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A QUANTITATIVE ANALYSIS OF THE GROWING
BUSINESS OF ORGANIZED CRIME: STRUCTURAL
PREDICTORS OF CROSS-NATIONAL DISTRIBUTION OF
HUMAN TRAFFICKING MARKETS AND TRAFFICKING IN
WOMEN IN TURKEY.

presented by
Onder Karakus

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PhD. degree in Criminal Justice

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5/08 KzlProlecc8Pres/ClRC/DateDue.indd

A QUANTITATIVE ANALYSIS OF THE GROWING BUSINESS OF ORGANIZED
CRIME: STRUCTURAL PREDICTORS OF CROSS-NATIONAL DISTRIBUTION OF
HUMAN TRAFFICKING MARKETS AND TRAFFICKING IN WOMEN IN
TURKEY.

By

Onder Karakus

A DISSERTATION

Submitted to
Michigan State University
in partial fulﬁllment of the requirements
for the degree of

DOCTOR OF PHILOSOPHY
School of Criminal Justice

2008

ABSTRACT
A QUANTITATIVE ANALYSIS OF THE GROWING BUSINESS OF ORGANIZED
CRIME: STRUCTURAL PREDICTORS OF CROSS-NATIONAL DISTRIBUTION OF
HUMAN TRAFFICKING MARKETS AND TRAFFICKING IN WOMEN IN
TURKEY.
By
Onder Karakus

Current knowledge on human trafﬁcking is mainly produced through the review
of ofﬁcial cases and statistics and interviews with former victims and key informants in a
single country or a number of countries in a single region. Therefore, structural forces
driving human trafﬁcking are simply inferred based on the individual (compositional)
characteristics of the victims and on the descriptive information about limited number of
countries where those individuals are recruited and/or exploited. This study attempted to
ﬁll this void by exploring the spatial distribution of human trafficking markets across the
world nations as well as within one particular country, Turkey. In this regard, three major
research questions have laid the foundation for overall discussion in this study.

First, the impact of certain macro structural factors on the distribution of human
trafﬁcking markets across a sample of 53 countries is examined to understand cross-
national distribution of human trafﬁcking markets. Acknowledging that human
trafﬁcking is generally nested in larger migratory processes, cross-national distribution of
human trafﬁcking is investigated based on the assumptions of migration systems theory,
which mainly explains the initiation and direction of migration and the role of trafﬁckers
as intermediary institutions in broader migration processes. Migration systems theory is

complemented with theoretical propositions drawn from the social disorganization

framework within criminology. Several sets of Ordinary Least Squares and Ordinal Logit

Regression Models indicated that poverty, urbanization. and the size of total population
have signiﬁcant positive impact whereas the proportion of younger population has
signiﬁcant negative impact on human trafﬁcking in a country.

Second, Rengert’s (1996) framework for the diffusion of illegal drug markets is
utilized to explore the distribution of human trafﬁcking markets across 81 cities in
Turkey. Several sets of Negative Binomial Regression (N BR) models revealed that
hierarchical diffusion and social disorganization have signiﬁcant impact on diffusion of
trafficking in women across 81 provinces in Turkey. Speciﬁcally, the cities with
international borders, larger population, more poverty, heterogeneity, residential
mobility, and family disruption were found to have higher number of human trafﬁcking
incidents and calls for rescue as reported by law enforcement officials and the
International Organization for Migration (IOM) ofﬁce in Turkey respectively.

Third and the ﬁnal analysis considered the extent to which human trafﬁcking,
illegal prostitution, and other forms of violent and property crime as well as organized
crime are associated at the city level. Related N BR models demonstrated that migrant
prostitution has signiﬁcant positive impact on human trafﬁcking incidents, calls for
rescue, total crime rates, and organized crime rates in the following years even when the
impact of hierarchical diffusion and social disorganization are held constant.

Overall, while globalization impacts the cross-national distribution of human
trafﬁcking through its differential impact on structural characteristics (i.e. poverty and
urbanization) of the world nations, distribution of human trafficking markets in Turkey is
mainly driven by the demand for the illegal services of trafﬁcking markets which thrives

upon existing social disorganization and disorder in a given community.

Copyright by
ON DER KARAKUS
2008

Dedicated to my mother. my sister. my wife, my daughters, and to the memory of

my father.

ACKNOWLEDGEMENT

I owe my gratitude to all those people who have contributed to the success of this
dissertation. My sincere appreciation and deepest gratitude goes to my advisor, Edmund
F. McGarrell, who continually stimulated my analytical thinking and greatly assisted me
with scientiﬁc writing. I have been fortunate to have an advisor who gave me the freedom
to explore on my own and at the same time the guidance to recover when my steps
faltered.

I am also very grateful for having an exceptional doctoral committee and wish to
thank Dr. Emine O. Evered, Dr. Vince Hoffman, and Dr. Steve Cherrnak for their
continual support and encouragement.

Many people on the faculty and staff of the School of Criminal Justice at
Michigan State University assisted and encouraged me in various ways during my course
of studies. I am especially grateful to Profs. Mery Morash, Mahesh N alla, Sheila Royo
Maxwell, Christopher Maxwell, Steve Dow, and David Carter for all that they have
taught me. Many thanks also go to Mary Lee VanderMoere, Melissa Christle, Pegg
Donahue, and Terri Bulock for their assistance and understanding.

I owe a special note of gratitude to Dr. Oguzhan Basibuyuk. His insightful
comments and constructive criticisms were thought-provoking and they helped me focus
my ideas throughout my research.

Finally, I would like to acknowledge and thank Turkish National Police and

extend many thanks to my colleagues for their patience and continuous support.

vi

TABLE OF CONTENTS

 

LIST OF TABLES

CHAPTER 1
INTRODUCTION
1. Statement of the Problem

 

2. Signiﬁcance of the Study

 

 

3. Organization of Dissertation

CHAPTER 2
THEORETICAL FOUNDATION

 

 

1. Migration Systems Theory and Global Human Trafﬁcking
2. Geography of Illicit (Drug) Markets in a Country:
Two-Step Drug Diffusion Model

 

3. Ecological Approach to Law Breaking:
Social Disorganization and Disorder

 

(1) Shaw and McKay’s Social Disorganization Theory

 

(2) Reformulation of Social Disorganization Theory and

 

Empirical Findings
(3) Contemporary Research on Social Disorganization:

 

Extension and Criticism of the Systemic Control Model
(4) Disorder, Decline, and More Serious Crime: Prostitution,
Human Trafﬁcking, and Other Serious Crimes

 

CHAPTER 3
LITERATURE REVIEW

 

1. Globalization, Migration, and Cross-national

 

Distribution of Human Trafﬁcking

 

2. National Distribution of Human Trafﬁcking
3. The Impact of Prostitution on the Distribution of

 

Human Trafﬁcking

CHAPTER 4
METHODOLOGY

 

 

1. Research Questions and Hypotheses
(3) How do macro-level structural characteristics inﬂuence
the cross-national distribution of human trafﬁcking? -

 

(2) How do hierarchical diffusion and social disorganization

 

inﬂuence trafﬁcking in women in Turkey?
(3) What is the impact of migrant prostitution on the distribution

 

of human trafﬁcking and violent and property crimes?

 

2. Data
(1) The United Nations Database

 

vii

10
11

13

13

17

22
23

26

31

36

42

42
50

61
68
68
68
71
74

76
77

 

(2) Census data and crime statistics----
3. Measurement of the concepts

 

(1) Cross-national analysis of human trafﬁcking

 

 

Dependent variable
Independent variables

 

Control variables

 

 

(2) Trafﬁcking in women in Turkey

 

Dependent variables
Independent variables

 

Control variables

 

 

4. Analytic Strategy

CHAPTER 5
ANALYSIS and FINDINGS

 

1. Cross-national distribution of human trafﬁcking markets
(1) Summary Statistics

 

 

 

(2) Bivariate Analysis

 

(3) Multivariate Analysis
2. Distribution of Human Trafﬁcking Markets in Turkey

 

(1) Summary Statistics

 

 

(2) Bivariate Analysis
(3) Multivariate Analysis

 

Negative Binomial Regression Model (NBRM)

 

 

3. Summary of Findings

CHAPTER 6
DISCUSSION and CONCLUSION

 

 

1. Discussion

(1) Cr oss-national Distribution of Human Trafﬁcking Markets .............

(2) Human trafﬁcking in Turkey

 

 

Hierarchical Diffusion
Social Disorganization

 

(3) Migrant prostitution, Human Trafﬁcking, and other
Serious Crimes

 

 

2. Conclusion and Policy Implications
3. Limitations and Future Research

 

 

BIBLIOGRAPHY --

viii

78
79
79
79
82
86
87
87
91
97
97

101
101
101
104
106
116
117
119
123
124
137

139
139
140
145
146
147

150
152
163

168

Table 1:

Table 2:

Table 3:

Table 4:

Table 5:

Table 6:

Table 7:

Table 8:

Table 9:

Table 10: Summary of Findings ------

LIST OF TABLES

 

Descriptive Statistics

 

Bivariate Correlations

OLS Regression Models for Total Trafﬁcking

 

Ordered Logit Regression Models for Trafﬁcking to and from the

 

Countries

 

Descriptive Statistics

 

Bivariate Correlations

Difference in human trafﬁcking incident rate, calls for rescue rate,
violent and property crime rate (per 10,000 population) means

 

between the cities with and without migrant prostitution

Negative Binomial Regression Models for Human Trafficking and
Migrant Prostitution

 

Negative Binomial Regression Models for Human Trafficking,

 

Total Crime, and Organized Crime

 

ix

103

105

108

113

118

120

122

128

131

136

CHAPTER ONE
INTRODUCTION
1. Statement of the Problem

Despite growing concern about human trafficking within the last decade,
empirical research on human trafﬁcking is still in its early stages (Adepoju, 2005; Ali,
2005; Bruckert and Parent, 2002; Calandruccio, 2005; Godziak and Collett, 2005;
Langberg, 2005; Lee, 2005; Piper, 2005). Existing research mostly estimates the scale of
the problem, maps routes and countries of origin, transit, and destinations; or reviews
legal frameworks and policy responses. As such, legal regulations and intervention
policies based on such limited research and information may create unintended side
effects (Salt, 2000).

Human trafﬁcking cuts across a wide array of concems at national, international,
and global level ranging from poverty, population growth, ethnic and gender
stratiﬁcation, human rights abuses, to transnational crime. Hence, it is not surprising to
see that human trafﬁcking is being approached in several ways such as a moral issue, a
gender issue, a human rights problem, a public order problem, a labor question, a
migration issue, and a matter of organized crime (Bales, 2005; Barry, 1984; Kwong.
2001; Okereke, 2005; Shelley, 2003). In addition to its impact on the victims, those who
are simply treated as commodities and imported, exported, and exploited by individuals
or transnational organized crime groups; human trafﬁcking is also believed to affect
origin, transit, and destination countries and pose a signiﬁcant threat to their international
relations, economy, and security because of the involvement of organized crime (Salt,

2000).

Human trafﬁcking is believed to be the fastest growing business of organized
crime. Speciﬁcally, trafﬁcking in women for the purposes of sexual exploitation is
claimed to be one of the most prolific areas of international criminal activity (Miko,
2004). According to the U.S. Federal Bureau of Investigation, human trafﬁcking
produces an estimated $9.5 billion of (illegal) revenue annually (US Department of State.
2005) whereas the value of the global trade in the market of trafﬁcking in women for the
purposes of sexual exploitation is estimated to be between $7 and 12 billion (Hughes,
2001; Tomasi , 2000).

Although the sex industry is claimed to be proﬁtable for the local economy as it
generates needs for secondary services and inputs ranging from food and clothing to
condoms and health services, huge amount of proﬁts generated out of that illicit market
are rarely invested in the countries of origin; in contrast, they are transferred out of the
country and laundered into western economies (Hughes, 2001). Moreover, the proﬁts
from that criminal transportation of human beings fuel other criminal activities since
human trafﬁcking and organized crime is mostly connected with money laundering, drug
trafﬁcking, document forgery, and human smuggling (US Department of State, 2005).
Yet, given the clandestine nature of the activities of smuggling and trafﬁcking, it is not
possible to measure the magnitude of the problem with a certain level of accuracy.

Between 600,000 and 800,000 women, men, and children are estimated to be
trafﬁcked across international borders each year. Moreover, women and girls, half of
whom are minors, constitute 80% of those trafﬁcked persons (US Department of state.
2005). The United Nations, on the other hand, placed the number of men, women, and

girls trafﬁcked across the international borders at 700,000 to 4 million in 2000. Recently,

more than 27 million people are estimated to have been trafﬁcked and exploited
worldwide (Bales, 2005). Forms of exploitation mainly include slavery, forced labor or
services, prostitution, and servitude (Granville, 2004). In fact, trafﬁcking in women can
not be limited to exploitation within the sex industry since it might also include other
forms of exploitations such as bonded labor, domestic service and even involuntary organ
transplants (Kelly, 2002). However, following the collapse of Soviet Union, the
international community had to pay more attention to the trafﬁcking in women for the
purposes of sexual exploitation as thousands of Russian and Ukrainian women were
trafﬁcked into sexual exploitation across the globe.

Even though trafﬁcking in women for sexual exploitation was not a new
phenomenon, the chaos in Russia was an important catalysis for human trafﬁcking to
spread to new parts of the world and become a larger part of the illicit economy (Stoecker
and Shelley, 2005 ).Until the end of the 19803, primarily the Asian women from Thailand
and Philippines were subject to human trafﬁcking (Stoecker and Shelley, 2005).
However, with the collapse of the USSR, the former Soviet republics have become the
major countries of origin of the women trafﬁcked into the sex industries across the globe
(Hughes, 2002). Basically, the process of post-Soviet socio-economic transition to market
economies was particularly traumatic for women. That is to say, females were at an
extremely precarious position since they were deprived of their jobs and were also
suffering from a collapsed welfare system (Haynes, 2004; Stoecker and Shelley, 2005).
Thus, trafﬁcking in human beings for sexual and labor exploitation in Europe has been
driven mainly by the collapse of the Soviet Union in the late 19803 (IOM, 2002).

Additionally, the conﬂicts in the former Yugoslavia was also another factor that

contributed to the same process since the Albanian maﬁa was used to abduct or lure
young women to Italy and other parts of the Europe and force them to work in brothels
until the arrival of peace keeping missions in Kosovo (Aronowitz, 2001).

Currently, Russian women are known to have been involved in prostitution in
over 50 countries as a result of trafﬁcking (Hughes. 2001). In general, 90 percent of
trafﬁcking victims in the EU. claimed to be originating from Central and Eastern
European countries (Mameli, 2002) and they are commonly trafﬁcked to North America,
Western Europe, and Japan (Kara, 2002; O’Neill, 1999). According to a report from the
United Nations Center for International Crime Prevention, Germany is the top destination
of the women trafﬁcked from Russia, and the United States is the second top destination
whereas Cyprus and Turkey are emerging as further European destination and transit
countries (Hughes, 2002). According to same report, besides Russia, other main countries
of origin for the trafﬁcked women are Ukraine, Thailand, Nigeria, Moldova, Romania.
Albania, China, Belarus, and Bulgaria (Mizus, Moody, Privado and Dougla, 2003).

In terms of trafﬁcking in women, Turkey is among the most affected countries
mainly because of its geographic location bordering and bridging the countries of origin
and destination in the region. The women from the former Soviet Union initially are
known to have engaged in small economic enterprise by selling clothes, which is called
the “suitcase trade” in Turkey following the demise of the Soviet regime (Williams and
Balaz, 2002; Yukseker, 2004). However, as soon as those women lost their purchasing
power and started to involve in lucrative tourism and entertainment business, there has
been signiﬁcant evidence that they have been trafﬁcked and recruited into forced

prostitution in Turkey. There were 469 people identiﬁed in Turkey as victims of human

trafﬁcking in 2005 all of whom were females and from the former Soviet Union except
for 11 women from Romania and only one woman from Uganda (IOM, 2006). Yet.
research on those women trafﬁcked into Turkey from the former Soviet Union is still
sparse (IOM, 2001). A

Two different yet overlapping models have been applied to the study of human
trafﬁcking so far. Relying on an economic approach, the ﬁrst model emphasizes
trafﬁcking as a business within broader international migration process while the second
approach takes a legalistic view by considering the trafﬁcking as a criminal activity. For
the former one, Salt and Stein (1997) provided a hypothetical model that presents
trafﬁcking networks as intermediary systems in the global migration, which facilitate the
movement of people between origin and destination countries. However, they do not
make a clear distinction between smuggling and trafﬁcking and this is the main drawback
of their model as reﬂected in the case studies of human trafﬁcking in Poland (Okolski,
2000), Hungary (Juhasz, 2000), and Ukraine (Klinchenko, 2000). w

The latter approach assumes the involvement of (organized) criminal networks as
trafﬁckers in human trafﬁcking by recruiting, transporting. or exploiting trafﬁcked
persons. Yet, there is an ongoing debate regarding the nature, structure and the role of
such organized criminal networks within human trafﬁcking (Bruinsma and Bemansko.
2004; Finckenauer, 2001), which simply seems to be reverberation of the disagreement
on the deﬁnition of organized crime (organized: Schelling, 1971 vs. disorganized crime:
Renter, 1983). Put another way, a homogeneous deﬁnition of organized crime networks

or syndicates without emphasizing local, regional. and international variations doesn’t

appear to be feasible (Allum & Sands. 2004; Levi. 1998; Mcillwain, 1999; Shelley. 2003;
Zhang & Chin, 2002).

There is no doubt that the current international trade in human beings is
perpetrated by the trafﬁckers composed of individuals or gangs of individuals (Stoecker,
2000). It is always possible for an individual pimp to get a woman into another country
for the purpose of prostitution. However, if a number of women are needed for a
prostitution or trafﬁcking operation, an organized way of transportation most likely to
appear (Hughes, 2002). Moreover, even if trafﬁcking starts with the involvement of a few
pimps, when prostitution is widely tolerated (or legalized/regulated in some countries like
Turkey), this initial process is most likely to attract and fuel the involvement of larger
organized crime groups in that tenitory in the long run under the pretext of protection
provided by that organized crime group. Thus, local small prostitution rings become
connected to larger organized crime groups and while providing the local pimps with a
future career in organized crime, this initial process also serves as an entrance point for
organized crime into that city or country since trafﬁcking in women is intertwined with
other related criminal activities (O’Neill, 1999). In this regard, the size and the scope of
trafﬁcking can range from individual pimping and control of local women to loosely
associated crime groups that focus on different aspects of trafﬁcking process, as well as
to major criminal syndicates with international connections for trafﬁcking women to
destination countries where they control the sex industry. And, when trafﬁcking is
perpetrated by a large number of loosely connected crime groups, this makes the
detection and crackdown of a trafﬁcking network relatively difﬁcult because of the vague

structure of involved groups.

Apparently, both approaches to human trafﬁcking prioritized the role and
structure of trafﬁckers with limited attention to the illegal human trafﬁcking market
itself. However, while running their illegal business, trafﬁckers, pimps, and recruiters
generally thrive on the vulnerability of the people who suffer from economic desperation
and disadvantage, lack of sustainable income, and poverty (Raymond and Hughes, 2001;
US Department of State. 2005). Many of the women subject to trafﬁcking are those with
poor job skills and almost no chance of employment in their country of origin
(Aronowitz, 2001). However, “researchers have glossed over or completely ignored the
broader socio-cultural and economic contexts in which migration, in general, and more
strictly trafﬁcking in human beings, takes place” (Adepoju, 2005, 84).

Moreover, people get involved in this international illicit trade to satisfy the
demand of the bosses of illegal labor and the clients of sexual and other services
(provided by the migrants), but not the demand of the trafﬁcker or smuggler (Okereke,
2005). In other words, human trafﬁcking is driven by sexual industry and the growing
demand for exploitable labor (US Department of State, 2005). Unfortunately, as indicated
by Hughes (2001), little or no attention is paid to the legitimacy of demand, though the
trafﬁckers and smugglers have simply taken the advantage of that demand (Okereke,
2005).

Ironically, unless the underlying contextual factors that lead to convergence of
trafﬁckers and victims of trafficking in the countries of origin and destination are
identiﬁed, the research focus merely on trafﬁckers and victims can have only limited
impact as the contextual factors that contribute to recruitment, transportation, and

exploitation of the victims by the trafﬁckers are left untouched. More dramatically. such

a limited approach puts the trafficked persons in a vicious cycle of exploitation. The
victims of human trafﬁcking are found to be re-victimized when they are returned to the
context in which they were initially victimized (Oxman-Martinez, Hanley, and Gomez,
2005).

Current knowledge on human trafficking is mainly produced through the review
of ofﬁcial cases and statistics and interviews with former victims and key informants in a
single country or a number of countries in a single region. Therefore, structural forces
driving human trafﬁcking are simply inferred based on the individual (compositional)
characteristics of the victims and on the descriptive information about limited number of
countries where those individuals are recruited and/or exploited. A representative sample
of world nations has never been subject to scientific inquiry to identify structural
correlates of human trafﬁcking with only one exception. That is, Bales (2005, 139) used
multiple regression analysis to identify structural correlates of human trafﬁcking in a
sample of 111 different countries. Creating two different measures of human trafﬁcking
as ‘trafﬁcking from’ and ‘trafﬁcking to’ a country, Bales found that human trafﬁcking
ﬂows from poor countries suffering from instability and corruption to relatively more
prosperous and stable nations.

However, the UN (2000) deﬁnition of human trafﬁcking does not make a
distinction between human trafﬁcking from, to, and in countries and all are embedded in
a single deﬁnition of human trafficking. Recent evidence also demonstrates that
classiﬁcation of the countries solely as ‘sending’ and ‘receiving’ points is quite difﬁcult
because several countries can simultaneously be sending, receiving, and transit points in

the process of human trafﬁcking. Put differently, developing countries are likely to serve

as both sending and receiving points for different countries in different regions. Relative
prosperity of a country might also be different in a regional and cross-national context.
Finally, this dual conceptualization of human trafﬁcking at such a broader level of
analysis also ignores human trafﬁcking which takes place within the borders of each
country.

In this case, while an ideal measure of human trafﬁcking should include at least
three major components of human trafﬁcking, that is, human trafﬁcking ‘from’, ‘to’, and
‘in’ a country; the geographic distribution of human trafﬁcking calls for further
investigation as the simple push and the pull factors offered by neoclassical approach to
international migration is not capable of explaining distribution of human trafﬁcking
because of its dual (as ‘from’ and ‘to’) focus on human trafﬁcking in space. Building on
existing research on human trafficking and drawing insights from theoretical frameworks
used to explain international migration, communities and crime, illegal drug markets, and
(migrant) prostitution; this study attempts to ﬁll this void by exploring the Spatial
distribution of human trafﬁcking at two different levels of analysis. Acknowledging that
human trafﬁcking is generally nested in larger international migration processes, the
cross-national distribution of human trafﬁcking is going to be investigated based on the
assumptions of migration systems theory as regard to the initiation and direction of
trafﬁcking and the role of traffickers as intermediary institutions in trafﬁcking process.
Then, Rengert’s (1996) conceptualization of illegal drug markets will be utilized to
explore the geography of human trafﬁcking markets across 81 cities in Turkey. Overall,
globalization is surmised to impact the global distribution of human trafﬁcking through

its differential impact on structural features of world nations while the distribution of

human trafﬁcking markets in Turkey is more likely to be driven by the demand for the
illegal services of trafﬁcking markets which thrive upon existing social disorganization
and disorder in a given community.

2. Significance of the Study

This study departs from conventional approaches to human trafﬁcking in several
ways. First, national and regional level studies on human trafﬁcking has largely drawn
upon individual level correlates of human trafficking to come up with contextual
explanations as regard to the distribution of human trafﬁcking across the globe. Thus,
available information about trafﬁcking markets has generally been limited to accounts of
identiﬁed victims of trafﬁcking and to those who provide social and legal support to these
victims. Therefore, in addition to the lack of contextual analysis on human trafﬁcking,
present knowledge based on individual level data might also be biased as it builds mainly
on individual case studies of assisted victims who might not necessarily represent the
entire population of trafﬁcked victims even in the country where they are exploited and
recruited. Hence, investigating the geographic distribution of human trafﬁcking markets
at the intemational and national level, this study provides the ﬁrst contextual analysis of
human trafﬁcking which is conceptualized as a process that involves both human
trafﬁcking from and to a country as deﬁned by the United Nations.

Second, even though a signiﬁcant number of studies on trafﬁcking in women has
been devoted to the investigation of causal factors in sending countries over the past
decade (Hughes, 2004), few studies have focused on the importance of demand and on
the places where exploitation of the victims takes place (Ali, 2005; Hughes, 2001).

Exploration of trafﬁcking in women for the purposes of sexual exploitation in Turkey,

10

thus, contributes to efforts aimed at understanding the role of demand for the services of
trafﬁckers on the distribution of trafﬁcking markets.

Third, through its interdisciplinary approach that borrows from the ﬁelds of
international migration, geography, criminology and criminal justice, the current study is
expected to contribute to all as it incorporates various concepts from these ﬁelds into the
study of human trafﬁcking as an understudied topic across these different disciplines.
More speciﬁcally, successful application of these multidisciplinary frameworks to the
analysis of human trafﬁcking across different countries with various cultural contexts
will also contribute to the theory as the power of a theory depends on the generalizability
of its assumptions across different times, places, and populations.

As long as the spatial distribution of human trafﬁcking markets across the nations
and in Turkey helps to identify certain contextual factors that signiﬁcantly contribute to
human trafﬁcking in a given country/community, the current study can also pave the way
for further investigation of physical and social environment(s) that drive/facilitate the
establishment and diffusion of human trafﬁcking markets.

3. Organization of Dissertation

The plan of this dissertation is as follows. Chapter two provides an overview of
the theoretical framework to be used in the course of this study. Chapter three reviews
available empirical research on human trafﬁcking in the light of proposed theoretical
framework to identify previous studies that could lend evidence for the relevance of
adapted framework and of the hypotheses to be specified in the following chapter.
Chapter four speciﬁes the hypotheses to be tested and the methodological design to be

employed. The measurement of independent and dependent variables to be used in the

11

analysis are also identiﬁed and defended in this chapter. Chapter ﬁve presents analysis of
the data and major research ﬁndings whereas the ﬁnal chapter, chapter six, extends the
discussion based on analyses and ﬁndings and concludes with implications of the

dissertation for both policy and future research.

12

CHAPTER TWO
THEORETICAL FOUNDATION
1. Migration Systems Theory and Global Human Trafﬁcking

Theories developed to understand the process of international migration process
provide different causal mechanisms operating at very diverse levels of analysis to
explain the initiation and persistence of migratory processes (Massey, Joaquin, Hugo,
Kouaocui, Pellegrino, and Taylor, 1993). Although economic pressures are among the
most inﬂuential factors instigating international migration, existence of economic
disparities between countries or individuals alone doesn’t always explain the speciﬁc
composition and direction of migratory process (Tsuda, 1999). In addition to the
economic motives of the immigrants, analyzing economic, historical, political, and ethnic
linkages between sending and host countries (Fujita, 2004), migration systems theory
mainly argues that migration is driven by pull and push factors speciﬁc to a particular
historical - structural context (Yamanaka, 2000). Put another way, migration systems
theory tries to integrate macro, micro, and meso assumptions of several other theories of
international migration. Thus, providing a broader conceptual framework, migration
systems theory expedites the analysis of the formation of speciﬁc migration systems
(Kritz, Lin, and Zlotnik, 1992).

Migration systems theory suggests that the migratory process is driven by the
existence of prior links between sending and receiving countries based on trade,
investment, political inﬂuence, colonization or cultural ties (Castles and Miller, 2003,
26). Unlike the neoclassical approach that neglects historical causes of movements and

ignores the role of the state, and in contrast to world systems approach that identiﬁes the

13

interest of capital as all-detenninin g and lacks attention to the individual motivations,
migration system theory focuses on the interaction among macro, micro, and meso
structures (Castles and Miller, 2003) and also puts the individual in the center of the
migration process (Rosewame, 2004). Therefore, the units of social analysis for which
the framework could apply can range from the individual migrant to the world system
(Lim, 1987, 418).

In general, Macro structures refer to large level institutional factors such as the
political economy of the world market, interstate relationships, and the laws, structures
and practices established by the states of sending and receiving countries to control
migration settlement. Micro structures are mainly composed of information about the
destination point and capabilities for organizing travel and ﬁnding work (cultural capital),
and informal social networks deve10ped by the immigrants themselves which include
personal relationships, family and household patterns, friendship and community ties, and
mutual help in economic and social matters. Intermediate or mesa structures refer to
individuals, groups, or institutions that simply beneﬁt from the migration of others as
individual or institutional entrepreneurs by mediating the process. Intermediaries can be
legal or illegal and may include: labor and education recruiting organizations; private
voluntary humanitarian, political or religious groups; travel and shipping agents;
smugglers; providers of false documentation and other intermediaries (Castles and Miller,
2003, 27-28; Lim, 1987.420).

According to Kritz et a1. (1992, 6), even though the economic, politic structures
and ties among the nation states deﬁne the system and the direction in which the

migration ﬂows are likely to occur, they do not explain why a subset of people migrates

14

or who is likely to become a migrant. In order to answer these questions. they say, we
need to look at the individual and institutional networks that assist with the mobilization
and recruitment of the migrants and organization of the migration. While most of such
individual and institutional networks operate legally and facilitates migration by reducing
the costs and risks (which also beneﬁts them), there are other networks of extra-legal
institutions or intermediaries which involve in the organization of the migration ﬂows to
take advantage of the migrants’ vulnerability. Extra-legal networks include the smugglers
and others who provide false documents to the migrants, and their clients expected to be
from the groups of people who can not qualify or afford for the legal services of formal
institutions (Kritz et al., 1992, 7).

Thus, individual or organized trafﬁcking networks can also be conceptualized as
extra-legal institutions under the scope of the migration systems theory, for such
organizations also take the advantage of the vulnerability of the victims who might
initially intend to leave the country for the same reasons as an immigrant. Given that the
victims of human trafﬁcking are also offered well-paid jobs at the beginning by the
trafﬁckers, they are likely to be motivated to migrate and/or accept the offers of the
trafﬁckers because of the same social, economic, and political conditions prevailing in
their country of origin.

However, since human trafficking mainly involves the deception of victims, it
departs from conventional deﬁnitions of international migration (Salt, 2000) and migrant
smuggling. Unlike regular migration and migrant smuggling (in which the smugglers are
paid by the immigrant to be transported to a certain destination), in human trafﬁcking, the

individual agency of a victim is almost irrelevant as the victims are simply lured.

15

recruited, and transported into the destinations where they are exploited. In other words,
trafﬁckers are capable of creating a new geography of international migration by
determining the choice of victims’ destination countries and routes (Salt and Stain, 1997,
474)

In this re gard, placing human trafficking in broader international migratory
process, migration system theory can provide a general framework for the analysis of
cross-national distribution of human trafficking. Since the trafﬁckers and vulnerable
victims who cannot qualify or afford for international migration are assumed to converge
in the process of international migration, certain contextual factors that stimulate the
convergence of potential victims and human trafﬁckers (in place) could impact the cross-
national distribution of human trafﬁcking as well. Even though such contextual factors
are not speciﬁed by migration system theory, as the political economy of the world
market and economic and trade relations between the world nations are proposed as
important macro level factors that shape international migration ﬂows, globalization and
related transformations might contribute to an increase in human trafﬁcking in
developing countries.

