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.

This study by the Coalition Against Trafficking Women was the first to research both contemporary international and domestic trafficking of women for sexual exploitation in the United States and to include primary research information from interviews with trafficked and prostituted women in the sex industry. Telephone and personal interviews were conducted with people who had experience with or knowledge of sex trafficking in the United States. This data collection consists of the verbatim questions and responses from the following groups of individuals who were interviewed: (1) international and United States women who had been or were in the sex industry in the United States, (2) law enforcement officials who had experience and expertise in sex-industry related cases or immigration, (3) social service workers who provided services to women in prostitution or might have come into contact with women from the sex industry and those providing services to immigrant populations, and (4) health care workers who provided services to women in prostitution or who may have come into contact with women in the sex industry. The research framework was developed to follow the path of trafficked women from their hometown, through their experiences in the sex industry, to their present place in life. Information was collected on trafficked women's backgrounds, roles and activities while in the sex industry, how they were controlled, and how they coped with their situations. Respondents were also asked about experiences with recruiters, traffickers, pimps, and customers. Additional information was gathered on the respondents' views on policies regarding trafficking and prostitution, the organization of the sex industry, and health and legal aspects of the business. Questionnaires for each group of interviewees were constructed according to the topics about which each group would most likely have knowledge or experience.

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Definition of the SDG indicator: Indicator 16.1.1 is the “number of victims of intentional homicide per 100,000 population, by sex and age” in the UN SDG framework.

Intentional homicides are unlawful deaths inflicted upon a person with the intent to cause death or serious injury.

Data for this indicator is shown in the interactive visualization.

Target: “Significantly reduce all forms of violence and related death rates” across all countries by 2030.

More research: Further data and research can be found at the Our World in Data topic page on Homicides.

Definition of the SDG indicator: Indicator 16.1.2 is “conflict-related deaths per 100,000 population, by sex, age and cause” in the UN SDG framework.

Data for this indicator is shown in the interactive visualization, using data from the Uppsala Conflict Data Program. It includes both deaths from conflicts within countries and between them.

Target: “Significantly reduce all forms of violence and related death rates” across all countries by 2030.

More research: Further data and research can be found at the Our World in Data topic pages on War and Peace and Terrorism.

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.

Human trafficking (HT) is a pervasive global issue affecting vulnerable individuals, violating their fundamental human rights. Investigations reveal that a significant number of HT cases are associated with online advertisements (ads), particularly in escort markets. Consequently, identifying and connecting HT vendors has become increasingly challenging for Law Enforcement Agencies (LEAs). To address this issue, we introduce IDTraffickers, an extensive dataset consisting of 87,595 text ads and 5,244 vendor labels to enable the verification and identification of potential HT vendors on online escort markets. To establish a benchmark for authorship identification, we train a DeCLUTR-small model, achieving a macro-F1 score of 0.8656 in a closed-set classification environment. Next, we leverage the style representations extracted from the trained classifier to conduct authorship verification, resulting in a mean r-precision score of 0.8852 in an open-set ranking environment. Finally, to encourage further research and ensure responsible data sharing, we plan to release IDTraffickers for the authorship attribution task to researchers under specific conditions, considering the sensitive nature of the data. We believe that the availability of our dataset and benchmarks will empower future researchers to utilize our findings, thereby facilitating the effective linkage of escort ads and the development of more robust approaches for identifying HT indicators. The dataset contains text sequences as inputs and labels as the arbitrary vendor IDs obtained by linking the phone numbers mentioned in Backpage escort market advertisements. To protect privacy, all personal information, except for the pseudonyms used by the escorts and the post locations, has been redacted so that it cannot be retrieved. For more details, kindly refer to our research attached to the submission. It is important to emphasize that this dataset should only be used for its intended purpose, research on authorship attribution of vendors on escort markets, and not other commercial/non-commercial purposes.

