The Ties That Bind: a Social Network Analysis of a Large Gang Sex Trafficking Network In

Kristina A. Lugo

2016

DEDICATION

To my late father, Fernando Lugo, who always assured me I could do anything, and his brother,

Eugeño Jiménez, who took over that job after he was gone.

And, to the victims of human trafficking I have had the privilege to encounter during the last

several years. You are stronger and more resilient than anyone I have ever met.

THE TIES THAT BIND: A SOCIAL NETWORK ANALYSIS OF A LARGE GANG

SEX TRAFFICKING NETWORK IN THE UNITED STATES

Kristina A. Lugo

ABSTRACT

This dissertation examines the effect of network position on the probability that a sex trafficking network member avoided indictment in a specific gang RICO prosecution, and what that says about the functioning of trafficking networks and their resistance to fragmentation strategies by law enforcement. Ties between members, particularly involving members that broker relationships between otherwise disconnected individuals or groups, are hypothesized to be conduits through which benefits flow, such as power, information, or influence. Others hypothesize that brokerage positions can hold disadvantages, so that an advantageous position in one network may be disadvantageous in another. An individual in a brokerage position might also use his/her power strategically to block the flow of benefits between others.

This study used police and court data to examine how brokerage, measured in different ways, influenced the probability of avoiding indictment. As of this writing, this is the first social network analysis of an individual human trafficking network in the United States, and the largest such study of a human trafficking network in terms of network size examined. It is distinctive in that it includes extensive data on both indicted and unindicted individuals in sufficient numbers to use broad boundaries and include the impact of porous network borders.

Avoiding indictment is the dependent variable, and it is used as a proxy for survival, which is an important utility that offenders and victims alike want to maximize. Rational utility ii

maximization by network members is important to consider when examining the operation of criminal trafficking networks as business enterprises. Results support the notion that relying on betweenness centrality alone to measure brokerage does not work as predictably well in larger networks; its effect was miniscule and not statistically significant in every model where degree centrality, or the sheer number of people one is connected to, was included. This is contrary to the results of several network analyses involving smaller networks for a few important reasons, including the presence of multiple redundant ties and the lengths of paths between individuals in larger, more mature networks. Further, the impact of degree centrality is shown to have a curvilinear effect that reverses direction after a certain point, and clustering coefficient emerged as a measure of brokerage worthy of more study. Important insights are also offered regarding the symbiotic relationships between perpetrators and victims.

On a practical level, the goal of this study was to explore the extent to which identifying brokers in a sex trafficking network could help law enforcement target network members that would best fragment a network, a strategy thought to reduce criminal networks’ ability to operate and exploit victims. The study shows that the size and complexity of the network, the presence of redundant ties, and the structure of components connected by cut points may call for different law enforcement strategies in dismantling larger gang sex trafficking networks.

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ACKNOWLEDGMENTS

There are numerous people without whom this dissertation would never have happened.

To my colleagues at International Justice Mission, especially Terence Fitzgerald, who have mentored me; American University professors Ed Maguire, Richard Bennett, Jon Gould, and others who taught and encouraged me. To Jay Albanese at Virginia Commonwealth University,

Andrew Fox at California State University, Fresno, Sharon Melzer at George Mason University, and John Picarelli at the National Institute of Justice, thank you for guiding and teaching me.

Thanks also to Francesco Calderoni at Università Cattolica del Sacro Cuore in Milan for his advice and feedback, to Jennifer Roberson for being a sounding board.

I also extend my sincerest gratitude to the task force members that investigated this case, including officers from the San Diego County Sherriff’s Department, and Special Agents from the Federal Bureau of Investigation, and Homeland Security Investigations for sharing their time with me for interviews; and to Ami Carpenter at University of San Diego, and Sheldon Zhang at

San Diego State University for connecting me to the members of the task force. Most of all, I thank the lead detective from the Oceanside Police Department, who led the investigation that helped so many victims and who gave untold time and access to collect the data needed for this study. None of this work would have been possible without you, and I am forever in your debt.

I also thank my family, including cousins Vanessa Jiménez, Adam Vana, Serena Jiménez

Ashby, Claudia Fortunato and many others, as well as my Aunt Pat Jiménez for their ongoing support. Last, but certainly not least, thank you to my sister, Mary Lugo, for not only being my copy editor but for her unwavering support over the last many years. I love you. Thank you.

TABLE OF CONTENTS

ABSTRACT ...... ii

ACKNOWLEDGMENTS ...... iv

LIST OF TABLES ...... vii

LIST OF ILLUSTRATIONS ...... viii

CHAPTER 1 INTRODUCTION ...... 9 General background: Human and sex trafficking ...... 18 The present study ...... 34 Roadmap for the dissertation ...... 38

CHAPTER 2 MAXIMIZING UTILITY AND THE PROBABILITY OF SURVIVAL IN A GANG SEX TRAFFICKING NETWORK: HUMAN TRAFFICKING AND NETWORK: THEORIES IN A BOUNDED RATIONAL CHOICE MODEL ...... 40

Victimization and perpetration in gang-controlled sex trafficking: How do they operate? ...... 41 Traditional Criminology and Organized Crime ...... 47 Bounded Rational Choice: Underlying Framework ...... 50 Network Theory ...... 57 Synthesizing Theory ...... 76 Conceptual model ...... 78 Research questions (Redux) ...... 80

CHAPTER 3 METHODOLOGY ...... 82

General methodology ...... 82 Research setting ...... 84 Data Sources ...... 86 Sampling and Boundaries ...... 93 Dataset construction ...... 97 Data analysis procedures...... 110

CHAPTER 4 FINDINGS: IS BROKERAGE PROTECTIVE? ...... 114

Case History ...... 115 Descriptive Statistics ...... 129 Social Network Analyses ...... 144 Summary ...... 164

CHAPTER 5 DISCUSSION AND CONCLUSION: IT’S WHO YOU KNOW ...... 168

Summary of Research and Findings ...... 168 Theoretical Implications and Generalizability ...... 172

Practical Implications...... 188 Limitations ...... 195 Future Research ...... 197 Practical and Policy Recommendations ...... 199 Conclusion ...... 201

APPENDIX A KEY INFORMANT INTERVIEW INSTRUMENT ...... 204

APPENDIX B INFORMED CONSENT FOR KEY INFORMANT INTERVIEWS...... 209

APPENDIX C KEY INFORMANT INTERVIEWS: INVITATION TO PARTICIPATE ...... 213

APPENDIX D AUXILLIARY REGRESSIONS ...... 215

APPENDIX E COMPARISON CASES: OTHER NETWORK PROSECUTIONS IN THE UNITED STATES ...... 220

APPENDIX F INSTITUTIONAL REVIEW BOARD APPROVALS (ORIGINAL AND MODIFICATION) ...... 221

REFERENCES ...... 223

LIST OF TABLES

Table 1. Variable Descriptions ...... 109

Table 2. Frequencies of Node Attribute Variables by Clique ...... 130

Table 3. Racial Breakdown ...... 131

Table 4. 2011 Whole-Network Descriptive Statistics ...... 132

Table 5. Triad Census 2011 Full Network ...... 135

Table 6: Network Measures over Time...... 138

Table 7: Correlations Matrix ...... 145

Table 8. Full Regression Models, Odds Ratios displayed ...... 150

Table 9. Principal Component Analysis: Network Measures ...... 154

Table 10. Mean Network Measures by Probability of Avoiding Indictment, Gang Rank, Gang Association, Clique, and Sex Trafficking Network Function ...... 158

Table 11. Bi-Component Analysis: Distribution Table ...... 161

Table 12. Centrality Scores for Identified Cut Points ...... 163

Table D1: Naïve Regressions (No Controls, Odds Ratios presented) ...... 215

Table D2: Full Regressions with Prior Police Contacts Removed ...... 216

Table D3: Test for Curvilinear Effect of Degree (Latent Variable) ...... 218

Table D4: Test for Curvilinear effect of Normalized Degree without Latent Variable ...... 219

Table E1: List of Comparison Indictments ...... 220

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LIST OF ILLUSTRATIONS

Figure 1: Triad Example ...... 59

Figure 2: Conceptual Model ...... 80

Figure 3: Relationships between pimps, bottoms, and the stable in the prostitution network .... 115

Figure 4A: Network Evolution: Increasing Complexity and Consolidation over Time ...... 139

Figure 4B: Network Evolution: Increasing Complexity and Consolidation over Time (continued)...... 140

Figure 4C: Network Evolution: Increasing Complexity and Consolidation over Time (continued)...... 141

Figure 5: Sociogram of Clique by Degree Centrality, 2011 ...... 142

Figure 6: Gang Rank and Victim Status by Degree Centrality ...... 143

Figure 7. Cutpoints in the 2011 network ...... 162

Figure F1: Original IRB Approval ...... 221

Figure F2: IRB Modification Approval ...... 222

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CHAPTER 1

INTRODUCTION

During a single moment in 2011, over 250 federal, county, and local law enforcement officers simultaneously fanned out in seven states across the United States to make arrests pursuant to an indictment following a multi-agency task force investigation into a large gang sex trafficking network based in San Diego County, California. This network, composed largely of members from three gang “sets,”1 but including many others, exploited minors and young adults for commercial sex2 via internet advertisement, mainly in hotel rooms. The network largely operated in one county, but did business or had connections in six other U.S. states.

Four years previously, the first minor victim was discovered by a local law enforcement officer who realized that something much bigger was going on than the simple juvenile prostitution incident for which he was called. As a result, a four year investigation led by local law enforcement from multiple jurisdictions began that grew to require federal support during the final eighteen months prior to the arrests, due to interstate jurisdiction, Federal charges involved, and the resources ultimately needed to build the prosecutions and break up the network. This trafficking ring contained over 120 individuals actively involved in the trafficking operation and over 250 additional identified associates and peripherally-involved persons.

1 A gang set, or clique, is like a neighborhood branch of a larger gang. For example, the Bloods and the Crips have branches in cities and towns across the country, and a single city may have several sets or cliques in different neighborhoods that all identify as “Bloods,” for example. In black gangs, these cliques appear to be more a manifestation of identity than of actual hierarchical organization, so no assumptions should be made that there is any sort of direction coming from a single organizing entity. Most gang sets operate somewhat independently, or in relation to others in their area, or to sets in other cities where family and friends may live (Papachristos, 2013). 2 Commercial sex is the exchange of a sexual act for anything of value (Dank et al., 2014). 9

Gang-controlled sex trafficking in this part of the country operates with a strict set of rules, especially for the victims, violations of which are punishable by violence that can sometimes be quite severe.3 Not all sex trafficking networks are this violent, but this one was known for using harsh physical control techniques including severe beatings, burnings, and in one case, spraying mace down the throat of a victim suspected of being a “snitch.” While these methods remained prominent throughout the life of the network, the network’s suite of techniques for managing and exploiting their victims grew more sophisticated over time to include more psychological and emotional means of control. Later in the life of the network, pimps and bottoms4 typically interspersed beatings, restriction on physical movement such as locking in hotel rooms, verbal abuse, and threats with “love bombing.” Love bombing is convincing a victim that her controller is in love with her, thus making her want to please her

“boyfriend” (see also J. Fox, 2013, pp. 599-600). Exercise of these means of control continued even from county jail or state prison via phone calls and letters or through other agents on the outside. Over 50 such victims were recovered during the four year period of investigation, and in the final of the two group indictments, almost 40 gang members and associates were prosecuted for racketeering, sex trafficking of adults and minors, drug distribution, and other charges.

3 It is important to note that victim-perpetrator relationships are complex, two-way relationships despite the power imbalance. This will be discussed in detail throughout the dissertation. For the purposes of this introduction, the term “victim” is kept because that is how they are identified in the indictment, and how they were classified by law enforcement and made eligible to receive support services. 4 Notes on terminology of “the life”: the word “pimp” is a loaded term with many cultural meanings beyond simply denoting one who manages and profits from the prostitution of another. However, like Dank et al. 2014, this study uses the word “pimp” because it was commonly found in all the interviews and police records used as data sources. It was also used by the network participants to describe themselves in police interviews, although victims just as commonly referred to pimps as their boyfriends. “Bottoms,” or “bottom bitches,” are female members of the network who typically fill the role of a “lead prostitute” in charge of recruitment, training new recruits, enforcing quotas for how much money each prostitute was to bring in daily, and enforcing the “rules of the game.” Bottoms were often also given the task of carrying out violence on behalf of the pimp so that he could mitigate risk and also keep being seen as a good guy. A bottom typically started as a regular prostitute that later moved up in rank. This position usually results in some perks and some relief from being the recipient of violence herself. Legally she falls into a gray area because she becomes both victim and offender (Dank et al., 2014; Petrunov, 2011; multiple in- person interviews with Law Enforcement). 10

Several more had also been indicted two years earlier, during the first group prosecution of this network. That prosecution was eventually rolled up into the second, larger prosecution undertaken in 2011.

This dissertation examines the effect of network position on the probability that a given sex trafficking network member avoided indictment, and what that says about the functioning of trafficking networks and their resistance to dismantlement strategies by law enforcement.

Indictment was chosen as the dependent variable because the level of evidence required to indict is higher than for arrest. This makes avoiding it a stronger indicator of success for a perpetrator, especially since almost all network members have been arrested at some point (hence their presence in police data). Ties between members, particularly involving members positioned as the bridge, or broker, between otherwise disconnected others, are hypothesized in network studies to be conduits through which some sort of benefit flows. These benefits may include power, information, money, protection, opportunity, or influence (Borgatti, Everett, & Johnson,

2013).

Others hypothesize that brokerage positions can be detrimental as well as favorable

(Kreager et al., 2015), so that an advantageous position in one network may be disadvantageous in another given concerns about remaining hidden from authorities and differences in operational practices. An individual in a brokerage position might also use his/her power to block benefits from accruing to others rather than facilitating their flow. For example, a broker might compartmentalize or keep information to him/herself, if that is of more benefit than sharing it between groups s/he deems threatening to his/her position.

This study uses police and court data of multiple types to examine how brokerage, measured in different ways, influences a network member’s probability of avoiding indictment.

Data were triangulated from multiple types of law enforcement investigation records, court documents, and investigator interviews, thus rendering the dataset more robust to bias than if one data source was used in isolation (Malm, Bichler, & Van De Walle, 2010; Morselli, 2009).

On a theoretical level, avoiding indictment is used as a proxy for survival—it represents the ability of a given trafficking network member to keep doing business or functioning uninterrupted. This is an important utility that offenders and victims alike want to maximize.

Rational utility maximization by network members is important to consider when examining the operation of criminal trafficking networks as business enterprises. Methodologically, this study explores the effectiveness and utility of three different network variables used to measure the effects and meaning of brokerage position, particularly in larger networks. On a practical level, the goal of this study is to explore the extent to which identifying brokers in a sex trafficking network may help police target network members that would best fragment the network, thus reducing its ability to operate efficiently and exploit victims for financial gain.

Context for the present case

So where does this case fit into the larger picture of the commercial sex economy in the

United States? In 2014, Meredith Dank and her team at the Urban Institute published the largest and most comprehensive multi-method study to date of this economy, and its findings provide important background information to help place this specific case in context. No other recent study compares to the breadth and depth of this multi-year project in giving a national snapshot of the size, shape, and operation of commercial sex market in the United States. Covering San

Diego, Seattle, Dallas, Denver, Washington, DC, Kansas City, Atlanta, and Miami, Dank et al. used existing datasets documenting sex markets from 2003 until 2007, and interviews were conducted with 261 local and federal law enforcement, prosecutors, convicted pimps/sex

traffickers, sex workers, and pornographers about the structure of the commercial sex economy, profits generated, networking within it, and changes in the market over time (Dank et al., 2014, p. 1). A number of important findings came out of their study that provide context for this case.

First, of the eight cities included in Dank et al. (2014), six cities were found to have gang5 involvement in sex trafficking and three reported the activity being dominated by gangs, including inter-gang cooperation (p. 58). Law enforcement from three cities reported significant hierarchical structures indicative of organized crime involvement. Five sites reported that offenders frequently communicate with one another. Three sites reported sophisticated money laundering operations. Four reported that many sex traffickers were former drug traffickers, and four reported sex traffickers concurrently dealing drugs. Three sites have significant Asian involvement in trafficking, two reported significant Eastern European involvement, and five have significant Latino involvement (Dank et al., 2014, p. 12).

Dank et al. estimated the size of San Diego’s commercial sex economy at $96.6 million in 2007, with detailed methodological caveats. Carpenter and Gates (2016) estimated that size to be much larger—$810 million in 2013 for San Diego County—by adding activities that occur around the recreation and gambling industries. Based on their interviews with facilitators they could access, Carpenter and Gates estimated that traffickers earn a mean gross income of

$670,025 annually, versus $528,000 annually as estimated in the Dank study. Carpenter and

Gates’ calculations are based on an average take of 75 percent from each transaction (p. 11).

None of these numbers are to be taken at face value, due to methodological limitations inherent

5 For the purposes of this study, the term “gang” is defined as “a group which has three or more members, generally aged 12–24, who share an identity, typically linked to a name and often other symbols, view themselves as a gang and are recognized by others as a gang, the group has some permanence and degree of organization, and the group is involved in an elevated level of criminal activity” (J. Fox, 2013, p. 596). This is consistent with the definition used by the National Gang Intelligence Center, which is the Federal Bureau of Investigation-coordinated clearing house for gang intelligence between Federal, state, and local law enforcement in the United States. 13

in calculating these “guestimates,” but they are provided here simply to establish that the market in which this gang network operates is substantial.

While gangs are typically thought of as having territories and engaging in turf wars, particularly in popular conceptions about street corner drug markets (Papachristos, Hureau, &

Braga, 2011), it was discovered by Dank et al. that rival gang cliques have begun cooperating with each other when it comes to prostitution. Indeed, previous literature on gang homicide describes ally relationships among gangs that change over time as often as rivalries do (Braga,

Apel, & Welsh, 2013; Decker & Curry, 2002; Nakamura, Tita, & Krackhardt, 2011). While a number of rules certainly apply, Dank et al. (2014) found that gangs have realized they can make more money working together and pooling their resources than they can working apart. Use of the internet to facilitate business also means the commercial sex market is now less based on geographic turf (Korsell, Vesterhav, & Skinnari, 2011; Rocha, Liljeros, & Holme, 2010), so there is no longer the need to keep others “off your corner.”

Pimps and sex workers6 cite many of the same reasons for becoming involved in commercial sex. These include neighborhood influence, family exposure to sex work, lack of job options, or encouragement from a significant other or acquaintance. Individuals who are struggling to meet basic daily needs like shelter and food are obvious targets for traffickers, especially minors who may have run away from home or been kicked out. Other vulnerabilities include mental illness, learning disabilities, loss of a caregiver or family support, history of physical, sexual or other abuse, recent discharge from a juvenile or drug treatment facility, living in foster care or in an area with concentrated disadvantage, or having family members already in

“the life” (Carpenter & Gates, 2016; Clawson, Dutch, Solomon, & Grace, 2009; Dank et al.,

6 In instances where both voluntary sex workers and sex trafficking victims may be involved, the term “sex worker” or “employee” will be used in this text for simplicity. 14

2014; Estes & Weiner, 2001; Polaris, 2015). Youth are considered easier targets, with the average age of entry estimated to be between 14.5 and 19 depending on the study – not 12 as cited in many advocacy campaigns (Carpenter & Gates, 2016; Marcus, Horning, Curtis, Samson,

& Thompson, 2014; Raphael & Myers-Powell, 2010).

Finally, most criminal justice stakeholders interviewed by Dank and colleagues felt the commercial sex economy was much larger than they could criminally investigate due to

“resource constraints, political will, or lack of public awareness about the prevalence of these crimes. Multiple offenders expressed the sentiment that ‘no one actually gets locked up for pimping’” (p. 3).

The present study examines a socially-networked gang case with internet-facilitated operations, in a jurisdiction with a human trafficking task force that can investigate at least some of these networked cases, but where perceived risk of apprehension is still very low. The study’s purpose is to examine and provide a tool that law enforcement can use to support such investigations, to understand the meanings of different network measures in the context of a sex trafficking case, and to show how they can be used to identify targets for infiltration when applied in practice.

Statement of the research problem

While the majority of sex trafficking and consensual pimping7 in the United States is committed by individuals or small groups of 2-3 individuals, domestic gang sex trafficking networks represent a type of trafficking that is being seen in cities, suburban communities, and on travel circuits throughout the United States (Clawson et al., 2009; Dank et al., 2014).

7 Use of the word “consensual” here refers to a pimp-sex worker relationship between adults with no force, fraud, or coercion involved – see section on the legal definition of human trafficking below. 15

Therefore, understanding the dynamics underlying domestic gang sex trafficking operations is an important empirical question with regard to reducing commercial sexual exploitation and trafficking of victims by these networks, and to reduce the amount of money gangs are able to earn from these activities and funnel into other enterprises.

While human trafficking is a crime with numerous social, cultural, psychological and other explanations, it is, at its heart, economically-driven. No matter the political, social, and economic environment that allows this crime to happen, or the personal, cultural, and socioeconomic vulnerabilities that place individuals at risk of victimization, people ultimately compel others into forced labor or prostitution because there are markets for cheap sex and labor, pools of desperate potential victims available, and money to be made. If this were not so, traffickers would seek some other avenue from which to profit. Difficulties in victim and offender identification and incomplete understanding of trafficking networks are two problems that have so far translated into low rates of prosecution of this widespread, yet hidden crime

(International Labor Organization [ILO], 2012; United Nations Office on Drugs and Crime

[UNODC], 2014; United States Department of State [USDOS], 2013). This dissertation focuses on the second of those two problems, incomplete understanding of trafficking networks, by examining a large U.S. gang criminal case via social network analysis.

Network theory often posits that the most valuable individuals to ensuring the survival and profitability of a network operation are not always the individuals at the top, but those who broker the most important business relationships between others in the network (Burt, 1992;

Granovetter, 1983; Morselli, 2014; Wasserman & Faust, 1994). These may be the same individuals, but often they are not—brokers may reap some profit or benefit from the enterprise even if they are not directly or extensively involved in the main activity outside of connecting

others in an advantageous way (Burt, 1992). This study explores several hypotheses around the applicability of this concept within a gang sex trafficking network and its members’ goals of continued survival and profit.

The network theoretical approach, and the social network analysis (SNA) method associated with it, take as a primary assumption that the observations in any given network are not independent of each other. Rather than a violation of statistical assumptions to be controlled for, this interdependence between individuals is the subject of interest (see, for example,

Morselli, 2014; Wasserman & Faust, 1994). In criminology, network analysis approaches have been used more in studies of other types of organized crime (e.g., Calderoni, 2012; Lupsha,

1983), of neighborhood and other social or group-based criminological theories (e.g., Kreager et al., 2015; McGloin & Kirk, 2010; Papachristos, 2013; Schaefer, 2012), and of gangs generally

(e.g., Papachristos, 2009; Papachristos et al., 2011), but rarely for studies of human trafficking.

The most important relationships or roles for ensuring survival of the network as its own entity, and of individuals within the network, will vary by time, place, and type of network. But, qualitatively-informed SNA offers a useful way to pinpoint the most important individuals in each context, because they may not always be who one would expect. SNA can be a useful tool that might help law enforcement target the individuals that will best fragment the network, reduce its operating capacity, and reduce its ability to hurt more victims.

The rest of this introductory chapter proceeds as follows. First, an overview of the issue of sex trafficking is presented, beginning with brief discussions of the definitional debates that impact how trafficking research is conducted. This is followed by a look at data and measurement issues in the field, a brief description of extant sex trafficking network typologies in which domestic U.S. gang sex trafficking is one type, the legal framework that has arisen

around human trafficking, and the challenges around investigating and prosecuting human trafficking cases. I then follow with a description of this research in which I clarify what this project does attempts to achieve, and provide a roadmap for the rest of the dissertation.

General background: Human and sex trafficking

What is human trafficking?

This overview of the issue of human and sex trafficking begins with the legal definition of the crime in the United States, as defined under the Trafficking Victims Protection Act of

2000 (TVPA) (8 U.S.C. § 1101):

Sex trafficking involves the recruitment, harboring, transportation, provision, or obtaining of a person for the purpose of a commercial sex act in which a commercial sex act is induced by force, fraud, or coercion, or in which the person forced to perform such an act is younger than age 18. A commercial sex act means any sex act on account of which anything of value is given to or received by any person. Types of sex trafficking include prostitution, pornography, stripping, live sex shows, mail order brides, military prostitution, and sex tourism. Labor trafficking is… the recruitment, harboring, transportation, provision, or obtaining of a person for labor services, through the use of force, fraud, or coercion for the purpose of subjection to involuntary servitude, peonage, debt bondage, or slavery. Labor trafficking situations may arise in domestic servitude, restaurant work, janitorial work, sweatshop factory work, migrant agricultural work, construction, and peddling.

The essence of human trafficking, as defined by U.S. federal law and further clarified using the Act-Means-Purpose (AMP) model, lies in the purpose: according to the TVPA, human trafficking consists of an Act (recruitment, harboring, transportation, or obtaining of a person) through Means (force, fraud, or coercion) for a specified Purpose (forced sex or labor); the other important point is that the victim is not free to leave his/her situation whether due to physical confinement, threats to themselves or their families, debt bondage, or other constraints that the perpetrator may use to prevent them from leaving (Bales, Trodd, & Williamson, 2009;

Kreidenweis & Hudson, 2015, pp. 68-69). For prosecutions, the only exception to having to

prove force, fraud or coercion in U.S. law is for sex trafficking of minors: by U.S. legal definition, a minor cannot legally consent to engage in commercial sex and, by virtue of being under eighteen years of age, law enforcement may consider that person a trafficking victim under the TVPA.8 Otherwise, legally proving the presence of human trafficking requires the presence of all three elements.

The United Nations also employs the AMP model in its definition of human trafficking under the Protocol to Prevent, Suppress and Punish Trafficking in Persons, Especially Women and Children of 2000 (also known as the Palermo Protocol), which was developed around the same time as the TVPA. The Palermo Protocol specifies further what is included in the means of force, fraud, and coercion: deception, abduction, “abuse of power or a position of vulnerability, or giving payments or benefits” (UNODC, 2014, pp. 15-16). These terms can be contentious between countries that view their meanings differently (Tyldum, 2010). Likewise, state human trafficking statutes in the U.S. do not necessarily line up with the Federal definition of human trafficking, and while their recent enactment and use is lauded and encouraged, challenges still exist in understanding and applying the new laws (Farrell, Owens, & McDevitt, 2014).

Transportation to another location may or may not be part of the equation in a trafficking situation (USDOS, 2015, p. 9), although movement is stipulated in the Palermo Protocol and by many scholars in their trafficking research (Tyldum, 2010, p. 2). Other factors inhibiting more clarity in definitions include “the lack of theoretical foundation of what trafficking actually is.

Significant conceptual clarity has been sacrificed in attempts to reach an international consensus”

8 Not all states view this as trafficking, however, and some still prosecute underage sex providers as prostitutes rather than treating them as victims. The TVPA does not cover all situations, and state statutes can and do differ. A major push over the last several years has been in the area of Safe Harbor laws at the state level; these require persons identified as trafficking victims to be directed into support services rather than arrested (Farrell & McDevitt, 2014). 19

on fighting trafficking, given that governments, scholars, and non-governmental organizations

(NGOs) have “fundamentally different views on prostitution, labour rights and migrant’s rights”

(Tyldum, 2010, pp. 7-8). These differing views sometimes create difficulty for countries in working together under international pressure to reach consensus on this issue.

Conceptual problems with definitions of trafficking extend to research, political, and policy contexts. These include the conflation of human trafficking and human smuggling

(Wheaton, Schauer, & Galli, 2010), the inability to clearly differentiate human trafficking and slavery (Bales et al., 2009; Kim, 2007), and the practical disputes over the legal definition of trafficking due to differences in moral agendas. One of the biggest ideological debates regarding sex trafficking is between those who view victims’ agency, or the ability of victims to make at least some decisions for themselves in their situations, as existing on a spectrum (Chuang, 2010;

Morselli & Savoie-Gargiso, 2014; Weitzer, 2014) versus those who embrace the moral panic view. The moral panic view states that all prostitution is trafficking and inherently violent, that no victim has any choice while entrapped, that all sex workers are victims, and therefore all prostitution and sex work should be abolished (see, e.g. Agustin, 2007; Chuang, 2010; Farley et al., 2004; Lederer, 2011; Marcus et al., 2014; Weitzer, 2013; Weitzer, 2014 for more detailed and critical discussions in this area).

The AMP model requiring all three elements to constitute trafficking is used in legal statutes by most countries and international bodies today, even if not enforced in an entirely consistent way. Similar differences between definitions in the research community also affect research designs, data collection, what gets counted, how it is interpreted, the scientific validity and generalizability of the research, and the policies and laws that result from it (Feingold, 2011;

Weitzer, 2013, p. 1348).

This dissertation takes the TVPA definition of human trafficking as its starting point, because that is what was relied upon during the investigation and prosecution of this case.

Nuances to be considered in order to better understand the actual functioning of sex trafficking relationships will be discussed throughout, especially in the findings chapter. There, through examination of individual relationships between traffickers and victims/survivors, it becomes clear that neither the line between perpetrator and victim, nor the assumption that the trafficker holds 100 percent of the power, is clear-cut.

Human trafficking: How does it manifest?

Human trafficking globally

According to Kevin Bales et al. (2009), slavery of all kinds (including human trafficking) has been with us throughout history, but it became universally illegal only recently after abolitionist movements drove the activity underground. Androff (2011) details several types of slavery: sexual, child, chattel, debt bondage, domestic servitude, contract, religious, and state slavery. The ILO offers one of the more conservative global prevalence estimates and suggests that there are approximately 20.9 million individuals in forced labor globally, of whom roughly

4.5 million are engaged in forced sexual exploitation; they also identified over 460 different trafficking flows around the globe (ILO, 2012). Several caveats about prevalence estimates apply, and are discussed in a later section.

Human trafficking in the United States

The U.S. Department of State’s 2004 Trafficking in Persons Report estimated that between 14,500 and 17,500 individuals were trafficked within the United States annually, but this estimate is not considered scientifically valid (Weitzer, 2013, p. 1349); it has since stopped offering estimates altogether because there was no defensible empirical basis for them. Now, the

State Department simply reports numbers of reported cases, arrests, and prosecutions (USDOS,

2015). While a large number of people are working on improving methodologies (see below, and see UNODC, 2014, p. 30 for a discussion of one of the working groups), a better national-level estimate of trafficking victim prevalence for the U.S. has not yet been put forth.

However, it is known that sex trafficking of adults and minors occurs in every state in the

U.S., thanks to trafficking hotline case report statistics from Polaris. Polaris runs the National

Human Trafficking Resource Center hotline, which receives calls and case tips from potential victims, victims’ family members, NGO representatives, and members of the general public

(Polaris, 2016b). They rank hotel-based sex trafficking, such as that which dominated the present case study, as the second most commonly reported type of sex trafficking in the United States, representing over ten percent of sex trafficking cases reported to the hotline in 2015 (Polaris,

2016b). Commercial front brothels9 were the number one sex trafficking venue reported to the hotline.

Assessments of available data on human trafficking

The state of knowledge about the size and scope of human trafficking is tenuous due to a number of methodological issues stemming from the challenges of measuring hidden populations and challenges with using official data to create estimates of hidden population numbers.

Further, the ideological debates mentioned above impact what ultimately gets counted in these estimates. Ronald Weitzer, one of the most vocal critics of global and national prevalence estimates, picks apart several common claims about human trafficking: that the number of trafficking victims worldwide is huge, that it is steadily growing, that it is the second or third

9 Commercial front brothels are establishments that advertise as a legitimate business, such as a bar, cantina, or massage parlor, but in which commercial sex acts are also available for purchase (Polaris 2015, 2016a). 22

largest organized crime globally, and that sex trafficking is more prevalent than labor trafficking

(Weitzer, 2010, 2013, 2014). The last claim is debunked even by the questionable international numbers themselves, in which sex trafficking usually represents approximately 10-15 percent of all human trafficking (Walk Free Foundation, 2014; ILO, 2012; UNODC, 2014; USDOS, 2015).

The other three he debunks via the fact that global reports are produced by collecting voluntary reports of trafficking cases submitted by each country. Nations usually compile their figures from police data, NGO data, and media accounts. Then, global and country estimates are created from these estimates.

This practice of creating estimates from estimates is used to generate numbers of both global prevalence and global profits, and several scholars believe that these problems render the accuracy of most prevalence numbers scientifically indefensible; clear, reproducible methodologies are seldom articulated by those publishing these reports (see also Feingold, 2011;

Gallagher, 2014; Guth, Anderson, Kinnard, & Tran, 2014). So, while global reports provide good background information about trafficking via the stories and descriptions they often contain, the prevalence numbers they publish must be viewed skeptically (Brunovskis & Surtees, 2010;

Clawson et al., 2009; Danailova-Trainor & Belser, 2006; Savona & Stefanizzi, 2007; Tyldum,

2010; Weitzer, 2013). In the meantime, scholars and practitioners continue to work on better estimation methods (David, 2014; ILO, 2012).

Weitzer (2014) also argues that micro-level research is much easier to conduct with scientific rigor, and that smaller local or regional studies can provide more valuable, policy- relevant data than less-accurate national- or international-level studies that rely on crimes reported to police. Several other scholars are also moving in this direction. Such work includes

Sheldon Zhang’s study of labor trafficking in San Diego County (2012), Zhang’s subsequent

study partnering with Research Triangle Institute counting agricultural labor trafficking victims in North Carolina (forthcoming), Dank et al’s (2014) and Carpenter and Gates’s (2016) work discussed previously, and a current (as of this writing) project by Abt & Associates that tests their own hidden populations methodology. Abt & Associates’ method involves accessing jails and service providers, screening a representative sample of inmates or clients for trafficking, and calculating estimates of trafficking prevalence.

The field continues to learn from these studies: as Zhang notes, his response-driven sampling method of measuring labor trafficking prevalence is limited in the amount of geography one study can cover and by access to potential victims (2012). However, more reliable and informative data can be gained by limiting the scope and applying rigorous methodology to data collection. Weitzer also advocates micro-level research for its qualitative value—its ability to better document “complexities in lived experiences” that can assist with developing context-specific policy and enforcement responses (Weitzer, 2014, Abstract).

Brunovkis and Surtees (2010) further argue the importance of the level of detail possible in smaller studies to question long-held assumptions about human and sex trafficking.

Sex trafficking typologies, including U.S. gangs

While current prevalence estimates require a number of caveats, typological analyses can complement numerical estimates by giving an idea of what sex trafficking looks like around the world. Trafficking networks vary in form and character depending on region, sub-type of trafficking, and more. This section starts with a brief identification of global and U.S.-based sex trafficking network types to provide some global context, while a more micro-level examination of the “American Pimp” sex trafficking type examined in this study opens the literature review in

Chapter 2.

Paola Monzini (2005) offers a very general typology of small, medium, and large sex trafficking organizations where complexity increases with size. Phil Williams (2008) offers a more elaborate typology consisting of opportunistic amateurs of very little operational sophistication, traditional criminal organizations similar in type to the Yakuza or La Cosa

Nostra, ethnically-based networks such as the Albanian networks described in the next paragraph, criminal-controlled legitimate businesses that engage in trafficking such as Russian and Nigerian networks that use legitimate employment agencies as fronts, and diversified transnational criminal organizations. Overall, Williams sees more flexible, adaptable, flat network structures that organize around transactions becoming more prevalent than the older- style criminal organizations with tight, vertical hierarchies (see also Zhang & Chin, 2003), and

Williams also sees transnational networks exploiting jurisdictional holes and situating certain functions, such as money laundering, in countries with lax legal structures.

Shelley (2007, 2010) identifies a global typology of six network types with different characteristics. First is the “violent entrepreneur” Balkan-Albanian model, referred to above.

They are often family-based, borne of war, violent during transport of victims, and often recruit through the “boyfriending” or “romeo” model common elsewhere that involves feigning love so that the recruit will submit. These victims work the greatest number of hours, suffer the most violence during exploitation, and the traffickers use proceeds more for conspicuous consumption and sending money home to family rather than re-investing it in the business (see also Leman &

Janssens, 2008). It is not unlike the U.S. African-American gang type with respect to victimization techniques. The post-Soviet Russian model is more organized, recruiting through legitimate and illegitimate employment agencies, with participants taking on more specialized functions. These traffickers are often more educated, former military, and use proceeds to fund

gangs, terrorist groups, and sometimes reinvest in the business (see also Leman & Janssens,

2008). Both of these types are shifting from ethnically-based, family relationships to more transnational, inter-ethnic ties as the increase in business and the need to outsource due to geographical spread demands it (e.g. using the Comorra Family in Italy for money laundering and moving operatives as needed throughout Europe).

Shelley’s third model is the trade and development model exemplified by the Chinese, which Shelley describes as being highly organized, concerned with long-term investment, and less violent than other models (Shelley, 2007, 2010). Zhang (2014) clarifies the “highly organized” descriptor, depicting a horizontally-based network of individuals and small groups that organize around transactions, based on skills that each brings to the table. The Mexico-U.S.

“supermarket model” operates smuggling networks that contain some human trafficking within the larger, more voluntary coyote smuggling operation. These networks can be violent and have been known to abandon people in the desert en route to destination. Heil (2012), in her ethnography of trafficking in Immokalee, Florida, describes how women may be trafficked for sex to serve male migrants who were smuggled or may have been trafficked for labor themselves. She also describes a chain-like network of interconnected gangs, with means of control including violence, misrepresenting work and living conditions, confiscating identification documents, threatening families at home, and threatening deportation.

Fifth, Shelley (2007, 2010) describes the “American Pimp” type, which is the type of network examined in the current study. The inner workings of this type are described in detail in the first part of Chapter 2. Sixth and finally, Shelley describes the Dutch attempt to regulate the legitimate prostitution market as a way to reduce trafficking, which is still illegal. However,

Huisman and Nelen (2014) note that trafficking still occurs there despite legalizing prostitution.

Further, cheaper prices for sex acts from trafficked individuals and higher operational costs from complying with regulations are driving down wages and simply causing displacement of trafficking rather than reducing it (Huisman & Nelen, 2014).

Another important type is trafficking and smuggling as they manifest in the Middle East.

This type is characterized by small ethnic, family, and friendship networks that span several countries and thousands of miles (Icduygu & Toktas, 2002), operating in sort of a chain formation similar to the cartwheel structure of the Snakehead network that smuggles individuals from China to the U.S. detailed by Zhang (2014). In this structure, small groups of operators are connected by brokers, but no single person knows too many people outside the next step in the chain (Zhang, 2014). The Middle Eastern form has more personal and family connections, where

Snakehead network connections may be more reputation-based due to the network’s global reach. The Middle East is the largest destination area for trafficked persons worldwide (UNODC,

2014) and is also an area that takes a long view of history and thus thinks of modern national borders as an irrelevant, artificial construction (Icduygu and Toktas 2002). The Nigeria-

Morocco/Italy network is also structured similarly, but it includes deception about work opportunities like the post-Soviet network, and perpetrators use threats that prey on victims’ religious beliefs about voodoo as a specific means of control (Monzini, 2005). Monzini also includes issues of gender and migration along with economic push factors for trafficking in her model. She describes a continuum of agency among victims who may get involved with at least partial knowledge of what they are getting into, traffickers’ choice of victims to meet market demands (young and submissive, for example), and the involvement of organized crime and legitimate employment agencies in providing infrastructure and reaping profits.

