By: Stephanie Savage
Introduction
Despite monumental progress in deterring human traffickers from harnessing and exploiting innocent victims, traffickers are still profiting off of forced labor and involuntary sexual activity. Every country still has their own accountability system to eradicate this detrimental phenomenon. Human trafficking is a multifaceted crime that challenges governments and policy makers because it is significantly fragmented. Human trafficking exists and thrives in today’s world, spawned from a vicious cycle of continuous crime. Policy makers and governments find it challenging to criminalize traffickers due to the lack of a complete and unanimous definition of trafficking, a lack of victim advocacy and treatment, and inadequate and erroneous statistics.
In order to assess an overview of human trafficking on a global scale, I plan to dissect the 2019 Trafficking in Persons Report alongside other scholarly articles and global databases to determine what specific factors bolster and catalyze higher human trafficking rates. To prevent more fragmentation from occurring, common myths that are believed to have caused human trafficking need to be addressed with the purpose of identifying more timely and appropriate roots.
I assess that countries with complicated political situations, humanitarian conflicts, weak and unstable governments, corrupt law enforcement, and a lack of amended legislation will report at a higher Tier level compared to relatively stronger governments. Furthermore, if countries have victim advocacy programs, then their Tier placement will be low. This research was collected in order to determine how the presence of victim advocacy programs, literacy rates, economic freedom, population below the poverty line, and education expenditures per gross domestic product affects Tier ranking. This research is important because if an accurate solution can be presented to governments or policy makers, then countries around the world can put plans into action to effectively combat human trafficking. By identifying where information gaps occur within previous research and we can ultimately locate possible weaknesses in the efforts to deter and eliminate human trafficking.
Literature Review
As stated previously, human trafficking is a multidimensional crime that is increasingly challenging to detect and criminalize. Researcher Christal Morehouse states “Despite the numerous references to the highly adaptive and complex nature of human trafficking, there has been a growing recognition by criminal justice researchers and practitioners that human trafficking is one of the most difficult crimes for the counter-trafficking community to understand, detect, and combat,” (Morehouse, 2009). Specific factors such as low literacy rates, high poverty rates, low education spending, and a lack of victim advocacy programs may accelerate the rates of trafficking, placing certain countries at a higher Tier ranking and therefore, degrading previous work to deter trafficking. Researcher Renata Konrad states “The interaction of poverty, development, and relevant policies affect the vulnerability of its population to trafficking,” (Konrad, 2019). It is commonly believed that a lack of relevant policy, development, and funding contribute to higher trafficking rates on a global scale.
Tier System
Before other factors of human trafficking are explored, it is important to describe the Tier system and how the United States Department of State has categorized each country in the Trafficking in Persons Report (TIP). The 2019 TIP report utilizes a Tier placement system in which countries are ranked comprehensively on government action to deter human trafficking.
Tier 1 is the highest ranking and what countries should strive to obtain. To clarify, if countries rank in the Tier 1 category, it does not mean there is no further progress to be made. This only means the efforts have been increasing in frequency and intensity or there have been more victims identified or new legislation passed. Tier 2 countries “do not fully meet the Trafficking and Violence Protection Act of 2000 (TVPA) minimum standards but are making significant efforts to bring themselves into compliance with those standards,” (USDS, 2019). In continuation, the Tier 2 watch list includes “countries whose governments do not fully meet the TVPA’s minimum standards but are making significant efforts to bring themselves into compliance with those standards” and “the absolute number of victims of severe forms of trafficking is very significant or is significantly increasing,” (USDS, 2019). The Tier 2 watch list also countries where “there is a failure to provide evidence of increasing efforts to combat severe forms of trafficking in persons from the previous year, including increased investigations, prosecution, and convictions of trafficking crimes, increased assistance to victims, and decreasing evidence of complicity in severe forms of trafficking by government officials,” (USDS, 2019). Lastly, Tier 3 includes “countries whose governments do not fully meet the TVPA’s minimum standards and are not making significant efforts to do so,” (USDS, 2019).
No country’s Tier placement is permanent and is assessed annually on performance. Each country should strive to be a Tier 1 status in order to effectively combat human trafficking and therefore combat human smuggling. Tier placement can fall or rise depending on whether countries make increased or decreased effort to adapt to their unique and ever-changing environments. Some countries fall into a special case scenario such as countries experiencing civil wars or extreme poverty. It is important to monitor each country in order to ensure no country is becoming a human trafficking safe haven or powerhouse.
