The motives and consequences of human mobility are shaped by various factors such as customary rules based on gender expectations, ethnicity, race, age and class. Among these, it can be argued that gender has the biggest impact on the migration experiences of men, women, boys, girls and persons identifying as lesbian, gay, bisexual, transgender and intersex (LGBTI). Thus, including gender considerations in policymaking and planning can contribute to individuals’ social and economic empowerment and promote gender equality; leaving such considerations out can expose them to further risks and vulnerabilities and perpetuate or exacerbate inequalities.
The New York Declaration for Refugees and Migrants calls for more migration data to be disaggregated by sex and age. It acknowledges that sex-disaggregated data allow for the identification and analysis of specific vulnerabilities and capacities of women and men, revealing gaps and inequalities. These data also enable the analysis of how gender norms might influence the experiences of women and men in migration processes, and in turn how their experiences might change gender norms. While it is important to consider the experiences of women and girls, which have sometimes been overlooked, it is equally important to also consider the experiences of men, boys and LGBTI persons, who are also exposed to forms of gender-based violence or vulnerabilities during different migration processes.
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Key terms and concepts that pertain to gender and migration are as follows:
According to the European Institute for Gender Equality(EIGE), sex-disaggregated data are “any data on individuals broken down by sex. Gender statistics rely on these sex-disaggregated data and reflect the realities of the lives of women and men and policy issues relating to gender” (EIGE, 2017).
According to the International Organization for Migration (IOM), gender “refers to the socially constructed roles and relationships, personality traits, attitudes, behaviors, values, relative power and influence that society ascribes to people based on their assigned sex. Gender is relational and refers not simply to women, men or other gender groups, but to the relationship between them. Although notions of gender are deeply rooted in every culture, they are also changeable over time and have wide variations both within and between cultures” (IOM, 2015).
According to the United Nations (UN) Entity for Gender Equality and the Empowerment of Women (UN Women), “gender is not only about women. It is important to emphasize that the concept of gender is not interchangeable with women. Gender refers to women, men [and other gender groups], and the often unequal relations between them. […] In practice, debates on gender often focus on women because they as a group have been most affected by gender inequality. However, [all gender groups] have important roles to play in working toward full equality. Consequently, a gender perspective does not mean women’s perspective” (UN Women Training Centre’s Glossary, 2017).
According to IOM’s LGBTI Glossary, gender identity “refers to each person’s deeply felt internal and individual experience of gender, which may or may not correspond with the sex they were assigned at birth or the gender attributed to them by society. It includes the personal sense of the body (which may involve, if freely chosen, modification of appearance or function by medical, surgical or other means) and expressions of gender, including dress, speech and mannerisms. ” (IOM’s LGBTI Glossary, 2017).
The share of female migrants has not changed tremendously in the past 60 years. However, more female migrants are migrating independently for work, education and as heads of households. Despite these improvements, female migrants may still face stronger discrimination, are more vulnerable to mistreatment, and can experience double discrimination as both migrants and as women in their host country in comparison to male migrants. Nonetheless, male migrants are also exposed to vulnerabilities in the migration processes. Therefore, gender-responsive data on migration have potential to promote greater equality and offer opportunities for disadvantaged groups.
Women comprise somewhat less than half, 125 million or 48.4 per cent, of the global international migrant stock (UN DESA, 2017). The share of female migrants has declined from 49.1 per cent in 2000 to 48.4 per cent in 2017, whereas the proportion of male migrants grew from 50.7 per cent in 2000 to 51.6 per cent in 2017 (ibid.). There were more male international migrant workers, 83.7 million or 55.7 per cent, than female, 66.6 million or 44.3 per cent, in 2013 (ILO, 2015).
Asia and Africa
From 2000-2017, the estimated stock of male international migrants grew tremendously by 73 per cent in Asia, to 46 million (UN DESA, 2017). This growth has been fueled by the increasing demand for male migrant workers in oil-producing countries of Western Asia. Similar developments can be observed in Africa, which experienced more growth among male migrants (41.8% during 2000-2017) than among female migrants (37.1%) (ibid.). The share of female migrants is much lower both in Asia (42.4%) and in Africa (47.1%) (UN DESA, 2017) Thus, male international migrants significantly outnumber female international migrants in these regions.
