European Institute for Gender Equality (EIGE)

ICF

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European Institute for Gender Equality, Gedimino pr. 16, LT-01103 Vilnius, Lithuania

EIGE.SEC@eige.europa.eu

+370 5 215 7444

05/02/2019

The European Institute for Gender Equality (EIGE) has undertaken three large-scale studies on female genital mutilation (FGM), in 2012, 2015 and 2017-2018. The data presented in this indicator were collected and/or computed as part of EIGE’s latest (2017-2018) study. The 2017-2018 study collected data on the number of girl asylum seekers and resident girl migrants from “FGM-practising countries” (see section 3.4 for the definition) and the prevalence of FGM in these FGM-practising countries. The data collection covered six countries (Belgium, Cyprus, France, Greece, Italy and Malta) for the period from 2011 to 2016. Based on this data collection, estimates of girls at risk of FGM were calculated using two different methodologies: the 2015 methodology and the 2018 revised methodology.

The rest of this section describes the 2012, 2015 and 2017-18 studies, as well as the 2015 and 2018 revised methodologies for estimating girls at risk of female genital mutilation.

The first EIGE FGM study, from 2012, provided an analysis of the situation of Female Genital Mutilation in EU Member States. It summarised the gaps in data collection on FGM across Europe and formed a basis on which EIGE has continued to build its collection of FGM prevalence data and the calculation of at-risk-rates. The study is available here: https://eige.europa.eu/gender-based-violence/eiges-studies-gender-based-violence/female-genital-mutilation#2012

The 2015 FGM study developed and piloted a standardised methodology for estimating the number/proportion of girls aged 0-18 at risk of female genital mutilation in a given Member State (girls at-risk). This methodology is referred to as “the 2015 methodology”. It focuses specifically on migrant girls or asylum-seeking girls from “FGM-practising countries”. For example, the girls risk rate applies to migrant girls aged 0-18 from FGM-practising communities only and not to all migrant girls or all girls in a given Member State. The methodology focuses on first-generation and second-generation migrants, and does not capture the situation of later generations of migrants.

The methodology is an ‘extrapolation-of-FGM-data-practising-countries-prevalence-data-method’, because it links a girl’s individual level of risk to what her level of risk would have been if she (or her parent[s]) had continued to reside in an FGM-practising country. The method seeks to determine the level of influence that the experience of residing in the EU has in changing girls’ level of risk. It makes use of a range of sources from both countries of destination and countries of origin. The 2015 study piloted this methodology to estimate girls at risk of FGM in three EU Member States (Ireland, Portugal and Sweden), covering only the year 2011.

EIGE’s 2017-2018 FGM study succeeded the 2015 study and introduced a second refined methodology for estimating girls at risk of FGM, reaching in particular the following conclusions:

• The normal age of cutting may not be the same for girls living in the EU and girls living in FGM-practising countries of origin. For girls living in the EU, the risk of cutting may be linked more to opportunity than to age, and extend past the median age of cutting in the country of origin.
• Whilst the risk of FGM is lower for second-generation girls than for first-generation girls, it is not eliminated entirely.

These conclusions were then used to update the assumptions within the methodology (see section 20.5 for more information on the risk estimation formula). This refined methodology is referred to as “the 2018 revised methodology”. Further information on the revisions from the 2015 to the 2018 revised methodology is available in Chapter 2 of EIGE’s 2017-2018 FGM study, available from: https://eige.europa.eu/rdc/eige-publications/estimation-girls-risk-female-genital-mutilation-european-union-report-0.

The 2017-2018 study applies both the 2015 and 2018 revised methodologies for estimating girls at risk of FGM to six EU Member States: Belgium, Cyprus, France, Greece, Italy and Malta, for the time period of 2011 to 2016 (dependent on data availability for the country), Section 20.5 in the metadata describes the method for calculating the at-risk- estimates in these six countries in more detail.

An overview of EIGE’s work on FGM and the three studies mentioned are available online: https://eige.europa.eu/gender-based-violence/eiges-studies-gender-based-violence/female-genital-mutilation.

