustice or inequality leads to rebellion.

Gurr and Harff‘s (1994) study is a prominent example. The authors go to great lengths to describe and document the historical injustices Middle Eastern Kurds have suffered, the argument being that these grievances are responsible for Kurdish political activity, trying to gain autonomy and establish their own state in the 1970s and 1980s (we see a continuation of this in present day post-war Iraq). Furthermore, the authors note the ways in which Kurds are ―different‖ from other ethnic groups. This same argument is offered for Miskitos in Nicaragua, the Chinese in Malaysia, and Turks in Germany.

27 A focus on difference and grievance is wide-spread in case studies and micro- level research on determinants of group rebellion, and for good reason. As Gurr and Harff

(1994, 27) state, two underlying factors are present in all cases of political conflict between ethnic groups and the state: ―people become more sharply aware of their common identity…[and they] become increasingly resentful about their unjust and unequal status in comparison with other groups.‖ This general view is carried through later work, as is the methodology (e.g., Gurr 2004, which uses many of the same case studies). Journalists‘ coverage of civil conflict tends to highlight popular grievances when explaining why conflict occurs where it does, neglecting to point out that peaceful individuals and groups also have grievances.

Horowitz (1985, 102-105) and Deutsch (1961), among others, note how modernization can occur at different rates across ethnic groups in a country, thereby creating a ―modernization gap‖ that might politicize on ethnic difference and fuel ethnic conflict (see also Gellner 1983). Whether one focuses on absolute levels of poverty, rates of growth, or inter-ethnic differences created by variable rates, there is a strong tradition of scholarship that points to some form of economic deprivation making rebellion and/or conflict more likely. Furthermore, modernization not only progressed at different rates across ethnic groups (see examples in Bates 1974), but scholars have shown a relationship between modernization and geography. The rural hinterlands, far from the centers of modernity and progress, are the last to feel the benefits of modernization (e.g.,

Bates 1974; Bates 2001). As an example of how chronic the effects of this process can be, Diaz-Cayeros (2009) examines the nature of poverty persistence in Mexico and finds that settlement areas isolated because of natural geography have found themselves in a

28 ―poverty trap‖ through the last several centuries. Some of these groups were able to escape the trap through assimilation, but many were not.

With respect to economic growth, deprivation, or inequality, rigorous tests of conventional hypotheses are not prevalent, and for good reason. The crucial problem is that appropriate data are difficult to come by. While survey or census data on income are available across some countries, the data are usually missing for some time periods and for poorer countries – precisely those that are judged to be at higher risk for onset. But, more importantly, the data are measured at the national level and are unsuited for testing the hypotheses that emerge from the grievance literature. Perception of inequality and grievance is relative and it makes the most sense to think of people in a country calculating whether there is a discrepancy between Gurr‘s ―is‖ and ―ought‖ based on how others in their country are faring.

A second prominent theory of rebellion that is usually thought to compete with accounts emphasizing economic grievances instead focuses on opportunity over grievances in telling the story of rebellion. In this account, grievances are argued to be more or less ubiquitous and thus cannot explain variation in rebellion across time and space. Instead, it is argued, it is people‘s opportunity to rebel that varies substantially.

The obstacle that must be overcome in order to have rebellion is not the holding of sufficient grievance or dissatisfaction, but rather, collective action (Tilly 1978). Groups, along whatever lines they form, must deal with the problem often plaguing rebellious efforts: individual interests do not neatly align with collective interests, leading to a free rider problem (Olson 1965). Lichbach (1990, 1052) formalizes the individual‘s decision of whether to rebel and concludes that rational actors ―neither rebel against inequality in

29 wealth nor inequality in income…inequality does not directly affect conflict.‖ Lichbach argues that any posited causal relationship between inequality and conflict is spurious:

―changes in economic and political conditions affect both inequality and strategic considerations, but only strategizing affects conflict.‖

The opportunity camp argues that it is relatively easy to find the disaffected poor and thus we should look elsewhere for explanations of rebellion. Opportunity has been conceptualized in at least two ways: an administratively and financially poor state apparatus and a citizen‘s opportunity cost of taking up arms.

First, Fearon and Laitin (2003) theorize that their cross-national results linking low GDP per capita to higher risk of civil war onset should be interpreted as economically and administratively weak states incapable of fending off civilian insurgencies. State strength, not popular grievance, is key. Second, scholars have investigated the ways in which groups overcome collective action problems and mobilize resources toward rebellion. One of the most prominent theories in this vein is that of

Collier and Hoeffler (2004), who argue that the extent to which countries rely on primary commodities for export revenue is the best predictor of civil war onset, and that this reliance creates more favorable opportunities for rebel organization financing. A civilian joins a rebellion when he is rewarded individually and the reward is worth his while, so rebel organizations must be able to offer selective incentives to joiners (see Weinstein

2007; Humphreys and Weinstein 2008), making rebellion a relatively more attractive option than sitting out. Easily extracted, or ―lootable‖, resources such as diamonds, are used to attract and reward new rebels, possibly making wars last longer (Ross 2004).

30 There are two points to be made about this research. First, because of inadequate data, cross-national studies of this kind have encountered difficulty in ruling out the possibility that political and economic grievances may predict rebellion by individual demographic groups. For example, standard measures of income inequality, such as that used by Collier and Hoeffler (2004) as a proxy for level of grievance, are aggregated at the national level. But in order to analyze whether economic deprivation correlates with a particular group‘s likelihood to rebel, a measure of inequality between specific groups is required (see, e.g., Stewart 2000). Second, even if theories emphasizing opportunity over grievance are correct, additional research is necessary to adjudicate between the candidate mechanisms that lead from increased opportunity to rebellion, such as a weak state apparatus or earnings foregone by rebelling.

The sub-national literature that does exist has relied either on case study evidence and/or the Minorities at Risk (MAR) dataset to uncover relationships between ethnic group characteristics and rebellion. This research agenda has collected data on groups experiencing political and economic discrimination, and resulting studies have suggested that geographically concentrated ethnic groups with historic ties to territory are more prone to rebellion. Results have been mixed on whether state-sponsored religious/political discrimination or economic disadvantage encourages rebellion, though case studies of different ethnic groups have suggested that grievances are influential.

Unfortunately, many of the inferences drawn from this line of research are biased by a common problem: the sample of ethnic groups in MAR includes only those that are perceived to be ―at risk‖, and the case study literature often over-represents accounts of rebellious (as opposed to peaceful) groups. Excluding groups that are not politically

31 active or over-sampling rebellious groups in the analysis can lead to an biased estimation of the effects of political/economic/cultural grievances on the probability of group rebellion.

The research question at the heart of this project pushes us to situate our analysis at a certain level of aggregation, the ethnic group. It bears repeating that though we might accept that poverty – by whatever mechanism – is a risk factor for country civil war onset, it does not follow that the same relationship is at work for ethnic groups in a given country. We can say the same about inferring such a relationship about ethnic group rebellion based on individual-level results of studies on insurgency participation. One of the advantages of this project is the ability to evaluate such claims at the appropriate level of analysis with the best available data. Others have done studies of the relationship between poverty or inequality and rebellion at levels of analysis other than the individual or the state, and I turn now to a brief discussion of those studies and why their results have not answered the research question under study here.

2.3.2 Inequality and Studies of Sub-national Rebellion

It is critical to note that the broad literature on poverty or inequality and rebellion, as well as studies on the spatial variation of economic development within countries, have led to multiple theories linking groups‘ economic status with rebellion, at least in part because the predictions are not always consistent. Horowitz (1985) predicts that poor groups in poor regions will try to secede more frequently than more advanced groups. In contrast to

Horowitz, Sorens (2005) finds that among democracies, relatively richer provinces vote in higher proportion for secessionist parties than poorer provinces and argues this is

32 because wealthier groups are the losers in democratic countries‘ redistributive policies.

Gourevitch (1979) argues that peripheral groups are likely to rebel when they are growing economically and the core is losing economic ground, with an implication that richer peripheral groups might be more likely to rebel.6 Hechter (1993) attempts to explain why certain peripheral regions retain their cultural distinction from the core and do not assimilate, and tests competing theories that predict these culturally distinct regions may or may not be more likely to rebel.

More recent studies have made use of micro-level economic data in developing countries to study economic growth and conflict, but most exhibit a strong disconnect between theory and research design. For example, some studies have investigated the relationship between the spatial location of ‗conflict zones‘ in a civil war and the socioeconomic status of respondents, sometimes using geocoded data giving the respondents‘ location. They point out the fact that there is likely to be spatial correlation on economic variables across sub-national units of analysis, and to help ameliorate this problem some scholars have cut countries into equal area grids and used those rectangles as the units of analysis when investigating where conflict events are most likely to occur

(e.g., Buhaug and Rød 2006; Hegre and Raleigh 2006). But, it is not at all clear what we should infer about the behavior from the results, and while this approach takes seriously such methodological issues as spatial autocorrelation (see Buhaug and Lujala 2005), the units of analysis in these studies have little or no real-world meaning. Accordingly, substantive interpretation of any empirical results can be difficult. The artificial spaces do not map onto boundaries that are politically, socially, or economically relevant.

6 ―Economic distress alone will not produce [ethnically-based politics], nor will ethnic subordination alone. So long as the core appears viable, the peripheries appear to accept their situation, even if they are poor and dominated‖ (p. 320).

33 Østby et al‘s (2006) and Østby‘s (2008) work improves upon this aspect of previous studies by focusing analysis on administrative regions within countries, mapping DHS respondents to those regions via geocoded survey data. The authors develop measures of inter- and intra-‗regional‘ inequality in developing countries and examine the relationship of those measures to geographic zones of conflict, as coded by

PRIO in the ACLED dataset. A recent collection of studies purports to show a relationship between inter-regional inequality and conflict, but with mixed results

(Stewart 2008). Østby (2008), for example, studies the relationship between inequality at the level of sub-national administrative regions and a country‘s likelihood of falling into civil war. Taking measures of inequality between the country‘s two largest groups as a proxy for relationships between all groups (p. 151), she finds that measures of social polarization and inter-group inequalities are positively associated with civil war onset. As the unit of analysis and dependent variable are quite different from those used here, however (e.g., Østby‘s work is essentially at the country level), it is not clear what the implication of that study is for the research question here.

Individual-level studies of participation in rebellion have found that measures of poverty are associated with greater probability of participation, though again, these studies do not examine inequality between groups, but rather individuals‘ values on economic proxy variables.7 Østby et al. (2006) look at the relationship between the sub- national location of conflict events/battles during a civil war and the economic status of

7 For example, Humphreys and Weinstein (2008), in their careful individual-level study of the determinants of rebel group participation in Sierra Leone, find that lack of education and poverty are associated with a higher risk of participation in the RUF. Similarly, in a study of the determinants of male youths joining rebel groups based in the oil-rich Delta region of Nigeria, Oyefusi (2008) also finds that low income and lack of education are risk factors. Lowi (2005, 232) describes the majority of membership in armed groups in Algeria‘s civil war as unemployed.

34 the conflict event‘s surrounding administrative district. They find evidence of a non- linear relationship between a region‘s relative deprivation and the presence of conflict events. Conflict events are most likely to occur in the most deprived and the most economically advantaged areas. Moreover, their wealth index measure is a positive predictor of conflict events occurring in a region, though the coefficient is not statistically significant. Finally, and contra expectations, they find that intra-regional socioeconomic inequality, in terms of household assets and education years, to be positively associated with conflict occurring in the region.

There are two points to be made about the approach taken in this dissertation toward measuring economic inequality across ethnic groups as compared to approaches in the work just mentioned. While one should consider the problems that arise from using sub-national units of analysis such as regions or districts, this is preferable to using artificial units of analysis because the former are likely to tell us more about individual and group behavior as the dependent variable is being linked more accurately to the appropriate individuals and/or groups. On this point I concur with the spirit of Østby et al‘s (2006) approach.

The second point is to highlight the differences between this paper‘s approach and that of work like that of Østby et al (2006). The unit of analysis in this paper is the actual geographic area principally inhabited by an ethnic group (as defined by language surveys taken in the field), not an administrative region. This area, I would argue, is more meaningful than an administrative district for purposes of studying rebellion, a behavior that often is conditioned on ethnicity, not on an administrative unit. It should be noted that our dependent variables are quite different: the outcome of interest here is the

35 principal ethnic composition of a rebel group fighting against the state, while for Østby and others, the dependent variable is usually zones or points of conflict across geographic space. We are asking two different, though perhaps related, questions: what are the features of an ethnic group that makes it most likely to be involved in a rebel group? vs. where in geographic space do conflict zones and points appear? This difference may explain some reasons for the divergent choices in the unit of analysis.

To summarize, findings from the current literatures on poverty and rebellion are insufficient for purposes of answering the research question of concern here. Country- level and individual-level findings do not necessarily transfer well to other levels of actor aggregation, such as ethnic groups. Studies that look between the individual and the state in terms of aggregation are unsatisfying because they either choose units of analysis that are artificially created and have little discernible linkage to actors whose behavior we are interested in, or the dependent variable being studied (e.g., conflict events) is not theoretically well connected to estimates of economic well-being.

2.4 Wealth’s Varying Effect on Rebellion

As accepted as the relationship between poverty and rebellion has become in the literature on mobilization toward political violence, it is worth examining in more detail to understand the ways it is inherently problematic. In the first place, it is not immediately obvious why poorer people or groups are better able to act collectively and form successful movements, given their low involvement in politics of all kinds and their lack of material resources. Organizing for collective action – particularly in the context of challenging the state and the risks involved in such a venture – is not an easy task. To

36 start and maintain a successful movement of this kind, one needs various kinds of resources. Indeed, the central issue involved in understanding many types of behavior involving political violence is that of how groups are able to act collectively (Olson

1965). Revolutions, rebellions, mobs, riots, and coups all require individuals to come together and solve this dilemma if they want to be successful in their aims. If they are not able to act collectively, they are just individuals acting in isolation from one another (if at all) and movements fail to develop. The standard argument in the literature relating wealth to rebellion – particularly in the context of group behavior – predicts that the poorer the group, the more likely it is to engage in various forms of political violence directed against the state. According to this reasoning, relative or absolute poverty serve as stronger motivation for rebellion, but it is not clear why this motivation should translate into a higher capacity to overcome problems of collective action.

The potential rebel needs financial resources to purchase arms with which to fight, which requires that your group of potential rebels has that money or is connected to those who have it. He would want a group with existing and strong connections to each other and with previous experience and knowledge of solving problems together. Poorer groups of people do not necessarily fulfill these conditions for successful mobilization.

Rather, it is the relatively wealthier groups in society who should be better able to act collectively and start and sustain violent movements designed to challenge the authority of the state.

The European experience illustrates this principle in the context of regional autonomy movements. Those groups that are relatively better off, better educated, or historically more culturally advanced are the groups that mobilize for purposes of gaining

37 concessions from the state. We see this in Catalonia and with the Basques of Spain

(Horowitz 1985, 250), the Walloons and Flemish in Belgium (Swenden and Jans 2006,

879), Bavarians in Germany, and northern Italians (Agnew 2001, 105).

Wealthier groups may have greater capacity to act collectively and mobilize toward violence, but do they have the will to do so? More specifically, should we expect these groups to have greater motivation than poorer groups? Horowitz (1985, 249-250) notes that ―[a]dvanced regions usually generate more income and contribute more revenue to the treasury of the undivided state than they receive. They believe that they are subsidizing poorer regions.‖ ―Advanced regions tend to complain of revenue-expenditure imbalances‖ (Horowitz 1985, 259). These and other examples suggest a reason why we could expect wealthier, more advanced groups to be motivated to rebel. Psychological grievance need not be the sole province of the poor. Grievance could very well develop – and indeed, has developed – among the wealthier groups in a society, as they feel that their wealth is being exploited by the poor or culturally inferior nationals. Writing of the way in which inter-ethnic inequality, broadly defined, can encourage group rebellion,

Frances Stewart observes that ―it is not necessarily the relatively deprived who instigate violence. The privileged may also do so, fearing a loss of power and position‖ (Stewart

2008, 12).

Indeed, in countries with some variant of federalism, political and financial institutions can encourage exactly the kind of imbalance that Horowitz describes. The fiscal interaction between center and region can lead to resentment among some sectors of the population (see Azam 2001). Where a sub-national government fails to meet its obligations to its constituents in terms of public service provision or entitlements, for

38 example, those constituents look to the center to bail out their region and to reestablish entitlements. Consequently, the center expends more of its revenues on the poorly performing regions (that is, regions depending heavily on fiscal transfers and grants from the center instead of local taxation to survive) and the better performing, richer, self- sufficient regions pay for the poorly performing regions (Rodden 2006, 8-9, chs. 3-4).

Nowhere do the rich want to subsidize the poor of society.

Contending that relatively wealthier ethnic groups have greater capacity to launch and maintain insurgencies than their counterparts and that they have sufficient motivation for doing so is not at odds with the scholarship on participation in insurgencies (e.g.,

Humphreys and Weinstein 2008; Oyefusi 2008; Lowi 2005, 232). At the organizational level, these movements require resources and entrepreneurs with connections to leaders and influence, which wealthier groups are more likely to be able to provide. However, poorer individuals within those groups may be more likely to join these movements and populate the insurgency‘s rank and file. Thus, the idea that relatively wealthier groups are better able and more likely to instigate insurgent movements is consistent with the individual-level empirical results that point to poverty as a predictor of participation.

My theory of ethnic group rebellion recognizes that not only might relative poverty and wealth be positively related to rebellion risk, but that the conflicting results and arguments in the extant literature on this question might be the result of the failure to theorize about how groups‘ economic well being could have different effects on rebellion depending on the goals and nature of the rebellion. There are many typologies that scholars have constructed to meaningfully categorize civil conflicts depending on the line of inquiry: ethnic vs. non-ethnic; identity vs. non-identity; traditional vs. irregular; etc.

39 My theory of rebellion that I advance in this chapter and test in the remaining part of the manuscript makes a distinction between civil wars in which the aim of non-state actors is to oust the existing government and take control of the apparatus of the state, and those conflicts in which the aim is instead much more limited in scope and involves issues of territorial and/or regional autonomy, and perhaps even secession.8 African examples of the first type – government takeover bids – are the civil wars in Liberia, Somalia,

Mozambique, and UNITA‘s struggle in Angola. Examples of the second type – territorial conflicts – are the Casamance conflict in Senegal, the conflict over Azawad in Mali, or

Ethiopia‘s conflict over Afar.9 The aims of these two types of conflict are extremely different. I argue that this difference should predict variation in the motivation to rebel in a certain way and in the capacity to be successful in that type of rebellion, and this prediction should help us make sense of the patterns of ethnic group rebellion.

Consider the type of rebellion whose underlying aim it is to take over the state.

This movement requires a good deal of manpower and material resources, as taking over the state usually means forcing the government‘s leadership from power and controlling the capital city. The aims of the movement necessitate expensive and direct confrontations with the state‘s military and counterinsurgent capacities. In line with this logic, I find that relatively wealthier ethnic groups are more likely than poorer groups to be involved in government takeover conflicts. There are three arguments explaining this finding.

8 While it is true that this latter type of rebellion can be usefully subdivided further into those that seek regional autonomy (separatism) and those that seek full independence (secession) (see Baker 2001), I do not make such a distinction here as the distinction is often blurry in practice and the objectives are extremely similar. 9 These two types of civil conflicts are coded by PRIO in their Armed Conflict Database (see chapter 3).

40 First, such movements require weapons and enough soldiers to confront the forces of the state directly. If the movement does not have the power to oust the sitting government by subduing the state‘s military forces, it aims to create enough instability through its threat to the state that regime change will occur – perhaps internally. In order to exert this kind of pressure, the movement would benefit from groups that are relatively wealthier.

Second, groups living closer to the capital and center of power do not have the luxury of a secessionist option. These groups are too close to the center for the government to allow them to physically separate from the state. To change their status in society, the most viable option available is try to take over the state.

Contrast these features with those of a conflict in which insurgents are seeking secession or some degree of autonomy from the national government. The requirements to begin and maintain this second type of insurgency are different. Secessionism and bids for regional autonomy are the province of groups living in the periphery – both in a geographical and a cultural sense of the term. Geographically, I will show that the farther away an ethnic group resides from the capital city, the more likely it is to be involved in this type of conflict. Three arguments explain this empirical finding, and they dovetail with those describing government takeover rebellions and they form the other half of my theory of ethnic group rebellion.

First, the requirements for beginning and sustaining a bid for autonomy or outright secession are fundamentally different from those for movements aiming to capture the state. Since secessionists do not have to face the state‘s forces directly, they can afford to be weaker than organizations trying to take over the state. Secessionist

41 movements can be more easily sustained without being as well funded or as well organized. As such, poorer groups could have better success being involved in secessionist bids.

Second, we should expect secessionist movements and bids for territorial autonomy to occur far from the state‘s center of power if we believe that the state is less able to project its counterinsurgency capability as we move away from the capital. To the extent that this type of power is more concentrated in and around the capital (and in

Africa, where many leaders worry about political violence like riots, protests, and coups at every turn, this seems an eminently reasonable assumption) and this power is costly to move and supply as distance from the center increases, there is greater opportunity for peripheral groups to be involved in such movements. Groups in the periphery face less risk of detection and punishment at the hands of the state than groups closer to the center, so the opportunity to rebel is greater. This is all the while assuming that the motivation for peripheral groups to be involved in secessionist bids is at least as great as for groups closer to the center; there is good reason to believe that peripheral groups‘ motivation is, in fact, greater than more centrally located groups, and this leads to the third argument.

Third, motivation to secede or gain greater autonomy/concessions from the state should be greater among peripheral ethnic groups than among those closer to the capital.

―Secessionist movements have also been observed in peripheral regions that have suffered losses in income and have become poorer relative to the center‖ (Berkowitz

1997). In Horowitz‘s (1985, 258) typology of secessionist dispositions by group and region within a country, he observes that ―backward‖ groups in ―backward‖ regions are the most frequent secessionists. These economically depressed (and sometimes culturally

42 distinct) regions are located in the country‘s periphery (e.g., Bates 1974; Bates 2001).

African groups located close to the capital are economically more developed and have greater political access than peripheral ethnic groups. This general spatial pattern of economic and political development – shown in chapter 4 – is due largely to political decisions made during the colonial period. Colonial governments of African states often developed and promoted groups and regions nearest the capital. Over time, these areas became not only the most developed in terms of infrastructure and public service delivery, but the people living in them became the most educated and over-represented in the civil service. As a consequence, peripheral groups are poorer and have the necessary motivation to become involved in these movements. Chapter 4 develops this argument more thoroughly.

In conclusion, the theory of ethnic group rebellion that I advance in the chapters that follow makes the principal claim that the state‘s inability to control certain territorial regions and the people that inhabit them is the most parsimonious and powerful predictor of which ethnic groups are most likely to rebel in civil war. Groups located farther from the capital city – the state‘s center of military, administrative, and political strength – are more likely to rebel. An ethnic group‘s location relative to the center can constrain or encourage it to become involved in a particular type of rebellion, either a separatist/secessionist bid over territorial autonomy, or an attempt to take over the state itself and oust the government. These types of rebellion attract ethnic groups with different profiles. Whereas territorial rebellions are most likely to involve peripheral groups who also happen to be quite poor, governmental takeover rebellions require ethnic groups that are better organized and wealthier. At the theory‘s foundation is the way in

43 which sub-national variation in the ability of the state to control its territory and population affects ethnic group behavior in civil war.

44 Chapter 3

Ethnic Group Data Sources and Collection Methods

As much of this dissertation is empirical in nature and because the data collected for the project features repeatedly in the chapters that follow, this chapter is devoted to a discussion of the sources and means of collection of the data so as to avoid repetitive explanation in later chapters. In completing the project, data were collected at the level of individual ethnic groups across Africa, so as to enable empirical testing of theories that call for such a level of analysis. In testing competing predictions of theories centered on state capacity and economic and political grievances, three main categories of data are explained below, in addition to a discussion of the creation of an original dataset of

African ethnic groups. First, data on the natural geography of ethnic groups‘ territorial regions are collected, as many of these features factor prominently as measures of the state‘s ability to project coercive capacity across territory. These factors include land area, ruggedness of terrain, road coverage, and existence of valuable natural resources.

Second, the collection of survey data on ethnic groups‘ relative economic well-being is described. These data are used to test competing predictions about poverty‘s effect on rebellion. Third, the data used to measure ethnic groups‘ political representation in national government is described.

3.1 Ethnic Groups

To perform the kind of analysis necessary to undertake this project, I had to decide how to define the unit of analysis. This meant undertaking the construction of a defined set of ethnic groups, which proved challenging. As students of ethnic politics are painfully

45 aware, studying political behavior in the shadow of ethnicity is a rather difficult affair, as not only are scholars undecided about what it means to conceive of ethnicity or how to define an ethnic group (Chandra 2006), but individuals may self-identify differently depending on the situation (e.g., Laitin 1998; Fearon and Laitin 2000; Adida 2010).

These concerns notwithstanding, however, an answer to the research question required the development of criteria that would define the units of analysis.

In constructing these criteria for the identification of ethnic groups that would serve as the basis of data collection and empirical analysis, I faced two competing pressures. A core concern with most available studies of ethnic group rebellion and other group-based forms of political violence is that their results are infused with bias because only groups that are deemed ―at risk‖ of political violence are included in the Minorities at Risk dataset, the principal source of data for these studies. To avoid such problems of bias, my list had to include an exhaustive set of ethnic groups, one that did not exclude any group. Fulfilling this criterion, however, does not help in deciding how an ―ethnic group‖ should be defined. Ideally, we would want to define ethnic groups along a dimension that corresponds to some sense of shared cultural identity, but is free from the charge that the means of classification is influenced by politics or even by conflict, as this would introduce a new source of bias. The list of ethnic groups identified in Ethnologue

(2005), a publication whose purpose is to ―provide a comprehensive listing of the known living languages of the world‖, arguably is prone to little or no bias of this kind, at as it is based solely on language as the division between groups. Its methods of carrying out this goal are fully described in its volumes. Linguists and translators conduct surveys to generate most of the volume‘s lists with no attention paid to any factor other than

46 language because the surveys are completed in service of the organization‘s primary goals of translation and literacy development in the vernacular. Critically, these surveys not only identify and classify the different language groups in a country, they also identify the geographic space principally inhabited by each group.

While such a list is attractive for reasons just noted, it is not ideal in light of the second competing pressure, that of relevance and practical collection of data. Many of the language groups identified in Ethnologue are minor, sometimes numbering only a few hundred people. This made collecting group-level data for such groups – for example, their economic status, political representation, and involvement in conflict – impossible because such data simply do not exist for most of these groups. To combat this problem, I turned to a list of ethnic groups developed by Fearon (2003) in his study relating to ethnic diversity across countries. Fearon‘s list attempts to mimic the perceptions of nationals as to the ordinarily understood ethnic divisions in each country. As such, the division of groups is not one-dimensional. It is at once linguistic, cultural, and even geographic. But, it closely mirrors other lists of African ethnic groups constructed by anthropologists, political scientists, and other scholars, which should give us more confidence that it is tapping into the relevant ethnic cleavages in each country, as seen by nationals. Only people groups comprising at least 1% of the country‘s total population are included in

Fearon‘s list.

To make use of the geographic nature of the Ethnologue data on ethnic groups, but also to enable collection of other types of data on group categories that are more conventionally in use, I developed a slightly adapted version of Fearon‘s list to use as the

47 units of analysis.10 This adapted set of groups then had to be matched in some way to the set of groups in Ethnologue. To do this, I matched Ethnologue‘s list of ethno-linguistic groups in each country to Fearon‘s list. The matching process was based on linguistic similarity between Ethnologue and Fearon groups: an Ethnologue group within a certain linguistic distance, as measured on a language tree created by Ethnologue, of a Fearon group was matched to it.11 Specific coding rules were devised to account for all contingencies, and in a few cases it was necessary to consult secondary sources in an effort to unpack the variety of ethno-linguistic groups contained in a Fearon category in order to code Ethnologue‘s groups into that category.12 The vast majority of each country‘s population was accounted for in the matching exercise. That is to say that while some Ethnologue groups could not be coded into a Fearon group based on ethno- linguistic similarity, these uncoded groups are almost exclusively small ones that compose far less than 1% of the country‘s population and thus are unlikely to bias results.

Based on this coding process, the unit of analysis in my data is a country-ethnic group-year for 48 African countries from 1980 to 2006. There are a total of 359 different ethnic groups in my data.

10 In a few cases, Fearon groups that were geographically based and not linguistically based were modified to reflect a linguistic basis of division (e.g., Southwest and Northwest in Cameroon; Middle Belt in Nigeria). 11 The measurement of linguistic distance is made possible by the way in which Ethnologue (and other linguists) classify languages into families, sub-families, and even lower groupings. The system resembles a genealogical tree, whereby one could determine how closely related he was to a certain person in his extended family by examining the tree. The classification system is fully described in Ethnologue. 12 Principal secondary sources are Yakan (1999); Scarritt and Mozaffar (1999); Olson (1996); and Morrison et al. (1989).

48 3.2 Dependent Variable: Ethnic Group Rebellion

The dependent variable is whether an ethnic group in a given country is involved significantly in a rebel group operating in a given year. The list of civil conflicts and associated rebel groups was taken from PRIO‘s Armed Conflict Dataset (v. 4-2006).