While some of the above types are manifested in the U.S. (American Pimp, Mexico-

Central American Supermarket model, Chinese Snakehead), other types are also prevalent in the

United States. One of the largest is the Asian Massage Parlor type, which exists all over the country, in which victims often live on site, have their documents confiscated, and have their movement severely restricted (Dank et al., 2014; Polaris, 2015, 2016b). This type is highly organized and engages in long-term resource and financial planning, as well as money laundering. Latino brothels are another type of commercial front brothel in the United States; they typically do not allow customers of other ethnicities due to concerns about detection

(Polaris, 2015). Trafficking in bars and cantinas tends to serve a wider range of clientele (Dank et al., 2014; Polaris, 2016a). Latino trafficking tends to be more organized than the American

Pimp model, especially the brothels, and they often remit money to their origin countries (Dank et al., 2014). Additional commonly-reported venues for U.S. pimp-controlled sex trafficking, besides hotel and street locations, include truck stops (L. Smith & Vardaman, 2010), escort services, and strip clubs (Polaris, 2015, 2016b). Within all of these types, but especially in street prostitution, exploited victims may be female, male, or part of the Lesbian, Gay, Bisexual,

Transgender and Queer (LGBTQ) communities, with each sub-market also exhibiting specific variation (Estes & Weiner, 2001).

Sex trafficking perpetration in these venues is under-researched by academics at this time. So far, sex trafficking research in the United States has focused more on the import of victims from other countries or on victimization/perpetration generally (Choo, Jang, & Choi,

2010; Countryman-Roswurm & Bolin, 2014; Hepburn & Simon, 2010; Srikantiah, 2007). Fewer studies examine how trafficking manifests differently by venue or type within the U.S.

However, the growing prevalence of gangs in the American pimp scene, beyond the role of providing security for hire, is noted as an important change in the domestic commercial sex market (Dank et al., 2014; Levitt & Venkatesh, 2007; Raphael & Myers-Powell, 2010). While they previously focused more on drugs, more gangs are now getting into a wider variety of money-making activities (Dank et al., 2014). With regard to sex trafficking, they are learning which “gangbanging” practices work in that industry and which do not—hence the newer practice of setting aside some previous rivalries in order to make more money, since the risk of apprehension is far lower than with drugs, robbery, and other crime types (Dank et al., 2014).

Operations were also noted to hide assets by renting homes or cars in other people’s names

(Dank et al., 2014), which indicates a growing sophistication past the “smoke it if ya got it” consumption-based approach to using proceeds noted by Shelley and by law enforcement in interviews for this study, even while they described similar practices occurring in the years closer to the takedown. Thus, organizational learning is present, and “it has been officially reported that the Bloods, Crips, Folk, Gangster Disciples, Latin Kings, Mara Salvatrucha, Starz

Up, Sur-13, and Hells Angels are [now] active in juvenile prostitution throughout the United

States” (J. Fox, 2013, p. 596).

Now that some context has been provided about prevalence of human trafficking and about different types of sex trafficking in the United States, and where gang sex trafficking fits into the larger picture, I turn attention to the legislative and prosecutorial framework that allowed for investigation and prosecution of the present criminal case. How did this particular gang network get caught? What legislative structures underpinned the approach used in the investigation? It is important to understand these, because proving the required elements for the prosecution drove the structure of the investigation and the gathering and analysis of evidence by

the task force. This next section explores the application of racketeering charges to trafficking network cases and how that influences investigation of gang network sex trafficking in the

United States. A roadmap describing the rest of the dissertation concludes the chapter.

Introduction of RICO to the network prosecution tool box

The 2008 renewal of the TVPA added a conspiracy provision to the Federal statute, and a

“reckless disregard” provision to the previous incarnation of the TVPA that required perpetrators to “knowingly use” force or coercion (K. N. Smith, 2010). This allowed Federal prosecutions to include those who “knowingly benefit from trafficking crimes,” and some Federal statutes were amended to include obstruction of trafficking investigations as prosecutable offenses (K. N.

Smith, 2010, p. 774). This allows for prosecution and culpability of individuals who may be facilitators of activity, or who broker relationships between other participants even if they do not participate in human trafficking themselves. With the 2003 renewal of the TVPA, human trafficking had been added as a predicate offense10 to the Federal Racketeer Influenced and

Corrupt Organizations (RICO) Act, which was originally enacted as part of the Organized Crime

Control Act of 197011.

State RICO statutes also exist, such as the Street Terrorism and Protection Act of 1988

(STEP) in California. The STEP Act was patterned after Federal RICO provisions, added specific gang penalty enhancements of up to 20 years, and also allowed for asset seizure where the gang is proven to be a continuing criminal enterprise just as Federal RICO does (J. Fox,

2013; Truman, 1995). Under STEP, gang membership for individuals was often substantiated in

10 A predicate offense in a RICO case is a criminal violation that can be used to substantiate the existence of a criminal enterprise. Full requirements for a Federal RICO prosecution are presented in Chapter 4 with the case description. 11 Federal RICO has been amended numerous times to reflect court decisions and known realities in applicable organizations and criminal activities. The current Federal RICO statute can be found in Title 18, Chapter 96 of the U.S. Code. 30

California by expert police officer testimony only, which was often contested in court if it was not corroborated by physical evidence or other witness testimony (Truman, 1995). This is important because police may use state standards to substantiate grounds for an arrest, and then the case gets transferred to the Federal level later. For example, arrests for acts that appear to be isolated crimes at first may be handled in terms of state or other statutes—but later an interstate enterprise is uncovered and those same acts become predicate acts that substantiate a Federal

RICO indictment.

Nationally, gangs were being prosecuted under the Federal RICO statute and the

Continuing Criminal Enterprise (CCE) statute targeted at drug organizations as early as the

1980s due to the attractiveness of powerful asset seizure provisions, and of being able to group charges against individuals together in order to target the whole enterprise in a single prosecution

(J. Fox, 2013; Truman, 1995). Its first use in a gang case that involved human trafficking was in

2004 with United States v. Pipkins. However, specific trafficking charges did not ultimately make it into the Pipkins indictment even with the 2003 amendment to the TVPA (K. N. Smith,

2010).

Federal RICO charges, which were originally designed in 1970 to target the “bosses” of criminal organizations, usually required directing the activities of others (Albanese, 2011). This

Federal RICO requirement has since been amended from the original to include knowledge of and consent to the activity of at least two other individuals for the common purpose of furthering the enterprise for at least two predicate acts, whether they directed the activity or not (J. Fox,

2013; K. N. Smith, 2010). In a human trafficking gang RICO indictment, other offenses can also be included if they furthered the enterprise (J. Fox, 2013). Under the TVPA, Title 18 of the U.S. code (see Footnote 11), certain civil rights statutes regarding slavery, and the 13th Amendment of

the United States Constitution that outlawed slavery, Federal law enforcement may take jurisdiction over human trafficking cases.12 They do so more often when the trafficking activity crosses state lines and when the cases involved are large. The first Federal RICO indictment that utilized human trafficking charges as predicate offenses, after the TVPA was amended to allow it, was the “Giant Labor” indictment in 2009; this organization engaged in labor trafficking with operations in 14 states (K. N. Smith, 2010). RICO statutes have since been used in several more group trafficking prosecutions; several of these cases are discussed later in Chapter 5. Use of

RICO in both gang and human trafficking cases is meant to signal that human trafficking is a much riskier crime than it used to be, one that can cause severe prison and financial penalties for trafficking network members (J. Fox, 2013).

However, at the same time, traffickers extensively read briefs of prosecutions and share them with their networks so they can adapt to new investigative and prosecutorial techniques

(Dank et al., 2014; J. Fox, 2013). The move away from street prostitution and into hotel rooms with online advertisement was one such adaptation in many markets (Dank et al., 2014), and now formerly less-organized networks are becoming more sophisticated as they learn which attempts to hide activities were discovered and used against others in prosecutions (Law enforcement interviews).

Investigation and prosecution: Human trafficking task forces

Gang sex trafficking cases require specific approaches to investigation and prosecution, and extensive resources given their size and complex nature. Hence, most successful

12 See https://www.fbi.gov/investigate/civil-rights/human-trafficking and https://www.dhs.gov/topic/human- trafficking for information about the Federal Bureau of Investigation (FBI)’s and the Department of Homeland Security, Immigration and Customs Enforcement, Homeland Security Investigations (HSI)’s roles and jurisdictions in investigating human trafficking cases under the Federal laws named above. Both agencies were part of the investigative task force on this case. 32

prosecutions are carried out by dedicated human trafficking task forces that include local, county, state, and Federal law enforcement, as well as a savvy and dedicated prosecutor (Brown,

2014)—all of which came together in the present case. Also necessary are community collaborators such as service providers, educators, medical providers, and other community service providers that victims might come into contact with. One of the most important investigative strategies is to follow the money (Brown, 2014), and in the present case, many connections leading to arrests were made by scouring giant spreadsheets of credit card charges from shared green dot cards13 and cross-referencing them to charges for different user names posting ads on Craigslist and other websites, as well as to hotel room bills for multiple individuals.14 This kind of activity is resource intensive, with a large part of it conducted in the present case by local detectives, by hand, without the benefit of Federal analyst resources during the early years of the investigation.

Recognizing the structure of how RICO charges are proven and substantiated, and how investigations and prosecutions are conducted by task forces, is important for comprehending how law enforcement put together such a case over a period of years—and how they gathered and assembled the collection of investigation and arrest records that formed the data sources for this study. The investigators needed to prove the predicate acts for a RICO prosecution, and thus needed to prove ongoing ties between individuals. It is also critical for understanding the methods that trafficking networks use to conceal their activities, because gang-affiliated traffickers are aware of the techniques used to investigate and prosecute them. This need to

13 A green dot card is a prepaid Visa or Mastercard used to purchase ads, pay for hotel rooms, rent cars, and handle other costs of doing business. 14 This kind of tool for detective work is removed from a detective’s arsenal if advertising on websites and use of credit cards is removed, as many advocates are calling for (Dank et al., 2014, Law enforcement interviews). Rather than reducing sex trafficking activity, or all prostitution activity as advocated by some, it will simply be driven further underground and harder to uncover. 33

conceal their activities has an impact on network structure and the distribution of tasks and labor between roles in the network. Tasks that involve more risk of exposure to law enforcement are likely to be given to individuals deemed more “disposable,” both to minimize risk to network survival, and to control the behavior of victims or other network members by having them assume the risk.

The present study

With that background in mind, this last section describes the present study and provides a roadmap for the rest of the dissertation. This study is a social network analysis of a large gang sex trafficking network, using archival data from the investigations and court proceedings associated with prosecution of the network. These data are supplemented by interviews about the investigation and the investigative process with law enforcement task force members and the

Assistant U.S. Attorney associated with the prosecution. Archival data was collected and interviews were conducted between May 2014 and May 2015, with the bulk of data collection conducted between July and October 2014 during two separate fieldwork trips to San Diego

County, where the case was investigated and prosecuted.

So far, many studies about sex trafficking networks and perpetrators (Dank et al., 2014;

Weitzer, 2014) have either been typological (Monzini, 2005; Shelley, 2010) or more general attempts to map the landscape (e.g., Androff, 2011; Aronowitz, Theuermann, & Tyurykanova,

2010; Dank et al., 2014). To complement these, there is good cause for employing an in-depth case study methods that can examine the inner workings of specific types of sex trafficking networks. This study will contribute to the larger literature examining how perpetrators operate not just individually, but relationally with one another—the type of fruitful study possible with micro-level research, as recommended by Weitzer (2014). This study will not only generate

deeper understanding of this type of sex trafficking in the United States, but also generate more nuanced and informed research questions that may be tested in future studies of similar networks or other types of networks (George & Bennett, 2005).

This social network analysis is supplemented by content analysis of the same case records in order to provide description of the history and development of the network throughout the dissertation, to describe its operations and operational processes, and inform interpretation of the network analysis results. Lastly, data from the indictments of a larger sample of gang and sex trafficking network prosecutions are used informally in the discussion (Chapter 5) to place this study in context and establish the extent of generalization possible from this study.

Research questions

This dissertation examines the effects of network position in sex trafficking networks on network member survival—operationalized as avoiding indictment in the prosecution of this case. Two assumptions underlie the hypotheses tested: a) in line with network theory, sex trafficking network members are not independent of one another, and b) in line with bounded rational choice, actors operate to maximize their utility. Utility may include survival, but also profit and status. Hypotheses are concerned with what type of protective benefits may flow through relationship ties between individuals, based on the idea that these ties are conduits for benefits such as power, information, protection, money, opportunity, or influence (Borgatti et al.,

2013). Individuals in strong brokerage positions, meaning that they bridge otherwise disconnected individuals or subgroups, are hypothesized to have a higher probability of avoiding indictment due to their potentially critical functions of controlling resource flow in the network—other individuals may be inclined to protect them because they want to keep those resources flowing. Thus, brokers are hypothesized to be in a better position to evade detection

versus someone who knows more people and is therefore more visible. Wasserman & Faust call these “auxiliary network studies,” because they account for and employ relational data into causal explanations, but focus on outcomes for individual actors (Borgatti et al., 2013;

Wasserman & Faust, 1994, p. 9). Secondary hypotheses test comparisons between different measures of brokerage, especially related to their effects in a larger network like this one.

Importance of the problem

If we can understand the impact of network position on individuals’ outcomes, and the impact that removing specific network members might have on the network as a whole, then the field will be one step closer to an empirically-driven theory of sex trafficking network behavior in the United States. Typological and descriptive studies provide a useful foundation for understanding sex trafficking and sex trafficking networks, but examining the differential effects of individuals’ positions in relation to others is important for understanding the decision making processes of individuals once they are involved in such a network; failure to understand these decision making processes can impact the success or failure of law enforcement and service provision efforts. The practical implication of the brokerage hypothesis, if supported, is that identifying brokers may help investigators identify individuals that may not otherwise be so visible, but whose removal may fracture the network in a way that reduces its ability to operate.

Why is social network analysis useful to answering these questions?

As mentioned, a core assumption behind social network analysis is that observations are not independent of each other, and this interdependence is the subject of interest rather than a violation of statistical assumptions to be controlled for. Therefore, SNA is an excellent tool for lending quantitative support to otherwise qualitative and potentially subjective criminal network

investigations in practice, and it is an appropriate method for testing scholarly theories about the impact of group- and subgroup-level dynamics on individuals. It is the impact of such dynamics on the outcomes for individual traffickers and their associates that are of interest in this study.

What this dissertation does not do

There are a number of important research questions that are beyond the scope of this dissertation. Questions regarding debates over the definition of human trafficking, root causes, ideological approaches, prevalence, and other issues alluded to in the above overview must be mentioned and understood for context, but hypotheses regarding them will not be tested here, nor will theories about them be offered. This dissertation also does not examine hypotheses related to predicting ties between individuals. Questions regarding why a tie is not observed between two individuals that would be expected to be there according to theory, for example, are not addressed here; nor are systematic examinations of which network attack type (targeting a broker vs. a top-dog, for example) would more efficiently fragment the network. Lastly, this is not an ethnography or cultural deep-dive into lived experiences in “the life” from within an African-

American street gang, nor does it purport to be so. These are all worthy and important inquiries, but this dissertation is limited to the questions noted in the previous section.

Contribution to the Literature

To date, little previous research has applied systematic SNA to examine human trafficking networks; in fact, Bright, Greenhill & Levenkova (2014) identify this as a specific theoretical vacuum in both the human trafficking and social network analysis fields. So, it is a special contribution of this research to apply social network analysis to a sex trafficking network because as of this writing, only four other such studies have been published. One looks at a

Nigeria-Italy trafficking network (Mancuso, 2014); another at a network in the United Kingdom

(Cockbain, Brayley, & Laycock, 2011); another at a Canadian gang network (Morselli & Savoie-

Gargiso, 2014); and most recently another Nigeria-Europe study (Campana, 2016). So, this study is one of only a handful as of this writing, and this avenue of investigation represents a large opportunity for understanding the inner workings of, and vulnerabilities in, human trafficking networks.

Roadmap for the dissertation

The rest of this dissertation proceeds as follows. Chapter 2 contains the literature review, and examines the literature on human trafficking. It begins with a subject matter primer on the internal dynamics of gang sex trafficking in the United States, and then explores economically- based theories of perpetration and the bounded rational choice approach to understanding these criminal enterprises. The second body of literature examined in Chapter 2 is network theory. I will explain current theory in that area, show examples of how it has been applied to gangs generally, to organized crime, and in those few trafficking network studies. The chapter concludes with the conceptual model built from these theories which underlies the hypotheses that will be tested.

Chapter 3 explains the methodology. The research design is presented, including how the fieldwork was carried out, how the dataset was constructed, how variables were defined, and how the analyses were carried out. Literature support for each of these decisions is presented in this chapter, and detail for each hypothesis and sub-hypothesis is also presented.

Chapter 4 contains the results. It begins with the story of how the network studied in the present case began, developed and changed over time, important factors that influenced this trajectory, and some anonymized detail about key influential players and how they may have

influenced critical junctures that changed network trajectory. The content for this was taken from the narrative portions of the police reports, interview transcripts, and interviews with the law enforcement that investigated the case. The social network analysis results are then presented, with additional qualitative detail added to support or explain what the results mean. Results show that brokerage position does have some impact, but that network size and the presence of a large number of redundant ties (multiple pathways possible between individuals) may mitigate its beneficial effects for an individual. Specific ways that may manifest for different types of trafficking network members, and implications for their survival probabilities, are explored.

Finally, Chapter 5 presents a discussion of these results and their implications by placing them in context with the literature, and by informally comparing this case to other U.S. gang and sex trafficking network prosecutions. Indictments were collected for 19 additional cases from

1980 to present. While these indictments obviously do not contain data on the individuals not indicted, they present rich detail about each of the cases that is helpful in assessing how generalizable the results from this research may be, and that can help frame future research questions to be conducted with other cases, either alone or comparatively. Recommendations and conclusions close out the chapter.

CHAPTER 2

MAXIMIZING UTILITY AND THE PROBABILITY OF SURVIVAL IN A GANG SEX TRAFFICKING NETWORK: HUMAN TRAFFICKING AND NETWORK THEORIES IN A BOUNDED RATIONAL CHOICE MODEL

Literature review

This research draws on two main bodies of scholarship—criminological theory and its applications to human trafficking, specifically economically-based models and typologies, and network theory. Applications of these theories in the literature on organized crime and gangs are reviewed secondarily, as they relate to the main model, but these two literatures provide the main subject matter and theoretical background. Network theory also provides the methodological basis for the case study. Within these literatures, scholars use the assumptions of bounded rational choice theory to examine the decisions of the players within their network structures, as well as network-level decisions within their market and law enforcement environments.

This literature search was conducted iteratively every few months during the project period in order to make sure the latest studies would be included. Searches covered the Google

Scholar, EBSCO, ProQuest Central, and JSTOR search engines and databases. Additional searches were also conducted on request by librarians at the National Criminal Justice Research

Service (NCJRS). General search terms used included “social network analysis,” “network analysis,” “human trafficking,” “sex trafficking,” “organized crime,” “gangs,” “gang sex trafficking,” “sex trafficking United States,” “rational choice theory,” “bounded rational choice,” and various combinations of all these terms. Additional targeted searches were conducted for topical items such as the history of using RICO in human trafficking prosecutions, for example.

Studies were chosen for inclusion if they met at least one of the following criteria: a) seminal

theoretical work; b) rigorous original research study; c) grey literature that is either well-known or new and cutting-edge; or d) literature reviews that assess state of a major topical or theoretical area.

The chapter begins with a descriptive, literature-based introduction to U.S. gang sex trafficking internal operations and characteristics, beginning with recruitment processes and getting into the means of control, the “rules of the game,” and other operational processes known and understood among gang networks. This is in contrast to the background overview of trafficking presented in Chapter 1. This is followed by reviews of human trafficking, bounded rational choice, and network theories. A conceptual model is presented at the end of the chapter.

Victimization and perpetration in gang-controlled sex trafficking: How do they operate?

“With the young girls, you promise them heaven, they’ll follow you to hell.”

- Harvey Washington, convicted pimp” (J. Fox, 2013, p. 592).

Recruitment

Recruitment of victims is an ongoing, but fairly easy process based on exploitation of vulnerability. As one former trafficker from Chicago described his targets: “girls who ran away from home or were put out by their parents. Ladies who were pretty but were on welfare, drop outs, you know you can smell desperation. If she is hungry, she will go” (Raphael & Myers-

Powell, 2010, p. 5). Replenishing supply as needed is also easy; some traffickers are known to rotate their “stables,” or groups of girls and women, to meet consumer demand for variety

(Raphael & Myers-Powell, 2010).

Recruitment may occur via social media, off the street, from strip clubs, in schools or after school hangouts, at local malls, and other such places where vulnerable victims may

frequent (Carpenter & Gates, 2016; Dank et al., 2014). Use of the Internet has exploded in the last several years and completely transformed the landscape. Geographical boundaries have expanded both for recruitment and soliciting customers, and it has helped move a great deal of activity off the streets and into hotel rooms, apartments, and other locations with better cover from law enforcement (Carpenter & Gates, 2016; Dank et al., 2014).15 Initial enticements usually promise potential victims a better lifestyle: nice clothes, cars, a nice home, opportunities to become a dancer or a model—“modeling shoots” are often then used to post the girls online once they have been made aware of what the real business is—all the while showering them with love they were not getting at home or elsewhere. This creates dependency on the pimp, especially if they have children (Dank et al., 2014; Polaris, 2015). It has been reported that victims can be

“turned out” in as little as two days if they are vulnerable enough (Polaris, 2015).

Recruitment of pimps by prostitutes is less commonly reported (Dank et al., 2014), and is mostly mentioned by pimps themselves (Marcus et al., 2014). Generally speaking, gang sex trafficking victims typically include female gang members sometimes initiated via a “sex-in,” where a recruit is forced to have sex with many gang members in rapid succession to cement her membership and desensitize her to the activity; those in “the stable,” who may have been enticed through any of the means above, or in rarer cases, kidnapped; and gang-associated girlfriends of members, family, friends, and acquaintances—these are the most likely targets for love-bombing

(J. Fox, 2013). The “girls in the stable” are the most likely to be confined to hotel rooms and subjected to severe violence, though violence is possible with all three groups (J. Fox, 2013).

15 While there has been a push from many activists particularly to shut down websites selling sex, like Backpage and Craigslist, and for credit card companies to prevent their cards being used to conduct business, law enforcement investigators interviewed for this study argue that doing so will take away primary tools they use for investigation. In fact, it was through these outlets that detectives broke this case and substantiated a great portion of the burden of proof that was needed to sustain the racketeering charges—specifically by connecting advertisements with shared credit cards used to pay for them. At the present time, some Internet and credit card companies have complied with these advocacy campaigns and others have not. 42

The “love-bombing” method used with girlfriends and associates may begin with building trust, then asking her to have sex with another gang member “just this once” for money, then discouraging them from having sex for free, and eventually engaging in sex work regularly as

“proof of love” for the pimp—even for victims that still live at home and attend school (Dank et al., 2014; J. Fox, 2013).

Pimp control: Dynamics and operations

Although research on traffickers and facilitators themselves is still in its infancy, it is safe to say that trafficker profiles globally and in the United States are not at all monolithic; they vary widely by place, culture, and victim profile (Dank et al., 2014; Marcus et al., 2014; Shelley,

2010; Weitzer, 2014). Within the United States, gang trafficker profiles vary even between type or ethnicity of gang; pimps and traffickers belonging to Latino gangs operate differently than pimps and facilitators in African-American gangs, for example (Carpenter & Gates, 2016, Law

Enforcement interview). The following discussion of pimp control dynamics and operations, and the above description of recruitment processes, applies to African-American gangs such as the one in the present case and does not apply to other types of gangs, unaffiliated pimps, and pimps that function more as hired managers for voluntary sex workers and do not engage in trafficking per se (Carpenter & Gates, 2016; see also Marcus et al., 2014; Weitzer, 2010, 2013; 2014 for more about the importance of this distinction.)

First, gang-associated pimps openly discuss with each other means of control to maintain power over victims, and they train new pimps on the same. As described above in the recruitment section, the first step is breaking down a victim’s psyche and resistance through a variety of techniques (Polaris, 2015). Psychological manipulation is a primary means of control, and the complex relationships that develop are often a significant obstacle for leaving. This is

especially true if the victim has had children with the pimp; threats to them can be used to keep her “in pocket” and working (Polaris, 2015).

Other means of control include economic abuse, which includes collecting most or all of the proceeds and only giving the victim an allowance for food, clothing, hair, nails, and a motel room in which to work (Polaris, 2015). Economic abuse may also involve establishing quotas that victims must meet each day; these can range from $500-1500 or more (Dank et al., 2014;

Polaris, 2015). Victims, bottoms, and pimps may add “john rips,” or client robberies, to the list of activities to increase revenue or meet a quota if the night has not been good (Dank et al., 2014,

Law enforcement interviews). Risky assets, such as apartment or car leases, or green dot cards used in financial transactions and to keep cash out of bank accounts, may be placed in victims’ names so the victims will not go to the police for fear of getting arrested (Dank et al., 2014, Law enforcement interviews).

Victims may also be subject to sexual abuse or rape by their pimps, beatings or other violence, isolation from others, physical branding or tattooing in visible and/or intimate locations to show ownership, confiscation of documents, control over access to drugs (both illegal, if the pimp allows his girls to use them, and those prescribed for medical conditions), monitoring or surveillance of movement, cell phones and internet activity, and confinement (Dank et al., 2014;

Polaris, 2015). The practice of allowing victims to use drugs varies, however, as a victim on drugs is harder to control and it is harder to trust that she will turn over all the money earned; when drug use is allowed or encouraged, it is often limited to softer drugs such as ecstasy and marijuana (Carpenter & Gates, 2016; Dank et al., 2014). Use of violence is often strategic to ensure that bruises and injuries can be easily covered; if the injuries inflicted are severe, she may be kept in isolation until they heal (Dank et al., 2014).

Pimps also use a widely known set of “rules of the game,” not only to control victims by enforcing compliance, but as a common language in the market. Rules for victims can include not looking at another pimp, not accepting black clients because they may be from a rival gang or may want to poach her to work for them, always giving a client the hotel room number next door so they can check him out first, meeting quotas, obeying the bottom as well as the pimp, using condoms with clients, and more (Law enforcement interviews). Victims may also be sold or traded between pimps: if a girl wants to “choose up” to a different pimp, she may pay a fee, or the new pimp may pay a fee to the old pimp (Dank et al., 2014). A girl may also choose up because she believes she will be treated more kindly by the new pimp, for example (Dank et al.,

2014). If a victim is a minor, she is also usually told which other girls or which bottom she must work with because someone over eighteen must pay for the hotel room (Dank et al., 2014, Law enforcement interviews).

After pimps receive their victims’ earnings and cover operational expenses (feeding and clothing their victims, paying for hotel rooms/ transportation), pimps involved in African-

American gangs are known for spending most of their money on visible consumption (Dank et al., 2014, Law enforcement interviews). However, recently the more sophisticated gangs are funneling some of the money into other criminal activities when network structures become more mature (Dank et al., 2014). Pimps are not known for keeping detailed records, although some keep their own books, and some victims and bottoms maintain records on customers and transactions either on paper or in shared laptops (Dank et al., 2014, Law enforcement interviews). Aside from hiding assets by leasing or placing in victims’ or family members’ names, or hiding titles or cash in others’ homes, some gang-affiliated pimps also have modeling or music businesses set up for tax and recruitment purposes (Dank et al., 2014).

There are a number of theorized causes of human trafficking victimization and perpetration on the individual level, in criminology and elsewhere, as well as theories about larger, structural causes that allow such exploitation and abuse to flourish. These causes are varied and feature interactions between political, social, historical, economic, cultural, psychological, human security, gender, migration, and other factors in different combinations between countries, regions, types of trafficking, networks, and individuals. For example, participants in the Balkans-Western Europe market generally traffic women from villages whose economies were gutted after the collapse of the Soviet Union; domestic traffickers of U.S. citizens may prey on runaways escaping abusive families; and some African networks may traffic women overseas who are escaping political violence (Leman & Janssens, 2008; Monzini,

2005; Shelley, 2003, 2010). Scholars and practitioners from many social science disciplines debate these complex interactions and which set of causes/priorities provides the dominant explanation (see, e.g., Albanese, 2008; Aradau, 2004; Aronowitz et al., 2010; Dean, 2009; Farrell

& Fahy, 2009; Lee, 2013; Monzini, 2005; Shelley, 2007, 2010; Weitzer, 2014 for discussions).

However, full elucidation of these debates is beyond the scope of this dissertation. In this study, I argue that the economic and business model approach deserves examination because it is more practical when seeking solutions. It gets away from purely moral and ideological arguments against human trafficking and informs realistic policy and enforcement strategies that might hit trafficking networks at vulnerable points in their operations in a practical, actionable way (see, e.g., Shelley, 2010). While the moral argument against forcing another to engage in commercial sex against his/her will appears obvious, especially in advocacy circles (Weitzer,

2010), it generally fails as a policy prescription when one attempts to use it to stop an actual trafficker from conducting business beyond raising awareness and likelihood that community

members may report identified activity. According to proponents of economic and organized crime approaches, making it difficult to operate and survive in business, directly disrupting profit flows, and cutting off traffickers’ ability to achieve their operational priorities and objectives works better to reduce prevalence and lends itself to specific, actionable policy and enforcement responses that agencies, municipalities, and countries can take on, operationalize, and execute

(Albanese, 2008; Aronowitz et al., 2010; Shelley, 2010; P. Williams, 2008).

Traditional Criminology and Organized Crime

There are several theories in traditional criminology that can explain human trafficking.

For example, Karakus (2008) combines economic and structural explanations and postulates that human trafficking markets in Turkey are driven by demand and allowed to thrive due to levels of existing social disorganization and disorder. Indeed, individuals vying for scarce resources while living in environments characterized by social disorganization (Park, 1936) can create divisions of labor between legal and illicit markets. Socioeconomic and racial divides between neighborhoods may manifest between the haves and the have-nots (Sampson & Wilson, 1995;

Shaw & McKay, 1942), and cultural transmission of criminal traditions in the absence of other strong social norms may occur (Shaw & McKay, 1942). Illicit labor market solutions (including forced sexual labor) can fill these spaces in the absence of “legitimate” solutions to problems.

Spatial distribution of crime in one neighborhood is also affected by what happens in other neighborhoods (Morenoff, Sampson, & Raudenbush, 2001), as seen in the differential spatial distribution of gang activity and violence in different areas (Papachristos, 2009; Tita &

Boessen, 2012). Over time, patterns of inequality allowed to persist in disadvantaged areas may lead to residents developing “cognitive landscapes” that include tolerance of crime as a legitimate option for achieving objectives (Sampson & Wilson, 1995), and transmitting these

norms socially and/or generationally, particularly when conventional social institutions in the community fail to support young people (see Papachristos & Kirk, 2006; Sutherland, 1937;

Thrasher, 1927 for discussions of cultural transmission and differential association theories applied to gang membership). The intrusion of more public social control—especially incarceration—can itself be criminogenic due to family disruption and the loss of strong familial support for children (Rose & Clear, 1998). Incarcerated individuals also learn more efficient and effective criminal business practices from one another (Dank et al., 2014) and even continue to operate from prison (J. Fox, 2013). All of these might be causes of engagement in sex trafficking, gang-associated or not, or causes that enable sex trafficking as an activity to happen in a neighborhood or city.

Related, anomie theory can be applied to sex trafficking generally, and gang sex trafficking specifically, due to the disjunction between aspirations and the means and opportunities available to attain goals (Durkheim, 1893; Merton, 1938; Messner & Rosenfeld,

1994), such as becoming famous rappers and clothing designers in the present case study. Strain theory (Agnew, 1985) suggests that goal blockage and a preponderance of negative stimuli can lead to the adoption of illegitimate coping strategies. In trafficking situations, strains may come from poverty, limited economic and/or migration options, gender norms, and globalization

(Cameron & Newman, 2008). Institutional anomie theory, specifically, proposes that economic institutions and aspirational norms dominate over all others in American society (Messner &

Rosenfeld, 1994) and globally (Passas, 2000), with the inability of individuals to achieve their goals through legitimate channels leading to crime when legitimate institutions fail to meet their needs (Rathbone-Bradley, 2006).

Additionally, in such institutional vacuums, mechanisms will form within illegal markets to provide governance and other services that an illicit group must have to efficiently conduct and manage operations, but that they cannot access via legitimate means. These include methods for dispute resolution, protection, and other “public goods” that are nevertheless in demand regardless of group legitimacy (Reuter, 1984; Skarbek, 2011). Different types of criminal groups may also share infrastructure when economies of scale make sense (Perri & Brody, 2011), a form of behavior seen when otherwise rival gang cliques cooperate to maximize profits and efficiency by sharing resources such as labor, green dot cards, drivers, and laptop computers, for example

(Dank et al., 2014, Law enforcement interviews).

Separately from traditional criminology, gender theories from radical feminist and moral abolitionist circles posit their own ideas about the patriarchal exploitation of women for prostitution (see Monzini, 2005; Weitzer, 2010 for more detail, and see mention of these debates in Chapter 1). The gendered markets hypothesis combines these with economic theory and posits that women are involved in perpetrating all kinds of criminal activities, such as human and drug smuggling, but in different roles than men, and that women’s participation rates are differentially impacted by blocked access to specific opportunities based on market demand and organizational context (Zhang, Chin, & Jody, 2007). Kleemans et al. (2014), in their examination of Dutch markets, suggest that this blockage may not be the impact of market forces on gender opportunities, but simple lack of access to brokers that can connect them to these opportunities.

In fact, these two reasons for blockage may be intertwined: market organization may be set up such that women have less access to brokers. Kleemans et al. note, however, that women’s interpersonal relationships, as spouses or family members of other network members, uniquely allow them to serve as transnational brokers themselves, and that they provide important services

in mediation and facilitating communications when language is a factor. Although the transnational dynamic is absent from the present case study, it could be argued that bottoms fill a similar role in domestic U.S. networks: they connect others via the personal relationships they have with their pimps. They provide mediation functions in times of conflict, although sometimes they also must carry out the violence used to enforce the rules.

Bounded Rational Choice: Underlying Framework

It is said that a good theory should be able to explain and predict behavior, and should be coherent, consistent, and should unify disparate aspects of a phenomenon to provide a clear account of the behavior under study (Wood & Alleyne, 2014, p. 19), and it should also be parsimonious and observable. Running through all of the above theories of crime—social disorganization, strain, anomie, institutional anomie, cultural transmission, differential association, gendered exploitation, and gendered market theories—are the themes of achieving economic goals and the choices people make to do so within the constraints they face. They differ on what factors may be the more important constraints on choices available, or in what way these factors may constrain information available during decision making, but maximizing utility with regard to personal position and accumulation of material or social resources is still the ultimate priority for most individuals.

Rational is defined here as purposive behavior chosen to benefit the offender in some way; it does not require the offender to go through or be able to complete a full cost-benefit analysis, or to consider all factors when making a decision (Simon, 1978). Thus, the assumptions behind bounded rational choice (Cornish & Clarke, 2014; Simon, 1957) underlie the framework chosen for this study. These assumptions are that individuals make decisions with the information, backgrounds, influences, and perspectives they have available, and that their goal in

making choices is to maximize benefits to themselves, whatever those benefits may be (Johnson

& Payne, 2014). It is a useful approach because the factors constraining choice within the network (e.g. network position, victim status, gender, gang membership, rank) can be allowed to vary by individual. This makes it a flexible approach that allows incorporation of a number of theoretical ideas in a given model.

Derek Cornish calls rational choice a perspective in criminology rather than a theory, as it cannot be tested to the exclusion of other theories that attempt, not very well according to

Cornish, to explain root causes of crime (Cornish & Clarke, 2014). Bounded rational choice perspective allows for several factors and explanations to shape the choices of offenders and others in the way they constrain the calculus available for making decisions. Criticisms, according to Cornish, center on the idea of offender rationality; however, these critics have often focused more on the rationality part than the bounded part of the theoretical perspective, and the concept of bounded rationality can be inclusive of other theories about root causes of crime

(Cornish & Clarke, 2014).

Bounded rationality also sets a low bar for how an offender decides an act will be of benefit to him/her. For example, Lattimore and Witte (2014) discuss prospect theory over expected utility calculations; prospect theory posits that people edit risky alternatives, or prospects, into simpler heuristics or representations in order to make quick decisions. This removes the strict rational choice assumption that people have fixed or definite preferences. It also removes the exclusion of cognitive limitations, or the expectation that individuals are fully aware of all information needed to make a decision, so that perfect knowledge and the ability to make informed calculations are not required in bounded rationality as they are in strict rational choice models. Here we find the idea of “satisficing” (Johnson & Payne, 2014; Simon, 1957), or

choosing the best “good enough” option out of what is available, with the information at hand and given the constraints faced by the individual.

Rational choice is also a theory of criminal behavior, like routine activities theory (Cohen

& Felson, 1979), not a theory of why some individuals commit crime in the first place while others do not. This is contrary to sociological criminology á la Sutherland (i.e., Sutherland,

1937), because it deals with proximal causes that are addressable by policy, rather than distal causes that are important, but not so easily addressable. James Q. Wilson often lamented that most criminological theories' explanatory variables are beyond the reach of policy—how does society eliminate relative deprivation or make mothers love their children, for example (Cornish

& Clarke, 2014)? The intent of rational choice is to be good enough for policy making, not to explain the root causes of everything, which is why Cornish and Clarke call it a perspective rather than a theory. Rational choice perspective is also never meant to be tested the same way other theories are in their ability to make predictions, which is why its basic tenets are used in this study as underlying assumptions about the purpose and behavior of a criminal business that profits rationally through crime (Albanese, 2008), but are not subjected to tests of falsification in and of themselves.

Additionally, according to Cornish and Clarke (2014), the purpose of rational choice perspective is to aid in crime reduction, not social transformation. As such, scholars using this perspective tend to be focused on needs of the victim, and they are not as uncomfortable with the notion of assigning criminal responsibility as proponents of more sociological theories may be.

Rational choice models also do not make such a hard distinction between offenders and non- offenders, but rather they allow for situational decision making and outcomes (Cornish & Clarke,

2014). For example, Chin & Finckenauer (2012) combined a bounded rationality approach with

examination of gender norms and restrictions in their study of sex work and sex trafficking between China and the United States, because it allowed for the examination of victim/participant options available and decision making agency within structural confines. It also allowed for the interrogation of assumptions about victims/participants and their behavior, up to and including whether they were victims at all. In fact, most of the adults interviewed by

Chin and Finckenauer turned out instead to be women who made conscious and autonomous choices to better their economic situations given what was available to them to do so.

Bounded Rational Choice as Applied to Commercial Sex

Levitt & Venkatesh (2007) situate their ethnography of street prostitution in Chicago in the economics of crime literature and analyze the division of labor between prostitutes and pimps, distribution of sex markets in the city, allocation of wages/ proceeds from work, pricing models for services, and strategic, rational choices made to sustain operations and mitigate risk.16

Korsell et al. (2011) delineate how money from trafficking operations must be paid and managed, and that to carry out these tasks long term and/or on large scale (a utility maximization goal), organization, labor, and management rules are required. Further, they note that operations and network survival must be protected from detection by law enforcement, from competing groups, and from traitorous individuals on the inside to maximize their probability of survival.