As far as possible, compliance with 1998 United Nations recommendations on the statistics of international migration (Revision 1), plus compliance with a large number of international legal frameworks (see 3.4 below).

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Female genital mutilation: comprises all procedures involving partial or total removal of the external female genitalia or other injury to the female genital organs for non-medical reasons.  

Asylum seeker (or asylum applicant): According to Eurostat, an asylum seeker is an asylum applicant awaiting a decision on an application for international protection, granting or refusing a refugee status or another form of international protection. ‘Application for international protection’ means an application for international protection, as defined in Article 2(h) of European Union Directive 2011/95/EU. i.e. a request by a third-country national or a stateless person for protection from a Member State, who can be understood to seek refugee status or subsidiary protection status, and who does not explicitly request another kind of protection. A “refugee” is defined in line with the Convention relating to the Status of Refugees done at Geneva on 28 July 1951, as amended by the New York Protocol of 31 January 1967. “Subsidiary protection” is defined in line with Article 2(f) and 2(g) of Qualification Directive 2011/95/EU.  

Country of birth: According to Regulation (EC) No 862/2007, ‘country of birth’ means the country of residence (in its current borders, if the information is available) of the mother at the time of the birth or, if not available, the country (in its current borders, if the information is available) in which the birth took place.

Country of destination: This is the EU Member State where a person originating from a country where female genital mutilation is commonly practised decides to establish her or his residence, or where she or he has asked for international

protection.

Country of origin: Unless otherwise stated, this covers an individual’s country of birth or the country of birth of their parents. For all FGM indicators, the countries of origin of the migrant population are FGM-practising countries   

Female genital mutilation risk estimation in an EU Member State: The number of girls (either born in an FGM-practising country or whose mothers’ countries of birth were FGM-practising countries) living in a Member State who might be at risk of female genital mutilation, expressed as a proportion of the total number of girls living in an EU Member State who originate from, or are born to a mother from, FGM-practising countries.

FGM-practising countries: Refers to 30 countries where female genital mutilation has been documented through national surveys: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Côte d’Ivoire, Djibouti, Egypt, Eritrea, Ethiopia, Gambia, Ghana, Guinea-Bissau, Guinea, Indonesia, Iraq, Kenya, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Somalia, Sudan, Togo, Uganda, Tanzania, Yemen.

First-generation migrant: First-generation migrants cover those who were born in an FGM-practising country to one or more parents who were also born in these countries, and who have established usual residence in an EU Member

State.

Irregular migrants: This refers to someone who does not fulfil, or no longer fulfils, the legal conditions for stay or residence in a country. In practice, national authorities are not normally able to track all individuals who are in this

situation.

Live births: Live births are the births of children who are breathing or showing evidence of life, i.e. beating of the heart, pulsation of the umbilical cord or definite movement of voluntary muscles, regardless of gestational age (Eurostat).

Migrant population: In these indicators, the migrant population covers both those who were born in an FGM-practising country to one or more parents who were also born in that country and who have established usual residence in an EU Member State (first generation); and those who were not born in an FGM-practising country, but who have at least one parent who was born in an FGM-practising country, and who are usually resident in an EU Member State (second generation).

Second-generation migrant: A second-generation migrant means a person who was not born in an FGM-practising country, but who has at least one parent who was born in an FGM-practising country, and who is usually resident in an EU Member State.

• Usual residence: According to Regulation (EU) No 1260/2013, ‘usual residence’ means the place where a person normally spends the daily period of rest, regardless of temporary absences for purposes of recreation, holidays, visits to friends and relatives, business, medical treatment or religious pilgrimage. The following persons alone shall be considered to be usual residents of a specific geographical area: (i) those who have lived in their place of usual residence for a continuous period of at least 12 months before the reference time; or (ii) those who arrived in their place of usual residence during the 12 months before the reference time with the intention of staying there for at least 1 year. 