As the nations become more connected to other countries through the spread of
formal and informal economic relations in the process of globalization, rapid social
change and displacements increase social disorganization (Howard, Newman, and
Pridemore, 2000, 148) which in turn decreases the level of social control and creates
more opportunities for-crime (Bursik and Grasmick, 1993). Put differently, “social
disorganization seems to be a mechanism through which globalization and economic

development affect crime rates” (Paulsen and Robinson, 2004).

16

In his analysis of the geography of illegal drug markets, Rengert (1996) also
emphasizes the role of community social disorganization and disorder on the
establishment and diffusion of illegal drug markets in the United States. Given the fact
that trafﬁcking markets are not equally distributed across a single country; Rengert’s
(1996) two step diffusion model of illicit drug markets can be applied to examine the
spatial distribution of human trafﬁcking as another type of illicit market (Eck, 1995) in a
given country.

2. Geography of Illicit (Drug) Markets in a Country: Two-Step Drug
Diffusion Model

Rengert (1996) proposes a two-step diffusion model to examine the spatial
processes that shape the distribution of illegal drug markets across different regions,
states, counties, cities, and neighborhoods in the United States. Two types of distributors
are deﬁned as active agents of this diffusion process: First, organized criminals who
market the drugs for personal proﬁt and second, the users who obtain and may sometimes
further distribute drugs for personal gratiﬁcation. In general, spatial distribution and
diffusion of the illegal drug market is driven by interdependent and reinforcing
motivations of these two active agents of the market.

Speciﬁcally, for the ﬁrst step of the model, the distribution of organized criminal
activity in illegal drug sales is expected to conform to a hierarchical pattern of diffusion.
In accordance with hierarchical diffusion theory, organized distribution and selling of
illegal drugs originates in the largest cities and subsequently ﬁlter down through an
ordered sequence of metropolitan centers of decreasing size in the urban system of the

United States. The main assumption behind this top-down diffusion of illegal markets is

17

that relative profitability of cities is expected to differ as a function of city size. Simply
put, because larger cities contain more potential customers than smaller urban centers,
more populated larger centers are expected to be selected as targets of illicit drug markets
and thus have widespread drug addiction earlier. Once the larger cities become saturated
with illegal drug markets, subsequently, relatively smaller cities are expected to be
chosen as new fertile markets.

The second step of the model, on the other hand, assumes a new pattern in the
area surrounding an established illicit drug market in each urban center. That is,
following hierarchical diffusion of illicit drug markets in accordance with the size of the
urban centers in decreasing order, local or neighborhood expansion of the markets takes
place in the region served by each center/city. In order to exploit the potential drug-
consuming population in each metropolitan area, illegal drug distributors expand their
activities outward from the central city into surrounding suburban areas in an expansion-
contiguous/neighborhood diffusion process and distant-minimizing effects that largely
shape the spatial patterns of drug distribution.

Thus, hierarchical and local diffusion of illegal markets are highly interdependent
in that while the expansion of illicit drug markets into immediate hinterland in each city
is driven by hierarchical diffusion, established central illicit markets survive on outward
dispersion of their activities. In this regard, Rengert ( 1996) offers two main factors that
impact the distribution of illicit drug markets in time and space: population and
geographic proximity. That is, the dispersion of illicit drug markets in a given
region/state/city/county mainly varies as a function of the size (population) of that

region/state/city/county. In addition to the size. geographic proximity to established illicit

18

markets or to the places where the illicit commodities are produced/imported/originated
also impacts the distribution of illicit markets.

In his analysis of the geography of illicit drug markets in the U.S., Rengert
demonstrates the ability of this two-step diffusion model in explaining spatial distribution
of illicit drug markets across different regions/states/cities/counties of the U.S. based on
ofﬁcial law enforcement statistics. Congruent with the hierarchical diffusion principles.
Rengert found that states that contain the nation’s largest cities, that is, California, New
York, Texas, and Florida experienced the highest number of drug law arrests in 1992. For
the impact of geographic proximity (of a state to the initial center of the production and
distribution of a speciﬁc type of drug), the results were also consistent with the
assumptions of the diffusion model. Namely, regional distribution of drug law arrests
exhibited different patterns for imported illicit drugs and marijuana which is produced in
the U.S. Among four different regions of the U.S., the north central region, which does
not contain a seacoast, experienced the lowest rate of imported illicit drugs of heroine and
cocaine. In contrast, the north central region had the highest location quotient (p.45) for
the arrests for sale and possession of marijuana, for this interior region is closest to where
much of the marijuana is produced in the U.S.

Moreover, among a sample of 24 U.S. cities, three port cities, Philadelphia,
Manhattan, and San Diego experienced the highest percentage of arrestees testing
positive for any type of drug whereas inner cities such as Omaha and Phoenix had the
lowest proportion of arrestees tested positive for any type of drugs. The hypothesized
temporal order of the hierarchical diffusion model was also supported by the examination

of the relative change in drug related arrests in the states of New York and Pennsylvania

19

at two different points in time. In both states, largest and smallest cities experienced the
smallest increase whereas the intermediate-sized cities had the greatest increase in drug
related arrests. This ﬁnding lends evidence for the main assumption of the hierarchical
diffusion model that illegal drug distribution ﬁlters down through the urban hierarchy in
the U.S. cities over time.

Rengert’s analysis provided consistent ﬁndings for the expansion of the illicit
markets in a region served by an established market as well (second step). The analysis of
the illicit drug marketing in the Philadelphia region indicated that while the central city
had the highest level of drug arrests because of the largest population and related
hierarchical diffusion, suburban counties had lower rates of drug rates mostly in accord
with their population size during the ﬁrst period (1976- 1978). However, as for the
increase in the drug arrests in the following period (1986-1988), the counties closer to the
central city experienced the highest proportion of change regardless of their population
size.

Finally, in his analysis of the distribution of illegal drugs at the retail level,
Rengert points to certain characteristics of the illicit markets quite different from the
characteristics of the legal markets. Although the overall diffusion model implies a
considerable level of similarity between legal and illegal markets in regard to the
distribution/diffusion of their activities, the lack of legal protection within the illicit
markets forces the market distributors to consider several other measures which in turn
impact location preferences and thus spatial distribution of illicit markets accordingly.
Simply put, in order to avoid apprehension by police, prevent violence and theft, and

increase the monetary return, illicit drug dealers want greater control over the exchange

20

of drugs and money. Therefore, Rengert argued, when selecting a location for drug sales.
the dealers must select a location “whose nearest residents are too disorganized to know
or care, or feel too helpless to confront the drug dealer who is operating in their
neighborhood” (p.110). Thus, at the retail level, Rengert relates the spatial distribution
and diffusion of illicit drug markets to the quality of life and viability of the communities.

According to Rengert, while the illicit drug markets increase property and violent
crime that mainly involve the distributors and addicts in the community, stable residents
can confront the problem with increased community organization, block watches and
greater demand for police services or apply target hardening strategies to prevent violent
and property crimes. However, in most cases, the residents decrease their outside
activities as the local streets become more disorganized and less safe. As a result of such
withdrawal from neighborhood life, law abiding residents who can afford to leave the
neighborhood move to safer places and prospective home owners and banks avoid
investing in these high-crime areas. This leads to further deterioration of the community
by decreasing the remaining stable residents’ ability to resist drug distributors and crime.
Rengert states that the process of deterioration and disorganization of formerly stable
communities through drugs-crime, crime-drugs cycle and its spatial expansion exist in
many parts of Philadelphia with the result that property and violent crimes are
concentrated in relatively small, contained drug sale neighborhoods.

In this context, in addition to the impact of population and geographic proximity
on the spatial distribution of illicit drug markets -as assumed by the hierarchical diffusion
model-; the cycle of drugs-crime and crime-drugs calls for the consideration of other

structural characteristics of the communities that increase social disorganization and

21

disorder in a community as both could negatively impact community resistance to the
establishment and diffusion of illegal markets and thus spatial distribution of these
markets.
3. Ecological Approach to Law Breaking: Social Disorganization and
Disorder

Having its roots in the studies of Querry and Guetelet in the 19m century (Burke
2001), structural/ecological approach to law breaking situates the individual in his/her
community or neighborhood mainly based on the contributions of Park and Burgess and
their human ecology model. Borrowing from the idea of studying plants and animals in
their natural habitat and assuming that the city is similar to a body with its different
organs, Park and Burgess developed what is generally known as the human ecology
model. Simply put, they hypothesized that the city of Chicago consisted of 5 concentric
zones and delinquency would prevail in Zone 11 or the ‘zone in transition’. Built in the
shadow of ageing factories, the ‘zone in transition’ was the least desirable-living area and
occupied by the inﬂux of the immigrants and those who were too poor to live in
elsewhere in the city (Burke, 2001). Therefore, the transitional zone was the most
unstable part of the city, and it was characterized mainly by the attrition of residents and
businesses and a break down in mechanisms of institutional social control (Reid, 2000).

Without ignoring the importance of individual factors, they argued, individual
experience was contingent upon where that individual lived; therefore, the
neighborhood/community level indicators could also precede the impact of individual
traits on criminal experience in a causal explanation of criminality (as well as

victimization and fear of crime). This notion of individual experience that is situated in

22

the neighborhood/geographical context was a signiﬁcant inspiration for the development
of the social disorganization perspective that dominated modern American criminology
since the early 20‘h century starting with the contributions of Shaw and McKay (Cullen &
Agnew 2006; Snell, 2001). A

(1) Shaw and McKay’s Social Disorganization Theory

Social disorganization theory is referred to as one of the earliest structural
approaches to the study of crime (Meier, Kennedy, and Sacco, 2001). Despite the
contributions of ecological work conducted before (i.e., by Breckinridge and Abbrott in
Chicago; Burgess in Kansas; and McKenzie in Columbus as cited in Snell, 2001, 26),
Clifford Shaw and Henry D. McKay’s social disorganization perspective is believed to
have set the foundations of modern American criminology in several ways. In theoretical
- terms, social disorganization perspective provided an empirical test of concentric zone
theory (Burgess, 1967) as well. However, given their long term research based on both
ofﬁcial data and case studies of delinquent careers (such as The J ackroller,1930; The
Natural History of a Delinquent Career,193l; Brothers in Crime,l938), rather than testing
a theory, it was a tremendous effort that fairly can be regarded as development of a
theory in itself. In methodological terms, relying on a 20 years of data collection
including juvenile court records, census reports, and housing and welfare records, it
exempliﬁed the ﬁrst longitudinal study of a city as regard to crime and criminality.

One of the most important findings documented in Shaw and McKay’s seminal
work Juvenile Delinquency and Urban Areas (in 1942; Shaw and McKay, 1972) was that
although the high delinquency rate neighborhoods were associated with African

American and immigrant populations, the rate of delinquency remained stable despite

23

almost entire population (ethnic/racial) shifts in these neighborhoods. Moreover, none of
the certain ethnic and/or racial group demonstrated stable/uniform rate of delinquency
across all the neighborhoods in the city in that period. In contrast, the delinquency rate of
a certain ethnic/racial group had a lot to do with the number of the members of that
speciﬁc group in high crime rate areas. This ﬁnding represented a remarkable departure
from psychological and biological explanations of law breaking and provided signiﬁcant
evidence for a sociological approach along with their other ﬁndings that indicated higher
delinquency rates in lower socioeconomic areas, in the neighborhoods closer to center of
the city and in heavy industrial areas.

Based on these ﬁndings, they offered a social disorganization perspective which
argued that higher crime rates in these neighborhoods were related to the characteristics
of the communities (which is also called contextual effect) rather than the psychological
or biological traits of the individuals (compositional effect) moved into these
communities (Cullen and Agnew, 2006). Although the association between structural
processes and delinquency rates did not offer a causal explanation for high crime rates
per se, Shaw and McKay applied case study methods and used life histories and extensive
interviews to develop their theoretical insights in regard to the relationship between
characteristics of the communities (i.e., mobility, poverty, and heterogeneity) and high
delinquency rates. The core of their theoretical argument was that rapid population
turnover in urban neighborhoods resulted in a breakdown in both formal organizations
and informal social controls.

Put differently. more consistent and uniform conventional values and norms in the

low delinquency rate areas and competing and conﬂicting values in high delinquency

24

neighborhoods (Shaw and McKay, 1972, 170) led Shaw and McKay to conclude that
there was no consensus over the values for or against criminal behavior and that there
were conﬂicting beliefs about the appropriate behavior in high delinquency
neighborhoods (Snell, 2001). This conﬂicting beliefs and lack 'of consensus and informal
control is also expected to promote intergenerational transmission of cultural values
supporting delinquency through exposure to such contradictory standards (Shaw and
McKay1972, 172) and association with delinquents and negative reinforcement by
delinquent groups (Shaw and McKay, 1972, 174). Moreover, Shaw and McKay also talk
about many opportunities for delinquent activities and lack of conventional opportunities
as well as about struggle for position in social order with such limited
means/opportunities in high delinquency neighborhoods (Shaw and McKay, 1972, 186-
187 ). Thus, besides their own contribution through the social disorganization framework,
Shaw and McKay also contributed to the development of control theories. cultural and
subcultural theories, Sutherland’s Differential association theory (Sutherland, Cressey,
and Luckenbill, 1992), and Merton’s (1938) anomie theory.

Unfortunately, such diversity of theoretical insights also gave rise to the criticism
of their work (Bursik, 1988; Bursik and Grasmick, 1993). Since they borrowed from
different theories, Shaw and McKay were mainly criticized because of being too eclectic
and contradictory in their explanations of the link between delinquency and
neighborhood characteristics. Komhauser (1978) argues that Shaw and McKay draw
freely on the elements of control, strain, and cultural conﬂict theories, yet the
implications of those theories are not consistent all the time (also see Akers, 1985 and

Liska et al., 1988). In fact, because of the lack of a clear deﬁnition of social

25

disorganization, some other scholars (i.e., Lander, 1954; Pfohl, 1985) confused this
concept with what it was intended to explain (Bursik, 1988).

(2) Reformulation of Social Disorganization Theory and Empirical Findings

To address the criticisms and demonstrate the relevance of social disorganization
perspective, Sampson (1986) tried to clarify the process through which ecological
characteristics of the neighborhood inﬂuenced the neighborhood crime and victimization
rates. Sampson (1986) argued that rapid population turnover, ethnic and racial
heterogeneity and disrupted families (Sampson, 1987) decrease the neighborhoods’
ability to socialize and supervise the youth because of a breakdown in social integration.
At the same time, Sampson asserted, rapid population growth or urbanization (Sampson,
1987) would exacerbate the situation because of the ebb and ﬂow of the people for
variety of reasons (i.e., work, recreation.) which further decrease the ability of the
neighborhood to control the environment. For the neighborhoods with low
socioeconomic status, it would also be quite difﬁcult to acquire the resources that could
help to address such problems.

Thus, the combination of these macro level characteristics (instability,
heterogeneity, family disruption, urbanization, and poverty) in the neighborhood was
expected to produce illegitimate opportunity structures and dysfunctional lifestyles
including violence and crime. On the one hand, research on these macro level correlates
of neighborhood delinquency rates did not always produce consistent ﬁndings as regard
to variation of crime rates across different neighborhoods (Chamlin, 1989; Hartnagel and
Lee, 1990; Land, McCall, and Cohen, 1990, 923; Loftin and Parker, 1985; Neuman and

Berger, 1988;). On the other hand, a considerable number of studies found signiﬁcant

26

relationship between macro level neighborhood characteristics and crime rates, though
sometimes they do not directly test social disorganization theory. To illustrate, prior
research found signiﬁcant positive relationship between poverty and crime: Crutchﬁeld,
Geerken, and Gove (1982), Miethe and Meier (1994), Mladenka and Hill (1976),
Patterson (1991). Sampson, (1986), Smith and Jarjoura (1988), Warner and Pierce
(1993); heterogeneity and crime: Smith and Jarjoura (1988), Warner and Pierce (1993);
instability/mobility and crime: Crutchﬁeld et al. (1982), Miethe and Meier (1994),
Patterson (1991), Warner and Pierce (1993); family structure (disruption) and crime:
Smith and Jarjoura (1988); urbanization and crime: Sampson and Groves, (1989).

Even though the notion of community integration and informal social control was
implicit in the speculative interpretation of the impact of the ecological characteristics on
neighborhood crime rates, the process through which such ecological characteristics
inﬂuenced (or failed to inﬂuence) neighborhood crime rates was not incorporated into
empirical tests of the Shaw and McKay community social disorganization theory until
1989 - when Sampson and Grove identiﬁed and tested the intervening role of relational
networks, supervisory capacity of the neighborhood, and organizational participation as
mediators of neighborhood characteristics on delinquency rates. Sampson and Grove
(1989) utilized data from Great Britain on 238 neighborhoods in 1982 and another
independent sample of residents from 300 neighborhoods in 1984 and found that social
disorganization (comprised of relational networks. supervisory capacity, and
organizational participation in the neighborhood) , mediated the impact of community
structural characteristics (low economic status, ethnic heterogeneity, residential mobility

and family disruption) on the rates of both criminal victimization and offending.

27

Sampson and Grove’s study is regarded as the ﬁrst and most complete study that
provides a convincing test of Shaw and McKay’s social disorganization theory (Bellair
1997, 679; Sun, Triplett, and Gainey. 2004). Thus, Sampson and Groves did not only
empirically explain and test the process through which the neighborhood ecological
characteristics inﬂuenced delinquency rates, but they also made an empirical distinction
between social disorganization and its manifestations. Empirical validity of Sampson and
Groves ﬁndings are supported by the reanalysis of their ﬁndings based on LISREL
analysis by Veysey and Messner (1999) and replication of both the original study based
on 1994 British Crime Survey and Veysey and Messner’s reanalysis (Lowenkamp,
Cullen, and Pratt. 2003), though Veysey and Messner (1999) and later Sun et a1. (2004)
reported mixed results as regard to the effects of structural antecedents on crime rates
and the mediating role of social disorganization factors between the former two. Yet. to a
large extent, structural characteristics of the neighborhoods predicted their measurement
of social disorganization and higher levels of social disorganization in turn, led to higher
levels of neighborhood crime — which was consistent with Shaw and McKay’s (1972)
conclusion.

Thus. with the contributions of Komhauser (1978), Bursik (1988), and Sampson
and Groves (1989), social disorganization has been deﬁned as the inability of a
community structure to realize the common values of its residents and maintain effective
social controls (Sampson, 2002). Furthermore, drawing from Komhauser’s explication
of control theory (1978) and also relying on Hunter’s (1985) description of three levels of
direct controls, Bursik and Grasmick (1993) extended social disorganization theory by

deve10ping a neighborhood control theory grounded in systemic theory of community

28

organization (Berry and Kasarda, 1977). A control oriented theoretical approach, Bursik
and Grasmick (1993) argued, could provide a better framework for understanding Shaw
and McKay’s argument. That is. rapid population turnover and heterogeneity could
decrease the ability of a neighborhood to control itself in order to prevent crime for
following reasons (Bursik, 1988, 521; Bursik and Grasmick, 1993, 33):

l. Institutions pertaining to internal control are difﬁcult to establish when
many residents are "uninterested in communities they hope to leave at the
ﬁrst opportunity" (Komhauser, 1978, 78).

2. The development of primary relationships that result in informal structures
of neighborhood control is less likely when local networks are in a
continual state of ﬂux (Berry and Kasarda, 1977).

3. Heterogeneity impedes communication and thus obstructs the quest to
solve common problems and attain common goals (Komhauser, 197 8, 75).

Even though Shaw and McKay mentioned the importance of family ties and
friendship networks, systemic control approach to the neighborhood control considers
these ties and networks as an essential part of regulation and socialization. More
speciﬁcally, Bursik and Grasmick assume that there is a consensus among the residents
of neighborhoods to live in an area relatively free from the threat of crime. Therefore, in
order to achieve this common goal, the community regulates itself and the behavior of the
residents and visitors to the neighborhood through social control. In this regard, the crux
of Bursik and Grasmick’s neighborhood control theory is that differences in
neighborhood crime and victimization (as well as fear of crime) can be explained by the

variations in the abilities of these neighborhoods to regulate and control the behavior of

29

their residents and visitors. And they adopt Hunter’s (1985) three level approach to local
control: private. parochial, and public level of control.

At the private level, rapid population turnover and instability make it difﬁcult to
establish and maintain intimate primary ties that promote social control through the
allocation or withdrawal of sentiment. esteem, and support. While instability impairs the
density of relational networks, racial and ethnic heterogeneity limit the breadth of such
networks. Parochial order is the second level of control that refers to the ability of the
neighborhood to supervise the behavior of its residents through broader level local
interpersonal networks and local institutions such as schools, churches, and voluntary
organizations. Instability and heterogeneity are also believed to weaken parochial control
as the people become less likely to know each other, like strangers in the same
neighborhood. Snell (2001) also incorporates family disruption as a component of
community self regulation refening to Sampson’s (1986) argument regarding the positive
impact of parents on effective socialization and supervision both in the family and in the
neighborhood.

Finally, public order is regarded as the most important component of the
community self regulation as the ability of local networks and institutions are believed to
be contingent upon external organizations that may even give rise to the development of
private and parochial networks through investment of their projects. Simply put, the
public level of control refers to the ability of the neighborhood to secure such external
resources in the fornr of public goods and services. Bursik and Grasmick (1993) offer two
forms of such control. First, the ability of the local community and organizations to

inﬂuence public service providers (i.e. municipalities and public-private decision making

30

agencies) in order to allocate economic resources to control neighborhood crime and the
second one is the relationship between the neighborhood and local police department.

Overall, according to the systemic control approach and speciﬁcally to Bursik and
Grasmick’s (1993) interpretation of systemic control under the neighborhood control
theory, density and breadth of private and parochial networks as well as the
neighborhood’s ability to secure external resources matters in terms of the
neighborhood’s ability to exert formal and informal social control-which in turn accounts
for the variation of crime/delinquency/victimization/fear of crime across different
neighborhoods. In this regard. the systemic control approach builds on Shaw and
McKay’s original discussion regarding the regulatory role of family networks and
networks of friends in the socialization process and extends social disorganization theory
by identifying two other sources of control (parochial and public). Thus, the causal
linkage between ecological characteristics of the neighborhoods and crime is mediated by
these three levels of control. Ecological characteristic of the neighborhoods are
considered important only when they support a breakdown of primary, parochial, and
public level control and thus promote a lack of consensus in the neighborhood.
(3) Contemporary Research on Social Disorganization: Extension and Criticism of

the Systemic Control Model

Following afore mentioned theoretical extension and clariﬁcation of the original
argument of Shaw and McKay. although recent research on systemic control model of
social disorganization produced quite consistent results regarding the positive impact of
neighborhood structural characteristics on social disorganization and crime rates (Bellair.

2000; Sun et al., 2004). the impact of social. disorganization factors and speciﬁcally local

31

friendship networks on crime is not as straightforward as assumed in Sampson and
Grove’s (1989) model. To begin with, Veysey and Messner’s (1999) afore mentioned
reanalysis of Sampson and Grove’s data through structural equation modeling produced
only moderate support for Sampson and Grove’s model in regard to the mediating role of
social disorganization factors. That is, while social disorganization mediated the impact
of low SES, racial heterogeneity, and residential mobility on crime, it had no impact on
the relationship between family disruption and crime. In fact, Vessey and Messner (1999)
goes beyond Sampson and Grove’s original framework when they identify both direct
and indirect impact of local friendship networks and organizational participation on crime
through unsupervised youth groups. Thus, besides its mediating role on the association
between local friendship networks and crime as well as on the relationship between
organizational participation and crime, unsupervised youth groups was the only
component of social disorganization that had direct impact on crime.

More recently, using the data from 36 neighborhoods in seven U.S. cities, Sun et
al. (2004) tested Sampson and Grove’s (1989) model of systemic control and they also
found a similar mediating impact of unsupervised youth groups on the relationship
between crime and neighborhood structural characteristics and also on the association
between crime and other social disorganization factors (local friendship networks and
organizational participation). Building on similar ﬁndings of Vessey and Messner’s
(1999, path diagram on p. 166) reanalysis of Sampson and Grove’s (1989) data, Sun et al.
(2004, path diagram on p.8) argued that unsupervised youth groups should be considered
as the failure of informal social control and added as another component to Sampson and

Grove’s (1989) model.

32

Therefore they speculated in favor of an extended systemic control model that
also incorporates unsupervised youth groups as another independent variable. According
to their argument, neighborhood structural characteristics (poverty, heterogeneity,
mobility, and family disruption) give rise to social disorganization reﬂected in the form
of weak relational ties and low organizational participation, and this in turn decreases the
community’s ability to exert informal social control as indicated in the form of
unsupervised youth and groups, and ﬁnally failure of effective informal social control
then directly increases law breaking in the community.

Even though Sampson and Grove’s (1989) systemic control model was
inspirational for the studies of neighborhood effects during the 19903 (Bellair 1997;
Veysey and Messner, 1999; Warner and Rountree. 1997), contemporary research on the
variation of delinquency across different neighborhoods have difﬁculties in terms of
explaining the relationship between informal social control and social and organizational
ties. For example, though their systemic control model treated social and organizational
networks as a source of informal social control, Warner and Rountree’s (1997 ) test of the
impact of local friendship networks on neighborhood crime rates indicates that whereas
local friendship networks have signiﬁcant negative effect on neighborhood rates of
assault, they have signiﬁcant positive effect on neighborhood rates of burglary. These
contradictory ﬁndings also gave rise to the subcultural criticism of the systemic control
model of social disorganization which was mainly about the failure of systemic control
model to account for higher crime neighborhoods with relatively strong relational

networks.

33

Acknowledging the controversial role of relational/social networks in the
neighborhood’s ability to regulate the behavior of its residents and visitors, Sampson,
Raudenbush, and Earls (1997 ) tested a new model which attempted to decouple the
impact of social networks and informal social control on crime through a new concept
incorporated into the systemic control model: collective efﬁcacy. Collective efﬁcacy
represents the degree of cohesion and trust among neighborhood residents as well as their
“willingness to intervene on behalf of the common good” (Sampson et al., 1997, 918;
Morenoff, Sampson. and Raudenbush, 2001). Put differently, whereas Sampson and
Grove’s (1989) model considers the density and prevalence of local friendship networks
as a source of informal social control, collective efﬁcacy represents relatively more direct
measure of informal social control (Sampson et al., 1997) as it also involves the measures
of residents’ trust, cohesion, and their willingness to intervene (also see Sampson,
Morenoff, and Earls. 1999).

Sampson et al.’s (1997) study on collective efﬁcacy found signiﬁcant negative
impact of collective efﬁcacy on homicide in 343 Chicago neighborhoods. Morenoff et al.
(2001) also found that while friendship and kinship ties among neighborhood residents
positively predict levels of collective efﬁcacy in Chicago neighborhoods, once the level
of neighborhood collective efﬁcacy was controlled, kinship and friendship ties didn’t
have a signiﬁcant impact on homicide rates. Relying on this ﬁnding, they argued that
collective efﬁcacy does not require interconnected neighborhood networks. In fact,
Sampson (2002) also confesses that he was wrong in terms of overstating the taken for
granted impact of social ties on informal social control (Sampson and Grove, 1989).

Thus. relationship between breadth and density of social ties and informal social control

34

was decoupled based on a distinction provided by the concept of collective efﬁcacy that
puts more weight on the mutual trust/cohesion and willingness to control in the
neighborhood rather than the prevalence of the networks. Therefore, the impact of social
ties on the crime rates was contingent upon the notion of collective efficacy in a given
neighborhood and collective efﬁcacy could vary across different neighborhoods as a
function of neighborhood structural characteristics (Sampson et al., 1997).

There is no doubt that Sampson et al (1997) was quite successful in terms of
addressing subcultural criticisms in terms of the ability of social disorganization theory in
explaining high crime neighborhoods with relatively strong networks/social ties. They
demonstrated that collective efﬁcacy or what we can also call social organization (as
opposed to disorganization) is not a simply having close relational/social ties, but rather
is the process of activating or converting social ties to achieve desired outcomes
(Sampson et al., 1999, 635). Thus. ﬁrst and foremost, there is an assumption about a
consensus on the desire of community residents to live in safe and orderly environments
free of (especially) predatory/violent crime (Sampson et al., 1997; also see Bursik, 1993
and Skogan, 1990) and the ability of residents to control group level processes and visible
signs of disorder is considered as a key mechanism that inﬂuences opportunities for law
breaking and interpersonal crime in a neighborhood.

Apparently, following the reformulation of social disorganization theory in
accordance with the underlying premises of systemic control, contemporary research
have put relatively more emphasis on the three levels of social control explained above.
However, contemporary research does not contradict but builds on Shaw and McKay’s

original argument about the impact of ecological characteristics of the neighborhoods on

35

law breaking and just clariﬁes the link between these structural antecedents and spatial
distribution of crime/victimization rates.

As far as the spatial distribution of the illicit markets is concerned, therefore, in
addition to the size of the community and geographic proximity as assumed by Rengert’s
(1996) hierarchical diffusion model, poverty, instability, heterogeneity, urbanization, and
family disruption can also impact establishment of illicit markets in that community
through their inﬂuence on informal and formal social controls prevailing in the society.
Simply put, the ability of the community to resist the establishment and activities of illicit
markets could be attenuated by communities’ ecological characteristics. To the extent
that these structural characteristics promote lack of primary, parochial, and public level of
control, related consequences such as lack of relational and friendship networks, weak
social bonds and cohesion. increased anonymity, decreased supervision, lack of
consensus and low organizational participation could beneﬁt the distributors of illegal
markets and facilitate their diffusion into the community. I

(4) Disorder, Decline, and More Serious Crime: Prostitution, Human

Trafﬁcking, and Other Serious Crimes

In addition to informal and formal social controls, contemporary research
incorporated disorder into the social disorganization framework to clarify the association
between community characteristics and crime. In general, disorder refers to violation of
norms regarding public behavior and is comprised of two dimensions: Social and
physical. “Social disorder is a matter of behavior: you can see it happen (public drinking.
or prostitution), experience it (catcalling or sexual harassment), or notice direct evidence

of it (grafﬁti, or vandalism). Physical disorder involves visual signs of negligence and

36

unchecked decay: abandoned or ill-kept buildings, broken streetlights, trash-ﬁlled lots,
and alleys strewn with garbage and alive with rats” (Skogan, 1990, 4).

Although some researchers avoided making a distinction between crime and
disorder (Garafalo and Laub, 1978; Sampson and Raudenbush, 2001, 2) and argued that
both had a common origin, others suggested that disorder needs to be distinguished from
more serious crime problems facing the same communities (Skogan,l990; Wilson and
Kelling, 1982). Using aggregated indicators based on citizen perceptions of disorder and
crime, Taylor, Bazemore. Boland, Clear, Corbett, Feinblatt, Berrnan, Sviridoff, and Stone
(1998) reported that (physical and social) disorder is distinct from community structure
and community crime rates at neighborhood level. Analyzing individual perceptions of
disorder and personal victimization of 13, 918 people from 12 U.S. cities, Worrall (2006)
also indicated that disorder represents a construct different from estimates of crime,
though not all of the disorder measures enjoyed discriminant validity.

According to Wilson and Kelling’s “Broken Windows” (1982) thesis, disorder
and crime are usually inextricably linked in a kind of developmental sequence at the
community level. Unchecked rule breaking, Wilson and Kelling argued, undermines
community social control and gives rise to serious crime. Prevailing disorder in the
neighborhood leads law-abiding residents to think that nobody in the neighborhood cares
about what is going on in their environment and this makes them withdraw from public
life as they assume that the neighborhood is disorganized and not safe (Lewis and Salem,
1986). Once the residents withdraw from the public life, they become less motivated to

intervene in physical and social disorder around them and such ongoing tolerance for

37

disorder also attracts outside trouble makers as a signal of increased opportunity for
delinquent or criminal behavior.

