Cities, Regions, and Rebels the Impact of Urbanization On

Cities, Regions, and Rebels: The Impact of Urbanization on Conflict in the Developing World

Item Type text; Electronic Dissertation

Authors Cobb, Matthew Ryan

Publisher The University of Arizona.

Rights Copyright © is held by the author. Digital access to this material is made possible by the University Libraries, University of Arizona. Further transmission, reproduction, presentation (such as public display or performance) of protected items is prohibited except with permission of the author.

Download date 26/09/2021 06:13:32

Link to Item http://hdl.handle.net/10150/641362

CITIES, REGIONS, AND REBELS

THE IMPACT OF URBANIZATION ON CONFLICT IN THE DEVELOPING WORLD

Matthew R. Cobb

SCHOOL OF GOVERNMENT AND PUBLIC POLICY

In Partial Fulfillment of the Requirements

For the Degree of

DOCTOR OF PHILOSOPHY

In the Graduate College

THE UNIVERSITY OF ARIZONA

2020

THE UNIVERSITY OF ARIZONA GRADUATE COLLEGE

As members of the Dissertation Committee, we certify that we have read the dissertation prepared by: Matthew Ryan Cobb, titled: Cities, Regions, and Rebels: The Impact of Urbanization on Conflict in the Developing World and recommend that it be accepted as fulfilling the dissertation requirement for the Degree of Doctor of Philosophy.

______Date: April 24, 2020 Prof. Alex Braithwaite

______Date: April 24, 2020 Prof. Jessica Maves Braithwaite

______Date: April 24, 2020 Prof. Jeffrey Kucik

______Date: April 24, 2020 Prof. Javier Osorio

______Date: April 24, 2020 Prof. Paul Schuler

Final approval and acceptance of this dissertation is contingent upon the candidate’s submission of the final copies of the dissertation to the Graduate College.

I hereby certify that I have read this dissertation prepared under my direction and recommend that it be accepted as fulfilling the dissertation requirement.

______Date: April 27, 2020 Prof. Alex Braithwaite Dissertation Committee Chair School of Government & Public Policy

Acknowledgements

I would like to thank my committee members for the guidance they have provided me as I have worked on this project. Alex Braithwaite, Jessica Maves Braithwaite, Jeffrey Kucik, Javier Osorio, and Paul Schuler have all been very patient with me and willing to provide advice, especially as I have constructed the theory behind this work. Their help has been critical not just to the completion of this project, but in many ways, my development as a scholar.

During this process, I have benefitted from various travel grants from the University of Arizona School of Government and Public Policy as well as a fellowship made available by the Charles E., Jr. Starnes Fellowship Fund. I have presented versions of my dissertation chapters at academic conferences including meetings of the Peace Science Society, the American Political Science Association, and the Four Corners Conflict Network and am grateful for the feedback provided to me in the process. Specifically, comments from provided at these conferences from T. David Mason, Monica Duffy Toft, and Cameron Thies have been immensely helpful in shaping this research into its current form.

As I have worked on this project, I have discussed this work with numerous colleagues, friends, and family members who have offered everything from moral support to methodological advice. Paul Bezerra, Joseph Cox, Sangmi Jeong, Logan Blair, Alejandro Beltran, Tiffany Chu, Minwoo Ahn, Michael McCammon, Mai Truong, Leah Pieper, Andrew Braden, and Matthew Spinks have all been particularly supportive in this regard. I would also like to thank my parents, Norman and Gail Cobb who have draft copies of every chapter of this dissertation.

Table of Contents

List of Tables and Figures……………………………………………….5

Abstract…………………………………………………………………...8

Chapter 1: Introduction……………….……………………………...…..10

Chapter 2: Background & Literature Review……………………………17

Chapter 3: Theory of Urbanization & Conflict………………….…….…46

Chapter 4: Global Country-Level Analysis……………………….…...…69

Chapter 5: Global Sub-State Analysis…………………………….……...109

Chapter 6: Analysis of Conflict in India’s Red Corridor………….……...154

Chapter 7: Conclusions………………………..……………….…………200

Appendices..……………………………………………….…………...... 213

Bibliography…….……….………………………………………...……...217

List of Tables and Figures

Chapter 1 Figure 1. Overview of Empirical Analysis of Dissertation Project….15 Chapter 2 Figure 1. World Urbanization Levels in 1950……………………….23 Figure 2. World Urbanization Levels in 1990……………………….24 Figure 3. World Urbanization Levels in 2018……………………….24 Figure 4. World Urbanization Levels by Region…………………….26 Figure 5. World Urbanization Rates by Region……………………...26 Chapter 4 Figure 1. Conflict Events by Fatality Threshold……………………..77 Table 1. ZINB Regression Results (Models 1-2)………….…………83 Table 2. ZINB Regression Results (Models 3-4)………….…………84 Table 3. ZINB Regression Results (Models 5-6)………….…………86 Figure 2. Pred. Conf. Event Counts by Number of Maj. Cities...……87 Table 4. ZINB Regression Results (Models 7-8)…………………….88 Table 5. ZINB Regression Results (Models 9-10)……………………90 Table 6. Logistic Regression Results (Models 11-14)………………..94 Table 7. Logistic Regression Results (Models 15-18)………………..95 Table 8. ZINB Regression Results (Models 19-20)…………………..97 Table 9. ZINB Regression Results (Models 21-22)…………………..99 Table 10. ZINB Regression Results (Models 23-24)…………………100 Table 11. Logistic Regression Results (Models 25-28)………………104 Figures 3-5. Predicted Risk of Gov’t Conf. onset by Δ GDP PC….....105 Chapter 5 Table 1. Logistic Regression Results (Models 1-4)…………………..124 Figures 1-2. Pred. Conflict Risk by Population Density………...……125

Table 2. Logistic Regression Results (Models 5-8)…………...………126 Table 3. Logistic Regression Results (Models 9-12)….………………125 Figures 3-5. Pred. Conflict Risk by Major City Count & Distance...…129 Table 4. Logistic Regression Results (Models 13-16)…………...……130 Table 5. Random Effects Logistic Reg. Results (Models 17-20)…..…132 Table 6. Logistic Regression Results (Models 21-24)……………..….134 Figures 9-11. Pred. Risk of Conf. by Pop Density & GDP PC……..…136 Table 7. Logistic Regression Results (Model 25-28)……………...…..139 Table 8. Logistic Regression Results (Models 29-32)…………………140 Table 9. Logistic Regression Results (Models 33-36)…………………141 Table 10. Logistic Regression Results (Models 37-40)………………..142 Table 11. Random Effects Logistic Reg. Results (Models 41-44)…….143 Table 12. Logistic Regression Results (Models 45-48)………………..144 Table 13. ZINB Regression Results (Models 49-50)…………………..146 Table 14. ZINB Regression Results (Models 51-52)…………………..148 Table 15. ZINB Regression Results (Models 53-54)…………………..149 Chapter 6 Figure 1. The States of India’s “Red Corridor”………………………...158 Figure 2. Annual Fatalities in Naxalite Conflict………………………..160 Figure 3. Indian States Involved in Operation Green Hunt…………….162 Table 1. Logistic Regression Results (Models 1A-2B)………...………173 Table 2. Logistic Regression Results (Models 3A-4B)……...…………175 Figures 4-5. Pred. Margins for Models 3A & 4A………………………176 Table 3. Logistic Regression Results (Models 5A-5B)………..….……179 Figures 6-7. Pred. Margins for Models 5A & 5B………………………180 Table 4. Logistic Regression Results (Models 6A-7B)……….………..181 Table 5. Logistic Regression Results (Models 8-11)………….………..183 Table 6. ZINB Regression Results (Models 14-15)…………….………185

Figure 8. Pred. Margins for Model 12…………………………………186 Table 7. ZINB Regression Results (14-15)……………………………190 Table 8. Random Effects Regression Results (Models 16-19)……...…192 Table 9. Random Effects Regression Results (Models 20-23)………...193 Table 10. Logistic Regression Results (Models 24-26)…………….….195 Figures 9-10. Pred. Margins for Models 24 & 25………………….…..196 Appendices Appendix A. Developing Countries Included in Analysis……………..213 Appendix B. Indian Districts Included in Analysis (States A-J)……….214 Appendix C. Indian Districts Included in Analysis (States M-O)……...215 Appendix D. Indian Districts Included in Analysis (States U-W)……...216

Abstract

The world's population is quickly urbanizing, a trend largely driven by the growth of cities in the Global South. In my dissertation, I analyze the consequences of this demographic phenomenon for civil conflict, another phenomenon associated with developing countries. In this research, I present a global study at the country level, showing that contrary to many people's concerns, urbanization does not usually lead to higher levels of urban-based civil conflict and may alleviate conflict violence in rural areas. Using geospatial data on conflict and demographics, I analyze the urbanization- conflict relationship at the subnational level, showing how these trends impact provincial and local administrative units. This includes a chapter specifically focused on local-level impacts of urbanization on India’s Naxalite insurgency, a long-running armed conflict affecting a wide swathe of the country’s territory.

Through my analysis, I find that contrary to the fears of many scholars and policymakers, urbanization does not normally create a risk of armed conflict communities in the developing world. Even when an area is experiencing economic decline, most models analyzed in this project show that economic hardship that coincides with urbanization does not increase the risk of conflict violence. Urbanization often corresponds with a decrease in the risk of armed conflict, especially for cases of high- fatality conflict events. The possible pacifying effect of urbanization may come from the ability of cities to provide citizens with increased economic and political opportunities and governments with a venue conducive to exercising their administrative capacities.

Areas nearby major cities are often much less to experience armed conflict compared to more remote areas. Communities within a urbanizing societies may also see a decline in

9 their risk of conflict if major cities nearby are growing significantly or if they are located in an area where the government has more security forces in place or is able to extract higher amounts of taxes.

This study is not a comprehensive examination of all factors that may affect the security of urbanizing societies, but it does provide a much clearer idea of what urbanization might mean for the risk of warfare in developing countries. The analysis presented in subsequent chapters provide an encouraging indication that despite all the social, economic, and political challenges associated with demographic changes, urbanization is often associated in a decline in conflict risk. I conclude with discussion of extensions for this line of research as well as policy recommendations for societies hoping to mitigate possibility of violence.

Chapter 1. Introduction

The world’s population is rapidly urbanizing, particularly in developing countries where impoverished rural people migrate to cities in large numbers to improve their livelihoods. For example, in its 2014 World Urbanization Prospects report, the United

Nations estimates that by 2050, two-thirds of the world’s population will live in cities, compared to just over half of today’s population. Urbanization is a complex demographic phenomenon with critical implications for a country's economic, political, and military situation. Defined simply as the percent of a country's population concentrated in cities, urbanization may occur when the rate of natural increase in urban areas' populations outpace those of rural areas (e.g. through relatively higher birth rates in cities) or through the migration of people from rural areas to urban ones (UN 2014, 14; McGranahan &

Satterthwaite 2014). Urbanization is not the same as urban growth, the increase in the absolute number of people living in urban areas. Urban growth may occur without urbanization, provided rural populations grow at equal or higher rates. Likewise, urbanization can occur without urban growth if the proportion of a country's population living in rural areas shrinks, such as through the out-migration of rural people (Rogers

1978).

Modernization theory emphasizes the economic importance of urbanization, which increases the urban workforce, a crucial resource for industrialization (e.g. Rostow

1959, Huntington 1971). While urbanization is often seen as a sign of economic

11 development,1 a factor often discussed as a negative correlate of civil war,2 it also creates significant challenges for the urbanizing country. In wealthy countries where the government has high capacity and cities have adequate infrastructure and resources to accommodate newcomers, this is less likely to be a problem. For example, the oil-rich

Sultanate of Oman has one of the world's fastest rates of urbanization (CIA World

Factbook 2018) but is highly efficient in constructing infrastructure and providing public services. As a result, Oman has maintained one of the world’s lowest levels of organized violence (UCDP 2017), terrorism (GTD 2016), and homicide (UNODC 2013). Other countries have not fared so well, struggling to contain violence in their growing cities.

For example, Honduras and Guatemala, the two fastest-urbanizing countries in Latin

America, have respectively experienced 31 and 22 incidents of organized violence since

2000 and are amongst the worst-ranked countries in the world in terms of their homicide rates (UNODC 2013). In this project, I attempt to answer the question of how a developing country’s patterns of urbanization and urban growth may impact its risk of internal conflict.

1.1. Urbanization as a Security Risk

Why might urbanization and urban growth lead to violence in some countries, but not in others? This is a key puzzle of my research, which I explain with an argument focused on the competition between militant groups and governments. In some countries with fast-growing cities, we might not necessarily see a change in the level of internal

1 See Rostow’s (1956, 45) discussion of economic growth, arguing that larger urban populations support the development of industrial enterprises; See Ravallion, Chen, and Sangraula (2007) and Njoh (2003) for empirical studies demonstrating a statistically significant link between urbanization and development. 2 See Fearon and Laitin (2003) or Hegre and Sambanis (2006) for empirical studies showing positive association of poverty and civil war.

12 conflict, but rather a change in the type of conflict. As the population becomes less rural and geographically more proximate to centers of government power—usually clustered in urban locations—rebel groups must consider how to focus their attacks in order to stay relevant, given changes in the country’s human geography. Shifting the geographic focus of attacks is a logical way to do this, especially when shrinking rural communities may deprive militants of certain advantages they traditionally enjoy while operating in the countryside. Perhaps less obvious is the way urban growth incentivizes militants to change the goals of their violence. As I argue, this can include a reduced emphasis on seizing control of territory—a form of violence more often associated with rural areas— and shift toward violence aimed at seizing control of government.

An important aspect of the puzzle of inconsistencies in the urbanization-violence nexus are factors underlying the growth of cities. Cities might grow by virtue of their higher birth rates, while others might grow due to human migration, which may occur under a variety of circumstances. I consider the risk of violence as it is associated with different types of internal migration, which may offer militants different opportunities. In all cases of urbanization, one can expect cities to become more crowded and busier. If a city does not have the resources to accommodate newcomers, the resulting competition for scarce resources can create a contentious environment in which political violence such as urban more easily break out.3 Because this expectation is based on the ratio of migrants to resources, patterns in violence would not vary according to the reason for the migrant’s movement to cities.

3 Competition amongst citizens in urban areas often involves non-violent vying for jobs (see Lipton 1977 for additional discussion), but some scholars argue that such situations as a possible motivator for political violence, perhaps even escalating to armed conflict (e.g. Goldstone 2002).

I argue that it is important to monitor the types of migration leading to urban growth as the type of migration and migrants carry specific consequences for armed conflict. Armed groups are especially well-positioned to take advantage of forced migrants whom they follow, recruit, and incorporate into smuggling networks. For example, scholars of Ugandan political violence (Nannyonjo 2005, Janmyr 2014) point out that internally displaced persons (IDPs) in Northern Uganda were heavily exploited by belligerent parties during the time of conflict. Both government-backed militias and rebel armies aggressively recruited IDPs into their ranks, causing the conflict to spread to new areas. This case illustrates the importance of considering varieties in migration type in security affairs, but more research is needed to determine how conflict dynamics relating to migration type will play out in the context of urbanization.

Addressing another aspect of this puzzle, I ask why migration may impact attitudes toward armed violence differently in some places than in others. I argue that rebel groups and governments may both be affected by people’s attitudes toward rural-to- urban migration, especially when people cast blame on one side or the other for the causes or negative consequences of migration into cities. Particularly in cases where rural-to-urban migration results from the forced displacement of rural people, demographic shifts associated with urbanization alter people’s perceptions of threats to their wellbeing. I argue that individuals’ attitudes toward urbanization and urban growth may stem from their personal experiences and have substantial impacts on belligerent parties’ abilities to fight.

1.2. Overview of Dissertation Project

This project is intended as a step forward in explaining how demographic changes impact the security of changing societies. Given the fast pace of urbanization trends across much of the developing world as well as projections showing these trends will continue for decades, it is necessary to learn more about the consequences of urbanization in the developing world.4 In the following two chapters, I provide a historical overview of urbanization in the world and explain how many scholars and governments fear urbanization as a potential source of many social and political difficulties, potentially bringing about violence. Fears about urbanization as a driver of violence are, I argue, often overstated and deserve greater scrutiny from empirical analysis. I do not dispute the fact that significant urban-based violence has occurred in many developing countries in the recent past. However, the experiences of these countries should not overshadow the benefits that most developing countries enjoy as their populations shift toward urban areas. Urbanization provides a variety of benefits to citizens and governments alike, and while the societies may have difficulty adjusting to major demographic shifts, I theorize that these benefits should, in most cases, reduce the risk of armed conflict.

This project tests the potential connection between urbanization and armed conflict at multiple levels of analysis. As depicted in Figure 1 below, the series of empirical analyses included in this project begin wide and become increasingly narrow and focused, giving the empirical analyses an inverted pyramid shape. I begin in Chapter

4 For example, see the United Nations World Urbanization Prospects reports for 2014 and 2018. These reports explain that urbanization is occurring a fast rates in many countries, particularly in the Global South. These reports also discuss estimates that urbanization will continue throughout much of the twenty- first century, creating challenges for policymakers whose societies must adapt accordingly.

4 with a country-level study which takes advantage of data on urbanization that is traditionally collected and reported at the country level. The country-level study makes use of widely used urbanization data to identify general trends in the relationship between urbanization and armed conflict. I then further analyze these trends with a variety of tests at subnational levels of analysis. In Chapter 5, I utilize georeferenced data and analytic tools to explore subnational variation in levels of urbanization and conflict. Both phenomena tend to vary greatly at subnational levels, so analysis at this lower level provides a richer understanding of demography’s important implications for security.

Finally, I focus in on India as an instructive case for more in-depth empirical analysis. As

I will explain in Chapter 6, India has struggled with armed insurgents for decades. While the Maoist rebels of India’s so-called “Red Corridor” region are well-known for operating in remote areas, some parts of the Red Corridor have seen quite significant urbanization and therefore provide an excellent opportunity to evaluate my theoretical expectations.

Figure 1. Overview of Empirical Analysis of Dissertation Project

This project provides value to the political science literature in several ways. First, it addresses questions unanswered in other literature regarding how demographic shifts may change the incentives for citizens to support rebel movements as well as the capacity for governments to suppress insurrectionists. Second, I will provide an explanation for why certain urbanization may alleviate conflict risk in some cases and exacerbate it in others. This enriches discussions about urbanization’s security implications, providing much more nuance to the discussion. This gets beyond the overwhelmingly negative tone of conversations about urbanization in the Global South which focuses heavily on the

“urbanization of poverty” or images of “cities under siege.”5 Third, in this project I utilize geospatial data and analytical tools to provide useful insight into claims about urbanization’s security implications. Urbanization is a phenomenon with important geographic characteristics. In my analysis, I take these into account in order to provide a fuller picture of how the effects of urbanization may cause conflict risk to vary across geographic spaces. By conducting research at these various levels of analysis, this study offers important insight into developing countries’ conflict processes and motivations for individual decision-making in urbanization processes.

5 This matter is discussed in greater detail in Chapters 2 and 3. For examples of works portraying urbanization as a source of poverty, conflict, and humanitarian crises, see Piel (1997), Davis (2006), Graham (2011), and Sampaio (2016, 2018).

Chapter 2. Background & Literature Review

What is urbanization and how does it affect the world? This is a basic question that must be answered before one can consider the nuances of its influence on armed conflict. In this section, I lay out several critical definitions and conceptualizations for urbanization and related phenomena critical to this study. I contextualize my discussion of urbanization with a brief review of historical patterns of world urbanization. Finally, I review literature on the urban-rural dynamics of armed conflict and the potential security threats that scholars and policymakers often attribute to urbanization.

2.1. What is Urbanization?

Before one can analyze the phenomenon of urbanization, it is important to first determine what urbanization means. The concept of a city or urban space is somewhat subjective, with countries’ governments using vastly different criteria for classifying geographic spaces as “urban areas.” Rural areas are generally used as a residual category, comprising all territories that a government has not classified as urban and what counts as urban varies widely from one country to another. According to the UN Statistical Handbook

(2018), Japan and defines urban spaces as settlements with a population threshold of

50,000 people, while the threshold is 20,000 in Syria, 4,000 in Vietnam, 500 in Papua

New Guinea, and just 200 in Iceland. Thailand and Sri Lanka categorize all municipal areas as urban, while in some island countries such as Samoa or the Maldives, the government simply considers the capital to be urban and all other spaces rural. Other countries include additional criteria; for example, India, Turkmenistan, and Botswana require certain percentages of an areas workforce to be engaged in non-agricultural labor,

18 while Nicaragua and Laos set infrastructural requirements such as access to electricity or piped water. This wide variety of definitions certainly does not prevent cross-national research on urbanization, but it does require scholars to be careful and specific when analyzing it.

Urbanization is a demographic phenomenon that is typically discussed both in terms of levels and rates. The former is the most traditional conception of urbanization, representing the proportion of a country’s population living in urban areas. This concept is related to, but not the same as conceptualization focusing on the rate of urbanization, meaning the growth of a country’s urban population relative to that of its rural population

(McGranahan & Satterthwaite 2014, Cohen 2006, Rogers 1978). While many discussions of urbanization tend to conflate these two concepts, the difference between them carries theoretical importance and deserves clarification. Throughout this study, I use the term

“urbanization level” in reference to more traditional conception of urbanization, which focuses on the overall share of a population inhabiting urban areas, rather than on the change in the proportion of a population living in urban areas. In discussing annual changes in a country’s urban population, I use the term “urbanization rate” in reference to the annual percent changes in a country’s urbanization level. While urbanization levels and rates may be related concepts, they come with their own unique theoretical consequences for conditions leading to armed conflict.6 I discuss this further in the following chapter.

6 Note that both these definitions of urbanization differ from the concept of urban extent, which refers the landmass occupied by a city or urban primacy, which refers to the concentration of a country’s population into one or several major cities.

Despite discrepancies in the way terms like “urban” or “urbanization” are conceptualized or measured, there is a general sense that both are related to an area’s population density. At its essence, a city is an agglomeration of people which necessarily exhibit a higher population density than one would find in rural areas, however defined

(Malpezzi 2013). Best, Jones, and Rogers (1974) suggest population density as a more objective alternative measurement of urbanization, getting around issues with problematic administrative definitions and a variety of later empirical studies of urbanization take the population density approach (Michaels, Rauch, & Redding 2012;

Chauvin et al. 2017; Urdal 2005). It is important to note that because urbanization levels refer to the proportion of a population living in urban spaces, meaning that if rural and urban areas’ populations grow at the same rate, the country’s overall urbanization level will not change. The world’s most heavily urbanized countries – Singapore, for example

– have populations that are entirely urban, reflected by an urbanization level of 100%.

Because all countries contain urban spaces, it is not possible to find a country whose population is entirely rural, although it is theoretically possible that a country could reach an urbanization level of zero if its entire population relocated to rural places.7

As a social science research topic, urbanization has historically been most heavily analyzed by sociologists and economists, whose perspectives on urbanization often demonstrate clear nexuses with political science. For example, some of the earliest works documenting the world population’s trend toward urban agglomeration was documented by the sociologist Kingsley Davis (1945). Sociologists – particularly those from the

7 According to data from United Nations’ World Urbanization Prospects (2018), the world’s least urbanized countries are Burundi and Papua New Guinea, where urbanites comprise approximately 13% of their countries’ overall populations. Alternatively, the populations of Singapore, Kuwait, Monaco, Anguilla, and Nauru are 100% urban.

Marxist tradition – have offered sharp and memorable critiques of urban life and social structures, ranging from Friedrich Engel’s criticism of Britain’s Industrial Revolution in

“The Great Towns” (2015 [originally 1845]) to Henri Lefebvre’s The Urban Revolution

(2003 [originally 1970]). In the early twenty-first century, sociologists have expanded discussion of urbanization characterizing it as an increasingly important component of globalization processes (Smart & Smart 2003) and are becoming increasingly important issues such as environmental protection and the function of governmental institutions

(Seto, Sánchez-Rodríguez, & Fragkias 2010).

Classic economic studies suggest that urbanization is deeply intertwined with economic development. Rostow (1956, 1959), for example, describes urbanization as part of an economic “take-off” phenomenon in which the pace of economic development accelerates, fueled in part by the availability of a large urban labor force to work in a growing industrial sector. As urbanizing societies grow, they tend to increase their involvement in non-agricultural activities, diversifying their economies and increasing the value of their potential exports. The role of cities in geographically concentrating economic activities is a key theme in many economic studies. Drawing on this idea, several works portray cities as crucial components of the modern economy, providing capital needed for markets to function. Highly developed countries’ populations and businesses are typically heavily concentrated in urban areas and poor countries typically become more developed as they converge on this trend (Todaro 1969; Harris & Todaro

1970). Emphasizing the importance of such trends, Duranton (2009) discusses cities as

“engines of economic growth” and Jacobs (1970, 1985) argues that because of their industrial output and their role as hubs of innovation, cities are essential to their

21 countries’ economic development as well as their strength in the global marketplace

(1970, 1985). While these studies do not ignore social problems that are common to cities, they put forth an important message that cities are crucial not just to advanced industrial economies, but also to those in lower-income countries where development is a key priority.

To a lesser extent, urbanization has been studied by political scientists, often in the context of urbanization’s role in countries’ democratization (Lipset 1959) or its propensity to deteriorate social capital (Putnam 2000).8 More recently, urbanization has become a topic of interest within the burgeoning “political demography” research program, which (Kugler & Kugler 2010). Political scientists interested in demography generally anticipate that the shift of the world’s population toward urban areas will, for better or worse, give cities a much more prominent position in both domestic and international political interactions. Political demographers often focus heavily on matters involving security, even if their portfolio of interests is, in fact, much wider. In the realm of interstate politics, Teitelbaum (2015) argues that international hierarchies are likely to shift due to recent demographic developments. Teitelbaum focuses heavily on factors like population size and international migration – two factors highly salient to urbanizing societies – which could significantly affect the future of international relations, but he does not address urbanization directly. Urbanization is a topic more commonly discussed in terms of intrastate political and security concerns, typically in the context of population pressures as a source of sociopolitical instability. For example, Goldstone, Marshall and

Root (2014) argue that urbanization stability of fragile states in the Global South is likely

8 It is important to note that much of Putnam’s (2000) discussion of social capital focuses not just on urbanization, but on the related subject of suburbanization, particularly in American society.

22 to be compromised by population pressures from urbanization occurring in those countries. Likewise, other scholars note that urbanization may trigger conflict in poor countries where young people comprise a large portion of the population (Urdal 2004,

2006). Further discussion of works pointing to urbanization as a cause of violence is provided in section five of this chapter as well as in the following chapter.

2.2. Background: Historical Trends in Urbanization

The modern world’s highly urbanized population is remarkable in the context of demographic history. The world’s first urban settlements emerged about seven thousand years ago, as human populations traded in nomadic lifestyles for sedentary ones. This pattern of sedentarization and population agglomeration started with the ancient civilizations of Egypt and Mesopotamia, which were home to early urban settlements and later became common worldwide over the course of centuries (Lampard 1965, 556-7).

However, urban settlements accounted only for a small portion of the global population throughout most of human history. At the dawn of the nineteenth century, only 3% of the world’s people lived in urban settlements, a proportion that would grow to more than

13% by the year 1900 (Lampard 1965, 552-4). This trend has accelerated quickly over time. Urbanites made up nearly half the world’s population by the start of the twenty-first century and will likely make up three-quarters by the twenty-second (United Nations

2014). In most parts of the world, urbanization has coincided with economic development, although this comes with some notable exceptions.

When the United Nations began recording statistics on urbanization in 1950, few countries’ populations were primarily concentrated in urban locations. The world’s most heavily urbanized countries were primarily concentrated in Western and Northern

Europe, North America, and Oceania, with a few notable exceptions. At that point in history, Japan, Argentina, Chile, Uruguay, Israel, Kuwait, and a smattering of small island countries stood out as major examples of non-Western countries to become majority-urban by the mid-twentieth century. The latter half of the twentieth century saw significant urbanization in many regions of the world. By this point, the populations of

Eastern Europe, Southern Europe, Latin America, and the Middle East had become overwhelmingly urban. By 2018, UN data observed that East Asia and Southern Africa had become primarily urban. Populations approached the 50% urbanization level in

Southeast Asia, Central Asia, North Africa, Central Africa, and West Africa.9

Figure 1. World Urbanization Levels in 1950 (UN 2018)

9 For reference, see data from the United Nations’ World Urbanization Prospects, 2018 Revision.

Figure 2. World Urbanization Levels in 1990 (UN 2018)

Figure 3. World Urbanization Levels in 2018 (UN 2018)

Figures 1-3 above graphically represent the world’s urbanization levels as measured by the UN at different points in history, based on data from the World

Urbanization Prospects report (UN 2018). It begins with the year 1950, the first time the

UN recorded countries’ urbanization levels. Next is a map from 1990, showing urbanization levels as they were at the end of the Cold War and shortly before the large spike in intrastate conflict violence that occurred in the 1990s. The third map shows world urbanization levels from 2018, which, at time of writing, was the most recent year for which the UN has made available data from the World Urbanization Prospects. To provide a better idea of how urbanization trends vary greatly across regions of the world,

Figure 4 depicts country urbanization levels averaged for the entire world, as well as those for six of the continents in the years 1950, 1990, and 2018. Finally, Figure 5 depicts urbanization rates averaged at the world and continent levels for the same three years.

Three trends are immediately apparent when comparing these graphs. First, the wealthier parts of the world are generally the ones that urbanized at earlier points in time. Second, urbanization rates are highest in the least developed regions of the world. The historical basis for these trends is discussed further below. Finally, as regions of the world reach higher urbanization levels, their rates of urbanization tend to slow down significantly.

This is a simple reflection of the fact that since countries cannot reach urbanization levels above 100%, the rates at which their urban population shares grow will inevitably slow as they approach the upper extremes of urbanization levels.

Figure 4. World Urbanization Levels by Region

Figure 5. World Urbanization Rates by Region

World Urbanization Rates by Region 2.5

1.5

0.5

% Change in Urban Share Urban Pop in %Change World Africa Asia Europe Latin North Oceania -0.5 America & America Caribbean

1950-1970 1970-1990 1990-2018

The West was the first region of the world to experience a dramatic wave of urbanization that began in the early nineteenth century. The Industrial Revolution, which started in the United Kingdom and spread to other European and North American countries created both incentives and a means for people to move to urban locations. The

27 technology of the Industrial area reduced the manpower needed for agricultural activities, created new jobs in urban areas, and funded the creation of infrastructure and bureaucracies needed to serve cities’ growing populations (Bairoch & Goertz 1986). The story of urbanization is strongly connected to Western economic history, as urban-based economic activities were critical parts of the Industrial Revolution and many key political developments in the nineteenth and twentieth centuries. As Western countries made technological progress and expanded their wealth during this time, they also improved their abilities to develop military equipment and military project force (Neiberg 2015;

Stearns 2018, 103-4; Bairoch 1982). These processes contributed to Western colonialism, which negatively impacted the development trajectories of many countries within their spheres of influence. While European countries built large cities and accumulated vast wealth and empires, their overseas influence delayed urbanization processes in many parts of the Global South (Bairoch 1982). For example, colonial-era residency restrictions and an emphasis on primary resource production blocked many people in Africa from moving to cities (Fox 2012, 297-8).

Compared to the Western countries that experienced urbanization at earlier points in history, the developing world was generally much slower to urbanize. Many countries from Latin America, Asia, and Africa remained focused on agricultural production for longer periods of time. They were also occupied by European powers who valued their colonies as sources of raw materials, often developing cities in coastal areas of their colonies, thereby facilitating the export of raw materials. This encouraged high levels of urban primacy – the concentration of people into a relatively small number of key cities while most of the countries’ land outside capitals or port cities went undeveloped or

28 underdeveloped (Gilbert & Gugler 1992, 47; Zuberi et al. 2003, 478). However, it is important to note that urbanization trends vary greatly between regions within the developing world. While these regions did not necessarily urbanize for the same reasons or in the same ways, there are certain factors particular to Latin America, Asia, and

Africa which impacted the urbanization of populations in those regions.

Latin American populations shifted toward urban localities earlier than was the case in most of Asia and Africa. The process began following independence from

European colonialism and accelerated in the twentieth century. This was driven primarily by rural people’s movement to cities for better economic opportunities and often involved high levels of urban primacy as rural people clustered into a relatively small number of cities (Brea 2003, 26-7; Portes & Canak 1981, 234-5). Movement into cities accelerated markedly starting in the 1930s and 1940s, primarily for two reasons relating to the region’s industrialization process. First, Latin American governments pursued policies of industrialization through import substitution, spurring the creation of jobs in cities.

Second, greater mechanization of farm equipment and improvements in agricultural practices lessened the need for workers in rural areas (De Oliviera & Roberts 1996, 257-

9). Modern-day Latin America remains, by far, the most heavily urbanized region of the developing world, with many of its constituent countries having populations that are on par with or even more urbanized than the populations of Western countries (Williams

2012, 69; UN 2018).

Unlike Europe and North America where urban agglomerations were spread across wide geographic spaces, Latin American populations tended to cluster into a much more limited number of cities. The result in many cases, was a sharp distinction between

29 densely populated cities and vast stretches of rural territory (Brea 2003, 30; Browning

1958, 114). Across Latin America, the massive differences between urbanization levels is linked to long-running historical patterns. It is also notable that South America’s

Southern Cone experienced significant urban growth at an early point in time, not just due to internal migration, but to international migration. The Southern Cone has historically been an attractive destination for European immigrants, most of whom have relocated to urban spaces within Argentina, Chile, and Uruguay, typically moving into urban spaces (Browning 1958, 118). As of 2018, the populations of all three of these countries had urbanization levels higher than 90% (UN 2018).

Central America’s shift towards urbanization has generally been slower than that of the Southern Cone countries. By comparison, Central America has historically relied much more heavily on agriculture and has seen its economic development stymied by warfare, foreign intervention, and difficulties with internal governance.10 These trends have historically given rural communities a much greater role in Central American economies. As of the early twenty-first century, most Central American countries are much less urbanized than many of their South American counterparts, particularly Belize, whose population is less than half urban and Guatemala and Honduras, whose urban population shares are both below 60%. In recent decades, Central America has experienced significant urban growth, with Guatemala and Honduras demonstrating the fastest rates of urbanization. Poor living conditions in rural areas of these countries

10 For fuller discussion of historical difficulties in Central America’s political and economic development, see Woodward (1984). This work provides discussion of historical factors that have suppressed the region’s traditionally rural labor force, hindered the development of industry – which normally would be associated with urban-based economic activities – and damaged much of the political and economic infrastructure needed for the region’s modernization processes.

30 sharply contrast with the promise of better-paid work in cities where export-oriented industries continue to expand (Aguilera 2017). Nevertheless, the region’s urbanization process has often been rocky, and many point to the region’s cities as a cautionary tale of about the urbanization of poverty and violence, particularly as homicide rates in the region are amongst the highest in the world (Aguilera 2017; Muggah 2015; Jütersonke,

Muggah, & Rodgers 2009; Rodgers & Muggah 2009).

Asia’s urbanization was quite varied, ranging from Japan, whose cities grew during an early urbanization period, to South Asia, which lagged far behind the rest of the continent in urban growth. Japan is notable as the first Asian country to become heavily industrialized, a process that contributed to its urbanization. Japan’s urbanization took root decades behind countries such as the United Kingdom and the United States, Japan’s industrialization but relatively early compared to many regions of the world. Beginning in the latter half of the nineteenth century, encouraged by government policies promoting capital investments and issuing loans to the country’s growing industries, most of which were largely concentrated in urban settings (Alexander 2000; Stearns 2018, 140-5; Harris

1982). Japan invested both in urban industries as well as non-farm industries in rural areas, a model that was later copied by many countries in East Asia during the twentieth century. This was the case in the fast-industrializing and urbanizing societies of Korea and Taiwan as well as the region’s poorer countries such as Malaysia and the Philippines

(Jones 1983, 24-5). This strategy was effective in building up urban industrial centers while simultaneously rural economies and disincentivizing rural-urban labor migration which could overwhelm major cities.

Following World War-II, East Asia experienced significant urban growth, closely related to the growth of urban-centered industries and reduced economic dependence on the agricultural sector. The region became home to many of the world’s megacities – usually discussed as agglomerations of ten million or more, although sometimes with lower thresholds – as populations shifted away from rural areas. Several of the newly industrialized countries moved into the upper echelons of world development rankings, a feat often attributed to urbanization and the presence of a strong industrial base in the region’s urban areas (McGee 2008). It is notable that the earlier period of East Asia’s urbanization process was often discussed as a case of “overurbanization,” cities grow faster than the economy or government administration can properly support. While the growth of cities corresponded with a growth of industry and creation of jobs, there are many people in countries like the Philippines, Thailand, Indonesia, etc. who remained still quite impoverished even after leaving agricultural, rural communities to live and work in urban settings (Jones 1983). This discussion later shifted toward talk of massive income disparities between the region’s rural and urban populations, but the overall industrialization accompanying the region’s urbanization processes is nevertheless quite remarkable (Jones 1997, McGee 2008).

South Asian, have predominantly rural populations (UN 2018) and have been historically slower in both industrialization and urbanization compared to many of their

East Asian counterparts (Jones 1983, 35). South Asia’s population remained much more heavily engaged in agriculture compared to East Asia (Jones 1983, 25) and although

South Asian cities increasingly attract migrants from rural communities, it is quite common for ex-rural migrants to return to their villages after working in cities for only a

32 short time (Keshri & Bhagat 2013). As of the early twenty-first century, South Asia’s population remains largely rural, but that may change as the region drives ahead with its development plans. South Asia’s cities are growing quickly, with a large number of megacities emerging and growing, usually with high levels of rural-urban migration.

While large cities now dominate the economies of South Asia, urbanization is frequently cited as a key contributor to the region’s severe issues with economic inequality, slum housing, and pollution (Misra 2013; Williams 2012, 74-5).11

Africa is a part of the world where urbanization began later and with less industrialization than other regions enjoyed. While a few African countries’ populations are heavily concentrated in cities – the populations of Gabon, Libya, and Sao Tome and

Principe are all more than 80% urban – the continent as a whole is predominantly rural

(UN 2018). Most of Africa had been colonial territories of European powers and, like many developing countries in Asia, did not become independent until the latter half of the twentieth century. During colonial rule, African countries typically developed few major cities, leading to strong patterns of urban primacy (Yamashita 2017, 48). Most countries in sub-Saharan Africa experienced surges in urbanization in their late colonial periods as

European powers invested more heavily in urban infrastructure and industry. Even when many of these countries experienced economic downturns in the following decades, urbanization continued at a fast pace, driven partly by increased international investment and aid targeted at urban communities (Fox 2012, 297-300). After achieving independence, African countries experience high levels of urban growth, owing both to internal migration as well as high birth rates. This has left many to question whether

11 India’s urbanization processes are further discussed in chapter 6, particularly as the growth of its cities may affect the risk of insurgent violence.

African cities can accommodate the high rates of growth, particularly since many of the continent’s cities have notoriously poor infrastructure and high numbers of unemployed young people (Yamashita 2017; Hove, Ngwerume, & Muchemwa 2013, 4).12

Africa has historically experienced lower levels of economic development than most other regions of the world, its urbanization processes often defying conventional expectations that urbanization should occur as part of an industrialization process (Fox

2012; Henderson, Roberts, & Storeygard 2013).13 As African countries have continued to urbanize, often at a fast pace, many have seen an increase in urban unemployment. Many of Africa’s growing cities have conditions that are quite poor by global standards in terms of infrastructure, poverty, the growth of slum settlements, and environmental degradation

(Cobbinah, Erdiaw-Kwasie, & Amoateng 2015, 68-9). Various empirical studies of the relationship between urbanization and economic development produce mixed results. For example, on one hand, Brückner (2012) finds that from 1960 to 2007, urbanization is significantly related to a decrease in GDP per capita growth rates in Saharan African countries. On the other hand, others find that even if Africa’s urbanization coincides with economic stagnation or recession, it may still correlate with progress in other aspects of development, such as improvements in technology (Henderson, Roberts, & Storeygard

2013) or greater access to key services like education or healthcare (Njoh 2003).

12 Findings by Urdal (2004) and Kunkeler and Peters (2011) indicate that urban environments are much more likely to experience criminal and political violence when populations are young and economic problems such as poverty and unemployment are severe. 13 Henderson and Kriticos (2018) point out that in Africa, agricultural workers make up a large portion of many cities’ workforces, indicating that urban growth in the continent is less connected to industrialization than one might expect based on the experiences of countries in other regions.

2.3. Urbanization in Developing and Developed Countries

Urbanization patterns in the developing world often differ from those of advanced industrialized societies, but they differ more on some factors than on others. In terms of similarities, it is important to note that in both developed and developing countries, historians and demographers highlight migration, rather than birth rates or definitional changes in designations for urban and rural areas as the primary driver behind urbanization (Stearns 2018; Ledent 1982). Although economic activities do not fully determine a population’s willingness or ability to concentrate in urban areas, historical trends indicate a generally positive relationship between urbanization and industrialization. This has generally been beneficial for most countries’ economies. Apart from historical discussions of urbanization during the Industrial Revolutions in Western countries, other works suggest that countries in the Global South have, for the most part, benefitted as well (Ravallion, Chen, and Sangraula 2007; Njoh 2003).14 The link between these two factors is perhaps weaker or less obvious in poorer countries of the contemporary era, but the ability of job opportunities to draw people toward cities is still highly relevant in those societies (Stearns 2018, 276). After all, people usually move in hopes of improving their circumstances, so rural-urban migration can be expected as long as conditions in the countryside are perceived to be worse than those in cities.

As for the differences between urbanization in developed versus developing countries, several patterns are highly notable. Whereas the Western world urbanized at an early point, largely through an organic process arising from market forces, later

14 Note that while these findings are somewhat encouraging, discussions of urbanization in the Global South are often influenced by works that point out the poor quality of infrastructure and living conditions in such countries. Refer to “Planet of Slums” (Davis 2006) for a more pessimistic view of this matter.

35 urbanization processes in other regions of the world were more significantly impacted by governments’ intentional efforts. “Urban bias” is often cited in countries economic development programs, as governments focus on growing industries in and providing public services to urban communities (Lipton 1977; Gilbert & Gugler 1992, 221; Kasarda

& Crenshaw 1991; 483-4). Development strategies concentrating government resources into the development of urban industries are quite prominent in the Japanese and Latin

American cases (Alexander 2000; De Oliviera & Roberts 1996, 257-9). International financial flows, including overseas development assistance, may have a similar effect, as seen in many African countries (Fox 2012, 300).

In some countries, governments invested in urban-based economic resources while also enacting polies that limited internal migration. China, for example, has historically used the “hukou” system of residency permits intended to protect labor markets and public administration programs from large or sudden movements of citizens from one area to another. The system classifies Chinese citizens as urban or rural residents and is often used as a tool to prevent rural people from moving to cities (Chan

& Zhang 1999). Other recent cases of governments manipulating urbanization patterns are seen in post-independence African countries. Governments of the region have sometimes spurred urbanization through import substitution policies which benefitted urban-based industries (Zuberi et al. 2003, 479), only to later discourage it due to concerns about urban crime and poverty (Turok & McGranahan 2013, 475). In India where local-level politicians in Mumbai and Kolkata rail against rural-urban migration, stirring up anti-migration sentiments, and in some cases have taken steps to prevent the newly arrived from accessing housing or registering to vote in cities (Abbas 2016).