United States Department of State and Human Trafficking
The United States Department of State collects data from around the world and publishes the annual Trafficking in Persons Report to determine country rankings. These rankings are again, based on a comprehensive analysis of government action to deter human trafficking within its borders. It is important to note that countries attempting to deter labor trafficking in its borders may receive a more severe ranking if there are high rates of a different form of trafficking. Scholars have argued that the United States may be incorrectly and unfairly placed at the top Tier, Tier 1, due to their involvement with publishing. However, upon further research, the United States continues to establish serious efforts annually to combat human trafficking and how it transforms within its borders.
There is a significant amount of agencies that currently dedicate their time and assets toward fighting human trafficking within the United States. To begin, Immigration and Customs Enforcement (ICE), an agency that works under the Department of State is a primary federal agency responsible for human trafficking because the United States is a high destination country that produces high levels of income (USDS, 2019). The Department of Homeland Security (DHS) is also a strong fighting force in the war against human trafficking as it develops campaigns that have the ability to work with law enforcement and governments. Furthermore, the Human Smuggling and Trafficking Center is an extension of ICE which essentially disseminates important information regarding the issue. Lastly, the Homeland Security Investigations agency, also an extension of ICE, focuses on victim advocacy and critical investigations (USDS, 2019).
Published within the 2019 Trafficking in Persons Report, Mike Pompeo, current U.S. Secretary of State has mentioned that 24.9 million people are affected which strips them of their natural born freedom and human dignity (2019). Secretary Pompeo has also stated that the annual TIP report is an “invaluable tool to arm ourselves with the latest information and guide our action at home and abroad,” (USDS, 2019). This annual report alone allows the United States to remain at a Tier 1 status because numerous agencies pour over global actions and data to create a comprehensive description of how each country is combatting human trafficking. This information is published honestly and fills the information gaps that need to be answered in order to move forward with defeating this phenomenon.
Sanctions
Furthermore, human trafficking is currently categorized as big business, or organized crime and therefore, has been inappropriately treated as such with little success. Researcher David Feingold states quite firmly that “trafficking involves mostly ‘disorganized crime’: individuals or small groups linked on an ad hoc basis” and that “there is no standard profile of traffickers,” (Feingold, 2005). Seeing as though there is a lack of a commonly understood and tangible definition for trafficking paired quite dangerously with a unique and random population of traffickers, the crime itself seems almost impossible to identify and prevent. Equally important, it is vital to mention that sanction do not necessarily deter human trafficking per popular belief. Sanctions emplaced by the United States are widely seen as a punishment, whereas human trafficking could be prevented more rapidly by providing incentives globally, instead of threats. This is a technique called positive reinforcement, often used with children or developing adults.
Feingold also states “international humanitarian agencies see the threat of U.S. sanctions against foreign governments as largely counterproductive” and “although some countries certainly lack candor and create false fronts of activity, others actively seek Uncle Sam’s seal of approval (and the resources that often follow) with genuine efforts to combat trafficking,” (Feingold, 2005). Fundamentally, it is arguable that sanctions are an outdated and inappropriate preventative measure and should be replaced by support through further resources, diplomacy, funding, and/or education. Logically speaking, incentives should be awarded to countries that are struggling, yet showing efforts to deter trafficking. Lower Tiered countries have found effective solutions to combat human trafficking, and where possible, should teach and guide struggling, higher Tiered countries.
The T visa crisis
Researcher Denise Brennen states, “We are now seventeen years into “fighting” trafficking in the United States, and the earliest mistakes, misconceptions, and myths still guide policy, media storylines, and the public’s understanding of human trafficking,” (Brennen, 2017). With this in mind, funding is not being placed appropriately for policy making because the data from which governments derive their information, is skewed and outdated. This matter alone generates almost inexorable obstacles and creates a false framework to combat human trafficking. One major intelligence gap occurs within visas and may be one of the biggest sources of human trafficking. Brennen acknowledges that, “The TVPA (Trafficking and Violence Protection Act of 2000) makes a new visa—a T visa—available for trafficked foreign nationals, allowing them to continue living in the United States,” (Brennen, 2017). After obtaining a T visa, individuals may then apply for the following documentation such as a green card and eventually, citizenship. Brennen also states that, “even though the law allows up to 5,000 T visas to be issued each year, 11 fewer than 7,000 have been given out since the passage of the legislation” but “just how few trafficking visas have been issued is little known – because the total number of T visas issued to date is not published in a single place, an accurate count requires tallying figures from different U.S. government documents,” (Brennen, 2017).
This complete lack of dissemination of data creates major intelligence gaps and therefore creates difficulty in assessing human trafficking statistics, bolstering fragmentation. In essence, this means that there is a problem with identifying victims or there is a problem with issuing T visas.