Three of the four types of conflict in that dataset are included; only strictly interstate conflicts (i.e., no associated rebel groups) are excluded by rule, though because the dataset does not begin until 1980, the colonial wars of earlier years are, in fact, excluded as well.13 Coups d‘état are counted as conflicts in PRIO‘s dataset if they meet the minimum casualty threshold, but I exclude coups from the majority of my analysis, and where coups are included in any statistical results, this is clearly noted.14

Secondary sources were consulted to determine the ethnic composition of as many listed rebel groups as possible for each country-year in the PRIO dataset. If an ethnic group is found to have been a significant participant in a rebel group for that year, a dummy variable is coded ―1‖. Recorded passive support from an ethnic group was not enough to garner a ―1‖; few lone rebels from an ethnic group were also not enough: rebel group members must be drawn in significant numbers from an ethnic group‘s population.

To be clear, only rebel groups are linked with ethnic groups in the coding of the dependent variable; the government side in the civil conflict is not linked with any ethnic

13 A conflict is defined in the PRIO dataset 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.‖ This has the consequence of including more conflicts than other data sets with higher battle-death thresholds. 14 Coups are excluded from my analysis for two reasons. First, the palace coup is carried out by a small number of people and often has little discernible connection to the general population. Second, even if we could be persuaded that the coup is not theoretically different from a popular rebellion, bloodless coups do not make it into the data as conflicts, so analysis would be problematically biased in that we are missing a considerable number of coups from the data. One could make the argument that including only civil conflicts that rise to an arbitrary death threshold is problematic for the same reasons, which is true, but this coding rule has become commonly accepted in the literature. Ethnic groups farther from the capital are still predicted to be much more likely than other ethnic groups to rebel in models that include coups.

49 groups. Research assistants worked from the same coding rules as they identified rebel group composition.

3.3 Ethnic Groups’ Geography

As a result of matching the adapted Fearon (2003) list to Ethnologue data, a total of 314 out of 359 groups in the dataset have associated geographic regions. This is because some groups in the Fearon dataset are not indigenous African groups and as such do not have historically defined areas of habitation in Ethnologue, which only provides information for indigenous groups. For example, the Lebanese and Syrians in Côte d‘Ivoire do not have an associated region in the country despite comprising approximately 2% of the population.15 Using various sources, I calculated data on ethnic groups‘ population, though my population data do not change over time.

To gather data relevant to the measurement of state capacity, I used Geographic

Information Systems (GIS) technology to locate ethnic groups‘ inhabited territory in space and calculate the land area of their geographic regions.16 To measure the ruggedness of terrain over these regions, I used data on elevation taken from a USGS data project that pools elevation data from various sources. The result is a digital elevation model (DEM) that gives an elevation value at 30-arc second (approx. 1 km) intervals.

Road coverage, a measure of state penetration in an area, was measured using data supplied by the Digital Charts of the World (v. 3.2). Unfortunately, this road data does not vary over time and the coverage date varies across countries. In general, the data

15 The lack of such data on these groups is not worrisome: Toft (2003) finds that non-native, immigrant groups are virtually never responsible for rebellion against the state and I found no evidence in my research to suggest otherwise. 16 Spatial data on ethnic groups‘ regional locations is adapted from Global Mapping International‘s ―World Language Mapping System‖ data, version 3.2, which corresponds to Ethnologue (2005).

50 reflect road coverage in an area during the 1980s and 1990s. A project involving the manual digitization of historical maps depicting advances in road networks across countries is underway to supply time series coverage of this variable, but it was not completed in time to use for this version of the manuscript.

Data on the location of natural resource wealth across ethnic groups‘ regions were generated based on the PETRODATA dataset (v. 1.1), published in Lujala et al. (2007).17

The locations of diamond mine sites are taken from the DIADATA project, published in

Gilmore et al. (2005) and Lujala et al. (2005).18

Most importantly for the theory I advance, the distance between the capital city and the group‘s region is a measure based on the centroid of the group‘s regional area.

The centroid is calculated as the polygon‘s center of gravity, the point about which the polygon would balance if it was made of a uniform thin sheet of material with constant density. It is computed using the location of the polygon‘s vertices, in much the same way that area is calculated. Since the Ethnologue groups, which are defined spatially, do not always form just one polygon when they are merged to reflect their new status as a larger group in the adapted Fearon list, the centroid has an advantage of locating the central point within (sometimes) several dispersed polygons.19

To illustrate how these geographical data appear together, Figure 3.1 is a map of the Central African Republic and the surrounding area. It shows these data interacted in ethnic groups‘ geographic space. Diamond mines are shown in the Banda area, while oil wells are located just across the border in Chad. Roads and ethnic group boundaries are

17 Available from: http://www.prio.no/CSCW/Datasets/Geographical-and-Resource/Petroleum-Dataset/. 18 Available from: http://www.prio.no/CSCW/Datasets/Geographical-and-Resource/Diamond-Resources/. 19 Future versions of this manuscript will include more complicated versions of this variable that are weighted by factors influencing transport cost and time required to travel to the ethnic group‘s region. This topic is discussed in the context of the case studies in chapter 7.

51 superimposed over a continuous elevation surface. Other maps using these data are found in the treatment of several conflict cases in chapter 7. 20

3.4 Ethnic Groups’ Political Representation

To measure an ethnic group‘s political representation, I used data identifying the ethnicity of countries‘ heads of state in Fearon, Kasara, and Laitin (2007), as well as the

Archigos leader dataset (v. 2.8-t.v.). These data originally were constructed to match the

Fearon list, so further matching was unnecessary except for in a few cases. Where a year saw multiple leaders, I coded a dummy equaling ‗1‘ for each ethnic group with a co- ethnic in office for some part of the year. Another dummy was constructed to reflect which ethnic group had a co-ethnic in office for the plurality of the calendar year.

3.5 Ethnic Groups’ Economic Status

Since the early 1980s, the Demographic and Health Surveys project has conducted health and economic surveys of populations in 75 countries around the world.21 The surveys provide data not only on health, but also on a range of household economic conditions.

DHS is part of the MEASURE program funded principally by USAID. The individual- level surveys are conducted on women ages 15-49. The surveys are representative at the national level and every effort is made to maximize the geographic extent of the coverage. Sample size for a survey is generally 5,000-6,000. Surveys and questions are described in Rustein and Rojas (2003) and sample weights, primary sampling units, and other survey design elements are discussed in Measure DHS+ (2004). Across all surveys

20 All geographic data were generated using ArcGIS (ArcInfo 9.2/9.3) and associated extensions. 21 Data available at: http://www.measuredhs.com/.

52 that I used, an ethnic group in a survey – after being coded into my list – averaged 1100 respondents. I collected all of the surveys that were conducted in African countries between 1980 and 2006 which recorded information on respondents‘ ethnicity, for a total of 52 surveys of 23 different countries.22

In order to make use of the DHS data for my project, I had to match the recorded ethnicity of the survey respondent to an ethnic group in my dataset. The recorded ethnicity of respondents did not always exactly match the list of ethnic groups already described. This was not a problem, however. In the first place, people refer to the same ethnic group or language using extremely dissimilar names, and this was sometimes the case in comparing the recorded DHS ethnicities to the ethnic list I use. Consultation of various sources enabled a match to be made in such cases. If a DHS ethnicity still could not be matched, usually all that was necessary was an exercise of the kind already performed in matching Ethnologue groups to the adapted Fearon (2003) ethnic group list by linguistic distance. That is, the DHS ethnicities sometimes posed the same problem: they were too specific and had to be subsumed under a larger ethnic grouping. If a DHS ethnicity still could not be matched to my list, it was placed into an ―other‖ category which the surveys use to capture non-nationals residing in the country. This category was usually a very small proportion of the total sample.

Next, following protocols given in DHS manuals, I used the individual-level data to generate statistics at the ethnic group-level for some of the included variables that

22 During this period, 35 of the 48 countries in my data experience at least one year of civil conflict (73%). Of the 23 countries represented by the collected DHS surveys, 20 of them experienced a civil conflict (87%). During this period, the total number of country-years coded as being in civil conflict (counting coups) is 289 out of 1279 (23%). Of the 52 country-years represented by the collected DHS surveys, 8 out of 52 are in conflict (15%). We can see that the collected surveys over-sample countries that have experienced conflict and under-samples conflict-years.

53 measure different aspects of a respondent‘s wealth. These variables include daily wages; the respondent‘s (and her partner‘s) years of completed education; employment status and type; materials used to make respondent‘s dwelling; type of toilet facility available; source of drinking water; access to electricity; whether respondent owns a radio, TV, motorcycle, car, or bicycle; and literacy. Figure 3.2 provides an example of this group- level data in the education level of ethnic groups in Benin in 1996, 2001, and 2006.

Though identical questions are asked across surveys, the response categories differ across countries, so I had to standardize the responses in order to make valid comparisons between variables across countries. For example, one question asks the respondent about her source of drinking water. Across the surveys, responses to this question included types of wells, pumps, streams, lakes, public taps, rainwater, ponds, boreholes, etc. I placed all question responses into two categories: good quality water sources, and bad quality water sources. This exercise in meaningful categorization was repeated for each of the variables listed above. Chapter 4 details the process by which these variables were used to create a wealth index that is used in the chapter‘s empirical analysis.

54 Figure 3.1. Geographical Data in Central African Republic

55 Figure 3.2. Benin Ethnic Groups‘ Educational Achievement over Time

56 Chapter 4

The Effect of Peripheral Location and Wealth on Group Rebellion

4.1 Introduction

This chapter is about identifying the effects of state capacity and ethnic group inequality on the motivation and opportunity to rebel against the state. Chapter 2 discussed how a dominant theory running through studies explaining variation in civil war onset or participation is that poverty is a substantial contributing factor. Regardless of the level of analysis – country, sub-national, or individual – there is considerable evidence supportive of the argument that poverty has an effect on rebellion, though other studies argue for the opposite relationship under certain circumstances. Does this relationship exist when we control for the state‘s capacity to control and monitor a sub-national region and its population, and if it does, does the effect exist for all types of conflict?

The arguments put forth in this chapter are situated at the heart of this ongoing debate. I test the core element of the theory discussed in chapter 2: that ethnic groups located in the periphery of the state are at heightened risk of being involved in violent rebellion. The theory presented in that chapter relating the periphery to rebellion outlined three main components, or mechanisms, that explain why peripheral groups are at higher risk. The first such mechanism deals with groups‘ relative economic standing and its effect on involvement in insurgency, and this chapter presents evidence suggesting that the conventional wisdom is only partly correct, and that the opportunity afforded groups by the state‘s inability to monitor and control people and territory is a more important factor. Ethnic groups are generally more likely to be involved in rebellion the farther away they are from the capital because of how the state‘s control and presence decreases

57 with this distance. In the periphery, ethnic groups are relatively poorer and thus have a combination of opportunity and motivation to become involved in insurgencies that seek concessions from the state on a limited basis. They are too far from the capital and too poor to mount a serious challenge to the state or threaten to overthrow its government.

Such rebellions are more likely to be taken up by ethnic groups that are relatively better off economically – possibly affording greater financial resources – and that find opportunity in another dimension of state weakness: the difficulty faced in controlling relatively large land areas and populations. While poverty is associated with rebellion of a certain kind, it is only operative as a risk factor for peripheral groups: poorer groups closer to the capital are not afforded the same kind of opportunity as their more far-flung neighbors and are less likely to be involved in any kind of rebellion at all.

This chapter proceeds as follows. The next section reviews the core elements of my theory of ethnic group rebellion, describing why ethnic groups located in the state‘s periphery are at higher risk of rebellion, and how groups‘ relative wealth impacts their behavior in this context. In the third section, I describe the methods I use to test implications of this argument and discuss the empirical results. A final section concludes and sets the stage for chapters 5 and 6 which test two theoretical claims that compete with my theory, namely: groups‘ political relationship with the center (chapter 5) and proximity to foreign sources of aid and ethnic kin (chapter 6).

4.2 The Periphery and State Capacity

The argument advanced in chapter 2 is that in spite of the conventional wisdom that poorer groups should be most likely to be involved in insurgency against the state, in fact,

58 the explanation that is most consistent with the data is that relative wealth is not as important a factor as the ethnic group‘s location relative to the center of state power. In fact, as this chapter shows, the evidence is more supportive of a theory that sees economic grievances as secondary to the ability of the state to exercise control over certain parts of its territory – in particular, the territory farthest away from its core. That is not to say that poverty is immaterial, but it appears as a risk factor only in a limited way, and is conditional on state capacity. This section first articulates my argument about the heightened risk associated with peripheral regions, as well as the more complicated relationship between relative wealth and rebellion risk.

I argue that the history of political and economic development in Africa puts certain ethnic groups at greater risk of rebelling against the state. This history makes ethnic groups that are situated farthest from the state‘s center of military and economic power at greatest risk of rebelling. If we believe that states have greater difficulty controlling territory that is farther from its center of power, rebellions should be more likely to be propagated by ethnic groups residing in the periphery.

The periphery is not only an indicator of state strength, however. Importantly, as we will see, the periphery is where the poorest groups in a country tend to live. Being poor and far from the state‘s center of power, these groups have one realistic option before them insofar as political violence is concerned. They should not launch or join insurgencies designed to overthrow the government and directly confront its military and counterinsurgent capability because these groups do not have the resources at their disposal to mount a well-run, well-financed, and well-equipped campaign. Instead, these groups‘ best option is to fight a low-level insurgency that seeks certain concessions from

59 the state (e.g., regional autonomy) and because the conflict area is far from the government‘s reach, the insurgency does not need to be as well-equipped to avoid being snuffed out.

Overthrowing the government and capturing the state usually requires that the capital city be taken, or that the sitting government believe that such capture is imminent and so capitulate. While being located some distance from the capital has its advantages for evading state capture, there are other factors that affect state capacity, particularly for ethnic groups engaged in an effort to overthrow the government. Just as the state has greater difficulty controlling territory in the hinterlands, large populations and large areas pose a similar problem and create opportunity for rebellion. In particular, large populations provide a larger recruiting pool from which to draw.

So far, this argument emphasizes the role of state weakness in explaining why we see certain ethnic groups rebelling, though this weakness manifests itself in slightly different ways for territorial-based insurgency and government takeover insurgency. I now explain the role poverty has in explaining variation in ethnic group rebellion.

Africa experiences more conflicts in which rebel groups intend to take over the state rather than secede or pursue regional autonomy (59% versus 41% according to coding by PRIO). This disparity – which is even greater if we factor coups d‘état into the equation – is due to the fact that gaining control of the central state apparatus may be more valuable than anything gained from territorial secession. To be successful, outside of a coup, a conflict designed to take over the state requires more organization and money to confront the forces of the state, and ethnic groups closer to the capital city tend to be relatively better off economically.

60 In contrast, peripheral groups suffer from lower economic development, as others have observed and which I show later in the chapter, and this may provide extra impetus to change the system because they find themselves on the bad end of economic inequality. But, the literature on economic deprivation and inequality makes competing predictions about whether relatively richer or poorer groups are more likely to rebel.

Moreover, the relationship between location (core and periphery) and economic standing has not been empirically demonstrated.

I show that peripheral groups are demonstrably poorer than the center, which is consistent with long-standing claims by Africanists but have not, to my knowledge, ever been systematically assessed. Further, my research adds to the literature on the interaction between political geography and economic status by arguing that political geography has a conditional effect on economic status as it relates to the potential to rebel in a certain type of conflict: territorial rebellion. It is this type of rebellion that is more likely to be associated with poor groups, precisely because the periphery offers the opportunity for these groups to be involved in territorial style rebellion via the state‘s absence, and the poorest groups tend to live in the periphery. Poverty may be motivational for rebellion, but poorer groups tend to live in the periphery, which limits their options insofar as political violence is concerned. Seeking to overthrow the government is probably not a viable option for such relatively poor, far-flung groups; their hope of gaining concessions from the government lies in involvement in insurgencies whose aims and organization are more limited in scope and require fewer resources. While poverty is associated with territorial rebellion, this relationship is

61 conditional on the ethnic group‘s location: it only holds for the most peripheral ethnic groups.

Insurgencies designed to take over the state, by virtue of their aims, should attract a different kind of ethnic group because the task at hand does not lend itself well to poor groups occupying the hinterlands. Taking over the government and reorienting the institutions that distribute state resources, while probably attractive to most ethnic groups, is not a viable option for groups unless they have the means at hand – in the way of financial resources and human capital – to mount a serious challenge to the state. We might expect, then, that only groups that are relatively more economically advanced are likely to be involved in rebellions of this kind.

In summary, through the pattern of state development in Africa produced an outcome of administratively and militarily weak states, geography allows certain ethnic groups to take advantage of the central government‘s weakness in these areas.

Furthermore, African states developed in such a way that the periphery is the most isolated and economically backward region of the state, and this relative poverty, coupled with state weakness, provides the right mix of both the opportunity and motivation for ethnic groups located in these areas to become involved in insurgencies waged over more limited claims such as territorial autonomy or minority rights. To wage the kind of insurgency whose aim is to reconfigure the existing state through the toppling of its government, poverty and peripheral location are not likely to be decisive factors. Instead, but similar to the way in which peripheral regions reify state weakness, the ability to recruit from a large population base that covers a large area is helpful for ethnic groups.

62 4.3 Research Design and Results

This section discusses the empirical approach used to test the claims of the theory just presented that relate state capacity and economic status to rebellion. Data are first presented only briefly, as chapter 3 discussed data sources and construction in detail.

Next, I describe the statistical models that are used to estimate relationships of interest, and finally, I discuss the results.

4.3.1 Data and Hypotheses

The unit of analysis used to return the results that follow is the country-ethnic group.

These 359 ethnic groups come from 48 African countries and are more fully described in chapter 3. The dependent variable is whether an ethnic group in a given country is involved significantly in any year (between 1980 and 2006) in a rebel group that fought a civil war against the state.

To measure the concept of state capacity, we draw on the data on political and natural geography also described in chapter 3, including the ethnic group‘s distance from the capital city. In cross-country studies, the percent of the country featuring rough terrain is a significant predictor of civil war onset, perhaps because the terrain both affords rebels opportunity to evade government detection and makes it more difficult for the government to project control in the area. Larger countries also are at higher risk of civil war onset, perhaps because larger populations require more layers of complicated government in order to administrate well, a decidedly more difficult task than controlling smaller numbers (Fearon and Laitin 2003).

63 A slightly different logic may operate between particular ethnic groups and the state, though the predicted effect is the same. Living in a relatively larger territorial area may give an ethnic group more of an incentive to rebel because the government may not be able to monitor the territory as effectively. By contrast, roads increase the government‘s ability to project territorial control, and measures of road density in an ethnic group‘s region might be expected to correlate negatively with rebellion. Countries with large populations have been shown to be at higher risk of falling into civil war, perhaps because a large population affords rebels a greater ability to evade government detection among non-rebels (see Kalyvas 2006, 181 ff.), so estimates of ethnic group population (as a share of the national figure) are used in the analysis. In the relationship between state and ethnic group, a larger population may be more costly for a government to control completely, thus providing greater opportunity to rebel. Summary statistics of these variables are presented in Table 4.1.

Besides measures of state capacity, we also care about measures of group economic standing within a country. Chapter 3 described the Demographic and Health

Survey data and how group-level variables measuring wealth on a variety of dimensions were constructed. These variables are used in the next section‘s analysis to demonstrate a first cut at uncovering the relationship between relative wealth and rebellion.

However, the drawback of using these proxies in an analysis of group rebellion is that each variable is measuring a different dimension of wealth and so it is not clear which variable is the best measure of the concept of group economic standing in the country. Thus, in addition to estimating the relationship between rebellion risk and these economic proxies individually, I create a group-level index that makes use of the

64 information contained in all of the proxies collectively and estimate the relationship between this index and rebellion risk. This index is attractive because it allows us a much more complete and holistic picture of a group‘s economic situation than do individual variables that lack such comprehensiveness.

To create the wealth index, I begin with the raw survey data collected by the

DHS, which is coded at the individual level. Then, I use principal component analysis to create a wealth index using economic proxy variables collected at the individual level and described in chapter 3 (e.g., variables on employment type, educational attainment, housing type, etc.). This wealth index is aggregated across ethnic groups within each country-year for which a survey is recorded, so that each ethnic group is given a value on the country-specific wealth index. This method allows us to take advantage of all available economic data in a given survey. For countries that are surveyed in multiple years, I simply take the average wealth index value across all years for each ethnic group and use this average value in the analysis.

It bears emphasizing that this index is country-specific: ethnic groups are compared to others within their own country only. The reason this is important is that even though survey data are not completely compatible across countries (i.e., all variables are not contained in all surveys), within a country the data are consistently available across ethnic groups. Since we are interested in comparing ethnic groups‘ economic standing within a country and not across countries, this approach is not hampered by cross-national inconsistency in the data.

Note that the index is constructed such that more positive values indicate relative wealth while more negative values indicate relative poverty.

65 4.3.2 Empirical Approach and Results

The question we want to answer is whether (a) peripherally located ethnic groups are more likely to rebel and (b) once we control for the ability of the state to monitor and control activity, does relative poverty impact rebellion risk? To answer this question, we fit the data to logistic regression models in order to estimate the relationship between ethnic group location, wealth, and rebellion.

Before explaining the results of the empirical analysis, it may be helpful to focus some attention on the distribution of the dependent variables, as the way in which the variables are distributed bears directly on the choice of model used to test the hypotheses.

Table 4.2 reports summary statistics for the dependent variables in the cross-sectional data: all rebellions; rebellions excluding coups; government takeover rebellions; and territorial rebellions. As the statistics indicate, the variables‘ distributions are highly skewed. Only a little over 20% of groups ever rebel at all in the period under study, and only 8% of the country-ethnic group-years in the period are conflict-years. Rebellion is a relatively rare occurrence.

We first examine the relationship between measures of state capacity and rebellion. We are principally interested in whether ethnic groups far from the nation‘s capital are at higher risk of rebelling, as this operationalizes the idea that a state‘s capacity to exercise effective control over territory is not only paramount to staving off rebellion, but it is most difficult to achieve in the hinterlands.

Results in Table 4.3 are based on estimating conditional fixed effects logistic models, where the dependent variable is an indicator for whether the ethnic group has

66 ever (between 1980-2006) been coded as forming a significant portion of a rebel group engaged in civil conflict against the state. The following equation is estimated:

, (1)

where is a dichotomous variable that takes ‗1‘ if ethnic group i in country c has ever been involved in rebellion; is the natural log of the distance in kilometers between the capital city and the ethnic group‘s centroid location; is the ethnic group‘s share of the country‘s land area; is the ethnic group‘s share of the country‘s population; is the kilometers of roads in the ethnic group‘s region divided by the region‘s land area; takes on ‗1‘ if a territorially neighboring group has rebelled during the period under study; and represent individual country fixed effects.

Note that Rebel represents four different dependent variables. The model with results reported in the first column in Table 4.3 includes all types of rebellion, including coups. The second column excludes coups. The third column estimates the effects of the variables on the likelihood of ethnic group involvement in rebellions that have as their aim capture of the state (coups still excluded). The fourth column restricts attention to rebellions that are waged over some territorial issue (coups still excluded).

As noted, all models include country fixed effects. These are included for an important reason. They are a powerful way to combat bias resulting from omitted variables that operate at the country level and influence all of the ethnic groups within

67 each country. In other words, we want to control, as best we can, for everything that is specific to the country so that any results we find are due to differences between ethnic groups within countries, and not to differences between countries. Also, standard errors are clustered on country. This clustering correction is introduced because we might think that observations within a country are correlated in some unknown way, inducing correlation in the error term within a country, but that different countries do not have correlated errors. Finally, we want to control for spatial autocorrelation – the possibility that the likelihood of rebellion by any ethnic group is partly a function of conflict involvement by nearby ethnic groups (see discussion in Buhaug and Rod 2006). To control for this possibility, a dummy variable is included that takes a value of ‗1‘ if the ethnic group‘s territory borders the territory of an ethnic group that has been in rebellion.23

When we estimate the probability of being involved in all types of rebellion

(even when coups are excluded), we see that there is a positive and highly significant relationship between rebellion risk and distance from the capital. This is the key result: in areas where the state‘s power and control is absent, ethnic groups are more likely to rebel. 24 For all rebellions, the share of area inhabited by an ethnic group is positively

23 The sign on this control variable is negative in table 1, but the sign is positive in a panel analysis (reported in chapter 5) and thus squares with what we intuitively would expect if there are contagion or spillover effects: ethnic groups are more likely to rebel in a given year if neighboring ethnic groups are also involved. 24 The results from models in Table 4.3 are robust to the inclusion of three other, different, measures of distance along with distance to the capital: whether the ethnic group‘s territory touches an international border; the distance between the group‘s centroid and the closest international border; and the distance between the group‘s centroid and the nearest major city that is not the capital. None of these variables change the size or significance of other variables in the models, except in one case. For territorial rebellion, the distance between the ethnic group and the closest major city is positively and significantly (p<.05) associated with rebellion (column 4). The size of the coefficient on distance to the capital is unaffected, but it achieves a higher level of significance than in the base model (p<.05). Other results remain unchanged.

68 associated with rebellion risk, a result supportive of the argument that state capacity is key. We return to this point in a moment.

Most of the other candidate factors are not helpful in predicting an ethnic group‘s risk of ever having rebelled against the state. Take two of the factors that could affect the opportunity for ethnic groups to rebel against the state. The standard deviation of elevation across a group‘s inhabited region is not a significant predictor, though much is made of this variable in the literature on civil war onset across countries. Within countries in Africa, it is not the case that groups living in the relatively roughest terrain within a country are more likely to rebel.25 This is no doubt due, at least in part, to the fact that there is very little variation in elevation across the African continent; in fact, only Australia is flatter. It may be that in areas with comparatively high variation in rugged terrain, this factor is a better predictor of rebellion. Neither is a comparatively high road density a predictor of a group‘s likelihood of rebellion in Africa.

We gain additional insight into the role of state capacity in giving opportunity for rebellion when we estimate separate models on the likelihood that an ethnic group has ever been involved in a secessionist rebellion, or that it has been involved in a rebellion designed to overthrow the government and capture the state.26 We see that distance is a positive and significant predictor of government takeover conflicts and a marginally statistically significant predictor of territorial conflicts. Note that the size of the coefficient on distance as a predictor of territorial conflict is 1.8 times larger than it is in

25 Measures of elevation besides the standard deviation – such as the maximum, minimum, mean, and median – similarly were not significant predictors of rebellion. 26 Conflict type is coded in the PRIO conflict data. Excluding coups d‘état, 20 conflicts (59%) are coded as government takeovers and 14 (41%) are coded as territorial conflicts. Of the 77 ethnic groups that have been involved in a rebellion, 20 were involved in secessionist or territorial conflicts, while 57 were involved in rebellions designed to capture the state.

69 the model predicting all rebellions, and larger than in the model predicting government takeover rebellion. In other words, while distance from the capital makes general rebellion more likely for an ethnic group, being far away from the capital is a greater risk factor for territorial rebellion.

The group‘s share of the national area and population are positive and significant predictors of government takeover type rebellion, consistent with the theory that state capacity manifests itself in different ways, particularly in how it affords ethnic groups opportunities to rebel in different types of rebellion. While being far from the capital aids the development of both government takeover bids and territorial insurgencies, this theoretically should be more beneficial for territorial rebellion, and the results bear this out. In contrast, a large population base over a large area of territory inhibits the state‘s ability to combat insurgency, which helps ethnic groups involved in government takeover rebellion.

In addition to the opportunity created by natural geography, we also are interested in how the economic status of ethnic groups – particularly relative to other groups – might affect the likelihood of rebellion. As discussed in chapter 3, however, the collected data are limited and missing data introduces not only bias but also limitations on the potential modeling approaches. Instead of modeling the risk of rebellion by year, I estimate the impact of an ethnic group‘s economic position on rebellion in the cross- sectional data. We will do this in two ways. First, we take the average of each economic proxy variable, for each ethnic group, across the years for which I have data, and estimate rebellion risk for the cross-section. Besides the proxy variables, I include measures of the average group member‘s annual wages. Specifically, the proportion of the group that falls

70 into each quartile of the national wage distribution is used, the first quartile being the poorest and the fourth quartile being the wealthiest. The equation estimated is as follows:

E S , (2)

where E is a vector of group-level economic proxy variables constructed from the DHS data; S is a vector of variables measuring state capacity (distance to capital, area share, and population share) that were statistically significant in estimates of equation (1) above; a spatial lag; and country fixed effects.

The second, and preferred, method of estimating the effect of group-level economic status on rebellion risk is to use the wealth index in place of the various economic proxy variables in (2) above.

Before discussing results, it is useful to take note of a simple, but powerful, descriptive fact from these data. Table 4.4 shows how group-level measures of economic standing correlate with the group‘s distance from the capital. In almost every case, groups are poorer as we move farther from the capital city (only the proportion of respondents who have piped water or whose partners are unemployed/agriculturally employed are the exceptions). This picture fits with the general literature on comparative political economy discussed above, namely, that economic growth in Africa and other similar developing countries progressed from the center outward, and the most economically depressed regions and groups of people are located in the country‘s periphery. This fact is important in interpreting the results on territorial rebellion below.

