Multivariate OLS and negative binomial regression results for non-terrorist violent crimes in general reveal statistically significant correlations between three structural disadvantage variables and rates of total violence, homicide, aggravated assault, rape, and robbery. Study results indicate that unemployment and family disruption have a significant positive impact on all types of non-terrorist violent crime rates. Lastly, low education is other structural disadvantage variable that is significantly

ii and positively associated with total violent, homicide, aggravated assault, and robbery crime rates in present study.

Zero inflated negative binomial analyses of terrorism-related crimes in Turkey show a number of important findings. Results indicate that among the structural disadvantage variables, poverty and residential instability have significant positive effects on terrorism-related crimes.

This study reveals important nuances to overall general findings, with completely different indicators of disadvantage predicting non-terrorist versus terrorist violence.

There are important differences between the nature of terrorism versus non-terrorist violence that might account for the differences in significance of specific indicators of disadvantage across these two categories. Non-terrorist and terrorism-related violent crimes have several differences in terms of motivation, opportunity structure, methods, and ideology. Additionally, non-terrorist violence crimes are usually preceded by social interaction. Many of these violent crime victims know their assailants and are involved in a dispute of some sort with their assailants. As such, from a macro viewpoint, they are most likely to be triggered by structural conditions that provide or indicate “relational stressors.”

On the other hand, in terrorism-related crimes, generally there is no social interaction between offender(s) and victim(s), so relational stress is not an issue. In contrast, political ideology is a typical motivation. Thus, dimensions of disadvantage that tap into social change and social injustice are logically more likely to be related to terrorist violence.

iii

iv ACKNOWLEDGEMENTS

I appreciate my dissertation committee members’ support during the dissertation process. Completion of this dissertation would not have been possible without their support. First, I thank Dr. Pamela Wilcox, my dissertation committee chair, for her intellect and energy. She continually stimulated my analytical thinking and greatly assisted me with scientific writing. Her theoretical and statistical insights are admirable. I also appreciate her patience and timely responses to my questions during that process.

Without her guidance and leadership, this dissertation would not have been finished.

I also wish to thank to the other dissertation committee members. Doctor John

Wright and Doctor James Frank did a lot for me to defend my dissertation with their endless support and their valuable suggestions. Doctor Melissa Moon also encouraged me during that process.

I believe in the common saying that “behind every successful man, there is a woman”. Finally, I owe special thanks to my wife for encouraging me and for her patience, love, understanding, and support throughout my pursuit of a doctoral degree.

She supported me at every stage of my studies. Without her encouragement, I could not finish this dissertation.

v TABLE OF CONTEXT

LIST OF TABLES ...... ix LIST OF FIGURES ...... x CHAPTER ONE: INTRODUCTION ...... 1 SIGNIFICANCE OF THE STUDY ...... 2 CHAPTER TWO: THEORETICAL FOUNDATIONS ...... 5 ANOMIE AND INSTITUTIONAL ANOMIE THEORY ...... 5 MERTON’S ANOMIE THEORY...... 5 INSTITUTIONAL ANOMIE THEORY ...... 7 EMPIRICAL RESEARCH ...... 10 MACRO-LEVEL GENERAL STRAIN THEORY ...... 12 MERTON’S STRAIN THEORY ...... 12 AGNEW’S GENERAL STRAIN THEORY ...... 14 MACRO LEVEL GENERAL STRAIN THEORY ...... 17 EMPIRICAL EVIDENCE ...... 18 SOCIAL DISORGANIZATION THEORY ...... 20 SYSTEMIC MODEL OF SOCIAL DISORGANIZATION THEORY ...... 21 EMPIRICAL STUDIES ABOUT SYSTEMIC MODEL ...... 23 COLLECTIVE EFFICACY MODEL OF SOCIAL DISORGANIZATION THEORY ...... 27 EMPIRICAL EVIDENCE ...... 29 CONFLICT THEORY ...... 30 ORIGINS OF MODERN-DAY CONFLICT THEORY ...... 31 EMPIRICAL EVIDENCE ...... 33 CONCLUSION ...... 35 CHAPTER THREE: REVIEW OF LITERATURE ...... 37 THE EFFECTS OF INEQUALITY ...... 60 POSITIVE EFFECTS OF INEQUALITY ON VIOLENCE ...... 61 NEGATIVE EFFECTS OF INEQUALITY ON VIOLENCE ...... 63 NULL EFFECTS OF INEQUALITY ON VIOLENCE ...... 63 POVERTY ...... 65 POSITIVE EFFECTS OF POVERTY ON VIOLENCE ...... 66 NEGATIVE EFFECTS OF POVERTY ON VIOLENCE ...... 67 NULL EFFECTS OF POVERTY ON VIOLENCE ...... 68 UNEMPLOYMENT ...... 68 POSITIVE EFFECTS OF UNEMPLOYMENT ON VIOLENCE ...... 69 NEGATIVE EFFECTS OF UNEMPLOYMENT ON VIOLENCE ...... 69 NULL EFFECTS OF UNEMPLOYMENT ON VIOLENCE ...... 70

vi RESIDENTIAL INSTABILITY ...... 71 POSITIVE EFFECTS OF RESIDENTIAL INSTABILITY ON VIOLENCE ...... 71 NEGATIVE EFFECTS OF RESIDENTIAL INSTABILITY ON VIOLENCE ...... 72 NULL EFFECTS OF RESIDENTIAL INSTABILITY ON VIOLENCE ...... 72 FAMILY DISRUPTION ...... 73 POSITIVE EFFECTS OF FAMILY DISRUPTION ON VIOLENCE ...... 73 NEGATIVE EFFECTS OF FAMILY DISRUPTION ON VIOLENCE ...... 74 NULL EFFECTS OF FAMILY DISRUPTION ON VIOLENCE ...... 75 LOW EDUCATION ...... 76 POSITIVE EFFECTS OF LOW EDUCATION ON VIOLENCE ...... 76 NEGATIVE EFFECTS OF LOW EDUCATION ON VIOLENCE ...... 76 NULL EFFECTS OF LOW EDUCATION ON VIOLENCE ...... 77 AGE-STRUCTURE ...... 77 POSITIVE EFFECTS OF PERCENT YOUNG ON VIOLENCE ...... 78 NEGATIVE EFFECTS OF PERCENT YOUNG ON VIOLENCE ...... 78 NULL EFFECTS OF PERCENT YOUNG ON VIOLENCE ...... 79 POPULATION DENSITY ...... 79 POSITIVE EFFECTS OF POPULATION DENSITY ON VIOLENCE ...... 80 NEGATIVE EFFECTS OF POPULATION DENSITY ON VIOLENCE ...... 80 NULL EFFECTS OF POPULATION DENSITY ON VIOLENCE ...... 80 REGION – SOUTH/SOUTHERNNESS ...... 81 POSITIVE EFFECTS OF REGION ON VIOLENCE ...... 82 NEGATIVE EFFECTS OF REGION ON VIOLENCE ...... 82 NULL EFFECTS OF REGION ON VIOLENCE ...... 82 OVERVIEW OF FINDINGS ...... 83 TERRORISM AS A SPECIAL CASE OF VIOLENCE ...... 86 TERRORISM: WHAT IS IT? ...... 87 DIFFERENCES BETWEEN TERRORISM AND ORDINARY VIOLENT CRIMES ...... 88 CAUSES OF TERRORISM ...... 90 MACRO-LEVEL TERRORISM RESEARCH ...... 91 CONCLUSION ...... 94 CHAPTER FOUR: A DESCRIPTION OF CRIME AND TERRORISM IN TURKEY ..... 95 ADMINISTRATIVE STRUCTURE OF TURKEY ...... 95 ADMINISTRATIVE DIVISIONS OF TURKEY ...... 95 VIOLENT CRIME IN TURKEY ...... 96 TRENDS OVER TIME IN PREVALENCE OF VIOLENCE IN TURKEY ...... 97 VARIATION IN VIOLENCE IN TURKEY BY LOCATION ...... 101 TERRORISM IN TURKEY ...... 106 TRENDS OVER TIME IN PREVALENCE OF TERRORISM IN TURKEY ...... 109 VARIATION IN TERRORISM IN TURKEY BY LOCATION ...... 110

vii CONCLUSION ...... 111 CHAPTER FIVE: DATA AND METHODOLOGY ...... 113 RESEARCH QUESTIONS AND HYPOTHESES ...... 113 DATA ...... 115 TURKISH NATIONAL POLICE DATA ...... 116 TURKEY’S STATISTICAL YEARBOOKS, CENSUSES ...... 117 OTHER DATA RESOURCES ...... 117 MEASURES OF VARIABLES ...... 118 DEPENDENT VARIABLES ...... 118 KEY INDEPENDENT VARIABLES: STRUCTURAL DISADVANTAGE ...... 119 UNEMPLOYMENT ...... 119 RESIDENTIAL INSTABILITY ...... 120 FAMILY DISRUPTION ...... 121 POVERTY ...... 121 ECONOMIC INEQUALITY ...... 122 LOW EDUCATION ...... 123 CONTROL VARIABLES...... 123 POPULATION DENSITY ...... 123 PERCENTAGE OF PEOPLE BETWEEN AGES 15-24 ...... 124 REGION (SOUTHEASTERN/ EASTERN DUMMY VARIABLE) ...... 125 DESCRIPTIVE STATISTICS ...... 129 CONCLUSION ...... 130 CHAPTER SEVEN: DISCUSSION AND CONCLUSIONS ...... 167 SUMMARY OF FINDINGS ...... 167 NON-TERRORIST VIOLENT CRIME ...... 167 TERRORISM-RELATED CRIME RESULTS ...... 171 SUMMARY: DISADVANTAGE AND TERRORIST V. NON-TERRORIST VIOLENCE ...... 173 POLICY IMPLICATIONS ...... 174 RESPONSES TO TERRORISM IN TURKEY: ADMINISTRATIVE STRUCTURE AND PROCESS...... 174 RESPONSES TO NON-TERRORIST CRIME IN TURKEY: ADMINISTRATIVE STRUCTURE AND PROCESS………………………………….…………………………...178

VIOLENT-CRIME AND TERRORISM PREVENTION: IMPLICATIONS OF FINDINGS……………………………………………………………………………………...179 LIMITATIONS OF THE STUDY AND RECOMMENDATIONS FOR FUTURE RESEARCH ...... 181 REFERENCES ...... 186

viii LIST OF TABLES

Table 1. Summary Findings from Prior Research for Structural Covariates and Violent Crimes...... 39 Table 2. Summary of relationships between violent crime rates and structural covariates ...... 84 Table 3. Descriptive Statistics ...... 129 Table 4. Hypotheses, Variables, and Data Sources ...... 131 Table 5. Bivariate correlations between non-terrorist violent crimes and independent/control...... 142 Table 6. Bivariate correlations between terrorism-related crimes and independent/control variables ...... 143 Table 7. Transformation results for skewed dependent variables ...... 145 Table 8. OLS Regression Models for Total Crime Rates (N=81) ...... 147 Table 9. Summary of hypotheses testing for total violent crimes ...... 149 Table 10. Negative Binomial Regression Model for Homicide (N=81) ...... 151 Table 11. Summary of hypotheses testing for homicide ...... 153 Table 12. OLS Regression Models for Aggravated Assault Rates (N=81) ...... 155 Table 13. Summary of hypotheses testing for aggravated assault rates ...... 157 Table 14. Negative Binomial Regression Models for Rape (N=81) ...... 158 Table 15. Summary of hypotheses testing for rape crimes ...... 159 Table 16. Negative Binomial Regression Models for Robbery (N=81) ...... 160 Table 17. Summary of hypotheses testing for robbery ...... 162 Table 18. ZINB Regression Model of Terrorism-related Crime (N=81) ...... 164 Table 19. Summary of hypotheses testing for terrorism-related crimes ...... 165 Table 20. Relationships between dependent and independent variables ...... 168

ix LIST OF FIGURES

Figure 1. Lorenz Curve and Gini Coefficient ...... 60 Figure 2. Census-defined regions of Turkey ...... 96 Figure 3. Total violent crime rates per 100,000 persons in Turkey between 1999 and 2008...... 97 Figure 4. Homicide crime rates per 100,000 persons in Turkey between 1999 and 2008 ...... 98 Figure 5. Aggravated assault crime rates per 100,000 persons in Turkey between 1999 and 2008 ...... 99 Figure 6. Robbery crime rates per 100,000 persons in Turkey between 1999 and 2008...... 100 Figure 7. Rape crime rates per 100,000 persons in Turkey between 1999 and 2008. ... 101 Figure 8. Regional differences for total violent crime rates between 1999 and 2008 ... 102 Figure 9. Regional differences for homicide crime rates between 1999 and 2008 ...... 103 Figure 10. Regional differences for aggravated assault crime rates between 1999 and 2008...... 104 Figure 11. Regional differences for robbery crime rates between 1999 and 2008 ...... 105 Figure 12. Regional differences for rape crime rates between 1999 and 2008 ...... 106 Figure 13. Terrorism-related crime rates per 100,000 persons in Turkey between 1999 and 2008...... 110 Figure 14. Regional differences for terrorism-related crime rates between 1999 and 2008 ...... 111 Figure 15.Organizational chart of Turkish National Police ...... 177

x CHAPTER ONE: INTRODUCTION

The main purpose of current study is to examine the association between structural disadvantage and occurrence of violent crimes and terrorism-related crimes across provinces in Turkey. While “structural disadvantage” is a broad term, it generally includes measures of poor social and economic variables. For the purposes of this study,

“structural disadvantage” consists of, specifically, measures of unemployment, residential instability, family disruption (i.e., divorce, single parents), poverty, economic inequality, and low education (see also, e.g., Blau and Blau, 1982; Liska and Chamlin,

1984; Messner and Golden 1992).

Similar to other societies, violence is a serious problem for Turkey. It causes frustration and distress in the community, and coping with violent crime is an important issue for government agencies in Turkey. Like other countries, it is also complicated to get a clear picture of the roots of violence in Turkey. It is seriously influenced by different social, economic, historical, political, and cultural factors (De Haan, 1997).

Besides a general violence problem, Turkey has also experienced an extensive and difficult struggle against terrorism for nearly four decades. The country has suffered from several terrorist movements looking to achieve their aims through violent means.

Specially, Turkey’s fight against Kurdistan Workers Party/ Kurdistan Freedom and

Democracy Congress (PKK/KONGRA GEL), which is a separatist terrorist organization, resulted in deaths of over 35,000 people and cost more than $100 billion since 1984.

Because of being in “its infancy, particularly in the empirical realm” (Sherley,

2006: p.17), terrorism literature has some weaknesses in terms of proper methodology

1 and general theoretical frameworks. In the terrorism literature, while information is available for psychopathological characteristics of terrorists (Crenshaw, 2000; Hudson,

1999), motivational factors for terrorist groups (Hoffman, 1995), evaluation of terrorist movements (Hoffman, 2001), and roots of terrorist activities at micro-level, there is little macro-level understanding on the same issue. In short, the relationship between terrorism and macro-level structural disadvantage is generally ignored by researchers (Lum et al.,

2005).

The current study will use provinces of Turkey as units of analysis, and will look at the effects of different structural characteristics of provinces in connection with violent and terrorism-related crime rates. During the analyses, total violent, homicide, robbery, aggravated assault, rape, and terrorism-related crime rates will be employed as dependent variables. Data for the study will be derived from different sources. For instance, data for all of the dependent variables will be obtained from Turkish National Police. Other data for independent variables will come from Turkish Statistical Institute, Ministry of Health, and Ministry of National Education.

Significance of the Study

This study differs from earlier research about terrorism-related crimes and violent crimes in several ways. First, as just indicated above, the majority of the empirical studies about terrorism in Turkey have looked at causes of crime at micro-level. For instance,

Ergil (1980), Songar (1984), Karacan (1984), Comertoglu (1995), Alkan (2002), and

Caglar (2006) examined psychiatric, psychological characteristics, and socioeconomic backgrounds of individuals who join terrorist groups in Turkey. On the other hand, only a

2 few studies (Koseli, 2007; and Basibuyuk, 2008) have looked at the relationship between terrorism-related crimes and some of the macro-level structural conditions in Turkey. The current study will go beyond this limited previous research by including more indicators of structural disadvantage.

In addition, previous research generally has examined either violent crimes or terrorism-related crimes. The present study will look at both terrorism-related crimes and violent crimes simultaneously. Including both violent and terrorism-related crimes in the same study will give opportunity to the reader to consider which specific macro-level factor(s) will have an effect on which particular type(s) of crime rates. Clear comparisons between non-terrorist violent crimes and terrorism-related crimes can be made from the current examination, in particular.

Organization of Dissertation

This study will unfold over six remaining chapters. In Chapter Two, macro-level theories of crime emphasizing the effects of structural disadvantage -- such as anomie- institutional anomie, macro-level general strain, social disorganization, and conflict theory -- will be explained in depth. Additionally, the empirical status of each theory will be presented.

In Chapter Three, the literature that identifies significant structural covariates that may impact the occurrence of violent crimes is reviewed. The emphasis of this review is on covariates that are indicators of structural disadvantage, but other structural factors related to area crime rates will also be considered. Also, in Chapter Three, terrorism will

3 be examined as a specific type of violence, and both micro and macro-level terrorism studies will be reviewed.

Chapter Four will provide a detailed descriptive discussion of the violence and terrorism problems in Turkey over the last ten years. Specifically, the prevalence of terrorist and non-terrorist violence will be examined between 1999 and 2008. Chapter

Four will also present regional differences in violent and terrorism-related crime rates in

Turkey.

In Chapter Five, research questions and research hypotheses will be indicated. In addition, detailed information about data and the sample will be specified. Also, operationalization of dependent, independent, and control variables will be presented, and the research design will be explained in this chapter.

Chapter Six will present the results of the study. Specifically, results of the bivariate and different multivariate analyses for violent and terrorism-related crimes will be presented and explained in detail in Chapter Six.

Finally, Chapter Seven will provide theoretical and policy implications of the results presented in Chapter Six. In addition, limitations of the study and suggestions for future research will be explained in Chapter Seven.

4 CHAPTER TWO: THEORETICAL FOUNDATIONS

In this chapter, main macro-level theories of crime with an emphasis on structural disadvantage, such as anomie- institutional anomie, general strain (macro level version), social disorganization, and conflict theory, will be explained in detail. In addition, the empirical status of each theory will also be presented. These theories will serve as theoretical justification for my examination of both non-terrorist violent crime and terrorist crime in Turkey. While these theories have a rich history of use in understanding non-terrorist crime (as will be show below), they are far-less explored when it comes to terrorist crimes. However, there is recent growing interest in understanding macro-level covariates of terrorism, including an examination of the effects of indicators of structural disadvantage (Testas; 2001; Shelley and Picarelli, 2002;

Schmid, 2003; Krueger and Maleckova, 2003; Blomberg et al., 2004; Li and Schaub,

2004; Sherley, 2006; Berrebi and Lakdawalla, 2007). It is not the purpose of this dissertation to test which of the theoretical perspectives on structural disadvantage is

“better” than the other in explaining terrorist versus non-terrorist crime in Turkey.

Rather, all are useful in justifying why disadvantaged contexts should have higher rates of both types of crime.

ANOMIE AND INSTITUTIONAL ANOMIE THEORY

Merton’s Anomie Theory

Merton’s social structure and anomie (1938) theory departs from psychological and Freudian explanations of crime by being based on Durkheimian sociology. Anomie

5 theory seeks to answer the question “What motivates individuals, who are considered to be naturally law-abiding, to engage in deviate behavior?” Durkheim argued that when crises erupt, individuals are exposed to an environment wherein expectations are unknown. It is in this state that anomie, or a lack of “norms and guides” of behavior, occurs. According to Durkheim, it causes an increase in deviant behavior and suicide.

Merton (1938) borrowed Durkheim's concept of “anomie” to form his own theory.

Merton’s theory differs somewhat from Durkheim's in that Merton (1938) argued that the real problem is not created by a sudden social change, as Durkheim proposed, but rather by cultural and social structures that hold out the same goals to all its members without giving them equal means to achieve them.

Merton contends that all social systems have two essential aspects: “culture structure” and “social structure” (Messner, 1988). The “culture structure” entails a society’s goals and the means for achieving these goals. The “social structure” refers to the organized relationships between members of a society. Therefore, the cultural structure defines the common goals, while the social structure determines access to the means to achieve those goals. When there is disjunction between the culture and social structures, anomie emerges, and it restrains the capacity of social controls. According to

Merton, malintegration is anomie.

Merton (1938) focused his theory on the United States and argued that this society is unique in that it is experiencing “malintegration.” First, the goals and means of achieving these goals are unbalanced in the United States. Specifically, there is a greater emphasis on monetary achievement than on the use of legitimate means to achieve this goal. According to Merton (1938), this is inherent to the American Dream. Therefore,

6 deviant behavior may be viewed as a symptom of dissociation between culturally defined goals and socially structured means. Overall, the goal of becoming economically successful is more highly valued than the legitimate means for achieving it. Furthermore, when the social structure fails to provide equal access to the legitimate means, an anomic state occurs, freeing members in society to employ innovative, yet illegal, means to acquire the universal goal, thereby increasing the crime rate. Therefore, Merton hypothesized that overall crime levels vary systematically with the degree of disjuncture between culture and social structures of social systems (Messner, 1988). More disadvantaged social structures are likely to experience higher rates of crime, especially if that disadvantage is out of balance with cultural goals.

Institutional Anomie Theory

Merton’s (1938) anomie theory was widely accepted in the 1950s and 1960s.

However, critics began rejecting the theory as an explanation of criminal behavior in the

1970s (Kornhauser, 1978). Merton’s anomie theory was then reviewed in the 1980s by

Messner and Rosenfeld. There are various similarities and differences between Messner and Rosenfeld’s (1994) institutional anomie theory and Merton’s (1938) anomie theory.

First, Messner and Rosenfeld (1994) are similar in that they both view culture and social structures as important. Like Merton (1938), Messner and Rosenfeld (1994) note that the American Dream fosters anomie. Messner and Rosenfeld (1994) expand this discussion by formulating four value commitments inherent in the American Dream: achievement orientation, individualism, universalism, and “fetishism” of money. First, achievement orientation refers to an individual’s worth in terms of what they achieve

7 rather than who they are. Second, individualism refers to the ideal that people are expected to succeed on their own, which facilitates a competitive environment. Third, universalism means that everyone (i.e., poor, rich, White, Black) is encouraged to pursue economic success. Finally, “fetishism” of money views money as the metric of success, which is inherently infinite. Taken together, these four value commitments give rise to the American Dream. Because the American Dream is emphasized more than the means to achieve it, and because legitimate means are not available across all social structures, an anomic environment results in which people pursue goals by any means possible

(Messner and Rosenfeld, 1994).

Where Messner and Rosenfeld (1994) and Merton (1938) differ is in the conceptualization of social structure. Merton (1938) focused on the inherent economic stratification that exists within the social structure. On the other hand, Messner and

Rosenfeld (1994) consider Merton’s anomie theory to be incomplete because it fails to fully address the roles of additional social institutions. Specifically, they identified four interdependent institutions: economy, polity, family, and educational system. Messner and Rosenfeld (1997) contend that non-economic institutions, when they are strong, are also important in that they can placate negative structural effects (of economic disadvantage).

They argue that a unique characteristic of the United States is the domination of the economy over all other social institutions. More specifically, Rosenfeld and Messner

(1995) state that the economy off centers the balance of power between the social institutions. Economic dominance is exhibited in three ways: devaluation, accommodation, and penetration. First, non-economic institutions are devalued relative

8 to the economy. For example, being a successful in business is more important than being a successful father. Second, when there is conflict between the institutions, the noneconomic roles are typically accommodated to economic roles; therefore, the economic institution is given preference. For instance, society views uprooting a family in order to pursue a job promotion as acceptable and necessary in order to climb the corporate ladder. Third, every sector of life is penetrated by the language, norms and logic of the economy. For example, education is seen more as a way of obtaining higher-paying jobs rather than learning. Since noneconomic institutions are relatively devalued, forced to accommodate to economic needs, and are penetrated by economic values, they are less able to achieve their social functions, resulting in a reduced capacity to exert social control (Rosenfeld and Messner, 1995). These weakened cultural and institutional controls result in higher levels of crime (Messner and Rosenfeld, 2001).

In sum, institutional anomie theory asserts that the American Dream fosters crime both directly and indirectly. First, it has a direct effect on crime through the establishment of anomie, which weakens social norms and their ability to regulate society. Second, it has an indirect effect on crime by contributing to the tilt in the institutional balance toward the economy within the social structure, which reduces the social control of other competing social institutions (Rosenfeld and Messner, 1995). Therefore, it is suggested that in order to reduce crime rates in America, there needs to be a significant change in the institutional balance of power.

9 Empirical Research

Although there have been a few tests of Merton’s macro-level anomie theory, there is more empirical research on Messner and Rosenfeld’s (1994) institutional anomie theory. Messner and Rosenfeld’s (1994) overall thesis is that culturally produced pressures to secure monetary rewards, coupled with weak controls from noneconomic social institutions, promotes high rates of crime.

Chamlin and Cochran (1995) conducted the first systematic test of Messner and

Rosenfeld’s (1994) institutional anomie theory. They argued that the effect of economic conditions on instrumental crime rates will depend on the vitality of noneconomic institutions. Therefore, they expected that poor economic conditions would affect instrumental crime especially when there is a simultaneous weakening of noneconomic institutions (N=50 states in 1980). Conversely, if these non-economic institutions were strong, the criminogenic effects of poverty should be mitigated. As expected by the theory, each product term (i.e., poverty rate x church membership, poverty rate x divorce- marriage ratio, and poverty rate x percent voting) significantly affected property crime rates. Specifically, higher levels of church membership, lower levels of divorce-marriage ratio, and higher levels of voting participation reduced the criminogenic effects of poverty on economic crime. In other words, poverty only positively affected the property crime rate insofar as it is not conditioned by other, noneconomic institutions. These findings provided partial support for institutional anomie theory.

Messner and Rosenfeld (1997) also tested institutional anomie theory by examining the relationship between the economic and political systems as they relate to homicide levels. Specifically, they used a “decommodification index” measure, which

10 served as a proxy for the balance between political and economic institutions. Their decommodification index represented “the ease of access to welfare benefits, their income replacement value, and the expansiveness of coverage across different statuses and circumstances” (Messner and Rosenfeld, 1997, p. 1398–1399). “Decommodification” reduced citizen reliance on the market for support (i.e., provide services and resources) and signaled that the balance of institutional power in market society has shifted from the economy to the polity. Therefore, based on institutional anomie theory, an increase in

“decommodification” should decrease anomie, thereby decreasing crime rates.

Specifically, Messner and Rosenfeld (1997) hypothesized a negative relationship between

“decommodification” and cross-national homicide rates (N= 45 nations). Their major finding was that the decommodification index did in fact generate a substantial and significant negative effect on the homicide rate. These results thereby provided support for institutional anomie theory.

Jensen (2002) criticized Messner and Rosenfeld’s (1997) test of institutional anomie. Specifically, Jensen (2002) challenged their description of American culture as being highly focused on economic goals relative to other nations. Also, according to

Jensen (2002), there were untested assumptions about the impacts of economic dominance on non-economic institutions. Finally, Jensen (2002) argued that Messner and

Rosenfeld (1997) failed to include variables from competing theories in their tests.

Jensen (2002) tested the validity of Messner and Rosenfeld’s (1997) institutional anomie theory and examined 43 nations by using data from the World Values Survey and the World Health Organization. The author first looked at the correlations between divorce rate, birth rate, marriage rate, importance of family, religion, leisure, and work,

11 and homicide rates. Contrary to Messner and Rosenfeld’s (1997) institutional anomie theory, Jensen’s (2002) two models without control variables revealed significant positive relationships between importance of religion and homicide rates, and homicide crime rates and the birth rate. Those findings did not support institutional anomie theory.

Moreover, contrary to the institutional anomie theory, the author revealed a significant positive association between burglary crime rates and decommodification.

MACRO-LEVEL GENERAL STRAIN THEORY

Macro-level general strain theory originated from earlier theoretical work on individual strain theory. I will review briefly the micro-level roots of macro-level strain theory, starting with Merton’s micro-level strain theory.

Merton’s Strain Theory

As described earlier, Merton (1938) focused on explaining why some societies, such as the United States, have higher crime rates than others. According to him,

American culture places an extraordinary emphasis on economic success, but also because this goal is universal. Poor people are not taught to be satisfied with their lot but rather are instructed to pursue the “American dream”.

However, in his work, Merton really talked about two distinct theories (anomie and strain) which he did not differentiate obviously. First, anomie theory (discussed above) was presented, referring to a deinstitutionalization of norms that takes place when there is a disjunction between the emphasis on institutional means and cultural goals

12 (Merton, 1938). As a second theory, Merton talked about was “strain theory,” which holds that when individuals are blocked from accessing the institutionalized means to culturally prescribed goals, they are more likely to follow illegitimate means to reach these goals (Merton, 1938). Strain theory has focused on explaining why some individuals within a society are more likely to engage in crime than others. According to the theory, individuals are pressured into crime. Most commonly, it has been argued that they are pressured into crime when they are prevented from achieving cultural goals like monetary success or middle-class status through legitimate channels. This is the central argument of the classic strain theories, like that of Merton. It is argued that everyone in the United States is encouraged to pursue the goals of monetary success or middle-class status. However, lower-class individuals are often prevented from achieving such goals through legitimate channels. Their parents do not equip them with the necessary skills and values to do well in schools. Then, society encourages everyone to pursue certain goals but then prevents large segments of the population from achieving these goals through legitimate channels. When individuals experience such goal blockage they are under a great deal of strain or pressure, and they may respond by engaging in crime.