An estimated 30 million people are subject to modernday slavery, including forced labor and sexual exploitation. Moreover, the trafficking of human beings is one of the fastest growing transnational criminal activities. Not only is it an abuse of the human rights of the victims involved, but it also incurs social, political, and economic costs for the countries it most impacts. Providing first an overview of the global phenomenon of modern-day slavery, this paper proceeds to study the Greater Mekong Subregion for whose states human trafficking represents a serious challengeone which requires a well-coordinated response to, among other measures, scrutinize labor contracts in risk economic sectors, enhance interstate cooperation, and more effectively identify and prosecute human traffickers.

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 was a process evaluation of three programs funded by the U.S. Department of Justice (DOJ) Office for Victims of Crime (OVC) to identify and provide services to victims of sex and labor trafficking who are U.S citizens and lawful permanent residents (LPR) under the age of 18. The three programs evaluated in this study were: The Standing Against Global Exploitation Everywhere (SAGE) Project The Salvation Army Trafficking Outreach Program and Intervention Techniques (STOP-IT) program The Streetwork Project at Safe Horizon The goals of the evaluation were to document program implementation in the three programs, identify promising practices for service delivery programs, and inform delivery of current and future efforts by the programs to serve this population. The evaluation examined young people served by the programs, their service needs and services delivered by the programs, the experiences of young people and staff with the programs, and programs' efforts to strengthen community response to trafficked youth.

The MATCHED dataset is a novel multimodal collection of escort advertisements curated to support research in Authorship Attribution (AA) and related tasks. It comprises 27,619 unique text descriptions and 55,115 images (in jpg format) sourced from Backpage escort ads across seven major U.S. cities–Atlanta, Dallas, Detroit, Houston, Chicago, San Fransisco, and New York. These cities are further categorized into four geographical regions—South, Midwest, West, and Northeast—offering a structured dataset that enables both in-distribution and out-of-distribution (OOD) evaluations. Each ad in the dataset contains metadata that links text and visual components, providing a rich resource for studying multimodal patterns, vendor identification, and verification tasks. The dataset is uniquely suited for multimodal authorship attribution, vendor linking, stylometric analysis, and understanding the interplay between textual and visual patterns in advertisements. All text descriptions are carefully processed to redact any explicit references to phone numbers, email addresses, advertisement IDs, age-related information, or other contact details that could be used to identify individuals or vendors. The structured metadata allows researchers to explore how multimodal features contribute to uncovering latent patterns in stylometry and vendor behaviors. A demi-data file showcasing the format and structure of our MATCHED dataset is attached with the entry. Given the sensitivity of the subject matter, the actual dataset resides securely on Maastricht University's servers. Only the metadata will be publicly released on Dataverse to ensure ethical use. Researchers interested in accessing the full dataset must sign a Non-Disclosure Agreement (NDA) and a Data Transfer Agreement with Prof. Dr. Gijs Van Dijck from Maastricht University. Access will only be granted under strict restrictions, and recipients must adhere to the ethical guidelines established by the university's committee. These guidelines emphasize the responsible use of the dataset to prevent misuse and to safeguard the privacy and dignity of all individuals involved.

Some existing human trafficking research examines how trafficking laws and regulations deter traffickers. This research, however, pays little attention to how states’ freedom of movement policies influences human trafficking. Existing policy debates suggest two possible effects. Europe’s experiences with open borders have led to claims that freedom of movement decreases the likelihood that traffickers are detected, thus making human trafficking in and out of states more likely. By contrast, movement restrictions could create an environment where people become more vulnerable to traffickers. We utilize data from 182 countries from 2001 to 2017 to test whether freedom of movement increases or decreases human trafficking flows. We find that it is necessary, theoretically and empirically, to consider freedom of foreign movement both locally and in a state’s neighborhood because freedom of movement increases human trafficking when the local and neighborhood practices diverge from each other.