71 As the models using the economic proxy variables are not the most preferred means of testing relevant hypotheses for reasons described above, the results from those models are reported in tables in an Appendix to this chapter. Each table represents a different dependent variable: all rebellions; rebellions excluding coups; territorial rebellions; and government takeovers. While the statistical significance of the economic proxy variables varies across models, a consistent relationship between a group‘s economic status and its risk of rebellion emerges from these results. There is a positive correlation between wealth and rebellion when we consider both types together in the same model; this same positive relationship is observed when we look only at the risk of being involved in government takeover bids. 27 In contrast, there is a consistent negative relationship between wealth variables and territorial rebellion. 28 This latter result particularly fits well with what we know about the spatial pattern of economic development and with the argument that not only do these groups have sufficient motivation to try to gain concessions from the state, their poverty and location mean that an insurgency with more limited aims is the most attractive option of the two types of rebellion.

Though the results are generally supportive of my theory, they are not terribly clean. Regression models using the constructed wealth index are able to impart a much broader and more complete picture of the relationship between rebellion and wealth.

Table 4.5 shows the results of this regression analysis. The results accord with results

27 There is the possibility that the unexpected sign on the coefficients in these results is due to the fact that we have eliminated the temporal element from the analysis. However, we would be more worried about this potential problem if the coefficient was signed the other direction. That is, if we found that poverty was associated with higher rebellion risk, we would worry that conflict makes groups poorer, and any argument positing the reverse causal association would be suspect. It is more difficult to think of how conflict would make groups richer, however, and lead to a spurious relationship between wealth and rebellion. 28 Recall that a positive sign on employment indicates greater poverty, as a ‗1‘ on this variable indicates unemployment or low-paying employment in agriculture.

72 from those reported in the Appendix: both distance and wealth are positively and significantly related to rebellion of all types. Columns 3 and 4 of Table 4.5 show the results when we look at government takeovers and territorial conflicts, respectively.

Though the signs are in the expected directions, neither distance nor wealth is statistically significant in predicting territorial rebellion. However, both distance and wealth are positive and statistically significant in predicting government takeover rebellion. Notice, also, how the coefficient on distance changes between the two models: the size for territorial conflicts dwarfs the result for government conflicts, as the theory would predict.

While these results are supportive of the portion of the theory that concerns government takeover rebellions – that wealthier groups should be more inclined to rebel, all else equal – they should be interpreted with caution. Tables 4.7 and 4.8 show ethnic groups categorized by relative wealth and distance to the capital and are analyzed in more detail below. Table 4.8, which depicts ethnic groups in countries experiencing government takeover rebellions, shows why the results on wealth‘s effect on government takeover rebellion should not be given much weight. Unlike countries experiencing territorial rebellion, for which we have economic data on almost all countries and their ethnic groups, we have data for only a few of the countries that experience government takeover rebellion. As noted in the table, out of 20 countries, the analysis includes economic data from only seven of them. Unless those seven countries are representative of the entire set, which is almost certainly not the case, the positive correlation between wealth and this type of rebellion may not hold for all countries in the group.

73 To this point, we have tried to analyze the independent relationships between poverty and state capacity measures on the one hand, and rebellion on the other. We might think, however, that there is an interactive effect of poverty and state capacity on rebellion, particularly given the fact that the periphery is generally poorer than the center

(Table 4.4) and this hypothesis is tested in Table 4.6. In essence, we are asking whether the effect of wealth on rebellion changes as we move farther away from the capital city.

Interestingly, we do find evidence of such a relationship, but only in the case of territorial rebellions. The reported coefficient for the wealth index (49.86) is the estimated effect on the probability of being involved in a territorial rebellion when the ethnic group‘s centroid is the capital city (i.e., the distance variable is zero). The coefficient from the interaction term shows that wealth‘s estimated effect actually turns negative when the natural logged value of distance is about 6 (or about 400 km) (i.e., poorer groups are more likely to rebel). This value for distance is above the mean (48% of groups with values for this variable are above 6) but signifies that the estimated negative effect of wealth operates on a substantial portion of the data, all else equal.

Table 4.7 makes this point in a different way. All groups for which we have data on group wealth and which reside in a country that experienced a territorial rebellion are plotted in a 3x3 matrix of group wealth against distance from the capital. Groups are placed in the terciles of wealth and distance variable distributions based on their individual values on these variables. We see that at low levels of distance, hardly any groups in each cell are involved in rebellion; the only group in the bottom third of the distance variable that rebels is the Diola of Senegal.29 In the medium values of distance, there is not much variation in rebellion across the wealth variable (20% to 37.5%). At

29 See chapter 7 for why this low value on distance is misleading.

74 high values of distance, the percentage in each cell that rebel reaches 75% for the poorest groups that are also the farthest away from the capital. This table also indicates why the results in Table 4.5 do not show an independent effect of wealth on rebellion: at high values of distance, there is a high percentage of groups that rebel in both the poorest and the wealthiest cells.

These results lend support to the argument that we should reconsider how we think of motivation and opportunity operating on different types of rebellion risk. While territorial rebellion is most closely associated with ethnic groups located in the periphery, government takeover rebellions are associated with other dimensions of state weakness: ethnic groups with relatively large shares of the population and land area. Just as we identified different aspects of state capacity as important for understanding rebellion, the relationship between relative poverty and rebellion is more complicated than the conventional wisdom would lead us to believe. Poorer groups may be more likely to be involved in territorial rebellion, particularly as their distance from the capital increases

(the interactive effect). In contrast, groups closer to the core tend to be economically better off relative to their peers and it is the government takeover rebellion type that is driving the positive correlation between wealth and rebellion in the full sample.

4.4 Conclusion

The research design employed in this chapter, with its focus on data collection at the level of an exhaustive set of ethnic groups, is arguably closer to identifying a broad pattern of the determinants of ethnic group rebellion than other studies that are hampered by lack of data or problems of bias associated with the unit or level of analysis. Indeed, by including

75 virtually all countries in the region and their ethnic groups in the analysis of rebellion, we should be more confident in our results and what they imply for a theoretical model of rebellion.

I have argued that the ability of the state to monitor and control its territory and population is the most important factor in determining which ethnic groups are more likely to be involved in any kind of rebellion. There is, in fact, a key characteristic that makes ethnic rebellion a higher probability outcome: being situated far from the country‘s power center. For reasons associated with how African states developed politically and economically, these groups are able to take fuller advantage of the state‘s absence, and they find extra motivation to rebel in their relative poverty. Importantly, however, these poor groups are probably more likely to participate in only one kind of rebellion: one that engages the state over some issue related to the ethnic group‘s territory and autonomy. Poorer groups closer to the capital do not, on average, have the resources or the opportunity afforded by state absence to launch such a rebellion. We find evidence linking economic status to government takeover rebellion, but it is marred by the fact that it comes from what should be considered a biased sample of data. However, that evidence does suggest that groups relatively better off economically are more likely to be involved.

As we have seen, the relationship between wealth and rebellion has not been articulated well in the literature on group rebellion (though, see Gourevitch 1979), nor has it been tested with the fine-grained data used here. When we consider that all rebellions are not the same in their motivations or goals, and separate them into secessionist and government takeover attempts, an even clearer picture emerges of the relationship between group wealth and rebellion. On its own, wealth is not a good

76 predictor of secessionist attempts, which are more likely to be instigated by poorer ethnic groups living in the periphery.

New questions are suggested by these findings. First, what are the factors common to government takeover rebellions that would make relatively well off groups more likely to participate? The analysis in this chapter suggests that, at least in Africa, secessionism is fueled by poor groups in the periphery, while relatively better off groups might be more likely to try to capture the state. Second, and related to the first question, are governments pursuing different strategies in trying to forestall secessionist conflict on the one hand, and government takeover rebellions on the other? The evidence presented here indicates that governments should treat these rebellions separately in terms of policy, and understanding how this is done should be a fruitful avenue for future work.

States like those under study that govern multiple nations within them already face a higher than average risk of internal conflict, relative to the rest of the world

(Human Security Report 2005, 4-24). The implication of this analysis is that that risk comes disproportionately from certain groups in society.

Taken together, the empirical results provide strong support for my theory of ethnic group rebellion that stresses the importance of state capacity and its interaction with economic status. Peripheral groups are at higher risk of rebellion, and I argue that political and economic development explains this finding through several mechanisms, one of which is the state‘s difficulty in monitoring and controlling the periphery, as well as larger ethnic group regions with large populations. In the next two chapters I address the other mechanisms linking the political geography of ethnic groups to rebellion: their lack of central government political representation and how this impacts revenue

77 distribution from natural resource wealth (chapter 5); and their proximity to transnational ethnic kin as a source of international support for insurgency (chapter 6).

78 Table 4.1. Summary Statistics of Geographic Variables N Mean Median Std. Dev. Min Max Pop Share 359 0.1279 0.06 0.1744 0.0100 0.980 Area Share 314 0.1222 0.0592 0.1680 0.00126 0.998 Road Density 310 0.0677 0.0625 0.0418 0 0.265 SD Elevation (log) 313 177.8 121.0 122.2 5.101 1000 Distance to Capital (log) 310 373.9 308.7 299.3 11.48 1819

Table 4.2. Cross-Tabulation of Dichotomous Dependent Variables Rebellion Frequency Percent 0 268 74.65 1 91 25.35 Total 359 100.00

Rebellion (No Coups) 0 282 78.55 1 77 21.45 Total 359 100.00

Government Takeover 0 299 83.29 1 60 16.71 Total 359 100.00

Territorial Rebellion 0 339 94.43 1 20 5.57 Total 359 100.00

79 Table 4.3. Estimates of the Relationship between Rebellion and Distance from Capital City (Conditional FE Logistic Regression) (1) (2) (3) (4) All Rebellions All Rebellions Govt Takeovers Territory VARIABLES No Coups No Coups No Coups

Distance to Capital† 0.629*** 0.965*** 1.220*** 1.766* (0.221) (0.285) (0.400) (1.007) Area Share 4.168** 5.184** 5.381*** 1.542 (1.910) (2.450) (1.880) (3.185) Population Share 4.096** 4.409 10.36*** -0.853 (2.014) (2.902) (2.620) (3.623) Std. Dev. Elevation† 0.0555 -0.0713 -0.198 -0.00256 (0.256) (0.312) (0.391) (0.495) Roads Density -0.128 -0.550 0.167 -1.441 (5.464) (7.103) (9.169) (19.18) Spatial Lag -0.792 -0.0697 -0.0653 -0.263 (0.486) (0.619) (0.730) (0.405)

Observations 244 174 140 62 Note: All models include country fixed effects. Robust standard errors clustered on country in parentheses. †Logged value. ***p<.01; **p<.05; *p<.10

80 Table 4.4. Correlation Coefficients: Group Economic Proxies and Distance from Capital Distance from Capital Education (respondent) -0.2434 Education (partner) -0.2633 Toilet -0.1117 Piped Water 0.0346 Electricity -0.1456 Radio -0.2512 TV -0.1158 Bicycle -0.1047 Motor Bike -0.0935 Car -0.0591 Good Floor -0.1156 Good Roof -0.2682 Good Walls -0.0253 Literacy -0.1890 Agricultural employment and unemployment (respondent) 0.2248 Agricultural employment and unemployment (partner) -0.0617

81 Table 4.5. Estimates of Relationship between Rebellion and Group-Level Wealth Index (Conditional FE Logistic Regression) (1) (2) (3) (4) All Coups Government Territorial VARIABLES Rebellions Excluded Takeovers Conflicts

Wealth Index 1.014** 0.852** 0.991* -0.380 (0.439) (0.372) (0.597) (0.762) Distance to Capital† 0.670*** 1.003*** 0.980** 3.528 (0.189) (0.257) (0.434) (2.820) Area Share 4.091* 4.826 5.270 3.312 (2.404) (3.346) (4.713) (4.731) Population Share 3.329 2.646 10.76** -0.0231 (2.631) (3.418) (5.331) (3.439)

Observations 125 84 57 38 Note: All models include country fixed effects. †Logged value. Robust standard errors clustered on country in parentheses. ***p<.01; **p<.05; *p<.10

82 Table 4.6. Estimates of Interactive Effect of Distance and Wealth on Rebellion (Conditional FE Logistic Regression) (1) (2) (3) (4) All Coups Government Territorial VARIABLES Rebellions Excluded Takeovers Conflicts

Wealth Index 7.423* 8.700** 4.045 49.86* (4.185) (4.356) (9.961) (26.17) Distance to Capital† 0.711*** 0.978*** 0.900* 3.869** (0.178) (0.265) (0.500) (1.924) Wealth x Distance -1.108 -1.346* -0.531 -8.239* (0.720) (0.784) (1.813) (4.290) Area Share 4.438* 5.091 4.755 2.861 (2.553) (3.369) (3.950) (5.389) Population Share 3.085 2.242 10.68** -2.076 (2.643) (3.288) (5.210) (2.693)

Observations 125 84 57 38 Note: All models include country fixed effects. †Logged value. Robust standard errors clustered on country in parentheses. ***p<.01; **p<.05; *p<.10

83 Table 4.7. Cross-Tabulation of Distance and Wealth for Ethnic Groups in Countries Experiencing Territorial Rebellion Wealth Index Low Medium High Low Tiv NGA Mande MAL Sarakole-Soninke MAL Sidamo ETH Peul (Fulani) MAL Wolof SEN Walayita/Sado ETH Serer SEN Diola SEN San NAM Djerma NIG Herero/Mbanderu NAM Gourmantche NIG Idoma-Igala NGA Nupe NGA Gurage ETH Distance Berber MOR Medium Hausa NIG Senufo MAL Soninke SEN Tuareg NIG Bozo MAL Yoruba NGA Hausa-Fulani NGA Peul (Fulani) SEN Ibo NGA Gambella ETH Oromo ETH Ibibio-Efik-Ijaw NGA Afar ETH Amhara ETH Edo NGA Beni-Shangul ETH Kaffa/Ometo ETH Ovambo NAM High Dogon MAL Mandinka SEN Songhai MAL Kanuri NIG Toubou NIG Kanuri NGA Tigre ETH Ogaden ETH Kavango NAM Arab MOR Notes: Table shows all groups (a) with available data on wealth and (b) residing in countries experiencing a territorial conflict. Categories of distance and wealth reflect group placement across variables‘ terciles (0-33%, 34-66%, 67-100%). Group names in red were involved in territorial rebellion at some point between 1980-2006. Countries experiencing territorial rebellions with no data on wealth: Angola and Sudan.

84 Table 4.8. Cross-Tabulation of Distance and Wealth for Ethnic Groups in Countries Experiencing Government Takeover Rebellion Wealth Index Low Medium High Low Teso UGA Naba CHD Mande, Senufo IVC Padhola UGA Kanembu CHD Baule IVC Basoga UGA Lagoon Type IVC Gisu UGA Agni-Attie IVC Toro UGA Susu GUI Banyoro UGA Kongo CON Bagwere UGA Teke CON Gurage ETH Baganda UGA Distance Lango UGA Medium Mesmedje/Kenga CHD Malinke GUI Kru IVC Mbete CON Kissi GUI Mbosi CON Kiga UGA Kirdi CHD Acholi UGA Banyarwanda UGA Ankole UGA Karamojong UGA Lugbara UGA Alur UGA Sebei UGA Kakwa UGA Madi UGA Amhara ETH Rwenzuru UGA Oromo ETH Tsonga MOZ Gambella ETH Afar ETH Sidamo ETH Beni-Shungul- ETH Gumuz Walayita, Sado ETH Kaffa, Ometo ETH High Lobi IVC Guerze/Kpelle GUI Sara CHD Maba CHD Toma GUI Tigre ETH Sanga CON Arab CHD Ogaden ETH Zambezi MOZ Makua-Lomwe MOZ

85 Notes: Table shows all groups (a) with available data on wealth and (b) residing in countries experiencing a government takeover conflict. Categories of distance and wealth reflect group placement across variables‘ terciles (0-33%, 34-66%, 67-100%). Group names in red were involved in government takeover rebellion at some point between 1980-2006. Countries experiencing government takeover rebellions with no data on wealth: Liberia, Sierra Leone, DRC, Burundi, Rwanda, Somalia, Djibouti, Eritrea, Angola, South Africa, Algeria, Sudan, Egypt.

86 Chapter 4 Appendix (Note: All models are conditional fixed effects logistic regressions.)

Appendix Table 4.1a. Estimates of the Relationship between Rebellion (All Types) and Wealth VARIABLES (1) (2) (3) (4)

Resp. Education 0.319** (0.152) Partner Education 0.297** (0.148) Toilet Facility 2.372* (1.267) Literacy 3.187** (1.561) Distance to Capital† 0.703*** 0.611*** 0.507** 0.570** (0.181) (0.205) (0.253) (0.257) Area Share 4.538 3.581 2.625 3.515 (2.817) (2.984) (2.920) (2.876) Population Share 3.721 4.661 3.823 4.321 (2.792) (3.320) (2.602) (3.359) SD Elevation -0.178 -0.172 -0.304 -0.279 (0.314) (0.332) (0.327) (0.329) Road Density -6.877 -9.480 -12.64** -8.641 (6.369) (7.186) (6.306) (6.766)

Observations 126 111 109 108 Notes: Robust standard errors clustered on country in parentheses. †Logged value. ***p<.01; **p<.05; *p<.10

87 Appendix Table 4.1b. Estimates of the Relationship between Rebellion (All Types) and Wealth VARIABLES (5) (6) (7) (8) (9)

Roof Quality 1.610 (0.997) Partner in Agriculture -3.311*** (1.204) Resp. in Agriculture -1.346* (0.692) Walls Quality -0.00434 (0.801) Floor Quality 0.540 (1.452) Distance to Capital† 0.885*** 0.564** 0.423 0.702** 0.451* (0.200) (0.244) (0.265) (0.294) (0.239) Area Share 1.031 2.980 3.183 -4.900 2.524 (4.622) (2.626) (2.472) (3.727) (2.790) Population Share 2.194 4.021 4.425 7.955** 4.100 (4.427) (2.626) (2.971) (3.230) (2.549) SD Elevation -0.407 -0.168 -0.116 -0.759*** -0.243 (0.386) (0.322) (0.295) (0.272) (0.296) Road Density -2.422 -8.213 -7.919 -5.902 -10.60 (9.155) (6.587) (6.353) (8.247) (7.101)

Observations 76 120 117 69 120 Notes: Robust standard errors clustered on country in parentheses. †Logged value. ***p<.01; **p<.05; *p<.10

88 Appendix Table 4.2a. Estimates of Relationship between Rebellion (No Coups) and Wealth VARIABLES (1) (2) (3) (4) (5) (6)

Resp. Education 0.137 (0.116) Toilet Facility 2.493 (1.580) Piped Water 2.764 (1.756) Bicycle 4.527*** (1.092) Car -4.924** (1.969) Roof Quality 2.333*** (0.812) Distance to Capital† 0.921*** 0.916** 1.150** 0.861*** 0.827** 0.954*** (0.262) (0.399) (0.581) (0.325) (0.338) (0.331) Area Share 5.068 2.975 3.139 4.260 4.285 3.770 (3.916) (3.709) (4.132) (4.009) (3.839) (4.756) Population Share 3.183 3.216 1.803 3.680 3.315 0.833 (3.673) (3.549) (3.325) (3.720) (3.664) (4.417) SD Elevation -0.445 -0.634* -0.590*** -0.468 -0.562* -0.659* (0.314) (0.373) (0.222) (0.351) (0.322) (0.346) Road Density -10.50** -17.94*** -18.73*** -21.63*** -15.80*** -1.875 (5.144) (5.924) (7.084) (5.940) (4.726) (7.642)

Observations 84 67 73 84 84 59 Note: All models include country fixed effects. Robust standard errors clustered on country in parentheses. †Logged value. ***p<.01; **p<.05; *p<.10

89 Appendix Table 4.2b. Estimates of Relationship between Rebellion (No Coups) and Wealth VARIABLES (7) (8) (9)

Partner in Agriculture -2.808*** (0.999) Literacy 2.391** (0.945) Wages (1st Q) 1.416 (6.398) Wages (2nd Q) -0.172 (3.794) Wages (3rd Q) 9.270*** (2.239) Wages (4th Q) 4.248 (11.77) Distance to Capital† 0.958*** 0.917*** 1.072 (0.316) (0.215) (1.101) Area Share 3.198 3.938 8.526** (3.420) (3.835) (3.743) Population Share 3.540 3.494 3.994 (3.586) (3.949) (7.804) SD Elevation -0.486 -0.660** -1.141*** (0.323) (0.310) (0.268) Road Density -11.98** -12.66** -20.96*** (4.850) (5.376) (5.778)

Observations 78 75 36 Note: All models include country fixed effects. Robust standard errors clustered on country in parentheses. †Logged value. ***p<.01; **p<.05; *p<.10

90 Appendix Table 4.3a. Estimates of Relationship between Territorial Rebellion and Wealth VARIABLES (1) (2) (3) (4) (5) (6) (7) (8)

Resp. Education -0.0688 (0.267) Partner Education -0.372 (0.287) Toilet Facility 2.888 (2.073) Piped Water -0.0992 (4.375) Electricity -4.227 (3.071) Radio -2.338 (4.680) Television -7.333* (4.399) Bicycle 1.736 (4.449) Distance to Capital† 3.580 3.891 1.819 3.493 3.998 3.633 4.301 3.314 (3.415) (3.852) (1.535) (3.929) (3.544) (3.159) (2.956) (2.776) Area Share 2.758 0.441 -0.0428 3.387 2.275 2.709 1.469 3.633 (5.694) (5.827) (3.660) (6.755) (6.183) (6.116) (6.797) (5.006) Population Share 0.420 0.971 0.756 0.0852 -0.203 0.619 -0.239 -0.0151 (4.429) (3.108) (4.267) (3.605) (2.596) (4.182) (2.504) (3.430) SD Elevation 0.0683 -0.533 -0.531 0.135 -0.285 0.0876 -0.727 0.0809 (1.092) (1.211) (0.795) (1.362) (1.443) (1.270) (1.676) (1.080) Road Density -6.799 -25.14 -31.34 -2.679 -8.560 -4.517 -15.82 -3.907 (35.40) (40.05) (25.81) (26.36) (33.68) (27.74) (40.67) (23.33)

Observations 38 38 27 38 38 38 38 38

91 Appendix Table 4.3b. Estimates of Relationship between Territorial Rebellion and Wealth VARIABLES (1) (2) (3) (4) (5) (6) (7) (8)

Car -21.61** (8.602) Roof Quality 4.871 (4.817) Floor Quality -3.208 (3.622) Walls Quality -0.278 (0.264) Literacy 0.979 (2.954) Time to Water Source 0.0423*** (0.0149) Resp. in Agriculture 7.700* (4.500) Partner in Agriculture 2.771 (2.108) Distance to Capital† 4.873 5.055*** 3.515 7.577*** 3.353 3.515 3.301 3.526 (3.130) (1.750) (3.143) (2.907) (3.299) (3.568) (2.987) (2.739) Area Share 2.785 7.317 1.655 -6.495 4.056 1.514 2.728 2.052 (6.903) (7.114) (6.781) (11.14) (5.085) (4.728) (5.655) (5.758) Population Share -0.570 -9.172 0.559 6.691 -0.373 0.909 -0.686 0.338 (2.879) (5.654) (3.380) (6.981) (4.782) (2.971) (2.552) (3.674) SD Elevation -1.718 -1.593 -0.000937 -3.925 0.171 0.299 -0.193 -0.0818 (1.743) (1.352) (1.432) (2.997) (1.026) (1.192) (1.067) (1.291) Road Density -20.08 16.41 -11.06 1.430 1.403 -1.459 -14.62 -10.69 (41.75) (41.44) (36.67) (87.15) (29.56) (16.07) (34.25) (26.29)

Observations 38 31 38 25 38 38 38 38

92 Appendix Table 4.4a. Estimates of Relationship between Wealth and Government Takeover Bids VARIABLES (1) (2) (3) (4) (5)

Resp. Education 0.0638 (0.163) Partner Education 0.268** (0.133) Toilet Facility 1.118 (2.948) Piped Water 1.287 (0.785) Electricity 0.243 (1.677) Distance to Capital† 0.152 0.0909 1.199** -0.101 0.130 (0.521) (0.652) (0.552) (0.615) (0.512) Area Share 0.452 -2.604 10.18 -3.193 0.249 (4.750) (8.467) (14.43) (8.323) (4.783) Population Share 7.889** 8.449** 10.98** 7.231* 8.030** (3.495) (3.510) (5.013) (3.803) (3.669) SD Elevation -0.580* -0.753** -0.660 -0.694** -0.595 (0.349) (0.369) (0.519) (0.322) (0.378) Road Density -19.56*** -26.22** -12.70 -29.16** -19.79*** (7.041) (11.16) (12.15) (13.43) (6.735)

Observations 57 51 40 46 57 Note: All models include country fixed effects. Robust standard errors clustered on country in parentheses. †Logged value. ***p<.01; **p<.05; *p<.10

93 Appendix Table 4.4b. Estimates of Relationship between Wealth and Government Takeover Bids VARIABLES (6) (7) (8) (9) (10)

Radio -0.654 (0.782) Television -0.555 (2.334) Bicycle 4.764*** (1.538) Car -4.122*** (1.455) Roof Quality 0.647 (1.150) Distance to Capital† 0.0536 0.0906 0.0849 0.0527 -0.452 (0.535) (0.538) (0.510) (0.508) (0.750) Area Share 0.402 0.331 0.385 0.486 -19.39 (4.702) (4.705) (5.760) (4.703) (14.71) Population Share 8.027** 8.002** 7.227* 7.835** 14.62* (3.569) (3.678) (3.980) (3.708) (7.801) SD Elevation -0.599* -0.575 -0.479 -0.569 -1.323*** (0.349) (0.395) (0.383) (0.364) (0.405) Road Density -20.56*** -20.29*** -28.49*** -22.21*** -29.79 (6.850) (6.722) (8.847) (6.499) (20.53)

Observations 57 57 57 57 39 Note: All models include country fixed effects. Robust standard errors clustered on country in parentheses. †Logged value. ***p<.01; **p<.05; *p<.10

94 Appendix Table 4.4c. Estimates of Relationship between Wealth and Government Takeover Bids VARIABLES (11) (12) (13) (14) (15) (16)

Walls Quality 0.0511 (0.552) Floor Quality -0.0260 (2.044) Resp. in Agriculture -1.706* (0.974) Partner in Agriculture -2.836** (1.362) Literacy 1.269 (1.073) Wages (1st Q) 5.860 (6.028) Wages (2nd Q) -1.680 (2.305) Wages (3rd Q) 12.89** (5.283) Wages (4th Q) 8.195* (4.817) Distance to Capital† -0.543 -0.00716 0.114 0.244 -0.107 0.808 (0.568) (0.581) (0.453) (0.650) (0.682) (1.271) Area Share -19.16 -4.592 0.228 -5.153 -5.816 32.26 (14.97) (7.835) (4.564) (7.935) (8.107) (39.29) Population Share 14.81* 9.353*** 7.751** 9.877*** 11.42*** 11.56 (7.578) (3.128) (3.670) (3.726) (3.574) (11.48) SD Elevation -1.296*** -0.800** -0.484 -0.817** -1.186*** -0.863* (0.405) (0.346) (0.374) (0.401) (0.303) (0.466) Road Density -30.00 -27.62** -18.39*** -25.87** -31.88*** -20.61 (19.09) (11.71) (6.762) (10.98) (10.89) (17.36)

Observations 39 51 57 51 48 Note: All models include country fixed effects. Robust standard errors clustered on country in parentheses. †Logged value. ***p<.01; **p<.05; *p<.10

95 Chapter 5

The Role of Political Representation and Cultural Difference on Rebellion

5.1 Introduction

In chapter 4, the core element of my theory on ethnic rebellion that emphasizes state weakness and resulting opportunity was empirically tested. This chapter considers and tests a prominent alternative theory that would explain ethnic rebellion: ethnic groups‘ political grievances. Similar to the logic discussed in the last chapter on the effect of inequality and poverty on rebellion, there is a considerable literature that links political inequality with political violence, including civil war. Using data on ethnic groups‘ political representation at the highest level of government, I am able to test whether ethnic groups that are poorly represented in this way are more likely to be involved in rebellion. Despite the fact that ethnic groups living in the periphery are disproportionately unlikely to have co-ethnic political representation at the center, 30 I find little evidence that such a lack of political representation, by itself, is associated with a higher risk of rebellion of any kind.

However, I find that ethnic groups that have valuable natural resources on their regional homeland but do not have a co-ethnic holding political power are more likely to rebel. I argue this is because the distribution of revenues gained from the sale of those natural resources are less likely to be distributed in a way that seems equitable to the ethnic group, thus encouraging rebellion. I find evidence of an interactive relationship between a lack of political representation and the ethnic group‘s land sitting on valuable

30 If we split groups into two categories using the median logged distance from the capital by country, we find that among groups closer to their capital city, about 45% have had a co-ethnic in power for at least one year during the time period under study. In contrast, among groups farther away from the capital, only 25% have had a co-ethnic in power, a statistically significant difference (χ2=4.65, p=0.03).

96 natural resources, such as oil and diamonds. This is consistent with, and adds context to, the arguments put forward in chapter 4 about the effect of wealth on rebellion: poorer groups in the periphery are more likely to rebel over issues regarding territory (which would include disputes over revenue distribution and ownership of natural resources).

