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Electronic Theses, Treatises and Dissertations The Graduate School
2017 In Search of an Attentive Public and Involvement in the Anti-Trafficking Movement Ashley Russell
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COLLEGE OF CRIMINOLOGY AND CRIMINAL JUSTICE
IN SEARCH OF AN ATTENTIVE PUBLIC AND INVOLVEMENT
IN THE ANTI-TRAFFICKING MOVEMENT
By
ASHLEY RUSSELL
A Dissertation submitted to the College of Criminology and Criminal Justice in partial fulfillment of the requirements for the degree of Doctor of Philosophy.
2017
© 2017 Ashley Russell
Ashley Russell defended this dissertation on July 5, 2017. The members of the supervisory committee were:
Marc G. Gertz Professor Directing Dissertation
Martin Kavka University Representative
Carter Hay Committee Member
Sonja E. Siennick Committee Member
The Graduate School has verified and approved the above-named committee members, and certifies that the dissertation has been approved in accordance with university requirements.
ii
In loving memory of William and Sara Russell
Dedicated to my parents, my Sherpas, David and Lois Russell
iii ACKNOWLEDGMENTS
I walked onto the campus of Florida State University as a freshman at 18 years old and I’ve spent the past decade in the College of Criminology. It takes a village to raise a child, and there are many people to thank for raising me. Dr. Gertz is the reason I came back the Ph.D. program after graduation. Thank you for seeing something in me that I did not see in myself. I believe my life and my career will be significantly better because of this experience and it would not have happened without you. Dr. Hay, from undergraduate classes to my dissertation committee, thank you for always being there to teach and to guide me. I have grown so much as a student and a professor because of you. Dr. Siennick, your guidance and critiques through comprehensive exams and my dissertation have been invaluable. Thank you for all that you have done over the past three years. Dr. Kavka, I appreciate your insight, your stories, and your willingness to jump on my committee without question! Thank you for being so thorough in reviewing my manuscript. Dr. Blomberg and Kevin Derryberry, thank you for giving me the opportunity to work with you in Alumni Development. It was a ton of fun and I learned a lot along the way! To the ones who went before me, Christi Metcalfe, Kevin Wolff, and Leslie Hill, thank you for always being available to assist and guide me. A special thank you to Wanda Leal, who is my patient wise owl that can act as a sounding board when I need to process a project out loud and can solve just about any problem I run into. From area paper to dissertation, your insight has been much appreciated. To Peter Lehmann, whose office door is always open to chat about life or work, I’m thankful for your brain and your love of coffee. To Malisa Neptune, I am so thankful for your friendship and constant support as we brave these waters together. To my family, my teammates, and my friends who have walked through this process with me, I’m sorry you had to deal with me, but I’m thankful you stuck by me anyways. It’s been a wild ride and I do not know if I would have enjoyed [survived] this experience without your laughter and support. Most importantly, thank you to my parents. Thanks for always supporting me and raising me to always finish what I’ve started, even when they are challenging. Jesus, take the wheel.
iv TABLE OF CONTENTS
List of Tables ...... vi Abstract ...... vii
1. INTRODUCTION ...... 1 1.1 Attentive Publics ...... 2 1.2 Current Study ...... 5 1.3 Overview ...... 8
2. LITERATURE REVIEW...... 9 2.1 Introduction to Human Trafficking ...... 9 2.2 Public Opinion Literature ...... 18 2.3 Media Literature...... 27
3. METHODS ...... 38 3.1 Data Collection ...... 38 3.2 Variables ...... 40 3.3 Analytic Plan ...... 47
4. RESULTS ...... 54 4.1 Descriptive Statistics ...... 54 4.2 Research Question 1: Is there an Attentive Public for Human Trafficking? ...... 55 4.3 Research Question 2: Is the Attentive Public Significantly Different from the General and Least-Attentive Publics? ...... 56 4.4 Research Question 3: What Factors Influence an Individual to become more Knowledgeable? ...... 57 4.5 Research Question 4: What Factors Influence an Individual to become Involved in the Anti-Trafficking Movement? ...... 59
5. DISCUSSION AND CONCLUSION ...... 82 5.1 Discussion of Key Findings ...... 84 5.2 Implications for Future Research ...... 90
APPENDICES ...... 93
A. DESCRIPTIVE TABLES ...... 93 B. LISTWISE MODELS ...... 94 C. IRB APPROVAL AND CONSENT ...... 96
References ...... 101
Biographical Sketch ...... 111
v LIST OF TABLES
Table 1: Demographics of Respondents ...... 52
Table 2: Descriptive Statistics (n=803) ...... 67
Table 3: Distribution of Respondents by Knowledge and Involvement (n=803) ...... 68
Table 4: T-tests Comparing Attentive Publics to Everyone Else (n=803)...... 68
Table 5: T-tests Comparing Least-Attentive Publics to Everyone Else (n=803)...... 68
Table 6: Bivariate Correlations including Information Source Use (n=803) ...... 69
Table 7: Bivariate Correlations including Trafficking Frequency (n=803)...... 71
Table 8: The Effect of Control Variables and Information Sources on Knowledge (n=803)...... 73
Table 9: The Effect of Information Source Use on Mediating Variables (n=803) ...... 74
Table 10: The Effect of Information Source Use on Involvement (n=803)...... 75
Table 11: The Effect of Trafficking Frequency on Mediating Variables (n=803) ...... 76
Table 12: The Effect of Trafficking Frequency on Involvement (n=803) ...... 77
Table 13: Significant Paths from Information Source to Involvement (Conceptual Model) ...... 78
Table 14: The Effects of Knowledge and Concern on Efficacy (n=803) ...... 79
Table 15: The Effects of Knowledge, Concern, and Efficacy on Involvement (n=803) ...... 80
Table 16: Significant Paths from Knowledge and Concern to Involvement (Conceptual Model) 81
Table 17: Frequency of Perceptions on Human Trafficking ...... 93
Table 18: Frequency of Involvement by Individual Acts ...... 93
Table 19: The Effect of Information Source Use on Involvement (Listwise Deletion) ...... 94
Table 20: The Effect of Trafficking Frequency on Involvement (Listwise Deletion) ...... 95
vi ABSTRACT
Research on attentive publics has worked towards identifying who is considered attentive, how the attentive public is different from the general public, and how policy makers should take their views into consideration (Devine, 1970; Adler, 1984). Attentive publics are often the heart of social movements that engage society in a topic through increasing awareness and involvement. The attentive public is not a disconnected elite group, but average individuals who are more likely to be highly knowledgeable on a topic, sustain interest over time, and are more likely to participate as well as encourage others to participate in tangible actions.
The size of an attentive public ebbs and flows over time, as society faces multiple issues at once, and it can take years of lobbying and engagement before any tangible results are seen at a higher level. One crime that has increased in awareness and importance is the issue of human trafficking. Accounts of men, women, and children being exploited for commercial sex or forced labor has become a hot topic of interest around the world, with estimates as high as 45.8 million people in some form of modern day slavery (Global Slavery Index, 2016). The current study uses a public opinion survey of Israeli citizens to identify if there is an attentive public, if its members are significantly different from the general and least-attentive public, and to better understand what factors of information sources, knowledge, concern, and efficacy influence an individual to get involved.
The findings from this study do identify the existence of an attentive public in Israel. The attentive public is different from the general and least-attentive public on a few characteristics, but the most important one is increased efficacy. Findings suggest that use of information source and the frequency in which they report on trafficking have varying influences on knowledge, concern, efficacy, and involvement. Implications for future research are discussed.
vii CHAPTER 1
INTRODUCTION
Human trafficking, also known as “modern day slavery”, is considered an old phenomenon with a new importance (Gozdziak and Collet, 2005). Human trafficking is the exploitation of men, women, and children through the use of force, fraud, or coercion.
Exploitation ranges from forced labor or debt bondage, to commercial sex trafficking and the removal and selling of organs. Accounts of human trafficking have been examined in at least one hundred and eighty-nine countries globally (US Department of State, 2016).
Gary Haugen, founder of International Justice Mission, the largest international anti- slavery organization, once said, “When our grandchildren ask us where we were when the voiceless and vulnerable of our era needed leaders of compassion and purpose, I hope we can say we showed up and that we showed up on time” (Haugen and Hunter, 2010). The charge set by
Mr. Haugen to be leaders during this time when trafficking is such a prominent issue was not meant just for those already in positions of power and influence, but for everyday average people to stand up for the oppressed by whatever means they can. Of course, one person cannot fight for the world’s oppressed people alone. Just as a spark can set a fire, the actions of one individual has the potential to build a movement. The collective actions of individuals can then grow to impact the lives of many.
As the global estimates of human trafficking victims steadily grows over the years from
27 million (Bales, 2007) to the most recent 45.8 million (Global Slavery Index, 2016), it’s no longer just a question of how does law enforcement handle the crime, but how does society as a whole combat this issue? More importantly, how does society make this issue a priority when there are so many global and local issues that we face on a daily basis? The answer lies in the use 1 of social movements that encourage everyone to action, which in turn raises awareness, financial support, and policy change on a specific issue. Key participants in social movements are known as the “attentive public”.
1.1 Attentive Publics
People gather information and construct meaning in the context of their personal lives, so unless the issue is of particular importance or outside forces promote action, the individual will dismiss involvement, continue their daily routine, and be described as apathetic (Hallahan,
2001). Gabriel Almond (1950) was the first to identify the concept of the “attentive public”, specifically in relation to foreign policy. The “general public” reacts to general stimuli and contains a variety of interests and groupings whereas the “attentive public” is the group of informed and interested individuals who make up the audience that are most likely to affect policy decisions among the elites (Almond, 1950). Almond, like Hallahan, suggests that most individuals are relatively apathetic to situations that arise outside of their daily routine, unless that situation start to affect them directly. “So long as there is no immediate need, sharply defined threat, the attitude is vague and indefinite. When the crisis becomes sharpened,
American responses become more specific” (Almond, 1950: 56). Almond believes the general public provides the “moods” or reaction to policy outcomes whereas the attentive public provides the “depth” to the policy discussion (Genco, 1984: 18)
Research about attentive publics has found that the attentive public is more than just interested, but actually active (Devine, 1970; Rosenau, 1974; Danley-Scott, 2006). Rosenau
(1974) found that the attentives were more likely to participate in letter writing to public officials and interest organizations to increase mobilization and support towards specific policies. Devine
(1970) found the attentive publics more likely to be reading the news and talking to neighbors
2 about political issues. Focusing on attentive publics is “a refinement that takes into account that most individuals find it too burdensome to worry often about the affairs of state and therefore delegate about decision making, within limits, to their representatives” (Adler, 1984: 147). The attentive public, compared to the elites, has a larger potential impact on the general public because of their unique position within society to reach the masses on a personal level as they engage with them in everyday situations (Campbell et al., 1954; Smith, 1989; Danley-Scott,
2006).
Current events have shown the importance and usefulness of social movements led by attentive publics on issues such as LGBTQ and women’s rights, the refugee crisis, and climate change. For example, since the early 1970s, there has been constant debate on same-sex marriage and the group of supporters has been rapidly growing. A law on domestic partnership was passed in 1984, but it was not until 1993 that the courts began overturning rulings on same-sex marriage bans starting with Hawaii. In 1996, same-sex marriage had an approval rating of 27% and slowly increased over time to 53% in 2013 (Gallup, 2013). By 2015, same-sex marriage was legal at both the state and federal level.
Even more recent examples of how attentive publics rise towards an issue and engage society in action can be seen in resistance movements like the March on Washington for women’s rights, as well as the reaction to Executive Order 1376, known as the travel ban. In light of the executive order, mass protests began across the country, resulting in a spike of financial support to many organizations working with refugees like the A.C.L.U. and the ban was blocked by a federal court (Almasy and Simon, 2017; Stack, 2017). The latest issue on the rise is the debate on climate change after the United States pulled out of the Paris agreement; now, states are forming the U.S. Climate Alliance to attempt implementing the agreement on the state
3 level (Comstock, 2017). Each of these movements have a core attentive public that were invested long before these large scale events and will continue to invest in these issues after the hype has passed. It is because of the work of the attentive public that issues such as the ones mentioned have gained awareness, a political presence, and have effectively impacted both public opinion and policy.
Anthony Downs (1972) argues that certain problems or topics will go through what is called the “Issue-Attention Cycle”. This cycle stems from a combination of the type of problem with how media engages the public with the issue. The cycle has five identified stages: the pre- problem stage, the alarmed discover and euphoric enthusiasm state, the realizing the cost of significant progress stage, the gradual decline of public interest stage, and the post-problem stage. Essentially, some experts on a subject will be aware of an issue first, but it will not take hold until the public becomes alarmed by the issue and feels compelled to “do something” about it. Once the general public engages in this “do something” attitude, they realize that to make actual change, it could mean personal sacrifice. With the realization of how difficult change is, individuals will feel discouraged, threatened, or bored with the topic (and are subsequently becoming “alarmed” by a new subject). The final stage leaves the subject in an indeterminate state where there is less attention and only spasms of interested by the public.
Downs (1972) believes that the topics most likely to go through this cycle are problems that do not affect the masses directly, have a high personal cost for the changes needed, and are not exciting enough to keep people entertained after a prolonged period of time. These three reasons could describe how a topic like human trafficking could end up in this “issue-attention cycle”. The estimate of 45.8 million people in slavery seems quite large, but only affects roughly
4 0.62% of the total global population (7.4 billion). With that in mind, it’s easy to comprehend why most people do not feel directly affected by it.
In addition to the percentage of people directly victimized being relatively low, to end the exploitation of others would have high personal costs to those who are affected indirectly by slavery. For example, it would require a higher cost to those benefiting from labor services (food, clothing, jewelry, hospitality, etc.) so that workers can make fair wages. It would also require massive changes and regulations in prostitution, in addition to a lower demand for that type of work. Keeping the topic of slavery entertaining is most likely the reason why the portrayal of trafficking tends to focus more on sex than labor; it needs to keep the audience captivated.
Although after a while, individuals can become desensitized to the story of sex trafficking and the audience will move on to the next new and entertaining topic to arise in the issue-attention cycle. This is why an attentive public are necessary. Its members can make the general public aware of an issue that most might not see as directly affecting their lives, compel the general public to a deeper engagement that can support the anti-trafficking movement and thereby influence state policy, and sustain interest over time.
1.2 Current Study
“Bad men need nothing more to compass their ends, than that good men should look on and do
nothing.” – John Stuart Mill (Mill, 1867)
As criminology tends to focus on why offenders commit crime, this dissertation will focus on how to combat trafficking by growing the anti-trafficking movement through attentive publics. Can an attentive public be identified for this issue? If so, what makes those who are attentive different than everyone else? In addition, what influences the creation of an attentive public so that we can grow the anti-trafficking movement to have a significant effect on policy
5 outcomes? Based on prior research in attentive publics, this dissertation will examine how various information sources shape opinions, knowledge, and concern about trafficking. In turn, those concepts will be used to examine an individual’s need to “do something”, followed by their tangible actions of involvement in the anti-trafficking movement.
The need for this type of research is threefold. First, aside from being a massive human rights violation against millions of people, human trafficking is an interdisciplinary problem that impacts everyone indirectly and everyone can play a role in its abolition. Second, as previously mentioned, current events on a variety of issues have shown the importance of attentive publics in rallying a movement and impacting policies to create real change. Third, prior research has identified how policy can be influenced by the public, more specifically a knowledgeable and involved attentive public, as well as how media can influence public opinions. Public opinion surveys like the one used in this study are useful because they can identified how salient an issue is, how information is perceived, if the information is accurate, and in turn provide an estimate of an individual’s knowledge and the likelihood that they will get involved.
This study addresses gaps in human trafficking literature, as previous research has examined the impact of all of these variables individually or in various relationships, but none have combined them into one study. While there have been a handful of studies measuring the media portrayal of trafficking, there has not been any follow up as to how this information influences individuals. The closest study to measure all of these components is Honeyman et al.
(2016), which looks at how public opinions influence willingness to action and outcome efficacy. The current study takes these concepts further to see what influences those opinions and measures if individuals actually participated rather than their willingness. This dissertation looks at the “big picture” systems view of how information on trafficking is related to the public and
6 how the public reacts to that information, which will ultimately help or hinder the anti-trafficking movement.
Ideally, this dissertation would have surveyed populations in multiple countries at the same time. Prior literature on both public opinion and media finds that the topic of trafficking has been studied in a variety of countries across the world. Israel was chosen because of its longstanding problem with trafficking, as well as its unique changes to law enforcement, visa policy, and media representation over time. Israel has gradually worked its way from a Tier 3 status in the Trafficking in Persons report to a current Tier 1 status, meaning they have gone from a country that does not address anti-trafficking efforts to a country that continues to work towards anti-trafficking measures. Israel has recently begun facing a renewed increase in trafficking due to changes in policy when trafficking was considered at its lowest. The type of trafficking, the victims of trafficking, and certain environmental factors that impact trafficking have also seen changes over time.
It will take a massive anti-trafficking movement to bring modern-day slavery to an end.
This specific study addresses what influences an individual to become more knowledgeable and involved, creating an attentive public in the anti-trafficking movement. By understanding these influences, more can be done to increase the size of an attentive public and engage the general public which would otherwise be indifferent. The attentive public is then more likely to influence those around them, impact anti-trafficking policy, and spread awareness among communities in an effort to decrease the prevalence of trafficking. This study is the first step in a long line of research to gain a basic understanding of how everyone can contribute their individual efforts in order to effect global change.
7 1.3 Overview
The chapters will proceed as follows. Chapter 2 will provide an in-depth literature review in three sections. The first section will highlight previous research on human trafficking. The second section will examine the impact of public opinion on policy in general, and then will discuss the current public opinion literature on human trafficking. The third section will address the previous literature on how various media (or information sources) shape public opinion, as well as highlight the findings from media analyses on human trafficking. Chapter 3 will discuss the data collection, variables used in the study, and the analytic plan. Chapter 4 provides a descriptive analysis and the results to the four research questions. Chapter 5 will discuss the findings, implications, and conclusions.
8 CHAPTER 2
LITERATURE REVIEW
2.1 Introduction to Human Trafficking
2.1.1 Current Definitions of Human Trafficking
“Slavery as a social and economic relationship has never ceased to exist during recorded history, but the form that it takes and its definition have evolved and changed” (Bales &
Robbins, 2001: 18). The term “human trafficking” has gained international attention since the
United Nations Convention against Transnational Crime in 2000. With over 300 laws about the first slave trade and trafficking, the convention’s main purpose was to create a standard for definitions, terminology, and practices (Bales, 2007). This convention passed the international law against human trafficking, the Protocol to Prevent, Suppress and Punish Trafficking in
Persons, especially Women and Children Supplementing the Convention to Prevent
Transnational Crime. In the protocol, human trafficking is defined as
the action of recruitment, transportation, transfer, harboring, or receipt of persons by means of the threat or use of force, coercion, abduction, fraud, deception, abuse of power or vulnerability, or giving payments or benefits to a person in control of the victim for the purposes of exploitation, which includes exploiting the prostitution of others, sexual exploitation, forced labor, slavery or similar practices, and the removal of organs. Consent of the victim is irrelevant where illicit means are established, but criminal law defenses are preserved. (Protocol Art. 3.b, Convention Art. 11.6)
There are three main elements to a human trafficking case. For example, the victim was recruited (criminal act) through fraud (the means to commit the acts) into a situation of forced labor (the form of exploitation). Since there is no one set way to traffic an individual, any series of combinations could relate to a case of trafficking as long as each of the three elements are met. A difficulty in defining human trafficking is that the international law is not exhaustive, but
9 it is the basis to which individual countries would form their own national human trafficking law.