BackgroundThere is very limited evidence on the health consequences of human trafficking. This systematic review reports on studies investigating the prevalence and risk of violence while trafficked and the prevalence and risk of physical, mental, and sexual health problems, including HIV, among trafficked people. Methods and FindingsWe conducted a systematic review comprising a search of Medline, PubMed, PsycINFO, EMBASE, and Web of Science, hand searches of reference lists of included articles, citation tracking, and expert recommendations. We included peer-reviewed papers reporting on the prevalence or risk of violence while trafficked and/or on the prevalence or risk of any measure of physical, mental, or sexual health among trafficked people. Two reviewers independently screened papers for eligibility and appraised the quality of included studies. The search identified 19 eligible studies, all of which reported on trafficked women and girls only and focused primarily on trafficking for sexual exploitation. The review suggests a high prevalence of violence and of mental distress among women and girls trafficked for sexual exploitation. The random effects pooled prevalence of diagnosed HIV was 31.9% (95% CI 21.3%–42.4%) in studies of women accessing post-trafficking support in India and Nepal, but the estimate was associated with high heterogeneity (I2 = 83.7%). Infection prevalence may be related as much to prevalence rates in women's areas of origin or exploitation as to the characteristics of their experience. Findings are limited by the methodological weaknesses of primary studies and their poor comparability and generalisability. ConclusionsAlthough limited, existing evidence suggests that trafficking for sexual exploitation is associated with violence and a range of serious health problems. Further research is needed on the health of trafficked men, individuals trafficked for other forms of exploitation, and effective health intervention approaches. Please see later in the article for the Editors' Summary

This project undertook the systematic collection of first-generation data concerning the nature, extent, and seriousness of child sexual exploitation (CSE) in the United States. The project was organized around the following research objectives: (1) identification of the nature, extent, and underlying causes of CSE and the commercial sexual exploitation of children (CSEC) occurring in the United States, (2) identification of those subgroups of children that were at the greatest risk of being sexually exploited, (3) identification of subgroups of adult perpetrators of sex crimes against children, and (4) identification of the modes of operation and other methods used by organized criminal units to recruit children into sexually exploitative activities. The study involved surveying senior staff members of nongovernment organizations (NGOs) and government organizations (GOs) in the United States known to be dealing with persons involved in the transnational trafficking of children for sexual purposes. Part 1 consists of survey data from nongovernment organizations. These were local child and family agencies serving runaway and homeless youth. Part 2 consists of survey data from government organizations. These organizations were divided into local, state, and federal agencies. Local organizations included municipal law enforcement, county law enforcement, prosecutors, public defenders, and corrections. State organizations included state child welfare directors, prosecutors, and public defenders. Federal organizations included the Federal Bureau of Investigation, Federal Public Defenders, Immigration and Naturalization Service, United States Attorneys, United States Customs, and the United States Postal Service. Variables in Parts 1 and 2 include the organization's city, state, and ZIP code, the type of services provided or type of law enforcement agency, how the agency was funded, the scope of the agency's service area, how much emphasis was placed on CSEC as a policy issue or a service issue, conditions that might influence the number of CSEC cases, how staff were trained to deal with CSEC cases, how victims were identified, the number of children that experienced child abuse, sexual abuse, pornography, or other exploitation in 1999 and 2000 by age and gender, methods of recruitment, family history of victims, gang involvement, and substance abuse history of victims.

Dataset

This dataset was created by Michal

Contents

This dataset contains aggregate data on violent index victimizations at the quarter level of each year (i.e., January – March, April – June, July – September, October – December), from 2001 to the present (1991 to present for Homicides), with a focus on those related to gun violence. Index crimes are 10 crime types selected by the FBI (codes 1-4) for special focus due to their seriousness and frequency. This dataset includes only those index crimes that involve bodily harm or the threat of bodily harm and are reported to the Chicago Police Department (CPD). Each row is aggregated up to victimization type, age group, sex, race, and whether the victimization was domestic-related. Aggregating at the quarter level provides large enough blocks of incidents to protect anonymity while allowing the end user to observe inter-year and intra-year variation. Any row where there were fewer than three incidents during a given quarter has been deleted to help prevent re-identification of victims. For example, if there were three domestic criminal sexual assaults during January to March 2020, all victims associated with those incidents have been removed from this dataset. Human trafficking victimizations have been aggregated separately due to the extremely small number of victimizations.