Using perceived measures of robbery victimization and physical and social
disorder, Skogan (1990) provided an empirical test of Wilson and Kelling’s “Broken
Windows” thesis based on a survey of 13,000 residents in 40 neighborhoods of six
different cities in the U.S. Analysis of the robbery victimization in 30 of the
neighborhoods demonstrated that the effects of poverty, instability, and racial
composition on robbery were mediated by disorder. Thus, Skogan (1990) incorporated
disorder into the social disorganization framework and concluded that crime and fear
were linked to disorder and disorder preceded crime in the neighborhoods surveyed.

Through his empirically supported disorder and decline model, Skogan (1990)
prioritized neighborhood change as the main focus of interest and demonstrated the
independent impact of disorder on neighborhood deterioration and decline. Neighborhood
change does not directly increase the crime or lower the quality of life as stable
neighborhoods are likely to react and ﬁghtback unwanted and destabilizing changes
through community organization and increased demand for formal control in the area.
However, in relatively unstable communities, an emergent disorder problem would fuel
more serious forms of disorder and crime in the community. Similar to Wilson and
Kelling’s (1982) argument about withdrawal from the community life, Skogan (1990)
also pointed to physical and psychological withdrawal from the neighborhood life in
times of ongoing disorder and crime. On the one hand, physical moves from the
community negatively inﬂuence the housing market and prevent investrrrent in the

neighborhood. Psychological isolation, on the other hand, undermines mutual

38

responsibility among the residents, weakens informal social control, reduces participation
in neighborhood affairs, and thus foreshadows a general decline in the community’s
organizational and political capacity (p.13). Overall, visible social disorder, including
drug dealing, prostitution, and sexually-oriented enterprises. provides direct behavioral
evidence of community social disorganization and feeding upon themselves, disorder and
crime contribute to the deterioration of local housing and business conditions as well
(Taylor 1995).

Bursik and Grasmick (1993) also contended that social disorganization theory
should be extended to also account for such feed back loops/processes. They suggested,
. .disorder and crime are not just the outcomes of the process of neighborhood
disorganization, but also that they are an integral part of that process” (p. 313). In fact,
contemporary research on social disorganization framework has largely draw upon
reciprocal relationships between disorder, informal social control, and fear of crime in the
community. Recently, using a sample of neighborhoods from three waves of the British
Crime Survey, Markowitz, Bellair, Liska, and Liu (2001) reported that while Low SES,
ethnic heterogeneity, family disruption, and residential instability increase disorder,
cohesion reduces disorder and mediates part of the effects of the exogenous variables on
disorder. Then they tested a three endogenous variable model including fear of crime.
disorder and cohesion and suggested that the effect of disorder on serious crime may
operate indirectly through increased fear and reduced cohesion (see also McGarrell,
Giacomazzi, and Thurman, 1997). Most recent studies on the disorder-crime nexus

demonstrated that disorder elicits more fear than crime (Kelling and Coles, 1996) and that

39

disorder has strong direct, indirect, and total effects on crime even with collective
efﬁcacy being controlled (Xu. Fiedler, and Flaming. 2005).

Finally. despite a considerable number of studies based on perceptive and/or
objective (i.e., observation; Taylor, 2001) measures of social and physical disorder, only
a limited number of studies looked at the relationship between more reactive measures of
disorder and crime. Of only two studies that focus on the relationship between ofﬁcially
recorded indicators of disorder and crime, Weisburd and Green-Mazerolle (2000)
investigated the relationship between street level drug hot spots, crime and disorder
problems in Jersey City, New Jersey. This exploratory study demonstrated that street
level drug hotspot activity, crime, and disorder problems cluster in certain areas of the
city and these areas characterized by drug hotspot activity had disproportionate share of
crime and disorder problems even compared to non-drug hotspot areas. Despite the lack
of controls for alternative explanations, this descriptive analysis of drug hotspots support
the idea of spatial association between disorder (illegal drug market activity) and crime
based on ofﬁcial data and deserves further investigation.

Most recently, a second study investigated the relationship between disorder and
crime adapting a longitudinal perspective based on county level arrest data in California.
Worrall (2006) sought to determine whether there is a link between arrests for disorderly
conduct and subsequent reduction in serious crime. Controlling for several other common
factors that are known to inﬂuence the crime rates, Worrall (2006) found that between the
years 1989 and 2000, arrests for disorderly conduct (arrests for disturbing peace and
public drunkenness divided by total arrests) could reduce certain crimes, particularly

felonious assault and burglary.

40

Overall. using perceptive, objective, and reactive measures of disorder and crime,
empirical research has provided substantial evidence on the spatial concentration of
(physical and social) disorder and crime at different levels of analysis. Recent studies
evince that tolerance for disorderly behavior leads to more disorder and crime in the
community. In this re gard, as an indication of existing social disorder, tolerance for
immigrant prostitution in a given community, which might also be considered as demand
for the sexual services. can also contribute to more serious crime and human trafﬁcking

in a community.

41

CHAPTER THREE
LITERATURE REVIEW

Given the dearth of cross-national research on the subject, comparative and
regional studies as well as national reports on human trafﬁcking are reviewed in this
chapter to identify signiﬁcant factors those could impact the geography of human
trafﬁcking at national and international levels in light of the proposed framework. In
order to recognize the impact of demand on human trafﬁcking in a country, prostitution
and trafﬁcking in women for the purpose of sexual exploitation related research has also
been included.

1. Globalization, Migration, and Cross-national Distribution of Human
Trafﬁcking

More of a generalization of diverse set of migration theories than a single theory
per se, migration systems theory conceptualizes migration as embedded in the broader
processes of economic, demographic, social, cultural and political change. At macro
level, political economy of the world market, interstate relationships, migration
regulations and their enforcement are all assumed to be inﬂuential on migration (Kritz,
Lim, and Zlotnik, 1992). More specifically, migratory movements generally arise from
prior links between sending and receiving countries based on trade, colonization,
investment. or political inﬂuence. Thus, whereas Algerian migration to France is
explained by the colonial presence of France in Algeria, Turkish presence in Germany is
mainly a result of labor recruitment by Germany in the 19603. Korean and Vietnamese
migrations to America, on the other hand, are seen as a consequence of long-term U.S.

military involvement (Castles and Miller, 2003).

42

Internal and international migration can thus be regarded as an integral part of
globalization -as globalization simply means increased worldwide interconnectedness
which, through the cross-border ﬂows of ﬁnance, trade, media, products, and people,
links previously isolated areas into the broader national and international markets based
on economic development. While economic development instigates the modernization of
agriculture, it also provokes social differentiation and displacement of labor. In the ﬁrst
place, this precipitates rural-urban migration, though recent industrialization might be too
slow to respond increasing demand for urban employment opportunities. And, as soon as
overseas employment opportunities become apparent, in addition to internal rural-urban
migration, international migration also grows (Castles, 2000).

Although population mobility is expected to follow other types of cross-border
ﬂows in the process of globalization, immigration (and related cultural difference) is not
always welcomed by economic and political powers as much as the ﬂow of capital and
commodities since the former is regarded as a potential threat to nationalidentity and
state sovereignty (Koser, 2005). But at the same time, urbanization of developing
societies and related displacement of the people give rise to both internal and
international migration as the evolution and concentration of production, distribution and
exchange in urban centers, primate (Jefferson, 1939) and global cities (Sassen, 1991)
create a strong demand for immigrant labor (Massey et al., 1993). Even though such
demand is generally met by formal institutions such as recruitment by governments,
employers, and recruitment organizations (as in the example of Turkish workers’

recruitment to Germany in 19603), extra-legal networks, such as smugglers and

43

trafﬁckers also emerge to exploit especially those who can not qualify or afford the legal
services of formal institutions (Kritz et al., 1992, 7).

In sum, according to the migration system framework, in addition to formal
recruiting agents, trafﬁckers also link global and local as well as the individual and
structure within the complex process of migration; yet, whereas the formal institutions do
that in a legal way, trafﬁckers operate illegally and simply prey on the vulnerability of the
immigrants, those who desire to migrate for better working and living opportunities in
urban centers or abroad but cannot afford or qualify for legal recruitment. To that end,
trafﬁckers sometimes make up success stories to lure people into forced labor and
exploitation. Although available empirical research on human trafﬁcking has not
exclusively relied on migration system framework, given that migration system theory
draws on different frameworks, several studies have provided support for various
assumptions of migration system theory and especially for the impact of globalization
and related urbanization on human trafﬁcking in developing countries in recent years.

The ﬁrst and may be the only cross-national level analysis of human trafﬁcking
was provided by Bales in 2005. Bales created an ordinal dependent variable for human
trafﬁcking from and to (from 1 to 4/least to most severe trafficking) 111 different
countries based on diverse set of sources of information ranging from ﬁeld research to
government reports about human trafﬁcking in these countries. Bales’s (2005, 139- 140)
cross-national multiple regression analysis based on afore mentioned rankings of (on
human trafﬁcking from 1 to 4) and demographic information about the countries
identiﬁed six factors that signiﬁcantly inﬂuence human trafﬁcking from a country

(governmental corruption, infant mortality rate, population below age of 14. level of food

production, population density, and conﬂict and social unrest in the country) and four
factors that signiﬁcantly inﬂuence human trafﬁcking to a country (the proportion of the
destination country’s male population over age sixty, the level of governmental
corruption, the level of food production, and low infant mortality). As Bales also
suggested, mainly the indicators of prosperity and stability impact the route of human
trafﬁcking from and to a country.

However, even though sending countries are largely represented by deve10ping
countries (Guinea, Burma, Georgia, Romania) in Bales’s data, on the receiving end we
see both deve10ping (Thailand) and developed countries (Germany) side by side and
sometimes only developing countries on both sides (Turkey, Czech Republic). Hence,
conceptualization of human trafﬁcking as including both human trafﬁcking to and from a
country as well as human trafﬁcking in a country might paint a different picture for the
geography of human trafﬁcking in and across the nations.

There is no doubt that the process of globalization and related social and
economic displacements across developing nations has promoted widespread migration
to the west in recent years (Laczko et al., 2002). Yet, the majority of irregular migration
has claimed to have taken place between developing countries on a global scale (Koser,
2005). It is estimated that there are several million irregular migrants in South and South
East Asia and three to ﬁve million in South Africa (DESA 2004, cited in Koser 2005). In
other words, irregular migration is not simply a South-North issue anymore (Koser, 2005)
and as overall migration increases, the incidence and severity of human trafﬁcking has

also escalated (Smit, 2004). The ﬂow of human trafﬁcking. thus. is not only toward the

45

developed countries but developing countries are also the emerging destinations of
human trafﬁcking today (Straubhaar, Mayer, Hefeker, Lammers, and Wohlers, 2004).

Asia has been characterized by the growth of a market-driven intraregional
migration for the past two decades as a result of growing global market integration of the
region (Lee, 2005). The number of illegal migrants moving across the countries in the
region is a lot more than the number of illegal migrants who leave the region. Yet,
differences in development within the region also impact these illegal ﬂows and
Malaysia, Thailand, J apan, and Republic of Korea have emerged as principal destinations
over the last 25 years. In the course of such illegal movements toward neighboring
countries in the region, more than 200,000 Bangladeshi women are trafﬁcked to India
and Pakistan whereas many underaged Nepali girls are recruited into prostitution in
Indian cities (Skeldon, 2000). Many non-Japanese women are also recruited into large
sex industry in Japan. Trafﬁckers in China are reported to intrude young women’s rural-
urban migration and force them into sexual services in neighboring countries (Lee, 2005).

Drawing upon several sources of information, a case study of human trafﬁcking
in Nepal (Smit, 2004) concluded that around 20, 000 children, men, and women are
trafﬁcked in Nepal annually. Trafﬁcking from rural to urban locations prevails as much
as trafﬁcking to neighboring countries especially India and to further destinations such as
South East Asia and the Middle East. Economic disparities between rural and urban
locations in Nepal and the booming Indian economy are indicated as root causes of
internal and international migration and human trafﬁcking in these countries.

Kan gaspunta’s (2003) descriptive analysis of the UNODC trafﬁcking trend

database also contrasts with the conventional belief that basically categorizes the

46

developing countries as source and industrialized/developed countries as the destination
points based on the assumptions of neoclassical perspective. A global database on
trafﬁcking trends was established under the Global Program against Trafﬁcking in
Human Beings (GPAT) of the United Nations Ofﬁce on Drugs and Crime (UNODC) in
order to systematically collect and collate open source information on trafﬁcking in
persons. Kangaspunta’s preliminary analysis of the database indicated that a total of 147
countries were mentioned at least once as a country of origin. At regional level, the Asian
countries were mentioned most as the countries of origin and they were followed by the
countries in the former Soviet Union region and Africa. There were relatively small
numbers of citations for industrialized countries as countries of origin. Out of 96
countries mentioned as transit countries in the database, Central and South Eastern
European countries were the most cited. When it comes to 150 countries those mentioned
as destination countries, although the favorite destinations involved industrialized
countries, Asia was as much an origin as a destination region. Moreover, countries in
Africa, Central Eastern Europe, Latin America and Commonwealth of Independent States
were also cited as destination points of human trafﬁcking.

The UN revealed information on human trafﬁcking across different countries
based on a more recent (UN CDC, 2006) version of the same database on human
trafﬁcking trends which documents trafﬁcking of human beings from 127 countries to be
exploited in 137 countries. In region wise analysis of the data, Africa is predominantly
deﬁned as an origin region for victims of trafficking who are trafﬁcked to Western
Europe and West Africa. West Africa is deﬁned as both origin and destination for victims

of trafﬁcking from Africa. Asia, on the other hand, is referred to as major origin as well

47

as destination country for trafficking victims. That is, Asian victims are reported to be
trafﬁcked to Asian countries. in particular to Thailand, Japan, India, Taiwan and
Pakistan. Victims from Commonwealth of Independent States and South Eastern Asia are
reported to be trafﬁcked into the countries in the region. Central and South Eastern
Europe is also reported as a destination country for the victims from the CIS region
whereas the victims from both CIS and Central and South Eastem Europe are trafﬁcked
to Western Europe and North America. Latin America and the Caribbean are also
reported mainly origin as well as destination regions for trafﬁcking victims. Finally,
Oceania, in particular Australia and New Zealand are predominantly reported as
destination spots for the victims from South Eastern Asia.

In the analysis of human trafﬁcking in the Commonwealth of Independent States
(ICMPD, 2005), victimization of rising numbers of women and children in almost all
twelve countries in the region is also deﬁned as related to economic transition processes
and ongoing restructuring of the labor market. Whereas trafﬁcking for sexual exploitation
is directed to Western Europe, South East Asia, and the Middle East; trafﬁcking for labor
exploitation mostly takes place toward Russia and Kazakhstan. In addition to such
intraregional and interregional ﬂows, trafﬁcking also occurs internally from rural areas to
urban areas for sexual exploitation especially in Belarus, Azerbaijan, Uzbekistan, and
Kazakhstan.

Many African countries have also become origin, transit, and destination points
for trafﬁckers and trafﬁcked victims (Surtees, 2005). Trafﬁcking in Africa takes several
different forms and includes large number of African children and women who are

trafﬁcked from rural to urban areas for labor and sexual exploitation. Intraregional

48

trafﬁcking also prevails among the countries in the region including Nigeria, Ghana, Cote
d’Ivoire, Senegal, Ethiopia. Kenya, Cameroon, Mali, Gabon and Niger. While South
African women are trafﬁcked to East Asia for sexual exploitation, the women from
China, Thailand, Eastern Europe, and Russia. on the other hand, are found to be
trafﬁcked to South Africa.

Apparently, not only the industrialized regions and the countries in those regions,
but also relatively less developed regions of the world and countries in these regions are
likely to be destination points of human trafﬁcking. Moreover, whereas the industrialized
regions and countries are mostly cited as destinations, deve10ping regions and countries
are more likely to be on both ends of human trafﬁcking if we were to conceptualize
human trafﬁcking as a process which simply involves recruitment, transportation and
exploitation of the victims. In this case. as long as human trafﬁcking is Operationalized as
a combination of human trafﬁcking from and to OR recruitment and exploitation in a
country, developed countries are likely to have less problem of human trafﬁcking
compared to developing countries. It’s because, foreign investment and related
urbanization and social and economic instability in developing countries would fuel
rural-urban migration and provide opportunities for the trafﬁckers to prey on those who
attempt to leave for better employment opportunities in urban centers in their own
country. As soon as urban employment opportunities fail to meet the demand in these
developing nations, then traffickers would also target those who become aware of
employment opportunities in developed countries or lure the people with fake promises

of such opportunities to exploit them in origin and more developed countries.

49

Overall, given the differential impact of globalization on developed and
developing countries and based on the review of the research as regard to the distribution
of human trafﬁcking across developed and developing countries; the level of
development and urbanization seem to be inﬂuential on cross-national distribution of
human trafﬁcking in light of the migration system approach. The following section, on
the other hand, focuses on the national distribution of human trafﬁcking markets.

2. National Distribution of Human Trafﬁcking

Migration systems theory provides a framework that can help to understand the
distribution of human trafﬁcking at broad level of analysis across the world nations. The
analysis of the distribution of human trafﬁcking in a single nation, on the other hand,
calls for further elaboration. As Sassen (1996 and 2000) argued, globalization and related
increase in international relations mainly based on economic investment and development
also have local as much as global impact. That is, large metropolitan cities are mainly the
places where globalization is embodied in the form of exchange of money and ideas or
the growth of immigrant presence in such places. Yet, human trafﬁcking in a country is
not limited to solely global or primate cities, though such places might be exploited
relatively more by the trafﬁckers.

Geographers suggest that the spread of innovations, epidemics, and urban decay
in space and time occurs in a similar pattern, reﬂecting the social, economic, and
geographic structuring of an underlying community (Wallace and Wallace, 1993). These
patterns, on decreasing spatial scales. are generally classiﬁed as hierarchical, spatially
contagious. and network diffusion. Hierarchical diffusion refers to the spread from larger,

more socially dominant cities to smaller ones, usually along the transportation

50

(national/intemational) networks. Spatial contagion or expansion diffusion involves the
radial spread from an infected geographic epicenter into nearby communities. Network
diffusion involves transmission along personal, domestic and community social networks
(Wallace and Wallace, 1993; Wallace, Huang, Gould, and Wallace, 1997).

Rengert (1996) explored the implications of these processes for the geography of
illegal drug markets in the U.S. Based on his analysis of ofﬁcial data on illegal drug use
and distribution across different regions, states, counties, cities, and neighborhoods,
Rengert found that the diffusion of illegal markets is driven by reinforcing motivations of
two active agents of the illegal drug markets, that is, drug trafﬁckers and the users. In the
ﬁrst place, the distribution of illicit drugs by the trafﬁckers originates in the largest cities
and subsequently ﬁlters down through an ordered sequence of metropolitan centers of
decreasing size, as the larger cities with more population are supposed to have more
potential customers compared to smaller urban centers. In addition to the cities with more
population, the cities that are closer to established illicit markets or to the places where
the illicit commodities are produced/imported/originated are also selected as targets by
drug trafﬁckers.

Then, in the second step of Rengert’s model, local or neighborhood expansion of
illegal markets takes place in each city, and illegal drug distributors expand their
activities outward from the central city into surrounding suburban area in an expansion
contiguous process in which distant-minimizing effects largely shape the spatial patterns
of drug distribution. However, lack of legal protection and the risk of apprehension in
illicit markets also inﬂuence market location preferences of illegal market distributors

and this in turn impacts the spatial distribution of illegal markets as well. In order to

51

avoid apprehension by police, prevent violence and theft, and increase the monetary
return, illicit drug dealers want greater control over the exchange of drugs and money.
Therefore, Rengert argued, illegal market distributors prefer the locations where the
residents are too weak and disorganized to resist the establishment and activities of illegal
markets.

Overall, population size. geographic proximity (to where the illicit goods are
produced or imported), and community disorganization and disorder are all expected to
shape the distribution of illicit drug markets in space and time in a given country.

Human trafﬁcking, on the other hand, is claimed to be more lucrative (Schauer
and Wheaton, 2006) than drug trafﬁcking. Trafﬁcking in women and children is reported
to be the third largest source of revenue for organized crime worldwide right behind drug
and ﬁrearm trafﬁcking by the U.S. congress in 2000 (Miko and Park, 2002). The UN
Ofﬁce on Drugs and Crime also noted that human trafﬁcking has reached epidemic
proportions and almost no country is immune (Fergus, 2005). Because of relatively fewer
risks incurred in the practice of human trafﬁcking and ease of moving and storing the
victims, human trafﬁcking is expected to supersede drug trafﬁcking in following years
(Schauer and Wheaton, 2006). Moreover, the same criminal groups that trafﬁc drug and
guns are also claimed to involve in human trafﬁcking as well (Raymond and Hughes,
2001).

UNODC undertook a pilot survey of forty selected organized groups in sixteen
countries in order to have a better understanding of the structural features of transnational
organized crime in 2002. Of the 40 organized crime groups in the survey, eight were

involved in human trafﬁcking and of these, only two groups were exclusively involved in

52

human trafﬁcking whereas for the remaining six groups, human trafficking was only one
of diverse set of activities they were involved in (UN ODC, 2006). Human trafﬁcking
networks in Ukraine are also reported to involve in a variety of criminal activities such as
trafﬁcking drugs, stolen cars, guns and conducting robberies. as well as murders (Hughes
and Denisova. 2001). Recent operations against human trafﬁcking networks in different
parts of Turkey also showed that human trafﬁckers are involved in drug trafﬁcking as
well (Hurriyet, 2007).

Given simultaneous involvement of criminal entrepreneurs in both drug and
human trafﬁcking and functional similarities especially between drug trafﬁcking and
trafﬁcking in women for the purpose of sexual exploitation as regard to the role of
community (dis)organization and (disorder) on the establishment and activities of the
markets, Rengert’s (1996) approach to illicit drug markets based on diffusion principles
could also apply to trafﬁcking in women as well. Simply put, as distributors of illegal
services -such as the exploitation of migrant labor and/or prostitution-, human trafﬁckers
would also desire more control over monetary exchange as they can’t rely on the civil
and criminal justice system to mediate the disputes and protect their business from
violent and property crimes. Hence, whereas large metropolitan centers could provide
more customers for illegal services of human trafﬁckers, social disorganization and
related structural features of a given community would also impact the distribution of
human trafﬁcking as trafﬁckers would look for more control over their illegal
transactions in order to be able to control potential arrest, theft, and violence (Eck, 1995).
Social disorganization and disorder in a community would also beneﬁt the consumers of

the illegal services of trafﬁckers and increase the demand for illicit services. for that

53

community would be less likely to regulate the behavior of its residents and visitors
(Bursik and Grasmick, 1993).

Reviewing the problem of human trafficking in ﬁve different regions of the world
(Asia and the Paciﬁc, Europe, Middle East, Latin America and Caribbean), in a 2005
report for the U.S. Congress, Miko (2005) argued that the ﬂow of human trafﬁcking was
mostly from developing countries to industrialized nations or toward neighboring
countries with relatively higher standards of living. Despite the lack of reliable statistics,
he contended, the former Soviet Union has emerged as the largest new source of
trafﬁcking in women for the purpose of sexual exploitation which is followed by Africa
and Latin America and the Caribbean. Whereas most of the victims are trafﬁcked to
Western Europe, North America, Asia and the Middle East, a considerable number of
women are also trafﬁcked to Eastern Europe as well. Miko (2005) asserted that the
victims are mostly taken to large metropolitan urban centers, tourist areas, or near
military bases where the demand is highest for the services they provide.

The United Nations (2003) also emphasized similar concentrations of human
trafﬁcking in major urban centers in the Asia region. Asia is estimated to be responsible
for approximately one-third of the total global trafﬁcking; and according to the UN, 60
percent of the women, men, and children are contended to be trafﬁcked to major regional
cities whereas the remaining 40 percent are claimed to be trafﬁcked to other parts of the
world (cited in IOM 2005, 112). Simply put, whereas intra-regional ﬂow of human
trafﬁcking targets regional metropolitan centers, inter-regional trafﬁcking exploits large

cities in other parts of the world.

54

In her review of research on trafﬁcking in women and children in Europe, Kelly
(2002) contends that sex industries are quite ﬂexible across the world and they shift their
locations as well as their structure and contents through market testing and also in
response to informal and formal social controls within the society. The level of
trafﬁcking in a given location. on the other hand, depends on the scale of the sex industry
and the applicable legal framework. The development and diffusion of human trafﬁcking
in Greece provides a good example for Kelly’s argument in that the scale of the sex
industry in Greece was quite small in 19803 when most of the women came from Poland
and Philippines. Yet, trafﬁckers used this period to test market demand and formal and
informal reaction from the state and public. The discovery of a conducive context was
followed by the growth of the sex industry in Greece and in the 19903, there were ten -
times more foreign women and children in the Greek sex industry than local women,
most of whom had been trafﬁcked. Trafﬁcking in women and children also had
considerable impact on the development of the sex industry in Greece (Lazos, 2001, cited
in Kelly, 2002) by increasing the demand as it spread out from Athens to larger cities and
villages. Border regions, especially those between the EU countries and the rest of the
Europe are also the places to where the women and children are trafﬁcked for the
purposes of sexual exploitation.

In their descriptive analysis of human trafﬁcking in the U.S., based on the
accounts of the former victims of human trafﬁcking and case studies of ofﬁcial reports in
6 different cities of the US, Bales and Lize (2005) identiﬁed poverty and economic
disruption in developing countries as the main causes of human trafﬁcking. Between

1011 and 2341 individuals are represented in twelve case studies originating from eight

55

different countries (Mexico, Guatemala, Argentina, Latvia, Cameroon, Ivory Coast,
China, India). The destinations of the victims, on the other hand, were almost exclusively
comprised of the States with the largest populations in the U.S. (such as New York/New
York City, Maryland, Florida, and Illinois/Chicago). Southwest Florida and Chicago
were referred as hot spots of trafﬁcking while Washington DC was cited as a newly
emerging port of entry.

Bales and Lize (2005) conceptualized human trafﬁcking as a process that mainly
involves recruitment, transportation and illegal entry of the trafﬁcked person, the
exploitation phase, or the subsequent phase of proﬁt laundering. Half of the cases they
investigated involved trafﬁcking operations in the form of simple links between single
individuals or agencies providing single service while one quarter of the cases involved
segmented businesses involving a criminal network and a legitimate transportation or
labor recruitment company. The remaining quarter of the cases involved relatively
sophisticated and complex networks dispersed across large geographic distances and long
periods of time. Trafﬁcked women were moved around an internal circuit through large
metropolitan areas including New York, Miami, Las Vegas, Houston, Reno, Seattle, and
San Francisco. According to Bales and Lize, traffickers in the US are engaged in a wide
range of crimes against victims (i.e., rape, homicide, assault) and against the state (fraud,
corruption of ofﬁcials, tax evasion, money laundering). The commission of other crimes
in addition to human trafﬁcking, they argued, was a rule for the trafﬁckers in the cases
they have investigated (pl 18).

Raymond and Hughes (2001) also point to concentration of sex industry and

trafﬁcking in women in large urban centers in their study on international and domestic

56

trafﬁcking of women in the U.S. based on the survey of the literature, case studies, and a
total of 128 interviews with international and U.S. women who are involved in the sex
industry, law enforcement ofﬁcials, social service workers, journalists, and researchers.
According to the researchers, sex trafﬁcking is often a mobile enterprise in both
domestic and international terms in that the trafﬁcked women are generally moved from
country to country, state to state, and city to city. International trafﬁcking was
documented in twenty states including: Washington, Oregon, California, Nevada,
Arizona, Colorado, Texas, Ohio, Illinois, Indiana, New York, New Jersey, Maryland,
Pennsylvania, Rhode Island, Massachusetts, Florida, Louisiana, South Carolina, Georgia,
Tennessee, Kentucky and Arkansas. Even though human trafﬁcking is generally assumed
to be concentrated in large cities, Raymond and Hughes argued, depending on the
demand, it can also be present in rural areas such as rural prostitution establishments
accessible to the migrant farm workers in Florida. This also indicates that trafﬁcking
networks are quite responsive to the demand through moving victims across urban and
rural locations and also establishing clubs, massage parlors, escort services, and brothels
in such places (p.23).

In a study of 40 trafﬁcked women working as prostitutes in Tapachula, Mexico,
Acharya (2004) reported that as a result of prevailing poverty and internal displacements
in the region, especially the young women are trafﬁcked from rural areas to local cities
and to other big, urban centers in Mexico such as Mexico City, Cancun, Acapulco and
Ciudad Juarez to work as prostitutes, table-dancers and bannaids. Some of those victims
are also trafﬁcked to the U.S. and Canada. While the peOple attempt to migrate to the city

to escape from poverty. Acharya argued, they become vulnerable to trafﬁckers as they

57

are not familiar with the urban environment and get lured into false promises of
employment and shelter in the city. There are also young women who are voluntarily sold
to the trafﬁckers by their parents because of poverty.

The second report on trafﬁcking to, from, and through South Eastern Europe
(Surtees, 2005) by IOM (which is also called ‘Regional Clearing Point’) includes
information about a total of 6.125 victims identiﬁed and assisted in the region between
2000 and 2004. While poverty and unemployment in the countries of origin are
mentioned as driving forces of human trafﬁcking, this report also supported the argument
about the concentration of trafﬁcking markets in major cities based on the information
about origin and destination countries of assisted victims. That is, unlike the common
perception that considers young women from the rural areas as the most vulnerable
category to trafﬁcking situations, most of the victims from different countries (i.e.,
Ukraine, Moldova, Bulgaria) in the region were trafﬁcked to and from urban locations. In
their 2004 report on EU-Enlargement, Migration, and Trafﬁcking in Women in South
Eastern Europe, Straubhaar, Mayer, Hefeker, Lammers, Wohlers (2004) indicated that
poverty, unemployment, ethnic heterogeneity, and lack of private (family) control at
home were related to the victimization of women in different parts of Europe. Based on
the proﬁles of trafﬁcking victims helped within the region, Straubhaar et al. (2004)
concluded that urban areas are more likely to be exploited by the trafﬁckers as these
locations are characterized by a lower degree of social control compared to small
communities and villages (p.50). Moreover, competition for opportunities of success is
also severe in urban locations. In smaller communities and villages, on the other hand,

the people are less likely to break community norms.

58

As far as human trafﬁcking in Turkey is concerned, Istanbul, the largest and most
industrialized city in the country, is the major destination of human trafﬁcking followed
by other large urban and tourism centers such as Antalya, Ankara, and Izmir. Attracting
international and internal population movements (Erder and Kaska, 2003), Istanbul hosts
large numbers of locals and foreigners that are willing to work and trade in an informal
economic environment. Even though irregular immigrants those who cannot qualify or
afford for formal employment could beneﬁt from this informal economy, the lack of
formal guardianship and control, on the other hand, creates an environment in which
deception and exploitation could ﬂourish easily. According to a 2003 report on human
trafﬁcking in T urkey, Istanbul has the largest number of illegal migration related
deportations from Turkey which mostly involves migrant prostitutes. Geographic
proximity to the countries of origin also seems to be relevant in Turkey as the
deportations of Georgians are quite low in Istanbul but high in Trabzon which is
relatively closer to the Georgian border. Although the police were used to apprehend
immigrant prostitutes during their regular controls in the city, with the advent of
enhanced communication and transportation, migrant prostitution and recruitment of
trafﬁcking victims into prostitution spread into the areas surrounding the urban locations
in the city in recent years (Erder and Kaska, 2003, 47).

