Migrants in Vulnerable Situations: evidence from the

Central and Eastern Mediterranean migration routes, and IOM’s global human trafficking database

Eliza Galos - Data Analyst - IOM Geneva*Flow monitoring survey analysis done with Laura Bartolini – IOM Rome

Presentation Outline

• Introduction– Defining “vulnerability”– IOM global database on identified victims of human trafficking

• New empirical evidence from the Mediterranean routes– Flow Monitoring Surveys (FMS) and human trafficking module– Central and Eastern Mediterranean survey samples– Research questions for the analysis – Survey implementation and methodology

• Analysis results - the probability of reporting human trafficking and exploitative practices

• Implications of the findings

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Vulnerability (1)

New York Declaration (2016):“develop, through a state-led process, non-binding principles and voluntary guidelines on the treatment of migrants in vulnerable situations”

What is vulnerability? An individual is vulnerable to something

SDGs and Global Compact on Migration• The concept is developed in IOM thematic papers:

https://www.iom.int/iom-thematic-papers• Towards a vulnerability model – IOM’s Migrant Assistance

DivisionIndividual and family/household factorsStructural- and community-levelCountry level

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Vulnerability (2)

Sources for vulnerability to abuse, exploitation and human trafficking?

• Human trafficking data – identified victims e.g. IOM’s global database on victims of human trafficking

However…

• Large spectrum of exploitation than just human trafficking • Increasing evidence, media coverage on vulnerabilities to widespread violence,

abuses, exploitation and practices that could amount to human trafficking (mostly qualitative)

Alternatives to measure/quantify?• Operationalising vulnerability to human trafficking in the migration context e.g.

IOM’s Flow Monitoring Surveys with the human trafficking/exploitative practices indicators

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IOM’s Global Database (1)

• There are no completely reliable figures on the global stock of victims of human trafficking – either identified or overall (identified and unidentified)

• IOM assists approximately 7,000 victims of human trafficking each year• IOM’s primary data on cases of assisted victims of trafficking registered in

MiMOSA (Migrant Management Operational System Application) • IOM’s global database of assisted victims of human trafficking being is the largest of this kind in the world, and continues to increase by approximately 5,000 cases per year.

– Over 46,000 cases– 140 countries of origin (nationality)– 150 countries of destination (exploitation/identification/screening)– 9,000 children

IOM’s Global Database (2)

Sex of identified victims of human trafficking by year

IOM’s Global Database (3)

Exploitation type among identified victims of human trafficking, by year

New data from IOM’s DTM

• Flow Monitoring Surveys– 1 year, 7 countries, 110 locations– Eastern Med Dec 2015-Nov 2016– Central Med June-Nov 2016– Baseline Module – HT Module captures individual experiences that might

amount to trafficking and exploitation

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Survey's country Central Eastern Total

Bulgaria 1,947 1,947

Greece 4,036 4,036

Hungary 889 889

Italy 6,485 6,485

Serbia 494 494

Slovenia 86 86

The FYR Macedonia 2,587 2,587

Total 6,485 10,039 16,524

1. Worked or performed activities without getting the expected payment2. Been forced to perform work or activities against will 3. Been approached by someone offering employment4. Been approached by someone with offers of an arranged marriage (for the

respondent or anyone in his or her family)5. Been kept at a certain location against will

What it is and what is not

• It can be linked to irregular migrants in vulnerable situations – depending on

definitions

• Regular vs irregular: a complex discussion, depending on the country.

• It is not identification

• It is an entry point with the population for onward referral and assistance

Survey Methodology

• Over 16,000 migrants - not a random sample but representative

• It is not a means of estimating the prevalence of human trafficking per se

• It is a way of quantifying people’s experiences and using those as indicators

• It can provide evidence of the kind of enabling environment within which human

trafficking happens and grows

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Sample structure

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• Eastern: 28 years old, 19% females, 52% married, 49% travelling with family members, 8% engaged in secondary migration

• 53% of the Central Med subsample reported journeys of 6 months or more, 56% of the Eastern Med subsample reported to have spent in transit less than 1 month

• Samples compare well with

total arrival populations by sea

in terms of nationality, sex, age

• Central: 23 years old, 13%

females, 81% single, 74%

travelling alone, 32% engaged

in secondary migration

Share of positive responses

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Share of individuals answering “yes” to one of the trafficking/exploitation indicators