As Bales (2007) points out, not every case of human trafficking will include the crossing of national borders or be run by an organized crime group, so each country must be prepared for whatever form of trafficking occurs domestically as well.
There were 117 countries that originally signed on to the protocol in 2000, and it has been ratified by 170 parties as of 2016. Part of the agreement when signing on was that each country would create their own national law. An important law to note is the Trafficking Victims
Protection Act (TVPA) passed by United States in 2000. The TVPA has been reauthorized and strengthened in 2003, 2005, 2008, and 2013 to stay up to standards with the increase in knowledge of human trafficking by the government, law enforcement, and anti-trafficking organizations; the last reauthorization was passed in 2013 (Farrell et al., 2013). The TVPA is important because it established the annual Trafficking In Persons (TIP) Report.
The TIP report is produced by the Department of State’s Office to Monitor and Combat
Trafficking in Persons every year, starting in 2001 covering eighty-two countries and the most recent report covering one hundred eighty-nine countries, and provides country narratives on the extent of their government’s effort to address human trafficking issues. The countries are placed in one of three tiers, with a tier ranking of one being the best and a ranking of three being the worst, based on their actions to comply with the TVPA’s minimum standards to eliminate trafficking through prosecution, protection, and prevention. Each country is then given recommendations on how to improve identified weaknesses. This report also establishes which countries receive funding from the United States for their anti-trafficking work.
When the TIP report was first published in 2001, Israel was ranked as a Tier 3 country.
Israel had not established any measures to combat the crime of human trafficking. In 2002, Israel
10 moved to a Tier 2 position and remained there until 2005. In June of 2006 the TIP report places
Israel on a Tier 2 watch list, which means that they had not made any significant improvements in their anti-trafficking efforts and funding would be cut if they fell to a Tier 3 placement. Four months later, the national law was officially passed in October of 2006. Israel’s Anti-Trafficking
Law defines trafficking as:
Anyone who carries on a transaction of a person for one of the following purposes or in so acting places the person in danger of one of the following, shall be liable to sixteen years imprisonment: (1) removing an organ from the person's body; (2) giving birth to a child and taking the child away; (3) subjecting the person to slavery; (4) subjecting the person to forced labor; (5) instigating the person to commit an act of prostitution; (6) instigating the person to take part in an obscene publication or obscene display; (7) committing a sexual offense against the person. (Prohibition of Trafficking in Persons Law 5766-2006)
With increased awareness on trafficking, the Knesset formed the Subcommittee on
Trafficking in Women and Prostitution. This subcommittee was formed under the Committee on the Status of Women and Gender Equality. One of their main long-term goals is to pass legislation based on the Nordic model, which criminalizes the purchase of sexual services and protects prostituted persons. While the original attempt to pass this legislation failed in 2012, the subcommittee is still working towards this goal to combat the demand side of prostitution and which also impacts sex trafficking. Israel became a Tier 1 country in 2012 and has continued to hold that position to this day.
2.1.2 Types of Trafficking
As previously mentioned, there are three main types of trafficking, however most trafficking research focuses on sex and labor. Sex trafficking is defined as when an individual
“engages in a commercial sex act, such as prostitution, as a result of force, fraud, or coercion”
(State Department, n.d.). Minors are considered victims without proof of force, fraud, or coercion. In cases of sex trafficking, there seems to be no limit to the amount of exploitation an
11 individual can take depending on the market demand and only pausing the exploitation if the individual requires medical attention. One victim recounted servicing up to 30 people per day, seven days a week for four years (Romo, 2015). Traditional sex trafficking operations are typically handled within brothels or with victims working on the street. New techniques by the traffickers include “mobile brothels” where the trafficker hands out cards to customers and then deliver the victims when called (Center for the Advancement of Human Rights, 2010). Victims have been increasingly solicited on the internet through websites like Backpage, where the CEO has recently been arrested on felony charges for pimping a minor, pimping, and conspiracy to commit pimping (Domonoske, 2016).
Labor trafficking is an umbrella term to include debt bondage and involuntary servitude.
Debt bondage is defined as the “status or condition of a debtor arising from a pledge by the debtor of his or her personal services or of those of a person under his or her control as a security for debt, if the value of those services as reasonably assessed is not applied toward the liquidation of the debt or the length and nature of those services are not respectively limited and defined” (State Department, n.d). Involuntary servitude is a condition of servitude induced by means of “any scheme, plan, or pattern intended to cause a person to believe that, if the person did not enter into or continue in such condition, that person or another person would suffer serious hard or physical restraint; or the abuse or threatened abuse of the legal process” (State
Department, n.d).
In cases of labor trafficking, victims are likely to work long hours in harsh conditions, may be routinely beaten, and have poor living conditions (Barrick et al., 2014). In some cases, debt bondage can keep an entire family enslaved, or pass down the bondage from generation to generation (Basu and Chau, 2004; Belser, 2005). Areas of labor trafficking include but are not
12 limited to agriculture, the hospitality industry, bars/clubs, factories, construction, brick kilns, fishing boats, rock quarries, diamond mines, and cocoa plantations.
One of the key events that increased prostitution and sex trafficking in Israel specifically was the Russian Aliyah beginning in 1969. Over 111,000 Soviet Jews immigrated to Israel between 1969 and 1975. During this time of increased immigration, prostitution became associated with the Russian women and this stereotype continued into the early 1990s. The first accounts of human trafficking come from the mid-1990s in the form of sex trafficking (Hotline for Refugees and Migrants; State Department, 2013). The first report was by Martina
Vandenberg and the Israel Women’s Network as they interviewed over fifty law enforcement officials, government officials, crisis workers, and prostitutes for a more in depth understanding of trafficking in Israel. Women were recruited from the former Soviet Union through classified ads in newspapers and opportunities to work abroad (Cwikel, 2006). Some of the women knew they would be working in prostitution, but not to the extent of exploitation that they would experience, and others were not aware at all (Cwikel, 2006). The sex industry in the 1990s was ruled by the mafia and many corrupt police officials would tip off brothel owners before a raid
(Israel Women’s Network, 1997). The Hotline for Migrants and Refugees states:
Public advocacy and media campaigns of the Hotline for Refugees and Migrants in 1999 and 2000 along with an Amnesty International report published in 2000 were unsuccessful is getting authorities to recognize the problem, although the same year the Knesset passed a law against the trafficking of women for the purpose of prostitution. Israeli authorities began recognizing that there is a problem only after the U.S. State Department placed Israel alongside Pakistan and Bahrain in Tier 3 of the annual Trafficking in Persons (TIP) Report in 2001 – the lowest possible level, indicating that Israel is doing nothing to combat human trafficking within its borders. That year and the coming years, most of the trafficking victims were women from FSU who were trafficked into Israel for the sex trade.
Israel has consistently been identified as a destination country for trafficking victims. In the early TIP reports (2001 – 02), the focus was specifically on foreign women for the purpose of
13 sexual exploitation. In 2003, male victims and situations of labor trafficking were identified within construction and has expanded over time to identify agriculture, caregiving, fishing, and other industries (State Department, 2016).
The TIP Report finds that sex trafficking victims are commonly identified from Ukraine,
Russia, Maldova, Belarus, China, and the Philippines. Some women were aware they would be working in prostitution, while others did not. Labor trafficking victims have been identified from
China, Romania, the Philippines, Thailand, Turkey, Jordan, and former Soviet countries. It was not until 2011 that reports of Israeli women being trafficking for sexual exploitation were considered by non-government organizations, however law enforcement did not confirm these cases (State Department, 2011). In labor cases, the recruitment agencies would charge anywhere from $1,000 to $20,000, which would place the victim in debt bondage until he or she could pay back the wages (Department of State, 2015). Thai workers on agricultural farms experience low wages, excessive work hours, had poor living conditions, and were unable to change employers
(Yaron, 2015; Department of State, 2015). Victims from various locations work in harsh conditions on fishing boats (Department of State, 2016). One of the most difficult situations to identify is exploitation of caregivers as they are isolated in private residences. Organ trafficking is acknowledged in Israel, however there has only been one document case from 2007.
2.1.3 Trafficking Discourse
“In addition to the polemical nature of trafficking debates within the United States, so too do shifting definitions of trafficking reveal under- lying teleological aims and nationalist agendas for countries seeking to address the problem. Trafficking thus emerges as an analytic catchphrase and dominant cognitive map for making sense of related and overlapping phenomena such as migration, commercial sex, and modern day slavery” (Musto, 2009: 281). Research has found
14 that the definition of human trafficking is often confused with two similar phenomena, human smuggling and prostitution (Musto, 2009). While the crimes are very similar in nature to human trafficking, there is a fine line that distinguishes them.
Many researchers have addressed the confusion between the definition of human trafficking and human smuggling, to which the latter is the “illegal entry of a person into a State
Party of which the person is not a national or a permanent resident” (Aronowitz, 2000;
Gallagher, 2001; Gallagher, 2002; Laczko, 2002; Salt, 2000). Bajrektarevic (2000: 66) emphasizes four main differences between trafficking and smuggling:
1. Smuggled persons always travel voluntarily; trafficked persons can either begin their
trip voluntarily or may have been coerced or kidnapped;
2. trafficked persons are used and exploited over a long period of time;
3. an interdependency occurs between the trafficked person and organized crime groups;
4. trafficked persons are eligible for further networking (recruitment for criminal
purposes).
Discourse also highlights a second differentiation needed between human trafficking and prostitution. While the act of prostitution, selling sexual acts in exchange for goods or money, is part of trafficking, the difference lies in the willingness of the individual committing the acts.
This is not to debate as to whether prostitution is a choice, but whether the individual can walk away from the situation without fear of harm to themselves or others.
The rise in trafficking has stirred a debate between feminist camps on whether prostitution should be abolished or regulated. Abolitionists focus on prostitution as an oppressive human rights issue, controlled by the demand of men, where consent is irrelevant
(Miriam, 2005; Desyllas, 2007; Inguez de Heredia, 2008; Cavalieri; Lozano, 2011). Activists in
15 favor of regulation see sex work as a legitimate occupation, giving women the agency to choose the lifestyle (Miriam, 2005; Desyllas, 2007; de Heredia, 2008; Cavalieri, 2011; Lozano, 2011).
As a labor option, the argument for regulation helps secure health and safety standards for the women (Miriam, 2005). Abolitionist question if legalization will lead to an increase in trafficking victims, even though the trade would be regulated. A few countries have examined whether legalization of prostitution would increase or decrease cases of trafficking (Ekberg,
2004; Jakobsson and Kotsadam, 2011; Cho, Dreher, and Neumayer 2013).
2.1.4 Responses to Trafficking in Israel
The National Anti-Trafficking Unit (NATU) was established in 2006 along with the penal code. NATU serves as the leading government agency that coordinates prosecution, protection, and prevention programs as well as policy efforts (State Report, 2015). A centralized anti-trafficking unit called SAAR, a Hebrew acronym for human trafficking, exploitation, and fraud, was created in 2009 (Department of State, 2012). The SAAR unit was disbanded in 2011 and cases were then handled by the regional districts and overseen by the Israel National Police
(Department of State, 2012). While the decentralization was designed to improve law enforcement on a more local level, the change was not received well by members from the
Supreme Court. "It means that dealing with this issue is dropping as a priority," she said. "All the functionaries appeared before the Knesset and all felt the same way. It took years for us to understand this crime. We delayed dealing with it because we didn't understand human trafficking and we didn't realize its scope" (Zarchin, 2011).
There are a handful of NGOs that work with victims of human trafficking. Some of the prominent NGOs include ATZUM/Justice Works, Kav LaOved – Worker’s Hotline, the Hotline for Migrants and Refugees, and Isha L’Isha – Feminist Center Haifa. Each of these organizations
16 works toward lobbying for improved legislation, increasing awareness of the general public, law enforcement, and the government through workshops, lectures, and training, as well as provide resources for victims in need. ATZUM and Isha L’Isha mostly focus on women, sex trafficking, and prostitution, whereas Kav LaOved and the Hotline for Migrants and Refugees focuses more on labor trafficking and labor violations.
The government has opened three shelters for rehabilitation. The first victim shelter established was Ma’agan in 2004. This shelter is specifically for women and children. The shelter can host up to 35 individuals and provides medical as well as psycho-social care. The police must be convinced of “preliminary evidence” that the individual is a victim of human trafficking prior to the individual being referred to one of the shelters (Hacker and Cohen, 2012).
The second victim’s shelter, Atlas, was established with 35 beds in 2007 for male victims. Atlas provides various training programs for its residents. The third shelter, Tesfa, was specifically for the specific needs of female victims coming from Eritrea and Ethiopia, was opened in 2014 and closed in 2015 due to low number of referrals. Along with the resident shelters, the government also provides transitional apartments. The shelters mainly host international victims; there have been cases in which an Israeli woman stayed at the Ma’agan shelter. For those not wanting to stay in a shelter, the Ministry of Social Affairs runs a day center with social workers, food aid, and psycho-social services that assists in victim transition (State Department, 2016). There are six centers for child sex trafficking victims and at-risk youth, providing medical care and rehabilitation services (State Department, 2016).
17 2.2 Public Opinion Literature
2.2.1 Public Opinion on Policy
The concept of public opinion dates back to the work of John Locke as he described men to be controlled by three laws: divine law, civil law, and the law of opinion (Speier, 2001). The study of public opinion and its impact on policy is based in the idea of democratic theory.
“Democratic governance assumes that the preferences of the citizens is reflected in policy outputs” (Manza and Cook, 2002: 630). Early studies of public opinion mainly covered issues in
Europe and the United States (Danley-Scott, 2006). In America, Lippmann (1922) initiated the idea that the general public was short sighted and misinformed, therefore the elite and opinion leaders we’re responsible for providing information and creating policy (Danley-Scott, 2006).
This idea was confirmed by Converse’s work (1964) highlighting the lack of developed political beliefs by citizens.
Early research on the effect of public opinion on policy begins with Miller and Stokes
(1963) as they measured the differences between congressman’s votes and their constituents.
Their study found that congruence on issues varied across multiple domains. Using correlations, there was little agreement between Representative and their district on issues of foreign involvement, significant agreement on the issues of social and economic welfare, and almost full agreement on the issue of civil rights. Miller and Stokes (1963) point out that there are two ways in which public opinion influences the representative. First, electing an official who shares similar views and values of the constituents and second, the representative follows perception of what the constituents want so that he is reelected. Their study finds that a representative’s roll call behavior is strongly influence by his own preference and his perception of the constituents.
18 Page and Shapiro (1983) also studied congruency of public opinion and policy outcomes, addressing a variety of policy issues at local, state, and federal levels between 1935 and 1979.
The sample for their study was based on the 357 instances of change in policy preference. Out of the cases that found a change in policy one way or the other, a majority (66%) were in congruence with public opinion after a one year lag. Congruence fluctuated with the type of policy and the time period. Page and Shapiro (1983) found the biggest increase in congruency between opinion and policy was when the opinion changed in a liberal direction (86% of the time) in comparison to a conservative change (53% of the time). When addressing issues of non- congruence, the authors found that most of the cases had very small opinion changes, temporary opinion changes, or the non-congruence link disappears all together when there is more than a one year lag. Policy is impacted the most when change is large, stable, and salient (Page and
Shapiro, 1983).
There have been mixed results on the effect of public opinion on policy (Burnstein,
2003). Some research believes there is little impact by public opinion (Converse, 1965), others believe opinion is important in the long run (Key, 1960). Burnstein (1998) reviews literature addressing the relationship between public opinion and various policies including, but not limited to, social welfare, economic growth, war/defense, civil rights, taxes, social investment, and abortion. Burnstein notes that many of the articles provided some kind of assurance of causal order going in the intended direction with the overall majority concluding that public opinion influenced policy.
Salience of an issue has been cited as a key component in responsiveness as people are more aware and attentive to the issue (Page and Shapiro, 1983; Burnstein, 2003). Page and
Shapiro (1983) indicate the proportion in which respondents answer “don’t know” or “no
19 opinion” to a survey question is an adequate measure of salience as offering a preference typically indicates interest and attention. The authors also found congruence between opinion and policy to be highest when the proportion to responses of “don’t know” were low (Page and
Shapiro, 1983). Burnstein (2003) found that most opinion-policy research does not address salience, but when it does, it is always significant.
Questions have been raised on the causal order between public opinion and policy. Does opinion influences policy, policy influence opinion, or is a cyclical pattern between the two
(Child, 1965, Easton, 1965; Burnstein, 1998; Hakhverdian, 2012; Bachner and Hill, 2014)?
Based on democratic theory, one perspective is that opinion should influence policy as political representatives adjust to the opinion of the constituents. Hakhverdian (2012) specifically addresses casual order by comparing three hypotheses of responsiveness (opinion influence policy), leadership (policy influences opinion), and counter response (policy influences opinion in the opposite direction). Using a scale ratio and granger causality tests to address causal order,
Hakhverdian found that the responsiveness hypothesis was supported, while the leadership and counter response hypotheses were not supported. Hakhverdian (2012) suggests that there are two occasions in which policy could influence opinion; if the issue is highly salient or when the source of information has a favorable view.
The cyclical pattern between public opinion and policy can be seen in David Easton’s
(1965) systems analysis. Simply stated, public opinion is one factor that influences the environment. The expectations, public opinion, motivations, ideology, interests, and preferences of people within the society are variables that shape the environment of each demand (Easton,
1965). The demands are put into a system to be handled by the authoritative decision makers
(Easton, 1965). The second input into a system is “support.” Support in “overt” form is
20 observable behavior. This support can be intentional behavior to encourage an institution or to direct change. On the other hand, unintentional support can come from protesting support for the opposition, regardless of whether they are invested in the alternative (Easton, 1965). “Covert” support is the attitude or the “supportive frame of mind” a person has, whether their observable behavior is overtly responding with action (Easton, 1965).
The decisions that are created as “outputs” from this system then feedback into the environment. The “feedback loop” gauges the reaction to the output and will affect public opinion and support levels towards the decision makers and the system as a whole. The feedback loop is important because without it, the system would not know how close it has come to its objectives and if it needs to redirect its goals and behavior (Easton, 1965).