Apparently, poverty and unemployment especially in developing countries and
related instability and internal rural-urban as well as international migration of the people
make them vulnerable to trafﬁcking situations with fake job and employment promises.
As for the distribution of human trafﬁcking in a country, trafﬁckers generally target

major urban centers that provide more customers and are closer to the source countries or

59

both. Once the exploitation of trafﬁcking victims gets started in a location, it also
penetrates into neighboring communities depending on the demand and reaction of the
local enforcement and community over time. Evidence from Ukraine also indicates a
similar diffusion of human trafﬁcking into smaller areas as regard to the recruitment of
victims from the countries of origin. That is, while the victims were recruited from the
cities in the early years, the recruiters in Ukraine are now moving into the countryside to
small towns and villages to recruit women into exploitation (Hughes and Denisova, 2001;
Pyshchulina, 2007).

According to Hughes and Denisova (2001), trafﬁckers target the regions and
cities based on the ease with which they can prey on the victims. As long as trafﬁcking in
women for the purposes of sexual exploitation is concerned, . .countries and cities
where there are large sex industry centers and where prostitution is legal or widely
tolerated” are more likely to be exploited by the trafﬁckers (Hughes 2001, 13).
Legalization of prostitution, pimping. and brothels, Hughes argued, causes an increase in
trafﬁcking in order to meet the demand driven by legitimized sex industry. Moreover.
when prostitution is legal, the dilemma of voluntary versus forced prostitution creates
problems in terms of prosecution of the trafﬁckers as law enforcement is expected to
prove that the women did not consent. From a criminological stand point, however, even
if prostitution is formally prohibited, an existing informal tolerance or demand for
prostitution would still increase trafﬁcking in a given community as prostitution is
regarded as a sign of physical and social disorder in that community. The following
section reviews prostitution as a sign of demand and social disorder and discusses the

impact of prostitution on the spatial distribution of human trafficking.

60

3. The Impact of Prostitution on the Distribution of Human Trafﬁcking

Studies on social disorder and disorganization suggested a close relationship
between physical and social disorder and more serious crimes in a community as
explained in the prior chapter. Empirical evidence demonstrated that disorder has direct
and indirect impact on more serious crime. Simply put, while poverty or low SES, ethnic
heterogeneity, family disruption, and residential instability increase disorder, disorder
directly or indirectly (through increased fear and/or reduced cohesion) increases serious
crime (Markowitz etal., 2001; Skogan, 1990; Wilson and Kelling, 1982; Xu, Fiedler, and
Flaming, 2005). Even though aggregate level perceptive measures of physical and social
disorder have been subject to substantial scientiﬁc inquiry, the relationship between more
speciﬁc and reactive indicators of disorder and serious crime haven’t been paid much
attention except only one study that looked at the relationship between street level drug
hot spots and serious crimes in Jersey City (Weisburd and Green-Mazorelle, 2000).

Skogan’s (1990) research in forty neighborhoods across six different U.S. cities
indicated that as a sign of both social and physical disorder, prostitution is likely to be
linked to more serious crime in the community. Moreover, disorder had relatively more
impact on crime compared to poverty, instability, and heterogeneity. In this regard,
considering the strong link between prostitution and human trafficking (Davis, 2000;
Ekberg, 2004; Farley and Barkan, 1998; Hughes, 2002; Schauer and Wheaton, 2006;
Thukral, 2005) in terms of involved risks, the services both offer, and the people they
beneﬁt (males-johns, pimps, trafﬁckers), an existing demand and tolerance for
prostitution might be more likely to give rise to human trafﬁcking in the city/community.

Moreover, former victims’ involvement in the victimization of others in order to avoid

61

reprisals from the traffickers may also fuel the continuance of human trafﬁcking in that
community (Altink, 1995). Finally. a presumed association between prostitution and
human trafﬁcking based on social disorder perspective would also help to better address
ongoing debate in human trafﬁcking literature as to whether. (existing demand for)
prostitution leads to trafficking or trafﬁcking creates its own market.

To the extent that prostitution underlies an existing social disorder and lack of
control in a given community, like other indicators of social and physical disorder,
prostitution can also account for the variation of human trafficking and other serious
types of crime across time and place. Since the trafﬁckers are assumed to select their
destinations through market testing and establish their businesses where the reaction from
the public and law enforcement are relatively weak (Kelly, 2002; Straubhaar et al., 2004).
prevailing social disorganization and disorder would provide a conducive context for
trafﬁcking situations, for the communities with social disorganization and disorder would
not be able to resist the establishment and diffusion of illegal markets. I

Motivated/likely offenders are also attracted to disorganized areas with social and
physical disorder because of the opportunities they offer (Skogan, 1990; Wilson and
Keling, 1982). In case of sexual exploitation of trafﬁcking victims, both trafﬁckers and
customers of their services and all other elements of the illegal market of human
trafﬁcking in that community (i.e., place managers) will be free of informal social control
without the regulative capacity of a disorganized and disordered neighborhood.
Furthermore, it is also argued that if local small prostitution rings become connected to
larger organized crime groups, they can also serve as an entrance point for organized

crime into that city or country since trafﬁcking in women is claimed to be intertwined

62

with other related criminal activities (Hughes, 2001; Hurriyet, 2007; O’Neill, 1999;
UNODC, 2006).

Cohen’s (1980) study on prostitution in New York City demonstrated that
population density, heterogeneity, and related anonymity (Cohen, 1980, 117) on the part
of both providers and customers of the illegal sex market contribute to emergence and
perpetuation of prostitution in a given context. Simon and Gagnon (1973) found that
many men are more likely to buy sex when social control over them is loosened (i.e.,
away from home). Browning and Wilbon (2003) also found that short term sexual
partnering practices were more likely to occur among the people living in disorganized
neighborhoods because of either weak social ties or of strong social ties with weak
collective efficacy. As for human trafﬁcking, Leuchtag (1995) contends that the women
are sold or rented to afﬂuent men who use anonymity to buy sex. As far as residential
stability is concerned, Cohen (1980) and Skogan (1990) concur that prostitution and
related services may be more likely to emerge in relatively stable parts of the city to
attract more customers without concerns about personal safety. Even if prostitution does
emerge in the slums or in the zone of transition, it is expected to border on stable
commercial areas to diminish the risk of intimidation by others (Cohen, 1980, 121).
Moreover, police’s or public ofﬁcials political will to conﬁne prostitution to a certain (red
light) district can also attenuate the predictive power of residential stability, for
prostitution is mainly pushed into non residential areas which is sometimes done through
negative zoning regulations (Mathews, 2005).

In the ﬁrst human trafﬁcking report of Turkey, Erder and Kaska (2003) supported

the relevance of Cohen and Skogan’s argument as regard to the relationship between

63

community structural characteristics and prostitution when they articulate the emergence
of immigrant prostitution and human trafﬁcking in Laleli -one of the districts of Istanbul
that is famous for migrant prostitution and human trafﬁcking. As the researchers
indicated, while the district was a well known historical and. residential area until the
19503, with the advent of an ofﬁcial policy favoring tourism beginning in the 19603, it
turned into a tourism center. Following the collapse of the former Soviet Union, however,
the district became a commercial center where internal and international immigrants met
each other in the course of newly emerged informal economy that was called “suitcase
trade” in the early 19903 (see also Yukseker, 2004). Economic development in Laleli was
also accompanied by fundamental social changes. That is, while middle class families
who could afford to leave the area deserted Laleli during that transformation process, a
transitional zone simultaneously emerged which is mainly comprised of irregular foreign
migrants and poor internal immigrants who live in lower class rental blocks surrounding
Laleli. Today, Laleli is well known for its hotels, malls, restaurants, nightclubs, bars and
it is also one of the most important hot spots of migrant prostitution and human
trafficking in Turkey.

While social disorganization and lack of social control in the community creates a
conducive context for prostitution to ﬂourish, normalization and legalization of
prostitution in the community, Hughes (2001) argued, increase men’s demand for such
services. This demand, on the other hand, is met through increased recruitment of women
and girls into prostitution which usually involves violence. deception or exploitation of
those who suffer from poverty, unemployment, and prior victimization (Hughes, 2001).

Put differently. trafﬁcking in women for the purposes of sexual exploitation is driven by

64

men’s demand for their services and the demand is increased and normalized by the
presence of prostitution in a given context. Thus, while social disorganization would lead
to more prostitution in a community, prevailing prostitution, on the other hand, would
fuel the demand for sexual services through the normalization of prostitution and this
would drive trafﬁcking in women to meet the demand in that community.

Current research on human trafﬁcking by international migration scholars already
indicates simultaneous emergence and continuance of migrant prostitution and human
trafﬁcking (for the purposes of sexual exploitation) in different parts of the world such as
the U.S., Italy, Spain, Turkey, Greece, Norway, and Korea (Agustin 2005; Brunovskis
and Tyldum, 2004; Don g-Hoon, 2004; Erder and Kaska, 2003; Fergus, 2005; Gulcur and
Ilkkaracan, 2002; Kelly, 2005a; Lazaridis, 2001; Schauer and Wheaton, 2006). The U.S.
government also insists that prostitution and human trafﬁcking are inextricably linked by
stating “Where prostitution is legalized or tolerated, there is a greater demand for human
trafﬁcking victims and nearly always an increase in the number of women and children
trafﬁcked into commercial sex slavery ” (United States Department of State, 2005, 19).

Exploring the results from two decades of legalization of brothel prostitution in
the state of Victoria. Australia. Sullivan and Jeffreys (2002) concluded that although
legalization was intended to minimize the harm in prostitution, related crimes, and to
control the expansion of the industry, that it conversely caused an increase in trafﬁcking
of girls and women into Victoria to meet the increased demand within expanding and
proﬁtable sex industry. Though the number of legal brothels were doubled following the
legalization of prostitution in Victoria, the researchers said, there are three times as many

illegal brothels as the legal ones. As regard to the functional distribution of trafﬁcking

65

markets, the researchers asserted that traffickers seem to operate where there are brothels
to place their goods without fear and harassment and thus legalization is perceived by the
trafﬁckers as a signal that shows them where they can develop their business. Simply put
“For trafﬁcking to occur there must exist in the destination country an economic context
in which enslaved workers can be exploited and a social context that allows treating
human beings in this way” (Bales, 2005, 156).

Overall, social disorganization and disorder lead to increased prostitution and
human trafﬁcking in the community as much as they would drive other violent and
property crimes mainly because of the inability of the community to regulate and control
the behavior of its residents and visitors. Prostitution, on the other hand, further increases
human trafﬁcking both as a sign of increased disorder and demand in a given context. As
the trafﬁckers are attracted to these locations along with other criminals, trafﬁcked
victims are also forced into other types of crimes such as pick-pocketin g, car theft, drug
smuggling or begging besides prostitution (Bales and Lize, 2005). As a sign of
community disorder, increased prostitution in the community is also considered to be
related to an increase in other types of crimes. Law enforcement ofﬁcers in the city of
Vyborg in St Petersburg indicated that as the city became a site for prostitution tourism
for Finnish men, there has been an increase in the robbery of Finnish men and murders
among rival prostitution gangs (Hughes, 2002). Even in the case of legal prostitution, the
lack of social control would lead to exploitation of prostituted women as much as it
would lead to other violent and property crimes. Of 854 people surveyed in a study of
prostitution in nine different countries including Canada, Colombia, Germany, Mexico,

South Africa, Thailand, Turkey, United States, and Zambia; 71 percent were exposed to

66

physical assaults in prostitution and 62 percent reported rapes in prostitution. Moreover,
59 percent of the women working in legal brothels of Gerrrrany indicated that legal
prostitution does not make them any safer from rape and physical assault (Farley, Cotton,
et al., 2004). Nevada, which is the only state with legal prostitution in 13 of its counties,
is also contended to have signiﬁcantly higher rates of sex crimes than all other states in

the U.S. in 19908 (Albert, 2001).

67

CHAPTER FOUR
METHODOLOGY

The purpose of this chapter is to clarify research questions and to specify a series
of research hypotheses to be tested based on the theoretical frames provided in the
previous chapter. Moreover, detailed information about two different data sets and the
operationalization of the variables is provided. Finally, an overview of the analytic
strategies that will be employed is also included in this chapter. Given that two
independent datasets are going to be analyzed with regard to cross-national and national
distribution of human trafﬁcking, related hypotheses and operationalization of the
variables are speciﬁed accordingly.

1. Research Questions and Hypotheses

(1) How do macro-level structural characteristics inﬂuence the cross-national

distribution of human trafﬁcking?

Migration system theory conceptualizes the migratory process as embedded in
broader social and economic changes which increase the interconnectedness across the
world nations especially based on economic development in the process of globalization.
Trafﬁcking markets, on the other hand, are expected to prey on the vulnerability of those
who cannot afford or qualify for the services of formal recruiting agents in the
development process. Thus, the nations in the process of economic development are
supposed to be exploited more by the trafﬁckers as economic growth in certain parts of a
country such as in global or primate cities would fuel both internal and international
migration and thus increase the number of vulnerable targets of the trafﬁcking markets.

As the evidence demonstrated, as far as a crossection of such movements in and across

68

the nations is concerned, developing countries are likely to have more incidents of human
trafﬁcking mainly because of ongoing rural-urban and international migration and related
social and economic transformations (Bales, 2005; Laczko et al., 2002; Straubhaar.
Mayer, Hefeker, Lammers, and Wohlers, 2004).

More speciﬁcally, structural changes such as shifts in urbanization and political
economy might break formal and informal social controls in the process of development
(Howard et al. 2000). That is. political-economic shifts that redirect the distribution of
public resources and jobs and services deﬁnitely impact social cohesion in a community
(Bursik, 1988; Bursik and Grasmick, 1993). Thus, social disorganization theory can be
applied to substantiate the contextual factors which contribute to vulnerability of
trafﬁcking victims in a given country as it also attributes higher crime rates in a nation to
a breakdown in formal and informal controls. Social disorganization theory suggests that
increased poverty, instability, heterogeneity, family disruption, and urbanization in an
area, including a nation, produce illegitimate opportunity structures and dysfunctional
lifestyles including violence and crime.

Although Shaw and McKay’s (1972) original social disorganization model has
been subjected to signiﬁcant revisions in terms of consideration of the factors which
mediate between the structural characteristics and crime and/or victimization rates, the
social disorganization perspective still seems to be worth exploring given substantial
empirical evidence indicating the ability of contextual factors to explain the spatial
distribution of crime/victimization rates at neighborhood, city, and state level (Kennedy,
Silverman. and Forde, 1991; Parker, 1989; Parker, McCall and Land, 1999; Rose and

McClain 1998; Sampson and Groves, 1989; Sampson etal., 1997; Smith et al., 2000).

69

Even though we are not living in Durkheim’s France or postwar US and/or city of
Chicago today, similar to the impact of modemization/industrialization; globalization and
related transformations and mobilization of goods and services have an impact on social
integration and cohesion through increased population, mobility/immigration,
heterogeneity, weak govemance/ governmental corruption, and poverty in the both ends
of such movements, especially in developing countries of the world (Bales and Lize,
2005; Castles, 2000; Coleman, 2003) . There are also plenty of studies which incorporate
the concepts of the social disorganization perspective (i.e., as control variables) into the
analysis of cross-national distribution of both violent and property crimes (i.e., Avison
and Loring, 1986; Braithwaite and Braithwaite, 1980; Gartner, Baker, and Pampel. 1990;
Gartner, 1990; Krahn, Hartnagel, and Gartrell, 1986; Messner, 1989; Neapolitan, 1994;
Ortega, Corzine, Burnett, and Poyer, 1992; Rosenfeld and Messner, 1991).

As far as human trafﬁcking is concerned, several qualitative studies and country
reports on human trafﬁcking have reported adverse effects of globalization of
transportation, markets, labor (Kelly, 2005a), and related impact of poverty, economic
deSperation and disadvantage (Raymond and Hughes, 2001; Brunovskis and Tyldum,
2004; Ahmad 2005; US Department of State, 2006); family breakdown and abusive
relationships (Acharya, 2006; Boontinand, 2005; Chaikin, 2006; Shelley, 2002;
Sulaimanova, 2005;); feminization of poverty and migration (Castles and Miller, 2003;
Dong-Hoon, 2004; J agori, 2005; Kofman, 1999); ethnic conﬂict and dislocations (Kelly,
2005; Kelly, 2005a); lack of political willingness, corruption, and weak governance
(Kelly 2005a; Shelley, 2002) on the emergence of the contemporary human trafﬁcking

problem. Thus. research already indicates the impact of globalization related breakdown

70

in formal and informal social controls on the emergence and continuance of human
trafﬁcking markets in different parts of the world.

Therefore, fueled by the differential impact of globalization on the structural
characteristics of the world nations, human trafﬁcking is hypothesized to have emerged
as a result of the inability of world nations to regulate the behavior of their residents and
visitors because of lack of formal and informal social controls (i.e. private, parochial, and
public controls: Bursik and Grasmick, 1993). Accordingly, the following relationships

are hypothesized:
H13: Poverty will have positive impact on human trafﬁcking in a country.
Hlbi Heterogeneity will have positive impact on human trafﬁcking in a country.
H10: Residential mobility will have positive impact on human trafﬁcking in a
country.
Hld: Urbanization will have positive impact on human trafficking in a country.
H16: Family disruption will have positive impact on human trafﬁcking in a
country.
H”: The size of total population will have positive impact on human

trafﬁcking in a country

(2) How do hierarchical diffusion and social disorganization influence
trafﬁcking in women in Turkey?

Rengert’s (1996) application of diffusion principles to the study of illegal drug
markets in the United States lends signiﬁcant evidence as to impact of population and

proximity to the places where illegal drugs are produced and exported on the spatial

71

distribution of illegal drug markets in a given area, whether it be a neighborhood, a city, a
state, or even a region. Socially disorganized communities, on the other hand, are also
found to be the favorite destinations of illegal markets, as these communities provide a
convenient context for illegal transactions and services because of the lack of formal and
informal social controls.

More recent studies have also pointed to the diffusion of property and violent
crimes as well as drug use (Frischer, Anderson, Hickman, and Heatlie, 2002; Rice and
Smith, 2002; Smith et al., 2000). Messner, Anselin, Baller, Hawkins, Deane, and Tolnay
(1999) investigated spatial patterning of county homicide rates across 78 counties in and
around the St. Louis metropolitan area and they found evidence of hierarchical and
contagious diffusion of homicides from the urban core of St.Louis to smaller urban areas
overtime. Cohen and Tita (1999) also examined homicides among youth gangs across the
neighborhoods in the city of Pittsburg and they found evidence for contagious diffusion
of youth-gang homicides into adjoining neighborhoods in the city. Frischer et al. (2002)
used individual surveys to analyze the diffusion of drug use in Scotland between 1993
and 1996 and found signiﬁcant evidence of drug use diffusion in accordance with the
town size and the proximity of the places to where a certain type of the drug is exported
or produced.

Given empirical evidence about crime diffusion and the similarities between
human trafﬁcking and illegal drug markets as regard to their structural and functional
features, and also considering simultaneous involvement of some criminal networks in
both human and drug trafﬁcking (Hurriyet, 2007 ; Raymond and Hughes, 2001; Shelley,

2003), human trafﬁcking markets are also expected to concentrate in large cities because

72

of the large number of available customers in a relatively more anonymous environment.
Moreover, socially disorganized cities would be more attractive targets for human
trafficking markets as they won’t be able to regulate the behavior of their residents and
visitors and thus create an unthreatening environment for both owners and customers of
human trafﬁcking markets.

In sum, human trafﬁcking markets are expected to follow a similar trend as illegal
drug markets in terms of their spatial distribution across different regions and/or cities in
a country. A crossection of such trends in a country could only provide evidence for
hierarchical diffusion in place but not time. In other words, with crossectional
information about human trafﬁcking markets in a country, it is not possible to focus on
spatial contagion (or expansion diffusion) and network diffusion as this would require
longitudinal data. However, given that expansion and network diffusion involve radial
spread from a hierarchically infected geographic epicenter into nearby communities, a
crossectional analysis of the diffusion of illegal markets can still produce information
about hierarchical diffusion and the impact of social disorganization on this diffusion.
Accordingly, the following relationships are hypothesized:

Hierarchical diﬁ‘usion:

H23; The cities with more population will have more human trafﬁcking

incidents.

sz; The cities with international border gates will have more human
trafﬁcking incidents.

Social disorganization:

H26 The cities with more poverty will have more human trafficking incidents.

73

H2d: The cities with more heterogeneity will have more human trafﬁcking
incidents.
H26; The cities with more residential mobility will have more human trafﬁcking

incidents.

H2f; The cities with more urbanization will have more human trafﬁcking

incidents.

Hzg; The cities with more family disruption will have more human trafﬁcking

incidents.

(3) What is the impact of migrant prostitution on the distribution of human

trafﬁcking and violent and property crimes?

Evidence showed that social and physical disorder have quite signiﬁcant direct
and indirect impact on more serious crimes (Markowitz et al., 2001; Skogan, 1990;
Wilson and Kelling, 1982; Xu, Fiedler, and Flaming, 2005). As a result of ongoing social
disorganization and lack of control in a community, social disorder is suggested to have
stronger impact on crime than do poverty, instability, and heterogeneity. In this respect.
as a sign of physical and social disorder, prostitution is also linked to more serious crime
in a community (Skogan, 1990). Even though disorder is generally measured based on
perceptive and objective measures of social and physical disorder in a community, some
other researchers also used official indicators (i.e., drug arrests, emergency calls for
service for narcotics, police arrests for vandalism, drunk driving, and other disorderly

behavior) to identify drug hot spots and to examine the relationship between drug hot

74

spots, disorder, and more serious crime (in Jersey City, Weisburd and Green-Mazorelle,
2000).

Despite the limited evidence as to the individual impact of prostitution on more
serious crime, the relationship between prostitution and human trafﬁcking has been
entertained for sometime by researchers (Brunovskis and Tyldum, 2004; Ekberg, 2004;
Erder and Kaska, 2003; Hughes, 2002; Lazaridis, 2001; Schauer and Wheaton, 2006).
While the impact of tolerance and demand for prostitution on increased incidents of
human trafﬁcking is documented in different parts of the world (Sullivan and Jeffreys,
2002), prostitution is also claimed to be linked to an increase in other types of crime and
disorder such as pick-pocketing, car theft, drug smuggling, begging, robbery, and
homicides by involving the women, their customers, or rival gangs and trafﬁckers as
offenders and victims of these various types of crimes (Bales and Lize, 2005; Hughes,
2002) -even if prostitution is legal in a speciﬁc community or country-(Albert, 2001).

Overall, based on the evidence regarding the association between prevailing
social disorganization and disorder in a community, and considering the link between
social disorder and more serious crimes, following relationships as to trafﬁcking in
women in Turkey are hypothesized:

Social disorganization and Prostitution:

H33, The cities with more poverty will have more illegal

prostitution.

H3b; The cities with more heterogeneity will have more illegal

prostitution.

H30 The cities with more residential mobility will have more illegal

75

prostitution.

H3d: The cities with more urbanization will have more illegal

prostitution.

H36; The cities with more family disruption will have more illegal
prostitution.

Prostitution and human trafﬁcking markets and other serious crimes:

H3f; The cities with illegal prostitution will have more human trafﬁcking

incidents in the following year.

H3g; The cities with illegal prostitution will have more property crime in a
given year.

H31]; The cities with illegal prostitution will have more violent crime in a given

year.
H3; The cities with illegal prostitution will have higher overall crime rates in a
given year.

H3; The cities with illegal prostitution will have higher overall crime rates in

the following year.

H316 The cities with illegal prostitution will have higher organized crime rates
in the following year.
2. Data
To explore the spatial distribution of human trafficking at global and national
level, data are collected from three main independent sources. These data include: 1) The

United Nations databases on social, demographic and economic development of the

76

world nations 2) Census data and 3) Official police crime records. More detailed
information about the collection of the data; operationalization and measurement of the
concepts are provided in the following subsections.

(1) The United Nations Database

A sample of 53 countries providing the most relevant and reliable information on
speciﬁed independent variables in accordance with hypothesized relationships has been
selected to analyze cross-national distribution of human trafﬁcking. Although the
information about the dependent variable is available for 111 countries, information on
independent and control variables is available for only 5 3 nations. In order to be able to
collect consistent information across different countries, almost all the information
regarding independent variables is retrieved from various databases of the United
Nations. The dependent variable, on the other hand, is constructed based on the
modiﬁcation of Bales’ (2005, 183-186) measurement of human trafﬁcking across 111
different countries as explained in detail in the following subsection on measurement
procedures.

Acknowledging that population trends are inextricably linked with the availability
of resources, the state of environment, and economic and social development, the
Population Division of the Department of Economic and Social Affairs of the United
Nations Secretariat has produced the PRED database, drawing on a variety of national-
level comparative data and documents dealing with aspects of population, resources,
environment and development]. While assembling the cross-national database for the

analysis of global distribution of human trafﬁcking, information about the nations’

 

1 PRED database is available at http://unstats.un.org/pop/Home.aspx

77

poverty, urbanization, annual p0pulation growth rate, heterogeneity and p0pulation is
retrieved from PRED database.

The same division of the UN also provides panel data on world population
prospects between 1950 and 20502. Information about one of the control variables, that is,
the proportion of the nations’ papulation at the age of 15-24, is retrieved from this
database. Information as regard to divorce rates comes from the United Nations
Demographic Year Book 2000 and 2003. The United Nations Demographic Yearbook
collects, compiles and disseminates ofﬁcial statistics on a wide range of topics including
marriage and divorce on an annual basis since 19483. Finally, information on the gini
index of inequality of the nations obtained from the UN Human development report 2005.
The measures of all these variables are based on the information provided by the
countries in the year 2000; yet, for a small number of nations that had missing
information for unemployment and divorce rates for the year 2000, information from the
preceding years was used (all on or after 1995).

(2) Census data and crime statistics

Census data and ofﬁcial crime statistics are utilized to examine trafﬁcking in
women in Turkey. Turkey is comprised of 81 provinces and has a national police
organization (TNP) serving in each of the 81 cities with a provincial head quarter and
personnel. Accordingly. statistical information about social and demographic
characteristics of these cities is obtained from Turkish Statistical Institute (TURKSTAT).
Crime statistics regarding the distribution of human trafficking markets and property and

violent crimes across cities are retrieved from annual crime statistics produced by the

 

2 see http://esa.un.org/unpp/index.asp?panel=2
3 see http://unstats.un.org/unsd/demographic/products/dyb/default.htrn

78

Turkish National Police. In addition to ofﬁcial crime statistics, the general directorate of
Turkish National Police also provides information about the number of international
border gates across 81 cities.

Census data include social, economic, and demographic information about the
cities in the year 2000. Crime statistics, on the other hand, include annual statistics
regarding the incidents of human trafﬁcking in 2006, homicides in 2003, burglaries in
2003; deportations of the immigrant women because of their involvement in prostitution
in 2003; public order crime rates in 2005, and ﬁnally, organized crime rates in 2005.
Turkish National Police produces different statistics for terror related, drug related,
organized, and for what is called “public order” crimes. Similar to the index crimes in the
United States, public order crimes include both violent crimes of murder, rape, robbery
and aggravated assault; and the property crimes of burglary, larceny, and motor vehicle
theft in Turkey. Organized crime incidents, on the other hand, consist of crime incidents
that were committed by organized crime groups.

In sum, census data are going to provide information about structural antecedents
of social disorganization in a city-which constitutes the social context for the analysis of
trafﬁcking in women in Turkey. Crime data, on the other hand, include annual crime
rates that will help to explore the diffusion of human trafﬁcking markets across the cities
and to investigate the impact of social disorganization and migrant prostitution on the
distribution of human trafficking markets and provincial crime rates.

3. Measurement of the concepts

(1) Cross-national analysis of human trafﬁcking

Dependent Variable:

79

Human traffging:

Bales (2005) has been the ﬁrst researcher who measured human trafﬁcking at the
cross-national level. In order to identify signiﬁcant predictors of ‘human trafﬁcking from’
and ‘human trafficking to’ a country, Bales (2005) createditwo separate dependent
variables for human trafﬁcking from and to a country. Using a systematic protocol, Bales
(2005. 96) retrieved information on human trafﬁcking from multiple physical and
electronic records including reports by national governments, reports by international
organizations, reports by experts, reports by non-governmental organizations, press
reports, and his ethnographic ﬁeldwork in ﬁve countries (Bales, 2004). Then, be
assigned two different points for each country on a 5 point ordinal scale -which ranges
from ‘0’ to ‘4’ (0: no trafﬁcking to 1=rare cases of trafﬁcking, 2=occasional but
persistent case of trafﬁcking, 3=regular cases of trafﬁcking in small numbers, 4: regular
cases of trafficking in large numbers) and indicates the level of human trafﬁcking to and
from a country. To increase the validity and reliability of his estimates, Bales (2005) had
several anonymous experts with personal knowledge on each country review his
estimates and reﬁned the country scores in accordance with the suggestions of the
experts.

Although Bales (2005, 128—129) adapted the United Nation’s deﬁnition of human
trafﬁcking, he operationalized human trafﬁcking in a country through two separate
measures, that is, human trafﬁcking to and from a country. However, the United Nations
deﬁned human trafﬁcking as:

(a) “Trafﬁcking in persons” shall mean the recruitment, transportation, transfer,
harboring or receipt of persons, by means of the threat or use of force or other
forms of coercion, of abduction, of fraud, of deception, of the abuse of power or
of a position of vulnerability or of the giving or receiving of payments or beneﬁts
to achieve the consent of a person having control over another person, for the

80

purpose of exploitation. Exploitation shall include, at a minimum, the
exploitation of the prostitution of others or other forms of sexual exploitation,
forced labor or services, slavery or practices similar to slavery, servitude or the
removal of organs;

(b) The consent of a victim of trafﬁcking in persons to the intended exploitation
set forth in subparagraph (a) of this article shall be irrelevant where any of the
means set forth in subparagraph (a) have been used;

(c) The recruitment, transportation, transfer, harboring or receipt of a child for
the purpose of exploitation shall be considered “trafﬁcking in persons” even if
this does not involve any of the means set forth in subparagraph (a) of this
article;

((1) “Child” shall mean any person under eighteen years of age (Article 3).

Hence, given the major elements of human trafﬁcking enumerated in the ﬁrst
paragraph (a), human trafﬁcking in a country should involve both trafﬁcking ‘to’ and
‘from’ a country as the deﬁnition of trafﬁcking embraces recruitment and exploitation as
well as different means to do both as vital elements of human trafﬁcking. Thus, when
talking about the spatial distribution of human trafﬁcking markets, one should consider
both recruitment and exploitation of the trafﬁcking victims in a country as major
indicators of the active presence of these markets. In fact, evidence provided in the
previous chapter also demonstrated that there are several regions and countries which
serve as favorite origin and destination points of human trafﬁcking simultaneously
(Kangaspunta, 2003; UN ODC, 2006). Moreover, a separated focus on trafﬁcking from
and to a country might also fail to consider internal trafﬁcking in a country as well (Bales
and Lize, 2005; ICMPD, 2005).

In this respect, considering the deﬁnition of trafﬁcking by the United Nations as
involving both recruitment and exploitation, and given the empirical evidence as regard
to the existence of recruitment and exploitation in the same countries, human trafﬁcking
in a country should better include the measures of both human trafficking from and to
that country. Therefore, in the analysis of cross-national distribution of human trafficking

across 53 countries. human trafﬁcking in a country will mean an aggregated measure of

81

both human trafﬁcking to and from a country. Exploratory factor analysis for Bales’
(2005) two items showed that these items were associated with a single latent construct
(Factor loadings > .85, explained variance: 72.50, eigenvalue: 1.45). Moreover, the
magnitude of internal consistency (Cronbach’s Alpha: .62) indicates an acceptable level
of reliability among the items (Cohen and Cohen, 1983). Thus, human trafﬁcking in a
country, the dependent variable, is operationalized as a factor based additive scale which
simply combines Bales’ (2005) ﬁve point human trafﬁcking to- and -from items and
ranges from 1 to 8.