While sitting on land that holds valuable natural resources increases rebellion risk relative to groups without such resources, the substantial increase in rebellion risk comes from a lack of political representation for those groups with natural resources.

The chapter proceeds as follows. Section 2 returns to the theory regarding political grievances and their effect on rebellion and discusses the extant literature on this question. This section also discusses the way in which natural resources – particularly oil and diamonds – are thought to impact civil war-related dependent variables, including country onset and insurgent organization. I present my argument linking political representation and natural resources to rebellion through a particular mechanism: the center‘s distribution of natural resource revenues across the country and how political representation might vary this distribution and encourage or discourage rebellion.

Section 3 presents descriptive statistics of the data used in the empirical analysis, which follows in section 4. Here, the existing literature‘s main hypotheses are tested alongside those implied by my argument. The evidence shows that political representation and natural resource wealth, on their own, are only important in predicting rebellion when we consider how they interact with one another to affect behavior. A final section concludes.

97 5.2 Political Grievances and Natural Resources

5.2.1 Political Representation and Political Violence

The way in which ethnic groups are (under)represented in national politics has long been recognized as a potential source of conflict instigation. Gurr (1970), Tilly (1978), and

Hibbs (1973) argue that repression increases levels of political dissent. Gurr (1969) and

Eckstein (1965) find that repression encourages violent behavior, while Lichbach and

Gurr (1981) suggest a U-shaped relationship between repression and violence. Gellner

(1983) worried particularly about a state in which a minority group ruled over the plurality group.31

For groups living in societies defined by colonial rule‘s legacy that encouraged control of the state (Horowitz 1985, 193), political exclusion might be particularly consequential for group rebellion. Ethnic groups lacking political representation have been linked to secessionism and takeover bids, and the issue of how to incentivize ethnic groups and their political agents to pursue peace instead of war has spawned a huge literature on such structural remedies as federalism (e.g. Lijphart 1977; Horowitz 1985,

601 ff.), or encouraging elite cooperation through the representation of multiple groups in key political institutions (Lijphart 1969). Indeed, debates waged in Congress and in the academy over just how to structure the new government of Iraq so as to ensure the highest probability of lasting peace between Sunnis, Shi‘ites, and Kurds is just the latest example of the attention paid to the perils of perceived or actual lack of political representation in the halls of power (e.g. Dawisha and Dawisha 2003; Brancati 2004;

Anderson and Stansfield 2005; Galbraith 2006).

31 On this question, cf. Cederman and Girardin (2007) and Fearon, Kasara, and Laitin (2007). The evidence suggests that this worry is overblown.

98 Control of the machinery of the state in African countries is rewarding – both for certain groups of the population and for the leaders themselves (Londregan et al. 1995,

3). Political power may be a means to an end (e.g., greater access to resources), but also an end in itself. Horowitz (1985, 187) speaks to this phenomenon in societies featuring

―unranked‖ ethnic groups: ―In short, power may be desired, not only for the lesser things it can gain, but for the greater things it reflects and prevents. Power in these two latter senses—confirming status and averting threat—usually entails an effort to dominate the environment, to suppress differences, as well as to prevent domination and suppression by others…The fear of ethnic domination and suppression is a motivating force for the acquisition of power as an end. And power is also sought for confirmation of ethnic status.‖

Even if we accept these cross-national results, however, we might still worry that at the sub-national level, ethnic groups without political representation are still more likely to rebel than those who do enjoy such representation. In Africa, there is reason to focus our attention on a particular type of ethnic group, defined by its geography. In my data, peripheral groups – those far from the capital – are less likely to gain control of the premiership, putting them at even further disadvantage relative to their peers for purposes of benefiting from government largesse. There are a number of studies that have examined leadership succession and risk factors for leadership change (e.g., Londregan et al. 1995; Goldsmith 2001; Govea and Holm 1998), but there are few or no studies relating political geography to the political representation of ethnic groups.

Besides basic political exclusion at the center, there is reason to believe that the particular non-coethnic occupying power could have ramifications for the excluded

99 group‘s participation in rebellion. We might think of political exclusion not in a binary sense, but in terms of a continuous variable, where some non-coethnics are better than others. Gellner (1997, 6) writes that nationalism‘s ―main concern is that the positions in

[the state] be manned by members of the ‗national‘ culture, the one which defines the unit. To put it in simple language: no foreigners may rule us!‖ In countries without a developed national culture, the concept of ―foreigner‖ vis-à-vis other ethnic groups may exist on a sliding scale: ethnic groups might be more apt to rebel if the premier is not only of a different ethnic group, but belongs to an ethnic group that is seen as significantly different from itself, especially if we think that greater ethnic difference in the executive‘s seat translates to perceived worse political representation than a more similar ethnic group‘s representative. This might be true if groups think that those significantly different from themselves are more likely to establish discriminatory policies toward them when they hold power.

5.2.2 The Political Economy of Natural Resources and Civil War

The literature on natural resources and conflict risk is not traditionally linked closely with theories of how political representation could impact rebellion. Countries that are more dependent on revenues from these resources‘ exportation are more likely to occur in countries that have them (Fearon 2004; Ross 2004; Humphreys 2005; Buhaug and Rod

2006). Sub-nationally, valuable natural resources are thought to encourage conflict between the national government and the resource-rich region over the equitable distribution of resulting revenues. Such disagreements over revenue sharing certainly

100 played a role, for example, in the separatist conflicts waged in Papua New Guinea (1989-

1997) over copper in Bougainville and in Nigeria (1967-1970) over oil in Biafra.

There are several theories for how natural resources could affect whether states become involved in civil war (see review in Ross 2006). Easily lootable resources might act as a source of funding for rebel groups, increasing their labor pool (Collier and

Hoeffler 2004) and even influencing how rebel groups fight (Weinstein 2007).

Dependence on natural resources could inhibit the development of state institutions (see

Humphreys, Sachs, and Stiglitz 2007) and democracy (Ross 2001), thereby contributing to state weakness and present more attractive opportunities for rebellion (Fearon and

Laitin 2003). Le Billon (2005) and Collier and Hoeffler (2005) argue that resources make secession more attractive to regions in which they are found.

The literature does make more specific predictions about the effects that particular natural resources should have on conflict outbreak. As other scholars have remarked

(e.g., Le Billon 2001; Ross 2003), when considering how natural resources might impact the likelihood of conflict, we should remember differences that exist across natural resources in the ease with which they can be extracted and exported. Alluvial diamonds are relatively lootable, while oil is a non-lootable resource: the latter requires considerable skill and equipment to extract and transport to market. As Ross (2003, 55) observes, lootable resources more easily and directly benefit local people and the poor than do non-lootable resources, simply because the extraction of the former depends more heavily on unskilled labor and less on skilled labor and capital. This insight would predict that diamond presence in an ethnic group‘s region is negatively related to territorial rebellion: since the extraction process is likely to benefit local people and not

101 require migratory labor, these ethnic groups may feel well compensated for the diamonds found on their land. Ross (2003) also predicts that we should not expect to see separatism in countries rich in lootable resources (e.g., alluvial gems, timber, drugs), based on an analysis of some cases of civil war.

Furthermore, if a resource is relatively un-lootable, like oil, its extraction process is less likely to benefit locals and instead benefit capital-rich foreign firms, skilled labor, and the government (Ross 2003, 56). The prediction that follows from this observation is that territorially based conflicts are more likely where oil is found (Ross 2006), since the local population is unlikely to feel compensated for the extraction and sale of the oil. It is unclear what prediction would follow for oil‘s effect on government takeover bids, based only on the fact that oil is relatively less lootable than diamonds.

The vast majority of this literature focuses on country civil war onset as the dependent variable of interest, the hypotheses regarding secessionist regions notwithstanding. Any hypotheses put forth regarding sub-national regions are not well tested and are often based on examination of a list of civil wars and noting which cases involved natural resource-rich regions and which did not. Particularly in examining how natural resources might impact ethnic groups‘ proclivity to be engaged in rebellion against the state, Ross‘s (2003, 48) call for new research is appropriate: ―To determine whether [his set of hypotheses linking natural resources to civil war] are valid beyond these scenarios – and hence have predictive and not just descriptive value – they should be tested with a different data set.‖ Using just such a data set, this chapter conducts a test of the traditional theory linking valuable natural resource wealth to an ethnic group‘s risk

102 of rebellion. This is the first test of its kind on such a comprehensive set of actors at the sub-national level and provides the most reliable evidence to date on this question.

The analysis goes farther than this, however, by suggesting a mechanism that explains these findings: political control of the distribution of the resource revenues. The logic behind the argument is straight forward. The presence of valuable natural resources

(whether lootable or non-lootable) on an ethnic group‘s land represents a potential source of income for that ethnic group and its people will want to be compensated justly when the state extracts and sells those resources. ―[R]esource-rich regions may feel that they have a particular claim on resource wealth and may be aggrieved if they see the wealth leaving their region and benefiting others. Such complaints have been raised in oil regions including Cabinda in Angola, Doba in Chad, and even in the small island of

Principe in Sao Tome and Principe‖ (Humphreys, Sachs, and Stiglitz 2007, 13). Ross

(2007, 245) identifies under-represented populations as particularly likely to feel this way: ―A large gap between real and expected incomes can lead to political and social unrest. This is a special danger in regions that are geographically peripheral, have little influence over the central government, and are populated by citizens with a distinct ethnic or religious identity.‖ If the ethnic group can be ‗bought off‘, the center and the ethnic group reap mutual gains from resource extraction and sale; if the distribution of revenues is perceived as unfair to the ethnic group sitting on those resources, the ethnic group is more likely to resort to violence against the state over natural resources. Thus, any positive relationship between the presence of oil and rebellion, according to this argument, is due to the way revenue is controlled and distributed at the center, and not

103 due to such revenue contributing to the financing of rebel group financing (see Fearon

2005, 487).

In order to test this argument, we need a measure of perceived equitability of revenue distribution. I propose that a useful proxy for this concept is whether one of the ethnic group‘s members wields significant political power. The assumption is that politically powerful co-ethnics are more likely to distribute the revenues in a way that the ethnic group perceives as fair, either because the distribution is in fact more favorable to the ethnic group than it would be otherwise, or because the ethnic group is more likely to accept as just a political settlement brokered by one of its own. This seems a safe assumption, given the literature on ethnic patronage. The center might decide to compensate the resource-rich region in any number of ways besides simply giving it a portion of the revenues or decentralizing ownership of resources, such as incentivizing mineral companies to hire local workers for the extraction process; restricting labor migration to the region; allow NGOs to arbitrate disputes between the local population and foreign firms over issues related to environmental protection and labor rights; or encouraging foreign firms to invest in local development (Ross 2007). The assumption is that co-ethnics in power are more likely to enact such policies favorable to their ethnic group than are politicians from a different ethnic group.

This argument adds a much-needed political element to the extant theories on the role that natural resources play in encouraging conflict. The standard story is that secessionism is more likely in countries where oil is present, but there are relatively few mechanisms explaining that variation, never mind the variation in sub-national behavior.

In other words, presumably all ethnic groups sitting on natural resources would like to

104 cash in on that wealth. Why do some resort to violence and others do not? My argument provides a political economy account of ethnic group rebellion that involves the way in which the center is likely to redistribute revenues and is based on a safe assumption, at least according to the literature on co-ethnic patronage: that those in political power are likely to redistribute central resources to benefit their co-ethnics. As Horowitz (1985,

194) notes, ―[i]n an array of societies, it is believed that officeholders will use their authority for the exclusive or disproportionate benefit of their own ethnic groups.‖32

5.2.3 Hypotheses

The previous subsections suggest several hypotheses regarding an ethnic group‘s political representation at the center, its region‘s natural resource wealth, and its risk of rebellion against the state. Conventional wisdom suggests two main hypotheses. First, politically marginalized groups will be more likely to rebel because this underrepresentation affects a group‘s motivation. Second, cultural similarity to the ethnic group holding political power should lessen the likelihood of rebellion, though not as much as having a true co- ethnic in power. This suggests the following hypotheses:

H1: Ethnic groups that do not have co-ethnic representation in the presidency are more likely to rebel against the state.

H2: The greater the cultural similarity to the ethnic group holding power, the less likely it is that a given ethnic group will rebel.

32 Though, see Kasara (2007) for the most empirically persuasive challenge to this standard theory of ethnic politics in Africa.

105

Third, natural resource-rich regions should be more likely to rebel, for any number of reasons just discussed, but we might expect natural resources to have the strongest effect on territorial rebellions. We can test two hypotheses related to this argument on lootable and non-lootable resources:

H3: The presence of oil fields in a group’s region should increase the risk of that ethnic group being involved in a territorial rebellion.

H4. The presence of diamond mines in a group’s region should decrease the risk of that ethnic group being involved in a territorial rebellion.

The argument I make is about co-ethnic political representation and its effect in mitigating the effect of natural resource wealth on ethnic group rebellion, and suggests the following two hypotheses.

H5: The presence of oil fields in a group’s region should increase the risk of that ethnic group being involved in a territorial rebellion, but oil’s impact should be greater if the head of state is not a co-ethnic.

H6: The presence of diamond mines in a group’s region should decrease the risk of that ethnic group being involved in a territorial rebellion, but diamonds’ impact should be less if the head of state is not a co-ethnic.

106

5.3 Data and Empirical Results

5.3.1 Data

To measure an ethnic groups‘ political representation, I used data on the ethnicity of countries‘ heads of state from Fearon, Kasara, and Laitin (2007), as well as the Archigos leader dataset (v. 2.8-t.v.). These data originally were constructed to match the Fearon list, so further matching was unnecessary except for in a few cases. Where a year saw multiple leaders, I coded a dummy taking the value of ‗1‘ for each ethnic group with a co-ethnic in office for some part of the year. Another dummy was constructed to reflect which ethnic group had a co-ethnic in office for the plurality of the calendar year.

When testing our hypotheses using time series data below, this coding of co- ethnic representation as an independent variable is appropriate. However, when conducting analysis on the cross-section of the data, we face a problem if we estimate co- ethnic representation in the same way. If in the cross-section we simply code whether an ethnic group has ever had a co-ethnic in power during the period under study, we immediately confront an issue of endogeneity: it could be that rebellion by an ethnic group in a given year impacts its chances of its co-ethnic attaining political power. If this were the case, regression analysis might show that having a co-ethnic in power has a positive effect on rebellion, and this result would be misleading.

To correct for this problem in the cross-sectional analysis that follows, I construct a new variable to measure co-ethnic political representation, which only affects the coding of ethnic groups involved in rebellion at some point in time. In the time series, I give the new co-ethnic variable the value ‗0‘ in every year after an ethnic group rebels,

107 forcing rebellion not to impact likelihood of having a co-ethnic in power. Thus, in the cross-section, all ethnic groups that are coded as having had a co-ethnic in power will not have gained that political representation via rebellion. This coding change results in a smaller portion of all ethnic groups (n=359) being coded as having a co-ethnic in power at some time during the study period, down to 72 (20%) from 92 (26%). This correction seems wise for analysis of the cross-section, however, and is used to address legitimate concerns of reverse causality in the results.

Finally, in order to test the hypothesis regarding cultural similarity between groups, for each year in the data I measured the linguistic distance between each ethnic group in a country and the ethnic group that held power for the plurality of that year.

Linguistic distance is measured simply by counting the number of language tree nodes that two ethnic groups have in common before the respective trees diverge.33 For example, if two languages are in the Niger-Congo family but one is part of the Atlantic-

Congo sub-family and the other is part of the Mande sub-family, then these two languages would have a linguistic similarity of 1. In this way, the greater the linguistic similarity between two ethnic groups, the larger the number.34

To test the hypotheses involving natural resources, I use data on the location of oil fields and diamond mines and pair this information with my data on ethnic group regions.

The data on oil fields are generated using PETRODATA, v. 1.1, a dataset constructed by

33 One can view this classification system at http://www.ethnologue.org. 34 See Holden (2002) for an applied example.

108 Thieme, Rød, and Lujala.35 The data on diamond mines is from the DIADATA project and is explained in Gilmore et al. (2005).36

5.3.2 Empirical Approach and Results

For the reasons outlined in the discussion of the empirical analysis in chapter 4, to test the hypotheses here we fit the data to various conditional fixed effects logistic models. The first hypothesis reflects conventional wisdom that politically unrepresented ethnic groups are more likely to rebel than groups who have had a co-ethnic occupy the country‘s presidency. In the cross-section, the following model is estimated:

x , (1)

where is a dichotomous variable that takes ‗1‘ if ethnic group i in country c has ever been involved in rebellion (tables report results from all rebellions, territorial, and government); is also dichotomous and reflects the coding of co-ethnic representation in power discussed above; x is a vector of group-level covariates found to be significant predictors of rebellion in chapter 4 and so are included here (logged distance to the capital, share of area, and share of population); the spatial lag variable takes ‗1‘ if a territorially neighboring group has rebelled during the period under study; and capture country fixed effects. Standard errors are clustered on individual countries.

35 Available from: http://www.prio.no/CSCW/Datasets/Geographical-and-Resource/Petroleum-Dataset/. 36 The data set on diamond mines used here divides diamond mines into four categories: total diamond mines; the number of ‗lootable‘ diamond mines; the number of ‗non-lootable‘ mines; and ‗known‘ subsets of lootable and non-lootable sites. Data on ‗lootable‘ mines are used in the analysis here. As for oil fields, only on shore (as opposed to off shore) fields are used.

109 Table 5.1 reports the results of estimating this model for all of the data, as well as predicting only government takeover rebellion and territorial rebellion. The first column reports results when the dependent variable is all rebellions (coups excluded). While the covariates seen to be significant from the analysis in chapter 3 remain statistically and substantively significant, we do not find evidence that having a co-ethnic in power reduces the likelihood that an ethnic group rebels. The sign on the coefficient is negative, as we would expect, but it does not approach conventional levels of significance. The second column reports results for government takeover rebellions. The results are unchanged: co-ethnic representation in the presidency does not lower risk of this type of rebellion, though again, the estimated effect is negative. Column 3 reports the results from the model when the dependent variable is territorial rebellion. While the group‘s distance to the capital remains marginally significant, the other standard covariates in the model are no longer statistically significant predictors. Again, we find no evidence that co-ethnic representation impacts the likelihood of this type of rebellion, either.37

Another of our hypotheses suggests that the cultural similarity between an ethnic group and the group holding the presidency should have an effect on rebellion. This hypothesis is tested measuring an ethnic group‘s cultural similarity to two different other ethnic groups. First, we can measure the cultural similarity between an ethnic group and the group that has enjoyed co-ethnic representation for the most number of years during the period under study. The rationale here is that such a group is likely to be quite politically powerful and thus its cultural similarity to other groups in the country should

37 There is not a statistically significant relationship between rebellion and the co-ethnic variable even in bivariate regression models of the kind shown in equation (1). Moreover, using a continuous version of the co-ethnicity variable – the percentage of country years the ethnic group has enjoyed co-ethnic representation – does not alter the results.

110 be the basis for testing the hypothesis at hand. Second, we can measure the cultural similarity between the ethnic group and the most populous ethnic group in the country.

The rationale is similar here, in that more populous groups might be more powerful.

Using these measures, we can estimate the following model, which simply substitutes in our measures of cultural similarity for co-ethnic representation in (1) above:

x (2)

The results for this hypothesis are also reported in Table 5.1. Model results in columns 4-6 use the measure of cultural similarity between the ethnic group and the most consistently powerful group. The results from analysis using this measure offer no support for the hypothesis that cultural similarity matters. While distance to the capital remains as a significant predictor of rebellion (and area and population are sometimes significant), cultural similarity is not. The coefficient on the variable is positive, which is counterintuitive according to the conventional way of thinking about this problem, but it is not statistically significant in any of the three models.38 Similarly, results using the second measure of cultural similarity are not significant; they look much the same as those shown in columns 4-6, and are not reported. For each model using a measure of cultural similarity, distance to the capital is a significant predictor of rebellion but similarity to the most populous or most powerful ethnic group in the country is not.39

To this point, we have only considered the possible effects of co-ethnicity on rebellion in the cross-section. We can also analyze these effects on behavior year to year.

38 Results are unchanged if the average cultural similarity to the group in power over the study period is used instead. 39 Results are unchanged if we omit population share from the right hand side.

111 To estimate the effect of co-ethnicity on rebellion, we estimate the following conditional fixed effects logistic model that includes a lagged dependent variable and different lag structures on the key independent variable, co-ethnic political representation in a given year. The dependent variable is the incidence of rebellion in year t.

(3)

The model includes the value of the dependent variable lagged one year to control for previous rebellion; various lag structures of the key independent variable; a contemporaneous spatial lag to control for spillover effects within a country; and country and year fixed effects. Results are reported in Table 5.2a. There is some difference between the results from the cross-sectional analysis and those in Table 5.2a. When we control for enough lags, the effect of co-ethnic political representation in the current year is marginally statistically significant and positive, suggesting that political representation does have some effect in reducing the probability of group rebellion.

We can do the same analysis on our measure of cultural similarity between the ethnic group and the group holding power in a given year. The model specification is identical to (3) above, substituting for These results also are reported in Table 5.2, columns 5-8. The current measure of cultural similarity is marginally significant, with an estimated negative effect on rebellion. That is, as an ethnic group becomes more culturally similar to the group holding political power that year, the less likely it is to be involved in rebellion in that particular year,

112 controlling for previous rebellion, neighbor groups‘ behavior, and country and year fixed effects.

This latter result lends slight support to the argument that ethnic difference is consequential for rebellion through the channel of political representation at the center, though given the null results on the co-ethnicity variable, it is not clear how much to make out of the results. In sum, we do not find strong evidence that lack of political representation, at least using the measures described here, affects the risk of ethnic group rebellion. It is worth emphasizing that these results are based on tests using the most comprehensive data on ethnic group political representation at the center, as well as ethnic group rebellion. The lack of evidence in support of the standard arguments regarding political grievances should provide the basis for re-evaluation of the argument itself, as well as the mechanisms through which political representation might matter. My argument involving ethnic political representation outlines a specific mechanism through which we should observe any effect on rebellion: how the center distributes revenues from natural resource wealth. We turn to analysis of standard arguments about the effect of natural resources on rebellion, as well as a test of my argument.

The presence of natural resources in an ethnic group‘s region might affect its likelihood of rebellion. To test this hypothesis, I use data described earlier on oil fields and diamond mines. As I do not have data on the time of discovery of these sites, I restrict analysis to the cross-section and examine whether these resources affect rebellion risk throughout the period under study. The model estimated is similar to those used in

Table 5.1 and is produced below:

113 x (4)

Table 5.3 reports results from this analysis, where refers to the number of oil fields or diamond mines found in the ethnic group‘s region; x refers to the vector of included standard control variables; and refers to country fixed effects.

Standard errors are clustered on individual countries.

Column 1 shows the effect of oil on any type of rebellion. While the estimated effect is positive, it is not close to being statistically significant. As we saw in our analysis of wealth‘s effect on rebellion, however, disaggregating the dependent variable into government takeover bids and territorial rebellions is revealing and shows why there is a null result when using all the data. Notice that as we observed in chapter 3, distance to the capital is still positive and significant, and area and population share are positive predictors of government takeover rebellions. We would expect that oil should have a positive effect on the probability of territorial rebellion, and indeed the results bear this out. Indeed, the positive (but insignificant) result for all rebellions is being driven by the strong relationship between territorial rebellion and oil.

Columns 4-6 repeat the analysis using lootable diamonds in an ethnic group‘s region as the key independent variable. The results constitute evidence that diamonds are associated with lower risk of rebellion, particularly for territorial rebellions. The results are insignificant for government takeover rebellions, and only marginally significant for all rebellions. Again, the result for all rebellions is being driven by the highly significant and substantively large and negative relationship between diamonds and territorial rebellions, and accords with the reasoning that lootable resources may have a negative

114 effect on territorial rebellion if the resources are easily transferred to the local population

(Ross 2003). This contrasts significantly with the results for oil‘s effect on territorial rebellion, which was significant and positive. Taken together, the results are supportive of the hypotheses about natural resources and fit well with the logic about the way in which lootability of resources should affect rebellion.

My argument predicts an interactive relationship between an ethnic group‘s political representation at the center and its natural resource base. In short, the prediction is that co-ethnic representation should exacerbate the effects of natural resources on the probability of rebellion, particularly territorial rebellion. The equation estimated is below and is similar to those above:

x (5)

where is the interaction between co-ethnic representation and natural resources (oil or diamonds) and the model is estimated on the cross-section of the data.

Table 5.4 reports the results from this estimation. The coefficient on oil or diamonds, , estimates the effect on the dependent variable of increasing the amount of natural resources in an ethnic group‘s region when the ethnic group does not have co-ethnic political representation (i.e., ). Likewise, estimates the effect of co-ethnic representation when no natural resources exist in the group‘s region. The effects of each of these variables on rebellion probability are interactive and require more explanation in order to understand how co-ethnic representation affects the way in which natural resources relate to rebellion.

115 Consider the predicted effect of oil on the probability of being involved in any type of rebellion as we vary whether the ethnic group has had co-ethnic representation at the center (column 1). Table 5.5 shows the change in predicted probability of all rebellions and of territorial rebellion in particular, as we increase the number of oil wells in an ethnic group‘s region. The model predicts little change – whether or not the group has had co-ethnic representation – as we increase the oil wealth in the region. By contrast, the change in predicted probability of being involved in territorial rebellion is dramatically affected by how political representation interacts with oil wealth. When the group has not had co-ethnic representation and we increase the number of oil wells in the region from zero to 5, the model predicts no difference. In contrast, when the group has had co-ethnic representation, the change is about 4%, a small decrease. This difference in predicted effect becomes larger as we increase the amount of oil found in the group‘s region. If we increase the number of oil fields from 5 to 10, again, there is no change in predicted probability for groups without co-ethnic representation. However, the same increase in oil for groups that have had co-ethnic representation sees a decrease in predicted probability of 96%.

The effect of co-ethnic representation on rebellion risk displayed here is remarkable. Thus, by itself political representation seems to have little effect on rebellion risk, contra conventional expectation. However, when we consider a particular mechanism through which it should have a mitigating effect on rebellion – the way in which revenues are distributed from the center – we find that it has a dramatic effect on exactly the kind of rebellion for which it should matter most, territorial rebellion. This result speaks to the need for scholars to think more analytically and specifically about the

116 mechanisms through which political representation could affect conflict risk at the sub- national level, as well as ways to test the implications of those mechanisms.40

The interactive effect of diamonds and co-ethnic representation on territorial rebellion risk also accords with the logic of my argument (Table 5.4). Table 5.3 shows that the independent effect of diamonds on territorial rebellion risk is negative and significant. The effect of diamonds, however, is also conditional on the nature of ethnic groups‘ political representation. The results show that, as in the case of oil, co-ethnic representation lessens the risk of rebellion, though the effect is not nearly as pronounced.

Table 5.5 also shows the change in predicted probability of territorial rebellion, as we increase the number of diamond mines in the group‘s territory, for groups that have co- ethnic representation and for those that do not have such representation. Compare the difference in the predicted probabilities between these two groups as we move from having no diamonds to having just one mine. The negative change for both groups is extremely large. For groups that have not had co-ethnic representation, this increase in diamonds decreases the predicted probability of rebellion by 0.859. For groups that have had co-ethnic representation, the decrease is greater, estimated at 0.994.

5.4 Conclusion

The objective of this chapter was to test the main hypotheses emanating from a standard theory of rebellion that emphasizes political grievances and their posited powerful effect

40 Note that the distribution of the oil fields variable is highly skewed to the left. That is, most ethnic groups do not have any oil in their regions (the value of the variable up to the 95th percentile is zero). Only 18% of the ethnic groups have at least 5 oil fields in their region, so the model‘s predictions for high values of oil are made based on few data points. Nevertheless, the results from this first model provide support for my argument, that co-ethnic representation lessens the pernicious effect that oil wealth has on territorial rebellion.

117 on ethnic groups‘ propensity to become involved in civil war against the state. This theory represents a prominent counterargument to my theory of rebellion that emphasizes state capacity and how measures of the state‘s inability to monitor and control its territory, such as the distance from the capital, group population, and group regional area, affect ethnic groups‘ opportunity to rebel. In chapter 4, we found that economic grievances were not as important as standard theories of political violence would suggest, and that wealth‘s effect on rebellion is conditional on political geography and operated.

Similarly, the results from the empirical analysis in this chapter are only weakly supportive, if at all, of the hypothesis that political grievances are the key to understanding why some ethnic groups rebel. Instead, my argument linking political representation to natural resource wealth through the specific mechanism of revenue distribution found much more support from the evidence.

Co-ethnic political representation did not emerge as a statistically significant predictor of any kind of rebellion in the cross-section, though there was weak evidence in the panel analysis that it reduced the likelihood of contemporaneous rebellion. There was little evidence that cultural similarity matters either, in that ethnic groups that were more culturally similar to the ethnic group that held power for the longest period of time were not less likely to rebel, as we might expect if we thought that political representation by more culturally similar ethnic groups would head off rebellious tendencies.

This is not to say that political representation is not important, but that we must specify the mechanism through which this power matters. While we found an independent effect of oil and diamonds on territorial rebellion risk (positive and negative, respectively), the effect of each conditional on co-ethnic political representation is quite

118 dramatic and illustrates a clear channel through which political representation does, in fact, play a role in ethnic group rebellion.