Conventional wisdom about internal migration focuses on economic incentives as critical to people’s decisions to migrate. Income disparities often have distinct urban-rural characteristics, a fact that is not unique to developing countries, but is nevertheless very important to their development (Todaro 1969, 1980; Harris & Todaro 1970).15 Sjaastad

(1962) characterizes “neo-classical” theories of migration as focusing economic incentives that drive people to relocate from one place to another, particularly in search of employment. Domestic labor mobility, according to this thinking, results from workers determining that their prospects for wage-earning abroad are better than their prospects at home, also taking into consideration the burden of moving to a new location. This is something that can occur at both the domestic and international levels. Based on research on internal migration within Nepal, Dudwick et al. (2011, 186) empirically demonstrate the preference for low-income people to migrate to wealthier areas, often urban ones.

They find that workers from impoverished rural areas systematically prefer to move to places that are close by—a matter of convenience—but that they also tend to move to areas with greater concentrations of wealth and usually higher population densities, highlighting the economic motivations at play in urbanization trends.

Finally, it is important to note that internal migration in the Global South is not necessarily from rural areas into cities. Research on migration patterns shows that when rural people decide to move, they often travel to other rural regions of their own country, rather than moving to a large cities (Lipton 1980). In some developing regions of the

15 Note that severe poverty in the contemporary world is heavily concentrated in rural areas, particularly in the developing countries (Castañeda et al. 2018). Rural areas of developing countries tend to suffer from worse problems with underdevelopment than do urban locales within the same country (Pierskalla 2016, Young 2013). Rural areas of wealthy countries may follow a similar pattern, as noted by Partridge and Rickman (2008) in the case of the United States.

37 world, migration from rural to urban areas is impermanent and therefore does not always cause the level of urban growth one might otherwise expect. For example, countries in

South Asia and various parts of Africa seem to lag behind the world in terms of urbanization levels, it is also the case that circular migration is common in some of these countries, motivating people to migrate. Circular migrants in these countries often move from rural areas to urban ones in search of better economic opportunities, then move back to rural areas (AfDB, OECD, & UNDP 2016, 160; Nayyar & Kim 2018, 18; Kasarda &

Crenshaw 1991, 475-6).

2.4. Urban-Rural Dynamics of Violence

Discussion of a possible urbanization-conflict nexus represents a significant shift away from traditional political science discussions of civil conflict as a rural phenomenon.16 A core insight of recent literature on political rebellion is that rebel groups operate best in rural areas where the government is less able to monitor them and where impoverished residents are likely to be more responsive to rebel recruitment messages (Kalyvas 2006;

Bienen 1984, 663-4; Collier 2003, 71). A great deal of political science literature dealing with violent conflicts in rural areas focuses on the concept of peasant rebellions or peasant revolts. This research concentrates on uprisings of low-income rural people whose livelihoods depend upon agricultural production and whose grievances are often

16 For example, Fearon and Laitin (2003) and Hegre and Sambanis (2006), find a positive association between rough terrain and intrastate conflict. They discuss the trend as stemming from the state’s difficulty in governing remote locations and the ability of rebels to hide in rugged, mountainous areas, all of which are rural.

38 connected to farming and land use problems (Popkin 1979, Desai & Eckstein 1990,

Seligson 1996, Gutierrez 2015).

Governments centered in major cities may sometimes be unable to fully exert their authority in the hinterland. This is particularly a problem for countries whose governments possess the fewest resources or administrative capacities. In remote parts of a country, rebels place themselves outside the government’s grasp, where they can better recruit, train, and operate large militias with minimal interference (Kalyvas 2006; Bienen

1984, 663-4; Collier 2003, 71). The voice of a rebel group may therefore be louder than that of the government in remote areas, a situation quite useful in growing a violent anti- government movement. This problem is highlighted by Kunkeler and Peters (2011), who point out that civil conflict is often a function of a rebel group’s ability to recruit young men in rural areas. This task becomes more difficult when young potential recruits have the option to improve their lives by moving to cities for better jobs, rather than fighting for change in the countryside.

Of course, urban areas may be the sites of conflict as well, but until recently, research on urban-based conflict has focused much more on criminal or intercommunal violence, rather than the rebel-versus-government conflicts studied in civil war research.

With so many of the rural poor migrating to cities, some fear that socioeconomic factors contributing to rural conflict could have the same effect in urban areas. Scholars of development have long pointed to rural-urban migration as a phenomenon that may lead to the “urbanization of poverty” along with an urbanizing population (Piel 1997; Davis

2006; Ravallion, Chen, & Sangraula 2007). The argument goes that as low-income individuals in the developing world relocate to cities, the move simply changes the

39 geography of poverty—instead of living in the countryside, many of a country’s poorest citizens live in cities with little hope of economic advancement. The construction of large slum communities and the high rates of urban crime and unemployment leave many people in fast-growing cities with low standards of living (Hove, Ngwerume, &

Muchemwa 2013; Moncada 2013).

As cities grow, demand for public services inevitably increase, sometimes much faster than the government’s capacity to supply them. Cities may be completely unprepared to handle large influxes of people, with governments unable to keep up with the rising demand for public services and infrastructure, housing markets, and job markets similarly failing to keep up with public demand. This is part of a phenomenon sometimes called “overurbanization” or “urbanization without growth,” in which urban population growth may far exceed economic growth, often leading to chronic problems of poverty (Fay & Opal 2000, Gugler 1982). This is an especially severe problem in developing countries, where government resources are especially low and where public services are performed on a very uneven basis, severely disadvantaging lower-income communities, especially in slum areas (Hove, Ngwerume, and Muchemwa 2013, 8;

Cohen 2006, 75-6). In addition to poverty and inequality, cities of the developing world are also highly vulnerable to natural disasters. People in crowded slum communities are particularly at risk during major events such as floods or earthquakes (Gencer 2013).

Urbanization brings groups together geographically, but not necessarily socially.

To make things worse, a denser urban environment may pit citizens against one another in competition for housing, work, or public services. This competition often greatly disadvantages the newly arrived (Lipton 1980) and is sometimes identified as a risk

40 factor for social discord, particularly when those involved come from different identity groups (Goldstone 2002). People may move to cities in search of better opportunities but may face hardships in the urban environment which may also create a motivation for violence. Earlier works suggest that competition between urbanites and ex-rural migrants is especially intense amongst lower-income individuals as people moving from rural regions are likely to be from lower socioeconomic strata (Goldstone 2002, 10; Hove,

Ngwerume, and Muchemwa 2013). Identity politics may further complicate this messy economic situation. Urbanization may change the ethnic or religious composition of a city or bring previously disparate groups into close proximity with one another, increasing the frequency of their interactions and opportunities for conflict (Goldstone

2002, Gubler and Selway 2012). Liddle (2017) points out that the gap between rich and poor is immense in large cities and inequalities between social classes are often exacerbated by urban growth. These demographic shifts are especially dangerous if they create or exacerbate inequalities in the wealth, political influence, or social status of one identity group relative to another.17

2.5. Urbanization Anxieties & the Threat of Urban Guerrilla Warfare

Scholars have pointed to urban settings as potential hotspots for a range of violent acts, both political and non-political. For example, sociologist Emile Durkheim argued that

17 Gurr’s (1970) theory of relative deprivation highlights the risk of growing inequalities as individuals from a worse-off group may accrue grievances that make violence seem like a more attractive option. Empirical research (e.g. Gubler and Selway 2012) demonstrates that “horizontal inequalities”—inequalities existing between groups, rather than between individuals—can increase a country’s risk of armed conflict. Huntington (1972) emphasizes that inequalities are often made worse in developing countries as they urbanize.

41 city life is associated with the condition of anomie – desires exceeding one’s circumstances – which may motivate criminal violence, including murder and suicide

(Krohn 1978). The risk of urbanization leading to large-scale social disorder is related to age-old debates about the economic, social, and political pressures associated with population pressures. In the late eighteenth century, the demographer and economist

Thomas Malthus argued in his “Principle of Population” essay that overpopulation may prove disastrous for societies. While populations tend to increase when resources when economic resources may support it, high population growth over time may stretch a population beyond the resources needed to sustain it, resulting in poverty, suffering, and perhaps even death (Malthus 1998 [originally 1798], Nekola et al. 2013). The tragic aspects of Malthus’ theory sometimes discussed explicitly in works about rural-urban migration. For example, Weiss-Altaner (1983) and Lipton (1989) point to resource shortages beginning in poorer rural areas critical in motivating movement to urban areas, which may create or exacerbate population pressures already existing in those areas.

The phenomenon of urbanization takes on Malthusian characteristics not just through the migration of rural people out of impoverished areas of the countryside, but also in the localized resource shortage problems that may arise in ever-crowded urban centers. Low standards of living in urban areas are often discussed as correlates of low- level violence, which may be of a political, not just a criminal nature (Moser 2004,

Moncada 2013). Karl Marx considered urban population pressures as a major source of population pressures that could provoke political conflicts based in large part on economic factors. He focused on matters of inequality, considering urbanization and urban poverty major potential sources of popular discontent which may contribute to

42 social unrest and even political revolution (Swyngedouw 2019). However, one should not assume that conflict potential stemming from poverty, population growth, or population concentration are limited to the class warfare discussed by Marx. Scholarship on relative deprivation (Gurr 1970) and horizontal inequalities (Stewart 2000, 2008) suggest that any major political, economic, or social disparities between different segments of a country’s population may create grievances strong enough to generate armed conflict. The population growth discussed by Malthus need not be the sole cause of conflict-inducing deprivation. The theory presented by Gurr and Stewart applies to other forms of deprivation, including those arising from demographic shifts that may disadvantage one or more societal groups, possibly angering them enough to fight.

Population pressures remain a common topic of discussions about security policy as scholars draw upon ideas about deprivation in predicting armed conflict. Prior research shows that the Malthusian logic of population pressures does not apply well to interstate conflict,18 the social and economic changes resulting from urbanization processes can alter the opportunities and willingness of domestic-level actors to engage in armed violence. Scholars with the most pessimistic views about the impact of urbanization on armed conflict frequently warn that overcrowded and poorly administered cities may become powder kegs of discontent (Taw & Hoffman 1994, Kilcullen 2013, Graham

2004). Focusing on deprivation-based ideas of conflict, a government’s inability to meet the needs of a growing urban population may spark public ire, which could trigger rebellion or support for militants. In such countries, influxes of large numbers of low-

18 For example, Tir and Diehl (1998) apply Malthus’ expectations regarding population pressures to analysis of interstate wars. They find that population increases do not heighten the risk that a state will either initiate or escalate military conflicts with other states.

43 income ex-rural individuals into cities could heighten the risk of anti-government violence. Large numbers of disadvantaged or alienated urban residents may motivate people to take up arms due to displeasure with government performance, a motivation for violence identified in prior research on conflict in developing countries (Raleigh 2015,

Raleigh & Hegre 2009).

Urbanization brings people geographically closer to centers of government, which may lead rebel groups to focus their attention on the institutions of government.

According to Schulz (2015), urbanization in many African countries is most prominent in capital cities, where large numbers of people moving into proximity of the government increases the risk that people will engage in violent struggles over matters of governance.

In theory, armed groups with governmental incompatibilities—meaning that rebels wish to gain control of the government—should be better able to pursue this goal when they are geographically closer to the seat of government. For this reason, it is unsurprising that prior research finds that civil conflicts over governmental incompatibilities are more likely to occur in urban areas than in rural ones (Buhaug & Rød 2006).

The “urbanization of poverty” is sometimes highlighted as a factor that may lead to the urbanization of conflict. Many scholars have identified a combination of urbanization and poverty in developing countries as a security threat, possibly leading to intrastate conflicts, especially urban-based insurgencies (Kilcullen 2013; Graham 2004,

2010; Sampaio 2016, 2018; Le Blanc 2013; Evans 2016; Beckett 2005; Norton 2003;

Taw & Hoffman 1994). Low urban living standards, they argue, may make people more willing to joining militant groups, possibly shifting the geographic foci of armed conflicts away from rural areas towards crowded, squalid cities of the Global South. Many of those

44 who speculate about an urbanization-conflict nexus do so without empirically testing core theoretical assumptions with empirical analysis. This speaks to a need for additional research to evaluate such claims.

Warnings about a possible urban future of armed conflict has also attracted great attention from policymakers. In the 1990s and early 2000s, a series of US military interventions in the developing world featured intense urban conflict, increasing scholars’ and policymakers’ interests in the security implications of urbanization. As a result of its deployments to urban combat zones, the US military developed detailed urban warfare doctrines, heavily emphasizing that continued urban growth in impoverished countries will necessitate future involvement in conflicts abroad.19 The North Atlantic Treaty

Organization (NATO) launched the NATO Urbanization Project in 2014 to study urban warfare (Bodnar & Collins 2019, Pendleton & Bodnar 2017) and the International

Committee of the Red Cross (ICRC) has also examined conflict as a future source of conflict-related humanitarian crises (Maurer 2016). As with the US military’s focus on urban conflict, discussions put forth by these groups also suggest that conflict will urbanize along with populations. In line with arguments put forward by scholars pessimistic about urbanization in low-income countries, they suggest that urbanization will contribute to intrastate conflict, creating crises that might necessitate external intervention.

Despite discussions of urbanization as a potential security threat, the empirical record is far from certain about a potential urbanization-conflict nexus. Qualitative

19 For example, in 2013, the US Joint Chiefs of Staff produced Joint Publication 3-06 on Joint Urban Operations provides detail on military operations in urban areas. It discusses rapid urbanization and problems with economic development in low-income countries as factors likely to contribute to armed conflict.

45 analyses provide numerous examples of urban-based insurgencies fueled, at least in part, by population pressures exacerbated by urbanization (Bakonyi, Chonka, & Stuvøy 2019;

Pirnie & O’Connell 2008; Rayburn et al. 2019; Sampaio 2018). Raleigh’s (2015) study of urbanization and conflict patterns in Africa finds that higher levels of urbanization are associated with a greater risk of armed conflict. Similarly, Schulz (2015) examines capital cities in Africa, finding that as capitals grow, so does the risk of civil conflict between governments and rebel groups over governmental incompatibilities. On the other hand, other large-N quantitative studies suggest that the relationship between urbanization and armed conflict is very weak (Buhaug & Urdal 2013), non-existent

(Urdal 2005, 2008), or perhaps even negative (Green 2012). This divergence in evidence creates uncertainty about whether urbanization is truly the security threat that so many have suggested.

Chapter 3. Theory of Urbanization & Conflict

In this chapter, I discuss the theoretical linkages between urbanization and armed conflict, drawing on literature from the fields of political science, sociology, and economics. I begin by explaining this project’s focus on intrastate armed conflict, as opposed to other types of violence. I then elaborate on the key aspects of urban environments and urbanization processes which I expect to influence conflict behavior.

Based on my theory of urbanization and conflict, I proffer several hypotheses in this chapter which are assessed in later chapters. The section concludes with a brief discussion of how this research benefits the political science literature and how findings derived from my analysis may also prove useful to policymaking communities.

In summary, I expect that in most cases, urbanization in the developing world should be associated with a lower risk of armed conflict. The process of urbanization should generate improved social, economic, and political opportunities for both rural and urban populations, alleviating grievances that might push people toward armed groups.

Even when people live in rural areas that are not subject to significant urbanization, a rural area’s proximity to large and growing cities should also afford its citizens better opportunities. For this reason, I expect proximity to urbanized or urbanizing areas to correlate negatively with political violence. There is, however, an important caveat to the expected negative relationship between urbanization and conflict. Although I identify a variety of ways that urbanization can improve people’s life opportunities, thereby decreasing their willingness to join or support armed groups, such things may not be possible in countries whose economic situations are deteriorating. In the face of rising

47 poverty and joblessness, I anticipate that the normal relationship between urbanization and conflict might become reversed, leaving an area more vulnerable to armed conflict.

3.1. Introduction of Theory

The urban-rural dynamics of armed conflict are increasingly discussed in scholarly literature, but there is much to be done to advance our understanding of what they mean for the urbanizing areas of the developing world. I identify several key aspects of urban- rural conflict dynamics which deserve scrutiny due to conflicting messages in scholarly literature and the interest in the topic shown by policymakers around the world. Before discussing the logic behind my hypothesis, it is first important to clarify the key concepts involved in this study, namely conflict and urbanization, as well as the way that theoretical considerations are connected to the scope of my analysis.

The outcome phenomenon I examine in this research is armed intrastate conflict, conceptualized according to the UCDP definition, which focuses on violence between governments and non-state actors.20 The violence analyzed here includes high-intensity warfare and lower-intensity armed insurgencies fought between governments and rebels.

Given the varieties of violence scholars have suggested are associated with cities or urbanization processes, it is important clarify this study’s focus on civil conflict and is not meant to explain all types of violence that could potentially occur in urbanizing countries. Isolating armed conflict from other forms of violence allows for an in-depth evaluation of claims regarding urbanization’s risk of triggering civil war. It also reduces

20 See the UCDP’s “definitions” page (UCDP 2018) for additional information about criteria for categorizing conflict situations.

48 the muddying of civil conflict with other concepts of violence, which come with separate sets of theoretical causes, dynamics, and effects. For example, prior works suggest that urbanization processes or urban settings are associated with higher levels of criminal violence (Glaeser & Sacerdote 1999; Muggah & del Frate 2007), but criminals and rebels have fundamentally different goals and may therefore carry different implications for policymakers. Rebel groups are inherently political, whereas criminals are not. Rebels therefore demand more than criminals do, seeking to capture, overthrow, or break free from governments.21 Criminal groups are profit-oriented and may seek to weaken or exploit governments, but are generally have much more limited goals and are less threatening than rebels to the state’s survival (Lessing 2015, Barnes 2017, Stepanova

2009).

Criminal violence and terrorist violence are commonly discussed as predominantly urban phenomena,22 but until relatively recently, rebel activities were thought to be more likely in rural settings. Rebel groups can more easily operate in remote or inaccessible locations where it is possible to operate a relatively large fighting force further from the government’s view (Fearon & Laitin 2003; Hegre & Sambanis

2006; Buhaug, Gates, & Lujala 2009). On the other hand, urban locations are more conducive to crime and terrorism because they are target-rich environments where individuals or small groups often go unapprehended (Glaeser & Sacerdote 1999;

21 While some scholars emphasize that rebels may fight against their governments for personal gain – e.g. the “greed-based” arguments proffered by Collier and Hoeffler (2004) – they still focus on conflicts in which rebel groups seek to change government’s composition or to achieve territorial independence. 22 Glaeser and Sacerdote (1999) discuss the tendency for cities to have much higher violent crime rates than rural areas. However, note that some quantitative studies show that urbanization is, in fact, not significantly associated with greater criminality (Fajnzylber, Lederman, & Loayza 2002; UNODC 2019). Barbara Crenshaw (1981, 384) mentions urbanization as a risk factor for terrorism, noting that terrorism is often characterized as “urban guerrilla warfare.”

Hinkkainen & Pickering 2013). However, criminal activity flourishes under conditions that are not perfectly identical to those involved in armed conflict. Those warning that urbanization will lead to armed conflict, particularly urban-based armed conflict (e.g.

Kilcullen 2013; Sampaio 2016, 2018), present an argument that is interesting precisely because of the way it differs from traditional assumptions about the advantages enjoyed by rebel groups. The government of an urbanizing country would not be surprised to find violent crime or terrorism concentrated mostly in urban areas as this already is the case.

However, a shift in rebel group activities away from their traditional rural locations toward cities would be a new situation that would carry meaningful implications for policymakers. A focus specifically on intrastate civil conflict is necessary to evaluate this possibility.

As discussed in the previous section, urbanization in this study is understood in both as the share of a population living in urban settings and the rate at which urban populations grow. Both conceptualizations of urbanization are relevant to a potential urbanization-conflict nexus and are empirically tested in later chapters of this project. In the sections below, I discuss my expectations regarding a possible urbanization-conflict nexus. I first lay out my theory that in most cases urbanization should be negatively associated with armed conflict in the developing world. Urbanization in poor countries is associated with socioeconomic advantages that I expect to decrease citizens’ willingness to join rebel groups, while also improving the government’s ability to suppress armed non-state actors. Being closer to major cities, I argue, should also improve socioeconomic conditions and opportunities for rural-urban migration. Citizens of rural communities might be closest to places where rebel strongholds are traditionally located, but the

50 opportunities made available by proximity to large and growing cities should decrease citizens interest in joining or supporting rebels. Finally, I expect that despite the many benefits associated with urbanization, the growth of cities may indeed prove perilous under certain circumstances. Countries with particularly high urbanization rates or particularly poor economic conditions may find themselves overwhelmed by the demographic shift. For such unfortunate countries, the benefits of urbanization may be out of reach and citizens may become aggrieved to the point of violence if the pressures of urbanization erode their opportunities and living standards.

3.2. Can Urbanization Ameliorate Armed Conflict?

Despite gloomy predictions of conflict in cities, there are reasons to expect that urbanization will not have any such negative effect. Social science research points to at least three factors that can reduce the ill effects of urbanization, which in turn may avert the risk of urban guerrilla warfare. First, urban areas typically have a much larger government presence than do rural ones.23 As people move into cities, it becomes harder for them to organize and train a rebel army, given the government’s ability to monitor and police security-related matters in cities. Criminal gangs or terrorist groups—other major security concerns for urban centers—may be small enough to evade detection or apprehension, but this is less so for the larger militias typically associated with rebellion

(White & White 1991, 127). Second, urban-dwelling citizens have better access to nonviolent means of advocating for political or social change. The proximity of urban

23 Governments tend to concentrate their resources in urban areas, especially, but not exclusively in capitals. See Gilbert and Gugler (1992) and Koren and Sarbahi (2017) for further discussion about governments’ greater presence in urban locations.

51 residents to government and to civil society organizations facilitates organization of social movements and efforts to lobby for policy changes (Walton 1998, Glaeser 1994).

These avenues for advocacy can, in many cases, render armed movements unnecessary in the eyes of people dissatisfied with the status quo. Thirdly, urbanization is often discussed as a correlate or component of economic development. Cities often serve as major commercial hubs, vital centers of economic activity for the countries in which they are located. If a city’s growth is a function of its economic vibrance, the growing economic opportunities for urban residents may alleviate socioeconomic grievances and raise the opportunity cost for participation in armed movements.

Poverty is one of the strongest correlates of intrastate conflict,24 so economic development accompanying urbanization processes may alleviate economic stressors that often motivate political violence. In his classic discussion of economic development processes, Rostow (1959) described urbanization as a factor necessary for a country’s economy to “take off,” propelling a society forward in terms of wealth and living standards. While urbanization is no panacea for poverty and is often blamed for creating socioeconomic inequalities,25 cross-national research has demonstrated a significant positive relationship between urbanization and economic development (Fox 2011; Chen et al. 2014; Bloom, Canning, & Fink 2008). The role of urbanization in development is apparent not just in long-term historical trends, but also in the way that people in developing countries view their opportunities and living standards in the context of urban

24 For example, Collier (2003) discusses a circular relationship between poverty and armed conflict in the developing world, a concept they term as the “conflict trap.” See Braithwaite, Dasandi, and Hudson (2016) for analysis of a potential causal relationship between poverty and conflict. 25 See Zhang (2016) for discussion of challenges of urbanization, including socioeconomic inequality, the growth of urban slum settlements, crime, pollution, etc.

52 and rural settings. Easterlin, Angelescu, and Zweig (2011) present survey-based research showing that in the developing world, people living in urban areas report significantly higher levels of perceived wellbeing compared to their rural counterparts. While this measure is subjective, it is notable that the urban-rural divide in perceived wellbeing is much higher in developing countries than industrialized ones. This is something that the researchers attribute to the starker disparity between economic and social opportunities afforded to urban and rural citizens of the developing world.

Urbanization is conventionally understood as a factor that prevents development, a factor that typically promotes peace. When a developing country experiences urban population growth—whether due to migration or through birth rates—empirical research provides mixed results regarding its ability to improve conditions. Urbanization is often discussed as a critical part of modernization or development (Huntington 1972; Rostow

1959; Tacoli, McGranahan, & Satterthwaite 2014) which can help alleviate poverty in developing countries and bring economic benefits to both urban and rural communities

(Liddle 2017; Imai, Gaiha, & Garbero 2017). Research shows that urbanization often leaves those remaining in rural areas better off. Out-migration from rural areas boosts urban demand for agricultural products as growing urban populations demand greater quantities of produce generated in rural settings. Additionally, when rural people move to cities for work, they have better opportunities to remit money back to their friends and family in the countryside, thus providing additional stimulus to rural economies (Cali &

Menon 2012; Ravallion, Chen, & Sangraula 2007).

Aside from studies associating higher incomes with a lower risk of civil war

(Collier 2003; Hegre & Sambanis 2006), research also indicates that the rate of economic

53 growth can affect a country’s conflict propensity. Higher rates of economic growth reduce the risk of conflict, whereas lower or negative rates are associated with greater risk (Miguel, Satyanath, & Sergenti 2004). If urbanization is, as many suggest, a sign of development or modernization, it should come with a lower risk of armed conflict.

Urbanization may coincide with a simultaneous increase of both incomes and income inequalities. Previous scholarship shows that even in the developing world where public services are weak and cities are overcrowded, urbanization does tend to coincide with forward progress in economic development (Chauvin et al. 2017; Ravallion, Chen, &

Sangraula 2007; Njoh 2003). This does not mean that urbanization is without problems or that concerns about the urbanization of poverty are unfounded. Other works find that economic changes associated with urbanization do indeed increase the gap between rich and poor (Zhang 2016, Ravallion 2014). However, this gap in wealth represents “vertical inequality,”26 which does not generally increase a country’s risk of war. Work by Stewart

(2000, 2008) and Østby (2011, 2016) indicates that socioeconomic disparities only present a risk of war when they are horizontal inequalities, meaning that disparities exist between a society’s distinct identity groups rather than between individuals.

As countries urbanize, impoverished rural citizens relocate to cities where rebel groups are at a disadvantage. Rebel groups concentrated in urban settings are generally less successful than their rural counterparts largely because they are easily found and overwhelmed in built-up areas where government forces are strongest. Connable and

26 Stewart (2000, 2008) identifies vertical inequalities as inequalities between individuals, rather than groups. She explains that because warfare is a type of collective action, it is difficult to mobilize people to fight based on grievances stemming hardships placed on an individual. Horizontal inequalities, which exist between groups are more dangerous because they may create clear divides between the groups and may generate grievances that motivate political violence.

Libicki (2010) for example, find that urban-based insurgents are much less likely than their rural counterparts to achieve their strategic goals, a trend they attribute to relative levels of government resources. Their study concludes that government forces’ heavier concentrations in urban areas allow the government to more easily monitor and respond to security crises in those areas. States with higher levels of overall resources – wealth, in other words – are less likely to experience insurgencies but more likely to defeat those that arise. Likewise, areas where government forces are most concentrated may better deter and more easily defeat armed groups. Joes (2007) arrives at a similar conclusion and identifies numerous campaigns where rebels struggled, often due to these relative disadvantages. The Irish Republican Army in Belfast, Maoist rebels in Sao Paulo, the

Tupamaros in Montevideo, and Chechen rebels in Grozny are all examples of high- profile rebel groups unable to overcome government forces in strategic cities.

As countries become more urban, those wishing to organize violence against their government will likely find themselves in a quite different operating environment than one would expect from a more rural society. Urbanization is sometimes discussed as a historical factor contributing not just to countries’ improvements of institutional capacities, but also the creation of democratic institutions to better govern their growing and diversifying populations (Dyson 2001, 78-80; Herbst 2006, 659-60; Lipset 1959). By increasing the population density of cities, urbanization simultaneously increases socioeconomic stresses on urban dwellers and increases their ability to address these problems through collective action. Although certain patterns of urbanization are

55 sometimes discussed as reducing citizens’ ability or willingness to organize,27 urban settings are often hubs of political activity (Walton 1998, Nicholls 2008).

Scholars of urban growth point out that cities are environments rich in resources and opportunities necessary for groups to exchange information about key issues and organize social or political groups to pursue desired reforms. As cities concentrate people into a geographic space, they also tend to attract political parties and special interest groups which provide urbanites with better opportunities to press for change (Miller and

Nicholls 2013; Nicholls 2008; Glaeser 1994). Various works have found that social movement organizations are more likely to form and flourish in more densely populated areas (Walton 1998, Knudsen & Clark 2013, Nownes 2004) and that civic participation increases as a city’s population density increases (Carr & Tavares 2014). In this way, urbanization benefits citizens interested in voicing their opinions by facilitating the organization of civil society groups that can push for change.

Apart from joining civic organizations, people who live in more densely populated areas can more easily assemble demonstrators to advocate change. Individuals dissatisfied with the status quo can engage in nonviolent civil resistance movements, a peaceful alternative to armed conflict for those seeking political change. Protest movements are typically centered in cities, often in capitals (Pinckney 2018). Even if government security forces can better suppress armed groups in cities, the large number of people living in urban areas may still become potential recruits for nonviolent political movements. Such movements are especially easy to organize in the contemporary era,

27 In his 2000 book Bowling Alone, Robert Putnam argues that, amongst other things, urban sprawl creates physical distance between people, making it more difficult for individuals to develop social connections to each other or to form organizations in their communities.

56 given the widespread access to modern communications technologies in many countries.

For example, in Egypt’s urban-based Arab Spring demonstrations, mass protests—largely nonviolent activities—were quickly organized via social media. Cairo’s large and politically discontented population provided a large pool of participants in close proximity to the country’s center of government. Their opportunity to affect change through urban protest movement made it unnecessary or undesirable for the government’s opponents to resort to armed violence (AlSayyad & Guvenc 2013, Nepstad

2013). Through these channels of grassroots organization, city life facilitates average citizens’ participation in politics without any involvement of armed actors. Even if grassroots campaigns take strongly anti-government positions, the generally nonviolent nature of civil resistance movements is important because such movements can directly substitute for armed conflict (Dunning 2011).28

As countries urbanize, tax revenue tends to increase. Given that urbanization often coincides with industrial growth, urbanizing countries typically enjoy an expansion of their tax bases, leading to greater public revenue. Governments in urbanizing countries often expand their capacity to collect taxes out of necessity in order administer the growing cities under their authority. Urban-based industries are also easier to tax compared to those based on agricultural production as they are often less reliant on informal labor (Khattry & Rao 2002, 1433). Empirical research demonstrates that in the developing world, urbanization is associated with higher levels of tax revenue (Mahdavi

2008, Khattry & Rao 2002). While tax revenue is critical to the function of any

28 It is also notable that while some governments may crack down on political dissent of any type, peaceful movements are less likely to provoke a harsh state response, making it more attractive for citizens to join a nonviolent campaign (Stephan & Chenoweth 2008).

57 government, it has special significance in places whose governments are faced with armed rebellion. Higher levels of tax revenue expand a government’s ability to fund security operations including internal policing and counterinsurgency operations.29

In summary, urbanizing societies have several advantages that should help them avoid the urban combat scenarios predicted by scholars and policymakers who characterize urbanization as a risk factor for civil conflict. Higher government presence in cities, availability of non-violent avenues for the politically aggrieved, and the potential for economic progress should help to avert an urban war. In the average developing country, I expect these factors to counterbalance the negative aspects of urbanization such as resource competition and inequality. Therefore, I propose the following hypothesis:

Hypothesis 1: If an area becomes more urbanized, it will enjoy a lower risk of

armed conflict.

3.3. The Influence of Urban Proximity

An area’s proximity to major cities may influence a population’s willingness or ability to participate in armed rebellions. The ability of cities to serve as engines of economic growth is a function of larger market size and higher economic productivity that occur within large population agglomerations (Duranton 2009). Because of cities’ economic productivity, communities tend to benefit from their proximity to cities. This is the case

29 The use of tax revenue to improve public service provision is, in this theory an indicator of government efforts to increase state capacity. Note that the extraction of tax revenue is itself is a common proxy measure for state capacity based on the assumption that governments must wield robust administrative capabilities in order to collect taxes from their citizens. See Hendrix (2010) for more detailed discussion about the extraction of tax revenues as an indicator of state capacity.

58 in both developed and developing countries as rural poverty tends to be more severe in remote locations situated furthest from urban centers (Partridge & Rickman 2008; Egan

& Bendick 1986). Prior research shows that economic development associated with urbanization can improve economic and social opportunities not just for urban dwellers, but also for people living in nearby rural areas. Economic activities tend to be most intense between people and organizations located geographically near each other, since closeness facilitates communication as well as the transportation of goods and labor

(Jacobs 1985, Krugman 1996, Egan & Bendick 1986). As a result, one can expect that rural people living nearby a large or growing city can draw more benefits from interacting with urban markets than can people living in more remote locations.

Proximity to urban areas produces greater opportunities for rural-urban migration, which can affect levels of armed conflict both by deepening rural-urban economic connections and by eroding rebel groups’ pools of potential recruits. People in the developing world are generally less able or willing to migrate to locations further from their home, even if both locations are within the same country. This is especially true of migrants from the lowest socioeconomic strata. Moving to relatively nearby cities allows migrants to expend fewer resources when moving and to maintain closer contact with friends and relatives in the area from which the left (Lucas 2001; Deshingkar &

Grimm 2005, 18). As the populations of impoverished rural regions grow, the regions where they live may experience population pressures that a Malthusian logic would normally expect to translate into socioeconomic tension. However, growing cities serve as pressure relief valves in many developing countries, providing rural people with an alternative venue to live and work. While rural-urban migration provides no perfect

59 guarantee of socioeconomic advancement, it relieves demographic stresses on rural communities by giving those dissatisfied with their lives a chance to do better elsewhere

(Camhis 2006, 92).

It is easier for people to move across short distances than long ones, which is why proximity to major cities is important for rural people considering moving. This is especially true in developing countries where migration often occurs in circular patterns, with rural people moving to cities temporarily for work, later returning to their homes in rural areas. In cases of temporary or circular migration, rural areas benefit both from increased job opportunities for citizens as well as those citizens’ ability to send remittances to family members in outlying rural areas (Potts 2009, Keshri & Bhagat

2013). As discussed earlier, these remittances may greatly benefit the rural communities that receive them. This is relevant for rural-based insurgencies because the migration of people out of rural areas inevitably reduces the pool of potential recruits available to rural-based rebel groups. Additionally, the flow of remittances into rural areas may alleviate poverty in communities that rebels are eager to exploit, reducing people’s incentives to cooperate with rebels.

State capacity – understood a government’s ability to implement policy – tends to be higher in urban areas and may therefore cause conflict patterns to vary according to an area’s proximate to major cities. Governments’ administration may take both civil and coercive forms, the former carrying the potential to reduce citizen’s grievances through improvements in living conditions, the latter enabling the government to suppress armed movements through force. Government administrative capacity to provide public services is usually greatest in urban areas where the government has relatively high levels of

60 resources and infrastructure (Gilbert & Gugler 1992, 56; Koren & Sarbahi 2017, 274).

Even in the developing world where public resources are meager, government administrative capabilities are generally much better in urban areas, allowing even the poorest urban citizens to enjoy better access to public services than is possible in the countryside (Enriquez, Sybblis, & Centeno 2017). Given the concentration of government resources in cities, it is reasonable to assume that a community’s location closer to a major city should increase its opportunities to benefit from those resources.

Coercive forms of state capacity including law enforcement and military resources that tend to be concentrated in or near urban centers, which should enable the government to forcibly suppress armed groups. Some studies have adapted Boulding’s

(1962) loss of strength gradient30 to spatial research on civil conflict, theorizing that states can better project coercive force to areas nearby capital cities, finding conflict risk increases as one gets further from a capital city (Schutte 2015; Buhaug, Gates, & Lujala

2009; Koren & Sarbahi 2017). While these studies focus on capital cities, expecting their level of coercive force resources to be especially high due to their political significance, it is not unreasonable to expect coercive capacity to be higher near all major urban centers.

If a rural region is geographically distant from the urban-based centers of government resources – regardless of whether the city is a capital – government security forces are likely to have difficulty accessing it relative to an area nearby the urban-based administrative center. Distance from major cities is a form of remoteness which may encumber state efforts to govern or control an area, not unlike the terrain-based features often cited as indicators of an area’s isolation. Just as rebel groups benefit from the

30 Boulding (1962) argues that geography mediates states’ ability to apply military force. Essentially, projecting force is easier for states attacking nearby targets compared to targets located further away.

61 presence of jungles or mountains which make a territory less accessible to state actors

(Tollefsen & Buhaug 2015), distance itself may form a similar barrier between states and rebels, advantaging militant groups operating in far-off regions.

Cities benefit their populations both by providing them better economic opportunities and the superior level of state capacity normally found in cities. These factors should both reduce people’s willingness to join armed groups and improve the government’s ability to suppress rebellion in places where the population is most concentrated. With these things in mind, I propose a second hypothesis:

Hypothesis 2: If an area is located closer to a major city or if a nearby major city

is growing at a fast rate, the risk of armed conflict in that area will be lower.

3.4. Circumstances in Which Urbanization Might be Harmful

The potential benefits of urbanization may not be available to all countries that are urbanizing. While the average developing country might not stand a high risk of armed conflict in urban areas simply because it is urbanizing, there are contexts in which urbanization could lead to greater levels of urban conflict. Two of these aspects or conditions of urbanization are especially likely to create a risk of violence, despite the benefits from urbanization discussed in the previous chapter. First, the rate of urbanization may simply be too much for a country to handle. If influxes of rural-urban migrants are especially large or sudden, they may overwhelm a city’s resources and lower living standards for people within the city. Second, if urbanization – even mild increases in urban populations – corresponds with weakening economic conditions the resulting decline in living standards may create grievances great enough to spark conflict. Both

62 matters are directly tied to the problem of resource shortages and threaten to create the conditions of squalor and deprivation often warned about by those most pessimistic about urbanization in the Global South.

There are several reasons why urbanization rate is harsher than urbanization level in straining society in general and the government in particular. Sudden or large influxes of newcomers may, at least for a time, increase competition for jobs, housing, utilities, infrastructure, and basic services beyond levels that governments and markets can satisfactorily provide. When a poor country experiences the “urbanization without development” phenomenon it is often associated with urbanization rates (Jedwab &

Vollrath 2015; Fay & Opal 1999). A rapid pace of urbanization does not guarantee an increase in wealth. Brückner (2012), for example finds that faster rates of urbanization are associated with lower rates of personal income growth in African countries. In some cases, a country may find that the rate of urbanization exceeds its capacity to sufficiently accommodate the growing number of people in its cities. In such cases, economic growth and infrastructure development may prove insufficient to sustain the population growth experienced in a fast-growing city, thereby resulting in low or decreasing living standards for many urban residents (Cohen 2006).

Regardless of a country’s urbanization level, sudden population pressures brought on by high rates of urbanization may be difficult for a developing country to bear.

Discussions abound about how crowded, poorly administered cities with underprivileged populations may become hotbeds of criminal violence (Jütersonke, Muggah, & Rodgers

2009; Muggah & del Frate 2007; Moncada 2013; Moser 2004, UNDP 2016), but urban violence is not necessarily of a criminal nature. Those who expect urbanization to fuel

63 political insurgencies cite the same population pressures as factors that can light the fuse of war (Kilcullen 2013, Graham 2004). It is therefore possible that while healthy and sustainable urbanization may decrease a country’s risk of armed conflict, the opposite will occur when the pace of urbanization is unsustainable. Militant groups in a growing city may exploit the situation for recruitment leading to more violence in years following major influxes of people into cities than one would expect during the average year.

Scholars of urbanization sometimes discuss urban growth in developing countries in the context of “over-urbanization,” meaning that the urban population growth is too great for the economy to accommodate. Influxes of new urban residents may overwhelm a city’s infrastructure as well as its labor and housing markets, possibly worsening problems of urban poverty and unemployment (Bienen 1984; Sovani 1964). Regardless of how many people live in cities during a given year, the rate at which people move to cities may change, thereby altering the socioeconomic strain on the communities receiving the most migrants. Of course, government resources and management capabilities vary widely across the developing world, but high rates of urbanization are likely to challenge even the governments that might otherwise be effective in providing for public infrastructure or services. Scholars and policymakers sometimes describe urbanization as being “sustainable” based, at least in part, on whether public services and infrastructure can be increased fast enough to handle the pace of urbanization (Cobbinah,

Erdiaw-Kwasie, & Amoateng 2015; Hiremath, et al. 2013; UNDP 2016).

The economic strain of urbanization may be especially painful to a country whose economic health is flagging. Fay and Opal (2000) present a strong warning that urbanization does not stop during economic downturns. Even though many developing

64 countries, especially in Africa, experienced positive levels of economic growth for much of the latter twentieth century, rural peoples’ motivation to seek better livelihoods in urban areas generally remained strong regardless of urban economic conditions. If a country urbanizes when its economy is doing very poorly, as in cases of a recession or times of increasing unemployment, the benefits outlined in the previous section may not be strong enough to prevent urban-based armed conflict. Essentially, the nightmare scenarios of urban guerrilla warfare may become likelier if economic problems make it difficult for a country to withstand the population pressures of urban growth.

Unlike wealthy countries which typically have sophisticated welfare states or resources needed to manage the harm of an economic crisis, developing countries have much less capacity to deal with a sudden economic downturn. In austere times, the populations of developing countries are highly vulnerable to the harms of critically low levels of public service provision (Seth & Ragab 2012). Such deprivation of resources or services may exacerbate internal divisions as social inequalities and uneven access to public services are felt more sharply during economic downturns (Gurr 1985). In some cases, economic downturns may affect one societal group more than another, creating the horizontal inequalities that Stewart (2000, 2008) warns often become deadly conflicts in poor countries. The chance of a government failing to provide adequate infrastructure and public services is greatest when the economy is weak. This has special significance for urban populations, which may be especially demanding of public resources.

Migrants to cities expect an improvement in their livelihoods and may be sorely disappointed if they move to a large city only to find that jobs are not available there.

Rural people who move to cities may lack the skills needed to be highly competitive in

65 the urban workforce or may not have enough information to judge the health of urban labor markets. For this reason, they may move to cities where they have difficulty finding employment or are otherwise forced into poor living conditions in the cities to which they migrate (Lipton 1977). The job search is likely to be especially problematic for ex-rural migrants when the country’s economy is in poor condition, particularly when unemployment is high. Prior research on developing countries’ economies shows that urban areas tend to have greater unemployment levels than rural ones (Zhang 2016, 247-

8) and that within cities, unemployment is especially severe amongst ex-rural migrants

(Cobbinah, Erdiaw-Kwasie, & Amoateng 2015, 67-8). In such a situation, the labor market may be unable to absorb influxes of low-skilled newcomers, leaving them struggling to make ends meet. Social and political grievances may arise if urban residents resent competing against newcomers for work or if newcomers are frustrated by unexpectedly scarce opportunities. These grievances may make the urban residents more amenable to joining and armed group (Goldstone 2002; Hove, Ngwerume, & Muchemwa

2013). This is especially true for the unemployed who will likely have a lower opportunity cost for participating in conflict. Essentially, participating in armed conflict becomes highly disadvantageous for those who would have to put their careers on hold in order to fight (Cramer 2010).

Because the effects of over-urbanization are concentrated in cities, I expect urban growth in countries with worsening economies to generate grievances primarily amongst urban residents. While I generally expect urbanization not to worsen the risk of armed conflict in cities, urbanization in a weak labor market should make things worse. If an area’s urbanization coincides with a spike in poverty or unemployment, conflict-inducing

66 grievances will be more likely to arise. People moving to cities may fail to find jobs, compete fiercely with other urbanites for scarce resources, and not experience an increased opportunity cost of conflict. Therefore, I propose a second hypothesis:

Hypothesis 3: If an area experiences high rates of urbanization during times of

economic difficulty, its risk of urban intrastate conflicts will increase.