Thus, Shelley argues for moving away from the human rights focus on trafficking to a market- based, cost-benefit approach (Shelley, 2010), and for framing it as a transnational or organized

16 The Levitt & Venkatesh study examined the voluntary prostitution market, where cooperative business relationships were set up with pimps that provided a service and took a cut of the proceeds, rather than sex trafficking relationships involving force, fraud, or coercion. However, sex trafficking occurs within these larger prostitution markets and is a subset of the activity. 53

crime problem (Shelley, 2007). Both of these approaches help to reframe potential policy responses (Cornish & Clarke, 2014) by helping to understand and predict trafficker actions.

Aronowitz et al. (2010) describe how human trafficking operations function through stages—recruitment, transport, exploitation, victim disposal and criminal proceeds—and then promote a risk assessment approach that targets the market rather than the network, because networks are replaceable. This is similar to the approach in this study, which is to target the network because individuals are replaceable. Based on Albanese’s (2007, 2008) model calling for delineation of factors impacting supply, competition, regulators, and demand variables that could limit the functioning of the market at each stage of trafficking, Aronowitz et al. propose a barrier model. This model focuses on disrupting the stages of trafficking by erecting barriers to carrying out business functions such as recruitment, housing victims, document production/ forging, transport, exploitation, and financial disposal/money laundering, and by identifying the strategic actors that might be hit at each stage in the process. This approach aims to raise the costs of survival as a trafficking network too high to continue. Albanese applies this intervention model similarly, noting that levels of functionality of each of the intervention points are things that can be objectively measured (Albanese, 2011). These approaches are practical and immediately applicable from a policy perspective, compared to attempting to change minds and hearts, or compared to recommendations to reduce income inequality and create more jobs

(Bales, 2007); these are definite problems, but gnarlier to design immediate policy around.

Wheaton et al. (2010) offer a specific bounded rational choice model of human trafficking and human smuggling, where human trafficking is an opportunistic response to increased demands for cheap labor or sex, and the tension between individuals’ economic needs to migrate and political restrictions placed on migration. Wheaton et al. build on the previous

work by Cornish and Clarke (2014) and Herbert Simon (1957), which includes the social, political, personal, cultural and other factors that act as constraints that bound, or shape, a rational choice to engage in trafficking. They also offer a great differentiation between trafficking and smuggling: human trafficking is a function of the demand for cheap labor or sex, while smuggling is a function of the demand for illegal migration services. They further outline the rational decision processes involved for supply, traffickers, and end users/exploiters. For example, persons may start out wanting smuggling services, but wind up trafficked when fraud enters the equation. Traffickers’ decision processes are examined as responses to market concepts such as ease of market entry/exit and product differentiation. Victim decisions when evaluating migration or employment opportunities presented by traffickers are made from positions of relative vulnerability, with options constrained by low education or skills and restrictions on immigration or movement. Finally, Wheaton et al. describe the roles of organizers, middlemen, business operational roles, and corrupt government and law enforcement that enable or facilitate operations. Their work focuses on trafficking across international borders, but can also be applied to internal domestic trafficking. Decisions to immigrate might be likened to decisions to leave an abusive parent’s home or to travel to another state with someone who turns out to be a trafficker.

I follow Wheaton et al.’s lead in applying a bounded rational choice framework to the problem of human trafficking, but on a slightly more “micro” level. Maximum utility calculations, whether conscious or unconscious, can be defined by the individual and may include profit maximization; maintaining or improving network position; physical survival and safety; network survival; maintenance of love, acceptance, or family ties; or more depending on the individual in question, their role, and their personal concerns. I argue that the assumptions of

bounded rational choice provide the best framework through which to understand the activity and operations in this network, because a rational choice approach undoubtedly underlies decision making for all participants in the operation. The various other causes for getting and staying involved in sex trafficking as victim or perpetrator can be included in any analytical model and allowed to vary by individual, network, and geographical area while maintaining core assumptions.

The bounded rational choice perspective and the economic market approach are complemented by network approaches to trafficking, which are discussed next after an introduction to network theory itself. In the current study, network position is hypothesized to be a main causal factor behind who in the network was indicted and who was not. Network position, or the nature and number of one’s ties to others, confers benefits or disadvantages to individuals that impact his/her survival, success, and ability to continue functioning in the network. While in some cases, an individual may be able to choose his/her network position strategically in order to maximize profit/position and minimize risk, in other cases individuals make rational choices requiring more satisficing given how their network constrains the choices available. In this way, the rational choice perspective can be used to help understand the choices and behaviors of both perpetrators and victims in the network, and does not require their separation into different groups. This is important theoretically, since perpetrators and victims have very strong ties with each other, and their relationships are more multifaceted than simply perpetrator-victim. The simpler conception of trafficking implies a 100 percent adversarial relationship, while the reality of their interactions is far more complex than that. Influence moves and information flows in both directions, even though the balance of power is far from equal.

Network Theory

General theoretical assumptions

In the previous section, the selection of bounded rational choice as the framework for analyzing survival outcomes in a large gang sex trafficking network in this study was explained.

Structural characteristics, including the nature and number of an individual’s relationships with others in the network, can constrain choices available to participants and influence the criteria by which the benefits of those choices are evaluated. Given this framework, network theory and network analysis form a natural vehicle through which to analyze such group dynamics. In this section, network theory is introduced. It begins by explaining basic concepts and assumptions, moving to an overview of social network analysis (SNA), which is the methodology that incorporates these assumptions. Examples of how SNA has been previously applied in studies of organized crime, gangs, and human trafficking follow that explanation. A synthesis of these theories and the resulting theoretical model appear in the final section of this chapter.

The first core assumption of network theory is that individuals are not independent of one another, and further, these interdependencies have consequences for behavior (Everton, 2012;

McGloin & Kirk, 2010; Scott, 2000; Wasserman & Faust, 1994). Rather than something to control for, these interdependencies are themselves the object of study (de Nooy, Mrvar, &

Batagelj, 2011; Everton, 2012). So, questions examined are relational in nature. The second core assumption is that relationships, or ties between individuals (also called actors, egos, or nodes) lead to structures (Wasserman & Faust, 1994). Standard social science methods usually do not empirically account for relational information; hence the usual statistical assumptions about independence of observations and identical distribution in their standard errors (Wasserman &

Faust, 1994), and the development of methods to help control for when they are not. For studies based on network theory, datasets are built that specifically map the relationship ties between 57

nodes. These ties are assumed to be conduits for the flow of resources (Everton, 2012). Research questions generally center on either the impact that ties have on individual outcomes, or the impact that tie structures have on the network as a whole (Bright et al., 2014; Everton, 2012;

McGloin & Kirk, 2010). Network theory assumes that relational processes are enduring and causal in and of themselves, but also that social networks are dynamic and change when actors and sub-groups enter or leave the network and when specific ties form, change, or dissolve

(Everton, 2012). Attributes of individuals are considered in relation to the ties between them, if the research question requires they be included at all (Bright et al., 2014; Everton, 2012;

Wasserman & Faust, 1994).

It is important also to note that there are approaches that centrally employ network ideas and measurements to study structures, and there are approaches that employ relational data and network ideas and measurements to study individual, actor-level outcomes. The latter are called auxiliary network studies (Borgatti et al., 2013; Wasserman & Faust, 1994, p. 9), of which this dissertation is one.

Network theory allows us to translate and formally model core concepts in the social sciences in relational terms (Wasserman & Faust, 1994). Used in basic research (Borgatti et al.,

2013), McGloin and Kirk (2010) posit that network analysis is a natural fit for studying numerous criminological theories, such as social disorganization, collective efficacy, social control, routine activities theory, differential association—any criminological theory where social and/or spatial relations are the theoretical variable of importance. In an applied usage

(Borgatti et al., 2013), network analysis is useful as a law enforcement tool to understand crime problems, to augment criminal network investigations, and to develop appropriate interventions

(Braga, Kennedy, Waring, & Piehl, 2001; Malm et al., 2010; Morselli, 2014; Papachristos et al.,

2011; van der Hulst, 2009).

The network approach is a marriage of sociological and social psychological theories and formal statistical and mathematical methods (Wasserman & Faust, 1994) that was inspired by theorists of social structures and the patterns of interaction that form them such as Durkheim,

Comte, Marx, and Weber (Everton, 2012). Network theory and methods grew out of three traditions: sociometric analysts from the gestalt and systems traditions in German psychology, who applied graph theory to visually map the relations of small groups; Harvard researchers in the 1930s that explored patterns of personal relations in larger groups and the formation of connected sub-groups called cliques;17 and Manchester anthropologists who built on the first two to study the structure of tribal community relations (Everton, 2012; Scott, 2000, p. 7). In the

1970s, all three strains came together at Harvard and modern social network analysis was born

(McGloin & Kirk, 2010).

In the gestalt tradition, Jacob Moreno, who migrated to the United

States in 1925, created sociometry, which investigated the impact of

“social configurations” on individual health and wellbeing; he saw these

configurations as resulting from interpersonal choice, friendship, and

repulsion (de Nooy et al., 2011; Wasserman & Faust, 1994). He saw

large-scale “social aggregates” such as “the economy” growing out of

these, which reflected the thought of German sociologists of the time

such as Weber and Simmel (Scott, 2000, p. 9). Absence of paths between Figure 1: Triad Example

17 Cliques in social network analysis are not to be confused with “cliques” in gangs. Cliques in gangs are age- or place-based sub-components (Klein, 1995), while the term “clique” in social network analysis describes the mathematical properties of a connected subgroup in which actors in the subgroup are more closely tied to one another than to others in the network (Wasserman & Faust, 1994). 59

subgroups can also have a determinant effect on social and individual outcomes by cutting individuals and groups off from resources (Scott, 2000). Moreno’s contribution was to visualize these social configurations in patterns of points representing the nodes and lines representing the ties, or paths, between them; these are called sociograms, which are still the visual representations of networks used today. Additionally, by giving paths between individuals positive or negative values indicating the tenor of a relationship, and assigning directionality if the tie between the dyad containing A and B can be defined as one-way, specific graph theoretical analyses can be conducted on networks for whom those properties apply (Scott, 2000, p. 13; Wasserman & Faust, 1994).

Regardless of whether directionality or signs are assigned to relationships, the smallest configuration in the building of networks is the triad—how C is connected to or impacted by the connection between A and B (Scott, 2000; Wasserman & Faust, 1994). If all are connected to each other, and have the same sign and directionality if those properties are noted, the triad is considered “balanced” (Scott, 2000). For example, in Figure 1, nodes 104, 471 and 472 are all connected to one another and all relationships are bidirectional, indicating a balanced triad. It is also transitive, meaning that each path between two actors in the triad is closed (de Nooy et al.,

2011, p. 231). More complex structures are composed of different configurations of connected or overlapping, and open or closed, triads that then make up cliques, clusters, or blocks within the whole network that transmit power, influence, ideas, and in medical research, even diseases. In

Figure 1, the fourth pink node connected to the triad mentioned above complicates the structure and results in a cluster of four, made up of two triads, and this cluster is connected to other parts of the larger network by the tie to node number 18, who serves as the broker through which they can reach others outside their group. Brokerage is discussed later and will refer back to this

figure. Pareto termed the kind of influence exerted by these sub-group (clique) structures the

“non-rational components of action” (Scott, 2000, p. 17), or in terms of this study, some of the

“bounds” in “bounded rationality.”

At Harvard, Homans saw this approach as a way to start breaking down Parson’s grand

“black box” systems theory of social order in a testable way, and he began reworking older studies in order to synthesize that theory out of abstraction using a ground-up approach by transforming the relational data into matrix form in order to discover patterns (Scott, 2000, pp.

22-23). After systems theorists reverted back to more psychological approaches, the Manchester anthropologists provided the next advances by switching the theoretical emphasis from integration and cohesion to the impact of conflict and change on social structures by focusing more on actual sets of observed relations than established, formal institutions (Scott, 2000, pp.

26-27).

John Barnes, Clyde Mitchell, Siegfried Nadel, Elizabeth Bott, and others worked together in the 1950s and 1960s to develop and legitimize the theoretical advance that forms of social relations can be separated from their contents—thus enabling comparative study of different types of networks by using similar methods combining sociometrics and sociograms with graph theory and formal mathematical models based on matrix algebra (Scott, 2000, p. 29). Graph theory here refers not to a diagram, but to a mathematical object containing vertices (the mathematical term for nodes) and directed arcs or undirected edges (ties, lines, or paths between them) (Borgatti et al., 2013). Types of networks studied included ego-centric networks, or networks originating around specific individuals; and global networks organized by specific types of ties, like kinship, event attendance, or alliance (Scott, 2000, p. 31). These relationships can vary in intensity, duration, and a number of other factors.

“Weak Ties” and “Structural Holes”

After social network analysis became a bit established as a set of theoretical propositions and as a method, Granovetter published a paper in 1974 called “Getting a Job” which grew into his later seminal work on weak ties (Scott, 2000). Granovetter (1983) distinguishes between strong ties—people we are very close to and with whom we may be connected in more than one way—and weak ties, or people who are further away from us in the network but who can connect us to others we do not otherwise know. Granovetter posits that weak ties can build bridges and broker relationships between people otherwise separated in a network. They can control the flow of information and other resources between groups as a sort of gatekeeper, which can make them very powerful. In Figure 1, node 18 fills such a role for the pink cluster containing nodes 104,

471, and 472.

According to Granovetter, people in broker positions tend to have more resources, be more successful, and be more immune to network shocks than those who stay in closed, dense networks with few links to outside others. Strong ties are useful as well, as they can often be relied upon in times of need, so strong ties and weak ties serve different purposes. A way to look at it in colloquial terms is that weak ties will get you the introduction, but strong ties will help you hide the body. A network without enough weak ties will also be fragmented, with isolated subgroups, and inefficient at diffusion of resources.

Burt (1992) expanded on Granovetter’s earlier work by introducing the concept of structural holes, which complements the idea of weak ties. Structural holes are entrepreneurial opportunities where a person can see two individuals who could be connected to the profit of both, but are not, and then can step in to be the broker that connects the two. It is a theory of competition not for relationships, but for the benefits of relationships, and it is a theory about 62

positioning oneself near future opportunities. A node’s competitive advantage comes from access to structural holes for which he/she can be the bridge, and possession of ties to the nodes he/she seeks to broker; Burt calls this position in a network the tertius gaudens. A broker is in the best position if he/she is structurally autonomous: free of structural holes on his/her own end so that others cannot also take advantage of these opportunities, but near many structural holes elsewhere in the network (Burt, 1992). To measure this, Burt came up with the structural holes coefficient, also known as the constraint coefficient, which is the proportion of possible connections around the individual that already exist (Borgatti, 1997; Burt, 1992; Masías et al.,

2016). The lower the coefficient, the more opportunities that still exist for the node to be a broker, and the less constrained the individual is in terms of opportunity to use her position to maximize her utility.

In Burt’s model, the relationship is the important unit of analysis because the specific people involved in the relationship are theoretically replaceable. Structure determines the actions of the individual, because the individual knows that he/she can be replaced, and is assumed not to want that. Therefore, individuals may try to vie for most competitive brokerage positions in order to reduce their vulnerability to replacement, although various constraints may limit the level to which this is possible for any given individual. This idea can be applied by law enforcement to assess vulnerabilities within a criminal network, or to where and in what function a criminal organization may decide to insert itself into the larger market for illicit or licit goods.

Several ideas emerge here that can help identify potential vulnerabilities in transnational criminal networks that may be useful for law enforcement. One is to identify vulnerable points where a network may be hit to cause the greatest fragmentation, or separation of clusters. In

Figure 1, removing node 18 would separate our pink cluster from the rest of the network and

leave isolated. Carley (2003) points out that it might be better to take out an emergent leader with many weak ties than a “top dog” who is at the center of the network, but whose immediate network contains a lot of redundant ties that render that individual easily replaced. In other words, if the top dog is taken out, there are already several others that can easily step in with little disruption to the network as a whole, while taking out a strategic broker that connects otherwise disconnected, but important subgroups may have a greater effect on breaking up the network. Another benefit for law enforcement is the ability to complement visual link analyses with social network calculations that can quantify the strength, quality, and direction of links between individuals (van der Hulst, 2009). Network-specific statistical tests may help in understanding processes behind resource diffusion within/between networks, and qualitative studies may complement these to determine exactly how processes of network formation/change and operational processes occur.

SNA Applied in Criminology: Organized Crime and Gangs

Co-offending patterns, and the effects co-offending has on individual offenders, is the most basic conceptual area in criminology where application of SNA makes sense. Traditionally, research on group-oriented criminological theories has viewed co-offending as a characteristic of an event, or as a manifestation of the power of “criminogenic social influence,” but researchers have only started studying position in a co-offending network as meaningful in and of itself in the last few decades. That it may potentially be predictive in its own right of crime patterns, mentorship of offenders, social control over offenders, and offender choices is still a relatively new idea in sociological criminology (McGloin & Nguyen, 2014). Research on criminal networks, particularly as dynamic networks rather than stable hierarchies, has been influential and SNA has been brought to bear in studies of street gangs, organized crime, and other criminal

network types. Co-offending is an important part of the functioning of criminal networks as well as a phenomenon that may occur on its own (McGloin & Nguyen, 2014). This section presents examples in organized crime and gang studies where SNA was used to study several concepts, including cohesion, network durability, spatial dynamics, network organizational principles, and brokerage. This is followed by the few published examples so far on human trafficking networks themselves.

Earlier literature focused on mapping out criminal organization structures generally, using sociograms to do so, as early as Whyte (1943) in Street Corner Society. One of the earliest modern applications of SNA to an organized criminal network was Lupsha’s (1983) study of the

New Purple Gang, a drug network operating in New York City in the 1970s, in which he analyzed the ethnic succession thesis18 and intergenerational patron-client ties using biographical data collected by law enforcement on the top 100 narcotics dealers. Lupsha used network ideas of cohesion and resource transfer and constructed sociograms, but used traditional statistical methods. Nevertheless, his is one of the first modern studies examining crime in this way. Law enforcement often uses link analysis to show connections between individuals, but Lupsha showed how network approaches can go beyond this to empirically examine questions about resource flow; network member roles, relations, and connection to drug activity; and can help law enforcement question assumptions and eliminate weak leads (Lupsha, 1983).

Cohesion

More recent literature examines the effects of interventions on social network properties, such as network cohesion (Bright et al., 2014), and the strength of ties on individual outcomes

18 The ethnic succession thesis is the proposition that if one ethnically-based crime group is dismantled, it will be replaced with another one. Patron-client ties are also stronger when the patron is a father and the client is the son, with resources transferring between them (Lupsha, 1983). 65

such as desistance (Pyrooz, Decker, & Webb, 2010) and trial verdicts (Masías et al., 2016). In

SNA, “cohesion is often measured as the density of the network—the extent to which members in a group are more or less connected to each other,” with individuals in more tightly-woven networks and networks more closed to outsiders likely to be more affected by group norms

(Papachristos, 2013, p. 52). Gang literature has focused on group consequences for criminal behavior for some time. For example, cohesive groups that are more unified under a common identity may facilitate stronger exchange of criminal definitions, or the learned beliefs about the acceptability of criminal behavior in line with differential association theory, leading to more crime (Klein, 1971, 1995; McGloin & Kirk, 2010). Modern applications of SNA to the study of cohesion in criminal networks show that cohesion can be reduced by fragmentation, and some of these studies even evaluate different attack strategies to find the most effective way to achieve this fragmentation (Bright et al., 2014).

Cohesion may be based less on an overarching group identity than on sets of interpersonal relationships within the gang and conflicts with other gangs or with police (Decker,

1996; Thrasher, 1927). However, in some cases, there is a more overarching “nation” structure that may monitor behavior at the smaller-group level (Venkatesh, 2000). This may be especially true if the gang is older and more traditional versus one that formed to conduct a specific criminal activity (Klein, 1995).

McGloin (2005), in her seminal study of four street gangs in New Jersey, captured five distinct relationship types that could overlap (e.g. siblings and co-defendants). Sociograms created from these data revealed that none of the Newark gangs were very cohesive in network analysis terms, as was expected. In other words, they did not have many redundant ties connecting subgroups into one large network, and network density was low. Rather, subgroups in

the Newark area were much more fragmented and isolated from one another than local law enforcement officers thought.

Papachristos introduced the idea that crime spreads through networks like a contagion in his seminal 2009 study, “Murder by Structure.” He argues that patterns of gang murder are better understood not by identifying determinants at the individual person level, but by “examining the social networks of action and reaction that create” them (Papachristos, 2009, Abstract), because the people involved typically know one another and murder arises from their interactions. Group honor is a function of cohesion and the ability to address perceived threats (Decker, 1996).

Norms around honor and face-saving demand that one murder across gang lines demands reciprocity (Decker & Van Winkle, 1994; Short & Strodtbeck, 1963).

Network Durability

Papachristos (2009) relies on Klein & Maxson’s and Eurogang’s definition of a gang as

“any durable, street-oriented youth group whose involvement in illegal activity is part of group identity” (Klein, 2005; Papachristos, 2009). Papachristos posits that a gang’s reputation, which communicates its social standing, is evaluated by this process of competition for dominance with other gangs. He found that patterns of network structure and influence on norms and behavior persist in the group even years after specific individuals have left.

But, generally speaking, constraints related to illegality of product and operations make it difficult for many larger criminal networks to form and sustain over long periods of time

(Calderoni, 2014). Indeed, smaller, more transient groups are the norm and larger, hierarchical, highly-structured groups, while better-known, are the exception rather than the rule (Calderoni,

2014). Most gang literature agrees, supporting the idea that most gangs are transient, relatively flat in structure, and largely decentralized (Curry & Decker, 2003; Decker, 1996; Decker & Van

Winkle, 1994; Klein & Maxson, 1994; McGloin & Kirk, 2010), although some are well- structured and entrepreneurial (McGloin & Kirk, 2010; Padilla, 1992; Skolnick, Corrrel,

Navarro, & Rabb, 1988).

Spatial Dynamics

Moving from cohesion and durability to spatial dynamics of criminal groups, Tita, Cohen and Engberg (2005) note that gangs may operate in a smaller “set space,” but defend a broader spatial area or “turf,” in which they come into contact and overlap with other neighborhoods; in fact, they may even live in other neighborhoods and travel into their turf or set space. Gang members also move over time, and illegal markets and “gang wars” create opportunities for gangs to interact with one another (Papachristos, 2009; Tita et al., 2005). These patterns of interaction may render the recent patterns of cooperative behavior between rival gangs in profit- oriented operations like sex trafficking (Dank et al., 2014, Law enforcement interviews) a bit less puzzling to law enforcement officers who have been surprised by this development.

The work of Tita and his colleagues also provides justification for studying gangs beyond simple geographic and membership boundaries, since gangs themselves do not strictly observe them (see also Venkatesh, 2000). Schaefer (2012), for example, finds that spatial proximity and social distance have separate effects on overall network structure (see also Radil, Flint, & Tita,

2010); specifically, ties between groups and clusters may skip over neighborhoods and be caused by other social factors, also contributing to co-offending patterns. Papachristos et al. (2011) agree, but still point to the primacy of place in the larger model that includes gang social ties.

Bouchard and Komarski (2014) further point out that gang members interact not just with one another, but also with a much larger set of criminally-involved associates that are not necessarily gang members. Therefore, Bouchard and Komarski recommend not taking gang boundaries for

granted but to let them emerge from the relational data. This is the approach taken in the present study, where several so-called “legitimate actors” were also involved in perpetration or facilitation, and whose exclusion would damage the validity of the research.

Network Organizational Principles

Van der Hulst (2009)’s study of adversary networks is framed in terms of how social capital shared within criminal networks facilitates crime. Van der Hulst operates from a perspective on criminal networks that is similar to Mintzberg’s assumptions on legitimate organizations—that networks organize their resources and members in order to meet common goals, create better access to needed resources, and increase efficiency (Mintzberg, 1979). First,

Van der Hulst points out that visual link analysis charts used alone can distort perceptions of relationships depending on layout; network measures, several of which will be fully defined in

Chapter 3, can augment understanding of a criminal network by providing quantitative underpinnings to assumptions about who is really central and what roles they truly play.

Second, a network that appears dense because there are many members may in fact be sparse, because only a small proportion of possible ties between members actually exist, and this has implications for the effectiveness of interventions. Likewise, a member may have only a few connections, but those connections bridge or broker otherwise separate components, such that his/her removal fragments the network and may disrupt efficiency and the flow of resources, at least for a period of time. Van der Hulst notes that SNA may be applied in threat assessments, hypothetical scenario-building, hypothesis testing, building evidence for prosecutions and investigations, identifying important players, and more.

Brokerage

So far, very few social network analyses of U.S. gangs, or even North American gangs, have examined the effect of brokerage position on individual or network outcomes. McGloin

(2005) created sociograms of New Jersey gangs that showed which members served as brokers between subgroups. This specific type of broker is called a cut point – very similar to node #18 identified in Figure 1. Different from brokers that may simply connect individuals in the same cluster, cut points that connect entire groups are a special kind of broker, the removal of which should cause greater fragmentation. McGloin recommended these individuals as potential targets for further reducing cohesion and sending a message of deterrence, although she did not test any hypotheses about the best way to do this in her 2005 study.

The other two gang studies addressing this issue at the time of this writing were graduate theses. The first, Gravel (2013), investigated social capital and role specialization in gangs and found that specialists in certain types of crime tended to have smaller personal networks and lower brokerage position in comparison to generalists involved in multiple types of crime. In his dissertation, A. Fox (2013) found that, combined with being a documented gang member versus an associate, brokerage position as measured by betweenness centrality (defined in the next paragraph) actually increased probability of arrest, rather than serving as protection as theorized by Burt.

There are a couple of different theories about why this may be. Like Fox, Morselli (2010) used network centrality measures to examine evidence presented at a trial of the Quebec Hell’s

Angels to distinguish which network members held vulnerable positions and which held strategic positions in relation to each other based on degree versus betweenness centrality. Centrality measures are quantifications of network prominence based on the patterns and structures of an individual’s ties to others (Wasserman & Faust, 1994). Degree centrality measures the number 70

of other people in the network one is connected to, and betweenness centrality measures the proportion of all relationships in the network that must pass through a given individual. Those relationships are relationships for which one serves as a broker—meaning the path between two individuals (A and B) must go through that third person (C) (Wasserman & Faust, 1994). The implication is that person C, as the broker between A and B, controls the flow of resources between A and B and thus has power associated with that control—A and B are motivated to protect C in order to keep that relationship intact.

In criminal network literature, it is argued that knowing more people makes one more visible, and therefore more vulnerable to law enforcement detection, while the combination of high betweenness (brokerage) and low degree centrality (visibility) serve as protection because that combination of factors means the individual (C in the present example) is potentially less visible to investigators, and holds an advantageous position over the flow of information or resources between A and B (Calderoni, 2014). Therefore, C should be in a better position to survive the impact of an investigation and prosecution of his/her criminal network than A or B.

Morselli’s (2010) tests supported the hypotheses that higher degree centrality increased one’s probability of arrest; and that higher betweenness centrality, as a measure of increased brokerage position, lowered one’s probability of arrest—the opposite of Fox’s conclusion.

Masías et al. (2016), in their examination of the Watergate and Canadian Caviar networks, found that for the Watergate network (N=61), the best predictor of verdict results was betweenness centrality, but this did not hold for the Canadian Caviar drug network. Morselli’s results depend on limited overlap between the two centrality scores, despite the fact that centrality measures are generally correlated with one another (Calderoni, 2014). But, there was enough variance between the two in Morselli’s 2010 study to allow the independent effects to be teased out.

However, this is not always true. So, Calderoni recommends combining SNA with other methods, such as triangulation with content analysis of judicial sources, intercepted conversations, and other data to identify which main tasks, statuses, and other individual attributes are associated with strategically positioned individuals (see, for example, Morselli,

2009; Natarajan, 2000). He then uses this information to disaggregate the effects of centrality measures between different types of network members. Indeed, Calderoni found that strategic position could be identified by comparing the impacts of centrality measures, individual characteristics, and the clustering coefficient (another network measure that captures the likelihood that any two of one’s connections are also connected to each other) on individual outcomes. A variation on Morselli’s hypotheses and Calderoni’s approach form the basis for the present inquiry and will be presented in detail with the formal model and in the methodology chapter.

Applications in trafficking literature

At the time of this writing, just four published studies have examined human trafficking networks via social network analysis, and none of them were in the United States. For the purposes of this literature review, SNAs on general prostitution topics, such as sex-buying patterns over the internet and their resulting disease risk, are not included. This section is limited to studies of human trafficking.

The first of these, in 2011, examined the potential for using social network analysis to examine domestic child sex trafficking networks in the United Kingdom (U.K.), using police data and using only analysis tools already available to police departments there, for the purpose of assessing the feasibility of adopting SNA for investigations in the field (Cockbain et al.,

2011). Data used were video interview recordings, case summaries, text messages, cell phone

videos, and charging documents. The authors’ exploratory analysis included only basic degree, betweenness and closeness centrality calculations, and only descriptive statistics were presented.

Findings included that “ringleaders” identified by police did not have higher centrality scores than other network members. Pre-existing social networks were found to precede offending networks. Closeness centrality scores, which measure the average distance between a node and its alters, or immediate connections, were fairly uniform in that network. The authors also found that the “loverboy” recruitment stereotype was insufficient for understanding offenders, but that having a “girlfriend figure” involved (a bottom, in U.S. terminology) aided recruitment.

However, Cockbain et al’s findings are questionable for at least three reasons. First, only charged offenders and victims were included; facilitators and offenders not charged were excluded, so lack of variation in the centrality scores may well be due to cutting the network boundaries too short. There was no way to tell whether police strategies might have been altered if network analysis was conducted that included all identified network members, not just those that were charged, which would have provided more insight into SNA’s utility in the field for investigative purposes. Second, the Ns were small – 25 perpetrators and 36 victims split into two networks; and third, nodes related to each other in multiple ways were dichotomized, so that a relationship either existed or did not. This disallowed possible weighting to understand the strength of specific ties. Weighting of degree centrality loses its explanatory power in larger networks, due to the already-large variation possible for degree centrality when there are many network members, but in smaller networks like Cockbain et al.’s, it can potentially yield a lot of important information.

The second study, Mancuso (2014), is an analysis of a Nigerian network trafficking women and girls to Italy for sex, and is focused on the role played by madams. This type of

network is ethnic and family-based, flexible and specialized, composed of independent cells that connect through personal contacts in the origin and destination countries. The trafficking process is controlled by madams, who are small in number (N=18) and perform several roles, and are usually former prostitutes themselves. Noting the dearth of knowledge about the relational structures of sex trafficking networks, Mancuso contributes one of the first micro-analyses completed to understand these processes. Using data from telephone conversations, Mancuso finds that not all madams are equally central in their brokerage position, and that those more likely to occupy a more powerful position are a) able to operate across national borders and b) connected to clusters of individuals whose influence also extends beyond national borders. Those with lower betweenness scores tended not to have those characteristics, and occupied smaller spheres of influence.

Campana (2016) also conducted a study on a Nigeria-Italy network. His data sources included two indictments at 600 pages, transcripts of phone conversations and text messages, details of a number of trafficking journeys between Africa and Europe, and background information about offenders against whom an arrest warrant was issued. His final network was constructed from the two month period of activities where complete event data was available, and it contained 58 individuals including 33 victims. He coded the data to create a two-mode matrix (58 actors by 16 events), as opposed to a one-mode matrix which would simply be actors by actors, without the event component. Of the perpetrators, 12 out of 25 actors participated in at least two events together; after that, the numbers fell off. The same two people were involved in

75 percent of the events.

Any co-offending was organized around transaction rather than long-term business relationships, but it may be argued that two months of data is not enough to make such a

determination. Coordination occurred depending on trafficking stage and role; for example, transportation between Nigeria and Italy required coordination between more individuals than activities occurring in a single location, such as housing and exploitation which can be handled by madams alone. Involving fewer people when possible can reduce monitoring costs, an item left out by Wheaton et al. (2010). So, madams were shown to be “independent of each other as well as independent of transporters. Transporters act as service providers contracted by the madams.” In analyses, Campana uses degree centrality only, and only in sociograms. No measures of brokerage were tested.

In a different vein, Morselli and Savoie-Gargiso (2014) used electronic surveillance data from a two-year police investigation of a prostitution ring in Montréal in order to study dynamics of coercion and control within the ring. Some of the prostitutes in the network fit the stereotypical victim role, but despite being recruited mostly as minors, there was a continuum of agency and position found, and some prostitutes held key positions of power and privilege in the network. Morselli and Savoie-Gargiso use a resource-sharing conceptual model to analyze the complex exchanges of network resources between prostitutes and pimps that they found, and they analyzed power disparity levels and patterns control or domination within those exchanges rather than simply making assumptions about their nature.

Their model focuses on necessities for “optimal transactions within an illicit enterprise”

(p. 250) and the diffusion of control in the face of changing dynamics in terms of how participants form partnerships in competitive settings. Their sample included 142 pimps, prostitutes, facilitators, and clients monitored during the police investigation, so it cast a wider net than Cockbain’s study. Ties were undirected to remove the assumption that power flowed only one way, and degree centrality, betweenness centrality, and clustering coefficients were

measured. Content analyses of surveilled conversations were also conducted to understand the precise nature of resource flow. Resources were categorized by theme such as providing protection, managing money, maintaining order, and so on. Frequencies of occurrence for each type were noted, and the specific contents of requests, who initiated them, and who the resource benefitted were discussed.

Morselli and Savoie-Gargiso found much information sharing and competition between prostitutes themselves, and a variety of management styles among pimps. For example, one pimp was very hands off, while another kept strict control with daily phone calls to each of his prostitutes to discuss earnings. Use of fraud and coercion by pimps was observed in the network, but the authors concluded that relations between pimps and prostitutes were much more of a two- way street than the current anti-trafficking narrative portrays, especially once a prostitute comes of age.

These four studies touch on different areas of trafficking research: dynamics of power and control between pimps and prostitutes, some of whom are survivors of trafficking; power conferred by brokerage position in a sex trafficking network; coordination activities in a sex trafficking network, and feasibility of SNA methods in the field. It can be seen that the application of social network analysis to human trafficking cases is still in its infancy.

Synthesizing Theory

The number of scholars who have studied perpetration in human trafficking networks is still small; most human trafficking research still focuses on victimology, prevalence measurement, demand, or socioeconomic and political root causes of trafficking. These are important areas of study to be sure, but human trafficking would not occur if there were no one doing the trafficking. I argue that sex traffickers are less likely to be stopped by moral argument

alone than by erecting obstacles to doing business (Albanese, 2007; Aronowitz et al., 2010) and better targeting of strategic players, such as brokers (Breiger, Carley, & Pattison, 2003; Morselli,

2014), that reduce their ability to operate profitably. In order to target sex trafficking networks and markets, we must first understand them as the businesses that they are.

Because sex trafficking networks, as businesses, are focused on profitability and survival, the assumptions of bounded rationality are an appropriate lens through which to examine their processes, the players, and their decisions. Due to the natural interdependence of network members—perpetrators, victims, and facilitators—network analysis methods are an ideal tool for studying entities whose goal is to ensure continuity even if specific players are removed. At the same time, individual network members also want to place themselves in the most advantageous position to maximize personal gain, or to at least “satisfice” among available options and ensure their own survival. They make decisions related to these goals based on the information, belief structures (personal and organizational), and resources available to them—decisions that will vary depending on role or function and are constrained by an individual’s position in the network in relation to others. Organized crime theory tells us that the decisions that ensure survival and profitability for illicit networks differ from the same decisions in licit networks, even though their goals are the same; for example, licit businesses want to reduce the expense of middlemen, while illicit businesses may want to add layers or place less important players in visible positions so as to avoid detection (Beare, 2003).

Few published studies so far have done social network analysis with human trafficking.

Most studies on trafficking networks have been typological or qualitative, but Bright et al. (2014) and others highlight the need to add SNA more prominently into this mix. Additionally, few gang studies have studied brokerage and the impact of individuals’ network positions within

different types of U.S. gangs. Previous studies have focused on predicting ties and predicting homicides, but not on studying different role structures within gangs in network terms. This study aims to contribute to both of these areas, with a distinctly criminological flavor.

Conceptual model

Figure 2 shows the conceptual model underlying the research questions addressed in this study. The main framework for decision making by individuals in a sex trafficking network, as described, is bounded rational choice. The first step in a bounded rational choice model involves calculating one’s maximum utility, whether done in the traditional economic sense or, more realistically, via shorthand and heuristics (Lattimore & Witte, 2014). A number of factors constrain the real or perceived utility-maximizing choices available to an individual.

Criminological theories such as social disorganization, strain and anomie, institutional anomie, differential association, gender norms, and more can be used to hypothesize about the nature of these constrained choices.

Testing these other theories is not part of this study, but it is assumed that at least one, and probably more, of these are at play as antecedent causes. For example, a potential victim may come from a poor family, with an abusive father, may attend an underperforming school without enough resources for struggling students, may be surrounded by gang and criminal activity, and may then be presented with opportunity to leave her abusive home and live the good life with a new boyfriend promising love and security. Likewise, a potential pimp may come from a similar background and a friend may tell him about all the money he and his friends are making, how easy it is, show him the new clothes and the new music video they are about to drop on YouTube, and how he will be respected if he joins the operation.

A number of utility types may be important to an individual, from standard financial profit to safety and physical survival, security, status, love relationships, and perceived upward mobility opportunities. An individual then weighs these goals against the constraints impacting him or her, within the frameworks shaping his/her perceived choices, and makes decisions about participation in the gang and in the sex trafficking operation—whether to join, whether to stay or leave, whether to conform to group expectations, and whom to connect with. These decisions result in the individual’s network position in relation to others: how central an individual is, how many people he/she is connected to and in what ways, and what level of access one has to structural holes over which one might become a broker.

It is important to remember that these decisions are made not just once, but over and over again as circumstances change and in response to others entering, leaving, or changing positions in the network, so this is a dynamic process. For the purposes of this study, however, survival is the dependent variable, and it is operationalized as having avoided indictment in the 2009 and/or

2011 network indictments. As shown below in Figure 2, all of these processes impact the probability that a given network member avoids indictment, or survives. The hypotheses tested are about how and whether brokerage position in the network is protective, as described earlier, and determines that survival. The hypotheses are outlined in Chapter 3.

Figure 2: Conceptual Model

Research questions (Redux)

As mentioned in the introduction, this dissertation examines how structural network dynamics impact the survival of individual members, associates, and their victims within the sex trafficking network. In this case, probability of indictment, or more specifically, probability of avoiding indictment, is a proxy variable for survival given that rich data were available for both indicted and unindicted individuals. The primary hypothesis is that the individual with the best brokerage position—who connects other members profitably but is not connected to too many people him/herself—is more likely to avoid indictment and survive after controlling for alternative explanations, because he/she is not as visible to police as someone with high degree centrality that is connected with more people (Morselli, 2010). The practical implication of this hypothesis, if supported, is that identifying brokers would help investigators identify individuals

that may not otherwise be so visible, but whose removal would significantly fracture the network

(Bright et al., 2014). The next chapter, Chapter 3, is the methodology chapter which describes the research design undertaken to examine these questions, the research process, and the methods used analyze the data collected.

CHAPTER 3

METHODOLOGY

This study is unusual in that it is a case study of one network, and also a social network analysis (SNA) in which the number of individuals involved makes for an N in the hundreds, and the number of ties between pairs create an N of almost 2,000. Therefore, the N in question depends on the level of analysis. This chapter will begin with justification for the general methodologies of case study and social network analysis, followed by discussions of the research design and the analyses performed on the data collected.

