Human Trafficking Statistics: Human trafficking remains a pervasive global issue, with millions of individuals subjected to exploitation and abuse each year. According to recent statistics, an estimated 25 million people worldwide are victims of human trafficking, with the majority being women and children. This lucrative criminal industry generates profits of over $150 billion annually, making it one of the most profitable illegal trades globally. As market research analysts, it's imperative to understand the scale and impact of human trafficking to develop effective strategies for prevention and intervention. Efforts to combat human trafficking have intensified in recent years, driven by increased awareness and advocacy. However, despite these efforts, the problem persists, with trafficking networks adapting to evade law enforcement and exploit vulnerabilities in communities. Through comprehensive data analysis and research, we can uncover trends, identify high-risk areas, and develop targeted interventions to disrupt trafficking networks and support survivors. In this context, understanding human trafficking statistics is crucial for informing policy decisions, resource allocation, and collaborative efforts to combat this grave violation of human rights. Editor’s Choice Every year, approximately 4.5 billion people become victims of forced sex trafficking. Two out of three immigrants become victims of human trafficking, regardless of their international travel method. There are 5.4 victims of modern slavery for every 1000 people worldwide. An estimated 40.3 million individuals are trapped in modern-day slavery, with 24.9 million in forced labor and 15.4 million in forced marriage. Around 16.55 million reported human trafficking cases have occurred in the Asia Pacific region. Out of 40 million human trafficking victims worldwide, 25% are children. The highest proportion of forced labor trafficking cases occurs in domestic work, accounting for 30%. The illicit earnings from human trafficking amount to approximately USD 150 billion annually. The sex trafficking industry globally exceeds the size of the worldwide cocaine market. Only 0.4% of survivors of human trafficking cases are detected. Currently, there are 49.6 million people in modern slavery worldwide, with 35% being children. Sex trafficking is the most common type of trafficking in the U.S. In 2022, there were 88 million child sexual abuse material (CSAM) files reported to the National Center for Missing and Exploited Children (NCMEC) tip line. Child sex trafficking has been reported in all 50 U.S. states. Human trafficking is a USD 150 billion industry globally. It ranks as the second most profitable illegal industry in the United States. 25 million people worldwide are denied their fundamental right to freedom. 30% of global human trafficking victims are children. Women constitute 49% of all victims of global trafficking. In 2019, 62% of victims in the US were identified as sex trafficking victims. In the same year, US Department of Health and Human Services (HHS) grantees reported that 68% of clients served were victims of labor trafficking. Human traffickers in the US face a maximum statutory penalty of 20 years in prison. In France, 74% of exploited victims in 2018 were victims of sex trafficking. You May Also Like To Read Domestic Violence Statistics Sexual Assault Statistics Crime Statistics FBI Crime Statistics Referral Marketing Statistics Prison Statistics GDPR Statistics Piracy Statistics Notable Ransomware Statistics DDoS Statistics Divorce Statistics

In 2021, 2,078 cases of human trafficking involving minors were reported to the National Human Trafficking hotline in the United States. 306 cases reported to the hotline in that year involved foreign nationals.

In 2023, a total of ******* victims of human trafficking were identified worldwide, the highest figure recorded during the observed period and an increase of over ****** compared to the previous year. The number of identified victims has risen sharply over the past decade, reflecting a troubling global trend.

This research project developed and fully documented a method to estimate the number of females and males trafficked for the purposes of sexual and labor exploitation from eight countries (Colombia, Ecuador, El Salvador, Guatemala, Mexico, Nicaragua, Peru, and Venezuela) into the United States at the Southwest border. The model utilizes only open source data. This research represents the first phase of a two-phase project and Provides a conceptual framework for identifying potential data sources to estimate the number of victims at different stages in traffickingDevelops statistical models to estimate the number of males and females at risk of being trafficked for sexual and labor exploitation from the eight countries, and the number of males and females actually trafficked for sex and laborIncorporates into the estimation models the transit journey of trafficking victims from the eight countries to the southwest border of the United StatesDesigns the estimation models such that they are highly flexible and modular so that they can evolve as the body of data expands Utilizes open source data as inputs to the statistical model, making the model accessible to anyone interested in using itPresents preliminary estimates that illustrate the use of the statistical methodsIlluminates gaps in data sources. The data included in this collection are the open source data which were primarily used in the models to estimate the number of males and females at risk of being trafficked.

Between 2003 and 2022, women were the majority of sex trafficking victims in almost every world region. In Southern Europe, ** percent of people forced to engage in sex against their will were women. Australia and New Zealand was the only region where men constituted a larger share of sex trafficking victims, with ** percent.