3.5. Contributions of Research

This research contributes to the field of political science by bringing greater clarity to the debate about the effects of urbanization in the developing world. The current literature provides mixed signals about the security-related consequences of urbanization in the developing world. Scholars who address this issue most directly, often suggest either that the shift of populations toward cities will trigger or worsen the risk of civil conflict

(Kilcullen 2013; Graham 2004, 2010; Sampaio 2016, 2018; Le Blanc 2013; Evans 2016;

Beckett 2005; Norton 2003; Taw & Hoffman 1994), while others suggest that urbanization will have no effect (Urdal 2005, 2008). This project encourages a different view of what urbanization means for the countries experiencing it. I argue that instead of threatening the Global South with armed conflict, urbanization is likely to decrease the risk of conflict in most countries. It is only when urbanization occurs within the context of severe economic deterioration that risk of intrastate conflict becomes meaningful. This does not mean that cities will be free from all forms of violence. After all, it is certainly possible for urbanizing, low-income countries to have serious problems with homicide or other forms of violent crime, even when there is no sign of armed rebellion.

My argument that conflict risk should decline with one’s proximity to major cities is novel for several reasons. First, the theory behind this argument focuses not on the state’s ability to project force, but on the socioeconomic benefits of urban areas themselves. Prior scholarship points to hazards associated with great distance from a country’s capital (Tollefsen & Buhaug 2015; Buhaug, Gates, & Lujala 2009; Raleigh

2010) or the ruggedness of terrain (Tollefsen & Buhaug 2015, Hegre & Sambanis 2006;

Raleigh 2010) as the government is likely to have difficulty monitoring or fighting in such locations. These arguments are extremely valuable but have thus far focused heavily on the state’s ability to manage conflict, rather than on communities’ incentives to participate in it. My work draws attention to the opportunities that cities create not just for urban communities, but also for rural ones.

Second, few prior studies attempt to quantitatively evaluate localities’ conflict propensities according to their distance from major cities. Of course, conflict-related research accounts for distance from administrative capitals (e.g. Raleigh 2010; Tollefsen

& Buhaug 2015; Buhaug, Gates, & Lujala 2009), but many major cities, including some of the largest or fastest-growing cities in the developing world, are not capital cities. And while Greig, Mason, and Hamner (2018) account for major cities other than administrative capitals, they do not evaluate the risk that conflict will occur in an area, but rather on the outcomes of conflicts already begun. Concerns about potential security risks faced by cities in the Global South focus on conflict propensities and rely on a logic that does not necessarily discriminate between cities based on their status as administrative capitals. This research therefore approaches the question of cities’ impacts on conflict propensity from an angle quite different from those taken in prior studies.

This represents a step forward in understanding the role of cities in the security of developing countries.

Third, while I theorize a great many benefits of urbanization for the security of the developing world, my argument is not naïve to the potential risks faced by urbanizing countries. It may well be the case that urbanization generates severe security risks, particularly regarding violent crime,31 however, the focus of this study is on armed intrastate conflict, a topic that is less well understood in the context of urbanization.

Despite the many ways that urbanization may lower the risk of armed conflict, many of these factors are tied to socioeconomic conditions, which may not be stable in developing countries, especially those undergoing significant demographic shifts. My theory explains why, urbanization may have a destabilizing effect in some societies, while the opposite is true in most cases.

In the following chapters, I evaluate the potential urbanization-conflict nexus from progressively smaller units of analysis, starting at the country-level, then drilling down to provinces, grid-squares, and districts. Through this variety of tests, I hope to achieve a greater degree of internal validity for the tests of my hypotheses, while also utilizing large-N quantitative techniques to identify trends that may be generalizable across cases. In Chapter 6, my focus on the conflict in India’s Red Corridor region, I also present a series of additional empirical tests to better evaluate the plausibility of various mechanisms which may theoretically impact the risk of conflict, given varying degrees of urbanization.

31 For example, see discussion by Hove, Ngwerume, and Muchemwa (2013) and Moncada (2013) for further discussion about the risk of violent crime in cities of the developing world.

Chapter 4. Global Country-Level Analysis

Many developing countries are quickly urbanizing, a trend that not only affects economic matters, but security as well. There are traditional concerns that urbanization brings with it a risk of violence as urban areas and their resources become subject to greater competition. At the same time, much of the literature on rebellion suggests that rebels favor rural areas to launch their violence against governments because they enjoy strategic advantages in peripheral areas where governments struggle to project their power. Perhaps we should not expect these groups to use urban areas to launch conflict, even if people move to those spaces. Furthermore, if urbanization results from displacement of individuals from rural to urban areas, then perhaps we could expect that out-migration would reduce expected levels of violence in rural areas. Analyzing intrastate conflict in developing countries from 1992-2016, I find that urbanization is associated with lower risk of rural conflict. Urbanization is generally not associated with an increase in conflict risk for urban areas, unless a country’s labor market is weak, providing fewer opportunities for those moving to cities.

4.1. Introduction

Based on previous works, we do not have a clear idea of how urbanization will impact political conflict in developing countries. Some argue that urbanization has no impact in urban-based conflict. For example, Urdal (2005) analyzes data on African countries, finding no relationship between the two phenomena, regardless of whether urbanization is conceptualized in terms of level or rate. Other literature identifies

70 urbanization as a major security threat to developing countries, particularly in urban areas. Scholars such as Goldstone (2002), Muggah (2012), and Kilcullen (2013) argue that social pressures created by urbanization are likely to create serious security challenges for developing countries unable to keep up with the pace of change, sparking both criminal and political violence. Shulz (2015) disaggregates African civil wars into those with territorial and governmental incompatibilities, focusing on the risk of civil war onset as countries become “metropolized,” meaning that their populations are more concentrated in capital cities. He argues that the concentration of government resources in capital cities should make conflict over governmental issues especially likely as capitals grow. However, this does not account for all urban growth, only for that of the capital city. While these studies approach the urban-rural dynamics of conflict from a variety of angles, they do not clearly demonstrate how urbanization might alter risks of different types of intrastate conflict at the country level.

The uncertainty about urbanization’s impact on armed conflict risk may be clarified by recognizing that urbanization may change the goals and locations of armed conflicts. For example, flows of people out of a rural area leave rural-based rebel groups fewer potential recruits. Rural and urban populations vary in terms of their economic and political interests, which give them different incentive structures for participating in armed conflicts. Citizens living in either setting may become willing to fight when they feel their interests are threatened. For rural people, disputes over control of land could easily present such a threat. Rural agricultural communities are economically tied to the land they live on, so if a population is more rural, citizens should be more willing to take up arms when the control of territory is at stake. Larger rural populations also provide

71 rebel groups a larger base of recruits with which to operate in rural areas, so I expect that urbanization will reduce the risk of conflict in rural areas. Although the government’s resources are likely to be most concentrated in cities, giving it an advantage in securing urban areas, urbanization may increase conflict risk when the economy is doing poorly. If people move to a city in an ailing economy, increased competition for jobs, housing, and other resources are likely to increase the risk of urban conflict.

I test my theory by coding armed conflicts according to their goals and locations.

I find that as a country's population moves from the countryside into cities, the country will experience a decreased risk of rural-based civil conflict violence, but that urban areas enjoy no such benefit. Furthermore, as countries urbanize, changes in conflict patterns vary according to the strength of the country’s labor market. The pacifying effect of high urbanization rates on rural-based conflict is reversed under conditions of higher unemployment. Under the right conditions, urbanization makes countries more peaceful by providing citizens with non-violent paths toward socioeconomic advancement, but when the labor market is weak, the growth of cities may generate population pressures, increasing the risk of urban-based conflict.

This research is intended to identify generalizable trends regarding the conflict in urbanizing countries throughout the developing world. I emphasize that conflict patterns are affected not only by the level of urbanization, but also the rate of urbanization. The latter of these two factors more closely reflects the phenomenon of rural-urban migration, a topic of keen interest for policymakers in developing countries. The results of this study suggest a need for analysis of subnational patterns of armed conflict as rural and urban areas are not equally affected by violence linked to the growth of cities. This study’s

72 focus on conflict trends at the country level establishes a foundation upon which to construct a research agenda on the micro-dynamics of conflict, particularly regarding countries with changing internal demographics. Finally, this study underscores the point that urbanization does not necessarily affect all countries in the same way. The economic environment in which urbanization occurs plays a key role in determining its impact on society, particularly regarding matters of war and peace.

4.2. Theoretical Contribution

The analysis featured in this chapter is intended to provide a baseline of understanding of a potential conflict urbanization-conflict nexus. Of this project’s three chapters of empirical analysis, this chapter focuses on the highest level of analysis – using annual country observations – in considering the impact of urbanization on armed conflict. This high level of aggregation is important for two reasons. First, it is useful in establishing general trends in the relationship between conflict and urbanization. This provides useful direction to the study, generating findings which can be examined in greater depth with the subnational analysis of the subsequent chapters.

Second, policy-related discussions of urbanization and data-collection efforts involving urbanization generally focus on country-level measures of the concept.

Urbanization is conventionally seen by demographers as the percent of a country’s population living in urban areas (McGranahan & Satterthwaite 2014, UN 2014). The task of creating specific conceptual or legal definitions of urban spaces belongs to each country’s government, a fact that has led to wide cross-country variation in standards

(UN Statistical Handbook 2018). The World Bank and United Nations regularly publish

73 statistics on urbanization based on the national-level concept and incorporating countries’ own operational definitions of “urban.” Despite the inconsistencies in these definitions, national-level measures of urbanization are widely used by policymakers across the international community, making analysis of them highly instructive for the purposes of this project. This chapter’s analysis takes advantage of country-level estimates of countries’ urban and rural populations that are widely available and most heavily used by policymaking and scholarly communities. This is important because although there are strong theoretical reasons to expect urbanization to yield subnational consequences for conflict and other social phenomena, governments and major international organization conventionally measure and study urbanization as a national-level phenomenon.

This study considers all three of the hypotheses posed in this project, focusing most heavily on the first and third. For reference, these hypotheses are as follows:

 Hypothesis 1: If an area becomes more urbanized, it will enjoy a lower risk of

armed conflict.

 Hypothesis 2: If an area is located closer to a major city or if a nearby major city

is growing at a fast rate, the risk of armed conflict in that area will be lower.

 Hypothesis 3: If an area experiences high rates of urbanization during times of

economic difficulty, its risk of urban intrastate conflicts will increase.

Hypothesis 1 and Hypothesis 3 receive the greatest attention in the analysis presented in this chapter as they are simplest to test at the country-level. The first hypothesis focuses on the concept of urbanization level, or the percent of a country’s population living in urban settings. The third focuses on the interactive effects of urbanization and changes in

GDP and unemployment, two economic indicators that are conventionally measured at

74 national levels. Country-level measures typically used in measuring these concepts makes the first and third hypotheses especially well-suited to the country-level analysis of this chapter. Because Hypothesis 2 focuses on the distance between localities and major cities, it is better suited to subnational study. Admittedly, this only partially addresses the logic of my second hypothesis which highlights the importance of measuring distances between geographic units at subnational levels. The national-level count of major cities should be understood as a preliminary evaluation of the theory. For a more thorough analysis of urban proximity as a negative correlate of armed conflict, refer to Chapter 5 and Chapter 6, which provide a more fine-grained analysis of data at the subnational level.

This study’s focus on conflict trends at the country level and establishes a foundation upon which to construct a research agenda on the micro-dynamics of conflict, particularly regarding countries with changing internal demographics. This study underscores the point that urbanization does not necessarily affect all countries in the same way. The economic environment in which urbanization occurs plays a key role in determining its impact on society, particularly regarding matters of war and peace.

Urbanization might be perfectly harmless in a country whose socioeconomic conditions are stable or improving but disastrous a country where conditions are deteriorating. The economic components of urbanization are sometimes critically important not just to a country’s development, but also its security.

The analysis presented here highlights the important role that major cities can play in reducing a country’s risk of armed conflict. If urbanization increases a country’s population density or increases the number of spaces designated as urban, the resulting

75 impact on conflict risk is likely to be quite mild if none of the country’s urban agglomerations is particularly large. This suggests need for greater analysis of major cities and security policies governments prescribe for them. It is also notable that while country-level analysis us inherently vulnerable to differences in the ways each country identifies urban spaces, the standard used for identifying major cities is more straightforward, applying the threshold of 300,000 city residents to all population agglomerations worldwide. This is reason for greater confidence in the validity of measurement of major cities, even though their influence is theoretically less relevant when measured at the country-level than at subnational levels.

The findings suggest a need for caution in securitizing discussions of urbanization. Urbanization in the developing world comes with plenty of challenges, but urban conflict is usually not one of them. The risks of such violence, if they materialize at all, are greatest when economic conditions deteriorate. Governments and international bodies should continue their focus on development efforts, gearing programs and policies for the specific needs of countries undergoing the often fast-paced urbanization processes. As a widespread demographic trend with long-term implications, the urbanization of the developing world comes with many implications that deserve close attention. This study is an important step toward a better understanding of this phenomenon.

4.3. Research Design

I test my theory at the country level using data on intrastate conflict, utilizing a global sample of country-year observations covering developing countries from the years 1990

76 to 2015. I operationalize the dependent variable, intrastate armed conflict, as an annualized count of each country’s armed conflict events as recorded in data published in the UCDP Georeferenced Event Dataset (UCDP GED), version 19.1 (Sundberg &

Melander 2013, Stina 2019). I present models which focus on all fatal conflict events – those involving at least one fatality in a year as indicated by the GED’s “best” variable – as well as those indicating severe conflict events as indicated by a best estimate of twenty-five or more casualties.32

Conflict events are coded as rural or urban based on place names listed in the

“where_coordinates” column of the UCDP GED.33 The naming convention used in coding the UCDP GED data includes the term “city” in all place names of capital cities and the term “town” in the place names of other urban areas. As the terms “municipality” and “suburb” also often refer to highly populated locations, I have also coded these areas as urban. Using India as an example, events occurring in “Mumbai Town,” “Bangalore

Suburbs,” and “New Delhi City” are all classified as urban.34 Rurality serves as a residual category for all events not categorized as urban.

Figure 1 below graphically demonstrates the distribution of rural and urban conflict events, based on fatality thresholds. For developing countries from 1990 to 2015,

32 According to UCDP conventions, a country is regarded as being in a state of “civil conflict” if it meets a threshold of twenty-five or more battle-deaths in a single year, regardless of how many events contributed to the accrual of those fatalities. Twenty-five fatalities from a single event is quite substantial considering that intrastate conflict events from 1990 to 2015 averaged less than ten fatalities and a median fatality count of two. For reference, 5.5% of all UCDP events from this time period resulted in twenty-five or more fatalities and 8.9% resulted in no fatalities. 33 Note that conflict events are only included if the UDCP “where_prec” indicator is valued 1-5, indicating a reasonable degree of accuracy in determining conflict events’ locations. 34 Many governments use localities’ status as incorporated municipalities to identify them as urban areas (UN Statistical Handbook 2018). While it is increasingly common for scholars and policymakers to discuss suburban areas as being qualitatively different from either urban or rural areas, suburbanization is traditionally seen by demographers and urban planners as a form of urbanization. See Keil (2018) and De Vidovich (2019) for further discussion of suburban areas and the process as suburbanization as they relate to urbanization.

77 most armed conflict events occurred in rural areas regardless fatality threshold. When considering all conflict events, even when the UCDP best estimate is zero, 35.2% occurred in urban areas and 64.8% occurred in rural ones. When considering only the fatal conflict events – those for which UCDP’s best fatality estimate is one or more – urban events comprise 35.6% of the sample, with rural events making up the remaining

64.4%. Urban settings are somewhat more common for higher-fatality events, which should perhaps not be surprising given that the denser clustering of people in urban areas creates a greater potential for fatalities for whatever violence occurs there. For conflict events generating more than twenty-five fatalities, 45.5% are urban and 54.5% are rural.

Figure 1. Conflict Event Counts by Fatality Threshold

Conflict Event Counts by Fatality Threshold 60000 54861 49714 50000 40000 29833 27485 30000 20000

Conflict Events Conflict 10000 2202 2634 0 0+ Fatalities 1+ Fatalities 25+ Fatalities Fatality Threshold

Urban Events Rural Events

The main set of independent variables analyzed in this study are indicators of countries’ urbanization, based on data from the World Bank. In accordance with the conceptualizations of urbanization heretofore discussed, the urbanization level variable denotes the percent of a country’s population inhabiting areas designated as urban. A

78 natural logarithmic transformation is employed to account for overdispersion in the urbanization level data. The urbanization rate variable indicates the percent change in a country’s urban population from one year to the next. Data for both variables are taken from the World Bank, which uses records from the United Nations Population Division to estimate the percentage of urban dwellers in a country’s population. For reference, the most rural country in the sample is Rwanda, where 5.4% of the population lived in urban areas in 1990. The most urban country in the sample is Argentina, whose population was nearly 92% urban by 2015. The lowest rate of urbanization in this sample was a -7.1%, experienced by Liberia in 1992, during a time of civil war. The highest rate of urbanization was 17.6% experienced by Rwanda in 1996, not long after a period of brutal conflict and genocide. If my expectations, as laid out in Hypothesis 1 are correct, then regression analysis should reveal a negative relationship between urbanization level and armed conflict.

To analyze the value of cities in deterring conflict, I include a count of the number of major cities in each country. The concept of “major city” is operationalized as cities with populations of at least 300,000 people. Data for this measure are taken from the

United Nations Urbanization Prospects (2018), which identifies major cities using population estimates taken in five-year increments. City counts for each country are carried forward to fill in values for the intervening years. A larger number of major cities should, in theory, provide a country’s population with greater opportunities for trade and migration between urban and rural communities, which should reduce the usefulness of engaging in conflict. Having a greater number of major cities implies that the average distance of a country’s rural communities from a major city should be lower. Countries

79 vary greatly in terms of the major cities within their borders. For example, Suriname and

Botswana go the entire 1990 to 2015 period without any cities reaching the 300,000 threshold, suggesting that the socioeconomic opportunities often attributed to life in or proximity to major cities are unavailable to people in those countries. Conversely, China and India have dozens of urban agglomerations large enough to classify as “major cities” throughout the entire time period.

To assess changes in a country’s economic health, I include two variables. First, is a measure of each country’s annual percent change in GDP per capita. This data is made available from the United Nations (UNDESA 2018) and reflects changes in the average citizen’s wealth from one year to the next. Note that UN estimates of GDP per capita are controlled for inflation, held at 2015 US Dollar values. Second, is a measure of annual percent change in a country’s unemployment rate. This measure is based on data collected by the International Labor Organization and made available by the World Bank.

If a country’s economic health is improving, then its annual change in GDP per capita should be positive and its change in unemployment should be negative.

Control variables included in this study account for a range of factors that may influence patterns of civil war. The population variable is an estimate of count of the country’s total population, available through the United Nations Population Prospects

(2018). This accounts for the positive relationship between population and conflict found in other studies. To account for wide variation across countries, I include a natural log transformation of this variable in the regression models. Development is measured in terms of GDP per capita, also based on data from the United Nations (UNDESA 2018).

Inclusion of this variable addresses the interrelationship between underdevelopment and

80 conflict, thoroughly discussed in the conflict literature (Collier 2003; Braithwaite, Hegre

& Sambanis 2006; Dasandi, & Hudson 2016). To account for wide variation across countries, I include a natural log transformation of both the population and GDP per capita variables in the regression models.

Democracy is represented with a dummy variable indicating whether a country’s Polity IV score is six or higher. This coding scheme is based on methods used elsewhere in the civil war literature and controls for the presence of democratic institutions which may allow aggrieved citizens to more easily achieve political changes through non-violent means. Mountainous terrain is a logged continuous variable indicating the percentage of a country’s territory that is mountainous. These statistics are based on data published by Fearon and Laitin (2003), who argue that because it is difficult for armies to patrol mountainous areas, larger expanses of such terrain afford rebels a convenient shelter from which to operate. Excluded population is operationalized as the percent of a country’s population that is excluded from power on the basis of ethnic identity. This variable is drawn from the EPR3 dataset (Wimmer, Cederman, &

Min 2009) and accounts for the presence of politically-salient ethnic divisions, which may generate enough grievances to spark internal violence, a phenomenon identified in other studies (Toft 2002, Ellingsen 2000). I account for the potential impact of past conflict onsets on a country’s conflict propensity by including a version of the urban conflicts event count variable, lagged by one year.

Because the outcome in this analysis is a count of events for which the overwhelming majority of observations have a value of zero, I employ zero-inflated negative binomial regression to analyze the effect of urbanization on urban conflict event

81 counts. In models relating to my third hypothesis, I utilize a series of interactions between urbanization rate and the two measures of economic health: changes in GDP per capita and unemployment. All explanatory variables are lagged by one year to better reflect their potential impact on the occurrence of armed conflict. For each model, I cluster standard errors by country in order to examine the urbanization-conflict relationship on a country-by-country basis. I present several models in this study. To further assess the validity of my findings, I conduct a series of robustness checks which are reported in detail in Section 4.5.

Country-level modeling for tests of the first and second hypotheses is based on the following equation:

푉 = 훽 + 훽푢푟푏푎푛푖푧푎푡푖표푛() + 푋() + 휀

In this formula, Vit is the risk of violence in country i and year t, β0 is the intercept, urbanization is the urbanization level or number of major cities in each country, X is a suite of control variables and εit is the error term for a country i in year t. Tests of

Hypothesis 3 focuses on the conditioning effect of changing economic factors on the urbanization-conflict relationship and is therefore represented with the following equation:

푉 = 훽 + 훽 푢푟푏 푟푎푡푒() ∗ 훽 ln (∆ 푒푐표푛 푐표푛푑푖푡푖표푛푠) + 푋() + 휀

Where Vit is the risk of violence in country i and year t, β0 is the intercept, urbanization rate is the annual change in the percent of a country’s population living in urban areas, ∆ econ conditions is the country’s annual change in unemployment or GDP per capita, X is a suite of control variables and εit is the error term for a country i in year t.

4.4. Results & Discussion

Findings from the zero-inflated negative regression models do not suggest support for my first hypothesis that more urbanized societies should experience lower levels of armed conflict. As shown in Table 1 and Table 2, there is no statistically significant relationship between urbanization level and urban-based armed conflict. This is the case both for less intense conflict events yielding as few as one battle death and for more intense conflict events yielding twenty-five or more fatalities.35 It also is the case for both the inflate stage and the counts stage of the regression, meaning that at the country level, urbanization is not a meaningful predictor of whether any conflict events will occur or the number of conflict events that will occur in a country unlucky enough to experience armed conflict.

35 Note that this trend remains the same even when conflict event counts are aggregated to include incidents with no fatalities.

Table 1. Zero-Inflated Negative Binomial Regression Results

Count of Urban Conflict Events Model 1 – Fatality Level 1+ Model 2 – Fatality Level 1+ (count equation) Coefficient Std. Error Coefficient Std. Error

Urb Level ln(t-1) 0.204 (0.268) Urb Rate (t-1) -0.065 (0.043) Pop ln(t-1) 0.191** (0.082) 0.192** (0.079) GDP Per Capita ln(t-1) 0.060 (0.145) 0.070 (0.113) Excluded Pop ln(t-1) 0.088 (0.358) 0.166 (0.376) Mountainous Terrain (t-1) 0.088 (0.099) 0.079 (0.093) Democracy (t-1) -0.279 (0.260) -0.294 (0.267) Lagged DV 0.016*** (0.003) 0.017*** (0.003) Constant -0.909 (1.187) -0.065 (1.184)

Probability of Urban Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Urb Level ln(t-1) 0.176 (0.403) Urb Rate (t-1) -0.069 (0.057) Pop ln(t-1) -0.271*** (0.105) -0.264*** (0.105) GDP Per Capita ln(t-1) 0.231 (0.161) 0.236* (0.143) Excluded Pop ln(t-1) -0.558 (0.635) -0.477 (0.644) Mountainous Terrain (t-1) 0.039 (0.128) 0.024 (0.125) Democracy (t-1) 0.346 (0.291) 0.333 (0.291) Lagged DV -2.128* (1.239) -2.133* (1.094) Constant 2.453 (1.581) 3.220** (1.465)

N 2779 2779 BIC 7147.3 7143.7 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Table 2. Zero-Inflated Negative Binomial Regression Results

Count of Urban Conflict Events Model 3 – Fatality Level 25+ Model 4 – Fatality Level 25+ (count equation) Coefficient Std. Error Coefficient Std. Error

Urb Level ln(t-1) 0.106 (0.270) Urb Rate (t-1) 0.018 (0.037) Pop ln(t-1) -0.011 (0.096) -0.012 (0.010) GDP Per Capita ln(t-1) 0.254** (0.121) 0.290*** (0.098) Excluded Pop ln(t-1) -0.057 (0.217) -0.100 (0.222) Mountainous Terrain (t-1) 0.031 (0.095) 0.019 (0.085) Democracy (t-1) -0.803** (0.344) -0.787** (0.333) Lagged DV 0.078*** (0.018) 0.078*** (0.018) Constant -1.018 (1.301) -0.912 (1.191)

Probability of Urban Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Urb Level ln(t-1) -0.085 (0.451) Urb Rate (t-1) 0.027 (0.079) Pop ln(t-1) -0.316** (0.124) -0.317** (0.124) GDP Per Capita ln(t-1) 0.647*** (0.185) 0.626*** (0.166) Excluded Pop ln(t-1) -0.702 (0.620) -0.726 (0.632) Mountainous Terrain (t-1) -0.142 (0.112) -0.134 (0.109) Democracy (t-1) 0.049 (0.447) 0.061 (0.441) Lagged DV -2.233*** (0.374) -2.232*** (0.369) Constant 1.699 (1.938) 1.454 (1.699)

N 2779 2779 BIC 2871.5 2871.7

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

The results for models with the city count variable are presented in Table 3 below.

They suggest a moderate level of support for my second hypothesis that having major cities nearby should correspond with lower levels of armed conflict. However, this is only the case for more intense conflicts, since the city count variable did not achieve statistical significance in Model 5, which accounts for events at or above the one-fatality threshold. As noted in the results for Model 6, countries with more major cities are less

85 likely to experience high-intensity conflict events than are countries with few or no major cities. Furthermore, the results show that amongst countries that experience high-intensity conflict events, those with larger numbers of major cities tend to experience fewer conflict events.

Figure 2 presents predictive margins for Model 6, showing estimated numbers of high-fatality urban conflict events a country can expect, given the number of major cities within its territory. If a developing country is non-democratic – as are the majority of countries in this sample – and experienced no high-fatality conflict events in the previous year, it can expect to experience 0.235 high-fatality events, given that the country has no cities of at least 300,000 people. The predicted number of high-fatality conflict events for such a country rises slightly to above 0.24 when there are twenty to forty major cities, and then falls slightly for each additional city. The last statistically significant number of major cities is 90, at which point the country can expect about 0.19 fatalities. The predicted number of events drops below 0.01 the country gets to 240 cities, although the effect of major cities on armed conflict is statistically insignificant at that level. While the substantive effect major cities have on severe conflict events are small at the national level, the overall negative trend is important to note as it suggests that cities may play an ameliorative role in armed conflict. This is true even given the curvilinear slope of the predictive margins.

Table 3. Zero-Inflated Negative Binomial Regression Results

Count of Urban Conflict Events Model 5 – Fatality Level 1+ Model 6 – Fatality Level 25+ (count equation) Coefficient Std. Error Coefficient Std. Error

City Count (t-1) 0.000 (0.007) -0.025*** (0.006) Pop ln(t-1) 0.185 (0.131) 0.169 (0.106) GDP Per Capita ln(t-1) 0.106 (0.118) 0.276*** (0.104) Excluded Pop ln(t-1) 0.150 (0.388) -0.268 (0.229) Mountainous Terrain (t-1) 0.058 (0.105) -0.030 (0.090) Democracy (t-1) -0.219 (0.285) -0.673* (0.346) Lagged DV 0.018*** (0.003) 0.076*** (0.021) Constant -0.447 (1.402) -2.196* (1.186)

Probability of Urban Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

City Count (t-1) 0.006 (0.004) -0.031** (0.014) Pop ln(t-1) -0.379** (0.149) -0.257 (0.163) GDP Per Capita ln(t-1) 0.277* (0.144) 0.674*** (0.180) Excluded Pop ln(t-1) -0.444 (0.639) -0.695 (0.619) Mountainous Terrain (t-1) 0.014 (0.129) -0.155 (0.111) Democracy (t-1) 0.373 (0.294) 0.197 (0.464) Lagged DV -2.075 (1.321) -2.268*** (0.358) Constant 3.769** (1.877) 0.841 (2.223)

N 2729 2729 BIC 6922.6 2736.3

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Figure 2. Predicted Conflict Event Counts by Number of Major Cities

Predictive Margins with 95% CIs .5 .4 .3 .2 .1 0 Predicted Number of EventsConflict Number Predicted Urban Fatal 0 100 200 300 Count of Major Cities, lagged

Table 4 displays findings for the models with interactive terms in which I test the logics of Hypothesis 3, that conflict should worsen when urbanization coincides with a country’s economic decline. In Model 7, I interact urbanization rate with changes in unemployment and in Model 8, I interact urbanization rate with changes in GDP per capita. The findings do not indicate support for my third hypothesis. By itself, increasing unemployment is correlated with an increase in the risk of a developing country experiencing armed conflict in urban areas. However, changes in unemployment produce no statistically significant conditioning effect on the relationship between urbanization and armed conflict. Likewise, declines in GDP per capita are associated with a larger number of conflict events in urban areas but do not significantly condition the relationship between urbanization and conflict.

Table 4. Zero-Inflated Negative Binomial Regression Results

Count of Urban Conflict Events Model 7 – Fatality Level 1+ Model 8 – Fatality Level 1+ (count equation) Coefficient Std. Error Coefficient Std. Error

Urb Rate (t-1) -7.717 (9.781) -0.077 (2.852) Pop ln(t-1) 0.242*** (0.077) 0.213*** (0.078) GDP Per Capita ln(t-1) -0.058 (0.121) 0.092 (0.116) Excluded Pop ln(t-1) 0.052 (0.415) 0.018 (0.376) Mountainous Terrain (t-1) 0.062 (0.096) 0.068 (0.099) Democracy (t-1) -0.095 (0.275) -0.223 (0.279) Δ Unemployment (t-1) 0.033 (0.027) Δ GDP Per Cap (t-1) -1.111** (0.458) Urb Rate (t-1) * Δ Unempl (t-1) 0.600 (0.801) Urb Rate (t-1) * Δ GDP Per Cap (t-1) -7.472 (19.180) Lagged DV 0.016*** (0.003) 0.017*** (0.003) Constant -0.052 (1.172) -0.634 (1.118)

Probability of Urban Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Urb Rate (t-1) -5.572 (22.99) -21.87 (13.800) Pop ln(t-1) -0.311*** (0.106) -0.306*** (0.108) GDP Per Capita ln(t-1) 0.205 (0.146) 0.211 (0.152) Excluded Pop ln(t-1) -0.264 (0.627) -0.297 (0.645) Mountainous Terrain (t-1) -0.015 (0.125) 0.006 (0.128) Democracy (t-1) 0.273 (0.293) 0.312 (0.308) Δ Unemployment (t-1) 1.580* (0.838) Δ GDP Per Cap (t-1) -2.245 (1.605) Urb Rate (t-1) * Δ Unempl (t-1) -1.714 (1.842) Urb Rate (t-1) * Δ GDP Per Cap (t-1) 63.18 (73.50) Lagged DV -2.066* (1.081) -2.535 (1.284) Constant 3.943** (1.683) 4.115** (1.623)

N 2675 2674 BIC 6755.5 6762.4

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

4.5. Robustness Checks

In order to more thoroughly assess the validity of my hypotheses and provide greater context to my findings, I run a series of additional tests, which I present in this section. I

89 begin with Model 9 and Model 10, presented in Table 5 below. These models essentially replicate the analysis done in Models 1 and 2, using rural conflict event counts instead of urban ones. If urbanization had a meaningful effect on national-level trends in conflict violence, then one may have expected it to impact violence in both urban and in rural locations. As the results presented in Table 5 indicate, is not significantly associated with the risk rural-based conflict when analyzed at the country level. However, higher rates of urbanization are associated with a lower risk of high-fatality conflict in rural areas, a finding significant at the 90% confidence level. Urbanization rate is not a significant predictor of the number of high-fatality conflict events that may occur in rural areas.

Nevertheless, the negative relationship between urbanization rate and conflict incidence suggests that urbanization – particularly that resulting from internal migration – may serve as a relief valve for rural communities.

Table 5. Zero-Inflated Negative Binomial Regression Results

Count of Rural Conflict Events Model 9 – Fatality Level 1+ Model 10 – Fatality Level 1+ (count equation) Coefficient Std. Error Coefficient Std. Error

Urb Level ln(t-1) -0.006 (0.748) Urb Rate (t-1) -3.484 (2.397) Pop ln(t-1) 0.350*** (0.124) 0.383*** (0.125) GDP Per Capita ln(t-1) 0.060 (0.099) 0.019 (0.111) Excluded Pop ln(t-1) 0.292 (0.490) 0.324 (0.495) Mountainous Terrain (t-1) 0.211* (0.120) 0.217 (0.132) Democracy (t-1) -0.107 (0.248) -0.108 (0.237) Lagged DV 0.008 (0.005) 0.007 (0.005) Constant -1.817 (1.217) -1.800 (1.230)

Probability of Rural Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Urb Level ln(t-1) -0.662 (0.443) Urb Rate (t-1) -19.95* (10.82) Pop ln(t-1) -0.164 (0.110) -0.214** (0.109) GDP Per Capita ln(t-1) 0.356** (0.144) 0.304** (0.145) Excluded Pop ln(t-1) -0.430 (0.542) -0.194 (0.590) Mountainous Terrain (t-1) -0.050 (0.115) -0.036 (0.118) Democracy (t-1) 0.485 (0.315) 0.337 (0.312) Lagged DV -2.699*** (0.690) -2.670*** (0.641) Constant 1.420 (1.480) 2.619* (1.521)

N 2783 2675 BIC 8082.4 7672.7

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Next, I assess my first hypothesis using an alternative operationalization of civil conflict. Instead of analyzing the influence of urbanization on conflict events, I instead consider civil conflict onset as the outcome. For Models 11-18, presented in Tables 6 and

7, I use dummy indicators for whether a civil conflict onset has occurred. Models 11-14 analyze the effect of urbanization level on conflict onset while Models 14-18 analyze the effect or urbanization rate on conflict onset. While urbanization rates are not the key

91 focus of my first hypothesis, the regression models in Table 7 are intended to shed more light on recent claims by various scholars that urbanization is a major risk factor for armed conflict. For these models, civil conflict onsets are identified as episodes of intrastate conflict in which fighting between government and a rebel group has resulted in twenty-five or more fatalities in a single year. Onset is measured as the first year in which violence reached the twenty-five battle-death threshold, with subsequent years of conflict dropped from the analysis. As the outcome variable is binary, I use logistic regression models to analyze the data.

Data for conflict onsets are taken from ETH Zurich’s GROWUP dataset (Girardin et al. 2015), which is based on data from the UCDP/PRIO Armed Conflict Dataset

(ACD) (Pettersson and Eck 2018; Gleditsch et al. 2002). The models in Tables 6 and 7 consider instances of civil conflict onset according to the conflicts’ contexts, whether they were fought primarily over territorial or governmental issues and whether the first fatal event in an armed conflict was in an urban or rural location. The distinction between territorial and governmental conflict is relevant because prior research suggests that territorial conflict is generally associated with rural areas and governmental conflict is more common in urban areas (Buhaug & Rød 2006) and is determined by the type of incompatibility associated with the conflict as recorded in the ACD and GROWup data.

The distinction between urban and rural onsets is determined by whether the first fatal event associated with an armed conflict episode occurred in a rural or urban location. As with the conflict events data used elsewhere in this chapter, designation as urban is based on whether the conflict’s first event occurred in a city, town, municipality, or suburb, as

92 per the place name listed for an event in the “where_coordinates” column of the GED.

Any event not containing these keywords in its place name is designated as rural.

Control variables included in the conflict onset models are like those used in the zero-inflated negative binomial models, with two important exceptions. First, as conflict- stricken countries rarely experience multiple successive years of conflict recurrence, it does not make sense to include a lagged version of the dependent variable as a way of accounting for time dependence in countries’ conflict behaviors. Instead, I include a war history variable which is simply a count of the number of previous intrastate conflicts a country experienced. This count does not discriminate between conflicts based on their associated incompatibilities or onset settings and accounts for countries conflict histories going back to the year 1946, where the GROWUP dataset’s records begin. Additionally, I include a “multiple conflicts” variable, which provides a count of the number of separate armed conflicts ongoing within a country. Together, these variables account for the possibility that conflict onset may be related to or triggered by conflict experiences with other conflicts, either in the past or concurrently with a present conflict. As with the count models, explanatory variables in the logistic regressions are lagged by one year and logged, where appropriate to deal with overdispersion.

As shown in Table 6, countries whose populations are more concentrated in urban locations are note more likely to see armed conflict in urban areas. This null finding provides some hope that despite discussions of the developing world’s cities as powder kegs of political violence, populations’ shifts toward urban areas is not associated with armed conflict in those areas. As a country’s population becomes more concentrated in

93 cities, violence throughout periods of conflict does not tend to geographically cluster in urban areas.

Despite discussions about conflict over governmental incompatibilities being more common in urban areas, it does not appear that countries become more likely to experience such conflict onsets as they become more urban. Nor does rural conflict onset or onsets of conflict relating to territorial incompatibilities become more or less likely as a society urbanizes. Furthermore, it is important to note that while urbanization level is associated with higher risks of urban-based conflict onset, urbanization level is not significantly associated with any type of civil conflict onset, at least when measured at the country-level. Results for Models 14-18 indicate that a faster pace of urbanization does not come with a higher risk of conflict, regardless of the setting or incompatibilities associated with the onset.

Table 6. Logistic Regression Results

DV: Civil Conflict Onset Rur Onset Urb Onset Terr Onset Gov Onset (Various Types) Model 11 Model 12 Model 13 Model 14

Urb Level ln(t-1) -0.396 0.670 -0.076 0.200 (0.272) (0.413) (0.312) (0.384) Pop ln(t-1) 0.166 0.157 0.184 0.129 (0.134) (0.101) (0.114) (0.133) GDP Per Capita lnt-1) -0.162 -0.465** -0.070 -0.581*** (0.141) (0.215) (0.175) (0.199) Excluded Pop ln(t-1) 0.523 0.219 0.278 0.851* (0.635) (0.725) (0.472) (0.461) Mountainous Terrain ln(t-1) 0.015 0.001 -0.025 0.102 (0.131) (0.114) (0.155) (0.115) Democracy (t-1) -0.544* -0.562 -0.683* -0.692** (0.326) (0.360) (0.372) (0.349) Multiple Conflicts (t-1) 0.504** 0.652** 0.389* -0.135 (0.206) (0.254) (0.223) (0.238) War History (t-1) 0.254*** 0.159** 0.245*** 0.159** (0.059) (0.074) (0.060) (0.072) Constant -2.810* -4.378*** -4.750** -1.630 (1.705) (1.682) (1.880) (1.816) N 2410 2384 2427 2387 BIC 737.5 641.9 709.0 687.2

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Table 7. Logistic Regression Results

DV: Civil Conflict Onset Rur Onset Urb Onset Terr Onset Gov Onset (Various Types) Model 15 Model 16 Model 17 Model 18

Urb Rate ln(t-1) 0.005 -0.119 -0.122 0.033 (0.092) (0.090) (0.118) (0.078) Pop ln(t-1) 0.178 0.156 0.221* 0.112 (0.138) (0.105) (0.119) (0.132) GDP Per Capita lnt-1) -0.311** -0.181 -0.338** -0.500*** (0.125) (0.167) (0.165) (0.160) Excluded Pop ln(t-1) 0.491 0.323 0.334 0.804* (0.625) (0.504) (0.725) (0.457) Mountainous Terrain ln(t-1) 0.047 -0.055 -0.036 0.089 (0.136) (0.107) (0.156) (0.111) Democracy (t-1) -0.548* -0.591 -0.718* -0.691** (0.333) (0.363) (0.386) (0.345) Multiple Conflicts (t-1) 0.521*** 0.594** 0.350 -0.136 (0.198) (0.243) (0.213) (0.233) War History (t-1) 0.246*** 0.179** 0.245*** 0.164** (0.056) (0.074) (0.056) (0.072) Constant -3.446** -2.495* -4.327** -1.488 (1.535) (1.447) (1.681) (1.576)

N 2410 2384 2427 2387 BIC 739.1 642.3 706.2 687.4

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

One of the problems with using national-level data on urbanization is that it is highly dependent on definitions of “urban” which are developed independently by each country’s government and often vary greatly from one country to the next. To avoid inaccuracies that may arise from having multiple standards for measuring this study’s key independent variable, it is useful to run additional tests using an indicator for urbanization which does not come with a separate standard for each country. Best, Jones, and Rogers

(1974) suggest population density as a more objective measure or proxy for urbanization.

A characteristic common to nearly all conceptualizations of cities is that they are

96 necessarily more populated than rural areas, condensing larger numbers of people into smaller areas of physical space. Urban areas therefore have higher population densities than do rural ones and because measurement of population density is straightforward – simply dividing the size of a population by the land area that contains it – including population density as a proxy for urbanization gets around definitional disparities. In

Models 19 and 20 below, I include zero-inflated negative binomial regressions in which the dependent variable is conflict event counts involving at least one fatality and urbanization is proxied with population density. Population density is calculated by dividing the country’s total population size by the number of square kilometers of land comprising the country’s territory. This is based on population data from the United

Nations (UNDESA 2018) and measurements of land area as reported in the CIA World

Factbook.36 A natural log transformation of population density is used as a proxy for urbanization level in Model 19 and percent change in annual population density measurements is used as a proxy for urbanization rate in Model 20.

As shown in Table 8, urbanization measured with the population density approach is not significantly correlated with country-level counts of armed conflict events. This is true both for the urbanization level and rate proxies in both the inflate and count stages of the regression models. This means that population density and changes in population density are not meaningfully associated with either the risk of a fatal conflict event occurring or the number of such events that occur in a country. While the coefficients for

36 Measurements of countries’ areas are taken primarily from the 2019 edition of the World Factbook. Most countries’ landmasses do not change at all over the 1990-2015 time period. However, several countries in the sample spilt during the time period under consideration, resulting in changes in their landmasses over time. For example, Sudan split in 2011 when South Sudan became an independent country. Where country’s landmasses changed, measurements for period prior to the split are based on measurements from the 1990 edition of the CIA World Factbook. Measurements for the period afterward come from the 2019 edition.

97 the urbanization proxies are in the negative direction, as my theory predicts, these findings suggest that my first hypothesis is unsupported, at least when studying country- level trends.