This dataset includes a " GUNSHOT_INJURY_I " column to indicate whether the victimization involved a shooting, showing either Yes ("Y"), No ("N"), or Unknown ("UKNOWN.") For homicides, injury descriptions are available dating back to 1991, so the "shooting" column will read either "Y" or "N" to indicate whether the homicide was a fatal shooting or not. For non-fatal shootings, data is only available as of 2010. As a result, for any non-fatal shootings that occurred from 2010 to the present, the shooting column will read as “Y.” Non-fatal shooting victims will not be included in this dataset prior to 2010; they will be included in the authorized dataset, but with "UNKNOWN" in the shooting column.

The dataset is refreshed daily, but excludes the most recent complete day to allow CPD time to gather the best available information. Each time the dataset is refreshed, records can change as CPD learns more about each victimization, especially those victimizations that are most recent. The data on the Mayor's Office Violence Reduction Dashboard is updated daily with an approximately 48-hour lag. As cases are passed from the initial reporting officer to the investigating detectives, some recorded data about incidents and victimizations may change once additional information arises. Regularly updated datasets on the City's public portal may change to reflect new or corrected information.

How does this dataset classify victims?

The methodology by which this dataset classifies victims of violent crime differs by victimization type:

Homicide and non-fatal shooting victims: A victimization is considered a homicide victimization or non-fatal shooting victimization depending on its presence in CPD's homicide victims data table or its shooting victims data table. A victimization is considered a homicide only if it is present in CPD's homicide data table, while a victimization is considered a non-fatal shooting only if it is present in CPD's shooting data tables and absent from CPD's homicide data table.

To determine the IUCR code of homicide and non-fatal shooting victimizations, we defer to the incident IUCR code available in CPD's Crimes, 2001-present dataset (available on the City's open data portal). If the IUCR code in CPD's Crimes dataset is inconsistent with the homicide/non-fatal shooting categorization, we defer to CPD's Victims dataset.

For a criminal homicide, the only sensible IUCR codes are 0110 (first-degree murder) or 0130 (second-degree murder). For a non-fatal shooting, a sensible IUCR code must signify a criminal sexual assault, a robbery, or, most commonly, an aggravated battery. In rare instances, the IUCR code in CPD's Crimes and Victims dataset do not align with the homicide/non-fatal shooting categorization:

1. In instances where a homicide victimization does not correspond to an IUCR code 0110 or 0130, we set the IUCR code to "01XX" to indicate that the victimization was a homicide but we do not know whether it was a first-degree murder (IUCR code = 0110) or a second-degree murder (IUCR code = 0130).
2. When a non-fatal shooting victimization does not correspond to an IUCR code that signifies a criminal sexual assault, robbery, or aggravated battery, we enter “UNK” in the IUCR column, “YES” in the GUNSHOT_I column, and “NON-FATAL” in the PRIMARY column to indicate that the victim was non-fatally shot, but the precise IUCR code is unknown.

Other violent crime victims: For other violent crime types, we refer to the IUCR classification that exists in CPD's victim table, with only one exception:

1. When there is an incident that is associated with no victim with a matching IUCR code, we assume that this is an error. Every crime should have at least 1 victim with a matching IUCR code. In these cases, we change the IUCR code to reflect the incident IUCR code because CPD's incident table is considered to be more reliable than the victim table.

Note: All businesses identified as victims in CPD data have been removed from this dataset.

Note: The definition of “homicide” (shooting or otherwise) does not include justifiable homicide or involuntary manslaughter. This dataset also excludes any cases that CPD considers to be “unfounded” or “noncriminal.”