2.2.2 Attentive vs. General Publics
The concept of how to define “publics” has been introduced and defined in a variety of ways to group individuals based on their attitudes and actions. Philip Converse (1964) argues that individuals are distributed into “issue publics” based on topics that are personally relevant and important to them. Kirk Hallahan (2001), in reference to public relations, recognizes that publics differ in their levels of activity and passivity.
Hallahan’s typology of publics is rooted in J. Grunig’s situational theory and has two components for criteria: knowledge and involvement. Knowledge refers to “beliefs and attitudes held in memory about a particular object, person, situation, or organization, based on every day experience or formal education (Hallahan, 2001). Involvement refers to “the degree to which an individual sees an object, person, situation, or organization as being personally relevant or having personal consequences” (Hallahan, 2001).
21 Hallahan (2001) describes the Non-Publics as those who have absolutely no knowledge of or involvement in an organization or situation. However, the moment these individuals do become aware, they should be moved to the inactive public group. Inactive Publics are defined as a group of individuals who have low knowledge and involvement in an organization or situation. The Aware Publics are defined as those knowledgeable about the organization or situation, but lack involvement. The Aroused Publics are those who have a low level of knowledge but have recognized a potential problem that needs to be addressed and are more likely to be actively involved even with their limited knowledge. The Active Publics are both highly knowledgeable and highly involved in an organization or situation and include leaders of social movements, interest groups, and those willing to invest time and effort to create change
(Hallahan, 2001).
Based on Almond (1950) and Lane and Sears (1964), Devine determines “levels of attentiveness – the lower ones being General or Non-Attentive Public, the higher levels being defined as the Attentive Public” (Devine, 1970: 35). Donald Devine (1970) tests whether an
“attentive public” can be identified within a democracy, if it is significantly different from the
“non-attentive public,” and if the opinions and support of the attentive public influence policy outcomes. Devine uses the input-process-output framework from Easton’s systems view to organize the model to which his attentive public hypothesis can be tested. Comparisons are then made between the percentages of support by attentive, non-attentive, least attentive, and the general public in relation to their influence on policy outcomes. The study finds that policy tends to follow the attentive public on certain topics like foreign policy and foreign aid, although the biggest influence to impact policy was to have a majority support of the general public (Devine,
1970).
22 Operationalizing “attentiveness” has varied among studies. Almond (1950) identified the attentive publics to have interest in general politics and national election campaigns, as well as talking about politics, self-exposure to political information, engaging in political activities, and caring about elections and politics. Devine (1970) began with fifteen provisional measures of attentiveness, but found only five to show a suitable difference of attentiveness compared to the non-attentive group. The five measures included attention to campaigns, following campaigns in the newspaper, reading about campaigns in magazines, attention to politics, and talking to others about how they should vote. Danley-Scott (2006) addressed three indicators of political knowledge, political discussant, and political interest. Genco (1984) argues that attentiveness by factual knowledge alone is a problematic indicator because some respondents might know the answer based on education, where others might know about the topic in general but are unable to answer the specific questions asked in a survey. Instead, attentiveness is operationalized by interest (or focused attention on a specific object), a sustained pattern of information acquisition, and level of information (Genco, 1984). These three measures are independent, yet combined to create a measure near attentiveness.
2.2.3 Public Opinion on Human Trafficking
Public opinion on human trafficking has been addressed in countries all over the world, including The United States (Del Carmen Balderas, 2006; Dennis, 2012; Jones, 2012; Farrell
NIJ; Farrell, Pfeffer, and Bright, 2015; Cunningham and Cromer, 2016; Mapp et al., 2016), Israel
(Herzog, 2008), Russia (Buckley, 2009), Nigeria (Olufayo and Omotosho, 2009; Omorodion,
2010), Japan (Otsuki and Hatano, 2009), Maldova (Robinson, 2011), Pakistan (Ali et al., 2013),
Bosnia and Herzegovina (Muftic, 2013), England (Dando, 2016), Australia (Honeyman, Stukas,
23 and Marques, 2016), Greece (Digidiki, Dikaiou, and Baka, 2016), as well as multi-country surveys of Europe (Bishop et al., 2013).
Much of the public perception research focuses specifically on sex trafficking, while a smaller portion addresses both sex and labor trafficking or use the term “trafficking” as a general statement. When addressing the perceptions of victims, research mostly addresses women and girls, with little insight into men and boys, especially in situations of sex trafficking. For example, even the survey by Buckley (2009) that began with a general term of “human trafficking” and included a victim reference to women, girls, boys, and men is biased in that subsequent questions focus specifically on sex trafficking and female victims rather than all types of trafficking or victim groups. The skewed amount of research focused on sex trafficking instead of including both types of trafficking, as well as the bias of victim gender, shows how even the research that is examined on public opinion gives preference to a specific type of trafficking and a specific victim population. A variety of samples have been used in these public opinion articles, from conveniences, to snowball sampling, and even a handful of randomized samples. Respondents range from the general public, university students, law enforcement officers, nurses, and even trafficking victims.
The questions of knowledge and public opinion have a wide range. Common research questions look at how the public defines trafficking, where victims come from, who is trafficked, who should handle the problem, and what are the causes or vulnerabilities leading to situations of trafficking (Del Carmen Balderas, 2006; Olufayo and Omotosho, 2009; Buckley, 2009;
Omorodion, 2009; Robinson, 2011). Trafficking is understood to be a form of slavery, but some think it’s a way of life or a victimless crime (Olufayo and Omotosho, 2009). Often times the definition of trafficking is confused with movement/behaviors or prostitution for money (Dando,
24 2016) or smuggling (Farrell et al., 2015). While all types of trafficking and victim populations were addressed, most respondents focused on women in prostitution (Robinson, 2011, Farrell et al., 2015). Most opinions agree that trafficking is a world-wide problem even occurring within their state, but do not necessarily believe it happens in their community.
In Nigeria, a majority of respondents believed family, friends, and local community members were the most common traffickers (Olufayo and Omotosho, 2009, Omorodion, 2009) compared to respondents in Russia who believe victims are more likely to be “dumped by criminal gangs” (32.8%) than sold by parents or friends (9.1%) (Buckley, 2009: 221). These questions are surprisingly rare in opinion surveys, but the findings in Nigeria are similar to the reality of traffickers in many countries. Causes and vulnerabilities to exploitation were described as poverty and unemployment (Buckley, 2009; Olufayo and Omotosho, 2009; Omorodion, 2009
Robinson, 2011), being trafficked for money or looking to make money (Dando, 2016), deception or force (Olufayo and Omotosho, 2009), corruption (Robinson, 2011) and criminal gangs (Buckley, 2009).
Opinion research has asked respondents who they believe should be in charge of handling the problem. Some opinions agreed with increased law enforcement (Del Carmen Balderas,
2006; Olufayo and Omotosho, 2009), while others believed the government should work towards improving people’s lives, while decreasing corruption, and increasing laws/enforcement to protect children (Buckley, 2009; Olufayo and Omotosho, 2009). The majority of respondents across studies were under the opinion that education and awareness were key responses in the fight against trafficking (Del Carmen Balderas, 2006; Olufayo and Omotosho, 2009; Buckley,
2009; Omorodion, 2009; Robinson, 2011). In Buckley (2009), when asked which institution was most likely to effective in combating trafficking, the phrase “no one” was the highest response,
25 showing the little faith respondents had in an institution to be effective. These sentiments were also reflected in the respondent’s answers during a focus group discussion (Buckley, 2009).
A second set of research addresses what factors shape public perceptions and concern for victims. Research has looked at various demographic characteristics (Jones, 2012; Ali et al.,
2013; Bishop et al., 2013), patriarchal or egalitarian attitudes (Herzog, 2008), just world theory
(Digidiki, Dikaiou, and Baka, 2016), attitudes towards buying and selling sex (Otsuki and
Hatano, 2009), as well as information sources where their perceptions were formed such as media or formal training (Omorodion, 2009; Dennis, 2012; Bishop et al., 2013; Dando, 2016;
Mapp et al., 2016). Media and myths play a large role in shaping perceptions and attitudes.
Aside from the general public, perceptions and training of first responders such as law enforcement and nurses, as well as some secondary players such as prosecutors, judges, and anti- trafficking coalitions have been examined (Del Carmen Balderas, 2006; Wilson and Dalton,
2008; Hounmenou, 2009; Dennis, 2012; Grubb and Bennett, 2012; Muftic, 2013; Farrell, Pfeffer, and Bright, 2016; Mapp et al., 2016). Most law enforcement studies examine how information sources, training, and experience with human trafficking have shaped their perceptions and definitions of trafficking. Mapp et al. (2016) had officers provide a definition of trafficking and most of their definitions were considered “low quality” meaning they showed no basic understanding, gave a weak definition, or confused it with other acts like smuggling.
Challenges to law enforcement knowledge include lack of awareness of anti-trafficking legislation (Newton, Mulcahy, and Martin, 2008), or the existence of the crime in their community (Farrell, McDevitt, and Fahy, 2010; Wilson, Walsh, and Kleuber, 2006). Farrell,
Pfiffer, and Bright (2015) studied the schemas used by law enforcement in cases of trafficking
26 and how that impacts their investigation. They found that officers without training often relied on myths or media representations, which distorts their knowledge and understanding of the case.
A smaller grouping of research has begun to address how perceptions influence willingness to act. Using the general public, Honeyman et al. (2016) measure variables of perception, knowledge, victim characteristics, concern, outcome efficacy, and cost of willingness to get involved with social action. The strongest predictor of social action was outcome efficacy, a feeling that the respondent’s actions could make an actual difference in the problem.
Specifically looking at law enforcement, Mapp et al. (2016) assessed the likelihood that a law enforcement officer would take action in a case of human trafficking. The findings showed that about a third of the sample would take some kind of action like engaging the victim for an interview, about a quarter of the sample would contact with more training for assistance, and less than a quarter said they would take action and contact others. In a comparison of trained and untrained officers, training led to more action and contact where the untrained officers were more likely to just contact someone for assistance (Mapp et al., 2016).
2.3 Media Literature
2.3.1 Impact of Media on Public Opinion and Social Activism
As seen in the previous section, public opinions play a role in impacting public policy.
There are a variety of things that shape and mold an individual’s opinion including age, race/ethnicity, SES, religion, and education. Typical political socialization comes from parents, school, peers, and the media (Pinkleton and Austin, 2004). The importance of addressing media in particular when assessing perceptions of crime and justice comes from prior research indicating that the general public gets much of their information from media sources (Roberts et al, 2003; Roberts and Stalans, 1997; Surette, 2007; Pinkleton et al, 2012; Donovan and Klahm,
27 2015; Roche, Pickett, and Gertz, 2016). For example, Chiricos and colleagues have found that media exposure impacts Americans’ fear of crime more than direct experience (Chiricos,
Padgett, and Gertz, 2000). The use of media has been found to accomplish three things: Framing and shaping the public knowledge and perception, foster participation in political activism, and impact both individual and collective efficacy.
Media is considered a “critical form of public knowledge” (Schudson, 1995) and provides an easier way for audiences to process information through “schemas” (Goffman,
1974). The media can influence public discourse about a topic as well as encourage action on the part of the public as well as policy makers (Sobel, 2014). Agenda setting is an important communication tool that refers to the relationship between the emphases the media places on a topic and how important the audience believes it to be (McCombs and Shaw, 1972). Audiences understand a topic’s level of importance based on salience, like being the lead story in the paper, an opening story in the news, or a topic that is frequently discussed (McCombs; Kiousis and Wu,
2008). Another important communications tool is issue framing, which is the representation of a topic through media that can influence how the audience understands the message (Scheufele and Tewksbury, 2007). The intention of framing is not to distort the information, but to simplify complex information so that the general public can become knowledgeable on a subject (Gans,
1979; Scheufele and Tewksbury, 2007).
In topics of crime and justice, prior research has found that media can impact perceptions of crime and justice by how it frames and shapes the information distributed to the public (Baum and Potter, 2008; Kiousis and Wu, 2008; Pinkleton et al., 2012; Hoffman, 2013; Pinkleton and
Austin, 2014; Donovan and Klah, 2015; Neubaum and Kramer, 2016; Roche, Pickett, and Gertz,
2016). News can be selective in how they portray the information to influence how the public
28 views the topic (Mcleod et al., 1995; Kiosis and Wu, 2008; Hoffman, 2013). Journalists are the gatekeepers that often determine which stories to cover based on their “newsworthiness” or ability to sell (Baum and Potter, 2008; Roche et al., 2016). Baume and Potter (2008) note that media feeds into the public’s demand of which topics to cover as they are the ultimate consumers while Beckett and Sasson (2004) find that television media will sensationalize crime news to attract crowds.
Media has been found to foster participation in political activism (Chaffee et al., 1994;
Baume and Potter, 2008; Booth-Perry, 2014). Originally, the effect of media was studied in terms of how voters paid attention to campaigns during election years (McCombs and Shaw,
1972). Technology has advanced beyond simply capturing the news on television as social media now enables direct communication between the general public and the justice system, whether its
“tweeting” from the courtroom or covering elections and motivating public outreach (Booth-
Perry, 2014). Internet news and social media have risen to become resources for engaging in the political process both online and offline (Bennett and Sergerberg, 2012; Booth-Perry, 2013,
2014; Velasquez and LaRose, 2014; Anstead and O’Loughlin, 2015; Neubaum and Kramer,
2016; Karamat and Farooq, 2016).
In Ober et al.’s survey of 169 representatives from 53 national advocacy/activist groups in the US, qualitative analysis reveal that “social media can facilitate civic engagement and collective action by strengthening outreach efforts, enabling feedback loops, increasing speed of communication, and being cost effective” (Ober et al. 2012 in Booth-Perry, 2014). In a study by
Karamat and Farooq (2016), social media was a platform used by college students to express their political views, follow political pages, and ask friends to participate. However, results have
29 also found that over half of the sample believed newspaper to be the more reliable source for information.
A variable that has begun to be addressed in the research area of media and activism is efficacy, both on an individual and collective level (Hayes, 2009; Vitka et al., 2011; Pinkleton et al 2012; Velasquez and Larose, 2014). Political efficacy is the belief that an individual’s political action can impact the political process (Campbell et al., 1954). External efficacy reveals an individual’s belief that they can influence public affairs and political decisions or that the individual will be responsive to the demands of the citizens (Pinkleton et al 2012; Velasquez and
Larose, 2014). Velasquez and Larose (2014) found that the more an individual felt the group could attain a goal, the more likely they were to participate in online collective actions, especially if there was increased interdependence between members. In the same way, media can be used to motivate individuals to become part of, and participate in, a larger anti-trafficking movement because each member is needed to obtain the goal.
2.3.2 Media and Human Trafficking
Since most people have little, if any, personal experience with crime, it is not entirely surprising that media coverage shapes the public's attitudes and perceptions about crime problems and the most appropriate solutions to criminal problems. The media has provided a vehicle for anti-trafficking stakeholders to convey messages to the public and legitimize particular problem frames. – Farrell and Fahy, 2009: 618
Cases of human trafficking have been examined in many media sources including newspapers, television, Internet news, and social media platforms as well as TV shows and movies. The importance of considering how trafficking is portrayed in media is because the mass media can influence both official policies and public perceptions of human trafficking (Vance,
2012; Riff, Lacy, and Fico, 2014; Sanford, Martinez, and Weitzer, 2016). Researchers have
30 looked at how the issues of trafficking are framed because the media has influence in agenda setting and gate keeping information to be dispersed to the public (Papadouka et al, 2016).
One major frame is the heavy emphasis on sex trafficking in comparison to labor, organs, or trafficking in general that incorporates all forms of exploitation. Wallinger (2010) reasons that human trafficking articles are popular and newsworthy as they mostly focus on sex trafficking.
The sensational cases of sex trafficking are similar frames to how crime is usually presented as violent and on the rise, when statistics tend to prove otherwise (Sanford et al., 2016). For example, it is estimated that there are more individuals in situations of forced labor than sexual exploitation. So what is the reasoning behind media’s focus on sex? First, sex sells and the plight of a young female being exploited for sex might be a more interesting read than a story about a farm worker or maid. A second influence could be that sex trafficking is identified more often than labor trafficking cases, making it easier for a journalist to report on it.
In addition to the type of trafficking, a second emphasized frame is the gender of the victim and offender. In prior years, US news coverage was found to focus more on sex trafficking of female victims (Farrell and Fahy, 2009; Vance, 2012; Johnston, Friedman, Sobel,
2015). For example, Denton (2010) completed a content analysis of 191 unique trafficking cases.
The victim was female in 42% of the cases, male in 9% of the cases, a combination of male and female in 44% of the cases, and the remaining were unidentified. The offenders were males within 63% of the cases, female in 9% of the cases, and a combination of male and female in
28% of the cases. In Brazil, the “Myth of Maria” highlighted an innocent family victim who was lured into sex trafficking (Blanchette, Silva, and Bento, 2013). This myth was a common frame that circulated various media sources and created a perception of trafficking that preceded formal research and essentially lead to a moral panic (Blanchette, Silva, and Bento, 2013).
31 Type of trafficking or victim gender aside, researchers have identified a handful of other frames such as security, crime, human rights, and immigration to identify how the crime has been shaped or may have changed over time (Farrell and Fahy, 2009; Denton, 2010; Pajnik,
2010; Johnston, Friedman, and Sobel, 2015). Each type of frame will shape a viewer’s understanding of what causes trafficking or how it happened, the impact this crime has on the victim and the viewer, and ultimately will shape feelings of how a viewer might respond to the situation (education or action). Farrell and Fahy (2009) assessed newspaper content in 2,462 articles on trafficking between the years 1990 and 2006. Three major frames were identified, grouped by different time periods. In the earlier years, when the term of trafficking was relatively unheard of, the articles were framed with a human right’s perspective, and more specifically, a woman’s issue. The frame shifted in the years 2002 to 2005 to defining trafficking as a crime and passing legislation. A third shift was identified from 2003 to 2006 where the focus was on national security as trafficking was linked to transnational crime and terrorism.
Pajnik (2010) identified four types of framing in Slovenian newspapers. The criminalization frame focused on the profitable, global criminal activity of trafficking, creating credible support for solutions trying to eradicate the issue. The nationalization frame, unlike the threat of the criminalization frame, calls attention to Slovenia as a transition country and directs the problem towards porous borders. The victimization frame highlights the distorted view of innocent female victims in sex trafficking. The regularization frame encourages stronger laws and increased penalties as a deterrent.
Johnston, Friedman, and Sobel (2015) examined the frames of sex trafficking in print and broadcast media between 2008 and 2012. The three biggest frames during the time period were sex trafficking as a crime, the problem with policy and legislation, and defining sex trafficking as
32 human rights. Crime was the most common frame throughout the entire study and often engaged in perspectives from law enforcement, politicians, government officials, of court officials. This framework tended to leave out both an explanation of the cause and a solution to the problem.