Nepal Case Study
Specifically, as a complete case study paired between the 2019 Trafficking in Persons Report and other data, Nepal solidifies the potential hypothesis detailed above. To begin, the United States Department of State has published that Nepal has not signed or acted upon the, “Protocol to Prevent, Suppress and Punish Trafficking in Persons, Especially Women and Children, supplementing the United Nations Convention against Transnational Organized Crime,” (USDS, 2019). Correspondingly, Konrad discusses that in Nepal, “most trafficking victims are women and girls, who are especially vulnerable due to economic insecurity, recent national disasters, and poverty, coupled with gender inequality,” (Konrad, 2019). Moreover, Nepal is ranked as a Tier 2 country in the 2019 Trafficking in Persons Report, has not accepted the United Nations protocol, and has been ravished by destabilizing economic insecurity which has created the perfect equation for high human trafficking rates.
Outside factors that directly affect Nepal include the fact that it is landlocked by other countries that also measure in at Tier 2 level including India and Pakistan (USDS, 2019). Nepal is also located in the vicinity of Bangladesh, a Tier 2 watchlist country, and Bhutan, a Tier 3 country (USDS, 2019). In particular, the United States Department of State has concluded that, “traffickers subject women and girls— predominately from Nepal and Bangladesh … to sex trafficking in India. Traffickers exploit Indian and Nepali women and girls in India as ‘orchestra dancers,’ where girls work for dance groups hired to perform at public functions but are subsequently subjected to sex trafficking,” (USDS, 2019). Ultimately, the Nepali government expresses enforcement and prevention efforts but, “does not fully meet the minimum standards for the elimination of trafficking but is making significant efforts to do so,” (USDS, 2019). Consequently, although Nepal has demonstrated efforts to prevent and deter human trafficking on a governmental level, outside factors are still allowing human trafficking to thrive.
As specifically stated above, Nepal has failed to criminalize all forms of human trafficking, possibly due to the lack of a commonly understood and tangible definition. The United States Department of State in regard to Nepal, has announced, “Its laws do not criminalize all forms of forced labor and sex trafficking, and despite a large number of Nepali male trafficking victims overseas, government protection efforts disproportionately focused on female victims,” (USDS, 2019). With that being said and knowing that most trafficking victims are girls and women from Nepal being ushered into sex trafficking as dancers, Nepal has done a great disservice to their population in the prevention and deterrence of trafficking.
To conclude, I assess that countries with complicated political situations, humanitarian conflicts, weak and unstable governments, corrupt law enforcement, and a lack of amended legislation will report at a higher Tier level compared to relatively stronger governments. Through holistic and extensive research, I plan to present appropriate and up-to-date factors such as the presence of victim advocacy programs, literacy rates, economic freedom, population below the poverty line, and education expenditures per gross domestic product that bolster current day human trafficking. By expanding on these variables, it will eliminate any existing falsehoods of this phenomenon in order to directly address the crime as it stands today. Lastly, these independent variables will be examined in conjunction with Tier placement on a 122-country analysis in order to determine trends and information gaps.
Theory and Hypotheses
If countries experience low literacy rates, then human trafficking rates will heighten or remain higher than usual. These two variables have an inverse relationship and might show that low literacy rates can be an indicator that a country has not put in significant efforts to educate their population about standard trafficking techniques. Furthermore, if countries experience low education expenditures per its annual gross domestic product, then literacy rates will measure below the average and human trafficking rates will increase. These variables also have an inverse relationship indicating that the more a country spends on education, the higher the literacy rates will be and therefore, produce lower rates of human trafficking.
Likewise, if countries experience higher rates of its population below the poverty line per the average, then human trafficking rates will increase or remain at a higher percentage. These variables have a correlating relationship in which both variables rise together or fall together. Higher poverty rates may indicate that countries are struggling economically. With that being said, countries that are struggling economically may allot a smaller percentage of their annual gross domestic product toward education expenditures. Moving forward, if countries are allotting a small percentage of its annual gross domestic product toward education expenditures, then literacy rates may begin to fall or remain at or below the average of 82.4 percent.
The dependent variable of this study is human trafficking rates (Tier placement) within the unit of analysis, countries. The independent variables collectively from the raw data are the presence of victim advocacy programs, education expenditures per annual gross domestic product, economic freedom, literacy rates, and percentage of population living below the poverty line. Other variables that are considered but are not based on raw data are whether or not countries possess a clear definition of human trafficking and whether or not countries act on fragmented statistics. This is important because without a unanimous and enforced definition, human trafficking will continue to be almost impossible to identify, criminalize, and prevent. Furthermore, a lack of victim advocacy programs and treatment will factor into this research, per the 2019 Trafficking in Persons Report which Tiers certain countries higher than others if the governments do not show a significant increase in advocacy for victims. This variable alone will almost certainly continue to raise human trafficking rates.