This conditional effect is strongest and only significant for territorial rebellion risk. In the case of oil, a non-lootable resource that has a positive impact on territorial rebellion risk, co-ethnic representation mitigates this effect and lowers the risk of rebellion as the number of oil fields in an ethnic group‘s region increased. The negative effect of diamonds on territorial rebellion is heightened for groups that have co-ethnic representation as the number of diamond mines increases in an ethnic group‘s region.

Together, these results provide powerful support for the argument that the political representation of ethnic groups is an extremely important factor in predicting a specific type of rebellion, but only when considered in interaction with natural resource wealth.

Furthermore, the evidence is consistent with the reasoning that such representation matters because decisions about how the center will redistribute wealth across sub- national groups and regions is a political process driven by politicians‘ desires to please co-ethnics. Governments run by co-ethnics are more likely to find ways to compensate members of their own ethnic group and thus lessen the risk of secessionism and conflict over territorial issues.

119 Table 5.1. Effect of Co-ethnic Representation and Cultural Similarity on Rebellion (Conditional Fixed Effects Logistic Regression) (1) (2) (3) (2) (4) (6) All Rebellions Government Territory All Rebellions Government Territory

Co-ethnic -1.025 -0.846 -0.732 (0.816) (1.086) (1.307) Cultural Similarity 0.0947 0.0815 0.0880 (0.0630) (0.0642) (0.0574) Distance to Capital† 0.862*** 1.244*** 1.142* 1.165*** 1.482*** 1.131** (0.286) (0.389) (0.662) (0.317) (0.503) (0.523) Area Share 5.332* 5.792** 0.407 5.708** 5.707*** 0.808 (2.904) (2.428) (2.246) (2.506) (1.957) (2.231) Population Share 5.069* 11.92*** -1.892 2.757 9.789*** -2.133 (2.824) (2.657) (4.247) (2.806) (2.991) (3.250) Spatial Lag -0.126 -0.631 0.510 0.0611 -0.280 0.496 (0.611) (0.690) (0.930) (0.609) (0.773) (0.859) Observations 174 140 62 167 128 57 Note: All models include country fixed effects. Robust standard errors, clustered on country, in parentheses. †Logged value. *** p<0.01, ** p<0.05, * p<0.1

120 Table 5.2a. Effect of Co-ethnicity on Rebellion (Conditional Fixed Effects Logistic Regression) (1) (2) (3) (4) VARIABLES All Rebellions All Rebellions All Rebellions All Rebellions

Co -ethnic (t) -1.519 -1.798* -1.757* -1.713* (0.997) (0.985) (.963) (0.954) Co -ethnic (t-1) 1.309 1.069 1.001 0.962 (0.905) (0.965) (0.913) (0.893) Co -ethnic (t-2) 0.458 0.604 0.545 (0.568) (0.638) (0.577) Co -ethnic (t-3) -0.137 -0.296 (0.401) (0.761) Co -ethnic (t-4) 0.221 (0.821) Spatial Lag (t) 1.594*** 1.526*** 1.474*** 1.460*** (0.386) (0.422) (0.429) (0.432) Rebellion (t-1) 5.137*** 5.105*** 4.995*** 4.922*** (0.352) (0.365) (0.362) (0.361)

Observations 4038 3856 3674 3492 Notes: All models include country and year fixed effects. Robust standard errors, clustered on country, in parentheses. *** p<0.01, ** p<0.05, * p<0.1

121 Table 5.2b. Effect of Cultural Similarity on Rebellion (Conditional Fixed Effects Logistic Regression) (5) (6) (7) (8) VARIABLES All Rebellions All Rebellions All Rebellions All Rebellions

Cultural Similarity (t) -0.0801 -0.112 -0.117* -0.115* (0) (0.0688) -0.0662 -0.0659 Cultural Similarity (t-1) 0.0424 0.0317 0.0147 0.0121 (0) (0.0592) -0.0482 -0.0451 Cultural Similarity (t-2) 0.0408 0.0608 0.0449 (0.0417) (0.0591) (0.0639) Cultural Similarity (t-3) -0.00659 -0.0344 (0.0452) (0.0590) Cultural Similarity (t-4) 0.0498** (0.0233) Spatial Lag (t) 1.556 1.468*** 1.400*** 1.412*** (0) (0.441) (0.455) (0.459) Rebellion (t-1) 5.181 5.152*** 5.029*** 4.982*** (0) (0.396) (0.395) (0.396) Observations 3775 3602 3429 3256 Notes: All models include country and year fixed effects. Robust standard errors, clustered on country, in parentheses. *** p<0.01, ** p<0.05, * p<0.1

122 Table 5.3. Effect of Natural Resources on Rebellion (Conditional Fixed Effects Logistic Regression) (1) (2) (3) (4) (5) (6) VARIABLES All Rebellions Government Territory All Rebellions Government Territory

Oil Fields 0.272 -0.543* 1.217*** (0.288) (0.289) (0.442) Diamond Mines -0.244* -0.0935 -11.05*** (0.130) (0.107) (2.266) Distance to Capital† 1.059*** 1.130*** 2.488** 0.915*** 1.278*** 1.027 (0.333) (0.342) (1.079) (0.256) (0.411) (0.663) Area Share 5.373** 5.684** 1.520 6.423** 6.453** 3.138 (2.542) (2.430) (2.300) (3.160) (3.247) (2.765) Population Share 4.060 10.98*** -2.989 4.222 10.86*** -2.771 (2.986) (2.547) (5.008) (2.778) (2.591) (4.232) Spatial Lag -0.432 -0.493 -0.704*** -0.0923 -0.532 0.344 (0.464) (0.624) (0.200) (0.584) (0.625) (0.844)

Observations 163 129 51 174 140 62 Notes: All models include country fixed effects. Robust standard errors, clustered on country, in parentheses. †Logged value. *** p<0.01, ** p<0.05, * p<0.1

123 Table 5.4. Interactive Effect of Natural Resources and Co-ethnic Representation on Rebellion (Conditional Fixed Effects Logistic Regression) (1) (2) (3) (4) (5) (6) VARIABLES All Rebellions Government Territory All Rebellions Government Territory

Oil Fields 0.688** -0.466 1.643*** (0.268) (0.320) (0.609) Diamond Mines -0.0381 0.0813 -7.734*** (0.163) (0.169) (1.065) Co-ethnic -0.710 -0.588 -0.148 -0.355 0.0574 -0.406 (0.884) (1.172) (1.650) (0.890) (1.360) (1.204) Interaction -0.713** -0.179 -3.670*** -0.643 -0.766 -3.899*** (0.303) (0.349) (0.443) (0.532) (0.557) (1.338) Distance to Capital† 1.052*** 1.077*** 2.258** 0.865*** 1.277*** 1.038 (0.368) (0.352) (0.991) (0.271) (0.391) (0.670) Area Share 5.719* 5.642* 2.015 6.438* 6.067* 3.256 (3.089) (3.058) (2.542) (3.335) (3.103) (3.039) Population Share 5.001 11.12*** -2.187 4.972* 11.74*** -2.458 (3.352) (2.294) (6.509) (2.840) (2.782) (5.187)

Observations 163 129 51 174 140 62 Notes: All models include country fixed effects. Robust standard errors, clustered on country, in parentheses. †Logged value. *** p<0.01, ** p<0.05, * p<0.1

124 Table 5.5. Changes in Marginal Effect of Political Representation on Rebellion

Change in Oil All Rebellions Territorial No Co-ethnic Co-ethnic No Co-ethnic Co-ethnic 0 to 5 wells 0.001 0 0 -0.043 5 to 10 wells 0 0 0 -0.96

Change in Diamonds All Rebellions Territorial

0 to 1 mines -0.859 -0.994 1 to 3 mines -.138 -0.00215 Note: Calculations based on results from Table 4.

Marginal effect of co-ethnicity variable on rebellion calculated as , where is the predicted probability of rebellion at different values of oil, holding other variables in the model at their mean values.

125

Chapter 6

Transnational Ethnic Ties as Encouragement for Ethnic Group Rebellion

6.1 Introduction

This chapter investigates the degree to which transnational ethnic ties affect the risk of ethnic group rebellion. The most prominent theory explaining foreign support for rebel groups in civil war posits that ethnic ties across borders affect leaders‘ decisions about when and where to get involved in neighbors‘ civil conflicts. Strong cultural ties between ethnic groups across borders, the theory goes, makes foreign involvement more likely, because leaders will aid foreign rebels to gain the support of key domestic constituencies.

An implication of this argument is that if an ethnic group has ethnic kin in a neighboring state, this should increase the likelihood of the group being involved in rebellion because it anticipates a higher probability of direct support from the neighboring government once conflict begins.

This argument about ethnic rebellion constitutes a rival explanation to my theory of ethnic group rebellion that focuses on the ways in which a state‘s ability to monitor and control territory and population varies across sub-national space and how this particular variation in opportunity makes ethnic groups more or less likely to rebel. The analysis in this chapter provides no evidence to support the claims of this competing theory, and instead reaffirms that factors measuring the state‘s ability to monitor its territory and population are better able to explain variation in ethnic group rebellion.

The lack of supportive evidence for that competing theory is significant. I use variables that more accurately measure the concept at the heart of the theory on domestic politics, that ethnic ties should matter if ethnic kin in the neighboring state are politically

126

powerful and/or connected. While this argument is implicit in the existing literature, it has not been measured as cleanly as in the analysis here. Second, I considerably broaden the scope of analysis on this research question. To date, research surrounding this question has focused almost exclusively on secessionist/separatist conflicts. The unfortunate consequence of this choice is the absence of theory and tests on conflicts where rebels are trying to take over the state itself, not separate themselves from it.

Particularly in Africa, we are left without an explanation for a full two-thirds of conflicts that have taken place there, since secessionism and autonomy movements are relatively less frequent occurrences.

The remainder of the chapter is structured as follows. The next section reviews a theory of foreign involvement in civil wars that emphasizes domestic politics and how ethnic transnational ties influence leaders‘ foreign policy decisions in this area. Based on the logic of the argument, specific hypotheses are derived to be tested. The third section describes the data used to test the hypotheses about ethnic ties and rebellion. The fourth section describes the empirical approach and the results of a large-n quantitative analysis of African civil wars fought between 1980-2006, using the original data set introduced in chapter 3 that locates ethnic groups‘ habitation across states and links ethnic groups to involvement in rebel groups fighting against the state. The results provide no support for the predictions of this competing theory of ethnic group rebellion. A final section concludes by discussing how the findings of this chapter lend support to my general theory of ethnic group rebellion.

127

6.2 Domestic Politics, Ethnic Ties, and Foreign Support for Parties in Civil War

Insurgency in Africa, perhaps more than any other region of the world, has been aided and probably prolonged because of international aid and support. States provide sanctuary, military arms, and even troop support to rebel groups operating in neighboring countries, as well as to governments. Even poorly-trained and equipped, nascent insurgencies can survive and even learn to thrive if they have sufficient support from other regimes.

Case studies of these conflicts routinely call attention to the role played by foreign governments in supporting both sides in a civil war. For example, the RENAMO rebel movement that fought against the FRELIMO government of Mozambique through the

1980s and into the early 1990s initially was funded and trained by the Rhodesian government; when that regime fell, South Africa took over as the rebel group‘s primary financial backer (Weinstein and Francisco 2005).

Charles Taylor‘s tiny National Patriotic Front of Liberia launched an insurgency against Samuel Doe‘s Liberian government in 1989. While vastly outnumbered and not as well equipped as the Liberian army and police, the NPFL was able to survive initially because of sanctuary in Côte d‘Ivoire, retreating across the border to evade capture after each assault into neighboring Liberia. Eventually, Doe‘s government was deposed and

Taylor acceded to power. For his part, Taylor backed the Revolutionary United Front insurgents fighting in neighboring Sierra Leone and provided safe haven for several

Ivoirian rebel groups fighting its civil war in the early 2000s (Salehyan 2009, 7-8, 15-16).

Perhaps the case that illustrates most effectively how integral is foreign involvement to the inception and continuation of civil wars is the Democratic Republic of

128

Congo‘s civil war from 1996 to the present day. Rwanda, Angola, and Uganda funded, trained, and even provided troops to the AFDL, a pro-Kabila group aiming to overthrow

Mobutu at the beginning of the conflict. Once Mobutu was overthrown and Kabila came to power, Uganda and Rwanda switched their support to emerging rebel groups seeking to wrest control of the state away from Kabila, while other countries provided direct military assistance to the Kabila regime (UCDP Database; Ndikumana and Emizet 2005).

The most basic facts of these examples illustrate much variation in international support for armed actors in civil war. They also suggest several possible explanations for why governments decide to support neighboring armed actors involved in civil war.

Angola may have supported Kabila‘s regime in an attempt to simultaneously root out

UNITA from its operating areas in DRC in an effort to make itself more secure from threats to its security. For its part, Rwanda may have aided rebels fighting against Kabila in the DRC to protect ethnic kin from his crackdown against those living in the Rwanda-

DRC border regions.

One of the domestic-level explanations of foreign involvement in civil war most often articulated in the literature is transnational ethnic linkages (e.g., Saideman and

Ayres 2000). In Africa, not only does the same ethnic group live in multiple countries, but national boundaries routinely bisect the traditional homelands of ethnic groups, arbitrarily designating different nationalities for members of the same ethnic group.

Given the largely artificial creation of African states (Herbst 2000), and the political salience of ethnicity (Van de Walle 2007), it seems natural to wonder whether the existence of transnational ethnic ties could have some effect on government policy with respect to neighboring conflicts.

129

In what ways might such ties matter? A promising answer is through the ethnic nature of the foreign country‘s domestic politics, and how domestic politics is influenced by events abroad. Saideman (2001) provides a theoretical account on these lines, arguing that governments become involved in others‘ conflicts if such involvement is popular among domestic audiences whose support is key for the government‘s political survival.

He reasons that such interventionist policies will be more likely where transnational ethnic ties exist between two states and the government contemplating involvement depends crucially on the support of the ethnic group whose kin is waging war against the state across the border. Saideman (2002) argues this theory better explains variation in foreign support of actors in civil war than do arguments centering on realist logic of external threat reduction or vulnerability to domestic and imitation secessionist attempts.

Countering arguments derived from the realist tradition of international politics that can be adapted to make predictions about ethnic group rebellion (Walt 1987;

Mearsheimer 2002; Schweller 1996; Schweller 1994), Saideman (1997; 2001) astutely points out that it is not enough to predict that a state is more likely to become involved in another state‘s civil war if a state perceives threat or harbors resentment toward the other state. In order to operationalize such a theory, we must have some theory of how perceptions of threat and vulnerability arise. Saideman theorizes that state decisions on involvement in others‘ secessionist struggles are based on domestic politics. In particular, he reasons that international ethnic ties affect these political decisions. A leader is more likely to lend support to secessionists in a neighboring state if the ethnic group on which he chiefly relies for domestic political support exists in a neighboring state – presumably because such action will resonate well with his domestic constituency. In particular,

130

―[w]hen an ethnic group has kin ruling a neighboring state, we should expect that state to help the ethnic group. Because the kin dominates the state, we should expect the support to be intense‖ (Saideman 2001, 168).

Saideman finds evidence supportive of this core claim in an analysis of state support for ethnic groups in neighboring countries using MAR data. If an ethnic group has ―dominant‖ kin in a neighboring state, that group is much more likely to receive support from that state. One critical implication of this argument is that ethnic groups with kin abroad, in anticipation of foreign support, should be more likely than other ethnic groups to rebel; this implication is not tested in the literature that focuses on determinants of foreign support to rebel groups.

Thus, from the domestic politics theory of foreign involvement in civil war comes a prediction about ethnic group rebellion that competes with my own about state capacity, and one that Saideman and most others studying foreign involvement in civil war do not address.41 The rest of the chapter is devoted to testing implications of this competing account.

6.3 Data on Transnational Ethnic Ties

To test theories about transnational ethnic ties and domestic politics, we use several variables designed to measure slightly different conceptions of ethnic kinship and why they should affect whether states support neighboring armed actors. Saideman‘s (2001) metric was whether the ethnic group had ―dominant‖ kin in a neighboring state, though it

41 A notable exception is Cederman et al. (2009), who use data from Eurasia and North Africa to show that the probability of a politically unrepresented ethnic group‘s involvement in civil conflict in their own state increases if ethnic kin reside in a neighboring state (thus providing a source of support for a rebel movement), but only if the ethnic group is large relative to other groups in the country.

131

is not clear how this was measured.42 The logic is that leaders will lend support to neighboring armed actors if such action will please a chief constituency. The assumption is that helping ethnic kin abroad – whether in government or in rebellion – is preferred by domestic ethnic groups to doing nothing. The question is how we should measure transnational ethnic ties, particularly those ties that link groups to politically powerful groups. Four different measures of this concept are used in the analysis that follows. All variables are measures of linguistic similarity and use mappings of similarity between languages in Ethnologue, as described in chapter 3. Note that for all variables, higher values indicate greater similarity.

The first variable measures the linguistic similarity between the ethnic group and the group in the neighboring country that has had a co-ethnic in executive office for the most number of years (1980-2004).43 This should be a measure of an ethnic group‘s affinity to the neighboring state‘s political leadership via the leader‘s co-ethnic constituency. The domestic politics theory of ethnic ties and rebellion tells us that this measure of ethnic kinship, in particular, should predict higher levels and frequency of foreign support for ethnic groups involved in civil war. If this is true, we also should observe that ethnic groups with strong ties to the most politically dominant group in the neighboring country can expect more foreign support and should be more likely to rebel in the first place.

The second variable measures the linguistic similarity of the rebelling ethnic group‘s linguistic similarity to a citizen in the neighboring country drawn at random. It is

42 Information supplementary to the source data is similarly silent on the question of what it means to be a ―dominant‖ group and how the concept is measured in the data. See versions of MAR data codebooks, the most recent available here: http://www.cidcm.umd.edu/mar/data/mar_codebook_Feb09.pdf. 43 Calculations based on leader ethnicity data in Fearon, Kasara, and Laitin (2007).

132

designed to measure how close the particular ethnic group is to the neighbor country‘s population at large and how broadly based any domestic support might be for the state to give aid to the ethnic group‘s rebel movement. The measure is simply the additive linguistic similarity measures between the ethnic group and each group in the neighboring country, with each similarity measure weighted by the ethnic group‘s share of population in the neighboring country. This is the ―expected‖ linguistic similarity.

Again, the domestic politics theory predicts that the greater this similarity, the more likely is foreign support for the ethnic group and so the more likely it is that the ethnic group should rebel.

The third variable measures the linguistic similarity between the ethnic group under analysis and its most ethno-linguistically similar ethnic group in the neighboring country.

A fourth variable records a ‗1‘ if the ethnic group under analysis has a true co- ethnic across the border, and ‗0‘ if not. The groups must be co-ethnics, not just similar.

Examples of groups common (not just similar) to more than country would be the Sara in

Chad and the Central African Republic, the Bakongo in Angola and the DRC, the Luo in

Kenya and Tanzania, and the Fulani in multiple countries across the middle of Africa.

Ethnic groups with true co-ethnics across the border should be more likely to rebel because they anticipate more foreign support for their cause.

Each of these four variables is calculated for each neighbor country of an ethnic group. For example, the Sanga ethnic group of Congo has as its neighbors Cameroon,

Central African Republic, Gabon, Democratic Republic of Congo, and Angola. For the

Sanga, the four measures of linguistic similarity are calculated for each of those

133

neighboring countries. Since the ethnic group can have only one value for analysis on its rebellion behavior, in the models that follow, the mean, minimum, and maximum values of those variables are used for each ethnic group.44

Variables previously used in analysis of rebellion – distance to the capital, area share, and population share – are also included in the models.

6.4 Empirical Approach and Results

To estimate the effect of transnational ethnic ties on the probability of ethnic group rebellion, we use the same approach as in previous chapters. Conditional fixed effects logistic models are estimated that include fixed effects for individual countries and whose standard errors are clustered on the country. As in previous analyses, we predict rebellions of different types (all rebellions, government takeover rebellion, and territorial rebellions) using our first three measures of ethno-linguistic similarity across borders.

The specific equations estimated are as follows:

x (1)

x (2)

x (3)

In each model, whether or not an ethnic group has ever rebelled is estimated for ethnic group i in country c. For each of the primary independent variables, either the mean, minimum, or maximum value from all of the ethnic group‘s country pairings is used.

is the similarity measure between the ethnic group and the most

44 The results in the tables that follow do not change if all variables are included in the same model.

134

politically powerful ethnic group in the neighboring country. is the expected similarity between a member of the ethnic group and a person in the neighboring country drawn at random. Finally, is the similarity between the ethnic group and its most similar ethnic kin in the neighboring country. The variables from the core models in chapter 4, distance to the capital, area share, and population share, are captured in x .

Tables 6.1-6.3 report the results of estimating each of the above three models when the dependent variable is all rebellion (Table 6.1), government takeover rebellion

(Table 6.2), and territorial rebellion (Table 6.3). Measures of state capacity – distance to the capital and group‘s share of area – are consistently statistically significant predictors of rebellion.

The results are not supportive of the theory that ethnic ties should be associated with a higher likelihood of rebellion: there is only one statistically significant variable measuring transnational ethnic ties: the minimum value of Powerful_Neighbor. That is, as this smallest value of the variable increases, the ethnic group is more likely to rebel, and the effect is both large – particularly for all rebellion and for government takeover rebellion – and highly significant. Other values of Powerful_Neighbor are not good predictors of rebellion, nor are the other two variables, Random_Neighbor and

Similar_Neighbor.

We also want to see if our fourth and most basic measure of cross-border ethnic ties, an indicator variable for true co-ethnic groups across borders, has any effect on rebellion. Tables 6.4-6.6 show the frequency of rebellion (by type) for ethnic groups depending on whether the group has a co-ethnic in any neighboring country. The ethnic

135

groups under consideration only come from those countries that experienced rebellion, since in the statistical analyses above, groups in countries that do not experience rebellion at all are dropped from the study. The tables also report results of Chi-squared tests of independence of the variables, that there is a statistically significant association between them. The test results for all three dependent variables show that we cannot reject the null hypothesis that there is no association between cross-border ethnic kin and rebellion of any type.

The results are not strongly supportive of the domestic politics theory‘s implied prediction that cross-border ethnic ties should be associated with higher likelihood of rebellion by those ethnic groups. However, while the results across our chosen measures are not consistent, the models predict that as the ethnic group‘s minimum similarity to the most powerful cross-border ethnic group increases, the risk of rebellion for that ethnic group increases. If this alternative theory of ethnic group rebellion based on domestic politics and ethnic constituencies should find any support in these data, it should be with this variable linking ethnic groups not just by their cultural similarity, but by their proximity to the foreign state‘s political center. While this result constitutes some support for this alternative theory, it is puzzling that none of the other measures of this variable

(or any of the other variables) fares well in the models. If increasing the minimum value of cultural similarity to a powerful group increases the rebellion risk, why would not the same be true for the mean or maximum values of this variable, as well? Furthermore, these tests only strengthen my argument that the most important factors contributing to the rebellion of ethnic groups is their distance from the center, their population, and their

136

region‘s size, each a different measure of the ability of the state to exercise effective control over its territory and people.

6.5 Conclusion

The weakness of African states and the difficulty governments have in administering and controlling their territory has given rise to the argument that states in the region tacitly agree to refrain from supporting insurgencies in other states, so as not to encourage rebellion in their own countries or neighbor support for such movements (Jackson and

Rosberg 1982; Neuberger 1986). If separatism and regime change is encouraged, according to this logic, states will live in constant fear of rebellion and intervention from abroad, making the difficult task of territorial and population control even more of a challenge. The issue with this line of argument is that it would seem we see far more intervention and support for armed actors from abroad than the theory would predict. The argument‘s logic implies an absence or at least a low incidence of support.

States may intervene in conflicts, supporting one side or another, either in an effort to increase their own security or shore up domestic political support for the regime.

The theory put forward by Saideman and others argues that foreign support for rebel groups, in particular, is a function of cross-national ethnic ties. This theory uses a specific dimension of domestic politics to explain this variation. Leaders depend on certain domestic constituencies for power, and in societies where politics is often conducted along ethnic lines, these constituencies are ethnic groups. If there are ethnic ties between a conflict-ridden state and its neighbor, and the political leadership in the neighboring state depends on an ethnic group who has kin fighting in the next state, the theory

137

predicts a higher likelihood of support for that ethnic group. By extension, this theory should predict that in anticipation of this higher likelihood of support, ethnic groups with stronger ethnic ties to groups in a neighboring state should be more likely to rebel.

In chapters 4 and 5, arguments from two alternate theories of ethnic rebellion were tested and found not to have as much empirical support as my theory that focuses on rebellion opportunities created through state incapacity. The theory described and tested in this chapter served as another alternative to my theory of ethnic rebellion. Measures of cross-border ethnic ties did not consistently predict a higher rate of ethnic group rebellion, as we would expect if the promise of foreign support was an important factor in determining behavior. Instead, measures of state capacity continue to fare well in predicting ethnic group rebellion of all types.

138

Table 6.1. Effect of Cross-Border Ethnic Ties on All Rebellion (Conditional Fixed Effects Logistic Regression) (1) (2) (3) (4) (5) (6) (7) (8) (9)

Powerful Neighbor (Mean) 0.0872 (0.0619) Powerful Neighbor (Min) 14.94*** (0.927) Powerful Neighbor (Max) 0.0545 (0.0508) Random Neighbor (Mean) 0.00179 (0.0574) Random Neighbor (Min) -0.00951 (0.116) Random Neighbor (Max) -0.00614 (0.0428) Similar Neighbor (Mean) -0.0120 (0.0576) Similar Neighbor (Min) -0.0186 (0.0736) Similar Neighbor (Max) -0.0177 (0.0684) Distance to Capital† 0.994*** 0.885*** 1.000*** 0.955*** 0.954*** 0.951*** 0.951*** 0.953*** 0.950*** (0.277) (0.266) (0.273) (0.272) (0.264) (0.274) (0.269) (0.267) (0.267) Area Share 5.485** 5.326** 5.498** 5.079** 5.088** 5.082** 5.105** 5.126** 5.074** (2.629) (2.570) (2.632) (2.471) (2.471) (2.469) (2.457) (2.462) (2.466) Population Share 3.415 3.103 3.442 4.163 4.168 4.163 4.161 4.169 4.169 (3.145) (2.997) (3.123) (2.814) (2.816) (2.811) (2.818) (2.830) (2.811) Observations 171 171 171 172 172 172 172 172 172 Note: All models include country fixed effects. Robust standard errors, clustered on country, in parentheses. †Logged value. *** p<0.01, ** p<0.05, * p<0.10

139

Table 6.2. Effect of Cross-Border Ethnic Ties on Government Takeover Rebellion (Conditional Fixed Effects Logistic Regression) (1) (2) (3) (4) (5) (6) (7) (8) (9)

Powerful Neighbor (Mean) 0.0257 (0.0674) Powerful Neighbor (Min) 16.99*** (1.003) Powerful Neighbor (Max) 0.0172 (0.0532) Random Neighbor (Mean) 0.0322 (0.0668) Random Neighbor (Min) -0.0781 (0.114) Random Neighbor (Max) 0.0224 (0.0508) Similar Neighbor (Mean) 0.0306 (0.0670) Similar Neighbor (Min) 0.0175 (0.0954) Similar Neighbor (Max) -0.00175 (0.0873) Distance to Capital† 1.283*** 1.193*** 1.285*** 1.297*** 1.266*** 1.298*** 1.296*** 1.277*** 1.271*** (0.422) (0.388) (0.423) (0.432) (0.412) (0.429) (0.407) (0.402) (0.406) Area Share 5.269** 5.219** 5.254** 5.143** 5.193** 5.151** 5.116** 5.139** 5.158** (2.177) (2.119) (2.190) (2.169) (2.158) (2.163) (2.117) (2.125) (2.155) Population Share 10.39*** 9.866*** 10.38*** 10.75*** 10.64*** 10.77*** 10.77*** 10.68*** 10.66*** (2.916) (2.987) (2.926) (2.769) (2.795) (2.765) (2.794) (2.761) (2.797) Observations 132 132 132 133 133 133 133 133 133 Note: All models include country fixed effects. Robust standard errors, clustered on country, in parentheses. †Logged value. *** p<0.01, ** p<0.05, * p<0.10

140

Table 6.3. Effect of Cross-Border Ethnic Ties on Territorial Rebellion (Conditional Fixed Effects Logistic Regression) (1) (2) (3) (4) (5) (6) (7) (8) (9)

Powerful Neighbor (Mean) 0.136 (0.189) Powerful Neighbor (Min) 3.521*** (0.423) Powerful Neighbor (Max) -0.0182 (0.109) Random Neighbor (Mean) 0.191 (0.148) Random Neighbor (Min) 0.870** (0.402) Random Neighbor (Max) 0.0464 (0.139) Similar Neighbor (Mean) 0.0377 (0.143) Similar Neighbor (Min) 0.159 (0.105) Similar Neighbor (Max) 0.0620 (0.122) Distance to Capital† 1.046* 0.991* 1.109** 1.058** 0.978* 1.102** 1.092** 1.096** 1.101** (0.535) (0.545) (0.530) (0.531) (0.532) (0.512) (0.524) (0.524) (0.514) Area Share 1.203 1.038 1.081 1.028 0.714 1.054 1.066 0.850 1.164 (2.339) (2.352) (2.206) (2.359) (2.465) (2.380) (2.353) (2.363) (2.271) Population Share -1.761 -2.006 -1.643 -1.945 -2.354 -1.723 -1.736 -2.045 -1.826 (3.287) (3.636) (3.487) (3.184) (3.315) (3.274) (3.247) (3.266) (3.397) Observations 57 57 57 57 57 57 57 57 57 Note: All models include country fixed effects. Robust standard errors, clustered on country, in parentheses. †Logged value. *** p<0.01, ** p<0.05, * p<0.10

141

Table 6.4. Relationship between Transnational Ethnic Kin and All Rebellion All Rebellion Neighboring Ethnic 0 1 Total Kin 0 123 47 170 (61.81) (61.04) (61.59) 1 76 30 106 (38.19) (38.96) (38.41) Total 199 77 276 (100.00) (100.00) (100.00) Pearson Chi-squared (1)=0.014 (p=0.91). Column percentages in parentheses.