Europe and Northern America
Female migrants comprise slightly more than half of all international migrants in Europe and Northern America. In 2017, the share of females among all international migrants reached 52 per cent in Europe and 51.5 per cent in Northern America (UN DESA, 2017). The larger portion of female migrants in these regions is because of a combination of two factors: the presence of older migrants in the population and the tendency of longer life expectancies of female migrants in comparison with males. Statistics show that women as a group live longer than men. Thus, these estimates show that older female migrants outlive older male migrants.
Latin America, Oceania and the Caribbean
In 2017, the number of female international migrants (50.7%) slightly outnumbered the proportion of male international migrants (49.3%) in these major areas. Moreover, during 2000-2017, the stock of female international migrants grew faster than that of male international migrants (UN DESA, 2017).
Data on gender and migration are collected and analyzed separately for male and female migrants. Although sex-disaggregated data are not always collected, major data sources that collect sex-disaggregated migration-related data are population censuses, administrative registers, and sample surveys such as labor force surveys and income and living condition surveys. Data from these data sources are compiled in databases. The following are the databases on migration disaggregated by sex.
The Population Division of the United Nations Department of Economic and Social Affairs (UN DESA) provides several international migrant stock datasetsfor all countries and areas and disaggregates data by sex, age and origin. UN DESA publishes datasets on a bi-yearly basis. Its latest dataset on the international migrant stock was published in 2017.
The International Labour Organization (ILO)’s Department of Statistics (ILOSTAT) has a database on Key Indicators of the Labour Market (KILM). This database provides datasets on labour migration that are grouped into three major themes: international migrant stock, nationals abroad and international migrant flow. These themes contain estimates of demographic stocks/flows and labour migrant stocks/flows and are predominantly disaggregated by sex and age. The database provides labour migration statistics for all countries and areas of the world on an annual basis.
The ILO report Global Estimates on Migrant Workers provides estimates on the share of labour migrant workers among the total international migrants and highlights regions and industries where international migrant workers are established. It also presents demographic characteristics of international labour migration specifically focusing on the proportion of female and male migrant workers in domestic work globally.
IOM’s Migration Law Database consolidates information on international migration law and frames it in a comprehensive manner. It draws together migration-related instruments including gender-related norms in the migration context. The database contains relevant international, regional and bilateral treaties, international and regional resolutions, declarations and other instruments.
IOM and Polaris pulled together existing data on human trafficking and created the Counter-Trafficking Data Collaborative (CTDC) repository. It contains publicly available de-identified data (43.741 records) on human trafficking disaggregated by sex and age.
BRIDGE is a gender and development research service at the Institute of Development Studies, which advocates for the significance of a gender perspective in endeavors to reduce poverty and promote social justice in the migration processes. Among other development-related objectives, BRIDGE focuses on gender aspects of migration. It also builds bridges between researchers, policymakers, and practitioners to inspire transformation in policy and practice.
The ILO Regional Office for Asia and the Pacific has a database called the International Labour Migration Statistics (ILMS) Database which compiles a variety of statistical sources on international migrants and international migrant workers. It generates statistical data from population and housing censuses, labour force surveys, household surveys, enterprise surveys and administrative records. The ILMS Database presents datasets on the international migrant stock, international migrant flow and nationals abroad. Data are broken down by sex, age, employment status, education, occupation, economic activity and origin.
The Gender Statistics Database, a database of the European Union (EU)’s European Institute for Gender Equality (EIGE), provides statistical evidence on numerous themes including migration from all over the EU and beyond, at the EU, Member State and European Level. It contains estimates on the immigrant stock, immigration and emigration flows, and migration and education. Data are disaggregated by sex, age, migration status and 55 other migration-related indicators. This database is used by the Member States to comply with the European Commission’s (EC) Strategy on Gender Equality and monitor their progress.
Data on population demography and migration are collected by Eurostat on a yearly basis. The Population (Demography, Migration, and Projections) database has a dataset on migration and citizenship data, which is divided into three major thematic groups: immigration, emigration and acquisition, and loss of citizenship. The estimates are mostly disaggregated by sex, age group, citizenship, country of birth, and ranking in the Human Development Index of the country of birth/citizenship/previous residence.