Merton presents five modes of adapting to strain caused by the restricted access to socially approved goals and means. Conformity is the most common mode of adaptation.

Individuals accept both the goals as well as the prescribed means for achieving those goals. Conformists will accept, though not always achieve, the goals of society and the means approved for achieving them. Individuals who adapt through innovation accept societal goals but have few legitimate means to achieve those goals, thus they innovate

(design) their own means to get ahead. In ritualism, individuals abandon the goals they

13 once believed to be within their reach and dedicate themselves to their current lifestyle.

They play by the rules and have a daily safe routine. Retreatism is the adaptation of those who give up not only the goals but also the means; they essentially “drop out” of society.

The final adaptation, rebellion, occurs when the cultural goals and the legitimate means are rejected. Rebellious individuals create their own goals and their own means, by protest or revolutionary activity.

Agnew’s General Strain Theory

Agnew (1992) built upon Merton’s (1938) traditional strain theory in several ways to create a “general strain theory.” Agnew (1992) identified three major types of strain, each referring to a different type of negative relationship with others. The first type of strain is the same as in traditional strain theories - the failure to achieve positively valued goals. Agnew (1992), however, further identified three subtypes of strains within this category. First, the gap between aspirations and expectations can lead to frustration, which may lead to crime. Second, the gap between expectations and actual achievements may lead to emotions, such as anger, disappointment, and resentment, which may motivate some individuals to reduce these emotions by acting out delinquently. Third, the gap between just/fair outcomes and actual outcomes (i.e., inequity) may lead to anger and frustration, which can lead to delinquency in order to restore equity.

As mentioned, Agnew (1992) also identified two additional sources of strain in addition to the failure to achieve positively valued goals. Specifically, the second type of strain is the removal, or threat of removal, of positively valued stimuli (e.g., death of a family member). This type of strain may lead to delinquency as the individual attempt to

14 prevent the loss, retrieve the loss, take revenge, or deal with the loss by using drugs. In addition, the third type of strain is the presentation, or threat of presentation, of negatively valued stimuli (e.g., physical abuse). This type of strain may lead to delinquency as the individual tries to escape from the negative stimuli, terminates it, seeks revenge, or deals with it by using drugs.

Agnew (1985, 1992) also expanded upon Merton’s traditional strain theory by stating that each type of strain increases the likelihood that individuals will experience negative affect, such as anger, disappointment, fear and depression. Anger is considered the most critical emotional reaction to strain, which increases the likelihood of criminal behavior. In other words, a psychological state of “negative affect” (i.e., anger) mediates the causal relationship between strain and delinquency.

Agnew (1992) developed traditional strain theories by arguing that there were legitimate and illegitimate ways for individuals to cope with strain and negative emotions. Specifically, general strain theory maintains that an individual may adapt to strain through the use of cognitive, behavioral, and emotional techniques. First, cognitive coping strategies included minimizing the importance of the adversity (i.e., “It’s not that important”), maximizing the positive outcomes (i.e., “It’s not that bad”), and accepting responsibility (i.e., “I deserve it”). Second, behavioral coping strategies included maximizing the positive outcomes (i.e., eliminating the source of strain) and revengeful behavior. Finally, emotional coping strategies act directly on the negative emotions that result from adversity. This may include meditation, relaxation, or using drugs. Agnew

(1992) contends that most individuals adapt to strain using these techniques; however, some individuals are constrained to adapting to strain in a delinquent manner. Agnew

15 (1992) identified variables that conditioned the response to strain. There are two important issues that distinguish delinquent and non-delinquent coping strategies. First, constraints to delinquent and non-delinquent coping can include factors such as individual coping resources, conventional social support, individual’s level of social control and access to illegitimate means. Second, factors that affect the disposition to delinquency include personality, temperament, past behavior and consequences, and procriminal beliefs.

Agnew (1992) also discussed characteristics of strain that increase negative affect, such as magnitude, recency, duration, and clustering. First, magnitude refers to the degree of loss in terms of the removal of positively valued stimuli or the degree of pain inflicted by the presentation of noxious stimuli. Second, recency implies that strains that occur most recently will be more stressful compared to older ones. Third, duration means that strains experienced for longer periods of time are more stressful. Lastly, clustering means that when there are several stressful events occurring over a short amount of time are more stressful because coping strategies become taxed.

Agnew (2001) also revised traditional strain theory by identifying the characteristics of strain that were most likely to have the greatest impact on the individual. He argues that strain is more likely to affect the individual when it is seen as unjust, high in magnitude, associated with low social control, and creates pressure or incentive to engage in crime. Therefore, it is the combination of an individual’s characteristics and the characteristics of the strain that he or she is experiencing that determined whether or not that individual is going to alleviate his or her strain by engaging in criminal behavior.

16 Macro Level General Strain Theory

As explained above, Agnew’s original strain theory was conceptualized as a micro level theory to explain individual level differences of crime. Later, Agnew (1999) extended and elaborated his individual level theory and presented macro level version of general strain theory by including community level predictors of crime.

In macro-level general strain theory, parallel with his micro level explanation,

Agnew (1999) proposed that strain or stress was a main cause of motivation to commit crimes. Specially, differences in crime rates across macro social units are because of

“differences in strain and in those factors that condition the effect of strain on crime”

(Agnew, 1999:126).

The main hypothesis of Agnew’s (1999) macro level general strain theory is that the aggregate level of strain within a neighborhood affects neighborhood violence level

(Agnew, 1999; Warner and Fowler, 2003). Neighborhood characteristics such as poverty, economic inequality, residential instability, and population density, increase neighborhood strain level. As a result, increased neighborhood strain raises the probability that inhabitants in these areas will experience negative emotions, such as frustration and anger, producing a highly charged environment that results in aggression and violent crime.

According to Agnew (1999), community level crime rates could best be understood as coming from both differences in levels of social control and motivation for crime, mainly motivation rooted in strain. Agnew recommended that community characteristics could affect strain levels by affecting the probability of residents failing to achieve positively valued goals, losing positive stimuli, and experiencing negative or

17 aversive stimuli. Increased levels of strain lead to increased rates of negative affect, such as frustration and anger. Neighborhoods with higher proportions of strained residents have a higher likelihood of those residents interacting with each other and leading to explosive situations (Warner and Fowler, 2003). On the other hand, the degree to which strain leads to crime is argued to be moderated by several variables, including social control levels and social support/social capital within the community.

Empirical Evidence

Traditional strain theory and general strain theory have both been widely empirically tested at the individual-level (Hirschi, 1969; Farnworth and Lieber, 1989;

Burton and Cullen, 1992; Burton et al., 1994; Agnew et al., 1996; Agnew and White,

1992; Paternoster and Mazerolle, 1994; Hoffman and Miller, 1998; Mazerolle and

Maahs, 2000; and Mazerolle et al., 2003). The overall evidence shows both significant and non-significant relationships between strain and crime. The focus of this section, however, will be on tests of macro-level general strain theory, as such studies are more relevant to the macro-level analysis that is undertaken in this dissertation.

Brezina et al. (2001) used school-level data from a sample of public high schools and carried out a study to asses Agnew’s (1999) macro-level version of strain theory. The authors looked at how macro level strain theory could be applied to aggressive behavior in the schools. Their results showed varied support for macro-level strain theory. For instance, they found a significant positive relationship between school level anger and conflict with peers, but this significant association disappeared when aggressive behavior was used as a dependent variable. The authors used HLM (hierarchical linear model) to

18 test directly macro level strain theory’s interpersonal-friction argument. Their HLM findings showed that controlling for individual level characteristics, history of aggressive behavior, and individual anger, schools with higher average levels of students anger had higher rates of fighting. Their HLM findings supported macro-level strain theory’s interpersonal- friction argument at the school level.

Warner and Fowler (2003) used survey data on 2,309 respondents in combination with neighborhood-level data from 66 neighborhoods and examined some central ideas of

Agnew’s (1999) macro level general strain theory. Indexes of neighborhood disadvantage and residential stability were employed as independent variables in their model. Their findings revealed that both stability and neighborhood disadvantage significantly affected neighborhood levels of strain. They also found that neighborhood level of strain significantly influenced neighborhood violence level.

Wareham et al. (2005) also tested Agnew’s macro level version of strain theory.

They used data from 430 students who were attending high school and looked at the degree to which community characteristics influenced individual-level strain. Their individual level model showed that individual strain, negative affect, and delinquency varied significantly within communities. On the other hand, their neighborhood level model did not fit their data. However, Wareham et al.’s (2005) supplementary analysis results “did produce evidence for the appearance of the conditioning effects of community disadvantage on the relationships between strain, negative affect and delinquency” (p. 129). In other words, macro-level disadvantage was important in that it accentuated the effects of individual-level strain on delinquency.

19 SOCIAL DISORGANIZATION THEORY

Social disorganization theory is developed by Shaw and McKay (1942). In order to fully understand the formulation of Shaw and McKay’s (1942) social disorganization theory, it is important to know the social context of the time period in which Shaw and

McKay were working. In the early 1900s, cities were expanding as a result of increased urbanization, immigration, and industrialization. This situation directs sociologists to examine the impact of the city growth on the society. During this time period, Park and

Burgess studied growth and development of urban areas and then they developed urban ecology theory. Park and Burgess (1925) state that city growth happens in an outward process with the inner core representing the oldest areas and the newest areas being located at the peripheries. According to Park and Burgess (1925), cities grow in a series of five concentric zones: the loop (i.e., the central business district), the zone of transition, the zone of working men’s homes, the residential zone, and the commuter zone. This city growth was intertwined with the processes of invasion and succession.

As the loop expanded, the areas surrounding the city center became run-down and eventually the least attractive areas of the city. As a result, while some people were able to move out of these conditions, others such as new immigrants were moving in because they could not afford anything else.

Shaw and McKay (1942) took into account Park and Burgess’s (1925) concentric zone model and examined aggregate juvenile delinquency rates across neighborhoods in

Chicago for their study. They used various delinquency measures such as contacted by police, referred to juvenile court, and sent to reform school and then they pinpointed the

20 delinquent’s home address on the city map. Their research discovered that the highest delinquency rate was in the area directly adjacent to the central business district, known as the zone of transition. In contrast, the lowest delinquency rate was in the area farthest away from the central business district which is known as the commuter zone. Shaw and

McKay’s (1942) results also showed that delinquency rates were inversely related to the zone’s distance from the central business district. The longitudinal feature of Shaw and

McKay’s (1942) research showed that Chicago’s zone of transition maintained the highest delinquency rate over a 40-year period, regardless of the its population characteristics. As a result, Shaw and McKay (1942) concluded that the cause of high crime rates was due to the conditions of the neighborhoods and not due to the characteristics of the individuals within these neighborhoods.

Shaw and McKay (1942) developed their social disorganization theory of crime in order to explain the zone differences in delinquency rates. After assessing their data, they found that the zones of transition were characterized by high concentrated poverty, ethnic heterogeneity, and high residential mobility. Therefore, they argue that these neighborhood characteristics of structural disadvantage led to “social disorganization” by weakening the community’s ability to provide informal social control and socialize youth, thereby leading to higher levels of crime.

Systemic Model of Social Disorganization Theory

Shaw and McKay’s (1942) social disorganization theory was an important in directing criminological attention to community and societal factors. Nevertheless, in the

1970s the social disorganization approach started losing its influence. Later, at the

21 beginning of the 1980s, renewed interest towards social disorganization theory was seen again.

The revitalization of social disorganization in the 1980’s is attributable to the work of theorists and researchers clarifying and reformulating Shaw and McKay’s (1942) model. Subsequent research in social disorganization theory has more directly emphasized the foundation of effective community organization in urban social networks.

For instance, Kasarda and Janowitz (1974) recognized the importance of control in Shaw and McKay’s social disorganization theory. Then, they developed the systemic model of community social dynamics in which extensive friendship and kinship bonds rooted in residential stability were hypothesized to strengthen neighborhood attachment. Similarly,

Korhnhauser’s (1978) elaboration of Shaw and McKay’s (1942) social disorganization theory stressed the intermediary role of aggregate-level weak social bonds in the link between structural disadvantage and a community’s capacity for informal social control.

These models are grounded in the assumption that urban social networks serve as the social infrastructure through which a community’s potential for self regulation is realized.

The systemic model of social disorganization theory explains that characteristics of the local environment, such as poverty, population turnover and heterogeneity, affect crime through the mediating effects of social ties, cohesion, and control among residents

(Bursik and Grasmick, 1993). Strong social ties and cohesive relationships between neighborhood residents are expected to facilitate responses to community problems

(Kasarda and Janowitz, 1974; Bursik and Grasmick, 1993).

Bursik and Grasmick (1993) provided more detail about this systemic model. In short, Bursik and Grasmick (1993) suggested the mediating role of three different levels

22 of community control: public, parochial, and private controls. Their extension of social disorganization theory showed how ecological factors influence different levels of control, which, in turn, regulated behavior, including criminal behavior.

Empirical Studies about Systemic Model

Sampson and Groves (1989) used 1982 British Crime Survey data and provided what is largely-considered the first test of the systemic model of social disorganization theory using a sufficiently-large number of macro-level units. They tested the effects of residential mobility, ethnic heterogeneity, urbanism, neighborhood SES and family disruption on three different measures of “social (dis)organization” including neighborhood friendship networks, organizational participation, and presence of unsupervised teenage groups. In addition, they looked at the effects of the measures of social disorganization on neighborhood crime rates. Consistent with the systemic model, their findings showed that indicators of neighborhood disadvantage affected various aspects of social disorganization. For instance, neighborhood residential stability had a powerful effect on local friendship networks. They also found that the social disorganization elements mediated the effects of social structural characteristics – like neighborhood SES and residential stability – on neighborhood crime rates.

In another study, Veysey and Messner (1999) replicated Sampson and Groves’s

(1989) test of social disorganization. Their results also partially supported the systemic model of social disorganization theory. They used structural equation modeling and found moderate relationship between crime rates and SES, ethnic heterogeneity, residential stability, family disruption, urbanization, local friendship networks,

23 unsupervised peer groups, and organizational participation. In addition, whereas SES, residential stability, and ethnic heterogeneity were mediated by social disorganization indicators, the effects of urbanism and family disruption were not fully mediated by social disorganization.

In another replication of Sampson and Groves’ (1989) study, Lowenkamp and his colleagues (2003) used 1994 British Crime Survey data. They employed SES, residential stability, family disruption, ethnic heterogeneity, and urbanization as social structural variables. Unsupervised teenage peer groups, local friendship networks, and organizational participation were used as intervening variables in the analyses.

Lowenkamp et al. (2003) first looked at the relationship between three social disorganization variables and five structural characteristics. Then, in the second model, they examined the effects of structural and intervening variables on neighborhood crime rates. Their findings were consistent with the results presented by Sampson and Groves

(1989). In short, indicators of structural disadvantage tended to weaken informal social control, thereby increasing neighborhood crime.

Warner and Pierce (1993) examined main and multiplicative effects of indicators of structural disadvantage on community rates of crime in Boston neighborhoods. They found that the effect of ethnic heterogeneity on crime rates in an area depended on the poverty level of the area (when areas had low poverty, heterogeneity increased burglary, but when areas had high poverty, heterogeneity decreased burglary). Also, according to their findings, the effect of poverty on assault and robbery was weakest in neighborhoods with residential stability. They stated that their findings were not similar with the social disorganization expectations because according to the social disorganization theory,

24 poverty, mobility, and heterogeneity should increase neighborhood crime rates by disrupting control. According to Warner and Pierce (1993), there were differences between cities that Shaw and McKay (1942) studied and the cities they studied. They stated that concentrated disadvantage areas were characterized by residential stability instead of mobility, and racial homogeneity instead of heterogeneity. Warner and Pierce

(1993) suggest that stability can increase frustration, resentment, and isolation, while homogeneity in contemporary U.S. inner-cities may indicate social isolation as opposed to social organization.

In another study, Bellair (1997) stated that frequent interaction between neighborhood residents may not be the only type of social ties which are effective at increasing neighborhood control, supervision, and intervention. He looked at the effects of ten measures of differing levels of social interaction between neighbors across several cities. According to his findings, infrequent interaction generated the greatest effect on neighborhood crime rates. For example, the percentage of residents who came together once a year or more was the most powerful predictor of social interaction on neighborhood burglary, theft, and robbery rates (Bellair, 1997). Bellair (1997) added that there was difference in the frequency of social interaction or ties between residents and infrequent ties are most effective for the control capacities of neighborhoods.

According to Pattillo (1998), all social ties were not crime-reducing. Although it is assumed in the systemic model that neighborhood social ties are important because they increase supervision and control within neighborhoods, Pattillo (1998) suggested that some forms of social ties reduced neighborhood crime control capacities. In her study, Pattillo (1998) found that social ties between community residents in a middle-

25 class African American Chicago neighborhood actually reduced the neighborhood’s use of formal control because of the residents’ overreliance on informal social control. Her research results showed that strong ties might actually decrease neighborhood control when ties existed between law-abiding and law-violating residents.

In two separate studies, Warner and Rountree (1997) and Rountree and Warner

(1999) looked at whether the effectiveness of social ties was dependent on the racial composition of the neighborhood or other demographic characteristics, such as the gender composition of neighbors. In the first study, Warner and Rountree (1997) found that the effectiveness of neighborhood social ties was conditional upon the racial composition of the neighborhood. They stated that social ties in Caucasian neighborhoods were more effective at reducing assault rates than in predominantly minority or mixed neighborhoods. In addition, Warner and Rountree (1997) confirmed social disorganization’s expectations that neighborhood ethnic heterogeneity decreases the formation and extent of social ties while residential stability increases social ties.

In their second study (1999), Rountree and Warner examined whether social ties were more or less effective among males or females. Their findings showed that neighborhood characteristics and demographic factors such as gender formed the effectiveness of social ties. Specifically, Rountree and Warner (1999) found that although males and females experience similar levels of social ties, only female social ties reduced neighborhood violent crime. Such ties were more likely in homogeneous and stable neighborhoods. Most importantly, female ties only controlled violent crime in neighborhood contexts with few female-headed families.

26 Collective Efficacy Model of Social Disorganization Theory

Although above mentioned extension of social disorganization theory attempts to address certain issues, some of them left unexplained by the systemic model.

Specifically, systemic theory focuses on the relationship between neighborhood ties (i.e., local friendship networks) and crime rates. The systemic model, however, cannot explain areas that either have few ties yet low crime rates or areas that have many ties yet high crime rates. For example, Bellair (1998) found that infrequent social ties could build social control and these ties are more effective than frequent social ties in terms of reducing crimes in neighborhoods. In another study, Wilson (1996) found that residents could be strongly interconnected in poor neighborhoods, but these strong ties did not create collective resources such as social control of criminal behavior.

Given such problems with the systemic model and taking into account urban changes, Sampson and his colleagues developed a new model. They suggested that the focus on ties in the systemic model is limited because it fails to notice the purposive action element (the activation of ties or the mobilization of resources). Their new approach was a response to that limitation of systemic model. In his new direction,

Sampson et al. (1997) moved away from community level correlations of social disorganization and focused on the theory of underlying social mechanisms. They believed that the nature of the cities and the meaning of the community changed. As a result of that change, strong neighborhood ties in urban areas did not exist. Because of the less frequent face to face social interactions, individuals had fewer intimate connections. In their new approach, Sampson et al. (1997) suggested a focus on mechanisms that facilitate social control without strong ties or associates.

27 Sampson et al.’s (1997) new social control model was known as “collective efficacy”. Neighborhood collective efficacy referred to social cohesion between neighborhood residents and it was characterized by their willingness to intervene and activate control for the common good of the neighborhood (Sampson, 2006). In other words, collective efficacy merged a cohesive neighborhood social organization with the shared expectations (Sampson, 2006). Collective efficacy mirrored shared beliefs in a neighborhood’s capability to achieve the intended effect and assumed active engagement between neighbors.

Sampson et al. (1997) stated that neighborhood efficacy level could vary between neighborhoods. According to Sampson et al. (1997), neighborhood’s demographic indicators such as concentrated poverty and residential instability were hypothesized to affect neighborhood disorder or violence through formal and informal mechanisms of social disorganization such as collective efficacy. Neighborhood disadvantage, racial heterogeneity, and residential instability were found to decrease levels of collective efficacy (Sampson et al., 1997, Sampson, 2006). In addition, communities low in economic status more often lack adequate monetary and other resources, experienced lower social cohesion and control, and had lower levels of collective efficacy. Residential instability was also hypothesized to disrupt a community’s network of social relations because it worked as a barrier to the development of friendship networks and local associational ties. Thus, neighborhood disadvantage, racial heterogeneity, and residential instability all contributed to social disorganization and prevented the formation of attributes such as trust and kinship needed for the development of social cohesion, social control, and collective efficacy.

28 Empirical Evidence

Sampson et al. (1997) tested the collective efficacy theory in their study. They examined violence rates around 340 Chicago neighborhoods and interview around 8,600 individuals. They analyzed three measures of neighborhood social structure: concentrated disadvantage, immigrant concentration, and residential stability in conjunction with collective efficacy. Their findings showed that social compositions of neighborhoods in terms of concentrated disadvantage, immigrant concentration, and residential stability were significantly related with collective efficacy in the predicted direction, and they explained approximately 70% of the neighborhood variation in collective efficacy. When they used multi-level modeling, they also found that collective efficacy consistently had one of the strongest inverse effects on the three measures of crime. In addition, when collective efficacy was entered into the equations, it tended to decrease the strength of the structural measures.

Sampson and Raudenbush (1999) examined the effects of collective efficacy in addition to disorder, concentrated disadvantage, immigrant concentration, and residential stability, on neighborhood crime. They concluded that concentrated disadvantage and collective efficacy were stronger and more proximate influences on neighborhood crime than disorder (except for robbery). In another study, Morenoff and his colleagues (2001) examined the relationship between collective efficacy and homicide. They used Chicago police incident data and Chicago Neighborhood Community Survey for their study. Their findings showed that structural disadvantage and collective efficacy were related with neighborhood homicide even after controlling for previous homicides. In addition, collective efficacy reduced the effects of social ties on neighborhood homicide rates

29 leading the authors to conclude that density of personal ties and organizations were predictive of higher collective efficacy. Finally, additional empirical evidence in favor of collective efficacy was provided by Pratt and Cullen (2005). Specifically, they conducted an extensive meta-analysis of approximately 200 empirical studies from 1960 to 1999. In general, they found that collective efficacy theory had a correlation of (-.30) with crime rates across studies. In addition, collective efficacy, once weighted by sample size, ranked fourth compared to other criminological theories.

CONFLICT THEORY

At a basic level, conflict theory focuses “on struggles between individuals and/or groups in terms of power differentials” (Lilly et al., 2007:149). Marxian conflict theorists argue that there is no consensus in the society, that the wealthy, elite, the haves, and bourgeoisie people in the society who have the money and power are the people within society that are setting the rules of the game.

One of the main concepts of Marxian economic theory is “surplus value”. In general terms, “surplus value” first leads to accumulation of capital in the hands of individual capitalists. This process inherently gives birth to two classes: Bourgeoisie and proletariat. Since capitalists want to increase their products more, they need dead labor

(technology). This step increases the size of problem populations through displacing more laborers. Economic depression and frustration also increase among problematic populations. This vicious cycle of “surplus value” causes crime to flourish in society. The flip side of the problem is that ruling class feels more threat from these problematic

30 populations due to flourishing crime. Therefore, the ruling class applies more coercive crime control policies through increasing police expenditures and police size.

Origins of Modern-day Conflict Theory

While rooted in classic work of Marx, modern-day conflict theory really developed into a popular theory of crime in the 1970s. During 1970s, several scholars

(Turk, Chambliss, and Quinney) were influenced by labeling theory and they started to develop different forms of criminological conflict theory.

Turk (1978) argued that we could look at the law as power. A conflict perspective defined power as control over resources. If you had the power, you had the control over resources within society. When we looked at that in terms of law, if you had power and control over the resources, you also had control over the law within a society. Turk

(1978) stated that if you controlled the ideology within a society and the way that you controlled the ideology within any given society was by controlling the media, you could easily foster a false consciousness. Turk stated that that was how the elite got their message across to the rest of us. The law was set up to benefit them and the law was used as a weapon against those who would impinge the best interest of the elite.

Chambliss (1978) was another scholar who wrote in the Marxist tradition. He argued that the most fundamental feature of an individual’s life was the relationship to the means and mode of production. According to Chambliss (1978), a capitalist system creates crime. He explained that inherently capitalism needed to grow and created a mass desire to consume more and more products. However, for this purpose, it was necessary to get people to do very tedious, very alienating, low-paying unrewarding jobs. In this

31 respect, on one hand, it invoked desire in people to buy and to consume more and more, and on the other hand, people, who worked with low wages and unrewarded conditions under a capitalist system, get alienated.

Chambliss (1978) as with other Marxists believed that criminal behavior is an expression of class conflict. Chambliss (1978) agreed very much with Turk (1978) in that

Chambliss argued that the states were acting on behalf of the elites. The elites passed laws that were designed to control the masses. Most crimes were not wrong within their very nature. Chambliss (1978) argued that a number of our laws were designed to protect the interests, the economic interests of the elites. Chambliss (1978) stated that capitalism was full of contradictions. Therefore, contradictions lead to conflict and subsequently lead to crimes. Criminal behavior is an expression of class conflict.

Quinney (1970) stated that people who had little effect during the formulation of law, the laws would be applied to them. The laws were produced by elites to protect their interests. The law was really shaped by the leagues. Quinney (1970) added that our laws were socially created. Quinney (1970) used “social reality of crime” notion to examine the role of the legal order in the society. According to Quinney (1970), crime was affected by society’s political, economic, and social structure. Quinney (1970) created the social reality of crime with putting together formulation of definitions of crime, application of definitions of crime, development of behavior patterns related with these definitions, and construction of an ideology of crime.

In sum, conflict theorists argue that both the laws and crime are consequences of a struggle for power. At the beginning of this struggle, a capitalist economic structure produces extensive poverty, where “resource deprivation” will be experienced by large

32 numbers of people in the community. Then, this resource deprivation increases crime.

According to Pratt (2001), poverty might cause crimes directly or indirectly. First, poverty might cause crime directly among individuals in “subordinate classes” who looked for daily survival. This means that, some individuals in the society have to commit certain criminal offenses such as theft to survive (Lilly et al., 2007). Second, poverty might cause crime indirectly when poverty-stricken group members questioned their social situation in the society. When they feel that they are treated unfairly, poverty- stricken groups “then would be more likely to organize and to bring the conflict out into the open, after which there would be polarization and violence” (Lilly et al., 2007:151).

Finally, this poverty can lead to crime indirectly through threat and coercive social control. Due to perceived threat among those in power, the actions of the poor are more likely to be defined as “criminal.” Chamlin (1989) explained this situation as “powerful groups and strata are able to translate their perceptions of threat into public policy and thereby affect the size and administration of crime control apparatuses” (p. 355).

Empirical Evidence

It is difficult to operationalize many of the dimensions of conflict theory into measurable concepts. For this reason, empirical support of the theory is relatively weak compared to other theories. Nevertheless, a number of studies exist testing a “threat hypothesis” of crime control, specifically. In these studies, minority group members were seen as a potential threat by the powerful groups in the society, and the ruling class thus responded by increasing its control of these minority groups. Such studies indicate that percent minority is related to such control measures as police size, arrest rates, and

33 incarceration rates (e.g., Chamlin, 1989(a); Jackson and Carroll, 1981; Liska and

Chamlin, 1984; Kent and Jackobs, 2005; and Holmes et al., 2008).

In addition to threat driving social control, the conflict perspective stresses that

“impoverishment itself is criminogenic and that there should be a direct empirical link between variables that proxy conditions of economic deprivation and crime rates” (Pratt and Lowenkamp, 2002, p.66). This aspect of conflict theory is most pertinent to this dissertation’s focus on the effect of structural disadvantage on crime. In general, empirical literature testing this aspect of conflict theory uses absolute economic deprivation (poverty), relative economic deprivation (economic/ income inequality), and unemployment as independent variables, and looks at their relationships with different crime rates.

Bailey (1984), for instance, looked at homicide rates at the city level and found that high poverty rates were related with high homicide rates. In his 1999 study, Bailey examined rape rates again at the city level and found significant relationship between absolute poverty, low SES, and rape rates. Loftin and Parker (1985) also studied at the city level in their examination of the relationship between poverty and homicide rates.

They found significant positive association between poverty and homicide rates. In another city level study, Williams and Flewelling (1988) employed homicide rates as dependent variable and found that resource deprivation levels were significantly positively related with all examined types of homicide rates.

Smith and Bennett (1985) studied at the SMSA level and used rate of rape as the dependent variable. They found that the strongest indicators of rape rates were poverty, racial composition, and family disruption. In another SMSA level study, Williams (1984)

34 looked at homicide rates and found significant positive relationship between absolute poverty and homicide rates.

Unlike many other conflict theory studies which measured economic deprivation with a single item, such as percent unemployed, the Gini coefficient, or the poverty rate,

Pratt and Lowenkamp (2002) looked at the overall effects of economic deprivation on crime. They employed a “composite of coincidental economic indicators” (CCEI) to measure national economic conditions. Their “composite of coincidental economic indicators” included four indicators: number of workers on nonagricultural payrolls, personal income, industrial production index, and series measuring manufacturing and trade sales. The authors employed ARIMA techniques to examine the association between economic conditions and monthly homicide crime rates. Homicide crime rates were disaggregated as felony murder and acquaintance homicide rates. Time series data about homicide crimes were derived from FBI’s Supplementary Homicide reports and

CCEI time series data were derived from US Department of Commerce. Consistent with conflict perspective, their results showed a negative association between disaggregated homicide rates and the composite of coincidental economic indicators (CCEI).