General methodology

Case Study

The case study approach is “a detailed examination of an aspect of a historical episode to develop or test… explanations that may be generalizable to other events” (George & Bennett,

2005, p. 5). George and Bennett (2005) identify four specific strengths of case study analysis: the potential for high conceptual validity, deriving new hypotheses, exploring causal mechanisms, and modeling and assessing complex causal relations (pp. 22-23). Yin (2014) posits that the case study approach should be used when the type of question posed is a “how” or “why” question, when the researcher does not have control over behavioral events such as s/he would have in an experiment, and when the focus is on contemporary events. All three apply here.

In a sense, SNA is used in this study as a quantitative tool for causal inference in a within-case analysis (Brady, Collier, & Seawright, 2010). The SNA, as I describe below, is the deductive piece—testing specific hypotheses about network relations and survival—occurring after a larger, partially inductive exploration of business and relational decision making

processes (George & Bennett, 2005) that was the process of building a valid relational dataset.

This process involved deep examination of archival records and interview transcripts to identify and characterize business processes, group norms, network history, porous network boundaries between gang members and others, deep detail about the nature and power dynamics of various types of relationships, and more. This inductive analysis was critical to ensuring that variables characterizing complex relationships were coded in an accurate and internally valid way, particularly for micro-level variables about gang sex trafficking activity that have never, as of this writing, been coded for a quantitative dataset before.

The decision to focus on a single network in this study was to allow for an in-depth look into this trafficking network’s formation, evolution, and operation in order to generate new insights into an understudied phenomenon—domestic sex trafficking in the United States, of

U.S. citizen victims, as carried out by African-American gangs—and to test specific hypotheses about network behavior that may inform future studies and assist law enforcement seeking to fragment or disrupt these kinds of networks.

I also chose to focus on this single network because the level of data access provided by the investigative task force, described in detail below, was far beyond what investigators of potential comparison cases were willing to provide. In order to ensure no validity concerns with trying to compare non-comparable data, and to take advantage of the unprecedented access to data on the present case, I decided to focus on this case alone to and to learn as much as possible from it. Some generality is traded off in favor of precision (Collier, Brady, & Seawright, 2010;

Yin, 2014), especially given the early stage of this kind of research on trafficking networks.

Other cases are referenced in the discussion chapter to assess generalizability, but the cases are not systematically compared.

Research setting

The case selected for this study was centered on three cliques, or “sets” as they are called locally, of the Crips in Oceanside, California. Oceanside is located in San Diego County. Several members of other gangs, including some Bloods, also participated in this trafficking network, with a total of eleven sets involved, plus unaffiliated associates. The network’s reach spread over seven states before the second of two group indictments was handed down in 2011, but the investigation was coordinated and conducted mostly in the hub, in San Diego County.

I chose this research setting, and this case, because it was one of the first Federal cases prosecuted that successfully brought RICO charges against a sex trafficking enterprise. It even included the asset seizure component, which was extremely rare for trafficking. It provided a unique opportunity to study the inner workings of a large operation. It was also the investigation that facilitated the formation of a very effective human trafficking task force located in San

Diego County that has gone on to investigate and prosecute more large gang trafficking cases.

This case was also chosen because sentencing was completed, which minimized any risk to survivors and others from accessing archival and administrative data.

As reported in Carpenter and Gates’ 2016 study of gang sex trafficking in San Diego

County as a whole, the county has a population of approximately 3 million, 1.3 million of whom live in the City of San Diego.19 Its economy is driven by three industries: tourism, the military and biotechnology. As a tourist, military, university, and border region, San Diego County has a high proportion of short and long term transient populations and visitors. According to the traffickers interviewed in Carpenter and Gates’ study, “sex trafficking piggybacks the hospitality industry… for two reasons: (1) there is a high population of potential buyers… male populations

19 See also http://www.census.gov/quickfacts/table/AGE275210/06073. 84

proximate to other sexual services such as strip clubs and massage parlors, and (2) a context where there is a low chance of getting caught or paying significant penalties for buying sex” (p.

43). Despite these conditions favorable to a profitable sex market, three of the largest Federal sex trafficking network prosecutions in the United States in the last six years occurred in San Diego

County—the present case, the BMS (Black Mob-Skanless) case in 2014, and the Tycoons case also in 2014. These other cases are reviewed in further detail in the Chapter 5.

Carpenter and Gates estimate the size of the commercial sex economy in San Diego

County to be equal in economic impact to the area to the San Diego Padres, and second only to drugs in the illicit economy (p. 109). Prostitution activity appeared to be evenly split between undirected activity and activity directed by a pimp (Carpenter & Gates, 2016, pp. 11-12). Eighty- five percent of the facilitators they interviewed were gang-affiliated, and there were 110 identified gangs in San Diego County. Among gangs perpetrating sex trafficking, the largest ethnic categories represented were white, African American, and Hispanic, although they acknowledge that Asian gangs in this area are under-researched. Carpenter and Gates estimate the number of victims/survivors to be between 3,417 and 8,108 annually, based on arrests and extrapolations from their interview data and area nonprofit records.20 Of these victims, only 70 were treated by victim services agencies. Only 29 dedicated beds were available in the county for sex trafficking survivors, with no beds available for men; Lesbian, Gay, Bisexual, Transgender and/or Queer (LGBTQ) individuals; or children. Ninety percent of confirmed commercial sexual exploitation victims were recruited in school. Survivors identified by Carpenter and Gates were

25 percent white, 28 percent black, 25 percent mixed race, 14 percent Latino/a, and 8 percent other race. 79 percent were born in the United States.

20 As with guestimates provided in Chapter 2, these numbers are to be taken with a grain of salt; they are provided here for the purpose of providing general context to the case setting. 85

Data Sources

Field Work

Field data collection, which included accessing archived investigation data onsite at local law enforcement agencies and process interviews with task force investigators from the local police department, the Sheriff’s office, and the Federal Bureau of Investigation (FBI), including an FBI Victim Witness Specialist attached to the case, was carried out during two separate trips to the area. The first, in July 2014, produced a few interviews and, at the end, the connection to the lead local detective who headed the task force that investigated this case and built the prosecution. It was this detective who provided access to the archived police investigation files.

The second, much shorter trip in October of 2014 yielded the investigative data itself, and the lead detective also spent an entire day walking me through it and providing extensive background information. Data included police reports, evidence reports, and other investigative files that, after signing a non-disclosure agreement with the police department and getting

Institutional Review Board (IRB) approval for my storage, data protection, and subject anonymity protection procedures, I was allowed to download and take home with me on a secured hard drive.

IRB approval was received on April 18, 2014, with a modification approved on

September 17, 2014 in which I amended my protocols to tighten data security and anonymization procedures in response to being granted access to a greater amount of data than initially anticipated. Final protocols to protect human subjects, even via accessing archival and administrative data, include complete anonymization of network members and associates in the final dataset and in the dissertation, and secure storage of data provided on network members and victims. Task force interviewees were also given control over whether they wanted to allow recording of their interviews, the level of information they wished to share for the dissertation 86

versus that which was to be kept off the record, and whether to grant permission to cite them by name. The IRB approval for protocol, #14077, and the approval for a modification from

September 2014, are available in Appendix F.

Archival documents covered a period of over 13 years, most of which fell between 2005 and 2011, which covers the active life of the sex trafficking business. They amounted to 23.7 GB of data, 13,888 separate files about incidents and individuals, and other materials compiled for the prosecution. They included police reports, evidence reports and summaries, victim and witness interview transcripts, gang charts and maps created by the Oceanside Police Department with help from a Federal analyst, and field interview (FI) cards. I was also granted access to watch recordings of interviews onsite with suspects and victims, from which I was allowed to take notes, with precautions taken to protect the identities of those involved. Follow-up interviews were conducted with task force investigators regarding specific questions via telephone on an as-needed basis. Two further task force interviews on the investigative and prosecutorial processes occurred in November 2014: the Assistant U.S. Attorney that prosecuted the case was available only at that time, and only by phone, and the Department of Homeland

Security, Immigration and Customs Enforcement, Homeland Security Investigations (hereafter

HSI) agent from the task force had been transferred to Washington, DC by the time of the study and so he was interviewed there.

Prior to conducting the fieldwork, I accessed media accounts via the internet, and the indictments and sentencing hearing transcripts from the case were accessed via Bloomberg Law.

Also accessed via Bloomberg Law were the publicly-available indictments from 22 other large trafficking network prosecutions, 19 of which are used in Chapter 5 for the purpose of assessing

generalizability from this case study.21 Further internet searches for media stories and public postings on social networking sites by individuals in the trafficking network after the takedown were also conducted periodically between October 2014 and June 2016.

Process Interviews

The process interviews conducted for background purposes were conducted using a semi- structured interview format that asked questions about the investigation or prosecution process

(depending on interviewee) and about the case itself. Respondents were invited to participate by email, and signed informed consent forms prior to participating in the interviews. Different investigators had different levels of knowledge regarding different parts of the case, so questions that were ultimately asked of each person from the bank of possible interview questions varied and were guided by the interview itself. Interviews were not recorded, per the preferences of the respondents; answers to questions were transcribed by computer for all the interviews except for the interviews at the local FBI field office. Per their standard procedure, I was not allowed to carry a computer or phone into the FBI facility, so for those interviews, notes were taken by hand. The invitation, informed consent form, and interview scripts/question banks used are located in Appendices A-C.

All questions asked were open-ended, for the purpose of facilitating discussion.

Interviews lasted anywhere from 45 minutes to two hours. The exception was the lead investigator, who spent two hours with me on my first visit, but then an entire day on the second visit when he provided my data. He also contributed further information and clarification on several points via phone calls and emails throughout the research process. The products of these

21 The other three cases turned out to be other RICO cases that were not related to gangs or human trafficking. 88

interviews are used in the description of the case history, in discussions of data validity, and in the discussion and recommendations chapter of this dissertation.

Data Validity and Management

There are several classes of data used in social network analysis; acquaintanceships, which include kinship, friendship, and weaker associate relationships as long as they endure over some length of time; joint event data, such as having attended the same party; and joint membership or activity, such as being members of the same club (Borgatti et al., 2013). These kinds of data may be collected and then compiled into an edgelist22 of relationship ties, which is then converted by network analysis software into an adjacency matrix or matrices (Borgatti et al.,

2013; Morselli, 2014; Wasserman & Faust, 1994) that can be analyzed. These processes are described over the course of the chapter. Data sources for past studies of criminal networks and gangs have included trial transcripts and indictments, complete investigation files, police/arrest records, or some combination (Bright et al., 2014; Framis, 2014; Lupsha, 1983; Morselli, 2014;

Morselli & Savoie-Gargiso, 2014; Papachristos, 2009). Rarely have they included all of the above.

The data access provided for this study was vast. I chose which files to include for coding, and which would be excluded, based on the demands of the research questions. Further, several folders contained duplicate files, so there was also a concern about avoiding duplication in counting relationship ties between individuals. For example, I did not want to code a separate instance of a tie between two individuals if one source was a summary built from raw reports I had already coded about the same event. Further information about the tie coding process follows below, but of the data provided, I relied on local crime reports, FI cards, dated

22 An edgelist is a list of all relationship ties at the dyad level, formatted for import into network analysis software. 89

photographs, organizational charts created by police crime analysts in I2 Analyst’s Notebook, jail mail, and interview transcripts. All were cross-referenced to create not only the relational dataset that maps all ties between individuals, but the complete dataset of node attributes for all individuals in the network (these variables are described below). Crime reports formed the largest type of data source, and each included not only the actual completed departmental forms plus all witness statements, but evidence summaries and photographs, booking forms, and any other documentation tied to the event such as investigation descriptions, if applicable.

Scholars have written that different data sources can result in different pictures of the same network, especially if taken in isolation, which is important to remember when considering police data. Fleisher (2005) separately used co-offending data, interviews, participant observation, and wiretap data, to create four separate pictures of the same network. These different data sources create network pictures that are quite different from one another, a finding confirmed by Rostami and Mondani (2015). Morselli (2009) found, using wiretap data to examine a drug gang in Montreal, that very few of the individuals central to operations were even gang members at all—showing that the strategies taken by the police department in that case based mostly on other data sources were really only hitting peripheral players. Therefore, data sources, triangulation, and recognition of data limitations are critical to interpretation of results, especially if there is no qualitative component to the SNA (Calderoni, 2014), and most researchers triangulate with multiple data sources to build the most complete networks possible

(McGloin, 2005; Morselli & Roy, 2008; Natarajan, 2000; Natarajan & Belanger, 1998;

Tremblay, Talon, & Hurley, 2001). This is the approach taken here. These limitations, as well as boundary specification decisions, have implications for who is identified as a core member and thus might be targeted by law enforcement (Bouchard & Komarski, 2014; Malm et al., 2010).

Other considerations with law enforcement data have to do with getting complete data about a network, particularly one that endeavors to stay hidden, and with the reliability of practices used by police to classify gang members. Thankfully, recent studies have shown that network centrality measure calculations are robust to a 10-20 percent missing data rate, so a large number of missing peripheral members will not impact network measure calculations too much as long as the goal is to understand something about network properties and not to create a photographic representation of the network (Berlusconi, 2013; Hegemann, Lewis, & Bertozzi,

2013). This means that police data will work well for this study.

The reliability of police identification of gang members is a little trickier. Police gang lists have been shown in many cases to be useful and reliable (A. Fox, 2013; Katz, Webb, &

Schaefer, 2000). California requires that the following four criteria must be met for a group to be defined as a gang (Frequently Asked Questions Regarding Identifying Gangs and Gang

Members, 2015):

 The group has a name (or identifiable leadership)

 The group claims a turf, territory, neighborhood, criminal enterprise, or causes or

contributes to the deterioration of a community through a pattern of criminal activity

 The group associates on a regular basis

 The group is involved in a pattern of criminal activity (two or more of the felonious

acts listed in PC 186.22f).

It is important to note that the identification of gang members was carried out by police officers in the normal course of completing individual police reports under California code—it was only later that these reports were used to build the case for the Federal indictment. The state of California permits, pursuant to the STEP Act described in Chapter 1, that the following are

acceptable criteria from which to identify someone as a gang member (Frequently Asked

Questions Regarding Identifying Gangs and Gang Members, 2015):

 Subject has admitted to being a gang member

 Subject has been arrested for offenses consistent with usual gang activity

 Subject has been identified as a gang member by a reliable informant/source

 Subject has been identified as a gang member by an untested informant

 Subject has been seen affiliating with already known gang members

 Subject has been seen displaying symbols and/or hand signs

 Subject has been seen frequenting gang areas

 Subject has been seen wearing gang dress

 Subject is known to have gang tattoos

The officers that completed police reports provided for this study named either two or more of the criteria listed above, or referenced past knowledge or “training and experience,” or both, as their justification for classifying someone as a gang member in their report. Surprisingly, officers did not refer to the CalGang intelligence database as a source of information in their reports. But, CalGang has been under fire for not being transparent with the public about how someone makes it into the database, and for containing wildly erroneous information (CBSNews;

Winston, 2016). For this study, explicit identification as a gang member by police in a written police report based on the above criteria, or in a gang organizational chart created by the crime analyst attached to the task force, was taken as sufficient, in line with previous research

(McGloin, 2005). But, these classifications still have possible margin for error. Data was triangulated between multiple reports and sources wherever possible with regard to gang membership, as well as for the gang rank, gang association level, and sex trafficking network function variables described later in this chapter.

Sampling and Boundaries

There were three sets of sampling choices in this study. Two are of secondary importance and relate only to data collected for background purposes. The first of those two is the choice of who to interview for the investigative process interviews conducted for the purposes of understanding the data collected, identifying validity issues with the data, and gaining context for where my results may have policy implications for future investigations. All multijurisdictional task force members involved in investigating and prosecuting the case were invited to participate. My goal was to understand how the investigative process worked for the task force, how labor was divided, what individuals thought went well and where there were challenges, and to gain their insights into the facts of the case itself. I contacted every member of the task force, with introductions from the Sherriff’s Office first and then from the lead local detective, and was able to interview an agent from every law enforcement body involved (local, county, and

Federal), the assigned FBI Victim Witness Specialist, and the Assistant U.S. Attorney (N=7).

The only individual I could not interview was the judge that presided over the prosecution, because he was still involved in sentencing and release decisions for convicted individuals. The second choice regarded the comparison cases chosen for the discussion section; there have not yet been very many network prosecutions in human trafficking cases, so I chose every gang sex trafficking or other human trafficking network prosecution for which there was an indictment document publicly available on Bloomberg Law (final N=19).

The third set of sampling choices is much more complex and affects the network analysis directly. They involve which data sources from the vast quantity acquired were coded for analysis, who was included from the data, and who was ultimately included in the final dataset for the network analysis itself. All archival data was coded via content analysis, with supplementation from the process interviews with task force members. 93

A few relations that appeared only in the indictment, but not in any of the files provided to me, were also included, because if they were included in the RICO indictment, participation in at least two predicate acts had to be substantiated. That indicates ongoing ties and network membership. Otherwise, source documents (mostly Federal) that simply aggregated information from the original local police data were excluded, because I had already coded the raw police reports used to compile those summary documents. Separate coding from the same data sources was conducted to record attribute data on individuals, such as age, address and other personal data that has since been de-identified, gang role, role in the sex trafficking operation, number of prior police contacts, etc. The full list of variables coded, and enumeration of the procedures used to do so, follow later. Additional data for the purpose of recording node attributes was gathered from provided individual criminal history files.

Data provided went as far back as 1999, and when coding began it was not possible to know who was going to become important over time and who was not among individuals not named in the indictment. The coding procedure went chronologically, starting with the 1999 documents and moving in order through 2011. All documents, many handwritten, were provided in PDF form, necessitating coding of every single person mentioned in every report— perpetrators, victims, and witnesses—by hand over the course of fourteen months. The goal was to capture the entire universe of individuals documented in the data, because only then could I know where to begin drawing the network boundaries. I did not want to draw a boundary too early and leave out an individual that seemed unimportant in 2005, but who became important later. The final raw “universe,” including gang members, victims, and those surrounding them, numbered 525 individuals.

There are instances in network analysis where sampling is conducted on the universe of individuals. Random samples may be drawn to conduct permutation tests on likelihood of different network structures forming, for example (Wasserman & Faust, 1994). Purposive sampling may be conducted for a number of reasons, such as to conduct analyses on a specific sub-component of the network, to assess structural equivalence (individuals who are not connected to each other but have the same network relationship structures – see Borgatti et al.,

2013), or for any number of other reasons.

For this purpose, which is to identify potential brokers, boundary decisions were driven not by official gang membership, but by the needs of the research question (Borgatti et al., 2013).

In this case, that requires including ties that sex trafficking network members also have to others outside the trafficking operation. The sex trafficking network is nested inside the larger gang network, and individuals have influential ties to people outside of the gang altogether. Thus, two successive cuts were made. The first was logical—individuals who were bystander witnesses at a single criminal event, never appeared again, and never had an identified relationship with another network member were cut. Victims of random auxiliary crimes committed by members, such as a robbery at an ATM where targeting was completely random, were also cut. This was simply to rid the sample of individuals who were truly superfluous, with no identified true relation with another network member.

The second cut to the nodes included was more strategic. As in most networks, there is a core of individuals with connections to a large number of others, and there are many more network members with only a few connections; occasionally, there are also isolates who appear in the data but who are connected to no one (Borgatti et al., 2013). It is thought by some that the inclusion of too many peripherally-connected individuals can skew centrality score calculations

toward zero, which may impact hypothesis test results, while others find that removing these nodes makes very little difference to the final calculations (Calderoni, 2016). Given the lack of substance in the relations of most of the individuals who only possessed one contact, individuals that had only one contact and had no identified role in any of the gangs, or in the sex trafficking operation even as a facilitator, were removed in line with previous literature (Calderoni, 2014;

Morselli, 2009; Natarajan, 2000, 2006). The final number of members, or nodes, included in the network is 375. Leaving the other 150 nodes in would have skewed centrality scores further toward zero, and there was no theoretically sound reason to keep bystanders in the sample.

There is a second sampling concern with network data. In addition to which individuals are included, it must also be determined which relationships will be included in the dataset and from which time period(s) or date range(s). The “complete universe” data included all relationships identified from 1999-2011, even though trafficking activity did not begin in any coordinated fashion until 2005. Again, I included all during coding because I wanted to be sure I did not exclude something that might be important later. After the dataset was complete, it was evident that data from prior to 2004 should be excluded, so relationships that ended in 2003 and earlier were cut, as well as individuals who did not appear again after 2003.

Records of criminal activity other than sex trafficking and prostitution operations remained included because these were still used to build the case that the network was a continuing criminal enterprise (CCE) under RICO, and because they established the strength and character of relations between network members that went beyond the sex trafficking operation.

They also helped identify brokers that connected members of the trafficking operation and profited from it, but who perhaps were not directly involved in trafficking activities themselves.

A relationship, for the purposes of this study, indicates an ongoing tie between any two individuals in the network, be they traffickers or facilitators, victims, associates of any kind, and in a couple of cases, repeat customers. Two people may also have more than one type of relationship at the same time, such as cousins and a gang relationship. Relations coded for the dataset included ongoing relationships and event-based relationships. Ongoing relationships include pimp-prostitute, pimp-bottom, bottom-prostitute (direct reporting relationship), siblings, parent-child, friends, gang relations, and “known associates” (police category). Event-based relationships included co-offenders, or co-attendance at a criminal event, party, or gang “hood” day. Complete definitions of all relationships coded follow in the next section. For the purposes of this SNA, all ongoing relationships are included. Event relationships are included as long as there were at least two of them between a pair of nodes, if the relationship was coded from one of the substantive counts in the RICO indictment, or if data about the ongoing relationship was confirmed in one or more police reports. All three criteria were chosen in order indicate substantial evidence of an ongoing relationship, the effects of which are the subject of interest, versus the potentially coincidental presence of two individuals at a single event.

Dataset construction

Copies of the police investigation files provided by local law enforcement were transferred to a secured external hard drive for me to take with me. I then coded these materials for relationships: 702 unique associations that contain two or more individuals were identified in the raw data, before the boundary decisions described above were made to exclude truly peripheral individuals. After boundaries were implemented and all dyad permutations were calculated for associations that contained more than two people, dyads in the final sample

numbered 1,966 ties between 375 individuals, from a total of 610 unique relationships. The number of ties between dyads constitutes the “N” in SNA (Borgatti et al., 2013; Scott, 2000).

I recorded ties, attribute data, and a “master list” of relationships containing detailed descriptions from each source file in a master Excel workbook. The first tab, Attribute Data, contains node-level data on each individual identified in the network. Names, aliases, and personally identifiable information (PII) were included until coding was complete to make certain no one was duplicated and that all references to a given individual could be referenced back correctly to source data during verification. Unique, anonymous identifier numbers for each person were also created during this process. After coding was completed and verified for accuracy against the source documents, all PII was deleted from the workbook per IRB specifications.

The second tab in the workbook contained the master list of unique relationships, plus additional descriptive coding for later analysis. Relationships were coded with columns given for source document name, source document number (e.g. 101), relationship number (e.g. 102), relationship code (in which the source document number and relationship number were combined, e.g. 101102), relationship start date, relationship end date, and geographic location of relationship (if event, including city-state and hotel name, if applicable). Also recorded was a relationship description field, which was an open text field containing all the individuals who were part of a given relationship (a dyad, or a larger number of people present at an event), plus detailed description of the relationship and/or what happened if not self-explanatory. For example, “siblings” would not require a lengthy description, but the events of a public shooting would require some detail. This was done to make it easy to find data later, and to remember what was described in a given source document. It was also used to facilitate the description of

the network’s evolution that opens the findings chapter, the interpretation of results in the discussion chapter, and to make the final dataset useful for future studies.

Relationship Types

As mentioned above, several types of relations were coded. “Pimp-Prostitute” is a trafficking or prostitution relationship that indicates that a victim worked for the male individual in the dyad. The word prostitute was used here to indicate the job function performed, not the willingness of the individual to participate in it, which varied from person to person. Issues concerning usage of the word “pimp” were discussed in a footnote in Chapter 1, as was the meaning of the term “bottom.” “Pimp-Bottom” indicates that the female in the dyad was the lead prostitute controlling others below her, reporting to the pimp in the dyad. “Bottom-prostitute” means that the victim identified in the dyad reported to that bottom as well as to her pimp. These relationships and categorical labels are taken directly from crime reports and survivor/suspect interview transcripts using these terms to describe relationships and events.

There was sometimes confusion if a witness statement referred to two individuals as boyfriend-girlfriend, but then described arrangements such as his organizing and supervising a date for her and collecting the money afterward; in such cases, relationship was inferred, and I triangulated that information with other police reports that identified the full nature of the business portion of the relationship wherever possible. In those cases, there was usually both a boyfriend-girlfriend and a pimp-prostitute or pimp-bottom relationship occurring simultaneously.

Family relationships such as siblings, parent-child, aunt-niece, or grandparent-grandchild are self-explanatory. “Friend” or “known associate” relationships were identified as such by police personnel completing police reports or by witnesses providing statements to police.

Gang rank relationships include “Big Homey” (top position, usually overseeing activities of a few Gangsters), “Gangster” (middle position), and “Baby Gangster” (low position, usually indicating a new member), in rank order. These individuals can be identified also by their shared gang aliases; for example, a Big Homey might be called “Big G,” a Gangster reporting to him called “G,” and a Baby Gangster below “G” would be called “Baby G.” Dyads containing these sorts of reporting relationships between specific individuals are noted by police in their reports and derived from witness statements, police specifying their own self-knowledge from previous work in their descriptions of events, or from organizational charts and other documents generated using crime analysis databases managed by the gang unit of the local police department. Again, there was no reference to CalGang in the data provided as a data source. There is potential for error in relying on police data only for this coding, so classifications were triangulated wherever possible by noting also physical evidence of gang ties, such as gang attire or photographs showing the individual throwing gang signs, and finding statements about membership and rank on the same individual in multiple crime reports. However, caveats still apply. “Associates” are durable relations between two individuals categorized as such by police, but where no more specific relational definition or description was found in any of the data sources.

These relations can change over time—victims get passed to, or choose up to, new pimps; gang members change allegiances; friendships start and end. But, each relationship identified in the dataset persisted for some period of time and was used to build the criminal case.

Start and end dates for every relationship are also recorded, if known. If a relationship is permanent, such as “siblings,” no dates are recorded. This was done to enable construction of the sociograms by year in Chapter 4 that show evolution of the network, and to enable time-series analyses in the future.

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The third worksheet in the master Excel file contains two columns: one for each node’s unique ID, and one for relationship code (the combination of source document and relationship number). For each relationship code, each member of the relationship is entered in a separate row. So, if network members 1, 2, 3, and 4 were all arrested together for example, the numbers 1,

2, 3, and 4 would be listed in separate rows one underneath the next, with each having the same relationship code listed to the right. A macro is then run to build out all the dyad permutations: for example, 1 is tied to 2, 1 is tied to 3, and 1 is tied to 4 in that scenario, and so on. This macro puts all these permutations into a fourth worksheet, which is the edgelist (Borgatti et al., 2013), or master relationship list used for the social network analysis.

The edgelist was imported into Pajek 32, version 4.03 (Mrvar & Batagelj, 1996-2016), a social network analysis software, and used to build a one-mode adjacency matrix, which consists of all network members listed along both the x and y axes of the matrix, with a “1” in the box for each matched pair if there is a relationship between them. If individuals were later found to have had multiple relationship types, these were weighted so that if a dyad had two relationship types—they were cousins and had a Big Homey-Gangster relationship, for example—a “2” would appear in the matrix instead of a “1,” and so on. This weight was assigned to indicate that being related in multiple ways indicates a stronger relationship (McGloin, 2005), although it was not ultimately used in the final model. Due to the size of this network, weighting created an outsized effect size for degree centrality, since degree already has a large amount of variation before adding the weights. Results for hypothesis tests also remained the same whether weighted degree or unweighted degree was used, so the simpler, unweighted degree was chosen.

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Individual (Node) Attribute Coding

As mentioned, a number of individual attributes were also coded. Some were coded simply by copying the value from the source data, such as recording race as it was entered in the police report, and some were derived via a number of means. Some are simply characteristics of the person, and some are variables that are later used to control for alternate reasons that an individual may have avoided indictment. In total, 43 node-attribute variables were coded, but not all were ultimately used. Details about this coding process follow.

Dependent variable

This case proceeded in two group indictments—the first, from 2009, was ultimately rolled into the second from 2011. The dependent variable in this study is a dummy variable coded 1 if the individual escaped both indictments and 0 if the individual was indicted in one or both criminal cases. Thus, avoiding indictment = survival = success.

A number of studies examine whether or not individuals were arrested as a proxy for visibility or vulnerability in the network (Bright et al., 2014; Calderoni, 2014; Morselli, 2010).

This case provided such rich data on both indicted and unindicted individuals, data that cover a long period of time, so I decided to take advantage of it and use indictment as the indicator of vulnerability since the burden of proof is much higher. Additionally, most members in this sex trafficking network get arrested often for any number of minor and serious offenses (hence their presence in police data), but arrest does not necessarily mean that proof of participation in a continuing criminal sex trafficking enterprise is present, whereas indictment in a RICO prosecution does.

Independent variables

Network Theoretical Variables

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Centrality measures

A number of concepts are used to describe relationships and positional strength through social network analysis, including several measures of centrality, density, and strength of network ties. There are many types of centrality measures, but two are used in this study as mentioned earlier in Chapter 2. The first is degree centrality, which is the number of nodes one is connected to; degree centrality may also be weighted by any number of factors indicating strength of the relationship (Borgatti et al., 2013; Scott, 2000; Wasserman & Faust, 1994), as discussed earlier. Degree centrality is an integer variable, whereas the other network variables tested here have a range from 0-1. Thus, degree centrality was normalized for more direct comparability, as is common practice (Bouchard & Komarski, 2014; Calderoni, 2014; Morselli,

2009), using the standard formula of x* = (x-m)/sd.

The second is betweenness centrality, which measures proportion of all paths between pairs of nodes in the network on which the node in question lies. Per the earlier example from

Chapter 2, A and B cannot reach one another without passing through C, who is gatekeeper or broker (Borgatti et al., 2013; Scott, 2000; Wasserman & Faust, 1994). Thus, betweenness centrality measures the proportion of all ties in the network that must pass through any given person in a position like C’s, and a higher betweenness centrality score indicates a stronger brokerage position because they control more relationships between others. Betweenness centrality is helpful for looking at structural holes, weak ties, and broker status, and can help identify whether a node is centered in one cluster or has ties to other clusters. A path is the route that connects one actor in a network to another, on which several actors may lie (de Nooy et al.,

2011)—think Six Degrees of Separation.

Other Brokerage Measures

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The clustering coefficient, mentioned in Chapter 2, measures the likelihood that any two of one’s direct connections are also connected to each other, and is an indicator of neighborhood density (Calderoni, 2014; Hanneman & Riddle, 2005). There are a few clustering coefficient measures: CC1 goes one step out from the node, CC2 goes out two steps and measures how many of a node’s connections’ connections are tied to each other, and so on. Following

Calderoni (2014), this study uses the simpler CC1 clustering coefficient. For interpretation,

Morselli (2009) viewed a higher clustering coefficient is an indicator that an individual’s personal network was perhaps denser than the rest of the network. So, Morselli viewed the clustering coefficient as a measure of direct connectivity similar to degree centrality rather than a measure of brokerage. Calderoni, on the other hand, interprets this higher density as less of a benefit in a criminal network, since density can mean higher information flow and low secrecy, and so a lower score may be an indication of brokerage advantage because it means that the individual can be the one controlling the flow of information between his/her connections. This study includes clustering coefficient in the model as a measure of brokerage using Calderoni’s interpretation of the measure.

The structural holes coefficient, also known as the constraint coefficient, measures the proportion of an individual’s connections that could be connected to one another, but are not. A lower coefficient indicates that an individual is less constrained and has fewer redundant contacts

(Borgatti, 1997; Burt, 1992). Individuals in tightly-connected cliques are likely to have higher structural holes coefficients, indicating more constraint in their ability to leverage a broker opportunity (Papachristos, 2006). Lower structural holes coefficients should also correspond to the presence of cut points23 and possibilities available for network fragmentation if they are

23 Cut points are defined in Chapter 2, and refer to individuals or brokers that connect otherwise disconnected subgroups, not just otherwise disconnected individuals. 104

removed. Someone with a lower structural holes coefficient has more opportunities to create social capital, positive or negative, between disconnected individuals and subgroups.

Tightly-connected cliques with many redundant ties, where nodes are likely to have higher clustering coefficients, are sharing the same information. In contrast, a broker that has opportunities to bridge structural holes can exert power by bringing in new information and contacts in an entrepreneurial way. These measures are used to understand how the structure of ties matters, not just number of ties. Thus, an individual may not be most central in terms of prominence (high degree), but s/he may be one holding the gang together. For the purposes of these analyses, secondary research questions look at which of these three measures of brokerage—betweenness centrality, clustering coefficient, or structural holes coefficient— performs better and provides more insight into power relations in this type of trafficking network.

Control Variables

Individuals’ first, middle, and last names, alias(es), birthdates, race, and addresses were recorded directly from police reports and criminal histories. As noted, this PII was ultimately deleted with the exception of race, per IRB, once its purpose of enabling cross-referencing and identification of unique individuals had been served.

The next set of variables has to do with gang membership and function in the sex trafficking network itself. First, members of multiple gang cliques, or sets, belonged to this pimping crew. “Clique” was coded for, by initials or name, with categories for “unknown” when there was no information, or “none” if they were confirmed to not be a member of any gang.

There is a qualitative difference between “unknown” and “none;” and there were sufficiently large numbers in each group, so both categories are included. This was the case for all variables

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for which “none” and “unknown” are both included as categories. The value for this variable for each individual was taken from police arrest or incident reports and from organizational charts produced by the crime analyst. A second variable was coded for “previous gang clique,” due to individuals changing allegiances, but this variable proved too unwieldy and information was not available for many individuals. Data were missing for too many different reasons to justify keeping the variable, and dates would also be needed to be able to use it in time series analysis, so this variable was ultimately cut and gang clique at the time of indictment was used in the regressions. Individual anecdotes about allegiance switches may be compiled and analyzed in a future study.

Variables were also coded for whether an individual had been incarcerated for greater than seven days during the 2005-2011 period, and if so, what the start and end dates were. This began as a control variable for a reason an individual could have avoided indictment, but this was also found not to be a valid construct. In fact, a number of pimps in the network continued operating and controlling business from prison—an important dynamic—rendering the explanatory power of alternate incarceration on avoiding indictment in this case moot. However, another variable—number of prior police contacts during the period (calculated by totaling the number of arrests, incarcerations, and FI reports) was useful as a control for the potential tautological bias of using police data—that police may focus on certain individuals over others because they have significant and ongoing contact with law enforcement anyway, and thus some individuals may be over-represented in the data (Rostami & Mondani, 2015).

The next set of variables have to do with gang position and role in the sex trafficking operation. These are Gang Association, Gang Rank, and Sex Trafficking Function. Gang

Association refers to level of embeddedness in the gang. Possible categories are “Member,”

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which indicates confirmed gang membership; “Associate,” which indicates direct involvement in gang activity and relationships with members, but no evidence of initiation into full membership; and “Facilitator,” which refers to individuals who provide services to the gang such as the hotel owners who provided lookout services to gang members using hotel rooms to conduct business, or repeat customers (for drugs or sex) that referred others to the network. Other categories were

“Unknown,” where there was no data about level of affiliation, or “None” where it was confirmed that a person had no ongoing relationship to the gang.

Gang Rank refers to the hierarchical rank an individual held in the gang. Possible values included “Big Homey,” “Gangster,” and “Little Gangster,” as described above, which are the three gang rank positions, as well as “Prostitute,” for females that were controlled by the gang, but not made actual gang members;24 “Gangster Girl” for bottoms that were gang-affiliated, perhaps sporting colors or tattoos or engaging in group activities, but who were also not made actual gang members,25 and “Unknown” or “None,” for the same reasons they were included in

Gang Affiliation. Females who were initiated and made official gang members were categorized according to their actual rank.

“Sex Trafficking Function” refers to the perpetration role held in the actual trafficking operation, since the actual pimping crew was a subset of the larger gang network. The main possible values here included “Pimp,” “Bottom,” “Customer,” “Unknown,” or “None.” Lesser classifications, grouped into the “Other” category due to very small numbers (N=10), included

“Lookout,” “Enforcer” (one tasked only with violence to enforce the rules), “Customer,”

“Logistics” (handled coordination of others getting to the next hotel, provision of laptop

24 An exhaustive search was conducted for studies that have coded for this dynamic previously; as of this writing, none were found. 25 Females that were made official gang members are simply categorized as “Gangster.” 107

computers, purchasing online advertising, and the like), “Hotel Manager” (if actively conspired by advising network members that police were on premises, e.g.), and “Credit Card” (providing a credit card for use in securing hotel rooms, e.g.).26 In cases where an individual held more than one role—for example, one individual was a pimp, a trainer, and a prolific recruiter—the value of “pimp” was assigned.

The next variable captures whether an individual was identified as a victim in the 2009 indictment or the 2011 indictment, respectively. This is a control variable for an alternate reason that an individual would have avoided indictment. Victims were identified in this dichotomous variable, and were not classified as having a perpetration function in the Sex Trafficking

Function variable. Finally, race and sex status were included.

I did attempt to use addresses to calculate socioeconomic status (SES) control variables for income and education based on census tract median values, but this approach was dropped for a couple of reasons. First, gangs are usually homophilous in terms of SES (van Mastrigt &

Carrington, 2014), meaning that most members come from similar backgrounds, and thus there is not normally enough variation in the usual SES controls to render them useful for analysis

(McPherson, Smith-Loving, & Cook, 2001; van Mastrigt & Carrington, 2014). This appears to be the case here as well—most of the members are African-American and from the same few neighborhoods, despite the spread of activity into many other areas. Second, the best one could hope for with this approach would be a neighborhood level indicator, which is not valid for an individual. Plus, about twenty percent of addresses that individuals gave to police were fake and did not even exist when searched on Google Maps. The only SES control variable that I ultimately kept was a dichotomous variable for whether an individual was transient, or homeless,

26 This is another variable for which I found no previous studies coding for it. 108

because it was the only potentially valid designation I could find in police data—information on things like education completed was simply not available.

Table 1, below, describes the variables used in the final models. As expected with the non-independent data associated with network studies, skewness and kurtosis test scores (UCLA,

2016) showed that the network measures were not normally distributed. This is accounted for later by selecting a penalized maximum likelihood model that relaxes assumptions of normality and independence to estimate the regressions.