The policy legislation frame often used an advocacy perspective and provided remedies including changes to policy, increasing punishments, and increasing advocacy efforts through
NGOs, training, and resources. The human rights frame focused more on the causes of trafficking, highlighting international issues such as globalization, war, and immigration policies, as well as vulnerable populations like those who have been sexually abused or are runaways/homeless.
Similar to how news on trafficking was framed to highlight sex and females, a majority of TV shows and movies portray the same message. For example, in 2008 the movie Taken was released in theaters internationally. The plot focused on a teenage girl traveling overseas with a friend to Paris who, by “chance” of sharing a cab with a stranger, was kidnapped by a gang that forced the girls into prostitution. Not only is this an inaccurate depiction of the realities of human trafficking, it only focused on the sex trafficking aspect. In 2012, the independent film Eden was released in the UK. Based on a true story, the plot centered on sex trafficking, but this time the recruitment was through a fake boyfriend and the location was in the United States. Eden provided a more realistic scenario of a sex trafficking case, but once again the sensationalizing of sex trafficking will have viewers believe that “sex trafficking” is the same as “human trafficking” rather than just one component of the crime. Documentaries on sex trafficking include Call + Response, Tricked, Cargo: Innocence Lost, A Dance for Bethany, The Day My
God Died, Finding Home, Nefarious, and many more. TV shows like CSI and Graceland have highlighted trafficking cases, focusing on both domestic and international sex trafficking.
33 Wolken (2006) discusses the skewed version of trafficking represented on channels like Lifetime that would shape how people understand trafficking and in turn would be less able to identify victims who do not fit the stereotypical version they have seen on TV.
The mass media, through product packaging and marketing, has successfully blurred the line between legitimate public education that responsibly informs and sex entertainment that misleads. It is under this false cover of "education" that a middle-aged suburban couple, Lifetime's typical viewers, can justify watching Human Trafficking even though the series most closely resembled a bondage pornography. How many people would have tuned in for a mini-series called Agricultural Trafficking. Sex trafficking has everything — beautiful helpless girls, drugs, violence, sex and money — it is the voyeur's dream come true. It is no wonder that the media both creates and perpetuates public focus and fixation on sex trafficking. (Wolken, 2006:16)
It’s often harder to find a show or movie that focuses on aspects of trafficking other than sex. Fortunately, Season 3 of American Crime Story in 2017 shifts gears and focuses on forced labor slavery as well. Documentaries such as The Dark Side of Chocolate and Black Gold, attempt to highlight the issue of forced labor in the chocolate and coffee industries, whereas Not my Life address a variety of slave practices in multiple countries and There is No Place for You
Here specifically highlights a young boy trying to escape the gold mining industry.
YouTube, while not home to full documentaries, has been a useful social media tool to provide information and tutorials to the general public. An ethnographic content analysis by Yick and Shapiro (2010) found that all the videos in the sample were focused on sex trafficking and highlighted trafficking as a modern day version of an old social problem, an epidemic, and an industry or business. Victims were portrayed as coerced and commodities while the perpetrators were corporations or general consumers (Yick and Shapiro, 2010). Many government and NGOs use YouTube as a tool public service announcements, information, and to highlight their organizations work. For example, the Department of Homeland Security hosts their “Blue
34 Campaign” video which addresses all forms of trafficking, victims, offenders, and ways to take action if you suspected a person to be trafficked.
While the key argument around media is its direct impact on public opinion, media research has begun to assess the role of the audience as active participants in communication
(Papadouka et al., 2016) and the impact on trafficking policy (Das et al., 2013; Sanford and
Weitzer, 2016). In Papadouka et al., (2016), the authors examine the role of the audience as
“active recipients” of media in how they observe and spread the information (this would be similar to someone within an “attentive public”). Their study identified the topics that were preferred by journalists and commentators. The results revealed that journalists preferred to cover trafficking victims, police action to child trafficking, government and charity response, and trafficking as modern day slavery. Commentators preferred topics such as opinions about the article, how people perceive or act toward human trafficking, and prostitution. Topics that were preferred equally between journalists and commenters were sex workers and paid sex, gender relations in human trafficking, workers and forced labor, and human rights in human trafficking.
Das et al. (2013) examined whether institutional implementation of anti-trafficking laws is influenced by press freedom. The authors argue that freedom of the press is associated with more economically and politically stable countries and that more freedom will give more exposure on trafficking through media and hold government officials accountable, therefore, countries with free press are more likely to be successful in combating human trafficking. Using a sample of 119 countries, the study looks at a country’s compliance with the 3 policy dimensions covered in the UN’s trafficking protocol, their World Press Freedom Index score, and their Economic Freedom of the World score, Income Inequality score, and Economic
Development score. The findings support the hypothesis that countries with more press freedom
35 are more likely to be successful in adherence to trafficking laws, even when including regional controls.
2.3.3 Trafficking Media in Israel After reaching the Tier 1 status in 2012, a comment was made in 2013 by Knesset member David Tsur of the HaTenua Party, the chairman of the subcommittee on Trafficking in
Women and Prostitution states, “the phenomenon of trafficking no longer exists in Israel”
(Mualem, 2013). In 2014, media sources still emphasized the decrease in trafficking, but highlighted a need to focus on prostitution. Between 250 and 300 brothels were identified in Tel
Aviv alone, not to mention the business in many other cities around Israel (Lee, 2014).
In August of 2015, the media reported on Israel’s signed agreement to allow visa free travel between Israel and Belarus even though Belarus was placed in the Tier 3 category and cited in the TIP report as “a source, transit, and destination country for men, women and children subjected to sex trafficking and forced labor” (State Department, 2015). MK Merav Michaeli, who serves on the Knesset, warned that this agreement will open up the gates to new human trafficking issues (Harkov, 2015). By December of 2015, reports of trafficking through visa waivers from the former Soviet Union were suspected. A police official discusses the differences between trafficking in the early years and the current situation stating, “The challenge to law enforcement stems from the fact that they arrive in Israel legitimately and most return to their countries and are not willing to testify” (Yaron, 2015).
In 2016, there was talk of significantly cutting the National Anti-trafficking
Coordinator’s office in the Justice Ministry, although there is no record that the reduction actually took place.
The Kav LaOved Workers Hotline warned: “Without the coordination done by the unit, it will be impossible to effectively combat trafficking, let alone eradicate it. With the unit's closing, the government will be de facto declaring that combatting human trafficking is
36 not on its agenda.” Idit Harel Shemesh, director of the Awareness Center, which combats prostitution-related trafficking, said, “The move may derive from a mistaken perception that trafficking has been contained and so fewer resources can be directed toward it. But trafficking in women from foreign countries is alive and well, even if its modus operandi has changed. Closing the unit will return us to darker times. (Kashti, 2016)
By 2017, Israel has reported seeing a renewed increase in sex trafficking due to the visa requirement waivers put in place for Ukraine, Moldova, Belarus, Russia and Georgia. The visa waivers were supposed to support tourism, but as predicted by MK Merav Michaeli, the opening of borders has led to a noticeable increase in exploitation. Population, Immigration and Border
Authority reported refusing entry to 50 people in 2015 under the suspicion of trafficking, whereas the 2016 report indicated around 300 women were denied entry (Yaron, 2017).
37 CHAPTER 3
METHODS
With prior research in mind, the purpose of the current study is to examine the impact of various information sources on knowledge, concern, efficacy, and involvement in the anti- trafficking movement. These variables will measure if there is an attentive public that can be identified for human trafficking and if the attentive public is significantly different from the general public and least-attentive public.
3.1 Data Collection
3.1.1 Israel
The data used for this dissertation were collected in Israel during a six-week period in
May and June of 2014. Paper surveys were distributed in three cities throughout Israel:
Jerusalem, Tel Aviv, and Eilat. These specific city locations were chosen based on the variety of people whom our survey team would come across in areas of mass public transportation. In
Israeli culture, it is normal for people of all class types, ages, and levels of religious belief to commute on public transportation. As noted in a study on travel behavior in Israel, residents traveled by bus at least once a month (Elias, Albert, and Shiftan, 2013; Pickett et al., 2014).
These three locations also represented areas of varying conservative and liberal views.
Distribution of the surveys was conducted by a survey team of sixteen criminology undergraduate students and two criminology graduate students. The students were trained prior to data collection on the topic of human trafficking and the proper way to interact with respondents. The students went out individually or in groups of two to speak with respondents in the bus stations. The males on the team had specific instructions to seek out Haredi Jews, the
Orthodox Jews who will not speak with female members of the survey team. After the 38 respondent completed the survey, the students would debrief the respondent on the survey if it were requested. The students turned in a report documenting their experiences during the field work.
3.1.2 The Sample
A nonprobability sample was drawn from the three locations in the study. The goal was to collect 1,000 surveys, 500 women and 500 men. Over 1,000 surveys were distributed and surveys with fewer than half of the questions answered were automatically dropped from the sample. Unfinished surveys were typically due to the respondent starting a survey and then needing to board their bus prior to completing the survey. A total of 803 surveys were kept for analysis. As noted in Table 1, the sample finds some similarity to the population in Israel based on 2014 statistics, although it is a little more male and Jewish.
It was required that the survey respondent be over the age of 18 and an Israeli citizen.
Each respondent was approached by a member of the survey team and asked for a verbal consent to voluntarily participate in a confidential, anonymous survey as part of a research project for a university. Respondents did not receive incentives for their participation. If the respondent said yes, the surveyor would ask if they were an Israeli citizen over the age of 18. A vast majority of these conversations took place in English. For the handful of respondents who did not speak English, the surveyor directed the respondent to the introduction of the survey and a consent form that was written in Hebrew.
3.1.3 The Survey
The survey was a public opinion survey designed to measure information sources use, and frequency of trafficking within information sources, knowledge, concern, efficacy, and involvement in anti-trafficking activities. To increase the reliability of the survey, many of the
39 questions and question structures were copied or modified from previous surveys used to measure the perceptions of trafficking as well as measuring the extent of the attentive public. The specific surveys used were from Caspary (1970), Devine (1970), Minnesota Advocates for
Human Rights (2003), Buckley (2009), Hepburn & Simon (2010), and Tverdova (2011). Other surveys were produced with similar questions after this dissertation survey was constructed, such as Farrell et al. (2015) and Honeyman et al. (2016), increasing confidence in the reliability of the survey questions. The survey had a total of 76 questions and was divided into 11 sections. The survey was originally written in English and was translated into Hebrew prior to survey distribution. The survey teams had both English and Hebrew copies on hand, depending on the needs of the respondent. The survey team could also use the English version to clarify any misunderstandings the respondent may have when filling out the survey.
3.1.4 Research Questions
This study has 4 primary research questions:
1. Is there an attentive public for the issue of human trafficking? 2. If there is an attentive public, is it significantly different from the general public and least-attentive public? 3. What factors influence an individual to become more knowledgeable about trafficking? 4. What factors influence an individual to become involved in the anti-trafficking movement?
3.2 Variables
3.2.1 Measures of Information Source Use and Frequency of Trafficking
Information sources include a variety of media sources as well as potential non-media influences. These individual sources are divided into two categories: how often the source is used per week (Information Source Use) and how frequently that source reports of human trafficking (Frequency of Trafficking). Information sources are independent variables to see how they affect Knowledge, Concern, Efficacy, and Involvement. Information sources have the 40 potential to shape the perceptions and understanding of an issue as well as how often you hear about an issue.
Information Source Use includes measuring how often the respondent used the following sources for information during the week: (a) TV News, (b) Haaretz newspaper, (c) Israel Today newspaper, (d) Internet, (e) Radio, (f) Friends and Family, and (g) Social Media (Facebook,
Twitter, Instagram). The two different newspapers were chosen to examine if the type of political leaning newspaper, Haaretz being a liberal paper and Israel Today being a conservative paper, influenced the frequency of trafficking discussion. The responses were measured with a 5 point
Likert type scale, 1=I do not use this information source, 2 = 1-2 days per week, 3 =3-4 days per week, 4=5-6 days per week, and 5=I use this information source every day of the week.
Trafficking Frequency within information sources includes the same seven sources, but measuring how frequently the respondent hears about trafficking through those sources. The responses were measured with a 5 point Likert type Scale, 1=Never, 2=Rarely, 3=Sometimes,
4=Frequently, 5=Very Frequently.
3.2.2 Measure of Knowledge
Knowledge is an independent variable and the first potential mediator in the analysis.
Knowledge was chosen to represent awareness of trafficking. Knowledge is an important variable to measure because, as Gary Haugen has said, “Nothing happens just because we are aware of modern day slavery, but nothing will ever happen until we are” (Graff, 2016). Taking action against a problem cannot take place before being aware of the problem. Knowledge was estimated based on responses to public opinion questions that had correct answers. Public opinion was used to estimate knowledge because in most cases, perception is reality. A distribution of opinions is provided in Table 17 of Appendix A.
41 The following twelve were statements based chosen to measure respondents knowledge:
(a) Human trafficking is the same as prostitution, (b) Human trafficking is the same as smuggling, (c) Trafficked individuals in Israel are mostly non-Israeli, (d) A majority of the victims trafficked in Israel for sex are women over 18, (e) A majority of the victims trafficked in
Israel for labor are men over 18, (f) trafficked individuals often make a conscious decision to go abroad for a better life, (g) Trafficked women are sometimes partly or fully aware of the possibility of being involved in commercial sex work, (h) Trafficked individuals expect to be held as slaves and do not think they will choose their working conditions, (i) A majority of trafficked individuals are poor, (j) trafficked individuals receive good payments for their services, (k) an entire family can be held as trafficked victims, and (l) trafficked individuals can enter into a country legally.
The categories of agree and disagree were collapsed and coded into a dichotomous variables with 0=incorrect and 1=correct. Respondents who answered a question with “do not know enough to have an opinion” were placed within the “incorrect” category. The dichotomous variables were then added to provide a score between zero and twelve, with twelve being the most knowledgeable.
3.2.3 Measures of Concern
Concern is another independent variable and the second potential mediator. Concern was chosen as a variable because it is a source of motivation for action. When an individual is faced with an issue, how they react to the issue will depend on how much they care about the issue or the consequences of the issue. Concern might also vary depending on an individual’s knowledge of an issue. With this in mind, the Concern variable includes questions that address victim
42 characteristics of age, gender, type of trafficking, and nationality as different factors that would influence Concern.
Concern was created using twelve items measuring how concerned a respondent was for
Israeli and non-Israeli trafficking victims. Measurement for Israeli victims was based on the following statements: (a) Israeli women being trafficked in Israel for sex, (b) Israeli men being trafficked in Israel for sex, (c) Israeli minors (under 18) being trafficked in Israel for sex, (d)
Israeli women being trafficked in Israel for labor, (e) Israeli men being trafficked in Israel for labor, and (f) Israeli minors (under 18) being trafficked in Israel for labor. Measurement for non-
Israeli victims was based on the following statements: (g) non-Israeli women being trafficked in
Israel for sex, (h) non-Israeli men being trafficked in Israel for sex, (i) non-Israeli minors (under
18) being trafficked in Israel for sex, (j) non-Israeli women being trafficked in Israel for labor,
(k) non-Israeli men being trafficked in Israel for labor, and (l) non-Israeli minors (under 18) being trafficked in Israel for labor. The responses were measured with a 5 point Likert type scale, with 1=Not Concerned, 2 =A Little Concerned, 3 =Somewhat Concerned, 4=Concerned, and
5=Very Concerned. The twelve statements were indexed to create a Concern index, with an alpha coefficient of 0.95. The index scores ranged from one to five, with five being the most concerned.
3.2.4 Measures of Efficacy
Efficacy is an independent variable and the third potential mediator. This variable is measured by the internal feelings of an individual and the actions they do in private, or as Easton
(1965) would call it, “covert support”. Efficacy was created using items measuring how much a person agreed or disagreed with the following statements: (a) I read articles/stories about human trafficking, (b) I watch documentaries about human trafficking, (c) I feel a responsibility to be
43 involved in anti-trafficking work, (d) I feel that my involvement can help make a difference, and
(e) I do not know how to get involved in anti-trafficking work.
Negative statements were included as a check against respondents circling all of the same answers without reading the individual statements and were reverse coded for analysis. The negative statements included: (f) I am not very interested in human trafficking, (g) Staying informed about human trafficking is a hassle, and (h) Learning about human trafficking takes too much time. These eight responses were measured with a 5 point Likert scale, with 1=Strongly
Disagree, 2 =Somewhat Disagree, 3=Neither Agree or Disagree, 4 =Somewhat Agree, and
5=Strongly Agree.
After running a factors analysis on all eight statements, only four statements, a through d, loaded together and were summed to create an Efficacy index, with an alpha coefficient of 0.72.
The index scores ranged from one to five, with five being the highest efficacy. Although not included in the Efficacy variable, the statements e through h are still used to provide more descriptive information on how the respondents feel. The last statement in this section, “I do not know how to get involved in anti-trafficking work”, was included specifically to see the number of individuals who support anti-trafficking work and want to get involved. This group is important because it identifies an area of weakness within social movements, namely lack of awareness for people to get involved in anti-trafficking work.
3.2.5 Measures of Involvement
The dependent variable Involvement is measured by tangible action. This variable was measured by how frequently respondents participated in the following activities: (a) I actively seek out information to learn more about human trafficking, (b) I try to influence people’s opinions on human trafficking, (c) I talk about human trafficking with family and friends, (d) I
44 share human trafficking articles/stories with family and friends, (e) I attend anti-trafficking meetings (f) I sign anti-trafficking petitions, (g) I contact my legislator to support anti-trafficking measures, and (h) I financially support anti-trafficking organizations. The frequency of these measures will show how much an individual is actively trying to take a stand against human trafficking. This is a measure of what Easton (1965) considers “overt support”, or observable actions that support a person, place, or issue. The responses were measured with a 5 point Likert type scale, 1=Never, 2=Rarely, 3=Sometimes, 4=Frequently, 5=Very Frequently. The eight measures were indexed for analysis, with an alpha coefficient of 0.90. The frequency index scores ranged from one to five, with five being very involved very frequently.
3.2.6 The Attentive Public
Operationalizing the Attentive Public is based on the typology provided by Hallahan
(2001), combining measures of knowledge and involvement, as well as the concepts of attentiveness outlined by Almond (1950) and Devine (1970). As previously mentioned,
Knowledge was created using twelve statements that were dummy coded to be true or false, and then summed to create a scale from zero to twelve. This scale was then broken down into three categories of low, medium, and high Knowledge. Low Knowledge was considered a score between zero and three. Medium Knowledge was considered a score between four and seven.
High Knowledge was considered a score between eight and twelve. Table 3 shows the distribution of Knowledge.
For this part of the study, the Involve item index was created using eight statements that were dummy coded (0= never participated, 1= has participated before) and then summed to create a scale from zero to eight. This scale was also distributed across three categories of low, medium, and high Involve. Low Involve was considered a score between zero and one. Medium
45 Involve was considered a score between two and four. High Involve was considered a score between five and eight. Table 3 shows the distribution of Involve.