As previously stated in the literature review, human trafficking is and was falsely categorized as big business, or organized crime and therefore, has been inappropriately treated as such with little success. If countries determine an appropriate definition for human trafficking per its unique needs, trafficking will be easier to criminalize. For example, countries with higher labor trafficking may focus more heavily on labor laws and labor enforcement, whereas countries that struggle with the recruitment of child soldiers may focus more heavily on education and enforcement of attendance at education facilities. Moreover, countries that experience higher rates of sex trafficking may focus on victim advocacy and areas that promote sexual activity such as hidden massage parlors, the adult entertainment industry, and prostitution. Ultimately, each country has its own responsibility to determine which type of trafficking is experienced at higher rates and then generate and enforce legislation from that point.
Additionally, sanctions have been placed on countries that do not show clear and significant effort in preventing human trafficking within its borders. As previously stated in the literature review, if countries do not measure up on the Trafficking in Persons Report, the United States may consider placing sanctions on the accountable countries. Countries may then start reporting false statistics to avoid sanctions and therefore, reinforce fragmented statistics which makes identifying and criminalizing human trafficking challenging. If countries report fraudulent statistics on literacy rates, education expenditures per the annual gross domestic product, and the population living below the poverty line, it produces difficulties in addressing deficiencies.
Furthermore, it is important to note that on a global effort we are well over a decade of fighting human trafficking and that fight is still based on outdated and erroneous statistics. This may be a source that is bolstering human trafficking in each and every country. Outdated statistics can only treat human trafficking as it was well over a decade ago, not the human trafficking we are experiencing today. This crime cannot and will never be based on one or two variables. Human trafficking will continue to be based on many interrelated variables that may or may not directly depend on each other. Human trafficking is a multifaceted crime that speaks no one language or occurs in one state or region, it is ever-changing and requires constant monitoring.
With that being said, this study is based on raw data variables and other qualitative variables. Certain factors must be taken into account such as developed or undeveloped legislation, protocols, country accountability, type of trafficking, population size, economic and government stability, current wars and war history, police enforcement, and corruption. As stated previously, it is a collective effort to determine how and why human trafficking is happening and how to address the phenomenon. Therefore, no definite statistical variables will determine that, it has to be a collective qualitative and quantitative effort.
Data and Methods
This research design consisted of both qualitative and quantitative exploration in order to develop a more all-encompassing view of the phenomenon. The units of analysis for this research were countries, ranging from Afghanistan to Zimbabwe. Countries that did not register information into the report or fact book were dismissed from further analysis as to eliminate producing more fragmented statistics. One of the most useful tools utilized during this research phase was the 2019 Trafficking in Persons Report developed by the United States Department of State alongside data derived from the CIA World Factbook. Dissecting and analyzing the 2019 Trafficking in Persons Report allowed for a categorized baseline of countries.
Alongside the raw data, scholarly articles were used to determine what specific factors bolster and catalyze higher human trafficking rates. The information derived from these scholarly articles were used explicitly to identify where information gaps occur and ultimately locate possible weaknesses in the efforts to deter and eliminate human trafficking. Ultimately, a control variable was not applied, and all data is as current and accurate as possible, and unique to each country. This study was based on cross tabulation analysis because multiple variables were compared against each other to support or dismiss a hypothesis. A cross tabulation test is a tool used to understand the correlation between multiple variables.
This research initially started with three interval level variables that were eventually converted into ordinal level variables. In order to complete a cross tabulation analysis, a frequency test was completed in order to obtain all ordinal level variables. The dependent variable of this research was Tier placement, which calculates human trafficking rates and government involvement into numerical categories. Each independent variable was split into smaller groups by selecting natural cut off points in order to make analysis more feasible and understandable.
To be specific, the population below the poverty line was initially represented in percentages. After converting this variable to an ordinal level variable and categorizing into natural cut off groups, three classifications were generated. Twenty percent or less created the group labeled one, twenty one percent to forty percent created the group labeled two, and forty-one or more created the group labeled three. For example, if a country had a thirty two percent poverty rate it would be placed in category two.
Literacy Rates also initially started as percentages derived from the CIA World Factbook. These percentages were converted into ordinal level variables by dividing them into groups through natural cut off points. Three classifications were generated through this frequency test. Zero percent to fifty nine percent created group one, sixty percent to ninety percent created group two, and ninety one percent to one hundred percent created group three. For example, if a country had a ninety three percent literacy rate, it would be placed in category three.