Independent variables:

mam:

As reviewed studies and reports on human trafﬁcking have concurred, compared
to developed countries, developing nations are more likely to struggle with human
trafﬁcking as both origin and destination points mainly because of poverty and lack of
employment opportunities -which creates a conducive context for the trafﬁckers to prey
on those who want to leave for better opportunities but can’t afford or qualify. While
testing the impact of poverty on cross-national distribution of crime, researchers
generally employ a Gross Domestic Product (GDP) or Gross National Product (GNP) per
capita measure, as an indicator of development and average income in a country (Bennett
and Bennett, 1983; Braithwaite and Braithwaite, 1980; Gartner , 1990; Howard,
Newman, and Pridemore, 2000. 156). In addition to serving as an indicator of poverty
and development, GDP is also considered as . .a proxy for the quality of government
institutions, including the police and justice systems” (Lederman, Loayza, and Menendez.

2002). Previous studies demonstrated that per capita GDP or GNP are negatively

82

associated with cross-national homicide rates, that is, as per capita GDP or GNP
increases. homicide rates tend to decrease (Fajnzylber, Lederman, and Loayza, 2002;
Ledermen et al., 2002; Neapolitan, 1994). Similarly, GDP per capita for the year 2000,
which refers to GDP (at constant U.S. dollars) divided by total population, is used to
measure the impact of poverty and level of development on cross-national distribution of
human trafﬁcking. The measure of GDP per capita in the year 2000 is retrieved from the
UN PRED database.

Urbanization:

Urbanization is considered as a key component in the process of social change
(Simpson and Conklin, 1989) and shifts in urbanization are suggested to break the
normative controls in the society (Howard et al., 2000). As far as human trafﬁcking is
concerned, development and related urbanization especially in developing countries
would beneﬁt trafﬁckers in terms of targeting those who look for employment
opportunities and exploiting them in an unguarded urban social envirdnment.
Urbanization is generally measured as the percentage of the population living in urban
areas (Krahn, Hartnagel, and Gartrell, 1986; Messner, 1989) and it was found to have
signiﬁcant impact on crossnational distribution of violent crime (Conklin and Simpson,
1985, cited in Messner, 1989). Hence, the percentage of the population living in urban
areas in 2000 as indicated by the UN PRED database is used as a measure of urbanization
across the countries.

Heterogeneity:

It is suggested that people might be reluctant lless inclined to guard or care about

the personal safety and private property of their neighbors they barely know or contact

83

(Howard et al., 2000). Previous research indicated signiﬁcant positive impact of ethnic
and linguistic heterogeneity on violent crimes (Conklin and Simpson, 1985; Gartner,
1990). In this regard, heterogeneity is expected to increase the incidents of human
trafﬁcking in a country mainly because of weak community ties and decreased level of
social control over the establishment and activities of human trafﬁcking markets. Prior
research generally employed different ethnolinguistic homogeneity indices (Messner,
1989; Li, 1995; Neapolitan 1998) as a measure of ethnic or linguistic heterogeneity.
Acknowledging that heterogeneity mainly refers to ethnic and linguistic differentiation
and given that human trafﬁcking mainly involves the exploitation of the victims in a
foreign country, the percentage of the foreign-boms in the population is used as a
measure of heterogeneity (Gartner 1990, 97; Howard et al., 2000, 158) and information
about both total and foreign-born population is retrieved from the UN PRED database.

Residential Mobility:

Like urbanization, residential mobility also leads to weaker community ties and
relatively weak social control as the people would be less likely to know each other in the
areas of massive population turmoil. At cross-national level, total population growth rate
is suggested as a cursor for residential mobility (Howard et al., 2000) and prior research
demonstrated signiﬁcant positive effect of total population growth rate on crossnational
violent crime rates (Krahn et al., 1986; Li, 1995). Human trafﬁcking markets are also
expected to beneﬁt from prevailing instability across a country as increased mobility
would decrease the ability of the communities to resist and control illegal markets’

penetration into their proximal social environment. The UN PRED database also provides

84

national population growth rates, and total population growth rates of the nations between
the years of 1995 and 2000 are employed as a measure of residential mobility.

Family Disruption:

Generally operationalized as a nation’s divorce rate (Simpson and Conklin, 1989;
Howard et al., 2000), family disruption is another factor that disrupts social cohesion and
integration (Sampson, Raudenbush, and Earls, 1997). Thus, family disruption could
contribute to diffusion of human trafﬁcking markets by reducing the control of both
offending and victimization. Prior research has already pointed to family disruption as a
signiﬁcant predictor of increased violent crime rates across the nations (Gartner, 1990).
The UN Demographic Year Book 2000 and 2003 (United Nations, 2006) are used to
operationalize family disruption as the rate of nations’ divorce rate per 1000 in the year
2000. Information about divorce rates of two nations, that is, Thailand and Sri Lanka, is
not available in these year books and this information is obtained from Gulnar Nugman
(2002) of the Heritage Foundation. The same information was also u3ed by other
researchers before (Rahman, Mittelhammer, and Wandschneider, 2003).

Total Population:

Although total population of a country is generally employed as a control variable
in cross-national analysis of crime rates, it is also considered as an important factor that
could undermine social integration and encourage anonymity (Rosenfeld and Messner,
1991). In this respect, a large population, especially when accompanied by poverty and
urbanization in developing countries, would contribute to establishment of human

trafﬁcking markets because of lack of control and increased number of suitable targets.

85

The measure of total population in a country in the year 2000 comes from the UN PRED
database.

Control Variables:

In addition to poverty, urbanization and indicators of social disorganization
enumerated above; two control variables are going to be employed as control variables
for the analysis of cross-national distribution of human trafﬁcking.

Income Inequality (Gini):

Prior research indicated signiﬁcant impact of income inequality as opposed to
absolute poverty on cross-national distribution of crime rates (Gartner, 1990; Lederman
et al., 2002; Messner, 1989). Income inequality is generally operationalized as the gini
index of a country. The Girri coefﬁcient ranges from 0 to 100, with 0 indicating perfect
equality and 100 indicating perfect inequality within a population (Howard etal., 2000).
Information about each country’s gini coefﬁcient in the year 2000 is obtained from the
UN Human Development Report 2005.

Population Aged 15-24:

The proportion of younger population aged between 15 and 24 is another common
control variable used in cross-national analysis of crime. However, the relationship
between this control variable and international crime rates is not clear yet. That is,
whereas some researchers found a signiﬁcant positive relationship (Ortega et al., 1992),
others found either negative (Bennett, 1991) or nonsigniﬁcant relationship (Neapolitan,
1994). Information about the proportion of the population aged between 15 and 24 in

2000 is retrieved from the UN database on world population prospects.

86

(2) Trafficking in women in Turkey

Dependent variables

Human Trafﬁcking:

Turkey has ratiﬁed the afore mentioned United Nations Convention (2000)
against Transnational Organized Crime and its additional Protocol to Prevent, Suppress
and Punish Trafﬁcking, Especially Women and Children in 2003, and incorporated
almost the same deﬁnition of human trafﬁcking into the Turkish Criminal Code (Article
80). Thus. human trafﬁcking is understood in accord with its globally accepted deﬁnition
by Turkish National Police across 81 cities in Turkey. In practical terms, however,
operationalization of human trafﬁcking as a crime is not quite as straight forward as in
other type of crimes as indicated by the small number of cases of human trafﬁcking in
most cities. In 2006, for example, there were no cases of human trafﬁcking in 58 cities
(72%) and only 23 cities (28%) had one or more incidents of human trafﬁcking in the
entire year (TNP, 2006). In this regard, following the previous research on the
distribution of uncommon crimes in and across the cities (Osgood, 2000; Osgood and
Chamber, 2000) the number of human trafﬁcking incidents as identiﬁed by the police in
the entire year of 2006 is used as a dependent variable for the analysis of the distribution
of human trafﬁcking markets in Turkey.

However, given the limitations of secondary data gathered by other people for
agency purposes, ofﬁcial police records might underestimate existing human trafﬁcking
in a city. Moreover, what events or individuals come to the police attention also depends
on the discretion and performance of the police organization as well (Hagan, 2006). Thus,

police reported measure of human trafﬁcking might have the confounding impact of

87

police discretion and performance. Therefore, an alternative measure of the distribution
of human trafﬁcking markets, that is, the number of calls for rescue as reported by the
International Organization for Migration (IOM) ofﬁce in Turkey for the entire year of
2006 is also used (IOM, 2006).

IOM ofﬁce in Turkey has established a hotline called “157 helpline” which is
mainly aimed at assisting law enforcement authorities in the rescue of human trafﬁcking
victims. Helpline is operated by consultants who are trained in counseling, emergency
assistance and sensitive information solicitation. Thus, among several services provided
through this helpline (i.e., assisting legal and illegal migrants and awareness raising in the
community), consultants also record the calls that are considered as potential cases of
human trafﬁcking under the “request for rescue” category of the 157 helpline database.
The distribution of these calls (requests for rescue) across the provinces is available in
2006 human trafﬁcking report of Turkey through IOM ofﬁce’s website
(www.countertrafﬁcking.org). Simply put, requests for rescue refer to the calls that
provide enough information to be considered as a potential case of human trafﬁcking.
Given that these calls are made not only by the victims themselves but also by their
relatives, friends, neighbors, and customers; and considering the fact that only limited
number of these calls result in the identiﬁcation and rescue of victims in the end, requests
for rescue can provide more adequate picture of the distribution of human trafﬁcking
markets across the cities. Therefore, in addition to the number of human trafﬁcking
incidents reported by police, the number of requests for rescue in each province are also
used as another measure of human trafﬁcking across 81 cities in Turkey and they are

referred as ‘calls for rescue’ in the remaining part of this study.

88

Migrant prostitution:

Prostitution is legal but limited to Turkish women in Turkey, and involvement in
prostitution is considered as a reason for immediate deportation for foreigners. Statistics
regarding the number of deported women because of their involvement in prostitution in
2003 is provided by the Turkish National Police. Prior research on prostitution, human
trafﬁcking, and social disorder suggested a positive relationship between prostitution and
human trafﬁcking and prostitution and more serious crime (Bales and Lize, 2005;
Skogan, 1990; Sullivan and Jeffreys, 2002). As for the situation in Turkey, like human
trafﬁcking, migrant prostitution is also speciﬁc to certain cities in Turkey. In 2003, 33
(41%) cities reported no prostitution related deportation, where 8 (10%) cities reported
one prostitution related deportation and remaining 40 (49%) cities had two or more
prostitution related deportations in the entire year (TNP, 2003). Therefore, following the
same approach taken for the measurement of human trafﬁcking above, migrant
prostitution is also operationalized as a count variable as the number of foreign women
deported because of involvement in prostitution in a city.

Violent crime:

Because of the mortal nature of the offense and higher availability, validity. and
reliability of the homicide measures (Howard et al., 2000), the number of homicides are
used as an indicator of violent crime rates across the cities in Turkey. Several prior
studies also used homicide rates to analyze distribution of violent crime rates across US
cities and counties (Baumer, 1994; Champlin and Cochran, 2004). Hence, the number of

homicides in the year 2003 is used as a measure of violent crime in each city in Turkey.

89

Statistics about the number of homicides in each city are obtained from the Turkish
National Police.

Property crime:

Burglary rates are used as an indicator of property crime in the previous work in
the context of communities and crime research literature (Harmon and Defronzo, 1998;
Lee and Ousey, 2001). Thus, the number of burglaries in each city in the year 2003 is
obtained from the Turkish National Police.

Total (public order) crime rate:

Turkish National Police provide Separate annual statistics for different types of
crimes. For example, terror, smuggling, human trafﬁcking, and (illegal) drug related
crimes and organized crime rates are reported separately. However, certain type of crimes
are aggregated under the name of “public order crimes” and TNP provides aggregated
annual statistics for these crimes which include murder, rape, robbery, aggravated assault.
burglary, larceny, and motor vehicle theft. As these include both property and violent
crimes, they constitute a measure for total/overall crime; therefore, the number of public
order crimes in the year 2003 and 2005 is used as indicator of the number of total crimes
in each city.

Organized crime rate:

Like in the U.S., there is not a universally accepted definition of organized crime
in Turkey, for Turkish law does not provide a legal deﬁnition for ‘organized crime’. In
general, organized crime incidents include any crime incidents that involve the use of
threat, violence, and other forms of coercion and that are committed only by organized

crime groups which are established for the purpose of generating (illegal) monetary profit

90

(KOM, 2008). In this re gard, organized crime incidents exclude terror and drug related
crimes even if they are committed by organized criminals (see KOM, 2004, 16 - for a
detailed explanation of activities that arrested organized crime groups were found to have
been involved in during 2003 and 2004).

Organized crime and common crime are suggested to be inﬂuenced by many of
the same factors. To illlustrate, rapid urbanization, industrialization, and social
disorganization are contended to facilitate organized crime as tolerance for deviant
behavior would create many criminal opportunities that might be exploited by criminal
networks (Finckenauer and Voronin, 2001). Accordingly, as an indicator of tolerance for
disorderly and deviant behavior, prevailing migrant prostitution is expected to have a
positive impact on the number of organized crime incidents in a city. The number of
organized crime incidents across 81 cities in the entire year of 2005 as provided by the
TNP Department of Anti-Smuggling and Organized Crime (generally known as ‘KOM’
in Turkish) is used as a measure of the distribution of organized crime incidents in
Turkey.

Independent variables:

A similar set of independent variables are being used at the Turkish city level as
were employed at the international country level:

BOV—erbLi

Poverty is assumed to deprive a society of necessary monetary and political
resources that could allow the pe0ple to address the issues of crime and disorder in their
community (Komhauser, 1978). When the residents are unable to harness or attract

resources that could help them to organize their community, they would be more likely to

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become disengaged in the community with weakening social ties and informal networks.
Past research on ecological predictors of crime has emphasized poverty as one of the key
determinants of urban crime rates (Oh, 2005; Lee, 2000; Parker, 1989). Poverty is
contended to be the most related factor to the geography of crime (Ackerrnan, 1998) and
several studies provided empirical evidence as to positive impact of poverty on violent
and property crime rates across U.S. counties, cities, and MSAs ( Bachman, 1991; Baller
et al., 2001; Baumer, 1994; Eitle et al., 2006). In an analysis of the distribution of terrorist
incidents in Turkey, Koseli (2006) also found a signiﬁcant positive impact of poverty on
terrorist incidents across all 81 cities in Turkey. Given the substantial evidence of the
positive impact of poverty on the geography of crime, poverty is expected to have a
similar effect on the distribution of human trafﬁcking markets in Turkey as cities with
more poverty would lack the resources to resist the establishment and diffusion of illegal
trafﬁcking markets in their communities. Poverty is generally measured as the proportion
of the population living under the poverty line in a geographic area. However, this
measure of poverty is not available in Turkey. Instead, similar to the measurement of
poverty in the cross-national context, gross domestic product (GDP) per capita in Turkish
Lira in the year 2000 is used as a measure of absolute poverty in each city.

Residential Mobility:

Considering that the development of trust and social ties among the residents of a
community would require some time (Sampson, 1997; Sampson and Groves, 1989),
residential stability is supposed to have a negative impact on the geography of crime as it
contributes to the establishment of friendship networks and local relational ties in the

community. While a sense of ownership of a community would emerge as a result of

92

strong social integration among stable residents, this could also allow them to address
their common problems such as resisting the establishment and diffusion of human
trafﬁcking markets through formal and informal social controls. Past research on violent
and property crimes has already indicated the negative impact of residential stability on
the distribution of crime across U.S. counties, MSAs , and cities (Bouffard and Mufﬁc,
2006; Eitle et al., 2006; Haynie and Armstrong, 2006). The percent of residents who have
lived in the same residence for the past five years (Eitle et al., 2006) is generally used as
an indicator of residential stability. However, as Bursik and Grasmick (1993) contended,
this indicator would fail to capture residential mobility if a large number of people left
the city where the remaining smaller population has lived more than ﬁve years.
Therefore, given that all of the people moving in and out of the city would have an
impact on social integration, the total number of people (per thousand population)
moving in and out of the city in the last ﬁve years is utilized as a measure of residential
mobility in this study. The total number of peOple moving in and out of each city during
the last 5 years is obtained from the 2000 census data.

Heterogeneity:

Ethnic, racial, or linguistic heterogeneity in the community can impede
communication among different groups because of the presence of diverse cultural values
and norms. Lack of communication, on the other hand, prevents the members of a
community to reach a level of consensus with respect to appropriate behaviors and goals
in their community (Elliott et al., 1996). In the case of human trafﬁcking, heterogeneity
would hamper any possible concentrated effort aimed at controlling the behaviors of

trafﬁckers and their customers in a given city. Past research provided evidence for the

93

positive impact of heterogeneity or diversity on the distribution of crime rates across
cities and counties (Baumer, 1994; Haynie and Armstrond, 2006). Even though a
heterogeneity index (Osgood and Chambers, 2000), or the proportion of a certain racial
or ethnic group in a community (Baumer, 1994), were employed as a measure of
heterogeneity in prior studies, such measures as the proportion of different ethnic and
racial groups is not available in Turkey; therefore, the proportion of foreigners as
percentage of total population in a given city in the year 2000 is used as a measure of
heterogeneity.

Family disruption:

According to Sampson (1986 and 1987), family disruption decreases involvement
in both formal and informal social control mechanisms. More speciﬁcally, family
disruption contributes to social disorganization by leading to decreased participation in
local voluntary organizations, disruption of the socialization and supervision of youths,
and to lack of active guardianship in an area (Sun et al., 2004). Family disruption, thus,
decreases the level of formal and informal social control over both trafﬁckers and
potential customers (youth) of their illegal services in the community. Family disruption
is generally operationalized as the number of divorces per 1,000 marriages and/or family
headed households in a geographic area and is found to have signiﬁcant positive impact
on the distribution of crime across U.S. cities and counties (Baller et al., 2001; Baumer,
1994; Liska and Bellair, 1995). Similarly, for the analysis of the distribution of human
trafﬁcking markets across the cities in Turkey, the number of divorces per 1,000

marriages is employed as a measure of family disruption based on 2000 census data.

94

Urbanization:

Urbanization is contended to produce criminal opportunities because of weak
social ties in the community. Even though urbanization increases interaction among the
people living in an urban area, it weakens local friendship and kinship networks and
impedes social participation in local affairs (Sampson and Grove, 1989, 782). Moreover,
“Economic development, industrialization, and urbanization both stimulate and facilitate
demand for goods and services... This demand may be for drugs, ﬁrearms, or sex
workers, or it may be for a service such as protection or resolving disputes when there is
no acceptable legal means of doing so” (Finchenauer and Voronin, 2001, 3). Thus, rapid
urbanization in certain cities could beneﬁt trafﬁcking markets because of increased
demand for their services in an environment which is comprised of more potential
customers coming in and out for work or recreation with relatively weak social controls.
Urbanization is generally measured as the proportion of the population living in urban
areas and demonstrated to have signiﬁcant positive impact on the distribution of property
and violent crime in the U.S. counties (Smith and Parker 1980; Kposowa et al., 1995).
TURKSTAT provides information about the percent of the population living in urban
areas in each city and this information is incorporated as a measure of urbanization for
the analysis of human trafﬁcking markets in Turkey. Speciﬁcally, the proportion of
population living in the city centers and in the districts with more than 100,000
population is used as a measure of urbanization, as the latter measure generally applies to
the central districts surrounding the city centers in metropolitan cities (i.e., Izmir.

Istanbul, and Ankara).

95

Total Population:

Mostly employed as a control variable (Kowandzic et al., 1998), increased
population size contributes to increased crime and victimization as it encourages
anonymity in the community. Several studies indicated signiﬁcant positive impact of
population size on crime across U.S. cities, MSAs, and counties (Blau and Blau, 1982;
Haynie and Armstrong, 2006; Eitle et al., 2006; Hannon and Defronzo, 1998). As far as
human trafﬁcking is concerned, population size could contribute to development of
human trafﬁcking markets because of increased number of potential customers and
increased anonymity which disrupts the level of control over the trafﬁckers and the
customers of their illegal services. Therefore, population size as provided in the 2000
census is incorporated as another independent variable into the analysis of human
trafﬁcking markets in Turkey.

Intematimal border gates:

Past research on the diffusion of illegal drug markets demonstrated that the
communities closer to the places where the illegal drugs are produced or exported are
more likely to have illegal drug markets (Frischer et al., 2002; Rengert, 1996). It may be
assumed that the cities bordering other countries or those with international airports and
seaports might be more likely to be exploited by trafﬁckers as such proximity would
decrease the cost and duration of transportation and thus the probability of detection and
apprehension. To see whether this impact of geographic proximity applies to distribution
of human trafﬁcking markets in Turkey, a dichotomous variable is created for the
presence of international border gates in each city. The cities with international border

gates are coded as 1 and the others without international borders are coded as 0.

96

Information about the presence and absence of international border gates is provided by
the Turkish National Police.
Control Variables:

Two control variables, unemployment and the proportion of population between
the age of 15 and 24 in a city. are going to be included in the analysis of human
trafﬁcking markets in Turkey.

Population Aged Between 15 and 24:

The impact of the proportion of the population between the age of 15 and 24 on
the dispersion of violent and property crimes across the cities and counties is not always
straight forward. Whereas some of the previous research noted signiﬁcant positive impact
of this speciﬁc population on crime rates (Hannon and Defronzo, 1998), others found
signiﬁcant negative (Eitle et al.. 2006) or nonsigniﬁcant impact (Liska and Bellair, 1995;
Bachman, 1991). Information about the proportion of the younger population in the cities
is retrieved from 2000 census.

Unermrloyment:

Unemployment rate, which is obtained from the 2000 census, is also incorporated
into the analysis of human trafﬁcking markets in Turkey. Similar to the impact of
younger population, the impact of unemployment on crime rates is also not consistent
across different studies (see Baller et al., 2001; Land et al., 1990).

4. Analytic strategy

There are two independent data sets used in the course of this study, that is, cross-

national and the national datasets. Cross-national data are utilized to investigate the

distribution of human trafficking markets across 53 world nations. The national data

97

about human trafﬁcking in Turkey, on the other hand, is built to investigate the
distribution of human trafficking markets across 81 cities in Turkey.

In order to explore the geography of human trafﬁcking markets, ﬁrst, a correlation
matrix for structural variables in each data set is estimated to examine bivariate
relationships and potential multicollinearity among independent and control variables.
Then, multivariate analyses is applied to test speciﬁed hypotheses enumerated above.
Multiple regression estimation is the main analytic tool employed in both cases.
Individual impact of each hypothesized structural characteristic on the cross-national
distribution of human trafﬁcking markets is estimated while controlling for the impacts of
all other structural (independent and control) variables. Three different models are
estimated for the distribution of human trafﬁcking across 53 countries. Moreover, the
ability of the same models in predicting human trafﬁcking “to” and “from” the world
nations is investigated as well.

As for the distribution of human trafﬁcking markets in Turkey, ten different
models are estimated. The ﬁrst six models look at the impact of the population size,
geographic proximity, and structural correlates of social disorganization as well as the
impact of other control variables on the distribution of migrant prostitution and human
trafficking markets across the cities. The following four models, on the other hand,
examine the impact of migrant prostitution on the distribution of human trafﬁcking
markets (in 2006), total crime (in 2005), and organized crime (in 2005) across the cities,
net of all other hierarchical diffusion, social disorganization, and control variables.

In these ﬁnal four models, migrant prostitution across cities in Turkey is

considered as a sign of social disorder. As explained in much more detail in previous

98

chapters, theory predicts that there will be an association between (nrigrant) prostitution
and human trafﬁcking and other violent and property crimes as well as organized crime
incidents. Also supported by reviewed literature, as a sign of social disorder and thus an
indicator of a given community’s inability to regulate its residents’ and visitors’
behaviors, prostitution is expected to fuel more serious crime including human trafﬁcking
in a given society. Simply put, visible social disorder, including drug dealing,
prostitution, and sexually-oriented enterprises (Taylor, 1995) leads law-abiding residents
to withdraw from public life as they assume that the neighborhood is disorganized and
not safe (Lewis and Salem, 1986; Skogan, 1990). Thus, while they become less
motivated to intervene in physical and social disorder around them, this ongoing
tolerance for disorder attracts outside trouble makers as a signal of increased opportunity
for delinquent or criminal behavior.

To test this premised relationship between prostitution and human trafﬁcking and
more serious crimes, ﬁrst, independent sample t tests are employed to see the impact of
the presence of migrant prostitution on the distribution of violent, property, overall
(public order) crime rates, and organized crime incidents across the cities in a given or
following year. Next, four different regression models are estimated. The ﬁrst two
models examine the impact of migrant prostitution (2003) on both measures of human
trafﬁcking across the cities. Then, the ﬁnal two models try to see if migrant prostitution
in 2003 also has signiﬁcant impact on the distribution of total (public order) crime and
organized crime incidents in 2005 when other structural correlates and controls are held

constant.

99

In sum, both bivariate and multivariate statistics are employed while analyzing the
data. When multiple regression is applied, OLS, logit, and negative binomial regression

models are used when appropriate.

100

CHAPTER FIVE
ANALYSES and FINDINGS

This chapter presents major research ﬁndings regarding cross-national distribution
of human trafficking markets and the distribution of human trafﬁcking markets in
Turkey. Given the focus of this dissertation on three main research questions as regard to
the distribution of human trafﬁcking at two different levels of analyses, this chapter is
divided into three major subsections corresponding to each research question. In the ﬁrst
section, the impact of macro level structural factors on cross-national distribution of
human trafﬁcking markets is investigated. In the second section, the diffusion of human
trafﬁcking markets across 81 cities in Turkey is examined. Speciﬁcally, the analysis
explores the impact of hierarchical diffusion and social disorganization on the
distribution of human trafﬁcking markets in Turkey. In the ﬁnal part, the impact of
migrant prostitution on the distribution of human trafﬁcking markets and other serious
crimes in Turkey is estimated. Overall, univarite/descriptive, bivariate, and ﬁnally
multivariate analyses are performed on both datasets. Multivariate analyses are conducted
using linear, logit, and count regression models when appropriate.

1. Cross-national distribution of human trafﬁcking markets

(1) Summary statistics

Information about a total of 53 countries is utilized for cross-national analysis of
the distribution of human trafﬁcking markets. Once again, the sample size is limited by
the availability of the information about independent variables used in the course of this
study. In fact, scarcity of the information about potential explanatory variables might

have also increased the possibility of omitted variable bias for multivariate analyses to be

101

provided; however, theoretical guidance provided by social disorganization framework
and the review of empirical studies on human trafﬁcking suggested the most relevant
indicators and control variables which are included in the present study. Moreover,
different countries from almost all regions of the world including Asia, Europe, North
America, South America, Central America and Caribbean, Africa, Middle East and North
Africa, Australia and Oceania are included in this available sample of world nations.
Descriptive statistics presented in Table 1 demonstrate general sample
characteristics. Although this study operationalized human trafﬁcking as a combined
measure of human trafﬁcking from and to a single country, additional two models with
regard to human trafﬁcking from and to a county are also going to be estimated to
promote the reliability of ﬁndings. Accordingly, summary statistics about all three
different dependent variables are provided below. On average, the levels of trafﬁcking
from and to countries are quite close to each other with a mean score of 2.25 and 2.32
respectively. Among 53 countries in the sample, 11 countries (21%) scored 4 (regular
cases of trafficking in large numbers) on Bales’ (2005) ﬁve point (0-4) ordinal human
trafﬁcking scale, whereas remaining 10 (19%) countries scored 3 (regular cases of
trafﬁcking in small numbers); 15 (28%) countries scored 2 (occasional but persistent
case of trafﬁcking); 15 (28%) countries scored 1 (rare cases of trafﬁcking); and 2 (4%)
countries scored 0 (no trafﬁcking) on the same scale. As far as human trafﬁcking to a
country is concerned, all countries in the sample were exposed to human trafﬁcking as a
destination country to some extent. Specifically, 9 (17%) countries scored 4; 14 (26%)
countries scored 3; 15 (28%) scored 2; and ﬁnally, 15 (28%) countries scored 1 on the

same scale. When these two measures are combined together into a single “Total

102

trafficking” variable, human trafﬁcking in 29 (55%) countries was less than average
ranging from 1 to 4. The remaining 24 countries, on the other hand, were exploited
relatively more as human trafﬁcking in these countries ranged from 5 to 8, which is

above the average.

Table 1. Descriptive Statistics (N=53)

 

 

Variable Mean SD Min Max
DEPENDENT VARIABLES

Total Trafﬁcking (a) 4.57 1.93 1.00 8.00
Trafﬁcking to a Country 2.32 1.07 1.00 4.00
Trafﬁcking from a Country 2.25 1.19 0.00 4.00
INDEPENDENT VARIABLES

Total Population (000) 58559.42 177825.90 1285.00 127397900
GDP per capita 10453.96 10571.75 656.00 34478.00
Urbanization 65.36 17.81 21.10 100.00
Heterogeneity 8.56 9.75 0.04 40.69
Residential Mobility 0.55 0.98 -1.30 2.90
Family Disruption 1.70 1.05 0.15 4.35
CONTROL VARIABLES

Population Aged 15-24 (%) 15.77 2.91 11.10 20.80
Gini 35.72 8.39 24.70 59.30

 

Note : (a) Additive Scale (Trafficking from a Country + Trafﬁcking to a Country)

As the nations in the sample represent a wide range of regions around the world,
there is considerable amount of variation in their characteristics as regard to independent
and control variables. The number of population ranges from 1,285 to 1,273,979
(thousand) with an average population of 58,5594 (thousand). The distribution of GDP
per capita also varies substantially in that it ranges from $ 656 to $ 34,478 with an
average per capita GDP level of $ 10,571.8 per capita. On average, the level of
urbanization across the sample is 65.4 percent and it ranges from 21 percent to 100
percent. There is considerable variation in terms of heterogeneity across the nations as it
ranges from .04 percent to 40.7 percent with an average of 9.8 percent. Family disruption

ranges from .2 to 4.4 with an average of 1.1 divorces per thousand population. Finally. of

103

two control variables, percentage of the population aged 15-24 ranges from 11.10 to
20.80 with a mean score of 15.77 percent, whereas GINI coefﬁcient ranges from 24.70 to
59.30 with an average of 35.72.

Considering extensive variation in some of independent and control variables, all
of the variables are checked for normal distribution prior to bivariate and multivariate
analyses. Univariate distributions revealed high degree of skewness for total population,
heterogeneity, and gini coefﬁcient. Given the small sample size, to maximize linear ﬁt
and thus facilitate linear modeling (Li, 1995; Messner, 1989), natural log transformations
(1n) are performed for these three variables and the transformed variables are utilized in
the following bivariate and multivariate analyses.

(2) Bivariate Analysis

Prior to multivariate modeling, bivariate correlations between dependent,

independent, and control variables have been examined. Only two hypotheses speciﬁed

in the previous chapter, that is. Hla and H 1f are supported by the following correlation

analyses in Table 2. First, a statistically signiﬁcant and negative relationship between
GDP per capita and Total Trafﬁcking (-.45, p<.01) indicates that poverty has a positive

impact on Total Trafﬁcking in a country and the nations with lower GDP per capita are

exposed to more human trafﬁcking (Hla)- Second, the signiﬁcant positive relationship

between Total Population (1n) and Total Trafﬁcking (.37 . p<.01) lends support for Hlf.