Based on prior literature and for the purpose of this study, those with low Knowledge and low Involve will be considered the “Least-Attentive Public”, whereas those with high Knowledge and high Involve will be considered the “Attentive Public”. The respondents that fall in the other combinations of Knowledge and Involve will be considered the “General Public”. The cut off points for each category were based on prior literature and current distributions on the scale.
Previous studies have considered anywhere from ten to thirty-nine percent as the “Attentive
Public” (Devine, 1970). Looking at the distribution of the two variables, the cut off for was chosen to include the top twenty percent within each variable as “high”, keeping in mind that when combined, not all of the individuals within the top twenty percent that will load high on both Knowledge and Involve. This will create a smaller “attentive” public.
3.2.7 Control Variables
There were seven demographic variables included in this survey. The demographics included are age, sex, ethnicity, religion, extent of religious belief, the city where the respondent lives, and education. The extent of religious belief was included in this survey because Israel has a range from secular to Orthodox. Respondents were also asked how many people they knew who actively participated as a prostitute as well as how many people they knew who actively participated in buying a prostitute.
Age and Education are considered continuous variables. Sex is a dichotomous variable coded 0= female and 1= male. Religion is a dichotomous variable coded 0= all else and 1=
Jewish. Ethnicity was kept out of the model to avoid multicollinearity with Religion. The four levels of religious belief are broken down into dichotomous variables. Secular is coded 0=no and
46 1=yes. Traditional is coded 0=no and 1=yes. Religious is coded 0=no and 1=yes. Orthodox is coded 0=no and 1=yes. The Secular variable was kept out of the model to avoid multicollinearity. Location is dichotomous variable coded 0=everywhere else and 1=Jerusalem.
The variable Prostitute is a dichotomous variable for knowing a prostitute and is coded 0=no and
1=yes. The variable Buy is a dichotomous variable for knowing someone who has bought a prostitute and is coded 0=no and 1=yes
3.3 Analytic Plan
Chapter 4 will examine the descriptive statistics and address the four research questions.
To answer the first research question, the Knowledge and Involvement variables will be distributed across three categories of low, medium, and high, and the cross tabulation of
Knowledge and Involvement will identify the Least-Attentive, General, and Attentive Publics. To answer the second research question, paired t-tests are conducted to compare Attentive Publics to the General and Least-Attentive Publics. For a comparison, paired t-tests will also be conducted comparing the Least-Attentive Publics to General and Attentive Publics.
To examine the factors that influence Knowledge and Involvement, ordinary least squares
(OLS) regression will be used for all of the models. To answer the third research question, this study will examine the various information sources, demographic characteristics, and control variables on an individual’s Knowledge. This will identify if there are certain groups of people or certain information sources that are more likely to be knowledgeable on trafficking.
To answer the fourth research question, analysis will be broken down into three parts: information source use, trafficking frequency, and Knowledge and Concern without information sources. First, regression equations test the direct effects of Information Source Use on the three possible mediators, Knowledge, Total Concern, and Efficacy. Second, the next set of regression
47 equations will address the relationship of the Information Source Use on Involvement, including the effects of the possible mediators. Third, regression equations will examine the direct effects of Trafficking Frequency within information sources on the three possible mediators:
Knowledge, Concern, and Efficacy. Fourth, regression equations will test for the effects of
Trafficking Frequency within information source on Involvement, including the three possible mediators. For a visual conceptual diagram of these relationships, see Figure 1.
The last two tables will examine the direct effects of Knowledge and Concern on
Efficacy, followed by the effects of Knowledge and Concern on Involvement, with the possible mediation effect of Efficacy. These last two models will be looking at the specific relationships of Knowledge and Concern without the influence of the various information sources.
3.3.1 Missing Data
Missing data occurred on more than one variable in the sample. Using listwise deletion would reduce the sample from n=803 to n=647 in the final regression model, representing a decrease of the sample size by nineteen percent. Patterns of missing data were examined first.
Eighty-one percent of the sample had complete data on all of the variables. For the rest of the data there were two patterns that were identified with one missing variable each, Religion and
Haaretz Frequency, and each of those patterns represented one percent of the sample. Each of the remaining patterns represented less than one percent of the sample.
Missing variables were compared to the observed variables to test for any significant differences by demographics. Variables Haaretz Use, Israel Today Use, and TV Frequency found significant differences by sex, with those missing for Haaretz and Israel Today more likely to be male and those missing on TV Frequency more likely to be female. Age lead to a significant difference for variables Haaretz Use, Internet Use, Friends and Family Use, and
48 knowing a prostitute, as those who were missing were significantly older than the respondents observed. Missing cases on the variable of Knowledge were found to be significantly different by religion, with those respondents missing more likely to not be Jewish compared to those observed.
All but three of the variables in the study had at least one case missing. The three full variables were Concern, Location, and Sex. Missing responses to how often information sources were used ranged from three to thirteen individuals. Missing responses to how frequently respondents heard about trafficking in the various information sources ranged from three to sixteen individuals. Creating the Knowledge scale resulted in seventy-six missing cases.
Imputation was run on the twelve individual variables before the scale was created as some respondents were only missing one or two answers, while others were missing more. Of the twelve individual variables that made up the Knowledge scale, the number of missing values ranged from two responses to seventeen responses. The index for Involvement only had two missing values and the index for Efficacy only had one missing value. Missing values in the control variables ranged from two to nine cases, except for knowing a prostitute, which was missing twenty-five cases and the control variable with the largest amount of missing data.
One potential reason for missing data could be an error in survey response. When inputting the survey data into the dataset, it was noticed that a handful of respondents seemed to randomly circle two responses for one item and then leave the item above or below it empty. It’s highly likely that these errors were due to respondents going through the survey quickly and not paying close attention to the coordinating response category or skipping a question all together.
Other potential reasons for non-response might be respondents who did not feel comfortable answering the questions due to the sensitivity of the topic or did not know enough to answer.
49 Considering the decrease was more than ten percent and the data could be considered missing at random aside from a few variables, multiple imputation was chosen to address the missing data and keep the sample size at n=803. Unlike listwise deletion, multiple imputation allows the observed values to remain in the model, rather than deleting the entire observation, leading to results that will be more efficient. Multiple imputation is a simulation-based statistical technique that begins by estimating the missing data a specified number of times, with the imputed values based on the observed values per case. The data is then analyzed across imputed data sets. Finally, the imputed estimates are pooled to produce the final models. The final estimates “take into account the sampling variability due to missing data” (StataCorp, 2013).
The variables for this study were imputed using a chained equation because it can accommodate arbitrary missing patters, as well as handling models with both binary and continuous variables included. The literature suggests on using anywhere from ten to twenty imputations, depending on the fraction of missing information (FMI); fifteen imputations were used for this study, especially when the overall FMI is below 10% on each model. Descriptive statistics for the variables in the regression models can be found in Table 2.
For the regression models, the mi estimate command was used for pooling procedures and the models were analyzed using Ordinary Least Squares (OLS) regression. There are slight differences in coefficients when comparing models using listwise and multiple imputation that should be noted. The biggest difference in the final model of Information Source Use (Table 10,
Model 4) is Friends and Family Use and Social Media Use are significant predictors in the imputed model, but not in the listwise model, and Age is a significant predictor in the listwise model, but not in the imputed model. The biggest difference in the final model of trafficking frequency by information source (Table 12, Model 4) is that Israel Today, Social Media, and
50 Knowledge are significant in the imputed model when they are not significant in the listwise model. Another difference is that Friends and Family and Sex are significant predictors in the listwise model, but not the imputed model. Despite the differences in significance, the differences in coefficients are relatively small all stay the same sign between the listwise and the imputed models. The listwise models are provided in Appendix B for comparison. Overall, while multiple imputation is a valid statistical inference for handling missing data, interpretation should be taken with caution.
51 Table 1: Demographics of Respondents Sample Population Sample n Frequency Frequency Gender 803 Male 463 58% 50% Female 340 42% 50%
Race/Ethnicity 803 Jewish 747 93% 76% Arab 38 5% 21% Other 18 2% 3%
Religion 801 Jewish 732 91% 75% Muslim 28 4% 18% Christian 17 2% 2% Other 25 3% 5%
Religiosity 798 Secular 332 42% 43% Traditional 170 21% 37% Religious 231 29% 11% Orthodox 65 8% 9%
Education 794 Some High School 50 6% - High School or GED 343 43% 26% Some University 174 22% - College Degree 142 18% 4% Graduate Degree 71 9% - Professional Degree 14 2% -
Location 803 Jerusalem 600 75% 12.4% Tel Aviv 182 23% 16.4% Eilat 21 2% -
Age Range 18 - 90 Sample mean= 27.8 years (SD=11.5)/ Population - 29.7 years Notes: Population frequency is based on rough estimates provided by the Israel Central Bureau of Statistics. http://www.cbs.gov.il/www/publications/isr_in_n14e.pdf *There is a 2% difference between Jewish Ethnicity and Jewish Religion, possibly due to response error.
52
TV News Knowledge Haaretz Newspaper Israel Today
Internet Efficacy Involvement Radio Friends and Family
Social Media Concern
Figure 1: Conceptual Diagram of the Relationship between Variables in the Study
53 CHAPTER 4
RESULTS
Chapter 4 covers five sections of results. The first section will provide descriptive statistics on the variables within the analysis. The four remaining sections will address each of the four research questions proposed; identifying if there is an Attentive Public, if they are different from the General and Least-Attentive Public, and what factors influence Knowledge and Involvement, the two key components of an Attentive Public.
4.1 Descriptive Statistics
Table 2 provides the descriptive statistics of the variables that will be used to answer the four research questions. Since the models are using the pooled estimates of multiply imputed data, the standard error is provided instead of the standard deviation. To provide a rough estimate of the standard deviation, the imputed value sets were combined and averaged. On average, the
Haaretz newspaper is the least used source for information and the Internet is the most used source for information. The Israel Today newspaper, TV news, and the Radio are used an average of two to three days per week, whereas Friends and Family, Internet, and Social Media are used an average of four to five days per week. Reports on human trafficking occur less frequently in the two newspapers, Haaretz and Israel Today, while the other five sources report on trafficking a little more frequently.
The sample of respondents had mean scores of 1.6 for Involvement 5.2 for Knowledge,
3.7 for Concern, and 2.7 for Efficacy. Respondents averaged 2.65 types of involvement activities out of the eight action items. On average, 17.6 percent of the sample knew someone working in prostitution and about 33.2 percent knew someone who bought a prostitute. About 91.2 percent of the sample is Jewish with 21.5 percent identifying as having Traditional beliefs, 28.8 percent
54 with Religious beliefs, and 8.2 percent with Orthodox beliefs. About 74.7 percent live in
Jerusalem and on average the respondents have completed high school and started some
University studies. The sample is roughly 58 percent male with an average age of 28 years old.
4.2 Research Question 1: Is there an Attentive Public for Human Trafficking?
As stated previously, Attentive Publics are defined by those individuals who are highly knowledgeable on trafficking as well as highly involved in the anti-trafficking movement. Table
3 examines the distribution of the respondents by the three categories of Knowledge and
Involvement, identifying the three types of publics. Seven percent of the respondents are considered the “Attentive Public”, scoring high on both Knowledge and Involvement. An attentive public has been identified, although it is small. As can be seen in the table, there are two groups of individuals who are close to being a part of the Attentive Public. The first group includes those who scored high on Knowledge and medium on Involvement. The second group includes those who scored medium on Knowledge and high on Involvement. These two groups are the most likely to become part of the Attentive Public with just a little more information or involvement.
On the other hand, sixteen percent of the respondents are considered the “Least
Attentive” public, scoring low on both Knowledge and Involvement. There is one group of individuals who have the potential to fall back into this category, those who scored low on
Knowledge and medium on Involvement. It is unlikely that individuals will become less knowledgeable, but they might become less involved. As Downs (1972) noted, people tend to get bored after a period of time and cease their activity in one movement while moving on to the next new and exciting issue. The general public represents the remaining seventy-seven percent, varying in levels of Knowledge and Involvement.
55 4.3 Research Question 2: Is the Attentive Public Significantly Different from the General
and Least-Attentive Public?
A series of t-tests were conducted to compare the differences of the Attentive Public from the General and Least-Attentive Publics as seen in Table 4. Since the variables of Knowledge and
Involvement create the three categories of publics, the publics were compared by Concern and
Efficacy, demographic variables of sex, age, religion, education, as well as knowing a prostitute, and knowing someone who buys a prostitute. When addressing Concern, there was not a significant difference between the Attentive Public and everyone else. On the other hand, when examining the publics by Efficacy, the Attentive Public has a significantly higher average than everyone else. Of the demographic variables, there was a significant difference in Sex and knowing a Prostitute for the Attentive Public compared to everyone else. There were no significant differences for Religion, Education, and Age. The Attentive Public knows significantly more people who works as prostitutes, but not that buy a prostitute. On average, the
Attentive Public tends to be different from everyone else by having higher feelings of efficacy, are more likely to be male, and are more likely to know someone who participates as a prostitute.
For a comparison to how the Attentive was different from everyone else, t-tests were also conducted to compare the Least-Attentive Public to the General and Attentive Publics as seen in
Table 5. There is a significant difference in Concern between the Least-Attentive Public and everyone else, with the Least-Attentive having higher concern. There was also a significant difference in Efficacy, with the Least-Attentive Public having a lower score on average than everyone else. For the control variables, the Least-Attentive were significantly different than everyone else based on Religion, Sex, Age, knowing a Prostitute, and knowing someone who
Buys prostitutes. There were no significant differences based on Education. On average, the
56 Least-Attentive Public tends to be different from everyone else by being more concerned, more likely to be female, younger, Jewish, and less likely to have feelings of efficacy as well as know less people who participate as a prostitute or buy a prostitute.
4.4 Research Question 3: What Factors Influence an Individual to become more
Knowledgeable?
Table 6 and 7 provide the bivariate correlations of all of the variables in the model. Table
6 shows that Knowledge is significantly correlated with all of the information sources used, except for the Israel Today newspaper and Friends and Family. Knowledge is significantly correlated with all of the demographic variables except for Religion and Traditional beliefs.
There is also a significant correlation between Knowledge, Prostitute, and Buy. Table 7 finds that
Knowledge is significantly correlated with the frequency of human trafficking reports across all seven information sources. Knowledge is significantly correlated with all of the demographic variables except for Location and Sex. There is also a significant correlation between Knowledge, knowing a prostitute, and knowing someone who buys a prostitute.
To exam the factors that influence an individual’s knowledge on human trafficking issues, Table 8 provides three models testing the effects of control variables and information sources. Model 1 in Table 8 examines a baseline of demographic characteristics and finds four significant predictors of Knowledge: Religious beliefs, Orthodox beliefs, Education, and Sex. In comparison to secular respondents, Religious and Orthodox respondents show a significant decrease in Knowledge. Respondents who are male or those with higher education are significantly more likely to be knowledgeable about human trafficking. While this model finds some significant predictors, the standardized betas are relatively low as is the adjusted R2 with the model only explaining six percent of the variance in Knowledge.
57 Model 2 in Table 8 adds measures of weekly information source use. Use of the Internet is the only significant positive predictor of Knowledge within the information sources.
Knowledge continues to have a significant positive relationship with males and a significant negative relationship with Religious respondents in comparison to secular respondents.
Respondents with Orthodox beliefs and increasing Education lose significance with the inclusion of information source use. Similar to Model 1, the standardized betas are relatively low, with Sex as the strongest standardized predictor (.12). The adjusted R2 increased by two percent to explain eight percent of the variance.
Model 3 in Table 8 examines measures of trafficking frequency within the various information sources to the baseline model. Again, the Internet frequency is the only significant positive predictor of Knowledge and its influence is twice the size of Internet use (.09 to .18) in
Model 2. Compared to the baseline in Model 1, Education and male respondents continue to have positive significant effects. Religious and Orthodox respondents continue to have a significantly negative effect on Knowledge compared to secular respondents. Traditional beliefs become significant with a negative effect on Knowledge compared to secular beliefs. The adjusted R2 only increases by two percent with the trafficking frequency measures, explaining eight percent of the variance in Knowledge.
Overall, there is a higher likelihood that males, respondents who are educated, and respondents with secular religious beliefs to be more knowledgeable on the subject of human trafficking. In addition, respondents who use the Internet more often during the week as a source of information or hear about trafficking more frequently on the Internet will be more knowledgeable on the subject of human trafficking.
58 4.5 Research Question 4: What Factors Influence an Individual to become Involved in the
Anti-Trafficking Movement?
Table 6 and 7 provide the bivariate correlations of all of the variables in the model.
Involvement is significantly correlated with six out of ten control variables; knowing a prostitute, knowing someone who buys prostitutes, Religion, Traditional beliefs, Religious beliefs, and Sex.
Table 6 shows that Involvement is significantly correlated with all of the information sources used, except for the Internet and Friends and Family. The potential mediators of Knowledge and
Efficacy are correlated with Involvement, but Concern is not. Just as correlation does not imply causation, lack of correlation does not mean that variables like Internet use, Friends and Family use, and Concern does not have an influence on Involvement. Considering their correlation to other variables in the model, they continue to be included to examine possible suppression effects. These variables might not be directly related with one another, but indirectly related based on the conditions of other variables, so the bivariate relationship does not provide enough information to fully explain the relationship.
Table 7 examines the correlations between trafficking frequency in the different information sources and the other variables within the model. The bivariate correlations find all seven information sources to be significantly positively correlated with Involvement. These correlations are also stronger than the correlations between information source use and
Involvement. The control variables significantly correlated with Involvement include Prostitute,
Buy, Religion, Traditional beliefs, Religious beliefs, and Sex. Of the three mediators being tested,
Knowledge and Efficacy find significant positive relationships. While not correlated with
Involvement, Concern is correlated with Knowledge and Efficacy.
59 Table 9 includes three models to assess direct effects of information source use on mediating variables of Knowledge, Concern, and Efficacy. To examine which factors influence an individual to become involved in the anti-trafficking movement, Table 10 examines four OLS models to test the influence of information source use on Involvement. Tables 11 and 12 conduct the same seven models by trafficking frequency within information sources. Table 11 includes three models to assess the direct effects of the trafficking frequency on the three mediators,
Knowledge, Concern, and Efficacy. Table 12 provides four OLS regression equations, similar to the models in the prior section, to examine the relationship of trafficking frequency reported in various information sources on Involvement in the anti-trafficking movement. Finally, Tables 13 and 14 look at the relationship between Knowledge, Concern, and Efficacy on Involvement without the influence of information sources.