CONCLUSION

This chapter has provided an overview of the numerous macro-level theories that suggest that structural disadvantage causes crime. In addition, the modest-to-high level of empirical support for each perspective was summarized. To recap, the prediction of

35 anomie and institutional anomie theory, macro-level general strain theory, social disorganization theory, and conflict theory -- that indicators of structural disadvantage are positively correlated with area rates of crime – serves as the theoretical basis for the hypotheses examined in this dissertation. While these theories have traditionally been applied to non-terrorist crime, these macro-level theories seem, logically, to be well- suited for application to understanding terrorism as well. This dissertation will explore whether that is, in fact, the case. Again, these various theories will not be tested “against one another.” The specific theoretical mechanisms implied in each of the perspectives will not be measured. Instead, the focus here is simply on whether the theoretically- supported link between disadvantage and crime applies to provinces in Turkey, and if so, whether it applies to both terrorist and non-terrorist crime.

36 CHAPTER THREE: REVIEW OF LITERATURE

In this chapter, the literature that identifies significant structural covariates that may impact the occurrence of violent crimes is reviewed. While some of this literature was reviewed in Chapter Two, it was done there more in the context of demonstrating the level of empirical support for specific theoretical perspectives. In contrast, the review in this chapter is on providing evidence regarding the effect on violent crime rates of the specific covariates that are indicators of structural disadvantage in this dissertation. In addition, an overview of the findings regarding the effects of control variables used in this dissertation is also provided. Finally, this chapter ends with a review of the relatively few studies examining macro-level structural covariates of terrorism.

The dependent variable used in the reviewed studies is some measure of area- level violent crime. Violent crimes comprise four types of offenses: murder and non- negligent manslaughter, forcible rape, robbery, and aggravated assault. According to the definition of Uniform Crime Reporting (UCR) Program, violent crimes involve force or threat of force.

The independent variables that will indicate structural disadvantage in this study are the following: (1) economic inequality (generally measured with Gini Index); (2) poverty (generally, the percentage of persons living below the poverty level); (3) residential mobility (usually measured as percentage of persons ages five and over who have changed residences in the past five years); (4) family disruption (percentage of divorced males ages 15 years and over, percentage of children 18 years and under not living with both parents); (5) unemployment (percentage of unemployed persons ages 16

37 years and over); and (6) low education level (percentage of persons with less than high- school education). I will also include as control variables the following: (1) age structure

(percentage of young males ages 15 to 29 or percentage aged 15-29 or 20-34); (2) population density (total resident population per square mile); and (3) region (usually measured as a dummy variable, controlling for “southern U.S.”).

In Table 3.1, the major findings from prior research are summarized. Table 3.1 gives some details for each study, such as author(s) name, unit of analysis, independent variable(s) (structural covariates), dependent variable(s) (outcomes), and main findings.

This table provides opportunity to the readers to examine major points from various studies in the violent crime- structural covariates’ association. The text that follows

Table 3.1 discusses the reviewed studies in relation to the specific key covariates and control variables. In that section of the chapter, I will summarize the overall finding from the body of research presented in Table 3.1 for each key correlate. Thus, I will leave the details of each of studies (data source, key measures, specific relationships found) to

Table 3.1. However, in my summary comments, I will provide an overview of a couple of key studies for purposes of example.

38 Table 3.1 Summary Findings from Prior Research for Structural Covariates and Violent Crimes.

AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Loftin and Hill US Bureau of the States (n=48) S: poverty, economic Poverty, economic inequality, family (1974) Census, US inequality, family disruption, disruption, and % ages 20-34 were positively Department of % ages 20-34, region related to the homicide rates. Health, education O: homicide and welfare

Parker and FBI, National States (n=48) S: structural poverty index There was statistically positive relationship smith (1979) Center for Health (infant mortality rate, % kids between structural poverty index and Statistics, not with both parents, % homicide rates (total and primate). In National Center families less than $1,000 addition, percent aged 20-34 was only for Educational income, % people less than 5 significantly related to primary homicide Statistics, Bureau year education), % aged 20- rates. Percent urban significantly positively of Census 34, region, % urban associated to total and non-primary homicide O: homicide rates.

Smith and UCR (N=16,163), States (n=48) S: structural poverty index, There was statistically positive relationship Parker (1980) Bureau of % urban, economic between total and primary homicide rates and Census, National inequality, % ages 20-34, structural poverty index, while this Center for Health region relationship was nonsignificant for non- Statistics, O: homicide primary homicide rates. There were not National Center statistically significant relationships between for Educational economic inequality, percentage ages 20-34, Statistics region and homicide rates.

39 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Williams and UCR, US Census, SMSA (n=25) S: economic inequality, Economic inequality had an effect on arrest Drake (1980) County and City unemployment rates for homicide, forcible rape, robbery, Data Book O: homicide, rape, robbery, and aggravated assault (with official data). assault

Watts and US Census Cities (n=152) S: poverty, economic There were significant positive relationships Watts (1981) inequality, unemployment, between major crime rates and percentage of population density female headed households, economic O: major crimes per 1,000 inequality, unemployment and poverty. population There was significant negative relationship between major crime rates and poverty.

Messner UCR, US Bureau SMSA (n=204) S: poverty, economic There was significant negative relationship (1982) of the Census inequality, region, % black, between poverty and homicide rates. The population size, population relationship between economic inequality density and homicide rates was nonsignificant. O: homicide Population size, % black, and south had significant positive relationship with homicide rates, but population density had negative relationship with homicide rates.

40 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Blau and Blau UCR, US Bureau SMSA (n=125) S: poverty, economic Criminal violence was positively related to (1982) of the Census inequality, family disruption, poverty. Income inequality increased the % black violence rate. When income inequality was O: homicide, rape, robbery, controlled the positive association between assault poverty and violence disappeared. Positive relationships were found between percent divorced and crime rates.

Carroll and UCR, US Bureau Cities (n=93) S: economic inequality, Income inequality had a strong positive Jackson (1983) of the Census unemployment effect on personal crimes (homicide, rape, O: homicide, rape, aggravated assault) and robbery rates. aggravated assault, robbery

Stack and UCR, US Bureau States (n=50) S: economic inequality, Income inequality, educational level, percent Kanavy (1983) of the Census family disruption, urban, and unemployment rate had unemployment, residential significant positive effect on rape rates. instability, educational level, urbanization. O: rape

41 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) DeFronzo Us Bureau of SMSA (n=39) S: poverty, unemployment, There were no significant relationships (1983) Labor Statistics, inequality, population size, between violent crime rates and poverty and UCR, US Bureau % ages 15-24 males, region economic inequality (in one model). There of the Census O: homicide, rape, robbery, was a significant positive relationship assault between percentage of unemployment and rape rates. Significant positive relationship was examined between population size, % black and crime rates. No significant correlation was found between percent 15-24 males and crime rates.

Messner UCR, US Bureau Cities (n=91 S: poverty, income Economic inequality and population between (1983) of the Census Southern, n= inequality, population 20-34 had no significant effect on homicide 256 non- density, population size, % rates in Southern and non-Southern cities. Southern) black, region, % ages 20-34 However, poverty only had a positive O: homicide significant effect on homicide rates in non- Southern cities. Population size and % black had significant positive effects on homicide rates.

Williams UCR, US Bureau SMSA (n=125) S: poverty, economic Poverty and family disruption had significant (1984) of the Census inequality, % divorced, positive effects on homicide rates, but population size economic inequality and population density O: homicide (in one model) did not have effect on homicide rates.

42 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Bailey (1984) UCR, US Bureau Cities (n=73) S: poverty, inequality, There were significant and positive of the Census region, population size, relationships between poverty and homicide population density, % ages rates. There were slight and nonsignificant 15-29 relationships between economic inequality, O: homicide population size, population density, % ages 15-29, South, and homicide rates.

Sampson UCR, US Bureau Cities (n=55) S: poverty, economic There were significant relationships between (1985) of the Census inequality, unemployment, homicide rates and poverty, and population population size size, but negative relationship between O: homicide unemployment and homicide rates. There was negative significant relationship between income inequality and male homicide arrest rates.

Simpson UCR, US Bureau MSA (n=125) S: income inequality, There was a significant positive relationship (1985) of the Census poverty, family disruption between poverty and rape rates, and income (percent divorced), % ages inequality and homicide rates. Family 15-29, population size, disruption and % black had significant region positive association with all crime rates, but O: homicide, rape, robbery, population size, income inequality (except assault for murder) and % age 15-29 did not have any significant effects on crime rates.

43 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Smith and UCR, US Bureau SMSA (n= 88) S: poverty, inequality, family Poverty was found to account for differences Bennett (1985) of the Census disruption in rape rates in communities. Percentage of O: rape divorced was also significantly positively related with rape rates. However, there was no relationship between inequality and rape rates.

Messner and US Bureau of the Neighborhoods S: economic inequality, Both positive and non-significant effect of Tardiff (1986) Census, New (n=26) poverty, family disruption poverty on rape rates were found in the York City Chief O: homicide study. However, no relationship was found Medical between rape rates and economic inequality. Examiner’s Percent divorced had significant positive Office (n=536) relationship with rape rates.

Rosenfeld UCR, US Bureau SMSA (n=125) S: economic inequality, Except for robbery, there was a significant (1986) of the Census population size relationship between income inequality and O: homicide, rape, robbery, violent crime rates. There were no significant assault relationships between unemployment, population size (in one model), and violent crime rates.

Blau and UCR, US Bureau SMSA (n=125) S: inequality, family There was significant positive relationship Golden (1986) of the Census disruption (% divorced), between inequality, percent divorced, region, population size population size, and crime rates. O: homicide, rape, robbery.

44 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Huff-Corzine National Center States (n=48) S: income inequality, Homicide rates for total population and et al. (1986) for Health poverty, urbanization, family whites were influenced by poverty and Statistics, US disruption, % ages 20-34, regional differences. Family disruption was Bureau of the region significantly positively related to homicide Census, National O: homicide rates. However, income inequality was not Center for significantly related to the homicide rates. Educational With Southernness index, percent ages 20-34 Statistics, UCR had significant positive relationship with nonwhite homicide rates in OLS regression. In addition, age 20-34 was significantly positively correlated to white homicide rates in OLS and normalized ridge regress analyses.

Baron and UCR, US Bureau States (n=50) S: economic inequality, Both economic inequality and poverty were Straus (1988) of the Census poverty, family disruption significantly positively associated with (family integration index), % homicide rates. Without income inequality age 18-24, region variable, family disruption is positively O: homicide related with homicide rates. No significant association was found between age percent 18-24 and homicide rates.

Williams and US Census, UCR Cities (n=168), S: poverty, family disruption, There were significant positive associations Flewelling (Supplementary Metro Areas population density between poverty, percent divorced, (1988) Homicide Report) (n=25), States. O: homicide population density, and homicide rates.

45 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Parker (1989) UCR Cities (n=299) S: poverty, economic Poverty was the most important predictor for (Supplemental inequality, population homicide rates. Inequality only had Homicide density, age 20-34, region significant negative relationship with average Report), US O: homicide homicide rates in 1969-71. In addition, Bureau of the population density had negative significant Census relationship with average homicide rates in 1969-71 and total homicide in 1973-75. Age structure was not related to homicide rates.

Land et al. UCR, US Bureau Cities (n=528 in S: resource deprivation/ There were significant positive relationships (1990) of the Census 1960, n=729 in affluence, family disruption between homicide rates and population 1970, n=904 in (% divorced), structure, resource deprivation, region, but 1980) unemployment, population negative relationship between homicide rates SMSA (n=182 structure, age 15-24, region and unemployment rate. There were in 1960, n=187 O: homicide statistically significant positive relationships in 1970, n=259 between percentages of population between in 1980) ages 15-29 and homicide rates at state level, States (n=50 in but no significant association at SMSA level. 1960, 1970, and At state level, there was no significant 1980) correlation between homicide rates and unemployment.

46 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Patterson Interview Neighborhoods S: poverty, income Poverty was more strongly related with (1991) (n=11,419) (n=57) inequality, population violent crime rates, but the relationship density, family disruption, depended on the type of crime. On the other residential instability hand, household income inequality and O: robbery, rape, assault percent single parent households had no significant effect on violent crime rates.

Harer and UCR, US Bureau SMSA (n=125) S: economic inequality, Inequality affected white violence rates and Steffensmeier of the Census poverty there was a significant positive relationship (1992) O: homicide, assault, rape, between white violence rates and inequality. robbery However, inequality had weak effect on black violent rates. In addition, there is significant negative relationship between overall poverty and rape rates.

Warner and US Bureau of the Neighborhoods S: poverty, residential There was a significant positive relationship Pierce (1993) Census, Boston (n=60) mobility, family disruption, between poverty and assault and robbery Police structural density rates. Residential mobility had both positive Department’s O: assault, robbery and negative significant relationships with Computer Aided crime rates. Family disruption was related to Dispatching robbery rates. System

47 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Kposowa et al. UCR, US Bureau Counties S: income inequality, Population density and urbanity were strong (1995) of the Census (n=3076), poverty, family disruption, predictors for violent crime rates. Poverty National Center counties with population density, followed by percentage of divorced, for Health less than 25,000 residential instability, residential instability, and population density Statistics population education, unemployment, were factors in homicide rates. In general, (n=1681), urbanization, region negative relationships between homicide counties with O: homicide, violent crime crime rates and income inequality were more than found (except counties with > 100,000 100,000 population). On the other hand, association population (n= between violent crime rates and income 408), Southern inequality was significant and positive. counties (n=1058), counties with more than 25% black (n= 405)

Krivo and UCR, US Bureau Census tracts S: poverty, residential Extremely disadvantaged communities had peterson of the Census (n=177) instability, family disruption, higher violent crime rates than less (1996) unemployment disadvantaged communities, and this was the O: homicide, assault, rape, case for both white and black communities. robbery In high disadvantaged communities, poverty, female headed households, and male unemployment were significantly and positively related with violent crime rates.

48 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Allen (1996) UCR, US Bureau National data S: economic inequality, Both poverty and economic inequality were of the Census poverty, unemployment not statistically significant related with O: robbery robbery rates. There was statistically significant positive relationship between unemployment and robbery rates.

Fowles and UCR, Bureau of SMSA (n= 28) S: poverty, economic There was a significant positive relationship Merva (1996) Census’s Current inequality between wage inequality and homicide and Population O: homicide, rape, robbery, assault. Poverty had a significant positive Survey assault relationship with all types of violent crimes.

Warner and US Bureau of the Census tracts S: poverty, residential Local social ties had little mediating effect Wilcox Census, Seattle (n=100) stability between community structure and crime Rountree Police O: assault rates. In white neighborhoods, there was (1997) Department, 1990 significant negative relationship between Survey Data (n= social ties and assault rates, while in racially 5,302) mixed neighborhoods this relationship disappeared. There was also significant positive relationship between poverty and assault rates.

49 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Bellair (1997) Victimization Urban S: family disruption, Residential stability was significant predictor survey in police neighborhoods residential stability of social interaction and its direct effect was service study (n=60) O: robbery decreased when social interaction is taken (n=12,019) into account. Social interaction had significant inverse effect on robbery crime rates.

Kovandzic et US Bureau of the Cities (n=190) S: economic inequality, Poverty and inequality had different effects al. (1998) Census, UCR poverty, family disruption, depending on the type of homicide. FBI’s unemployment, region, Inequality was related to family and stranger, Supplementary percent young, population while poverty was only related with Homicide density, population change acquaintance homicide (no relationship for Reports, US O: homicide stranger and family homicide). On the other Department of hand, percent black was related to all three Health and types of homicide and came out as the Human Services strongest predictor in each model. (n=31,589) Unemployment was significantly positively correlated to acquaintance homicide rates. There was negative association between percent young and family homicide.

50 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Shihadeh and US Bureau of the Cities (n=100) S: economic deprivation, Economic deprivation had significant direct Ousey (1998) Census, UCR high school dropouts, region positive relationship with homicide rates in O: homicide white and black populations. Region had significant direct negative association with homicide rates in white population. There was no significant correlation between high school dropouts and homicide rates.

Parker and US Bureau of the Cities (n=196) S: local opportunity structure There was a significant positive relationship McCall (1999) Census, UCR (%of unemployed), between economic deprivation, local economic deprivation opportunity structure, and interracial (poverty, inequality), family homicide rates. Racial inequality only disruption (% of children not significantly positively related with black living with both parents, % interracial homicide rates. No significant of divorced males), racial relationship was found between % divorced inequality, O: homicide and disaggregated homicide rates.

Wilcox US Census, Census tracts S: poverty, residential There was a significant negative relationship Rountree and Seattle Police (n=100) stability), family disruption between residential stability and violent Warner (1999) Department, 1990 (female household) crime rates. No significant association was Survey Data (n= O: violent crime found between violence and poverty. 5,302) Significant positive correlation between violence and female households was found.

51 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Peterson et al. US Census, Census tracts S: economic deprivation Residential instability had significant (2000) Columbus Police (n=177) (poverty), residential positive relationships with violent crime rates Department, instability, young males (except homicide). Economic deprivation had UCR, Columbus O: homicide, forcible rape, significant positive association with total Department of robbery, violent crime rates. They found significant Parks and Aggravated assault positive correlation between economic Recreation, State deprivation and rape and aggravated assault of Ohio rates. No significant association was Department of examined between young males and crime Liquor Control, rates. Columbus Metropolitan Housing Authority

Lee (2000) US Bureau of the Cities (n=121) S: poverty, region, education, City disadvantage and poverty concentration Census, UCR age between 15-24 variables were significantly positively related O: homicide to homicide arrest rates in both white and black populations in one model. Age between 15 and 24 was negatively related to homicide arrest rates for only white population. Region was negatively associated with black homicide arrest rates.

52 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) McNulty US Bureau of the City blocks S: structural disadvantage, There was significant positive relationship (2001) Census, UCR (n=400) residential stability, age between structural disadvantage and violent between 15-24 crime rates. Residential stability and percent O: violent crime age 15-24 had significant negative association with violent crime rates in all of the models.

Morenoff et al. US Census, Neighborhoods S: poverty, residential On the 1990 census and 1995 PHDCN data, (2001) Project on Human (n=343) stability, population density concentration disadvantage had significant Development in O: homicide positive relationships with homicide rates. Chicago No significant relationship was found Neighborhoods between population density and homicide Community rates. Survey (n=8,782), Chicago Police Department

Baller et al. US Bureau of the Counties (n= S: poverty, inequality, family Their spatial lag models revealed significant (2001) Census, National 3,085) disruption (female headed positive relationship between resource Center for Health households), percent deprivation variable and homicide rates for Statistics divorced, population density, all examined years. In addition, population population size, structure component only significantly unemployment associated with homicide rates in 1980 and O: homicide 1990. They found significant negative association between unemployment and

53 county homicide rates in 1970, 1980, and1990.

Phillips (2002) US Bureau of the Metropolitan S: poverty, inequality, % Percent divorced, residential instability, and Census, National standard areas divorced, male income inequality were significantly Center for Health (MSA) (n= 129) unemployment, % with positively related to homicide rates in white Statistics (NCHS) college education, region, population. Income inequality, male residential instability unemployment, and region were significantly O: homicide positively associated with homicide rates in black population. Unemployment was not related to homicide rates for white and Latino populations. Poverty was significantly correlated with homicide rates in Latino population (no relationship in black and white population). Percent college education was significantly negatively related to homicide rates in all population.

Stucky (2003) U.S. Department Cities (n=958) S: deprivation index (% Significant positive relationship was found of Commerce, poor, % unemployed, % between violent crime rates and deprivation UCR female headed households), index. Percent age 18-24 was significantly population density, % negatively related to violent crime rates. population change, % However, no significant relationship was population age 18 to 24 found between crime rates and percent O: homicide, robbery, population change. aggravated assault, rape.

54 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Pratt and World Health Nations (n=46) S: economic inequality In their all models economic inequality was Godsey (2003) Organization, O: homicide significantly positively related to nations’ United Nations homicide rates. Statistics Division

Velez et al. FBI’s Cities (n= 126) S: poverty, age structure, College graduate gap between blacks and (2003) Supplementary unemployment, college whites was significantly positively related to Homicide graduate gap between blacks homicide rates. Poverty, age structure, family Reports, Center and whites, family disruption, unemployment, and region were for the Study and disruption, region. not significantly associated with homicide Prevention of O: homicide rates. Violence, US Census

Baumer et al. NCVS (n=3,327 Census tracts S: poverty, unemployment, The neighborhood variation in violence (2003) assault, n= 468 poverty, family disruption crimes reflected the neighborhoods’ robbery), US O: robbery, assault socioeconomic conditions, and individual’s Census social class.

Kubrin (2003) US Census, St. Census tracts S: neighborhood Neighborhood disadvantage and population Louis (n=111) disadvantage, residential size had positive significant relationships Metropolitan instability, family disruption with all four types of homicides. Residential Police (% divorced males), % instability was only significantly positively Department young males related to overall and felony homicides. (n=2,161) O: homicide

55 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Browning et al. US Bureau of the Neighborhoods S: poverty, unemployment, Concentrated disadvantage, unemployment (2004) Census, Chicago (n=343) concentrated disadvantage had significant positive, and population homicide data, (family disruption, % black) density had significant negative relationship Project on Human residential stability, with homicide rates. Development in population density Chicago O: homicide Neighborhoods Community Survey (n=8,782)

Lee and FBI (SHR), Counties S: poverty, unemployment, Divorce rate and concentrated poverty were Bartkowski Census of Church (n=3,099) family disruption (female significantly positively related with both (2004) and Church headed households), divorce juvenile and adult homicide rates. Percentage Membership, US rates, high school dropout of female headed households and Census O: homicide unemployment were significantly negatively related to homicide rates.

Neumayer United Nations Countries S: income inequality, When more representative sample was used, (2005) Crime Surveys, (n=59) unemployment rate, and country-specific fixed effects were UN, World Bank urbanization rate, female controlled, income inequality is no longer a labor force participation rate, statistically significant determinant of violent proportion of males in the crime. Unemployment had significant age group 15 to 64 positive relationship with robbery rates. O: robbery Males in the age group 15 to 64 did not have significant correlation with robbery rates.

56 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) MacDonald US Bureau of the Cities (n=159) S: concentrated disadvantage There was a significant positive relationship and Gover Census, FBI’s (% black population, % between concentrated disadvantage and city (2005) Supplementary female head of households, homicide rates. In addition, percent divorced Homicide Reports % persons living in poverty, also had significant positive relationship with and the % unemployed), % city homicide rates. divorced, population density O: homicide

Barber (2006) INTERPOL Countries S: illegitimacy ratio (single Significant relationship was found between (n=39) parenthood), income violent crime rates and single parenthood inequality ratios, but this association disappeared for O: homicide, rape, assault single parenthood ratios 18 years earlier. No significant relationship was found between income inequality and violent crime rates.

Lee (2006) UCR, Inter- Counties S: disadvantage index (% Significant positive relationship was found university (n=902) county population living between disadvantage index, population Consortium for below the poverty line, % turnover, and violent crime rates in all of the Political and unemployed, % female models. Age 15-24 was significantly Social Research headed households, and % negatively related to violent crime rates in (ICPSR), 2000 population without high two models. Region was positively Census of school degree), age 15-24, associated with violent crime rates in two Churches region. O: homicide, models. robbery, aggravated assault

57 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Stolzenberg et National Incident- Cities (n=91) S: economic inequality, city White-to-black inequality was significantly al. (2006) based Reporting disadvantage factor (% related to violent and black-on-black crime System (NIBRS), households with public rates. Economic inequality was not US Bureau of the assistance income, % significantly associated with violent crime Census population without high rates. City disadvantage was significantly school degree, and % female positively related to total violent crime, headed households ages 15– white-on-white, and white-on-white crime 64 with children), rates. Region and unemployment rate did not unemployment, region significantly related to violent crime rates. O: violent crime

Hipp (2007) US Bureau of the Census tracts S: income inequality, family Inequality had significant positive Census, police (n=3,409) disruption (divorced), relationship with homicide, robbery, and department crime unemployment aggravated assault crime rates. In addition, reports, economic O: homicide, robbery, when income inequality level was taken into census aggravated assault consideration, poverty’s effect on homicide and robbery crimes became nonsignificant. No relationship was found between unemployment and violent crime rates.

58 AUTHOR(S) DATA UNITS OF STRUCTURAL FINDINGS SOURCES AND ANALYSIS COVARIATES (S) AND SAMPLE OUTCOME MEASURES (O) Lee et al. Supplementary Counties S: resource deprivation They found significant positive relationship (2008) Homicide Report (n=934) (female headed households, between resource deprivation variable and (SHR), poverty, educational white homicide rates in their three models in inequality), percent age 15- rural counties. In one of their model, 24, population size, significant positive association between residential instability homicide and percent age 15-24 was found. O: homicide Residential instability was positively related to homicide rates in three models.

Barber (2009) United Nations, Countries (n=62 S: income inequality, The author found significant negative World Health for UN data, population density, percent relationship between homicide rates and Organization n=67 for WHO urban population density. Significant positive data) O: homicide correlation was examined between inequality and homicide in both data sets.

59 THE EFFECTS OF INEQUALITY

Economic inequality is generally measured with the Gini index of income inequality. The Gini coefficient is computed by dividing entire area between Lorenz curve and perfect equality line to the area below the 45 degree perfect equality line

(Figure 3.1). The Gini index of income inequality ranges between 0 and 1. The value of zero represents perfect equality and the value of one represents the maximum level of inequality (Gini, 1921; Druckman and Jackson, 2008).

Figure 3. 1 Lorenz Curve and Gini Coefficient (Adopted from Wolff, E. N., 1997).

Gini coefficient = A / (A+B)

There are diverse results with respect to the impact of the economic inequality on violent crimes. Forty of the studies reviewed in Table 3.1 included inequality as a measure. Of those forty studies, nineteen studies found a positive relationship between

60 different types of violent crimes such as robbery, aggravated assault, rape, and homicide and economic inequality (Loftin and Hill, 1974; Danziger and Wheeler, 1975; Williams and Drake, 1980; Jackobs, 1981, Watts and Watts, 1981; Blau and Blau, 1982; Carroll and Jackson, 1983; Stack and Kanavy, 1983; Rosenfeld, 1986; Blau and Golden, 1986;

Baron and Straus, 1988; Harer and Steffensmeir, 1992; Kovandzic et al., 1998; Fowles and Merva, 1996; Parker and McCall, 1999; Baller et al., 2001; Phillips, 2002; Pratt and

Godsey, 2003; and Hipp, 2007). In contrast, three studies found negative relationship

(Sampson, 1985; Parker, 1989; and Kposowa et al., 1995), and eighteen studies found a null relationship (Bailey, 1984; Smith and Bennett, 1985; Smith and Parker, 1980;

Messner, 1982; DeFronzo, 1983; Messner, 1983; Jackson, 1984; Williams, 1984;

Simpson, 1985; Messner and Tardiff, 1986; Huff Corzine et al., 1986; Parker, 1989;

Patterson, 1991; Allen, 1996; Neumayer, 2005; Barber, 2006; Stolzenberg et al., 2006; and Barber, 2009).

Positive Effects of Inequality on Violence

Blau and Blau (1982) theoretically linked the racial socioeconomic inequality and violent crime in their seminal study. They emphasized ascriptive forms of social differentiation. The authors speculated that inequality in any form was a potential source of violence, but inborn inequalities were especially conducive to such behaviors.

According to Blau and Blau (1982), one of the ways in which ascriptive inequality allegedly generated conflict and violence was by its effect on the overall social climate of the community. They hypothesized that in a society with a formal commitment to democratic principles, inequality based on race interfered with social integration. The

61 essence of the argument was that inborn inequalities generated hostile sentiments and at the same time weakened support for social norms that operated to restrain such sentiments.

Blau and Blau’s (1982) unit of analysis was SMSAs. They used data on the largest 125 SMSAs in the United States, and they hypothesized that variation in rates of urban criminal violence resulted from differences in racial inequality in socioeconomic conditions.

Blau and Blau’s (1982) results showed that socioeconomic and economic inequalities between races increased criminal violence rates, and when economic inequalities were controlled the positive association between poverty and violence disappeared. Thus, their study is widely-cited as supporting the notion of relative deprivation, as opposed to absolute deprivation, as a cause of crime.

In another SMSA level analysis, Harer and Steffensmeier (1992) examined the relationship between violent crime rates of blacks and whites and economic inequality.

Besides total inequality (Gini coefficient), they also included within- and between- race measures of inequality in their analyses. They looked at robbery, homicide, rape, and assault rates and found that a high level of inequality was related with high crime rates for whites. On the other hand, there was a weak relationship between inequality and violence rates for blacks.

Pratt and Godsey (2003) used 46 nations as a sample in their study. Income inequality was measured differently by the authors as the ratio of median incomes of the richest to the poorest 20% of residents. Their study results revealed significant positive relationship between economic inequality and crime.

62 Negative Effects of Inequality on Violence

Sampson (1985) examined the ecological determinants of disaggragated homicide rates for 55 cities with the population more than 250,000 in the US in 1970. He used racial income inequality as one of the independent variables in his model, and the regression results showed that there is a significant negative relationship between homicide rates and racial income inequality.

Parker (1989) used cities as units of analysis (N= 299). The homicide data was derived from Supplemental Homicide Reports. The author looked at average homicide and total homicide rates in the study. As a result, Parker (1989) found a significant negative relationship between economic inequality and average homicide rates in 1969-

71.

In their county-level analysis (N=3,076), Kposowa et al. (1995) tested the relationship between income inequality and homicide and violent crimes. Except for counties with more than 100,000 population, their findings showed a significantly negative relationship between homicide rates and income inequality.