As expected, countries with larger populations are exploited more by human trafﬁcking

markets. However, bivariate correlations do not support the remaining four hypotheses

104

(Hlb, H10, Hld. H18) about the impact of Heterogeneity, Residential Mobility,

Urbanization, and Family Disruption on Total Trafﬁcking.

Table 2. Bivariate Correlations

 

l 2 3 4 5 6 7 8 9

1- Total Trafﬁcking -
2- GDP per capita -.45** -
3. Residential

Mobility "06 '05 '
4- Heterogeneity (In) --36** .47** -.60 -
5- Urbanization -. 16 .63** .08 .49** -
6- Family Disruption -.15 .37** -.29* .44** .42** -
7' 53:31 Powlauon .37** .10 .14 -.36** .00 .12 -
8. Population Aged ** H ** H M

15_24 (%) .21 -.80 .34 -.41 -.53 -.45 -.05 -
9- Gini (In) .10 -.32* .53** -.19 -.08 -.40** .12 .50** -

 

Note: Entries are Pearson Correlation Coefﬁcients; *p<.05, **p<.01 (2-tailed tests).

In contrast, the sign of all these independent variables are in the opposite
direction, though only one of them, heterogeneity, had a signiﬁcant impact on Total
Trafﬁcking in a country. Heterogeneity was hypothesized to have a positive impact on
cross-national distribution of human trafficking; yet, it has a signiﬁcant negative impact
(-.36) on total Trafﬁcking. However, considering the limitations of a given bivariate
relationship in terms of its validity, discussion about counterintuitive findings are going
to be provided following the multivariate analyses as, for example, certain other factors
explaining both independent and dependent variable in a bivariate analysis can invalidate
this (spurious) relationship in a multivariate context. To illustrate, this may be the case
for the relationship between Heterogeneity and Total Trafﬁcking when the signiﬁcant
positive relationship between GDP per capita and Heterogeneity and the negative
relationship between GDP per capita and total trafﬁcking are taken into consideration.

Even though four independent variables and both control variables are not signiﬁcantly

105

related to total trafﬁcking, they are going to be retained in the following multivariate
models because there is theoretical and empirical justiﬁcation for their expected inﬂuence
on the distribution of crime rates.

As for bivariate correlations between independent and control variables, a
common problem in macro level analysis of crime is that structural characteristics of
population aggregates are generally intercorrelated. Extremely high intercorrelations
between independent variables can give rise to multicollinearity in a multivariate context.
Speciﬁcally, a correlation between independent variables exceeding .70 can create large
standard errors for regression coefﬁcients and generate unstable parameter estimates
(Pratt and Godsey, 2003). Even though bivariate correlations between independent and
control variables are generally less than .70, the high correlation between GDP per capita
and Population Aged 15-24 (.80. p<.01) signals a potential problem for multicollinearity
(Gujarati, 2003). A number of different age cohorts as an indicator of the proportion of
younger population in a country (i.e. percentage of population aged 15-29, 15-34, 15-39)
have been included in bivariate analyses; however, correlation between GDP per capita
and the percentage of all these age groups always exceeded .70. Therefore, Population
Aged 15-24 is retained for multivariate analyses presented below. However, additional
tests for multicollinearity will be conducted in the following multivariate anlaysis.

(3) Multivariate Analysis

Informed by empirical research on human trafficking and mainly guided by
migration systems and social disorganization theories, the following multivariate analyses
are intended to estimate exploratory models for cross-national distribution of human

trafﬁcking across 53 countries. Of nine different models estimated below, the ﬁrst three

106

models investigate the impact of macro level structural factors on the distribution of total
human trafﬁcking, whereas the remaining six models examine whether the inﬂuence of
the same structural factors holds for human trafﬁcking from and to 53 countries in the
sample. Table 3 and Table 4 present the results of Ordinary Least Squares and Ordered
Logit Regression analyses.

First, as the base model, an OLS regression model is estimated by regressing total
trafﬁcking on independent variables GDP per capita, urbanization, heterogeneity (1n),
residential mobility, family disruption, and total population (In) in Model 1. This ﬁrst

model explains 45% of variance in total trafﬁcking and supports three hypotheses as

regard to the impact of poverty (Hla), urbanization (HM), and total population (H If) on

cross-national distribution of human trafﬁcking. As expected, poverty, urbanization, and
total number of population have a positive impact on total trafﬁcking in a country
controlling for the impact of residential mobility, heterogeneity, and family disruption. In
other words, countries with more poverty, urbanization, and total population are exploited

more by human trafﬁcking markets net of the impact of remaining independent variables.

The other three hypotheses as regard to the impact of heterogeneity (Hlb), residential

mobility (ch), and family disruption (H16), however. are not supported by the ﬁrst

model. That is, heterogeneity (1n), residential mobility, and family disruption do not have
signiﬁcant impact on cross-national distribution of human trafﬁcking when GDP per
capita, urbanization, and total population (ln) are held constant.

In the second model, the introduction of control variables gini (1n) and population

aged 15-24 provides a better ﬁt to the data and results in 8 % increase in R2 (AF: 3.79,

107

p<.05). Moreover, coefﬁcients of GDP per capita and total population are also increased
in the second model, whereas the impact of urbanization on total trafﬁcking remained the

same.

Table 3. OLS Regression Models for Total Trafficking (N=53)

 

 

 

 

 

Model 1 Model 2 Model 3
Total Traffickipg Total Trafﬁckipg Total Traffickipg
Variable B (SE) VIF B (SE) VIF B (SE) VIF
Constant -2.32 - 8.56a - 4.34 -
(2-13) (4.96) (4.76)
Independent
Variables
GDP per capita -.0001*** 1.88 -.0002*** 4.88 -.0002*** 4.87
(.00003) (.00004) (.00004)
Urbanization .03** 1.92 .03... 2.06 .033 2.14
(92) (.02) (.02)
Heterogeneity (In) .04 2.11 .10 2.15 .17 2.21
(.21) (.20) (.19)
Residential Mobility -.34 1.22 .19 2.13 .09 2.02
(.24) (.30) (.27)
Family Disruption -.30 1.73 -.43 1.89 -.38 1.91
(.26) (.26) (.25)
Total Population (In) .66** 1.49 .70*** 1.57 .66*** 1.58
, (.18) (.17) (.16)
Control Variables
Population Aged 15- - -.35* 4.77 — -
24 (.15)
Gini (1n) - -1.42 1.93 -.95 1.99
(1.26) (1.21)
P0pulation Aged 15- -.77** 4.17
19 ' ’ (.22)
P0pulation Aged 20- .44 4.61
24 - ' (.33)
F 6.20*** 6.16*** 6.89***
R2 .45 .53 .59
AIC 3.80 3.72 3.61

 

Note: Entries are unstandardized coefﬁcients and numbers in parenthesis are
standard errors; ap<.10; *p<.05; **p<.01; ***p<.001

Thus, the impact of poverty, urbanization, and total population on cross-national

distribution of human trafﬁcking remains signiﬁcant even when inequality and the

108

proportion of younger population in a country are controlled. Heterogeneity, residential
mobility and family disruption do not have signiﬁcant impact on total trafﬁcking in the
second model either. Even though such macro level structural characteristics can hardly
be regarded as favoring a speciﬁc theoretical framework; the results of Model 1 and
Model 2 support migration systems theory (as regard to its broad discussion about
trafﬁckers) and also provide partial support for social disorganization framework (as
regard to the impact of poverty and urbanization on the distribution of crime). However,
the lack of impact of heterogeneity, residential mobility, and family disruption on the
distribution of crossnational human trafﬁcking is not consistent with social
disorganization theory. Further theoretical implications of these ﬁndings are provided in
the following chapter.

An unexpected ﬁnding in the second model, however, is signiﬁcant negative
impact of the percentage of younger population on total trafﬁcking in a country. In spite
of sizable higher correlation between percent of population aged 15-24 and per capita
GDP, several model selection procedures (i.e., stepwise regression, forward selection,
and backward elimination) suggested that this control variable be retained in Model 2
together with per capita GDP and total population (In). Once this variable is retained in
Model 2, mean VIF inﬂates from 1.92 to 2.67 with the highest VIF score of 4.88 (for
GDP per capita). Although not perfect, VIF scores for per capita GDP and percentage of
population aged 15-24 are quite lower than the VIF score of 10, which is regarded as an
indicator of high collinearity (Gujarati 2003; Neter, Wassennan, and Kutner, 1990;

DeMaris, 2004; Lim, Bond. and Bond, 2005f.

 

4 However, see Allison (1999) for a more conservative discussion about the level of VIF scores in terms of
multicollinearity.

109

Removing percentage of population aged 15-24 from Model 2, on the other hand,
results in a statistically signiﬁcant 6% decrease in R2 (AF: 5.55, p<.05) and again the
same variables (GDP per capita, urbanization, and total population) signiﬁcantly predict
total trafﬁcking in a country. In both cases, neither the reduced nor full versions of Model
2 present evidence for heteroscedastic error variance5 ; and examination of residuals does
not indicate any outlying cases that could unduly inﬂuence the regression coefﬁcients”.
Thus, despite a certain level of multicollinearity mainly caused by the retention of this
variable, percentage of the population aged 15-24 is not dropped from Model 2 to avoid
model misspeciﬁcation. However, the signiﬁcant impact of younger population on total
trafﬁcking in the opposite direction demanded further investigation as presented in the
ﬁnal Model 3.

In fact, even though social disorganization theory and empirical studies on cross-
national distribution of crime, specifically on violent crime/homicide, found positive
impact of this age cohort (or slightly different cohorts of younger population) on the
distribution of crime, there are others who found negative impact of younger population
on crime rates at different levels of analysis (Bennett, 1991; Lee, 2006). In their analysis
of adult homicides across US counties, Lee and Bartkowski (2004) found signiﬁcant
negative impact of population aged 15-29 on adult homicide and they suggested that a
large proportion of younger population (15-29) may indicate limited social integration

and interaction as smaller segment of population would be attached through intimate

 

5 Breusch —Pagan/Cook - Weisberg test for heteroscedasticity indicated constant variance (p>X2(2.27)
=. 13).

6 For Model 2, studentized residuals range from -2.08 to 1.95. and thus. both are in the acceptable range.
Hat values (leverages) range from .3 to .41, again all lower than the threshold of 3p/n =.45. DFBETAS
range from -.48 to .54. and thus. all less than the critical value of l (Agresti & Finlay. 1997).

110

relationships. Simply put, when there is a large young population (and a corresponding
smaller older population), since younger people are less likely to integrate/interact with
others compared to older people in the community, there will be less interaction among
the people in the community and this will decrease the possibility of/opportunity for
victimization and offending. This may be the case for human trafﬁcking as well based on
human trafﬁcking literature which often talks about victims as being recruited and
exploited by their signiﬁcant others whom the victims trusted in. Speciﬁcally, relatives
and neighbors are as much likely as strangers to recruit and exploit trafficking victims
(see, for example, Brunovskis and Tyldum, 2004; INCIDIN, 2002). If this is the case.
once the percentage of the population aged 15-24 is disaggregated into two groups as the
proportion of population aged 15-19 and 20-24, the countries with more aged 20-24
population would have more human trafﬁcking as this category is older and thus might be
more likely to integrate and interact with others through several ways such as education,
employment, and entertainment —which might increase the opportunities and possibilities
for offending and victimization. On the other hand, having more of aged 15-19
population would decrease such interactions and have a negative impact on offending and
victimization and thus on human trafﬁcking as well.

The ﬁnal model, Model 3, attempts to elaborate this speculated impact of younger
population on cross-national distribution of human trafﬁcking. As demonstrated, once the
proportion of population is divided into two groups, as expected, the proportion of
younger cohort (aged 15-19) has a signiﬁcant negative impact on total trafficking
whereas the older group does not have a signiﬁcant impact at all. Entering these two

variables signiﬁcantly contributes to linear ﬁt of the model (R2=.59) and Akaike

111

Information Criterion (AIC) statistics also favors this ﬁnal model, Model 3, as a better
and more parsimonious ﬁt among all three models estimated7. While per capita GDP and
population remained signiﬁcant together with the percentage of population aged 15-19 in
Model 3, efﬁciency of urbanization is reduced in this ﬁnal model, though it is still
cautiously signiﬁcant (p<.10). Retention of other independent variables heterogeneity,
residential mobility and family disruption is based on theoretical considerations, though
they do not signiﬁcantly contribute to any of the models estimated.

Given the scarcity of quantitative studies on human trafﬁcking, in addition to
afore mentioned model selection and speciﬁcation procedures, the following ordered
logit models for human trafficking to-and-from a country are also estimated to further the
reliability of OLS regression results. Ordered/ordinal logistic regression (OLR) is
preferred for the analyses of trafﬁcking from and to countries, for both variables are
coded as ordered/ordinal (response) variables in Bales’s human trafﬁcking scales (0=no
trafﬁcking; 1= rare cases of trafﬁcking; 2=occasional but persistent cases of trafﬁcking;
3=regular cases of trafﬁcking in small numbers; 4=regular cases of trafﬁcking in large
numbers). Even though response variable in an OLR model is comprised of multiple
categories, OLR produces only one set of coefﬁcients for each parameter. Therefore, an
underlying assumption of OLR, which is called “the proportional odds assumption” or
“the parallel regression assumption”, is that the coefﬁcients of the parameters in the
equation would not vary signiﬁcantly if they were estimated separately. The importance

of this assumption is that since the slopes are expected to be same, once this assumption

 

7 For Model 3. studentized residuals are lower than 3 (they range from -1.96 to 2.19 with the latter one as
the highest score). Leverages are also lower than the suggested limit of .51 (3p/n) ranging from .05 to .44.
DFBETAS are also in acceptable limits within the range of -.62 to .42. Finally, Breusch -Pagan/Cook —
Weisberg test for heteroscedasticity indicates constant variance (p>X2( 1.37) =.24).

112

is violated, separate models are needed to describe the relationship between each pair of

outcome groups. Investigation of this assumption across all six OLR models indicates

that there is no significant difference in the coefﬁcients between models 8.

Table 4. Ordered Logit Regression Models for Trafﬁcking to and from the countries

 

 

 

 

 

(N=53)
Traﬂicking from a country Trafﬁcking to a country
M r 4 Model; M M M Mede_12
Variable B (SE) B (SE) B (SE) B (SE) B (SE) B (SE)
Independent
Variables
GDP per capita -.0003*** -.0004*** -.0004*** -.00005 -.0001** -.0001*
(.00006) (.0001) (.0001) (.0003) (.00006) (.00006)
urbanization 04 a 05* 04 a 04 a .048 .02
(.02) (.03) (.03) (.02) (.02) ('0‘)
Heterogeneity (In) .08 .17 .27 .10 .14 .24
(.27) (.28) (.29) (.25) (.26) (.26)
Residential -.91** -.07 -.25 -.09 .53 .52
Mobility (.34) (.44) (.46) (.29) (.39) (.39)
Family Disruption -.31 -.55 -.51 -.35 -.49 -.48
(.34) (.37) (.38) (.34) (.36) (.37)
Total Population .80** .91** .87** .74** .84** .86**
(In) (.26) (.28) (.28) (.24) (.26) (.26)
Control Variables
Gini (1n) - -1.08 -.67 - -1.59 -1.43
(1.74) (1.77) (1.69) (1.69)
Population Aged - -.59** - - _.413 -
15-24 (.25) (.21)
Population Aged - - -l .07** - - -.98**
15-19 (.36) (.33)
Population Aged - - .36 - - .55
20-24 (.52) (.46)
Model x2 5230*“ 59.54*** 63.71*** 1528* 20.97** 26.36**
Pseudo R-Squared .33 .38 .41 .10 .14 .18

 

a p<.10; *p<.05; **p<.01; ***p<.001

 

8 A likelihood-ratio test of proportionality of odds across response categories tests the null hypothesis that
there is no difference in the coefﬁcients between models. All LR tests across six models are non-signiﬁcant
(p>.05). Brant test is also used to examine individual coefﬁcients estimated in the ﬁnal models 3 and 6 and
it indicates that parallel regression assumption is not violated for individual coefﬁcients either. Finally.
investigation of goodness of ﬁt indicated that both models ﬁt the data well as pearson and deviance
statistics are both nonsigniﬁcant.

113

Table 4 presents the results of OLR analyses for trafﬁcking from and to 53
countries. Independent and control variables are entered into the models in the same
manner they were entered into OLS models above; that is, starting with base models with
no control variables, two additional models are estimated with the introduction of control
variables gini coefﬁcient and proportion of population aged 15-24 into the second and
gini coefﬁcient and percentage of population aged 15-19 and percentage of population
aged 2024 into the third models for each of the dependent variables-trafﬁcking from and
trafﬁcking to a country-. In general, the same indicators that signiﬁcantly predict total
trafﬁcking in a country also predict trafﬁcking from and to a country, though with
ﬂuctuations in their conﬁdence levels.

For human trafﬁcking from a country, both per capita GDP and population (In)
have signiﬁcant impact on trafﬁcking from a country across all three models. The
countries with more per capita GDP are exploited less by human trafﬁcking markets,
whereas countries with larger p0pulation are targeted more as source countries.
Urbanization is also positively associated with human trafﬁcking from a country, though
it is cautiously signiﬁcant in Model 1 and Model 3. Unexpectedly, residential mobility
has a signiﬁcant negative impact on human trafﬁcking from a country in the ﬁrst model
without controls. Once the proportion of younger population is introduced into the second
and third models (no matter it is in combined ‘aged 15-24’ or disaggregated ‘aged 15-19’
form), however, this control variable(s) negatively impact human trafﬁcking from a
country and the signiﬁcant impact of residential mobility disappears. Thus. it can be
speculated that the negative impact of residential mobility on trafﬁcking from a country

might also stem from its negative impact on social integration, which might be helpful for

114

the trafﬁckers especially at the recruitment phase of human trafﬁcking as to be elucidated
in the next chapter.

As for human trafﬁcking to a country, however, the discriminatory powers of all
three models (7, 8, and 9) are relatively less than Model ’4, 5, and 6 (be aware that ﬁnal
three models are estimated with a different dependent variable ‘trafﬁcking to a country’).
Although urbanization has signiﬁcant positive impact on trafﬁcking to a country in the
first two models, the results should be interpreted cautiously (.06<p<.10); and, in the ﬁnal
Model 9, it does not have significant impact on human trafﬁcking to a country at all.
Poverty, total population (In), and the proportion of younger population (aged 15-19). on
the other hand, have signiﬁcant impact on human trafﬁcking to the countries in the
sample. Simply put, the countries with more poverty, larger population, and with smaller
younger population (aged 15-19) are targeted more by human trafﬁcking markets as
destination places.

To sum up, both OLS and OLR models provide certain level of support for the
hypothesized impacts of structural factors on cross-national distribution of human
trafficking in the light of migration systems theory, social disorganization theory, and of
previous research on human trafficking and on cross-national distribution of crimes.
Poverty. urbanization, and total population provide evidence for the cross-national
distribution of human trafﬁcking in the hypothesized direction, whereas proportion of
younger population in a country has an unexpected negative impact on human trafﬁcking.
Even though this ﬁnding seems to be counterintuitive at ﬁrst, the ﬁndings of previous
research regarding the impact of the proportion of younger population on the distribution

of crime rates was not consistent either; yet. human trafﬁcking literature helps to clarify

115

such unexpected relationship between younger population and human trafﬁcking to some
extent and more discussion regarding the findings of OLS and OLR models is going to be
provided in the following chapter.

The next section examines trafﬁcking in women across 81 cities in Turkey. Thus,
it attempts to shed light on the relationship between structural factors and human
trafficking by focusing on a specific (if not single) type of human trafﬁcking in a
culturally homogeneous context with a structurally heterogeneous sample of 81
provinces. Observed total trafﬁcking in Turkey is 8, which is the maximum value on
combined human trafﬁcking scale. The predicted value for human trafﬁcking in Turkey
is 6.45 based on afore estimated ﬁnal OLS model (Model 3) of total trafﬁcking in a
country. Apparently, both observed and predicted values of human trafﬁcking in Turkey
signals an opportunity to look at the relationship between structural factors and
distribution of human trafﬁcking markets across different cities. Additional factors to be
considered in the Turkish context are expected to inform the limitations of cross-national
models as well.

2. Distribution of Human Trafﬁcking Markets in Turkey

In this section. human trafﬁcking in Turkey is going to be investigated in the light
of hierarchical diffusion, social disorganization, and social disorder frameworks. In this
regard, three different models, one for each framework, are going to be estimated for the
distribution of human trafﬁcking markets across 81 cities in Turkey. Moreover, the
impact of hierarchical diffusion and social disorganization on the distribution of migrant

prostitution is also going to be examined. Finally. the relationship between migrant

116

prostitution and human trafﬁcking, property and violent crimes as well as organized
crime incidents is going to be investigated in bivariate and multivariate context.

(1) Summary statistics

Information about all 81 provinces of Turkey is included in the dataset for the
analysis of the distribution of human trafﬁcking markets and other crimes in Turkey.
General characteristics of these 81 cities as regard to independent and dependent
variables are presented in Table 5. Human trafﬁcking incidents and calls for rescue are
two different measures of human trafﬁcking in Turkey for the entire year of 2006. The
Turkish National Police recorded 153 human trafﬁcking incidents in 23 provinces (28%)
in 2006, which range from 0 to 45 incidents with a mean score of 1.89. The IOM office in
Ankara, on the other hand, reported a total number of 579 calls for rescue in 36 provinces
(44%) in 2006, which range from 0 to 218 calls with 7.07 calls on average (IOM, 2006;
also see www.countertrafﬁcking.org).

As an indicator of total crime in Turkey (excluding drug and terror related
incidents), public order crimes in 2005 range from 232 to 135,834 with 6,021.74 crimes
on average, whereas the same type of crimes were relatively less in the year 2003 as they
range from 178 to 82,529 with a mean score of 3,972.90. The number of organized crime
incidents recorded by the TNP during the entire year of 2005 ranges from 0 to 43 with
2.78 incidents on average. Homicides are considered as a measure of violent crime in the
year 2003 and they range from 0 to 465 with 22.55 homicides on average. Property
crimes, measured as the total number of burglaries in 2003, range from 21 to 39,118 with

1,479.15 burglaries on average.

117

Table 5. Descriptive Statistics (N=81)

 

 

Variable Mean SD Min Max
DEPENDENT VARIABLES

Human Trafﬁcking Incidents ’06 1.89 6.12 .00 45.00
Calls for Rescue ’06 7.07 28.71 .00 218.00
Public Order Crime ’05 6021.74 15831.79 232.00 135834.00
Organized Crime ’05 2.78 5.99 .00 43.00
Public Order Crime ’03 3972.90 10072.49 178.00 82529.00
Homicide ’03 22.55 56.19 .00 465.00
Burglary ’03 1479.15 4749.91 21.00 39118.00
INDEPENDENT VARIABLES

Total POPulation 837085.50 123146202 93584.00 1001873500
GDP per capita 1479.57 744.06 453.00 4696.00
Urbanization .32 .17 .06 .89
Heterogeneity 2.38 8.38 .02 72.48
Residential Mobility 15.47 4.26 8.73 36.90
Family Disruption .11 .06 01 .28
International Border .58 .49 .00 1.00
Migrant Prostitution ’03 2191 68.95 00 432.00
CONTROL VARIABLES

POPulation Aged 15-24 (%) 20.61 2.13 16.55 30.86
Unemployment 7.85 3.1 1 3.60 17.40

 

The independent variables are intended to represent hierarchical diffusion, social

disorganization, and social disorder across the cities. Diffusion is represented by total

population and the presence of international borders in a city. Total population range

from 93,584 to 10,018,735 with a mean population of 837,085. The presence of

international borders in a city is dummy coded and it ranges from 0 to 1 with a mean

score of .58. For social disorganization, traditional antecedents of social disorganization

have been utilized. Per capita GDP is used to measure absolute poverty and it ranges

from 453 to 4,696 with 1,479.57 (Y eni Turkish Liras/YTL) on average. Urbanization

ranges from .06 to .89 with 32% of people living in the city centers on average.

Heterogeneity ranges from .02 to 72.48 with a mean score of 2.38. Residential mobility

118

ranges from 8.73 to 36.90 with a mean score of 15.47. The ﬁnal indicator of social
disorganization is family disruption and it ranges from .01 to .28 with .11 divorces per
thousand marriages on average.

Migrant prostitution is going to be used as both a dependent and an independent
variable in the following bivariate and multivariate analysis procedures. It is considered
as a single measure of social disorder and it ranges from 0 to 432 deportations with 21.91
deportations on average. Finally, from two control variables, the proportion of younger
population (15-24) ranges from 16.55 to 30.86 with a mean score of 20.61 whereas
unemployment ranges from 3.60 to 17 .40 with a mean score of 7 .85.

Before examining the bivariate relationships between independent and dependent
variables, natural log transformation is performed for two independent variables, total
population and heterogeneity, given the considerable positive skewness (West, Finch. and
Curran, 1995) in both variables’ distribution (Table 5).

(2) Bivariate Analysis

Both bivariate correlations and independent sample t tests are used to examine
hypothesized relationships between independent and dependent variables in bivariate
context. Moderate to strong bivariate correlations are found between the measures of
human trafﬁcking, total crime, and organized crime in Turkey (Table 6). This already
indicates concentration of crime and disorderly behavior in certain places in Turkey.
Moreover, human trafﬁcking incidents are positively correlated with international borders
(.25), total population (.44), heterogeneity (.48), urbanization (.28), and migrant

prostitution (.77).

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120

Calls for rescue is also positively correlated with the same independent variables
of international borders (.19), total population (.48), heterogeneity (.38), urbanization
(.38), and migrant prostitution (.84); however, correlation between calls for rescue and
the presence of international borders is not signiﬁcant (p=.08). Thus, positive relationship
between both measures of human trafﬁcking and ﬁve independent variables of

international borders. total population, urbanization, heterogeneity, and migrant

prostitution lends support for the hypotheses H2a, H21), H2d , H2f , and H3f respectively.

N amely, the cities with international borders and more population, heterogeneity,
urbanization, and with more migrant prostitution also have more human trafﬁcking
incidents and more calls for rescue.

In fact, the same set of independent variables is also positively and significantly
correlated with total crime and organized crime as well. In other words, besides having
more human trafﬁcking incidents and calls for rescue, the cities with international
borders and more population, urbanization, heterogeneity, and with more migrant
prostitution also have more total crime and organized crime. Additionally, unemployment
is also positively correlated with total crime (.22) and organized crime (.24).
Unexpectedly, GDP per capita is positively correlated with both measures of human
trafﬁcking. Other independent (residential mobility and family disruption) and control
variables (population aged 15-24 and unemployment), however, are not signiﬁcantly
correlated with the measures of human trafﬁcking.

As mentioned, migrant prostitution is considered both as a dependent and an
independent variable in this study. In order to examine the impact of migrant prostitution

on the distribution of human trafﬁcking and other crimes, independent sample t tests have

121

been performed with unequal variances assumed and non-directional hypotheses (two-
tailed), in which average human trafﬁcking incident rates, calls for rescue rates, and other
crime rates (per 10.000 population) of cities without migrant prostitution is subtracted
from average incident rates, call rates. and other crime rates (per 10,000 population) of
cities with migrant prostitution.

Table 7. Difference in human trafﬁcking incident rate, calls for rescue rate,

violent and property crime rate (per 10,000 population) means between the
cities with and without migrant prostitution (N=81)

 

 

Variable Mean Difference t P
Human Trafﬁcking Incidents

’06 per 10,000 population .027 3.224 .002
Calls for Rescue ’06 per

10,000 population .049 2.592 .012
Public Order Crime ’05 per

10.000 population 22.790 3.306 .001
Organized Crime ’05 per

10,000 population 1.657 2.430 .017
Homicide ’03 per 10,000

population .077 3.236 .002
Burglary ’03 per 10,000

population 5.565 3.160 .002
Public Order Crime ’03 per

10.000 population 11.069 3.254 .002

 

As presented in Table 7, the cities with migrant prostitution have signiﬁcantly
more human trafﬁcking, more violent and property crime, more total crime, and more
organized crime in a given and following year. Thus, signiﬁcant mean differences
between the cities with and without migrant prostitution as regard to the number of

human trafﬁcking incidents and other violent, property, total crimes, and organized crime

incidents support the hypotheses H3f, H3g, H3h, H3i, H3]; and H3k. As a dependent

variable, on the other hand, migrant prostitution is positively correlated with

heterogeneity (.43), urbanization (.28), and total population (.42). which indicates that the

122

cities with more heterogeneity, more urbanization, and more population also have more

migrant prostitution and thus, lend support for the hypotheses H31, and H3d.

Finally, as a ﬁrst step for identifying multicollinearity, correlations between
independent and control variables are inspected and they do not indicate any serious
problem since bivariate correlations are quite lower than the generally accepted threshold
of .70 (Pratt and Godsey, 2003). However, further investigation of potential problems for
multicollinearity is going to be carried out in multivariate context in the following
section.

Overall, bivariate correlations and independent sample t tests provide substantial
support for hypothesized relationships between the measures of human trafﬁcking and
hierarchical diffusion, social disorganization, and social disorder. Although speciﬁed
relationships between human trafﬁcking and residential mobility and family disruption;
and, between migrant prostitution and residential mobility, family disruption, and GDP
per capita are not supported in the bivariate analyses, considering the theoretical model
and prior empirical evidence, all these independent variables and two other control
variables are going to be retained in multivariate analyses.

(3) Multivariate Analysis

Multivariate analyses are aimed at estimating exploratory models for the
distribution of human trafﬁcking markets across 81 cities in Turkey. The preceding
bivariate analyses evinced that hierarchical diffusion, social disorganization, and social
disorder have considerable impact on the distribution of human trafﬁcking markets in the

hypothesized direction. Hence, the following multivariate analysis procedures mainly

123

attempt to model the impact of hierarchical diffusion, social disorganization, and social
disorder on the distribution of human trafﬁcking markets in Turkey.

In this regard, the ﬁrst six models (Model 10 through Model 15) look into the
impact of hierarchical diffusion and social disorganization on both measures of human
trafficking and on migrant prostitution. Then, four additional models Model 16 through
Model 19) are estimated to examine the impact of migrant prostitution on the distribution
of human trafﬁcking incidents, calls for rescue, total crime, and organized crime while
controlling for the impact of hierarchical diffusion and social disorganization.

Negative Binomial Regression Model (NBRM)

A common analytical strategy in modeling the distribution of aggregate level
crime rates is using least squares estimation procedures. Yet, as it is clear from the above
presented descriptive statistics (Table 5), the distribution of the measures of human
trafﬁcking, migrant prostitution, total crimes, and organized crime incidents in Turkey
are all highly skewed with no or rare incidents/calls/deportations/crimes in several cities.
In this case, error distribution also becomes increasingly skewed because of rare and zero
counts, which violates the OLS assumption of normal distribution of residuals especially
in the cities with relatively less population. Moreover, if population based rates - instead
of incident/call/deportation/crime counts- are used as dependent variables, Osgood (2000,
22-23) argued, precision of the estimated crime rates depends on the population sizeg.
Thus, variation of population sizes across the provinces violates the OLS assumption of

homogeneity of error variance by causing larger errors of prediction for crime rates (i.e.,

 

9 To illustrate. while each additional count of incident/call/deportation/crime corresponds to half an
incident/call/deportation/crime per 100.000 in a city with 200.000 population, it will correspond to .01 per
100.000 incident/call/deportation/crime in a city with 1.000.000 population.