4.5.1 Frequency of Information Source Use
Table 9 shows three ordinary least squares regression estimates testing the direct effect of the multiple information sources on Knowledge, Concern, and Efficacy. Model 1 identifies the
Internet as a significant positive predictor of Knowledge, along with two significant control variables; Knowledge is higher for males with secular views. In this model, Sex is the strongest predictor of Knowledge. Model 2 identifies Friends and Family as the information source with a significant positive effect on Concern. Three control variables found significant effects on
Concern, notably, concern is higher among respondents who are Jewish, female, and those who do not know someone working as a prostitute. Surprisingly, knowing a prostitute shows a significant decrease in Concern. The standardized beta scores find Sex and Friends and Family to be the strongest predictors of Concern. The model only explains about ten percent of the variance in Concern.
60 Table 9 Model 3 identifies the use of the Haaretz and Israel Today newspapers as the only information source use to be significant positive predictors of Efficacy. A notable change in the significance of control variables is that knowing a prostitute now has a significant positive relationship with Efficacy, which was the opposite impact it had on Concern. Religious respondents were found to have a significant negative effect on Efficacy compared to secular respondents. A notable finding across these three variables is that different information sources are significant predictors of each variable. The only predictors that are significant in more than one model are those with Religious beliefs compared to secular, Sex, and knowing a prostitute; although, Sex and Prostitute have different effects depending on the outcome variable.
Table 10 provides four OLS regression equations used to examine the relationship between information source use and Involvement including the mediating variables of
Knowledge, Concern, and Efficacy. Model 1 includes the control variables and the information sources to create a baseline, with Models 2 through 4 adding in each of the three mediators. The baseline Model 1 establishes three positive significant effects of information sources on
Involvement; Haaretz newspaper, the Radio, and Social Media. Two significant negative effects of information sources on Involvement were identified; the Internet and Friends and Family.
These two variables were not correlated with Involvement in the bivariate Table 6, however due to their correlations with other information sources, the significant predictors indicate a suppression effect. Prostitute and Religion are the only two significant control predictors indicating that respondents knowing a prostitute and respondents who are Jewish are significantly less likely to be involved. The baseline model has an adjusted R2 of .21.
Model 2 adds Knowledge as a significant positive predictor into the equation creating two notable changes. First, use of Friends and Family as an information source is no longer a
61 significant predictor of Involvement, indicating a mediating effect of Knowledge. Second,
Prostitute went from a negative effect in Model 1 to a positive effect in Model 2 (and remains positive through Model 4). The size of the coefficients are consisted across models. There is a slight increase in the adjusted R2, from .21 to .22.
Model 3 includes Concern as a significant positive predictor into the equation. The use of
Haaretz newspaper, the Radio, and Social Media continue to hold a significant positive effect on
Involvement. Prostitute continues to have significant positive effect, while Internet and Religion continues to have a significant negative effect. All three variables find very slight increase in their coefficients. The Friends and Family variable was not significant with the inclusion of
Knowledge, but adding in Concern brings it back to significance. The inclusion of Concern did not impact the explained variance of the model. In Model 4, Efficacy is added into the equation as a positive significant predictor. The effect of Concern on Involvement loses significance, indicating a mediating effect by Efficacy. Other minor changes to the model with the inclusion of
Efficacy include a minor reduction in the coefficients of Haaretz newspaper, Internet, Social
Media, Knowledge, Prostitute, and Religion. Including Efficacy into the full model shows a minor, partial mediating effect and a significant increase in the adjusted R2 from .22 to .29.
Overall, Table 10 indicated a couple different conditioning effects between suppression and mediation. Model 4 provides the largest explanatory power in Table 10 by including
Knowledge, Concern, and Efficacy with the information sources. Efficacy was found to be significant partial mediator in the relationships between Haaretz use and Involvement, with
Efficacy fully mediating the effect of Concern. While nine variables remained significant in the final model, the coefficients are relatively small except for Efficacy, Haaretz newspaper use, and
Prostitute.
62 4.5.2 Trafficking Frequency by Information Source
Model 1 in Table 11 finds the trafficking frequency on the Internet to be the only information source to be a significant positive predictor of Knowledge. Five control variables were also found to be significant predictors of Knowledge: increasing education and males have positive effects, whereas respondents with Traditional, Religious, or Orthodox beliefs have a decrease in Knowledge compared to individuals with secular views. Model 2 identifies trafficking frequency on TV News to be the only information sources as a significant positive predictor of Concern. Concern was also found to be higher among females, Jewish respondents, and those who do not know someone working in prostitution. TV News and Religion do not have significant bivariate correlations with Concern in Table 7, signifying potential suppression effects by including the other information sources and control variables. Model 3 identifies trafficking frequency in the Haaretz newspaper and Social Media to be the only information sources to have significant positive predictors of Efficacy. The only significant control variable for Efficacy is Age. These three models identify various information sources and control variables to have direct effects, however, the standardized coefficients and the adjusted R2 are relatively low, with each model only accounting for eight percent of the variance in Involvement.
Table 12 includes the results for the four equations addressing frequency of human trafficking reports in information sources on Involvement. Model 1 includes only the information sources and controls as a baseline for the other models. Model 1 finds trafficking frequency in the Haaretz newspaper, the Israel Today newspaper, the Radio, and on Social Media to be significant positive predictors of Involvement. TV News is also a significant predictor, but with a negative effect. Involvement was significantly higher among those who know a prostitute and are non-Jewish respondents. Model 2 includes Knowledge as a significant positive predictor of
63 Involvement. The inclusion of Knowledge had relatively no impact on the model other than increasing the adjusted R2 by one percent.
Model 3 adds Concern into the model as a significant positive predictor of Involvement.
Adding Concern slightly increased the strength of the unstandardized coefficients for TV News, the Radio, Social Media, Prostitute, and Religion. The inclusion of Concern does not have any mediating effects on TV News, in fact the TV News coefficient becomes slightly stronger. Adding
Concern also brings Friends and Family and Sex into significance for the first time in this table, indicating a suppression effect. Model 4 includes Efficacy into the final equation as a significant positive predictor of Involvement. Efficacy fully mediated the effect of Concern, and subsequently the effect of Friends and Family, on Involvement. A very slight decrease in both the unstandardized and standardized coefficients for the Haaretz newspaper, Social Media,
Knowledge, and Prostitute indicate a small, partial mediation effect by Efficacy. By including
Knowledge, Concern, and Efficacy into the equation, the explanatory power of the model increased from an adjusted R2 of .27 in Model 1 to .33 in Model 4. Table 13 provides and outline of the significant paths from information sources to Involvement that were supported, along with their direction, based on the conceptual diagram from Figure 1.
4.5.3 The Effect of Knowledge and Concern on Involvement, Mediated by Efficacy
To gain further understanding of the relationship between Knowledge and Concern on
Involvement, Table 14 first examines the direct relationships between Knowledge and Concern on Efficacy. Both Knowledge and Concern find significant direct effects on Efficacy, with
Concern having a stronger and more significant effect on Efficacy than Knowledge. Both models also find Prostitute and Age to be significant positive predictors of Efficacy, and Religious
64 beliefs to have a significant negative effect. Overall, being older, non-Jewish, more knowledgeable, more concerned, and knowing a prostitute increases Efficacy.
Table 15 examines six equations testing the mediating effect of Efficacy on the relationship between Knowledge, Concern, and Involvement. Models 1 and 3 in Table 15 provide the baseline measures for the effects of Knowledge and Concern on Involvement. Model
1 finds Knowledge to be a significant positive predictor of Involvement, along with a significant positive effect by Prostitute and a significant negative effect by Religion. The two significant control variables are also found to have stronger coefficients than Knowledge. Model 3 finds that
Concern on its own does not have a significant effect on Involvement, although Prostitute and
Religion remain significant. Models 2 and 4 include Efficacy to test for its impact on the relationships. Model 2 finds that including Efficacy causes a very slight decrease in the coefficients of Knowledge, Prostitute, and Religion, indicating a slight mediating effect. Efficacy is a significant predictor in Model 4, again showing slight decreases in the coefficients of
Prostitute and Religion.
Model 5 and 6 include Knowledge and Concern in the same model and find that without the inclusion of Knowledge, Concern is a significant predictor of Involvement, although small.
The relationship of Concern is then fully mediated by Efficacy when it is included in Model 6.
Noting the non-significant bivariate correlation in Table 6, the non-significant effect of Concern on Involvement from Model 3, the significant effect of Concern on Involvement after the inclusion of Knowledge, along with the slight increase in the R2 from Model 1, there is an indication of a suppression effect. Table 15 finds that Efficacy is not only the strongest predictor of Involvement, but its inclusion also increases the explained variance in each model. Table 16
65 provides the significant paths from Knowledge and Concern to Involvement that were supported, along with their direction, based on the conceptual diagram from Figure 1.
66 Table 2: Descriptive Statistics (n=803) Estimated Variable Mean Std. Error Std. Deviation Min Max Involvement Index 1.59 .03 .76 0 5 TV News Use 2.47 .05 1.43 1 5 Haaretz Use 1.84 .04 1.25 1 5 Israel Today Use 2.24 .05 1.33 .59 5 Internet Use 3.98 .05 1.31 1 5.39 Radio Use 2.69 .05 1.42 1 5 Friend & Family Use 3.67 .05 1.29 1 5 Social Media Use 3.55 .06 1.57 .75 5 TV News Freq 2.27 .04 1.09 1 5 Haaretz Freq 1.81 .04 1.07 1 5 Israel Today Freq 1.90 .04 1.08 .82 5 Internet Freq 2.80 .04 1.21 1 5 Radio Freq 2.09 .04 1.14 1 5 Friends & Family Freq 2.17 .04 1.20 .81 5 Social Media Freq 2.42 .05 1.24 1 5 Knowledge 5.19 .09 2.55 0 12 Concern 3.69 .04 1.06 1 5 Efficacy 2.69 .03 .98 1 5 Involve Item Index 2.65 .09 2.66 0 8 Prostitute .18 .01 .38 0 1 Buy . 33 .02 .47 0 1 Religion .91 .01 .28 0 1 Traditional .21 .01 .41 0 1 Religious .29 .02 .45 0 1 Orthodox .08 .01 .27 0 1 Location .75 .02 .43 0 1 Education 2.85 .04 1.16 1 6 Age 27.87 .41 11.53 18 90 Sex .58 .02 .49 0 1
67 Table 3: Distribution of Respondents by Knowledge and Involvement (n=803) Low Medium High Knowledge Knowledge Knowledge Total Low 16% 22% 7% 44% Involvement (126) (173) (57) (356) Medium 6% 19% 5% 31% Involvement (47) (156) (43) (246) High 3% 15% 7% 25% Involvement (27) (121) (53) (201) 25% 56% 19% 100% Total (200) (450) (153) (803)
Table 4: T-tests Comparing Attentive Public to Everyone Else (n=803) Attentive Public Everyone Else Mean SD Mean SD t Concern 3.65 (.83) 3.69 (1.1) -0.26 Efficacy 3.4 (.82) 2.64 (.98) 5.5*** Sex .72 (.45) .57 (.50) 2.1* Religion .87 (.34) .92 (.28) -1.2 Education 3.04 (1.27) 2.84 (1.15) 1.2 Age 27.8 (9.4) 27.9 (11.7) -0.03 Prostitute .28 (.45) .17 (.37) 2.2* Buy .38 (.49) .33 (.47) .73 Notes: *p<.05, **p<.01, ***p<.001
Table 5: T-tests Comparing Least-Attentive Publics to Everyone Else (n=803)
Least - Attentive Everyone Else
Public Mean SD Mean SD t Concern 3.88 (1.19) 3.65 (1.0) 2.3* Efficacy 2.29 (.96) 2.77 (.97) -5.1*** Sex .49 (.50) .59 (.49) -2.1* Religion .98 (.15) .90 (.30) 2.8** Education 2.80 (1.14) 2.86 (1.17) -.56 Age 25.6 (8.9) 28.3 (11.9) -2.4* Prostitute 0.08 (.27) .19 (.39) -3.0*** Buy .22 (.42) .35 (.48) -2.9*** Notes: *p<.05, **p<.01, ***p<.001
68 Table 6: Bivariate Correlations including Information Source Use (n=803) 1 2 3 4 5 6 7 8 9 10 (1) Involvement 1.00 (2) Tv News Use .16*** 1.00 (3) Haaretz Use .31*** .23*** 1.00 (4) Israel Today Use .19*** .35*** .29*** 1.00 (5) Internet Use -.02 .30*** .17*** .17*** 1.00 (6) Radio Use .20*** .37*** .18*** .35*** .19*** 1.00 (7) Friends and Family Use -.01 .16*** .09** .15*** .31*** .18*** 1.00 (8) Social Media Use .09** .23*** .17*** .16*** .49*** .10** .37*** 1.00 (9) Knowledge .17*** .14*** .13*** .06 .14*** .09** -.03 .07* 1.00 (10) Concern -.01 .03 -.06 -.05 .07* .03 .18*** .06 -.18*** 1.00 (11) Efficacy .36*** .16*** .19*** .16*** .03 .08* .01 .06 .13*** .18*** (12) Prostitute .24*** .02 -.01 .08* .02 .07* .02 .05 .09** -.12*** (13) Buy .10** -.02 -.01 .01 .01 .03 -.82* .09** .12*** -.06* (14) Religion -.22*** -.03 -.16*** -.02 .02 -.04 -.02 -.02 -.06 .13*** (15) Traditional .08* .11** .02 .06 .04 .01 .03 .12*** .00 .01 (16) Religious -.10** -.17*** -.15** .13*** -.04 -.01 .09** -.03 -.14*** .04 (17) Orthodox -.04 -.18*** -.08* -.06 -.20*** -.04 -.04 -.28*** -.08* .01 (18) Location -.00 -.15*** .00 .02* -.11** -.06 .07* -.09** -.08* .02 (19) Education -.05 -.03 .07 -.05 .10** .03 -.08* -.04 .09** -.06 (20) Age .03 .17*** .07 .08* -.10** .16*** -.28*** -.23*** .08* -.05 (21) Sex .09** .03 .04 .08* -.02 .09** -.14*** -.12*** .17*** -.24*** Notes: *p<.05, **p<.01, ***p<.001
69 Table 6: Continued 11 12 13 14 15 16 17 18 19 20 21 (11) Efficacy 1.00 (12) Prostitute .08* 1.00 (13) Buy .02 .31*** 1.00 (14) Religion -.04 -.11*** -.01 1.00 (15) Traditional .06 .05 .13*** .04 1.00 (16) Religious -.11** -.04 -.18*** .11*** -.33*** 1.00 (17) Orthodox -.03 .01 -.03 .04 -.16*** -.19*** 1.00 (18) Jerusalem -.01 -.06 -.14*** .07* -.01 .25*** .10** 1.00 (19) Education -.00 -.03 -.02 .01 -.06 -.04 -.05 -.12*** 1.00 (20) Age .10** -.02 -.02 .02 .03 -.20*** -.00 -.22*** .28*** 1.00 (21) Sex .04 .03 .18*** -.13*** -.01 -.23*** .06 -.10** .01 .16*** 1.00 Notes: *p<.05, **p<.01, ***p<.001
70 Table 7: Bivariate Correlations including Trafficking Frequency (n=803) 1 2 3 4 5 6 7 8 9 10 (1) Involvement 1.00 (2) Tv News Freq .21*** 1.00 (3) Haaretz Freq .35*** .42*** 1.00 (4) Israel Today Freq .34*** .55*** .51*** 1.00 (5) Internet Freq .32*** .50*** .41*** .46*** 1.00 (6) Radio Freq .36*** .49*** .49*** .58*** .48*** 1.00 (7) Friends and Family Freq .36*** .42*** .39*** .48*** .56*** .49*** 1.00 (8) Social Media .39*** .41*** .39*** .40*** .61*** .42** .61*** 1.00 (9) Knowledge .17*** .13** .089** .07* .19*** .09** .08* .10** 1.00 (10) Concern -.01 .06 -.06 -.05 -.07 -.04 -.02 -.02 -.18*** 1.00 (11) Efficacy .36*** .20*** .22*** .17* .22*** .19*** .23*** .24*** .13*** .18*** (12) Prostitute .24* .01** .08* .13* .13*** .12*** .17*** .16*** .09** -.12*** (13) Buy .10** .02 -.01 .01 .05 .03 .03 .10** .12*** -.06 (14) Religion -.22*** -.00 -.09** -.07* -.10** -.11** -.11** -.13*** -.06 .13*** (15) Traditional .06** .10* .02 .09** .10** .06 .12*** .13*** .00 .01 (16) Religious -.11** -.15*** -.13*** .02 -.10** -.08* -.10** -.10** -.14*** .04 (17) Orthodox -.05 -.11** -.06 -.02 -.05 .00 -.05 -.10** -.08* .01 (18) Location -.00 -.14*** -.03 -.02 -.03 -.02 -.01 -.04 -.08* .02 (19) Education -.05 -.03 .05 -.17** -.03 -.05 -.09* -.09* .09** -.06 (20) Age .03 .17*** .11** .14*** .02 .16*** .02 -.08* .08* -.05 (21) Sex .09* .01 .04 .04 .03 .02 -.04 -.02 .17*** -.24*** Notes: *p<.05, **p<.01, ***p<.001