Table 8. Zero-Inflated Negative Binomial Regression Results

Count of Urban Conflict Events Model 19 – Fatality Level 1+ Model 20 – Fatality Level 1+ (count equation) Coefficient Std. Error Coefficient Std. Error

Pop Den ln(t-1) -0.406 (0.404) Δ Pop Den (t-1) -0.301 (2.806) Pop ln(t-1) 0.201** (0.082) 0197** (0.080) GDP Per Capita ln(t-1) 0.116 (0.110) 0.081 (0.120) Excluded Pop ln(t-1) 0.046 (0.373) 0.198 (0.394) Mountainous Terrain (t-1) 0.067 (0.086) 0.058 (0.100) Democracy (t-1) -0.218 (0.282) -0.207 (0.284) Lagged DV 0.017*** (0.003) 0.017*** (0.003) Constant -0.578 (1.176) -0.413 (1.145)

Probability of Urban Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Pop Den ln(t-1) -0.623 (0.504) Δ Pop Den (t-1) -19.52 (13.48) Pop ln(t-1) -0.255** (0.103) -0.308*** (0.107) GDP Per Capita ln(t-1) 0.280** (0.139) 0.197 (0.149) Excluded Pop ln(t-1) -0.567 (0.637) -0.249 (0.636) Mountainous Terrain (t-1) 0.012 (0.126) -0.003 (0.127) Democracy (t-1) 0.375 (0.295) 0.327 (0.305) Lagged DV -2.076* (1.160) -2.483* (1.459) Constant 2.704* (1.438) 4.159*** (1.600)

N 2783 2675 BIC 7147.2 6746.5 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

In Models 19 and 20, I again use changes in population density to proxy for urbanization rate, this time interacting the urbanization rate proxy with changes in unemployment and GDP per capita to further evaluate my third hypothesis. Models 21 and 22 test my theory using urban-based conflict events, while Models 23 and 24 add context to the results by considering rural-based conflict events. As shown in the results for all four of these models, changes in a country’s economic health do not appear to interact with urbanization rates to meaningfully impact the occurrence of conflict events or the number of such events that may occur. This is the case both for urban- and rural- based conflict events.

Table 9. Zero-Inflated Negative Binomial Regression Results

Count of Urban Conflict Events Model 21 – Fatality Level 1+ Model 22 – Fatality Level 1+ (count equation) Coefficient Std. Error Coefficient Std. Error

Δ Pop Den (t-1) -0.949 (2.488) -0.077 (2.852) Pop ln(t-1) 0.191** (0.080) 0.213*** (0.078) GDP Per Capita ln(t-1) 0.061 (0.121) 0.092 (0.116) Excluded Pop ln(t-1) 0.164 (0.401) 0.018 (0.376) Mountainous Terrain (t-1) 0.043 (0.101) 0.068 (0.099) Democracy (t-1) -0.201 (0.293) -0.223 (0.279) Δ Unemployment (t-1) -1.052 (0.942) Δ GDP Per Cap (t-1) -1.111* (0.474) Δ Pop Den (t-1) * Δ Unempl (t-1) 21.61 (31.00) Δ Pop Den (t-1) * Δ GDP Per Cap (t-1) -7.472 (19.18) Lagged DV 0.017*** (0.003) 0.017*** (0.003) Constant -0.167 (1.137) -0.634 (1.118)

Probability of Urban Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Δ Pop Den (t-1) -19.520 (13.070) -21.870 (13.800) Pop ln(t-1) -0.311*** (0.108) -0.306*** (0.108) GDP Per Capita ln(t-1) 0.209 (0.147) 0.211 (0.152) Excluded Pop ln(t-1) -0.376 (0.651) -0.297 (0.645) Mountainous Terrain (t-1) 0.003 (0.133) 0.006 (0.128) Democracy (t-1) 0.349 (0.316) 0.312 (0.308) Δ Unemployment (t-1) 0.626 (1.519) Δ GDP Per Cap (t-1) -2.245 (1.605) Δ Pop Den (t-1) * Δ Unempl (t-1) -42.340 (62.580) Δ Pop Den (t-1) * Δ GDP Per Cap (t-1) 63.18 (73.500) Lagged DV -2.731* (1.487) -2.535** (1.284) Constant 4.101*** (1.548) 4.115** (1.623)

N 2582 2674 BIC 6514.9 6762.4

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

100

Table 10. Zero-Inflated Negative Binomial Regression Results

Count of Rural Conflict Events Model 23 – Fatality Level 1+ Model 24 – Fatality Level 1+ (count equation) Coefficient Std. Error Coefficient Std. Error

Urb Rate (t-1) -0.016 (0.047) -0.011 (0.045) Pop ln(t-1) 0.393*** (0.128) 0.390*** (0.123) GDP Per Capita ln(t-1) 0.001 (0.117) 0.026 (0.114) Excluded Pop ln(t-1) 0.320 (0.499) 0.336 (0.492) Mountainous Terrain (t-1) 0.212 (0.135) 0.217* (0.129) Democracy (t-1) -0.110 (0.241) -0.099 (0.241) Δ Unemployment (t-1) -0.744 (0.476) Δ GDP Per Cap (t-1) -0.291 (1.097) Urb Rate (t-1) * Δ Unempl (t-1) 0.050 (0.101) Urb Rate (t-1) * Δ GDP Per Cap (t-1) -0.088 (0.177) Lagged DV 0.007 (0.005) 0.007 (0.005) Constant -1.735 (1.349) -1.947 (1.344)

Probability of Rural Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Urb Rate (t-1) -0.072 (0.067) -0.061 (0.064) Pop ln(t-1) -0.220* (0.113) -0.197* (0.110) GDP Per Capita ln(t-1) 0.309** (0.148) 0.340** (0.155) Excluded Pop ln(t-1) -0.369 (0.559) -0.312 (0.579) Mountainous Terrain (t-1) -0.040 (0.116) -0.008 (0.117) Democracy (t-1) 0.321 (0.319) 0.356 (0.314) Δ Unemployment (t-1) -0.041 (0.486) Δ GDP Per Cap (t-1) -0.104 (2.050) Urb Rate (t-1) * Δ Unempl (t-1) -0.036 (0.177) Urb Rate (t-1) * Δ GDP Per Cap (t-1) -0.325 (0.372) Lagged DV -2.650*** (0.735) -2.695*** (0.621) Constant 2.516* (1.513) 1.980 (1.555)

N 2578 2670 BIC 7438.6 7702.8

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

I further assess my expectations from Hypothesis 3 using logistic regression models to analyze the interactive effects of urbanization and economic health on conflict onset. This includes the same four types of conflict onset used earlier in this section regarding conflicts’ incompatibilities as the setting of their first fatal conflict events.

101

Models 25-28, presented in Table 11, include an interaction between urbanization rate and changes in unemployment. Likewise, Table 12 displays Models 29-32, which include an interaction between urbanization rate and changes in GDP per capita.

In these models, it appears that the relationship between urbanization and conflict onset is not significantly affected by changes in a country’s unemployment levels.

Regardless of whether conflict onset is based on territorial or governmental incompatibilities or whether the violence begins in an urban or rural area, these results show that an interaction of changing unemployment and urbanization do not seem to impact it. This is notably different from the findings in Model 7 that the risk of an urban- based conflict event occurring in a developing country is greater when urbanization coincides with rising unemployment. However, changes in GDP per capita appear to have a greater impact on the relationship between urbanization and conflict. As shown in the results for Model 32, the onset of conflict over governmental incompatibilities are less likely in fast-urbanizing countries where income levels are rising. These findings suggest support for Hypothesis 3. They also differ from the null findings for Model 8, which considers a count of urban-based conflict events. Although the interaction between unemployment rate and changes in GDP per capita displayed the expected negative sign in Model 8, it was not statistically significant, unlike the interaction terms in Models 30 and 32.

102

Table 11. Logistic Regression Results

DV: Civil Conflict Onset Rur Onset Urb Onset Terr Conf Gov Conf (Various Types) Model 25 Model 26 Model 27 Model 28

Urb Rate (t-1) 0.081 -0.063 -0.058 0.068 (0.096) (0.086) (0.131) (0.075) Pop ln(t-1) 0.224 0.190* 0.238* 0.123 (0.156) (0.112) (0.130) (0.135) GDP Per Capita lnt-1) -0.298** -0.341* -0.178 -0.508*** 0.132) (0.174) (0.188) (0.173) Excluded Pop ln(t-1) 0.774 0.263 0.599 0.974** (0.622) (0.473) (0.724) (0.432) Mountainous Terrain ln(t-1) 0.060 -0.086 -0.001 0.076 (0.131) (0.010) (0.153) (0.107) Democracy (t-1) -0.430 -0.613 -0.674* -0.837** (0.355) (0.378) (0.403) (0.360) Multiple Conflicts (t-1) 0.848*** 1.090*** 0.441 0.043 (0.286) (0.315) (0.312) (0.249) War History (t-1) 0.222*** 0.163** 0.223*** 0.137* (0.063) (0.077) (0.059) (0.075) Δ Unemployment (t-1) 0.046 0.146 0.185 -1.111 (0.250) (0.252) (0.313) (1.463) Urb Rate (t-1) * Δ Unempl (t-1) 0.011 -0.043 -0.066 0.407 (0.156) (0.157) (0.188) (0.430) Constant -4.179** -2.725* -4.596** -1.379 (1.744) (1.478) (1.876) (1.642)

N 2143 2120 2161 2124 BIC 671.1 604.0 663.0 660.7 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Predictive Margins for Model 32 are presented in Figures 3-5. These show when a country’s rate of change in GDP per capita are at their lowest levels – in this sample that means an annual change of -0.67% – a country’s risk of conflict over governmental incompatibilities declines as urbanization rates increase. This means going from a risk of about 91% when the urbanization rate is at -7% to a risk of 54% when the urbanization rate is zero, to just under 33% when the urbanization rate is 3%. Predictive margins for

103 higher urbanization rates are statistically insignificant under these conditions. When GDP per capita remains steady in such a country, the risk of conflict involving governmental incompatibilities is about 1% when urbanization rates are at their lowest levels, 3% when the urbanization rate is zero, and 12% when the urbanization rate is 13%. Conflict risk estimates become statistically insignificant when the urbanization rate goes beyond that level. When the annual GDP per capita is increasing by 123%, the fastest rate seen in this sample, the estimate of conflict risk by urbanization rate is statistically insignificant until urbanization rate approaches extreme levels. For example, the estimated risk of governmental conflict onset is 90% when the country has an urbanization rate of 14% and nearly 99.7% when the urbanization rate reaches 18%.

104

Table 12. Logistic Regression Results

DV: Civil Conflict Onset Rur Onset Urb Onset Terr Conf Gov Conf (Various Types) Model 29 Model 30 Model 31 Model 32

Urb Rate (t-1) 0.076 0.009 -0.057 0.124* (0.087) (0.103) (0.125) (0.069) Pop ln(t-1) 0.205 0.214* 0.243* 0.145 (0.150) (0.111) (0.124) (0.135) GDP Per Capita lnt-1) -0.279** -0.243 -0.168 -0.404** (0.126) (0.156) (0.179) (0.164) Excluded Pop ln(t-1) 0.510 0.179 0.391 0.819* (0.604) (0.510) (0.730) (0.456) Mountainous Terrain ln(t-1) 0.061 -0.060 0.001 0.098 (0.135) (0.104) (0.159) (0.112) Democracy (t-1) -0.497 -0.463 -0.715* -0.713** (0.345) (0.355) (0.404) (0.342) Multiple Conflicts (t-1) 0.924*** 0.999*** 0.467 0.039 (0.281) (0.284) (0.317) (0.234) War History (t-1) 0.225*** 0.168** 0.230*** 0.143* (0.060) (0.076) (0.057) (0.075) Δ GDP Per Cap (t-1) -0.846 -5.393*** -2.212 -5.846*** (1.431) (1.587) (1.672) (1.623) Urb Rate (t-1) * Δ GDP Per Cap (t-1) 0.318 0.275 -0.0435 0.667** (0.310) (0.277) (0.339) (0.267) Constant -4.057** -3.946** -4.646** -2.558 (1.707) (1.548) (1.842) (1.708)

N 2213 2188 2231 2192 BIC 697.4 605.1 683.7 666.1

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

105

Figures 3-5. Predicted Risk of Governmental Conflict Onset by Δ GDP PC

Predictive Margins with 95% CIs, Δ GDP PC at Min Predictive Margins with 95% CIs, Δ GDP PC at 0 .6 1.5 .4 1 .2 .5 0 0 Probability of Governmental Conflict Onset Governmental Conflict Probability of Onset Governmental Conflict Probability of -.2 -10 0 10 20 -10 0 10 20 Urbanization Rate, lagged Urbanization Rate, lagged

Predictive Margins with 95% CIs, Δ GDP at Max 2 1 0 Probability of Governmental Conflict Onset Conflict of Governmental Probability -1 -10 0 10 20 Urbanization Rate, lagged

4.6. Conclusion

In this chapter I have presented a country-level analysis of my theoretical expectations regarding the relationship between urbanization and armed conflict. The goal of this chapter has been to provide an initial analysis of my theory and to identify trends which can be further analyzed at lower levels of analysis in Chapters 5 and 6. Given discussions of urbanization often focus on the concept as a country-level trend and data collection relating to urbanization is conventionally done at the country-level, the country-level analysis is a natural starting point for this analysis.

The empirical analysis conducted in this chapter does not support my first hypothesis at all. However, it does suggest a moderate degree of support for my second

106 and third hypotheses. Neither the main models presented in section 4.4 nor the robustness checks in section 4.5 show any significant decline in conflict risk in societies that are more urban. On the contrary, one of the models – Model 13 – demonstrates that more urbanized countries are more likely to experience conflict onsets whose violence begins in urban areas. This suggests that while highly urbanized societies might be more likely than their more rural counterparts to experience a conflict that begins in an urban setting, fatal events within a given conflict are not necessarily more likely to occur in urban areas.

The evidence is somewhat supportive of my second hypothesis, as a higher number of major cities is indeed correlated with a lower risk of high-fatality armed conflict events. This finding does not hold when lower-fatality events are included in the analysis. These findings suggest that the potential pacifying effect of urbanization depended, at least somewhat, on the level of violence in question. Lower-intensity violence appears not to vary according to how many cities a country has, even though the chance of experiencing deadlier violence may decline. A larger number of major cities should provide governments with greater tax revenue and citizens with more opportunities to conduct business or to internally migrate in such of a better life. I further explore some of these potential benefits in subsequent chapters, particularly in Chapter 6.

Empirical analysis from both the main models as well as the robustness checks provides moderate support for my theory that the effect of urbanization on conflict is moderated by economic wellbeing. High levels of urbanization combined with worsening economic conditions should generate much higher security risks than one can expect from urbanization in a strong or stable economy. This is bolstered with findings that more urban-based conflict events occur in fast-urbanizing societies with rising unemployment

107 and that rising GDP per capita is associated with lower risk of conflicts over governmental incompatibilities or which begin in urban areas for fast-urbanizing societies. Unemployment and falling incomes indicate financial hardship, making the population of an affected area more vulnerable to whatever negative effects their society may incur through urbanization. While urbanization in a positive economy should not place terrible strains on a population, urbanization in a bad economy may generate grievances that lead to conflict. The findings of this study suggest that this is the case.

A population’s shift toward urban settings is a complex phenomenon, sometimes viewed positively as a catalyst for economic take-off and sometimes negatively as factor that sparks the growth of slums and urban discord. This research suggests that urbanization can fill all these roles and therefore should not be categorized simply as a sign of progress or as an omen of destruction. The country-level findings presented in this chapter show that urbanization is sometimes associated with armed conflict, especially when a country’s economy is deteriorating. In such situations, people in urban areas are likely to feel the sense of deprivation.37 However, declining conflict risk in the face of rising incomes or when more agglomerations grow large enough to count as major cities provides reasons for optimism.

Of course, urbanization and armed conflict are phenomena that do not affect all parts of a country equally. Some parts of a country may be bustling metropolises and others remote villages; some may be bloody warzones and others may be completely untouched by war. For this reason, the trends identified in this chapter require analysis at

37 This point is most closely related to Robert Gurr’s theory of relative deprivation. Refer to Sections 2.5 and 3.4 for more detailed discussion about how and why deprivation may trigger violence, particularly in the context of urbanization.

108 the substate level. The finer-grained assessment accomplished by studying smaller units of analysis will yield a clearer view of an urbanization-conflict nexus. In the following two chapters, I accomplish this, first with a cross-national analysis of developing countries broken down by provinces and grid-cells, second, with a district-level study of

India’s conflict-stricken Red Corridor region.

109

Chapter 5. Global Sub-State Analysis

I present in this study a quantitative subnational analysis, breaking developing countries into their constituent provinces and using georeferenced data to more thoroughly examine the security consequences of urbanization. Urban growth makes cities more politically important but may strain the state's capacity to administer urban areas. While most studies of intrastate conflict focus on national-level conflict trends or on violence near capital cities, there is still much to learn about how demographic changes of a population may lead to geographic changes in conflict events. Urbanization and conflict are both phenomena that tend to vary significantly at subnational levels and the research presented in this chapter should therefore capture the nuance in the relationship between these two factors more effectively than is possible with a country-level study. Consideration of subnational data is therefore crucial to our understanding of how urbanization and urban growth impact conflict trends in urbanizing countries. In this study, I use georeferenced data on civil conflict events to analyze changes in subnational levels of conflict during periods of urban growth. I find empirical support for both hypotheses and provide commentary about their implications for national security policy in urbanizing countries.

5.1. Introduction

In the search for understanding of a possible urbanization-conflict nexus, the need for subnational analysis is based on two key facts. First, civil conflict tends to vary significantly across different parts of the same country. Raleigh et al. (2010) find that on average, armed conflicts only cover 15% of a country’s territory. This means that while

110 some parts of a war-torn country may be devastated by conflict, others may emerge completely unscathed, experiencing conditions quite like peacetime. Even when an area within a state experiences conflict violence, the severity or frequency of the violence may vary substantially from other conflict-affected areas. For example, in their analysis of

Nepal’s decade-long civil war, Do and Iyer (2018) find that while most of Nepal’s districts experienced conflict violence, the number of fatalities as a proportion of the national population vary greatly across districts. Those in the Western side of the country tended to experience the most severe violence, while those in the East experienced more moderate levels. Studies of armed conflict have attributed both social and natural phenomena to internal variations in conflict. For example, subnational risk or levels of conflict violence is found in some countries to correlate with poverty (Tollefsen 2017), government performance (Møller 2018), crop yields (Harari & La Ferrara 2018), and rainfall (Raleigh & Kniveton 2012).

Second, urbanization varies significantly within countries, but there is wide acceptance of the idea that urban areas are necessarily more densely populated than are rural ones.38 As of 2018, World Bank data reveals that 55% of the world’s population is urban, suggesting that people throughout the world inhabit a diverse array of settings.

With a few exceptions for entirely urbanized countries like Singapore or Monaco, countries’ populations are dispersed across urban and rural areas. The coexistence of urban and rural population centers represents subnational variation in countries’

38 The agglomeration of people across a geographic space to form cities necessarily results in higher population density within a given space. For further discussion of this idea, see Frey and Zimmer (2001), Malpezzi (2013), Lewis and Dijkstra and Poelman (2014) who acknowledge the wide-ranging variety of methods for identifying and measuring urban spaces, but emphasize that population density is a foundational characteristic of most conceptualizations of cities. Furthermore, see Best, Jones, and Rogers (1974) for discussion of population density as an alternative means for measuring urbanization.

111 urbanization. Within a country, patterns of urbanization may vary according to specific circumstances, often insofar as they affect people’s livelihoods. For example, coastal areas have long been logistical hubs for seaborne trade, making them logical locations to build cities. Coastal areas are home to most of the world’s major cities, including the gargantuan “megacities” whose populations of ten million or more people are increasingly studied by policymakers and social scientists (Barragán & Andrés 2015).

The literature on urbanization also discusses populations as tending to concentrate in a capital city or in a large “primate” cities. Such work highlights political and economic factors that serve as magnets for resources into key cities, leaving rural areas as well as less-important cities less well-off (Yamashita 2017, Pierskalla 2016). Rates of urban growth are often highest in “per-urban” areas – geographic spaces just outside of major cities – which develop as economic activities intensify near major cities and as a city’s growing population expands, spilling outward (Ravetz, Fertner, & Nielsen 2013).

Subnational variation in urbanization can be attributed to other factors including, but not limited to, human capital development and accumulation in large cities (Flückiger &

Ludwig 2018), disparities between urban and rural wages (de Brauw, Mueller, & Lee

2014; Todaro 1968, 1980), or natural or manmade disasters in rural areas (Marchiori,

Maystadt, & Schumacher 2012). In any event, urbanization levels and rates may vary greatly within a country, making it impossible to get a full view of the country’s urbanization without considering subnational conditions.

112

5.2. Theoretical Contribution

The primary value of this chapter is that it utilizes subnational units of analysis to study the relationship between two phenomena which vary greatly below the country level. The country-year analysis presented in the previous chapter is useful for establishing a baseline of understanding about a possible urbanization-conflict nexus and exploring generalizable trends that can be applied broadly to the developing world. It also takes advantage of the country-year urbanization data most used in large-N cross-national studies of the concept. However, the country-level aggregation obscures the specific points of variation in both urbanization and conflict, somewhat muddling the relationship between the two factors. In this chapter, I aim to provide greater clarity by testing my theory at the subnational level. This chapter includes tests of all three of my hypotheses, capitalizing on the smaller unit of analysis to provide a more refined analysis of each. For reference, these hypotheses are as follows:

 Hypothesis 1: If an area becomes more urbanized, it will enjoy a lower risk of

armed conflict.

 Hypothesis 2: If an area is located closer to a major city or if a nearby major city

is growing at a fast rate, the risk of armed conflict in that area will be lower.

 Hypothesis 3: If an area experiences high rates of urbanization during times of

economic difficulty, its risk of urban intrastate conflicts will increase.

In order to conduct subnational analyses, I divide the developing world into smaller units, namely provinces and grid cells of 0.5 decimal degrees. The core set of models presented in this chapter are based on province-level, a unit of analysis that comes with two advantages. First, provinces are concrete, easily identifiable units. While

113 they may vary in size, shape, population, etc., provinces do not overlap international boundary lines and are generally recognized by governments and citizens alike as being meaningful spatial entities. Second and relatedly, many political administrative decisions are made at subnational levels and the existence of provinces therefore provides a simple but politically relevant way to divide up the world for subnational analysis. Nearly all countries have provinces or some equivalent subnational administrative division.

According to the CIA World Factbook (2019), Singapore is the only country in the world that has no first order administrative divisions. Although there has been a trend over time for countries, to create new sub-national units of administration, the number and composition of provinces or first-order administrative is less subject to dramatic changes than one would find with lower-level units such as districts (Grossman & Lewis 2014).

In a series of robustness checks presented in section 6.6, I present analyses utilizing the PRIO-Grid system of 0.5 decimal degree grid cells, which are approximately

2500 kilometers at the Earth’s equator. As spatial units, grid cells are often smaller than provinces and much more uniform in both size and shape, which adds objectivity to inter- unit comparison. However, unlike provinces, the grid cells are apolitical, their dimensions not matching any

5.3. Research Design

To test my theory, I analyze a sample of province-year observations, drawing on the first administrative levels of developing countries from 1990 to 2015.39 The unit of analysis is

39 Note that temporal ranges differ across the variables used to operationalize the dependent variable in this study. Nightlights data ranges from 1992-2013. The population density and world cities population data range from 1990 to 2015. This accounts for differences in sample size across the statistical models used in this study.

114 province-year, allowing the models to account for sub-national variation in intrastate conflict levels. Provinces are defined as the first-order administrative units as recorded by the Database of Global Administrative Areas (GADM), version 3.4. Provinces included in this study are those associated with independent states as recorded by the Correlates of

War (COW) project, and which are considered developing countries. For the purposes of this study, I identify countries as developing if they are listed as low- or middle-income countries according to the World Bank’s 2020 lending groups scheme.40 The dataset for this study was constructed using the Environmental Systems Research Institute’s

(ESRI’s) ArcGIS software to associate georeferenced variables with GADM-Level 1 administrative units. All geospatial data in this study are processed using the World

Geodetic System 1984 (WGS84) data to define data projections.

Data aggregation at the province level provides at least two advantages. First, because it is a lower level of aggregation than country-level boundaries, it enables one to account for sub-national variations in both demographic conditions and conflict behavior.

Just as civil war does not uniformly affect all parts of a country’s territory, neither does urbanization. Sub-national units of analysis are necessary to account for these facts.

Second, the administrative boundaries represented by the GADM-1 polygons are politically meaningful. Although they provide a lower resolution than is possible with approaches that divide the world into apolitical grid squares, the focus on province boundaries accounts for spaces of subnational governance. Policies implemented by

40 Note that very few countries classified by the World Bank as low- or middle- income in 1990 have moved into the high-income category. Several notable exceptions include Portugal, Saudi Arabia, and Oman. Such countries have been excluded from the sample as they were classified as high-income for a large portion of the 1990-2015 time period.

115 governors and other sub-national political actors may have some bearing on an area’s conflict propensity.

Armed civil conflict is the outcome of interest in this study. I operationalize this variable with events data from the UCDP Georeferenced Event Dataset (UCDP GED), version 19.1 (Sundberg & Melander 2013, Stina 2019). The sample includes fatal intrastate conflict events involving violence between rebel groups and government forces, regardless of which side initiated the fighting. Prior research shows that the severity of intrastate conflict may be worse in settings that are impoverished (Chaudoin, Peskowitz,

& Stanton 2017) or undemocratic (Lacina 2006). As my theoretical arguments expect urbanization to influence conflict levels through its effect on economic and political opportunities, it is also likely that the expected benefits of urbanization should be especially effective in reducing the risk of more severe forms of conflict. I therefore include a low-threshold conflict variable which incorporates all conflict events involving at least one fatality as well as a high-fatality conflict variable which includes only events which generate twenty-five or more fatalities. Fatality levels for each event are determined by the GED’s “best estimate” variable. A cumulative twenty-five battle deaths in a year is the threshold at which the UCDP considers countries to be in a state of civil conflict, so reaching twenty-five fatalities in a single event is quite substantial.

Conflict events are aggregated by province to provide annual event counts for each province belonging to a developing country.

Urbanization is the main independent variable in this study and is operationalized in two ways. The first method uses population density as a proxy for urbanization. This variable is calculated as the natural log of the estimated number of people per square

116 kilometer. Population values are provided in five-year increments with data for years

1990 and 1995 coming from the Global Population of the World (GPW) version 3 dataset; and data for 2000, 2005, 2010, and 2015 coming from the GPW version 4 dataset. Both datasets are made available by the Socioeconomic Data and Applications

Center (SEDAC). The GPW datasets provide population estimates adjusted according to data provided by the UN’s World Population Prospectus for greater accuracy. To provide full coverage of population estimates for all years 1990 to 2015, I use a linear interpolation of the 1990, 1995, 2000, 2005, 2010, and 2015 data to estimate values for the intervening years.41 The population estimates are divided by their provinces’ geographic areas, as measured in square kilometers.

As a secondary proxy for urbanization, I include an annualized measure of average nighttime light emissions from for province of countries from the world. Data for this variable are taken from the US Air Force’s Defense Meteorological Satellite

Program/Operational Line Scanner (DSMP/OLS) nighttime lights records. This data is made available by the National Oceanic and Atmospheric Administration (NOAA) and provides annual composite images of the world at night, controlling for cloud cover.

Brightness of lights is considered a proxy for urbanization in this study, along the lines of demographic research from other works (Zhang & Seto 2011; Ma et al. 2012).42 Light

41 Many methods exist for interpolating data and while some scholars recommend cubic spline interpolation for use with demographic data, as it produces estimates with smoother fitted lines, allowing for more accurate or realistic estimates. However, the cubic spline algorithm sometimes generates a substantial number of negative values for estimates of population sizes, particularly in less-populated provinces. As negative population sizes are impossible, the linear interpolation algorithm, which does not produce negative values, is more appropriate for use with this data. See McNeil, Trussell, and Turner (1977) for further information about using cubic splines to interpolate demographic data. 42 Note that there is not consensus in the academic literature regarding the most efficient usages of nighttime lights data. Bennett and Smith (2017) discuss nighttime lights data as being a useful proxy for a range of factors, including urbanization, economic activity, and population. Recent works from the field of

117 saturation measures from the DMSP OLS light sensors are measured on a scale of 0-63, with higher numbers indicating greater levels of light radiance.43 Radiance scores are provided in 30 arc-second grids. In processing this data, I have summed the gridded radiance scores by province, then divided the summed radiance value by the province’s geographic area. This procedure generates a variable indicating the density of a province’s nighttime lights. To account for overdispersion in the data, I take a natural log of this variable. The nighttime lights data ranges from 1992 to 2013, yielding fewer observations for models in which nighttime light emissions are used as a proxy for urbanization.44

For models analyzing my second hypothesis that proximity to large or growing cities should decrease conflict risk, I include several measures of cities and urban proximity. First is a count of the number of major cities in a province. As with the analysis in the previous chapter, cities are identified as “major” if the United Nations

Urbanization Prospects (2018) records its population as 300,000 or higher. As the UN estimates city populations in five-year increments, city counts aggregated for each province are carried forward to fill in values for the intervening years. The largest number of major cities in any province in this study is 36, however the typical province in this sample has no major cities.

conflict studies have used nighttime lights as a proxy for such things as wealth (Weidmann & Schutte 2017) and state capacity (Koren & Sarbahi 2017). 43 See Mellander, Lobo, Stolarick, & Matheson (2015) for more details about the basics of nighttime light data, how it is collected, and how researchers have previously applied it to economic analysis as a substitute for GDP measures. 44 Note that prior research finds that nighttime lights emissions are more useful in tracking urbanization in developed countries, rather than in the developing world. In some countries, underdeveloped infrastructure may result in lower light emission levels, resulting in low population estimates for an area under study. For further discussion of this phenomenon, see Zhang and Seto (2013).

118

Urban proximity is operationalized in this study with a natural log transformation of the number of kilometers from province’s borders to the nearest major city outside the province. This measure is especially important for provinces which may have no major cities of their own but may be located close to a major city in a neighboring province. To account for the influence of cities’ growth, I include measures for the average population growth rate of all major cities within a province as well as the population growth rate of the nearest external city. These variables are also based on United Nations (2018) estimates provided in five-year increments, with a linear interpolation algorithm used to estimate city population growth values for the intervening years.

In models assessing my third hypothesis that urbanization can make conflict more likely if an area’s economic health is declining, I measure economic health using two variables. First is a measure of annual change in unemployment levels. This variable is calculated using an annualized country-level unemployment estimates published by the

World Bank, based on data from the International Labor Organization. As with the population data in this study, a linear interpolation process is used to generate estimates for years with missing unemployment data. Second, I proxy economic health as the annual change in a province’s per capita income. This variable is calculated using data from Kummu, Taka, and Gauillaume (2018).

Control Variables

 Population: The total number of people estimated to live in each province during

a given year. This variable is calculated according to the process described in the

discussion of population density, but without regard to the size of a province’s

landmass.

119

 GDP Per Capita: A measure of the average per capita income in each province,

calculated in terms of US Dollars, held constant at 2011 levels. Derived from the

gridded GDP (purchasing power parity) estimates provided by Kummu, Taka, and

Gauillaume (2018). Total annual GDP levels are aggregated by province and then

divided by the provinces population to estimate the average per capita income for

residents of each province. Population data in this calculation are derived from the

Gridded Population of the World (GDP) datasets described above.

 Distance from Capital: A measure of the number of kilometers from a country’s

capital to the centroid of each province, based on shortest path between the two

points using great-circle distances. All distances are expressed in kilometers and

are calculated using the Haversine formula.45 This variable accounts for the

possibility that it may be more difficult for governments to administer areas far

from the capital, affecting the risk of conflict in outlying areas.

 Ethnic Diversity: A count of the total number of politically relevant ethnic groups

living in each province. This accounts for the possibility that greater ethnic

diversity may adversely affect a country’s conflict propensity as ethnic identities

become politized, a phenomenon suggested in other studies (Wegenast & Basedau

2014, Bleaney & Dimico 2011). This value is derived from the Georeferencing of

Ethnic Groups (GREG) dataset (Weidmann, Rød, & Cederman 2010), which, in

turn, is derived from demographic research compiled by Soviet Union’s Narodov

Mira Atlas. Values for the GREG data do not vary over time.

45 See Robusto (1957), Gleditsch and Ward (2001), and Mahmoud and Akkari (2016) for further information about distance calculations using the Haversine Formula.

120

 Mountainous Terrain: The percent of a province’s landmass that is covered in

mountainous terrain. This variable is derived from data in ESRI’s World

Ecological Facets Landform Classes dataset. All areas recorded as having high

mountains, low mountains, scattered high mountains, or scattered low mountains

are coded as being mountainous. Values for the mountainous terrain measurement

remain constant across all years. This variable controls for the possibility that

rebel groups may be better able to evade capture by taking refuge in mountainous

areas, a factor which Fearon and Latin (2003) find worsens a country’s risk of

armed conflict.

 Democracy: A dummy variable indicating whether a province belongs to a

country with a Polity IV score of six or higher. This binary coding method is the

same as that used by Goldstone et al. (2010). It controls for the role of regime

type in determining either a society’s level of conflict-inducing grievances or the

availability of non-violent means of seeking political change.

 Nearby Conflict: The log-transformed number of kilometers from a province’s

border to the nearest fatal conflict event outside the province. This controls for the

possibility that a province’s conflict propensity may be influenced by conflict

violence occurring nearby. This addresses the problem of spatial autocorrelation

discussed in geographic research,46 as well as the neighborhood effects identified

in conflict research (e.g. Goldstone et al. 2010) identifies as key predictors of

conflict. The further the distance of “nearby” conflict events, the safer the

province’s neighborhood.

46 See Miller (2004) for discussion of spatial autocorrelation and Tobler’s First Law of Geography that entities geographically closer to one another tend to share more characteristics than those far apart.

121

 Lagged Dependent Variable: The number of conflict events that a province

experienced in the previous year. This variable accounts for the possibility that

conflict events in one year may form a pattern of violence, thereby increasing the

risk of conflict in the following year.

All statistical models used in this study involve negative binomial regression, which is appropriate, given that the dependent variable is an event count. To differentiate between levels of urbanization and changes in level of urbanization, I include models with rate of change statistics for each of the urbanization variables. To better consider the interactive effect of urbanization and unemployment, I include models with unemployment levels as well as a variable for the rate of change in unemployment. All explanatory variables are lagged by one year to better show their influence on conflict levels. Standard errors are clustered by province to better examine the potential urbanization-conflict nexus on a province-by-province basis.

Subnational modeling for tests of the first and second hypotheses is based on the following equation:

푉 = 훽 + 훽푢푟푏푎푛푖푧푎푡푖표푛() + 푋() + 휀

In this formula, Vit is the risk of violence in spatial unit i and year t, β0 is the intercept, urbanization a spatial unit’s population density, number of major cities, or distance from a major city, X is a suite of control variables and εit is the error term for a spatial unit i in year t. Tests of Hypothesis 3 focuses on the conditioning effect of changing economic factors on the urbanization-conflict relationship and is therefore represented with the following equation:

푉 = 훽 + 훽 푢푟푏 푟푎푡푒() ∗ 훽 ln (∆ 푒푐표푛 푐표푛푑푖푡푖표푛푠) + 푋() + 휀

122

Where Vit is the risk of violence in spatial unit i and year t, β0 is the intercept, urbanization rate is the annual change in the percent of a spatial unit’s population density,

∆ econ conditions is the spatial unit’s annual change in unemployment or GDP per capita,

X is a suite of control variables and εit is the error term for a spatial unit i in year t.

5.4. Results & Discussion

The results of the regression models provide mixed findings regarding my first hypothesis, that conflict risk should decline as an area becomes more urbanized. Results for Model 1 show that when a province is more densely populated, it stands a higher chance of experiencing conflict events at the low threshold of one fatality. This finding indicates that Hypothesis 1 is not well supported in the context of fatal conflict violence with a low fatality threshold. However, the story is different when considering major conflict events. As shown in Table 1, provinces are less likely to experience conflict events with twenty-five or more fatalities when they are more urbanized. The negative coefficients for both variables are statistically significant at the 99% confidence level, providing a strong indication that more urbanized provinces are less likely to experience high-fatality conflict events. This is the case whether urbanization is proxied as a spatial unit’s density of population or nighttime light emissions.

To ease the interpretation of the logistic regression results, I have calculated probabilities of conflict occurrence at both the 1- and 25-fatality thresholds, as depicted in Figure 1 and Figure 2. These predictions are for a hypothetical district in a non- democratic country which experienced no conflict in the previous year and where all other variables are held at their mean values. Under such conditions, as a country’s

123 population density increases from the lowest to the highest levels seen in this sample,47 its risk of experiencing a conflict event with one or more fatalities will increase from

0.6% to 6.9%. At the same time that the urbanizing province sees its risk of low-threshold conflict increase, it will enjoy a decline in risk of high-fatality conflict events, falling from 4.7% at the lowest levels of population density to 0.9% at the highest levels of population density.

47 The regression models presented here use log transformations of the population density measurement. For context, raw population density scores in this sample range from just 0.003 persons per square kilometer to 56,518 persons per square kilometer at the maximum.

124

Table 1. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 1 Model 2 Model 3 Model 4

Pop Density ln(t-1) 0.056** -0.107*** (0.022) (0.038) Night Light Density (t-1) -0.023 -0.118*** (0.018) (0.026) Pop ln(t-1) 0.158*** 0.173*** 0.193*** 0.176*** (0.021) (0.037) (0.021) (0.037) GDP Per Capita lnt-1) -0.090*** -0.304*** -0.089** -0.175*** (0.027) (0.038) (0.038) (0.064) Ethnic Groups (t-1) 0.007 0.008 -0.004 0.007 (0.012) (0.014) (0.013) (0.015) Mountainous Terrain ln(t-1) 0.183 -0.617* 0.481** -1.129*** (0.209) (0.362) (0.205) (0.407) Democracy (t-1) -0.084 -0.665*** -0.044 -0.603*** (0.058) (0.095) (0.061) (0.105) Distance to Capital ln(t-1) 0.203*** -0.010 0.166*** 0.011 (0.030) (0.053) (0.031) (0.053) Nearest External Conflict (t-1) -0.126*** -0.219*** -0.135*** -0.220*** (0.006) (0.007) (0.007) (0.008) Lagged DV 2.495*** 1.686*** 2.440*** 1.774*** (0.103) (0.108) (0.121) (0.121) Constant -5.242*** -2.873*** -5.306*** -4.669*** (0.316) (0.512) (0.446) (0.824)

N 60,046 60,046 49,004 49,004 BIC 23,216.3 8456.4 18,513.7 6390.9

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

125

Figures 1-2. Predicted Conflict Risk by Population Density

Predictive Margins with 95% CIs Predictive Margins with 95% CIs .1 .08 .08 .06 .06 .04 .04 .02 Probability of ConflictThreshold) (1-Fatalityof Probability Probability of Conflict (25-Fatality ofConflict Probability Threshold) (25-Fatality .02 0

-6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 8 9 10 11 Population Density, logged, lagged Population Density, logged, lagged

Hypothesis 1 focuses on the concept of urbanization level, based on theoretical expectations that citizens in more urbanized areas will have greater opportunities for economic and political participation and that governments will have greater tax revenue and tactical advantages over rebel groups. However, for the purpose of comparison, I present models in Table 2 showing how conflict risk varies according to changes in urbanization rate. As shown in these models, increases in population density are associated with higher conflict risk, while increases in nighttime light emissions are associated with lower conflict risk. The findings for Model 5 and Model 6 suggest support for the logic of Malthusian arguments that population growth results in crises.

Models 7 and 8 present a much rosier picture of urbanization, but also raises questions about methods of measuring urbanization at the subnational level. The divergence in findings suggest that although population density and nighttime lights are both used as proxies for urbanization in academic research,48 they may perhaps capture different aspects of urbanization. This is discussed further in section 5.6.

48 For examples of empirical studies operationalizing urbanization with population density, see Michaels, Rauch, and Redding (2012), Chauvin et al. (2017), and Urdal (2005). For examples of studies

126

Table 2. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 5 Model 6 Model 7 Model 8

Δ Pop Density ln(t-1) 0.070** 0.083** (0.032) (0.035) Δ Night Light Density (t-1) -0.008* -0.018* (0.004) (0.010) Pop ln(t-1) 0.185*** 0.113*** 0.191*** 0.126*** (0.018) (0.030) (0.018) (0.033) GDP Per Capita lnt-1) -0.099*** -0.287*** -0.110*** -0.293*** (0.027) (0.039) (0.029) (0.043) Ethnic Groups (t-1) -0.002 0.021 -0.003 0.021 (0.012) (0.014) (0.013) (0.014) Mountainous Terrain ln(t-1) 0.386* -0.874** 0.565*** -0.984** (0.200) (0.366) (0.207) (0.410) Democracy (t-1) -0.100* -0.693*** -0.073 -0.710*** (0.058) (0.095) (0.061) (0.105) Distance to Capital ln(t-1) 0.172*** 0.081* 0.156*** 0.053 (0.028) (0.045) (0.029) (0.051) Nearest External Conflict (t-1) -0.124*** -0.219*** -0.136*** -0.220*** (0.006) (0.045) (0.007) (0.007) Lagged DV 2.551*** 1.748*** 2.397*** 1.740*** (0.103) (0.109) (0.116) (0.115) Constant -5.134*** -3.171*** -4.990*** -3.140*** (0.314) (0.514) (0.326) (0.544)

N 57,953 57,953 52,675 52,675 BIC 22210.3 8001.0 20014.3 7122.6 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

The regression results indicate that Hypothesis 2 is mostly supported, although not in all models. Table 3 presents results for tests assessing Hypothesis 2, that conflict risk should be lower in areas that are near a large or growing city. Models 9, 10, and 11 demonstrate support for this hypothesis. If a province is home to a major city – one with at least 300,000 citizens, as per UN records – it stands a significantly lower risk of armed

operationalizing urbanization with nighttime lights emissions, see Zhang and Seto (2011) and Ma et al. (2012).

127 conflict. This is true regardless of whether conflict is operationalized at the 1- or 25- fatality threshold. Figure 3 below depicts the marginal effect of major cities on conflict risk in a hypothetical province in a non-democratic society with no conflict events in the previous year. As the number of major cities in the province increases, the predicted number of conflict events at the 1-fatality threshold declines significantly from 5% to 2%.

Under the same conditions, a province’s risk of conflict at the 25-fatality, threshold will decline from 1.95% when there are no major cities to 0.02% at the highest number of major cities.

Not all provinces contain major cities, but that does not mean that the benefits of urban proximity are unimportant to their risk of conflict violence. As suggested by the results for Model 11, the presence of a major city in another province nearby should extend some conflict-relieving benefits to a province without any major cities of its own.

The negative relationship between conflict occurrence and distance to a major city outside of one’s province suggests that a province is safer when further from its borders.

Figure 5 below shows that the risk of conflict at the 1-fatality threshold is 6.47% when the distance to the nearest external major city is minimal, and 3.9% when distance is maximized. This finding is contrary to the expectations stated in Hypothesis 2. This finding does not apply to the risk of major conflict events resulting in twenty-five or more deaths.