Education expenditures per Gross Domestic Product is also an independent variable that was divided into groups by natural cut off points. This variable was divided by less than five percent creating group one and five percent or higher creating group two. For example, if a country had 2 percent of its Gross Domestic Product spent on education, it would be placed in group one.
Furthermore, the presence of victim advocacy programs was measured through a country case study within the 2019 Trafficking in Persons Report. If a country had victim advocacy programs, it would be categorized into group one. If a country did not have victim advocacy programs, or was failing to accommodate some of its population, such as males, it would be placed into category two. Economic Freedom was also split into groups for each country. This information was derived from the Economic Freedom Rating Index and groups countries from free to repressed. If a country is free, it would be categorized into group one, if a country is moderately free, it would be categorized into group two. In continuation, if a country is mostly unfree, it would be categorized into group three. Lastly, if a country is repressed, it would be categorized into group four.
After converting all variables into ordinal level variables, they were then organized based on relevance to the hypotheses. The order is as follows; victim advocacy, education expenditures per GDP, economic freedom, literacy rates, and percent of the population living below the poverty line. Through these independent variables, specific questions were prompted to better understand this selected group of one hundred and twenty-two countries. For example, ‘what is the government doing to bolster and improve education’, ‘what type of environment does the population live in’, and ‘what is the condition of the people?’
This research was proven significant through a chi-squared measurement. A chi-square test, better known as Pearson’s Chi-Square measures the magnitude of discrepancy between the observed data and the data expected to be obtained with a specific hypothesis. For this data, the focus is on the significance value derived from the test. Statistical significance is a measurement of how possible it is that a relationship between variables has been found in error. In our discipline, it is generally measured at the .05 level, accepting 5% or less risk of error for a finding to be deemed significant. To measure the strength of a relationship if one existed, a Gamma Test was used, which is a tool that measures a relationship between two pieces of data, and checks the strength of that relationship. Gamma values range from -1 to 1, with values close to -1 being strong inverse correlations, values close to 1 being strong positive correlations, and values near 0 showing little to no relationship.
As shown below, victim advocacy measured statistically significant at .014 which means that if countries have victim advocacy programs, it is statistically proven that their Tier ranking will be lower (Table 1). Based on gamma, victim advocacy measures strong with a .459 (Table 2).
Table 1 – Victim Advocacy
| Chi-Square Test | |||
| Value | df | Asymptotic Significance (2-sided) | |
| Pearson Chi-Square | 10.650a | 3 | .014 |
| Likelihood Ratio | 10.930 | 3 | .012 |
| Linear-by-Linear Association | 9.647 | 1 | .002 |
| N of Valid Cases | 122 | ||
| a. 0 cells (.0%) have expected count less than 5. The minimum expected count is 7.00. |
Table 2 – Victim Advocacy
| Gamma Test | |||||
| Value | Asymptotic Standard Errora | Approximate Tb | Approximate Significance | ||
| Ordinal by Ordinal | Gamma | .459 | .125 | 3.431 | .001 |
| N of Valid Cases | 122 | ||||
Table 3 – Victim Advocacy & Tier Placement
| Cross Tabulation Test | |||||
| Victim Advocacy Program | Total | ||||
| Yes | No | ||||
| Tier Placement | Tier 1 | Count | 14 | 6 | 20 |
| % within Victim Advocacy Program | 23.0% | 9.8% | 16.4% | ||
| Tier 2 | Count | 35 | 27 | 62 | |
| % within Victim Advocacy Program | 57.4% | 44.3% | 50.8% | ||
| Tier 2 Watch List | Count | 8 | 18 | 26 | |
| % within Victim Advocacy Program | 13.1% | 29.5% | 21.3% | ||
| Tier 3 | Count | 4 | 10 | 14 | |
| % within Victim Advocacy Program | 6.6% | 16.4% | 11.5% | ||
| Total | Count | 61 | 61 | 122 | |
| % within Victim Advocacy Program | 100.0% | 100.0% | 100.0% |
Education Expenditures per GDP measured statistically significant at .023 which means that countries that spend more on their education, rank lower on Tier level (Table 4). Having an educated population can reduce human trafficking within country borders. Based on gamma, education expenditures per GDP measured strong at -.450 (Table 5).