71 Table 7: Continued 11 12 13 14 15 16 17 18 19 20 21 (11) Efficacy 1.00 (12) Prostitute .08* 1.00 (13) Buy .02 .31*** 1.00 (14) Religion -.04 -.11** -.01 1.00 (15) Traditional .06 .05 .13*** .04 1.00 (16) Religious -.11** -.04 -.18*** .11** -.33*** 1.00 (17) Orthodox -.03 .01 -.03 .04 -.16*** -.19*** 1.00 (18) Jerusalem -.01 -.06 -.14*** .07* -.01 .25*** .10** 1.00 (19) Education -.00 -.03 -.02 .01 -.06 -.04 -.05 -.12** 1.00 (20) Age .10** -.02 -.02 .02 .03 -.20*** -.00 -.22*** .28*** 1.00 (21) Sex .04 .03 .18*** -.13*** -.01 -.23*** .07 -.10** .01 .16*** 1.00 Notes: *p<.05, **p<.01, ***p<.001
72 Table 8: The Effect of Control Variables and Information Sources on Knowledge (n=803) Model 1 Model 2 Model 3 b SE Beta b SE Beta b SE Beta Prostitute .45 .25 .07 .43 .25 .06 .32 .25 .05 Buy .33 .20 .06 .36 .21 .07 .34 .20 .06 Religion -.14 .32 -.02 -.10 .33 -.01 -.06 .32 -.01 Traditional -.42 .24 -.07 -.39 .24 -.06 -.48* .24 -.08 Religious -.75** .23 -.13 -.56* .25 -.10 -.69** .24 -.12 Orthodox -1.1** .35 -.12 -.70 .37 -.08 -1.0** .35 -.11 Location -.00 .22 -.00 -.05 .22 .01 .01 .22 .00 Education .16* .08 .07 .14 .08 .07 .17* .08 .08 Age .00 .01 .02 .00 .01 .01 .00 .01 .01 Sex .65** .19 .12 .64*** .19 .12 .66*** .19 .13 TV News Use - - - .13 .07 .07 - - - Haaretz Use - - - .13 .08 .06 - - - Israel Today Use - - - -.01 .08 -.00 - - - Internet Use - - - .18* .08 .09 - - - Radio Use - - - .03 .07 .02 - - - Friends and Family Use - - - -.08 .08 -.04 - - - Social Media Use - - - .01 .07 .01 - - - TV News Freq ------.06 .11 .03 Haaretz Freq ------.06 .10 -.02 Israel Today Freq ------.00 .12 .00 Internet Freq ------.39*** .10 .18 Radio Freq ------.02 .11 .01 Friends and Family ------.04 .10 -.02 Freq Social Media Freq ------.04 .09 -.02 Constant 4.6 .48 - 3.4 .64 - 3.5 .55 - Adjusted R2 .06 .08 .08 Notes: *p<.05, **p<.01, ***p<.001
73
Table 9: The Effect of Information Source Use on Mediating Variables (n=803)
Model 1 Model 2 Model 3 Knowledge Concern Efficacy b SE Beta b SE Beta b SE Beta TV News Use .13 .07 .07 .01 .03 .02 .05 .03 .07 Haaretz Use .13 .08 .06 -.04 .03 -.05 .10*** .03 .12 Israel Today Use -.01 .08 -.00 -.04 .03 -.06 .08** .03 .11 Internet Use .18* .08 .09 .04 .03 .05 -.03 .03 -.04 Radio Use .03 .07 .02 .03 .03 .04 -.01 .03 -.01 Friends and Family Use -.08 .08 -.04 .13*** .03 .16 -.01 .03 -.01 Social Media Use .01 .07 .01 -.02 .03 -.03 .03 .03 .04 Prostitute .43 .25 .06 -.33** .10 -.12 .20* .10 .08 Buy .36 .20 .07 .06 .08 .03 -.04 .08 -.02 Religion -.10 .32 -.01 .33** .13 .09 .02 .12 .01 Traditional -.39 .24 -.06 .01 .10 .01 -.02 .09 -.01 Religious -.56* .25 -.10 -.02 .10 -.01 -.23* .10 -.10 Orthodox -.70 .37 -.08 .11 .15 .03 -.10 .14 -.03 Jerusalem -.05 .22 .01 -.04 .09 -.01 .10 .09 .04 Education .14 .08 .07 -.06 .03 -.07 -.01 .03 -.02 Age .00 .01 .01 .00 .00 .04 .01 .00 .08 Sex .64*** .19 .12 -.47*** .08 -.22 -.00 .07 -.00 Constant 3.4 .64 - 3.3 .26 - 2.1 .25 - Adjusted R2 .08 .10 .06 Notes: *p<.05, **p<.01, ***p<.001
74
Table 10: The Effect of Information Source Use on Involvement (n=803) Model 1 Model 2 Model 3 Model 4 b SE Beta b SE Beta b SE B b SE Beta TV News Use .03 .02 .05 .03 .02 .05 .02 .02 .05 .02 .02 .03 Haaretz Use .15*** .02 .25 .15*** .02 .24 .15*** .02 .25 .13*** .02 .21 Israel Today Use .03 .02 .05 .03 .02 .05 .03 .02 .06 .01 .02 .02 Internet Use -.08*** .02 -.13 -.08*** .02 -.14 -.09*** .02 -.15 -.08*** .02 -.13 Radio Use .07*** .02 .13 .07*** .02 .13 .07*** .02 .12 .07*** .02 .13 Friends & Family Use -.04* .02 -.08 -.04 .02 -.07 -.05* .02 -.08 -.04* .02 -.07 Social Media .04* .02 .09 .04* .02 .09 .04* .02 .09 .04* .02 .08 Knowledge - - - .03** .01 .10 .03*** .01 .11 .02** .01 .08 Concern ------.07** .02 .09 .02 .02 .02 Efficacy ------.21*** .02 .27 Prostitute -.41*** .07 .21 .40*** .07 .20 .42*** .07 .21 .36*** .07 .18 Buy .02 .06 .01 .01 .06 .00 .00 .06 .00 .02 .05 .01 Religion -.39*** .09 -.14 -.38*** .09 -.14 -.40*** .09 -.15 -.39*** .08 -.14 Traditional .07 .07 .04 .08 .07 .04 .08 .07 .05 .08 .06 .04 Religious -.05 .07 -.03 -.03 .07 -.02 -.03 .07 -.02 .01 .07 .01 Orthodox -.05 .10 -.02 -.02 .10 -.01 -.03 .10 -.01 -.01 .10 .00 Location .07 .06 .04 .07 .06 .04 .07 .06 .04 .04 .06 .03 Education -.03 .02 -.04 -.03 .02 -.05 -.03 .02 -.04 -.03 .02 -.04 Age -.00 .00 -.02 -.00 .00 -.02 -.00 .00 -.02 -.00 .00 -.04 Sex .06 .05 .04 .04 .05 .02 .07 .05 .04 .05 .05 .03 Constant 1.6 .18 - 1.5 .18 - 1.3 .20 - 1.0 .19 - Adjusted R2 .21 .22 .22 .29 Notes: *p<.05, **p<.01, ***p<.001
75
Table 11: The Effect of Trafficking Frequency on Mediating Variables (n=803)
Model 1 Model 2 Model 3 Knowledge Concern Efficacy b SE Beta b SE Beta b SE Beta TV News Freq .06 .11 .03 .12** .04 .13 .03 .04 .04 Haaretz Freq -.06 .10 -.02 -.03 .04 -.03 .09* .04 .10 Israel Today Freq .00 .12 .00 -.06 .05 -.07 -.01 .04 -.01 Internet Freq .39*** .10 .18 .02 .04 .02 .04 .04 .04 Radio Freq .02 .10 .01 -.02 .04 -.02 .00 .04 .00 Friends & Family Freq -.04 .10 -.02 -.01 .04 -.01 .05 .04 .06 Social Media Freq -.04 .09 -.02 -.01 .04 -.02 .09** .04 .12 Prostitute .32 .25 .05 -.31** .10 -.11 .11 .10 .04 Buy .34 .20 .06 .02 .08 .01 -.03 .08 -.02 Religion -.06 .32 -.01 .30* .13 .08 .04 .12 .01 Traditional -.48* .24 -.08 .03 .10 .01 -.02 .09 -.01 Religious -.69** .24 -.12 .01 .10 .01 -.16 .09 -.07 Orthodox -1.0** .35 -.11 .14 .14 .04 -.10 .14 -.03 Location .01 .22 .00 -.03 .09 -.01 .11 .08 .05 Education .17* .08 .08 -.06 .03 -.06 -.01 .03 -.01 Age .00 .01 .01 -.00 .00 -.00 .01* .00 .09 Sex .66*** .19 .13 -.48*** .08 -.23 .04 .07 .02 Constant 3.5 .55 - 3.8 .23 - 1.8 .21 - Adjusted R2 .08 .08 .08 Notes: *p<.05, **p<.01, ***p<.001
76 Table 12: The Effect of Trafficking Frequency on Involvement (n=803) Model 1 Model 2 Model 3 Model 4 b SE Beta b SE Beta b SE Beta b SE Beta TV News Freq -.08** .03 -.11 -.08** .03 -.11 -.09** .03 -.12 -.09** .03 -.13 Haaretz Freq .10*** .03 .15 .11*** .03 .15 .11*** .03 .15 .09*** .03 .13 Israel Today Freq .07* .03 .10 .07* .03 .10 .07* .03 .11 .07* .03 .11 Internet Freq .01 .03 .02 -.00 .03 -.00 -.00 .03 -.01 -.01 .03 -.01 Radio Freq .09** .03 .13 .09** .03 .13 .09** .03 .13 .09*** .03 .13 Friends & Family .05 .03 .08 .05 .03 .08 .05* .03 .08 .04 .03 .07 Freq Social Media .10*** .02 .18 .10*** .02 .18 .10*** .03 .18 .09*** .02 .15 Knowledge - - - .03** .01 .09 .03*** .01 .10 .02** .01 .08 Concern ------.06** .02 .09 .03 .02 .03 Efficacy ------.18*** .02 .23 Prostitute .29*** .07 .14 .28*** .07 .14 .30*** .07 .15 .27*** .07 .13 Buy .04 .05 -.02 .03 .05 .02 .03 .05 .01 .03 .05 .02 Religion -.34*** .09 -.13 -.34*** .08 -.13 -.36*** .09 -.13 -.36*** .08 -.13 Traditional .02 .06 .01 .03 .06 .02 .03 .06 .02 .03 .06 .02 Religious -.07 .06 -.04 -.05 .06 -.03 -.05 .06 -.03 -.02 .06 -.01 Orthodox -.10 .09 -.04 -.07 .09 -.03 -.08 .09 -.02 -.06 .09 -.02 Location .07 .06 .04 .07 .06 .04 .07 .06 .04 .05 .06 .03 Education -.01 .02 -.02 -.02 .02 -.03 -.01 .02 -.02 -.01 .02 -.02 Age .00 .00 .01 .00 .00 .01 .00 .00 .01 -.00 .00 -.01 Sex .09 .05 .06 .07 .05 .05 .10* .05 .06 .08 .05 .05 Constant 1.1 .14 - .97 .15 - .71 .17 - .57 .17 - Adjusted R2 .27 .28 .29 .33 Notes: *p<.05, **p<.01, ***p<.001
77
Table 13: Significant Paths from Information Source to Involvement (Conceptual Model) Involvement Involvement w/o with Mediators Knowledge Concern Efficacy Mediators TV News Use n.s n.s n.s n.s n.s Haaretz Use + n.s n.s + + Israel Today Use n.s n.s n.s + n.s Internet Use -- + n.s n.s -- Radio Use + n.s n.s n.s + Friends and Family Use -- n.s + n.s -- Social Media Use + n.s n.s n.s + TV News Freq -- n.s + n.s -- Haaretz Freq + n.s n.s + + Israel Today Freq + n.s n.s n.s + Internet Freq n.s + n.s n.s n.s Radio Freq + n.s n.s n.s + Friends and Family Freq n.s n.s n.s n.s n.s Social Media Freq + n.s n.s + + Notes: + = Positive significance, -- = Negative significance, n.s. = No significance
78
Table 14: The Effects of Knowledge and Concern on Efficacy (n=803)
Model 1 Model 2 b SE Beta b SE Beta Knowledge .04** .01 .11 - - - Concern - - - .20*** .03 .21 Prostitute .20* .10 .08 .28** .10 .11 Buy -.06 .08 -.03 -.05 .08 -.02 Religion -.05 .13 -.01 -.12 .12 -.04 Traditional .02 .09 .01 -.00 .09 -.00 Religious -.22* .09 -.10 -.25** .09 -.11 Orthodox -.17 .14 -.05 -.24 .13 -.07 Jerusalem .11 .09 .05 .13 .08 .06 Education -.03 .03 -.03 -.01 .03 -.01 Age .01** .00 .10 .01** .00 .10 Sex -.02 .07 -.01 .11 .07 .05 Constant 2.4 .20 - 1.8 .22 - Adjusted R2 .03 .06 Notes: *p<.05, **p<.01, ***p<.001
79 Table 15: The Effects of Knowledge, Concern, and Efficacy on Involvement (n=803) Model 1 Model 2 Model 3 Model 4 b SE Beta b SE Beta b SE Beta b SE Beta Knowledge .04*** .01 .13 .03** .01 .09 ------Concern ------.04 .03 .05 -.01 .02 -.02 Efficacy - - - .25*** .02 .32 - - - .26*** .03 .34 Prostitute .40*** .07 .20 .35*** .07 .16 .43*** .07 .21 .36*** .07 .18 Buy .02 .06 .01 .03 .06 .03 .03 .06 .02 .04 .06 .03 Religion -.50*** .09 -.19 -.49*** .09 -.18 -.52*** .09 -.19 -.49*** .09 -.18 Traditional .08 .07 .04 .08 .07 .04 .06 .07 .03 .06 .07 .03 Religious -.09 .07 -.06 -.03 .06 -.02 -.11 .07 -.07 -.05 .06 -.03 Orthodox -.12 .10 -.04 -.07 .10 -.02 -.16 .10 -.06 -.10 .10 -.04 Location .10 .06 .06 .07 .06 .04 .10 .06 .06 .07 .06 .04 Education -.04 .02 -.06 -.03 .02 -.05 -.03 .02 -.05 -.03 .02 -.04 Age .00 .00 .04 .00 .00 .01 .00 .00 .04 .00 .00 .01 Sex .04 .05 .03 .05 .05 .03 .09 .06 .06 .06 .05 .04 Constant 1.7 .15 - 1.1 .15 - 1.7 .17 - 1.3 .17 - Adjusted R2 .12 .22 .10 .21 Notes: *p<.05, **p<.01, ***p<.001
80
Table 15: Continued Table 16: Significant Paths from Knowledge and
Concern to Involvement (Conceptual Model)
Model 5 Model 6 Involvement Involvement
b SE Beta b SE Beta w/o with Mediator Efficacy Mediator Knowledge .04*** .01 .14 .03** .01 .09
Concern .05* .03 .07 -.00 .02 -.00 Knowledge + + +
Efficacy - - - .25*** .03 .32 Concern + + n.s. Prostitute .41*** .07 .21 .35*** .07 .17
Buy .01 .06 .01 .03 .06 .02 Notes: + = Positive significance, n.s. = No significance
Religion -.52*** .09 -.19 -.49*** .09 -.18
Traditional .08 .07 .04 .08 .07 .04 Religious -.08 .07 -.05 -.03 .07 -.02 Orthodox -.12 .10 -.04 -.07 .10 -.03 Jerusalem .10 .06 .06 .07 .06 .04 Education -.04 .02 -.06 -.03 .02 -.05 Age .00 .00 .04 .00 .00 .01 Sex .07 .06 .04 .05 .05 .03 Constant 1.5 .18 - 1.1 .17 - Adjusted R2 .12 .21 Notes: *p<.05, **p<.01, ***p<.001
81 CHAPTER 5
DISCUSSION AND CONCLUSION
The study presented in this dissertation used a public opinion survey of Israeli residents to see if an Attentive Public existed for the issue of human trafficking, as well as examine the factors that influence an individual to become knowledgeable on the issue and involved in the anti-trafficking movement. Prior literature identifies Attentive Publics in politics and social movements as individuals that sustain interest in a topic, are integrated within society, and are likely to influence those around them (Almond, 1950; Campbell et al., 1954; Devine, 1970;
Smith, 1989; Danley-Scott, 2006). As stated earlier, individuals that are part of the Attentive
Publics are classified as highly knowledgeable, highly involved in a specific topic, and have the influence to impact policy outcomes.
Addressing public opinion of trafficking is important because public opinion has been known to impact policy outcomes (Miller and Stokes, 1963; Page and Shapiro, 1983; Burnstein,
2003). Opinion impacts policy by the tangible acts of individuals as they try to sway the policy makers in their favor. These acts include talking about the issue, signing petitions, calling legislators, or financially supporting anti-trafficking organizations. Members of an Attentive
Public are more likely to engage in more than one of these acts as well as try to influence the people around them to do the same. Prior literature finds that the media can shape public opinion through tactics of agenda setting and issue framing (McCombs and Shaw, 1972; Scheufele and
Tewksbury, 2007). This study used a variety of media sources and influencers (friends and family) to examine a wide range of information sources that might shape knowledge, concern, and efficacy in relation to human trafficking.
82 This study advances the literature in two ways. First, the concept of an Attentive Public has not previously been studied in the area of human trafficking. The identification of an
Attentive Public with an estimate of its size allows researchers to gauge how large or sustainable a social movement can be. The members of an Attentive Public are the key players that engage the general public with a cause which can lead to policy change; their interest in a topic is fairly stable and will not sway as the interest of the General Public will. In addition to identifying an
Attentive Public, this study has identified Efficacy as one of the key differences between the
Attentive Public and everyone.
Second, this study provides insight into the factors that contribute to an individual’s
Knowledge and Involvement in the anti-trafficking movement, as well as which media sources are likely have a significant influence. Findings from this study also provide information on the relationships between Knowledge, Concern, and Efficacy on Involvement. Understanding the relationships of these three concepts to involvement provides insight into where and how
Attentive Publics should focus their effort to grow the anti-trafficking movement.
There are six limitations that should be noted. First, this study is based on a convenience sample and is not generalizable to the total population of Israel. While not generalizable, the study still has a similar distribution in the sample compared to the population statistics from
Table 1. Second, this study was cross-sectional. While capturing an estimate of an attentive public at one time is useful, a better indicator would be to look at the ebb and flow of the attentive public in a longitudinal study as changes in salience of the issue and policies are likely to change over time. Third, the survey had missing data on most of the variables. An assessment of the missingness found a range of one value to 76 depending on the variable. Multiple imputation was conducted to account for the missing values. Fourth, while the project attempted
83 get sample a range of locations, the majority of the respondents came from Jerusalem. Fifth, the study assessed two specific newspapers to compare conservative and liberal perspectives. These are just two of the possible newspapers in Israel, therefore other newspaper or a generic newspaper term might have resulted in different outcomes. Finally, the questions constructed for the knowledge variable were based off previous opinion survey questions, but are still susceptible to biases in validity.
5.1 Discussion of Key Findings
Several conclusions can be drawn from the analyses presented. First, an Attentive Public does exist in Israel on the issue of human trafficking. Based on this sample, when compared to everyone else, those in the Attentive Public are higher in efficacy, are more likely to be male, and more likely to know someone who works as a prostitute. By identifying only three significant differences between the Attentive Public and everyone else, the findings align with prior literature that describes the Attentive Public as average, everyday people, rather than an elite group that is distant and disengaged from the general public.
Other than knowing more and participating more often, the two important takeaways are that the Attentive Public are more likely to feel like they can make a difference as well as know someone who works as a prostitute. By knowing someone in the sex trade, they might have a better understanding of how and why individuals found themselves in their current situations, the violence that occurs, or that not everyone is a willing participant in the trade. While knowing a prostitute has a significant negative effect on Concern, it does have a significant positive effect on Efficacy, noted in Table 9 and Table 14. As seen in the analysis on Involvement, Efficacy is one of the strongest predictors.
84 The most effective way to increase the size of the Attentive Public would be to increase feelings of efficacy, which the findings in Table 11 suggest increasing the frequency of trafficking reported on Social Media and in the Haaretz newspaper and the findings in Table 14 suggest increasing Knowledge and feelings of Concern. The anti-trafficking movement can use this finding as a way to focus their efforts in reaching members of the General Public by creating more awareness that increases a sense of responsibility and belief that the individual can make an impact. Most importantly, this study finds that there needs to be an increase awareness of how to engage in the anti-trafficking movement as over sixty percent of the respondents agree that they want to get involved in the anti-trafficking movement but do not know how. While the most beneficial acts might demand a little more effort on part of the respondent (i.e. giving financially or contacting their legislators), if ways to participate are well known, easy to complete, and makes the respondent feel as if they can make a legitimate difference, then the respondent might be more inclined to participate.