Note: In some instances, the police department's raw incident-level data and victim-level data that were inputs into this dataset do not align on the type of crime that occurred. In those instances, this dataset attempts to correct mismatches between incident and victim specific crime types. When it is not possible to determine which victims are associated with the most recent crime determination, the dataset will show empty cells in the respective demographic fields (age, sex, race, etc.).

Note: The initial reporting officer usually asks victims to report demographic data. If victims are unable to recall, the reporting officer will use their best judgment. “Unknown” can be reported if it is truly unknown.

The set contains a directory of phones "hotlines" for people affected by human trafficking

In China, the illegal adoption of missing persons and especially of missing children is a major public safety issue that affects social and family stability. Recent work has established a trafficking network developed from a volunteer-managed database of missing people that identifies and locates node cities and critical paths of illegal adoption. In order to evaluate locations where trafficking can be identified and provide direct advice for affected families, this study analyses the temporal and spatial distribution of the missing population and explores factors that affect their transfer. We use spatiotemporal information to construct multiple random forest models to predict the locations of missing person transfer on a larger spatial scale. The proposed independent models achieve very high levels of accuracy: provinces potentially entered, destination regions, relative distances and relative directions. Moreover, an integrated city-level forecasting algorithm can effectively locate the city a missing person was trafficked to. From our driving factor analysis, the transfer paths are strongly correlated with source provinces and regions. The study also shows that the transfer of missing persons is driven by multiple factors rather than by a single element.

The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding. The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine. The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators. The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables. MAIN RESULTS Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level. Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time. Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion. Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively). HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing. Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis. Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care. Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women. Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