Table 1. Variable Descriptions

Variable Variable Obs Type Mean Std. Dev. Min Max P(Avoid Indictment) 375 Dichotomous 0.89 0.32 0 1 Normalized Continuous Degree 375 0-1 0.15 0.19 0 1 Betweenness Continuous Centrality 375 0-1 0.005 0.013 0 0.12 CC1 Cluster Continuous Coefficient 375 0-1 0.62 0.38 0 1 Structural Holes Coefficient 375 Continuous 0.48 0.32 0.07 1.28 None, Prostitute, Gangster Girl, Baby Gangster, Gangster, Big Gang Rank 375 Ordinal Homey, Unknown Gang Association 375 Ordinal None, Member, Associate, Facilitator, Unknown ST Function 375 Categorical None, Pimp, Bottom, Customer, Other, Unknown Clique 375 Categorical 5 main, plus “Other” Prior Contacts 375 Integer 4.69 8.56 1 62 Victim 375 Dichotomous 0.17 0.37 0 1 Race 375 Categorical Black, White, Hispanic, Asian, Filipino, Samoan, Unknown Sex 375 Dichotomous 0.65 0.48 0 1 Transient 375 Categorical Yes, No, Unknown

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Data analysis procedures

Data analysis proceeds in several stages. After conducting node-level variable descriptives, whole-network descriptives were calculated in the network visualization software

Pajek that include network density, or the proportion of possible ties that actually exist in the network; number of ties (edges); number of nodes (network size); mean degree centrality

(average number of other individuals that each node is connected to); number of components; and a triad census. The triad census describes how many of each of sixteen different configurations of triad exist in the observed network; the shapes of these have implications for the overall structure such as tendencies toward clustering, transitivity (the idea that if A is friends with C, and B is friends with C, then A is friends with B), and hierarchical clustering

(Wasserman & Faust, 1994). These properties and their implications are described in detail when the results are presented in Chapter 4.

Together, these network descriptives give a snapshot of the data, along with sociograms to visually show the structure and components of the network. Sociograms were generated for each year for which there is data from 2005-2011, with more detail in the 2011 sociogram because that was the year of the main indictment. The sociograms-by-year will be additive – each successive year will contain the data from the previous year(s) as well, to give an idea of what data was available to police at different times during the investigation. The purpose is simply to show evolution of network size and complexity. The 2011 sociogram, which represents the network at the time of the 2011 indictment, will illustrate the complete data that was available to police and that was provided for this study. It will also be more detailed and show more individual, component, and network attributes.

Component analysis was conducted to determine whether the network has separate, isolated components held together by a few cut points, or whether the entire network is 110

connected by a number of redundant ties (Wasserman & Faust, 1994). As discussed previously, in brokerage terms, a cut-point is a potential broker whose removal would split a component into two or more components and fragment the network (Everton, 2012; McGloin, 2005).

Social Network Analyses

Degree centrality and betweenness centrality scores, and clustering and structural holes coefficients, were calculated for each node in the network in preparation for hypothesis testing.

Whole-network equivalents were also calculated to get a sense of general network characteristics. A correlation matrix was created that contains all variables used in the model.

As stated, the dependent variable is the probability of avoiding indictment, or

P(AvoidIndict). Avoiding indictment indicates survival in the network, and the main hypothesis being tested is whether having higher betweenness centrality, hypothesized to be indicative of a better broker position in the network, increases that probability:

H1a: Higher Betweenness --> Higher P(AvoidIndict)

As described earlier, auxiliary hypotheses are that having higher degree centrality, or knowing more people, lowers that probability; that a higher clustering coefficient decreases that probability; and that a lower structural holes coefficient increases that probability:

H1b: Higher Degree --> Lower P(AvoidIndict)

H1c: Higher CC1 Clustering Coefficient --> Lower P(AvoidIndict)

H1d: Lower Structural Holes --> Higher P(AvoidIndict)

Because the dependent variable is essentially a node attribute, and not a variable with a matrix structure involving predicting ties or characteristics of ties, the statistical method chosen for hypothesis testing is not a network method such as a quadratic assignment procedure (QAP) or an exponential random graph model (ERGM). While no perfect method has been found thus

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far for testing the effects of interdependent relations on node-level variables, consensus at this time supports modeling centrality scores as independent variables in standard regressions

(Hofstra, Corten, & Buskens, 2015; Skvoretz, 1991) in which other node-level attributes can be used as controls. For this study, a penalized maximum likelihood (penalized MLE) model was selected. Designed by Gary King, the penalized MLE is a logit model that was originally designed for handling rare events, but has the property of not being written with the assumptions of independent observations or normality in the data (King & Zeng, 2001). This is critical for thinking about network data in these terms. The model is estimated by importing the node attribute data into Stata, with centrality scores and clustering coefficients as attribute values, and running the firthlogit command (Coveney, 2015b).

Centrality measures and network measures, as one might suspect due to the non- independent character of the data, tend to be correlated with one another (Valente, Coronges,

Lakon, & Costenbader, 2008). Principal component analyses are estimated on the network measures to determine the level of overlap between them, in order to determine which brokerage measure may be capturing something most conceptually different from degree centrality.

Specifically, I look at whether the clustering coefficient and degree centrality are measuring the same concept, as posited by (Morselli, 2009). Then, I look at whether degree and betweenness are measuring the same concept, given their large amounts of overlap in some previous studies discussed in Chapter 2. This is the secondary set of hypotheses tested in this study:

H2: Degree and Clustering Coefficient measure the same concept

H3: Degree and Betweenness measure the same concept

The resulting latent variables are then also tested in the regression model.

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If network measures are too correlated in the network under study, it can be difficult to draw inferences about the impact of strategic position on probability of survival. When this happens, there are other post analyses that can be useful for learning more about the impacts of these positions. When confronted with the same dilemma in his study of the ‘Ndrangheta in Italy,

Calderoni (2014) broke his analyses down and looked at centrality scores by task, by clustering coefficient, and by a status level index he created. He found that engagement in specific tasks accounted for brokerage positioning in that network, rather than leadership position or status.

This contrasted with the model of the ‘Ndrangheta as a hierarchical organization. Mancuso

(2014) also breaks down mean centrality scores by role to discover attributes of madams with more brokerage power (higher betweenness centrality) than others in her Nigerian madam sex trafficking network study.

Thus, post-analysis means tests were conducted that examine the impact of centrality scores and clustering and structural holes coefficients broken down by gang rank, sex trafficking network function, gang association level, and gang clique in order to gain a more complete picture of the causal mechanisms at play in this network. This may also help uncover differences in values for network measures between different participant types in the trafficking network.

For example, how do gang members and non-gang members, victims, or the hotel managers who facilitated the operation vary with respect to degree centrality and betweenness centrality or clustering coefficient as measures of brokerage? Lastly, a visual cut point analysis was run using the 2011 sociogram to identify potential individuals whose removal may facilitate network fragmentation. From here, the dissertation moves into Chapter 4. The chapter begins with a descriptive telling of the Oceanside Crips network history, and then presents the results in the order described above.

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CHAPTER 4

FINDINGS: IS BROKERAGE PROTECTIVE?

This chapter presents the results of analysis of the quantitative dataset built from the 263 police incident reports, the indictments, the interviews with the investigative task force members, and the supplementary court and police documents described in Chapter 3. The chapter begins with a description of the case history that covers the network’s beginnings, relational dynamics, growth, operational patterns, the “rules of the game” of prostitution followed by this network, the investigation and prosecution of the network, and how all of these play into the structure of the data and determine some of the choices made for validity purposes. It proceeds next with node- level and network-level descriptive statistics, sociograms by year (with the most detail covering the final network that was prosecuted), and the regression results from testing the hypotheses regarding network position and its impact on probability of avoiding indictment, which is the proxy variable for survival as described previously.

Before going into the “story” of the network, which precedes the quantitative results, a little background about trafficking network structure is provided. As will be seen later in this chapter, there are a large number of pimps in the network, some identified bottoms that serve as surrogates for their pimps, and these pimps and bottoms together to manage “stables” of trafficking victims and other prostitutes. A few pimps had large stables of 5-8 minor and adult females, but most had stables of 2-4. These sub-units are then connected to one another through friends, family, and gang membership. These connections are also used for recruitment, business efficiencies, exerting power, and controlling the flow of resources including information, money, and even food when victims were involved. One sub-unit might look like this one in Figure 3:

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Figure 3: Relationships between pimps, bottoms, and the stable in the prostitution network

These sorts of relationship clusters then repeat throughout the network, and each of these individuals also connects to many other individuals in different ways. Note that the arrows depicting the relationships in this diagram are bi-directional: while there is often a stark power imbalance, none of these relationships are completely one way. Additionally, all of these relationships shift and evolve over time. In gang rank, Big Homeys, Gangsters, and Baby

Gangsters also connect to one another and to others in a similar way.

Case History

When police in a neighboring town answered what appeared to be a routine juvenile prostitution call involving V11,27 they had no idea what they were walking into. Supported by

27 All network members are referred to by anonymization codes to protect human subjects. Each individual was numbered for analysis purposes, and no two individuals share the same unique ID number. These numbers carry throughout the network sociograms, tables, and all references to individuals. The letters placed before numbers in text and tables indicate the individual’s role: V=Victim, P=Pimp, B=Bottom, C=Customer, O=Other, and U=Unknown. In this first reference, 11 is the individual’s unique ID number and the V indicates that person 11 is a victim. 115

her father’s encouragement, V11 eventually gave testimony some weeks later that served as the launching pad for an investigation that blew the lid off a gang sex trafficking network with hundreds of members and associates, both core and peripheral, spanning seven states. The investigation was led by a local police detective that eventually assembled a task force consisting of local and county police, the FBI, HSI, and the U.S. Attorney’s Office to uncover the extent of this network that advertised via social media, Craigslist, Backpage.com, and other websites, and operated mostly out of budget hotel rooms.

This pimping28 crew,29 which started with a small number of individuals and grew to include several interconnected and cooperating gang cliques, or “sets” as they are called locally, began in earnest around 2005. Originally, this crew of then-teenagers also wanted to form a music label, design clothes, and achieve success in multiple arenas, but it was the prostitution activity that lasted out of all those ambitions. Around 2005, P3, one of the most prolific recruiters and who had learned the business himself from P18, took B4, P15, and P16 to Las

Vegas. They were all about fifteen years old at that time. It was here that P3 began training P15 and P16 how to pimp, and teaching B4 how to prostitute (before she was a bottom30). After serving a stint in juvenile prison for the prostitution activity that occurred during that Vegas trip, the group brought the lessons learned about “the rules of the game” home. These rules are

28 “Pimp” as a verb, or “pimping,” is used here in line with how this network’s members referred to themselves. Pimping here refers to managing and/or coercing girls and women into prostitution with exploitative work conditions. Use of the word “pimp” or “pimping” in this research is in the context of what occurred in this case, and the “rules of the game” described for this type of group, and is not intended to be a blanket, generalizable definition for pimps in all types of sex trafficking networks, among which there is wide variation. 29 “Crew” is used in this case because the group formed by the members of multiple gang cliques for the purpose of establishing the prostitution business did not meet the state code for definition a gang in its own right. Thus, many of the core members were members of individual gangs and of this crew, though some eventually left their original gangs and just stayed in the crew. This is reflected in which clique they are assigned later in the regression models. As a reminder, gang distinctions were originally made during each arrest over the years according to state codes; the case only went to Federal jurisdiction later. Gang membership was also not a requirement for the Federal indictment—only involvement in racketeering activities. 30 The definition of a “bottom” or “bottom bitch” is given in Chapters 1 and 3; it refers to a female that functions as a “head” prostitute, helping her pimp manage the other victims and employees. 116

described later in this section. P18 himself was originally recruited by a sex worker in Los

Angeles, and had taught what he learned about how to pimp to other members of his gang clique.

P15, in addition to learning from P3 in Las Vegas, was taught how to pimp by his sisters.

In turn, he taught many others to pimp after being released from his juvenile sentence, and he then “rolled up”31 into the “Insane Crips Gang” (ICG)32 clique in 2007 before switching completely to the semi-independent “Niggas Wit Swagg” (NWS) pimping crew. P15 had several minor females working for him, and one of his earliest, B41, eventually became his bottom. It is common for a juvenile sex worker to eventually move up to the position of bottom33 once she has been working for a while, if she has a good relationship with her pimp, if she has grown older and possibly had children, and if the customer base in the area prefers a younger “product.”

In B41’s case, her productivity slowed after becoming pregnant with her pimp’s child, so she moved into a bottom position.34

P15 eventually returned to prison in 2006, but was still looked to for leadership. Many pimps who were incarcerated at different points, like P15, still controlled their prostitutes35 via

31 “Rolled up” is slang for officially joining a gang. 32 All of these gang sets, or cliques, were previously identified publicly in press releases about the indictment from the United States Attorney’s Office, Southern District of California (see, for example, https://archives.fbi.gov/archives/sandiego/press-releases/2011/sd041811.htm). Individuals are still referred to here by anonymous code to protect human subjects. 33 Despite the title of “bottom,” which is used to remind the holder of it how lowly she is still viewed, this is actually a promotion of sorts over simply being a prostitute into a more supervisory role. 34 All bottoms in this case started as juvenile prostitutes. Additionally, their relationships with their pimps are complicated: there are often children involved, and there is usually also a complex and ongoing domestic violence component. There was not enough evidence for the prosecution that the bottoms were still under duress at the time they were committing violence of their own, though the lead detective suspects that they probably were. But, due to lack of admissible evidence proving otherwise, they were charged for the criminal behavior they engaged in after turning eighteen even though, conceptually if not legally, the line between victim and perpetrator is blurry when it comes to bottoms. It was also not entirely uncommon for some minors recruited into prostitution to eventually join the gang as a full-fledged member, such as B43 or V167. In both cases, moving into one or both of these positions was a way to assert some power over their own lives, even if they were relegated mostly to sex work and abusing other women, and kept out of other parts of gang life. A few exceptions to that rule, like V167, were assigned group leadership in some non-prostitution-related criminal events. 35 “Prostitutes” is used here to indicate that not all sex workers identified can be assumed to be victims. It can be inferred from this data that most of those discovered were likely victims, but there is potential for error, for example, 117

letters and phone calls, recruited more victims while in prison, provided leadership to the group from within prison walls, and kept money earned by the business either on their own books or via family members. P15 even still had the authority to “gift” a victim (V69) to another pimp via a birthday card while in prison.

In this case, there were two separate group indictments, and the first was eventually rolled into the second after the prosecution filed a motion to have the case declared “complex,” and after evidence was uncovered that the network was much larger than it initially appeared.

According to the lead detective, the Las Vegas event referred to above was from the original indictment, as were videos seized for evidence that were taken by a regular customer (C6) and involved numerous minor females. The details from those events were what indicated to investigators that their original nine defendants were involved in a much larger operation. The criminal investigation focused on one gang clique initially, but it became apparent that three cliques were heavily involved with NWS—“Insane Crip Gang” (ICG), “Deep Valley Crips”

(DVC), and “Crook, Mob, Gangsters” (CMG). There were also additional network members from several other cliques and even from rival gangs, such as the Bloods.

The two biggest differences between the 2009 indictment and the final 2011 indictment that resulted in full prosecution were the addition of more people and substantive counts between

2009 and 2011, and the addition of racketeering (RICO) charges in the superseding 2011 indictments.36 The RICO charges were added with the idea that minimum sentences would then be longer due to the added penalties involved with RICO cases (see Chapter 1 for discussion of

in cases where the police report does not include a victim statement or where the charges were not for sex trafficking but general pandering, etc. 36 In 2011, there was an initial indictment, a first superseding indictment, and the second superseding indictment in which the racketeering charges were added. So, there was an initial indictment in 2009 and three indictments total in 2011, because the first 2011 indictment was amended twice before the prosecution was concluded. The individuals named in all three 2011 indictments remained the same; the only changes involved the addition of more charges. 118

requirements for a RICO prosecution, and later in this chapter). The racketeering charges were added to the indictment because their addition can result in sentences of up to 20 years longer than for the substantive counts alone. However, during adjudication the substantive counts were dropped, and most sentences in the present case ranged between just one to five years due to subsequent plea deals made—especially deals for reduced sentences in exchange for information.

Just four individuals received sentences that were ten years or longer. According to task force interviews, subsequent prosecutions of other gang sex trafficking networks in the area have learned from that experience to push for not dropping the substantive counts.

Task force interviews indicated that many of those who received reduced sentences provided information about fellow network members. This pattern of informing was so widespread that NWS became known in the larger community no longer as “Niggas Wit

Swagg,” but “Niggas Who Snitch”—indicating that loyalty in this network was fleeting, and that while many members from potentially rival sets cooperated in the enterprise, trust between them was low.37 This may be one reason why this network was known as one of the more violent in the area—if trust is lacking, more violent means of ensuring compliance with group norms may be employed (Desroches, 2003; Icduygu & Toktas, 2002; Skarbek, 2011). However, as described in previous chapters, this trend of different gangs and cliques cooperating in profit-oriented business activities has continued. This has happened despite the risk of longer sentences if they get prosecuted for racketeering, and despite the risk that their compatriots may inform on them in return for reduced sentences. This is perhaps due to perceptions that the risk of getting caught is

37 The data sources for this research did not provide information regarding who “snitched” on whom during the plea agreements, which would have been interesting to know. The sentencing hearing transcripts merely described negotiations between defense attorneys and the prosecution around sentence lengths for each. 119

still small, while the potential for making money is high, increasing, and far more certain (Dank et al., 2014; Raphael & Myers-Powell, 2010).

V11 was one of the victims identified on C6’s video, which was the basis of the first indictment. This video was estimated to be from around 2005-2006, although police could not date it exactly. Once convinced to cooperate, although she was under numerous credible threats, had previously been beaten severely by a couple of pimps and several bottoms,38 and labeled a

“snitch,” V11 informed police in detail about many victims and bottoms who arranged dates with

C6 and others, as well as their pimps. These identifications uncovered not just gang clique memberships, but a number of network members who were family members, in romantic relationships, parents to children, and many other types of connections that can impact an individual’s network position and perceived power. For example, V11 herself was recruited by

P3 and “turned out”39 by P1, who was also B4’s brother. V11 was later pimped by P106 and

P126 after P1 was incarcerated—it was not uncommon to pass victims from pimp to pimp as needed.

It was after V11 was provided appropriate safety protections, and assured that she would be treated as a victim in need of services rather than arrested for prostitution, that she eventually gave a two-day interview outlining the entire network as she knew it. This was when the police investigation began in earnest. The investigation was being built as early as 2007-2008, but

V11’s interview in 2009 was the catalyst to taking it full-scale and building the interagency task force. The lead investigator put together the events prior to 2007 via earlier arrest records and investigation archives, so the sociograms by year below show what was possible to put together

38 It is not uncommon for some pimps to relegate some or all violent rule enforcement to their bottoms in order to insulate themselves and protect their standing, especially if the prostitute in question is not one of his “bitches.” 39 “Turned out” refers to successfully moving a victim from recruitment/grooming into actual work with customers. 120

via data available during earlier years, but this does not mean that detectives were aware of or investigating the case that early on. V11’s statements, later augmented by several others, paint a picture of a loose but interconnected network of gang cliques that attended one another’s “hood days,”40 engaged in the prostitution business in a coordinated fashion, but still clashed in other areas of gang life as documented in police reports detailing altercations outside of the trafficking- related offenses.

Rules of the game41

V11’s pimp was not violent in the beginning. When P1 recruited her initially, he romanced her and convinced her he loved her in the typical “romeo” or “loverboy” fashion discussed in Chapter 2. By this method, the pimp convinces the victim that he loves her and that they are headed for a big, rich, comfortable life together—making her willing to do anything for him in order to keep him. Others recruited were runaways or teenagers from troubled homes that were attracted not only to love, but to the ability to make money to support themselves or meet basic needs even if they had to give most or all of it to their pimp in the end. Once P1 convinced

V11 to prostitute for him, he got one of his other workers to teach her “the rules of the game.” As described by a police officer in his synopsis of an early interview with V11:

I asked [V11] who taught her “The Rules” of prostitution and she told me it was [P1]. He told her that when she is walking “The Track,” to walk against the flow of traffic so potential johns could see her face. He said to never date42 a black guy because he could be a rival pimp. He said that she had to always wear a condom. [P1] told [V11] to keep her head down when she was around his friends and not to look them in the eyes. He said that when she got into a car, she should touch the john’s crotch or have him touch her breasts to try and identify undercover police officers. [P1] told her that if she sees a cop while she is walking the streets to walk the other way or cut through stores and parking lots to make it hard for them to catch up to her. [P1] told her to never use her real name

40 A “hood day” is an annual event where a gang throws a celebration and inducts new members via “jumping in,” or making them fight, or by other means. A “sex-in” may be involved for females, where a female that wants to be a gang member has to let all the males in the gang have sex with her one after another. 41 “The game” and “the life” refer to the world of prostitution. 42 “Date” within the rules of the game refers to a commercial sex act with a client. 121

and always give her street name to the police. I asked [V11] how much she charged for sex acts and she told me the following: $40 for head, $120 for a quickie, $150 for half an hour, $200 for an hour, and $200 for a two-girl special. [V11] said that [P1] told her how much to charge and that those prices were standard for local prostitutes. When she received money from a trick at a motel room, she was to put the money in her bra, or slide it underneath the motel room door and [P1] would retrieve it from the outside. I asked [V11] if she was allowed to keep any of the money and she said no. She had to give all of the money to [P1] and he would even search her to make sure she gave him all of the money. [V11] said that [P1] would give her money for food, clothes, hair and nails, but she had to make at least $100 a day before he would give her money to eat.

The average victim in this network completed approximately 6-10 dates per day. Some victims may have only worked a couple of weeks, whereas others had been in “the life” for years.

V11 further went on to describe how ads were posted on the Craigslist and Backpage websites using various pimps’, but mostly bottoms’, credit cards; and how P2 often drove them around and rented hotel rooms in exchange for a place to stay. Sometimes there would be ecstasy, marijuana, and/or alcohol to relax the victims, especially those new to the game; drugs and alcohol might also be around for when victims, pimps, and bottoms would party.

There was some variety among views expressed by pimps about “the game,” even while they adhered to the same rules. As described in a police interview with one pimp:

I (police officer) asked [P124] about the prostitution activities of NWS and he said he doesn’t “Mack.” He said all he does is get a girl and tell her he loves her. She can be working; she just pays him. He said he doesn’t pimp; he said, “None of the [guys] really pimp like that, we just juicin’ we call it milkin’ cows.” He said people look at it differently. I tried to reiterate what he said and he admitted that it works both ways; they take care of him and he takes care of them. He said it’s not like pimping; where he’d say, “Give me my money.” [P124] said he does things for them and they do things back.

Granted, this was a police interview where a pimp was trying to avoid being charged; this same individual was described using quite severe violence with victims in other arrest reports.

However, this still provides some insight into how pimps from this group viewed themselves, and the variety that exists among them in how they communicate their perceptions of their activities. Some were remorseful, others described all activities fully and with no remorse, and

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still others described holding a more peripheral role such as a driver and tried to dissociate themselves from the core activities.

Later in this interview, P124 described himself as trying to back off from “the life” a little bit, and explained that NWS had started “getting out of hand” with the level of their criminal activities and showing off their perceived status to others. He described this inflated sense of status as the result of running a profitable enterprise where others (their victims) did all the work and gave them the money, and because of some YouTube music videos about “the life” that they had created and were proud of, which spoke to their other ambitions.

“Renegading,” or working without a pimp, was also a punishable offense in the area where this network operated. One identified female had been chased by a moving car, threatened repeatedly, and robbed for suspicion of renegading. Perhaps this is one of the reasons that a prostitute (victim or not) might recruit a specific male to be her pimp: to provide protection and help her avoid trouble for renegading in areas of the country where renegading poses risk, to choose a pimp she thinks may treat her better than others, and to help watch out for police while she is in the middle of conducting business. This is important, as it illustrates the symbiotic relationship between pimp and prostitute—even in trafficking cases where a victim may want to escape one pimp, but still feel unable to leave the network.

Division of Labor

But as far as labor specialization, specific tasks were normally divided out as needed or convenient during any given day or week. V11 described no formal division of labor except between victims/prostitutes, bottoms, and pimps. Pimps carried out multiple tasks that fell into three main categories: recruitment, management/operations, and rules enforcement. Recruitment took place in malls, the beach, at parties, via MySpace and Facebook, through family and

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friends, and at the local high school. There was also a female peer-pressure element, as victims were expected to recruit more victims to work. Management of business operations occurred mostly in San Diego County, but crossed state lines when pimps took victims with them to work while they were traveling. These activities included posting ads, monitoring tricks/dates, renting hotel rooms, and collecting and handling money.

Lastly, enforcement of rules was accomplished via one or more of several means, carried out by pimps, bottoms, and one male enforcer that did not serve any other role in the sex trafficking part of the gang’s operation. These included threats, from mild threats up to and including threats to victims’ lives or their children’s lives; beatings and physical abuse that ranged from mild to severe; monitoring or confiscating cell phones to prevent communicating with family or friends; stealing victims’ identification; keeping all the money and stealing victims’ allowances as well when they broke a rule; locking victims in hotel rooms or apartments; keeping a bottom or another female in the hotel room with victims during all dates, and withholding love. For example, from the synopsis of one police interview, V59 explained the nature of threats she regularly faced:

On one occasion while working as prostitute for [P3] they were watching a TV. On the show they were watching a prostitute and a door man were killed, chopped up, and buried in the desert near Las Vegas. [P3] made the statement “that is something I would do.” [V59] told me that she believed that [P3] would indeed commit murder to prevent his incarceration… [V59] said that she was not allowed outside of the rooms, and even though [P3] initially told her that the door was open… any time she walked toward the door in a hotel room, [P3] would yell “Where the fuck do you think you’re going?”. [V59] said that she was treated like a prisoner, and that she had no more freedom than she does now in Juvenile Hall.43

43 In interviews, investigators described the regrettable use of the Juvenile Justice system in some cases to “keep victims safe.” There is debate about placing victims in a criminal system this way. Police in this case were at a loss regarding what to do, due to lack of secure shelter available in the area for human trafficking victims. This is a policy issue under examination and development in the area, particularly in light of Safe Harbor laws meant to direct victims into services rather than the criminal justice system. 124

Other interview transcripts and synopses told similar stories.

Thus, any male identified as a perpetrator is classified as a pimp in the sex trafficking network function variable described in Chapter 3, or “ST Function,” because they carry out all of the functions described above to varying degrees. There were a few people who did hold one of several specific support roles, such as providing a credit card, serving as lookout, the complicit hotel managers, the “enforcer,” and parents hiding their children’s culpability, money and weapons from police. There were only ten of those individuals identified in the data sources, so they are grouped together in “other” for the quantitative analysis.

Network Assets

The network also had shared assets such as laptops and cell phones (“trick phones”) that were dedicated entirely to the business, and a number of those were eventually seized and forensically examined for evidence. On one of these laptops was a spreadsheet that provided some of the only financial evidence from this case. The network dealt almost entirely in cash, but some was put on prepaid “Green Dot” cards used by the victims and bottoms to pay for hotels, advertisements, and occasionally rental cars. Cards were generally put only in the females’ names to reduce exposure for the pimps. The lead detective described the spreadsheet as great evidence because he was able to use it to backtrack dates and verify activity as far as where people were, who posted ads for whom, and whose card was charged for a number of the substantive counts in the main indictment. Otherwise, there was no money trail. Spending was consumption based: “They smoked it as fast as they got it,” according to the lead detective. Some were even “sneaker pimps” because they had no car; thus the need to share resources between network members.

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Structure of investigation

“Big Homey” is the highest gang rank, followed by Gangster and Baby Gangster. Big

Homeys were targeted first in this investigation because of their leadership positions, which consist of member recruitment and training, both in commission of crimes and group expectations, and direction of criminal activities. A Big Homey “puts you on,” and then mentors the recruit in gang life, norms, and customs. Knowledge of this pattern determined the investigation procedures used because, per detective interviews, “if a Big Homey is a pimp, his guys will also be pimps.” As it turned out, there were many Big Homeys in the unindicted category and many more Gangsters and Baby Gangsters who were indicted. Starting with the Big

Homeys in investigative practice after securing such detailed victim statements and interviews uncovered a great deal of the network, but the position may have been somewhat protective against indictment in the end, with only two Big Homeys indicted in the RICO prosecution.

However, this cannot be determined for sure with the extant dataset.

RICO Requirements for Racketeering Charges

RICO provisions under the Organized Crime Control Act of 1970 have been amended several times over the years to recognize the various structures and organizational characters of corrupt organizations, organized crime activities, and other criminal activities that involve a pattern of racketeering different from the mafia-like organizations feared in the 1970s (Pierson,

2013). RICO was applicable in this case, like several other gang-related federal RICO cases, because of the various amendments to the original RICO statute. A relevant example of amending RICO is when Congress passed the Trafficking Victims Protection Reauthorization

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Act of 2003,44 which added human trafficking as a predicate offense for RICO cases. The burden of proof to sustain Federal RICO charges is as follows:

 The enterprise must be defined  The defendant must be connected to the enterprise—by association, if not as an official member  Every indicted defendant must be involved in 2+ RICO predicate acts  Every indicted defendant must be proven to have known and agreed to two RICO predicate acts committed by one other member.45

The ability to prove all four of these requirements determined who was indicted in this case, so substantial visibility to law enforcement can be inferred to be necessary in order to amass that level of evidence. Charging decisions depended on whether there was enough evidence according to the above standards; whether witnesses were available, reliable, “findable,” and willing; and whether physical evidence that could corroborate unreliable witnesses’46 testimony was available. Prosecuting all members under a single RICO indictment, instead of individually charging each member in separate prosecutions, was the strategy used in order to utilize sentencing enhancements possible under RICO. This is intended to communicate a deterrent message to other gang networks in the area.

A RICO prosecution was also chosen because the web connecting the network members was determined by the prosecuting attorney to be too complicated to separate the charges, given that there were often 10 or more people associated with a single count (Law Enforcement and

Task Force Interviews).47 Therefore, prosecutors focused on how the sex trafficking business fit

44 See Pub. L. No. 108-193, 117 Stat. 2875 (2003) (codified as amended in 22 U.S.C. §§ 7101-7110 (2003)). The Trafficking Victims Protection Act has been reauthorized and amended further in 2005, 2008, and 2013. 45 http://uscode.house.gov/statutes/pl/91/452.pdf; https://www.justice.gov/usam/usam-9-110000-organized-crime- and-racketeering. 46 A witness was described by members of the investigative task force as unreliable if he/she was prone to running away, if he/she changed his/her mind, if his/her story changed due to the effects of sustained trauma, etc. 47 A filing to have the court classify this as a “complex case” enabled the RICO prosecution to go forward as a group indictment for this reason. 127

into the larger gang network, which is the approach taken in boundary specification for this study. Indeed, one of the original Assistant U.S. Attorneys (AUSAs) wanted to include all criminal activity uncovered in order to take down the gang altogether, but in the end that proved unwieldy. So, while the investigation and prosecution initially focused on the entire network, the prosecution ended up limiting the indictment to sex trafficking activities and peripheral crimes that were directly tied into the trafficking operation, despite earlier efforts to cast a wider net.

Hence, the importance of including the categories of “none” and “unknown” in the control variable of “Sex Trafficking Function” in the statistical models that follow later in this chapter.

Some of the other criminal activities also undertaken by the group included robberies of businesses and “john rips” where a prostitution customer is lured into a hotel room by a prostitute and then beaten and robbed by the group. Other members were also involved in drug trade and in more standard types of crime like burglary, assaults, check fraud, and a few murders.

Due to the overlap of activities and relationships, and the fact that some of these auxiliary crimes were also used to build the RICO case and were charged in the indictment because they materially benefitted the trafficking business, boundaries in this network were found to be fluid.

Thus, an expansive view was taken by investigators and prosecutors regarding who is part of the trafficking network. Indeed, a few of the most connected people in the network, as identified in these analyses, could not be identified as having a specific sex trafficking function in the available police data, even though they connected some of the biggest players to each other. This is an important justification for not limiting network boundaries too tightly—identifying these less obvious types of brokers can open up further avenues for law enforcement investigation in establishing network cases.

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Now that we have some history about the case, the network dynamics and evolution, and the investigation and prosecution that gave the data its structure, discussion will move into the quantitative results from the data. The chapter proceeds next with node-level, or individual member-level descriptive statistics, followed by network-level descriptives. Next, sociogram pictures built from available police data by year are shown, with more detail in the sociogram for the final 2011 network. Finally, the regressions used to test the hypotheses regarding network position and its impact on probability of avoiding indictment are presented, along with the post- analyses described at the end of Chapter 3 that disaggregate some of the results for deeper understanding.

Descriptive Statistics

Node-level Variable Descriptives

Table 2 provides frequencies by gang clique for several node attribute variables, with sums for each value at the right. As expected, those identified specifically with the pimping crew, NWS, represent 30 of the 43 individuals indicted. Ten were members of other cliques, and three were materially involved but not members of any gang. Pimps came mostly from members identified with NWS. NWS was the value assigned to an individual if he/she was a member of the crew alone, or of both the crew and a separate clique, but not to individuals that did not claim

NWS as an allegiance. Bottoms were a little more evenly spread between cliques, and people in

“other” sex trafficking functions were pretty evenly split between known clique allegiances, no allegiance, or unknown allegiance. Gang Association, or gang embeddedness, shows no discernable patterns in the frequency table.

Gang Rank is a little more mysterious. There were many individuals who were identified with a clique, but for whom there was no information in the data about what rank they held.

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Table 2. Frequencies of Node Attribute Variables by Clique

Clique DVC ICG CMG NWS DVB Other None Unknown Total Total N 99 59 21 56 10 22 24 84 375 P(AvoidIndict) No 1 5 4 30 0 0 3 0 43 Yes 98 54 17 26 10 22 21 84 332 ST Function None 90 49 13 6 8 21 20 71 278 Pimp 7 2 5 38 2 0 0 2 56 Bottom 1 5 3 5 0 1 0 2 17 Customer 0 0 0 0 0 0 2 2 4 Other 0 1 0 3 0 0 2 4 10 Unknown 1 2 0 4 0 0 0 3 10 Gang Association None 0 0 0 0 0 0 18 2 20 Member 51 12 13 43 1 14 0 5 139 Associate 48 47 8 11 9 6 1 16 146 Facilitator 0 0 0 2 0 0 3 4 9 Unknown 0 0 0 0 0 2 2 57 61 Gang Rank None 14 27 3 5 6 2 22 8 87 Gangster Girl 1 4 3 5 0 1 0 0 14 Baby Gangster 3 0 0 1 0 0 0 0 4 Gangster 49 12 9 42 2 10 0 5 129 Big Homey 4 0 3 3 0 1 0 1 12 Unknown 28 16 3 0 2 8 2 70 129

Note: Gang Cliques are as follows: NWS = Niggas Wit Swagg; DVC = Deep Valley Crips; ICG = Insane Crips Gang; CMG = Crooks Mob Gangsters: DVB = Deep Valley Bloods

These individuals were thus categorized as “unknown,” which may signify the limited usefulness of gang rank as an explanatory variable. Other individuals were confirmed as “Associate” in

Gang Association, so would have no rank because they were not full members. These were classified as “none” as opposed to “unknown.” It will be shown later that none of the node attribute controls in Table 2 were statistically significant in the regressions when compared to the network measures, which was surprising. One would expect that holding a certain role in the gang, for example, would have an important impact on one’s outcome—individuals naturally

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seek certain roles because they perceive that those roles will give them certain benefits. But, despite their statistical insignificance in the regressions, they prove interesting later in the post- regression analyses.

Demographics showed that 11 individuals, or 2.9 per cent of network members, were transient (homeless). One hundred thirty-three individuals, or 35.57 percent, were female. Only

62 of those were victims. Table 3 shows that 218 individuals were identified as black, 46 were white, 47 were Hispanic, and smaller numbers were Filipino, Asian, Samoan, Other, or

Table 3. Racial Breakdown

Race Freq. Percent Cum. Black 218 58.1 58.1 White 46 12.3 70.4 Hispanic 47 12.5 82.9 Filipino 2 0.5 83.5 Asian 4 1.1 84.5 Samoan 9 2.4 86.9 Other 2 0.5 87.5 Unknown 47 12.5 100 Total 375 100

Unknown. It is important to remember that the gangs involved in this network are predominately

African American in culture and origin, identifying mostly as Crips and Bloods, but some members, associates, and others involved in activities as perpetrators, enablers, or victims come from different backgrounds.

Means were provided for the individual-level network coefficient variables in Table 1,

Variable Descriptions, in Chapter 3. To recap here, the mean degree centrality was 10.5, meaning that the average network member was connected to 10.5 other people. Normalized for fair comparison to the other node-level network measures that have a range of 0 to 1, the mean normalized degree centrality is 0.147. Mean betweenness centrality is 0.005; mean Clustering 131

Coefficient is 0.622; and mean Structural Holes Coefficient is 0.475. The meanings of all of these are discussed below when the regression results are presented.

In addition to these, the average network member had 4.688 contacts with police prior to the indictment that could be identified from the investigation data provided, and the mean probability of avoiding indictment was 88.5 percent.

Whole-Network Descriptives

Table 4 provides initial network-level descriptive statistics for the final 2011 network as understood at the time of the law enforcement takedown operation and the 2011 indictment.

Table 4. 2011 Whole-Network Descriptive Statistics

Network Density 0.0280 Number of Ties/ Dyads 1,966 (1,362 with value/weight = 1) Number of Nodes/ Network Size 375 nodes Mean Degree Centrality 10.4853 (S.D. 13.3557) Number of Hierarchical Clusters 17; largest = 261 vertices Network Clustering Coefficient 0.4436 Watts-Strogatz Clustering Coefficient 0.7472 Betweenness Centralization 0.1169

Network density for the final network is 0.028, which means that of all possible ties that could exist between individuals, only 2.8 per cent of them actually exist. In a network as large as this one, this sparse level of density is not unusual. The very size of this network impacts results later and provides interesting insights, given that many network studies in criminology tend to be of smaller networks (see examples in Chapter 3), or of networks where data was not available on more individuals. There are 1,966 edges, or ties, between 375 nodes, or individuals. 1362 of those had a weight of one, meaning that 604 ties were weighted greater than one—indicating multiple types of relationships existing concurrently between those pairs of individuals.

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Weighted degree centrality was tested in the regressions, but weighting made no difference in hypothesis test results because of the already-large variation in degree. So, the plain, unweighted, normalized degree centrality is used in the regressions presented here.

Next a hierarchical clustering procedure was run. Hierarchical clustering is a process used to identify nodes that are structurally similar with regard to their personal networks, whether or not they are connected to one another (de Nooy et al., 2011). First, the hierarchical clustering process groups nodes, also called vertices, that are most similar with respect to ties into a cluster; then, it groups the next pair of most-similar nodes or clusters, and so on until all vertices have been joined (de Nooy et al., 2011, p. 308). This clustering process created 17 clusters that each contain nodes with similar tie structures, with the largest cluster containing 261 vertices. This indicates that 261 individuals in the network are similarly-situated with respect to the structure of their relationships with others, meaning that the types of advantages that may be available to them due to the structures of their relationships are also similar. Hierarchical clustering was used here to examine structural equivalency, due to the size and sparseness of the network, instead of blockmodeling which works better with smaller networks (de Nooy et al.,

2011, p. 299). There are clustering-related hypotheses that may be tested, but for this dissertation, clustering was done for descriptive purposes. The very large cluster of 261 individuals that are all similarly-situated indicates that there is some regularity in personal network structures for a great number of individuals in the network as a whole, which may also mean there are a large number of redundant ties, or multiple paths through which A may reach B.

This idea will come up again later.