Null Effects of Inequality on Violence

Messner (1982) looked at the effects of poverty versus inequality on urban homicide rates. He used 204 SMSAs as units of analysis and ran his model by using

“murder and non-negligent homicide” rates as a dependent variable, the Gini coefficient for an inequality measure, and percentage of population below poverty line for an absolute poverty measure. Messner’s (1982) initial OLS regression results showed a significant positive relationship between the Gini coefficient and homicide rates, and a

63 negative relationship between percent poor and homicide rates. Because of a multicollinearity problem, however, Messner ran two different regression models, putting the Gini coefficient and “percent poor” into separate models. At that time, the Gini coefficient failed to have a significant relationship with homicide rates, while percent poor had a negative relationship with homicide rates.

Bailey (1984) replicated and extended Messner’s (1982) absolute and relative economic deprivation study. He used cities instead of SMSA’s as unit of analysis and looked at three years of homicide rates (1950, 1960, and 1970) instead of only one year of homicide rates. The Gini index of income inequality is used to measure relative economic deprivation and the relationship between economic inequality and homicide rates was examined. Like Messner (1982), Bailey (1984) also did not find any significant relationship between relative economic deprivation and homicide rates.

Messner and Tardiff (1986) presented an analysis of the relationship between urban neighborhood homicide rates and economic inequality. Their multiple regression analysis failed to support the hypothesis regarding high degree of economic inequality would cause high rates of homicide in the neighborhood. In their study, income inequality was weakly (non-significantly) related with neighborhood homicide rates.

As a final example, Neumayer (2005) measured income inequality with the Gini coefficient and the ratio of the top to the bottom income quintiles. The author concluded that when more representative sample was used, and country-specific fixed effects were controlled, income inequality was no longer a statistically significant determinant of violent crime.

64 POVERTY

Besides inequality, other indicators of disadvantage are used in several studies of violent crime rates, including poverty. The effect of poverty on crime was touched on in a few of the studies reviewed above, examining relative versus absolute deprivation.

However, the effect of poverty on crime is given fuller attention in this section.

The UN provides a broader definition of poverty. Poverty is defined as:

… a human condition characterized by the sustained or chronic deprivation of the resources, capabilities, choices, security and power necessary for the enjoyment of an adequate standard of living and other civil, cultural, economic, political and social rights (United Nations Committee on Economic, Social and Cultural Rights, 2001: p.1).

This condition of poverty is generally measured by percentage of an area’s population living below the US government’s established poverty level. Although the wide range of theoretical supports for the hypothesized positive relationship between poverty and crime, the empirical evidence has been inconsistent (Table 3.1). As summarized in Table 3.1, forty-five studies included poverty as a measure. Of those forty-five studies, thirty-two studies found positive relationship between poverty and different types of violent crimes such as robbery, aggravated assault, rape, and homicide rates (Loftin and Hill, 1974; Parker and Smith, 1979; Smith and Parker, 1980; Watts and

Watts, 1981; Messner, 1983; Williams, 1984; Bailey, 1984; Sampson, 1985; Simpson,

1985; Smith and Bennett, 1985; Huff-Corzine et al., 1986; Williams and Flewelling,

1988; Baron and Straus, 1988; Parker, 1989; Land et al., 1990; Patterson, 1991; Warner and Pierce, 1993; Kposowa et al., 1995; Krivo and Peterson, 1996; Fowles and Merva,

1996; Warner and Wilcox Rountree, 1997; Parker and McCall, 1999; Peterson et al.,

65 2000; Lee, 2000; Baller et al., 2001; Morenoff et al., 2001; Kubrin, 2003; Browning et al., 2004; Lee and Bartkowski, 2004; MacDonald and Gover, 2005; Lee, 2006; and Lee et al., 2008). In contrast, two studies found a negative relationship (Messner, 1982; Harer and Steffensmeier, 1992), and eleven found no relationship (Blau and Blau, 1982;

DeFronzo, 1983; Simpson, 1985; Messner and Tardiff, 1986; Allen, 1996; Kovandzic et al., 1998; Wilcox Rountree and Warner, 1999; Phillips, 2002; Stucky, 2003; Velez et al.,

2003; and Hipp, 2007). Examples of studies falling into each of these three categories are provided below.

Positive Effects of Poverty on Violence

Messner (1983) looked at the local disparities in the economic correlates of homicide rates. Official crime data from Uniform Crime Reports was used to compute city homicide rates. A sample of cities from southern (N= 91) and nonsouthern (N=256) regions was selected for the analyses, and the proportion of persons below the official poverty line was used to measure poverty in the study. To avoid one year fluctuation, average of three year (1969-1971) for homicide rates was computed for the analyses.

Messner’s (1983) study results showed significant positive relationship with urban homicide rates and poverty in nonsouthern cities.

In other research, Sampson (1985) used 55 cities’ homicide crime rates as his dependent variable, and looked at the relationship between poverty and homicide rates.

Sampson (1985) found a significant positive relationship between poverty and city homicide rates. Warner and Wilcox Rountree (1997) used data for 100 census tracts from Seattle. Poverty was measured by the proportion of population in each census tract

66 living below the poverty line. Warner and Wilcox Rountree’s (1997) results revealed significant positive relationship between poverty and assault crimes. In addition, while the poverty-heterogeneity interaction had a significant negative relationship with assault crimes, the poverty-stability interaction was significantly and positively associated with official assault rates. In a final example of a positive effect of poverty, Lee and

Bartkowski (2004) used Supplementary Homicide Reports to examine juvenile homicide rates in 3,099 counties in the United States. Because of having discrete and non-normally distributed dependent variable, the authors employed negative binomial regression models during the analyses. They used “concentrated poverty” as a measure of poverty in their analysis and found a significant positive correlation between concentrated poverty and juvenile homicide rates.

Negative Effects of Poverty on Violence

Messner (1982) used SMSAs as units of analysis (N=204) and examined the relationship between poverty and homicide rates. Poverty was measured by the percent of the population below the United States Social Security Administration’s poverty line.

Study results revealed a significant negative correlation between percent poor and homicide rates for 204 SMSAs in 1970.

Harer and Steffensmeier (1992) looked at the association between violent crime rates and economic inequality. They used SMSA data for 1980 which was derived from the UCR’s data on violent index crimes. Poverty was employed as a control variable in the models. Their regression results for aggregate offense rates showed a significant and negative relationship between rape rates, specifically, and overall poverty.

Null Effects of Poverty on Violence

DeFronzo’s (1983) study looked at the association between violent crime rates and standard-of-living differences between SMSAs (N=39). The author employed

“adjusted percentage poor” to measure poverty. In his regression analyses, DeFronzo

(1983) found no significant relationship between violent crime rates and the “adjusted percentage poor” variable.

In another examination, Kovandzic et al. (1998) disaggregated homicide rates and estimated three separate equations for family, acquaintance, and stranger homicide rates between 1989 and 1991. Poverty was measured as the percent of the population below the Unites States Security Administration’s poverty line (Bureau of the Census, 1994).

According to their multivariate OLS regression analysis, poverty was not related to family and stranger homicide rates.

UNEMPLOYMENT

A variety of studies found a relationship between unemployment and crime rates.

Unemployment is usually measured by using the unemployment rate, which is defined as the percentage of those in the labor force who are unemployed. Study results regarding the violent crime- unemployment association showed inconsistent findings. Twenty studies included unemployment as a measure in different models. Of those twenty studies, ten studies found a positive relationship between unemployment and different types of violent crimes such as robbery, aggravated assault, rape, and homicide (Watts

68 and Watts, 1981; DeFronzo, 1983; Stack and Kanavy, 1983; Land et al., 1990; Krivo and

Peterson, 1996; Allen, 1996; Kovandzic et al., 1998; Parker and McCall, 1999; Browning et al., 2004; and Neumayer, 2005). However, five studies found a negative relationship

(Carroll and Jackson, 1983; Sampson, 1985; Baller et al., 2001; Phillips, 2002; and Lee and Bartkowski, 2004), and five studies revealed a null relationship (Williams and Drake,

1980; Kposowa et al., 1995; Velez et al., 2003; Stolzenberg et al., 2006; and Hipp, 2007).

Positive Effects of Unemployment on Violence

Watts and Watts (1981) measured unemployment as percentage of the labor force that was unemployed. They found a significant positive relationship between major crime rates and unemployment across 152 cities. In another study, DeFronzo (1983) studied

SMSAs as units of analysis and found a significant positive relationship between percentage unemployment and rape rates.

In their study, Stack and Kanavy (1983) found significant positive association between state rape rates and unemployment. Krivo and Peterson (1996) found that male unemployment was significantly and positively related with violent crime rates. Allen

(1996) also found a statistically significant positive correlation between unemployment and robbery rates. Thus, this positive association has been found for a variety of different measures of violence.

Negative Effects of Unemployment on Violence

Carroll and Jackson (1983) used average unemployment rate for each city in their sample (N=93) and found that unemployment rate was not significantly associated with

69 robbery rates. In their spatial lag model, Baller et al. (2001) found a significant negative association between unemployment and county homicide rates in 1970, 1980, and 1990.

In another study, Lee and Bartkowski (2004) also found significant negative association between homicide rates and unemployment. In his study, Sampson (1985) found a negative relationship between nonwhite homicide rates and unemployment.

Null Effects of Unemployment on Violence

Williams and Drake (1980) used both official and unofficial statistics to compute crime rates. Official statistics were derived from FBI and unofficial statistics were obtained from the US Bureau of the Census. The authors did not find significant association between homicide, aggravated assault, and robbery crime rates and percent unemployment with official and unofficial data.

In their county level analysis Kposowa et al. (1995) used different sample sizes based on the population, region and race. Their regression results showed that unemployment did not have a significant relationship with homicide rates in counties with more than 100,000 population, southern counties, and counties with less than 25,000 population. In addition, no significant correlation was found between total violent crime rates and unemployment.

In another examination, Stolzenberg et al. (2006) separately looked at total unemployment rates, white unemployment rates, and Black unemployment rates.

Unemployment rate was measured by percent of the civilian labor force that was unemployed. Their OLS regression estimates for 91 cities did not show significant correlation between any of the three types of unemployment rates and crime rates.

70 RESIDENTIAL INSTABILITY

Residential mobility/instability is also thought to indicate a disadvantaged social structure. It is generally measured by looking at the percentage of people ages five and over who have changed residences in the past five years. Study results about the relationship between violent crime and residential instability are mixed. Sixteen studies included residential instability as a measure. Of those sixteen studies, twelve studies found a positive relationship between residential instability and different types of violent crimes such as robbery, aggravated assault, rape, and homicide crime rates (Patterson,

1991; Warner and Pierce, 1993; Miethe and Meier, 1994; Kposowa et al, 1995; Bellair,

1997; Warner and Wilcox Rountree, 1997; Wilcox Rountree and Warner, 1999; Peterson et al., 2000; McNulty, 2001; Phillips, 2002; Kubrin, 2003; and Lee et al., 2008). No study found a negative relationship, but four studies found that the relationship was null

(Stack and Kanavy, 1983; Morenoff et al., 2001; Stucky, 2003; and Browning et al.,

2004).

Positive Effects of Residential Instability on Violence

Patterson (1991) examined the association between economic conditions and crime rates at the neighborhood level (N=57). The author found a significant positive association between crime rates and residential instability. In another study, Kposowa et al. (1995) revealed significant positive relationships between homicide crime rates and residential instability for counties with more than 100,000 population and counties with less than 25,000 population.

71 Kubrin (2003) disaggregated homicide crime counts as “general altercation homicide,” “felony homicide,” “domestic male to female homicide,” and “domestic female to male homicide” at the census tract level (N=111) for the city of St. Louis. She also examined a total homicide rate. Kubrin’s (2003) regression results showed that percent residential mobility was significantly positively related to overall and felony homicide rates.

Negative Effects of Residential Instability on Violence

No study found a negative relationship between residential instability and violent crime rates.

Null Effects of Residential Instability on Violence

Stack and Kanavy (1983) focused on the forcible rape rates in 50 states with using

FBI’s data. Their multiple regression analyses revealed no significant relationship between residential instability and rape rates.

Stucky (2003) used cities (N=958) as a unit of analysis and examined homicide, robbery, aggravated assault, and rape crimes in the study. Because of having highly skewed dependent variable, negative binomial regression model was employed to look at the association between percent population change and city violent crime. In all of the equations, percent population change was not significantly related to violent crime rates.

72 FAMILY DISRUPTION

Family disruption is another structural covariate thought to indicate disadvantage, and it is measured differently across studies. For example, researchers include the divorce rate/ percentage of divorced, percent single parenthood, or percent female headed households with children to measure family disruption among aggregate units.

Twenty-six of the reviewed studies included family disruption as a measure. Of those twenty-six studies, eighteen studies found a positive relationship between family disruption and different types of violent crimes such as robbery, aggravated assault, rape, and homicide rates (Loftin and Hill, 1974; Blau and Blau, 1982; Williams, 1984;

Simpson, 1985; Smith and Bennett, 1985; Blau and Golden, 1986; Messner and Tardiff,

1986; Huff-Corzine et al., 1986; Williams and Flewelling, 1988; Baron and Straus, 1988;

Land et al., 1990; Warner and Pierce, 1993; Kposowa et al., 1995; Krivo and Peterson,

1996; Wilcox Rountree and Warner, 1999; Baller et al., 2001; Lee and Bartkowski,

2004; and Hipp, 2007). No study found a negative relationship, but eight studies reported a null relationship (Stack and Kanavy, 1983; Patterson, 1991; Kovandzic et al., 1998;

Parker and McCall, 1999; Phillips, 2002; Kubrin, 2003; Velez et al., 2003; and Barber,

2006).

Positive Effects of Family Disruption on Violence

Blau and Blau (1982) used data for the 125 largest SMSAs in the US. Their percent divorced independent variable was calculated by dividing number of divorced

73 and number of separated to SMSA’s population over age 14. According to their OLS regression analysis results, percent divorced was significantly positively associated with total violent, murder, rape, robbery, and assault crime rates.

In another study, Simpson (1985) reported significant positive relationships between murder, rape, robbery, assault, and total violent crime rates and percent divorced.

Kposowa et al. (1995) ran different models (OLS, WLS, multiple linear, and nonlinear) with different numbers of counties and looked at the relationship between violent crime rates and structural covariates. They used percent divorced as a measure of family disruption. Their regression results for counties have more than 100,000 population, less than 25,000 population, and southern counties showed positive significant associations between violent crime, homicide and percent divorced.

Lee and Bartkowski (2004) used divorce rates as a control variable in their analysis and, in their negative binomial regression model, they found a significant positive relationship between county homicide rates and divorce rates. Then, they used female-headed households as an additional variable, and they found a significant positive association between juvenile and adult homicide rates and female-headed households.

Negative Effects of Family Disruption on Violence

No study found a negative relationship between family disruption and violent crime rates.

74 Null Effects of Family Disruption on Violence

Patterson (1991) examined serious violent crime rates at neighborhood level

(N=57). The author used percentage of single parent households with children between ages 12-20 as a measure of family disruption. He did not find significant association between violent crime rates and percent single-parent households.

Parker and McCall (1999) incorporated both percent of divorced males and percent children not living with both parents to measure family disruption. The former variable was calculated by dividing total number of children age 18 and under without both parents to total number of children age 18 and under. The latter variable was computed by dividing the total number of divorced males to the total number of males over 15 years. The measure of percent divorced males was not significant in any of their disaggregated homicide models.

Barber (2006) conducted a country level study (N=39) and used “illegitimacy ratio” (proportion of total births to single mothers) as an independent variable.

Illegitimacy ratios were measured in 1972 and 1991. The year 1972 was examined because children who born in that year were 18 years old in 1990 and they were at “their peak age of criminal offending by 1990” (Barber, 2006: p.445). The author found a significant positive relationship between homicide crime rates and illegitimacy ratio in

1991, but there was no significant association between illegitimacy ratio in 1972 and homicide crime rates controlling for sex ratios and the Gini coefficient.

75 LOW EDUCATION

A final indicator of structural disadvantage in this study is low education (i.e., high-school dropouts). Three studies included low education level as a measure. Of those three studies, one study found positive relationship (Lee and Bartkowski, 2004), no study found negative relationship, and two studies found null relationship (Kposowa et al.,

1995; and Shihadeh and Ousey, 1998) between low education and different types of violent crimes such as robbery, aggravated assault, rape, and homicide.

Positive Effects of Low Education on Violence

Lee and Bartkowski’s (2004) macro-level homicide research examined the relationship between age specific homicide rates and violence. The authors used high school dropouts as education level variable. Lee and Bartkowski’s (2004) analyses revealed significant positive correlation between high school dropouts and juvenile and adult homicide rates.

Negative Effects of Low Education on Violence

No study found a negative relationship between low education and violent crime rates.

76 Null Effects of Low Education on Violence

In county level analyses, Kposowa et al. (1995) reassessed the structural covariates of violent crimes in the United States. Various research techniques and strategies were employed for looking at the association between homicide and violent crime rates and structural covariates. Education was not significantly related to homicide and violent crime rates in the models.

Shihadeh and Ousey (1998) used racially disaggregated Uniform Crime Report and Census data to examine the correlation between structures of the cities and homicide rates. A Black high school dropout rate was employed as an education variable. Black dropout was the proportion of black population over age 25 that did not have high school diploma. The authors did not find significant correlation between black education level and black homicide rates.

AGE-STRUCTURE

While not a measure of “disadvantage,” age- structure is typically controlled in most macro-level studies of crime, and it will be controlled in this dissertation as well.

Different age groups have been selected by researchers for measuring “age structure.”

For instance, some authors use “percent 15-24,” while others select 20-34 or 15-29 as the age range to control. Overall, all are attempting to measure the percentage of late adolescence and/or young adults (peak crime years) in the population.

Controls for age structure exhibit inconsistent relationships with violent crime rates across studies. Twenty studies included age-structure as a measure. Of those twenty

77 studies, four studies found a positive relationship (Loftin and Hill, 1974, Huff-Corzine et al., 1986; Land et al., 1990; and Lee et al., 2008), five studies found a negative relationship (Kovandzic et al., 1998; Lee, 2000; McNulty, 2001; Stucky, 2003; and Lee,

2006), and eleven studies found no relationship (Parker and Smith, 1979; Smith and

Parker, 1980; DeFronzo, 1983; Messner, 1983; Bailey, 1984; Simpson, 1985; Baron and

Straus, 1988; Parker, 1989; Peterson et al., 2000; Kubrin, 2003; and Neumayer, 2005) between age-structure (percent young)and different types of violent crimes.

Positive Effects of Percent Young on Violence

Loftin and Hill (1974) used percent ages 20-34 as a measure of age structure and found that percent aged 20-34 was positively related to the homicide rates. Land et al.

(1990) used city, SMSA, and state-level data in their analyses. The authors included percent aged 15-24 as an age-structure variable, and they found a significant positive association between age and homicide rates at the state level in 1960, 1970, and 1980.

Lee et al. (2008) examined the regional variation in the effect of structural factors on homicide crime rates. They selected counties (N=934) as their unit of analysis. Negative binomial regression model was employed, and they found a significant positive relationship between percent aged 15-24 and homicide crime rates in one of their models.

Negative Effects of Percent Young on Violence

Kovandzic et al. (1998) disaggregated homicide crime rates into three subgroups.

Their OLS regression analyses about family, acquaintance, and stranger homicide rates

78 revealed significant negative relationship between family homicide rates, specifically, and percent young.

Lee (2000) focused on homicide crime rates at the city level. According to his

OLS regression model predicting white homicide arrest rates, the percent of whites aged

15-24 was significantly negatively associated with white homicide arrest rates.

McNulty (2001) logged violent crime rates in Atlanta, and used a Weighted Least

Squares regression model. The author examined all, White, and Black neighborhoods separately, and found a significant negative association between percent aged 15-24 and violent crime rates across all three types of neighborhoods.

Null Effects of Percent Young on Violence

Parker and Smith (1979) and Smith and Parker (1980) used percent aged 20-34 and found no significant relationship between this age structure variable and crime rates.

In their studies, Bailey (1984) and Simpson (1985) used percent ages 15-29, and they found weak relationships between percent ages 15-29 and crime rates.

POPULATION DENSITY

Thirteen studies included population density as a measure. Of those thirteen studies, three studies found positive relationship (Williams and Flewelling, 1988;

Kposowa et al., 1995; and Morenoff et al., 2001), five studies found negative relationship

(Messner, 1982; Parker, 1989; Kovandzic et al., 1998; Browning et al., 2004; Barber,

2009), and five studies found a null relationship (Messner, 1983; Bailey, 1984; Patterson,

79 1991; Stucky, 2003; MacDonald and Gover, 2005) between population density and different types of violent crimes such as robbery, aggravated assault, rape, and homicide.

Positive Effects of Population Density on Violence

Williams and Flewelling (1988) used 168 cities with 100,000 population or more in the United States. Research results showed total homicide rate, acquaintance other, and stranger other homicide rates had significant positive correlation with population density.

Similarly, Kposowa et al.’s (1995) county-level analyses found significant associations between population density and homicide rates in counties with more than

100,000 population, and Southern counties.

Negative Effects of Population Density on Violence

Messner (1982) found a negative correlation between logged population density and homicide rates at the SMSA level. In addition, Kovandzic et al. (1998) also found a negative relationship between family homicide rates and logged population density in their city-level examination. In other research, Barber’s (2009) country-level analysis revealed a significant negative correlation between homicide rates and population density.

Null Effects of Population Density on Violence

Messner (1983) disaggregated city homicide rates by region and did not find a significant correlation between population density and homicide rates. Bailey (1984) also examined city homicide rates, and he did not find a significant relationship between

80 population density and homicide rates. MacDonald and Gover (2005) also reported a null correlation between population density and youth homicide rates.

REGION – SOUTH/SOUTHERNNESS

Region was incorporated as a control variable in many previous studies of crime rates, often measured with a dummy variable indicating whether the cases being analyzed were located in the southern region of the United States. This control is presumed to be necessary due to the South’s historically higher rates of violence. More recent studies using U.S. macro-level data have also begun to control for location in the western region of the United States.

Overall, nineteen studies included region as a measure. Of those nineteen studies, eight studies found a positive relationship (Messner, 1982; Blau and Golden, 1986; Huff-

Corzine et al., 1986; Parker, 1989; Land et al., 1990; Kposowa et al., 1995; Phillips,

2002; and Lee, 2006), two studies found negative relationship (DeFronzo, 1983; and

Shihadeh and Ousey, 1998), and nine studies found a null relationship (Loftin and Hill,

1974; Parker and Smith, 1979; Smith and Parker, 1980; Bailey, 1984; Simpson, 1985;

Baron and Straus, 1988; Kovandzic et al., 1998; Velez et al., 2003; and Stolzenberg et al.,

2006) between region and different types of violent crimes such as robbery, aggravated assault, rape, and homicide.

81 Positive Effects of Region on Violence

Huff-Corzine et al. (1986) used National Center for Health Statistics data to compute three-year averages of homicide rates for whites, non-whites, and total population. Gastil’s “Southernness Index” and percent population born in the South were employed as region variables. The “Southernness Index” was significantly, positively correlated to white, non-white, and total state homicide rates.

Parker (1989) included a dummy variable for southern region and examined average homicide rates for 1969-1971 and 1973-1975, total homicide rates, and other felony homicide rates. The “South” dummy variable was significantly positively related to average homicide 1996-1971 rates.

In county-level analyses, Lee (2006) used a “South” dummy variable as well.

OLS regression analysis of violent crime index showed a significant positive association between location in the south region and violent crime rates.

Negative Effects of Region on Violence

DeFronzo (1983) also employed a dummy variable to measure the potential effect of the region on violent crime variation in an SMSA-level analysis. The author found negative relationship between region and robbery rates, specifically.

Null Effects of Region on Violence

Two different variables were employed in Loftin and Hill’s (1974) analyses to examine the relationship between place and homicide rates. Neither a “Southernness

Index” nor a “Confederate South” dummy variable had a significant correlation with state

82 homicide rates. Parker and Smith (1979) also found no significant relationship between total, primary, and non-primary homicide rates and region. Velez et al. (2003) used

“South” and “West” as region variables, and they did not find any significant association between homicide rates and the region variables.

OVERVIEW OF FINDINGS

Regardless of the general agreement across various theoretical perspectives

(reviewed in Chapter 2) regarding the relationships between indicators of disadvantage and violent crime rates, it is worthwhile to point out that research results reviewed thus far in this chapter reveal inconsistent and contradictory findings. A summary of the disparity in findings is presented in Table 3.2 below.

Land et al. (1990) examined the potential causes of inconsistent research findings in a now-seminal study. Land et al. (1990) examined structural covariates of homicide rates across places and times. They used cities, SMSAs, and states as unit of analysis, and they examined homicide data for these different units in three different time periods:

1960, 1970, and 1980. Their sample was comprised of 528 cities in 1960, 729 in 1970, and 904 in 1980, while the corresponding numbers for SMSAs in their sample was 182,

187, and 259.

Land et al. (1990) conclude that much of the inconsistency in earlier studies was experienced because of collinearity. In order to overcome collinearity problem, the authors computed factor analysis for 11 structural covariates and came up with two factors. One factor was “population structure” and the other one was “resource

83 deprivation/affluence”. Percentage divorced, percentage ages 15-29, unemployment rate, and cultural violence (South) stood alone in the baseline model.

Two factors in the analysis remained pretty much invariant in their magnitude across places and time periods. However, the nature of relationships for percentage ages

15-29, unemployment rate, and region (South) varied across different units of analysis.

For instance, a positive effect of “South” only appeared at the city level of analysis.

Table 3.2. Summary of relationships between violent crime rates and structural covariates

Variable Number Number Of Number Of Number Of Of Total Positive Negative Null Studies Relationship Relationship Relationship Studies Studies Studies Inequality 40 19 3 18 Poverty 45 32 2 11 Unemployment 20 10 5 5 Residential 16 12 0 4 Instability Family Disruption 26 18 0 8 Education 3 1 0 2 Age-Structure Index 20 4 5 11 Population Density 13 3 5 5 Region 19 8 2 9

Since Land et al. (1990), other researcher have used similar indexes. For example, Kubrin (2003) studied census tracts (N=111) as unit of analysis and looked at the association between structural covariates and homicide rates. Some independent variables in Kubrin’s (2003) model had collinearity problems, and factor analysis and scale construction was used to solve that problem. Percent poverty, percent unemployment, percent Black, and percent children not living with both parents were

84 combined in the “neighborhood (economic) disadvantage” factor. Kubrin’s (2003) results revealed a significant positive association between neighborhood disadvantage, population size and all types of homicides.

Stolzenberg et al. (2006) used a “city disadvantage” factor which included percentage of households with public assistance income, percentage of population ages

25 and over that never graduated from high school, and percentage of households headed by a single female aged 15–64 with children. They found that the “city disadvantage” variable was significantly, positively related to total violent crime, white-on-white, and white-on-black crime rates.

Pratt and Godsey (2003) employed a “human development index,” which included as components adult literacy rates, expected average life at birth, GDP per capita, and the sum of primary, secondary, and tertiary school enrollment. Their

Weighted Least Squares regression models revealed no significant correlation between the “human development index” and homicide rates.

MacDonald and Gover (2005) used a “concentrated disadvantage” index which consisted of percentage of the Black population, percentage of female head of households, percentage of persons living in poverty, and the percentage of the population that was unemployed. They, first, revealed a significant, negative relationship between concentrated disadvantage and city homicide rates. After dropping 12 cities from their model, however, concentrated disadvantage had a significant positive relationship with city homicide rates.

Lee (2006) created a disadvantage index that included the percent of the county population living below the poverty line, percent unemployed, percent female headed

85 households, percent population without a high school degree, and percent of the county that was black. His study results showed a significant positive association between violent crime rates and the disadvantage index.

Lee et al. (2008) used “resource deprivation” index which contained female headed households, poverty, and educational inequality variables. They found a significant positive relationship between resource deprivation and White homicide rates in rural counties.

Stucky (2003) included percent poor, percent unemployed, percent of female headed households, and percent of owner-occupied homes in a deprivation index. The author found a significant positive relationship between city-level violent crime rates and the city-level deprivation index.

TERRORISM AS A SPECIAL CASE OF VIOLENCE

Most all of the criminological literature examining structural covariates of violent crime rates has focused on non-terrorist violence. While non-terrorist violence will be examined in this dissertation, the structural covariates of non-terrorist violence in Turkey will be compared to similar models estimating terrorist violence in Turkey. Thus, previous literature on causes of terrorism – especially that using a macro-level approach - is relevant. Below, I will first define terrorism, delineate it from “non-terrorist violence,” and review relevant macro-level research findings on its causes.

86 Terrorism: What is it?

The word terrorism originally comes from the Latin language in which “terrere”, means great fear (Juergensmeyer, 2000). During the French Revolution, the word

“terreur” was used in French to refer policy which was used to defend French Republic government against counterrevolutionaries (Cronin, 2002). The word was then transformed in English as the word “terror”.

Definitions of terrorism differ widely, and it is thus difficult to find a single description of terrorism that consists of all possible characteristics of terrorism. For instance, while a certain event can be defined as an act of terror in one country, the same sort of incident can be defined as a “freedom fight” by another. While there is no agreement on a single definition of terror and terrorism, terrorism is defined by the

United States Department of State (1983) as follow:

(1) the term "international terrorism" means terrorism involving citizens or the territory of more than one country; (2) the term "terrorism" means premeditated, politically motivated violence perpetrated against non-combatant targets by subnational groups or clandestine agents; and (3) the term "terrorist group" means any group practicing, or which has significant subgroups which practice, international terrorism (Section 2656f(d) of Title 22 of the United States Code).