128

Table 3. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 9 Model 10 Model 11 Model 12

Maj Cities in Province (t-1) -0.098*** -0.131*** (0.030) (0.041) Dist to Maj External City ln(t-1) -0.056* 0.042 (0.029) (0.047) Pop ln(t-1) 0.185*** 0.113*** 0.191*** 0.126*** (0.018) (0.030) (0.018) (0.033) GDP Per Capita lnt-1) -0.090*** -0.285*** -0.111*** -0.285*** (0.027) (0.039) (0.028) (0.040) Ethnic Groups (t-1) -0.005 0.020 0.000 0.020 (0.012) (0.013) (0.012) (0.014) Mountainous Terrain ln(t-1) 0.403** -0.779** 0.306 -0.820** (0.197) (0.350) (0.202) (0.360) Democracy (t-1) -0.065 -0.684*** -0.074 -0.701*** (0.056) (0.096) (0.057) (0.096) Distance to Capital ln(t-1) 0.177*** 0.063 0.193*** 0.042 (0.028) (0.045) (0.029) (0.051) Nearest External Conflict (t-1) -0.126*** -0.216*** -0.127*** -0.218*** (0.006) (0.007) (0.006) (0.007) Lagged DV 2.484*** 1.704*** 2.491*** 1.703*** (0.103) (0.107) (0.103) (0.107) Constant -5.961*** -3.779*** -4.749*** -3.303*** (0.371) (0.614) (0.378) (0.622)

N 60,046 60,046 60,046 60,046 BIC 23,177.7 8455.7 23,222.0 8469.5 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

129

Figures 3-5. Predicted Conflict Risk by Major City Count and Distance

Predictive Margins with 95% CIs Predictive Margins with 95% CIs .06 .02 .015 .04 .01 .02 .005 Probability Conflict Threshold) (1-Fatality Probability of 0 Probability Threshold) Probability Conflict (25-Fatality of 0

0 10 20 30 40 0 10 20 30 40 Major Cities in Province, lagged Major Cities in Province, lagged

Predictive Margins with 95% CIs .08 .07 .06 .05 .04 Probability Threshold) Conflict of Probability (1-Fatality .03 -.8 .2 1.2 2.2 3.2 4.2 5.2 6.2 7.2 8.2 9.2 Distance to Major External City, logged, lagged

The results presented in Table 4 provide mixed support for my second hypothesis that the growth of major cities should have a pacifying impact on nearby areas. Model 13 shows that when major city population growth is calculated according to the population changes of cities within one’s own province and, for provinces with no major cities, the growth of the nearest major external city, the coefficient is positive and statistically significant at the 90% confidence level. This finding is evidence against Hypothesis 2 in the context of conflict events at the 1-fatality threshold. However, Model 14 suggests that the trend is reversed when considering major conflict events, a finding in line with

Hypothesis 2. When the sample is narrowed to include only the provinces containing no

130 major cities, the population growth of the nearest external city still has an impact. In such a case, a province will see a significantly lower risk of a fatal conflict event when the nearest major city from another province experiences population growth at a faster rate.

Table 4. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 13 Model 14 Model 15 Model 16

Δ Maj Cities Pop (t-1) 3.762** -7.569** (1.561) (3.108) Δ Maj External City Pop ln(t-1) -0.548*** 0.714 (0.206) (0.505) Pop ln(t-1) 0.183*** 0.120*** 0.186*** 0.112*** (0.018) (0.030) (0.018) (0.030) GDP Per Capita lnt-1) -0.101*** -0.284*** -0.101*** -0.284*** (0.027) (0.039) (0.027) (0.039) Ethnic Groups (t-1) -0.002 0.021 -0.003 0.021 (0.012) (0.014) (0.012) (0.014) Mountainous Terrain ln(t-1) 0.387* -0.887** 0.373* -0.864** (0.200) (0.368) (0.201) (0.366) Democracy (t-1) -0.106* -0.684*** -0.104* -0.690*** (0.057) (0.098) (0.057) (0.098) Distance to Capital ln(t-1) 0.172*** 0.0826* 0.172*** 0.0804* (0.028) (0.045) (0.028) (0.045) Nearest External Conflict (t-1) -0.126*** -0.218*** -0.124*** -0.220*** (0.006) (0.007) (0.006) (0.007) Lagged DV 2.535*** 1.742*** 2.551*** 1.714*** (0.103) (0.109) (0.103) (0.109) Constant -5.094*** -3.264*** -5.118*** -3.189*** (0.315) (0.516) (0.314) (0.514)

N 57,953 57,953 57,953 57,953 BIC 22,207.6 7996.2 22,204.4 7998.9 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Figures 6-8 below show the predictive margins for conflict based on findings from Models 13-15. As the nearest major city’s population growth moves from minimum to maximum a province in a non-democratic country with no conflict in the previous year

131 will experience a modest increase in risk of fatal conflict a the 1-fatality threshold, going from 4.6% to 5%. This trend, of course, goes against the predictions of Hypothesis 2.

However, Hypothesis 2 is supported in tests of the effect of city population growth on high-fatality conflict events. Going from minimum to maximum population growth rates in the nearest major city, a province’s risk of a high-fatality conflict event declines from

2% to 1.7%. And when limiting the sample to provinces with no major cities of their own, the distance to the nearest major external city demonstrates the expected negative relationship with conflict risk. The risk of a fatal conflict event in such a province falls from 7.7% when the nearest major city has the lowest possible population growth rates and 2.9% when population growth rates are at their maximum level. This finding does not hold when considering only the risk of high-fatality conflict events.

132

Figures 6-8. Predicted Conflict Risk by Changes in City Growth

Predictive Margins with 95% CIs Predictive Margins with 95% CIs .055 .024 .022 .05 .02 .018 .045 .016 Probability of Conflict (1-Fatality Threshold)ofConflict Probability (1-Fatality Probability of Conflict (25-Fatality Conflict Threshold) Probability (25-Fatality of .04 .014 -.013 -.011 -.009 -.007 -.005 -.003 -.001 .001 .003 .005 .007 .009 .011 .013 -.013 -.011 -.009 -.007 -.005 -.003 -.001 .001 .003 .005 .007 .009 .011 .013 Population Growth Rate of Nearest Major City, lagged Δ Major City Population, lagged

Predictive Margins with 95% CIs .1 .08 .06 .04 Probability of Conflict Threshold) (1-Fatalityof Conflict Probability .02

-.95 -.8 -.65 -.5 -.35 -.2 -.05 .1 .25 .4 .55 .7 .85 1 Δ Nearest External City Pop, lagged

Tests for Hypothesis 3 are presented in Tables 5 and 6. These tests seek to evaluate the conditioning effect of a province’s economic health on the relationship between urbanization are armed conflict. The interaction between urbanization rate and country-level changes in unemployment have no impact on conflict risk, as shown in

Models 17-20. It is notable that country-level measures of unemployment are also statistically insignificant in these models, even when considered outside of the interaction with urbanization rate. The models in Table 6 use changes in provincial measures of GDP per capita to assess economic health, presenting mixed findings for Hypothesis 2. Results for Model 17 do not support my theory and instead show that when incomes and population density both increase, a province stands a higher chance of experiencing at

133 least one fatal armed conflict event. The finding is the opposite when urbanization is proxied with nighttime light emissions and the fatality threshold for conflict events is set at twenty-five. Increases in nighttime light emissions have a negative and statistically significant relationship with conflict occurrence in all the interactive models in which it is included, while changes in population density present a significant positive association with conflict in Model 21. Ultimately, the results for these models provide no definitive answer about whether worsening economic hardships during times of higher urbanization produce population pressures that bring a province to the point of armed conflict.

As Model 24 was the only province-level test of Hypothesis 3 to return a statistically significant coefficient I present predictive margins for it in Figures 9-11 below. The predictive margins calculations show that if a province in a non-democratic country with no conflict in the previous year experiences the lowest – negative – change in levels of GDP per capita, then going from the lowest to highest rates levels of city population growth corresponds to a drop in conflict risk from 5% to 3.3%. When growth of GDP per capita is held at its mean, a shift from lowest to highest rates of city population growth correspond to an S-curve in the relationship between changes in population density and conflict risk. With no change in GDP per capita, a province’s risk of experiencing a fatal conflict event go from 1.2% when the nearest external city’s population shrinks by 1% to 4.7% when the city’s population remain stagnant, to more than 99% when the city’s population grows by 6% or more per year.

134

Table 5. Random Effects Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 17 Model 18 Model 19 Model 20

Δ Pop Density (t-1) 0.046 0.067 (0.113) (0.103) Δ Night Light Density (t-1) -0.013* -0.016* (0.007) (0.008) Δ Unemployment (t-1) -0.104 0.034 -0.110 0.091 (0.133) (0.205) (0.131) (0.200) Pop ln(t-1) 0.321*** 0.137*** 0.321*** 0.134*** (0.036) (0.034) (0.037) (0.035) GDP Per Capita lnt-1) -0.221*** -0.328*** -0.212*** -0.317*** (0.044) (0.045) (0.045) (0.047) Ethnic Groups (t-1) 0.037 0.040** 0.030 0.041** (0.024) (0.019) (0.024) (0.019) Mountainous Terrain ln(t-1) 1.122*** -1.122*** 1.282*** -1.087** (0.409) (0.428) (0.414) (0.436) Democracy (t-1) -0.731*** -0.813*** -0.664*** -0.812*** (0.075) (0.102) (0.078) (0.104) Distance to Capital ln(t-1) 0.207*** 0.092** 0.172*** 0.062 (0.048) (0.044) (0.052) (0.048) Nearest External Conflict (t-1) -0.073*** -0.207*** -0.084*** -0.211*** (0.006) (0.007) (0.007) (0.008) Δ Pop Den (t-1) * Δ Unempl (t-1) -0.390 1.661 (3.810) (6.616) Δ Night L. Den (t-1) * Δ Unempl (t-1) -0.015 -0.033 (0.023) (0.044) Lagged DV 1.528*** 1.274*** 1.387*** 1.245*** (0.074) (0.100) (0.079) (0.103) Constant -7.269*** -3.671*** -7.043*** -3.495*** (0.616) (0.576) (0.618) (0.584) N 55,344 55,344 52,897 52,897 BIC 19289.2 7450.7 18520.9 7063.7 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

135

Table 6. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 21 Model 22 Model 23 Model 24

Δ Pop Density (t-1) 1.396* 0.554 (0.730) (1.557) Δ Night Light Density (t-1) -0.008* -0.021* (0.004) (0.012) Δ GDP PC (t-1) -0.0411 -0.750* 0.148 -0.680 (0.213) (0.404) (0.226) (0.479) Pop ln(t-1) 0.184*** 0.115*** 0.192*** 0.133*** (0.018) (0.030) (0.018) (0.034) GDP Per Capita lnt-1) -0.221*** -0.328*** -0.212*** -0.317*** (0.044) (0.045) (0.045) (0.047) Ethnic Groups (t-1) -0.003 0.021 -0.003 0.020 (0.012) (0.014) (0.013) (0.014) Mountainous Terrain ln(t-1) 0.395** -0.829** 0.557*** -0.943** (0.200) (0.367) (0.207) (0.413) Democracy (t-1) -0.731*** -0.813*** -0.664*** -0.812*** (0.075) (0.102) (0.078) (0.104) Distance to Capital ln(t-1) 0.172*** 0.0828* 0.156*** 0.057 (0.027) (0.045) (0.029) (0.051) Nearest External Conflict (t-1) -0.124*** -0.218*** -0.136*** -0.219*** (0.006) (0.007) (0.007) (0.007) Δ Pop Den (t-1) * Δ GDP PC (t-1) 1.407* 0.518 (0.768) (1.605) Δ Night L. Den (t-1) * Δ GDP PC (t-1) -0.036 -0.070* (0.032) (0.040) Lagged DV 2.549*** 1.735*** 2.396*** 1.724*** (0.103) (0.110) (0.116) (0.117) Constant -5.200*** -3.226*** -5.004*** -3.251*** (0.318) (0.534) (0.325) (0.558) N 57,950 57,950 52,673 52,673 BIC 22,224.8 8016.8 20,034.1 7136.3 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

136

Figures 9-11. Predicted Risk of Conflict by Population Density and GDP Per Capita

Predictive Margins with 95% CIs, Δ GDP Per Cap at Min Predictive Margins with 95% CIs, Δ GDP Per Cap at Mean 1.5 .15 .1 1 .05 0 .5 -.05 0 Probability ofConflict Threshold) Probability (1-Fatality Probability of Conflict Conflict Probability Threshold) (1-Fatality of -.1 -1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 -1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Δ Population Density, lagged Δ Population Density, lagged

Predictive Margins with 95% CIs, Δ GDP Per Cap at Max 1 .8 .6 .4 .2 Probability of Conflict Threshold) Conflict Probability (1-Fatality of 0

-1 1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 Δ Population Density, lagged

Regarding the control variables included in this analysis, there are several trends worth mentioning. Several of the geographic control variables consistently presented predictable, but meaningful results in these models. Areas further from the national capital are more likely to experience conflict violence. In line with applications of

Boulding’s (1962) loss of strength gradient to domestic security affairs, this suggests that state capacity does indeed decay across space. Violence far from the seat of government is likely due to logistical difficulties in projecting force to more distant regions. Second, conflict occurrence is negatively correlated with the distance between one’s own province and the location of the nearest conflict event outside of that province. This

137 demonstrates that arguments for neighborhood effects or the spatial autocorrelation phenomenon are indeed at play in this subnational conflict analysis. Mountainous terrain correlates positively with low-threshold conflict and negatively with high-threshold conflict. As expected, GDP per capita and democracy are negative correlates of conflict while population size is a positive correlate.

5.5. Robustness Checks

As a robustness check on models presented in the previous section, I run the same statistical tests on models in which the province year is replaced by grid cells of approximately 2,500 square kilometers. These grid cells are drawn from the PRIO-Grid geographic data structure, which is intended to facilitate analysis of conflict and other political phenomena at subnational levels (Tollefsen, Strand, & Buhaug 2012). Although the cells themselves are apolitical units, not representing any political or administrative boundary, they are smaller than the average province, enabling me to test my hypotheses on a smaller geographic scale.49 Values for variables in the grid-cell-level analysis are drawn from the same sources as those used in the province-level analysis. Calculations for the spatial variables have been performed with ESRI’s ArcGIS software, using the

World Geodetic System 1984 (WGS84) datum for spatial reference. Except for the random effects logits, all grid-cell-level models include standard errors clustered by grid- cell to provide a better understanding on the effect of conflict at the grid-cell-level.

49 The average grid cell has an area of approximately 2,500 square kilometers, the average province in this sample is nearly eight times larger, representing an area of about 19,400 square kilometers.

138

Table 7 displays tests of Hypothesis 1, that urbanization level should correspond with fewer conflict events. Finding for the grid-cell-level analysis are less supportive of

Hypothesis 1 than were the findings at the province-level. In this analysis, greater population density corresponds to an increased risk of a high-fatality conflict event and nightlight density corresponds to an increase in conflict events at the one-fatality threshold. While this evidence goes against my first hypothesis, increases in nightlight density still correlate negatively with the risk of high-fatality conflict, as in the province- level analysis of Model 4. The relationships between conflict and changes in population and nightlight density are presented in Table 8. Here, it seems that urbanization rate only influences conflict risk when proxied as nightlight density and where the threshold of conflict is at the 1-fatality level.

139

Table 7. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 25 Model 26 Model 27 Model 28

Pop Density ln(t-1) 0.068 0.282* (0.053) (0.162) Night Light Density (t-1) 0.013** -0.103*** (0.006) (0.021) Pop ln(t-1) 0.348*** 0.447*** 0.274*** 0.272*** (0.049) (0.150) (0.013) (0.030) GDP Per Capita ln(t-1) -0.011* -0.019 -0.007 -0.007 (0.006) (0.013) (0.005) (0.015) Ethnic Groups (t-1) 0.155*** 0.178*** 0.157*** 0.173*** (0.016) (0.041) (0.015) (0.042) Mountainous Terrain ln(t-1) 0.096 -0.786* 0.062 -0.555 (0.125) (0.409) (0.127) (0.408) Democracy (t-1) 0.133*** -1.727*** 0.117*** -1.588*** (0.044) (0.192) (0.044) (0.193) Distance to Capital ln(t-1) -0.152*** -0.190* -0.160*** -0.167* (0.036) (0.010) (0.036) (0.101) Nearest External Conflict (t-1) -0.738*** -0.740*** -0.738*** -0.733*** (0.010) (0.020) (0.010) (0.021) Lagged DV 3.340*** 3.441*** 3.350*** 3.552*** (0.090) (0.846) (0.090) (0.840) Constant -4.204*** -6.217*** -3.536*** -5.865*** (0.446) (1.363) (0.297) (0.800)

N 1,036,629 1,036,629 1,033,104 1,033,104 BIC 36,084.6 4734.3 36,076.4 4706.4

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

140

Table 8. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 29 Model 30 Model 31 Model 32

Δ Pop Density ln(t-1) -0.217 -0.791 (0.438) (1.632) Δ Night Light Density (t-1) -0.020*** -0.009 (0.005) (0.018) Pop ln(t-1) 0.285*** 0.174*** 0.284*** 0.176*** (0.011) (0.026) (0.011) (0.056) GDP Per Capita lnt-1) -0.008* -0.010 -0.008* -0.010 (0.005) (0.013) (0.005) (0.013) Ethnic Groups (t-1) 0.153*** 0.191*** 0.153*** 0.191*** (0.015) (0.041) (0.015) (0.041) Mountainous Terrain ln(t-1) 0.098 -0.847** 0.0430 -0.463 (0.125) (0.424) (0.143) (0.469) Democracy (t-1) 0.123*** -1.696*** 0.128*** -1.696*** (0.044) (0.196) (0.044) (0.196) Distance to Capital ln(t-1) -0.154*** -0.191* -0.158*** -0.193* (0.036) (0.107) (0.036) (0.107) Nearest External Conflict (t-1) -0.740*** -0.747*** -0.741*** -0.747*** (0.010) (0.021) (0.010) (0.021) Lagged DV 3.316*** 3.471*** 3.276*** 3.474*** (0.090) (0.852) (0.090) (0.852) Constant -3.681*** -4.170*** -3.639*** -4.160*** (0.278) (0.743) (0.278) (0.744)

N 997,112 997,112 993,764 993,764 BIC 35,401.0 4431.4 35,381.4 4431.7 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Findings for robustness checks relating to Hypothesis 2 are presented in Table 9 below. Here the findings are quite different from those at the province level and provide mixed support for Hypothesis 2. Whereas the presence of major cities in a province corresponds to a lower risk of conflict – a finding in line with my expectations – the coefficient for the major city count variable are positive in the grid-cell-level analysis.

This shows that while a province with more major cities is less likely to experience conflict, a grid cell with more major cities is more likely to experience conflict.

141

Regarding the grid-cell analysis of the distance to major external cities, it appears that as stated in Hypothesis 2, conflict does become more likely in cells that are far from the nearest external city. While these findings reflect positively on my theory, they differ from the province-level findings, which showed a negative relationship between fatal conflict risk and distance to the nearest major external city. Additionally, the grid-cell- level analysis returned no significant findings regarding effect of major cities’ population growth and conflict propensity.

Table 9. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 33 Model 34 Model 35 Model 36

Maj Cities in Grid Cell (t-1) 0.148* 0.533*** (0.078) (0.198) Dist to Maj External City ln(t-1) 0.054* 0.269*** (0.032) (0.098) Pop ln(t-1) 0.280*** 0.165*** 0.305*** 0.270*** (0.012) (0.025) (0.015) (0.038) GDP Per Capita lnt-1) -0.007 -0.005 -0.007 -0.007 (0.027) (0.039) (0.005) (0.014) Ethnic Groups (t-1) 0.157*** 0.181*** 0.154*** 0.169*** (0.016) (0.040) (0.015) (0.041) Mountainous Terrain ln(t-1) 0.0917 -0.840** 0.099 -0.740* (0.124) (0.409) (0.125) (0.408) Democracy (t-1) 0.131*** -1.739*** 0.138*** -1.687*** (0.044) (0.193) (0.044) (0.193) Distance to Capital ln(t-1) 0.177*** 0.063 -0.160*** -0.225** (0.028) (0.045) (0.036) (0.101) Nearest External Conflict (t-1) -0.740*** -0.745*** -0.737*** -0.738*** (0.010) (0.020) (0.022) (0.036) Lagged DV 3.337*** 3.428*** 3.342*** 3.475*** (0.090) (0.820) (0.010) (0.020) Constant -3.692*** -4.042*** -4.571*** -8.251*** (0.277) (0.682) (0.555) (1.695)

N 1,036,629 1,036,629 1,036,629 1,036,629 BIC 36,082.1 4732.8 36,082.5 4727.3 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

142

Table 10. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 37 Model 38 Model 39 Model 40

Δ Maj Cities Pop (t-1) 0.253 0.809 (0.209) (0.941) Δ Maj External City Pop ln(t-1) 0.268 0.821 (0.210) (0.946) Pop ln(t-1) 0.285*** 0.176*** 0.285*** 0.176*** (0.011) (0.026) (0.011) (0.026) GDP Per Capita lnt-1) -0.008* -0.010 -0.008* -0.010 (0.005) (0.013) (0.005) (0.013) Ethnic Groups (t-1) 0.153*** 0.191*** 0.153*** 0.191*** (0.015) (0.041) (0.015) (0.041) Mountainous Terrain ln(t-1) 0.094 -0.861** 0.094 -0.860** (0.125) (0.425) (0.125) (0.425) Democracy (t-1) 0.124*** -1.692*** 0.124*** -1.692*** (0.044) (0.196) (0.044) (0.196) Distance to Capital ln(t-1) -0.154*** -0.188* -0.154*** -0.188* (0.035) (0.107) (0.035) (0.107) Nearest External Conflict (t-1) -0.740*** -0.747*** -0.740*** -0.747*** (0.010) (0.021) (0.010) (0.021) Lagged DV 3.319*** 3.468*** 3.319*** 3.468*** (0.090) (0.857) (0.090) (0.857) Constant -3.683*** -4.201*** -3.683*** -4.203*** (0.278) (0.740) (0.278) (0.740)

N 997,160 997,160 997,160 997,160 BIC 35,400.2 4431.2 35,400.1 4431.1 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

The grid-cell-level analysis shows that changing economic conditions do not significantly condition the relationship between urbanization and conflict. As shown in

Tables 11 and 12, none of the interactions between urbanization and changes in unemployment or changes in GDP per capita are statistically significant. Note that in modeling these variables, it was necessary to take a natural log transformation for the changes in nightlight density as well as GDP per capita as overdispersion was much more

143 profound in the grid-cell measurements of these variables than was true at the province level.

Table 11. Random Effects Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 41 Model 42 Model 43 Model 44

Δ Pop Density (t-1) -0.091 -0.467 (0.175) (0.893) Δ Night Light Density ln(t-1) -0.021*** -0.014 (0.005) (0.020) Δ Unemployment (t-1) -0.172 -0.102 -0.165 -0.0892 (0.110) (0.357) (0.109) (0.352) Pop ln(t-1) 0.351*** 0.180*** 0.351*** 0.181*** (0.013) (0.037) (0.013) (0.037) GDP Per Capita lnt-1) -0.005 -0.009 -0.006 -0.009 (0.044) (0.045) (0.005) (0.014) Ethnic Groups (t-1) 0.185*** 0.166*** 0.185*** 0.166*** (0.019) (0.052) (0.019) (0.052) Mountainous Terrain ln(t-1) -0.126 -1.023** -0.127 -1.018** (0.140) (0.456) (0.140) (0.456) Democracy (t-1) 0.043 -1.745*** 0.045 -1.748*** (0.045) (0.205) (0.045) (0.205) Distance to Capital ln(t-1) -0.168*** -0.191* -0.168*** -0.191* (0.040) (0.105) (0.040) (0.105) Nearest External Conflict (t-1) -0.739*** -0.795*** -0.741*** -0.797*** (0.011) (0.033) (0.011) (0.033) Δ Pop Den (t-1) * Δ Unempl (t-1) -0.749 1.543 (1.412) (2.949) Δ Night L. Den ln(t-1) * Δ Unempl (t-1) 0.012 0.016 (0.026) (0.106) Lagged DV 2.472*** 2.645*** 2.429*** 2.631*** (0.076) (0.928) (0.077) (0.929) Constant -5.135*** -5.070*** -5.101*** -5.076*** (0.321) (0.867) (0.321) (0.868) N 955,134 955,134 955,170 955,170 BIC 33,989.5 4134.2 33,973.3 4134.8 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

144

Table 12. Logistic Regression Results

1+ Deaths 25+ Deaths 1+ Deaths 25+ Deaths DV: Civil Conflict Dummy Model 45 Model 46 Model 47 Model 48

Δ Pop Density (t-1) -0.873 -0.200 (0.689) (1.784) Δ Night Light Density log(t-1) -0.023*** -0.010 (0.006) (0.019) Δ GDP PC (t-1) 0.116*** 0.003 0.113*** 0.000 (0.197) (0.454) (0.184) (0.177) Pop ln(t-1) 0.265*** 0.151*** 0.266*** 0.149*** (0.012) (0.029) (0.012) (0.029) GDP Per Capita lnt-1) -0.221*** -0.328*** -0.212*** -0.317*** (0.019) (0.043) (0.019) (0.043) Ethnic Groups (t-1) 0.151*** 0.198*** 0.151*** 0.197*** (0.012) (0.014) (0.013) (0.014) Mountainous Terrain ln(t-1) 0.0148 -0.925** 0.008 -0.930** (0.135) (0.459) (0.135) (0.460) Democracy (t-1) 0.0722 -1.729*** 0.0794* -1.728*** (0.047) (0.214) (0.047) (0.214) Distance to Capital ln(t-1) -0.174*** -0.095 -0.178*** -0.098 (0.040) (0.136) (0.040) (0.136) Nearest External Conflict (t-1) -0.761*** -0.757*** -0.762*** -0.759*** (0.011) (0.022) (0.011) (0.022) Δ Pop Den (t-1) * Δ GDP PC ln(t-1) 0.108 -0.406 (0.085) (0.503) Δ Night L. Den ln(t-1) * Δ GDP PC ln(t-1) 0.093 -0.098 (0.070) (0.157) Lagged DV 3.235*** 3.559*** 3.193*** 3.551*** (0.095) (0.884) (0.095) (0.883) Constant -4.199*** -4.581*** -4.137*** -4.511*** (0.348) (0.966) (0.346) (0.962) N 811,684 811,684 808,324 808,324 BIC 30,598.5 3937.3 30,581.1 3937.2 Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

In addition to the logistic regression models presented thus far, I have also calculated zero-inflated negative binomial regression models, where possible, to account for conflict risk operationalized with a count of conflict events, rather than as a simple dummy variable. Negative binomial regression is ideal in cases where the dependent variable is an event count and, in this case, zero-inflated models where chosen because in

145 each year throughout the 1990-2015 time period, the overwhelming majority of spatial units in this analysis did not experience armed conflict. Note that due to problems with model convergence, it was not possible to re-run all earlier analysis from this chapter using zero-inflated negative binomial regression. However, the count of armed conflict events is a useful way of operationalizing armed conflict as the alleged threat posed by urbanization to political stability would, if true, likely cause the frequency of armed conflict events to increase. For the grid-cell analysis, none of the models involving interaction terms were able to converge. Below I present results for the zero-inflated negative binomial regressions for grid-cell models that converged successfully.

Table 13 presents models testing my first hypothesis for counts of conflict events at the 1- and 25-fatality thresholds. The findings show that when all fatal conflict events are aggregated by grid-cell, higher population density corresponds to a lower risk of conflict occurrence as well as a lower number of predicted events in cells unfortunate enough to experience armed conflict. This finding supports my first hypothesis and differs substantially from the findings in the logistic regression models presented in

Tables 1 and 7, which show that provinces and grid cells with higher population densities are more likely to experience at least one fatal conflict event. The regressions presented in Table 13 reveal no such trend for counts of high-fatality conflict events. However, this null result differs markedly from the positive coefficient found in the grid-cell-level logistic regression.

146

Table 13. Zero-Inflated Negative Binomial Regression Results

Count of Conflict Events Model 49 – Fatality Level 1+ Model 50 – Fatality Level 25+ (count equation) Coefficient Std. Error Coefficient Std. Error

Pop Den ln(t-1) -0.536*** (0.188) 0.083 (0.739) Pop ln(t-1) -0.315* (0.176) 0.141 (0.729) GDP Per Capita ln(t-1) 0.019 (0.123) -0.034 (0.048) Ethnic Groups (t-1) 0.083** (0.039) 0.177 (0.114) Mountainous Terrain (t-1) -0.467 (0.389) 1.396 (0.906) Democracy (t-1) -0.198* (0.103) -1.025* (0.582) Distance to Capital ln(t-1) -0.157 (0.114) -0.229 (0.141) Nearest External Conflict ln(t-1) 0.055** (0.024) 0.294** (0.137) Lagged DV 0.297*** (0.030) 0.225 (0.522) Constant 1.060 (1.554) -5.135 (6.137)

Probability of Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Pop Den ln(t-1) -0.529*** (0.141) -0.195 (0.872) Pop ln(t-1) -0.656*** (0.130) -0.286 (0.862) GDP Per Capita ln(t-1) 0.027** (0.011) -0.008 (0.050) Ethnic Groups (t-1) 0.088 (0.358) 0.166 (0.376) Mountainous Terrain (t-1) 0.088 (0.099) 0.079 (0.093) Democracy (t-1) -0.380 (0.094) 1.026 (0.721) Distance to Capital ln(t-1) 0.087 (0.099) -0.024 (0.187) Nearest External Conflict ln(t-1) 1.124*** (0.027) 1.545*** (0.111) Lagged DV -20.79*** (1.801) -9.205*** (1.455) Constant 2.541** (1.175) -2.321 (7.413)

N 1,036,629 1,036,629 BIC 44,863.2 5101.6

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Tables 14 and 15 display count models testing Hypothesis 2 in which I expect the presence of major cities to correspond with fewer conflict events. The results show that grid cells with more cities are less likely to experience a conflict event, but that the number of cities is not a useful predictor of the number of conflict events that may occur.

However, at the 25-fatality threshold a larger number of major cities corresponds to a

147 larger number of high-fatality conflict events. This is so despite the number of major cities not being a useful predictor of high-fatality conflict incidence. Taken together, these models present mixed findings for my hypothesis. It appears that Hypothesis 2 is more meaningful when applied to lower-level conflict events than to higher ones.

Regarding the influence of distance to major city on conflict event counts, I find that distance corresponds to a lower risk of high fatality conflict events occurring but does not meaningfully predict the number of such events that will occur. This finding goes against the expectations of my hypothesis. For events at the 1-fatality threshold, there is no statistically significant relationship between conflict and distance from major cities.

148

Table 14. Zero-Inflated Negative Binomial Regression Results

Count of Conflict Events Model 51 – Fatality Level 1+ Model 52 – Fatality Level 25+ (count equation) Coefficient Std. Error Coefficient Std. Error

City Count (t-1) 0.342 (0.283) 1.128** (0.444) Pop ln(t-1) 0.148*** (0.045) 0.007 (0.196) GDP Per Capita ln(t-1) -0.002 (0.010) -0.011 (0.031) Ethnic Groups (t-1) 0.095** (0.039) 0.184 (0.120) Mountainous Terrain (t-1) -0.474 (0.372) 1.171 (0.801) Democracy (t-1) -0.192* (0.101) -0.945 (0.597) Distance to Capital ln(t-1) -0.133 (0.123) -0.144 (0.153) Nearest External Conflict ln(t-1) 0.038* (0.021) 0.229* (0.139) Lagged DV 0.299*** (0.031) 0.336 (0.549) Constant -2.349*** (0.877) -4.509 (2.865)

Probability of Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

City Count (t-1) -0.170*** (0.202) 0.096 (0.538) Pop ln(t-1) -0.178*** (0.038) -0.118 (0.208) GDP Per Capita ln(t-1) 0.002 (0.009) -0.004 (0.036) Ethnic Groups (t-1) -0.113*** (0.036) -0.006 (0.141) Mountainous Terrain (t-1) -0.518 (0.317) 2.105** (0.910) Democracy (t-1) -0.369*** (0.092) 1.157 (0.729) Distance to Capital ln(t-1) 0.109 (0.101) 0.049 (0.192) Nearest External Conflict ln(t-1) 1.118*** (0.027) 1.526*** (0.117) Lagged DV -24.40*** (1.418) -9.240*** (1.366) Constant -1.029 (0.744) -4.151 (3.202)

N 1,036,629 1,036,629 BIC 44869.9 5088.1

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

149

Table 15. Zero-Inflated Negative Binomial Regression Results

Count of Conflict Events Model 53 – Fatality Level 1+ Model 54 – Fatality Level 25+ (count equation) Coefficient Std. Error Coefficient Std. Error

Dist to Maj City ln(t-1) -0.105 (0.094) -0.188 (0.154) Pop ln(t-1) 0.132** (0.057) -0.003 (0.201) GDP Per Capita ln(t-1) -0.002 (0.011) -0.018 (0.029) Ethnic Groups (t-1) 0.090** (0.040) 0.202 (0.125) Mountainous Terrain (t-1) -0.516 (0.377) 1.185 (0.822) Democracy (t-1) -0.189* (0.106) -1.026* (0.543) Distance to Capital ln(t-1) -0.119 (0.124) -0.019 (0.138) Nearest External Conflict ln(t-1) 0.045** (0.023) 0.312** (0.134) Lagged DV 0.295*** (0.031) 0.103 (0.514) Constant -1.003 (1.552) -1.857 (3.980)

Probability of Conflict Event (inflations equation) Coefficient Std. Error Coefficient Std. Error

Dist to Maj City ln(t-1) -0.081 (0.072) -0.413** (0.198) Pop ln(t-1) -0.209*** (0.045) -0.233 (0.204) GDP Per Capita ln(t-1) 0.003 (0.009) -0.005 (0.033) Ethnic Groups (t-1) -0.112*** (0.036) 0.023 (0.140) Mountainous Terrain (t-1) -0.536* (0.320) 1.987** (0.922) Democracy (t-1) -0.366*** (0.095) 0.965 (0.648) Distance to Capital ln(t-1) 0.123 (0.104) 0.057 (0.186) Nearest External Conflict ln(t-1) 1.114*** (0.027) 1.516*** (0.116) Lagged DV -21.79*** (1.725) -9.306*** (1.273) Constant 0.207 (1.329) 2.464 (4.733)

N 1,036,629 1,036,629 BIC 44,895.2 5097.6

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

5.6. Conclusion

Overall, the findings here suggest mixed findings for my hypotheses. I shall discuss each of them in order, providing reflection on what the findings mean for my theoretical arguments. First, there is no overwhelming evidence in the analysis of this chapter of urbanization level as a factor that aggregates or alleviates conflict risk. It appears, particularly in the province-level models that urbanization may reduce the chance of

150 severe armed conflict, although this effect is washed out or reversed when accounting for fatal conflict violence at lower levels of severity. A major exception to this comes from the count model that shows that conflict occurrence and counts vary inversely with population density. While this does not robustly reject arguments that urbanization may threaten security in the quickly urbanizing countries of the developing world, these findings, in combination with one another, do provide some good news. Provinces that become more urban are less likely to experience severe incidents of conflict violence.

Although robustness checks present a mixed view on whether this finding holds up to analysis at lower levels, this trend seems fairly, though not perfectly, consistent at the province level.

Perhaps the strongest findings in this chapter are those which consider the presence of major cities as a factor that influences conflict propensity. At the province level, a larger presence of major cities appears to significantly reduce the risk of armed conflict. This finding is consistent in the provincial-level models although in the grid-cell analysis, findings diverge between the count and logit models. There is also some evidence that being geographically closer to a major city outside one’s own province is good for peace. This is very important given that the modal city count for provinces in this sample is zero. Essentially, people can benefit from the opportunities granted to them by cities whether those cities are in the same jurisdiction or in another one nearby.

Evidence regarding urban growth rates as a conflict-alleviating factor are less clear as findings are mixed at the province level and null at the grid-cell level. As with population density, the growth of major city populations is a factor that appears to simultaneously alleviate the risk of severe conflict events and aggregate the risk of lower level events.

151

Of my three hypotheses, the third received the weakest support from the tests presented here. Changes in unemployment appear to have no impact on subnational conflict risk, either by themselves or through their interactions with the urbanization proxy variables. Year-to-year changes in GDP per capita have minimal impact on conflict, showing negative relationship with high-fatality conflict both by itself at the province level and in interaction with nightlight density at the grid-cell level. These findings are significant only at the 90% confidence level; however, they do indicate that worsening economic conditions may, in some cases, compound the security risks associated with urbanization in the Global South.

One notable result of my empirical tests is that results have sometimes diverged significantly between models operationalizing urbanization with population density and those using nightlight density. For example, Models 5 and 6 show that urbanization rate is associated with higher risk of armed conflict when proxied with changes in population density, while the opposite is true in Models 7 and 8 where urbanization is proxied with changes in nightlight emissions density. This suggests that perhaps nighttime lights capture a different aspect of urbanization than one would expect with a population density measure. This underscores concerns raised by Zhang and Seto (2013) regarding the use of nighttime lights to measure urbanization in developing countries, where population growth may outpace infrastructural development. Because infrastructure is necessary to generate lights during nighttime, measurements of light emissions are more directly tied to the presence of infrastructure than the presence of people who may or may not have access to electricity. Although governments often focus on both population agglomeration and infrastructure in identifying areas as urban, population agglomeration

152 is a much more prominent component of most countries’ definitions (UN Statistical

Handbook 2018). Population agglomeration is also much more central to the logics of the urbanization-conflict nexus about which I have theorized in this project.

On its face, the role of infrastructure in serving urban settlements may seem to suggest that perhaps nightlight emissions may better proxy economic development than urbanization and indeed, nightlights are sometimes used in other research as proxies for economic development. However, the data analyzed here do not clearly show that that nightlights better indicate economic conditions than demographic ones. In the province- level data, the log-transformed nightlight density scores have a 0.637 correlation with log-transformed population density and a 0.449 correlation with log-transformed GDP per capita. This suggests that even if economic development is needed to support the construction of infrastructure that generates nighttime lights, the lights themselves are more closely related to population density than to incomes.

By considering variations in subnational levels of urbanization and conflict, the research presented in this chapter has sought to more thoroughly parse out dynamics critical to security-oriented discussions of global demographic patterns. As the developing world continues to urbanize, this research will remain highly relevant to such discussions in both academic and policymaking circles. Building upon the country-level dynamics studied in the previous chapter, several aspects of the large-N subnational analysis are particularly notable. First, it appears that subnational analysis is more effective than country-level analysis at identifying an impact of urbanization level on armed conflict. Second, the anticipated negative relationship between urbanization and conflict level identified in the subnational analysis seems more effective in explaining

153 variation in high-fatality conflict. Third, the presence of large cities within a geographic space appears to have a pacifying effect in developing countries. In the country-level study, this effect was only seen for high-fatality conflict, but at the province level, the trend is also apparent at the 1-fatality threshold. Fourth, the conditioning effect of economic health on armed conflict seems quite weak in studies at either the country-level or the subnational level.

154

Chapter 6. Analysis of Conflict in India’s Red Corridor

For decades, a Maoist insurgency has simmered throughout a large stretch of rural India known as the “Red Corridor.” Levels of violence in the Red Corridor have varied greatly over time and across districts within the region. In this study, I explore the social and economic factors that have contributed to reductions in violence in some districts but have left others severely affected by the conflict. I argue that the conflict dynamics in the

Red Corridor are influenced by levels of urbanization and the proximity of areas to large and growing cities. Through a quantitative analysis of demographic data, I demonstrate that insurgent violence has abated most in rural areas geographically proximate to fast- growing cities, whose labor markets provide the rural poor with greater opportunities to improve their livelihoods. Urbanization itself is generally much less influential than proximity to a major city in determining conflict risk. I conclude with discussion of the broader policy implications of human migration for developing countries affected by internal conflict.

6.1. Introduction

The effects of urbanization on armed conflict become clearer when analyzing specific cases of armed conflict containing significant urban-rural dynamics. For demographic reasons, analysis of India is especially instructive as India is a vast country, significantly affected by internal conflict and quickly urbanizing, albeit at a late point in history. At the dawn the twenty-first century, only 18% of India’s households lived in urban areas, according to Indian government records (Census of India 2001). While this level of

155 urbanization is far below what one would normally find in other countries, particularly in advanced industrial societies, the forces of urbanization are strong in India. By the 2011 census, the Government of India found that 31% of the population lived in urban areas, a remarkable increase in just a decade (Census of India 2011).

While the UN’s Department of Economic and Social Affairs (DESA) reported in 2018 that India had more rural-dwelling citizens than any other country, it also projected that India would be amongst the top three countries experiencing a growth in urban-dwelling population by mid-century (UN 2018a). Importantly, this increase in urban population is not due simply to natural increase – birth rates – in cities, but to massive levels of rural-urban migration. Every year, mass numbers of Indians move to cities looking for improved economic and social opportunities (Government of India

2017). DESA ranks many of India’s urban agglomerations on its list of the world’s fastest growing cities and expects demographic patterns to result in a decrease in the country’s rural population by more than one-hundred million people (United Nations 2018a, United

Nations 2018b).

The economic disparity between urban and rural regions of India’s is immense, with poverty most concentrated in rural areas. People from chronically poor rural areas are especially likely to migrate, sometimes seeking work in other rural areas, sometimes seeking work in major cities. It is also notable that circular migration (otherwise known as “return migration”) is the dominant pattern of migration in India, with most rural people moving to cities only temporarily for work, then returning to their home villages.

This pattern of internal migration is important to the Indian economy, allowing rural people to find work in cities and to remit money back to their families in rural areas

156

(Deshingkar & Akter 2009, Chandrasekhar & Sharma 2015, Cali & Menon 2012). Cities play a critical role in the growth of India’s wealth as well as its poverty reduction. For example, Tripathi (2013a) finds a strong relationship between urban agglomerations and economic growth in India, while other scholars find that economic growth is strongest in

Indian districts that are more urbanized or located closer to cities (Das, Ghate, &

Robertson 2014; Cali & Menon 2012). However, the economic benefits of urbanization or rural-urban migration are not felt equally across India. Scholars have pointed to India’s urbanization processes as both widening the gap between the urban rich and poor

(Tripathi 2013b) as well as expanding the country’s middle class, which is largely concentrated in cities (Beinhocker, Farrell, & Zainulbhai 2007, Kapur 2017).

As with many aspects of Indian society, the urban-rural divide plays a significant role in security matters. India’s long-running communist insurgency is based primarily in rural areas and is fought by armed groups whose political messaging often emphasizes the economic and social deprivations of rural communities and historically disadvantaged groups (Scanlon 2018). The so-called Naxalite insurgency has continued for decades, affecting a large swathe of territory across ten of India’s largest states. In most years, fatalities generated by the conflict have been well below the one-thousand battle-death level that conflict scholars conventionally use to identify wars (UCDP 2018), but the armed movement has nevertheless proven to be quite deadly and difficult to snuff out.

In this chapter, I consider the case of India’s insurgency-ridden “Red Corridor” region to assess the alleged risks of urbanization for developing countries. I conduct this analysis using measures of two concepts relating to urbanization: Population density and

157 proximity to major cities, expecting both to be associated with lower risk of armed conflict. To summarize the results of this study, I find mixed support for the hypothesis that conflict risk declines as an area becomes more urbanized. I find much stronger support for my second hypothesis that proximity to major cities is associated with lower risk of armed conflict. These findings suggest that urbanization processes may disincentivize armed conflict by providing people with greater life opportunities or nonviolent alternatives to pursue their political interests.

6.2. The Naxalite Insurgency: Background & Theoretical Interest

India’s large rural population has long suffered from severe poverty and various forms of social and economic inequalities. These pressures have generated profound political difficulties for India, including insurgent violence, beginning in the late 1960s. In nearby

China, the Cultural Revolution began in 1966, promoting communist messages which gained significant traction in rural Indian communities frustrated by their lack of opportunities. The communist ideologies of China’s Mao were especially apparent in the rhetoric of a 1967 uprising in the town of Naxalbari, in West Bengal’s Darjeeling district.

In this case, frustrated peasants inspired by Maoism fought to seize territory from local landowners (Ahuja & Ganguly 2007, 257; Gupta 2007, 162; Lynch 2016, 9). While the insurgents were defeated by security forces, it was not the end of communist insurgency in India. Similar militias rose up in many rural districts across multiple states, forming a diagonal stripe across India. Nicknamed for the town where the insurgency began, the

“Naxalites” or “Naxal” insurgents concentrated their attacks against government officials and landowners. Violence was especially acute in areas within the states of Chhattisgarh,

158

Jharkhand, Uttar Pradesh, Andhra Pradesh (including the districts that were split off from

Andhra Pradesh in 2014 to form the new state of Telangana), Odisha, Maharashtra,

Madhya Pradesh, and West Bengal (Gupta 2007, 159). Areas affected by Naxalite violence are known collectively as the “Red Corridor” (Mukhopadhyay & Banik 2013;

Harriss 2011; Gupta 2007). Figure 1 provides cartographical depiction of the Red

Corridor region.