The separate, individual-level clustering coefficient described in Chapter 3 is used and discussed later in the regression analyses (Calderoni, 2014; de Nooy et al., 2011). Here, a

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network-level clustering coefficient was also calculated. This is a weighted average of the clustering coefficient for each individual member (de Nooy et al., 2011, p. 344). In this case, a weighted mean of 44.37 percent of any individuals’ connections are also likely to be connected to each other. This tells us the level of transitivity in the network, as measured by the number of two-paths48 that are closed (see the triad census table below for illustration, particularly triad type 11-201). It also gives a sense of structural hole opportunities available, even though it is likely that these averages are skewed by the presence of a core of highly connected individuals with a large number of sparsely-connected people at the edges. The Watts-Strogatz network clustering coefficient is unweighted and much less precise (de Nooy et al., 2011, p. 344), but it is also given in the table above.

Lastly, betweenness centralization for the network is 0.1169. This is a different level of measurement from the mean betweenness centrality for individuals. Based on Freeman (1979), network betweenness centralization is “the [actual] variation in the betweenness centrality of vertices divided by the maximum variation in betweenness centrality scores possible in a network of the same size” (de Nooy et al., 2011, p. 151). The relatively low betweenness centralization score here is due to the low min-max values for actor-level betweenness centralities in this network, shown in Table 1 in Chapter 3, but the fact that it is at the higher end of the min-max range reflects the level of heterogeneity in betweenness centrality values between actors within that range (Wasserman & Faust, 1994, pp. 191-192). Were this study examining two or more networks, this network betweenness centralization could be used to compare the level of brokerage, or the prevalence of individuals that connect others, between them.

48 A two-path is a path between individuals of length 2, meaning the path is not direct and must pass through one other person (de Nooy et al., 2011, p. 239). 134

Table 5. Triad Census 2011 Full Network

Type # Triads (Ni) Expected (ei) (ni-ei)/ei Model

3-102 651686 18313.5 34.59 Balance

16-300 7684 0 1826059.87 Balance

1-003 8030588 7352565.92 0.09 Clusterability

4-021D 0 18313.5 -1 Ranked Clusters

5-021U 0 18313.5 -1 Ranked Clusters

9-030T 0 1055.37 -1 Ranked Clusters

12-120D 0 15.2 -1 Ranked Clusters

13-120U 0 15.2 -1 Ranked Clusters

2-012 0 1271146.79 -1 Transitivity

14-120C 0 30.41 -1 Hierarchical Clusters

15-210 0 0.88 -1 Hierarchical Clusters

6-021C 0 36626.99 -1 Forbidden

7-111D 0 1055.37 -1 Forbidden

8-111U 0 1055.37 -1 Forbidden

10-030C 0 351.79 -1 Forbidden

11-201 28917 15.2 1900.83 Forbidden

Note: See Wasserman and Faust (1994), Chapter 14.

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Table 5 shows the triad census for this network. This breakdown of number of triads by type describes implications for overall network structure, such as tendencies toward clustering, transitivity (the idea that if A is friends with C, and B is friends with C, then A is friends with B), and the hierarchical clustering described above (Wasserman & Faust, 1994). Each of the triad types, or ways that any three people can be connected, has implications for the flow of information and influence. This is an undirected network, in that most relationships are mutual even if the balance of power is not even, so directional arrows are not applicable for this study.

As well, triads for every possible combination of three people in our N of 375 are included, so that is why there are many that do not show lines between two or more members.

As can be seen by looking at the illustrations of possible triad structures in Table 5, of all the possible triad variations, only a few are evident in this network. That is because, as an undirected network, there are no one-way arrows between nodes that would make the other types possible; there are no purely unidirectional relationships. The most common type is 1-003, where none of the three people are connected, because this is a large, sparse network. Next most- common is 3-102, where two individuals are connected, but the third is excluded, again simply an indication of sparseness. Third is 11-201, where two individuals are connected, but not directly—they must pass through the third. This is the triad that illustrates brokerage, and there are 28,917 such triads in this network. Fourth, and least common, is the case where all three individuals are connected (16-300), illustrating transitivity (defined in the previous paragraph).

Transitivity is an indication of balance, but also of a lack of structural holes that may be converted into a brokerage advantage because all three members in the triad are already connected.

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Sociograms

Sociograms are visual representations of the network and can be used to illustrate a variety of characteristics and relations. First, to show evolution of the network from 2005 (NWS inception) until 2011 (final indictment), sociograms were generated for each year from the available police data sources, based on relationship start- and end-dates. Shown in Figures 4A,

4B, and 4C, these sociograms-by-year are additive and build upon one another. It is important to note that these are based on data available to police at each point in time, whether police had actually collected it by then or not. This is especially important regarding the earlier years, since investigation of the group as a networked enterprise did not begin in earnest until 2007-2008.

Relationships of a permanent nature, such as siblings, are included throughout, as are relationships for which no begin/end dates were available. Since these represent police knowledge about the network, relationships that have end dates, such as an event or a romantic relationship, remain included throughout because they were still used as evidence in building the prosecution. The purpose of the study is to determine probability of avoiding indictment, as a proxy for survival, and so even if a relationship ceased to be functional due to the end date, it was still part of the evidence that impacted whether a participant was indicted.

These sociograms-by-year give a picture of what level of data was available to police at different times during the investigation. The purpose of Figures 4A-4C and Table 6 is simply to show evolution of network size and complexity over time. Since the data is available and all possible relationships dated, future studies may conduct time-series analyses for different research questions. But, since this study focuses on the probability of being included in a single pair of indictments (the smaller, 2009 indictment and the final 2011 indictment), all sociograms after this point relate to the complete network data used to compile and support them.

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Table 6: Network Measures over Time.

2005 2006 2007 2008 2009 2010 2011 # Vertices 220 244 267 291 359 365 375 # Edges 430 517 857 1075 1841 1948 1965 # Components 25 19 14 11 10 10 10 Largest Component 153 199 229 256 332 338 349 (Vertices) (70%) (82%) (86%) (88%) (93%) (93%) (93%) Network Density 0.0178 0.0174 0.0241 0.0255 0.0286 0.0293 0.0282 Average Degree 3.9091 4.2377 6.4195 7.3883 10.2563 10.6740 10.5082

For illustrative purposes, Table 6 shows descriptive measures for the network for each year for which we have data, and for which a sociogram is shown in Figures 4A, 4B, and 4C. As evident in the sociograms, the number of vertices grew from 220 in 2005 to 375 in 2011. The

220 vertices in 2005 are quite disconnected, as can be seen from the many isolated groups in that sociogram and the fact that there were only 430 ties, or edges. Remember that these relationships always include familial relationships that have no start or end dates. The largest component in

2005 had 153 vertices, meaning that even if connections were sparse, 153 of the individuals were connected to each other in some way even if it might take many steps to get from one to the other

(see Wasserman & Faust, 1994, pp. 190-191 for further discussion of components). Further illustrating the sparseness, network density in 2005 was just 0.0178, and the average person was connected to 3.91 other people.

The network continued to grow in complexity by year, with most statistics increasing while number of components decreased, meaning that nodes were building bridges and becoming the connectors of different components (assuming brokerage positions). The highest network density and average degree were present in 2010, with those numbers decreasing just slightly in 2011. This may have been, perhaps, because people were leaving the group, or it might have been due to slightly better concealment efforts on the part of some individuals, since

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2005

2006

2007

Figure 4A: Network Evolution: Increasing Complexity and Consolidation over Time

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2008

2009

2010

Figure 4B: Network Evolution: Increasing Complexity and Consolidation over Time (continued).

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2011: Final Network at time of Indictment

Figure 4C: Network Evolution: Increasing Complexity and Consolidation over Time (continued). the group was aware that it was being targeted for investigation at that time according to detectives. However, the decreases in those measures are very small, so they might also simply be a mathematical consequence of the increase in peripheral members from 2010 to 2011.

The two sociograms that follow these annual pictures show more detail about the network in 2011. Figure 5 shows the interconnectedness of the different gang cliques that cooperated in this network. Each color represents a different clique, and the size of each vertex represents its degree centrality. So, the larger the vertex, the more individuals that vertex is connected to. As can be seen, those who claimed allegiance to the pimping crew (NWS, the blue nodes), either by itself or in a dual allegiance to the crew and their own clique, were located right in the middle.

They served as the connectors for many groups and often had higher degree centrality, though there are quite a few small blue dots as well. DVC (yellow) and ICG (green) also had several members with high degree centrality, with CMG (red) showing a lesser role. Most members of

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DVC are on the right side in the center, core area, and members of ICG are mostly on the left, but this sociogram still shows how members from different cliques were quite interconnected, and they were connected most often by members of the pimping operation. Were their relationships completely rivalrous, there would be much more separation of colors with fewer intersections, since most intersections would only occur during conflict events such as a fight or an inter-gang shooting.

Figure 5: Sociogram of Clique by Degree Centrality, 2011

Blue = NWS Yellow = DVC Red = CMG Green = ICG Orange = Unknown White = FWS (Females Wit Swagg, all-female clique)

Figure 6 shows degree centrality by gang rank and victim status. Blue in this sociogram represents gangster, the middle and most common rank, so to see large vertices (higher degree centrality) for gangsters is not surprising. Yellow represents a victim, so even while victims are under duress, they still wind up connected to a large number of other members in the network. 142

This is also a reflection of the fact that victims are likely to place themselves in the center of their own stories, so there may be a slight overrepresentation of victims in the relational dataset since the network was built out by police by using their interviews and statements as the starting

Figure 6: Gang Rank and Victim Status by Degree Centrality

Pink = Big Homey Blue = Gangster Yellow = Victim Red = Baby Gangster Orange = Unknown Turquoise = none Green = Gangster Girl

point. The few well-connected red-dots are Baby Gangsters, and green represents a Gangster Girl that holds no other rank in the gang.

Pink, of which there are not many visible here, represents a Big Homey. There are just three that are very visible, and only one of those was indicted. This is, however, a two- dimensional representation of a three-dimensional network; of the different visual angles tested, this angle presented the clearest most complete picture, but some nodes are still hidden behind 143

others. It is not entirely surprising that so few Big Homeys show visibly, given that there are only 12 identified in the police data, but it is noteworthy that only three have high enough degree centralities to show clearly in the sociogram given the level of influence they are thought to hold over other members.

Social Network Analyses

This chapter will now move from descriptive individual and network-level statistics into testing the hypotheses outlined at the end of Chapter 3. First, in Table 7, a correlation matrix is presented for all the variables included in the regressions. As a reminder, all variables included are at the individual level, including the centrality measures and coefficients calculated for each actor based on their relationships with others. Further, as mentioned on page 109 in Chapter 3, their correlations are affected by skewness present due to the non-independent nature of the data and this is accounted for in the choice of regression model estimated.

As shown, the centrality measures, the clustering coefficient, and the structural holes coefficient are all highly correlated with one another. This is normal with network measures because of the interdependence of observations and the fact that they are all calculated from the same matrix, although not all network measures are correlated equally (Valente et al., 2008).

Surprisingly, normalized degree centrality and betweenness centrality are 80 percent positively correlated in this network, but previous studies show that this is not unusual although the size of the correlation will vary between networks (Calderoni, 2014; Valente et al., 2008).

Degree is negatively correlated with the clustering coefficient and the structural holes coefficient, both defined in Chapter 3, although these mean different things. The clustering coefficient, as described earlier, measures the likelihood that any two of an actor’s connections are also connected to one another. A higher clustering coefficient indicates that there are fewer

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Table 7: Correlations Matrix

P Struct (Avoid Normal CC1 -ural ST Prior Indict- ized Between Cluster- Holes Gang Funct Conta Set/ Gang Tran- ment) Degree -ness ing Coef. Coef. Rank ion Victim cts Clique Asso. sient Race Sex P(Avoid Indictment) 1 Normalized Degree -0.73* 1 Between- ness Centrality -0.63* 0.81* 1 CC1 Clustering Coef. 0.18* -0.11* -0.24* 1 Structural Holes Coef. 0.40* -0.65* -0.39* -0.30* 1 Gang Rank 0.26* -0.36* -0.22* 0.00 0.36* 1 ST Function 0.03 0.02 -0.02 0.02 -0.01 -0.07 1 Victim 0.16* 0.07 -0.03 0.06 -0.14* -0.34* -0.04 1 Prior Contacts -0.71* 0.60* 0.63* -0.15* -0.35* -0.27* -0.04 -0.11* 1 Set/ Clique 0.18* -0.23* -0.16* -0.03 0.27* 0.55* 0.02 -0.14* -0.21* 1 Gang Association 0.16* -0.24* -0.14* -0.09 0.32* 0.57* -0.05 -0.15* -0.18* 0.75* 1 Transient 0.25* -0.20* -0.18* 0.09 0.11* 0.17* -0.06 0.02 -0.28* 0.10 0.07 1 Race 0.14* -0.10 -0.08 0.10* 0.02 0.10* 0.09 0.05 -0.16* 0.21* 0.12* 0.42* 1 Sex -0.13* 0.05 0.09 0.02 0.01 0.03 0.09 -0.59* 0.16* 0.04 0.06 -0.02 -0.09 1

Note: * = p<.05

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structural holes that might be exploited for gain, meaning that a higher clustering coefficient would indicate a weaker brokerage position and potentially less secrecy, and is hypothesized to reduce the probability of being indicted. Similarly, a lower value for the structural holes/ constraint coefficient indicates that there are more structural holes available to exploit for that actor, so a higher structural holes coefficient is hypothesized to increase the probability of avoiding indictment according to theory. These two measures, however, are negatively correlated with each other. Betweenness centrality is also negatively correlated with the clustering and structural holes coefficients, which is surprising since all three are intended to measure existing brokerage power or brokerage opportunity. The potential differences between them that may explain these variances, and the utility of each measure in capturing the effects of brokerage position, are explored via the regression models, the principal component analysis, and other post-regression analyses.

Among the control variables, every variable except “sex trafficking function” is significantly correlated with the dependent variable of avoiding indictment. Surprisingly, one’s job function in the sex trafficking business was not statistically significant in any of the correlations—one would have expected that to be a very important factor not only for individuals’ indictment outcomes, but in their relationships to each other. However, most of the other control variables were significantly correlated with at least some network measures with the exception of the clustering coefficient, which was significantly correlated only with prior police contacts and race.

Regression Models

As described in Chapter 3, several hypotheses were tested regarding different measures of network centrality, types of connections, and their influence on whether a given individual in

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the network was able to avoid indictment. Again, avoiding indictment is used as a measure of survival for the individual, and would be considered the positive and desired outcome by any network participant. Any rational decisions made by a trafficking network member would assume that avoiding indictment is a utility to be maximized.

A number of penalized maximum likelihood (MLE) logit models are run for all four network measures, taking them in different combinations and treating them as node attributes, using the firthfit package in Stata. The penalized MLE method of estimation is chosen because it relaxes assumptions regarding normality and independence of observations, which is necessary for testing hypotheses about interdependence. The author of the firthfit Stata package notes that the standard errors for the individual coefficients are not to be trusted on their own (Coveney,

2015a), however, so several measures of model fit are included. Two of these are key to understanding which model fits these data best.

First, Tjur's Coefficient of Discrimination, more simply known as Tjur’s D, is a relatively new R2 calculation that has been accepted by many as more appropriate for certain types of logistic regression, including the penalized MLE model used here. This is because Tjur’s D relaxes the assumptions about linearity inherent in traditional R2 and Pseudo R2 formulae

(Allison, 2013). It is also provided with the “firthfit” Stata package that was recently created to generate valid goodness of fit tests for the firthlogit model; the standard goodness of fit tests that

Stata runs with the firthlogit command do not produce valid results (Staudt, 2016). Tjur’s D is defined as “the [absolute] difference between the averages of fitted values for successes and failures, respectively” (Tjur, 2009, p. 366). So, the larger the value (or the closer it is to 1.0), the greater the model’s ability to predict the observed outcomes. It can also be used to compare different types of regression models for their predictive power.

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Second, the Akaike’s Information Criterion (AIC) is used to identify the more parsimonious model. Unlike many of the R2 measures, “the information measures have penalties for including variables that do not significantly improve fit. Particularly with large samples, the information measures can lead to more parsimonious but adequate models” (R. Williams, 2016b, p. 3). Additionally, the fits of different models can be compared, even if they are not nested.

With AIC, the lower the coefficient, the better the model fit, and the same is true for the

Bayesian Information Criterion (BIC), also reported here. Both are looked at together: the Tjur’s

D to assess the model’s predictive power, and the information criteria to assess parsimony when the Tjur’s D values might be too similar to make a meaningful distinction regarding which model fits the data better. Further, it is to be noted that the author of firthfit package states that it was not written to work with calculating marginal effects (Coveney, 2015a), so odds ratios are reported instead. The regression models, shown in Table 8, contain the four network variables and the controls defined and operationalized in Chapter 3.

Evaluating models 1-9 first, degree centrality is the consistently statistically significant theoretical variable, wiping out the significance of the other three, in all models where degree is included. Those models have the highest Tjur’s D scores, and generally the lower AIC and BIC values. All network centralities and coefficients are statistically significant in the models in which they are tested alone, but the other coefficients’ Tjur’s D scores are lower, from .79 for betweenness centrality down to.598 for the clustering coefficient, and .702 for the structural holes coefficient. Among models containing normalized degree centrality in varying combinations with the other variables, all five have Tjur’s D values between .816 and .823, which are almost indistinguishable. Looking then at the information criteria, the models with

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degree alone, and degree and the clustering coefficient together, appear to be the most parsimonious, with AICs of 12.72 and 14.234, and BICs of 55.92 and 61.36, respectively.

Of all the control variables included in the model, the only one that was statistically significant was, again, the number of prior police contacts. This was the variable introduced to control for possible tautology involved in using police data to create this network. The models were run without the prior police contacts variable as well; in these, whether or not the individual was an identified victim became the significant control variable. All others remained statistically insignificant, with the exception of gang rank when betweenness centrality was tested alone. For the models without the prior police contacts variable, the Tjur’s D values averaged about ten points lower than the models presented here, indicating less predictive power, and the information criteria scores were higher, indicating less parsimony.

Model 9, with all four variables, showed that a higher degree centrality was the statistically significant predictor of avoiding indictment, with a Tjur’s D of .819 and the AIC and

BIC values at 24.89 and 79.87, respectively. That said, the full model did not have the highest

Tjur’s D or the lowest information criteria values. Models VP4 and VP7, which contain both degree and betweenness, but not the structural holes coefficient, have slightly higher Tjur’s Ds at

.824 and .823, respectively, with the model containing degree and betweenness alone (model 4) demonstrating the lower information criteria values. Examining the information criteria values closer, the model with degree centrality alone, Model 1, is the best performing out of models 1-9.

Model 5, with degree and the clustering coefficient, nearly ties with model 1 but has just slightly higher information criteria. Clustering coefficient remains statistically insignificant in this model, and the direction of its effect is the opposite of what was originally hypothesized (higher clustering coefficient increased the probability of avoiding indictment rather than lowering it),

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Table 8. Full Regression Models, Odds Ratios displayed

MODEL MODEL MODEL MODEL MODEL POVGAvoidIndict 1 MODEL 2 MODEL 3 4 MODEL 5 MODEL 6 MODEL 7 8 9 10 Degree Centrality 0.00006** 0.00042** 0.00009** 0.00047** 0.00120* (0.00013) (1.0E-03) (0.00018) (0.00112) (0.00335) Betweenness 8.7E-82** 3.9E-24 5.4E-7911** 3.6E-21 2.0E-20 Centrality (5.2E-78) (1.4E-22) (3.3E-77) (1.4E-19) (7.6E-19) CC1 Clustering 5.32* 2.24 1.02 1.50 1.71 Coefficient (3.82) (2.53) (1.11) (1.89) (2.18) Structural Holes 1702.7*** 2.50 Coefficient (3459.2) (5.10) 0.025*** Factor 1 (Degree) (0.027) Factor 2 0.374 (Clustering) (0.354) Gang Rank 1.03 1.02 1.04 1.03 1.03 1.02 1.03 1.03 1.03 1.01 (0.03) (0.02) (0.03) (0.04) (0.03) (0.02) (0.04) (0.02) (0.03) (0.02) ST Function 0.99 1.02 1.00 1.00 0.99 1.02 1.00 1.00 1.00 0.99 (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.01) (0.02) (0.02) Victim 28.21 11.08 31.55 24.13 28.29 11.03 23.98 19.83 20.81 43.54 (49.76) (18.99) (48.16) (44.58) (49.94) (18.91) (43.82) (31.28) (37.73) (79.74) Prior Contacts 0.85** 0.84** 0.83** 0.84** 0.85** 0.84** 0.85** 0.83** 0.85** 0.82** (0.04) (0.04) (0.03) (0.04) (0.04) (0.04) (0.04) (0.04) (0.04) (0.07) Set/Clique 0.98 0.98 0.99 0.98 0.98 0.98 0.98 1.00 0.98 0.99 (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) 0.03 Gang Association 1.01 1.06 0.99 1.01 1.01 1.06 1.01 1.00 1.01 1.00 (0.03) (0.04) (0.03) (0.03) (0.03) (0.04) (0.03) (0.02) (0.03) (0.02) Transient 1.01 1.04 1.02 1.02 1.01 1.04 1.01 1.02 1.01 1.00 (0.02) (0.03) (0.02) (0.02) (0.02) (0.03) (0.02) (0.02) (0.02) (0.02) Race 0.99 0.98 0.99 0.98 0.99 0.98 0.99 0.99 0.99 0.99 150

(0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) Sex 4.18 1.92 3.38 3.97 4.13 1.92 3.86 2.54 3.43 7.61

(4.42) (1.95) (2.15) (4.39) (4.36) (1.94) (4.20) (1.83) (3.74) (8.92)

Number of obs = 375 375 375 375 375 375 375 375 375 375 Wald chi2(13) = 37.76 28.96 45.8 32.78 37.17 29.18 33.36 41.34 33.47 21.34 Prob > chi2 = 0 0.0013 0 0.0006 0.0001 0.0021 0.0009 0 0.0014 0.03 Log-likelihood Full Model 4.639 -3.097 -18.842 1.953 4.883 -3.089 1.926 -7.156 1.553 14.612 Information Criteria AIC 12.722 28.193 59.685 20.095 14.234 30.178 22.148 36.311 24.894 -5.233 BIC 55.918 71.389 102.881 67.218 61.357 77.301 73.198 79.507 79.871 41.9 R^2 Cox-Snell/ML 0.412 0.396 0.331 0.413 0.41 0.393 0.016 0.372 0.006 0.433 Cragg-Uhler/ Nagelkerke 1.037 0.976 0.838 1.015 1.039 0.975 2.869 0.941 -3.279 1.119 Efron 0.828 0.804 0.608 0.839 0.83 0.803 0.838 0.712 0.836 0.901 McFadden 1.049 0.968 0.8 1.02 1.052 0.968 2.884 0.924 -3.292 1.159 McFadden (adj) 0.933 0.856 0.68 0.897 0.924 0.844 -9.832 0.808 35.401 1.028 McKelvey & Zavoina 0.784 0.89 0.719 0.815 0.78 0.884 0.805 0.803 0.798 0.884 Tjur's D 0.816 0.79 0.598 0.824 0.816 0.788 0.823 0.702 0.819 0.886

Note: * = p < .05; ** = p < .01. Numbers italicized and in parentheses are standard errors.

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but its inclusion in one of the two best-fitting models is still interesting. While a higher clustering coefficient increased the possibility that an individual would avoid indictment, perhaps indicating that the individual would be in possession of information that s/he could give to police to avoid indictment personally, that very same dynamic may be disadvantageous to the network as a whole. This idea supports Calderoni’s assertion that high clustering coefficients, as an indication of low secrecy, can hurt a criminal network. In fact, the network-level clustering coefficient was 0.44, which is quite high. Thus, a high clustering coefficient might protect an individual, but hurt the network.

However, although the differences are slight, degree centrality (knowing more people) is the strongest predictor of avoiding indictment in these analyses—contrary to Hypothesis 1A, that a higher degree centrality would lower the probability of indictment. This is explored later after the means tests in the post-analyses. Additionally, although the effect of betweenness centrality on avoiding indictment was positive, as expected, the effect size was miniscule and it was not statistically significant. The models featuring it also had lower explanatory power when degree was not included.

But, given the correlation between network centralities and coefficients, a natural question is: to what extent are these variables really measuring separate concepts? Results of a principal component analysis are presented next to answer the secondary hypotheses around this question. Model 10 from Table 8, which tests the latent network variables estimated in the principal component analysis, is discussed after that.

Principal Component Analyses

Morselli (2009) hypothesized that the number of people one is connected to (degree centrality) and the likelihood that any pair of them that are connected to one another (clustering

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coefficient) were both measures of direct connectivity. Calderoni (2014) suggests that a higher clustering coefficient may indicate lower secrecy; if all one’s friends are connected to each other, it could hurt a criminal organization. In the present case, the investigators and prosecutor confirmed that many of those indicted gave information on others as part of their plea deals; others may have cooperated with police earlier in the investigation and gave information before the indictment was handed down. This was mentioned anecdotally during interviews, but not documented in the investigative records. So, while the clustering coefficient was not statistically significant (p < .695), this concept will also be explored further in the post-analyses.

Table 9 shows that the four actor-level network measures load on two components.

Degree and betweenness both load mostly on component 1. The clustering coefficient loads almost entirely on component 2, indicating that it is measuring something different than the others. This provides support for hypothesis 2b, that degree centrality and the clustering coefficient are measuring separate concepts (Calderoni, 2014). The structural holes coefficient loads on both components. Both component 1 and component 2 have eigenvalues over 1.0, revealing that the latent variable behind degree centrality and the latent variable behind the clustering coefficient are both statistically significant in explanatory power. Additionally, the correlation between the two components is only -0.0087, which supports Calderoni’s conclusion rather than Morselli’s (2009) assertion that degree and the clustering coefficient are measuring the same concept.

Thus, the principal component analysis suggests that degree and betweenness are measuring mostly the same thing rather than different concepts, supporting hypothesis 3b from

Chapter 3, while the clustering coefficient is measuring something different. Previous studies testing the differences between degree and betweenness cited in the literature review mostly

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involved smaller networks, so it may be possible that network size contributed to the different results seen here. Or, this result may be due to the tightly connected center component and the presence of redundant ties. If individuals are able to reach each other through more than one broker, they may have less motivation to protect any specific one, so betweenness centrality may have less predictive power for individuals’ ability to avoid indictment under those circumstances.

Table 9. Principal Component Analysis: Network Measures

Rotated Factor Loadings (PCF, Oblique, Promax) Variable Comp. 1 Comp. 2 Uniqueness Degree 0.7556 -0.0676 0.4237 Betweenness 0.803 -0.2728 0.2769 CC1ClusteringCoefficient -0.0811 0.9406 0.1075 StructuralHolesCoefficient -0.719 -0.5378 0.2007 Correlation Matrix of the Principal Components Variable Comp. 1 Comp. 2 Component 1 (Degree) 1 Component 2 (Clustering) -0.00876 1

Component Eigenvalue Difference Proportion Cumulative Component1 1.74028 0.48933 0.4351 0.4351 Component2 1.25095 0.61225 0.3127 0.7478 Component3 0.6387 0.26862 0.1597 0.9075 Component4 0.37008 0.0925 1 LR test: independent vs. saturated: chi2(3) = 247.72 Prob>chi2 = 0 Number of obs = 375 Retained factors = 2 Number of parameters = 6

In regression model 10, predicted values for the latent variables estimated in the principal component analysis were tested. The latent variable behind degree and betweenness remained the statistically significant predictor of avoiding indictment, and the effect size was much larger—0.025 vs. betas with five decimal places in models 1-9. Importantly, the test statistics for the latent variable model show a markedly better fit than the other nine models. The Tjur’s D for 154

the latent variable model is .886, versus an average of .81 for the other models, and the information criteria are also improved—AIC is -.5233, while the BIC is 41.9—much lower than even the best performing model out of the first nine shown in Table 8.

Post Analyses

In an attempt to understand the impact of centrality measures and network coefficients further, the next analyses loosely follow Francesco Calderoni’s approach in his 2014 study of the

‘Ndrangheta in Italy. If network measures are too correlated in the network under study, it can be difficult to draw inferences about the impact of strategic position on probability of survival, or whatever the dependent variable of interest may be. Calderoni (2014) thus separated his analyses and looked at centrality scores by task and by a status level index he created. Mancuso (2014) also breaks down mean centrality scores by role to discover attributes of madams with more power. Here, the impacts of the centrality scores, structural holes coefficient, and clustering coefficient are broken down by gang rank, sex trafficking network function, and gang association. Then, the means for each variable are tested in order to shed more light on the processes at play in this network. This may help clarify some differences in the effects of network position between gang members and non-gang members, victims, or the hotel managers and lookouts who facilitated the business. Table 10 shows that the differences between most of these means were statistically significant at the .001 level, with the exception of most of the mean clustering coefficient values, which were not statistically significant between different categories.

Mean normalized degree was almost .50 greater for those who did not avoid indictment versus those who were successful, which is the hypothesized relationship, but this appears to be in contradiction to the regression results that showed higher degree centrality was protective—

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raising questions about a possible nonlinear relationship between degree centrality and avoiding indictment. It is conceivable that, as degree centrality gets very high, there are diminishing returns for each additional person added. Thus, the predicted effects of degree centrality may change direction the larger the network becomes.

To test for this, separate regressions were estimated to test for curvilinear effects, one utilizing the latent degree variable and the other testing the original normalized degree centrality measure. Both showed a curvilinear effect for degree centrality, but the effect was not statistically significant in either model, although the p-value in the normalized degree model was borderline (.065). Both models had similar Tjur’s D scores to the latent variable model, which was model 10 in Table 8. The model testing the curvilinear effect of the latent variable had a

Tjur’s D of 0.882, while the model testing that effect for normalized degree had a Tjur’s D of

0.888, indicating slightly better predictive power. However, the model testing the curvilinear effect of the latent variable had better information criteria scores (AIC = -.5892; BIC = 45.068) versus the normalized degree model (AIC = 12.917; BIC = 71.821), indicating that the latent variable model is still the more parsimonious. The full regression tables for these curvilinear tests are located in Appendix D, Tables D3 and D4.

This indicates that the latent variable model is still a marginally better fit for the data, but that the effect of degree centrality on the probability of indictment changes direction after its value reaches a certain point—after a while, one can know so many people that the increased visibility ceases to be a risk factor, and brokerage loses its power as a protective factor in relation to it. Thus, these measures may have tipping points the larger the network becomes—a network position that is detrimental in a smaller network may be beneficial in a larger one, and vice versa.

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Returning to the means tests, mean betweenness centrality was higher for those who were indicted as well. Given the close correlation between degree and betweenness in this network, this is not surprising. Clustering and structural holes coefficients were both higher for those who avoided indictment. That seems unusual since beneficial values for each of those two coefficients move in opposite directions. However, the difference between means is much smaller for the clustering coefficient than it is for the structural holes coefficient. This may explain why the clustering coefficient was mostly statistically insignificant, but its uniqueness in the principal component analyses and its presence in the best-fitting model still makes it interesting.

Within gang rank, Gangster Girls tended to have higher mean degree centrality, and only Big

Homeys had higher mean betweenness values, meaning that they brokered connections between more people. Big Homeys also tended to avoid indictment. Gangster Girls had lower clustering coefficients, indicating potential higher secrecy in line with their higher betweenness scores, but their structural holes coefficient was also low which indicates fewer structural holes around them available for future exploitation. This means that although Gangster Girls tended to broker more relationships, as evidenced by their higher betweenness scores, their lower structural holes coefficients indicated that their opportunities to expand their brokerage positions were limited.

This makes sense, given their functions as bottoms in the sex trafficking business. As a reminder, females that were full-fledged gang members and that participated in other gang enterprises were categorized simply as Gangsters—Gangster Girls were not official gang members, and served only in this one capacity in the hierarchy outside of their personal relationships with gang members.

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Table 10. Mean Network Measures by Probability of Avoiding Indictment, Gang Rank, Gang Association, Clique, and Sex Trafficking Network Function

Mean Mean CC1 Mean Normalized Mean Clustering Structural Degree Betweenness Coef. Holes Coef. N P(AvoidIndict) No 0.5293*** 0.0283*** 0.4314*** 0.1239*** 43 Yes 0.0365*** 0.0018*** 0.6463*** 0.5208*** 332 Gang Rank None 0.1235*** 0.0037*** 0.6506 0.4322*** 87 Gangster Girl 0.3370*** 0.0091*** 0.5006 0.2649*** 14 Baby Gangster 0.1514*** 0.0036*** 0.7337 0.2770*** 4 Gangster 0.1953*** 0.0085*** 0.5986 0.3847*** 129 Big Homey 0.0857*** 0.0136*** 0.7675 0.3327*** 12 Unknown 0.1009*** 0.0007*** 0.6213 0.6371*** 129 Gang Association None 0.0775*** 0.0003*** 0.5768 0.6826*** 20 Member 0.1832*** 0.0084*** 0.6235 0.3885*** 139 Associate 0.1259*** 0.0037*** 0.6518 0.4426*** 146 Facilitator 0.4335*** 0.0083*** 0.7110 0.2997*** 9 Unknown 0.0986*** 0.0005*** 0.5469 0.7091*** 61 Clique None 0.1626*** 0.0028*** 0.6546 0.5695*** 24 DVC 0.0984*** 0.0020*** 0.6330 0.4658*** 99 ICG 0.1406*** 0.0042*** 0.7093 0.4157*** 59 CMG 0.2106*** 0.0089*** 0.5358 0.3492*** 21 NWS 0.3229*** 0.0181*** 0.5111 0.3146*** 56 FWS (Female Clique) 0.0859*** 0.0016*** 0.7612 0.3256*** 10 Other 0.0858*** 0.0007*** 0.6812 0.5476*** 22 Unknown 0.0966*** 0.0008*** 0.6002 0.6387*** 84 ST Function None 0.0978*** 0.0016*** 0.6439 0.5311*** 278 Pimp 0.0311*** 0.0190*** 0.5173 0.2729*** 56 Bottom 0.3123*** 0.0079*** 0.5279 0.2872*** 17 Customer 0.2430*** 0.0132*** 0.5142 0.4893*** 4 Other 0.3535*** 0.0101*** 0.7515 0.3528*** 10 Unknown 0.0873*** 0.0016*** 0.6594 0.4908*** 10

Notes: *** = p < .001. Means Tests (except P(AvoidIndict), which is a 2 sample t-test) use Wilk’s Lambda, Pillai’s Trace, Lawley-Hotelling Trace, and Roy’s Largest Root to determine F-scores for significance.

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Gang Association, or embeddedness, has interesting results as well. Facilitators— individuals who are tied to activities such as complicit hotel managers and lookouts, but who are not otherwise associated with the group—had the highest mean degree. They tied with Members for mean betweenness, far above Associates. They also had lower clustering and structural holes coefficients, but keep in mind that there is an N of only nine in this category. Members have the next highest mean degree, a lower mean clustering coefficient than Associates (supporting the idea that Members’ personal networks may have a slightly higher level of secrecy), and the next lowest structural holes coefficient. So, Members may have more opportunities for future brokerage available to them than Associates have.

Not surprisingly, among cliques, the pimping crew (NWS) had the highest mean degree centrality at 0.3229 since they were the focus of the investigation. But, remember that individuals with dual allegiances to both the crew and a clique were classified into the crew for purposes of analysis. Next in line were ICG and CMG. NWS also had higher mean betweenness, lower clustering coefficient, and more structural holes available to exploit (lower structural holes coefficient) than members of other cliques or of no clique. As the center of the network and the main brokers between others, these all make sense.

Last, in Sex Trafficking Function, pimps had lower mean normalized degree centrality, at

.03, compared to Bottoms, Customers, and Other (which included drivers, enforcers, logistics, etc.—but note the low N for that category as well). Perhaps this is because pimps’ larger N allows for a larger range of values in degree. Customers had higher mean betweenness than all other categories except for pimps, perhaps indicating the role of a repeat customer as a magnet for business. Those in “Other” roles had a much higher clustering coefficient at .75, compared to the other active roles that hovered just over .50, which might not be too surprising since those in

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specialized roles may meet their connections via the introductions of others in the network. For example, a broker might connect people to a specific hotel manager in order to further the business, and thus the hotel managers’ connections may have all known each other previously

(leading to the hotel manager’s higher clustering coefficient). This might be a useful hypothesis to test in a follow-up study about ego networks and the temporal order in which connections are made. Finally, Customers (N=4) had the highest mean structural holes coefficient, indicating the fewest opportunities to broker new connections, while pimps and bottoms had the highest. But, remember that sex trafficking function was not statistically significant in the regressions.

Component and Cut Point Analyses

Lastly, a component analysis is presented to show the possibilities of fragmenting the network by removing specific identified brokers called cut points. A cut-point is a potential broker that, if removed, would split a network component into two or more components. These are different than other brokers that may only connect individuals in the same component.

Betweenness centrality applies to both of these types of brokers, but in the case of many redundant ties, only the removal of a cut point has the potential to fragment the network and reduce its ability to operate at its previous capacity as described in detail in Chapter 3. With the removal of a cut point, an entire component would be cut off from information or resources that flow through that broker once s/he is removed (Everton, 2012; McGloin, 2005). As a law enforcement strategy, this sort of process could be run in iterative fashion to weaken a network.

Proceeding from the earlier analyses, a search for components of at least three members each was run in Pajek using the bi-component procedure (de Nooy et al., 2011) to determine whether the network has separate, isolated components connected by cut points, or if the entire network is connected (Wasserman & Faust, 1994). A bi-component is “a component of

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minimum size 3 that does not contain a cut vertex;” a cut-point connects it to other bi- components, but is not considered part of the bi-component itself (de Nooy et al., 2011, p. 400).

The largest bi-component contains 251 members. The next largest bi-component has 65 members, and the rest, which are all very small, are shown in Table 11 below.

After this, a sociogram was run to show the cut points connecting each component. Each cut point, if removed, would fragment the network because one or more components would then be separated (McGloin, 2005). It should be noted that, in this network, the largest component connects most of the members multiple ways (redundant ties), so the amount of fragmentation possible here is limited with a cut point removal strategy unless taken in successive waves as

Table 11. Bi-Component Analysis: Distribution Table

Representative Component # Nodes Freq% CumFreq CumFreq% NW Mbr ID 0 65 17.33 65 17.33 121 1 3 0.8 68 18.13 399 4 5 1.33 77 20.53 204 5 4 1.07 81 21.6 104 8 3 0.8 88 23.47 365 9 3 0.8 91 24.27 217 10 8 2.13 99 26.4 441 14 251 66.93 356 94.93 1 15 3 0.8 359 95.73 138 16 3 0.8 362 96.53 268 17 3 0.8 365 97.33 280 Sum 365 97.33 100 Unknown 10 2.67 Total 375 100

described above, chipping away at that large center component in Figure 7 over time. This might be desirable if an infiltration approach is desired—for example, strategically removing brokers

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or using brokers to spread information in the network that will change its behavior (Braga et al.

2001)—as opposed to a one-time takedown of all the most valuable members.

In Figure 7, each vertex color represents a different identified bi-component. Each of the cut points connecting different bi-components is colored grey and highlighted with a blue hexagon around it. The largest component, with 251 interconnected nodes, is the dark red one in the middle. The next largest, the aqua one with 65 nodes at the lower left, has several people connecting it to multiple cut point members and to several other components, which makes it impossible to separate from the main red component by removing just one or two cut points. As a reminder, bi-components do not represent clique or any other node attribute variable—only the structure of ties between connected groups. The idea is that, when one of the cut points is removed, the differently-colored groups connected by that cut point will splinter apart.