In Turkey, Anti-Terror Law (Act No. 3713) defines terrorism as follow:

Terrorism is any kind of act done by one or more persons belonging to an organization with the aim of changing the characteristics of the Republic as specified in the Constitution, its political, legal, social, secular and economic system, damaging the indivisible unity of the State with its territory and nation, endangering the existence of the Turkish State and Republic, weakening or destroying or seizing the authority of the State, eliminating fundamental rights and freedoms, or damaging the internal and external security of the State, public order or general health by means of pressure, force and violence, terror, intimidation, oppression or threat (Article 1).

From an academic viewpoint, terrorism is defined by Schmid and Jongman (1988) as:

… an anxiety-inspiring method of repeated violent action, employed by clandestine individual, group, or state actors, for idiosyncratic, criminal or political reasons, whereby –in contrast to assassination- the direct targets of violence are not the main targets. The immediate human victims of violence are generally chosen randomly (targets of opportunity) or selectively (representative or symbolic targets) from a target population, and serve as message generators. Threat –and violence- based communication process between terrorist (organization), (imperiled) victims, and main targets are used to manipulate the main target (audience(s)), turning it into a target of terror, a target of demands, or a target of attention, depending on whether intimidation, coercion, or propaganda is primarily sought. (p.28)

Schmid and Jongman’s (1988) definition gives more details about characteristics of terrorist events and contains several features of common terrorism definitions. Finally,

Goodwin’s (2006) terrorism definition is one of the most commonly accepted ones, describing terrorism as “deliberate use of violence in order to influence some audiences”

(p.2028).

Differences between Terrorism and Ordinary Violent Crimes

Terrorism differs from other violent crimes in some respects. All terrorist actions are criminal and different types of criminal acts, such as murder, kidnapping, arson, bombing, are committed by terrorists. The main difference between terrorism and

“normal” violent crime is the motivation and goals behind such actions. Terrorism employs violence as a strategy to achieve certain objectives. Achieving a political objective is substantial notion which separates terrorist actions from other standard criminal acts (Crenshaw, 2000).

88 Besides motivational and goal differences, terrorism and other violent crimes also differ in terms of opportunity structure. According to Bodrero (2000), standard criminals are “undisciplined, untrained, and oriented towards escape”. On the other hand, terrorists

“have prepared for their mission, they are willing to take risks, and they are attack- oriented” (White, 2003:21).

According to Shelley and Picarelli (2002), terrorist groups use violence to achieve their organizational objectives and to remove their political opponents. Terrorists hit their targets in public places such as shopping malls and office buildings. Additionally, during terrorist attacks, innocent individuals, females, and kids who are not related to the targeted organization are killed (Shelley and Picarelli, 2002).

Similarly, standard criminals mainly look for personal gain and immediate goals during criminal action. In contrast, terrorists use crime to support their ideology instead of personal gain, and terrorist actions may have long-term as well as immediate aims. For instance, as a type of violent crime, “standard” homicide crime is committed by an individual to satisfy his/her immediate individual gain, but homicides committed by terrorist organizations may have a long-term aim to conquer or change the structure of the government (Sherley, 2006).

Hoffman (1998) summarizes the difference between terrorists and ordinary criminals as follows:

Perhaps most fundamentally, the criminal is not concerned with influencing or affecting public opinion: he simply wants to abscond with his money or accomplish his mercenary task in the quickest and easiest way possible so that he may reap his reward and enjoy the fruits of his labors. By contrast, the fundamental aim of the terrorist’s violence is ultimately to change ‘the system’ – about which the ordinary criminal, of course, couldn’t care less (p. 42).

89 Another difference between terrorism and ordinary violent crimes is that terrorist organizations use information technology to support their illegal activities. To improve their intelligence capability, terrorist groups use information from open media sources and the internet. As an example, in the September 11 terrorist attacks in the United States, many of the terrorists communicated with each other via internet and they used anonymous local business internet service centers to send or read e-mail messages

(Shelley and Picarelli, 2002).

Causes of Terrorism

Sherley (2006) reviewed both international and domestic terrorism literature and concluded that it “is in its infancy, particularly in the empirical realm” (p.17). According to Sherley (2006), past research terrorism studies generally concentrated on history of terrorism (Garrison, 2003; Jentleson, 2003; Ross, 1993), motivational factors for terrorist groups (Hoffman, 1995), psychopathological characteristics of terrorists (Crenshaw,

2000; Hudson, 1999), and evaluation of terrorist movements (Hoffman, 2001).

Previous terrorism literature also has limitations in terms of proper methodology and general theoretical frameworks. Although information is available for characteristics of terrorists and reasons for terrorist activities, the associations between terrorism and socioeconomic, religious, and political factors are often omitted by researchers (Lum et al., 2006).

In another literature review of terrorism research, Schmid and Jongman (1988) found that almost 80% of the terrorism literature was not research-based. The authors stated that “there are probably few areas in the social science literature in which so much

90 is written on the basis of so little research” (p.179). Later, Lum and his associates (2006) reviewed the terrorism literature and found that research on terrorism started increasing after September 11th, 2001 terrorist attacks. Parallel with Schmid and Jongman’s (1988) findings, Lum et al. (2006) examined that only 3% of reviewed terrorism studies had an empirical foundation.

In sum, most previous research on terrorism has not included statistical and/or experimental analyses. However, there are important exceptions to this overall trend.

Empirically-based micro-level research on terrorism is reviewed in Appendix A. The macro-level research that is more relevant to this dissertation is reviewed below.

Macro-Level Terrorism Research

Empirical studies about the structural correlates of terrorism reveal inconsistent findings. For example, Schmid (2003) used only univariate analyses without statistical controls, and looked at the relationship between terrorism and development. Schmid

(2003) found a weak relationship between area rate of poverty and terrorism.

Blomberg et al. (2004) examined the association between terrorism and economic conditions at the country level (N=127) between 1968 and 1991. Their results revealed a positive relationship between income level, democracy, and levels of terrorism.

In another country level (N=112) study, Li and Schaub (2004) looked at the effect of economic globalization on the number of international terrorism incidents within countries between 1975 and 1997. Their dependent variable was the number of terrorist events in a year for each country. They used ITERATE1 data sets for their dependent

1 ITERATE - International Terrorism: Attributes of Terrorist Events. The ITERATE project attempts to generate data about the characteristics of international terrorist groups, their activities which have

91 variable. Economic integration was measured with trade, foreign direct investment, and investment portfolios. Size of the country, level of democracy, government capability to control terrorist activities, number of past year’s terrorist incidents, and interstate military conflict were used as control variables in the analysis. They did not find a significant association between economic integration and numbers of international terrorist incidents. They found a significant negative relationship between economic globalization and the number of international terrorist incidents within the country. In addition, they found that the size of the country was positively related with the number of international terrorist incidents within the country. Also, the country’s democracy level was significantly positively associated with number of international terrorist incidents. Lastly, the authors found a geographical pattern for the international terrorist incidents’ distribution. Countries in the Middle East had the highest proportion of terrorist incidents, followed by countries in Europe.

Testas (2001) looked at the association between economy and political violence in

Algeria over time. He measured economic decline with the percentage decrease in the country’s Gross Domestic Product. The author concluded that Algeria’s economic decline caused political violence and instability. It was proposed that economic collapse in Algeria caused dissatisfaction among citizens promoted ethnic hostilities and increased the attractiveness of economic motivations offered by rebels for recruits.

Krueger and Maleckova (2003) examined the causal connection between education, poverty, and terrorism in Palestine by using evidence from public opinion polls (N=1,357), conducted by the Palestinian Center for Policy and Survey Research.

international effect. Different sources, such as major daily newspapers, foreign broadcast daily reports, and global news networks are used for gathering information.

92 The authors did not find significant association between the educational and economic levels of Palestinian people and their support for armed attacks against Israeli people. In addition to this individual-level analysis, Krueger and Maleckova (2003) used country- level terrorism data from ITERATE and ran a negative binomial regression model. They tested the interactions between Gross Domestic Product per capita, civil liberties, political freedom and did not find significant associations among these variables. The authors also found no correlation between low income, level of education, and number of international terrorist incidents originating from each country between 1997 and 2002.

Sherley (2006) also looked at which macro level predictors foster terrorist events.

She used countries (N=144) as units of analysis and picked data from ITERATE and

World Bank. Sherley (2006) also used MIPT Terrorism Knowledge Base, Transparency

International, POLITY IV data, and state religiosity data for the analysis.

Number of terrorist incidents per country for 15 year period (1987-2001) and number of terrorists originating from each country were used as dependent variables in

Sherley’s analysis. Different economic, social, and political variables from the year 2001 were employed as independent variables in the study. Sherley’s (2006) regression analyses showed that country’s democracy level, religious cleavage, state religiosity, corruption level, and development level were significantly negatively related to the country’s level of terrorism. When number of terrorists originating from the country

(origin countries) was employed as a dependent variable, country’s democracy level and state religiosity were positively related to number of terrorists, and religious cleavage was negatively associated with this dependent variable.

93 Berrebi and Lakdawalla (2007) looked at the temporal and spatial predictors of terrorism risk in Israel. They used a database from Israeli Foreign Ministry, the National

Insurance Institute, the Israeli Defense Forces, and the archives of two newspapers to examine terrorist incidents in Israeli between 1949 and 2004. They found that terrorist attacks were more common in larger and crowded cities, and less common in places without Jewish inhabitants. In addition, proximity to international borders and terrorist home bases were found significant predictors of being attacked by terrorists.

CONCLUSION

As review in this chapter, numerous studies have examined relationships between structural covariates and non-terrorist violent crimes. A growing but still small number of studies about terrorism-related crimes have also examined macro-level correlates.

Like studies of non-terrorist violent crime, these terrorism studies have revealed inconsistent associations across studies. This dissertation will extend the literature on structural covariates of crime by explicitly comparing structural covariates of terrorist versus non-terrorist violence in Turkey, with an emphasis on indicators of structural disadvantage. The purpose of the next chapter will be to situate this thesis in a historical overview of crime and terrorism in Turkey.

94 CHAPTER FOUR: A DESCRIPTION OF CRIME AND TERRORISM IN

TURKEY

The primary goal of this chapter is to describe violent crime and terrorism in

Turkey in order to provide a context for the analysis of the structural covariates of crime and terrorism to be presented later. First, brief information about Turkey’s administrative divisions will be given. Then, crime rate trends between 1999 and 2008 and violent-crime variation in Turkey by location will be described. Finally, types of terrorist activities, types of terrorist organizations, terrorism-related crime rate trends between 1999 and

2008, and variation in terrorism by region will be described.

ADMINISTRATIVE STRUCTURE OF TURKEY

Administrative Divisions of Turkey

Turkey is divided into 7 census-defined regions: Central Anatolia, Eastern

Anatolia, Southeastern Anatolia, Black Sea, Marmara, Aegean, and Mediterranean

(Figure 4.1). Within these seven regions, there are 81 provinces for governmental purposes. The numbers of provinces within each of these seven census-defined regions are as follows: Central Anatolia: 14, Eastern Anatolia: 13, Southeastern Anatolia: 9,

Black Sea: 17, Marmara: 10 and Aegean: 10, Mediterranean: 8.

The 1982 Constitution of the Republic of Turkey established Turkey's current centralized administrative system. In Turkey, each province is administered by a governor who is appointed by the Council of Ministers, and they are approved by the

95 President. The governors are representatives of the central administration at the province level, and they are responsible to the Ministry of Interior. In addition, the national police organization has security directorates in each province, and the National Police

Headquarter as a central organization in Ankara. Province security directors are served under the supervision of governors in the provinces.

Figure 4.1. Census-defined regions of Turkey.

Map used with permission from Turkish National Police.

VIOLENT CRIME IN TURKEY

In this chapter, I use the Main Crime Database of Turkish National Police to examine the annual frequencies and trends in total violent crime, homicide, aggravated assault, robbery, and rape crime rates over the past decade (1999-2008). The Main Crime

Database of Turkish National Police consists of yearly crime numbers for terrorism related crimes, organized crimes, smuggling crimes, riots, traffic accidents, and public

96 order crimes. Parallel to the FBI’s Uniform Crime Report “index crimes” in the United

States, “public order crimes” in Turkey consist of violent crimes such as homicide, assault, rape, and robbery and property crimes such as burglary, motor vehicle theft, larceny, and arson. In the information to follow, rates of crime across the past ten years in

Turkey are described for total violence, homicide, aggravated assault, robbery, and rape.

Trends over Time in Prevalence of Violence in Turkey

Figure 4.2 is a graph for the annual total violent crime rates in Turkey between

1999 and 2008. Total violent crime rates were stable between 1999 and 2002, but then increased until 2006. In 2006, the total violent crime rate had increased almost twofold compared to 2002. Then, it decreased to less than 150 for per 100,000 individuals in

2007, but rose again in 2008, and reached its highest level over the 10-year period.

Figure 4.2. Total violent crime rates per 100,000 persons in Turkey between 1999 and 2008.

Total Violent Crime Rates per 100,000 persons in Turkey (1999-2008)

250 200

150 100 persons 50

Crime rate per 100,000 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Years

Figure 4.3 shows yearly homicide crime rates trend in Turkey between 1999 and

2008 period. As seen from graph, the homicide rate was nearly 2.5 per 100,000 population in 1999. It increased sharply in 2000. After a one-year decrease in 2001, it started to rise again and reached a rate of more than 3 per 100,000 persons in 2005. Then, the homicide rate began a sharp decrease in 2007 and reached its lowest level over the

10-year period examined in 2008.

Figure 2.3. Homicide crime rates per 100,000 persons in Turkey between 1999 and 2008

Homicide Crime Rate per 100,000 persons in Turkey (1999-2008)

3,5 3 2,5 2 1,5 persons 1 0,5 Crime rate per 100,000 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Years

98 Aggravated assault crime rates are observed in the Figure 4.4. Assaults were steady between 1999 and 2002. After 2002, assault rates began increasing, and after two years of increase, assault decreased sharply in 2006. After 2006, aggravated assault rates rose sharply into 2007 and 2008.

Figure 4.4. Aggravated assault crime rates per 100,000 persons in Turkey between 1999 and 2008

Aggravated Assault Crime Rates per 100,000 persons (1999-2008)

200 180 160 140 120 100 80 60 40

Crime rate per 100,000 persons 20 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Years

99 As displayed by Figure 4.5, a substantial rise in the national robbery rate began in

2000 and ended in 2006. Robbery reached its highest level over the 1999-2008 period in

2006. During the 2000-2006 time period, the robbery rate in Turkey increased nearly sevenfold. Conversely, the rate started to decrease sharply after 2006, but it rose again in

2008.

Figure 4.5. Robbery crime rates per 100,000 persons in Turkey between 1999 and 2008.

Robbery Crime rates per 100,000 persons in Turkey (1999-2008)

14 12 10 8 6 persons 4 2

Crime rate per 100,000 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Years

100 Figure 4.6 displays trends in the rate of rape. Considering the 1999-2008 period, the rape rate in Turkey was at its lowest level in 1999. It substantially increased from

1999 to 2000, however. Figure 4.6 reveals that the rape rate then decreased in 2001, but then began increasing again until reaching its highest level in 2006. It decreased sharply in 2007 and 2008 in comparison to the peak in 2006.

Figure 4.6 Rape crime rates per 100,000 persons in Turkey between 1999 and 2008.

Rape Crime Rate per 100,000 persons in Turkey (1999-2008)

2,5

1,5

1 persons

0,5 Crime rate per 100,000 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Years

Variation in Violence in Turkey by Location

As stated before, Turkey is split into seven regions and each region consists of different numbers of provinces. At this point, variation in violence in Turkey will be examined by focusing on year and location. For each region, total violent crime, homicide, aggravated assault, rape, and robbery crime rates are demonstrated separately between 1999 and 2008.

101 First, Figure 4.7 shows total violent crime rates between 1999 and 2008 for the census-defined seven regions of Turkey. In general, the highest total crime rates are in the

Aegean region (except 2006 and 2007) followed by Central Anatolia and Marmara. In

2006, the Mediterranean region, and in 2007, the Central Anatolia region had the highest total violent crime rate across the country. On the other hand, generally, Southeastern

Anatolia has the lowest total violent crime rates between 1999 and 2008. In 2006, however, the Black Sea region had a lower total violent crime rate than the Southeastern

Anatolia region.

Figure 4.7. Regional differences for total violent crime rates between 1999 and 2008

Regional Differences for Total Violent Crime Rates (1999-2008)

250

200

150

100

Crime rates per 100,000 persons 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Central Anatolia Marmara Aegean Mediterranean Black Sea Eastern Anatolia Southeastern Anatolia

102 Figure 4.8 reveals the regional differences for homicide rates from 1999-2008.

The Mediterranean region has the highest homicide rates across the country in the examined time period, followed by Marmara. Except for 2001 and 2007, Eastern

Anatolia had the lowest rates for homicide crimes.

Figure 4.8. Regional differences for homicide crime rates between 1999 and 2008

Regional Differences for Homicide Rates (1999-2008)

5 4,5 4 3,5 3 2,5 2 1,5 1 0,5 Crime rate per 100,000 persons 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Central Anatolia Marmara Aegean Mediterranean Black Sea Eastern Anatolia Southeastern Anatolia

Figure 4.9 displays assault rates over time across region. In terms of aggravated assault rates between 1999 and 2008, the Aegean region has the highest rates (except for

2006 and 2007). In 2006, the Mediterranean region and, in 2007, the Central Anatolia

103 region had the highest aggravated assault rates (Figure 4.9). On the other hand, the

Southeastern Anatolia region tended to have the lowest aggravated assault rates (except

2006).

Figure 4.9. Regional differences for aggravated assault crime rates between 1999 and

2008

Regional Differences for Aggravated Assault Rates (1999-2008)

250

200

150

100

50 Crime per 100,000 persons

0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Central Anatolia Marmara Aegean Mediterranean Black Sea Eastern Anatolia Southeastern Anatolia

Figure 4.10 displays over-time trends in robbery by region. In the examined time period, the Marmara region has the highest rates of robbery. Following Marmara, the

Aegean and Mediterranean regions have similar robbery crime rates. Contrary to other

104 violent crime rates, the Black Sea Region has the lowest rates for robbery. Following

Black Sea Region, Southeastern and Eastern Anatolia Regions have similarly lower robbery rates in comparison to other regions between 1999 and 2008.

Figure 4.30. Regional differences for robbery crime rates between 1999 and 2008

Regional Differences for Robbery Crime Rates (1999-2008)

20 18 16 14 12 10 8 6 4

Rate perRate 100,000 persons 2 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Central Anatolia Marmara Aegean Mediterranean Black Sea Eastern Anatolia Southeastern Anatolia

As Figure 4.11 shows, the Mediterranean region has the highest rape crime rates between 1999 and 2008. Rape crime rates are extremely high in the Mediterranean region compared to other six regions. The Aegean region has the second highest rape crime

105 rates, and the Marmara region follows it. In general, the Black Sea region has the lowest rape crime rates over the time period examined (except in 2006 and 2007).

Figure 4.41. Regional differences for rape crime rates between 1999 and 2008

Regional Differences for Rape Rates (1999-2008)

3,5

2,5

1,5

0,5 Crime rate per 100,000 persons 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Central Anatolia Marmara Aegean Mediterranean Black Sea Eastern Anatolia Southeastern Anatolia

TERRORISM IN TURKEY

Turkey has been experiencing terrorism problem for nearly four decades. In

Turkey, terrorist events come from various left-wing, right-wing, Al-Qaeda related transnational terrorism groups, and separatist groups (Bal and Laciner, 2001). The

Turkish National Police’s anti-terrorism department has indicated that there are currently twelve active terrorist groups in Turkey.

106 These groups are as follows: Kurdistan Workers’ Party/ Kurdistan Freedom and

Democracy Congress (PKK/KONGRA GEL), Kurdistan Revolutionary Party, Kurdistan

Democratic Party, Turkish Hezbollah (Party of God), Great Eastern Islamic Riders/Front

(IBDA-C), Caliphate State, Jerusalem Army, Revolutionary People’s Liberation

Party/Front (DHKP-C or Dev-Sol), Marksist-Leninist Communist Party (MLKP),

Turkish Communist Party/ Marksist-Leninist-Partisan (TKP/ML- TIKKO), Maoist

Communist Party (MKP), and Al Qaeda Terrorist Organization in Turkey.

The main idea of the separatist terrorist groups in Turkey is to establish an independent Marxist- Leninist Kurdish state in the Turkey’s Southeastern region, where the Kurdish population predominates. The final goal of these terrorist groups is to create

“greater Kurdistan” which incorporates territories from north Syria, north Iraq, and west

Iran (Sozen, 2006). According to Turkish National Police’s anti-terrorism department

(TEMUH) records, separatist terrorist groups in Turkey are as follows: Kurdistan

Workers’ Party/ Kurdistan Freedom and Democracy Congress (PKK/KONGRA GEL),

Kurdistan Revolutionary Party, and Kurdistan Democratic Party.

Kurdistan Workers’ Party/ Kurdistan Freedom and Democracy Congress

(PKK/KONGRA GEL) is the main Kurdish separatist terrorist group related to the majority of terrorist attacks in Turkey and responsible for the deaths of over 35,000 people since 1984. PKK/KONGRA GEL terrorist group was first established in 1978 and started terrorist actions in 1984. They generally target military and police forces, and they use guerilla tactics to weaken government’s authority (Ozeren and Cinoglu, 2006). At the beginning, PKK/ KONGRA GEL only hit targets in the Southeastern region, but they later spread their terrorist activities to other provinces and districts across Turkey.

107 The main foundation of right-wing terrorist groups is religion. Most of these terrorist organizations have a leader, and, under the authority of that leader, they have a consultation committee. In addition, regional, provincial, and district supervisors are functioning under the consultation committee. Right-wing terrorist groups espouse similar organizational structures, with different units -- such as a social division, a transmitting division, an intelligence division, and a military division (Caglar, 2006).

Each division has specific duties and responsibilities to achieve the aims of the organization. According to Turkish National Police’s anti-terrorism department

(TEMUH) records, right-wing terrorist groups in Turkey are as follows: Turkish

Hezbollah (Party of God), Great Eastern Islamic Riders/Front (IBDA-C), Caliphate State, and Jerusalem Army (Dilmac, 1997; and Criss, 1995).

Left-wing terrorist groups support a Marxist-Leninist agenda. Their aim is to demolish the existing political system and to set up a Marxist regime. Left-wing terrorist group members tend to be well-educated, and the structures or terrorism organizations are very rigid in Turkey. In addition, left-wing terrorist groups are tight-knit organizations, and it is very difficult to identify terrorists in these groups (Ozeren and Cinoglu, 2006).

According to Turkish National Police’s anti-terrorism department (TEMUH) records, left-wing terrorist groups in Turkey are as follows: Revolutionary People’s Liberation

Party/Front (DHKP-C or Dev-Sol), Marxist-Leninist Communist Party (MLKP), Turkish

Communist Party/ Marxist-Leninist-Partisan (TKP/ML- TIKKO), and Maoist Communist

Party (MKP).

Turkey also has one Al Qaeda transnational terrorism group operating within its borders. Turkey is the target of Al Qaeda terrorist group because Turkey has positive

108 relationships with the United States and European countries. Especially, after the

September 11 attacks, Al Qaeda sharply increased their terrorist activities in Turkey.

According to Cline (2004), in Turkey, “(t)he security structure’s major concern regarding foreign terrorism in recent years has been Al Qaeda” (p.322).

In Turkey, the various terrorist organizations use different methods -- including smuggling, extortion, burglary, shootings, kidnapping, arson, assault, guerilla tactics, and political actions -- to achieve their aims (Ozeren and Cinoglu, 2007). Different groups tend to emphasize particular methods. For instance, while one terrorist group generally uses guerilla tactics or targets police/military security check-points, another one kidnaps and tortures people to realize their ideologies.

The most common terrorist activities in Turkey are: attacks against police stations, attacks on military bases, attacks on civilian people, attacks on/at official buildings, attacks aimed at political parties, and armed conflicts with security forces. Use of explosives and suicide bombings are common in the various attacks.

Trends over Time in Prevalence of Terrorism in Turkey

Figure 4.12 shows annual terrorism-related crime rates in Turkey between 1999 and 2008. In the examined ten-year period, terrorism-related crime was at its highest level in 1999. Then, it started to decline sharply through 2002. In 2003, terrorism-related crime increased again, and the rate was stable between 2003 and 2004. After 2004, terrorism generally started to decline once again, though it rose slightly between 2007 and 2008.

109 Figure 4.52. Terrorism-related crime rates per 100,000 persons in Turkey between 1999 and 2008.

Terrorism-related Crime Rates per 100,000 persons (1999-2008)

6 5 4 3

persons 2 1

Crime rate per 100,000 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Years

Variation in Terrorism in Turkey by Location

As seen in Figure 4.13, Southeastern and Eastern Anatolia suffer from terrorism more than do the other five regions of Turkey. In all examined years, Eastern Anatolia had the highest terrorism-related crime rates. The Southeastern Anatolia region ranks second, after Eastern Anatolia, in terms of prevalence of terrorism-related crimes.

Terrorism-related crime rates for the other five regions do not approach the levels seen in

Southeastern or Eastern Anatolia during the examined time period.

110 Figure 4.63. Regional differences for terrorism-related crime rates between 1999 and

2008

Regional Differences for Terrorism-related Crime Rates (1999-2008)

20 18 16 14 12 10 8 6 4 2 Crime rate per 100,000 persons 100,000 per rate Crime 0 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Central Anatolia Marmara Aegean Mediterranean Black Sea Eastern Anatolia Southeastern Anatolia

CONCLUSION

In present chapter, the current situations of violent crime and terrorism problems in Turkey were described. At the beginning, brief information about Turkey’s administrative divisions was presented. Then, violence and violent crimes in Turkey were explained for 1999-2008 time period. Afterward, the terrorism problem in Turkey, trends

111 and regional differences for terrorism-related crimes between 1999 and 2008 were examined in detail.

The examination of violent and terrorism-related crimes in Turkey by location showed important differences between regions. For instance, while Aegean Region had the highest crime rates for total violent and aggravated assault crimes, Mediterranean

Region experienced highest rape and homicide crime rates. In terms of robbery crime,

Marmara Region had the maximum crime rates between 1999 and 2008. On the other hand, Eastern Anatolia and Southeastern Anatolia Regions had the highest levels of terrorism-related crime rates respectively in the examined time period.

112 CHAPTER FIVE: DATA AND METHODOLOGY

After having provided the descriptive overview of crime and terrorism trends in

Turkey in the previous chapter, this chapter focuses on the research questions, research hypotheses, data, measures of variables, and methodology used in the cross-province analysis of the structural covariates of crime and terrorism. First, research questions are clarified, and research hypotheses are indicated. Next, detailed information regarding data and sample are explained. Afterward, operationalization of dependent, independent, and control variables are presented. Finally, I provide detailed information about my research design for the study.

RESEARCH QUESTIONS AND HYPOTHESES

Research Question 1: How do indicators of structural disadvantage influence the occurrence of non-terrorist violent crimes across provinces in Turkey?

The indicators of structural disadvantage that will be emphasized in addressing this question include unemployment, residential instability, family disruption, poverty, economic inequality, and low education. The specific hypotheses related to this first research question are presented below.

H1a: Provinces that experience a greater percentage of unemployed residents will

experience higher rates of total violent crime than provinces where the

percentage of unemployed is lower.

113 H1b: Provinces that experience greater residential instability will experience

higher rates of total violent crime than provinces where the residential instability

is lower.

H1c: Provinces that experience high levels of family disruption will have higher

rates of total violent crime than provinces that have low levels of family

disruption.

H1d: Provinces with higher poverty will have higher rates of total violent crimes

than provinces that have lower poverty rates.

H1e: Provinces with higher economic inequality will have higher rates of total

violent crime than provinces that have lower economic inequality rates.

H1f: Provinces that have higher percentages of persons with less than high-school

education will have higher total violent crimes that provinces that have lower

percentages of persons with less than high-school education.

Total violent crime rates will also be disaggregated, and similar hypotheses will be explored for homicide, robbery, aggravated assault, and rape, individually.

Research Question 2: How do indicators of structural disadvantage influence the occurrence of terrorism-related crimes across provinces in Turkey?

Again, the indicators of structural disadvantage that will be emphasized in addressing this question include unemployment, residential instability, family disruption, poverty, economic inequality, and low education. Specific hypotheses related with this second research question are presented below.

114 H2a: Provinces that experience a greater percentage of unemployed residents will

experience higher rates of terrorism-related crime than provinces where the

percentage of unemployed is lower.

H2b: Provinces that experience greater residential instability will experience

higher rates of terrorism-related crime than provinces where the residential

instability is lower.

H2c: Provinces that experience higher levels of family disruption will have higher

rates of terrorism-related crime than provinces that have lower levels of family

disruption.

H2d: Provinces with higher poverty will have higher rates of terrorism-related

crimes than provinces that have lower poverty rates.

H2e: Provinces with higher economic inequality will have higher rates of

terrorism-related crimes than provinces that have lower economic inequality

rates.

H2f: Provinces that have higher percentages of persons with less than high-school

education will have higher terrorism-related crimes that provinces that have lower

percentages of persons with less than high-school education.

DATA

Given the hypotheses stated above, the basic research design for this study includes secondary data analysis, with the data coming from several different resources.

115 The primary data sources for this study are Turkish National Police, Turkish Statistical

Institute, Ministry of Health, and Ministry of National Education.