124

per 100,000) based on smaller populations in the sample. Therefore, using linear
regression models for counts of human trafﬁcking incidents, calls for rescue, migrant
prostitution, and total crime can result in inefﬁcient, inconsistent, and biased estimates
(Long, 1997, 217).

Given the shortcomings of the OLS regression models, Poisson based regression
models are suggested as an alternative analytic approach for the analysis of discrete count
data. The basic poisson regression model, however, assumes that events occur
independently over time and it also has a restrictive distributional feature termed
‘equidispersion’, which means that the mean and variance of the distribution are the
same. As far as the distribution of human trafﬁcking, migrant prostitution, total crime,
and organized crime in Turkey is concerned, occurrence of one may certainly precipitate
additional trafﬁcking/prostitution/crime incidents. Moreover, since the variation of these
dependent variables exceed their means. using basic poisson model with this
overdispersed data can result in underestimation of the standard errors of the coefﬁcients
and thus produce inefﬁcient signiﬁcance tests (Cameron and Trivedi, 1998).

Thus, instead of the basic poisson model, a variation of basic poisson model, the
Negative binomial regression model (NBRM) is preferred for the analysis of the
distribution of human trafﬁcking, migrant prostitution, and total crime in Turkey. NBRM
accommodates overdispersion by allowing a disturbance term (or) into the relationship
between the regressors and outcome (DeMaris, 2004, 365). Simply put, whereas the basic
Poisson model assumes that the variation in the expected count is caused by observed
heterogeneity and different values of the parameters; in NBRM, variation in the predicted

mean is assumed to be produced by both regressors and the residual variance parameter

125

(or) introduced into the model. Finally, even though NBRM is utilized to analyze count
outcomes, when natural logarithm of population size is included in NBRM as an offset
variable (with a fixed coefﬁcient of one), per capita incidents/calls/deportations/crime
rates become constant across provinces with different population sizes, controlling for
other explanatory variables. Yet, when the impact of population size is estimated rather
than ﬁxed, as this is the case for the following models, a value of one should be
subtracted from the coefﬁcient of population size. Then, for the signiﬁcance test, the

estimated value of the coefﬁcient should be compared to the value of one rather than zero

(i.e., b-1/SEb; Osgood, 2000, 39-40). This transformation, which can be thought of as

equivalent to converting the counts to per capita rates, adjusts for the impact of
population size and allows to control for the impact of population size.

Tables 8 and 9 present the results of N BR analyses for human trafﬁcking
incidents, calls for rescue, migrant prostitution, total crime, and organized crime in
Turkey. Indications of model ﬁt are based on minus two times the log likelihood (-2LL),
spearman’s correlation (r) between observed and predicted values (Osgood, 2000), and
AIC statistic (Hilbe, 2007), which are provided for each individual model below. The
significance test associated with -2LL is based on a likelihood test which compares the
full model to base (intercept-only) model. Spearman r, on the other hand, demonstrates
the success of the model in rank ordering the cities by incident/call/deportation/crime
counts. AIC is the acronym for the Aikake Information Criterion, which is also based on
log-likelihood function. Typically, the preferred model is the one with the lowest AIC
statistic (Hilbe, 2007, 27). Although the interpretation of pseudo R2 is not as

straightforward as it is in an OLS model, the values of the same type of pseudo R2

126

(McFadden's pseudo R2) are included to provide information about the discriminatory
power of different models. The signiﬁcance test associated with “or” (the residual
variance parameter), on the other hand, is based on a likelihood ratio test which tests that
dispersion is equal to zero. Since absence of overdispersion would favor ordinary poisson
regression model, a significant likelihood ratio test associated with or coefﬁcient indicates
that N BRM provides a better ﬁt to data compared to PRM.

The ﬁrst six models examine the impact of hierarchical diffusion and social
disorganization on the distribution of human trafﬁcking incidents, calls for rescue, and
migrant prostitution as presented in Table 8.

As demonstrated in the Models 10, 12, and 14, presence of international borders

in a city signiﬁcantly increase per capita human trafﬁcking incidents, calls for rescue, and

migrant prostitution. Speciﬁcally, the cities with international borders have [100*(eb-1) =

988%] almost ten times more human trafficking incidents, 327% (three times) more calls
for rescue, and 707% (seven times) more migrant prostitution compared to cities without
international borders when only the indicators of hierarchical diffusion are entered into
the models.

Although the size of the total population was almost the strongest predictor of all
three outcomes in bivariate context, when the presence of international borders in a city is
controlled, it does not have signiﬁcant impact on the distribution of human trafﬁcking
incidents and calls for rescue. Unexpectedly, the size of population (In) has a signiﬁcant
negative impact on migrant prostitution rate in a city controlling for the presence of

international borders.

127

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Apparently, since human trafﬁcking in Turkey exclusively involves foreigner
women, rather than the size of the population of the cities, the presence of international
borders becomes more important in terms of the distribution of migrant prostitution and
human trafﬁcking in Turkey. Once the indicators of social disorganization are also
entered into the models 11, 13, and 15 together with the controls, however, the signiﬁcant
impact of the presence of international borders on migrant prostitution disappears. Yet,
the presence of international borders still has a signiﬁcant impact on human trafﬁcking
incidents (reduced model estimated with 80 cases) and calls for rescue.

Before going into the detailed interpretation of parameter estimates, it should be
noted that diagnostic procedures using residual analyses based on standardized deviance

values indicated that —whereas model 13 and 15 ﬁtted the data well (with standardized

. . . d
dev1ance values 1n the range of -3 to 3), 1n the model 11, the 23r case turned out to be a

potential outlier with a deviance value of 3.30, although it does not have signiﬁcant
inﬂuence on parameter estimates as its leverage is lower than the critical value of .33
(3p/n) as suggested by Cameron and Trivedi (1998). Therefore, an alternative model is
estimated with 80 cities by dropping this potential outlier and the results presented next to
the full model under Model 11. Standardized deviance values in this reduced model are in
the acceptable range of -3 to 3 with most of the values less than 2. Comparison of AIC
and 0t statistics as well as spearman correlation values across the full and reduced models
demonstrated that the reduced model ﬁts the data better with relatively more
discriminatory power (R2=.26). Thus, although the cities with international borders do
not have signiﬁcantly more human trafﬁcking incidents in the full Model 11, in the

reduced model, the cities with international borders have 1128% (or almost 11 times)

129

more human trafficking incidents compared to cities without migrant prostitution. The
cities with international borders also have 103% more per capita rescue calls (Model 13)
compared to cities without international borders controlling for remaining independent
and control variables.

As for the impact of social disorganization; poverty (p<.10), heterogeneity (1n),
and residential mobility all have signiﬁcant impact on human trafﬁcking incidents in the
expected direction in the reduced model, though heterogeneity was the only explanatory
variable that had signiﬁcant impact in the full model 11. Speciﬁcally, in the reduced
model, one standard deviation increase in gdp per capita leads to 42% decrease in human
trafﬁcking incidents whereas one standard deviation increase in heterogeneity (1n) and
residential mobility corresponds to 248% and 83% increase in human trafﬁcking
incidents respectively. As for rescue requests, in addition to the presence of intemational
borders, the size of total population (p<.10), residential mobility, and family disruption
(p<.10) also have signiﬁcant positive impact on the distribution of» per capita calls for
rescue controlling for other independent and control variables. To be more speciﬁc, each
standard deviation increase in population (In), residential mobility, heterogeneity (1n),
and family disruption corresponds to 51%, 68%, 132%, and 38% increase in per capita
calls for rescue respectively. On the other hand, heterogeneity (In) is the only signiﬁcant
predictor of per capita migrant prostitution in a city. One standard deviation increase in
heterogeneity results in corresponding 119% increase in migrant prostitution rates.
Hence, social disorganization also seems to have signiﬁcant impact on the distribution of
human trafﬁcking incidents, calls for rescue and migrant prostitution while controlling

for the impact of hierarchical diffusion and control variables.

130

Table 9. Negative Binomial Regression Models for Human Trafﬁcking, Total Crime, and
Organized Crime (N=81)

 

 

 

 

Model 16 Model 17 Model 18 Model 19
Trafﬁcking Incidents ’06 Calls for Public Order Organized
Egﬂ £138_0 Rescue ’06 Crimes’05 Crime ‘05
Variable B (S.E.) B (S.E.) B (S.E.) B (S.E.) B(S.E.)
Constant -22.7l*** -28.32*** -21. 19*** -4.77*** -9.47*
(6.17) (6.19) (3.90) (1.20) (3.90)
Hierarchical Diffusion
Total Population (ln) .27 .48 .413 —.02 -.12
(.42) (.38) (.24) (.09) (.25)
International Border 1.188 275* .69* .14 .72*
(.68) (1.16) (.33) (.11) (.34)
Social Disorganization
GDP per capita —.0004 _ 00063 -.0002 0002* .0006“
(.0004) (.0003) (.0002) (.0001) (.0002)
Heterogeneity ( In) .30 .45* .36** -.05 -.18
(.18) (.18) (.11) (.04) (.11)
Residential Mobility .14* .18** .11* -.007 .009
(.07) (.07) (.05) (.01) (.05)
Urbanization .17 -.50 -1.46 1.82*** 1.76*
(1.65) (1.51) (1.06) (.36) (.91)
Family Disruption 2.82 4.29 5.113 -.07 -.54
(4.18) (3.91) (2.80) (1.06) (2.51)
Social Disorder
Migrant Prostitution Rate .61 ** .51** .35** .23** .34*
(In) (.21) (.19) (.13) (.07) (.17)
Control Variables
Population Aged 15-24 .12 .18 .03 -.04 -.20*
(%) (.15) (.15) (.09) (.03) (.09)
Unemployment -.05 -.08 .05 -.07*** -.03
(.11) (.12) (.07) (.02) (.06)
-2LL l70.48*** 150.61*** 273.24*** 1354.46*** 238.68***
Pseudo R2 .22 .29 .29 .04 .14
on .89*** .51*** .37*** .16*** .28***
AIC 2.40 2.18 3.67 17.02 3.24
Spearmanr 59*“: .64*** .80*** 92*“: 73*“:

 

a
p<.10; *p<.05; **p<.01; ***p<.001

The ﬁnal four models, Model 16, 17, 18, and 19 (Table 9) examine the

relationship between migrant prostitution and the distribution of human trafﬁcking, total

crime, and organized crime in Turkey while controlling for the impact of hierarchical

diffusion, social disorganization, and control variables. It should be noted that instead of

131

 

migrant prostitution counts across the cities, migrant prostitution rate per 100,000
population was used as an explanatory variable in the following models. Moreover,
because of severe positive skewness (Pridemore and Freilich, 2006; West et al., 1995) in
migrant prostitution rates across the provinces, natural log transformation was performed
for this variable after adding 1 to the cities with 0 cases of migrant prostitution as the log
of 0 is undeﬁned (Osgood, 2000).

As hypothesized, migrant prostitution has significant positive impact on all four
outcomes. Speciﬁcally, one standard deviation increase in migrant prostitution results in
70% increase in human trafﬁcking incidents (or 57 % increase in the reduced model with
80 cases), 36% increase in calls for rescue, 21% increase in total crime, and 34% increase
in organized crime. In more practical terms, based on the ﬁnal three models, a 10%
increase in migrant prostitution leads to 6.2% increase in human trafﬁcking incidents (or
5.2 % increase in the reduced model with 80 cases), 3.6 % increase in calls for rescue, 2.3
% increase in total crime, and 3.5 % increase in organized crime incidents in a city when
all other parameters in the models are held constant”).

Apart from the signiﬁcant positive impact of migrant prostitution on all four
outcomes, cities with international borders (p<.10) and more residential mobility appear

to have more human trafﬁcking incidents (Model 16). Yet, residual analyses based on

. . . . . rd
standardized dewance values indicated that once again, the 23 case turned out to be a

potential outlier in the full model 16, although its inﬂuence on parameter estimates was

 

x
'0 An increase of x in an explanatory variable multiplies the ﬁtted mean incident/call/crime rate by e
(Osgood, 2000. 39).

132

not signiﬁcantly high“. Therefore, an alternative model is estimated with 80 cities by
dropping this potential outlier and the results of this reduced model presented next to the
full model under Model 16.

In addition to the signiﬁcant impact of the presence of international borders,
residential mobility, and migrant prostitution in the full model, heterogeneity and gdp per
capita also have signiﬁcant impact on human trafﬁcking incidents in the expected
direction in this reduced model. Comparison of AIC and or statistics as well as Spearman
correlation values across full and reduced models also indicate that the reduced model ﬁts
the data better with relatively more discriminatory power (R2=.29).

As far as calls for rescue are concerned, the cities with international borders and
more population (p<.10), heterogeneity, residential mobility, and family disruption have
more calls per capita (Model 17).

For total crime, the cities with more income, urbanization, and less unemployment
appear to have more total crime. Even though the positive relationship between per capita
gdp and total crime as well as the negative relationship between unemployment and total
crime appears to be counterintuitive considering what is expected from social
disorganization perspective; given Bursik and Grasmick’s (1993) argument about the

relationship between social disorganization theory and routine activities theory as regard

 

“ Standardized deviance values were within the acceptable range in the models 17, 18, and 19 since they
are less than 3 and even lower than 2 for most of the cases. For the model 16, however, only one case. that
is. case number 23 turned out to be a potential outlier since its standardized deviance was 3.22. Therefore
an additional model is estimated with 80 cases by dropping out case 23, although its leverage (.05) was less
than the critical value of .37, which is equal to three times the number of parameters divided by the number
of observations (3p/n) as suggested by Cameron and Trivedi (1998). Moreover, potential problems of
multicollinearity across all models were also investigated using OLS estimation procedures. All VIF scores
are lower than the critical limit of 10 suggested in the literature (Gujarati 2003; Neter, Wasserman, &
Kutner, 1990; DeMaris. 2004). For the models 16 (both reduced and full models). and 17 the average VIF
score is 2.02 with a maximum VIF score of 2.65 for total population.

133

to both theories’ focus on social control and guardianship, it can be speculated that cities
with more income and thus relatively more social and ﬁnancial interaction could increase
the crime rate because of increased residential and commercial use of land. Put
differently, the cities with more income and relatively more social and commercial
interactions can be considered as attractive places especially for property crime and
disaggregated measures of public order crime rates, such as separate violent and property
crime rates across the cities might help to better address this relationship. Increased
unemployment, on the other hand, would decrease such interactions and also increase the
guardianship back at home or in the neighborhood. In fact, the signiﬁcant positive impact
of urbanization on public order (total) crimes also supports such argument in that
urbanization would also decrease guardianship and control by increasing anonymity
because of ebb and ﬂow of workers, visitors, foreigners into these cities.

Finally, as expected, organized crime is inﬂuenced by almost the same factors as
total crime, for organized crime also prevails in the cities with higher industrialization
and urbanization. The results also concur with Finckenauer and Voronin’s (2001)
argument as regard to the distribution of organized crime in that as they suggested, the
cities with international border gates, higher income and urbanization also have more
organized crime incidents. On the other hand, the proportion of younger population has
an unexpected negative impact on the distribution of organized crime incidents in
Turkey. This unexpected ﬁnding might have emerged as a result of the fact that even
though younger people are generally known to be more likely to offend and victimized, a
signiﬁcant proportion (96 %) of the members of criminal organizations arrested in

Turkey in recent years was 21 years old or older (KOM, 2002). Thus, a larger proportion

134

of younger population in a city may have negative impact on establishment and activities
of criminal organizations through its negative impact on recruitment by these
organizations.

Based on afore provided information about model selection and ﬁt considerations,
whereas the models of hierarchical diffusion (Model 10, 12, and 14) produce limited
information about the distribution of human trafﬁcking and migrant prostitution in
Turkey; in general, inclusion of social disorganization and disorder measures as well as
of control variables provides better ﬁt to data in the Models 11, 13, 15, 16, and 17. A11
ten models signiﬁcantly contribute to our understating about the distribution of human
trafﬁcking markets and other serious crimes in Turkey (as opposed base/intercept-only
models) and signiﬁcant alpha (0t)coefﬁcient in all models demonstrate that NBRM
provides a better ﬁt as opposed to PRM because of overdispersion present in the data. As
far as ﬁt statistics are concerned, however, the ﬁnal four full models which consider the
impact of all three factors (diffusion/disorganization/disorder) and‘controls appear to be
better in explaining the distribution of human trafﬁcking and other crimes in Turkey.
Spearman correlation statistics, on the other hand, favor the Model 17 in terms of its
success in rank ordering the cities as regard to the distribution of per capita calls for
rescue.

Overall, all three approaches, that is, hierarchical diffusion, social disorganization,
and disorder signiﬁcantly contribute to our understanding of the distribution of human
trafficking in Turkey as supported by both bivariate and multivariate analyses.

The impact of each approach summarized in Table 10 by referring back to the

models 15, 16, 17, 18, and 19. This table is also intended to provide information about

135

speciﬁed hypotheses as to whether they are supported/accepted (A) or rejected (R) in the

multivariate context based on the information from the full models of human trafﬁcking,

migrant prostitution, total crime, and organized crime. For migrant prostitution, Model 15

only provides partial support for social disorganization (H313) and no support for

hierarchical diffusion. In this model, heterogeneity (In) is the only signiﬁcant factor that

inﬂuences the distribution of migrant prostitution in Turkey controlling for other

indicators.

Table 10. Summgy of F indigs

 

 

 

 

Model 15 Model 16 Model 17 Model 18 Model 19
Migrant Prost. HT Inc. Calls for Total crimc Organized
’03 ’06 Rescue ’06 ’05 crimc ’05
Variable
Hierarchical
Diffusion
a
Total Population (In) N (Hza/R) N (Hza/R) s (Hza/A) N N
a N *
international Border N (sz/R) s (sz/A) 3* (sz/A) 3
Social
Disorganization b
GDP percapita N (H3a/R) N (H2C/A ) N (Hzc/R) 3* SM
Heterogeneity (In) S*** (H3b/A) N (HZd/A ) 3** (HZd/A) N N
Residential Mobility N (H3c/R) 8* (Hze/A) 8* (Hze/A) N N
Urbanization N (H3d/R) N (HZf/R) N (HZf/R) S*** 8*
Family Disruption N (Baa/R) N (HZg/R) sa (H2g/A) N N
Social Disorder
Migrant Prostitution
Rate ' S**(H3f/A) S** (H3f/A) S** (H3j/ A) S*(H3k/A)
Control Variables
POpulation Aged 15-
24 (%) N N N N -S*
Unemployment N N N -S*** N

 

a
p.<10. *<.05. **p<.01, ***p<.001; N=Nonsigniﬁcant. S=Signiﬁcant. A=Accepted. R=Rejected.

b
Note: These two hypotheses are supported in the reduced model 16 estimated with 80 cases.

136

Provincial distribution of both measures of human trafﬁcking, on the other hand,

is well explained by all three approaches in the hypothesized direction. Model 16

supports the hypotheses H21). H2O, sz, H26, and H3f whereas Model 17 lends support

for the hypotheses H23, sz, sz. H26. Hzg and H3f. Thus. hypothesized relationships

between the distribution of human trafﬁcking markets and hierarchical diffusion, social
disorganization, and disorder were supported in these two models. Finally, signiﬁcant

positive impact of migrant prostitution on total crime and organized crime lends support

for the hypotheses H3 and H3k respectively.

Summary of Findings

The ﬁndings presented in this chapter provide considerable support for all three
major research questions as regard to the cross-national distribution of human trafﬁcking;
distribution of human trafﬁcking markets in Turkey; and the impact of migrant
prostitution on the distribution of human trafﬁcking markets in Turkey.

OLS and OLR models for cross-national distribution of human trafﬁcking
indicate that poverty, the size of the total population (In), and urbanization have
signiﬁcant positive impact on the distribution of human trafﬁcking markets across the
sample of 53 countries. The proportion of younger population in a country (proportion of
population aged 15-19), however, has signiﬁcant negative impact on cross-national
distribution of human trafﬁcking. The same indicators signiﬁcantly impact human
trafﬁcking from a country and to a country as well as total human trafﬁcking in a

country, though the impact of urbanization depends on the age cohort of the younger

137

population included in the models — especially in the ﬁnal OLR model (Model 9) in
which it does not have a signiﬁcant impact on human trafﬁcking to a country at all.

NBR models for the distribution of human trafﬁcking markets in Turkey
demonstrate that Hierarchical diffusion, social disorganization, and social disorder are all
signiﬁcantly related to the distribution of human trafﬁcking Markets in Turkey (Table 9).
While the presence of international borders, heterogeneity (ln), residential mobility, and
migrant prostitution (1n) have signiﬁcant positive impact on human trafﬁcking incidents;
as expected, per capita gdp has signiﬁcant negative impact on human trafficking
incidents. On the other hand, cities with international borders, larger population, higher
heterogeneity (1n), residential mobility, family disruption, and migrant prostitution have
signiﬁcantly higher number of calls for rescue. Finally, migrant prostitution has a
signiﬁcant positive impact on per capita total crime rates and organized crime incidents
controlling for the impact of social disorganization and control variables.

The following chapter is going to provide an extended discussion about the results
of bivariate and multivariate analyses presented in this chapter. Limitations and the
implications in both theoretical and practical terms are also going to be mentioned along

with suggestions for future research.

138

CHAPTER SIX
DISCUSSION and CONCLUSION
1. Discussion

This study revolves around three major research. questions as regard to the
distribution of human trafficking markets. Fist, the impact of macro level structural
factors on cross-national distribution of human trafficking markets has been examined
with a speciﬁc focus on the impact of globalization and social disorganization. Then, the
role of hierarchal diffusion and social disorganization in explaining the distribution of
human trafﬁckin g markets in Turkey is inspected. Finally. this study also sought to
discover the impact of social disorder on the distribution of human trafﬁcking markets
and other serious crime by looking at the relationship between migrant prostitution and
the measures of human trafficking, violent crime, property crime, total crime, and
organized crime in Turkey.

Analysis of cross-national distribution of human trafﬁcking markets utilizes the
measure of human trafﬁcking across the world nations provided by Bales (2005). In the
current study, however, operationalization of human trafﬁcking in a country differs from
what is suggested by Bales’s study (2005). Moreover, estimated models are mainly based
on theoretical concepts suggested by communities and crime research literature.
Investigation of human trafﬁcking in Turkey and the impact of migrant prostitution on
the distribution of human trafﬁcking and other serious crime, on the other hand, is guided
by hierarchical diffusion and social disorganization frameworks, as suggested by Rengert
(1996) and the research literature on the distribution of illegal markets (i.e., drug and

prostitution) and serious crime.

139

This chapter is intended to discuss all three research questions based on the
findings presented in the previous chapter and also in comparison to ﬁndings of previous
research including the recent ﬁndings in human trafﬁcking and communities and crime
research literature. In the end, limitations and implications for policy and future research
are also discussed.

(1) Cross-national Distribution of Human Trafﬁcking Markets

Macro structural factors such as poverty, heterogeneity, residential mobility,
urbanization, and family disruption were hypothesized to inﬂuence cross-national
distribution of human trafficking markets when relative deprivation and proportion of
younger population in a country is controlled. Not surprisingly, the results indicate that
absolute poverty (or what we can also call the level of development in the course of this
study), urbanization, and the size of total population have signiﬁcant positive impact on
cross-national distribution of human trafﬁcking. The proportion of younger population,
on the other hand, has an unexpected signiﬁcant negative impact on cross-national
distribution of human trafficking markets. The remaining independent variables of
residential mobility, heterogeneity, family disruption and control variable of gini
coefﬁcient (as the measure of relative deprivation), however, do not have signiﬁcant
impact on cross-national distribution of human trafﬁcking markets.

In general, the ﬁndings concur with the notion that rapid industrialization and
related urbanization is problematic especially for the countries/communities which are
deprived of necessary resources to address the problems of crime and social disorder
(Paulsen and Robinson, 2004). In this regard, the process of globalization and related

transformations in deve10ping countries play a signiﬁcant role in shaping cross-national

140

distribution of human trafficking. Simply put, human trafﬁckers target the nations with
relatively more poverty, urbanization, and population since it is easier to exploit and
recruit more potential victims for more potential customers in a social and economic
context where potential victims are more likely to risk their lives for ‘so called’ better
employment and living opportunities in metropolitan centers (in their or neighboring
countries); and, where both the trafﬁckers and their customers, on the other hand, can
beneﬁt from increased anonymity and lack of informal and formal social controls. Even
though social disorganization theory mainly focuses on informal social controls, the
systemic control interpretation of the disorganization framework refers to public level
control (i.e., law enforcement) as one of the most important yet under-tested components
of community control (Bursik and Grasmick, 1993). Accordingly, given that per capita
GDP is also considered as an indicator of the quality of government institutions such as
police and justice systems (Lederman, Loayza, and Menendez, 2002), lower level of per
capita GDP can also beneﬁt to trafﬁckers in this respect.

Despite the signiﬁcant positive impact of poverty, urbanization, and the size of
total population on cross-national distribution of human trafﬁcking markets,
heterogeneity, residential mobility, and family disruption do not have signiﬁcant impact
on cross-national distribution of human trafﬁcking markets. Even though this seems to be
contrary to theorizing, certain characteristics of human trafﬁcking markets and
speciﬁcally of the recruitment of victims can help to explain these unexpected ﬁndings.
Namely, given the fact that a considerable number of victims are recruited by their
signiﬁcant others including their relatives, friends, and even parents (Brunovskis &

Tyldum 2004; IN CIDIN, 2002), it can be speculated that this would require development

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of certain level of trust between the trafﬁckers/recruiters and their victims. Yet,
heterogeneity, higher residential mobility and family disruption in the society would
prevent the development of such informal relationships/networks and interactions that
could beneﬁt to the trafﬁckers and recruiters. In fact, the unexpected signiﬁcant negative
impact of the younger cohort (aged between 15-19) on cross-national distribution of
human trafﬁcking can also be regarded as supporting evidence for this argument since the
countries with large proportion of younger population would be the ones with less
intimate ties (because of relatively less number/proportion of adult population). Actually,
the negative impact of the proportion of younger population on the distribution of crime
rates has received empirical support from both previous (Bennett 1991) and recent (Lee
and Bartkowski, 2004; Lee. 2006) studies on the distribution of crime rates at different
levels of analysis.

Afore mentioned results apply to the full model of human trafﬁcking in a country
in which a combined measure of human trafﬁcking from and to a Country was used. Yet,
the same set of indicators also appears capable of explaining the distribution of human
trafﬁcking from and to world nations separately. Speciﬁcally, whereas exactly the same
indicators that predict total trafﬁcking in a country also signiﬁcantly predict human
trafficking from a country; urbanization is dropped from the ﬁnal OLR model (Model 9)
that examines human trafﬁcking to a country. In other words, only poverty, total
population, and proportion of younger population have signiﬁcant impact on human
trafﬁcking to a country. There are two main reasons for why signiﬁcant positive impact

of urbanization might be more relevant in an origin country (or for ‘human trafﬁcking

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from a country’ to another country) when human trafﬁcking in a country is disaggregated
into two separate incidents as human trafficking from and to a country:

First, urbanization is likely to increase the number of potential targets who are
willing to risk their lives for presumed better living and employment opportunities in
unguarded urban areas in origin countries. As far as the destination countries (or
trafﬁcking to a country) are concerned, they are assumed to be targeted mainly for the
purposes of exploitation of the victims those who are already recruited from an origin
country. In this case, higher population and poverty in the destination countries would
provide anonymity and lack of informal and formal social controls as desired by the
trafﬁckers in a destination country.

The second reason for the non-signiﬁcant impact of urbanization on human
trafﬁcking to a country, on the other hand, can relate to the different market solutions
adopted by illegal markets, which can also account for the negative impact of the
proportion of younger population on human trafﬁcking to a country. There are two major
solutions adopted by illegal markets in an effort to avoid apprehension by police, prevent
violence and theft, and increase the monetary return; routine activity markets and
network solutions (see Eek, 1995, 67). When the market adopts network solutions at the
retail level, which is likely to be the case for the exploitation of victims in destination
points, they are more likely to spread out a large geographic area compared to routine
activity markets (Eck, 1995, 74). Therefore, as long as trafﬁckers are able to operate in a
destination country where they can beneﬁt from anonymity and lack of informal and
formal social controls -as suggested by larger population and higher poverty-.

urbanization may not have a signiﬁcant impact on human trafﬁcking to a country

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especially when the trafﬁckers adopt network solutions and prefer to spread out a large
geographic area to prevent detection. Additionally, urbanization would impair
development of a secure informal network between the trafﬁckers and their customers.
The negative impact of the prOportion of younger population on trafﬁcking to a country
may also support this argument about network solutions. As mentioned, a larger
proportion of younger population would mean lower levels of integration and interaction
because of decreased adult population. When it is implemented by trafﬁckers, however,
network solutions would require trafﬁckers to serve to the people whom they know and
trust, which in turn would necessitate development of informal networks and intimate
relationships between the trafﬁckers and their customers-which again, might be more
difﬁcult in an urban context.

To sum up, the results from the analysis of cross-national distribution of human
trafﬁcking markets concur with the results of Bales’s multiple regression analysis of
human trafﬁcking from and to countries to some extent”. Bales fOund that prosperity and
stability had signiﬁcant positive impact on human trafﬁcking to a country whereas they
were negatively related to trafﬁcking from a country. Accordingly, he argued that the
level of development and stability signiﬁcantly determine the route of human trafﬁcking

across the countries. However, in the sample of 53 countries used in this study, once

 

‘2 Given that the current study applied almost completely different theoretical framework from the one used
in Bales’ (2005) research and thus conceptualized human trafﬁcking and its correlates quite differently; it
would not be appropriate to expect the results to be in complete agreement with the results of Bales’ (2005)
study. One of the important variables that is used in Bales’ (2005) study but omitted in the present research
was corruption. The corruption index was intended to be included as a control variable in this study as well;
however, the correlation between gdp per capita and corruption index was .88 (p<.01). Therefore. concern
about potential multicollinearity among these two aggregated measures in a small sample was the major
reason for the exclusion of corruption from the multivariate analysis. High bivariate correlation between
gdp per capita and corruption. on the other hand, supports the notion that gdp can be considered as . .a
proxy for the quality of government institutions, including the police and justice systems” (Lederman.
Loayza, and Menendez. 2002. 511). Consequently, in addition to theoretical justiﬁcations. such analytical
issues should also be considered while trying to compare both studies.

144

human trafﬁcking is constructed as an aggregated measure of human trafﬁcking from and
to a country. reasonably, prosperity or development in a given country impacts the level
of total trafﬁcking rather than the route of trafﬁcking. Moreover, the full model of human
trafﬁcking in a country (Model 6) can also account for human trafﬁcking in either
direction when human trafﬁcking is disaggregated as human trafﬁcking from and to
countries.

Thus, utilizing a sample of 53 countries and borrowing from communities and
crime literature and international migration theories, the present study extends Bales’s
(2005) argument about cross-national distribution of human trafﬁcking markets in both
theoretical and methodological terms. Methodologically, the tested models indicated that
human trafﬁcking from and to a country can be considered as a part of the same
construct. Theoretically. the problem of human trafﬁcking across the world nations is
well explained by criminological insights drawn from social disorganization and related
systemic control framework using an available sample of 53 countries and Bales’s
measurement of human trafﬁcking to and from world nations.

(2) Human trafﬁcking in Turkey

Analysis of human trafﬁcking in Turkey sought to explore the impact of
hierarchical diffusion, social disorganization, and migrant prostitution on the distribution
of human trafﬁcking markets across 81 cities. Moreover, the relationship between
migrant prostitution, which is considered as a sign of social disorder, and other serious
crimes is also examined. All three different frameworks signiﬁcantly contribute to our
understanding of the distribution of human trafﬁcking markets in Turkey, though their

contribution does not remain the same across different models.