Figure 1. The States of India’s “Red Corridor”

India’s rurality and patterns of rural-urban migration are profoundly important for the conflict between the government and insurgents. Despite the rural setting of the original uprising in Naxalbari, the militant Maoist movement built up steam in urban areas, fueled by the participation of radicalized intellectuals (Gupta 2007, 164; Lynch

2016, 9-10). As of the early twenty-first century, India’s Maoists have expressed strong interest in recruiting urban people into their revolutionary movement. They consider an

159 urban revolution key to creating a communist government in India (Chakrabarty 2009,

82-4), but have not gained much traction in trying to operate in cities. Naxal violence has historically concentrated on targets in rural areas and militant activities are centered in the countryside, anchored in rural areas where poverty is especially severe, and education is especially poor (Borooah 2008, Ahuja & Ganguly 2007, Mukhopadhyay & Banik

2013). Naxal militants often generate revenue through racketeering, demanding money from villagers in the form of protection payments. The militias typically recruit and mobilize villagers with messages emphasizing the economic and political disadvantages of rural Indians, blaming the government for citizens’ hardships. Individuals from ethnic minority groups – commonly understood as citizens belonging to “scheduled castes” or

“scheduled tribes” as designated by the government of India – are often targeted for recruitment by rebels who capitalize on social inequalities facing those communities, compounding grievances also linked to poverty (Scanlon 2018, 341-3; Mahadevan 2012,

Borooah 2008). Annual casualty figures have remained in the hundreds quite consistently for much of the recent past. Figure 2 depicts aggregates Naxal-related conflict fatalities for all of India from 2005 to 2018, based on data published by the South Asia Terrorism

Portal (2019).

160

Figure 2. Annual Fatalities in Naxalite Conflict, 2005-2018

Annual Fatalities in Naxalite Conflict, 2005-2018 1400

1200

1000

800

600

400 Conflict Fatlities Conflict

200

0 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 Year

Indian law creates a strict divide between the responsibilities of the state and central governments. While the central government does participate in counterinsurgency campaigns, efforts to defeat the Naxalites are led by state governments

(Routray 2013a, 651). For this reason, it is important to consider the resources available to the states. States within the Red Corridor vary greatly in terms of wealth, which has affected state governments’ abilities to fund counterinsurgency operations. Some states, such as Andhra Pradesh and Telangana have created highly professionalized police units to suppress the insurgency in their states. On the other hand, some of India’s poorest states, including worst-hit Jharkhand and Chhattisgarh, have historically struggled much more in responding to the insurgency. Low resources have hampered both law enforcement programs to crack down on the insurgency as well as development programs to direct citizens away from it (Harriss 2011, Miklian 2011). The low government resources have also contributed to other problems. For example, in 2005, Chhattisgarh’s state government launched a disastrous attempt to fight the insurgency on a shoestring

161 budget by organizing villagers into vigilante groups. Alternatively, Jharkhand’s state government officials have often accepted bribes from both legal and illegal mining operations friendly with the Naxals and have therefore accordingly shied away from cracking down on them (Miklian 2011, 38-42).

In 2009, the Government of India launched a program later nicknamed

“Operation Green Hunt,” seeking to bolster counterinsurgency operations by providing higher levels of money and training to some states of the Red Corridor, as well as sending large numbers of police officers to bolster their law enforcement manpower. Operation

Green Hunt has been active since 2009 in the states of Chhattisgarh, Jharkhand, Andhra

Pradesh, Telangana, and Maharashtra (Sethi 2010). Operation Green Hunt marked a major change in India’s domestic security policy, massively escalating the use of force in reasserting government control in states whose hinterlands were most profoundly influenced by left-wing extremist groups. During this time, the Government of India more than doubled the number of center-controlled law enforcement units of the Central

Armed Police Forces (CAPF) and stepped up its collaboration and intelligence sharing with state-level law enforcement organizations (Routray 2013b, 62-3). While at first the operation corresponded with a major increase in conflict-related violence, as depicted in

Figure 2, fatalities have since leveled off (SATP 2019). For a cartographic depiction of the Indian states involved in Operation Green Hunt, see Figure 3 below.

162

Figure 3. Indian States Involved in Operation Green Hunt

While rural Indians seeking opportunities in major cities, may not necessarily limit themselves to cities nearby – or even cities in India, for that matter – Indian migrants are generally from lower-income backgrounds and prefer to seek work in places closer to home (Government of India 2017). Indian migrants commonly migrate in circular patterns, moving to a major city for work and later returning to their villages. The pattern of circular migration not only reinforces the narrow geographic range of migration but is also noted as a source of economic stimulus for rural areas due to associated employment opportunities and remittance payments (Bhagat 2017,

Government of India 2017). In the next section, I describe the research design with which

I test my theory in the context of the Naxalite insurgency.

163

6.3 Theoretical Contribution

As a country with a long history of low-level armed conflict and its society increasingly impacted large-scale urbanization processes, India is an ideal case to test the hypotheses laid out in Chapter 3. For reference, these hypotheses are as follows:

 Hypothesis 1: If an area becomes more urbanized, it will enjoy a lower risk of

armed conflict.

 Hypothesis 2: If an area is located closer to a major city or if a nearby major city

is growing at a fast rate, the risk of armed conflict in that area will be lower.

 Hypothesis 3: If an area experiences high rates of urbanization during times of

economic difficulty, its risk of urban intrastate conflicts will increase.

Analysis of the Naxalite insurgency gives better context to my theory, demonstrating how the trends and phenomena I theorize about will play out in a single country. India is a diverse country and the broad swath of area affected by Naxalite violence is quite diverse both in terms of levels of violence, urbanization, policymaking, and so forth. Despite differences in these areas, the insurgency in the Red Corridor is all linked to the same Maoist movement whose roots date back to 1967 Naxalbari uprising and its aftermath. This examination of the Red Corridor involves a large-N quantitative analysis, as was the case in Chapter 2 and Chapter 3, but the focus on India provides a greater opportunity to demonstrate the internal validity of my theory.

If my theory is strongly supported by trends in the Naxalite insurgency from

1990 to 2015, then I should find several outcomes in the empirical results. First, as the

Red Corridor districts become more densely populated – urbanization being a factor that contributes heavily to increases population density – the levels of conflict-related

164 violence should decrease. In such districts, urbanization will decrease conflict risk by some combination of increasing citizens’ economic and political opportunities as well as increasing governments’ tactical advantages and tax bases. A focus on India is further beneficial because the great availability of data on Indian society enables a deeper consideration of these potential mechanisms than is possible with a global analysis. This is largely due to the Indian government’s ability to collect and willingness to publish data to a much greater extent than one can normally expect from most developing countries.

See Section 6.6 for the results and discussion of these additional tests.

Second, if my theory is correct, then we should see less conflict violence in

Red Corridor districts located closer to major cities. Since its independence and especially since liberalizing its economy in 1991, India cities have demonstrated significant population growth. This trend coincides with India’s economic growth and has involved significant rural-urban migration as people have relocated to take advantage of the country’s more dynamic labor markets (Tumbe 2016). If my theory is correct, this should have a significant impact on Naxalite rebels’ efforts at mobilizing rural communities to revolt against the government. Urban markets may provide rural communities with economic stimulus and better opportunities to move, undermining people’s economic motives to fight against the government and incentivizing people to physically move away from areas where the Naxalites are most active.

Finally, it is important to remember that although the Naxal movement has been most active in rural areas, it has historically shown interest in extending its reach into urban areas as well (Chakrabarty 2009, Lynch 2016). As discussed previously, cities have many characteristics that normally make them difficult operating environments for

165 rebel groups. However, deteriorating economic conditions in urbanizing areas within the

Red Corridor may produce population pressures that Naxal militias can harness for recruitment and mobilization purposes. The research design discussed below outlines tests I have set up to evaluate the three hypotheses and explore the ways in which urbanization processes influence conflict behavior in the Red Corridor region.

6.4. Research Design

To test my theoretical expectations, I consider district-level data from India’s Red

Corridor, covering the years 1991 through 2015. Districts and states included in this study are based on administrative boundaries as defined in India’s 2011 census. The “Red

Corridor,” is operationalized as the states most effected by Naxalite violence, which is largely concentrated in Andhra Pradesh, Bihar, Chhattisgarh, Jharkhand, Madhya

Pradesh, Maharashtra, Odisha, Telangana, Uttar Pradesh, and West Bengal (Sharma &

Singhal 2011, 768; Gupta 2007, 159).50 A conflict occurrence dummy serves as the dependent variable in this analysis. Data are drawn from the Uppsala Conflict Data

Program’s (UCDP) Georeferenced Events Database (GED), version 18.1 (Sundberg &

Melander 2013, Croicu & Sundberg 2017). For this variable, an observation is recorded as a one if a district experienced at least one fatal conflict event in a given year, zero otherwise. Data on conflict events are drawn from the “best” casualty estimate variable included in the UCDP GED.

50 Note that the state of Telangana, heavily affected by Naxalite violence, did not split from Andhra Pradesh until 2014, so Telangana is classified as part of the Red Corridor and its constitutive districts classified as belonging to Andhra Pradesh until that year.

166

To assess the impact of urbanization and urban proximity on the occurrence of armed conflict, I include models using four separate measures of relevant “urban” factors: population density, the percent of a district’s population designated as “urban” by the Census of India, the growth rate of the major city nearest to the district, and the distance to the nearest major city. Population density data are based on population count data from the Gridded Population of the World (GPW), version 3 (CIESIN & CIAT

2005) and GPW, version 4 (CIESIN 2016) that are divided by district land area, measured in square kilometers.51 Based on the assumption that cities are more densely populated than rural areas, population density serves as a proxy for urbanization. The percent of a district’s population considered “urban” is derived from the Socioeconomic

High-Resolution Rural-Urban Geographic Dataset on India (SHRUG) dataset, version 1.4

(Asher 2019), which provides counts of rural- and urban-dwelling population counts for village and sub-district locations, based on census data originally collected by the Census of India in 1991, 2001, and 2011. District-level measures of urban population are generated by aggregating population data from each district’s constituent villages and sub-districts, then dividing the district’s urban population count by the total population size. Values for the urban population percentages are carried forward from one census year to the next to fill in variables for subsequent years, until the next round of census data became available.52 To avoid dropping observations when calculating the natural

51 Note that values are based on GPW population estimates that have been adjusted to match United Nations (UN) World Population Prospectus (WPP) country totals. 52 Note that due to the low number of observations available for this variable, it was not possible to create a reliable imputation of the urban population data. Because values are carried forward in time, temporal variation in observation values is low, despite the variable’s robust spatial variation.

167 logarithm of this variable, all values of this variable are increased by 0.001 before being logged.

Growth rate for the nearest major city is based on city-level population estimates provided in the United Nations World Population Prospectus (WPP) (2009) and rendered spatially by Ahlenius (2010). The concept of “major city” is operationalized as an Indian city included in the WPP dataset, which tracks the population growth of the world’s largest cities, providing population estimates in five-year increments. A cubic spline interpolation algorithm is employed to generate estimates for city populations in the intervening years.53 Population growth is measured as the annual percent change in cities’ estimated population levels for the major city geographically closest to the centroid of a given district. Finally, distance to the nearest major city is operationalized as the number of kilometers from a district’s centroid to the location of the nearest major city, based on city coordinates provided by Ahlenius (2010). Values for this variable are based on great-circle distances calculated using the Haversine formula.54

In addition to the independent variables discussed above, the models in this study include the following control variables:

 District Population: A logged estimate of the total number of people living in a

district during a given year. As with the population density measure, this variable

is calculated from the GPW 3 (CIESIN & CIAT 2005) and GPW 4 (CIESIN

2016) datasets. As this variable is measured in five-year increments, cubic spline

53 While many methods exist for interpolating data, cubic spline interpolation is ideal for use with demographic data as it produces estimates with smoother fitted lines, allowing for more accurate or realistic estimates. See McNeil, Trussell, and Turner (1977) for further information about using cubic splines to interpolate demographic data. 54 See Robusto (1957), Gleditsch and Ward (2001), and Mahmoud and Akkari (2016) for further information about distance calculations using the Haversine Formula.

168

interpolation is used to generate estimated population counts for the intervening

years.

 Major City Population: A logged estimate of the population level for each the

major cities designated as a “nearest major city” to a given district from the Red

Corridor. As this variable is measured in five-year increments, a cubic spline

algorithm is used to generate estimated values for the intervening years. Note that

this control is only included in Model 3, in which major city population growth

rate serves as the primary independent variable.

 Distance from State Capital: A logged measure of the number of kilometers from

a district’s centroid to the capital city of the state in which the district is located.

Given that state governments take a leading role in India’s counterinsurgency

operations, the state capitals are particularly important as administrative centers.

This variable accounts for the possibility that administration of state policies

might be easier for the state to manage in areas geographically closer to the seat

of government.55

 GDP Per Capita: The logged average annual income for a person living in a

district. Values are derived from data published by Kummu, Taka, and Guillaume

(2019).

 Percent Scheduled Castes and Tribes: The logged percentage of a district’s

population comprised of people from scheduled castes or scheduled tribes. This

55 Note that of the Red Corridor states in the 1990-2015 period, Maharashtra is the only one to have two capital cities, Mumbai and Nagpur. For this reason, distance from the capital for Maharashtra’s is recorded according which of the capitals is geographically closest to a district’s centroid. If Mumbai is closest, then the logged number of kilometers to Mumbai is recorded. Alternatively, if Nagpur is closest, then the logged number of kilometers to Nagpur is recorded.

169

variable is derived from Indian census data as recorded in the SHRUG dataset

(Asher et al. 2019) and accounts for the fact that although Naxalite militias are

generally led by relatively more-educated people often from middle class

backgrounds, rank and file Naxal fighters are often recruited from historically

marginalized ethnic or social groups (Scanlon 2018, 341-3). As with the SHRUG-

derived urban population variable, this measure of scheduled castes and tribes is

aggregated from all a district’s constituent villages and sub-districts and dividing

by a district’s total population. As this variable is measured in censuses of 1991,

2001, and 2011, values have been imputed to generate estimates for the other

years in the sample.56

 Police Strength: The number of police officers (expressed in thousands) in a state

divided by the state’s population. This includes both armed and civil police

officers employed by state and local governments. As counterinsurgency

operations in India are conducted primarily by law enforcement organizations

(Routray 2013a), this variable indicates the capacity of states to suppress Naxalite

insurgents.

 Operation Green Hunt Dummy: A binary indicator of whether a district is located

within a state that received assistance under the Operation Green Hunt scheme

during a given year. This accounts for the possibility that levels of conflict

violence may have changed as a result of resources transferred to state

governments and their counterinsurgency programs through Operation Green

56 Linear interpolation and extrapolation algorithms have been used in imputing these values as they generate fewer negative values than would be possible with a cubic spline algorithm. Any negative values generated with through interpolating or extrapolating the scheduled caste and tribe data have been dropped from the analysis, although this involves only a small number of observations.

170

Hunt. For reference, Operation Green Hunt was active in the states of

Chhattisgarh, Jharkhand, Andhra Pradesh (including the districts which came to

form the state of Telangana in 2014), and Maharashtra from 2009 onward.

 Lagged Conflict Dummy: A binary indicator of whether a fatal conflict event

occurred in a district during the previous year. This variable accounts for temporal

dependence in armed conflict risk.

Geospatial data rendering and calculations for this analysis are made using

ArcGIS software, using the World Geodetic System 1984 (WGS84) datum to identify locations of relevant points and polygons used in calculating variables’ values. I use logistic regression models to analyze these data, given that they are well suited for use with binary outcome variables. Using population density as an indicator of a district’s level of urbanization, Model 1 and Model 2 are used as a test of Hypothesis 1, that conflict risk should decline as an area urbanizes. Model 3 and Model 4 are tests of

Hypothesis 2, that conflict risk should decline in areas closer to cities, especially as those cities are growing. Except for the geographic distance variables which do not change from year to year and the major city growth rate variable which measures population growth from the previous year to the current year, all explanatory variables are lagged by one year to better assess their impact on conflict levels. In each logit model, robust standard errors are clustered by district to provide better insight into the district-by- district variation levels of armed conflict, given the set of variables specified in each model. All models contain 6,976 observations from 306 districts from the states of

India’s Red Corridor.

171

A potential inefficiency of the data used in the logit models discussed thus far is that they include data measured at different levels of analysis. For example, armed conflict and population density are district-level measures, whereas variables for police strength and participation in Operation Green Hunt are state-level measures. To account for this, I also utilize random effects logit models without clustering standard errors.

Random effects models allow variables’ slopes and intercepts to vary in a regression, which can help get around potential problems that come with using variables from different levels of analysis, as the nesting of districts within states reduces the independence of observations from one another for districts nested within the same state

(Bell & Jones 2015; Harrison et al. 2018). The spatial units included in this analysis are all based on Indian district boundaries as determined by the 2011 Census of India districting scheme.

Modeling for tests of the first and second hypotheses is based on the following equation:

푉 = 훽 + 훽푢푟푏푎푛푖푧푎푡푖표푛() + 푋() + 휀

In this formula, Vit is the risk of violence in district i and year t, β0 is the intercept, urbanization a district’s population density, number of major cities, or distance from a major city, X is a suite of control variables and εit is the error term for a district i in year t.

Tests of Hypothesis 3 focuses on the conditioning effect of changing economic factors on the urbanization-conflict relationship and is therefore represented with the following equation:

푉 = 훽 + 훽 푢푟푏 푟푎푡푒() ∗ 훽 ln (∆ 푒푐표푛 푐표푛푑푖푡푖표푛푠) + 푋() + 휀

172

Where Vit is the risk of violence in district i and year t, β0 is the intercept, urbanization rate is the annual change in the percent of a district’s population density, ∆ econ conditions is the district’s annual change in unemployment or GDP per capita, X is a suite of control variables and εit is the error term for a district i in year t.

6.5. Results & Discussion

Empirical tests using logistic regression models reveal little support for

Hypothesis 1, which states that conflict risk should decline as in more urbanized areas. In evaluating this theory with variables that proxy both for urbanization and urbanization rate, the relationship between urbanization and conflict is statistically insignificant in both the logistic and random effects logistic regression models. For reference, statistical results for Models 1A-2B are displayed in Table 1. It is worth noting that the coefficients for the urbanization variables are negative in Models 1 and 2 as predicted, even though the coefficients ability to predict armed conflict occurrence is indistinguishable from zero. In the next section, I include robustness checks for this hypothesis which yield further insights into the urbanization-conflict nexus but based on the results from the first two models, it appears that Hypothesis 1 is unsupported by the empirical tests.

173

Table 1. Logistic Regression Results

Basic Models Random Effects Models DV: Fatal Conflict Dummy Model 1A Model 2A Model 1B Model 2B

Pop Density ln(t-1) -0.086 -0.018 (0.053) (0.092) Δ Pop Density (t-1) -5.740 -4.783 (10.58) (16.43) Pop ln(t-1) 0.045 0.158 0.075 0.156 (0.123) (0.220) (0.153) (0.313) GDP Per Capita ln(t-1) 0.298 0.308 1.758*** 1.753*** (0.299) (0.300) (0.415) (0.415) % Sched Castes & Tribes ln(t-1) -0.063* -0.034 -0.060 -0.056 (0.037) (0.032) (0.048) (0.044) Police Strength (t-1) 0.214 0.260 0.740*** 0.745*** (0.204) (0.200) (0.229) (0.227) Distance to State Capital ln -0.156** -0.133** -0.281** -0.279** (0.061) (0.062) (0.127) (0.126) Operation Green Hunt (t-1) 0.162 0.145 -0.414* -0.419* (0.165) (0.165) (0.228) (0.228) Neighbor Fatal Conflict (t-1) 1.925*** 1.918*** 1.717*** 1.715*** (0.134) (0.134) (0.140) (0.140) Lagged DV 2.402*** 2.409*** 1.441*** 1.441*** (0.133) (0.134) (0.133) (0.133) Constant -4.155*** -3.665** -9.230*** -8.782*** (1.197) (1.497) (1.629) (2.201)

N 6976 6976 6976 6976 BIC 3171.7 3175.1 3040.6 3040.5

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Table 2 displays regression results for tests of Hypothesis 2, which predicts the risk of armed conflict to be lower in areas near large or growing cities. Models 3A-4B test this hypothesis and indicate robust support for it. As show in Model 3A and 3B, an area’s conflict risk tends to decline when the population of the nearest major city is growing. This suggests support for my theory that major cities can reduce the willingness of people in nearby rural communities to engage in conflict as the cities provide them

174 with better opportunities to instead conduct business or migrate. Model 4A shows that when a district is located further away from a major city, it is more likely to experience an armed conflict event, a finding significant at the 95% confidence level. Although distance to the nearest major city did not return a statistically significant coefficient in the random effects model, Model 4B, the coefficient is positive, as expected.

To make my results more interpretable, I include marginal effects plots below in Figures 4 and 5, which correspond to Model 3A and Model 4A, respectively. In calculating the expected risk of an armed conflict event occurring, I model the marginal effects on a hypothetical Red Corridor district which is not involved in Operation Green

Hunt, where there was no armed conflict in the previous year, and where all other explanatory variables are held at their median values. The predictive margins for Model

3A show that there is a 7% risk that such a district will experience armed conflict when its population remains at the lowest levels of urbanization. When its population reaches the highest levels of urbanization seen in the sample, the same district will see its risk of armed conflict reduced by more than half, falling to about 3%.

175

Table 2. Logistic Regression Results

Basic Models Random Effects Models DV: Fatal Conflict Dummy Model 3A Model 4A Model 3B Model 4B

Δ Nearest Major City Pop ln(t-1) -13.29** -37.88*** (5.384) (6.804) Distance to Maj City ln(t-1) 0.265** 0.170 (0.109) (0.172) Pop ln(t-1) 0.060 0.0644 0.074 0.071 (0.123) (0.124) (0.156) (0.153) GDP Per Capita lnt-1) 0.092 0.295 0.702 1.570*** (0.318) (0.291) (0.449) (0.412) % Sched Castes & Tribes ln(t-1) -0.036 -0.040 -0.058 -0.068 (0.034) (0.032) (0.044) (0.045) Police Strength (t-1) 0.268 0.304 0.730*** 0.833*** (0.209) (0.210) (0.234) (0.231) Distance to State Capital ln -0.145** -0.289*** -0.261* -0.284* (0.067) (0.104) (0.134) (0.169) Operation Green Hunt (t-1) 0.045 0.132 -0.547** -0.482** (0.177) (0.173) (0.228) (0.229) Pop of Major City ln(t-1) 0.040 0.066 0.247 0.426** (0.106) (0.0966) (0.176) (0.173) Neighbor Fatal Conflict (t-1) 1.880*** 1.868*** 1.611*** 1.667*** (0.136) (0.135) (0.143) (0.142) Lagged DV 2.375*** 2.387*** 1.359*** 1.408*** (0.136) (0.136) (0.132) (0.134) Constant -3.332** -5.012*** -6.820** -12.49*** (1.604) (1.432) (2.416) (2.122)

N 6976 6976 6976 6976 BIC 3175.2 3173.7 3024.9 3041.2

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

176

Figures 4 – 5. Predictive Margins for Models 3A & 4A

Predictive Margins with 95% CIs Predictive Margins with 95% CIs .1 .08 .08 .06 .06 .04 .04 .02 .02 Probability of Fatal Conflict of Fatal Event Probability Conflict of Fatal Event Probability 0 0

.5 1.5 2.5 3.5 4.5 5.5 6.5 7.5 8.5 9.5 .44 .94 1.44 1.94 2.44 2.94 3.44 3.94 4.44 4.94 5.44 Δ Major City Pop, logged, lagged Distance to Nearest Major City, logged

To assess the impact of urbanization in areas with declining economies, I conduct a series of tests in which district urbanization rates are interacted with various measures of economic conditions. Results from these tests are presented in Table 3 and

Table 4 below and generally do not support my theoretical expectations as stated in

Hypothesis 3. As shown for models 5A-7B, higher urbanization rates are not significantly associated with armed conflict in districts whose states experience changes in either urban or rural unemployment. This holds across both the standard logistic regression models as well as the random effects models. Compared to the state-level measures of urban and rural unemployment, district-level changes in GDP per capita are more important in determining or predicting a district’s conflict risk. However, the interaction effects of urbanization rates and economic conditions goes against my theoretical expectations, at least in Models 5A and 5B.

Wealth is typically considered a negative correlate of armed conflict

(Braitwaite, Dasandi, & Hudson 2016; Collier 2003), but in Models 5A and 5B, the interaction of urbanization rate and GDP per capita on armed conflict produce a

177 significant positive coefficient in the regression results. This indicates that higher urbanization rates are more strongly associated with armed conflict in areas where people’s incomes are quickly increasing, compared to those where incomes are stagnant or falling. In Figures 6 and 7, I present predictive probabilities for the risk of conflict in

Red Corridor districts, based on analysis from Model 5A. Figure 6 represents the risk of armed conflicts occurring in districts with no prior conflict in the previous year, which are not included in Operation Green hunt, and where income growth is negative, given the lowest rate of economic growth possible in the sample of Red Corridor states. In such a district, conflict risk declines as urbanization rates increase. One can expect a 97% chance of armed conflict occurring in such a district during times when urbanization is at the lowest levels – falling by 10% in a year – found in the sample of Red Corridor districts. The risk of armed conflict falls to 12% when urbanization levels are at zero and less than 4% when urbanization rates are at 2%. Estimated conflict risk in a hypothetical district becomes statistically significant at higher levels of urbanization, given that citizens’ incomes are falling fast in that district. Figure 7 shows how the urbanization- conflict relationship plays out in a district with the highest levels of per capita GDP growth. Calculating predicted probabilities of conflict in such a district, I find that when a district’s population density is shrinking or holding steady, the estimated risk of armed conflict is statistically insignificant. When urbanization rates rise to 2% in a district with the fastest-growing income levels, the risk of armed conflict is estimated at 10%. Finally, when the urbanization rate is at or above 10%, the estimated risk of an armed conflict event is greater than 99%.

178

Because rural and urban unemployment statistics do not significantly impact mediate the relationship between urbanization and conflict, findings from the models utilizing the GDP per capita variable are somewhat surprising. The idea that rising incomes should correlate with an increase in violence goes against conventional wisdom that prosperous societies are less likely to experience severe violence. A variety of factors may have created this trend, but they require further analysis. At this point, it is possible, however, to speculate about several reasons why this trend may be so. For example, in

1963, Mancur Olson’s predicted that rapid economic growth could create conditions for internal strife, including war, should the growth disrupt the country’s internal status quo.

Second, it is possible that Naxalite militias may intentionally target places where wealth is increasing. This is not normally what one would expect from rebel groups, given the ways money disincentivizes violence. However, India’s Maoists, as a communist political group, are particularly sensitive to the socioeconomic inequalities that often come with growth. It is also possible that the Naxalites’ disdain for the wealthy may motivate them to select targets in places where people are wealthier. For example, Naxals’ ideology condemns the upper class and Naxalite militias have a history of targeting landowners and others who they see as representing social injustice (Ahuja & Ganguly 2007, Gupta

2007).

179

Table 3. Logistic Regression Results

Basic Model Random Effects Model DV: Fatal Conflict Dummy Model 5A Model 5B

Δ Pop Density (t-1) -409.0** -410.7*** (170.4) (155.7) Δ GDP Per Cap (t-1) -4.996 -3.837 (3.189) (3.155) Pop ln(t-1) 0.191 0.211 (0.233) (0.329) GDP Per Capita ln(t-1) 0.188 1.469*** (0.311) (0.424) % Sched Castes & Tribes ln(t-1) -0.033 -0.059 (0.031) (0.044) Police Strength (t-1) 0.281 0.780*** (0.202) (0.228) Dist to State Capital ln -0.137** -0.288** (0.062) (0.127) Operation Green Hunt (t-1) 0.131 -0.430* (0.164) (0.228) Neighbor Fatal Conflict (t-1) 1.918*** 1.736*** (0.136) (0.141) Lagged DV 2.429*** 1.439*** (0.135) (0.133) Δ Pop Density * Δ GDP Per Cap 381.4** 382.4*** (158.1) (145.6) Constant 2.167 -3.482 (3.895) (4.073)

N 6976 6976 BIC 3183.1 3042.6

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

180

Figures 6 – 7. Predictive Margins for Models 5A and 5B

Predictive Margins with 95% CIs Predictive Margins with 95% CIs 1.5 1.5 1 1 .5 .5 0 Probability of Fatal Conflict Conflict Fatal Eventof Probability Probability of Armed Conflict Event Conflict Probability ofArmed 0 -.5 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 -10 -8 -6 -4 -2 0 2 4 6 8 10 12 14 Δ Pop Density (%), lagged Δ Pop Den (%), lagged

181

Table 4. Logistic Regression Results

Basic Model Random Effects Model DV: Fatal Conflict Dummy Model 6A Model 7A Model 6B Model 7B

Δ Pop Density (t-1) 4.576 -8.580 28.96 -0.145 (45.60) (28.42) (51.67) (33.61) Pop ln(t-1) 0.163 0.160 -0.306 0.157 (0.220) (0.220) (0.920) (0.315) GDP Per Capita ln(t-1) 0.336 0.374 1.877*** 1.930*** (0.305) (0.305) (0.426) (0.428) % Sched Castes & Tribes ln(t-1) -0.0347 -0.035 -0.0580 -0.059 (0.0313) (0.031) (0.0444) (0.044) Police Strength (t-1) 0.234 0.236 0.698*** 0.749*** (0.200) (0.202) (0.233) (0.228) Dist to State Capital ln -0.132** -0.130** -0.278** -0.277** (0.062) (0.062) (0.129) (0.128) Operation Green Hunt (t-1) 0.168 0.147 -0.373 -0.452** (0.165) (0.166) (0.233) (0.229) Neighbor Fatal Conflict (t-1) 1.909*** 1.932*** 1.726*** 1.732*** (0.137) (0.136) (0.142) (0.142) Lagged DV 2.415*** 2.422*** 1.426*** 1.423*** (0.134) (0.133) (0.133) (0.133) Δ Urban Unemployment (t-1) -0.372 -0.555 (0.788) (0.912) Δ Rural Unemployment (t-1) -0.472 -0.428 (0.395) (0.523) Δ Pop Den (t-1) * Δ Urb Unemp (t-1) -10.32 -33.36 (43.63) (49.43) Δ Pop Den (t-1) * Δ Rur Unemp (t-1) 2.745 -4.264 (23.12) (27.72) Constant -3.360* -3.391** -8.981*** -8.979*** (1.785) (1.623) (2.384) (2.281)

N 6968 6968 6968 6968 BIC 3191.6 3187.2 3048.9 3048.4

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

6.6. Robustness Checks & Additional Analysis

To better understand the ways that urbanization might affect an area’s conflict propensity, I include a battery of additional tests. First, I include a series of models using a different version of the dependent variable. While the other models only account for

182 conflict events in which the best estimate of fatalities referenced in the UCDP GED data is at least one, this excludes events which were either non-fatal or in which fatalities were suspected but not confirmed. I run additional logistic regression models for which I code an observation with a one if any conflict event occurred in the district during that year, zero otherwise. As with the main models for this chapter, all explanatory variables described in this section are lagged by one year. Results for models accounting for all conflict events are presented in Table 5. I also use a count of all fatal conflict events in a district-year observation as an alternative method of operationalizing the dependent variable. Instead of a binary indicator of whether a fatal event occurred – the dependent variable from the main models – I include an annual count of fatal conflict events in each of the Red Corridor districts. Of the district-year observations covered in this study, approximately 90% experienced zero conflict events. To account for this low number of positive observations by using zero-inflated negative binomial regression analysis for these count models. Results for these models are presented in Tables 6 and 7.

In Table 5, I present Models 8-11, in which the dependent variable covers all conflict events occurring in the Red Corridor districts, regardless of the UCDP’s best estimate of fatality levels. Results for these models are remarkably similar to those in

Models 1-4, whose dependent variables are conflict events for which the UCDP’s best estimate of fatality levels is above zero. As with the logistic regressions for models for the fatal conflict events, these models return statistically insignificant negative coefficients for the relationship between conflict and urbanization, defined both in terms of level and rate. The robustness checks represented in Model 8 and Model 9 therefore do not provide support for Hypothesis 1. As with the analysis utilizing fatal conflict events

183 as the dependent variable, Models 9 and 10 both suggest support for Hypothesis 2. As expected, Model 10 shows that the probability of conflict events declines as the populations of major cities grow. Additionally, conflict risk is higher for districts located further away from major cities.

Table 5. Logistic Regression Results

DV: Any Conflict Dummy Model 8 Model 9 Model 10 Model 11

Pop Density ln(t-1) -0.075 (0.053) Δ Pop Density (t-1) -3.024 (11.120) Δ Nearest Major City Pop ln(t-1) -13.88*** (5.281) Dist Nearest to Major City ln (t-1) 0.268** (0.110) Pop ln(t-1) 0.070 0.137 0.083 0.087 (0.123) (0.223) (0.123) (0.123) GDP Per Capita ln(t-1) 0.348 0.358 0.129 0.344 (0.296) (0.297) (0.314) (0.288) % Sched Castes & Tribes ln(t-1) -0.061* -0.036 -0.038 -0.042 (0.036) (0.031) (0.034) (0.032) Police Strength (t-1) 0.219 0.261 0.269 0.305 (0.205) (0.203) (0.211) (0.211) Dist to State Capital ln -0.169*** -0.149** -0.162** -0.308*** (0.062) (0.062) (0.068) (0.104) Operation Green Hunt (t-1) 0.191 0.177 0.074 0.161 (0.161) (0.161) (0.174) (0.170) Neighbor Fatal Conflict (t-1) 1.933*** 1.928*** 1.887*** 1.876*** (0.133) (0.133) (0.135) (0.134) Nearest Major City Pop ln(t-1) 0.036 0.065 (0.105) (0.095) Lagged DV 2.377*** 2.383*** 2.347*** 2.360*** (0.127) (0.128) (0.130) (0.130) Constant -4.135*** -3.880** -3.231** -4.984*** (1.173) (1.536) (1.580) (1.410)

N 6976 6976 6976 6976 BIC 3237.9 3240.7 3239.9 3238.6

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

184

In another test of my first two hypotheses, I run zero-inflated negative binomial models to determine whether urbanization or being close to a large and growing city may influence a district’s conflict propensity. These models are depicted in Tables 6 and 7.

With these models, conflict propensity is measured in terms of a district’s total number of fatal conflict events, not just with a dummy indicator of whether any such event occurred.

Unlike with the logistic regression models, the negative binomial models indicate a moderate level of support for Hypothesis 1. As shown in Table 12, there is a negative relationship between urbanization level and the number of conflict events that occur, a finding that is statistically significant at the 90% confidence level.

It is worth noting that in Models 12 and 13, urbanization level is a significant predictor only of the number of conflict events and does not provide a good indication of whether any event will occur at all. However, unlike the logistic regression models discussed in the previous section, the zero-inflated negative binomial model in Model 13 does not return a statistically significant coefficient for the urbanization rate variable.

This indicates that although conflict violence tends to be lower in districts with high population densities, a meaningful increase in an urban population will not drive up the risk of internal conflict.

185

Table 6. Zero-Inflated Negative Binomial Regression Results

Count of Fatal Conflict Events Model 12 Model 13 (count equation) Coefficient Std. Error Coefficient Std. Error

Pop Density ln(t-1) -0.123* (0.075) Δ Pop Density (t-1) 38.91 (27.630) Pop ln(t-1) -0.054 (0.169) -0.646* (0.384) GDP Per Capita lnt-1) 0.199 (0.314) 0.282 (0.346) % Sched Castes & Tribes ln(t-1) -0.039 (0.047) 0.004 (0.046) Police Strength (t-1) 0.433*** (0.160) 0.466*** (0.152) Distance to State Capital ln 0.153 (0.114) 0.185 (0.118) Operation Green Hunt (t-1) -0.315* (0.162) -0.353** (0.157) Neighbor Fatal Conflict (t-1) 0.794*** (0.275) 0.814*** (0.267) Fatal Conflict Events (t-1) 0.133** (0.028) 0.136*** (0.026) Constant -2.750** (1.166) -6.210** (2.420)

Probability of Fatal Conflict (inflations equation) Coefficient Std. Error Coefficient Std. Error

Pop Density ln(t-1) 0.015 (0.090) Δ Pop Density (t-1) 33.24 (20.28) Pop ln(t-1) -0.109 (0.193) -0.645* (0.342) GDP Per Capita lnt-1) 0.122 (0.475) -0.058 (0.477) % Sched Castes & Tribes ln(t-1) 0.032 (0.074) 0.031 (0.055) Police Strength (t-1) 0.428 (0.335) 0.372 (0.321) Distance to State Capital ln 0.354** (0.164) 0.350** (0.161) Operation Green Hunt (t-1) -0.333 (0.316) -0.341 (0.317) Neighbor Fatal Conflict (t-1) -1.641*** (0.248) 1.618*** (0.243) Fatal Conflict Events (t-1) -2.258*** (0.227) -2.268*** (0.220) Constant 0.555 (1.735) -2.430 (2.556)

N 6976 6976 BIC 5827.8 5832.0

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

186

Figure 8. Predictive Margins for Model 12

Predictive Margins with 95% CIs .5 .4 .3 .2 Predicted Of Predicted Events Number .1 0

-7.5 -6.5 -5.5 -4.5 -3.5 -2.5 -1.5 -.5 .5 1.5 2.5 Population Density, logged, lagged

Next, I run a series of models in which I interact urbanization with a series of explanatory variables relating to the mechanisms through which I theorize urbanization and proximity to major cities may decrease an area’s conflict propensity. As discussed in

Chapter 3, urbanization is associated with a variety of factors which I expect will reduce a population’s willingness to support an armed movement or aid government forces hoping to suppress insurgents. These factors include economic gains from industrialization associated with the industrialization of urbanizing areas, expanded opportunities for urbanites to participate nonviolently in politics, the government’s tactical advantages in suppressing insurgencies in urban areas, and the government’s ability access greater amounts of tax revenue from urban areas. To determine if these factors have influence over urbanization’s impact on conflict levels, I include tests in which I interact urbanization with the following variables:

187

 GDP Per Capita: The same district-level measure of GDP per capital used in the

earlier models.

 Subnational Democracy: A state-level measure of electoral democracy in India,

based on the “n_small_index5” variable as coded by Harbers, Bartman, and van

Wingerden (2019). This is a continuous measure based on a 0-2 scale that

measures subnational levels of democracy in India, based on party turnover in

state-level legislative elections and a measure of contestation in statewide

elections. The later of these is based on a curvilinear transformation of the

effective number of parties participating in legislative elections and the

normalized percentage of seats controlled by opposition parties in the state

legislature.57 This measure of subnational democracy is used as a proxy measure

of citizens’ opportunities to peacefully seek political change. This is based on the

assumption that such opportunities are more likely to exist in a more competitive

democratic environment.

 Police Strength: The same state-level measure of police officers per thousand

citizens as is used in thus far in this study. Joes (2007) and Connable and Libicki

(2010) argue that government security forces have advantages in more urbanized

areas due to the state’s greater presence and capacities in those areas. If this logic

holds, the risk of armed conflict should be lower in areas where state security

forces have a stronger presence.

57 Note that while Harbers, Bartman, and Wingerden (2019) have another index of subnational democracy, which also considers whether states have “clean” elections. Because a lack of political violence is used as a determinant of whether elections are clean, inclusion of such an index variable in my models would create an endogeneity problem, given that political violence is the outcome I analyze. I have instead used the “small” index, which sidesteps endogeneity issues by excluding consideration of clean elections.

188

 Tax Revenue: A state-level measure of the annual amount of tax revenue

extracted by a state government. This data is derived from data published by the

Reserve Bank of India.58 In order to account for overdispersion in the data, I

employ a natural logarithmic transformation of the tax revenue data.

Next, I conduct additional tests of Hypothesis 2 to evaluate the ways in which proximity to urban areas may influence a district’s conflict propensity. I proposed two theoretical reasons why an area might benefit from proximity to cities. I theorized that the presence of a large city nearby affords rural people an opportunity to move to the city, physically removing themselves from the village settings from which rebel forces often recruit. Additionally, I contend that cities may decrease conflict risk by stimulating the economies of nearby areas, thus decreasing socioeconomic grievances in those areas. I assess the role of urban “economic engines” on the urbanization-conflict nexus using an additional set of models in which I interact urbanization with the GDP per capita of the wealthiest neighboring district. This is done with a log-transformed measure of GDP per capita as per the Kummu, Taka, and Guillame (2018) data. Neighbors, in this case, are understood as the Red Corridor districts directly contiguous to one’s own district. The utility of this test turns on the logic that the presence of wealthy neighbor districts provides both economic opportunities and incentives for rural-urban migration.

Models 14 and 15, shown in Table 7, present zero-inflated negative binomial models which further test the assumptions of my second hypothesis. The findings are only significant for Model 14, which shows that as a major city’s population grows, the

58 For years 2002-2015, data on state tax revenues in India comes from the Reserve Bank of India’s “State Finances: A Study of Budgets.” Tax revenue data from other years comes from the state-wise details of revenue receipts found in the Reserve Bank of India’s Handbook on State Government Finances (2010).

189 districts nearby are likely to see their conflict levels go down. Distance from a major city, however, does not appear to influence the predicted number of fatal conflict events a district is likely to incur. Predictive margins for Model 12 are displayed below in Figure

8. I predict that if a district has no armed conflict in the previous year is not part of

Operation Green Hunt and has all control variables held at their mean values, the country will go from a predicted 0.25 conflict events at the lowest levels of urbanization. This number falls to 0.7 conflict events when a district experiences the highest levels of urbanization. While these predicted counts are low to begin with, the predicted count falls my more than two-thirds when one moves from the highest to lowest levels of urbanization.

190

Table 7. Zero-Inflated Negative Binomial Regression Results

Count of Fatal Conflict Events Model 14 Model 15 (count equation) Coefficient Std. Error Coefficient Std. Error

Δ Nearest Major City Pop ln(t-1) -19.77*** (7.251) Dist to Nearest Major City ln(t-1) 0.065 (0.167) Pop ln(t-1) -0.096 (0.161) -0.074 (0.165) GDP Per Capita lnt-1) 0.272 (0.393) 0.392 (0.396) % Sched Castes & Tribes ln(t-1) 0.013 (0.030) 0.014 (0.044) Police Strength (t-1) 0.368** (0.173) 0.533*** (0.162) Distance to State Capital ln 0.121 (0.118) 0.127 (0.172) Operation Green Hunt (t-1) -0.358** (0.163) -0.352** (0.168) Neighbor Fatal Conflict (t-1) 0.604** (0.273) 0.693*** (0.264) Nearest Major City Pop ln(t-1) 0.158 (0.128) 0.175 (0.147) Fatal Conflict Events (t-1) 0.127*** (0.028) 0.131*** (0.030) Constant -3.404 (2.221) -4.801** (2.342)

Probability of Fatal Conflict (inflations equation) Coefficient Std. Error Coefficient Std. Error

Δ Nearest Major City Pop ln(t-1) 1.864 (8.352) Dist to Nearest Major City ln(t-1) -0.175 (0.189) Pop ln(t-1) -0.160 (0.191) -0.142 (0.191) GDP Per Capita lnt-1) 0.261 (0.502) 0.050 (0.497) % Sched Castes & Tribes ln(t-1) 0.040 (0.047) 0.045 (0.053) Police Strength (t-1) 0.258 (0.335) 0.349 (0.322) Distance to State Capital ln 0.290* (0.157) 0.391** (0.189) Operation Green Hunt (t-1) -0.175 (0.305) -0.287 (0.308) Neighbor Fatal Conflict (t-1) -1.720*** (0.244) -1.661*** (0.238) Nearest Major City Pop ln(t-1) 0.043 (0.169) -0.015 (0.176) Fatal Conflict Events (t-1) -2.163*** (0.213) -2.213*** (0.228) Constant -0.820 (2.515) 0.622 (2.329)

N 6976 6976 BIC 5827.9 5843.5

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

In my theory, I discuss several ways in which certain benefits of urbanization may contribute to a negative relationship between urbanization and armed conflict. These factors include urbanization’s ability to improve average people’s lives through increased

191 wealth and the inclusion of greater levels of political opportunities. I also theorized that urbanization could help the government suppress armed movements by concentrating the population in areas where the government has a tactical advantage or by helping to generate tax revenue with which governments can fund counterinsurgency operations. In

Tables 8 and 9, I present random effects logistic regression models in which I interact urbanization levels and rates with variables representing these four benefits.