Second, based on the descriptive statistics in Table 2 and the distributions found in Table
17 and 18 in Appendix A, many individuals are lacking an adequate amount of information on human trafficking to form an opinion, while those who have an opinion tend to align with some of the typical human trafficking misunderstandings. Common misunderstandings of trafficking include the comparison to prostitution, smuggling, and the process by which victims are relocated to other countries. These misunderstandings were addressed in the public perceptions.
While the crimes of prostitution and smuggling look similar to trafficking in the physical acts of sex or traveling across a border, it is important that the public understand the exploitive nature of trafficking as non-voluntary sex or that trafficked victims, when transported, are typically indebted to their transporter and are subjected to various forms of exploitation. This survey
85 showed that over half of the sample agrees that trafficking is the same as prostitution (65%) and smuggling (47%). This indicates that respondents’ perspectives tend to align with common trafficking myths rather than understanding the victimization associated with trafficking, which may be influenced by the media sources they use. It could also influence their perceptions of the victim population, how concerned they should be, and if they feel compelled to get involved.
Another trafficking myth is that victims enter other countries illegally (smuggled), when in reality it is more common for victims to enter another country legally on various forms of visas
(tourist, student, work). Only a quarter of the respondents were able to indicate that victims can enter another country legally (26%).
The response category “Do not know enough to have an opinion” was included in the survey to address salience of the issues, similar to the work addressed in Page and Shapiro
(1983). The importance of examining salience via “do not know” responses is that it assists in identifying when, instead of having a misinformed perception, the public is not receiving enough information in general to understand the realities of trafficking or the populations that are at risk of victimization in Israel. When salience is low, less people will be able to form an opinion, impacting how society as a whole can react to crime. This is why various forms of information sources are so important to public perceptions; each source provides information to the public in their own way, shaping the information that is provided through agenda setting and how the information is viewed through issue framing.
These findings highlight the need to provide accurate information on human trafficking to the general public along with improved dissemination of the information. In Israel, the lack of information on the issue could be the result of a government that suggests “the phenomenon of trafficking no longer exists in Israel” (Mualem, 2013). As we have seen in 2017, this type of
86 perception influenced a change in policy that eventually led to an increased amount of trafficking victims in Israel (Yaron, 2017). The public perceptions suggest a specific need for more information on who the victims are in Israel. The more people are aware of the problem and confident enough in their understanding, the probability of reporting the crime or being able to recommend places to go for help should increase. In the same way, proper awareness of the problem can also lead to more involvement in the anti-trafficking movement, which mainly focuses on supporting victims and advocating for better anti-trafficking policies.
Third, the majority of people do not frequently participate in the anti-trafficking movement. As noted in Table 2, the mean for Involvement frequency was a 1.59 out of 5 and on average individuals have on average participated in close to 3 out of 8 involvement items. A distribution of the individual items is located in Table 18 of Appendix A. Of the action items individuals could participate in, respondents were more likely to talk about trafficking or try to influence those around them to learn more about trafficking. Close to twenty percent of respondents will sometimes seek out more information on their own, share information they have found with others, or sign an anti-trafficking petition. Only about ten percent of respondents will sometimes attend anti-trafficking meetings, contact their members of government to voice their opinion, or financially support an anti-trafficking organization. Oftentimes it’s those acts of involvement like attending meetings, contacting government, and financially supporting organizations that are the most important and the most effective in making a difference. These findings suggest the need for more engagement in activities that will take more effort on behalf of the individual, but will also be more effective in the long-term anti-trafficking movement.
Fourth, there are very few variables identified that significantly predict Knowledge. Out of the demographic characteristics noted in Table 8, only males, individuals with secular beliefs,
87 and those with higher education have a higher likelihood of being knowledgeable. Out of all seven information sources, the Internet is the only source identified as a significant predictor of
Knowledge. This finding was consistent across both models of information source use and trafficking frequency within information sources. The lack of significance for the other six sources is unexpected, but suggests three possible causes. One possible cause would be that the other sources do provide information on trafficking, but it might not be accurate. Another possibility is that the sources providing information are the ones that are not used as often as others. The third possibility is that trafficking is simply not reported consistently, with only sporadic attention more likely to be missed within any source. The lack of significant predictors as well as the low R2 also suggests that there are other influencers to Knowledge that have not been identified by this study.
Fifth, based on the results in Tables 9 and 11, different information sources have significant direct effects the concepts of Knowledge, Concern, and Efficacy, and the effects vary by how often a source is used and how frequently trafficking is discussed in those sources. In
Table 9, the use of the Internet predicts Knowledge, the reliance on Friends and Family for information predicts Concern, and both newspapers predict Efficacy. These relationships make sense on face value. For example, the Internet can provide a lot of information quickly and emphasizes the use of technology for information gathering or news. The relationship between
Friends and Family with Concern suggest that individuals will be concerned with the same things that concern those around them. This barely scratches the surface on the influence of close relationships on behavior, but begins to support the idea that the Attentive Public can influence those relationships that are close to them. The influence of newspaper use on Efficacy finds that both a liberal and conservative paper generate similar feelings of responsibility and ability to
88 make a difference. This could be that newspaper articles provide information on how to get involved, or that newspapers in general provide a sense of legitimacy and desire for political engagement.
In Table 11, the frequency of trafficking reports in the Internet and in the Haaretz newspaper continues to predict Knowledge and Efficacy, respectively. Increased reports of trafficking in TV News significantly predicts Concern, rather than Friends and Family, suggesting that Concern might vary by type of source in combination with how often those sources push the trafficking narrative. Friends and Family are a trusted source of information, but if they do not discuss trafficking often, they are likely to not make an impact. On the other hand, even though people do not use TV News as an information source as often as other sources, if the news consistently reports on trafficking, it’s likely that people will pay attention to it. The
Israel Today newspaper loses its significant effect on Efficacy and is replaced by Social Media.
Similar to the significance of the Internet, an increased frequency of trafficking reports on social media highlight the accessibly of information through technology. It goes even further to suggest that specific internet platforms, like social networks, are a good place to rally like-minded individuals to engage in social movements, similar to the findings of Booth-Perry (2014),
Velasquez and LaRose, (2014), and Karamat and Farooq (2016).
Sixth, when addressing information source use, the information sources (Internet and
Friends and Family) that predict a positive effect on variables of Knowledge and Concern in
Table 9 predict a negative effect on Involvement in Table 10. Similarly, when addressing trafficking frequency, the information sources (Internet and TV News) that have a positive effect on Knowledge and Concern in Table 11, either have a negative effect on Involvement, or no effect at all in Table 12. In addition, throughout the models, multiple suppression effects were
89 identified as well as some partial and fully mediating effects. These findings highlight the complexity of these relationships as variables play a conditioning role depending on the relationship and that some variables have influence only through indirect relationships.
Finally, the results of this study indicate that a variety of information sources used and the frequency in which they report on trafficking have significant effects on Involvement, with some findings in unexpected directions. Knowledge and Efficacy do not entirely mediate the relationships between these information sources, but do have significant relationships with some information sources and assist in providing explanatory power within the models. Concern is the most complex variable as it only becomes a significant predictor with the inclusion of
Knowledge, but is also fully mediated by the inclusion of Efficacy.
5.2 Implications for Future Research
This study in particular could be extended for more in-depth analysis. One would be to examine the information sources on the individual public opinions, rather than a combined score of knowledge, to address how salience and accuracy is distributed among sources. A comparison could be made between those that do have an opinion and those who do not.
A second study would look further into how information sources influence the different subgroups of concern (by type of trafficking and victim characteristics), rather than a total combined score of concern. In addition, research can go further into the relationships between the subgroups of concern. For example, comparing if respondents are more concerned for sex versus labor trafficking, adult vs minor victims, female vs male victims, or if there is an ethnocentric effect where respondents are more concerned for Israeli victims compared to non-
Israeli victims. These different subgroups of concern would also be assessed by their impact on
90 efficacy. Does concern for a particular type of trafficking or victim characteristic influence a respondent’s feelings of efficacy?
Two of the main research questions of this study involved identifying the significant predictors of the two key components of an Attentive Public, Knowledge and Involvement. While significant predictors were identified, the models explained eight percent of Knowledge and thirty-three percent of Involvement at best. Future research should continue to search for better predictors of Knowledge and Involvement to further understand what influences the Attentive
Public. The identification of Efficacy as a strong predictor of Involvement is useful for the anti- trafficking movement, however more research is still needed. While this study found Knowledge and Concern to be significant predictors of Efficacy, the models continued to have a relatively low explanatory power of six percent at best. Future research into what factors increase Efficacy will assist in growing the Attentive Public. By knowing these factors, the anti-trafficking movement as a whole has a better chance of increasing the amount of highly knowledgeable and highly involvement individuals from the general public and, in turn maximizing efforts for policy change.
Longitudinal studies would be beneficial in examining these concepts even further.
Future research should combine public opinion surveys with a content analysis to gain a better understanding of how media sources frame issues of trafficking and how that aligns with respondent knowledge. A comparison could be made across media sources to identify the sources providing the more accurate message as well as the most frequent. Another study could examine public opinion of trafficking pre and post-legislative decision. This type of study would be an in-depth look into a systems analysis view of how public opinion influences decision
91 makers and their policy outputs. A study like this would be able to watch the actual ebb and flow of Attentive Publics in the anti-trafficking movement similar to the study by Devine (1970).
This study addresses the beginning efforts and influences in a much larger system of the anti-trafficking movement. The findings from this study indicate that information sources, knowledge, concern, and efficacy all play their own role within an individual’s decision to get involved with the anti-trafficking movement, and when used in the correct way, could lead to an increase in the Attentive Public. Further research can extend this analysis to measuring how much the desires of the Attentive Public compares with policy outputs delivered. This study is only one piece in working towards the elimination of human trafficking, but by understanding the level of knowledge, concerns, and involvement of the public, future activist groups can better equip the public to play a larger role in influencing policy changes.
92 APPENDIX A
DESCRIPTIVE TABLES
Table 17: Frequency of Perceptions on Human Trafficking No Agree Disagree Opinion Human trafficking is the same as prostitution (n=799) 65% 28% 7%
Human trafficking is the same as smuggling (n=794) 47% 45% 8%
Trafficked individuals in Israel are mostly non-Israeli (n=801) 51% 12% 37%
A majority of the victims trafficked in Israel for sex are women 39% 16% 45% over 18 (n=799)
A majority of the victims trafficked in Israel for labor are men 25% 28% 47% over 18 (n=793) Trafficked individuals often make a conscious decision to go 44% 20% 36% abroad for a better life (n=793)
Trafficked women are sometimes partly or fully aware of the 47% 25% 28% possibility of being involved in commercial sex work (n=793)
Trafficked individuals expect to be held as slaves and do not 36% 36% 28% think they will choose their working conditions (n=783)
A majority of trafficked individuals are poor (n=789) 63% 18% 19% Trafficked individuals receive good payments for their services 17% 60% 23% (n=791) An entire family can be held as trafficked victims (n=796) 55% 14% 31%
Trafficked individuals can enter into a country legally (n=797) 26% 34% 40%
Table 18: Frequency of Involvement by Individual Acts Very Never Rarely Sometimes Frequently Frequently Seek Out Info (n=801) 57% 26% 11% 3% 2% Try to Influence Others (n=800) 54% 21% 16% 6% 3% Talk about trafficking (n=799) 50% 26% 16% 6% 2% Share info about trafficking (n=793) 59% 22% 12% 4% 3% Attend an anti-trafficking meeting (n=797) 82% 8% 5% 3% 2% Sign a Petition (n=792) 69% 15% 9% 4% 3% Contact Representative (n=794) 83% 7% 4% 4% 2% Financially Support (n=797) 81% 8% 5% 4% 2%
93 APPENDIX B
LISTWISE MODELS
Table 19: The Effect of Information Source Use on Involvement (Listwise Deletion) Model 1 (n=714) Model 2 (n=651) Model 3 (n=651) Model 4 (n=650) b SE Beta b SE Beta b SE B b SE Beta TV News Use .03 .02 .07 .03 .02 .06 .03 .02 .06 .02 .02 .04 Haaretz Use .15*** .02 .26 .12*** .02 .23 .12*** .02 .23 .10*** .02 .19 Israel Today Use .03 .02 .05 .02 .02 .03 .02 .02 .04 .00 .02 .00 Internet Use -.06** .02 -.12 -.06** .02 -.11 -.06** .02 -.11 -.05* .02 -.09 Radio Use .06** .02 .12 .05** .02 .12 .05** .02 .11 .06** .02 .12 Friends and Family Use -.04* .02 -.08 -.03 .02 -.06 -.04 .02 -.07 -.03 .02 -.05 Social Media Use .03 .02 .07 .02 .02 .05 .02 .02 .05 .01 .02 .03 Knowledge - - - .02* .01 .09 .03** .01 .11 .02* .01 .08 Concern ------.06** .02 .10 .01 .02 .02 Efficacy ------.21*** .02 .32 Prostitute .42*** .07 .22 .41*** .07 .23 .42*** .07 .24 .35*** .06 .20 Buy -.02 .05 -.01 -.05 .06 -.04 -.05 .05 -.04 -.02 .05 -.02 Religion -.25** .09 -.10 -.19* .10 -.07 -.21* .10 -.08 -.23** .09 -.09 Traditional .09 .06 .06 .10 .07 .06 .10 .07 .06 .10 .06 .06 Religious -.09 .07 -.06 -.11 .07 -.08 -.10 .07 -.07 -.05 .06 -.03 Orthodox -.03 .10 -.01 -.06 .10 -.02 -.05 .10 -.03 -.05 .09 -.02 Location .08 .06 .05 .06 .06 .04 .07 .06 .05 .04 .06 .03 Education -.01 .02 -.02 -.00 .02 -.01 .00 .02 .00 .00 .02 .00 Age -.00 .00 -.05 -.00 .00 -.06 -.00 .00 -.06 -.01* .00 -.09 Sex .06 .05 .04 .04 .05 .03 .07 .05 .05 .05 .05 .04 Constant 1.5 .17 1.4 .18 1.1 .20 .90 .19 Adjusted R2 .19 .17 .17 .26 Notes: *p<.05, **p<.01, ***p<.001
94 Table 20: The Effect of Trafficking Frequency on Involvement (Listwise Deletion) Model 1 (n=712) Model 2 (n=648) Model 3 (n=648) Model 4 (n=647) b SE Beta b SE Beta b SE Beta b SE Beta TV News Freq -.06* .03 -.09 -.09** .03 -.15 -.10*** .03 -.15 -.09*** .03 -.14 Haaretz Freq .11*** .03 .16 .07** .03 .11 .07** .03 .11 .05* .03 .08 Israel Today Freq .04 .03 .06 .03 .03 .05 .03 .03 .05 .04 .03 .07 Internet Freq .04 .03 .06 .06 .03 .12 .06* .03 .11 .05 .03 .10 Radio Freq .06* .03 .09 .07** .03 .12 .07** .03 .12 .07** .03 .12 Friends and Family Freq .08** .03 .13 .08** .03 .15 .08** .03 .14 .07** .03 .13 Social Media Freq .06* .03 .11 .03 .02 .07 .03 .02 .07 .01 .02 .02 Knowledge - - - .02* .01 .08 .02** .01 .09 .02 .01 .07 Concern ------.06** .02 .09 .02 .02 .03 Efficacy ------.19*** .02 .29 Prostitute .26*** .06 .14 .28*** .07 .16 .29*** .07 .17 .24*** .06 .14 Buy -.01 .05 -.00 -.03 .05 -.02 -.04 .05 -.03 -.01 .05 -.01 Religion -.21* .09 -.08 -.16 .09 -.06 -.18 .09 -.07 -.21* .09 -.08 Traditional .04 .06 .02 .04 .06 .02 .03 .06 .02 .03 .06 .02 Religious -.07 .06 -.05 -.12* .06 -.09 -.12 .06 -.08 -.08 .06 -.06 Orthodox -.10 .09 -.04 -.16 .09 -.06 -.16 .09 -.07 -.13 .09 -.05 Location .09 .06 .06 .04 .06 .03 .05 .06 .03 .04 .05 .02 Education -.00 .02 -.01 .01 .02 .01 .01 .02 .02 .01 .02 .02 Age .00 .00 .03 -.00 .00 -.02 -.00 .00 -.02 -.00 .00 -.06 Sex .12** .05 .09 .09 .05 .07 .11* .05 .08 .09* .05 .07 Constant .89 .15 .94 .15 .70 .18 .59 .17 Adjusted R2 .24 .22 .23 .30 Notes: *p<.05, **p<.01, ***p<.001
95 APPENDIX C
IRB APPROVAL AND CONSENT
96
97
98
99 May 2014
FLORIDA STATE UNIVERSITY Informed Consent Form A Study of Human Trafficking Perceptions in Israel Ashley Russell- Principal Investigator Dr. Marc Gertz – Faculty Supervisor Purpose of Research Our research is focused on looking at opinions about issues relevant to Israelis, such as perceptions of human trafficking, media attention on the issue of human trafficking, and concerns about the crime of human trafficking.
Specific Procedures to be Used You will be participating in a completely anonymous and voluntary survey. You will be asked to answer questions on the topics of human trafficking and prostitution. The survey will ask about your perceptions of human trafficking and your concern for trafficking victims, along with your opinion on prostitution and if you know anyone involved in prostitution. The survey should take 10 minutes to complete.
Benefits to the Individual Subjects will be helping to further the understanding of human trafficking in Israel.
Risks to the Individual Minimal risk is anticipated for the participants of this study. You may have concerns about filling out a survey, but we guarantee confidentiality to the extent allowed by law.
Confidentiality Your answers will not and cannot be linked back to you by the researchers. No identifying data will be used in any write-up or publication.
Voluntary Nature of Participation Please note that participation is entirely voluntary. You have the right to skip over questions, refuse to participate, or withdraw from participation at any time without penalty.
Human Subject Statement If you have any questions or concerns regarding our study or your participation, you may contact the Faculty Supervisor Marc Gertz directly by email at [email protected], by phone +001-850-644-7382 or the Institutional Review Board at Florida State University
Human Subjects Office 2010 Levy Avenue, Suite 276-C Tallahassee, FL 32306 Ph: +001-850-644-7900
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110 BIOGRAPHICAL SKETCH
Ashley Russell, Ph.D., completed her B.S., M.S., and Ph.D. degrees in the College of
Criminology and Criminal Justice at Florida State University. Ashley served as the Assistant to the Director of Alumni Development for three years and taught as a Graduate Student Instructor, covering classes on crimes against humanity and comparative criminology. Ashley works on contract with International Justice Mission, the largest international anti-slavery organization, as a quantitative data analyst. She also volunteers as an IJM Florida Advocacy Leader, engaging the state of Florida in a variety of advocacy efforts. Her research interests include human trafficking, crimes against humanity, prosecution and sentencing behavior, and life course criminology.
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