Figure 7. Cutpoints in the 2011 network

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Below, in Table 12, are the centrality scores, indictment status, and node attribute values for the cut points identified in Figure 7. All but two were pimps—the lookout and the network member with no identified sex trafficking function were not indicted. Removing either one of them would fragment the network, although the bi-components that would be cut out are small.

Interestingly, Number 46 had the highest normalized degree centrality of any member in the network. He is not an official gang member, but as a lookout, it is reasonable to expect that a lot of people knew him. Numbers 18 and 19, two of the most prominent pimps in the case, had slightly lower degree and betweenness centralities, but advantageous structural holes coefficients. There is a wide range of clustering coefficients present below, but these were largely statistically insignificant in the regressions as mentioned before.

Table 12. Centrality Scores for Identified Cut Points

Number of new network Struct fragments CC1 ural Ind ST Gang if Between Clusteri Holes icte Func- Associa- Gang ID removed Degree -ness ng Coef. Coef. d? tion tion Rank Clique 17 1 0.9578 0.1214 0.2612 0.1699 Yes Pimp Member Gangster CMG 18 2 0.3099 0.0009 0.6190 0.1410 Yes Pimp Member Gangster ICG 19 2 0.3380 0.0102 0.4275 0.2605 Yes Pimp Member Gangster NWS 22 1 0.7606 0.0560 0.3124 0.1477 Yes Pimp Member Gangster NWS 24 1 0.4648 0.0097 0.6705 0.2335 Yes Pimp Member Gangster NWS 32 1 0.4648 0.0080 0.3295 0.1568 Yes Pimp Member Gangster NWS Baby 33 1 0.1972 0.0214 0.2527 0.2501 Yes Pimp Associate Gangster NWS 35 1 0.4225 0.0055 0.4989 0.1367 Yes Pimp Associate Gangster NWS 46 1 1.0000 0.0808 0.2551 0.1721 No Lookout Facilitator None NWS 257 1 0.2254 0.0076 0.3583 0.2580 No None Member Gangster CMG

It would be interesting to understand the causal direction between centrality measures

(network position) and sex trafficking function, since the trafficking function appears to be the

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explanatory factor in the Table 12, and yet it was statistically insignificant throughout the regressions when the influences of other variables were included. It might be suggested that individuals develop relationships first, and then move into network functions based on those relationships, but this is a question for a future study.

Summary

This study examined whether higher brokerage position, a position where an actor in the network controls the flow of information and resources between others in a sex trafficking network, increases the probability that an individual will avoid indictment. The dependent variable is the probability of avoiding indictment, or P(AvoidIndict), because that would be the positive outcome in the mind of any perpetrator in the network, and would be one of the drivers behind decisions they would make personally or as a group. Avoiding indictment is a proxy for survival, and the main hypothesis tested is whether an actor’s higher betweenness centrality, or proportion of relationships between others that must pass through that individual, increases that individual’s probability of survival. The three auxiliary hypotheses are that having higher degree centrality, or knowing more people, lowers that probability; that a lower clustering coefficient increases the probability of avoiding indictment due to the higher secrecy it may afford, and that a lower structural holes coefficient increases the probability of avoiding indictment due to the actor having more structural holes opportunities available to exploit to his/her advantage.

The first two hypotheses are not supported by this study. In this network, higher degree centrality appears to increase the probability of avoiding indictment, and higher betweenness centrality is not only highly correlated with degree in this case, but it is statistically insignificant in all models where degree is included. Even though its effect is positive as predicted, the effect size is miniscule. It is thought that this is a normal outcome in larger, sparser networks, although

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the results of the means tests indicate that there may be tipping points—the effects of different network properties may have diminishing returns and maybe even reverse direction after some point, depending on network size and the presence of redundant ties. For example, mean degree centrality was shown to be higher in the means tests for those who were indicted, as predicted by hypotheses, but tests for nonlinear effects showed that the effects of degree centrality reverse direction after a certain point so that very high values in a large network prove to be protective rather than to increase risk. This explains the net protective effect against indictment shown in the first regressions. These various results will be compared against others in the discussion chapter.

Clustering coefficient was not statistically significant except in the model where it was tested alone, but it was shown to measure a different concept from the other network measures in the principal component analysis. In the regressions, it was positively associated with avoiding indictment. The later analyses indicate that while its effect might be beneficial for an individual’s survival, it might hurt the network as a whole; indeed, when push came to shove, many shared what they knew with police and prosecutors in order to save themselves while the network took severe blows. This supports Calderoni’s idea that higher clustering coefficients may mean lower secrecy, which can be harmful to the continued survival of a criminal network, but these results provide more detailed insights into potential differences in effects at the individual and network levels of analysis. The structural holes coefficient, like the other network measures, was only statistically significant in the models where degree was not included.

The latent variable model that tested the predicted values for the principal components behind degree/betweenness centralities and the clustering coefficient was the best-fitting of regression models 1-10. The latent variable behind degree was still the statistically significant

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predictor, but the inclusion of both latent variables resulted in the best-predicting and most parsimonious model of all that were fitted to these data.

Subsequent means tests of network measures by several node attribute variables showed statistically significant differences in mean coefficients, especially in gang rank, gang association, and sex trafficking function. Within gang rank especially, Gangster Girls had higher mean degree centralities, and only Big Homeys had higher mean betweenness values, meaning that they brokered connections between more people. Gangster Girls had lower clustering coefficients, indicating higher secrecy in line with their higher betweenness scores, but their structural holes coefficient was also low, indicating fewer structural holes around them available for future exploitation. This means that while Gangster Girls tended to broker more relationships while working as bottoms, as evidenced by their higher betweenness scores, their lower structural holes coefficients indicated that their opportunities to expand their brokerage positions were limited. Similar connections between network functions and centrality measures and coefficients provide interesting insights into the interplay between role and network positions.

Finally, bi-component analysis identified ten cut points, or specific brokers whose removal would fragment otherwise independent components away from the network. Their network measures each covered a wide range, but it was notable that the only two of the ten who were not indicted were a lookout and a gang member who connected many, but had no identified function in the sex trafficking part of the organization. It is a subject for a future study whether network position determines a member’s sex trafficking function, or vice versa, since sex trafficking function was statistically insignificant in all models. In any case, most of the components that would splinter off if these ten cut points were removed are small, due to the size of the largest two components and the large number of redundant ties between them. A cut point

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strategy to infiltrate this network would need to be undertaken in iterative fashion to make a real dent in it.

Chapter 5, the discussion and conclusions chapter, follows next. In Chapter 5, the results from this case will be compared with indictments from other sex trafficking network cases to determine, at least on a general level, how generalizable this case might be. Further, the results are explored further and tied back to the literature to discuss the contribution of this research to the human trafficking and network analysis fields. Limitations and future research trajectories are outlined, and conclusions and recommendations are presented.

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CHAPTER 5

DISCUSSION AND CONCLUSION: IT’S WHO YOU KNOW

Summary of Research and Findings

This dissertation examines the effect of network position on the probability that a given sex trafficking network member avoided indictment in this Federal case that was centered in

California, but spanned seven states, and what that says about the functioning of trafficking networks and their resistance to fragmentation strategies by law enforcement. Ties between members, particularly involving members that bridge or broker otherwise disconnected individuals or groups, are hypothesized in network literature to be conduits through which some sort of benefit flows, such as power, information, money, protection, opportunity, or influence

(Borgatti et al., 2013). Others hypothesize that brokerage positions can also hold disadvantages

(Kreager et al., 2015), so that an advantageous position in one network may be disadvantageous in another, given concerns about visibility to authorities and differences in illicit business practices. As well, an individual in a brokerage position might use his/her power strategically to block the flow of information or benefits between groups or members rather than facilitate it.

This study used police and court data of multiple types to examine how brokerage, measured in different ways, influenced a network member’s probability of having avoided indictment in either or both of the two indictments involved with this case. As of this writing, this is the first completed study of an individual human trafficking network in the United States, and the fifth such completed study of a trafficking network in the world. It is also the largest such study in terms of network size examined, and it is distinctive in that it includes extensive data on both indicted and unindicted individuals in sufficient numbers to use broad boundaries

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and include the impact of porous network borders (Bouchard & Komarski, 2014). Data were triangulated from multiple types of law enforcement investigation records, court documents, and investigator interviews, thus rendering the dataset more robust to bias than if one data source was used in isolation (Malm et al., 2010; Morselli, 2009).

Avoiding indictment is the dependent variable and it is used as a proxy for survival—the ability of a given trafficking network member to keep doing business or function uninterrupted.

In line with bounded rational choice perspective, survival is an important utility to be maximized on the individual and group levels when examining criminal sex trafficking networks as business enterprises. Betweenness centrality, as a measure of brokerage, held as positively correlated with avoiding indictment, consistent with hypotheses. But, its effect was miniscule and statistically insignificant whenever degree centrality was included in the model. Degree centrality, or the number of people a given network member knows, was much more powerful. It rendered the effects of all measures of brokerage—betweenness centrality, the clustering coefficient, and the structural holes coefficient—statistically insignificant in all models in which it was included.

Although betweenness and degree loaded on the same latent variable in the principal component analyses, this difference in the regressions indicates that the number of people one is connected to was the stronger force at work in a network of this size, rather than the connections they facilitated. When compared with Masías et al.’s 2016 study modeling criminal verdict outcomes, where betweenness centrality was the predictive factor for the Watergate network

(N=61) and effective size of the network was predictive for the Caviar network (N=110), the question then becomes at what network size does degree centrality overtake betweenness centrality and other brokerage measures in predicting judicial outcomes, controlling for other

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covariates? While Masías et al. also focus on judicial outcomes, the qualitative nature of the conspiracies is different.

Nevertheless, there may be a tipping point related to network size, particularly in the area of redundant ties. In cases where an individual has multiple potential brokers to choose from to get from A to B, any given broker’s power will be reduced, and the individual’s inclination to protect them will not be as strong. Tests for nonlinearity in this network showed that once the value for degree centrality gets very high, the direction of its effect reverses, suggesting support for the difference seen by Másias et al. based on effective network size.

Methodologically, this study explores the effectiveness of different network variables used to measure brokerage position, particularly in larger networks. These results support the notion that relying on betweenness centrality alone to measure brokerage does not work as predictably well in larger networks, confirming some earlier research (A. Fox, 2013; Morselli,

2010), perhaps due to longer paths that must be bridged between nodes when more people are involved, and perhaps due to the presence of redundant ties. Paths between individuals are shorter when the size of the network is smaller, which can create more efficiency in such networks (Gould & Fernandez, 1989; Morselli & Roy, 2008). Larger networks may share some infrastructure or economies of scale, which can be of operational benefit to the enterprise and be exploited for other criminal businesses as happened later in this network, but such infrastructures may also have a bureaucratic effect of slowing things down.

The clustering coefficient measured a different concept than degree or betweenness centralities according to principal component analysis, as hypothesized by Calderoni (2014), since it takes into account the connections between an actor’s ties. Although the latent clustering coefficient variable was still statistically insignificant in terms of predicting the probability of

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avoiding indictment when the latent degree variables were tested in the regressions, the clustering coefficient has emerged as a measure worthy of more study because it loaded on a very different concept related to brokerage than betweenness centrality does. Betweenness centrality measures the proportion of connections between others that must flow through a given individuals, whereas the clustering coefficient measures the likelihood that any pair of ones’ connections are also connected to each other. Higher denseness among one’s connections, as captured by the clustering coefficient, appears to imply that more information may be flowing through the criminal network in a potentially detrimental way. Specifically, it appears that while clustering coefficient may be beneficial to the individual (higher coefficient increased the probability of avoiding indictment), it may be harmful to the network if the individual uses that benefit to give information to the police or prosecution.

On a practical level, the goal of this study was to explore the extent to which identifying brokers in a sex trafficking network could help law enforcement target network members that would best fragment the network, thus reducing the network’s ability to operate efficiently and exploit victims for financial gain. The main theoretical hypothesis tests and the cut point analyses contribute to identifying the type of fragmentation that may be possible using a brokerage approach, depending on network, and tests of the secondary hypotheses provide some insight as to the best measures of brokerage that crime or intelligence analysts might use for different types of networks. The centrality measures are available in I2 Analyst’s Notebook, a crime analysis package with an SNA module commonly used by law enforcement in the field, but the clustering and structural hole coefficients are not included (IBM, 2012). These may be things that IBM wants to investigate adding to their package in the future, if more studies prove them to be useful in the field.

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Cut point analyses showed that in the final form of the network just prior to indictment, removing those special kinds of brokers that connect entire components would only break off small components in the 2011 network. However, when looking at the sociograms by year in

Figures 4A-4C in Chapter 4, one can see that such an approach might have had a greater impact if undertaken earlier. Removing 39 individuals in one day, as was done by the task force, was probably the best approach to achieving a quick fragmentation by 2011 due to the number of redundant ties that had formed by then. Otherwise, a cut point removal strategy would have had to be undertaken iteratively, which would have taken more time and would have also allowed for new brokers to take the place of those removed during each iteration. So, removing just the ten cut points would have been less effective in 2011 than it might have been in 2008.

Theoretical Implications and Generalizability

Other Trafficking Network Studies that Use Social Network Analysis

So where does this study fit in with the other published social network analyses of human trafficking networks? While new studies are beginning to come out on global trafficking flows

(Denton, 2016) and national-level U.S. trafficking flows (Price, 2016, thesis embargoed until

2018), this study is best compared with the four published studies on individual networks discussed in the literature review. Cockbain et al. (2011), who used i2 Analyst’s Notebook to analyze a U.K. sex trafficking network, put some puzzling limitations on their study such as eliminating individuals that were identified, but not charged. Individuals identified as ringleaders by police did not have higher centrality scores than other members, but this is perhaps because so many other individuals were cut out due to their network boundary choices. Their study could also have identified other individuals police might have charged, as the present study did, but who were more hidden in the network. For example, from the present case, person 257 had 172

middling coefficients and no identified sex trafficking function, but was an identified gang member and a cut point between clusters – such a person would be worthy at least of more investigation due to the potential for undiscovered important ties, but Cockbain et al.’s boundary decisions would have excluded him from the analysis. These choices may have limited the recommendations about the usefulness of SNA to law enforcement that were possible from

Cockbain et al.’s results.

Cockbain et al. also looked at victims as a separate network, which this study does not.

Victim status is taken as a node attribute here, but relationships between pimps and victims are seldom one way streets. Victims often connected others as a broker would, even if they may not have had the power to leverage that position for purposes other than ensuring their own physical survival. A decision to exclude them from the network, or to treat them separately unless all network functions are treated separately, can be a mistake in human trafficking and sex trafficking networks because that decision relies on assumptions about victimhood that may not be accurate—reference the earlier discussion in Chapter 3 on the debates over whether victims have decision making power. In a study on interdependence between network members, dividing individuals into separate networks seems as though it would miss important dynamics that could impact results.

While Cockbain et al.’s was an important first study of sex trafficking networks using social network analysis, the present study extends that work and provides important contributions in terms of how boundary decisions might affect results, especially in a network with porous borders, and with victims who are not completely devoid of agency. Victims, too, have survival decisions to make and may engage in behavior protecting their pimps, etc., that benefits the network even if it might hurt them in the long run.

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Morselli & Savoie-Gargiso (2014) used SNA to conceptualize a resource-sharing model in a trafficking and prostitution network in Montreal. Using rational choice logic, they found that pimps did not hold 100 percent of the power in the pimp-victim or pimp-prostitute relationship.

“Prostitutes often held key positions and privileged roles within the network… pimps’ and prostitutes’ relationships involved complex exchanges of network resources,” both material and non-material (Morselli & Savoie-Gargiso, 2014, p. 247). The descriptive data and the results of the relationship coding process from the present study confirm this notion, and Morselli and

Savoie-Gargiso’s work was also used as support for the boundary decisions made in this case.

As covered in the literature review, Campana (2016) and Mancuso (2014) both look at sex trafficking from Nigeria to Europe. Campana looked at costs of monitoring victims’ activities and debt repayments, and showed that traffickers and madams in that trafficking flow operated largely as independent contractors, while transporters were more likely to utilize economies of scale. It can be inferred that transporters would have more of a brokerage position because they connect madams on one end and victims and recruiters on the other, but this was not his primary research question. Methodologically, Campana used degree centrality only, and only in sociograms—not in his regressions. In the description of NWS history that opened

Chapter 4, there were certainly monitoring costs associated with maintaining and controlling victims—costs that were shared between pimps, bottoms, and even other victims under duress— but these were not quantified in the data, nor were they the focus of the present study.

Mancuso (2014), on the other hand, looks specifically at brokerage, but in a much smaller network (N=86, with focus on the 18 madams). In Mancuso’s study, betweenness centrality was used to measure brokerage, and combined with content analysis of case documents and transcripts, she found that not all madams were equally central with regard to brokerage position.

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Those that were more likely to have a powerful position as measured by higher betweenness centrality operated across national borders and were connected to other clusters with international influence. This was an advantage not shared by madams with lower betweenness centrality that perhaps operated more locally. This implies that if they were not a cut point themselves, these madams were likely connected to a transporter that was a cut point.

This is a different dynamic than the domestic network in the present study. While victims in the present case were taken on trips that crossed state lines, there was no need for connections that facilitated crossing national borders and clearing immigration checkpoints versus those that brokered other relationships, perhaps between cliques. Mancuso also shows the usefulness of betweenness as a measure of brokerage, while the present study shows its limits when applied to networks of larger size, as evidenced by the small effect size of betweenness and the outsize influence of degree. However, Mancuso’s study also begs the question of differences in the characteristics of international versus domestic networks. In addition to size, the influences of degree and betweenness in the present case may also be a function of the purely domestic nature of the network, a characteristic shared by up to 80 percent of trafficking operations in the United

States (Carpenter & Gates, 2016). It would be interesting to conduct a comparative study on this point with a similar network situated in the United States, but that has international dimensions.

Human Trafficking Literature

Within the trafficking literature, this study makes contributions to human trafficking typologies, and in understanding coercion and control dynamics. Regarding typologies, this study adds insight into the domestic gang manifestation of the American Pimp human trafficking model in Shelley’s six-category typology of trafficking networks (Shelley, 2007, 2010). While the network focused on making as much profit as possible, financial management seldom

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branched out beyond a few spreadsheets, if anything was documented at all. Law enforcement interviews and police records confirm patterns of consumption spending and little reinvestment into the business, as documented by Shelley. It was shown in the data that payment of most expenses was routed through victims and bottoms, and taken off the top of their earnings (see also Dank et al., 2014). Pimps then generally kept the rest and spent it freely to signal their status to others.

The major United States trafficking types include brothels, many run by closed Hispanic networks; highly organized Asian massage parlors with strong ties to their origin countries; domestic gang trafficking that has risen in prominence in many cities; Russian and Eastern

European topless bar networks; and small groups and individuals of all races and ethnicities engaged in trafficking either via internet, escort services, or on the street (Dank et al., 2014). The conclusions from this study apply to the domestic gang network type in this U.S. typology, and will be compared against court documents for other gang case indictments later in the Practical

Implications section of this chapter for the purpose of assessing generalizability to similar network cases.

Data from the case documents and law enforcement interviews also confirm the assertion in empirically-based literature that the dynamics of coercion and control are not as clear-cut as some advocates and legislative language would have us believe. Pimp-victim, pimp-bottom, and bottom-victim relationships are generally characterized by an imbalance of power—many times severe—but none of those relationships could be definitively described as one-way in the present case. This precluded assigning directionality to any of these relationships during coding, and confirmed the cautions against over-generalization and against denial of victim agency advocated by Morselli and Savoie-Gargiso (2014), Chin and Finckenauer (2012), Weitzer

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(2013), and Marcus et al. (2014). Even when coerced by severe violence, restrictions on movement, threats, and other means of control, it was clear from the data that the victims in this case still felt they were choosing their “boyfriends” and this way of life. As such, while some victim-witnesses ran away before the prosecution due to being threatened, it took others several attempts before successfully exiting the life because they simply were not ready.

While other scholars maintain that the effects of prolonged trauma mean that victims and/or voluntary sex workers really have no control over what happens to them while in their trafficking situations (Farley et al., 2004; Lederer, 2011; Miriam, 2005), victims in this case still made choices between perceived available options within the bounds of their circumstances to ensure their survival. In some cases, these choices involved facilitating relationships between other network members. These are bounded rational choice decisions. As horrendous as violent sex trafficking exploitations is, it is a mistake to treat victims as individuals incapable of making their own choices or of knowing what is best for them—especially when offering support services to help them exit the life. While they may be constrained by their trafficking situations, such a view harms their recovery, as such advocates often continue to make choices for them instead of empowering them to make their own—thus resulting in victims’ feeling that they have simply left once imprisonment situation for another (Soderlund, 2005).

Bounded Rational Choice Perspective

This study was based on the assumptions of bounded rational choice. Through this view, traffickers and trafficking networks operate in pursuit of maximizing utilities of profit and business continuity—no matter what other social, economic, psychological, cultural, or other factors may introduce boundaries or limits to their decision making or options available—see

Albanese (2007), Aronowitz et al. (2010), Icduygu & Toktas (2002), Shelley (2010), and

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Wheaton et al. (2010). Those authors describe several approaches that focus on varying structures of supply and demand, division of labor, and labor specialization—all of which explore the workings of markets and the identification of pressure points that may disrupt them.

This study relies on these assumptions rather than testing them, using them instead to formulate the rationale behind the network theory hypotheses tested, but this study still contributes to the rational choice theoretical discussion.

Derek Cornish, in his introduction to the new 2014 edition of Cornish and Clarke’s 1986 classic, The Reasoning Criminal, rightly calls rational choice a perspective rather than a theory.

It is a set of assumptions and way of looking at crime that is not testable in a way that excludes all other theories, as is the focus of much of sociological criminology. Rather, in the bounded rationality form, it can incorporate many other criminological theories as factors that constrain choices. Rather than seeking root causes, its purpose is to predict behavior, much like routine activities theory. This makes it more readily applicable in policy than other theories that, for example, may prescribe reducing income inequality as a prerequisite for reducing crime. In the long term, that is important, but it is unlikely to be something that can be incorporated practically into everyday practice by existing communities and law enforcement agencies.

Rational choice also does not make a hard distinction between offenders and non- offenders (Cornish & Clarke, 2014). This makes it possible to apply the same theoretical assumptions to pimps, bottoms, victims, and even bystanders when examining a case like this.

And, it is clear by taking a more expansive view with network boundary decisions that all these types of individuals interact with each other and affect each other’s outcomes via their network ties. We might separate these groups into separate networks to study other research questions, but that is less reflective of the reality in which they are all intertwined. Despite potential

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qualitative differences, neither offenders nor non-offenders function in a vacuum. Rather, they influence one another’s behavior and decisions directly. Regardless of what may make a non- offender more susceptible to recruitment, for example, bounded rational choice can help us understand more immediate decisions applicable to network disruption strategies in practice.

Delving a little deeper, Lattimore and Witte (2014) discuss prospect theory over expected utility. Prospect theory allows for editing risky alternatives, or prospects, into simpler heuristics or representations in order to make quick decisions. This is far more realistic than thinking about individuals making fully-conscious expected utility calculations that also require more complex information. This also leads to the notion of “satisficing,” or choosing the best “good enough” option, rather than calculating the maximum possible utility (see also Johnson & Payne, 2014;

Simon, 1957). The narrative police report data documenting decision making by victims in this case is certainly supportive of the satisficing concept, or making survival decisions targeted toward achieving the perceived best of limited outcomes. The process involved in this may be interesting to explore in a future human trafficking victimology study. Deeper qualitative investigation of the “satisficing” process by perpetrators would also be interesting to undertake, given the differential effects of network position on network member outcomes in the indictment, and the fact that these relational variables rendered almost all other factors statistically insignificant.

Lastly, Norrie (2014) looks at how the idea of boundaries that constrain one’s choices impacts the idea of criminal responsibility. This is a large part of the theoretical debate around structural theories of crime such as anomie and strain (Agnew, 1985; Merton, 1938), and the practical debate about whether and how bottoms should be criminally charged, since they are usually part victim and part perpetrator. All of the six task force investigators interviewed for this

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research,49 were ambivalent and uncomfortable about criminally charging bottoms, no matter how violent. While they could live with the line they drew around decisions to commit specific acts of violence that were not explicitly directed by a pimp, especially if the bottom had also attained eighteen years of age, they still felt discomfort. This is because they knew that bottoms in this network often started out as trafficking victims themselves, and later decisions to commit violence on behalf of their pimps were rarely 100 percent theirs. Thus, how to determine criminal responsibility?

Morselli and Roy (2008), in their SNA study of stolen vehicle networks, also looked at crime scripts—the step-by-step procedural “guides” to criminal business practices in a given group, how these scripts may be “cast,” or recruited for, and rational choices to change them in response to changing law enforcement tactics or market conditions. The “rules of the game” in this sex trafficking network constitute such a crime script. Morselli and Roy’s boundary choices and measures of brokerage will be discussed in the next section, but given the dominance of degree centrality in predicting who avoids indictment in the present study, it is interesting to postulate how this massive indictment may have changed the “rules of the game” crime script in order to better protect against police detection in the future. Over the four years of the investigation alone, the network became flatter, renting of hotel rooms moved more from victims’ and bottoms’ credit cards to use of customers’ cards, use of Green Dot cards to launder money increased, and activities moved further out of the streets and into hotel rooms. In the beginning, there had still been a fair share of street prostitution activity. According to law enforcement interviews in 2014, the network was still operating on some level despite the indictment, but possibly further underground. All these adaptations are rational responses to

49 The task force had nine members, with other officers from all four agencies helping on an as-needed basis. 180

changes in the market environment (Albanese, 2011), such as shifts in law enforcement strategy after identifying potential weaknesses or pressure points that can be exploited.

One might have expected, in order to better manage risk of exposure and resist node attacks, that networks throughout the market might have voluntarily fragmented or moved to more of a cartwheel structure, such as the Chinese snakehead networks where information is more compartmentalized (Zhang, 2014). But even without that sort of change, African-American gangs are still remarkably resistant to node removal once they become as tightly connected as the case from this study (Papachristos, 2009) in terms of redundant ties between components. This can be seen from the positions of cut points available for removal by 2011. Their removal would have only fragmented off small, peripheral components by that point. Earlier interventions may have resulted in greater fragmentation from a single sweep, due to fewer redundant contacts.

Later on, there were too many to make that level of impact possible in a single cut (see the many connections between the aqua and red components in Figure 7 of Chapter 4, for example). This is also likely why all the brokerage measures were less statistically significant than degree for this network—because of the strength in numbers and redundant contacts.

It is important to note that the goal of fragmentation as the way to disrupt criminal networks has so far been taken as a somewhat untested assumption in criminal network literature. Most studies described so far assume that fragmentation is the goal, whereas it is possible that decentralized networks are more resistant to shock (Bakker, Rabb & Milward,

2012). For example, Al Q’aeda, the African National Congress (ANC), and the Liberation Tigers of Tamil Eelam (LTTE) in Sri Lanka are able to keep operating even if individual cells are targeted. The disadvantage of decentralization for a network, however, is that there are fewer redundant ties available for continued resource flow if a cut point is removed. Thus, while

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smaller, decentralized components may continue in operation, if they are dependent on others for any sort of resources aside from inspiration, fragmentation may reduce their operational capacity; while business may continue, a shock may still cause efficiencies to be reduced

(Bakker, Raab & Milward, 2012). As well, a more centralized has the benefit of more redundant ties to protect it from the impact of removing specific members. So, while a more centralized network may have a more difficult time recovering from a shock, it also takes a much bigger shock to truly disrupt operations (Bakker, Raab & Milward, 2012).

Ability to recover quickly also depends on which kinds of resources were being transmitted by the brokers and how easy it might be to find those resources somewhere else; for example, information versus material goods require different types of supply structures (Malm &

Bichler, 2011). Additionally, the drive to come back in the same form is stronger with identity based groups, such as politically-motivated organizations, while profit-motivated organizations are generally more willing to adapt and change form in response to more minor shocks as long as the profits keep flowing (Bakker, Raab & Milward, 2012).

Thus, in the present case, by 2011 there was a strongly connected central component with many redundant ties, indicating that a blitz approach arresting many people at once may be more effective at achieving fragmentation than a cut point removal strategy—indeed, this is how the takedown went in 2011. And, while the product is not a physical good like guns or drugs, there is movement and management of people involved, indicating some need for infrastructure, even if it is less complex in a domestic network that is not worried about facilitating international border crossings. Finally, because the organization is profit-motivated, with low loyalty between members, they may be more inclined to adapt and change their structure in order to keep profits

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flowing, versus trying to rebuild the same structure. This will challenge law enforcement monitoring the remains of the group going forward.

Thus, we see that the effectiveness and impacts of fragmentation as a strategy to fight criminal networks vary by network size and type. The amount of generalizability possible as to type of rational response to expect from node removal (fragmentation vs. consolidation, horizontal vs. vertical structure, concealment measures, e.g.) is limited from just this one case, but the insights generated can inform analyses of more trafficking network cases utilizing this framework are needed to start identifying predictable patterns of adaptation beyond the anecdotal.

Network Literature

This study makes three contributions to network literature: tests of network positional impacts on individual survival in a gang sex trafficking enterprise; post-tests of different measures of brokerage in a large gang context; and after hypothesis testing was completed, some suppositional examination of boundary selection implications for the validity of criminal network analyses. The details of how the hypothesis test results fit into the debates about specific network and brokerage measures in studying various criminal networks were covered in the introduction to this chapter.

From a broader perspective, the position of a gang member or other perpetrator in relation to others, especially that measured by degree centrality, was the most significant predictor of whether an individual was able to avoid indictment. The only other consistently significant predictor was number of prior contacts with police, which was introduced as an attempt to control for possible tautology in using police data to predict criminal justice outcomes.

But, even when the police contacts variable was removed from the models as a robustness check,

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the only non-network variable that was significant was victim status. This confirms Fox’s (2013) result about the impact of network position on individual outcomes generally, but in this study

(N=375), degree overtook betweenness, in contrast with his results in a network ranging in size from 94-169 over the course of five years.

So, further studies of networks of different sizes, purposes, and time frames would be needed to examine the comparative importance of degree centrality versus betweenness centrality in criminal networks. Are the impacts of these measures of network connectivity helpful or harmful to a network member’s goal of survival? In the present study, degree centrality was negatively correlated with avoiding indictment in the correlations matrix, but in the regressions each additional individual that one was connected to increased the probability of avoiding indictment after a certain point when number of prior police contacts was controlled for—this indicates that using the number of prior police contacts as a control for potential bias in the data was useful. In the fully specified model, the direction of the net effect was the same when prior police contacts was removed, but the effect size was larger and the goodness-of-fit statistics were poorer. This indicates that the introduction of prior police contacts as a control variable had some impact on results. However, exploration of the causes of increased police contacts, such as proactive police decisions in how to focus the investigation versus increased criminality by certain individuals causing a reactive response, or some interaction of these, is a subject for a future study. Quadratic regressions testing for a curvilinear effect also show that higher degree centrality does have an overall negative effect on avoiding indictment in a large network, although the curvilinear effect found was not statistically significant. This is likely because all those indicted had a generally large number of connections, but the direction of impact changes after a certain point due to diminishing returns.

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Either way, degree centrality is the most significant variable impacting the ability to avoid indictment in this large network, and it negated the statistical significance of any beneficial effects from brokerage. It may be that either in a network of this size, or in networks with horizontal structure and the particular culture of African American gangs in this area, that Burt’s

1992 theory of the advantages of brokerage does not appear to apply in this case. Perhaps, “in cultural contexts that prioritize collectivism… actors not bound to a group through multiple ties

[may be] viewed with suspicion” (Kreager et al., 2015, p. 21). Under those circumstances, brokerage advantage might also disappear. While loyalty is prized within the group, it also happened that loyalty went out the window after law enforcement and prosecutors became a threat to individual survival. Large numbers of network members sold one another out in exchange for plea deals or other advantages as early as the first, smaller indictment in 2009—to the point that NWS began to stand not for “Niggas Wit Swagg,” but “Niggas Who Snitch.”50

But, while loyalty as a feature of group identity may not have been as dominant a cultural factor in this network as perhaps in some others, it is possible that not having multiple ties would leave one vulnerable to suspicion in day-to-day life given the levels of trust required between pimps by the rules of the game. This may render brokerage potentially less significant here for individual survival than it might be, perhaps, in drug networks where brokers tended to head up distribution operations that involve transport between disconnected groups in a supply chain

(Framis, 2014), or in networks requiring international connections in a similar way (Mancuso,

2014).

The implications of network boundary decisions in creating social network datasets have been discussed throughout this chapter, but the main points to note are that these decisions can

50 Note, though, that this “snitching” occurred beginning after indictment, and thus would not be a control variable involved with avoiding indictment in the first place. 185

take too narrow or too broad a view if theoretical and the qualitative nature of the network is not carefully considered, and mistakes in these decisions can also bias results. For example,

Cockbain et al. (2011) appear to have had access to data on individuals who were not charged in their U.K. sex trafficking network, but chose to leave them out. This may have reduced the amount of variation possible on network measures between individuals and left out important insights. As discussed earlier, in gang studies the borders are not iron-clad either. Individuals move in and out of the gang and interact with non-gang members and associates regularly. If we want to understand the impact of network position on individual outcomes, clear boundaries between members and non-members cannot be assumed, but must be allowed to emerge from the data (Bouchard & Komarski, 2014; Fleisher, 2005). Indeed, four individuals indicted in this case—a lookout, the hotel managers, and a customer—were neither gang members nor associates but still centrally involved.

Morselli and Roy (2008) also left out legitimate actors in their study of coercion and control in a Montreal prostitution network. Fox (2013) went two steps out from each gang member, in line with previous research and given that his data source was a city-wide gang database as opposed to a single large network. Each of these decisions impacts research results, so consideration of tradeoffs must be undertaken carefully, thoughtfully, explicitly, and in light of the research questions asked. This study took an expansive view, but removed bystanders and victims of auxiliary crimes (such as random ATM holdups), leaving only those with evidence of some ongoing relationship regardless of type. Otherwise, assuming boundaries based on gang membership alone may have significantly skewed results by creating a network that was too small to reflect reality. Likewise, leaving bystanders and similar people in a network that are not materially connected might have diluted the results.

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In sum, this study contributes to the human trafficking and network literatures by showing the impact of network structure on the individual sex trafficking perpetrator’s ability to survive and continue operating in the U.S. gang context. The data and results help to flesh out the

American Pimp typological model of sex trafficking (Dank et al., 2014; Shelley, 2003, 2007,

2010), and confirm the complexities of relationships between pimps, bottoms, and victims since it was not possible to characterize any of those relationships as unidirectional, despite power imbalances (Marcus et al., 2014; Morselli & Savoie-Gargiso, 2014; Weitzer, 2013). It also explored the functioning of this network in the larger market environment (Albanese, 2007;

Carpenter & Gates, 2016), something that will be discussed further by looking at comparison case indictments in the next section.

Relying on the assumptions of bounded rational choice for actor survival decisions and outcomes in criminal network enterprises, regardless of perpetrator or victim status (Cornish &

Clarke, 2014; Morselli & Roy, 2008; Wheaton et al., 2010), this study posits that in larger networks, survival outcomes will improve if a given member knows a very high number of people, versus having a broker status, with caveats around trust culture in the network and when controlling for prior police contacts. Secondarily, in this larger network, degree and betweenness centralities loaded on a single component, while clustering coefficient (how many of one’s connections are connected to one another) is shown to measure a separate concept.

While not statistically significant in regressions, the differences in mean centrality scores between individuals at different gang ranks and levels of gang embeddedness indicated the need for differential interpretations of network measure meanings. For example, Big Homeys and

Gangster Girls appeared more insulated against indictment due to higher mean betweenness centralities. However, Big Homeys direct and mentor the gang members who report up to them,

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while Gangster Girls only direct activities of victims. Gangster Girls also had higher mean degree centralities and lower clustering coefficients, so they were known by more people but may have had enjoyed more secrecy because their connections did not necessarily know one another. However, their structural holes/constraint coefficients were also low, indicating that they may have fewer structural holes around them available to connect those individuals for future gain—a dynamic that is not surprising since Gangster Girls that serve as bottoms in the sex trafficking business are fairly well-controlled by their pimps. Lastly, gang association, or level of embeddedness, showed that non-member facilitators had the highest mean degree and tied with gang members for betweenness, far above gang associates. This supports the use of considering broader, porous boundaries when studying gang operations.

Practical Implications

It is important to be careful about generalizing from case studies (Natarajan & Belanger,

1998). While the previous section situated this study in the larger trafficking and network literatures, this section begins with a comparison of the NWS case with several other U.S. gang indictments, including both sex trafficking operations and other gang criminal enterprises, to understand how similar these dynamics are to other networks in the United States. The second part of this section looks at the practical applications of this sort of network analysis as a technique for use in the field.

Generalizability to Other Gang Network Cases

First, it is important to note that, although not definitively quantifiable (Feingold, 2011;

Weitzer, 2013), it is generally thought that most sex trafficking in the United States is perpetrated by small groups and individuals that may operate alone, or that may share information but are not criminally affiliated in a common enterprise (Dank et al., 2014; Marcus

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et al., 2014). This section assesses generalizability to other gang network cases only. Gang sex trafficking is a phenomenon that appears to be spreading to more cities in the United States

(Dank et al., 2014), but caution should be taken not to overstate its prominence as a share of the total commercial sex market without more national-level evidence.

The indictments of 19 other gang network cases from 1981-2014 were compared based on type of gang or trafficking, size, location, number of defendants, number of cliques, and brief summaries of case facts in terms of business practices, means of control used, and criminal charges prosecuted. Some of these cases utilized RICO charges, while others used conspiracy statutes, and seven cases (five RICO, two conspiracy) involved asset forfeiture of criminal proceeds, or of assets used to further the business. All cases were gang enterprise prosecutions.

This was a cursory comparison for the sole purpose of determining how prevalent the type of network studied in the present case may be. The comparison might also be used to identify some most-similar or most-different cases for deep comparative analysis in a future study. The complete list of cases compared is in Appendix E, Table E1.

This purposive sample of cases covers several regions of the country and several types of gangs. Locations identified reflect the U.S. state the case was prosecuted in regardless of interstate dynamics, and were chosen specifically to give a sample that reflected the entire country. Geographically speaking, there were three other cases in San Diego County among the comparison group, with two others also in California, two each in Kansas and New York, and one each in Pennsylvania, Maryland, Texas, Virginia, Tennessee, Minnesota, North Dakota,

Florida, Arizona, and Washington State. Numbers of individuals prosecuted ranged from two to

119. Types of gangs included African American, Hispanic, Native American, Asian, and

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Caucasian motorcycle, with some engaged in sex trafficking and some in other criminal enterprises.

First, the rules of the game were similar across the African American gang indictments in

California—see U.S. v. King et al. 2014 (Tycoons, San Diego) and U.S. v. Pittman et al. 2012

(Black Mob/Skanless case, San Diego)—a shared dynamic also confirmed by Carpenter and

Gates (2016) in their recent analysis of gang sex trafficking in San Diego County. Also similar in this regard were the cases of State of Washington v. Clark 2012; U.S. v. Strom et al. 2012

(Fairfax County, VA); and USA v. Cephus et al. 2009 (Indiana/Illinois); all of these are African

American gang cases. These cases also share the flat, loose network structure, large membership numbers, and cooperative behavior between cliques in the present case. Structurally, while cliques do cooperate with one another, the Hispanic gangs in the San Diego area tend to be more hierarchical with more military, regimented structure and rules involving stricter command and control structures, including payment of “taxes” up the hierarchy by “soldiers” carrying out business activities (see also Carpenter & Gates, 2016). This may be due to the international structure of some gangs, like MS-13, where control is more centralized than some of the more identity-based African American gangs.