The unit of analysis is either the number of migrant girls from FGM-practising countries, the number of asylum-seeking girls from FGM-practising countries, the number of migrant girls from FGM-practising countries who are at risk of female genital mutilation or the number of asylum-seeking girls aged 0-18 from FGM-practising countries who are at risk of female genital mutilation. For all indicators, the girls will fall into a pre-defined age band (either 0-9, 10-18 or 0-18). The specific units of analysis are available as drop-down option in each indicators’ variables:

• Time period (reference year);
• Country (Belgium, Cyprus, France, Greece, Italy and Malta);
• Country of origin (see section 3.7);
• Age (0-9, 10-18 or 0-18).

The target population is either:

• all migrant girls (first- and second-generation), from 30 FGM-practising countries of origin, resident in a Member State, or
• all asylum-seeking girls from the same 30 FGM-practising countries (described in section 3.7) resident in a Member State.

Depending on the indicator, the age categories used are either 0-9, 10-18 or 0-18.

The 2017-2018 study for which data is presented here covers resident migrant girls and girl asylum seekers from “FGM-practising countries" in Belgium, Cyprus, France, Greece, Italy and Malta. FGM-practising countries include: Benin, Burkina Faso, Cameroon, Central African Republic, Chad, Côte d’Ivoire, Djibouti, Egypt, Eritrea, Ethiopia, Gambia, Ghana, Guinea‑Bissau, Guinea-Conakry, Indonesia, Iraq, Kenya, Liberia, Mali, Mauritania, Niger, Nigeria, Senegal, Sierra Leone, Somalia, Sudan, Togo, Uganda, Tanzania, Yemen. 

Note that in the 2015 study (data not presented in the Gender Statistics Database but available from the report available online: https://eige.europa.eu/rdc/eige-publications/estimation-girls-risk-female-genital-mutilation-european-union-report) Indonesia was not included as an FGM-practising country). In the 2017-2018 round of research, this list has been updated also to include Indonesia, another country where female genital mutilation is documented through national surveys.  

The years of coverage are between 2011 and 2016, depending on the country’s data availability (see section 5).

At a minimum, the methodology for estimating girls at risk of female genital mutilation can almost always be applied to years covered by an EU-wide census. As of 2018, the most recent such year is 2011.

No legal acts are directly applicable, although the European Commission has specifically assigned EIGE with the role of developing and improving the risk estimation methodology on FGM (Commission Communication, ‘Towards the elimination of female genital mutilation’ COM/2013/0833 final’). Furthermore, States Parties to the Istanbul Convention are obliged to collect statistical data on all forms of violence covered by the Convention, including FGM.

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This will generally be handled by national statistical offices. In some cases, they may aggregate key data if the full disaggregation endangers anonymity (for example, suppressing one-year age bands for certain migrant communities). 

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As mentioned above, EIGE has published two studies so far that include risk estimation data (2015 and 2017-2018), available online at https://eige.europa.eu/gender-based-violence/eiges-studies-gender-based-violence/female-genital-mutilation.

It is expected that future studies will be published containing risk estimation data, at the same link.

No regular news releases.

Ad hoc publication, available here: https://eige.europa.eu/gender-based-violence/eiges-studies-gender-based-violence/female-genital-mutilation.

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Available details on the methodology for estimating girls at risk of female genital mutilation are included in EIGE’s FGM studies. In particular, users should refer to chapter 2 of the 2017-2018 study, which explains the 2018 revised methodology and compares the 2015 and 2018 revised methodologies. The study is available online, from https://eige.europa.eu/rdc/eige-publications/estimation-girls-risk-female-genital-mutilation-european-union-report-0

Information on quality management and assessment is documented as part of methodological information in EIGE’s FGM studies (see row 10.6), as well as throughout the individual country chapters in the 2017-2018 study available online from: https://eige.europa.eu/rdc/eige-publications/estimation-girls-risk-female-genital-mutilation-european-union-report-0.