Table 6.5. Relationship between Transnational Ethnic Kin and Government Takeover Rebellion Government Takeover Rebellion Neighboring Ethnic 0 1 Total Kin 0 138 32 170 (61.88) (60.38) (61.59) 1 85 21 106 (38.12) (39.62) (38.41 Total 223 53 276 (100.00) (100.00) (100.00) Pearson Chi-squared (1)=0.041 (p=0.84). Column percentages in parentheses.

Table 6.6. Relationship between Transnational Ethnic Kin and Territorial Rebellion Territorial Rebellion Neighboring Ethnic 0 1 Total Kin 0 154 16 170 (61.35) (64.00) (61.59) 1 97 9 106 (38.65) (36.00) (38.41) Total 251 25 276 (100.00) (100.00) (100.00) Pearson Chi-squared (1)=0.067 (p=0.80). Column percentages in parentheses.

142

Chapter 7

Evaluating Theory’s Predictions in Senegal, Niger, Guinea, and Chad

7.1 Introduction

This dissertation seeks to identify the factors that make some ethnic groups more likely than others within the same country of rebelling in civil conflict against the state. The theory of ethnic group rebellion I advance here was tested using data from across African civil wars over a 25 year period. In addition to testing elements of my theory, the statistical analysis cast doubt on two prominent theories of rebellion, that poverty and political grievances should make ethnic groups more likely to rebel. Instead of these factors, the analysis pointed to a factor that I interpret as a measure of the state‘s ability to monitor and control its territory and population, the distance between the ethnic group‘s region and the capital city, the country‘s center of administrative and military capacity.

The analysis indicated that while a theory of state capacity can explain both the determinants of territorial and secessionist rebellions, as opposed to those designed to take over the state itself, there are important differences between the two types, particularly in how they are affected by the poverty and political representation of ethnic groups.

In this chapter, I present four case studies of civil wars to complement the statistical analysis. In these case studies I examine the immediate historical context of each civil war and advance a narrative that reviews what scholars identify as principal factors leading to the conflict and the involvement of different actors. In so doing, the case studies primary purpose is to illustrate elements of my general theory in particular

143

cases. While the statistical analysis is meant to be useful for identifying general patterns of behavior over a large set of cases and testing explanations for those patterns, the case studies are meant to make use of the particularities of specific cases to learn whether the argument about state capacity is helpful for explaining these cases or whether there are important patterns of behavior left unexplained and in need of further theoretical development.

As these narratives constitute a selection of cases from a larger set, a word about case selection is in order. I do not base my case selection on any standard procedure of selection, such as Mill‘s principle of similarity and difference, identification of outliers, close familiarity with the cases, or even random selection. Instead, I had three simple criteria that result from the availability of data more than anything else.

First, the country had to have experienced a civil war. Analysis of peaceful countries would not help in evaluating predictions from the statistical models, since the use of country fixed effects effectively drops ethnic groups in countries that have never experienced a civil war. The variation on the dependent variable that has been studied to this point is that occurring between ethnic groups that rebelled and those that did not, in the same country.

The second criterion was that a reasonable amount of data coverage on ethnic groups‘ economic well being had to exist. As explained in chapter 3, the economic group- level data I use to evaluate whether poverty is a determining factor in ethnic group rebellion are not available for all countries, and some countries are surveyed more than once. I narrowed my set of cases to those countries which (a) had been surveyed the most

144

number of times (in order to follow change over time) and (b) included the greatest number of different economic measures within each survey.

Finally, from this list I chose two countries that experienced territorial rebellion and two countries that experienced government takeover style rebellions, as this distinction factors prominently in my theory of rebellion. Chad and Guinea underwent government takeover style rebellions, while Senegal and Niger suffered from regional rebellions over territorial and sovereignty issues.

In line with the purpose of inquiry, each case study follows a specific format. The narrative begins with a brief history of the civil war, including the main actors involved and how it ended. The second section investigates why certain ethnic groups became involved in the conflict. While alert to the possibility of other factors being important for involvement that are unidentified by the statistical analysis, this section focuses on five broad possible determinants of civil conflict involvement discussed in previous chapters: the state‘s capacity to project control across its territory, groups‘ relative economic well- being, groups‘ political representation, natural resource wealth, and cross-border ethnic kin. In addition to an examination of each case‘s history, this section makes use of the economic survey data for the countries in order to analyze wealth‘s possible effect on particular cases of rebellion. The analysis also examines constructed maps of the ethnic groups‘ regions and their important characteristics that are based on the geographic data I have collected in order to evaluate arguments about state capacity, natural resource wealth, and foreign ethnic ties in leading to rebellion.

145

7.2 The Casamance Rebellion in Senegal (1990-2003)

7.2.1 History of the Conflict

While the civil conflict in southern Senegal began in 1990 as far as many data sets are concerned, the separatist movement based in the Casamance region, south of the Gambia, had much earlier political origins. In 1947, the political party Mouvement des forces démocratiques de Casamance (MFDC) was formed to articulate the political aspirations of the regional population, which consists mostly of the Diola ethnic group. A deal with the center may have been made, wherein (eventual) President Senghor promised to invest central resources in Casamance and to grant the region independence within 20 years of

Senegal‘s national independence in 1960 in exchange for co-optation of the regional political party into a national group (Humphreys and Mohamed 2005). In the early 1980s, with no sign of regional independence or autonomy from the center in sight, the MFDC organized peaceful demonstrations against the government. In response to sporadic clashes between the local population and government forces, the MFDC gradually morphed into a rebel movement in the mid-1980s and eventually clashed definitively with government forces in 1990.

Despite negotiations being held between the MFDC and the government through the 1990s, the issue of Casamance‘s (and effectively, the Diola‘s) relationship with the central state was avoided. Also key to the continuation of hostilities was the fracturing of the MFDC into multiple rebel and political organizations over time: Front Nord, the faction that ceased military action after the first accord was signed in 1991; Front Sud, the more progressively violent faction; and several factions that splintered from Front

Sud as the war dragged on.

146

While hope for peace was raised in 2000 with the election of a new president in

Wade, who seemed keen to end the conflict swiftly, negotiations remained complex because of the number of parties involved and a solution to the question of the Diola region proved elusive. Further accords were signed in 2001 and 2004 and while there is no definitive end to the conflict politically, since there has been no significant violence between the two sides since 2003, the conflict‘s end is dated to that year.

7.2.2 Explaining Ethnic Group Involvement

State Capacity and Rebellion

The people of Senegal are divided into six main ethnic groups in my data: Wolof (43% of the population), Peul/Fulani (24%), Serer (15%), Mandinka (7%), Diola (5%), and

Soninke (1%). Figure 7.1 shows the general locations of each of these groups‘ main inhabited regions. As with all of the maps, the groups‘ regions are color coded but are semi-transparent so that a digital elevation map of the terrain can show through. Lighter, white areas indicate mountains, while darker areas indicate low lying regions. Large black circles represent the locations of known oil and natural gas deposits.

The Wolof, while the most populous group, occupy only about 22% of the land area relatively near the capital of Dakar near the Atlantic coast. The Peul/Fulani group are much more widespread and their region runs from down the center of the country from north to south. The most peripheral groups are the Mandinka, in the far southeast corner of the country (and also in Casamance); the Soninke, occupying a small pocket of land due east of the capital city and bordering Mali and Mauritania; and the Diola on the

Atlantic Coast of the Casamance region.

147

In general, the statistical models found a positive correlation between distance from the capital and rebellion. Peripheral location was a particular risk factor for territorial rebellion: while the statistical significance of the coefficient was reduced when the model restricted itself to predicting territorial rebellion, the substantive impact of the coefficient was more than double that of the all-inclusive model.

The only ethnic group to rebel in Senegal is the Diola, but according to my data, the Diola are only fourth from the top in their distance from the capital. As the map would suggest, the weighted centers of the Mandinka and Soninke groups are the farthest from the capital; these are followed by the Peul/Fulani, and then by the Diola, who are a mere 250 kilometers from the capital as opposed to 520 kilometers for the Mandinka. If state capacity decreases with distance, and if this constitutes such a favorable opportunity for ethnic groups to rebel, we would have a high expectation that it would be the

Mandinka and Soninke groups that would rebel, not the Diola.

While the pacific nature of the Mandinka and Soninke, in particular, do not fit well with my argument about state capacity and its absence in the periphery, a closer examination of the map and the natural geography of the country may help us make more sense of the Diola‘s involvement in the context of my argument. While the Diola are relatively close to the capital, as the crow flies, Gambian territory creates a buffer between the region and the capital, increasing the cost of maintaining a significant government presence in the region or sending military forces around the Gambia to the

Diola area to stave off rebellion. In effect, Gambia‘s buffer radically increases the cost- distance between the Diola‘s region and the capital and makes the region much more

―peripheral‖ than the data suggest. This observation suggests a methodological

148

improvement for the project. Instead of measuring geographic distance from the capital to ethnic group regions, future improvements should include a more theoretically informed measure of this dimension of state capacity that includes a measure of distance that is weighted by factors such as mountainous terrain, swamp land, and percent of area without roads.

The statistical analysis of ethnic group rebellion did not find consistent evidence that rough terrain in an ethnic group‘s region makes rebellion more likely, a surprising finding given its prominence in theories of state capacity and cross-country analysis of civil war onset risk (Fearon and Laitin 2003). Studies of individual countries‘ conflict experiences often point to different aspects of terrain as a means of avoiding the state.

The Senegalese conflict is an example of this, as the MFDC sought refuge in the dense forests and mangrove swamps of the Casamance region, particularly in the 1980s when the nascent rebel movement needed time to train its fighters (Humphreys and Mohamed

2005). This illustrates the difficulty of identifying and operationalizing one aspect of terrain that is useful for predicting ethnic group rebellion, as the knowledge that dense forests and swamp land lay close by may have encouraged rebellion among the Diola, while other studies point to desert and mountains as particularly helpful, as we will see in the case of Niger. These examples notwithstanding, mountainous terrain seems to be a quality geographic proxy for the ease with which potential rebels can further evade state detection, and the Senegal case provides no reason to question the statistical null findings on this variable, as the there is very little variation in elevation across the country and certainly no mountains to speak of – as is the case for much of Africa.

149

Poverty

The conventional wisdom on the economic aspects of this conflict is that the Diola rebelled because Casamance was poor relative to other regions of the country and/or because the regional population wanted to gain control of valuable agricultural resources in the area such as cannabis and timber. Humphreys and Mohamed (2005) provide convincing evidence to the contrary on this point, arguing that while these resources may have contributed to the duration and intensity of the conflict, they were not instrumental in its onset as they were of little value when the conflict began. Furthermore, in terms of relative grievances, Casamance was not necessarily the most underserved region in the country, based on several measures of pre-war infrastructure (Humphreys and Mohamed

2005, 270-271).

Indeed, the survey data collected on the economic well-being of Senegalese individuals and used in the statistical analysis in chapter 4 corroborate this point. Figure

7.5 shows the group-level wealth index values constructed for each Senegalese ethnic group from individual-level data for each year that a survey was conducted. Note that more positive values indicate better well-being, while more negative values indicate relative poverty. For the statistical analysis, the group average over all three years was used; this graph shows how group well-being changes over time. Perhaps surprisingly, given the refrain that Casamance and its population were poor relative to the rest of

Senegal, these data indicate otherwise. In fact, the Diola rank first or second according to the wealth index for each of the three years. In 1986, before the war begins, the Diola are second only to the Wolof in terms of economic well-being. If relative poverty were a

150

driving factor in group rebellion, we would expect the Peul/Fulani, Serer, or Malinke groups to rebel, as they are the most economically aggrieved according to these data.

The statistical analysis of wealth‘s effect on territorial rebellion in chapter 4 produced mixed results. There was scant evidence that wealth had an independent effect on rebellion (table 4.5), but the evidence also suggested an effect of group wealth on rebellion that is conditional on the group‘s geographic location relative to the capital.

Nearer the capital, there is a positive effect of wealth on territorial rebellion, but at high values of distance, the effect of wealth is negative, meaning that poorer groups are more likely to rebel. Table 4.7 showed that the Diola is somewhat of an outlier in that it is the only ethnic group of its kind that are relatively close to the capital but relatively wealthy that rebels. If we reconsidered the Diola‘s ―distance‖ from the capital and took into account how Gambia makes it much farther from the center than my data would suggest, its behavior might make more sense in light of the model. We might place it in the category of ethnic groups that are relatively far from the capital and wealthy, and where

60% of that current category of groups rebelled.

Political Representation

Statistical analysis on the effect that political exclusion has on territorial rebellion, at least in the cross-section, indicated no statistically significant relationship between this measure of political grievance and rebellion. The Diola are not remarkable among

Senegalese groups on this measure. While they have never had a co-ethnic occupy the office of the presidency, neither has the Peul/Fulani, the Mandinka, or the Soninke. The

Wolof have enjoyed co-ethnic representation for 96% of the years under study.

151

Since the Casamance conflict was, by all accounts, mainly fought over the political relationship between the region and the center, this issue merits more discussion.

It may be that the Senegalese government thought that Diola political representation at the center would stave off rebellion, as two Casamançais ministers were added to the national cabinet in the mid-1980s and the mayor of Ziguinchor, the regional capital, was replaced by a Casamançais (Humphreys and Mohamed 2005, 251). Not only were the

Diola of Casamance politically isolated from the center, which does not set them apart from other ethnic groups, but there are claims that this region and its people are historically and culturally distinct from the rest of the country.

The statistical models do not account for such a factor, to their detriment, as often studies of territorial rebellion and secession point to the ―distinctiveness‖ in some way or another of the rebelling group. In the case of the Diola, religion and language are usually discussed as the cultural factors that set the group apart from others and led to the desire to politically distance themselves from the state. Linguistically, the Diola are not as distinct from other ethnic groups as, say, the Mandinka, whose language derives from a different language sub-family than the Fulani and the Wolof. However, it is reported that the Diola do not speak the primary lingua franca of the country, Wolof, and this contributes to their distinctiveness and inability to assimilate; (though, this is probably endogenous either to a desire to separate or to being cut off economically from the rest of the country by The Gambia). Furthermore, the Diola tend to have beliefs that are a mixture of traditional animism and Christianity, while the rest of the population is

Muslim.

152

Natural Resources

The natural resource wealth that the statistical analysis focused on was that emanating from oil and diamonds. The country has no diamond mines, but there is some oil wealth, as depicted in Figure 7.1 near the capital in the Wolof region. Recently, foreign companies have pursued exploration in the offshore oil and natural gas fields, some of which are depicted in the map. A South African company, Energy Africa, signed an agreement with Senegal‘s state oil company, Petrosen, in 2003 which gave it 90% interest in the license and the opportunity to explore the St. Louis block of oil off the coast north of the capital.45 The country does not depend heavily on oil, however. Data compiled on fuel exports shows that Senegal‘s oil production is relatively low, given that the country‘s fuel exports to GDP ratio and the percentage of its exports that are fuel are both below the world mean since 1980.46

The models in chapter 5 identified a positive relationship between oil in an ethnic group‘s region and its propensity to be involved in territorial rebellion. The Wolof fit this profile, except that they have enjoyed co-ethnic representation in the presidency for most of the period under study, and the models found that this factor dramatically reduced the likelihood that the ethnic group would rebel. There is no indication from the history of the conflict that oil had anything to do with the conflict.

45 ―South African Entity to Explore Senegalese Oil.‖ Pan African News Agency. July 25, 2003. See also: Alexander‘s Gas and Oil Connections. ―Energy Africa signs exploration deals for North Sea and Senegal.‖ July 28, 2003. Available from: http://www.gasandoil.com/goc/company/cne33449.htm. Accessed August 18, 2010. 46 Author‘s calculations based on replication data in Fearon (2005).

153

Transnational Ethnic Kin

The Casamance conflict was hardly confined to Senegal. The MFDC took refuge in neighboring Guinea-Bissau in order to evade government detection, and the Senegalese government accused the government of Guinea-Bissau of providing the MFDC with arms and safe harbor. In 1998, a high ranking military officer in Guinea-Bissau staged a revolt against the government, which brought in Senegalese troops to fight for the sitting government while the MFDC took up arms to protect the officer, who allegedly had been providing the group with aid. Once Kumba Yalla was elected president of Guinea-Bissau in 1999, his government largely ceased cooperation with the rebels.47

Every ethnic group in Senegal has true co-ethnics in Gambia; the Fulani have co- ethnics in Guinea; and the Fulani and Mandinka have co-ethnics in Guinea, and this leaves aside co-ethnics in neighboring Mauritania and Mali. Strong ethno-linguistic, and thus cultural, ties exist between many groups in Senegal and neighboring Guinea and

Guinea-Bissau. Thus, while the Diola in the MFDC were able to find refuge across the border and in so doing avoid state capture, the fact that they have co-ethnics in neighboring countries does not distinguish them from other ethnic groups. The MFDC enjoyed material support from other governments, but it is not clear how the theory of foreign support based on domestic politics and transnational ethnic ties discussed in chapter 6 would predict this. In Guinea-Bissau, the state was controlled throughout the

1980s and 1990s by a president from the Mandjako-Papel ethnic group; in Guinea the president was Susu; and in Gambia the president was Mandingo until 1995, when a Diola assumed control of the state. If the Diola rebelled in part because they expected foreign

47Uppsala Conflict Data Program. UCDP Database: www.ucdp.uu.se/database. Uppsala University. Accessed August 18, 2010.

154

aid, it was not because they had strong ethnic ties to neighboring governments or those leaders‘ key constituencies.

7.3 Niger’s Conflicts over Air, Azawad, and Eastern Niger (1991-1997)

7.3.1 History of the Conflict

Depending on how one counts them, Niger experienced either two or three conflicts in the 1990s. As the data I use for identifying conflicts (PRIO) counts them as two, this is reflected in my analysis: one over northern Tuareg territory and one over territory in

Eastern Niger. The coding decision makes sense, as the ―missing‖ conflict over federalism can be thought of as a precursor to the territorial conflict over the future of

Tuareg regions Air and Azawad.

The territorial conflict that involved the Tuareg ethnic group began as a dispute with the center over autonomy. When the Front de libération de l'Aïr et l'Azaouad

(FLAA) began articulating its political ambitions, it officially wanted administrative autonomy for all of the country‘s ethnic groups, not secession for the north. But as time passed, in practice the FLAA‘s demands were particular to the fate of the Tuareg population and its homeland. A peace agreement was signed in 1993 with the government, but Tuareg rebels splintered and formed the Coordination de la résistance armée (CRA) and continued the conflict, sporadically, until 1997 when parties signed a ceasefire agreement.48

The CRA‘s stated aim beginning in 1994 was regional political and economic autonomy for the Tuareg regions of Air and Azawad in the Tuareg homeland region in

48 The conflict began anew in 2007 under the Mouvement Nigérien pour la Justice (MNJ), but this time period falls outside the scope of the study. See Abdalla (2009) for an analysis of the conflict since it resumed.

155

northern Niger. The CRA wanted increased Tuareg representation in the central government and substantial increased state investment in these areas, similar to the Diola demands for Casamance in Senegal. The CRA and the government signed peace accords in 1994 and 1995, with the CRA achieving much of its aims. However, as in 1993, the group splintered and a faction continued fighting until 1997.

The second, brief, conflict occurred simultaneously between 1995 and 1997 in

Eastern Niger. Similar to the Tuareg dominated rebel groups, the Front démocratique du renouveau (FDR) demanded regional autonomy for the Toubou areas in the eastern part of the country and the movement was populated by both Tuareg and Kanuri (Gastil 1997,

386). Both ethnic groups are coded as rebelling during this conflict. Though a peace deal was struck with the government in 1998, the political status of those eastern regions was not resolved.

7.3.2 Explaining Ethnic Group Involvement

State Capacity

Niger features six main ethnic groups. The Hausa make up about 54% of the population, followed by the Djerma-Songhai (24%), the Tuareg (11%), the Kanuri (4%), the Toubou

(1%) and the Gourmantche (1%). Figure 7.2 depicts the ethnic group regions and physical geography of Niger. The Tuareg, a nomadic pastoral ethnic group, occupy a swath of territory running along the Malian and Algerian borders northward into the

Azawad region. This region spans western Niger as well as parts of southeastern Algeria and eastern Mali. Historically, the area was inhabited by Tuareg groups. While most of the country is relatively flat, the Air Mountains rise out of the desert of northern Niger

156

and can be seen in Figure 7.2 as the bright white oval mass in the Tuareg region. This region, as well as those inhabited by the Kanuri and Toubou groups located in the east, is farthest from the capital city of Niamey tucked away in the western corner of the country.

The distance of the Kanuri to the capital, relative to the Gourmantche in particular, is a point I return to in the discussion of economic grievances below to further illustrate the argument about state capacity and rebellion.

Poverty

Figure 7.6 plots the constructed wealth index for Nigerien ethnic groups in the years for which survey data exist. Notice that the data‘s negative values across groups and years are consistent with what economic statistics on growth and development indicate: Niger is a desperately poor country, ranking at or near the bottom in virtually every category of development scored by the UN‘s Human Development Index.49 However, we do have data for 1992, before either conflict begins in earnest. The wealth index statistics on the

Tuareg and the Kanuri from 1992 fit well with the model‘s prediction of a negative relationship between wealth and territorial rebellion for peripheral groups. The Toubou, according to the data, are not as impoverished as the other two rebelling groups.50

A comparison between the Kanuri and the Gourmantche provides an illustration of my argument about the interaction between state capacity and group wealth as it affects territorial rebellion. The groups make up about 4 and 1% of the population and land area, respectively. They are both traditionally sedentary farmers, and in 1992 and

1998 their well-being measures are quite similar. A key difference between the two is

49 See http://hdr.undp.org/en/. 50 The Toubou value in 1998 is a function of extreme under-sampling of the group in this survey and should not be taken as representative.

157

their distance from the capital city. The Gourmantche lie just outside the capital, while the Kanuri occupy the eastern and northeastern part of the country. My theory explains their divergent behavior in conflict by noting how geography affects the opportunity of two equally aggrieved groups. Their peripheral residence affords the Kanuri the opportunity to engage in rebellion that was not well-funded or trained and lacked central leadership. But, the FDR survived and did reasonably well in part because the state could do little to stop it. In contrast, the Gourmantche‘s proximity to the capital keeps the group from engaging in territorial style insurgency, and its poverty makes a bid to take over the government quite difficult, since such an endeavor requires an insurgency that can engage directly with the state‘s military and police capacities.

Political Representation

Unlike Senegal, political representation in the presidency in Niger is much more evenly distributed. During the period under study, the Djerma-Songhai group occupies the office for a little over half the years, the Hausa have it for 25%, and the Kanuri for the remaining 20%. There was no statistical evidence to support the claim that any co-ethnic political representation in the center decreases the likelihood of any kind of rebellion in the cross-section, but as we observed in Senegal, this measure may not capture the degree to which a group is politically marginalized.

Like the Diola, the Tuareg are seen as culturally and historically distinct from other groups in the country. One report puts it thus: ―Since independence, the [Tuareg] has been physically, politically, economically and socially separated from the country's centre of power in the south. Other ethnic groups have dominated all governments since

158

independence, subordinating the Tuaregs in numerous ways, for instance by prohibiting the use of their language‖ (UDCP Database Niger Report). The post-independence political establishment has been dominated by the Djerma-Songhai, and the French colonial establishment played favorites with this group before independence. The first main political party, the Parti Progressiste Nigerien (PPN), was dominated by Djerma-

Songhai for 30 years, as was the military‘s officer corps (at least 70% Djerma-Songhai).

Political unrest led to the president calling a National Conference that convened for four months in 1991 and created a transitional, caretaker system to govern until elections 15 months later. There is no doubt that the Tuareg have not been well represented in central government.51

As with the Diola of Senegal, a reading of the history of the Tuareg and Toubou, their way of life, and relationship with the modern state leaves one with the sense that the statistical model‘s measures of political and economic grievances are incomplete. The

Tuareg and Toubou‘s nomadic pastoralism distinguishes them from other groups and their involvement in rebellion is an example of a weak finding across Africa in my research that nomadic groups are more likely than subsistence agriculturalists or pastoralists to be involved in territorial insurgency (results unreported). Indeed, the rebellion of the Tuareg in neighboring Mali and the Toubou in neighboring Chad reinforces the idea that these groups‘ cultural distinctiveness has made it difficult for the groups to assimilate into the Nigerien, Malian, and Chadian cultural and political nations.

The statistical analysis provides a reason for caution here, however. It is easy to point to the political exclusion of the Toubou and Tuareg groups in Niger and infer that this was not only a determining factor in the groups‘ bid for regional autonomy and

51 For a review of Nigerien political history, see Ibrahim (1994).

159

federalism, but that such marginalization is a general risk factor for other countries. As noted in this subsection, there are other groups in Niger that have experienced at least as much political exclusion, given that the only group to enjoy the benefits of central revenue redistribution and political patronage was the Djerma-Songhai. My theory does not discount political exclusion and discrimination as important in specific cases, but rather than political exclusion – which applies to the Gourmantche and Hausa as much as to the Tuareg and Toubou – a more general explanation of rebellion that focuses on the ability of the state to control territory and population explains the Niger case quite well, as illustrated in the comparison between the Kanuri and the Gourmantche.

Furthermore, the Tuareg, Toubou, and Kanuri do not appear to be more culturally dissimilar from the most dominant and powerful in their country than other ethnic groups, at least along ethno-linguistic lines. While the Tuareg, Toubou, and Kanuri have very little in common with the politically dominant Djerma-Songhai using this measure, the same can be said for the Hausa and the Gourmantche, which are actually even more culturally distant in terms of language.

Natural Resources

Figure 7.2 shows the location of oil resources in the eastern part of the country, in the

Toubou region. Though oil was discovered in 1975 (Lujala et al. 2005), Niger‘s oil production is nonexistent as of 2009 (CIA Factbook), and its proven reserves do not make the top ten for African countries (Gökay 2006, 219-225). The fact that there is no oil revenue for the center and periphery to dispute means this issue cannot be considered integral to the conflict. This suggests an improvement for the model: instead of a simple

160

count of oil wells over territory, a more appropriate measure that connects to the concept of negotiation over revenue sharing might be to take into account (a) the estimated reserves held in the wells and (b) whether and when production has begun. The fact that

Niger‘s oil has entered the model in the same way that Nigeria‘s oil has, for example, constitutes a source of systematic measurement error and should be corrected.

Transnational Ethnic Kin

While some Nigerien ethnic groups are found in neighboring countries (Gourmantche in

Burkina Faso; Tuareg in Mali; Hausa in Nigeria; and Toubou in Chad), this case illustrates why the statistical models find little, if any, evidence that transnational ethnic ties or ties to the government in power are significant predictors of rebellion. The model considers not only the co-ethnics noted above, but also counts the non-co-ethnic pairings between those groups and other neighboring countries.

According to data compiled by Kristian Gleditsch and Idean Salehyan on foreign support of rebel groups, Chad allegedly supplied the FDR with military equipment during the Eastern Niger rebellion. While it is not clear when this support may have been extended, the Chadian presidency during the conflict was occupied by a member of the

Zaghawea-Bideya (Beri) ethnic group, not Toubou, which supplies a particular example of evidence against the hypothesis that Chad‘s government decided to aid the FDR based on how such a decision would play with co-ethnics in Chad. Some sources claim that while the two speak different languages, the Zaghawea-Bideya are closely related culturally to the Toubou, based on similar systems of social organization, patrilineal clans, and Muslim identity (e.g., Olson 1996, 550), and the statistical model would not

161

account for this kind of similarity. However, the burden would be on defenders of this domestic ethnic politics argument to articulate why such a Chadian government would want to aid a neighboring Toubou movement that is not clearly linked to it through ethnic ties.

7.4 Guinea and the RFDG Insurgency (2000-2001)

7.4.1 History of the Conflict

Guinea has suffered from frequent political instability since its independence. According to data compiled by Marshall and Marshall (2007), Guinea has seen eleven successful or attempted coups between 1960 and 2006, and the most recent coup occurred in 2008 when Captain Moussa Dadis Camara took over the country upon President Lansana

Conté‘s death. Guinea was ruled by dictatorship from independence until the first president, Sékou Touré, died in 1984, and then by Conté until multiparty (if flawed) elections in 1993. The elections were the end stage of a political transformation of sorts that saw the military dictatorship give way to a civilian-military transitional council.