Additional indictments describing networks that share cooperative behavior between cliques include U.S. v. Adan, et al. 2010 (Somali gang, Minneapolis, MN); U.S. v. Campbell et al. 2007 (African American gang, Kansas, no sex trafficking but other criminal enterprises); U.S. v. Najera et al. 2012 (Hispanic Nortenos gang, Kansas); U.S. v. Espudos et. al. 2011 (Nortenos gang, San Diego); and People v. Lam 2006 (Asian gang, Monterrey, CA). Regarding the dynamics of cooperative behavior, law enforcement interviews from the present study revealed that conflicts seem to be changing from clique-based to race-based, with more murders and

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competition occurring along racial lines. Thus, the lines are less clear now within the African-

American gang community in San Diego County. Interviews conducted by Dank’s team included in the Urban Institute’s 2014 study of eight cities confirmed this.

Several other structural differences between gang networks are evident as well. U.S. v.

Morsette, a Native American gang case with one defendant, was reservation-based and appeared to be centrally directed by one person enforcing very harsh rules, while U.S. v. Francisco et al. in

Arizona was a larger, flatter Native American gang that identified themselves with the Bloods and had nine defendants. Asian gangs with large memberships, such as the “Black Dragons” in

People v. Lam (three indicted, 40-50 identified) were involved with extortion from existing brothels, a venue also common among Hispanic gangs but not used by African-American gangs.

29 individuals from three cliques that engaged in a tightly-coordinated operation were indicted in

U.S. v. Adan et al., a Somali gang in Tennessee and Minnesota that trafficked very young girls

(aged 14 and under) in apartment-based exploitation and by taking them to college parties called

“African parties.” The Somali pimps set up all the dates via phone calls out to clients, rather than making their victims drum up their own business via the internet, but they provided all transportation—at times picking minor girls up from school to do dates. They also took on gang aliases similar to African American gangs. So, the Somali gang shared some structural characteristics with African American gangs, but their trafficking venues and business practices were different.

One of the most violent cases of all was the Hell’s Angels motorcycle outlaws case in

Florida prosecuted in United States v. Starrett, 1995. Means of control and business practices here were highly regimented and extremely violent, making the present case look almost like child’s play in comparison. Both shared cooperative business practices between network

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members and both had strict rules of the game that all must follow. But, the rules were different with the Hell’s Angels as far as who a member’s “old lady” could talk to, where she could go, and how her movements were controlled. According to the indictment, there was less romancing of victims with Hell’s Angels than outright kidnapping or forcing into slavery, and many more victims in the Hell’s Angels case ended up murdered for breaking the rules or attempting to escape. Victims in the present case, while also suffering severe violence, were often able to get away even if it took multiple attempts.

While deeper comparative study is certainly the next step, this initial, cursory look shows that specifics around network structure, business practices, means of control with victims, and inter-clique cooperation are most similar between African American gangs, with some parallels that can be drawn with other gang network types on the basic structural level–-structural parallels that can be explored and tested with social network analysis methods. However, differences tend to appear along the lines of gangs’ ethnic origins and cultures. There may be less role specialization in smaller gangs, like the Black Dragons that cooperate only opportunistically, and

African American gangs. More specialization may occur among Hispanic gangs that have a more military structure; while he Espudos et al. indictments did not involve trafficking, they described a sophisticated business enterprise with specialized accounting, “tax” collection, strict command and kick-back structures, and more. Levels of violence used to control victims (or other network members, if not a sex trafficking case) vary as well. However, with slight regional variations, the differences in gang culture related to their ethnic origins51 and structures seem to hold across different areas of the United States.

51 While the actual make up of NWS and associated cliques in the present case comprised several ethnicities, the Crips and the Bloods are historically African American gangs. These cliques operated according to the group norms that have been traditional with these gangs outside of the evolution in inter-gang cooperation in criminal businesses and the shifts in intergang conflicts from turf to race. Thus, while some individuals from other races were involved 192

Implications for Practice

A big element of interest in social network analysis is how it can be applied in the field as a crime analysis tool to assist in investigations. The reason for brokerage studies is to understand whether targeting these individuals can result in more effective attack strategies for reducing sex trafficking and other criminal networks’ ability to operate. Everton (2012), in Disrupting Dark

Networks, talks about kinetic and nonkinetic attacks. For example, a kinetic attack focuses on taking key individuals out of the network via tactics like broker removal through prosecution, conviction and sentencing. A nonkinetic use of brokers might be to feed those individuals propaganda or information that changes what the network will do—such as in terrorist networks

(Everton, 2012), or via projects like Operation Ceasefire’s attempt to reduce gang violence in

Boston (Braga et al., 2001).

Identification of key individuals in terms of degree centrality and brokerage position can be useful for both such strategies; in fact, customized use of both strategies (kinetic and nonkinetic) at the same time could be quite useful. Bright et al. (2014), Calderoni (2014),

Mancuso (2014), and the present study show the importance of pairing network analysis with node attribute data to understand the impact of network position and individual characteristics on potential outcomes. Bright et al. provide a way to simulate such attacks on computer, before spending scarce law enforcement resources to carry them out, to determine a) how much one strategy will fragment the network over another and b) how many iterations each strategy may require to cause enough fragmentation to functionally dismantle the network. As discussed earlier, the impact of fragmentation on network operations will depend on its level of

in this network, it is still considered an African American gang with regard to its culture and characteristics when compared against, for example, Hispanic gangs or white motorcycle gangs. 193

centralization, the presence of redundant ties, which resources flow through its brokers, and whether or not it is identity- or profit-motivated.

Investigating and prosecuting network cases, especially large network cases, is expensive. Law enforcement officers interviewed stated that, beyond a certain number of network members, carrying out such an investigation well and managing the level of data to build the prosecution gets almost unwieldy. A skilled network analyst in the field dedicated to such investigations could contribute such information management and data analysis to assist experienced police detectives and investigators. This individual would be able to provide some quantitative evidence that either confirms or helps investigators modify strategies before resources are deployed, potentially increasing chances for success, or after field interviews are conducted by investigators to develop a coherent approach for interdiction. Such insights would also provide understanding that the effects of targeting brokers versus “head honchos” may be different for different trafficking network types, and show the impact of network size on the results. In short, one dismantlement strategy may not fit all.

As fusion centers and crime analysis departments across the country continue to grow and increase, the feasibility of employing network analysts from a staffing perspective will also continue to increase—especially as departments are realizing that having a sufficient number of analysts allows their officers to spend more time in the field. Most departments are also using i2

Analyst’s Notebook, or something like it, for their crime analysis work, and it has the capacity for social network analysis. Continuing education and training in network methods are making analyses like these more possible in the field as more police chiefs and departments begin to see its potential, although in order to be truly practical in real time, police records really need to be

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computerized and searchable. Otherwise, the process of hand-coding paper reports can render the tool impracticable. Thankfully, more departments are moving in this direction.

In the law enforcement interviews for this study, one common refrain from all investigators was that, while the level of cooperation and teamwork they experienced felt unprecedented in such an inter-jurisdictional task force due to dedication to a common purpose, the one thing they all wished the team had was an additional analyst to help with these kinds of tools. The analyst they did have put together organization charts and the like, but did not conduct much analysis of this kind due to a lack of time and resources.

Instead, the lead detective of the local police department spent an enormous amount of time making connections between individuals by hand-sifting through gargantuan amounts of evidence. He completed a herculean job that led to this landmark indictment, but tools like SNA are available that can make the jobs of officers easier. Could an indictment have been possible earlier with an additional analysis like this one, thus reducing the number of victims sexually exploited by this network before the takedown? It is impossible to say, but having a dedicated analyst with enough resources to identify cut points, clusters when lines between cliques/sets are blurry, and the most-connected individuals even if they appear less obvious, may have allowed investigators to use their time more efficiently given the constant challenge of limited resources at the local level. The possibility is there, especially for jurisdictions that may be investigating multiple individual cases and networks simultaneously.

Limitations

Even with the wealth of data and robustness of the research design, all research has its limitations. But, as long as researchers are explicit about them, a lot may be learned (Tyldum,

2010). As discussed earlier, there is potential tautology in using police data, but it is still used

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ubiquitously in studies of crime. There is potential missing data due to using police data only and not having knowledge the still-hidden parts of the full network. However, centrality scores are robust to a certain amount of missing data related to peripheral individuals (Berlusconi, 2013), and the breadth of data made available by the investigative team is unprecedented for a study of this kind--outweighing some of the drawbacks. There is no shortage of different investigative data types available for future research, and the sheer quantity of raw investigative data provided made it possible to build one of the most complete networks of this kind.

Second, data was not available for all the control variables one might like to include in a study like this, such as socioeconomic and educational status, due to the fact that they are not collected in police data. Although gangs tend to be homophilous in nature (McPherson et al.,

2001), differences in family history, education, and economic resources might have affected perceived options available to perpetrators and victims to achieve their survival goals—as well as their decisions to participate in or leave the network.

There is also potential bias in relying on police identifications for coding of certain variables. Sex trafficking network function, gang rank, gang association, and gang clique were taken directly from police reports and other files, and I made no assumptions in this regard— instead preferring to classify a variable value as “unknown” if it was not named specifically.

While triangulation between multiple sources was used as much as possible to minimize this, there is still potential bias inherent when officer determinations are relied upon without being able to verify via non-police sources.

Third, while decisions about boundary specifications were carefully made, and an expansive definition was selected in order to reduce the possibility of leaving out potentially important nodes by following the logic of the investigation, this approach results in a much

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larger N, which may have impacted the network measure calculations. But, on balance, it was the less biased approach. The tradeoff involved with that choice was a potential bias problem caused by missing/ excluded data, vs. having a larger denominator that affects the calculations. In this study, I chose to prioritize reducing missing data bias.

Lastly, this is not a comparative study, so generalizations described earlier are limited.

However, this study may form the basis for future comparative work, both in the United States and in other countries, by providing a model and a case to compare to. Weitzer (2014) points out the greater utility of micro-level research over macro-level research on human trafficking, because conclusions may be more defensible than those of current macro-level studies due to greater control over, and transparency with, designing study parameters.

Future Research

Several recommendations for future research come out of this study. Several such opportunities have been discussed throughout the text of this dissertation. This section highlights three major directions for future work that can include this dataset alone, or in comparison with other cases.

First, based on the main hypothesis tests that show degree centrality to be central in predicting the probability of avoiding indictment in this large network, future work should examine the question: at what network size does degree centrality overtake betweenness centrality and other brokerage measures in predicting judicial outcomes, after controlling for alternate explanations? If brokerage position is not universally beneficial to individuals, in what type of network is this the case, and is there some “tipping point” at which the balance shifts from brokerage to degree? Further, where is the tipping point within degree centrality at which the effect of an individual’s score becomes protective rather than a risk factor? These future 197

studies should look at the impacts of qualitative differences between network types in type of criminal enterprise, setting, culture/ethnicity, or business practices on network member and whole-network survival outcomes in this regard. For law enforcement agencies looking to apply social network analysis because it is trendy, it is important to recognize that not every problem needs the same kind of hammer to attack it. Understanding where these tipping points are, and how to shift and pivot use of network analysis to the case, would be a wonderful contribution for future network analyses on sex trafficking networks, and other types of criminal networks, too.

The present study provides valuable input into shaping such future studies.

Second, and in the same vein, further ego network analyses could get at the qualitative dimensions behind variance in the clustering coefficient between individuals with different network roles. For example, customers with high clustering coefficients may have those because trafficking network members are working together to provide that customer with services, and those already-connected individuals are the only individuals the customer knows. Delving deeper into what the second network principal component captured so strongly in the clustering coefficient means when applied in practice is also important. Does it signify reduced secrecy, which could be exploited to the individual’s benefit, but to the detriment of the network if loyalty is low as it appears here? What is its real conceptual difference from degree centrality (Morselli

2009 vs. Calderoni 2014)? Is it better for one’s connections to be connected to one another also, to reduce the hit the network would take if one were removed, or is it better to have more structural holes available that an individual can exploit for his/her personal benefit? Attack simulations that test the impact of different law enforcement targeting strategies (Bright et al.,

2014), combined with qualitative analysis of more case investigations, may be useful here.

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Third, time series analyses of all these questions, and to test the impacts of different kinetic or nonkinetic attack strategies at different points in a network’s life cycle, would be of great theoretical and practical value. Expanding into link prediction, alone or via a time-series approach, is also investigatively useful—where do we not see ties, but structural equivalence or other network characteristics indicate that there should be ties? This could be helpful to law enforcement in uncovering more relationships in places that were not readily apparent

(Berlusconi, Calderoni, Parolini, Verani, & Piccardi, 2016)—especially for building a RICO indictment. And lastly, as a result of means test results, time series analyses could be used to test the causal direction between whether network position predicts sex trafficking function, gang rank, and gang association, or the other way around. This will also have direct application in law enforcement in terms of lead generation.

Practical and Policy Recommendations

Law enforcement and investigative agencies should explore introducing or expanding the use of social network analysis as another tool available for investigating sex trafficking networks and other types of criminal networks. In all research recommendations above, as in delivery and evaluation of services to trafficking victims, research-practitioner partnerships and whole- community task forces are strongly recommended—especially with police, but branching out to fusion centers, trafficking victim service agencies, schools in areas with high in-school victim recruitment, and others that may be involved in community efforts to reduce commercial sexual exploitation and assist victims in recovery.

Of utmost importance is the application of network analysis methods by analysts in the investigative field. This work shows that it may be possible, in partnership with academics if necessary when large network cases are at stake, especially if police records are computerized

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and searchable. Analytical capacity building in this area and application in strategy-building can help law enforcement reduce the ability of trafficking networks to operate, if undertaken thoughtfully and carefully—and with constant, conscious attention to the potential for bias lest agencies be accused of using it to unfairly target individuals. This study shows that controls may be put in place to at least partially minimize this possibility, such as triangulating information from multiple sources. It also points out how incorporating qualitative case knowledge can contextualize and aid in interpretation of results, so that the two together can provide deep insight into trafficking network operations. Understanding the impacts that network dynamics have on individuals can create a rich picture of the trafficking network and different possibilities for disrupting it.

Specific applications in the field that come out of this particular study are as follows.

First, while the balance of power between perpetrator and victim is highly unequal, it is not a one-way relationship and these data substantiate that. This is critical in understanding how to deal with victims, who are first and foremost concerned with their own survival, who are calculating whether law enforcement or their pimps present the greater threat to that at any given moment, and who may love their pimps even while they fear them. Second, there are differences in how law enforcement should develop an attack strategy based on network size and complexity, the structure of components connected by cut points, and the presence or absence of redundant ties. The ability to identify all these is only possible with enough investigative and analytical resources to build the most complete network possible from available data. Critically, the difference in whether the strategy chosen is successful may differ greatly based on these factors, as can be seen in the sociograms by year in Chapter 4—a cut point removal strategy may have worked well in this network in 2007 or 2008, but by 2011 there were so many redundant

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ties that the blitz-style approach was more effective. Choosing the correct strategy can have a real impact on reducing the exploitation and brutalization of victims by this kind of network, while an incorrect choice may have less beneficial consequences, or even lead to victims being in more danger if they are suspected of snitching.

Conclusion

Operations in NWS evolved during the four years of the active investigation. For example, social media prevalence was still in its infancy in 2008-2009. MySpace was still bigger than Facebook among certain crowds, few instant messaging chats were saved (and later retrievable by police), and not everyone had a smart phone yet. During the intervening years, pimps stopped having their victims walk “The Blade” or other strolls in order to avoid police contacts, they began locking victims in hotel rooms to reduce their visibility, and they taught victims how to answer police questions to avoid detection by such means as lying about their name and age since it is more plausible for a minor to have no identification. Lines between hierarchical gang cliques/sets began to disappear—Crips sets in Oceanside now associate with each other and go to each other’s hood days, which has resulted in potentially protective consolidation in numbers. Shared network resources, both material and labor, create efficiencies in the sex trafficking operation. For example, one female can “land,” get a room, earn a little money, and then turn it into six rooms to be shared across the enterprise.

The operation that started in Oceanside also moved into other states and other crime types, such as drugs, robberies, murders, gun trafficking, and burglaries. The infrastructure created through the growth and increased sophistication of the sex trafficking business began to be used to move stolen goods, such as cellular phones and laptops. With these economies of scale, larger profit margins became possible. Thus, while the 2011 indictment might have

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“broken” the network for a while, a lot of shared learning may still be there to leverage for a comeback in a different form.

This study, while focused on 2011, reveals a network that developed and evolved during the course of all these changes. Relationship ties of all kinds between network members were the conduits along which resources and knowledge were shared; efficiencies were built; means of control over victims were put in place and enforced; and methods of concealment, recruitment, and risk management were taught, adapted and expanded. While further exploration remains to be done with regard to similarities and differences between network members and the root causes for their involvement, once someone is involved in a trafficking network, it remains that a great deal of the explanatory power behind perpetrator survival probabilities comes from who the perpetrator connected to while far less comes from individual characteristics. It is also critical to remember that victims are still making active survival decisions as well. Pimp-victim relationships are seldom one way, as evidenced by these data and the subsequent inability to code them as unidirectional for the network analysis. Moving away from the “perfect victim” narrative into a model where victims are viewed as the multi-dimensional, decision-making individuals that they are is crucial for helping survivors with true, empowering recovery.

This dissertation asked two main questions. First, how does network position in a human trafficking network impact survival outcomes for participants? Results show that brokerage position may be protective, but only up to a certain network size and/or complexity. The presence or absence of redundant ties, or multiple ways for members to connect to others that can help them achieve their goals, may create a tipping point where the sheer number of people one knows becomes more protective even if one is more visible and theoretically more vulnerable. One might speculate that this is because it makes it easier to hide evidence. However,

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investigation into the clustering coefficient, as a potential indicator of lower secrecy in this low- loyalty network, suggests that being connected to many people that also share information with one another can be protective for the individual, if s/he shares that information with law enforcement to save him/herself, while ultimately harming the network. However, qualitative data about network culture can mean that same social network analysis measures and results may have different meanings in different networks, so that information must be taken into account when interpreting the meanings of network measures.

Second, this research asked how the answer to the first question impacts possible attack strategies that law enforcement may use in the field against gang sex trafficking networks. This research, looked at in tandem with studies of other trafficking and criminal networks, supports the idea that while brokers may be effectively targeted for removal to fragment the network, or used by law enforcement to spread information to others as in Operation Ceasefire (Braga et al.

2001), a blitz approach may be required in larger networks that have many redundant ties. A blitz approach, where all key players identified for prosecution are removed at once, may minimize harm to victims that may otherwise be targeted for retaliation once word begins to spread that the network is under fire—if sufficient victim services are also lined up and in place so that victims may choose to enter a safer situation after the arrests of their perpetrators.

Put these together, and the study of the dynamics of interdependence between network members can be a powerful theoretical and investigative tool, as well as a tool to inform victim services approaches, to help reduce sex trafficking perpetrated by gangs in the United States.

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APPENDIX A

KEY INFORMANT INTERVIEW INSTRUMENT

Semi-Structured Key Informant Interview

This interview is designed to take approximately 1 hour to make scheduling easier with busy law enforcement and court officers. If additional time is needed, schedule a follow-up interview.

This will be a semi-structured interview driven by the evidence to be discovered during the archival research, which the IRB must provide permission for before I can begin. Follow-up questions on that evidence will be then be drafted and asked in Part IV. It is understood that respondents may not remember every detail of a case, or that questions might address a detail s/he did not think of at the time, so respondents are advised not to speculate if they are unsure of an answer.

I. Complete informed consent.

II. Case:

Defendant(s):

Sentencing date:

Court:

Key Informant:

Name:

Title:

Affiliation (Court or Investigative Agency):

Role in case (Have KI confirm):

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Date of Interview:

Location:

III. General Questions (Semi-structured: meant to launch discussion. Length of time needed

depends on how long probing/clarifying questions take following each of these questions.

Aim for 30 minutes or less, depending how much time is needed for Part IV. Ask about

investigating and/or trying the case depending on role – investigating for law enforcement,

trying for judges, both for prosecutors.)

a. Thank you for confirming your specific role in the case. What about this case do you

think went really well? What made this prosecution successful?

b. What was/were your biggest challenge(s) in investigating this case (Law Enforcement,

Prosecutors, Defense Attorneys)? How did you overcome them? (Get specific)

a. Case-related (for analysis – use to identify limitations of data)

b. Process-related (trial or investigation processes - for discussion only)

c. What was/were your biggest challenge(s) in trying this case (Prosecutors, Defense

Attorneys, Judges)? How did you overcome them? (Get specific)

a. Case-related (for analysis – use to identify limitations of data)

b. Process-related (trial or investigation processes - for discussion only)

d. Can you talk specifically about the process of investigating (Law Enforcement,

Prosecutors, Defense Attorneys) this case given the involvement of the multi-agency task

force (both cases in this study involved a multi-agency task force)? What worked well

and what areas presented difficulty, particularly in coordinating evidence, searches, and

interviews?

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a. Case-related

b. Process-related

e. Can you talk specifically about the process of trying this case (Prosecutors, Defense

Attorneys, Judges) given the involvement of the multi-agency task force (both cases in

this study involved a multi-agency task force)? What worked well and what areas

presented difficulty, particularly in coordinating evidence, searches, and interviews?

a. Case-related

b. Process-related

f. Was this case typical of sex trafficking network cases you have investigated (Law

Enforcement, Prosecutors, Defense Attorneys) personally? What made it so?

g. Was this case typical of sex trafficking network cases you have tried personally

(Prosecutors, Defense Attorneys, Judges)? What made it so?

h. What was unique specifically to this case?

i. Is there anything else particularly memorable regarding your experience on this case that

you would like to share?

a. Case-related

b. Process-related

IV. Evidence-Driven Questions (Far more specific, and may require covering in a separate

interview depending on research status at time of interview. Length of time needed for this

section will be determined by what information is still missing after archival data is collected

in case.)

a. I have been through the physical evidence presented in court, police reports, documents

filed with the court, and sentencing transcripts. The questions that follow address gaps

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remaining in the evidence that I request your help with, if you know the answers. If you do, please also tell me how you know the answer(s) such as: police report, first-hand interview, excluded testimony or evidence, etc. Supplementary questions may concern one or more of the following areas if the court documents do not address them:

i. Specific sex trafficking network members

1. Personal demographics

2. Personal skill sets

3. Personal resources

ii. Specific relationships (personal, financial, working – quantifiable and

descriptive data)

1. Between members

2. Between clusters of members

iii. Network operations

1. Tracing processes/procedures such as

a. Operations

b. Finance

c. Supply

d. Transport

e. Demand

f. Adaptation

g. Network protection from law enforcement, competitors

i. Risk management

2. Market Dynamics

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a. Ease of entry/exit

b. Competitive practices to increase market share

c. Amount of money generated by business

3. Other businesses engaged in by the network

a. Interrelations with the sex trafficking business

Thank you very much for your participation.

 Make sure Key Informant has contact information.

 Schedule follow-up interview if 1-hour time slot was not enough.

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APPENDIX B

INFORMED CONSENT FOR KEY INFORMANT INTERVIEWS

Consent to Participate in Research

Identification of Investigators & Purpose of Study

You are being asked to participate in a research study conducted by Kristina Lugo from the

Department of Justice, Law and Criminology at American University. The purpose of this study is to examine the organizational dynamics of sex trafficking networks. This study will contribute to the student’s completion of her doctoral dissertation.

Research Procedures

Should you decide to participate in this research study, you will be asked to sign this consent form once all your questions have been answered to your satisfaction. This portion of the study consists of key informant interviews of court and law enforcement officers that participated in the investigation of one of the two cases under analysis. You will be asked to answer to a series of questions related to network members and the ties between them. This interview will be audio-recorded, but only with your permission.

Time Required

Participation in this interview will require about one hour of your time. If additional time is needed, mutually-agreeable arrangements will be made. If additional interviews are required, total time required for them is not expected to be more than two hours.

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Risks

The investigator does not perceive more than minimal risks from your involvement in this study, since the interview will cover a case where sentencing is completed and the trial evidence is publicly available on request. However, you may request that your name not be attributed to a specific comment if you wish. Please be advised, though, that given the public nature of the cases perfect anonymity cannot be guaranteed. Therefore, you may also refuse to answer any question.

Benefits

There is no financial compensation for participating in this study. However, while participation in this study may bring no immediate personal benefit to participants, potential benefits from participation in this project include affiliation with a policy-relevant study showing how scientific network analysis, combining computer simulations with qualitative case study, can identify patterns that help law enforcement better target resources to dismantle trafficking networks.

Confidentiality

The results of this research will be presented at the dissertation defense at American University and at academic criminology conferences. They will also be published. If desired, results of this project will be coded in such a way that the respondent’s identity will not be attached to the final form of this study, but perfect anonymity of key informants already publicly-associated with the case cannot be guaranteed.

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Participation & Withdrawal

Your participation is entirely voluntary. You are free to choose not to participate. Should you choose to participate, you can withdraw at any time without consequences of any kind. You may also refuse to answer any individual question without consequences.

Questions about the Study

If you have questions or concerns during the time of your participation in this study, or after its completion or you would like to receive a copy of the final aggregate results of this study, please contact:

Kristina Lugo Professor Richard Bennett

Justice, Law & Criminology Justice, Law & Criminology

American University American University

[email protected] Telephone: (202) 885-2956

[email protected]

Questions about Your Rights as a Research Subject

Anthony Ahrens Matt Zembrzuski

Chair, Institutional Review Board IRB Coordinator

American University American University

(202)885-1714 (202)885-3447 [email protected] [email protected]

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Giving of Consent

I have read this consent form and I understand what is being requested of me as a participant in this study. I freely consent to participate. I have been given satisfactory answers to my questions. The investigator provided me with a copy of this form. I certify that I am at least 18 years of age.

I give consent to be audio taped during my interview. ______(initials)

I give consent for the investigator to share the content of my interview with other researchers after completion of the study (optional). ______(initials)

______

Name of Participant (Printed)

______

Name of Participant (Signed) Date

______

Name of Researcher (Signed) Date

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APPENDIX C

KEY INFORMANT INTERVIEWS: INVITATION TO PARTICIPATE

Key Informant Interview Invitation

(Email. The same information will be presented if the invitation is extended by phone.)

Dear (Title, Name, Agency),

My name is Kristina Lugo, and I am a PhD. candidate in the Department of Justice, Law and

Criminology at American University conducting dissertation research on sex trafficking network dynamics. One of the cases I am analyzing, using case study methods and quantitative social network analysis, is (case name/number). You served as the (role) on this case, and I would like to request your gracious participation in an interview concerning the case.

Prior to meeting with you, I will examine the evidence presented at trial on this case as well as all documents and transcripts filed with the court to assemble not only an “organizational chart,” but to collect more detailed information on relationships between specific individuals, operational dynamics, and the relative importance of different relationships in the network. The purpose of the interview will be to fill in holes in the data and to gain your personal perspective on the difficulties you face when trying or investigating these types of cases. Through this study,

I hope to show how quantitative social network analysis utilizing this information, combined with qualitative case analysis for richer detail, can provide an additional tool to help law enforcement better target resources in these investigations.

This interview should take about one hour of your time. I will be in (city) on (dates) and will meet with you at the time and place of your choosing while in town. If you are not available during this time, I am happy to arrange an interview by Skype or during a subsequent visit. If

213

you give me permission, I would like to audio record the interview. If we discover that more time is needed for follow-up questions, a mutually-acceptable time and interview format can be arranged.

Please let me know if you would be willing to participate in this research. If you are please respond with your availability, and your interview time/ location preferences, by (date). Once you agree to participate, I will send you a copy of the informed consent form necessary for participation in this research.

Thank you in advance for your participation, and don’t hesitate to ask me any questions.

Gratefully,

Kristina Lugo

PhD Candidate

Department of Justice, Law & Criminology

American University

Washington, DC

Email: [email protected]

Phone: 678-231-3892

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APPENDIX D

AUXILLIARY REGRESSIONS

Table D1: Naïve Regressions (No Controls, Odds Ratios presented)

MODEL MODEL MODEL MODEL MODEL MODEL MODEL MODEL MODEL MODEL P(AvoidIndict) BV1 BV2 BV3 BV4 BV5 BV6 BV7 BV8 BV9 BV10 0.00002*** 0.00007*** .00003*** 0.00006*** 0.00019*** Degree (Norm) (0.00003) (0.00013) (0.00004) (0.00011) (0.00041) 2.4E- 1.39E- 5.1E- Betweenness 63*** 1.1E-13 60*** 5.0E-08 43*** 3.4E-09 (5.0E-62) (2.9E-12) (2.9E-59) (1.3E-06) (1.1E-41) (9.0E-08) 4.57*** 4.47 1.73 3.66 3.29 4.32 Clustering Coef (1.96) (3.93) (1.15) (3.37) (2.65) (4.17) Structural Holes 2718.44*** 177.49*** 3.09 Coef. (3817.29) (273.55) (5.05)

Number of obs 375 375 375 375 375 375 375 375 375 375 Wald chi2(13) 65.35 49.98 12.58 61.69 62.67 50.69 61.05 31.71 50.86 60 Prob > chi2 0 0 0.0004 0 0 0 0 0 0 0 Log-likelihood Full Model -57.762 -78.325 -124.325 -60.272 -56.213 -77.625 -59.253 -96.879 -68.599 -59.37 Info. Criteria AIC 119.523 160.65 252.65 126.544 118.426 161.25 126.506 197.757 145.197 128.739 BIC 127.377 168.504 260.50 138.325 130.207 173.03 142.214 205.611 160.905 148.374 R^2 Cox-Snell/ML 0.325 0.258 0.034 0.327 0.328 0.257 0.328 0.167 0.29 0.326 Efron 0.556 0.449 0.01 0.556 0.567 0.449 0.566 0.189 0.489 0.565 McFadden 0.561 0.416 0.05 0.552 0.57 0.418 0.557 0.261 0.484 0.555 McFadden (adj) 0.546 0.402 0.035 0.53 0.547 0.396 0.527 0.246 0.454 0.518 McKelvey& Zavoina 0.559 0.535 0.092 0.579 0.584 0.528 0.585 0.635 0.646 0.597 Tjur's D 0.567 0.46 0.024 0.57 0.576 0.459 0.575 0.192 0.492 0.57 Notes: *** = p < .001. Numbers italicized and in parentheses are standard errors. 215

Table D2: Full Regressions with Prior Police Contacts Removed

MODEL MODEL MODEL MODEL MODEL MODEL MODEL MODEL MODEL MODEL P(AvoidIndict) VPX1 VPX2 VPX3 VPX4 VPX5 VPX6 VPX7 VPX8 VPX9 VPX10 Degree (non- 0.82** 0.82** 0.83** 0.82** 0.85** normalized) (0.03_ (0.03) (0.03) (0.03) (0.04) 3.06E- 1.52E-58** 0.01 58** 483789.50 8.11E-13 4.15E+11 (9.06E- (17600000 (1.59E+13 Betweenness (4.28E-57) (0.19) 57) ) (2.35E-11) ) CC1 5.54** 2.89 0.95 3.64 1.46 7.42 Clustering Coef. (3.19) (3.48) (0.73) (4.86) (2.37) (12.29) 2.82E+14* 2.76e+10* * * 7.33 Structural (2.29E+11 Holes Coef. (2.10E+15) ) (18.09) 1.02 1.03 1.04** 1.02 1.02 1.03 1.02 1.02 1.02 1.02 Gang Rank (0.02) (0.02) (0.02) (0.02) (0.03) (0.02) (0.03) (0.02) (0.02) (0.03) 1.01 1.01 1.02 1.01 1.01 1.01 1.01 1.02 1.02 1.01 ST Function (0.02) (0.01) (0.01) (0.02) (0.01) (0.01) (0.01) (0.02) (0.02) (0.02) 30080.56* 58048.54** 309.0714** 28.70* 54252.26** 42652.95** 301.67** * 278.94** 200.65** 6482.97** (155959.70 (147771.30 (111461.50 Victim ) (638.40) (42.85) ) ) (624.96) (79255.56) (471.38) (341.25) (17989.50) 1.00 1.00 1.01 1.00 1.00 1.00 1.00 1.00 1.00 1.00 Clique (0.02) (0.02) (0.01) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) Gang Associa- 0.99 0.99 1.00 0.99 0.99 0.99 0.99 0.97 0.97 0.99 tion (0.03) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) 1.03 1.03 1.03 1.03 1.03 1.03 1.03 1.04 1.03 1.03 Transient (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) 0.98 0.98 1.00 0.98 0.98 0.98 0.98 0.98 0.98 0.98 Race (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) (0.02) Sex 3.62 1.80 0.80 3.60 3.02 1.81 2.84 3.20 -2.64 2.42

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(2.84) (1.03) (0.39) (2.80) (2.36) (1.05) (2.23) (2.62) (2.12) (1.99) Number of obs 375 375 375 375 375 375 375 375 375 375 Wald chi2(13) 38.46 33.77 28.19 38.32 39.25 33.67 38.7 34.55 33.45 41.54 Prob > chi2 = 0 0.0001 0.0009 0 0 0.0002 0.0001 0.0001 0.0004 0 Log- likelihood Intercept only -94.502 -101.361 -97.987 -97.531 -0.054 -100.55 -96.731 -98.235 -100.125 -96.51 Full Model -0.37 -29.407 -52.918 -3.816 -0.048 -29.074 -3.554 -12.111 -14.312 -3.735 Information Criteria AIC 20.74 78.813 125.835 29.631 22.097 80.148 31.107 44.223 52.624 33.47 BIC 60.009 118.082 165.105 72.827 65.293 123.344 78.23 83.492 99.747 84.52 R^2 Cox-Snell/ ML 0.395 0.319 0.214 0.393 0 0.317 0.392 0.368 0.367 0.39 Cragg- Uhler/ Nagelkerk e 0.997 0.763 0.525 0.97 0.11 0.764 0.972 0.903 0.888 0.97 Efron 0.767 0.594 0.275 0.767 0.769 0.594 0.77 0.717 0.723 0.774 McFadden 0.996 0.71 0.46 0.961 0.11 0.711 0.963 0.877 0.857 0.961 McFadden (adj) 0.89 0.611 0.358 0.848 -202.491 0.601 0.839 0.775 0.737 0.827 McKelvey & Zavoina 0.868 0.775 0.698 0.866 0.867 0.773 0.86 0.972 0.952 0.835 Tjur's D 0.765 0.58 0.279 0.763 0.766 0.579 0.764 0.702 0.703 0.76 Note: Odds ratios presented. ** p = .01.

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Table D3: Test for Curvilinear Effect of Degree (Latent Variable)

P(AvoidIndict) Odds Ratio Std. Err z P>z [95% Conf. Interval] Factor1 (Degree Latent Var) 0.005 0.010 -2.7 0.007 0.00 0.23 Factor1_2 (Degree^2) 2.47 1.73 1.29 0.196 0.63 9.71 Factor2 (Clustering Latent Var) 0.41 0.37 -0.98 0.325 0.07 2.45 Gang Rank 1.01 0.03 0.23 0.816 0.96 1.06 ST Function 0.99 0.02 -0.7 0.483 0.95 1.02 Victim 53.55 105.27 2.02 0.043 1.14 2523.92 Prior Police Contacts 0.81 0.09 -1.77 0.077 0.65 1.02 Clique 0.99 0.03 -0.44 0.658 0.93 1.04 Gang Association 1.01 0.03 0.2 0.84 0.96 1.06 Transient 1.00 0.02 -0.09 0.926 0.96 1.03 Race 0.99 0.02 -0.41 0.681 0.95 1.03 Sex 7.48 9.73 1.55 0.122 0.58 95.78 Information Criteria AIC -5.982 BIC 45.068 R^2 Tjur's D 0.882

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Table D4: Test for Curvilinear effect of Normalized Degree without Latent Variable

P(AvoidIndict) Odds Ratio Std. Err z P>z [95% Conf. Interval] Normalized Degree 2.3E-10 2.1E-09 -2.41 0.016 4E-18 2E-02 Normalized Degree^2 2.5E+07 2.3E+08 1.85 0.065 3E-01 2E+15 Betweenness Centrality 1.8E-20 5.7E-19 -1.39 0.164 3E-48 1E+08 CC1 Clustering Coefficient 1.9E+01 4.3E+01 1.34 0.181 3E-01 1E+03 Structural Holes Coefficient 3.7E+02 1.4E+03 1.55 0.121 2E-01 7E+05 Gang Rank 1.0E+00 2.2E-02 0.18 0.856 1E+00 1E+00 ST Function 9.7E-01 2.1E-02 -1.49 0.136 9E-01 1E+00 Victim 2.5E+01 4.6E+01 1.73 0.083 7E-01 9E+02 Prior Police Contacts 8.4E-01 7.6E-02 -1.95 0.051 7E-01 1E+00 Clique 9.9E-01 2.3E-02 -0.35 0.726 9E-01 1E+00 Gang Association 1.0E+00 2.4E-02 0.08 0.938 1E+00 1E+00 Transient 9.9E-01 1.7E-02 -0.42 0.674 1E+00 1E+00 Race 1.0E+00 1.7E-02 -0.27 0.788 1E+00 1E+00 Sex 4.5E+00 5.9E+00 1.15 0.249 3E-01 6E+01 Information Criteria AIC 12.917 BIC 71.821 R^2 Tjur's D 0.888

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APPENDIX E

COMPARISON CASES: OTHER NETWORK PROSECUTIONS IN THE UNITED STATES

Table E1: List of Comparison Indictments

Name/ Case ID Year Location People v. Dominguez, 121 Cal.App.3d 481, 175 Cal.Rptr. 445 (App. 5th Dist.) 1981 CA, 5th District United States v. Starrett, 55 F.3d 1525 (11th Cir.) 1995 S. Florida People v. Lam, No. B184441, 2006 BL 157631 (Cal. App. 2d Dist.) 2006 Monterey, CA US v. Campbell et al. 2007 Wichita, KS US v. Morsette et al. 2008 North Dakota USA v. Cephus et al. 2009 N. Indiana-Illinois Nashville, TN; Minneapolis, US v. Adan, et al. 2010 MN Nortenos Gang in ST: 3 related cases, all US v. Espudos et. al. (3 diffferent case numbers) 2011 San Diego County, CA US v. Baires et al. 2011 Brooklyn, NY US v. Ventura et al. 2011 Capitol Heights, MD State of Washington v. Clark, 170 Wn. App. 166, 283 P.3d 1116 (App. Div. 1) 2012 Seattle, WA US v. Pittman, et al. 2012 San Diego County, CA US v. Bell, et al. 2012 Los Angeles, CA US v. Strom et al. 2012 Fairfax County, VA US v. Najera et al. 2012 Dodge City, KS US v. Lockhart, et al. 2013 El Paso, TX US v. Rivera et al. 2013 Brooklyn, NY & Scranton, PA US v. Francisco et al. 2013 Scottsdale, AZ San Diego County, CA, AZ, US v. King et al. - Tycoons 2014 TX

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APPENDIX F

INSTITUTIONAL REVIEW BOARD APPROVALS (ORIGINAL AND MODIFICATION)

Figure F1: Original IRB Approval

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Figure F2: IRB Modification Approval

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