Table 4 – Education Expenditures per GDP
| Chi-Square Test | |||
| Value | df | Asymptotic Significance (2-sided) | |
| Pearson Chi-Square | 9.556a | 3 | .023 |
| Likelihood Ratio | 9.646 | 3 | .022 |
| Linear-by-Linear Association | 7.917 | 1 | .005 |
| N of Valid Cases | 122 | ||
| a. 1 cells (12.5%) have expected count less than 5. The minimum expected count is 4.93. |
Table 5 – Education Expenditures per GDP
| Gamma Test | |||||
| Value | Asymptotic Standard Errora | Approximate Tb | Approximate Significance | ||
| Ordinal by Ordinal | Gamma | -.450 | .136 | -3.112 | .002 |
| N of Valid Cases | 122 | ||||
| a. Not assuming the null hypothesis. | |||||
| b. Using the asymptotic standard error assuming the null hypothesis. | |||||
Table 6 – Education Expenditures per GDP & Tier Placement
| Cross Tabulation Test | |||||
| % GDP on Education | Total | ||||
| less than 5% of GDP | 5% or higher | ||||
| Tier Placement | Tier 1 | Count | 8 | 12 | 20 |
| % within % GDP on Education | 10.1% | 27.9% | 16.4% | ||
| Tier 2 | Count | 39 | 23 | 62 | |
| % within % GDP on Education | 49.4% | 53.5% | 50.8% | ||
| Tier 2 Watch List | Count | 21 | 5 | 26 | |
| % within % GDP on Education | 26.6% | 11.6% | 21.3% | ||
| Tier 3 | Count | 11 | 3 | 14 | |
| % within % GDP on Education | 13.9% | 7.0% | 11.5% | ||
| Total | Count | 79 | 43 | 122 | |
| % within % GDP on Education | 100.0% | 100.0% | 100.0% |
Literacy rates also measure statistically significant, but not as significant as the other independent variables. Literacy rates were .006 statistically significant in which countries that have literacy rates below sixty percent could not transform into a Tier 1 ranking (Table 7). Countries with literacy rates over sixty percent were noticed in the Tier 1 category, but that ranking could be due to many other outside factors and not just literacy rate alone. Based on gamma, literacy rates measured strong at -.415 (Table 8).
Table 7 – Literacy Rates
| Chi-Square Test | |||
| Value | df | Asymptotic Significance (2-sided) | |
| Pearson Chi-Square | 18.279a | 6 | .006 |
| Likelihood Ratio | 22.385 | 6 | .001 |
| Linear-by-Linear Association | 8.140 | 1 | .004 |
| N of Valid Cases | 122 | ||
| a. 4 cells (33.3%) have expected count less than 5. The minimum expected count is 2.07. |
Table 8 – Literacy Rates
| Gamma Test | |||||
| Value | Asymptotic Standard Errora | Approximate Tb | Approximate Significance | ||
| Ordinal by Ordinal | Gamma | -.415 | .106 | -3.658 | .000 |
| N of Valid Cases | 122 | ||||
| a. Not assuming the null hypothesis. | |||||
| b. Using the asymptotic standard error assuming the null hypothesis. | |||||
Table 9 – Literacy Rates & Tier Placement
| Cross Tabulation Test | ||||||
| Percent of population that can’t read | Total | |||||
| Less than 60% can read | 60%-90% can read | More than 90% can read | ||||
| Tier Placement | Tier 1 | Count | 0 | 1 | 19 | 20 |
| % within Percent of population that can’t read | 0.0% | 2.6% | 29.2% | 16.4% | ||
| Tier 2 | Count | 11 | 21 | 30 | 62 | |
| % within Percent of population that can’t read | 61.1% | 53.8% | 46.2% | 50.8% | ||
| Tier 2 Watch List | Count | 5 | 10 | 11 | 26 | |
| % within Percent of population that can’t read | 27.8% | 25.6% | 16.9% | 21.3% | ||
| Tier 3 | Count | 2 | 7 | 5 | 14 | |
| % within Percent of population that can’t read | 11.1% | 17.9% | 7.7% | 11.5% | ||
| Total | Count | 18 | 39 | 65 | 122 | |
| % within Percent of population that can’t read | 100.0% | 100.0% | 100.0% | 100.0% |
Economic Freedom was statistically significant measuring at .000 (Table 10). Economic Freedom was quite strong, based on gamma, measuring at .695 (Table 11).