These first elections did little to quell political unrest, however, as evidenced by continued coup attempts throughout the 1990s.

Guinea is part of a West African hot spot of civil conflict, with each of its neighbors suffering a civil war at some point during the period under study. The conflicts in Liberia and Sierra Leone were particularly harmful for the Guinean civilian population and the government, as the porous borders and inability of the state to patrol those areas led not only to large influxes of refugees from neighboring Sierra Leone, Liberia, and

Guinea-Bissau, but also to foreign rebel groups (e.g., Charles Taylor‘s own ULIMO and

162

his supported RUF in Sierra Leone) establishing bases in Guinean territory and terrorizing civilians (UDCP Database). Guinea is a prime example of the fluid nature of civil conflict in Africa (see Salehyan 2009), in that the Liberian government and rebels fought on Guinean soil and Conté sent troops to Liberia to defend Samuel Doe‘s regime against Charles Taylor‘s attempt to take Monrovia in the early 1990s.

In 2000, the Rassemblement des forces démocratiques de Guinée (RFDG), until then a relatively unknown rebel group made up primarily of members of the Malinke ethnic group, began an insurgency in southeast Guinea near the Liberian and Sierra

Leonean borders. Heavily supported by Charles Taylor and led by exiled Guinean military officers who fled after a 1996 coup attempt, the rebels‘ stated aim was to overthrow Conté‘s government and establish a purer form of democracy. The conflict between the RFDG and Conté‘s regime played out over roughly two years and on two primary fronts: along the Sierra Leonean border near the capital of Conakry, and in the southeast, along the Liberian border. Near the capital, the RFDG fought directly with the government‘s forces, but along the Liberian border, the conflict merged with the Liberian forces‘ cross-border forays into Guinea to eradicate Liberian rebel bases. Throughout, the

RFDG moved back and forth between Guinea and Sierra Leone and Liberia (UDCP

Database).

After RFDG attacks on the capital and Guinean forces‘ pursuit of rebels in the southeast in 2001, the presidents of Guinea, Sierra Leone, and Liberia convened for a joint summit in which they pledged their commitment to border security and repatriation of refugees (O‘Toole and Baker 2005). The conflict‘s intensity declined dramatically by the end of 2001 and is no longer considered active.

163

7.4.2 Explaining Ethnic Group Involvement

State Capacity

Guinea‘s main ethnic groups are the Malinke (30%), Fulani (28%), Susu (16%), Kissi

(7%), Guerze/Kpelle (4.2%), and Toma (2%). Their primary inhabited regions are marked in Figure 7.3. Like many West African countries, the capital city is located on the coast, and Conakry is surrounded by traditionally Susu land. The Fulani occupy a more mountainous terrain in the northeast, whereas the other ethnic groups occupy more low- lying areas in the east and southeast.

While distance from the capital, as a measure of state capacity, was found to have some power in predicting involvement in government takeover rebellion, the theory advanced in chapters 2 and 4 articulated how ethnic groups involved in this type of conflict might have a different risk profile from those involved in territorial rebellion.

While peripheral location was found to be of some help in predicting involvement in government takeover rebellion, the goals and requirements of this type of insurgency suggested that being far from the capital might not be as important as having a large population base from which to draw recruits and evade state capture. The Malinke are the most populous group in the country, but there is nothing in the case study literature suggesting that the Malinke‘s population was instrumental in establishing the RFDG.

Undoubtedly, however, the peripheral location of the second front of the conflict, noted above, helped the RFDG evade state capture. From the map we can see that the

Susu and Fulani ethnic groups encircle the capital and the other groups form the periphery, with the Guerze/Kpelle and Toma being farthest away. While the Malinke are not the farthest from the capital, their home region is relatively far away and their

164

proximity to international borders enabled aid to flow in from foreign governments. The statistical analysis did not a find a relationship between an ethnic group‘s proximity to an international border and rebellion risk, though in the Guinea case this proximity helped rebel efforts.

Poverty

Figure 7.7 plots the wealth index values for surveyed ethnic groups in 1999 and 2005.

Unfortunately, the surveys did not sample members of the Fulani ethnic group so we do not have data on their economic well-being relative to other groups. Between 1999 and

2005, each ethnic group becomes better off economically, according to the data. The data from the 1999 survey provides a pre-war picture of the relative economic status of ethnic groups. There is considerable horizontal inequality across ethnic groups in Guinea. The

Susu is the only group in either survey that records a positive value on the wealth index, and the gap between them and the Toma, the poorest group, is extremely large. Following the Susu are the Malinke, the Kissi and the Guerze/Kpelle, and the Toma.

The predicted effect of wealth on government rebellion in the regression analysis was positive and significant. The Malinke are second behind the historically privileged

Susu in economic well-being, which is consistent with the argument that wealthier groups may be more likely than poorer groups to try and take over the state.

Political Representation

Politically, the national government was extremely centralized and power was vested heavily in the reigning dictator. Touré, a Malinke, ruled until his death in 1984. Conté, a

165

Susu, continued on until 2008. None of the other ethnic groups has had a co-ethnic in the presidency, and not only was the presidency and army controlled by Conté, he appointed regional governors, and without a legislature in the 1980s effectively directed all aspects of national policy through executive decrees (O‘Toole and Baker 2005, 55). Like Touré before him, Conté hand-picked Susu co-ethnics to occupy high ranking positions in the government (U.S. Department of State). The groups politically marginalized historically are the Fulani, Kissi, Guerze/Kpelle, and Toma.

As the conflict was fought over control of the state and the ousting of Conté from power, clearly who had the ability to wield political power was an important factor in fomenting rebellion. The Guinean case suggests a different way to think about political representation as a risk factor: previous co-ethnic representation coupled with current exclusion could be more problematic than complete historical exclusion. In the statistical analysis of cross-sectional rebellion risk, the Malinke are coded as having had a co-ethnic in power and thus their rebellious behavior contributes to the null result on this variable.

The group controlled the presidency (and effectively the state) until 1984 and were then pushed to the side politically by the Susu. In line with the argument that wealthier groups may be more likely to try and take over the state, perhaps some Malinke wanted to retake the state and restore the group to a place of political prominence. This would suggest that groups that have held power in the past but then lose it to another ethnic group may be more likely than either groups in power or those that have never held it to rebel. Even in light of the lack of statistical results for political grievances of the highest level being a key factor in explaining variation in rebellious behavior, this possibility cannot be ruled out by the preceding analysis but could be tested.

166

Natural Resources

Mined natural resources constitutes a significant portion of Guinea‘s exports and revenue.

At least since the early 2000s, the mining sector accounts for more than 80% of exports,

25% to 30% of government revenues, and 17% to 20% of GDP (Bermúdez-Lugo 2004).

Bauxite, aluminum ore that is strip mined, is the largest source of mining revenue for the state, and Guinea has the largest bauxite reserves in the world. As Figure 7.3 shows,

Guinea has a considerable number of known diamond mines in the southeastern part of the country. A 2004 estimate put the country‘s diamond resources between 25 and 30 million carats. During the conflict, the number of metric tons of diamonds produced in

Guinea steadily rose, from 327,000 in 2000 to 740,000 in 2004 (Bermúdez-Lugo 2004).

The vast majority of diamond resources in the early 2000s was extracted via a joint venture between a Canadian company (85%) and the government (15%), though several companies held exploration permits and mined in alluvial sites around the country.

There is nothing in the case history that indicates diamonds or disputes over diamond revenues were a source of conflict or integral to the formation of the RFDG.

The statistical analysis did not find a significant relationship between diamond wealth in an ethnic group‘s region and its propensity to be involved in a rebellion designed to take over the state, though the estimated effect was negative. The Guinean case does not suggest a need to question or alter the model.

Transnational Ethnic Kin

Not only are there linguistic similarities between Guinean ethnic groups and those in neighboring countries, there are true co-ethnics. In Senegal, both the Fulani and Malinke

167

are found. Liberia has Toma, Guerze/Kpelle, Kissi, and Malinke. Guinea-Bissau has

Malinke and Fulani. The Kissi and Susu are found in Sierra Leone, and Fulani are also in

Mali. Thus, there exists a rich set of cross-border ethnic ties that theoretically could encourage rebellion in Guinea if ethnic groups thought such ties might serve to encourage foreign support of their rebel movement or ethnic kin‘s territory across the border could act as safe sanctuary.

We know that the conflict featured foreign support of both the rebels and government forces, with Charles Taylor‘s Liberia supporting the RFDG and rebels hiding out in Guinea-Bissau, and the government receiving some support from the Liberian rebel group ULIMO. It is impossible to know whether the RFDG would ever have formed had it not been for Taylor‘s backing, and thus if the conflict should be seen exclusively as a proxy war between Taylor and Conté. Regardless, the reason Taylor and

Conté each backed foreign rebels seems to have had little to do with satisfying domestic political constituencies. Instead, leaders backed rebels as a means of punishing neighboring regimes for their intervention, as a kind of tit-for-tat dynamic ensued. It is instructive, on this point, to consider that the leadership summit which effectively ended the Guinean conflict did not involve rebel leaders but rather the presidents of the region‘s countries who required some institutional help in coordinating on a standard of non- interventionist behavior to protect their regimes. While the RFDG benefitted from neighboring countries in which they had ethnic kin, a story that emphasizes neighbors‘ domestic ethnic politics as a source of motivation for Malinke rebellion does not fit well here. The case also suggests what is probably a necessary condition for the domestic ethnic politics argument to hold: a form of audience costs. Dictators may not be

168

consistently concerned with popular support, and in economically underdeveloped contexts, popular support may not be easily mobilized or even existent in the first place.

If the theory is more likely to hold in cases where political leaders are more concerned and motivated by popular support, then Africa, with its history of dictatorship, is probably not where we should expect the argument to explain behavior well.

7.5 Chad’s Evolving Civil War

7.5.1 History of the Conflict

Almost since its independence from France in 1960, Chad has been involved in a civil war that has involved multiple ethnic groups and, by the best estimates, resulted in over

38,000 battle-related deaths between 1966 and 2008 (Lacina and Gleditsch 2005), besides conflict-related deaths. A review of the chronology of the civil war since 1966 is beyond the scope of this chapter, so readers are urged to consult other sources for a more complete understanding of the complexity of this conflict (e.g., Burr and Collins 1999).

The Front de libération nationale du Tchad (FROLINAT) launched a rebellion in

1966 against the autocratic government of François Tombalabye. Much as the conflict in

Sudan is framed, the rebellion was in protest of the government‘s perceived favoritism of the Christian south at the expense of the more Muslim north. FROLINAT splintered in

1971 and after a brief interim government that included northerners and southerners,

Hissèn Habré‘s Forces armées du nord (FAN) gained control of the state in 1982. During the 1980s, Libya backed other rebel groups fighting against Habré‘s government. In

1990, Idriss Deby, heading the MPS organization based in Sudan, ousted Habré from power. In 1991, a new rebel group, the Mouvement pour la démocratie et le development

169

(MDD), made up of Habré loyalists, launched attacks against Deby‘s government from

Nigeria. Other groups emerged to vie for control of the state throughout the 1990s and numerous accords were signed by the government with various groups in an effort to end the conflict. By 2000, only the MDJT remained as a viable organization, but after its leader was killed by government forces in 2002, the group splintered and fighting continued. A peace agreement was signed in 2005 but fighting continued, with new rebel groups forming and backed by Sudan. The conflict is considered ongoing.

7.5.2 Explaining Ethnic Group Involvement

State Capacity

Chad is a highly ethnically diverse country. In my data population is divided between eleven main ethnic groups: Sara (34% population share), Arab (32%), Kirdi (5%), Maba

(5%), Toubou (4.4%), Naba (3%), Kanembu (2.3%), Moundan (2.2%), Zaghawea-Bideya

(2%), the Mesmedje/Kenga/Babalia/Diongor/Saba cluster (1.3%), and the Mubi/Karbo

(1%). The PRIO data, which identify rebel groups operating during a conflict and which I link to ethnic groups, count at least ten distinct groups fighting against the state between

1980 and 2006 (more emerged after that date). Based on research of the rebel groups, I link the following ethnic groups to at least one rebel group during the conflict: Sara,

Arab, Toubou, Kanembu, Moundan, and Zaghawea-Bideya.

Figure 7.4 shows the locations of the ethnic regions. The groups involved in rebellion are located across the country, with the Sara in the south, the Arabs and Toubou in the north, the Kanembu in the west, the Moundan in the southwest, and the Zaghawea-

Bideya in the northeast. The plethora of rebel groups fighting for control of the

170

government over the years indicates the state‘s continued inability to combat them effectively or prevent their emergence. The Zaghawea-Bideya, Toubou, and Maba are farthest from the capital, but only two of the those three are coded as being involved in rebellion. The history of the rebel groups‘ activities indicates a relative ease of movement; the government usually confined itself to defending the capital or regional centers from onslaught. The political instability associated with each successive regime, coupled with the vast territory to patrol, made for an extremely weak state, but also makes it more difficult in this case to point to relative differences in state capacity across sub-national space. More specific to government takeover rebellions, the two groups with the highest share of population in the country, the Sara and Arab groups, were involved in rebellion. This is consistent with the statistical models‘ results that point to population share as a risk factor, but this case does not shed any light on the specific mechanism through which large populations are more likely to rebel.

Poverty

Like Niger, Chad is desperately poor. The Sara, Moundan, Kirdi, Naba, and Mubi/Karbo are all traditionally sedentary farmers, practicing subsistence agriculture to make their living. While the Maba and Zaghawea-Bideya also engage in some farming, they are also pastoralists, raising livestock that supplements their agricultural production. Finally, the Toubou, some Arab tribes, the Kanembu, and Mesmedje cluster are nomadic pastoralists, though the Kanembu and Mesmedje cluster also practice some farming, as well (Olson 1996). The rebellion that began in 1965 does not seem to have poverty or inequality at the root of the original Toubou insurgency. Rather, part of the motivation

171

was a reaction against the government‘s efforts to tax and control the movements of the

Toubou, which affected their traditional nomadic lifestyle and identity as a cultural nation

(Burr and Collins 1999). To my knowledge, there are no reliable comprehensive data at the ethnic group level dating as far back as the 1960s; indeed, as Figure 7.8 indicates, it is still difficult to obtain data on all ethnic groups in the country, even today. As in other cases, the Tombalbaye government favored its co-ethnics in the distribution of civil service positions and in decisions on locations of early infrastructural development. Case studies point to a great drought across the Sahara in the 1960s and its particular effect on nomads‘ herds and grazing areas as an exacerbation of the early conflict, as water scarcity led to renewed inter-ethnic and inter-tribal disputes across the northeast frontier of the state, spilling over into western Sudan.

In truth, one can only speculate as to how much poverty or inequality played a role in the rebellion of various ethnic groups over the last 35 years. Since every single ethnic group in the country is properly labeled poor, an argument linking any ethnic group to rebellion via their economic status can, on its face, seem convincing. Yet it is important to remember that the key component of the economic grievances argument is the relative status of individuals and groups. It is not clear from a reading of the case‘s history that one or more groups were particularly worse off than others, especially in the first stages of the rebellion, or that such inequality was instrumental in the rebellion of different ethnic groups over time. Rather, one might argue that the Toubou insurgency that began the conflict was about preserving a particular way of life and that this conflict early on in Chad‘s history helped continue the state‘s weakness and enable other ethnic

172

groups later to launch bids to control the center that were utterly separate from the

Toubou rebellion in 1965.

Political Representation

After independence, Ngartha Tombalbaye, an ethnic Sara, assumed the presidency and responsibility for a country with ethnic and religious divisions and a politically unconsolidated frontier left over from French colonialism. Within a few years of taking the presidency, southern soldiers stationed in the BET (northern prefecture) clashed with

Toubou tribesmen. The clash, coupled with government troop presence in the area and their mission of imposing new taxes on the region, helped turn the minor incident into the beginning of a region-wide conflict (Burr and Collins 1999, 32-33). The nomadic herdsmen of the north did not welcome southern black officials and military personnel asserting authority over the region and the weak insurgency gained strength as multiple groups acted on similar antipathy toward the Sara government and the sporadic clashes with government forces developed into a full-fledged movement (FROLINAT) in the late

1960s.

The colonial and Tombalbaye regimes were responsible for the political marginalization of non-Sara groups, particularly those in the north and east. That we do not observe each of those politically excluded groups rebelling simply reminds one of the point made through the statistical analysis, which is that such variation does not generally predict rebellion. Between 1980 and the present, Chad has had only two ethnic groups represented in the presidency, Oueddei and Habré, both Toubou, from 1980 to 1990, and

Idriss Déby, a Zaghawea, ever since.

173

This case illustrates a shortcoming of the way in which the statistical modeling approach captures political representation of ethnic groups. Since the conflict in Chad began 15 years before the beginning of the statistical analysis, the data are misleading, in a way, in coding the Toubou as having a co-ethnic in the presidency for 10 years and also rebelling between 1980 and 2006. The reason this could be problematic is that when the

Toubou originally rebelled in 1965, the group did not have political representation at the center in any meaningful way, much less in the presidency. Though this should not be problematic in the simple cross-sectional analysis performed in chapter 5 of political exclusion‘s effect on rebellion, the fact that the Toubou had been in rebellion long before

1980, during a time without political representation, is biasing the time series analysis against finding a statistically significant negative result on co-ethnic political representation. The degree to which this bias is problematic for the entire analysis depends on whether this Toubou example is more wide spread throughout other cases of civil conflict. Having not collected the necessary data prior to 1980, I do not know whether this possibility is reason for concern, or if the Toubou in Chad are a unique case.

The political relationship between peripheral groups and the center read as important factors in the early stages of the conflict, which resemble a territorial conflict more than a classic government takeover. Only later, when more rebel groups emerge and foreign intervention from Libya and Sudan, in particular, change the scope of the conflict does it take on the character of multiple parties competing to capture the state.

174

Natural Resources

Figure 7.4 depicts the location of major sites of oil extraction in Chad, which has been exporting oil on a large scale since 2003 (Gökay 2006, 138). A 1,600-kilometer pipeline built from Chad‘s southernmost oil fields (the Bolobo, Komé, and Miandoum fields depicted in the Sara area in Figure 7.4) to the coast through Cameroon made production much more profitable. As an example of the power the president in many of these African countries over how oil revenues are spent, the government changed laws that pushed oil profits toward policies designed to alleviate poverty, an original prerequisite for World

Bank funding of the pipeline project.52 The entire project was financed by a consortium of private interests in addition to the World Bank, at a total production cost of $3.7 billion dollars. Prior to the pipeline‘s production, it was estimated that the pipeline would enable

Chad to realize $2 billion in profits over the anticipated 25-year projected, an estimate that has turned out to be quite conservative (Pegg 2005).

Most of the oil in Chad was discovered in the mid-1970s (Lujala et al. 2005), and since large-scale production did not begin until late 2003, its role in encouraging conflict has been relatively recent. The portions of the pipeline agreement that cover the revenue stream the oil-rich area in the Sara region receives from the project‘s profits illustrate the logic of my argument linking oil wealth and co-ethnic political representation to rebellion risk. According to the agreement signed with the World Bank, the region around the

Doba fields in southern Chad was to receive 5% of the overall 90% of oil royalties that the state received (10% are placed in an account to fund Chad‘s post-oil future). While

52 Lydia Polgreen, ―Chad Backs Out of Pledge to Use Oil Wealth to Reduce Poverty,‖ New York Times (December 13, 2005). See Pegg (2005, 11 ff) for details on the agreement between the World Bank and Chad that governed how oil revenues were to be used, and discussion of the entire project‘s early failures to alleviate poverty and spur infrastructural development as intended, particularly in the area of public health.

175

this percentage may seem to some a reasonable and just compensation package given the lucrative nature of oil profits, the agreement gives the president discretion to change this figure within five years of the agreement‘s signing.

This example illustrates the importance of the ways in which institutions are designed to compensate oil-rich regions and stave off rebellion. Nigeria has a much longer history in dealing with this problem. Initially, regions from which oil was taken received a fixed percentage of the taxes levied on the oil (not the profits); in 1960, this was set at 50%. By 1975, this figure had decreased to just 20%, and subsequently, this system was scrapped in favor of giving regions just 1.5% of overall government profits from oil sales (Frynas 2001, 32). If Chad‘s government reduces the regional payment stream in a similar manner, it may find itself battling yet another conflict over oil wealth distribution, much like that of the Niger Delta region in Nigeria.

The history of Chad does not, however, help interpret a weak statistical finding

(Table 5.3) that oil wealth in a region actually reduces the probability of that ethnic group being involved in a government takeover conflict. The argument linking oil wealth and political representation theoretically applies more to territorial rebellion, but as the discussion of the recent beginning in oil production in a politically unrepresented ethnic group‘s region suggests, a new and distinct territorial conflict may yet emerge from disputes over revenue sharing.

Transnational Ethnic Kin

Foreign support of rebels in Chad, Sudan, and Libya during this period by various governments is North Africa‘s answer to West Africa‘s set of regional conflicts, of which

176

Guinea and Senegal served as prime examples. Arab tribes span the surrounding countries of Sudan and Libya, while the Toubou are also found in Niger and the Sara in

Central African Republic. Gleditsch and Salehyan‘s data record extensive military support for FROLINAT from Libya and Sudan dating to early in the conflict, while the

US, France, and Zaire aided the government at various times. Was this support motivated by transnational ethnic ties and domestic politics?

For its part, Libyan support of Chadian insurgents is not unique, as Libya aided many different rebel groups in various countries across Africa during this period. While

Arabs did participate in the Chadian insurgency, so did other, non-Arab groups, so

Sudan‘s consistent harboring of Chadian rebels and support of them may have less to do with Khartoum needing to appease domestic Arab groups than with a desire to destabilize its neighbor. Burr and Collins (1999, 37) report that early in the Toubou-dominated rebellion, Tombalbaye was ―infuriated‖ by Khartoum‘s open support of the insurgency in the north because Tombalbaye consistently had refused to send aid to southern Christian rebels in Sudan, with whom he sympathized because of ties in ―race‖ and ―religion‖. If such ties existed, they could help explain early Sudanese support for the northern Toubu and Arab rebellion against the southern Sara government; the north-south, Muslim-

Christian divide in Chad mirrors that in Sudan. There is no evidence, however, that ethnic groups rebelled because they anticipated foreign support for their cause, though after initial Libyan and Sudanese support, Chadian ethnic groups may have updated their expectations.

177

7.6 Conclusion

This chapter supplemented the statistical analysis of ethnic group rebellion by examining ethnic group involvement in four individual civil conflicts in more detail. The primary purpose was to trace the histories of each conflict in order to see how well the predictions of the statistical models are borne out in particular cases. Overall, these cases illustrate the core theoretical claim of the dissertation: peripheral ethnic groups are more likely to rebel. This was true in Senegal, particularly if we consider Gambia‘s effect on the Diola‘s proximity to the capital; in Niger, where the most peripheral groups were involved in rebellion; and in Guinea, where peripheral location and international borders helped rebels evade Guinean forces. Only in Chad is the evidence mixed on this question: with so many ethnic groups participating in the civil war that has lasted so long, both peripheral and centrally-located groups have been involved.

The cases also illustrated the predicted effect of wealth on likelihood of rebellion.

In Guinea, which experienced a government takeover conflict, the second wealthiest group (according to the wealth index) was involved in rebellion, though it is not clear what would explain this finding. In Niger, peripheral poor groups were involved in rebellion.

In each of these cases, no clear pattern appears as to the relationship between political representation and ethnic group rebellion. While politically excluded ethnic groups do rebel, this is not consistently the case. Similarly, the relationship between rebellion and transnational ethnic ties is not clear cut. Again, we can find instances in these cases of groups that rebel and also have ethnic ties to politically powerful groups in neighboring countries, but we also find groups with such ties that do not rebel. In

178

summary, the most consistent finding to emerge from the case studies is that peripheral ethnic groups are at higher risk of rebellion.

179

Figure 7.1. Senegal‘s Casamance Rebellion

180

Figure 7.2. Niger‘s Separatist Rebellions

181

Figure 7.3. Guinea and the RFDG Insurgency

182

Figure 7.4. Chadian Rebellion

183

Figure 7.5. Relative Poverty of Senegalese Ethnic Groups

184

Figure 7.6. Relative Poverty of Nigerien Ethnic Groups

185

Figure 7.7. Relative Poverty of Guinean Ethnic Groups

186

Figure 7.8. Relative Poverty of Chadian Ethnic Groups

187

Chapter 8

Findings and Future Research

Competing theories of ethnic group rebellion and of participation in civil war more generally exist, in part, because of the difficulty associated with testing the validity of the competing claims. The paucity of quality data is largely to blame for this difficulty, which has led previous research to focus on single cases or a biased sample of cases in large-n studies of this question. The research design and collected data in this dissertation considerably improve on scholarship in this respect. The large geographic and temporal scope of the project, coupled with the rich data collected on a complete set of ethnic groups, all serve to enhance the credibility of the results and defend against charges of bias and concerns over external validity typically levied against these studies.

Moreover, given the size and scope of the statistical analysis, the emergence of the core result linking ethnic groups‘ peripheral location to rebellion – particularly territorial rebellion – is remarkable. Consider that this result follows from analysis on perhaps the most culturally diverse continent in the world, on both North Africa and Sub-

Saharan Africa, both during the Cold War and after its conclusion. This variation underscores the strength of this relationship, as it holds across such a wide array of places, peoples, and times.

I use proximity to the capital city as a measurement of the state‘s ability to monitor, administer, and control its territory and population, and interpret the key result as evidence that sub-national variation in state capacity to exercise a monopoly of violence over territory – and not a group‘s political representation, its relative economic

188

status, or its ties to foreign governments – is most important for explaining which ethnic groups are more likely than others to be involved in civil conflict.

I argue that Africa‘s history of economic development follows a particular spatial pattern, whereby the state‘s peripheral regions develop most slowly and thus these ethnic groups not only have the best opportunity to rebel against the state, but also motivation to fight for an altered and more beneficial political relationship with the center. The traditional link made between poverty and rebellion only holds for a particular type of rebellion and for a particularly situated ethnic group, that which inhabits the extreme periphery. Ethnic groups involved in bids to capture the state may be more likely to be wealthy (though, because of the data coverage, we can have less confidence in this result), and are more likely to have larger populations relative to other ethnic groups in the country. These groups‘ large populations makes it easier for insurgents to blend in and avoid state capture.

Alternate theories of ethnic group rebellion did not find as much support in the evidence. Certainly, the expected relationship – either the size or direction – between poverty and rebellion did not emerge from the data. The results suggest several avenues for future research. Is the positive correlation between wealth and involvement in government takeover rebellion spurious or is it reflective of a solid result? If there is such a relationship, what mechanism explains it? My theory articulated here posits one possible way to account for the finding, but does not provide any test of the hypothesis.

Analysis of the relationship between political representation and rebellion risk yielded little evidence of a strong relationship in the cross-section, though lagged dependent variable models using panel data found a weak negative relationship between

189

lagged co-ethnic representation in the presidency and propensity to rebel in the current period. The more interesting finding is that which provides a specific way through which political representation seems to impact the likelihood of rebellion: the center‘s control of natural resource revenue distribution. Co-ethnic representation in government dramatically reduces the likelihood that ethnic groups with oil on their land will be involved in territorial rebellion. If the posited mechanism accounting for this result is correct, that co-ethnics in government provide more favorable schemes of revenue redistribution for their kin, then this analysis, along with the case study of Chad, implied that governments would do well to favorably compensate ethnic groups on whose land oil is found in order to stave off rebellion.

The wisdom of this policy implication is immediately countered by an issue raised in the introduction, that of endogenous group formation and the fluid nature of ethnic identity.53 If the Chadian government negotiates a mutually satisfactory revenue sharing agreement with the Sara who inhabit the land on which the Doba oil fields are located, why should we expect this policy to reduce the risk of rebellion over this issue? Instead, the government‘s activity invites groups from within the Sara ethnic group (e.g., tribal) groups to lobby for similar payments. The Sara ethnic group cannot be treated as a single entity under this scenario; rather, the government should expect an infinite number of

―Sara‖ groups to emerge in response to its initial attempt to settle the revenue distribution issue. This exact process played out in the Nigerian government‘s attempt to compensate owners of land in the oil-rich Niger Delta region: the government continued to face new groups of people who claimed ownership over the land and rights to revenues from its natural resources (Frynas 2001).

53 I thank David Laitin for raising this issue.

190

This suggests that governments face a non-trivial cost in pursuing a strategy that attempts to buy off ethnic groups who inhabit oil-rich land, and under certain circumstances might be better off financially retaining complete control over oil revenues and dealing with a low-level insurgency in the region than avoiding rebellion but being forced to give up increasing amounts of revenue over successive rounds of negotiation with emerging groups. A fruitful avenue of research along these lines would be the development of a formal model that outlined the conditions under which the government pursues each strategy.

Several other avenues of research are suggested by the overall results. First, implicit in the coding linking ethnic groups to rebel groups is a decision to treat ethnic groups as individual actors. To what degree does such a decision obscure important variation in micro-level decision making? The coding scheme is useful for purposes of research because it allows us to make certain inferences at the ethnic group level, but is it problematic if it ignores any intra-ethnic variation in preferences?