National statistical offices in the EU are expected to follow comprehensive Quality Control procedures for all data that is publicly available. Furthermore, both public and non-public national data were collected for the 2017-2018 study using the EURO-SDMX Metadata Structure, meaning that information is held on all core metadata components of the data provided (where relevant). Quality and comparability issues for both the population data and the risk estimations are summarised in the country chapters of EIGE’s 2017-2018 FGM study. Finally, when undertaking data collection, national researchers confirmed the degree to which their data was in conformity with the statistical classifications described in section 3.4.

The current FGM indicators employ a standardised methodology for calculating risk across different Member States, and the current data available is positive in that it was collected using a standardised tool with comprehensive metadata. This allows for greater transparency in the exact coverage of data held. Nonetheless, due to gaps within national data collection systems, there are limitations when it comes to the data’s precision (see section 13.1) and comparability across countries and over time (see sections 15.1 and 15.2). There is a critical need for more systematic collection of the data needed to undertake robust FGM risk estimation using EIGE’s methodology, as described in section 12.3 below. In the long term, it may also be worthwhile to consider measuring FGM prevalence as part of large-scale surveys in countries of destination.

The focus of the statistical data is to provide a comprehensive overview about the communities where FGM risk is considered to exist, and, in particular, the number/proportion of girls of risk within these communities. This overall picture is an important indicator for designing and implementing government measures that will protect girls at risk of FGM, and can support state obligations under key international legal frameworks such as the Istanbul Convention. Whilst these are estimates only, they remain important for enabling governments to design more tailored and appropriate interventions and awareness-raising campaigns on FGM.

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At present, Member States are not collecting a range of important data needed for more accurate FGM risk estimation.

The following indicators are proposed to collect data on the female migrant population:

• the number of female resident migrants (aged 0–18) from FGM-practising countries;
• the number of female asylum seekers from FGM-practising countries;
• female live births to mothers from FGM-practising countries.

For regular, more robust estimations of female genital mutilation risk, it will be important to gather the data necessary to populate the three indicators above, and to disaggregate these data by sex, country and region of birth, generation (based on country of birth), mother’s and father’s country of birth, one-year age intervals, age at arrival, years since migration. Statistical agencies, border agencies and other administrative sources should cooperate closely to strive for these additional forms of disaggregation. Making these adjustments in national data collection systems will require commitment and collaboration across different agencies, as well as specific attention to protect the privacy and rights of the populations being targeted.

Existing data about migrant and asylum-seeking populations from different Member States is limited by the lack of key disaggregations in some cases (one-year age bands, generation, region of origin, etc.), as well as some other important gaps. This reduces the accuracy of some risk estimations, due to the need to use a range of proxies and make assumptions. For example, considering the six Member States in the 2017-2018 study:

• Reliable and complete data on second-generation migrants are missing for many countries (Greece, Italy, Malta), because information on individuals’ parents’ country of birth is not routinely collected. This can create underestimation, especially in the estimation of the High Scenarios. In each country, because of the lack of data, the estimations were calculated by using expedients that allow for more precise calculations. For example, in Greece, the second generation was estimated as a fraction of the total residence permit-holders and did not include girls that do not have or need residence permits (potentially leading to underestimation). In Malta and Italy, second-generation data were calculated using information on the number of live births to resident as well as non-resident mothers. However, data were provided only in grouped age brackets, and therefore it was necessary to apply a hypothesis of uniform distribution to estimate births by single years.
• In many cases (Greece, France, Italy, Malta), as indicated in the country chapters of EIGE’s 2017-2018 FGM study, data is not available in one-year breakdowns for migrant populations and is instead provided in 5-year groupings (0-4, 5-9, 10-14, 15-19). To estimate the number of girls aged 0-18, it was thus necessary to assume an equal distribution of girls across the years of 15-19 age category, then to exclude 19-year-olds on this basis.
• A breakdown by region of origin is not available for any of the six Member States. This undermines the accuracy of the FGM risk estimations, as it is not possible to tell whether individuals come with regions with above- or below-average FGM prevalence rates. FGM risk and prevalence can be extremely variable within an FGM-practising country, depending on the region of origin, as is shown by the regional FGM prevalence rates available through the Demographic and Health Surveys (DHS) Program (more information from the DHS Program website, available at https://dhsprogram.com/).