Turkish National Police Data

Turkey is composed of 81 provinces, and each province has its own local police organization and its provincial headquarter. Different divisions, such as “Public Order”,

“Anti-terror”, “Smuggling”, “Traffic”, and “Intelligence”, are providing security services at the province level. In addition, Turkish National Police has a main headquarter in

Ankara (capital) which has identical units to those in the provinces, at the national level.

As indicated in Chapter Four, Turkish National Police generates different aggregated crime statistics for terrorism-related crimes, organized crimes, smuggling crimes, riots, traffic accidents, and “public order” crimes. Parallel to the FBI’s Uniform

Crime Report index crimes in the United States, “public order” crimes in Turkey consist of both violent type of crimes such as homicide, assault, rape, and robbery and property crimes such as burglary, motor vehicle theft, larceny, and arson.

In Turkey, crimes are reported within a chain of command to the Turkish National

Police Main Headquarter. Such data originate from the responsible unit at the local level within the provinces. Then, the responsible local department notifies the regional headquarter of the province about the crime. Later, regional headquarters send crime reports to the Turkish National Police Main Headquarter. At that point, reported crime is recorded to the Main Crime Database.

In the current examination, data for rates of terrorism-related and non-terrorist violent crime (homicide, robbery, assault, rape, and total non-terrorist violent crime) are

116 derived from the Turkish National Police Data for 2006, 2007, and 2008. To control and reduce the impact of atypically low or high crime rates in any one year, in the course of the study, three years averages of province level crime rates are used for all types of crimes.

Turkey’s Statistical Yearbooks, Censuses

Information regarding social and demographic characteristics of provinces is drawn from 2003 and 2008 Turkey’s Statistical Yearbook, based on 2000 and 2007 censuses respectively. Turkey’s Statistical Yearbook, in which official statistics associated with many characteristics of the society are presented, is published by Turkish

Statistical Institute.

In current study, data at the province level for percent divorced, residential instability, percent unemployment, population density, economic inequality, and percentage of people between ages 15-24 are obtained from both the 2003 and 2008

Turkey’s Statistical Yearbook. For all of the above mentioned variables, to prevent potential one- year fluctuation, two-year averages (average of 2003 and 2008 values) are calculated and used in the analyses. These averages represent structural conditions within provinces between 2000 and 2007.

Other Data Resources

Besides the Main Crime Database of Turkish National Police and Turkey’s

Statistical Yearbooks from Turkish Statistical Institute, additional data sources are also used for other variables. For example, statistics for percent Green Card holders are

117 derived from Ministry of National Health, and education data come from Ministry of

National Education’s yearly statistics. For those variables also, averages of 2003 and

2008 values are used in the analyses. More specific information regarding the actual measurement of each study variable is addressed below.

MEASURES OF VARIABLES

Dependent Variables

The dependent variables in this study are the province-level rates per 100,000 people of non-terrorist violent crimes and terrorism-related crime. The dependent variable of terrorism-related crime is operationalized by calculating each province’s terrorism-related crime rates obtained from the Turkish National Police Data. In addition, the same data source is used for measurement of homicide, rape, assault, robbery, and total violent crimes.

In Turkish National Police data, terrorism-related crimes are counted as follows.

If a person commits a terrorism-related crime, such as bombing, and kills 10 people, this act appears in the official records as 1 terrorism-related crime; it does not appear in the homicide crime data. Further, the total number of victims is recorded in a subset of information specific to terrorism, not homicide. In this way, the Turkish government tracks the exact number of victims who are killed in terrorist attacks as opposed to non- terrorist homicides. As a result, terrorism related and non-terrorism violent crime homicides are counted separately from each other, and there is no problem with “double- counting” in terms of measurement of the dependent variables.

118 To compute the above mentioned violent and terrorism-related crime rates for each province, first, for each year, the number of offenses reported for each of the observed crime categories is divided to that year’s province population and result is multiplied by 100,000 for 2006, 2007, and 2008 separately (creating annual rates per

100,000 persons):

Crime rate = # of offenses * 100,000

province’s total population

Then, the three years’ crime rates are summed and result is divided to 3 to find three years’ average crime rate:

Average crime rate = 2006 crime rate + 2007 crime rate + 2008 crime rate

KEY INDEPENDENT VARIABLES: STRUCTURAL DISADVANTAGE

Unemployment

There has been a substantial amount of research about the links between unemployment and crime rates. Unemployment is generally measured by using the unemployment rate, which is defined as the ratio of unemployed population in the labor force at least 16 years old to the total population at least 16 years old (in the labor force).

In current study, percent unemployment for each province is obtained for years

2000 and 20007 from Turkish Statistical Institute’s 2003 and 2008 Turkey’s Statistical

119 Yearbook. Unemployment percentage is computed by dividing the unemployed population in the labor force at least 16 years old by the total population at least 16 years old in the labor force. A two-year average of percent unemployment for 2000 and 2007 is computed for the analyses.

Residential Instability

Residential instability is usually measured by looking at the percentage of individuals, ages five and over, who have changed residences in the past five years

(Osgood and Chambers, 2000; Morenoff et al., 2001; and Ross et al., 2000). According to

Bursik and Grasmick (1993), using the percentage of individuals with ages five and over who have changed residences in the past five years to measure population instability may give deceptive information in some conditions. For instance, if massive population changes occurred in the neighborhoods, cities, or SMSAs, the remaining small part of the population who has resided in those areas more than five years would not be an efficient indicator for measuring residential instability.

In present study, Bursik and Grasmick’s (1993) warning is taken into account and residential instability is measured as follows. First, the number of residents moving in and moving out for each province in a year is summed separately for 2000 and 2007

(using the 2003 and 2008 versions of the Turkey’s Statistical Yearbook). Next, this amount is divided by the province’s population. To create a two-year average, 2000 and

2007 residential instability rates are summed and result is divided by two.

120 Family Disruption

As reviewed in Chapter Three, various studies have examined the relationship between family disruption and crime using different variables, such as percent divorced and percent female headed households. In general, study results have revealed significant positive association between family disruption variables and crime rates (e.g., Sampson and Wooldredge, 1987; Warner and Pierce, 1993; Miethe and Meier, 1994; Krivo and

Peterson, 1996; and MacDonald and Gover, 2005).

In current study, family disruption is measured by using information on the number of divorces and marriages for each province from the Turkish Statistical

Institute’s 2003 and 2008 Turkey’s Statistical Yearbook, based on 2000 and 2007 censuses respectively.

To compute”family disruption” for each province, first, the number of divorces in

2000/2007 is divided by the number of marriages in 2000/2007 separately. Then, a two- year average rate of family disruption is created by summing the ratios of divorces to marriages in 2000 and 2007 and dividing by two.

Poverty

Even though there is extensive theoretical support for the positive association between poverty, and crime, the empirical evidence has been inconsistent. While some studies do not report any relationship between violent crime rates and poverty, others indicate significant positive or negative association among poverty and crime rates (e.g.,

Blau and Blau, 1982; Messner, 1982; Simpson, 1985; Messner and Tardiff, 1986; Warner and Wilcox Rountree, 1997; and Lee and Bartkowski, 2004). Most of those previous

121 studies measured “poverty” as percentage of city population living below the US

Government’s established poverty level.

In Turkey, there is no information available for provinces regarding number or percentage of people living under the poverty line. Instead, percent Green Card holders is used as a measure of poverty. According to Green Card Law (1992), “green cards can only be given to Turkish citizens who are not covered by any social welfare institution and who do not have enough economic resources to cover their medical expenses” (Act

Number 3816). Parallel to the Medicaid system in the United States, the Green Card is a kind of health insurance for people who cannot afford their medical expenses.

Sullivan (1993) and Rauh et al. (2001) used number of Medicaid users as an indicator of poverty in the United States. Parallel to that idea, Koseli (2003) and Basibuyuk (2008) used percent of Green Card holders in each province to measure poverty in Turkey.

In this study, percent Green Card holders is used as a measure of poverty and information for Green Card holders is obtained from Ministry of Health for the years

2003 and 2008. Percentage of Green Card holders is computed by dividing number of people who have Green Cards by the province’s population. A two-year average poverty rate is computed by summing the two years’ poverty percentages and dividing the result by two.

Economic Inequality

Economic inequality is another significant structural covariate used in crime studies. Economic inequality is usually measured with the Gini index of income inequality. The Gini index of income inequality ranges between 0 and 1. The value of zero

122 represents perfect equality and the value of one represents the maximum level of inequality

(Gini, 1921; Druckman and Jackson, 2008).

In current study, income inequality is measured with the Gini coefficient for each province. Gini coefficient values are derived from Turkish Statistical Institute’s 2003 and

2008 Turkey’s Statistical Yearbook, representing 2000 and 2007 data, respectively. A two-year average of Gini coefficient values is used during analyses.

Low Education

As a sixth key indicator of structural disadvantage, a measure of low education level is used in the current examination. Low education level is measured by computing the percentage of a province’s population aged 25 and older who have less-than-high- school education. It is computed by dividing population aged 25 and older who did not graduate from high school by the total population aged 25 and older. Education data is derived from Ministry of National Education’s yearly statistics for 2003 and 2008. A two- year average is calculated for analyses.

CONTROL VARIABLES

Population Density

According to social disorganization theory, population density influences community’s capacity to build and maintain strong systems of social relationships. In addition, densely populated neighborhoods are assumed to have higher crime rates.

However, as reviewed in Chapter Three, different studies have used population density and found inconsistent evidence of a relationship between crime rates and population density. Nonetheless, it is controlled in this study. In current research, population density

123 data is obtained from Turkish Statistical Institute’s 2003 and 2008 Turkey’s Statistical

Yearbook, based on 2000 and 2007 censuses. Population density is population per one square kilometer. It is computed by dividing province population by surface area of the province. The average of province population density is computed by summing 2000 and

2007 population densities and then dividing the result by two.

Percentage of people between ages 15-24

According to Lee and Bartkowski (2004), including a variable about the proportion of the population in late adolescence/ young adulthood in an examination of crime would be valuable because this specific age group in the population is known as a crime-prone age group, and higher percentages of a population in this age group may be related to high crime rates. As reviewed in Chapter Three, different studies used different age groups for age structure index variable. For example, while some studies used the 15-

24 age range, others selected 20-34 or 15-29 as the age range of interest.

In present study, age structure is used as a control variable in the analyses. It is measured as ratio of the 15-24 years old population in the province to the total province population. Data is derived from Turkish Statistical Institute’s 2003 and 2008 Turkey’s

Statistical Yearbook, based on 2000 and 2007 censuses respectively. Like other independent and control variables, a two-year average of percentage of people between ages 15-24 is computed and is used in the analyses.

124 Region (Southeastern/ Eastern Dummy Variable)

Region is used as another control variable in present study. Like previous studies

(e.g., Koseli, 2003; Demirci and Suen, 2007; and Basibuyuk, 2008), a dummy variable is produced for region to examine whether location of the province has any effect on terrorism-related crime rates. Because of their historical tendency of experiencing higher terrorism-related crime rates, twenty three provinces which are located in South Eastern

Anatolia and Eastern Anatolia regions are coded as (1), and all other fifty eight provinces are coded as “0”. For non-terrorist violent crimes, region will not be controlled because as indicated in Chapter Four, different regions have high rates for different types of violent crimes.

ANALYTIC STRATEGY

This study is primarily based on the analysis of distribution of terrorism-related and non-terrorist violent crimes across 81 provinces in Turkey. Since all of the dependent variables are continuous, ordinary least squares (OLS) regression analysis will be applied by using SPSS Version 15.0 and STATA 9.1 software.

In the course of the study, first, descriptive statistics and zero-order correlations between terrorism-related crime rates, independent variables, and control variables will be examined. In addition, bivariate relationships between non-terrorist violent crime rates, independent variables, and control variables will be observed. Both of the correlation matrixes are used to examine significant bivariate relationships and potential multicollinearity problems between independent and control variables. According to

125 Blalock (1972) and Hanushek and Jackson (1977), if independent variables are highly intercorrelated in multivariate regression analyses, it may cause biased and unstable assessments. As a starting point, looking at bivariate correlations between independent variables may be helpful in this procedure. A zero-order correlation, (.70) or higher between two independent variables is one indicator of a multicollinerity problem

(Hanushek and Jackson, 1977). Indicators will be combined into indexes as necessary to address multicollinearity (Land et al., 1990).

After looking at bivariate correlations, different multivariate regression models are estimated by using rates of terrorism-related crime, total violent crime, homicide, assault, robbery, and rape rates as dependent variables to test enumerated research hypotheses. In multivariate OLS regression analyses, two different models are used for the multivariate regression analysis of each crime rate. In the first stage, only percent unemployment, family disruption, residential instability, percent Green Card holders, economic inequality, and low education variables are included in the analyses. Then, along with these key independent variables, population density, region (only for terrorism-related crimes), and percent people ages between 15-24 are also added to the models.

Given that utilized datasets include all of the essential information for dependent, independent, and control variables for all provinces across the country, there are no missing values in the analyses. In addition, because of using directional research hypotheses, one-tailed tests are employed in the study.

Throughout multivariate regression analyses, linearity, normality, homoscidasticity, and multicollinearity assumptions will be taken into consideration. For

126 instance, tolerance and variance inflation factor (VIF) values will be reported in the models to determine multicollinearity problem. Besides, other main ordinary least squares assumptions (normality, homoscidasticity, and linearity) are also examined during analyses of each dependent variable. If any of the assumptions are violated, necessary treatment methods such as transformation (taking natural log of skewed variable), elimination of cases from the analyses, or alternative modeling specifications

(i.e., WLS, Poisson regression, negative binomial regression) will be employed.

For instance, because the Poisson regression model copes with problems often inherent in count data, it might be useful for some models. According to Osgood (2000),

Poisson regression is the best method to examine counts of rare events such as violent offenses. Paternoster and Brame (1997) and Osgood (2000) have recommended using negative binomial model instead of Poisson regression, when “overdisperson” is a feature. On the other hand, when dependent variable is “overdispersed” and has “excess zeros”, negative binomial model would not give precise results. In that situation, Zero

Inflated Negative Binomial (ZINB) model is used because this model “allows for "excess zeros" in count models under the assumption that the population is characterized by two regimes, one where members always have zero counts, and one where members have zero or positive counts” (Greene, 1997: p.996).

DESCRIPTIVE STATISTICS

Province level descriptive statistics for dependent, independent, and control variables are presented in Table 5.1. According to the results shown in Table 5.1, the

127 average of total crime rates is 139.03 per 100,000 persons. It ranges from 44.93 to 277.46 with the standard deviation of 57.33825. Homicide crime rates per 100,000 persons ranges from 0.25 to 4.22. Mean value for homicide crime rates is 1.9065, and standard deviation value is 0.92134. In terms of aggravated assault rates, it has a mean value of

128.51, and ranges from 41.34 to 262.08 with the standard deviation of 53.45988. The mean value for rape rates is 1.3703, and its minimum and maximum values are 0.05 and

4.75 respectively. Robbery crime rates range from 0.39 to 18.06, with an average of

5.1955, and a standard deviation of 0.94986. On the other hand, terrorism-related crime rates range from 0 to 23.24 with the mean value of 2.799. The minimum value of 0 indicates that some provinces do not have terrorism-related crimes in the examined time period. The standard deviation value is 5.247 for terrorism-related crime rates.

Descriptive statistics for six independent variables are also presented in Table 5.1.

According to the results, unemployment has a mean value of 14.91, and standard deviation value is 2.995. The minimum and maximum values for unemployment are 9.1 and 21.5 respectively. Residential instability ranges from 9.32 to 33.02, with an average of 15.762, and a standard deviation of 3.894. Family disruption variable’s mean value is

11.4185, and standard deviation value is 5.68786. Family disruption values range between 1.32 and 27.06. Poverty variable ranges from 1.43 to 30.73, with a mean value of 20.0693, standard deviation value is 6.1811. The average and standard deviation values for inequality are 37.66 and 2.984 respectively, and inequality variable ranges between 30 and 44. Final independent variable is low education, and its mean value is

39.527, with a standard deviation of 9.2771. Low education ranges from 17.522 to 63.21.

128 Table 5.1. Descriptive Statistics

Descriptive Statistics (N=81)

Variable Mean Standard Min Max Skewness Kurtosis Deviation Dependent Variables

Total violent crime 139.03 57.338 44.93 277.46 0.454 -0.664 rates Homicide rates 1.906 0.92134 0.25 4.22 0.510 -0.442 Aggravated assault 128.51 53.459 41.34 262.08 0.519 -0.487 rates Rape rates 1.37 0.949 0.05 4.75 1.553 2.859 Robbery rates 5.195 3.765 0.39 18.06 1.247 1.391 Terrorism-related 2.799 5.247 0 23.24 2.697 6.829 crime rates

Independent Variables

Unemployment 14.91 2.9949 9.1 21.5 0.455 -0.82 Residential instability 15.762 3.894 9.33 33.02 1.426 4.191 Family disruption 11.418 5.687 1.32 27.06 0.375 0.32 Poverty 20.069 6.181 1.43 30.73 -0.892 0.622 Inequality 37.66 2.984 30 44 -0.176 0.08 Low education 39.527 9.277 17.52 63.21 0.11 0.081

Control Variables

Region 0.2716 0.447 0 1 1.046 -0.928 Percent young 17.99 3.4777 8.31 27.51 0.168 0.484 Population density 87.024 80.93 11 470 2.588 8.076

Descriptive statistics of three control variables are also presented in Table 5.1.

Region is measured by dummy variable. Its mean and standard deviation values are

0.2716 and 0.44756 respectively. Percent young ranges between 8.314 and 27.51, with a mean value of 1.799, and a standard deviation of 0.034777. Last control variable

129 population density’s average is 87.024 with a standard deviation of 80.93. Population density ranges from 11 to 470.

CONCLUSION

In the current research, data from Turkish National Police is used to compute non- terrorist and terrorist violent crime rates. This analysis will provide an unprecedented province-level analysis of terrorist and non-terrorist violence in Turkey, with an emphasis on the effects of structural disadvantage. Table 5.2 presents an overview of all hypotheses and relevant data sources described throughout this chapter. First, total violent crime hypotheses, then homicide, aggravated assault, rape, robbery, and terrorism-related crime hypotheses are presented in the Table 5.2.

130 Table 5.2. Hypotheses, Variables, and Data Sources.

Hypotheses Variables Source of Data

H1a: Provinces that experience greater Dependent variable: total violent Dependent variable: Turkish percentage of unemployed residents will crime rates National Police (2006, 2007, 2008) experience higher rates of total violent crime Independent variable: unemployment Independent variable: Turkish than provinces where the percentage of rate Statistical Institute (2003, 2008) unemployed is lower.

H1b: Provinces that experience greater Dependent variable: total violent Dependent variable: Turkish residential instability will experience higher crime rates National Police (2006, 2007, 2008) rates of total violent crime than provinces Independent variable: residential Independent variable: Turkish where the residential instability is lower. instability Statistical Institute (2003, 2008)

H1c: Provinces that experience high levels of Dependent variable: total violent Dependent variable: Turkish family disruption will have higher rates of crime rates National Police (2006, 2007, 2008) total violent crime than provinces that have Independent variable: percent Independent variable: Turkish low levels of family disruption. divorced Statistical Institute (2003, 2008)

H1d: Provinces with higher poverty will have Dependent variable: total violent Dependent variable: Turkish higher rates of total violent crime than crime rates National Police (2006, 2007, 2008) provinces that have lower poverty rates. Independent variable: percent Green Independent variable: Ministry of Card holders Health (2003, 2008) H1e: Provinces with higher economic Dependent variable: total violent Dependent variable: Turkish inequality will have higher rates of total crime rates National Police (2006, 2007, 2008) violent crime than provinces that have lower Independent variable: Gini coefficient Independent variable: Turkish economic inequality rates. Statistical Institute (2003, 2008)

131 Hypotheses Variables Source of Data

H1f: Provinces that have higher percentages Dependent variable: total violent Dependent variable: Turkish of persons with less than high-school crime rates National Police (2006, 2007, 2008) education will have higher total violent crime Independent variable: percent people Independent variable: Ministry of than provinces that have lower percentages less than high school education Education (2003, 2008) of persons with less than high-school education.

H1g: Provinces that experience greater Dependent variable: homicide rates Dependent variable: Turkish percentage of unemployed residents will Independent variable: unemployment National Police (2006, 2007, 2008) experience higher rates of homicide crime rate Independent variable: Turkish than provinces where the percentage of Statistical Institute (2003, 2008) unemployed is lower.

H1h: Provinces that experience greater Dependent variable: homicide rates Dependent variable: Turkish residential instability will experience higher Independent variable: residential National Police (2006, 2007, 2008) rates of homicide crime than provinces where instability Independent variable: Turkish the residential instability is lower. Statistical Institute (2003, 2008)

H1i: Provinces that experience high levels of Dependent variable: homicide rates Dependent variable: Turkish family disruption will have higher rates of Independent variable: percent National Police (2006, 2007, 2008) homicide crime than provinces that have low divorced Independent variable: Turkish levels of family disruption. Statistical Institute (2003, 2008)

H1j: Provinces with higher poverty will have Dependent variable: homicide rates Dependent variable: Turkish higher rates of homicide crime than Independent variable: percent Green National Police (2006, 2007, 2008) provinces that have lower poverty rates. Card holders Independent variable: Ministry of Health (2003, 2008)

132 Hypotheses Variables Source of Data

H1k: Provinces with higher economic Dependent variable: homicide rates Dependent variable: Turkish inequality will have higher rates of homicide Independent variable: Gini Coefficient National Police (2006, 2007, 2008) crime than provinces that have lower Independent variable: Turkish economic inequality rates. Statistical Institute (2003, 2008) H1l: Provinces that have higher percentages Dependent variable: homicide rates Dependent variable: Turkish of persons with less than high-school Independent variable: percent people National Police (2006, 2007, 2008) education will have higher homicide crime less than high school education Independent variable: Ministry of than provinces that have lower percentages Education (2003, 2008) of persons with less than high-school education.

H1m: Provinces that experience greater Dependent variable: aggravated Dependent variable: Turkish percentage of unemployed residents will assault rates National Police (2006, 2007, 2008) experience higher rates of aggravated assault Independent variable: unemployment Independent variable: Turkish crime than provinces where the percentage of rate Statistical Institute (2003, 2008) unemployed is lower.

H1n: Provinces that experience greater Dependent variable: aggravated Dependent variable: Turkish residential instability will experience higher assault rates National Police (2006, 2007, 2008) rates of aggravated assault crime than Independent variable: residential Independent variable: Turkish provinces where the residential instability is instability Statistical Institute (2003, 2008) lower.

H1o: Provinces that experience high levels of Dependent variable: aggravated Dependent variable: Turkish family disruption will have higher rates of assault rates National Police (2006, 2007, 2008) aggravated assault crime than provinces that Independent variable: percent Independent variable: Turkish have low levels of family disruption. divorced Statistical Institute (2003, 2008)

133 Hypotheses Variables Source of Data

H1p: Provinces with higher poverty will have Dependent variable: aggravated Dependent variable: Turkish higher rates of aggravated assault crime than assault rates National Police (2006, 2007, 2008) provinces that have lower poverty rates. Independent variable: percent Green Independent variable: Ministry of Card holders Health (2003, 2008)

H1r: Provinces with higher economic Dependent variable: aggravated Dependent variable: Turkish inequality will have higher rates of assault rates National Police (2006, 2007, 2008) aggravated assault crime than provinces that Independent variable: Gini coefficient Independent variable: Turkish have lower economic inequality rates. Statistical Institute (2003, 2008)

H1s: Provinces that have higher percentages Dependent variable: aggravated Dependent variable: Turkish of persons with less than high-school assault rates National Police (2006, 2007, 2008) education will have higher aggravated assault Independent variable: percent people Independent variable: Ministry of crime than provinces that have lower less than high school education Education (2003, 2008) percentages of persons with less than high- school education.

H1t: Provinces that experience greater Dependent variable: rape rates Dependent variable: Turkish percentage of unemployed residents will Independent variable: unemployment National Police (2006, 2007, 2008) experience higher rates of rape crime than rate Independent variable: Turkish provinces where the percentage of Statistical Institute (2003, 2008) unemployed is lower.

H1u: Provinces that experience greater Dependent variable: rape rates Dependent variable: Turkish residential instability will experience higher Independent variable: residential National Police (2006, 2007, 2008) rates of rape crime than provinces where the instability Independent variable: Turkish residential instability is lower. Statistical Institute (2003, 2008)

134 Hypotheses Variables Source of Data

H1v: Provinces that experience high levels of Dependent variable: rape rates Dependent variable: Turkish family disruption will have higher rates of Independent variable: percent National Police (2006, 2007, 2008) rape crime than provinces that have low divorced Independent variable: Turkish levels of family disruption. Statistical Institute (2003, 2008)

H1w: Provinces with higher poverty will Dependent variable: rape rates Dependent variable: Turkish have higher rates of rape crime than Independent variable: percent Green National Police (2006, 2007, 2008) provinces that have lower poverty rates. Card holders Independent variable: Ministry of Health (2003, 2008) H1x: Provinces with higher economic Dependent variable: rape rates Dependent variable: Turkish inequality will have higher rates of rape Independent variable: Gini coefficient National Police (2006, 2007, 2008) crime than provinces that have lower Independent variable: Turkish economic inequality rates. Statistical Institute (2003, 2008)

H1y: Provinces that have higher percentages Dependent variable: rape rates Dependent variable: Turkish of persons with less than high-school Independent variable: percent people National Police (2006, 2007, 2008) education will have higher rape crime than less than high school education Independent variable: Ministry of provinces that have lower percentages of Education (2003, 2008) persons with less than high-school education.

H1z: Provinces that experience greater Dependent variable: robbery rates Dependent variable: Turkish percentage of unemployed residents will Independent variable: unemployment National Police (2006, 2007, 2008) experience higher rates of robbery crime than rate Independent variable: Turkish provinces where the percentage of Statistical Institute (2003, 2008) unemployed is lower.

135 Hypotheses Variables Source of Data

H1aa: Provinces that experience greater Dependent variable: robbery rates Dependent variable: Turkish residential instability will experience higher Independent variable: residential National Police (2006, 2007, 2008) rates of robbery crime than provinces where instability Independent variable: Turkish the residential instability is lower. Statistical Institute (2003, 2008)

H1ab: Provinces that experience high levels Dependent variable: robbery rates Dependent variable: Turkish of family disruption will have higher rates of Independent variable: percent National Police (2006, 2007, 2008) robbery crime than provinces that have low divorced Independent variable: Turkish levels of family disruption. Statistical Institute (2003, 2008)

H1ac: Provinces with higher poverty will Dependent variable: robbery rates Dependent variable: Turkish have higher rates of robbery crime than Independent variable: percent Green National Police (2006, 2007, 2008) provinces that have lower poverty rates. Card holders Independent variable: Ministry of Health (2003, 2008) H1ad: Provinces with higher economic Dependent variable: robbery rates Dependent variable: Turkish inequality will have higher rates of robbery Independent variable: Gini coefficient National Police (2006, 2007, 2008) crime than provinces that have lower Independent variable: Turkish economic inequality rates. Statistical Institute (2003, 2008)

H1ae: Provinces that have higher percentages Dependent variable: robbery rates Dependent variable: Turkish of persons with less than high-school Independent variable: percent people National Police (2006, 2007, 2008) education will have higher robbery crime less than high school education Independent variable: Ministry of than provinces that have lower percentages Education (2003, 2008) of persons with less than high-school education.

136 Hypotheses Variables Source of Data

H2a: Provinces that experience greater Dependent variable: terrorism-related Dependent variable: Turkish percentage of unemployed residents will crime rates National Police (2006, 2007, 2008) experience higher rates of terrorism-related Independent variable: unemployment Independent variable: Turkish crime than provinces where the percentage of rate Statistical Institute (2003, 2008) unemployed is lower.

H2b: Provinces that experience greater Dependent variable: terrorism-related Dependent variable: Turkish residential instability will experience higher crime rates National Police (2006, 2007, 2008) rates of terrorism-related crime than Independent variable: residential Independent variable: Turkish provinces where the residential instability is instability Statistical Institute (2003, 2008) lower.

H2c: Provinces that experience high levels of Dependent variable: terrorism-related Dependent variable: Turkish family disruption will have higher rates of crime rates National Police (2006, 2007, 2008) terrorism-related crime than provinces that Independent variable: percent Independent variable: Turkish have low levels of family disruption. divorced Statistical Institute (2003, 2008)

H2d: Provinces with higher poverty will have Dependent variable: terrorism-related Dependent variable: Turkish higher rates of terrorism-related crime than crime rates National Police (2006, 2007, 2008) provinces that have lower poverty rates. Independent variable: percent Green Independent variable: Ministry of Card holders Health (2003, 2008)

H2e: Provinces with higher economic Dependent variable: terrorism-related Dependent variable: Turkish inequality will have higher rates of terrorism- crime rates National Police (2006, 2007, 2008) related crime than provinces that have lower Independent variable: Gini coefficient Independent variable: Turkish economic inequality rates. Statistical Institute (2003, 2008)

137 Hypotheses Variables Source of Data

H2f: Provinces that have higher percentages Dependent variable: terrorism-related Dependent variable: Turkish of persons with less than high-school crime rates National Police (2006, 2007, 2008) education will have higher terrorism-related Independent variable: percent people Independent variable: Ministry of crime than provinces that have lower less than high school education Education (2003, 2008) percentages of persons with less than high- school education.