Goal 16Promote peaceful and inclusive societies for sustainable development, provide access to justice for all and build effective, accountable and inclusive institutions at all levelsTarget 16.1: Significantly reduce all forms of violence and related death rates everywhereIndicator 16.1.1: Number of victims of intentional homicide per 100,000 population, by sex and ageVC_IHR_PSRC: Number of victims of intentional homicide per 100,000 population, by sex (victims per 100,000 population)VC_IHR_PSRCN: Number of victims of intentional homicide, by sex (number)Indicator 16.1.2: Conflict-related deaths per 100,000 population, by sex, age and causeVC_DTH_TOCV: Number of conflict-related deaths (civilians) per 100,000 population (Per 100,000 population)VC_DTH_TOCVN: Number of conflict-related deaths (civilians), by sex, age and cause of death (Number)VC_DTH_TOCVR: Conflict-related death rate (civilians), by sex, age and cause of death (%)Indicator 16.1.3: Proportion of population subjected to (a) physical violence, (b) psychological violence and (c) sexual violence in the previous 12 monthsVC_VOV_PHYL: Proportion of population subjected to physical violence in the previous 12 months, by sex (%)VC_VOV_ROBB: Proportion of population subjected to robbery in the previous 12 months, by sex (%)VC_VOV_SEXL: Proportion of population subjected to sexual violence in the previous 12 months, by sex (%)Indicator 16.1.4: Proportion of population that feel safe walking alone around the area they liveVC_SNS_WALN: Proportion of population that feel safe walking alone around the area they live (%)Target 16.2: End abuse, exploitation, trafficking and all forms of violence against and torture of childrenIndicator 16.2.1: Proportion of children aged 1–17 years who experienced any physical punishment and/or psychological aggression by caregivers in the past monthVC_VAW_PHYPYV: Proportion of children aged 1-14 years who experienced physical punishment and/or psychological aggression by caregivers in last month (% of children aged 1-14 years)Indicator 16.2.2: Number of victims of human trafficking per 100,000 population, by sex, age and form of exploitationVC_HTF_DETVFL: Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)VC_HTF_DETVOP: Detected victims of human trafficking for other purposes, by age and sex (number)VC_HTF_DETVOG: Detected victims of human trafficking for removal of organ, by age and sex (number)VC_HTF_DETVSX: Detected victims of human trafficking for sexual exploitaton, by age and sex (number)VC_HTF_DETV: Detected victims of human trafficking, by age and sex (number)Indicator 16.2.3: Proportion of young women and men aged 18–29 years who experienced sexual violence by age 18VC_VAW_SXVLN: Proportion of population aged 18-29 years who experienced sexual violence by age 18, by sex (% of population aged 18-29)Target 16.3: Promote the rule of law at the national and international levels and ensure equal access to justice for allIndicator 16.3.1: Proportion of victims of violence in the previous 12 months who reported their victimization to competent authorities or other officially recognized conflict resolution mechanismsVC_PRR_PHYV: Police reporting rate for physical assault, by sex (%)VC_PRR_SEXV: Police reporting rate for sexual assault, by sex (%)VC_PRR_ROBB: Police reporting rate for robbery, by sex (%)Indicator 16.3.2: Unsentenced detainees as a proportion of overall prison populationVC_PRS_UNSNT: Unsentenced detainees as a proportion of overall prison population (%)Indicator 16.3.3: Proportion of the population who have experienced a dispute in the past two years and who accessed a formal or informal dispute resolution mechanism, by type of mechanismTarget 16.4: By 2030, significantly reduce illicit financial and arms flows, strengthen the recovery and return of stolen assets and combat all forms of organized crimeIndicator 16.4.1: Total value of inward and outward illicit financial flows (in current United States dollars)Indicator 16.4.2: Proportion of seized, found or surrendered arms whose illicit origin or context has been traced or established by a competent authority in line with international instrumentsVC_ARM_SZTRACE: Proportion of seized, found or surrendered arms whose illicit origin or context has been traced or established by a competent authority in line with international instrumentsTarget 16.5: Substantially reduce corruption and bribery in all their formsIndicator 16.5.1: Proportion of persons who had at least one contact with a public official and who paid a bribe to a public official, or were asked for a bribe by those public officials, during the previous 12 monthsIU_COR_BRIB: Prevalence rate of bribery, by sex (%)Indicator 16.5.2: Proportion of businesses that had at least one contact with a public official and that paid a bribe to a public official, or were asked for a bribe by those public officials during the previous 12 monthsIC_FRM_BRIB: Bribery incidence (% of firms experiencing at least one bribe payment request)Target 16.6: Develop effective, accountable and transparent institutions at all levelsIndicator 16.6.1: Primary government expenditures as a proportion of original approved budget, by sector (or by budget codes or similar)GF_XPD_GBPC: Primary government expenditures as a proportion of original approved budget (%)Indicator 16.6.2: Proportion of population satisfied with their last experience of public servicesTarget 16.7: Ensure responsive, inclusive, participatory and representative decision-making at all levelsIndicator 16.7.1: Proportions of positions in national and local institutions, including (a) the legislatures; (b) the public service; and (c) the judiciary, compared to national distributions, by sex, age, persons with disabilities and population groupsSG_DMK_PARLCC_JC: Number of chairs of permanent committees, by age sex and focus of the committee, Joint CommitteesSG_DMK_PARLMP_LC: Ratio for female members of parliaments (Ratio of the proportion of women in parliament in the proportion of women in the national population with the age of eligibility as a lower bound boundary), Lower Chamber or UnicameralSG_DMK_PARLSP_LC: Number of speakers in parliament, by age and sex , Lower Chamber or UnicameralSG_DMK_PARLCC_LC: Number of chairs of permanent committees, by age sex and focus of the committee, Lower Chamber or UnicameralSG_DMK_PARLMP_UC: Ratio for female members of parliaments (Ratio of the proportion of women in parliament in the proportion of women in the national population with the age of eligibility as a lower bound boundary), Upper ChamberSG_DMK_PARLSP_UC: Number of speakers in parliament, by age and sex, Upper ChamberSG_DMK_PARLCC_UC: Number of chairs of permanent committees, by age sex and focus of the committee, Upper ChamberSG_DMK_PARLYR_LC: Ratio of young members in parliament (Ratio of the proportion of young members in parliament (age 45 or below) in the proportion of the national population (age 45 or below) with the age of eligibility as a lower bound boundary), Lower Chamber or UnicameralSG_DMK_PARLYP_LC: Proportion of youth in parliament (age 45 or below), Lower Chamber or Unicameral (%)SG_DMK_PARLYN_LC: Number of youth in parliament (age 45 or below), Lower Chamber or Unicameral (Number)SG_DMK_PARLYR_UC: Ratio of young members in parliament (Ratio of the proportion of young members in parliament (age 45 or below) in the proportion of the national population (age 45 or below) with the age of eligibility as a lower bound boundary), Upper ChamberSG_DMK_PARLYP_UC: Proportion of youth in parliament (age 45 or below), Upper Chamber (%)SG_DMK_PARLYN_UC: Number of youth in parliament (age 45 or below), Upper Chamber (Number)Indicator 16.7.2: Proportion of population who believe decision-making is inclusive and responsive, by sex, age, disability and population groupTarget 16.8: Broaden and strengthen the participation of developing countries in the institutions of global governanceIndicator 16.8.1: Proportion of members and voting rights of developing countries in international organizationsSG_INT_MBRDEV: Proportion of members of developing countries in international organizations, by organization (%)SG_INT_VRTDEV: Proportion of voting rights of developing countries in international organizations, by organization (%)Target 16.9: By 2030, provide legal identity for all, including birth registrationIndicator 16.9.1: Proportion of children under 5 years of age whose births have been registered with a civil authority, by ageSG_REG_BRTH: Proportion of children under 5 years of age whose births have been registered with a civil authority (% of children under 5 years of age)Target 16.10: Ensure public access to information and protect fundamental freedoms, in accordance with national legislation and international agreementsIndicator 16.10.1: Number of verified cases of killing, kidnapping, enforced disappearance, arbitrary detention and torture of journalists, associated media personnel, trade unionists and human rights advocates in the previous 12 monthsVC_VAW_MTUHRA: Number of cases of killings of human rights defenders, journalists and trade unionistsVC_VOC_ENFDIS: Number of cases of enforced disappearance of human rights defenders, journalists and trade unionists (Number)Indicator 16.10.2: Number of countries that adopt and implement constitutional, statutory and/or policy guarantees for public access to informationSG_INF_ACCSS: Countries that adopt and implement constitutional, statutory and/or policy guarantees for public access to informationTarget 16.a: Strengthen relevant national institutions, including through international cooperation, for building capacity at all levels, in particular in developing countries, to prevent violence and combat terrorism and crimeIndicator 16.a.1: Existence of independent national human rights institutions in compliance with the Paris PrinciplesSG_NHR_IMPL: Proportion of