Irregular migrants constitute a ‘hidden population‘ for which it is not possible to have an overview of prevalence and risk of female genital mutilation among girls.

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Increasing the harmonisation of data collection on migrant populations will be an important priority in future years for improving data on FGM risk. Whilst the methodology used to calculate the FGM indicators is standardised across countries, there are important limits to cross-country comparability, particularly it comes to the exact girls covered under the category of “migrants”, and related sub-categories. For example, as mentioned above, there are key challenges in gaining an accurate picture of the size of the second generation in most Member States. Specifically:

• In Belgium, Italy and Malta, population/census data was combined with births data to estimate the size of the second generation, although not all second-generation girls are likely to be captured, and there are key gaps in births data. 
• In Greece and Cyprus, for recent years, it is necessary to make use of data on those with residence permits to establish the size of the second generation. The generation breakdown is not yet available for this data, meaning that it is approximated. Furthermore, using this data does not capture second-generation girls who may not need a residence permit to live legally in the country. This means that, again, the number of second-generation girls at risk is likely significantly underestimated.
• In France, generational breakdowns are available, meaning it is not necessary to use live births or permits data to project the size of the second generation. 

There is also deviance from the international concepts in use (for example, for Belgium, in relation to first generation migrants). These types of discrepancies reduce the comparability of the data from the different Member States. Further country-specific problems for estimating girls at risk resulting from the available data were identified for each Member State are explained in more detail at the start of each country chapter in EIGE’s 2017-2018 FGM study. 

In some Member States, there is a lack of comprehensive data on the migrant population in non-census years, meaning that different data sources are used depending on the year. This creates breaks in comparability over time. Specifically:

• In Belgium and Cyprus, the data sources used in 2011 (linked to the census) were different to those used in later years.
• In Greece, the generation breakdown for the migrant population in the years after 2011 is estimated (using 2011 census data).
• In Italy, there are some important gaps in births data for all available years, but the nature of the gap differs depending on the year.

The reason for publishing these results is the strong need to have access to FGM data more frequently than on a 10-yearly basis (the classic timeline for censuses).

Although not EU-wide, there are some important national and cross-national studies on FGM that also use an extrapolation method to calculate FGM risk for girls living Europe. These differ in some important respects, for example in the age categories they use, and whether they take into account whether one or both parents are from an FGM-practising country (an approach that, due to data limitations, is not currently feasible under EIGE’s current methodology). For more information, see Chapter 1.4 of EIGE’s 2017-2018 FGM study, available online from: https://eige.europa.eu/rdc/eige-publications/estimation-girls-risk-female-genital-mutilation-european-union-report-0. 

There are challenges to cross domain coherence, of which the most common is the lack of complete age and/or generation breakdowns for non-census years (meaning it is often necessary to approximate these using the more detailed data from the census). The reason for publishing these results is the strong need to have access to FGM data more frequently than on a 10-yearly basis (the classic timeline for censuses). .

Variables were rigorously reviewed by the study team at the initial data collection stage. No revisions are made after the publication for the data.

There is no fixed revision schedule.

The data required to undertake FGM risk estimation comes from both countries of origin and countries of destination.

Country of origin data is drawn generally from the Demographic and Health Survey (DHS) Program (more information from the DHS Program website, available at https://dhsprogram.com/) or (in rare cases) the Multiple Indicator Cluster Surveys (MICS) released by UNICEF (more information from the UNICEF MICS website, available at http://mics.unicef.org/). The following country of origin data is required:

National prevalence rates for women/girls aged 15 to 19. This is the youngest group of adults considered to be in ‘final cut status’, i.e. either having undergone FGM or no longer at risk of FGM. 

National median age of FGM occurrence for women/girls aged 15 to 19. In cases where this information is not available specifically for this age group, the age group 15 to 49 will be considered instead.

