Introduction

According to the United Nations Office on Drugs and crime, human trafficking is defined as “a global crime that trades in people and exploits them for profit. People of all genders, ages and backgrounds can become victims of this crime, which occurs in every region of the world. Traffickers use violence, fraudulent employment agencies, and fake promises of education and job opportunities to trick, coerce and deceive their victims. The organized networks or individuals behind this lucrative crime take advantage of people who are vulnerable, desperate or simply seeking a better life”. (United Nations Office of Drugs ad Crime, 2019). However, human trafficking is a worldwide issue which is not correctly being addressed, mainly due to the lack of information in the majority of the countries due to the inability of track the amount of people being victims of human trafficking. In fact, the UN estimates that traffickers are compelling 27.6 million people into forced labor or sex trafficking. If 27.6 million victims exist and governments are only identifying 115,324 victims, the the world only identifies less than half of 1 percent pf the estimated victims. The data is sourced from the designated authorities for the identification of victims of trafficking, including law enforcement, critinal justice system, and National Referral Mechanisms (NRMs) when available.
Data collection method (COLL_METHOD) Last updated: 2023-03-31 Data are collected by the United Nations Office on Drugs and Crime (UNODC) from national authorities with the annual Questionnaire for the Global Report on Trafficking in Persons (GLOTIP). National focal points working in national agencies responsible for trafficking in persons, statistics on crime and/or the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the GLOTIP questionnaire to UNODC. Following the submission, UNODC checks the data for consistency and coherence with other data sources. Member States that are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union channel their responses through Eurostat. Data submitted by Member States through other means or taken from other sources, namely official websites of national authorities or governments’ reports are added to the dataset after review by Member States.


install.packages("tidyverse",dependencies=TRUE)
install.packages("gapminder")
install.packages("plotly")
install.packages("wbstats")
install.packages("remotes")
remotes::install_github("caldwellst/goalie")
install.packages("countrycode")
install.packages("flextable")
install.packages("tufte")
install.packages("tmap")
install.packages("patchwork")
install.packages("ggrepel")
install.packages("ggplot2")
install.packages("sf")
intall.packages("dplyr")

Objects, functions

my_countries=c("ALLCOUNTRIES")
my_years <- c("2003,2022")

Placeholder (you should not see this)

In order to obtain our data and due to the relevance that the SDGs, concretely, 16.2.2. Goal 16 of the SDGs, aims to promote the peaceful and inclusive societies for sustainable development, providing access to justice for all and building effective, accountable and inclusive institutions at all levels. Focusing in the SDG 16.2, its aim is to end abuse, exploitation, trafficking and all forms of violence against torture of children. In this case focusing on the number of victims of human trafficking per 100,000 population, by sex, age and form of exploitation (16.2.2) We have decided to analyze the data from https://unstats.un.org/sdgs/indicators/database/, using the package [@goalie] to download official SDG indicators data. Before proceeding we need to make the relevant R packages available to our session with library. We have decided to analyzed this data because every year is made a report based on it, and it is the only database available for every report based on the topic.

Now, in order to apply the SDG 16.2.2 to our project we have used the code “VC_HTF_DETVFL”. Then, we open the said database by reading the table: “POV”.

library(goalie)
library(tidyverse)
pov=sdg_data("VC_HTF_DETVFL")

Our first analysis will be based on creating a table that will show us the countries that had been registered less or equal to 10 victims of human trafficking per year, which is a very little quantity. For analyzing it, we have manipulated the data by removing those parts of the table that were unnecessary for our research and also by removing the missing data (“NaN”) on the number of victims of human trafficking registered (“Value”). Since there were many countries that did not have data registered of the victims of human trafficking.

Table 1: Equal or less than ten human trafficking victims

library(dplyr)
pov <-pov |> select(-TimeCoverage)
pov <-pov |> select(-UpperBound)
pov <-pov |> select(-LowerBound)
pov <-pov |> select(-BasePeriod)
pov <-pov |> select(-Source)
pov <-pov |> select(-GeoInfoUrl)
pov <-pov |> select(-FootNote)
pov <-pov |> select(-Nature)
pov <-pov |> select(-Reporting_Type)
pov <-pov |> select(-Units)

pov<-subset(pov,Value!="NaN")

library(flextable)
pov_filtered <-pov |> filter(Value <= 10) |> flextable()
pov_filtered