Eurostat’s database on asylum and managed migration is based on data collected from the Member States’ Ministries of Interior and related Immigration Agencies. The database presents data on asylum, residence permits and the enforcement of immigration legislation (EIL). Data on asylum and residence permits are mostly disaggregated by sex and age group.
OECD’s Gender, Institutions and Development database (GID-DB) presents comparative data on gender-based discrimination in social institutions such as legal, cultural and traditional practices and covers 160 countries for 2014. The database consists of variables such as the legal age of marriage, early marriage rates, parental authority in marriage and after divorce, violence against women, reproductive integrity, female genital mutilation, and other gender topics. This database provides datasets on the Social Institutions and Gender Index (SIGI), which measures the extent to which social institutions are discriminatory, to reveal main drivers of gender inequality and their impact on women’s empowerment opportunities. This dataset has 21 variables of discriminatory social institutions that are grouped into five sub-indices such as discriminatory family code, restricted physical integrity, son bias, restricted civil liberties and restricted resources/assets.
By disaggregating and analyzing data by the variables “female” and “male” migrant, researchers produce broad migration statistics on these two different groups. However, to understand more thoroughly the gender patterns present throughout the migration processes, more qualitative studies and inclusion of specific relevant questions in surveys are needed to reveal the power imbalances in migration decisions, labor markets, remittance sending/utilization, and the impact of migration on social relations in the households and communities experiencing out-migration.Back to top
Data strengths & limitations
Sex-disaggregated data are essential to question erroneous gender stereotypes such as labeling only women as a vulnerable migrant group. Information broken down by sex also provides a better understanding of the gender dimension of migration. The data sources listed above mostly provide disaggregated data on male and female migrants, which enable users to reveal differences, inequalities between these two groups. Subsequently, these particular data sources can be helpful to ensure the equal opportunity of male and female migrants to benefit from migration. By using sex-disaggregated data sources, policymakers will be able to initiate effective programs based on solid, accurate and reliable data. Nevertheless, there are some limitations to existing data and data sources:
Sex disaggregated data are not always collected, particularly in certain contexts such as displacement situations. For example, the Global Internal Displacement Database (GIDD) provides a limited amount of sex-disaggregated data on internal displacement because data are not collected by sex; data are instead collected by household. Similarly, data on migrants’ deaths are only occasionally disaggregated by sex because it is highly contingent on the identification of bodies (IOM, 2017). There are other reasons why data are rarely disaggregated by sex. Some authorities: have low statistical capacities to produce more granular data; are unwilling to collect and disaggregate data; and/or aim to protect migrants’ post mortem privacy and therefore disseminate aggregated data.
The collection of data and definition of gender are not always comprehensive enough: The collection of data at the national level should, but does not always, include gender mainstreaming methodologies to capture the experiences of people who identify as something other than male or female. Moreover, the definition of gender, which is currently viewed as being the same thing as sex or equated to female migrants, should be more comprehensive to include the different needs of men and persons identifying as LGBTI.
Analysis is limited because some data producers do not disaggregate data by sex. For example, the World Bank Migration Database, which among other data contains remittances data, mostly contains sex-aggregated data, which makes it difficult to extract gender-driven inequalities and exclusions. Moreover, this sort of limited data often distorts the reality and risks equating women/girls and men’s migration experiences and outcomes. Thus, it becomes difficult to distinguish gender differences in remittance sending, amount of money, frequency, channels, reasons, among other issues.
Despite attempts to disaggregate migration data by sexual orientation and gender identity, data are hardly ever disaggregated by LGBTI identification: “Leave no one behind” is the principle which is at the core of the Sustainable Development Goals for 2030. One of the most vulnerable and marginalized groups, which should not be left behind, are persons identifying as LGBTI. Producers of migration and gender statistics should incorporate such variables as gender identity and sexual orientation to collect data on LGBTI persons’ experiences and inequalities in migration processes. However, this particular disaggregation should not compromise the security and well-being of LGBTI people in countries and regions where discriminatory laws and policies exist and prejudiced customary rules prevail.