Table 10 – Economic Freedom
| Chi-Square Test | |||
| Value | df | Asymptotic Significance (2-sided) | |
| Pearson Chi-Square | 56.192a | 9 | .000 |
| Likelihood Ratio | 50.996 | 9 | .000 |
| Linear-by-Linear Association | 36.150 | 1 | .000 |
| N of Valid Cases | 122 | ||
| a. 7 cells (43.8%) have expected count less than 5. The minimum expected count is 1.49. |
Table 11 – Economic Freedom
| Gamma Test | |||||
| Value | Asymptotic Standard Errora | Approximate Tb | Approximate Significance | ||
| Ordinal by Ordinal | Gamma | .695 | .072 | 7.256 | .000 |
| N of Valid Cases | 122 | ||||
| a. Not assuming the null hypothesis. | |||||
| b. Using the asymptotic standard error assuming the null hypothesis. | |||||
Table 12 – Economic Freedom Rating & Tier Placement
| Cross Tabulation Test | |||||||
| Economic Freedom Rating (W,M,P) | Total | ||||||
| totally free | moderately free | mostly un-free | repres-sed | ||||
| Tier Placement | Tier 1 | Count | 12 | 6 | 2 | 0 | 20 |
| % within Economic Freedom Rating (W,M,P) | 66.7% | 15.0% | 3.9% | 0.0% | 16.4% | ||
| Tier 2 | Count | 5 | 27 | 26 | 4 | 62 | |
| % within Economic Freedom Rating (W,M,P) | 27.8% | 67.5% | 51.0% | 30.8% | 50.8% | ||
| Tier 2 Watch List | Count | 1 | 6 | 14 | 5 | 26 | |
| % within Economic Freedom Rating (W,M,P) | 5.6% | 15.0% | 27.5% | 38.5% | 21.3% | ||
| Tier 3 | Count | 0 | 1 | 9 | 4 | 14 | |
| % within Economic Freedom Rating (W,M,P) | 0.0% | 2.5% | 17.6% | 30.8% | 11.5% | ||
| Total | Count | 18 | 40 | 51 | 13 | 122 | |
| % within Economic Freedom Rating (W,M,P) | 100.0% | 100.0% | 100.0% | 100.0% | 100.0% |
Lastly, the population below the poverty line was statistically significant measuring at 0.47 (Table 13). Based on gamma, had a somewhat strong relationship measuring at .196 (Table 14).
Table 13 – Population Below the Poverty Line
| Chi-Square Test | |||
| Value | df | Asymptotic Significance (2-sided) | |
| Pearson Chi-Square | 12.748a | 6 | .047 |
| Likelihood Ratio | 16.979 | 6 | .009 |
| Linear-by-Linear Association | 2.579 | 1 | .108 |
| N of Valid Cases | 122 | ||
| a. 2 cells (16.7%) have expected count less than 5. The minimum expected count is 2.87. |
Table 14 – Population Below the Poverty Line
| Gamma Test | |||||
| Value | Asymptotic Standard Errora | Approximate Tb | Approximate Significance | ||
| Ordinal by Ordinal | Gamma | .196 | .124 | 1.549 | .121 |
| N of Valid Cases | 122 | ||||
| a. Not assuming the null hypothesis. | |||||
| b. Using the asymptotic standard error assuming the null hypothesis. | |||||
Table 15 – Population Living Below the Poverty Line & Tier Placement
| Cross Tabulation Test | ||||||
| Countries in three groups re poverty | Total | |||||
| 20% or less below poverty | 20%-40% below poverty | more than 40% below poverty | ||||
| Tier Placement | Tier 1 | Count | 12 | 8 | 0 | 20 |
| % within Countries in three groups re poverty | 24.5% | 16.7% | 0.0% | 16.4% | ||
| Tier 2 | Count | 20 | 29 | 13 | 62 | |
| % within Countries in three groups re poverty | 40.8% | 60.4% | 52.0% | 50.8% | ||
| Tier 2 Watch List | Count | 10 | 9 | 7 | 26 | |
| % within Countries in three groups re poverty | 20.4% | 18.8% | 28.0% | 21.3% | ||
| Tier 3 | Count | 7 | 2 | 5 | 14 | |
| % within Countries in three groups re poverty | 14.3% | 4.2% | 20.0% | 11.5% | ||
| Total | Count | 49 | 48 | 25 | 122 | |
| % within Countries in three groups re poverty | 100.0% | 100.0% | 100.0% | 100.0% |
Analysis and Findings
Through cross tabulation analysis, it was noticeable that all measured independent variables were statistically significant per chi-squared and represented a strong relationship per gamma. Some results were not as statistically significant or strong when measuring relationship strength, but all measured within reasonable parameters. This analysis was able to prove my hypotheses in which certain outside factors does affect human trafficking rates.
To begin, it seems as though countries with higher education spending have higher Tier placements. This may be the case because higher education can eliminate language barriers, create better careers and higher pay, and allow families and communities to avoid desperate situations. Furthermore, countries that are poor rank lower which may be true because they lack the funds to promote victim advocacy programs and other aiding systems. For example, countries that commit in the five percent range ranked higher. This means that if countries commit to spend more toward education, somehow, it lowers human trafficking rates. Logically, more children will be off the streets and in class and more men and women will have jobs that are able to support their families.