Models 16-19 return statistically insignificant coefficients for the interaction of urbanization with GDP per capita as well as subnational democracy scores. This indicates that neither wealth nor subnational democracy levels make meaningful impacts on the way urbanization influences districts’ conflict propensity. The story is somewhat different in Table 9, which shows that conflict risk is likely to be significantly lower if urbanization occurs in a district belonging to a state with a larger police force. Conflict levels also appear to decline somewhat in cases where population density levels are high and tax revenue goes up. These findings are significant at the 90% and 95% confidence level, respectively. Based on these interaction effects, it is possible to say that despite the threats attributed to urbanization, demographic shifts are likely to be much smoother in districts which possess larger police forces, or which take in larger amounts of tax revenue.

192

Table 8. Random Effects Logistic Regression Results

DV: Fatal Conflict Dummy Model 16 Model 17 Model 18 Model 19

Pop Density ln(t-1) -0.024 -0.192* (0.417) (0.108) Δ Pop Density (t-1) 104.400 7.314 (113.700) (25.23) Pop ln(t-1) 0.074 0.220 0.037 0.131 (0.235) (0.328) (0.104) (0.318) GDP Per Capita ln(t-1) 1.729*** 2.319*** 0.111 1.244*** (0.415) (0.747) (0.229) (0.414) % Sched Castes & Tribes ln(t-1) -0.061* -0.056 -0.073** -0.068 (0.048) (0.043) (0.031) (0.045) Police Strength (t-1) 0.744*** 0.747*** -0.005 0.481** (0.229) (0.227) (0.181) (0.231) Dist to State Capital ln -0.287** -0.281** -0.161*** -0.260** (0.128) (0.127) (0.058) (0.127) Operation Green Hunt (t-1) 0.407* -0.415* 0.154 -0.415* (0.228) (0.227) (0.186) (0.227) Neighbor Fatal Conflict (t-1) 1.734*** 1.729*** 1.837*** 1.606*** (0.141) (0.141) (0.117) (0.143) Democracy (t-1) -0.399** -0.784*** (0.165) (0.304) Pop Den ln(t-1) * GDP Per Cap ln(t-1) -0.000 (0.100) Δ Pop Den (t-1) * GDP Per Cap ln(t-1) -32.970 (34.350) Pop Den ln(t-1) * Democracy ln(t-1) 0.072 (0.073) Δ Pop Den (t-1) * Democracy ln(t-1) -6.501 (14.090) Lagged DV 1.434*** 1.430*** 2.365*** 1.331*** (0.133) (0.133) (0.106) (0.134) Constant -9.148*** -10.420*** -2.658*** -5.791*** (1.769) (2.792) (0.975) (2.276)

N 6976 6976 6976 6976 BIC 3047.2 3046.2 3167.3 3020.1

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

193

Table 9. Random Effects Logistic Regression Results

DV: Fatal Conflict Dummy Model 20 Model 21 Model 22 Model 23

Pop Density log(t-1) -0.172 0.349 (0.191) (0.243) Δ Pop Density (t-1) 52.960* -44.51 (30.24) (35.95) Pop ln(t-1) 0.072 0.157 0.081 0.151 (0.154) (0.310) (0.154) (0.318) GDP Per Capita ln(t-1) 1.708*** 1.701*** 2.136*** 2.088*** (0.415) (0.413) (0.514) (0.506) % Sched Castes & Tribes ln(t-1) -0.064 -0.057 -0.057 -0.056 (0.048) (0.044) (0.047) (0.043) Police Strength ln(t-1) 0.916*** 1.714*** 0.760*** 0.734*** (0.301) (0.500) (0.231) (0.227) Dist to State Capital ln -0.285** -0.286*** -0.261** -0.263** (0.127) (0.127) (0.129) (0.126) Operation Green Hunt (t-1) -0.392* -0.424* -0.403* -0.337 (0.228) (0.228) (0.238) (0.234) Neighbor Fatal Conflict (t-1) 1.735*** 1.738*** 1.742*** 1.732*** (0.141) (0.141) (0.141) (0.141) Tax Revenue (t-1) -0.244** -0.297* (0.115) (0.163) Pop Den ln(t-1) * Police Strength (t-1) 0.139 (0.156) Δ Pop Den (t-1) * Police Strength (t-1) -56.000** (25.300) Pop Den ln(t-1) * Tax Revenue ln(t-1) -0.081* (0.049) Δ Pop Den (t-1) * Tax Revenue ln(t-1) 9.339 (7.305) Lagged DV 1.436*** 1.420*** 1.433*** 1.452*** (0.133) (0.133) (0.134) (0.134) Constant -9.262*** -9.602*** -9.580*** -8.753*** (1.631) (2.220) (1.801) (2.406)

N 6976 6976 6976 6976 BIC 3046.4 3042.2 3051.3 3052.4

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

Finally, I attempt to better assess my hypothesis regarding urban proximity with a robustness check that counts the number of major cities near each district. Based

194 on great-circle distance measurements from district centroids to the locations of India’s major cities (WPP 2009, Ahlenius 2010), I provide a count of the number of major cities within distance bands of 50, 100, and 200 kilometers of each district’s center-point. The logic behind this test is based on the assumption that the presence of one nearby major city provides opportunities for migration and economic exchange which can reduce a population’s support for armed groups, then the presence of multiple nearby major cities should have an even stronger pacifying effect.

Results from these tests are displayed in Models 24-26. In each case, the coefficients for the urbanization proxies were negative, as expected. However, the relationship between urbanization and conflict only reaches conventional levels of statistical significance in Models 24 and 25. This indicates that the risk of armed conflict is significantly lower if there are more major cities within fifty or one hundred kilometers of the centroid of one’s district. This suggests that if communities can benefit from being geographically close to a major city, they indeed also benefit from having multiple major cities nearby. The benefits of major cities appear to diminish as one becomes further away from a major cities. This is indicated by the lower risk of armed conflict in locations within shorter distance bands of major cities and the insignificance of the number of major cities within the much broader 200-kilometer radius from the center of a district. Predictive margins for Models 24 and 25 in Figures 9 and 10, respectively. For a hypothetical district without conflict in the previous year and not located in a state involved in Operation Green Hunt, there is approximately a 6% chance of armed conflict if the district does not have any major cities located within either fifty or one hundred

195 kilometers. In both models, as the district moves toward its maximum number of major cities within the given distance band, the risk of a fatal conflict event falls to about 2%.

Table 10. Logistic Regression Results

DV: Fatal Conflict Dummy Model 24 Model 25 Model 26

Major Cities Within 50 km -0.383** (0.171) Major Cities Within 100 km -0.166** (0.077) Major Cities Within 200 km -0.02 (0.040) Dist to Nearest Major City ln 0.098 0.070 0.089 (0.095) (0.096) (0.099) Pop of Nearest Major City ln(t-1) 0.059 0.077 0.079 (0.125) (0.125) (0.123) GDP Per Cap ln(t-1) 0.325 0.269 0.281 (0.293) (0.294) (0.295) % Sched Castes & Tribes ln(t-1) -0.039 -0.038 -0.037 (0.033) (0.032) (0.033) Police Strength (t-1) 0.337 0.313 0.300 (0.208) (0.206) (0.211) Distance to State Capital ln -0.194** -0.165** -0.120* (0.081) (0.064) (0.064) Operation Green Hunt (t-1) 0.103 0.127 0.127 (0.171) (0.171) (0.171) Neighbor Fatal Conflict (t-1) 1.887*** 1.898*** 1.907*** (0.136) (0.135) (0.136) Lagged DV 2.381*** 2.394*** 2.405*** (0.134) (0.135) (0.135) Constant -4.720*** -4.353*** -4.794*** (1.438) (1.456) (1.452)

N 6976 6976 6976 BIC 3174.3 3176.4 3182.2

Standard errors in parentheses * p<0.10, ** p<0.05, *** p<0.01

196

Figures 9-10. Predictive Margins for Models 24 & 25

Predictive Margins with 95% CIs Predictive Margins with 95% CIs .08 .08 .06 .06 .04 .04 Pr(Conflict_Fatal_Dummy) .02 .02 Probability ofFatal Conflict Probability Event 0 0

0 1 2 3 0 1 2 3 4 5 6 Major Cities within 50 km Major Cities within 100 km

6.7. Conclusion

In this chapter, I have attempted to test my hypothetical expectations regarding a potential conflict-urbanization nexus with analysis of India’s Red Corridor region. Based on the findings from the regression models analyzed here, several important trends are apparent. These findings provide little support for hypothesis 1, which holds that urbanization should vary inversely with armed conflict. In most of the regression models, urbanization levels yield statistically insignificant correlations with armed conflict.

However, several important exceptions to this trend appear in random effects logit and zero-inflated negative binomial regressions presented as robustness checks. These models indicate a modest level of support for the claim that urbanization should alleviate conflict violence, being associated either with a decline in the risk of conflict or a decline in the estimated number of conflict events a district may incur. It is also notable that while urbanization level was sometimes significantly associated with reduced conflict risk in the Red Corridor districts, urbanization rate was not. This suggests that while urban- dwelling populations may be more peaceful than rural ones, the urbanization process itself does not make a society any more peaceful. In fact, urbanization rates do return

197 significant positive coefficients in some models, suggesting that while urban populations are less conflict prone, moving to cities may not create conditions of peace immediately.

Conflict risk may go up as an area’s population shifts from rural to urban settings.

The empirical evidence provides much greater support for Hypothesis 2, that conflict risk should decline in areas that are closer to cities or where the nearest major city is growing. Most of the models support expectation, finding that as one gets farther from cities, the probability of conflict increases. Conversely, high rates of growth in major cities or the presence of more cities nearby a district are associated with lower risk of conflict in the district. This study does not directly analyze either the business connections between rural and urban communities or the movement of people within

India’s districts or states. However, the findings for urban proximity are quite robust, suggesting that perhaps people from rural communities are less interested in supporting an armed movement if they can improve their lives by trading with or move to urban communities.

Tests regarding the condition effects of deteriorating economic conditions on the relationship between urbanization and conflict yield results contrary to my expectations in Hypothesis 3. While I expected to find that conflict should be worse when urbanization rates are high and the economy is moving in the wrong direction, it appears that neither urban nor rural unemployment have any meaningful effect when interacted with urbanization. Against my expectations, logistic regression analysis with the fatal conflict dummy as the dependent variable shows that urbanization may cause conflict risk to worsen as the economy grows. However, the finding did not hold up in robustness checks using random effects models, which may suggest that the positive and significant

198 coefficient for the urbanization-income interaction is an artefact of the model in which it was run. Nevertheless, the potential for rising incomes in urbanizing locations to worsen conflict is one that require further investigation in future analyses as it is not clear why this might occur. While rising incomes may alleviate deprivations that bode poorly for a country’s peace prospects, my findings suggest that this is not necessarily what one can expect in areas experiencing rapid urbanization. Other works suggest that economic growth can be politically destabilizing (Olson 1963) or that growth unevenly distributed across a country’s population may generate conflict-inducing horizontal inequalities

(Stewart 2008), or that Naxalite militias may target wealthier areas due to ideological reasons (Ahuja & Ganguly 2007, Gupta 2007). The conflict literature may benefit from future analysis of how these possibilities may contribute to armed conflict.

In testing the potential mechanisms by which urbanization may affect armed conflict, I find that while wealth and subnational variations in democracy are usually negative correlates of armed conflict, they do not necessarily play an important role in determining the conflict propensity of urbanizing societies. Neither variable significantly mediated the urbanization-conflict relationship in the random effects models. However, larger police forces and higher levels of tax revenue appear to reduce the risk of conflict in urbanizing areas. Taken together, these findings suggest an important role for government in overseeing urbanization processes. Police are important because they support public safety and are responsible for fighting India’s armed insurgents. Tax revenue is important because it is used to fund government operations, including both security operations and other forms of public services.

199

India’s Red Corridor is an excellent example of a region where armed insurgency has lasted for decades and always with strong urban-rural dynamics. It also an extremely diverse region, varying greatly in terms of conflict levels, urbanization processes, state capacity, etc., making it an excellent location to test the theories I have proposed in this project. As with the analyses presented in earlier chapters, India’s experience shows that although urbanization is a complex process which sometimes comes with significant difficulties, it may reduce the risk of armed conflict. Urbanization levels are especially important in this regard as the most densely populated areas are typically ones that avoid Naxalite violence. This provides an encouraging view of India’s slow but powerful and ongoing demographic shift. Furthermore, it appears that governments may play a constructive role in guiding urbanization processes in a peaceful direction. This study also provides reason to expect that as India’s population becomes increasingly urbanized, Naxalite violence will eventually decline. This bodes very well for a country that possesses so many large and growing cities.

200

Chapter 7. Conclusions

This dissertation project began as a search for a better understanding of the security implications of major demographic changes. Existing research suggests that security in urbanizing societies may be jeopardized by threats ranging from criminal activity to destruction from natural disasters.59 However, discussions of urbanization also abound with claims attributing the demographic shift to warfare, often based heavily on anecdotes rather than large-N empirical analysis. As the world’s population continues to grow and becomes more heavily concentrated in urban areas, policymakers will need a clearer view of whether or how armed conflict may occur in urbanizing societies. In this project, I have presented theory regarding urbanization’s ability to increase or decrease the risk of armed conflict in the Global South. I have tested this theory in large samples of the developing world at various levels of analysis using a variety of methods to operationalize both urbanization and conflict. I have also used the simmering insurgency in India’s “Red Corridor” region as an opportunity to more closely analyze the dynamics of a notoriously durable conflict across a range of subnational jurisdictions, each exhibiting different degrees of urbanization.

In many ways, my findings present a mixed account of urbanization, suggesting that policymakers should view it neither as a grave threat to national security nor as a perfect antidote for socioeconomic ills generally attributed to conflict.

Nevertheless, the balance of my empirical analyses evinces a positive role for urbanization in matters of security. Cities in the developing world may come with a

59 See Moncada (2013) and Gencer (2013) for further discussion of crime and natural disaster vulnerability, respectively.

201 multitude of problems, but armed conflict need not be one of them. In many cases, urbanization processes and citizens’ access to cities may, in fact, contribute to peace. In the subsequent sections, I review key findings from this project, discuss their implications for the alleged urbanization-conflict nexus, and provide commentary on what additional research or policy should be pursued based on my findings.

7.1. Reviewing Key Findings

By concentrating large numbers of people in a geographic space, the process of urbanization creates both risks and opportunities for a growing society. As discussed in

Chapter 3, these risks and opportunities may build or alleviate grievances which can impact a society’s chance of intrastate conflict. Regarding these factors, I hypothesized three things. First, the risk of armed conflict should decline as an area becomes more urban. Essentially, a population’s concentration in urban areas affords citizens economic and political opportunities that decrease their interest in anti-government violence. It also increases the government’s capacity to reduce conflict through its increased administrative capacity and tactical advantages in cities. Second, I predict that areas will be less conflict-prone when they are located close to cities or when nearby cities are growing. This is because cities are large economic marketplaces, providing people in outlying areas with opportunities to work and absorbing internal migrants who choose to leave unhappy situations in rural communities. Third, I hypothesize that if urbanization does lead to violence, it will most likely be in cases where urbanization coincides with worsening economic conditions. Essentially, a bad economy is likely to underperform the expectations of citizens hoping for socioeconomic advancement and increase the

202 hardships – and political grievances – of people crowding into impoverished cities. Each of the preceding three chapters included tests for all three of these hypotheses.

My first hypothesis receives no support in the global, country-year study of

Chapter 4. Whether conflict is measured with low- or high-fatality threshold events or with onset of various types of intrastate conflict, urbanization level did not correlate positively with armed conflict in the country-level analysis. The findings are somewhat different at the sub-national level, where urbanization somewhat alleviates the risk of high-fatality conflict events, even though conflict with lower fatality thresholds becomes more common. In the India study, the urbanization level appears to have no influence on the risk of conflict occurring, although it is the case that more urbanized districts seem to have fewer conflict events, even if urbanization itself is not a good predictor of which districts will experience conflict violence in the first place.

Tests of my second hypothesis portray urbanization in the best light as findings in all three chapters show that having large cities nearby is usually good for peace. At the country-level, this finding holds only for high-fatality conflict, although the growth of major cities’ populations does correspond with a decrease in the risk of lower-levels of armed conflict. The presence of major cities is associated with lower risk of conflict in the province-level analysis of all developing countries and in the district-level analysis of the Red Corridor, although findings in the grid-cell-level models produced mixed findings. Although proximity to major cities is associated with a higher risk of single- fatality-threshold violence in provinces, it is associated with lower conflict risk in the grid-cell-level models as well as the district-level analysis of the Red Corridor. Increases in major cities’ populations were also associated with lower levels of conflict in a number

203 of models across the levels of analysis. This suggests that regardless of a country’s overall level of urbanization, places located near major cities can still benefit from the growth of those cities.

Interactions between urbanization rate and changes in economic conditions return null results in most of the models, with several notable exceptions. At the country level, urbanization rates that coincide with increases in GDP per capita are associated with very substantial declines in the risk of civil conflict over governmental incompatibilities. In some of the models at the province-level analysis, increases in GDP per capita interacted with urbanization correspond to lower levels of high-fatality conflict, but higher levels of conflict measured at the one-fatality threshold. In the Red

Corridor analysis, changes in GDP per capita produce a positive coefficient when interacted with population density, suggesting an area will experience a greater risk of violence when urbanization coincides with rising incomes. Taken together, these findings suggest that people should be cautious in tying Malthusian logics to discussions of a possible urbanization-conflict nexus.

Urbanization in areas with varying degrees of economic health produces several other notable findings. One is that changes in unemployment do not appear to play a meaningful role in motivating conflict. Much has been written about the effects of urbanization on labor markets, but my findings indicate that even when labor markets deteriorate, such conditions do not turn urbanization into a recipe for disaster, at least not as far as armed conflict is concerned. Subnational analysis shows that changes in income level often do not influence the urbanization conflict relationship either, but where they do, it is often in the opposite direction from what Malthusian arguments – including the

204 one I proffered in my third hypothesis – would expect. It is not immediately clear why conflict in urbanizing areas might increase when incomes rise, even though income itself is usually a negative correlate of armed conflict. This may be an avenue for future research. However, it is possible to speculate that increases in wealth correspond to disruptive social changes creating clashes between competing factions of society. If true, this would be in line with Olson’s (1963) arguments in his essay “The Rapid Growth of

Wealth as a Destabilizing Force.” Alternatively, it is possible that rebel groups may target attacks against the wealthy and therefore take great interest in wealth accumulating in a growing city.

The case of India is highly instructive because of the large number of jurisdictions affected by the conflict, the diversity of the affected areas, and the high availability of data of India’s subnational administrative units, especially compared to other developing countries. The focus on a single case with so much available data made it possible to conduct additional sets of tests to assess the influence of factor underlying the relationship between urbanization and armed conflict. These include subnational levels of wealth, democracy, police strength, and tax revenue. Despite wealth and democracy being highly influential predictors of country-level conflict trends, they do not condition the relationship between urbanization and conflict in my analysis of India.

What I find is that as a district urbanizes at a faster rate, a larger number of police officers corresponds to a lower risk of conflict. The negative coefficient for the interaction of urbanization rate and police strength is especially meaningful because if police were simply hired in or deployed to areas already beset with violence, one would normally expect the interaction of these variables to demonstrate a positive relationship with armed

205 conflict. This indicates that a fast-urbanizing community may experience significant population pressures during urbanization – something that may come with important political and security implications – it will be better off if its government maintains a larger police force.

Second, analysis of the Red Corridor shows that if a district is more densely populated, it’s risk of conflict will be lower where the government extracts higher levels of tax revenue. Tax revenue is an interesting factor because of its obvious connection to wealth – more wealth equals a higher tax base – as well as its frequent use as a measurement of state capacity.60 I have argued that tax revenue is critical for states to fund basic public services which keep citizens happy as well as security measures which can suppress efforts at armed insurrection. The findings are in line with anecdotal accounts of ineffective and sometimes disastrous efforts by cash-strapped state governments to wage counterinsurgency campaigns.61 While the regressions presented in

Chapter 6 do not prove a causal link between tax revenues and state capacity, the findings do provide useful context regarding efforts to secure urbanizing societies.

7.2. The Urbanization-Conflict Nexus Assessed

It is easy to overstate the security risks associated with urbanization. Armed conflict does occur in urban settings from time to time, and it is certainly important for communities of scholars and practitioners to direct their attention to it. However, it is also important that the understanding of urbanization’s security implications be understood in greater

60 See Hendrix (2010). 61 See Harriss (2011) and Miklian (2011) for further discussion of problems with counterinsurgency programs and related policymaking that are linked to government resource shortages.

206 context, something I have striven to provide through this project. The findings presented in the previous section provide reason for a realistic, yet optimistic view of what urbanization means for the developing world. This does not mean that urbanization in the developing world will always go smoothly, either in terms of security, or other factors.

But it does mean that the role of urbanization is much less dangerous than conventional wisdom might imply.

Urbanization should not be characterized as a major risk factor for armed conflict. Across the models tested in this project, urbanization level and urbanization rate both show mixed findings as correlates of armed conflict. However, the balance of my findings supports the notion that cities – including the presence of, proximity to, and growth of cities – correlate negatively with armed conflict. Urbanization in subnational spaces often corresponds to reductions in high-fatality conflict events, but in many models shows promising results for low-threshold violence as well.

This does not mean that cities are necessarily free from all violence, conflict- related or not. Certainly, many works discuss urban areas as major centers of criminal violence (e.g. Moncada 2013; Jütersonke, Muggah, & Rodgers 2009; Rodgers & Muggah

2009; Hove, Ngwerume, & Muchemwa 2011), but even in such cases, this does not automatically translate into armed rebellions against the state or the intense urban-based warfare that some might warn about (e.g. Kilcullen 2013, Graham 2011, Sampaio 2019).

Conflict violence is not endemic to urbanization processes. In most cases, a population’s shift toward urban areas with a decline in conflict risk. Scholars and practitioners would do well to avoid projecting experiences of urban warfare in places like Mogadishu,

Srebrenica, or Baghdad into predictions of urban-based insurgency across the rapidly

207 urbanizing Global South. Conflict may still occur in urban areas, but such cases are the exception, not the rule. In most places, the circumstances surrounding urbanization are not nearly so dire.

7.3. Limitations of Research & Avenues for Future Research

This project comes with several limitations worth noting as well as several avenues which I identify for future research. As with many concepts in social scientific research, a key limiting factor for any research on urbanization is that there is no universally agreed- upon way of defining or measuring urbanization. The wide variation in countries’ definitions means that country-level estimates of urban and rural population sizes – data that are used widely in studies of politics, economic development, sociology, urban planning, etc. that discuss urbanization – inherently result in apples-to-oranges comparisons when incorporated into large-N cross-national analysis. While factors such as population density and nightlight emissions sidestep this issue by avoiding problems of mismatched or even arbitrary definitions, they come with their own problems. Population density and nightlight emission density can, of course, be measured at subnational levels, but the resulting scores imply that a level of urbanization is uniform across whatever geographic space is used as the unit of analysis. This fails to capture the internal variation of a province or a grid cell whose landmasses include some areas that are densely settled and others are unpopulated or sparsely populated. Measuring cities at 300,000 people at least provides a standard uniform across all countries, but some may debate about matters of urban extent or how to measure the populations of metropolitan areas. In this study, I have incorporated a wide variety of approaches to measure and analyze urbanization.

208

While these operationalizations cover many of the ways people think about or study urbanization, I acknowledge that any measure, by itself is subject to disputes regarding conceptualization and operationalization.

This study has covered the 1990-2015 time period, providing insight into how the countries and their constituent administrative units have experience urbanization and how that urbanization may or may not have contributed to conflict in the post-Cold War era.

While civil conflict became much more prominent after the Cold War than it was during it (Lacina 2004), the developing world’s trend toward urbanization was already well underway by the time the Cold War ended (Gilbert & Gugler 1992). It is possible that broadening the temporal scope of research on the relationship between conflict and urbanization may yield greater insight into the potential threats, if any, urbanization may pose.

Urbanizing areas of many countries, particularly developing countries, are affected by violence that may come in many forms. This project has focused on armed conflict, something that enabled a deep dive into an underexplored question about whether urbanization may bring about warfare. This focused approach comes with benefits such as simplicity, coherence, and depth, but it does not provide a comprehensive look at all security concerns policymakers might face or worry about.

Even if cities do not become tomorrow’s battlefields, they may still find themselves beset with crime and terrorism or faced with more abstract security threats such as public health emergencies, environmental degradation, or natural disasters. All of these factors may pose serious risk to human life and are likely to affect urban communities differently from rural communities. These are factors which policymakers should carefully consider

209 and are avenues through which scholarly research could provide additional timely and relevant analysis to those who need it.

In Chapter 6, I used the analysis of India’s Red Corridor to elucidate several key factors which I argued have theoretical importance to the relationship between urbanization and conflict. The Red Corridor provides an instructive example of how urbanization may impact an ongoing conflict, but there is still additional room for analysis of this case. More qualitative analysis of urbanization policy and approaches to counterinsurgency could yield very important additional insights not just into the relationship between urbanization and conflict, but also in the ways governments might manage risks connected to both these phenomena. India’s federal system provides many subnational jurisdictions whose policies regarding urbanization and conflict management may be compared. Such analysis can help in devising practical solutions for developing countries which must deal with population pressures and security threats under less-than- ideal conditions including situations involving severe resource shortages, corruption, coordination between government bodies, etc. India is certainly not the only country which might make for good policy analysis opportunities, but it would be a logical place to start in building upon the research presented here. Future studies could certainly benefit from closer analysis of urbanization and security policies in other parts of the

Global South.

7.4. Policy Implications & Concluding Thoughts

Urbanization is a complex phenomenon that has occurred over the course of millennia and continues to change our world today. In a best-case scenario, urbanization may

210 herald the economic take-off envisioned by Rostow (1956) and at worst, it may realize the worst nightmares envisioned by Thomas Malthus, creating resource shortages and strife. These situations provide much food for thought, but in practice, most countries fall somewhere in the middle of this spectrum. Urbanizing societies often experience significant growing pains, but as my research shows, positive outcomes are quite possible, even in poor countries. War is not inevitable for urbanizing societies and most developing countries can expect the risk of civil war to decline as their populations shift away from villages toward cities.

Lipton’s (1977) writing on “urban bias” accused developing countries’ governments of favoring investments in urban communities and markets over rural ones.

This he attributed to governments’ expectations that the growth of cities can yield greater long-term economic gains for a country hoping to improve its economy. Given the findings of this dissertation project, it perhaps is natural for one to wonder if a null or negative relationship between urbanization and conflict should be understood as a green light for countries to lean heavily into urban-oriented development strategies. While this analysis does not evaluate the effectiveness of development policies, it does provide reason to believe that as cities grow, they may play a useful role in disincentivizing citizens from engaging in armed conflict. If a government has an existing problem with a rural-based rebellion akin to the Naxalite insurgency of the Red Corridor, governments might do well to increase rural citizens’ access to cities. This can benefit rural societies and reduce support for anti-government movements either by providing rural citizens with opportunities to migrate or opportunities to earn more money through connections to larger urban marketplaces. This should not be seen as encouragement for governments to

211 increase their urban biases in policymaking, but it does demonstrate the value of interconnectivity between rural and urban communities.

My analysis of the Red Corridor case suggests that state capacity plays a critical role in conditioning the relationship between urbanization and conflict. The value of this role is present in both to the coercive and administrative aspects of the state capacity concept. The ability to extract tax revenue, often viewed as an important indicator of government administrative capacities (Hendrix 2010) is shown to beneficially condition the impact of urbanization on conflict in the Red Corridor.

Urbanizing societies are likely to find government resources stretched thin as they grow, something that may strain or test the limits of the state’s capabilities. If a state focuses on increasing the capabilities of its government bodies, it may better address the population pressures of urbanization with improved public services, reducing the public’s grievances. But among the various forms of state capacity, coercive capabilities are particularly relevant where physical security is a strong and immediate concern. A country or other jurisdiction experiencing rapid urbanization is likely to find its risk of armed conflict lower if more security forces are available during the process. As I have discussed in the case of India, this means a combination of armed and unarmed police officers which may be drawn from various levels of government. If the government of a rapidly-urbanizing society – especially one with a history of armed violence – is short on resources, it must be strategic in deciding where to deploy security forces, something likely to involve coordination between levels of government.

If urbanization is not a major risk factor for armed conflict, should policymakers give up on training for possible future urban warfare? In short, the answer

212 is clearly ‘no.’ Although the opportunities afforded to citizens and governments through the growth of cities may often make a society more peaceful, at least in some regards, they do not eliminate the threat of violence. Even if major conflicts become less likely as a society urbanizes, the fact that armed conflict in urban or rural areas remains possible means that government and humanitarian agencies should be prepared to deal with such scenarios. The findings of this research should therefore not be interpreted as a reason to forego readiness efforts needed to respond to conflicts in urban areas. Even if a quickly urbanizing society never experienced armed conflict, is also possible that military, police, emergency services, and charitable organizations may need to respond to urban based emergencies for phenomena not included in these analyses. Expertise in urban operations may, for example, be needed if a government is forced to respond to crime, terrorist activity, natural disasters, or other crises which may fall outside of civil conflict.

This project is not intended as a last word on urban security but rather as an important step toward understanding conflict as it relates to urbanization, an important topic in the emerging field of political demography. The twenty-first century is likely to be a period of profound demographic changes for the world. As urbanization continues rapidly in much of the Global South, many governments will experience challenges across a variety of political, economic, and social concerns. The findings of this study should come as some small comfort to those concerned about the risk of war in urbanizing countries. However, there is still much work to do in fully understanding security threats faced by these countries’ governments and how best to manage these threats. Through this research, I hope to have created some progress toward this understanding.

213

Appendices

Appendix A. Developing Countries Included in Analysis

Afghanistan Albania Algeria Angola Argentina Armenia Azerbaijan Bangladesh Belarus Benin Bolivia Bosnia & Herzegovina Botswana Brazil Bulgaria Burkina Faso Burundi Cambodia Cameroon Cape Verde Central African Republic Chad China Colombia Comoros Congo – Brazzaville Congo – Kinshasa Costa Rica Côte d’Ivoire Croatia Cuba Cyprus Djibouti Dominican Republic Ecuador Egypt El Salvador Equatorial Guinea Eritrea Ethiopia Fiji Gabon Gambia Georgia Ghana Guatemala Guinea Guinea-Bissau Guyana Haiti Honduras India Indonesia Iran Iraq Jamaica Jordan Kazakhstan Kenya Kyrgyzstan Laos Lebanon Lesotho Liberia Libya Macedonia Madagascar Malawi Malaysia Mali Mauritania Mauritius Moldova Mongolia Montenegro Morocco Mozambique Myanmar (Burma) Namibia Nepal Nicaragua Niger Nigeria North Korea Pakistan Panama Papua New Guinea Peru Philippines Romania Russia Rwanda Senegal Sierra Leone Solomon Islands Somalia South Africa South Sudan Sri Lanka Sudan Suriname Swaziland (Eswatini) Syria Tajikistan Tanzania Thailand Timor-Leste Togo Tunisia Turkmenistan Uganda Ukraine Uzbekistan Venezuela Yemen Yugoslavia (Serbia) Zambia Zimbabwe

214

Appendix B. Indian Districts Included in Analysis (by State, A-J)

Andhra Pradesh

Adilabad Anantapur Chittoor E. Godavari Guntur Hyderabad Karimnagar Khammam Krishna Kurnool Mahbubnagar Medak Nalgonda Nizamabad Prakasam Ragnareddy SPS Nellore Srikakulam Visakhapatnam Vizianagaram Warangal W. Godavari YSR (Kadapa)

Bihar

Araria Arwal Aurangabad Banka Begusarai Bhagalpur Bhojpur Buxar Darbhanga Gaya Gopalganj Jamui Jehanabad Kaimur (Bhabua) Katihar Khagaria Kishanganj Lakhisarai Madhepura Madhubani Munger Muzaffarpur Nalanda Nawada W. Champaran E. Champaran Patna Purnia Rohtas Saharsa Samastipur Saran (Chhapra) Sheikhpura Sheohar Sitamarhi Siwan Supaul Vaishali

Chhattisgarh

Bastar Bijapur Bilaspur Dantewada Dhamtari Durg Janjgir-Champa Jashpur Kabeerdham Korba Koriya Mahasamund Narayanpur Raigarh Raipur Rajnandgaon Surguja Uttar Bastar Kanker

Jharkhand

Bokaro Chatra Deoghar Dhanbad Dumka Garhwa Giridih Godda Gumla Hazaribagh Jamtara Khunti Kodarma Latehar Lohardaga Pakur Palamu W. Singhbhum E. Singhbhum Ramgarh Ranchi Sahibganj Saraikela-Kharsawan Simdega

Note: Districts and states based on India’s 2011 census designations

215

Appendix C. Indian Districts Included in Analysis (by State, M-O)

Madhya Pradesh

Alirajpur Anuppur Ashoknagar Balaghat Barwani Betul Bhind Bhopal Burhanpur Chhatarpur Chhindwara Damoh Datia Dewas Dhar Dindori E. Nimar Guna Gwalior Harda Hoshangabad Indore Japalpur Jhabua Katni Mandla Mandsaur Morena Narsimhapur Neemuch Panna Raisen Rajgarh Ratlam Rewa Sagar Satna Sehore Seoni Shahdol Shajapur Sheopur Shivpuri Sidhi Singrauli Tikamgarh Ujjain Umaria Vidisha W. Nimar

Maharashtra

Ahmadnagar Akola Amravati Aurangabad Bhandara Bid Buldana Chandrapur Dhule Garchiroli Gondiya Hingoli Jalgaon Jalna Kolhapur Latur Mumbai Mumbai Suburban Nagpur Nanded Nandurbar Nashik Osmanabad Parbhani Pune Raigarh Ratnagiri Sangli Satara Sindhudurh Solapur Thane Wardha Washim Yavatmal

Odisha

Anugul Balangir Baleshwar Bargarh Bauda Bhadrak Cuttack Debagarh Dhenkanal Gajapati Ganjam Jagatsinghapur Jajapur Jharsuguda Kalahandi Kandhamal Kendrapara Kendujhar Khordha Koraput Malkangiri Mayurbhanj Nabarangapur Nayagarh Nuapada Puri Rayagada Sambalpur Subarnapur Sundargarh

Note: Districts and states based on India’s 2011 census designations

216

Appendix D. Indian Districts Included in Analysis (by State, U-W)

Uttar Pradesh

Agra Aligarh Allahabad Ambedkar Nagar Auraiya Azamgarh Baghpat Baghpat Bahraich Ballia Balrampur Banda Bara Banki Bareilly Basti Bijnor Budaun Bulandshahr Chandauli Chitrakoot Deoria Etah Etawah Faizabad Farrukhabad Fatehpur Firozabad Gautam BN Ghaziabad Ghazipur Gonda Gorakhpur Hamirpur Hardoi Jalaun Jaunpur Jhansi Jyotiba Phule Nagar Kannauj Kanpur Dehat Kanpur Nagar Kansiram Nagar Kaushambi Kheri Kushinagar Lalitpur Lucknow Mahamaya N. Maharajganj Mahoba Mainpuri Mathura Mau Meerut Mirzapur Moradabad Muzaffarnagar Pilibhit Pratapgarh Rae Bareli Rampur Saharanpur Sant Kabir Nagar Sant Ravi Das N. Shahjahanpur Shrawsti Siddharth Nagar Sitapur Sonbhadra Sultanpur Unnao Varanasi

West Bengal

Bankura Barddhaman Birbhum Dakshin Dinajpur Darjeeling Haora Hugli Jalpaiguri Koch Bihar Kolkata Maldah Murshidabad Nadia North 24 Parganas W. Medinipur E. Medinipur Puruliya South 24 Parganas Uttar Dinajpur

Note: Districts and states based on India’s 2011 census designations

217

Bibliography

Abbas, Rameez. “Internal Migration and Citizenship in India.” Journal of Ethnic and Migration Studies 42.1 (2016): 150-168.

African Development Bank, OECD Development Centre, and United Nations Development Programme. African Economic Outlook 2016: Sustainable Cities and Structural Transformation. AfDB, OECD, UNDP, 2016.

Aguilera, Ana I. “How Urbanization is Transforming Central America.” In Central America Urbanization Review: Making Cities Work for Central America. Eds. Augustin Maria, Jose Luis Acero, Ana I. Aguilera, and Marisa Garcia Lozano. World Bank (2017): 27-64.

Ahlenius, Hugo. “World Large Urban Areas, 1950-2020.” Nordpil, 2010.

Ahuja, Pratul, and Rajat Ganguly. “The Fire Within: Naxalite Insurgency Violence in India.” Small Wars & Insurgencies 18.2 (2007): 249-274.

Alexander, Arthur J. Japan’s Economy in the Twentieth Century, JEI Report. Japan Economic Institute, 2000.

AlSayyad, Nezar, and Muna Guvenc. “Virtual Uprisings: On the Interaction of New Social Media, Traditional Media Coverage and Urban Space During the ‘Arab Spring’.” Urban Studies 52.11 (2015): 2018-2034.

Asher, Sam, Tobias Lunt, Ryu Matuura, and Paul Novosad. “The Socioeconomic High- Resolution Rural-Urban Geographic Dataset on India (SHRUG), version 1.4.” Working paper, 2019.

Bairoch, Paul, and Gary Goertz. “Factors of Urbanisation in the Nineteenth Century Developed Countries: A Descriptive and Econometric Analysis.” Urban Studies 23.4 (1986): 285-305.

Bairoch, Paul. “International Industrialization Levels from 1750 to 1980.” Journal of European Economic History 11.2 (1982): 269-333.

Bakonyi, Jutta, Peter Chonka, and Kirsti Stuvøy. “War and City-Making in Somalia: Property, Power and Disposable Lives.” Political Geography 73 (2019): 82-91.

Barnes, Nicholas. “Criminal Politics: An Integrated Approach to the Study of Organized Crime, Politics, and Violence.” Perspectives on Politics 15.4 (2017): 967-987.

Barragán, Juan Manuel, and María de Andrés. “Analysis and Trends of the World's Coastal Cities and Agglomerations.” Ocean & Coastal Management 114 (2015): 11-20.

218

Bhagat, Ram B. “Migration and Urban Transition in India: Implications for Development.” United Nations Expert Group Meeting on Sustainable Cities, Human Mobility, and International Migration, 2017.

Beckett, Ian. “The Future of Insurgency.” Small Wars and Insurgencies 16.1 (2005): 22- 36.

Beinhocker, Eric D., Diana Farrell, and Adil S. Zainulbhai. “Tracking the Growth of India's Middle Class.” McKinsey Quarterly 3 (2007): 50-61.

Bell, Andrew, and Kelvyn Jones. “Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data.” Political Science Research and Methods 3.1 (2015): 133-153.

Bennett, Mia M., and Laurence C. Smith. “Advances in Using Multitemporal Night-Time Lights Satellite Imagery to Detect, Estimate, and Monitor Socioeconomic Dynamics.” Remote Sensing of Environment 192 (2017): 176-197.

Best, RH, AR Jones, and AW Rogers. “The Density-Size Rule.” Urban Studies 11.2 (1974): 201-208.

Bienen, Henry. “Urbanization and Third World Stability.” World Development 12.7 (1984): 661-691.

Bleaney, Michael, and Arcangelo Dimico. “How Different are the Correlates of Onset and Continuation of Civil Wars?” Journal of Peace Research 48.2 (2011): 145-155.

Bloom, David E., David Canning, and Günther Fink. “Urbanization and the Wealth of Nations.” Science 319.5864 (2008): 772-775.

Bodnar, Jozef and Sue Collins. “NATO Joint Military Operations in an Urban Environment: A Capstone Concept.” Three Swords Magazine 34 (2019): 93-96.

Boulding, Kenneth E. Conflict and Defense: A General Theory. New York: Harper, 1962.

Braithwaite, Alex, Niheer Dasandi, and David Hudson. “Does Poverty Cause Conflict? Isolating the Causal Origins of the Conflict Trap.” Conflict Management and Peace Science 33.1 (2016): 45-66.

Brea, Jorge A. Population Dynamics in Latin America. Vol. 58. No. 1. Washington, DC: Population Reference Bureau, 2003.

Brown, Graham K., and Arnim Langer. “Horizontal Inequalities and Conflict: A Critical Review and Research Agenda.” Conflict, Security & Development 10.1 (2010): 27-55.

219

Browning, Harley L. “Recent Trends in Latin American Urbanization.” The Annals of the American Academy of Political and Social Science 316.1 (1958): 111-120.

Brückner, Markus. “Economic Growth, Size of the Agricultural Sector, and Urbanization in Africa.” Journal of Urban Economics 71.1 (2012): 26-36.

Buhaug, Halvard, Scott Gates, and Päivi Lujala. “Geography, Rebel Capability, and the Duration of Civil Conflict.” Journal of Conflict Resolution 53.4 (2009): 544-569.

Buhaug, Halvard, and Jan Ketil Rød. “Local Determinants of African Civil Wars, 1970– 2001.” Political Geography 25.3 (2006): 315-35.

Buhaug, Halvard, and Henrik Urdal. “An Urbanization Bomb? Population Growth and Social Disorder in Cities.” Global Environmental Change 23.1 (2013): 1-10.

Cali, Massimiliano, and Carlo Menon. “Does Urbanization Affect Rural Poverty? Evidence from Indian Districts.” World Bank Economic Review 27.2 (2012): 171-201.

Camhis, Marios. “Sustainable Development and Urbanization.” In The Future of Sustainability. Ed. Marco Keiner. Springer (2006): 69-98.

Carr, Jered B., and Antonio Tavares. “City Size and Political Participation in Local Government: Reassessing the Contingent Effects of Residential Location Decisions Within Urban Regions.” Urban Affairs Review 50.2 (2014): 269-302.

Castañeda, Andrés, Dung Doan, David Newhouse, Minh Cong Nguyen, Hiroki Uematsu, and João Pedro Azevedo. “A New Profile of the Global Poor.” World Development 101.1 (2018): 250-267.

Cederman, Lars-Erik, Andreas Wimmer, and Brian Min. “Why do Ethnic Groups Rebel? New Data and Analysis.” World Politics 62.1 (2010): 87-119.

Center for International Earth Science Information Network (CIESIN), Columbia University. Gridded Population of the World, Version 4 (GPWv4). NASA Socioeconomic Data and Applications Center (SEDAC), 2016.