Country of destination data is drawn from national statistical offices, in particular census data, population registers and birth registers. The following country of destination data is required:

Number of female migrants aged 0–18 in an EU Member State, originating from countries where FGM is commonly practised. Data disaggregation (where possible):
■ Country of origin, age and first/second generation
■ Region of destination within EU Member State

Number of female asylum seekers aged 0-18 in an EU Member State, originating from countries where FGM is commonly practised.
Data disaggregation (where possible):
■ Country of origin, age and first/second generation

Number of female live births in an EU Member State to mothers originating from countries where FGM is documented.
Data disaggregation (where possible):
■ Country of origin of the mother

If the necessary data is missing at national level, relevant data that can be considered are as follows:

• Population on 1 January split by: five-year age group, sex and country of birth – Eurostat (code: migr_pop3ctb).
• Migrants’ with legal residence permits – Eurostat (code: migr_resvas).

Ideally, data on the region of origin would also be collected. As FGM prevalence can strongly vary within an FGM-practising country dependent on the region, using regional prevalence rates and individuals’ region of origin would provide the most accurate risk estimate. However, the region of origin of resident migrants and asylum seekers is not collected for the six destination countries covered in the study, so that these regional prevalence rates cannot be linked.

The following data by region is required to undertake this regional estimation:

■ Regional prevalence rates for women/girls aged 15 to 19: The regional prevalence rates are available for all 30 countries in which FGM is documented (EIGE 2015). In cases where regional prevalence rates are not specifically available for age group 15 to 19, age group 15 to 49 could be considered instead.

■ Number of female migrants aged 0–18 in an EU Member State, originating from countries where FGM is commonly practised, disaggregated by their region of origin in this country.

■ Number of female asylum seekers aged 0-18 in an EU Member State, originating from countries where FGM is commonly practised, disaggregated by their region of origin in this county.

■ Number of female live births in an EU Member State to mothers originating from countries where FGM is documented, by region of origin of the mother, region where the birth took place or region of usual residence of the mother (and father).

Baseline tends to be EU-wide censuses, so every 10 years. Estimates can be prepared more frequently for some Member States.

The 2017-2018 study involved the ad-hoc collection of data in the six Member States from national statistical offices, birth registration offices, and all other sources as indicated in the report. In addition, qualitative research (24 focus group discussions) was undertaken with FGM-affected communities to gain the insights needed to refine the 2015 methodology for estimating girls at risk of female genital mutilation.

A standardised data collection tool was used in the six Member States, with important metadata collected according to the SDMX structure.

Indicators on the number of girls aged 0-18 at risk of FGM from FGM-practising countries 

The risk estimation formula (using the 2015 method) is as follows:

x_c = (a_c=first * p_c * (1 – m_c=first)) + (a_c=second * p_c * (1 – m_c=second))

where:

• x_c  is the number of girls at risk of FGM originating from a particular country c where FGM has been documented in the reference year (2011 to 2016);
• a_c=first is the number of first-generation girls from country c that are below the national median age of FGM occurrence in country c in the reference year (2011 to 2016);
• a_c=second is the number of second-generation girls from country c that are below the national median age of FGM occurrence in country c in the reference year (2011 to 2016);
• p_c  is the national prevalence rate of FGM in country of origin c in the reference year (2011 to 2016); and
• m_c=first is the migration and acculturation factor, which estimates how FGM prevalence differs between first generation migrants and the population of the country of origin c.
• m_c=second is the migration and acculturation factor, which estimates how FGM prevalence differs between second generation migrants and the population of the country of origin c.

The risk estimates are expressed in a range, with a high and low scenario.

The High Scenario assumes: migration and acculturation do not affect FGM prevalence for first- or second-generation migrants (i.e. m_c=first = 0 and m_c=second = 0).

The Low Scenario assumes: migration and acculturation do not affect FGM prevalence for first-generation migrants but the FGM practice disappears amongst second-generation migrants (i.e. m_c=first = 0 and m_c=second = 1).