This dataset consists of 38 interviews on family reunification with people who migrated to Finland as refugees or asylum seekers. The interviewees had either not yet completed the family reunification process or had given up on bringing their family to Finland because the process had been too difficult. The data were collected as part of the 'Family Separation, Migration Status and Everyday Security: Experiences and Strategies of Vulnerable Migrants' research project funded by the Academy of Finland. Main themes of the interviews included the interviewees' experiences of the family reunification process as well as their everyday life in their home country and in Finland. The interviewees were also asked how it felt to be apart from their family, whether the separation had affected their relations with their family, and whether they had found anyone in Finland who they could trust. Interview questions also focused on the interviewees' plans for the future. Background information varied in each interview. All interviews included the interviewee's age group, gender, and original home country as background information. Additionally, many interviewees mentioned how far along they were in the family reunification process, as well as their mother tongue and family structure. The data were organised into an easy to use HTML version at FSD. 30 of the interview transcripts are in English and 8 in Finnish. The dataset is only available in the original languages.

Police-reported organized crime, by most serious violation (homicide and attempted murder, assault, sexual violations, kidnapping and hostage taking, human trafficking, robbery and theft, firearm and weapons violations, extortion and criminal harassment, arson, forgery and fraud, child pornography, criminal organization involvement, probation and court violations, drug possession and trafficking, and other violations), Canada (selected police services), 2016 to 2023.