Center for International Earth Science Information Network (CIESIN) and Centro Internacional de Agricultura Tropical (CIAT). Gridded Population of the World, Version 3 (GPWv3). NASA Socioeconomic Data and Applications Center (SEDAC), 2005.

Central Intelligence Agency. World Factbook 1990. https://theodora.com/wfb/1990/index.html (accessed 01 MAR 2020).

Central Intelligence Agency. World Factbook 2018. https://www.cia.gov/library/publications/the-world-factbook (accessed 20 JUL 2018). [urbanization statistics calculated based on estimates for the 2015-2020 period].

220

Central Intelligence Agency. World Factbook 2019. https://www.cia.gov/library/publications/the-world-factbook (accessed 01 MAR 2020).

Chakrabarty, Bidyut and Rajat Kumar Kujur. Maoism in India: Reincarnation of Ultra- Left-Wing Extremism in the Twenty-First Century. Routledge, 2009.

Chan, Kam Wing, and Li Zhang. “The Hukou System and Rural-Urban Migration in China: Processes and Changes.” The China Quarterly 160 (1999): 818-855.

Chandrasekhar, S., and Ajay Sharma. “Urbanization and Spatial Patterns of Internal Migration in India.” Spatial Demography 3.2 (2015): 63-89.

Chaudoin, Stephen, Zachary Peskowitz, and Christopher Stanton. “Beyond Zeroes and Ones: The Intensity and Dynamics of Civil Conflict.” Journal of Conflict Resolution 61.1 (2017): 56-83.

Chauvin, Juan Pablo, Edward Glaeser, Yueran Ma, and Kristina Tobio. “What is Different about Urbanization in Rich and Poor Countries? Cities in Brazil, China, India and the United States.” Journal of Urban Economics 98 (2017): 17-49.

Chen, Mingxing, Hua Zhang, and Weidong Liu, and Wenzhong Zhang. “The Global Pattern of Urbanization and Economic Growth: Evidence from the Last Three Decades.” PloS One 9.8 (2014): e103799.

Cobbinah, Patrick Brandful, Michael Odei Erdiaw-Kwasie, and Paul Amoateng. “Rethinking Sustainable Development Within the Framework of Poverty and Urbanization in Developing Countries.” Environmental Development 13 (2015): 18-32.

Cohen, Barney. “Urbanization in Developing Countries: Current Trends, Future Projections, and Key Challenges for Sustainability.” Technology in Society 28.1-2 (2006): 63-80.

Collier, Paul. Breaking the Conflict Trap: Civil War and Development Policy. World Bank Publications, 2003.

Collier, Paul, and Anke Hoeffler. “On Economic Causes of Civil War.” Oxford Economic Papers 50.4 (1998): 563-573.

Collier, Paul, and Anke Hoeffler. “Greed and Grievance in Civil War.” Oxford Economic Papers 56.4 (2004): 563-595.

Collier, Paul, Anke Hoeffler, and Måns Söderbom. "On the Duration of Civil War.” Journal of Peace Research 41.3 (2004): 253-273.

221

Connable, Ben and Martin C. Libicki. How Insurgencies End. RAND Corporation, National Defense Research Institute, 2010.

Cramer, Christopher. Unemployment and Participation in Violence. World Bank, 2011.

Crenshaw, Martha. “The Causes of Terrorism.” Comparative Politics 13.4 (1981): 379- 399.

Croicu, Mihai and Ralph Sundberg. “UCDP GED Codebook, version 18.1.” Department of Peace and Conflict Research, Uppsala University (2017).

Das, Samarjit, Chetan Ghate, and Peter E. Robertson. “Remoteness, Urbanization, and India’s Unbalanced Growth.” World Development 66 (2015): 572-587.

Database of Global Administrative Areas. “GADM Database of Global Administrative Areas,” version 3.4, 2018.

Davis, Kingsley. “The World Demographic Transition.” The Annals of the American Academy of Political and Social Science 237.1 (1945): 1-11.

Davis, Mike. “Planet of Slums.” New Perspectives Quarterly 23.2 (2006): 6-11.

De Brauw, Alan, Valerie Mueller, and Hak Lim Lee. “The Role of Rural–Urban Migration in the Structural Transformation of Sub-Saharan Africa.” World Development 63 (2014): 33-42.

De Oliviera, Orlandia and Bryan Roberts. “Urban Development and Social Inequity in Latin America.” In The Urban Transformation of the Developing World. Ed. Josef Gugler. Oxford University Press, 1996: 253-314.

Desai, Raj, and Harry Eckstein. “Insurgency: The Transformation of Peasant Rebellion.” World Politics 42.4 (1990): 441-465.

Deshingkar, Priya, and Shaheen Akter. “Migration and human development in India.” Human Development Research Paper. United Nations Development Program, 2009.

Deshingkar, Priya, and Sven Grimm. “Internal Migration and Development: A Global Perspective.” International Organization for Migration, No. 19. United Nations Publications, 2005.

De Vidovich, Lorenzo. “Suburban Studies: State of the Field and Unsolved Knots.” Geography Compass 13.5 (2019): e12440.

Dijkstra, Lewis, and Hugo Poelman. “A Harmonised Definition of Cities and Rural Areas: The New Degree of Urbanisation.” European Commission Working Paper 01/2014, 2014.

222

Do, Quy-Toan, and Lakshmi Iyer. “Geography, Poverty and Conflict in Nepal.” Journal of Peace Research 47.6 (2010): 735-748.

Dunning, Thad. “Fighting and Voting: Violent Conflict and Electoral Politics.” Journal of Conflict Resolution 55.3 (2011): 327-339.

Duranton, Gilles. “Cities: Engines of Growth and Prosperity for Developing Countries?” In Urbanization and Growth. Eds. Michael Spence, Patricia Clarke Annez, and Robert M. Buckley. World Bank (2009): 67-113.

Dyson, Tim. “A Partial Theory of World Development: The Neglected Role of the Demographic Transition in the Shaping of Modern Society.” International Journal of Population Geography 7.2 (2001): 67-90.

Easterlin, Richard A., Laura Angelescu, and Jacqueline S. Zweig. “The Impact of Modern Economic Growth on Urban–Rural Differences in Subjective Well-Being.” World Development 39.12 (2011): 2187-2198.

Egan, Mary Lou, and Marc Bendick. “The Urban-Rural Dimension in National Economic Development.” The Journal of Developing Areas 20.2 (1986): 203-222.

Ellingsen, Tanja. “Colorful Community or Ethnic Witches' Brew? Multiethnicity and Domestic Conflict During and After the Cold War.” Journal of Conflict Resolution 44.2 (2000): 228-249.

Engels, Friedrich. “The Great Towns.” The City Reader. Routledge, 2015 (originally 1845): 77-86.

Enriquez, Elaine, Martin Sybblis, and Miguel Angel Centeno. “A Cross-National Comparison of Subnational Variation.” American Behavioral Scientist 61.8 (2017): 908- 931.

Environmental Systems Research Institute (ESRI). World Ecological Facets Landform Classes. ESRI, 2018.

Evans, Michael. “Future War in Cities: Urbanization’s Challenge to Strategic Studies in the 21st Century.” International Review of the Red Cross 98.1 (2016): 37-51.

Fajnzylber, Pablo, Daniel Lederman, and Norman Loayza. “What Causes Violent Crime?” European Economic Review 46.7 (2002): 1323-1357.

Fay, Marianne, and Charlotte Opal. Urbanization Without Growth: A Not-So-Uncommon Phenomenon. The World Bank, 2000.

223

Fearon, James D., and David D. Laitin. “Ethnicity, Insurgency, and Civil War.” American Political Science Review 97.1 (2003): 75-90.

Fox, Sean. “Urbanization as a Global Historical Process: Theory and Evidence from Sub‐ Saharan Africa.” Population and Development Review 38.2 (2012): 285-310.

Frey, William H. and Zachary Zimmer. “Defining the City.” In Handbook of Urban Studies. Ed. Ronan Paddison. Sage Publications (2001): 14-35.

Flückiger, Matthias, and Markus Ludwig. “Geography, Human Capital and Urbanization: A Regional Analysis.” Economics Letters 168 (2018): 10-14.

Gates, Scott. “Recruitment and Allegiance: The Microfoundations of Rebellion.” Journal of Conflict Resolution 46.1 (2002): 111-130.

Gencer, Ebru A. “Natural Disasters, Urban Vulnerability, and Risk Management: A Theoretical Overview.” In The Interplay Between Urban Development, Vulnerability, and Risk Management. Springer (2013): 7-43.

Geneva Declaration Secretariat. “Lethal Encounters: Non-Conflict Armed Violence.” Global Burden of Armed Violence 2008. Geneva Declaration on Armed Violence and Development, Small Arms Survey (2008): 67-88.

Girardin, Luc, Phillipp Hunziker, Lars-Erik Cederman, Nils-Christian Borman, and Manuel Vogt. (2015) GROWup—Geographical Research on War, Unified Platform. ETH Zurich.

Gilbert, Alan and Joseph Gugler. Cities, Poverty, and Development: Urbanization in the Third World, 2nd ed. Oxford University Press, 1992.

Glaeser, Edward L. “Cities, Information, and Economic Growth.” Cityscape 1.1 (1994): 9-47.

Glaeser, Edward L. and Bruce Sacerdote. “Why is There More Crime in Cities?” Journal of Political Economy 107.S6 (1999): S225-S258.

Gleditsch, Kristian S., and Michael D. Ward. “Measuring Space: A Minimum-Distance Database and Applications to International Studies.” Journal of Peace Research 38.6 (2001): 739-758.

Gleditsch, Nils Petter, et al. “Armed Conflict 1946-2001: A New Dataset.” Journal of Peace Research 39.5 (2002): 615-637.

Global Terrorism Database. National Center for the Study of Terrorism, 2018 (https://www.start.umd.edu/gtd/) accessed 20 JUL 2018.

224

Goldstone, Jack A., Robert H. Bates, David L. Epstein, Ted Robert Gurr, Michael B. Lustik, Monty G. Marshall, Jay Ulfelder, and Mark Woodward. “A Global Model for Forecasting Political Instability.” American Journal of Political Science 54.1 (2010): 190-208.

Goldstone, Jack A. “Population and Security: How Demographic Change Can Lead to Violent Conflict.” Journal of International Affairs (2002): 3-21.

Goldstone, Jack A., Monty G. Marshall, and Hilton Root. “Demographic Growth in Dangerous Places: Concentrating Conflict Risks.” International Area Studies Review 17.2 (2014): 120-133.

Government of India. “India on the Move and Churning: New Evidence.” Economic Survey 2016-17. Government of India, Ministry of Finance (2017): 264-284.

Government of India. Census of India 2011. Office of the Registrar General & Census Commissioner, Ministry of Home Affairs, 2011.

Graham, Stephen, ed. Cities, War, and Terrorism: Towards an Urban Geopolitics. Blackwell Publishing, 2004.

Graham, Stephen. Cities Under Siege: The New Military Urbanism. Verso Books, 2011.

Green, Elliott. “The Political Demography of Conflict in Modern Africa.” Civil Wars 14.4 (2012): 477-498.

Greig, J. Michael, T. David Mason, and Jesse Hamner. “Win, Lose, or Draw in the Fog of Civil War.” Conflict Management and Peace Science 35.5 (2018): 523-543.

Grossman, Guy, and Janet I. Lewis. “Administrative Unit Proliferation.” American Political Science Review 108.1 (2014): 196-217.

Gubler, Joshua R., and Joel Sawat Selway. “Horizontal Inequality, Crosscutting Cleavages, and Civil War.” Journal of Conflict Resolution 56.2 (2012): 206-232.

Gugler, Josef. “Overurbanization Reconsidered.” Economic Development and Cultural Change 31.1 (1982): 173-189.

Gupta, Dipak K. “The Naxalites and the Maoist Movement in India: Birth, Demise, and Reincarnation.” Democracy and Security 3.2 (2007): 157-188.

Gurr, Ted Robert. “On the Political Consequences of Scarcity and Economic Decline.” International Studies Quarterly 29.1 (1985): 51-75.

Gurr, Ted Robert. Why Men Rebel. Routledge, 1970.

225

Gutierrez, Francisco. “Agrarian Inequality and Civil War Revisited.” Geography Compass 9.8 (2015): 423-431.

Harari, Mariaflavia, and Eliana La Ferrara. “Conflict, Climate, and Cells: A Disaggregated Analysis.” Review of Economics and Statistics 100.4 (2018): 594-608.

Harbers, Imke, Jos Bartman, and Enrike van Wingerden. “Conceptualizing and Measuring Subnational Democracy Across Indian States.” Democratization 26.7 (2019): 1154-1175.

Harrison, Xavier A., et al. “A Brief Introduction to Mixed Effects Modelling and Multi- Model Inference in Ecology. PeerJ 6 (2018): e4794.

Harris, Chauncy D. “The Urban and Industrial Transformation of Japan.” Geographical Review (1982): 50-89.

Harris, John R., and Michael P. Todaro. “Migration, Unemployment and Development: A Two-Sector Analysis.” American Economic Review 60.1 (1970): 126-142.

Harriss, John. “What is Going on in India's ‘Red Corridor?’ Questions about India's Maoist Insurgency.” Pacific Affairs 84.2 (2011): 309-327.

Hegre, Havard and Nicolas Sambanis. 2006. “Sensitivity Analysis of Civil War Onset.” Journal of Conflict Resolution 50, no. 4: 508-535.

Henderson, J. Vernon, and Sebastian Kriticos. “The Development of the African System of Cities.” Annual Review of Economics 10 (2018): 287-314.

Henderson, J. Vernon, Mark Roberts, and Adam Storeygard. Is Urbanization in Sub- Saharan Africa Different? The World Bank, 2013.

Hendrix, Cullen S. “Measuring State Capacity: Theoretical and Empirical Implications for the Study of Civil Conflict.” Journal of Peace Research 47.3 (2010): 273-285.

Herbst, Jeffrey. “Population Change, Urbanization, and Political Consolidation.” In The Oxford Handbook of Contextual Political Analysis. Eds. Robert E. Goodin and Charles Tilly. Oxford University Press, 2006.

Hinkkainen, Kaisa, and Steve Pickering. “Strategic Risk of Terrorist Targets in Urban vs. Rural Locations.” Working Paper delivered at the Peace Science Society Workshop in Disaggregation in Terrorism Studies, Philadelphia, USA, October. Vol. 9, 2013.

Hiremath, Rahul B., P. Balachandra, Bimlesh Kumar, Sheelratan S. Bansode, and Janakaraj Murali. “Indicator-Based Urban Sustainability—A Review.” Energy for Sustainable Development 17.6 (2013): 555-563.

226

Hove, Mediel, Emmaculate Tsitsi Ngwerume, and Cyprian Muchemwa. “The Urban Crisis in Sub-Saharan Africa: A Threat to Human Security and Sustainable Development.” Stability 2.1 (2013): 1-14.

Huntington, Samuel P. “Civil Violence and the Process of Development.” Adelphi Papers 11.83 (1972): 1-15.

Huntington, Samuel P. “The Change to Change: Modernization, Development, and Politics.” Comparative Politics 3.3 (1971): 283-322.

Imai, Katsushi S., Raghav Gaiha, and Alessandra Garbero. “Poverty Reduction during the Rural–Urban Transformation: Rural Development is Still More Important than Urbanisation.” Journal of Policy Modeling 39.6 (2017): 963-982.

Jacobs, Jane. Cities and the Wealth of Nations: Principles of Economic Life. Vintage, 1985.

Jacobs, Jane. The Economy of Cities. Vintage, 1970.

Janmyr, Maja. “Recruiting Internally Displaced Persons into Civil Militias: The Case of Northern Uganda.” Nordic Journal of Human Rights 32.3 (2014): 199-219.

Jedwab, Remi, and Dietrich Vollrath. “Urbanization Without Growth in Historical Perspective.” Explorations in Economic History 58 (2015): 1-21.

Joes, Anthony James. Urban Guerrilla Warfare. University Press of Kentucky, 2007.

Joint Chiefs of Staff. Joint Urban Operations. Joint Chiefs of Staff, Joint Publication 3- 06, 2013.

Jones, Gavin W. “Structural Change and Prospects for Urbanization in Asian Countries.” Papers for the East-West Population Institute, no. 88, 1983.

Jones, Gavin W. “The Throughgoing Urbanisation of East and Southeast Asia.” Asia Pacific Viewpoint 38.3 (1997): 237-249.

Jütersonke, Oliver, Robert Muggah, and Dennis Rodgers. “Gangs, Urban Violence, and Security Interventions in Central America.” Security Dialogue 40.4-5 (2009): 373-397.

Kalyvas, Stathis N. The Logic of Violence in Civil War. Cambridge University Press, 2006.

Kapur, Devesh. “How Will India’s Urban Future Affect Social Identities?” Urbanisation 2.1 (2017): 1-8.

227

Kasarda, John D., and Edward M. Crenshaw. “Third World Urbanization: Dimensions, Theories, and Determinants.” Annual Review of Sociology 17.1 (1991): 467-501.

Keil, Roger. “Extended Urbanization, “Disjunct Fragments” and Global Suburbanisms.” Environment and Planning D: Society and Space 36.3 (2018): 494-511.

Keshri, Kunal, and Ram B. Bhagat. “Socioeconomic Determinants of Temporary Labour Migration in India: A Regional Analysis.” Asian Population Studies 9.2 (2013): 175-195.

Khattry, Barsha and J. Mohan Rao. “Fiscal Faux Pas?: An Analysis of the Revenue Implications of Trade Liberalization.” World Development 30.8 (2002): 1431-1444.

Kilcullen, David. Out of the Mountains: The Coming Age of the Urban Guerrilla. Oxford University Press, 2013.

Knudsen, Brian B., and Terry N. Clark. “Walk and be Moved: How Walking Builds Social Movements.” Urban Affairs Review 49.5 (2013): 627-651.

Koren, Ore, and Anoop K. Sarbahi. “State Capacity, Insurgency, and Civil War: A Disaggregated Analysis.” International Studies Quarterly 62.2 (2018): 274-288.

Krohn, Marvin D. “A Durkheimian Analysis of International Crime Rates.” Social Forces 57.2 (1978): 654-670.

Krugman, Paul. “Urban Concentration: The Role of Increasing Returns and Transport Costs.” International Regional Science Review 19.1-2 (1996): 5-30.

Kugler, Tadeusz and Jacek Kugler. “Political Demography.” Oxford Research Encyclopedia of International Studies. Oxford University Press, 2010.

Kummu, Matti, Maija Taka, and Joseph HA Guillaume. “Gridded Global Datasets for Gross Domestic Product and Human Development Index Over 1990–2015.” Scientific Data 5 (2018): 180004.

Kunkeler, Josjah, and Krijn Peters. “The Boys Are Coming to Town: Youth, Armed Conflict and Urban Violence in Developing Countries.” International Journal of Conflict and Violence 5.2 (2011): 277-291.

Lacina, Bethany. “Explaining the Severity of Civil Wars.” Journal of Conflict Resolution 50.2 (2006): 276-289.

Lacina, Bethany. “From Side Show to Centre Stage: Civil Conflict After the Cold War.” Security Dialogue 35.2 (2004): 191-205.

Lampard, Eric E. “Historical Aspects of Urbanization.” In The Study of Urbanization. Eds. Philip M. Hauser and Leo F. Schnore. John Wiley & Sons, Inc., 1965: 519-54.

228

Le Blanc, Joerg. “The Urban Environment and Its Influences on Insurgent Campaigns.” Terrorism and Political Violence 25.5 (2013): 798-819.

Ledent, Jacques. “Rural-Urban Migration, Urbanization, and Economic Development.” Economic Development and Cultural Change 30.3 (1982): 507-538.

Lefebvre, Henri. The Urban Revolution. Translated by Neil Smith. University of Minnesota Press, 2003 (originally 1970).

Lessing, Benjamin. “Logics of Violence in Criminal War.” Journal of Conflict Resolution 59.8 (2015): 1486-1516.

Liddle, Brantley. “Urbanization and Inequality/Poverty.” Urban Science 1.4 (2017): 35.

Lipset, Seymour Martin. “Some Social Requisites of Democracy: Economic Development and Political Legitimacy.” American Political Science Review 53.1 (1959): 69-105.

Lipton, Michael. “Migration from Rural Areas of Poor Countries: The Impact on Rural Productivity and Income Distribution.” World Development 8.1 (1980): 1-24.

Lipton, Michael. “Responses to Rural Population Growth: Malthus and the Moderns.” Population and Development Review 15 (1989): 215-242.

Lipton, Michael. Why Poor People Stay Poor: A Study of Urban Bias in World Development. London: Canberra: Temple Smith; Australian National University Press, 1977.

Lucas, Robert EB. “The Effects of Proximity and Transportation on Developing Country Population Migrations.” Journal of Economic Geography 1.3 (2001): 323-339.

Lynch, Thomas Francis. India's Naxalite Insurgency: History, Trajectory, and Implications for US-India Security Cooperation on Domestic Counterinsurgency. National Defense University Press, 2016.

Ma, Ting, Chenghu Zhou, Tao Pei, Susan Haynie, and Junfu Fan. “Quantitative Estimation of Urbanization Dynamics Using Time Series of DMSP/OLS Nighttime Light Data: A Comparative Case Study from China's Cities.” Remote Sensing of Environment 124 (2012): 99-107.

Mahadevan, Prem. “The Maoist Insurgency in India: Between Crime and Revolution.” Small Wars & Insurgencies 23.2 (2012): 203-220.

229

Mahdavi, Saeid. “The Level and Composition of Tax Revenue in Developing Countries: Evidence from Unbalanced Panel Data.” International Review of Economics & Finance 17.4 (2008): 607-617.

Mahmoud, Hagar, and Nadine Akkari. “Shortest Path Calculation: A Comparative Study for Location-Based Recommender System.” 2016 World Symposium on Computer Applications & Research (WSCAR). IEEE, 2016.

Malpezzi, Stephen. “Population Density: Some Facts and Some Predictions.” Cityscape 15.3 (2013): 183-202.

Malthus, Thomas. “An Essay on the Principle of Population.” Electronic Scholarly Publishing Project, 1998 (originally 1798).

Marchiori, Luca, Jean-François Maystadt, and Ingmar Schumacher. “The Impact of weather anomalies on migration in sub-Saharan Africa.” Journal of Environmental Economics and Management 63.3 (2012): 355-374.

Marshall, Monty G., Ted R. Gurr, and Keith Jaggers. Polity IV Project: Political Regimes Characteristics, 1800-2016. Center for Systemic Peace (CSP), 2014.

Maurer, Peter. “War in Cities: What is at Stake?” International Review of the Red Cross 98.2 (2016): 639-646.

McGee, T.G. “Managing the Rural-Urban Transformation in East Asia in the 21st Century.” Sustainability Science 3.1 (2008): 155-167.

McGranahan, Gordon, and David Satterthwaite. “Urbanisation: Concepts and Trends.” London: IIED, 2014.

McNeil, Donald R., T. James Trussell, and John C. Turner. “Spline Interpolation of Demographic Data.” Demography (1977): 245-252.

Mellander, Charlotta, José Lobo, Kevin Stolarick, and Zara Matheson. “Night-Time Light Data: A Good Proxy Measure for Economic Activity?” PloS One 10.10 (2015): e0139779.

Michaels, Guy, Ferdinand Rauch, and Stephen J. Redding. “Urbanization and Structural Transformation.” The Quarterly Journal of Economics 127.2 (2012): 535-586.

Miguel, Edward, Shanker Satyanath, and Ernest Sergenti. “Economic Shocks and Civil Conflict: An Instrumental Variables Approach.” Journal of Political Economy 112.4 (2004): 725-753.

Miklian, Jason. “Revolutionary Conflict in Federations: The Indian Case.” Conflict, Security, & Development 11.01 (2011): 25-53.

230

Miller, Harvey J. “Tobler’s First Law and Spatial Analysis.” Annals of the Association of American Geographers 94.2 (2004): 284-289.

Miller, Byron, and Walter Nicholls. “Social Movements in Urban Society: The City as a Space of Politicization.” Urban Geography 34.4 (2013): 452-473.

Misra, RP. “South Asia: Land, People, and Economy.” In Urbanisation in South Asia: Focus on Mega Cities. Cambridge University Press (2013): 1-42.

Møller, Fenja Søndergaard. “Performance Legitimacy and Conflict in African Provinces.” Studies in Conflict & Terrorism (2018): 1-22.

Moncada, Eduardo. “The Politics of Urban Violence: Challenges for Development in the Global South.” Studies in Comparative International Development 48.3 (2013): 217-239.

Moser, Caroline O. “Urban Violence and Insecurity: An Introductory Roadmap.” Environment and Urbanization 16.2(2004): 3-16.

Muggah, Robert. “A Manifesto for the Fragile City.” Journal of International Affairs 68.2 (2015): 19-36.

Muggah, Robert. “Researching the Urban Dilemma: Urbanization, Poverty and Violence.” IDRC, 2012.

Muggah, Robert, and A. Alvazzi del Frate. “More Slums Equals More Violence: Reviewing Armed Violence and Urbanization in Africa.” Geneva: UN Development Programme, 2007.

Mukhopadhyay, Jyoti Prasad and Nilanjan Banik. “The Red Corridor Region of India: What Do the Data Tell Us?” MPRA Paper No. 58616, 2013.

Nannyonjo, Justine. “Conflicts, Poverty and Human Development in Northern Uganda.” The Round Table 94.381 (2005): 473-488.

National Oceanic and Atmospheric Administration (NOAA) National Centers for Environmental Information. Version 4 DMSP-OLS Nighttime Lights Time Series. https://ngdc.noaa.gov/eog/dmsp/downloadV4composites.html (accessed July 24, 2019).

Nayyar, Gaurav, and Kyoung Yang Kim. India's Internal Labor Migration Paradox: The Statistical and the Real. The World Bank, 2018.

Neiberg, Michael S. “Warfare in History” International Encyclopedia of the Social and Behavioral Sciences 2.25: 414-419.

231

Nekola, Jeffrey C., Craig D. Allen, James H. Brown, Joseph R. Burger, Ana D. Davidson, Trevor S. Fristoe, Marcus J. Hamilton, Sean T. Hamilton, Astrid Kodric- Brown, Norman Mercado-Silva, and Jordan G. Okie. “The Malthusian–Darwinian Dynamic and the Trajectory of Civilization.” Trends in Ecology & Evolution 28.3 (2013): 127-130.

Nepstad, Sharon Erickson. “Mutiny and Nonviolence in the Arab Spring: Exploring Military Defections and Loyalty in Egypt, Bahrain, and Syria.” Journal of Peace Research 50.3 (2013): 337-349.

NGDC, NOAA. “Version 4 DMSP-OLS Nighttime Lights Time Series.” National Oceanic and Atmospheric Administration-National Geophysical Data Center, 2013.

Nicholls, Walter J. “The Urban Question Revisited: The Importance of Cities for Social Movements.” International Journal of Urban and Regional Research 32.4 (2008): 841- 859.

Njoh, Ambe J. “Urbanization and Development in Sub-Saharan Africa.” Cities 20.3 (2003): 167-174.

Norton, Richard J. “Feral Cities.” Naval War College Review 56.4 (2003): 97-106.

Nownes, Anthony J. “The Population Ecology of Interest Group Formation: Mobilizing for Gay and Lesbian Rights in the United States, 1950–98.” British Journal of Political Science 34.1 (2004): 49-67.

Olson, Mancur. “Rapid Growth as a Destabilizing Force.” Journal of Economic History 23.4 (1963): 529-552.

Østby, Gudrun. “Polarization, Horizontal Inequalities and Violent Civil Conflict.” Journal of Peace Research 45.2 (2008): 143-162.

Østby, Gudrun, et al. “Population Pressure, Horizontal Inequality and Political Violence: A Disaggregated Study of Indonesian Provinces, 1990–2003.” The Journal of Development Studies 47.3 (2011): 377-98.

Partridge, Mark D., and Dan S. Rickman. “Distance from Urban Agglomeration Economies and Rural Poverty.” Journal of Regional Science 48.2 (2008): 285-310.

Pendleton, Gordon and Jozef Bodnar. “Update on Joint Urban Operations and the NATO Urbanization Project.” Three Swords Magazine 21 (2017): 56-61.

Pettersson, Therése and Kristine Eck. “Organized Violence, 1989–2017.” Journal of Peace Research (2018): 535-547.

Piel, Gerard. “The Urbanization of Poverty Worldwide.” Challenge 40.1 (1997): 58-68.

232

Pierskalla, Jan H. “The Politics of Urban Bias: Rural Threats and the Dual Dilemma of Political Survival.” Studies in Comparative International Development 51.3 (2016): 286- 307.

Pinckney, Jonathan. “When Civil Resistance Succeeds: Building Democracy After Popular Nonviolent Uprisings.” International Center on Nonviolent Conflict Press, 2018.

Pirnie, Bruce R. and Edward O’Connell. Counterinsurgency in Iraq (2003 – 2006). RAND Counterinsurgency Study, vol 2. RAND (2008): 21-34.

Potts, Deborah. “The Slowing of Sub-Saharan Africa's Urbanization: Evidence and Implications for Urban Livelihoods.” Environment and Urbanization 21.1 (2009): 253- 259.

Popkin, Samuel L. The Rational Peasant: The Political Economy of Rural Society in Vietnam. University of California Press, 1979.

Putnam, Robert D. Bowling Alone: The Collapse and Revival of American Community. Simon and Schuster, 2000.

Raleigh, Clionadh. “Urban Violence Patterns Across African States.” International Studies Review 17.1 (2015): 90-106.

Raleigh, Clionadh. “Seeing the Forest for the Trees: Does Physical Geography Affect a State’s Conflict Risk?” International Interactions 36.4 (2010): 384-410.

Raleigh, Clionadh, and Håvard Hegre. “Population Size, Concentration, and Civil War. A Geographically Disaggregated Analysis.” Political Geography 28.4 (2009): 224-238.

Raleigh, Clionadh, and Dominic Kniveton. “Come Rain or Shine: An Analysis of Conflict and Climate Variability in East Africa.” Journal of Peace Research 49.1 (2012): 51-64.

Raleigh, Clionadh, Andrew Linke, Håvard Hegre, and Joakim Karlsen. “Introducing ACLED: An Armed Conflict Location and Event Dataset: Special Data Feature.” Journal of Peace Research 47.5 (2010): 651-660.

Ravallion, Martin. “Income Inequality in the Developing World.” Science 344.6186 (2014): 851-855.

Ravallion, Martin, Shaohua Chen, and Prem Sangraula. “New Evidence on the Urbanization of Global Poverty.” Population and Development Review 33.4 (2007): 667- 701.

233

Rayburn, Joel D., Frank K. Sobchak, Jeanne F. Godfroy, Matthew D. Morton, James S. Powell, and Matthew M. Zais. The US Army in the Iraq War, vol. 1. United States Army War College Press, 2019.

Ravetz, Joe, Christian Fertner, and Thomas Sick Nielsen. “The Dynamics of Peri- Urbanization.” In Peri-Urban Futures: Scenarios and Models for Land Use Change in Europe. Springer (2013): 13-44.

Reserve Bank of India. Handbook of Statistics on State Government Finances. Reserve Bank of India, 2010.

Reserve Bank of India. “State Finances: A Study of Budgets.” Reserve Bank of India, 2019. https://www.rbi.org.in/Scripts/AnnualPublications.aspx?head=State%20Finances%20:%2 0A%20Study%20of%20Budgets (accessed February 1, 2020)

Robusto, C. Carl. “The Cosine-Haversine Formula.” The American Mathematical Monthly 64.1 (1957): 38-40.

Rodgers, Dennis and Robert Muggah. “Gangs as Non-State Armed Groups: The Central American Case.” Contemporary Security Policy 30.2 (2009): 301-317.

Rogers, Andrei. “Migration, Urbanization, Resources & Development, and Discussions.” Alternatives for Growth: The Engineering and Economics of Natural Resources Development. NBER, 1978. 147-238.

Rostow, Walt W. “The Take-Off into Self-Sustained Growth.” The Economic Journal 66.261 (1956): 25-48.

Rostow, Walt W. "The Stages of Economic Growth.” Economic History Review 12.1 (1959): 1-16.

Routray, Bibhu Prasad. “India's Internal Wars: Counterinsurgency Role of Central Police Forces.” Small Wars & Insurgencies 24.4 (2013a): 648-668.

Routray, Bibhu Prasad. “Policy on Left-Wing Extremism.” In National Security Decision-Making in India. S. Rajaratnam School of International Studies, 2013b.

Sambanis, Nicholas. “What is Civil War? Conceptual and Empirical Complexities of an Operational Definition.” Journal of Conflict Resolution 48.6 (2004): 814-858.

Sampaio, Antônio. “Before and After Urban Warfare: Conflict Prevention and Transitions in Cities.” International Review of the Red Cross 98.901 (2016): 71-95.

234

Sampaio, Antônio. “The New Frontlines are in the Slums.” Foreign Policy, July 3, 2018. https://foreignpolicy.com/2018/07/03/the-new-frontlines-are-in-the-slums/# (accessed August 13, 2018).

Scanlon, Eric. “Fifty-One Years of Naxalite-Maoist Insurgency in India: Examining the Factors that Have Influenced the Longevity of the Conflict.” Asian Journal of Peacebuilding (2018): 335-351.

Sharma, Sarita and Vipin Kumar Singhal. “Naxalism: A Challenge in Internal Security in India.” Indian Journal of Political Science 72.3 (2011): 765-772.

Schulz, Nicolai. "Dangerous Demographics? The Effect of Urbanisation and Metropolisation on African Civil Wars, 1961–2010.” Civil Wars 17.3 (2015): 291-317.

Schutte, Sebastian. “Geography, Outcome, and Casualties: A Unified Model of Insurgency.” Journal of Conflict Resolution 59.6 (2015): 1101-1128.

Seligson, Mitchell A. “Agrarian Inequality and the Theory of Peasant Rebellion.” Latin American Research Review 31.2 (1996): 140-157.

Seth, Anuradha and Amr Ragab. “Macroeconomic Vulnerability in Developing Countries: Approaches and Issues.” International Policy Centre for Inclusive Growth, Working Paper no. 94, 2012.

Sethi, Aman. “Green Hunt: The Anatomy of an Operation.” The Hindu. 05 February 2010 (updated 15 December 2016). https://www.thehindu.com/opinion/op-ed/Green- Hunt-the-anatomy-of-an-operation/article16812797.ece (accessed 01 January 2020).

Seto, Karen C., Roberto Sánchez-Rodríguez, and Michail Fragkias. “The New Geography of Contemporary Urbanization and the Environment.” Annual Review of Environment and Resources 35 (2010): 167-194.

Sharma, Sarita and Vipin Kumar Signhal. “Naxalism: A Challenge in Internal Security of India.” Indian Journal of Political Science 72.3 (2011): 765-772.

Sjaastad, Larry A. “The Costs and Returns of Human Migration.” Journal of Political Economy 70.5, Part 2 (1962): 80-93.

Smart, Alan, and Josephine Smart. “Urbanization and the Global Perspective.” Annual Review of Anthropology 32.1 (2003): 263-285.

South Asia Terrorism Portal (SATP). Fatalities in Left-wing Extremism: 2005-2019. SATP, 2017. https://www.satp.org/satporgtp/countries/india/maoist/data_sheets/fatalitiesnaxal05- 11.htm (accessed 01 January 2020).

235

Sovani, Nilcanth V. “The Analysis of ‘Over-Urbanization.’” Economic Development and Cultural Change 12.2 (1964): 113-122.

Stearns, Peter N. The Industrial Revolution in World History. Routledge, 2018.

Stepanova, Ekaterina. “Armed Conflict, Crime and Criminal Violence.” SIPRI Yearbook 2010: Armaments, Disarmament and International Security (2010): 37-60.

Stephan, Maria J., and Erica Chenoweth. “Why Civil Resistance Works: The Strategic Logic of Nonviolent Conflict.” International Security 33.1 (2008): 7-44.

Stewart, Frances. “Crisis Prevention: Tackling Horizontal Inequalities.” Oxford Development Studies 28.3 (2000): 245-262.

Stewart, Frances. “Horizontal Inequalities and Conflict: An Introduction and Some Hypotheses.” Horizontal Inequalities and Conflict. Palgrave Macmillan, London, 2008.

Stina, Högbladh. 2019. “UCDP GED, Codebook version 19.1.” Department of Peace and Conflict Research, Uppsala University.

Sundberg, Ralph, and Erik Melander. “Introducing the UCDP Georeferenced Event Dataset." Journal of Peace Research 50.4 (2013): 523-352.

Swyngedouw, Erik. “The Urbanization of Capital and the Production of Capitalist Natures.” In The Oxford Handbook of Karl Marx. Eds. Matt Vidal, Tomás Rotta, Tony Smith, and Paul Prew. Oxford University Press, 2019.

Tacoli, Cecilia, Gordon, McGranahan, and David Satterthwaite. World Migration Report 2015: Urbanization, Rural-Urban Migration, and Urban Poverty. London: International Organization for Migration (IOM), 2014.

Taw, Jennifer and Bruce Hoffman. The Urbanization of Insurgency: The Potential Challenge to U.S. Army Operations. RAND Corporation, 1994.

Teitelbaum, Michael S. “Political Demography: Powerful Trends Under-Attended by Demographic Science.” Population Studies 69.sup1 (2015): S87-S95.

Tir, Jaroslav and Paul F. Diehl. “Demographic Pressure and Interstate Conflict: Linking Population Growth and Density to Militarized Disputes and Wars, 1930-89.” Journal of Peace Research 35.3 (1998): 319-339.

Todaro, Michael P. “A Model of Labor Migration and Urban Unemployment in Less Developed Countries.” American Economic Review 59.1 (1969): 138-148.

Todaro, Michael P. “Urbanization in Developing Nations: Trends, Prospects, and Policies.” Journal of Geography 79.5 (1980): 164-174.

236

Toft, Monica Duffy. “Indivisible Territory, Geographic Concentration, and Ethnic War.” Security Studies 12.2 (2002): 82-119.

Tollefsen, Andreas Forø. “Experienced Poverty and Local Conflict Violence.” Conflict Management and Peace Science (2017): 0738894217741618.

Tollefsen, Andreas Forø, and Halvard Buhaug. “Insurgency and Inaccessibility.” International Studies Review 17.1 (2015): 6-25.

Tollefsen, Andreas Forø, Håvard Strand, and Halvard Buhaug. “PRIO-GRID: A Unified Spatial Data Structure.” Journal of Peace Research 49.2 (2012): 363-374.

Tripathi, Sabyasachi. “Do Large Agglomerations Lead to Economic Growth? Evidence from Urban India.” Review of Urban & Regional Development Studies 25.3 (2013a): 176- 200.

Tripathi, Sabyasachi. “Is Urban Economic Growth Inclusive in India?” Margin: The Journal of Applied Economic Research 7.4 (2013b): 507-539.

Turok, Ivan, and Gordon McGranahan. “Urbanization and Economic Growth: The Arguments and Evidence for Africa and Asia.” Environment and Urbanization 25.2 (2013): 465-482.

United Nations. United Nations Demographic Yearbook 2018. UN Department of Economic and Social Affairs, 69th ed., 2019.

United Nations. “World Urbanization Prospects 2014.” UN Department of Economic and Social Affairs, 2014.

United Nations. “World Urbanization Prospects 2018: Highlights.” UN Department of Economic and Social Affairs, 2018.

United Nations. “Global Study on Homicide 2013.” UN Office on Drugs and Crime, 2013.

United Nations. “Homicide, Development, and the Sustainable Development Goals.” UN Office on Drugs and Crime, 2019.

United Nations. “The World’s Cities in 2018: Data Booklet.” UN Department of Economic and Social Affairs, 2018.

United Nations Development Programme. Sustainable Urbanization Strategy: UNDP’s Support to Sustainable, Inclusive, and Resilient Cities in the Developing World. UNDP, 2016.

237

Uppsala Conflict Data Program (UCDP). “Definitions.” 2018. https://www.pcr.uu.se/research/ucdp/definitions (accessed 13 NOV 2018).

Urdal, Henrik. “A Clash of Generations? Youth Bulges and Political Violence.” International Studies Quarterly 50.3 (2006): 607-629.

Urdal, Henrik. “The Devil in the Demographics: The Effect of Youth Bulges on Domestic Armed Conflict, 1950-2000.” Social Development Papers 14 (2004).

Urdal, Henrik. “People vs. Malthus: Population Pressure, Environmental Degradation, and Armed Conflict Revisited.” Journal of Peace Research 42.4 (2005): 417-434.

Urdal, Henrik. “Population, Resources, and Political Violence: A Subnational Study of India, 1956–2002.” Journal of Conflict Resolution 52.4 (2008): 590-617.

Walter, Barbara F. “Bargaining Failures and Civil War.” Annual Review of Political Science 12 (2009): 243-261.

Walter, Barbara F. “The Critical Barrier to Civil War Settlement.” International Organization 51.3 (1997): 335-64.

Walton, John. “Urban Conflict and Social Movements in Poor Countries: Theory and Evidence of Collective Action.” International Journal of Urban and Regional Research 22.3 (1998): 460-481.

Wegenast, Tim C., and Matthias Basedau. “Ethnic Fractionalization, Natural Resources and Armed Conflict.” Conflict Management and Peace Science 31.4 (2014): 432-457.

Weidmann, Nils B., Jan Ketil Rød, and Lars-Erik Cederman. “Representing Ethnic Groups in Space: A Sew Dataset.” Journal of Peace Research 47.4 (2010): 491-499.

Weidmann, Nils B., and Sebastian Schutte. “Using Night Light Emissions for the Prediction of Local Wealth.” Journal of Peace Research 54.2 (2017): 125-140.

Weiss-Altaner, E. R. “Rural Exodus in the Third World: A Malthusian Crisis?” History of European Ideas 4.2 (1983): 183-201.

White, Robert W., and Terry Falkenberg White. “Revolution in the City: On the Resources of Urban Guerrillas." Terrorism and Political Violence 3.4 (1991): 100-132.

Williams, Donald C. “Urban Growth around the World.” Global Urban Growth: A Reference Handbook. ABC-CLIO (2012): 49-84.

Wimmer, Andreas, Lars-Erik Cederman, and Brian Min. “Ethnic Politics and Armed Conflict: A Configurational Analysis of a New Global Data Set.” American Sociological Review 74.2 (2009): 316-337.

238

Woodward, Ralph Lee. “The Rise and Decline of Liberalism in Central America: Historical Perspectives on the Contemporary Crisis.” Journal of Interamerican Studies and World Affairs 26.3 (1984): 291-312.

World Bank. Open Budgets Portal. BOOST Data Lab, 2018. http://boost.worldbank.org/boost-data-lab (accessed 28 JUL 2018).

Yamashita, Akio. “Rapid Urbanization in Developing Asia and Africa.” Urban Development in Asia and Africa. Springer, Singapore (2017): 47-61.

Young, Alwyn. “Inequality, the Urban-Rural Gap, and Migration.” Quarterly Journal of Economics 128.4 (2013): 1727-1785.

Zhang, Qian and Karen C. Seto. “Can Night-Time Light Data Identify Typologies of Urbanization? A Global Assessment of Successes and Failures.” Remote Sensing 5 (2013): 3476-3494.

Zhang, Qingling, and Karen C. Seto. “Mapping Urbanization Dynamics at Regional and Global Scales Using Multi-Temporal DMSP/OLS Nighttime Light Data.” Remote Sensing of Environment 115.9 (2011): 2320-2329.

Zhang, Xiang Quan. “The Trends, Promises, and Challenges of Urbanisation in the World.” Habitat International 54 (2016): 241-252.

Zuberi, Tukufu, et al. “Population and African Society.” Annual Review of Sociology 29.1 (2003): 465-486.