138 CHAPTER SIX: ANALYSES AND RESULTS

In this chapter, first, bivariate correlations between dependent, key independent, and control variables will be examined. Then, six different multivariate analyses for total violent crime, homicide, aggravated assault, rape, robbery, and terrorism-related crimes will be presented. For OLS analyses, two different models for each crime will be created to test the research hypotheses. For analyses based on Poisson-type regression (including negative binomial regression and zero-inflated negative binomial regression), one model will be estimated for each crime.2

Bivariate Analysis

Bivariate correlation results for non-terrorist and terrorism-related violent crimes are presented in Table 6.1 and Table 6.2 separately. According to the bivariate a number of relationships represented in the research hypotheses are supported, at least at the bivariate level (H1a, H1c, H1f, H1g, H1k, H1l, H1m, H1o, H1s, H1t, H1v, H1x, H1y, H1z, H1ab, H1ad, and H1ae). A discussion of specific significant bivariate relationships follows below.

Consistent with prior research and expectations, unemployment was significantly and positively related to total violent crime (.355, p<.01), homicide (.466, p<.01), aggravated assault (.324, p<.01), rape (.511, p<.01), and robbery (.405, p<.01) in bivariate correlations. In addition, except for homicide crime, parallel with prior

2 In OLS models, only key independent variables measuring structural disadvantage are included in an initial model, with controls for age structure and population size/density added in a final model. However, this sort of modeling strategy does not really make sense in Poisson-type regression, as specifying logged population size as either an offset or a control variable is necessary to get meaningful results. As such, models estimating crime counts without controls for population size are not reported here.

139 examinations, family disruption exhibited statistically significant and positive association with the other four non-terrorist violent crimes.

The bivariate relationship between economic inequality and non-terrorism violent crime was only significant and positive for homicide (.259, p<.01), rape (.245, p<.05), and robbery crimes (.238, p<.05) in the bivariate analysis. Consistent with previous research findings, low education was significantly and positively related to total violent crime (.246, p<.05), homicide (.630, p<.01), aggravated assault (.198, p<.05), rape (.230, p<.05), and robbery (.537, p<.01) in bivariate correlations. Namely, provinces that have higher percentages of persons with less than high-school education had higher non- terrorism violent crimes than provinces that have lower percentages of persons with less than high-school education.

Contrary to the expectations and previous research, the relationship between poverty and non-terrorism violent crime was actually in the opposite direction to what was predicted in research hypotheses. In bivariate analyses, poverty was statistically significantly and negatively related to total violent (-.452, p<.01), homicide (-.421, p<.01), aggravated assault (-.426, p<.01), rape (-.420, p<.01), and robbery crimes (-.465, p<.01).

On the other hand, bivariate correlation results for terrorism-related crime rates

(Table 6.2) and independent variables showed significant positive correlation between terrorism-related crime rates and unemployment (.255, p<.05), residential instability

(.334, p<.01), and poverty (.308, p<.01).

An additional feature of bivariate correlation is the ability to identify high intercorrelations among independent variables. Multicollinearity is a condition in which

140 two or more independent variables are highly associated. According to Agresti and Finlay

(1999), a high correlation between independent variables can cause inflated or large standard errors for regression coefficients and create unstable parameter estimates. One of the basic methods of identifying multicollinearity is to look at the correlation coefficients between independent variables. A correlation coefficient higher than .70 is commonly accepted as a sign of a multicollinearity problem in the model (Gujarati,

2003). None of the bivariate correlation coefficients among independent variables presented in Table 6.1 and Table 6.2 are higher than 0.56.

Although not the focus of this research, correlations among different non-terrorist violent crimes are also displayed in Table 6.1. It is clear from these correlations that total reported violent crime in Turkey is almost completely driven by aggravated assault specifically, with the correlation between those two rates above .9. Although not reported in either Table 6.1 or 6.2, it should be noted that the correlation between non- terrorist violent crimes and terrorist-related violent crimes were all negative, ranging from -.138 to -.314.

141 Table 6.1. Bivariate correlations between non-terrorist violent crimes and independent/control variables

Correlations

1 2 3 4 5 6 7 8 9 10 11 12 13 1. Total

crime rates 2. Homicide .458(**) crime rates 3.Aggravated assault crime .995(**) .395(**) rates 4. Rape .613(**) .548(**) .568(**) crime rates 5. Robbery .611(**) .727(**) .533(**) .646(**) crime rates 6.Unemploy .355(**) .466(**) .324(**) .511(**) .405(**) 7.Residential .060 -.138 .077 .057 -.116 -.095 instability 8. Family .484(**) .172 .489(**) .390(**) .235(*) -.059 .057 disruption 9. Poverty -.452(**) -.421(**) -.426(**) -.420(**) -.465(**) -.056 -.156 -.381(**) 10. Inequality .104 .259(**) .080 .245(*) .238(*) .126 -.209(*) -.110 -.088 11.Less than .246(*) .630(**) .198(*) .230(*) .537(**) .354(**) -.295(**) -.194(*) -.338(**) .232(*) high school 12. Percent -.032 .263(**) -.068 .111 .275(**) .481(**) -.023 -.400(**) .179 .122 .328(**) age 15-24 13. Population .191(*) .624(**) .140 .228(*) .532(**) .128 -.090 .083 -.498(**) .224(*) .501(**) .263(**) density

** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

142 Table 3.2. Bivariate correlations between terrorism-related crimes and independent/control variables

Correlations

Terrorism- related crime Unemploy Residential Family Less than percent age Population rates ment instability disruption Poverty Inequality high school Region 15-24 density Terrorism-related crime rates

Unemployment .255(*)

Residential instability .334(**) -.095

Family disruption -.363(**) -.059 .057

Poverty .308(**) -.056 -.156 -.381(**)

Inequality -.165 .126 -.209(*) -.110 -.088

Less than high school -.104 .354(**) -.295(**) -.194(*) -.338(**) .232(*)

Region .563(**) .285(**) .080 -.557(**) .554(**) -.234(*) -.110

Percent age 15-24 .492(**) .481(**) -.023 -.400(**) .179 .122 .328(**) .549(**)

Population density -.159 .128 -.090 .083 -.498(**) .224(*) .501(**) -.200(*) .263(**)

* Correlation is significant at the 0.05 level (2-tailed).

** Correlation is significant at the 0.01 level (2-tailed).

143 MULTIVARIATE ORDINARY LEAST SQUARES (OLS), NEGATIVE BINOMIAL, AND ZERO-INFLATED NEGATIVE BINOMIAL (ZINB) REGRESSION ANALYSES

According to Thompson (2006), “(m)ultiple regression analysis is a statistical technique that can be used to investigate relationships between a single outcome variable and two or more predictor variables” (p.216). The generic multiple regression equation for predicting values of a dependent variable in a population is:

Y= α + β1X1 + β2X2 + … + βkXk

For the current study, the specific equation of interest is:

Y = α + B unemployment X unemployment + B residential instability X residential instability + B family disruption X family disruption + B poverty X poverty + B economic inequality X economic inequality + B low education

X low education + B age-structure X age-structure + B population density X population density

This general model is most appropriate for ordinary least-squares (OLS) specification. However, as noted in Chapter Five, some of the dependent variables examined here are not necessarily well-suited for OLS regression. Negative binomial regression is the most appropriate to use with a count dependent variable that is rare and/or skewed; it is viewed as better than Ordinary Least Squares regression in such instances for numerous reasons. First, Ordinary Least Squares assumes a normal distribution but because there are few homicide, rape, and robbery crimes in most provinces in Turkey, skewed distributions for those dependent variables are apparent.

Also, if base populations are small, even small changes in counts of rare events can cause large fluctuations in rates. Using negative binomial regression instead is thought to produce more accurate results that those from studies that use OLS to analyze rates of

144 rare events across aggregate units (Osgood, 2000). In addition, when the dependent variable contains many zero values, Ordinary Least Squares regression results produce biased and inconsistent estimates (Long, 1997). In that situation, the zero-inflated negative binomial (ZINB) model is appropriate.

In the current study, OLS is used for analysis of total violent crime and aggravated assault crimes. Negative binomial regression is used for analysis of homicide, rape, and robbery crimes, and zero-inflated negative binomial (ZINB) regression is use for analysis of terrorism-related crimes. As justification for this overall modeling strategy, Table 6.3 shows the skewness of all dependent variables and the smoothing that occurs after log-transformation of the skewed dependent variables. Since negative binomial regression and ZINB regression control for the logged population in each aggregate unit, they result in the sort of smoothing that is seen upon logging a dependent variable (Osgood, 2000).

Table 6.3. Transformation results for skewed dependent variables Variable Mean Standard Min Max Skewness Kurtosis Deviation Dependent Variables Homicide rates 1.906 0.921 0.25 4.22 .510 -0.442 Homicide rates (LOG) 0.222 0.239 -0.6 0.63 -0.757 0.866

Rape rates 1.370 0.949 0.05 4.75 1.553 2.859 Rape rates (LOG) 0.027 0.344 -1.27 0.68 -1.095 2.556

Robbery rates 5.195 3.765 0.39 18.06 1.247 1.391 Robbery rates (LOG) 0.600 0.334 -0.41 1.26 -0.349 -0.022

Terrorism-related 2.799 5.247 0 23.24 2.697 6.829 crime rates Terrorism-related 0.094 0.551 -1.08 1.37 0.474 0.07 crime rates (LOG)

145 Multivariate Regression Analysis for Total Violent Crime Rates

In OLS models for total violent crime, a three-year average (2006, 2007, and

2008) homicide rate is used as the dependent variable in the analyses. Table 6.4 shows the OLS regression results for total violent crime rates. Model 1 estimates a baseline equation, comprised of only six key independent variables (unemployment, residential instability, family disruption, poverty, economic inequality, and low education), whereas

Model 2 incorporates two additional control variables (age structure and population density). A summary of hypotheses testing for total violent crimes is demonstrated in

Table 6.5.

Model 1 explains 47.6 % of the variance in total violent crime rates. The F value is 11.212 and significant at p<0.01 level in the first model. Multivariate regression results for Model 1 demonstrate consistent findings for the relationship between total violent crime rates and unemployment and family disruption. Two of the research hypotheses

(H1a and H1c) are thus supported in Model 1. Firstly, unemployment has a positive significant effect on total violent crime rates (B = .590, p<0.01). Controlling for other structural disadvantage variables, total violent crime rates are higher in provinces where unemployment is higher. Furthermore, family disruption is also significantly and positively associated with total violent crime rates (B= .481, p<0.01). That is, provinces that experience higher levels of family disruption will have higher rates of total violent crime than provinces that have lower levels of family disruption, while holding the other variables in the model constant. The strongest predictor of total violent crime rates in

Model 1 is family disruption because it has the largest standardized coefficient value (β=

.478).

146 On the other hand, no statistically significant associations are found between total violent crime rates and residential instability, poverty, economic inequality, and low education in Model 1. The hypotheses H1b, H1d, H1e, and H1f are thus not supported in first multivariate regression model. In second model, beside unemployment and family disruption, low education also has significant relationship with total violent crime rates.

Table 6.4. OLS Regression Models for Total Crime Rates (N=81)

Variables Model 1 Model 2

B SE β VIF B SE β VIF

Independent Variables Unemployment .590** .173 .308 1.158 .585* .198 .305 1.499

Residential .160 .139 .109 1.267 .157 .143 .107 1.311 instability Family disruption .481** .101 .478 1.417 .484** .106 .480 1.554

Poverty -.154 .101 -.166 1.683 -.192 .114 -.207 2.124

Inequality .159 .170 .083 1.104 .184 .172 .096 1.124

Low education .115 .069 .186 1.661 .142* .074 .230 2.014

Control variables

Percent youth -.006 .196 -.004 1.971

Population density -.008 .008 -.117 1.887

F 11.2** 9.46**

R² .476 .485

* p< 0.05, ** p< 0.01

147

Multicollinearity is a possible dilemma for multivariate regression analyses when two or more independent variables in the same model are highly associated with each other. If there is a high covariation between two independent variables, then it could be hard to find out the precise proportions of explained variation in the dependent variable that should be attributed to each independent variable discretely. While the bivariate correlations (discussed above) showed that multicollinearity was unlikely, further evidence of this is shown with presentation of variance inflation factors in Table 6.4.

In the case of multicollinearity, Variance Inflation Factor (VIF) values will be above 4.0 (Allison, 1999). In Model 1, multicollinearity does not seem to be a problem because Variance Inflation Factor (VIF) values range from 1.104 to 1.683, which are considerably below the cut-off value of 4.0 (Allison, 1999).

Besides the six key independent variables, Model 2 also includes two control variables (percent young and population density) and examines the effects of those variables on the total violent crime rates. Three research hypotheses (H1a, H1c, and H1f) are supported in Model 2. Different from Model 1, in Model 2 a significant positive correlation between low education and total violent crime rate is observed.

According to results from Model 2, provinces that experience greater percentage of unemployed residents experience higher rates of total violent crime than provinces where the percentage of unemployed is lower, while holding the other variables in the model constant. (B= .585, p<0.05). Additionally, in Model 2, a significant correlation is found between total violent crime rates and family disruption. That is, controlling for other variables, provinces with higher rates of family disruption will experience higher

148 total violent crime rates than provinces that have low levels of family disruption (B=

.484, p<0.01). Lastly, provinces that have higher percentages of persons with less than high-school education experience higher total violent crime than provinces that have lower percentages of persons with less than high-school education (B= .142, p<0.05).

Parallel with Model 1, the strongest predictor of the total violent crime rates is family disruption variable (β= .480). In contrast, no significant correlations are found between total violent crime rates and residential instability, poverty, economic inequality, percent young, and population density.

Model 2 explains 48.5 % of the variance in total violent crime rates. Also, multicollinearity does not appear to be a problem for second model because all the VIF scores range between 1.124 and 2.124, which are below the cut-off value 4.0. In Model 2, the F value is 9.467 and statistically significant at p<0.01 level.

Table 6.5. Summary of hypotheses testing for total violent crimes Hypotheses Model 1 Model 2

H1a: Provinces that experience greater percentage Supported Supported of unemployed residents will experience higher rates of total violent crime than provinces where the percentage of unemployed is lower.

H1b: Provinces that experience greater residential Not supported Not supported instability will experience higher rates of total violent crime than provinces where the residential instability is lower.

H1c: Provinces that experience high levels of Supported Supported family disruption will have higher rates of total violent crime than provinces that have low levels of family disruption.

H1d: Provinces with higher poverty will have Not supported Not supported higher rates of total violent crime than provinces that have lower poverty rates.

149 Hypotheses Model 1 Model 2

H1e: Provinces with higher economic inequality Not supported Not supported will have higher rates of total violent crime than provinces that have lower economic inequality rates.

H1f: Provinces that have higher percentages of Not supported Supported persons with less than high-school education will have higher total violent crime than provinces that have lower percentages of persons with less than high-school education.

Negative Binomial Regression Analysis for Homicide

In a negative binomial analysis of homicide crime, a three-year average (2006,

2007, and 2008) number of homicide incidents is used as the dependent variable. Table

6.6 shows the negative binomial regression results for homicide. Summary of hypotheses testing for homicide is demonstrated in Table 6.7.

Results for the homicide analysis show statistically significant positive correlations between homicide crime and unemployment, family disruption, and low

3 education. Three research hypotheses are thus supported in this model (H1g, H1i, and H1l) .

Holding the other variables constant in the model, provinces with greater percent of unemployment experience higher rates of homicide crime than provinces where the percent of unemployment is lower (coefficient = .033, p<0.01). Additionally, controlling for other variables, provinces with higher rates of family disruption will experience higher homicide than provinces that have low levels of family disruption (coefficient =

3 I also ran a model for homicide crime with using regular Poisson regression given the non-significant over-dispersion and did not find any substantive differences between negative binomial and Poisson regression. Like negative binomial model, in Poisson model, unemployment, family disruption, and low education had statistically significant positive correlations with homicide crime.

150 .026, p<0.01). Lastly, low education level significantly positively influences homicide crime (coefficient = .022, p<0.01), while holding the other variables in the model constant. As a control variable, population size has statistically significant positive relationship with homicide crime (coefficient = 2.482, p<0.01).

Table 6.6.Negative Binomial Regression Model for Homicide (N=81)

Variables

Coefficient SE

Independent Variables

Unemployment .033** .009

Residential instability .005 .012

Family disruption .026** .008

Poverty -.004 .008

Inequality .003 .011

Low education .022** .005

Percent youth .020 .014

Population size 2.482** .130

Dispersion parameter 2.86e-10

Intercept -14.094

Log likelihood χ ² 252.24

Prob > chi2 .000

* p< 0.05, ** p< 0.01

151 On the other hand, residential instability, poverty, and economic inequality variables fail to display any statistically significant net effect on homicide crime. Three research hypotheses (H1h, H1j, and H1k) are thus not supported in negative binomial model for homicide crimes.

The alpha value shown in Table 6.6 is the estimate of the dispersion parameter for the negative binomial regression model. If the dispersion parameter alpha value equals zero, a Poisson model should be employed instead of negative binomial. Conversely, if alpha value is significantly greater than zero, it is an indicator of overdispersion, and the relationship between dependent and independent variables are estimated better with negative binomial regression model than Poisson regression model (Long, 1997; and

Cameron and Trivedi, 1998). In Table 6.6, the dispersion parameter value is not significantly greater than zero. Thus, a Poisson model was also estimated. As stated in footnote 3 of this chapter, I did not find any substantive differences between negative binomial and Poisson regression results for homicide.

Log likelihood χ ² in Table 6.6 is the test statistic that is calculated as negative two times the difference of the likelihood for the null model and the fitted model.

Additionally, Prob > chi2 value is the likelihood of getting a log likelihood χ ² test statistic under the null hypothesis, which assumes that all of the regression coefficients are simultaneously equal to zero. It is, therefore, the odds of getting a log likelihood χ ² test statistic, if there is in fact no effect of the independent variables. The small p-value from the Log likelihood χ ² test shown in Table 6.6 (<0.000) would lead to the conclusion that at least one of the regression coefficients in the negative binomial model for homicide crime is not equal to zero (UCLA Academic Technology Services).

152 Table 6.7. Summary of hypotheses testing for homicide

Hypotheses Result

H1g: Provinces that experience greater percentage of Supported unemployed residents will experience higher rates of homicide crime than provinces where the percentage of unemployed is lower.

H1h: Provinces that experience greater residential Not supported instability will experience higher rates of homicide crime than provinces where the residential instability is lower.

H1i: Provinces that experience high levels of family Supported disruption will have higher rates of homicide crime than provinces that have low levels of family disruption.

H1j: Provinces with higher poverty will have higher Not supported rates of homicide crime than provinces that have lower poverty rates.

H1k: Provinces with higher economic inequality will Not supported have higher rates of homicide crime than provinces that have lower economic inequality rates.

H1l: Provinces that have higher percentages of persons Supported with less than high-school education will have higher homicide crime than provinces that have lower percentages of persons with less than high-school education.

Multivariate OLS Regression Analysis for Aggravated Assault Rates

Table 6.8 shows the OLS regression results for aggravated assault rates. Model 1 estimates a baseline equation including only six key structural disadvantage independent variables (unemployment, residential instability, family disruption, poverty, economic inequality, and low education), while Model 2 incorporates age-structure and population

153 density as well. A summary of hypotheses testing for aggravated assault crimes is demonstrated in Table 6.9.

In Model 1, 43.8 % of the variance in aggravated assault is explained by the independent variables. The F value is 9.63 and significant at p<0.01 level. Multivariate regression results for Model 1 show statistically significant positive correlations between aggravated assault rates and unemployment and family disruption. In Model 1, two of the research hypotheses (H1m and H1o) are thus supported. First, while holding the other variables in the model constant, unemployment has a positive significant effect on aggravated assault rates (B = .519, p<0.01) (H1m). In other words, controlling for other variables, aggravated assault rates are higher in provinces where unemployment is high.

In addition, family disruption has significant positive impact on aggravated assault rates

(B= .452, p<0.01) (H1o). Namely, provinces that experience high levels of percent divorced will have higher rates of aggravated assault than provinces that have lower levels of percent divorced, while holding the other variables in the model constant. The strongest predictor of aggravated assault in Model 1 is family disruption; it has the largest standardized coefficient value (β= .481).

On the other hand, no statistically significant associations are found between aggravated assault crime rates and residential instability, poverty, economic inequality, and low education in Model 1. The hypotheses H1n, H1p, H1r, and H1s are thus not supported in Model 1.

154

Table 6.8. OLS Regression Models for Aggravated Assault Rates (N=81)

Variables Model 1 Model 2

B SE β VIF B SE β VIF

Independent Variables Unemployment .519** .167 .291 1.158 .525** .190 .294 1.499

Residential .157 .135 .114 1.267 .157 .137 .114 1.311 instability Family disruption .452** .097 .481 1.417 .451** .102 .480 1.554

Poverty -.130 .098 -.150 1.683 -.172 .110 -.198 2.124

Inequality .128 .164 .071 1.104 .158 .165 .088 1.124

Low education .089 .067 .155 1.772 .124* .071 .215 2.014

Control variables

Percent youth -.033 .188 -.022 1.971

Population density -.010 .008 -.148 1.887

F 9.63** 7.47**

R² .438 .454

* p< 0.05, ** p< 0.01

Beside the six key independent variables, Model 2 also includes two control variables (percent young and population density) and looks at the effects of those variables on the aggravated assault crime rates. Three research hypotheses (H1m, H1o, and

H1s) are supported in Model 2. Different from Model 1, a significant positive correlation

155 between low education and aggravated assault crime rate is observed in Model 2 (B=

.124, p<0.05).

Provinces that experience greater percentages of unemployed residents experience higher rates of aggravated assault than provinces where the percentage of unemployed is lower, while holding the other variables in the model constant. (B= .585, p<0.05).

Additionally, in Model 2, significant correlation is found between assault and family disruption. That is, controlling for other variables, provinces with higher rates of family disruption experience higher aggravated assault than provinces that have low levels of family disruption (B= .484, p<0.01). Lastly, provinces that have higher percentages of persons with less than high-school education experience higher aggravated assault crime than provinces that have lower percentages of persons with less than high-school education (B= .124, p<0.05). Parallel with Model 1, the strongest predictor of the total violent crime rates is family disruption variable (β= .480). In contrast, no significant associations are examined between aggravated assault rates and residential instability, poverty, economic inequality, percent young, and population density in Model 2. As a result, three research hypotheses (H1n, H1p, and H1r) are not supported in Model 2.

Model 2 explains 48.5 % of the variance in aggravated assault crime rates and multicollinearity does not appear to be a problem. All the VIF scores range between

1.124 and 2.124, which are below the standard cut-off value 4.0. In Model 2, the F value is 7.47 and statistically significant at p<0.01 level.

156 Table 6.9. Summary of hypotheses testing for aggravated assault rates

Hypotheses Model 1 Model 2

H1m: Provinces that experience greater percentage Supported Supported of unemployed residents will experience higher rates of aggravated assault crime than provinces where the percentage of unemployed is lower.

H1n: Provinces that experience greater residential Not supported Not supported instability will experience higher rates of aggravated assault crime than provinces where the residential instability is lower.

H1o: Provinces that experience high levels of Supported Supported family disruption will have higher rates of aggravated assault crime than provinces that have low levels of family disruption.

H1p: Provinces with higher poverty will have Not supported Not supported higher rates of aggravated assault crime than provinces that have lower poverty rates. H1r: Provinces with higher economic inequality Not supported Not supported will have higher rates of aggravated assault crime than provinces that have lower economic inequality rates.

H1s: Provinces that have higher percentages of Not supported Supported persons with less than high-school education will have higher aggravated assault crime than provinces that have lower percentages of persons with less than high-school education.

Negative Binomial Regression Analysis for Rape

In a negative binomial model for rape, a three-year average (2006, 2007, and

2008) number of rape incidents are used as the dependent variable. Table 6.10 shows the negative binomial regression results for rape. Summary of hypotheses testing for rape crimes is demonstrated in Table 6.11.

157 Table 6.10. Negative Binomial Regression Models for Rape (N=81)

Variables

Coefficient SE

Independent Variables

Unemployment .083** .019

Residential instability .003 .019

Family disruption .048** .013

Poverty -.021 .129

Inequality .041* .019

Low education .009 .008

Percent youth .027 .023

Population size 2.089** .223

Dispersion parameter .075**

Intercept -13.835

Log likelihood χ ² 169.96

Prob > chi2 .000

* p< 0.05, ** p< 0.01

Results for the analysis of rape show statistically significant positive relationships between rape and unemployment, family disruption, and economic inequality. Three research hypotheses are supported in the model (H1t, H1v, and H1x). Holding the other variables constant, provinces with greater percent of unemployment experience higher

158 rates of rape crime than provinces where the percent of unemployment is lower

(coefficient = .083, p<0.01). Moreover, controlling for other variables, provinces with higher rates of family disruption will experience higher rape rates than provinces that have low levels of family disruption (coefficient = .026, p<0.01). Finally, economic inequality significantly positively affects rape (coefficient = .040, p<0.01), while holding the other variables in the model constant. As a control variable, population size also has a statistically significant positive correlation with rape (coefficient = 2.090, p<0.01).

In Table 6.10, the dispersion parameter value is significantly greater than zero, and therefore overdispersion is indicated in the model. As a result, this dispersion parameter value shows that negative binomial is the appropriate regression model for looking at correlation between rape and key structural disadvantage independent variables. The small p-value from the Log likelihood χ ² test (<0.000) suggests that at least one of the regression coefficients in the negative binomial model for rape is not equal to zero (UCLA Academic Technology Services).

Table 6.11. Summary of hypotheses testing for rape crimes

Hypotheses Result

H1t: Provinces that experience greater percentage of Supported unemployed residents will experience higher rates of rape crime than provinces where the percentage of unemployed is lower.

H1u: Provinces that experience greater residential Not supported instability will experience higher rates of rape crime than provinces where the residential instability is lower.

159 Hypotheses Result

H1v: Provinces that experience high levels of family Supported disruption will have higher rates of rape crime than provinces that have low levels of family disruption.

H1w: Provinces with higher poverty will have higher Not supported rates of rape crime than provinces that have lower poverty rates.

H1x: Provinces with higher economic inequality will Supported have higher rates of rape crime than provinces that have lower economic inequality rates.

H1y: Provinces that have higher percentages of Not supported persons with less than high-school education will have higher rates of rape crime than provinces that have lower percentages of persons with less than high- school education.

Negative Binomial Regression Analysis for Robbery

In a negative binomial model for robbery, a three-year average (2006, 2007, and

2008) number of robbery incidents is used as the dependent variable. Table 6.12 illustrates the negative binomial regression results for robbery. A summary of hypotheses testing for robbery is demonstrated in Table 6.13.

Table 6.12. Negative Binomial Regression Models for Robbery (N=81)

Coefficient SE

Independent Variables

Unemployment .047* .023

Residential instability .002 .021

Family disruption .039** .014

160 Independent Variables Coefficient SE

Poverty -.027 .014

Inequality .017 .022

Low education .019* .010

Percent youth .057* .025

Population size 2.515** .243

Dispersion parameter .242**

Intercept -13.871

Log likelihood χ ² 184.55

Prob > chi2 .000

* p< 0.05, ** p< 0.01

Results for the robbery analysis demonstrate statistically significant positive associations among robbery crime and unemployment, family disruption, and low education. Three research hypotheses are thus supported (H1z, H1ab, and H1ae). Holding the other variables constant, provinces with greater percent of unemployment experience higher rates of robbery than provinces where the percent of unemployment is lower

(coefficient = .047, p<0.05). Moreover, controlling for other variables, provinces with higher rates of family disruption will experience higher robbery rates than provinces that have low levels of family disruption (coefficient = .039, p<0.01). Lastly, low education level is significantly positively related to robbery (coefficient = .019, p<0.05), while holding the other variables in the model constant. Both of the control variables (percent

161 youth and population size) have statistically significant positive correlations with robbery

(coefficient = .057, p<0.05 for percent youth and coefficient = 2.516, p<0.01 for population size).

The dispersion parameter value (.242) in Table 6.12 is significantly greater than zero. As stated before, it is an indicator of overdispersion, and the relationship between dependent and independent variables are estimated better with negative binomial regression model as opposed to a Poisson specification (Long, 1997; and Cameron and

Trivedi, 1998). In addition, Prob > chi2 value is the probability of getting Log likelihood

χ² test statistic under the null hypothesis. The null hypothesis assumes that all of the regression coefficients are equal to zero. Otherwise stated, it is the probability of getting

Log likelihood χ ² test statistic, if there is in fact no effect of the independent variables.

The p-value of <0.000 suggests that at least one of the regression coefficients in the negative binomial model for rape crime is not equal to zero (UCLA Academic

Technology Services).

Table 6.13. Summary of hypotheses testing for robbery

Hypotheses Result

H1z: Provinces that experience greater percentage of Supported unemployed residents will experience higher rates of robbery crime than provinces where the percentage of unemployed is lower.

H1aa: Provinces that experience greater residential Not supported instability will experience higher rates of robbery crime than provinces where the residential instability is lower.

162 Hypotheses Result

H1ab: Provinces that experience high levels of family Supported disruption will have higher rates of robbery crime than provinces that have low levels of family disruption.

H1ac: Provinces with higher poverty will have higher Not supported rates of robbery crime than provinces that have lower poverty rates.

H1ad: Provinces with higher economic inequality will Not supported have higher rates of robbery crime than provinces that have lower economic inequality rates.

H1ae: Provinces that have higher percentages of persons Supported with less than high-school education will have higher robbery crime than provinces that have lower percentages of persons with less than high-school education.