Continuing on, there does not seem to be a whole lot of difference between Tier 1 countries that spend generally the same amount of money on education compared to Tier 3 countries. It seems as though poor countries essentially have their hands tied and wealthy countries choose not to do more. For example, rich authoritarian regimes might be able to control spending more than a democratic country, and therefore allot more or less funding.
The first hypothesis of this study was, if countries experience low literacy rates, then human trafficking rates will heighten or remain higher than usual. From the data and research, it was observed that countries experiencing low literacy rates below sixty percent had a majority of eleven countries in Tier 2 status. Tier 2 is not a good category to be ranked at, but it is also not the worst. Countries with literacy rates below sixty percent could not enter Tier 1 status as if there were a barrier keeping them out. This variable could be that deciding factor of developed and not developed. If a country is not developed, how would human trafficking issues be focused on and combatted effectively? Statistically, this hypothesis can be accepted even though the relationship was not as strong as other results.
The second hypothesis of this study was, if countries experience low education expenditures per its annual gross domestic product, then literacy rates will measure below the average and human trafficking rates will increase. Looking back, education expenditures per the GDP measured the most statistically significant with a strong relationship. Countries that commit less than five percent of its GDP had a majority of thirty-nine countries in Tier 2 status. Tier 2 status is still a struggling country, which means that this hypothesis can be accepted. In Tier 2 status there are thirty-nine countries that spent less than five percent on education and twenty-three countries that spend more than five percent. This high-low relationship shows a pattern that countries spending less on education will rank lower or struggle more.
The last hypothesis of this study was, if countries experience higher rates of its population below the poverty line per the average, then human trafficking rates will increase or remain at a higher percentage. This relationship was strong, and the results were statistically significant which means this hypothesis can be accepted. For example, countries that experience more than forty percent of its population living below the poverty line could not transform into a Tier 1 country, again, as if there were a barrier between entering that status. In conclusion, all three hypotheses of this study were accepted by rejecting the null.
Discussion and Conclusion
Practically, this data and research can be utilized by policy makers, governments, NGOs, and non-profit groups around the world in order to assess which countries need help, and where. Through this research, common myths were addressed, and other issues were clearly pointed out, such as a lack of a concrete and unanimous definition of human trafficking. It is an overarching message of this research that human trafficking should be completely illegal but cannot be yet due to a lack of a common definition. Each country is experiencing its own type of blended form of human trafficking, and each country must act accordingly in that specific scenario.
This research can provide a solution to policy makers, advocates, and representatives by expressing strong relationships and correlations between the previously stated variables. Countries around the world can take this information, data, and, research template in order to put plans into action and effectively combat the human trafficking it is experiencing within its own borders. The findings in this research support the previously mentioned literature. Specifically, countries around the world are not meeting the minimum standard for the issuing of T visas, which shows a clear failure to identify and assist human trafficking victims. This is an issue that even the United States can improve on. Moving forward, future scholars can take this research and broaden it with more independent variables or utilize more up to date information as countries continue to grow.
Overall, this research revealed clear statistically significant data and strong relationships between Tier placement and the five independent variables. When researching human trafficking it is important to obtain accurate and up to date information, so more data fragmentation does not occur. This 122-country analysis on human trafficking was possible through the use of the 2019 Trafficking in Persons Report, the CIA World Factbook, the economic freedom rating index, and other qualitative scholarly journals and articles. In conclusion, further research needs to be done in this field in order to stay one step ahead of the phenomenon and future scholars need to continue to monitor these variables alongside others.
References
Brennen, Denise. 2017. “Fighting Human Trafficking Today: Moral Panics, Zombie Data, and the Seduction of Rescue.” Wake Forest Law Review 52(2): 477–96. https://search-ebscohost-com.ezproxy.ycp.edu:8443/login.aspx?direct=true&db=bth&AN=124408246&site=ehost-live&scope=site
Feingold, David A. 2005. “Human Trafficking.” Foreign Policy 150: 26–30. https://www-jstor-org.ezproxy.ycp.edu:8443/stable/30048506?pq-origsite=summon&seq=1#metadata_info_tab_contents
Konrad, Renata A. 2019. “Designing Awareness Campaigns to Counter Human Trafficking: An Analytic Approach.” Socio-Economic Planning Sciences 67: 86–93. doi: https://doi.org/10.1016/j.seps.2018.10.005
Morehouse, C. 2009. “Combating Human Trafficking Heidelberg: Germany VS Research.” 10.1080/15614263.2017.1291560
United States Department of State. 2019. “2019 Trafficking in Persons Report.” Washington DC: United States Department of State. https://www.state.gov/wp-content/uploads/2019/06/2019-Trafficking-in-Persons-Report.pdf