The 2018 revised methodology is different to the 2015 methodology. As part of the 2017-2018 study, the 2015 methodology for estimating the risk of FGM was reviewed. The conclusions of this review fed into the 2018 revised methodology. These conclusions entailed that:

• The normal age of cutting may not be the same for girls living in the EU and girls living in FGM-practising countries of origin. For girls living in the EU, the risk of cutting may be linked more to opportunity than to age, and extend past the median age of cutting in the country of origin.
• Whilst the risk of FGM is lower for second-generation girls than for first-generation girls, it is not eliminated entirely. This means it is not realistic to assume that the migration and acculturation factor for second-generation girls has a value of 1. With this assumption in mind, the value of m_c=second was updated to 0.5.

Subsequently, the risk estimation formula (2018 revised method) is as follows:

x_c = (a_c=first * p_c * (1 – m_c=first)) + (a_c=second * p_c * (1 – m_c=second))

where:

• x_c  is the number of girls at risk of FGM originating from a particular country c where FGM has been documented in the reference year (2011 to 2016);
• a_c=first is the number of first-generation girls from country c that are younger than, or the same age, the ‘median of FGM occurrence, increased by its standard deviation’ in country c in the reference year (2011 to 2016);
• a_c=second is the number of second-generation girls from country c that are younger than, or the same age, the ‘median of FGM occurrence, increased by its standard deviation’ in country c in the reference year (2011 to 2016);
• p_c  is the national prevalence rate of FGM in country of origin c in the reference year (2011 to 2016); and
• m_c=first is the migration and acculturation factor, which estimates how FGM prevalence differs between first generation migrants and the population of the country of origin c.
• m_c=second is the migration and acculturation factor, which estimates how FGM prevalence differs between second generation migrants and the population of the country of origin c.

The risk estimates are expressed in a range, with a high and low scenario.

The High Scenario assumes: migration and acculturation do not affect FGM prevalence for first- or second-generation migrants (i.e. m_c=first = 0 and m_c=second = 0).

The Low Scenario assumes: migration and acculturation do not affect FGM prevalence for first-generation migrants; second-generation migrants remain at risk but are half as likely as first-generation migrants to experience FGM (i.e. m_c=first = 0 and m_c=second = 0.5).

All girl asylum seekers from FGM-practicing countries, irrespective of the methodology used, are assumed to be first-generation (i.e. born in an FGM-practising country). Therefore, the High and Low Scenarios explained above do not apply to asylum seekers. The migration and acculturation factor for second generation migrants (mc=second), which is the varying component between the High and Low Scenarios (either 0 in the High Scenario or 0.5 in the Low Scenario), is not included in the calculation for asylum seekers. Only the migration and acculturation factor for the first generation (mc=first) is included and this is always 0.

It is important to be aware that in rare cases some girls might technically be second-generation, if they are born whilst their mother is in an asylum reception centre. Due to limited data, risk estimates for asylum-seekers are required treat these girls as first-generation.

Detailed information about the 2015 and 2018 revised methods for calculating at-risk-rates is available in EIGE’s latest Step-by-step guide, online: https://eige.europa.eu/rdc/eige-publications/estimation-girls-risk-female-genital-mutilation-european-union-step-step-guide-2nd-edition

Indicators on the proportion of migrant girls aged 0-18 at risk of FGM from an FGM-practising country

It is calculated by dividing the number of migrant girls aged 0-18 at risk of FGM from this country of origin, by the overall number of migrant girls aged 0-18 from this country of origin. The assumptions used to calculate the number of girls at risk (in the Low and High Scenario) is described above.

Indicators on the proportion of asylum-seeking girls aged 0-18 at risk of FGM from an FGM-practising country

It is calculated by dividing the number of asylum-seeker girls aged 0-18 at risk of FGM from this country of origin, by the overall number of asylum-seeker girls aged 0-18 from this country of origin. The assumptions used to calculate the number of girls at risk (in the Low and High Scenario) is described above.

Due to limited data availability, in some cases it is necessary to combine some datasets and employ proxies. See section 13.1 above.