Goal	Target	Indicator	SeriesCode	SeriesDescription	GeoAreaCode	GeoAreaName	TimePeriod	Value	Time_Detail	Age	Sex
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	8	Albania	2,021	10	2,021	<18Y	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	32	Argentina	2,014	10	2,014	<18Y	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	32	Argentina	2,015	10	2,015	<18Y	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	36	Australia	2,010	10	2,010	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	56	Belgium	2,016	10	2,016	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	56	Belgium	2,022	10	2,022	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	68	Bolivia (Plurinational State of)	2,014	10	2,014	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	70	Bosnia and Herzegovina	2,011	10	2,011	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	112	Belarus	2,018	10	2,018	18+	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	112	Belarus	2,018	10	2,018	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	124	Canada	2,014	10	2,014	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	124	Canada	2,018	10	2,018	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	170	Colombia	2,016	10	2,016	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	170	Colombia	2,019	10	2,019	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	170	Colombia	2,021	10	2,021	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	218	Ecuador	2,014	10	2,014	<18Y	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	250	France	2,018	10	2,018	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	340	Honduras	2,018	10	2,018	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	348	Hungary	2,016	10	2,016	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	348	Hungary	2,017	10	2,017	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	368	Iraq	2,019	10	2,019	18+	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	368	Iraq	2,019	10	2,019	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	368	Iraq	2,020	10	2,020	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	368	Iraq	2,020	10	2,020	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	372	Ireland	2,017	10	2,017	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	372	Ireland	2,020	10	2,020	18+	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	372	Ireland	2,020	10	2,020	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	376	Israel	2,015	10	2,015	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	380	Italy	2,016	10	2,016	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	392	Japan	2,016	10	2,016	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	428	Latvia	2,018	10	2,018	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	440	Lithuania	2,021	10	2,021	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	442	Luxembourg	2,017	10	2,017	18+	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	442	Luxembourg	2,022	10	2,022	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	484	Mexico	2,021	10	2,021	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	498	Republic of Moldova	2,018	10	2,018	<18Y	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	498	Republic of Moldova	2,018	10	2,018	18+	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	516	Namibia	2,018	10	2,018	<18Y	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	516	Namibia	2,018	10	2,018	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	554	New Zealand	2,018	10	2,018	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	554	New Zealand	2,018	10	2,018	18+	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	591	Panama	2,020	10	2,020	18+	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	600	Paraguay	2,019	10	2,019	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	604	Peru	2,008	10	2,008	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	634	Qatar	2,021	10	2,021	<18Y	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	634	Qatar	2,022	10	2,022	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	642	Romania	2,020	10	2,020	<18Y	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	642	Romania	2,022	10	2,022	<18Y	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	688	Serbia	2,019	10	2,019	18+	FEMALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	688	Serbia	2,019	10	2,019	18+	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	690	Seychelles	2,021	10	2,021	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	694	Sierra Leone	2,017	10	2,017	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	694	Sierra Leone	2,022	10	2,022	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	703	Slovakia	2,013	10	2,013	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	756	Switzerland	2,022	10	2,022	ALLAGE	BOTHSEX
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	764	Thailand	2,021	10	2,021	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	792	Türkiye	2,018	10	2,018	18+	MALE
16	16.2	16.2.2	VC_HTF_DETVFL	Detected victims of human trafficking for forced labour, servitude and slavery, by age and sex (number)	826	United Kingdom of Great Britain and Northern Ireland	2,015	10	2,015	<18Y	MALE

As it is possible to observe in the table created there are many countries in which there are equal or less to 10 victims of human trafficking from years 2013 to 2022. Some examples could be Albania in 2021, Colombia in 2016, 1019 and 2021, Israel in 2015, Panama in 2020, Sierra Leone in 2017, and 2022, or Iraq in 2019,and 2020. Since the data source came from National focal points working in national agencies responsible for trafficking in persons, statistics on crime and/or the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the GLOTIP questionnaire to UNODC. In accordance with the observations on the data registered, and taking into account that is an official source and that the majority of projects concerning human trafficking are based on this data set, it could be conclude that the majority of data of human trafficking in all the countries is not registered. However for making clear the statement we have created the following graphic, We have calculated the median of the years in the axis (“X”) and the mean of the value that represents the victims of human trafficking. In order to make it more clear we have introduce the variable “less10”, in the sense that those countries that have less than ten victims of human trafficking in a year will be in blue, and those that have more than ten will be in red.