These data are part of NACJD's Fast Track Release and are distributed as they were received from the data depositor. The files have been zipped by NACJD for release, but not checked or processed except for the removal of direct identifiers. Users should refer to the accompanying readme file for a brief description of the files available with this collection and consult the investigator(s) if further information is needed. This study examined life histories and experiences of individuals involved in the sex trade in New York City. Also interviewed were twenty-eight criminal justice policymakers, practitioners, and community representatives affiliated with New York City's Human Trafficking Intervention Courts (HTICs). The collection contains 1 SPSS data file (Final-Quantitative-Data-resubmission.sav (n=304; 218 variables)). Demographic variables include gender, age, race, ethnicity, education level, citizenship status, current housing, family size, sexual orientation, and respondent's place of birth.

In 2023, a total of ***** human traffickers were convicted worldwide, an increase of approximately ***** compared to the previous year. However, the number of convictions remains lower than levels recorded prior to the COVID-19 pandemic.

SDG indicator 16.2.2 - graph for the 166 referrals alleged human trafficking

Table showing the number of cases related to human trafficking in 2023

Despite public attention to the problem of human trafficking, it has proven difficult to measure the problem. Improving the quality of information about human trafficking is critical to developing sound anti-trafficking policy. In support of this effort, in 2013 the Federal Bureau of Investigation incorporated human trafficking offenses in the Uniform Crime Reporting (UCR) program. Despite this achievement, there are many reasons to expect the UCR program to underreport human trafficking. Law enforcement agencies struggle to identify human trafficking and distinguishing it from other crimes. Additionally, human trafficking investigations may not be accurately classified in official data sources. Finally, human trafficking presents unique challenges to summary and incident-based crime reporting methods. For these reasons, it is important to understand how agencies identify and report human trafficking cases within the UCR program and what part of the population of human trafficking victims in a community are represented by UCR data. This study provides critical information to improve law enforcement identification and reporting of human trafficking. Coding criminal incidents investigated as human trafficking offenses in three US cities, supplemented by interviews with law and social service stakeholders in these locations, this study answers the following research questions: How are human trafficking cases identified and reported by the police? What sources of information about human trafficking exist outside of law enforcement data? What is the estimated disparity between actual instances of human trafficking and the number of human trafficking offenses reported to the UCR?

This study was conducted to explore the effects prostitution legislation has on sex trafficking rates. This issue holds paramount importance in the fields of legal studies and human rights. By leveraging advanced machine learning techniques to analyze data from the Counter-Trafficking Data Collaborative (CTDC), encompassing 180 countries, this study aims to uncover the relationship between various prostitution legislation types and sex trafficking occurrences. The exploration begins with extensive cleaning, merging, and filtering of the CTDC dataset, integrating it with prostitution legislation data from the World Population Review. This process ensures a harmonized dataset that accurately reflects the global landscape of sex trafficking in relation to legislative frameworks. The machine learning model initially concentrated on prostitution legislation as a key variable but evolved to include a broader range of factors like registration year, population, growth rate, gender, and citizenship. This expansion was crucial in developing a more accurate and holistic model.This study offered a nuanced exploration of the impact of prostitution legislation on sex trafficking, employing sophisticated data analysis and machine learning models to parse through extensive data. The advanced RandomForestClassifier was key in the research, achieving an 87% accuracy rate for predicting instances of sex trafficking and demonstrating the need to incorporate diverse predictive features. Notably, the analysis emphasized the importance of the legislative feature in accurately predicting sex trafficking, despite the inclusion of other variables to improve overall model precision. These findings underscore the significance of a multifaceted approach, considering factors like demographics and socio-economic indicators, to gain a comprehensive understanding of sex trafficking trends.Complementing the machine learning insights, a logistic regression model scrutinized the specific effects of different legislative approaches on sex trafficking. The analysis revealed that legislative frameworks such as legalization, abolitionism, decriminalization, and neo-abolitionism have a considerable influence on reducing sex trafficking rates, suggesting their potential as effective legal strategies. Alternantively, prohibition legislation is found to corrrelate with significantly higher sex trafficking rates. These results serve as a critical resource for policymakers and advocates engaged in the development of informed, evidence-based approaches to address the global challenge of sex trafficking.

The National Referral Mechanism (NRM) is a framework for identifying victims of human trafficking and ensuring they receive the appropriate protection and support.

The NRM is also the mechanism through which the UKHTC collects data about victims. This information contributes to building a clearer picture about the scope of human trafficking in the UK.

The NRM was introduced in 2009 to meet the UK’s obligations under the Council of European Convention on Action against Trafficking in Human Beings. At the core of every country’s NRM is the process of locating and identifying “potential victims of trafficking” (PVoT).

The NRM grants a minimum 45-day reflection and recovery period for victims of human trafficking. Trained case owners decide whether individuals referred to them should be considered to be victims of trafficking according to the definition in the Council of Europe Convention.