10 countries with the highest number of reported incidents, by route

Central Med Eastern Med

Libya 94.2 Turkey 54.9

Algeria 2.4 Bulgaria 16.3

Sudan 0.9 Iran 11.1

Niger 0.5 Greece 10.5

Bangladesh 0.4 Macedonia 2.3

Egypt 0.4 Pakistan 1.3

Mali 0.2 Albania 0.5

Nigeria 0.2 Afghanistan 0.5

Burkina Faso 0.1 Serbia 0.4

Senegal 0.1 Hungary 0.3

Other 0.5 Other 2.0

Sex and age of individuals who responded positively, by route

Travel mode of individuals who responded positively, by route

Research Questions & Methodology

• Questions1) What personal attributes and journey characteristics predict vulnerability in transit?2) Are specific national groups more likely to report direct experiences? 3) And all other conditions being equal, are migrants traveling on the Central Med route more vulnerable than those on the Eastern Med route?

• Multi-level logistic regression– Total sample, Central Med, Eastern Med– Outcome variable (1,0): Vulnerability = Positive responses to at least 1 out

of 5 indicators– Predictors: sex, age, marital status, education level, travel mode,

secondary migration, time spent in transit, cost of the journey, country in crisis(conflict), departure from country in crisis(push factors), first-line family members at destination

– 2nd level: country level (origin)

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Results (1)

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Central Med

Eastern Med

Average marginal effects of “days spent

in transit”Profile of the most vulnerable migrants

Is male

No education, primary or secondary

Close family in country of destination

Travelling alone

Secondary migration

Has departed from in a country in crisis (conflict)

Has left a country of origin in crisis (war/conflict)

Has spent more time in transit

High cost of the migration journey (over USD 5,000)

Is travelling along the Central Med route

Results (2)

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0%

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40%

50%

60%

70%

80%

90%

100%

0%

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40%

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100%

• Central Med: West Africans - the highest vulnerability• East Med: Bangladeshis, Afghans and Somalis with highest predicted

probabilities, followed by Syrians and Pakistanis. • North Africans have the lowest predicted probabilities on both routes

Predicted probabilities of positive responses by route, first 12 nationalities

Results (3)

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Effect of age groups on vulnerability: comparison with children, total sample.

Results (4)

• Children travelling without their families experience similar patterns of vulnerability to the rest of the migrants

• Higher education levels are significantly associated with a lower vulnerability to trafficking and exploitation

• The longer the journey, the more likely the child is to experience one of the human trafficking and exploitative practices surveyed

• Children travelling without their families are more vulnerable to human trafficking or exploitation in transit, if they originate in a country from where they were forced to migrate (e.g. war) than children travelling alone who left for other reasons

More vulnerable:• Children travelling alone than children travelling with a group of

non-family members • West African children travelling without their families are more

vulnerable

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• Vulnerability of men and boys: challengingexisting assumptions about women and children

• Route and journey’s characteristics associated with the migration process are significant

• Secondary migration movements are common (17% of the sample) and increase vulnerability

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Discussion (1)

Discussion (2)

• Libya - whether as transit country, initial destination, or as country of departure – is the country where most events took place

• Country in conflict and vulnerability

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Strengths and limitations of DTM data (1)

1. Not a random sample: selection bias and under-over representation of some groups (weights?)

2. Identification bias: some profiles are more likely to participate and to share experiences

3. Data collection: not (yet) specific questions on type of exploitation (sexual/labour) and on physical and sexual violence, or forced involvement in armed conflicts

4. Standardized and stylized measure: – No in-depth assessment but ability of cross-country

comparisons (multi-sited research) – Not an estimation of prevalence of HT per se, but a way of

quantifying people’s experiences, outlining the contours of the environment which populations are forced to pass through

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5. The multilevel regression model would benefit from more second level variables, such as GDP/unemployment rate etc in country of origin and a rethink on how to operationalize “country in crisis” or “country in post-crisis context”

6.Explore vulnerability to exploitation also in the country of origin/displacement context, as the experience in transit can be linked to that (e.g. internal trafficking)

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Strengths and limitation of DTM data (2)

Implications of findings

• Continuous efforts to provide a reliable and comprehensive picture– DTM FMS are complementary with the Global HT

Database of IOM

– DTM FMS additional questions

– Need to include policy measures/adjustments in the analysis

• Policy recommendations all go towards safer and legal routes to Europe

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