Second, the project raises questions of external validity beyond Africa and the need to define more explicitly the scope conditions of the theory. This is not just a question of whether the same results would hold in Southeast Asia or Latin America, but rather a need to articulate the necessary and sufficient conditions for which we expect the theory to hold. Is it necessary to have Africa‘s pattern of political and economic development in order for the state to have such a weak hold on the periphery? Other research on political violence at the sub-national level, referenced in chapter 2, suggest that this is not a scope condition for the theory, but one could raise similar questions that might not be answered as easily.

191

Third, a related issue is whether this theory holds more or less strongly for different parts of Africa. Most studies of anything related to Africa ignore either North

Africa or Sub-Saharan Africa, assuming the two regions are so distinct as to render any comparison of limited value. A useful extension of the analysis here would be to test, to the extent permitted by the data, the elements of the candidate theories on certain regions of Africa, perhaps during and after the Cold War.

Finally, one of the most obvious ways of improving on the results here involves creative identification and subsequent data collection of other direct measures of state capacity. This proves an exceedingly difficult task, as most such measures of the state‘s coercive capacity that vary across time or space are endogenous to conflict occurrence or perceived risk of it. Nevertheless, being more specific about the particular elements of state capacity that matter for discouraging rebellion, and more accurately mapping measures to those concepts promises to be a fruitful way forward in furthering our understanding of this complicated relationship.

192

References

Abdalla, Muna A. 2009. ―Understanding of the Natural Resource Conflict Dynamics: The Case of Tuareg in North Africa and the Sahel.‖ ISS Paper no. 194. Pretoria, South Africa: Institute for Security Studies.

Adam, Hussein M. 1999. ―Somali Civil Wars.‖ In Civil Wars in Africa: Roots and Resolution, ed. Taisier M. Ali and Robert O. Matthews, 169-92. Montreal: McGill-Queen‘s University Press.

Adida, Claire L. 2010. ―Immigrant Exclusion and Insecurity in Africa.‖ Ph. D. Thesis. Stanford University.

Agnew, John. 2001. ―Regions in Revolt.‖ Progress in Human Geography 25, no. 1: 103- 110.

Anderson, Benedict. 1983. Imagined Communities. London: Verso.

Anderson, Liam and Gareth Stansfield. 2005. ―The Implications of Elections for Federalism in Iraq: Toward a Five-Region Model.‖ Publius 35, no. 3 (Summer): 359-82.

Azam, Jean-Paul. 2001. ―The Redistributive State and Conflicts in Africa.‖ Journal of Peace Research 38, no. 4: 429-44.

Baker, Bruce. 2001. ―Separating the Sheep from the Goats among Africa‘s Separatist Movements.‖ Terrorism and Political Violence 13, no. 1: 66-86.

Bates, Robert H. 1974. ―Ethnic Competition and Modernization in Contemporary Africa.‖ Comparative Political Studies 6, no. 4 (January): 457-84.

———. 1986. ―Modernization, Ethnic Competition, and the Rationality of Politics in Contemporary Africa.‖ Governing Black Africa 6, no. 4: 457-84.

———. 2001. Prosperity and Violence: the Political Economy of Development. New York: W. W. Norton.

Baum, Robert M. 1990. ―Emergence of a Diola Christianity.‖ Africa: Journal of the International African Institute 60, no. 3: 370-98.

Berkowitz, Daniel. 1997. ―Regional Income and Secession: Center-Periphery Relations in Emerging Market Economies.‖ Regional Science and Urban Economics 27: 17- 45.

193

Bermúdez-Lugo, Omayra. 2004. ―The Mineral Industry of Guinea.‖ U.S. Geological Survey Minerals Handbook. Besançon, Marie L. 2005. ―Relative Resources: Inequality in Ethnic Wars, Revolutions, and Genocides.‖ Journal of Peace Research 42, no. 4: 393-415.

Brancati, Dawn. 2004. ―Can Federalism Stabilize Iraq?‖ The Washington Quarterly 27, no. 2 (Spring): 7-21.

Brustein, William and Margaret Levi. 1987. ―The Geography of Rebellion: Rulers, Rebels, and Regions, 1500 to 1700.‖ Theory and Society 16, no. 4 (July): 467-95.

Buhaug, Halvard, Lars-Erik Cederman, and Jan Ketil Rød. 2008. ―Disaggregating Ethno- Nationalist Civil Wars: A Dyadic Test of Exclusion Theory.‖ International Organization 62, no. 3: 531-551.

Buhaug, Halvard and Päivi Lujala. 2005. ―Accounting for Scale: Measuring Geography in Quantitative Studies of Civil War.‖ Political Geography 24: 399-418.

Buhaug, Halvard, and Jan Ketil Rød. 2006. ―Local Determinants of African Civil Wars, 1970-2001.‖ Political Geography 25: 315-35.

Burr, J. Millard and Robert O. Collins. 1999. Africa’s Thirty Years War: Libya, Chad, and the Sudan, 1963-1993. Boulder, CO: Westview Press.

Caselli, Francesco and Wilbur John Coleman. 2006. On the Theory of Ethnic Conflict. Cambridge, MA: National Bureau of Economic Research.

Cederman, Lars-Erik and Luc Girardin. 2007. ―Beyond Fractionalization: Mapping Ethnicity onto Nationalist Insurgencies.‖ American Political Science Review 101, no. 1 (February): 173-85.

Cederman, Lars-Erik, Luc Girardin, and Kristian Skrede Gleditsch. 2009. ―Ethnonationalist Triads: Assessing the Influence of Kin Groups on Civil Wars.‖ World Politics 61, no. 3 (July): 403-37.

Central Intelligence Agency. The World Factbook. Available from: https://www.cia.gov/library/publications/the-world-factbook.

Chandra, Kanchan. 2006. ―What Is Ethnic Identity and Does It Matter?‖ Annual Review of Political Science 9 (June): 397-424.

Collier, Paul. 2009. Wars, Guns, and Votes. New York: HarperCollins.

Collier, Paul and Anke Hoeffler. 2004. ―Greed and Grievance in Civil War.‖ Oxford Economic Papers 56: 563-95.

194

———. 2005. ―The Political Economy of Secession.‖ In Negotiation and Self- Determination, ed. Hurst Hannum and Eileen F. Babbitt. Lanham: Lexington Books.

Davies, James. 1962. ―Toward a Theory of Revolution.‖ American Sociological Review 6 (February): 5-19.

Dawisha, Adeed I. and Karen Dawisha. 2003. ―How to Build a Democratic Iraq.‖ Foreign Affairs (May/June).

De Soysa, Indra. 2002. ―Paradise Is a Bazaar? Greed, Creed, and Governance in Civil War, 1989-99.‖ Journal of Peace Research 39, no. 4: 395-416.

Deutsch, Karl W. 1953. ―The Growth of Nations: Some Recurrent Patterns of Political and Social Integration.‖ World Politics 5, no. 2 (January): 168-95.

———. 1961. ―Social Mobilization and Political Development.‖ The American Political Science Review 55, no. 3 (September): 493-514.

Diaz-Cayeros, Alberto. January 21, 2009. ―Indian Identity, Poverty and Colonial Development in Mexico.‖ Manuscript. University of California, San Diego.

Eckstein, H. 1965. ―On the Etiology of Internal Wars.‖ History and Theory 4, no. 2: 133- 63.

Fearon, James D. 2003. ―Ethnic and Cultural Diversity by Country.‖ Journal of Economic Growth 8, no. 2 (June): 195-222.

———. 2004. ―Why Do Some Civil Wars Last So Much Longer Than Others?‖ Journal of Peace Research 41, no. 3: 275-301.

———. 2005. ―Primary Commodity Exports and Civil War.‖ Journal of Conflict Resolution 49 (August): 483-507.

Fearon, James D., and David D. Laitin. 2000. ―Violence and the Social Construction of Ethnic Identity.‖ International Organization 54, i. 4 (November): 845-77.

———. 2003. ―Ethnicity, Insurgency, and Civil War.‖ American Political Science Review 97, no. 1 (February): 75-90.

———. June 16, 2005. ―Random Narratives - Cameroon.‖ Manuscript. Stanford University.

Fearon, James D., Kimuli Kasara, and David D. Laitin. 2007. ―Ethnic Minority Rule and Civil War Onset.‖ American Political Science Review 101, no. 1: 187-93.

195

Foucher, Vincent. 2005. ―La guerre des dieux? Religions et séparatisme en Basse Casamance.‖ Canadian Review of African Studies 39, no. 2: 361-88.

Frynas, Jfdrzej George. 2001. ―Corporate and State Responses to Anti-Oil Protests in the Niger Delta.‖ African Affairs 100, no. 398: 27–54.

Galbraith, Peter W. 2006. The End of Iraq: How American Incompetence Created a War without End. New York: Simon & Schuster.

Gastil, Raymond D., ed. 1997. Freedom in the World: The Annual Survey of Political Rights, 1996-1997. New York: Freedom House.

Gellner, Ernest. 1983. Nations and Nationalism. Second edition, 2006. Malden, MA: Blackwell Publishing.

———. 1997. Nationalism. New York: New York University Press.

Gilmore, Elisabeth, Nils Petter Gleditsch, Päivi Lujala, and Ketil Rød. 2005. ―Conflict Diamonds: A New Dataset.‖ Conflict Management and Peace Science 22, no. 3: 257-92.

Gleditsch, Kristian Skrede. 2002. All Politics Is Local: The Diffusion of Conflict, Integration, and Democratization. Ann Arbor: MI: University of Michigan Press.

———. 2007. ―Transnational Dimensions of Civil War.‖ Journal of Peace Research 44, no. 3: 293-309.

Gleditsch, Nils Petter; Peter Wallensteen, Mikael Eriksson, Margareta Sollenberg & Håvard Strand. 2002. ―Armed Conflict 1946-2001: A New Dataset.‖ Journal of Peace Research 39, no. 5: 615-637.

Global Mapping International. World Language Mapping System, Version 3.2. Colorado Springs, CO.

Goemans, Hein, Kristian Gleditsch, and Giacomo Chiozza. ―Archigos: A Data Base on Leaders 1875 - 2004.‖ Available from: http://mail.rochester.edu/~hgoemans/data.htm.

Gökay, Bülent, ed. 2006. The Politics of Oil. London: Routledge.

Goldsmith, Arthur A. 2001. ―Risk, Rule and Reason: Leadership in Africa.‖ Public Administration and Development 21: 77-87.

Gordon, Raymond G., ed. 2005. Ethnologue: Languages of the World. Fifteenth ed. Dallas: SIL International.

196

Gourevitch, Peter Alexis. 1979. ―The Reemergence of ‗Peripheral Nationalisms‘: Some Comparative Speculations on the Spatial Distribution of Political Leadership and Economic Growth.‖ Comparative Studies in Society and History 21, no. 3 (July): 303-22.

Govea, Rodger M. and John D. Holm. 1998. ―Crisis, Violence and Political Succession in Africa.‖ Third World Quarterly 19, no. 1: 129-48.

Greene, William H. 2003. Econometric Analysis. Fifth edition. Upper Saddle River, NJ: Prentice Hall.

Gurr, Ted Robert. 1969. ―A Comparative Study of Civil Strife.‖ In Violence in America: Historical and Comparative Perspectives, edited by H. D. Graham and Ted Robert Gurr. New York: Signet.

———. 1970. Why Men Rebel. Princeton, NJ: Princeton University Press.

———. 1993a. Minorities at Risk: A Global View of Ethnopolitical Conflict. Washington, DC: United States Institute of Peace Press.

———. 1993b. ―Why Minorities Rebel: A Global Analysis of Communal Mobilization and Conflict since 1945.‖ International Political Science Review 14: 161-201.

———. 1996. ―Minorities, Nationalists, and Ethnopolitical Conflict.‖ In Peacemaking into the 21st Century, edited by Chester A. Crocker and Fen Osler Hampson. Washington, DC: United States Institute of Peace Press.

———. 2000. People versus States: Minorities at Risk in the New Century. Washington, DC: United States Institute of Peace.

Gurr, Ted Robert and Will H. Moore. 1997. ―Ethnopolitical Rebellion: A Cross-Sectional Analysis of the 1980s with Risk Assessments for the 1990s.‖ American Journal of Political Science 41, no. 4 (October): 1079-1103.

Harff, Barbara, and Ted Robert Gurr. 2004. Ethnic Conflict in World Politics. Boulder, CO: Westview Press.

Harty, Siobhan. 2001. ―The Institutional Foundations of Substate National Movements.‖ Comparative Politics 33, no. 2 (January): 191-210.

Hechter, Michael. 1993. Internal Colonialism. New Brunswick: Transaction Publishers.

Hegre, Håvard and Nicholas Sambanis. 2006. ―A Sensitivity Analysis of the Empirical Literature on Civil War Onset.‖ Journal of Conflict Resolution 50, no. 4: 508-535.

197

Hegre, Håvard, and Clionadh Raleigh. September 19, 2006. ―Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis.‖ Working Paper. Presented to the Meeting in the Environmental Factors in Civil War Working Group, Centre for the Study of Civil War, PRIO.

Herbst, Jeffrey. 2000. States and Power in Africa. Princeton, NJ: Princeton University Press.

Hibbs, D.A., Jr. 1973. Mass Political Violence: A Cross-National Causal Analysis. New York: John Wiley.

Holden, Clare Janaki. 2002. ―Bantu Language Trees Reflect the Spread of Farming across Sub-Saharan Africa: A Maximum-Parsimony Analysis.‖ Proc. R. Soc. Lond.: 793-99.

Horowitz, Donald L. 1985. Ethnic Groups in Conflict. Berkeley, CA: University of California Press.

Human Security Centre. 2005. Human Security Report: War and Peace in the 21st Century. New York: Oxford University Press.

Humphreys, Macartan. 2005. ―Natural Resources, Conflict and Conflict Resolution.‖ Journal of Conflict Resolution 49: 508-37.

Humphreys, Macartan, Jeffrey D. Sachs, and Joseph E. Stiglitz. 2007. ―What Is the Problem with Natural Resource Wealth?‖ In Escaping the Resource Curse, ed. Macartan Humphreys, Jeffrey D. Sachs, and Joseph E. Stiglitz, 1-20. New York: Columbia University Press.

Humphreys, Macartan and Jeremy M. Weinstein. 2008. ―Who Fights? The Determinants of Participation in Civil War.‖ American Journal of Political Science 52, no. 2 (April): 436-55.

Humphreys, Macartan and Habaye ag Mohamed. 2005. ―Senegal and Mali.‖ In Understanding Civil War Vol. 1, ed. Paul Collier and Nicholas Sambanis, 247- 302. Washington, DC: The World Bank.

Ibrahim, Jibrin. 1994. ―Political Exclusion, Democratization and Dynamics of Ethnicity in Niger.‖ Africa Today 41, no. 3: 15-39.

Jackson, Robert H. and Carl G. Rosberg. 1982. ―Why Africa‘s Weak States Persist: The Empirical and Juridical in Statehood.‖ World Politics 3 (15): 1-24.

Kalyvas, Stathis. 2006. The Logic of Violence in Civil War. New York, NY: Cambridge University Press.

198

Kasara, Kimuli. 2007. ―Tax Me If You Can: Ethnic Geography, Democracy, and the Taxation of Agriculture in Africa.‖ American Political Science Review 101, no. 1: 159-72.

Kedourie, Elie. 1960. Nationalism. London: Hutchinson.

Kiyaga-Nsubuga, John. 1999. ―Managing Political Change: Uganda under Museveni.‖ In Civil Wars in Africa: Roots and Resolution, ed. Taisier M. Ali and Robert O. Matthews, 13-34. Montreal: McGill-Queen‘s University Press.

Korpi, Walter. 1974. ―Conflict, Power and Relative Deprivation.‖ American Political Science Review 68: 1569-78.

Lacina, Bethany. 2010. ―The Origins of Political Violence: Autonomy Demands and Civil Conflict in India, 1951-1989.‖ Ph. D. Thesis. Stanford University.

Lacina, Bethany and Nils Petter Gleditsch, 2005. ―Monitoring Trends in Global Combat: A New Dataset of Battle Deaths.‖ European Journal of Population 21 (2–3): 145–166.

Laitin, David D. 1998. Identity in Formation: The Russian-Speaking Populations in the Near Abroad. Ithaca, NY: Cornell University Press.

Le Billon, Philippe. 2001. ―The Political Ecology of War: Natural Resources and Armed Conflict.‖ Political Geography 20, i. 5 (June): 561-84.

———. 2005. Fuelling War: Natural Resources and Armed Conflict. New York: Routledge.

Lichbach, Mark Irving. 1987. ―Deterrence or Escalation? The Puzzle of Aggregate Studies of Repression and Dissent.‖ Journal of Conflict Resolution 31, no. 2 (June): 266-97.

———. 1990. ―Will rational people rebel against inequality? Samson‘s choice.‖ American Journal of Political Science 34, no. 4 (November): 1049-1076.

———. 1994. ―What makes rational peasants revolutionary?: Dilemma, paradox, and irony in peasant collective action.‖ World Politics 46, no. 3 (April): 383-418.

Lichbach, Mark Irving, and Ted Robert Gurr. 1981. ―The Conflict Process: A Formal Model.‖ Journal of Conflict Resolution 25, no. 3: 3-29.

Lijphart, Arend. 1969. ―Consociational Democracy.‖ World Politics 21, no. 2 (January): 207-25.

———. 1977. Democracy in Plural Society. New Haven: Yale University Press.

199

Lindstrom, Ronny, and Will H. Moore. 1995. ―Deprived, Rational or Both? 'Why Minorities Rebel' Revisited.‖ Journal of Political and Military Sociology 23: 167- 90.

Londregan, John, Henry Bienen, and Nicolas van de Walle. 1995. ―Ethnicity and Leadership Succession in Africa.‖ International Studies Quarterly 39, no. 1 (March): 1-25.

Lowi, Miriam R. 2005. ―Algeria, 1992-2002: Anatomy of a Civil War.‖ In Understanding Civil War, Volume 1: Africa, ed. Paul Collier and Nicholas Sambanis. Washington, DC: The World Bank.

Lujala, Päivi, Nils Petter Gleditsch, and Elisabeth Gilmore. 2005. ―A Diamond Curse? Civil War and a Lootable Resource.‖ Journal of Conflict Resolution 49 (2005): 538-562.

Lujala, Päivi, Jan Ketil Rød, and Nadia Thieme. 2007. ―Fighting over Oil: Introducing a New Dataset.‖ Conflict Management and Peace Science 24, no. 3: 239-256.

Macro Publishing Inc. 1996. Sampling Manual. DHS III Basic Documentation 6.

Marshall, Monty G. and Donna Ramsey Marshall. January 10, 2007. ―Coup d‘Etat Events, 1960-2006.‖ Center for Systemic Peace.

Mearsheimer, John. 2002. The Tragedy of Great Power Politics. New York: W. W. Norton & Company.

Measure DHS+. 2004. Description of the Demographic and Health Surveys Individual Recode Data File. Calverton, MD: ORC Macro.

Miguel, Edward, Shanker Satyanath, and Ernest Sergenti. 2004. ―Economic Shocks and Civil Conflict: An Instrumental Variables Approach.‖ Journal of Political Economy 1, no. 4: 725-53.

Miguel, Edward, and Mary Kay Gugerty. 2005. ―Ethnic Diversity, Social Sanctions, and Public Goods in Kenya.‖ Journal of Public Economics 89: 2325-68.

Moore, Will H. 1998. ―Repression and Dissent: Substitution, Context and Timing.‖ American Journal of Political Science 42, no. 3: 851-73.

Moore, Will H., and Keith Jaggers. 1990. ―Deprivation, Mobilization and the State.‖ Journal of Developing Societies 6: 17-36.

Morrison, Donald George, Robert Cameron Mitchell, and John Naber Paden. 1989. Black Africa: A Comparative Handbook, second ed. New York: Paragon House.

200

Muller, Edward, and Mitchell Seligson. 1987. ―Inequality and Insurgency.‖ American Political Science Review 81 (June): 425-52.

Murdoch, James C. and Todd Sandler. 2002. ―Economic Growth, Civil Wars, and Spatial Spillovers.‖ Journal of Conflict Resolution 46, no. 1: 91-110.

Murshed, S. Mansoob and Scott Gates. 2005. ―Spatial-Horizontal Inequality and the Maoist Insurgency in Nepal.‖ Review of Development Economics 9 (1): 121-134.

Ndikumana, Leonce and Kisangani F. Emizet. 2005. ―The Economics of Civil War: The Case of the Democratic Republic of Congo.‖ In Understanding Civil War: Evidence and Analysis, Vol. 1 Africa, ed. Paul Collier and Nicholas Sambanis, 63- 121. Washington, DC: The World Bank.

Neuberger, Benjamin. 1986. National Self-Determination in Postcolonial Africa. Boulder: Lynne Rienner Press.

O‘Toole, Thomas and Janice E. Baker. 2005. Historical Dictionary of Guinea, 4th ed. Oxford, UK: The Scarecrow Press.

Olson, James Stuart. 1996. The Peoples of Africa: An Ethnohistorical Dictionary. Westport, CT: Greenwood Press.

Olson, Mancur. 1965. The Logic of Collective Action: Public Goods and the Theory of Groups. Cambridge, MA: Harvard University Press.

Østby, Gudrun. 2008. ―Polarization, Horizontal Inequalities, and Violent Civil Conflict.‖ Journal of Peace Research 45, no. 2: 143-62.

Østby, Gudrun, Ragnhild Nordås, and Jan Ketil Rød. 26-29 April 2006. ―Regional Inequalities and Civil Conflict in 21 Sub-Saharan African Countries, 1986-2004.‖ Prepared for presentation at the workshop on Polarization and Conflict, Cyprus.

Oyefusi, Aderoju. 2008. ―Oil and the Probability of Rebel Participation Among Youths in the Niger Delta of Nigeria.‖ Journal of Peace Research 45 no. 4: 539-55.

Pegg, Scott. 2005. ―Can Policy Intervention Beat the Resource Curse? Evidence from the Chad-Cameroon Pipeline Project.‖ African Affairs 105: 1-25.

Posner, Daniel N. 2004. ―The Political Salience of Cultural Difference: Why Chewas and Tumbukas Are Allies in Zambia and Adversaries in Malawi.‖ American Political Science Review 98, no. 4: 529-545.

Reynal-Querol, Marta. 2002. ―Ethnicity, Political Systems, and Civil Wars.‖ Journal of Conflict Resolution 46, no. 1: 29-54.

201

Rodden, Jonathan A. 2006. Hamilton’s Paradox: The Promise and Peril of Fiscal Federalism. New York: Cambridge University Press.

Ross, Michael L. 2001. ―Does Oil Hinder Democracy?‖ World Politics 53, no. 3: 326-61.

———. 2003. ―Oil, Drugs, and Diamonds: The Varying Roles of Natural Resources in Civil War.‖ In The Political Economy of Armed Conflict, ed. Karen Ballentine and Jake Sherman, 47-72. London: Lynne Rienner.

———. 2004. ―What Do We Know about Natural Resources and Civil War?‖ Journal of Peace Research 41, no. 3 (May): 337-56.

———. 2006. ―A Closer Look at Oil, Diamonds, and Civil War.‖ Annual Review of Political Science 9 (June): 265-300.

———. 2007. ―How Mineral-Rich States Can Reduce Inequality.‖ In Escaping the Resource Curse, ed. Macartan Humphreys, Jeffrey D. Sachs, and Joseph E. Stiglitz, 237-55. New York: Columbia University Press.

Russett, Bruce. 1993. Grasping the Democratic Peace. Princeton, NJ: Princeton University Press.

Rustein, Shea Oscar, and Guillermo Rojas. 2003. Guide to DHS Statistics. Calverton, MD: Demographic and Health Surveys, ORC Macro.

Saideman, Stephen M. 2001. The Ties that Divide: Ethnic Politics, Foreign Policy, and International Conflict. New York: Columbia University Press.

———. 2002. ―Discrimination in International Relations: Analyzing External Support for Ethnic Groups.‖ Journal of Peace Research 39, no. 1: 27-50.

———. 1997. ―Explaining the International Relations of Secessionist Conflicts: Vulnerability Versus Ethnic Ties.‖ International Organization 51, no. 4 (Autumn): 721-53.

Saideman, Stephen M. and R. William Ayres. 2000. ―Determining the Causes of Irredentism: Logit Analyses of Minorities at Risk Data from the 1980s and 1990s.‖ Journal of Politics 62 no. 4 (November): 1126-1144.

Salehyan, Idean. 2009. Rebels Without Borders: Transnational Insurgencies in World Politics. Ithaca, NY: Cornell University Press.

Salehyan, Idean and Kristian Skrede Gleditsch. 2006. ―Refugee Flows and the Spread of Civil War.‖ International Organization 60, no. 2: 335-366.

202

Sambanis, Nicholas. 2001. ―Do Ethnic and Non-Ethnic Civil Wars Have the Same Causes? A Theoretical and Empirical Inquiry (part 1)‖ Journal of Conflict Resolution 45, no. 3 (February): 259-282.

Sambanis, Nicholas and Ibrahim Elbadawi. 2000. ―Why Are There So Many Civil Wars in Africa? Understanding and Preventing Violent Conflict.‖ Journal of African Economies 9, no. 3 (December): 244-69.

Scarritt, James R. and Shaheen Mozaffar. 1999. ―The Specification of Ethnic Cleavages and Ethnopolitical Groups for the Analysis of Democratic Competition in Contemporary Africa.‖ Nationalism and Ethnic Politics 5, no. 1 (Spring): 82-117.

Schweller, Randall L. 1994. ―Bandwagoning for Profit: Bringing the Revisionist State Back In.‖ International Security 19, no. 1 (Summer): 72-107.

———. 1996. ―Neorealism‘s Status-Quo Bias: What Security Dilemma?‖ Security Studies 5, no. 3: 90-121.

Scott, James. 1976. The Moral Economy of the Peasant: Rebellion and Subsistence in Southeast Asia. New Haven, CT: Yale University Press.

Sorens, Jason. 2005. ―The Cross-Sectional Determinants of Secessionism in Advanced Democracies.‖ Comparative Political Studies 38: 304-26.

Stewart, Frances. 2000. ―Crisis Prevention: Tackling Horizontal Inequalities.‖ Oxford Development Studies 28, no. 3: 245-62.

———. 2002. ―Horizontal Inequalities: A Neglected Dimension of Development.‖ Centre for Research on Inequality, Human Security, and Ethnicity Working Paper. Queen Elizabeth House, University of Oxford.

———. 2008. ―Horizontal Inequalities and Conflict: An Introduction and Some Hypotheses.‖ In Horizontal Inequalities and Conflict, ed. Frances Stewart. New York: Palgrave Macmillan.

Swenden, Wilfried and Maarten Theo Jans. 2006. ―‘Will It Stay or Will It Go?‘ Federalism and the Sustainability of Belgium.‖ West European Politics 29, no. 5 (November): 877-894.

Tilly, Charles. 1978. From Mobilization to Revolution. Reading, MA: Addison-Wesley.

———. 1990. Coercion, Capital, and European States, AD 990-1992. Malden, MA: Blackwell Publishing.

Toft, Monica Duffy. 2003. The Geography of Ethnic Violence. Princeton, NJ: Princeton University Press.

203

Tong, James W. 1991. Disorder under Heaven: Collective Violence in the Ming Dynasty. Stanford, CA: Stanford University Press.

UCDP/PRIO Armed Conflict Dataset, v.4-2006. Available from: http://www.pcr.uu.se/research/UCDP/data_and_publications/datasets_archive.htm .

Uppsala Conflict Data Program Database. Available from: http://www.pcr.uu.se/research/UCDP/index.htm. Accessed 2 November 2009.

U.S. Department of State. March 12, 2010. ―Background Note: Guinea.‖ Available from http://www.state.gov.

Van de Walle, Nicolas. 2007. ―Meet the new boss, same as the old boss? The evolution of political clientilism in Africa.‖ In Patrons, Clients, and Policies: Patterns of Democratic Accountability and Political Competition, ed. Herbert Kitschelt and Steven I. Wilkinson. New York: Cambridge University Press.

Walter, Barbara F. 2006a. ―Building Reputation: Why Governments Fight Some Separatists but Not Others.‖ American Journal of Political Science 50, no. 2: 313- 30.

———. 2006b. ―Information, Uncertainty, and the Decision to Secede.‖ International Organization 60, no. 1: 105-35.

Weber, Eugen. 1976. Peasants into Frenchmen. Stanford, CA: Stanford University Press.

Weidmann, Nils B. 2009. ―Geography as Motivation and Opportunity: Group Concentration and Ethnic Conflict.‖ Journal of Conflict Resolution 53, no. 4 (August): 526-543.

Weingast, Barry and De Figueiredo, Rui. 1999. ―Rationality of Fear: Political Opportunism and Ethnic Conflict.‖ In Jack Snyder and Barbara Walter, eds., Military Intervention in Civil Wars. New York: Columbia University Press.

Weinstein, Jeremy M. 2007. Inside Rebellion. New York: Cambridge Unviersity Press.

Weinstein, Jeremy M. and Laudemiro Francisco. 2005. ―The Civil War in Mozambique: The Balance Between Internal and External Influences.‖ In Understanding Civil War: Evidence and Analysis, Vol. 1 Africa, ed. Paul Collier and Nicholas Sambanis, 157-92. Washington, DC: The World Bank.

Yakan, Muhammad Zuhdï. 1999. Almanac of African Peoples and Nations. New Brunswick, NJ: Transaction.

204