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Hierarchical Diffusion:

The organization of human trafficking markets in Turkey is best interpreted in the
light of theoretical insights drawn from Rengert’s (1996) study on hierarchical diffusion
of illegal drug markets in the U.S. Originally, the size of population and geographic
proximity to established illicit markets or to the places were the illicit commodities are
imported were suggested as two main factors that impact the distribution of illicit drug
markets in time and space. Whereas the larger cities contain more potential customers
than smaller urban centers, the cities closer to the places where illicit products are
produced or imported decrease the costs by minimizing the distance between origin
countries and the markets. As applied to this study, the size of the total population and the
presence of international borders in a city were hypothesized to increase the number of
human trafﬁcking markets since human trafﬁcking in Turkey almost exclusively involved
sexual and/or labor exploitation of foreigner women (IOM, 2006; see
www.countertrafﬁcking.org).

As expected, when these two indicators of hierarchical diffusion are entered into
the models (10, 12, and 14), the presence of international borders had considerable
positive impact on the distribution of human trafﬁcking markets in Turkey. Similar to the
diffusion of illegal drug markets in the U.S. (Rengert, 1996), human trafﬁcking markets
are concentrated in the cities with international borders where it is easier to transport and
recruit trafﬁcked women into exploitation immediately. The size of the population, on the
other hand, did not have signiﬁcant impact on the measures of human trafﬁcking whereas
it had signiﬁcant negative impact on migrant prostitution when the presence of

international borders and control variables held constant. This may be caused by the

146

relationship between the size of total population and one or more of omitted independent
variables which are included in successive models, for correlations between the size of
total population and other independent variables (i.e., negative correlation between total
population and residential mobility and positive correlation between total population and
per capita GDP) might suppress the relationship between total population and the
measures of human trafﬁcking and migrant prostitution -unless they are controlled.
Social Disorganization

The hierarchical pattern of diffusion observed in the distribution of organized
criminal activity in human trafﬁcking markets is mainly driven by proﬁt-oriented
concerns in legal markets. However, the lack of legal protection within the illicit markets
forces organized criminals to consider additional precautions which in turn impact their
market location preferences and thus distribution of illegal markets in space and time.
Simply put, to avoid apprehension by police, to prevent violence and theft, and to
increase the monetary return, Rengert (1996) contended, organized criminals target the
places whose residents are too disorganized to confront the operation of illegal markets in
their community. Thus, drawing upon communities and crime literature and speciﬁcally
assumptions of Shaw and McKay’s (1972) social disorganization framework; ﬁrst,
poverty, heterogeneity, residential mobility, urbanization, and family disruption were
hypothesized to have signiﬁcant positive impact on the distribution of migrant
prostitution and human trafﬁcking markets, for these independent variables are
considered as classical antecedents of community social disorganization. Second, given
recent arguments as regard to the impact of social disorder on the distribution of serious

crime (Weisburd and Green-Mazerolle, 2000; Worrall, 2006) and considering the fact

147

that prostitution is also regarded as a sign of social (and physical) disorder (Skogan,
1990; Taylor, 1995); migrant prostitution was also hypothesized to have significant
positive impact on the distribution of human trafﬁcking markets as well as on the
distribution of violent, property, and total crime rates. '

As expected, social disorganization had significant positive impact on the
distribution of human trafﬁcking and migrant prostitution when the indicators of
hierarchical diffusion, proportion of the younger population (aged 15-24), and
unemployment are held constant. While heterogeneity emerged as the only factor that
signiﬁcantly increases per capita migrant prostitution rates across the cities (Model 15),
presence of international borders, poverty, heterogeneity, and residential mobility had
signiﬁcant impact on the distribution of human trafﬁcking incidents. Finally, the size of
total population, presence of international borders, heterogeneity, residential mobility,
and family disruption had signiﬁcant positive impact on the distribution of per capita
calls for rescue in a city. Thus, even though the distribution of migrant prostitution
provided partial support for theorizing, distribution of human trafﬁcking incidents and
calls for rescue produced substantial empirical support for hypothesized relationships
based on hierarchical diffusion and social disorganization frameworks since both
frameworks significantly explain the distribution of human trafﬁcking incidents and calls
for rescue in the expected direction.

Limited partial support provided by the distribution of migrant prostitution for
theorizing may be related to the nature of this outcome variable. Speciﬁcally, the events
or individuals that come to police attention also depend on the discretion and

performance of the police organization. Thus, the measures of both human trafﬁcking

148

incidents and migrant prostitution might have the confounding impact of police discretion
and performance (Hagan, 2006), although human trafﬁcking incidents provided a
relatively better ﬁt. Calls for rescue, on the other hand, can be considered more adequate
indicator of the distribution of human trafﬁcking markets as these calls are made not only
by the victims themselves, but also by the relatives, friends, customers, or neighbors of
the victims. To be more precise, calls for rescue refer only to the calls which are
considered as potential cases of human trafﬁcking with enough information. Although a
total of 579 calls for rescue were placed during the entire period of 2006, only 112
victims among a total of 246 victims (rescued by law enforcement) were rescued
through 157 helpline in 2006. Hence, given the fact that only 19 percent of all rescue
requests (112 out of 579) directed to the law enforcement authorities result in
identiﬁcation and rescue of the victims, compared to identiﬁed human trafﬁcking
incidents reported by law enforcement, calls for rescue appear to be more accurate
representation of human trafﬁcking markets in a province.

Thus, as long as human trafﬁcking incidents and per capita calls for rescue are
regarded as the measures of organized criminal activity within human trafﬁcking
markets; both hierarchical diffusion and social disorganization frameworks are relevant in
explaining the distribution of human trafficking markets across 81 cities in Turkey. That
is, the cities with international borders, larger population, more poverty, heterogeneity,
residential mobility, and family disruption are exploited more by organized criminals
running human trafﬁcking markets. Simply put, these provinces provide more conducive
context for the traffickers, where they can easily recruit their victims for more potential

customers with less concern about informal and formal controls and about reaction from

149

the local community. The signiﬁcant positive impact of heterogeneity on migrant
prostitution, on the other hand, suggests that communities with higher heterogeneity
would be less likely to confront the problem of migrant prostitution or other types of
social disorder since they would be less likely to reachiconsensus about their common
problems and their solution.

Overall, the signiﬁcant positive impact of hierarchical diffusion and social
disorganization on the distribution of human trafﬁcking markets supports Rengert’s
(1996) argument as regard to the distribution of illicit markets in space. Namely, human
trafﬁcking markets, like their legal counterparts, seek to increase their proﬁts by targeting
the places with more potential customers and with shorter distance to countries where
trafﬁcked women are recruited from. Yet, as Rengert (1996) argued, illicit markets differ
from the legal markets in terms of their concerns about legal protection and therefore they
limit their market selection to the places where they can have more control over their
operations and illegal exchanges.

(3) Migrant prostitution, Human Trafﬁcking, and other Serious Crimes

Communities and crime research literature suggests a positive relationship
between social disorganization, disorder, and more serious crime (Wilson and Kelling,
1982; Skogan, 1990). As mentioned, the distribution of migrant prostitution in Turkey
has provided partial support for the social disorganization framework. Accordingly,
migrant prostitution is conceptualized as a sign of social disorder in this ﬁnal part of the
study and it is hypothesized to have signiﬁcant positive impact on the distribution of
human trafﬁcking, violent crime, property crime, total crime, and organized crime rates

in Turkey.

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Bivariate analyses demonstrated that the cities with migrant prostitution have
significantly more average violent crime, property crime, and total crime in a given year.
Moreover, the same cities also have signiﬁcantly more human trafﬁcking incidents, total
crime, and organized crime in the following years as well (Table 7). As expected, migrant
prostitution also had signiﬁcant positive impact on the distribution of human trafﬁcking
incidents, calls for rescue, total crime, and organized crime net for the impact of
hierarchical diffusion, social disorganization, and other controls in the multivariate
context (Models 16, 17, 18, and 19). To be more precise, when migrant prostitution is
introduced into previous models with hierarchical diffusion, social disorganization, and
control variables, it is found that more human trafﬁcking incidents take place in the cities
with international borders, more poverty, more residential mobility, more heterogeneity,
and with more migrant prostitution. As for calls for rescue, the cities with international
borders, more population, heterogeneity, residential mobility, family disruption, and more
migrant prostitution have more per capita calls for rescue. Finally, migrant prostitution
also had signiﬁcant positive impact on the distribution of total crime and organized crime
incidents in the year 2005 when social disorganization and other control variables held
constant.

Overall, as a single and official indicator of community social disorder, migrant
prostitution had significant impact on the distribution of human trafﬁcking, violent crime.
property crime, total crime, and organized crime incidents in the hypothesized direction.
Both measures of human trafﬁcking in the final models (Models 16 and 17) demonstrated
that all three frameworks of hierarchical diffusion, social disorganization, and disorder

signiﬁcantly contribute to our understanding of the distribution of human trafﬁcking

151

markets in Turkey. The final two models (Model 18 and 19) also points to the ability of
migrant prostitution in explaining the distribution of serious crimes as a sign of social
disorder. Moreover, these ﬁnal two models also help to clarify a legitimate concern about
whether concentration of human trafﬁcking, migrant prostitution, and other serious crime
result from uneven distribution of crimes across the cities. Apparently, whereas the
distribution of human trafﬁcking incidents and calls for rescue are determined by quite
similar processes of hierarchical diffusion and social disorganization, the distribution of
total crime and organized crime is determined by different factors such as higher income
level, higher urbanization. and migrant prostitution.
2. Conclusion and Policy Implications

The primary purpose of this dissertation has been to investigate how macro level
structural factors, social disorganization and hierarchical diffusion, and social disorder
impact the distribution of human trafﬁcking markets. Based on three major research
questions and related hypotheses derived from international migration studies and
communities and crime research literature; the analyses of human trafﬁcking markets in
53 countries and then in 81 cities in Turkey demonstrated that certain macro level
structural factors, social disorganization and hierarchical diffusion, and ﬁnally social
disorder all have signiﬁcant inﬂuence on the distribution of human trafﬁcking markets in
the hypothesized direction.

The results of the analyses of the cross-national distribution of human trafﬁcking
markets identify poverty, urbanization, total population size, and the proportion of
younger population as the most signiﬁcant factors that determine human trafﬁcking in a

country. Moreover, except for urbanization, the same factors also determine human

152

trafﬁcking from and to a country as well. The failure of heterogeneity, residential
mobility, and family disruption can arise mainly from two potential issues. First, despite
Bales’s and his colleagues’ extensive efforts to increase the validity of the measurement
of human trafficking to and from different countries; since they also rely on different
formal and informal resources in their measurement, the hidden nature of crime and
related under—representation of human trafﬁcking in certain countries is likely to bias
final country ratings. If the countries with higher heterogeneity, residential mobility, and
family disruption are underrated, this might cause non-signiﬁcant relationship between
these structural characteristics and human trafﬁcking in a country and this possibility
needs to be explored before ruling out the importance of these three explanatory
variables. This does not seem to be the case for the measures of poverty, urbanization,
total population, and proportion of younger population, however, as they have consistent
signiﬁcant impact across different measures of human trafﬁcking. A second problem is
that in order to increase the sample size, because of the scarcity of information about
different structural characteristics of the nations, only crude measures of heterogeneity,
residential mobility and family disruption are included in this study and their adequacy
for cross-national comparisons needs to be more fully established with future research.
Overall, the analyses of human trafﬁcking markets across 53 countries supported
the notion that as the nations become more connected in both social and financial terms
within the process of globalization, rapid industrialization and urbanization produce a
conducive context for the traffickers where they can prey on higher numbers of potential
victims who are suffering from poverty and looking for better employment and living

opportunities in unguarded urban locations. Additionally, intimate relationships among

153

the adult population and speciﬁcally between the trafﬁckers and potential victims are also
speculated to be important in determining the level of human trafﬁcking in a country. Put
differently, higher levels of poverty and urbanization in especially largely populated
countries increase both recruitment and exploitation of potential victims as these
countries lack in necessary informal and formal control mechanisms to help their citizens
to confront organized criminals and their illegal exchanges within human trafﬁcking
markets. Finally, as a possible interpretation of an unexpected negative relationship
between the proportion of younger population and human trafﬁcking in a country; the
lower proportion of younger population (aged 15-19) and correspondingly higher number
of adult population in a country may serve for both recruitment and exploitation purposes
of the trafﬁckers as human trafﬁcking markets also thrive on intimate relationships and
informal networks because of their major concerns about building trust relationships and
avoiding the potential risk of apprehension and violent and property crime.

Apparently, even though recent empirical studies and government reports in the
ﬁeld of human trafﬁcking generally prioritized and suggested the deterrence of trafﬁckers
as a major strategy to prevent human trafﬁcking; the current study points to the
importance of certain structural factors that basically form the context in which such
deterrence oriented policies (would) take place. To be more speciﬁc, even though general
and speciﬁc deterrence could contribute to prevention efforts, structural characteristics of
the nations precede volunteered or forced prevention efforts against human trafﬁcking
markets (in time). Accordingly, any global, regional, or national level policy initiative to
confront organized criminals within human trafﬁcking markets should ﬁrst consider

structural characteristics of the nations as certain characteristics (once again, poverty,

154

urbanization, the size of the total population, and proportion of the younger population)
have signiﬁcant impact on the distribution of human trafﬁcking markets. This would
apply to both origin and destination countries, as estimated models produced similar
results for human trafﬁcking in, from, and to a country.

Consequently, this study can help to identify the countries that are at relatively
more risk of being targeted by human trafﬁckers. Thus, international community,
governments, governmental and non- governmental organizations, activists, and law
enforcement agencies can allocate their resources to prevent human trafficking in and
across the world nations accordingly. Given that the countries with more poverty,
urbanization, and larger population are at more risk, prevention strategies that fail to
address the problem of absolute poverty and that unable to provide better living and
employment opportunities are unlikely to be successful and may even be
counterproductive by displacing human trafﬁcking across time and/or places.
Additionally, poor people are recruited by the trafﬁckers who are able to operate in
multiple countries through their cross border connections and this deﬁnitely calls for
international cooperation among the law enforcement authorities. However, given that
human trafﬁcking organizations are composed of loosely coupled networks including
legal business owners (i.e., employers, hotel owners) and even corrupted ofﬁcials, this
might undermine the power of deterrence and prosecution oriented strategies. Therefore,
formal social control needs to be complemented by informal social control. At national
level, this can be facilitated by activists and non- governmental organizations through the
allocation of some of their resources for the education and mobilization of the public.

Law enforcement agencies, as suggested by community policing philosophy, can also

155

contribute to this multi agency task of community building and organizing process, for
community policing entails increasing police and government responsiveness and
involves the formation of police and community partnerships in addition to crime control
(McGarrell et al., 1997).

In this regard, the analysis of the distribution of human trafﬁcking markets in
Turkey complements the above argument about the relationship between structural
factors and human trafficking, though these two datasets are independent from each
other. An ideal approach would have multiple cities nested in these 53 countries and
would model both cross-national and city level effects using multilevel analysis
techniques (i.e., Hierarchical Linear Model/HLM). Unfortunately, data is not readily
available at city level. Given certain limitations about sampling of the countries and
measurement of independent and dependent variables across these countries, however,
analysis of human trafﬁcking in Turkey can provide an opportunity to have a closer look
at the relationship between structural characteristics of the communities and the
distribution of human trafﬁcking markets in a relatively more homogeneous context,
although it is almost impossible to know whether city level ﬁndings would still hold
when cross-national level structural factors (i.e., poverty and urbanization) are controlled.

Additionally, human trafﬁcking in Turkey can be regarded as representing a more
speciﬁc type of human trafﬁcking as it mainly involves foreign women who are
trafﬁcked into Turkey for the purposes of sexual exploitation and/or forced labor. Among
all trafﬁcking victims helped by IOM ofﬁce in Turkey in 2006, 93 percent of the victims
were trafﬁcked for the purposes of sexual exploitation, 5 percent was trafﬁcked for

forced labor, and remaining 2 percent was exposed to both sexual and labor exploitation.

156

Moreover, all of these victims were females coming from the former Soviet Union
countries except for two women from Iran and two women from Bulgaria (IOM, 2006).
The city level analysis of the distribution of human trafﬁcking markets in Turkey
provides stronger support for the relationship betweenstructural characteristics of the
communities and proliferation of human trafﬁcking markets. Speciﬁcally, similar to the
diffusion of illegal drug markets (Rengert, 1996), human trafﬁcking markets generally
target the cities with international borders as they are closer to the countries where the
trafﬁcked women are recruited from. Moreover, certain community characteristics such
as poverty, heterogeneity, residential mobility, family disruption, and the size of the total
population are also found to have significant positive relationship with the distribution of
human trafﬁcking markets as suggested by social disorganization framework.
Accordingly, organized criminals in the illegal business of human trafﬁcking mainly
prefer the cities which promise higher proﬁt with relatively lower risk of apprehension
and criminal victimization. Higher proﬁt is granted mainly by international borders which
facilitate the supply of the victims and by larger populations that create higher demand or
more potential customers for the services of human trafﬁcking markets. Higher poverty,
heterogeneity, residential mobility, and family disruption, on the other hand, decrease the
ability of the communities to regulate and control the behavior of suppliers and
consumers of human trafﬁcking markets who are likely to be their residents and visitors.
Finally, migrant prostitution, which is considered a sign of social disorder
(Skogan, 1990; Taylor, 1995), also was found to have signiﬁcant positive impact on
provincial distribution of human trafficking markets net for the impact of hierarchical

diffusion and social disorganization. The results indicate a clear relationship at the city

157

level between rates of migrant prostitution, human trafficking, total crime, and organized
crime. In fact, these relationships can also be reinforcing with crime and disorder
attracting prostitution and human trafﬁcking and with prostitution and trafﬁcking
reinforcing disorder, fear, and crime. Thus, in general, the results are in agreement with
findings of communities and crime research literature and more speciﬁcally with the
research on the distribution of illegal (drug) markets (Rengert, 1996).

In this regard, analysis of the distribution of human trafﬁcking markets in the
Turkish context extends afore mentioned argument about the relationship between certain
community characteristics and the proliferation of human trafﬁcking markets and points
to potential prevention efforts.

First, since the cities with international borders, certain structural characteristics,
and migrant prostitution are at higher risk for being targeted by trafﬁckers, focusing on
these cities, law enforcement ofﬁcials can identify speciﬁc situations and micro places
that facilitate both migrant prostitution and human trafﬁcking and allocate resources for
prevention efforts accordingly.

Second, given the fact that the cities with international borders are exploited more
by human trafﬁcking markets compared to the cities without international borders and
that the victims are exclusively comprised of foreign women recruited generally from the
former Soviet countries. early identification of potential victims at the border gates could
considerably decrease the number of victimizations. Therefore, in addition to the need for
advanced recording of individual arrival and departure information by law enforcement

ofﬁcials at the border gates, more research on the individual characteristics of the victims

158

that could serve to the early identiﬁcation purposes of law enforcement authorities at the
international borders is warranted.

Third, in addition to the cities with international borders, the cities with more
population, poverty, heterogeneity, mobility, and family disruption are found to be
exploited more by human trafficking markets as these antecedents of community social
disorganization might decrease the ability of the cities to confront the establishment of
human trafﬁcking markets. Going back to the discussion of systemic control model
(Bursik & Grasmick, 199 3) provided in the chapter two, community social
disorganization calls for the reinforcement of primary, parochial, and public level control
in the community. In other words, prevention of human trafﬁcking goes beyond solely
enacting and implementing human trafﬁcking regulations but asks for a comprehensive
response to the problem. Thus, analysis of the distribution of human trafﬁcking markets
also lends support for the importance of community policing movement as a potential
policy implication. In addition to crime control, the goals of community policing
strategies include diagnosing and managing problems in the community; increasing
police and government responsiveness; and building police and community partnerships
to address the issues of crime and disorder (McGarrell et al., 1997; Thurman, Zhao, and
Giomazzi, 2001; Trojanowicz, Kappeler, and Gaines, 1998; Zhao, Scheider, and
Thurman, 2002).

The forth implication of this study is based on the finding that among the cities
with international borders and social disorganization those reporting more migrant
prostitution have signiﬁcantly more human trafﬁcking, total (public order) crime, and

organized crime. In line with the community policing philosophy, law enforcement focus

159

on the cities with migrant prostitution is likely to expedite the diagnosis of underlying
physical, social, and environmental problems that lead to more prostitution, human
trafficking, and other serious crimes. Consequently, a problem oriented approach which
prioritizes the control of opportunities (Green, 1996; Mazerolle and Ransley, 2006) that
create migrant prostitution, human trafﬁcking, public order crime, and organized crime is
supported as well. In this regard, to increase police and government responsiveness,
problem oriented policing offers a variety of problem solving strategies including formal
and informal interventions that range from the allocation of necessary resources and
implementation of criminal and civil laws to collaboration with place managers and
owners as well as the mobilization of the local residents (Goldstein, 1990; Mazerolle and
Ransley, 2006). Targeting all parts involved in migrant prostitution and human
trafficking including the prostitutes, clients, trafﬁckers, and the owners and managers of
the places that facilitate migrant prostitution and human trafﬁcking can be considered as
potential implications. Considering the fact that human trafﬁcking is believed to be
perpetrated by a large number of loosely connected crime groups (which makes the
detection and prosecution relatively difﬁcult), exposing the clients to publicity or
notifying those who have inﬂuence on clients’ conduct; enforcing zoning, nuisance
abatement, and business license regulations against properties used for prostitution; and
redeveloping the area economy can help the community to better address the problems of
migrant prostitution and human trafﬁckin g13 . More importantly, all these and other
interventions targeting migrant prostitution and human trafﬁcking must have available

social services to help the prostitutes and trafﬁcked victims. In fact, activists and

 

‘3 See www.copcenter.org for a detailed list of possible interventions suggested to tackle the problem of
street prostitution.

160

nongovernmental organizations helping trafﬁcking victims could also consider helping
migrant prostitutes to quit prostitution given the significant relationship between migrant
prostitution and human trafﬁcking”.

A potential response in a given city, however, ’should be based on a reliable
analysis of prostitution and human trafﬁcking problems in that speciﬁc city and the
effectiveness of potential responses could be measurable at the end of the intervention
process. Put differently, before any intervention takes places, as mentioned, each city
should locate speciﬁc micro places, situations, or the c0pping zones (Skogan, 1990, 30)
that produce migrant prostitution and human trafﬁcking to be able to identify underlying
physical, social and economic problems in the community. However, almost all of afore
mentioned potential responses would require money, resources, and changes in the local
community environment and it will be difﬁcult to prevent migrant prostitution, human
trafﬁcking, or any other problem of crime and disorder without community support. This,
on the other hand, leads the discussion into the third strategy of community policing that
involves building police and community partnerships to address the issues of crime and
disorder. Speciﬁcally, law enforcement would deﬁnitely need community support and
participation to insure legitimacy and allocate resources for such comprehensive
intervention strategies. At the same time, police officials become ‘problem managers’
(Green, 1996; Mazerolle and Ransley, 2006; McGarrell, Giacomazzi, and Thurman,
1999) by cooperating with the community and leading them into the right direction while

tackling the issues of crime and disorder. Communities and crime research literature has

 

‘4 Even though the present study reveals a signiﬁcant relationship between migrant prostitution and human
trafﬁcking at city level, more incidents of human trafﬁcking and other serious crimes in the cities with
migrant prostitution put prostituting women at higher risk of victimization.

161

already pointed to successful results in terms of the prevention of disorder, including both
drugs and prostitution (Eck, 1995; McGarrell et al., 1999; Weisburd et al., 2006;) and-
also demonstrated diffusion of the crime control beneﬁts without displacement (Braga et
al., 1999; Weisburd and Green, 1995). As for human trafﬁcking, as Bales and Lize
(2005) argued, neighborhood watch projects can also be promising for the prevention of
human trafﬁcking incidents, especially in the cities where the exploitation of women
takes place in private homes through the network solutions (Eck, 1995).

Finally, concentration of migrant prostitution, human trafﬁcking, and other
serious crimes in certain cities and signiﬁcant positive impact of migrant prostitution on
provincial distribution of human trafﬁcking, total crime, and organized crime in Turkey
suggests formal and informal control of (migrant) prostitution. This, on the other hand,
contrasts with radical feminist arguments rejecting a dual (volunteered/forced)
conceptualization of prostitution in the name of emancipation of the prostituting women
as free sex workers (Doezema, 1998; Chapkis, 1997; Kempadoo' and Doezema, 1998;
Naggle, 1997;). However, in addition to ﬁndings of foregoing analyses, several other
empirical ﬁndings supporting an existing link between prostitution and prevailing
tolerance for prostitution (Cohen, 1980) and suggesting an association between migrant
prostitution and human trafﬁcking (Davis, 2000; Ekberg, 2004; Farley and Barkan, 1998;
Hughes, 2002; Schauer and Wheaton, 2006; Thukral, 2005) concur that formal control of
prostitution can contribute to community based efforts by indicating an existing political
will and determination reﬂected in the formal intervention of law enforcement ofﬁcials
(also see McGarrell, Giacomazzi, and Thurman, 1997; and Silver and Miller, 2004 for a

positive relationship between satisfaction with police services and informal social

162

control). As for the relationship between prostitution and human trafficking, when
prostitution is legal, Hughes and Denisova (2001) argued, the dilemma of voluntary
versus forced prostitution creates problems in terms of prosecution of the trafﬁckers as
law enforcement is expected to prove that the women'did not consent. Once again, for all
afore mentioned formal and informal interventions to work in the expected direction, law
enforcement authorities and cooperating communities would need to have available
social services that they could offer to the (migrant) women to quit prostitution.

3. Limitations and Future Research

Although the ﬁndings are generally consistent with theoretical predictions and
specified hypotheses, conclusions must be drawn cautiously because of several
methodological issues raised in the course of this research. Measurement of independent
variables and sampling strategy were two major concerns in cross-national analysis of
human trafﬁcking. Measurement of human trafﬁcking, the problem of causal order, and
unit of analyses used in the analysis of provincial distribution of human trafﬁcking
markets in Turkey were three other issues that require consideration.

In cross-national analysis of human trafﬁcking markets, ﬁrst, the measures of
independent variables, speciﬁcally the measures of heterogeneity and residential mobility
were rather crude. Even though better operationalization of these two explanatory
variables is available in previous research, using more adequate measures further
decreases the sample size, for speciﬁc information about other necessary explanatory
variables is not available for every country. This also relates to the second limitation of
cross-national analysis in that the selection of countries for cross-national analysis of

human trafﬁcking markets is also limited by available information about explanatory

163

variables. Although the measurement of human trafﬁcking, the dependent variable,
provided by Bales (2005) was available for 111 different countries, the necessary
information about explanatory variables was only available for 53 countries included in
the ﬁnal sample. Thus, any other factor that might have compounded the selection
process might also have inﬂuenced the results of cross-national analysis of human
trafﬁcking markets. Therefore, given that the representativeness of the sample is
problematic, ﬁrm conclusions should better await until replication of these results with
larger sample of countries and with more adequate measures of heterogeneity and
residential mobility.

As far as the analysis of human trafficking markets in Turkey is concerned, it is
quite difﬁcult to specify temporal order of variables which is the major problem with
cross-sectional research. Moreover, the communities and crime research literature points
to the reciprocal relationship between disorder and crime, for they are considered not
only as the outcomes of the process of neighborhood disorganization, but also as an
integral part of that process (Bursik and Grasmick, 1993, 313). Accordingly, the
signiﬁcant impact of migrant prostitution on the distribution of human trafﬁcking
markets and other serious crime does not necessarily prove a casual link between migrant
prostitution and human trafﬁcking and serious crime. Although the measure of migrant
prostitution precedes the measures of human trafﬁcking and serious crime in time (2003
vs. 2006 and 2005), the relationship between migrant prostitution and human trafﬁcking
and serious crime may be more complex. To be more speciﬁc, a feedback-loop

arrangement could exist where migrant prostitution, human trafﬁcking, and serious crime

164

are mutually reinforcing (Skogan, 1990). Therefore, more research on the causal order of
these relationships is warranted.

Measurement of human trafﬁcking and migrant prostitution in Turkey emerges as
another challenge for future researchers. The discrepancy between two different measures
of human trafﬁcking, that is, human trafﬁcking incidents reported by police (incidents
take place in 23 cities) and calls for rescue reported by the IOM ofﬁce in Turkey (calls
for rescue come from 36 cities) already underlines the fact that the measurement of
human trafﬁcking is under-reported in these data. Although this is true for all crime data,
157 helpline statistics (IOM, 2006) also demonstrate that it is particularly so for human
trafﬁcking where victims are unlikely to report to the authorities. The IOM ofﬁce in
Turkey (2006) reported that only 19 percent of calls for rescue were made by the victims
themselves whereas remaining 81 percent of calls were placed by others including
friends, customers, relatives, and neighbors of the victims. In this regard, even though
calls for rescue might be considered more adequate representation of human trafﬁcking in
a city, different reporting and/or recording practices across 81 cities still remain as a
possible threat to the patterns observed in the results. Thus, an extension of this research
should consider alternative measurement of the distribution of migrant prostitution and
human trafﬁcking markets by either looking for different (i.e., self reported or perceived)
measures or combining available (secondary) measures from various institutions into a
single construct. To illustrate, in addition to the number of deported women, public health
records about provincial distribution of STDs might help to measure overall demand for

sexual services in a city.

165

For the unit of analysis, even though the present research looks at the relationship
between the distribution of human trafﬁcking markets and community characteristics at
the city level, the communities and crime literature suggests that the real variation in
terms of structural characteristics, crime, disorder, fear, and formal and informal social
control is at the neighborhood level (Bursik and Grasmick, 1993; Sampson, 2002; Taylor,
2000). Hence, future research should move to the neighborhood and block level of
analysis to truly test the relationship between disorder, crime, and human trafﬁcking.
This could also help identifying micro places and situations that contribute to
establishment and proliferation of human trafﬁcking markets as well.

Finally, culture and country speciﬁc factors also need to be considered in Turkey
where the people live in a society with a blend of Islam and secularism. Turkey has a
strong tradition of family and community lives and thus individual behavior is subject to
relatively higher social control exercised by family, friends, and religion. Accordingly,
foreigner women who are involved in prostitution are viewed as A ‘ ‘threats to family
sanctity” especially by women and this also leads to stress between wives and husbands
(Gulcur and Ilkkaracan, 2003). On the other hand, brothel prostitution is legal only for
Turkish women and regulated by the government. Consequently, such social and legal
restrictions surrounding migrant prostitution might have also contributed to women
trafﬁcking in Turkey as the foreigner women involved in prostitution are devoid of both
formal and informal protection. In fact, brothel prostitution might be another factor that
contributes to human trafﬁcking in Turkey. Simply put, as in the example of the state of
Victoria, Australia (Sullivan and Jeffreys, 2002) mentioned in chapter three, legal

prostitution might drive trafﬁcking in women to meet increased demand within

166

expanding and proﬁtable sex industry and this remains another important question for
further research on human trafﬁcking in Turkey.

Afore mentioned methodological reservations aside, the current study has
demonstrated that communities and crime research literature has applicability to the
global and local issue of human trafﬁcking, although it has been developed in the western
context and employed for the study of crime other than human trafﬁcking so far. In this
regard, this study marks a signiﬁcant shift in the study of human trafﬁcking, which has
almost exclusively focused on individual characteristics of trafﬁcking victims without a
systematic consideration of structural factors in which these individuals were recruited or

exploited.

167

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