Series Name: Detected victims of human trafficking for forced labour servitude and slavery by age and sex (number)Series Code: VC_HTF_DETVFLRelease Version: 2021.Q2.G.03 This dataset is part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 16.2.2: Number of victims of human trafficking per 100,000 population, by sex, age and form of exploitationTarget 16.2: End abuse, exploitation, trafficking and all forms of violence against and torture of childrenGoal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levelsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

Series Name: Detected victims of human trafficking for other purposes by age and sex (number)Series Code: VC_HTF_DETVOPRelease Version: 2020.Q2.G.03 This dataset is the part of the Global SDG Indicator Database compiled through the UN System in preparation for the Secretary-General's annual report on Progress towards the Sustainable Development Goals.Indicator 16.2.2: Number of victims of human trafficking per 100,000 population, by sex, age and form of exploitationTarget 16.2: End abuse, exploitation, trafficking and all forms of violence against and torture of childrenGoal 16: Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levelsFor more information on the compilation methodology of this dataset, see https://unstats.un.org/sdgs/metadata/

In 2022, ** percent of all reported human trafficking victims were trafficked for sexual exploitation, a substantial decline compared to the ** to ** percent range reported in most years between 2008 and 2018. Since 2011, approximately ** percent of victims have consistently been trafficked for forced labor.

What is the Counter-Trafficking Data Collaborative?

The Counter-Trafficking Data Collaborative is the first global data hub on human trafficking, publishing harmonized data from counter-trafficking organizations around the world. Launched in November 2017, the goal of CTDC is to break down information-sharing barriers and equip the counter-trafficking community with up to date, reliable data on human trafficking.

The global victim of trafficking dataset

The CTDC global victim of trafficking dataset is the largest of its kind in the world, and currently exists in two forms. The data are based on case management data, gathered from identified cases of human trafficking, disaggregated at the level of the individual. The cases are recorded in a case management system during the provision of protection and assistance services, or are logged when individuals contact a counter-trafficking hotline. The number of observations in the dataset increases as new records are added by the contributing organizations. The global victim of trafficking dataset that is available to download from the website in csv format has been mathematically anonymized, and the complete, non k-anonymized version of the dataset is displayed throughout the website through visualizations and charts showing detailed analysis.

Where do the data come from?

The data come from a variety of sources. The data featured in the global victim of trafficking dataset come from the assistance activities of the contributing organizations, including from case management services and from counter-trafficking hotline logs.

How are the global datasets created?

Each dataset has been created through a process of comparing and harmonizing existing data models of contributing partners and data classification systems. Initial areas of compatibility were identified to create a unified system for organizing and mapping data to a single standard. Each contributing organization transforms its data to this shared standard and any identifying information is removed before the datasets are made available.

How is the individual-level data protected?

Step 1

Counter-trafficking case data contains highly sensitive information, and maintaining privacy and confidentiality is of paramount importance for CTDC. For example, all explicit identifiers, such as names, were removed from the global victim dataset and some data such as age has been transformed into age ranges. No personally identifying information is transferred to or hosted by CTDC, and organizations that want to contribute are asked to anonymize in accordance to the standards set by CTDC.

Step 2

In addition to the safeguard measures outlined in step 1 the global victim dataset has been anonymized to a higher level, through a mathematical approach called k-anonymization. For a full description of k-anonymization, please refer to the definitions page.

IOM collects and processes data in accordance to its own Data Protection Policy. The other contributors adhere to relevant national and international standards through their policies for collecting and processing personal data.

How to interpret the data?

These data reflect the victims assisted/identified/referred/reported to the contributing organizations, which may not represent all victims identified within a country. Nevertheless, the larger the sample size for a given country (or, the more victims displayed on the map for a given country), the more representative the data are likely to be of the identified victim of trafficking population.

A larger number of identified victims of trafficking does not imply that there is a larger number of undetected victims of trafficking (i.e. a higher prevalence of trafficking).

In addition, samples of identified victims of trafficking cannot be considered random samples of the wider population of victims of trafficking (which includes unidentified victims), since counter-trafficking agencies may be more likely to identify some trafficking cases rather than others. However, with this caveat in mind, the profile of identified victims of trafficking tends to be considered as indicative of the profile of the wider population, given that the availability of other data sources is close to zero.

How does human trafficking case data relate to prevalence data?

There are currently no global or regional estimates of the prevalence of human trafficking. National estimates have been conducted in a few countries but they are also based on modelling of existing administrative data from identified cases and should therefore only be considered as basic baseline estimates. Historically, producing estimates of the prevalence of trafficking based on the collection of new primary data through surveys, for example, has been difficult. This is due to trafficking’s complicated legal definition and the challenges of a...

Human trafficking Africa statistics from 2005 to 2019

Statistics on the number of professional capacity-building trainings for preventing human trafficking

Financial overview and grant giving statistics of Center for Combating Human Trafficking

Financial overview and grant giving statistics of Human Trafficking Institute