Graphic 1: Mean of Human trafficking victims

pov$Value<-as.numeric(pov$Value)

agrupado_pais<-pov|>
  group_by(GeoAreaName)|>
  summarise(Media=mean(Value), Año_Mediano=median(Time_Detail))|>
  mutate(less10=(Media<=10))

ggplot(agrupado_pais, aes(x=Año_Mediano, y=Media, col=less10))+geom_point()+
  scale_y_log10()+geom_text(label=agrupado_pais$GeoAreaName,size.unit = "mm", size=2.5,hjust=1.1, alpha=0.5, col="blue")

As seen in the present graph, it is clear that data about human trafficking is lacking in every country and year. Those countries that have a mean of less than ten percent of victims are; between 2010 and 2015 (the ones in blue); Mauritania, Trinidad y Tobago, Uruguay, Guinea; between 2015 and 2020: Georgia, Ecuador, United Arab Emirates, Croatia, Kuwait, Belice, Costa Rica, Zambia, Iraq, Iceland, and Niger. from 2020 until now only Syria. Considering Syria as an example,Syria has a lot of problems nowadays due to the civil war, is not possible that in the country there are less than 10 victims of human trafficking. For instance, when it comes to 2010 and backwards, we can observe very few data, not just regarding the number of counties, but also the low number when it comes to the number of victims, in this year the most remarkable country seems to be Ghana, representing a bit more than a hundred cases in that same year, taking into account that Ghana’s population in 2010 was of 26 million approximately, and that Ghana is a country where organized crime is a daily order. Moving on time, we can observe that the vast majority of the countries represented in this graph are located in the 10-100 victims, numbers that remain lower than they should be in reality, taking into account the previously analyzed methods mentioned, the number of population and the presence of organized countries. In that frame, we can see countries such as Spain or Bulgaria, which are countries with a high migration rate, and which therefore, should have a more representative number of human trafficking victims, as well as, as we previously stated, have big populations. It is noticeable how in the 100-1000 victims area the number of countries decreases, having a substantial bigger amount at the lowest part and being reduced while going to the top, here we can start seeing those countries that best address the lack of information regarding human trafficking victims, or where this issue is more prominent than in other states, this is the case of the United Kingdom or Burkina Faso respectively. Apart from this we find it essential to remark on the only country above 1000, India, which had a total population of 1.396 million inhabitants in 2020, and the percentage of migration of 26.5% in rural areas and 35% in urban ones. Therefore, this leads us to believe that, although it represents the country with the biggest number of victims reported, it remains unbelievable compared to population and migration rates.

Hypothesis

As we previously saw in the data provided, the relationship between the number of victims of human trafficking in each of the countries analyzed is not in accord to what it is supposed to be. Therefore, it is essential to specify the reasons why this issue is being poorly addressed. As a hypothesis, human trafficking has mostly to do with triggers within the country they had been trafficked from and the fear of and being deported back. Following this, in the majority of cases, victims of human trafficking do not report their situation to local authorities, as their situation in the current country they are in might be better than the one they were facing in their home countries. This situation presents a lack of respect for fundamental rights for the victims, being enrolled into forced sex or labor.

Conclusion

We have analyzed the data of the SDG 16.2 which is called: Detected human trafficking for forced labor, servitude, and slavery by age and sex. We have made a table that represent the countries that have less or equal to 10 victims of human trafficking reported, and we have made a graphic that represent the number of human trafficking per year, changing the color to the ones that represent less than ten to make it more understandable. As it is possible to observe, from 2010 onwards there are more victims of human rights reported, which could represent the improvement of methods. However, the number of cases are quite law in all the regions and years, meaning that there is lack of information regarding the dataset, and taking into account that the data is official, then we can conclude that much of the data is missing. Our hypothesis establish that the majority of cases are not registered. However, the majority of data is missing, since it is not possible that human trafficking had that low number of cases in this regions. The data obtained from the SDG 16.2, comes to be obtained from the United Nations Office on Drugs and Crime (UNODC) from national authorities with the annual Questionnaire for the Global Report on Trafficking in Persons (GLOTIP). National focal points working in national agencies responsible for trafficking in persons, statistics on crime and/or the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the GLOTIP questionnaire to UNODC. Following the submission, UNODC checks the data for consistency and coherence with other data sources. Member States that are also part of the European Union or the European Free Trade Association, or candidate or potential candidate to the European Union channel their responses through Eurostat. Data submitted by Member States through other means or taken from other sources, namely official websites of national authorities or governments’ reports are added to the dataset after review by Member States. Data providers, therefore come to be National focal points working in national agencies responsible for trafficking in persons, statistics on crime and/or the criminal justice system and nominated by the Permanent Mission to UNODC are responsible for compiling the data from the other relevant agencies before transmitting the GLOTIP questionnaire to UNODC.

Unspoken victims of human traffiking

Global Development Indicators, Global Studies

Violeta Olalla Ruiz and Andrea Mateos Denche

21 may., 2024