Zero-Inflated Negative Binomial Regression Analysis for Terrorism-related Crime

Sometimes when analyzing a response variable that is a count variable, the number of zeros may seem excessive. When analyzing a dataset with an excessive number of outcome zeros, a zero-inflated model should be considered. In terrorism- related crimes, 18 provinces within 81 provinces in Turkey did not experience any terrorism-related crimes during the examined time period. Because of excessive number of zeros, a zero-inflated negative binomial model (ZINB) is used for analysis of terrorism-related crimes. In a zero-inflated negative binomial model, a three- year average (2006, 2007, and 2008) number of terrorist incidents is used as the dependent variable. Table 6.14 shows zero-inflated negative binomial (ZINB) regression results for terrorism-related crime. A summary of hypotheses testing for terrorism-related crimes is demonstrated in Table 6.15.

163 Table 6.14. ZINB Regression Model of Terrorism-related Crime (N=81)

Coefficient SE

Independent Variables

Unemployment .054 .047

Residential instability .122** .034

Family disruption .0009 .0321

Poverty .035** .016

Inequality .037 .042

Low education .011 .019

Region .840** .336

Percent youth .015 .045

Population size 2.131** .457

Dispersion parameter .569**

Intercept -15.788

Log likelihood χ ² 70.44

Prob > chi2 .000

Vuong test 5.25**

* p< 0.05, ** p< 0.01

Results for the analysis of terrorism display statistically significant positive relationships between terrorism-related crime and residential instability and poverty. Two research hypotheses are therefore supported in the model (H2b and H2d). Holding the other

164 variables constant, provinces with greater residential instability experience higher rates of terrorism-related crime than provinces where residential instability is lower (coefficient =

.122, p<0.01). Furthermore, according to the zero-inflated negative binomial regression results, controlling for other variables, provinces with higher percentages in poverty experience higher terrorism-related crime rates than provinces that have lower levels of poverty (coefficient = .035, p<0.01).

In addition to the effects of indicators of structural disadvantage, region and population size are statistically positively correlated with terrorism-related crimes

(coefficient = .840, p<0.05 for region and coefficient = 2.132, p<0.01 for population size). Namely, provinces in southeastern and eastern part of the country have more terrorism-related crimes compared to other provinces. The small p-value from the Log likelihood χ ² test, <0.000, would direct researcher to conclude that at least one of the regression coefficients in the zero-inflated negative binomial model for terrorism-related crimes is not equal to zero (UCLA Academic Technology Services).4

Table 6.15. Summary of hypotheses testing for terrorism-related crimes Hypotheses Result

H2a: Provinces that experience greater percentage of supported unemployed residents will experience higher rates of terrorism-related crime than provinces where the percentage of unemployed is lower.

H2b: Provinces that experience greater residential ported instability will experience higher rates of terrorism- related crime than provinces where the residential instability is lower.

4 A supplemental model for terrorism was run which included non-terrorist violent crime as an independent variable. Non-terrorist violence was significantly, negatively related to terrorist violence. However, no other results presented in Table 6.14 or 6.15 were substantively changed.

165 Hypotheses Result

H2c: Provinces that experience high levels of family Not supported disruption will have higher rates of terrorism-related crime than provinces that have low levels of family disruption.

H2d: Provinces with higher poverty will have higher Supported rates of terrorism-related crime than provinces that have lower poverty rates.

H2e: Provinces with higher economic inequality will Not supported have higher rates of terrorism-related crime than provinces that have lower economic inequality rates.

H2f: Provinces that have higher percentages of persons Not supported with less than high-school education will have higher terrorism-related crime than provinces that have lower percentages of persons with less than high-school education.

CONCLUSION

This chapter presented bivariate and multivariate analyses of six different dependent variables. In general, many indicators of structural disadvantage were related to violent crime in Turkey, as hypothesized. However, the specific indicators of disadvantage that were important seemed to vary across terrorist versus non-terrorist violence. A summary of these findings and a fuller discussion of key differences in terrorist versus non-terrorist violence will be addressed in the final chapter that follows.

166 CHAPTER SEVEN: DISCUSSION AND CONCLUSIONS

This study looked at the relationships between indicators of structural disadvantage non-terrorist violent crimes and terrorism-related violent crimes at the province-level in Turkey by using official crime reports, census data, and various institutional statistics. In this chapter, study results will be summarized and discussed in detail. In addition, findings of previous studies will be compared with the current study’s results. Policy implications, limitations of the current study, and recommendations for future research will also be provided in this chapter.

SUMMARY OF FINDINGS

Multivariate analyses of total violent crime, homicide, aggravated assault, rape, robbery, and terrorism-related crimes in Turkey showed a number of significant results.

A summary of the relationships between examined crime types and indicators of structural disadvantage is provided in Table 7.1. This summary shows a coherent set of findings in that nearly all non-terrorist violent crimes were predicted by the same indicators of disadvantage. In contrast, the indicators that were not significantly associated with non-terrorist violence were significant in a model of terrorist violence.

Non-terrorist Violent Crime

167 assault, rape, and robbery. First, results indicate that unemployment has a significant positive impact on total violent, homicide, aggravated assault, rape and robbery crime rates. Those relationships are consistent with much previous research. For instance, as reviewed in Chapter Three, many previous studies reported that unemployment was positively related to various non-terrorist types of violent crime (e.g., Watts and Watts,

1981; DeFronzo, 1983; Stack and Kanavy, 1983; Land et al., 1990; Allen, 1996; Krivo and Peterson, 1996; Morenoff et al., 2001; Browning et al., 2004; and MacDonald and

Gover, 2005).

Table 7.1. Relationships between dependent and independent variables

Total Homicide Aggravated Rape Robbery Terrorism- violent assault related crime crime Unemployment + + + + + NS

Residential NS NS NS NS NS + instability

Family + + + + + NS disruption

Poverty NS NS NS NS NS +

Economic NS NS NS + NS NS inequality

Low education + + + NS + NS

(+) statistically significant positive relationship, (-) statistically significant negative relationship, (NS) null relationship.

168 Family disruption is another structural disadvantage variable that had significant positive influence on all types of non-terrorist violent crime rates across provinces in

Turkey. Results presented in Chapter Six showed that total violent, homicide, aggravated assault, rape, and robbery crime rates were higher in the provinces where there is higher family disruption. Those finding are also consistent with several previous studies that look at the effects of family disruption on non-terrorist violent crime rates. For example,

Blau and Blau (1982) used percent divorced as a measure of family disruption in their

SMSA-level seminal study, and their multivariate OLS regression analysis results revealed a significantly positive association between percent divorced and total violent crime rates. In another study, Simpson (1985) used a logit transformation of percent divorced in his study and found a significant positive relationship between total violent crime rates and percent divorced in one of his models. Additionally, Smith and Bennett

(1985) and Blau and Golden (1986) examined SMSA level data and their study results revealed that percentage of divorced was significantly and positively related with crime rates. Miethe and Meier (1994) also found a significant correlation between crime rates and family disruption.

Numerous previous studies have also found a positive association between family disruption and homicide specifically, consistent with the current examination (e.g., Baller et al., 2001; Baron & Strauss, 1988; Browning et al., 2004; Kposowa et al., 1995; Land et al., 1990; Lee & Bartkowski, 2004; MacDonald & Gover, 2005; Williams & Flewelling,

1988). Beyond homicide, previous studies have also found positive relationship between family disruption and other specific types of violent crime, as found here. For example,

Simpson (1985) used logit transformation of percent divorced in his study and found

169 significant positive relationships between murder, rape, robbery, assault, and total violent crime rates and percent divorced. In another study, Messner and Tardiff (1986) found significant positive association with rape rates, specifically, and percent divorced at neighborhood level.

Low education is another structural disadvantage variable that was significantly and positively associated with total violent, homicide, aggravated assault, and robbery crime rates in present study. This finding is consistent with the findings of previous studies that look at the impact of low education on crime rates. For example, Drapela

(2006) found statistically significant and positive relationship between high rates of school dropouts and involvement in violent crimes. In another study, Santas (2007) used counties as level of analysis and found that when high school dropout rates increased, crime rates also increased in both urban and rural counties. Additionally, Lee and

Bartkowski’s (2004) study showed a significant positive correlation between high school dropouts and juvenile and adult homicide rates specifically.

On the other hand, multivariate OLS and negative binomial results do not show significant relationships between non-terrorist violent crime and residential instability, poverty, and economic inequality (with the exception of economic inequality’s positively association with rape only). While largely contrary to hypotheses, other previous studies have also found such effects to be null for both general measures of violent crime and homicide specifically (Smith and Parker, 1980; Messner, 1982; Messner, 1983;

DeFronzo, 1983; Bailey, 1984; Messner and Tardiff, 1986; Huff-Corzine et al., 1986;

Parker, 1989; Miethe et al., 1991; Kposowa et al., 1995; Vessey and Messner, 1999;

Neumayer, 2005; and Hipp, 2007).

170 Terrorism-related Crime Results

Zero-inflated negative binomial (ZINB) analyses of terrorism-related crimes in

Turkey show a number of important findings. Parallel to the expectations, the results indicate that between structural disadvantage variables, poverty has a significant positive effect on terrorism-related crimes. Even though several researchers are doubtful regarding relationship between terrorism-related crimes and poverty (Kruger and

Maleckova, 2002; Berrebi, 2003; Piazza, 2006; and Abaide, 2004), the findings are consistent with numerous other studies that suggest a positive relationship between poverty and terrorism (Auvinen and Nafziger, 1999; Fearon and Laitin, 2003; Li and

Schaub, 2004; and Burgoon, 2006).

In Turkey, an average of 20.1 percent of residents hold a Green Card, which is used to measure poverty in this study. However, this percentage of Green Card holders is considerably higher in provinces in the southeastern and eastern part of the country where terrorism-related crimes are also high. For example, the percentage of Green Card holders is almost twice the national average, increasing to 38.26 percent, in southeastern and eastern provinces.

Residential instability is another structural disadvantage variable that has a statistically significant and positive effect on the terrorism-related crimes across Turkey.

The results presented in Chapter Six show that the number of terrorism-related crimes is higher in provinces where residential instability rates are high. This finding is consistent with the findings of previous studies that examine the effects of residential instability on terrorism-related violent crimes, including political violence. For example, Galvin (2002) states that people who were torn from their rural roots tend to become isolated and prone

171 to crime as a reaction to the disappointment and grievances associated with urban life. In addition, Karpat (1976) and Bayat (2007) emphasize the positive effect of residential instability on political extremism in Turkey.

According to the multivariate zero-inflated negative binomial regression results, region also has a statistically significant positive impact on terrorism-related crimes.

Provinces which are located in southeastern and eastern part of the country experience more terrorism-related crimes than provinces in other regions. None of the prior studies about terrorism in Turkey used region variable in their analyses as used in current research, so it is not possible to look at consistency of this finding with previous results.

On the other hand, the zero-inflated negative binomial (ZINB) regression model did not reveal significant correlations between terrorism-related crimes and unemployment, family disruption, income inequality, and low education. While there was a statistically significant and positive correlation between unemployment and terrorism- related crime in bivariate analysis, this significant association disappeared when other independent variables were introduced in multivariate analysis.

While unemployment, family disruption, income inequality, and low education were hypothesized to negatively affect terrorism, null effects make some sense in hindsight. As an example, low education was hypothesized to have a negative effect on terrorism, however its null effect is not altogether surprising. In some circumstances, terror organizations may offer greater benefits such as group leadership or some other critical positions in the organization to the educated individuals. Also, well- educated individuals are more likely to be involved in political issues because political participation requires some level of awareness and knowledge regarding political topics.

172 Since terrorism is so closely linked to political ideology, it is not surprising that percent with less-than-high-school education does not predict terrorism. Other null effects for terrorism are discussed in the conclusion below, specifically comparing results across terrorism v. non-terrorism categories.

Summary: Disadvantage and Terrorist v. Non-Terrorist Violence

Theoretically, disadvantage should fuel various types of violence. Generally speaking, this broad claim was supported by the current research. However, this study also revealed important nuances to this overall general finding, with completely different indicators of disadvantage predicting non-terrorist versus terrorist violence. There are important differences between the nature of terrorism versus non-terrorist violence that might account for the differences in significance of specific indicators of disadvantage across these two categories. As stated in previous chapters, non-terrorist and terrorism- related violent crimes have several differences in terms of motivation, opportunity structure, methods, and ideology. For instance, violence in the form of homicide, assault, and rape are typically preceded by social interaction. Many of these violent crime victims, particularly homicide and rape victims, know their assailants and are involved in a dispute of some sort with their assailants. As such, from a macro viewpoint, they are most likely to be triggered by structural conditions that provide or indicate “relational stressors.” High rates of unemployment and poor education put stress on families and interpersonal relationships. Family disruption is an even more obvious indicator of macro-level “relational stress.” On the other hand, in terrorism-related crimes, in general there is no social interaction between offender(s) and victim(s), so relational stress is not

173 an issue. In contrast, political ideology is a typical motivation. Thus, dimensions of disadvantage that tap into social change and social injustice (i.e., instability and poverty) are logically more likely to be related to terrorist violence. Those differences may be the reason why totally different structural disadvantage variables have significant correlations with non-terrorist and terrorism-related violent crimes.

POLICY IMPLICATIONS

The results summarized above may have implications for law enforcement and other government activities surrounding crime and terrorism in Turkey. While replication is necessary before strong policy recommendations are made, at least preliminary discussion of policy implications is warranted. This section addresses these policy implications. First, an overview of the administrative structure of Turkish organizations fighting both terrorism and non-terrorist crime are described. The practices within these organizations are also described. This description is followed by a discussion of how the findings in this dissertation suggest that such practices could be supplemented with other approaches.

Responses to Terrorism in Turkey: Administrative Structure and Process

Counterterrorism activities in Turkey are administered by the Ministry of Interior.

Under the management of the Ministry of Interior, two main units are responsible for dealing with terrorism. According to Turkish Provincial Administration Law (Law number 5442), while the Turkish National Police (TNP) is responsible in urban areas to

174 provide security, the Gendarmerie General Command (JGK) fights against terrorism in the rural areas across the country.

Additionally, the National Intelligence Agency (MIT), which is affiliated with the

Prime Minister, has responsibilities to gather intelligence about national security. The

National Intelligence Agency’s (MIT) duties and responsibilities are stated in law.

According to the 4th Article of Law no. 2937, the Turkish National Intelligence

Organization is responsible “to produce national security intelligence on immediate and potential activities carried out in or outside the country to target the territorial and national integrity, existence, independence, security, Constitutional order and all elements that constitute the national strength of the Republic of Turkey, and to deliver this intelligence to the President, the Prime Minister, the Secretary General of the

National Security Council and to relevant institutions.”

When the National Intelligence Agency (MIT) requires, the Ministries and all of the other state organizations are responsible to collect information and intelligence, and send out acquired information and intelligence immediately to the MIT. In addition, during their duties, all of the state organizations are responsible to give all kinds of support and assistance to the National Intelligence Agency (MIT) staff.

Turkish National Police (TNP) has different units that address terrorism. In Figure

7.1, the organizational chart of Turkish National Police is presented. The Counter- terrorism Department, Intelligence Department, and Special Operations Department are the main counter-terrorism components in the Turkish National Police (TNP).

Turkish National Police’s (TNP) Counter-terrorism Department has responsibilities to analyze terror incidents and organizations, to develop new policies and

175 preventive strategies, to create operational and investigational guidelines, to coordinate and supervise the struggle against terrorist organizations, and to identify and capture terrorists and their supporters (General Directorate of Security, 2008).

The Intelligence Department of Turkish National Police manages the intelligence- gathering part of the struggle against terrorism. After gathering intelligence about terrorism, the Intelligence Department analyzes the information, and other related departments are informed to take preventive measures and to carry out operations.

The Special Operations Department is responsible for carrying out operations against terrorist groups. The Special Operations Department staff is intensively trained and equipped specially to deal with all types of terrorist attacks. When terrorist targets are identified, the Special Operations Department is informed and they carry out operations against targets. Beside terrorism-related crimes, the Special Operations Department is also responsible for hostage rescue operations (Ozeren and Yilmaz, 2006).

176 Figure 7.1.Organizational chart of Turkish National Police

GENERAL DIRECTORATE OF TURKISH POLICE

DIRECTOR GENERAL

SECRETARIAT OFFICE POLICE ACADEMY DIVISION OF PRESS, PROTOCOL POLICE COLLEGE &PUBLIC RELATIONS EXPERT CHIEFS OF RESEARCH LEGAL AFFAIRS BOARD PLANNING AND CO-ORDINATION DEPARTMENT OF INTELLIGENCE DEPARTMENT OF SPECIAL OPERATIONS POLICE INSPECTION BOARD

DEPUTY DIRECTOR DEPUTY DIRECTOR DEPUTY DIRECTOR DEPUTY DIRECTOR DEPUTY DIRECTOR GENERAL GENERAL GENERAL GENERAL GENERAL

DEPARTMENT OF DEPARTMENT OF DEPARTMENT OF DEPARTMENT OF DEPARTMENT OF MAIN SECURITY ADMINISTRATIVE PERSONNEL AFFAIRS FOREIGN RELATIONS COMMAND AND CNTR. & FINANCIAL AFFAIRS DEPARTMENT OF DEPARTMENT OF ANTI DEPARTMENT OF COUNTER-TERRORISM -SMUGGLING AND DEPT. OF SUPPLY DEPARTMENT OF EDUCATION&TRAINING ORGANISED CRIME AND MAINTENANCE PROTECTION DEPARTMENT OF DEPARTMENT OF PUBLIC ORDER DEPARTMENT OF DEPARTMENT OF INTERPOL DEPARTMENT OF DEPARTMENT OF CONSTRUCTION HEALTH SERVICES DEPARTMENT OF COMMUNICATION AND REAL ESTATE FORENSIC POLICE FOREIGNERS, BORDERS LABORATORIES DEPARTMENT OF AND ASYLUM DEPARTMENT OF EXPERTS BOARD OF SOCIAL AFFAIRS DEPT. OF TRAFFIC DIRECTORATE OF DATA PROCESS CIVIL DEFENSE PLANNING AND SUPPORT PROTECTION OF DEPARTMENT OF PRESIDENCY DEPARTMENT OF ARCHIVE AND RESEARCH, PLANNING DEPT. OF TRAFFIC DIRECTORATE OF AND CO-ORDINATION PRACTICE AND SUPRV. PROTECTION OF PRIME DOCUMENTATION MINISTRY DEPARTMENT OF DEPARTMENT OF TRAFFIC EDUCATION DIRECTORATE OF PROT. AVIATION &RESEARCH OF PARLIAMENT TRAFFIC RESEARCH CENTRE

Organizational chart is used with permission from Turkish National Police.

177 In addition, to fight effectively against terrorism, a bill for the establishment of a

“Public Order and Security Undersecretariat” was submitted to Grand National Assembly of Turkey (TBMM) by the Ministry of Interior in 2009. One of the key purposes of the new unit will be to provide the coordination between intelligence-gathering institutions. In

Turkey, National Intelligence Organization (MİT), General Directorate of Security,

Turkish General Staff, and Gendarmerie General Command (JGK) are the most important institutions for gathering intelligence. Those four institutions are affiliated with different national departments, and this may sometimes cause coordination problems between different units. For instance, whereas the General Directorate of Security (TNP) and the

Gendarmerie General Command (JGK) are affiliated with the Ministry of Interior, the

National Intelligence Organization (MİT) and the Turkish General Staff are affiliated with the Prime Minister.

The new bill aims to set up coordination between those different units, and prevent security drawbacks in ensuring law and order. After establishment of “Public

Order and Security Undersecretariat,” all the intelligence about terrorism will be sent to the undersecretariat by all of the intelligence-gathering institutions, and that information will be evaluated in this new unit.

Responses to Non-Terrorist Crime in Turkey: Administrative Structure and

Process

Parallel with terrorism-related crimes, activities about non-terrorist violent crimes are also administered by the Ministry of Interior in Turkey. Under the management of

Ministry of Interior, Turkish National Police (TNP) and Gendarmerie General Command

(JGK) are responsible for dealing with non-terrorist violent crimes across the country.

178 The Department of Public Order is the main unit in the Turkish National Police

(TNP) for handling violent crimes, and this department works in coordination with the

Department of Forensic Police Laboratories to investigate non-terrorist violent crimes.

The main duties of the Department of Public Order are minimizing incidents/crimes against public order by establishing preventive strategies, analyzing crimes/incidents, providing investigative guidelines to coordinate provincial divisions, and coordinating activities of the provincial divisions.

Violent-Crime and Terrorism Prevention: Implications of Findings

The administrative structures and processes for addressing both terrorist and non- terrorist violence described above are along the lines of centralized “traditional” policing, with an emphasis on investigating reported crime, and attempting to arrest and punish offenders. They are consistent with a deterrence-based “get tough” philosophy to crime reduction. As stated in previous chapters, terrorism differs from other type of violent crime in some important respects, such as ideological, motivational, and goal differences and opportunity structure. Differences between two types of crimes may require employing some different policies for terrorism-related and non-terrorist violent crime reduction.

The “get tough” approach is based on the idea of harsher penalties for more serious crimes and repeat offenders. Relying only on deterrence policy in terrorism crimes and paying no attention to broader social origins of the phenomenon may yield particularly unsatisfactory results. In terrorism-related crimes, using merely severe punishment methods and retribution is unlikely to solve problem because terrorism-

179 related crimes are based on ideology instead of personal gain. Moreover, in some circumstances, a “get tough” approach for terrorism may even lead to counterproductive outcomes. For instance, institutionalized violence (i.e. police/government-sponsored) against terrorists may cause new individuals to support or to join terrorist organizations.

Because of this, in the fight against terrorism, the government should maintain balance between “getting tough on crime” and personal rights in its procedures and strategies.

Further scholarly research is an important and necessary issue to highlight the links between different aspects of structural disadvantage and terrorist crime. Though

Turkey has suffered from terrorism dilemma for more than three decades, academia in

Turkey does not pay adequate attention to this social problem. Without studying and looking into its roots and causes in the society, crime cannot be stopped. Law enforcement’s response alone is not enough. As a result, in coordination with law enforcement agencies, universities should focus on crime. It would be ideal to establish criminal justice departments and crime research centers in universities to explore the causes of crime, including terrorism, in Turkey.

Such study is important, because it can provide new ways to solve the crime problems. Despite the evidence of presented here regarding the importance of structural disadvantage, there has been little or no attention given to the role of social policies as

“crime prevention” in Turkey. Poor economic conditions are generally considered as

“terrorist breeding grounds” (Li and Schaub, 2004), and the findings reported in the present research seem to also point out that dealing with absolute poverty can be an effective policy option for reducing or preventing terrorism-related crimes. Specifically, an effective fight against terrorism depends on effective administration strategies. As an

180 example, government can combat poverty which generates terrorism through providing more economic support to the citizens in provinces with higher number of terrorism incidents and through improvement of existing social welfare programs in those areas.

Reducing poverty accordingly may also help to decrease the residential instability rate in these provinces, which is another significant predictor of terrorism-related crimes in

Turkey.

Though this study suggest that province-level social welfare programs might help curb terrorism, crime prevention policy must extend beyond the province level, given the cross-national nature of terrorist organizations. Thus, international cooperation is an important aspect of struggle against terrorism. It is beneficial for countries to coordinate with others to deal with terrorism. The United Nations Office on Drug and Crime

(UNODC) initiated “Strengthening the Legal Regime against Terrorism” global technical program in October 2002 to provide assistance to the member states for establishing effective mechanism for international cooperation against terrorist activities. This program mainly aims to build up enhanced mechanisms for national and transnational counter-terrorism cooperation between member countries by respecting civil liberties and human rights. As a member of United Nations, Turkey should expand its cooperation with other countries and benefit from their experiences about terrorism.

LIMITATIONS OF THE STUDY AND RECOMMENDATIONS FOR FUTURE

RESEARCH

Even though the study findings are consistent with previous research and specified research hypotheses in general, some methodological issues have to be taken

181 into consideration. First, the present study is a cross-sectional analysis. In cross-sectional studies, the researcher cannot fully identify causality. While the independent variables in this study were all obtained prior to the crimes that served as measurement of the dependent variable, it was not possible to look at the possible reciprocal association between non-terrorist violent crimes or terrorism-related violent crime and any of the independent variables (e.g. unemployment or residential instability) without a repeated cross-sectional design. Thus, the results presented here do not represent the effects of disadvantage on crime net of the effects of crime on structural disadvantage.

Secondly, in the current research, data from the Turkish National Police were used to compute non-terrorist and terrorist violent crime rates. There were some limitations with respect to police crime data that may have an effect on the accuracy of the study outcomes. For instance, police crime statistics only show crimes that were reported to the police and do not count crimes that were not reported by the victim(s) or anyone else. In other words, official crime statistics are contingent on either crimes being detected by the police or individuals reporting crimes to the police. As a consequence of unreported and unknown crimes, the official statistics used here undoubtedly underestimated crime rates.

Additionally, the unavailability of other data sources to check the validity of police crime data in Turkey is another limitation of the current study. For example, in the

United States, National Crime Victimization Survey (NCVS) is designed to complement official crime statistic program (UCR). National Crime Victimization Survey (NCVS) covers rape, robbery, aggravated assault, burglary, theft, and motor vehicle theft crimes.

In NCVS approximately 100,000 persons at 12 or over in 87,000 households are

182 interviewed across the United States (Bureau of Justice Statistics, 2009). Those selected people are interviewed every 6 months for 3.5 years period about their victimization experiences during this time period. National Crime Victimization Survey results showed that nearly 49% of personal robbery, 48% of household larceny over $50, and 21% of business robberies were not reported to police (Hindelang and Gottfredson, 1976). As a result of those unreported crimes to police, official crime statistics have substantial validity problems. This comparison, however, is not available for Turkish Police’s crime data. Nonetheless, it is suspected that official statistics in Turkey also seriously under- represent actual crime incidents.

Moreover, categorization of crimes known to police and processing errors at different phases of data entry might also cause serious reliability problems in official crime statistics. Further, discretion by the police during crime recording phase might also affect accuracy of the statistics and limit the overall reliability of police data. Another concern related to official crime statistics is that crime recording may be influenced by the demands of administration. This problem also affects the accuracy of the official crime statistics. Finally, all of these potential problems with police data might vary across provinces, making it particularly difficult to predict variation in crime across provinces in a meaningful way.

Another limitation of present examination is related with the aggregate nature of terrorism crime data. Current data about terrorism-related crimes only gives total numbers of terrorism incidents for each province. As a result of this limitation, it is not possible to look at the relationship between specific types of terrorism crime such as

183 suicide bombing, attacks against official buildings, and attacks on civilian people and the indicators of structural disadvantage.

Finally, some explanatory variables in current examination could be measured more precisely for better estimation of the relationship between dependent variables and predictors. For instance, in current study, as an independent variable, poverty is measured by using the percent of each provinces population who have Green Cards. This computation does not precisely reflect the number of people below the poverty level, but there are no other data in Turkey that measure percent of people below the poverty line in each province.

Despite the above mentioned limitations, this analysis provided an unprecedented province-level analysis of terrorist and non-terrorist violence in Turkey, with an emphasis on the effects of structural disadvantage. Thus, it serves as a foundation for understanding macro-level variation in crime in Turkey. Replication and extension of this research are called for. Ideally, future studies would utilize data not bound by the limitations discussed above. Hopefully new data collection methods for the study of crime can be developed by academia and police experts. If so, the data should include more and/or better information regarding indicators of structural disadvantage, demographics of places, information on the offenders, and information about crime events (whether or not reported to the police). Such data would be useful to better demonstrate the roots and potential causes of crimes.

In addition, future research should strive to understand more fully different types of terrorism. Terrorist crime data should be disaggregated in a new database to allow

184 examination of the associations between structural disadvantage and specific types of terrorism.

Finally, although current study used provinces as units of analysis, a study of smaller aggregate units (i.e. neighborhoods) might reveal different results. Therefore, future studies should move from the province-level to the neighborhood level, for example, in order to observe whether the correlations between the structural disadvantage variables and non-terrorist or terrorism-related violent crimes reported here are robust.

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200 APPENDIX A

MICRO-LEVEL RESEARCH ON TERRORISM

Krueger and Maleckova (2003) recognized the need for research regarding relationship between poverty, education, and terrorism, and they performed a study to investigate this association. Krueger and Maleckova (2003) used both macro and micro level data in their study. In micro level part of their study, the authors used biographical information such as economic status, education, age, marital status, and region of residence about 129 Hezbollah militants killed in operations, and compared their information with Lebanese population’s general characteristics. After comparison, the authors found that two factors (having secondary-school education and living above the poverty line) were positively related to involvement in Hezbollah militant activities.

According to Russell and Miller’s (1983) findings, urban terrorists’ age range was between 22 and 25. Parallel with Russell and Miller’s (1983) findings, Krueger and

Maleckova’s (2003) results revealed that Hezbollah militants generally tended to be between 18 and 25 years when they died.

Berrebi (2003) used micro level data in his study to examine the relationship between poverty, education, and terrorism. The author looked at the biographies of 285 suicide bombers between 1993 and 2002. He found that almost 55% of suicide bombers were graduated from high school, whereas nearly 15% of the Palestinian population had the same level of education. In addition, when they compared poverty levels of suicide

201 bombers and population, they found that suicide bombers were less likely to come from disadvantaged poor families.

In another micro level study, Benmelech and Berrebi (2007) examined the effects of human capital on the production of suicide bombers. They measured suicide bomber’s education level by using a dummy variable (1 equals for suicide bombers who went beyond high school education). Their results showed that 18% of the suicide bombers had beyond high school education, whereas only 8% of the Palestinian population had same level of education. They also found significant correlation between city’s population size and frequency of terror attacks. In addition, they found evidence that terror organizations in Palestine use more educated and older suicide bombers for more important targets in

Israel. In addition, the authors found that when more educated and older suicide bombers attacked to the targets, they were less likely to fail or to be caught by the authorities.

202

Table of Contents