The Invisibility of Migrant Women in National Data

Migration is often described using broad, gender‑neutral data, even though it is a gendered process. Women and men migrate for different reasons and experience migration, settlement, and integration differently. These experiences are further shaped by race, ethnicity, age, class, caregiving responsibilities and immigration status, which influence vulnerability and access to rights. When data systems fail to reflect these intersections, the realities of migrant women are reduced to overly simple categories.

This article examines how gaps in national and local data make migrant women statistically invisible. It focuses on data that is not collected, or collected but not broken down in ways that allow gendered and intersectional analysis.

Understanding Representation of Migrant Women in Data

National statistics use different definitions, such as nationality, country of birth, length of stay or visa status, and this shapes how migrant experiences appear in data. Broad categories often fail to record interaction with gender, caregiving responsibilities, race, immigration status or economic participation. This leaves the experiences of migrant women obscured.

For example, Home Office statistics show that women form the majority of partner visa holders, but this is not linked to employment or housing outcomes. Office for National Statistics labour-market datasets indicate that migrant women have lower employment rates and higher economic inactivity but they do not include information about visa status or whether someone is subject to No Recourse to Public Funds (NRPF). This means regional data can only show where risks are concentrated, not measure specific vulnerabilities faced by people on partner visas such as financial dependency, which can increase the risk of domestic abuse.

At the national level, Census data offers a useful snapshot of the population by country of birth and nationality, but its ten‑year cycle leaves large gaps in up‑to‑date demographic information. This makes it harder to interpret other data on migrant communities as migration patterns and policies change quickly. The Census identifies only those who arrived in the past 12 months, meaning long‑term migrants are less visible and early‑stage indicators dominate. It also does not record visa status, limiting policymakers’ ability to analyse local populations by immigration category or understand differences between visa groups.

Evidence from the Migration Observatory helps contextualise these gaps, but fragmented data makes it difficult to draw clear conclusions and highlights the need for better-integrated administrative systems.

These gaps matter because they shape which issues are recognised and how policy responses are designed. Policymakers require accurate and timely information on migration to plan effectively for healthcare, housing, employment, and public spending.

Employment

Employment outcomes for migrant women depend on education, English proficiency, caring responsibilities, and recognition of qualifications. However, national datasets rarely capture how these factors interact with immigration status.

The Labour Force Survey (LFS) allows analysis by characteristics such as sex, nationality and country of birth. However, it does not consistently enable analysis that captures immigration status, visa route, or conditions such as NRPF. National Insurance number (NINo) statistics for adult overseas nationals provide a useful indicator of new registrations that may suggest labour market entry, but they don’t confirm employment or offer insight into job quality, earnings, insecurity, or later work outcomes. These gaps limit understanding of migrant women’s economic participation, especially where outcomes are shaped by visa restrictions. This makes it harder for government to design strategies to reduce unemployment among groups facing additional barriers.

Health

Local health planning relies on national indicators and routine datasets. Most routine administrative health data is collected for clinical and service delivery purposes and does not record immigration status or visa conditions. This creates a significant blind spot, making it difficult to understand how immigration-based constraints affect health outcomes, including the impact of wider determinants of health.

GP registration data is often used to measure populations and access to primary care, but it misses people who move frequently, face language or digital barriers, need support to register, or avoid services due to distrust. Many migrant women fall into at least one of these groups. Additionally, area-based deprivation measures such as the Index of Multiple Deprivation do not identify immigration status, or migration-related barriers (including language and entitlement constraints), leaving migrant women under‑represented in priority-setting.

Evidence from Doctors of the World shows that barriers to access can delay engagement with care, but this pattern remains hard to monitor or address when immigration status is not recorded in routine datasets used for local planning.

Housing

Housing datasets present some of the clearest gaps. The statutory homelessness dataset (H-CLIC), does not require local authorities to record immigration status or NRPF. As a result, migrant women who are ineligible for assistance due to their immigration status are absent from homelessness statistics, even when they present in crisis or flee abuse. This undermines local authorities’ ability to understand housing need across communities, including within the context of the Violence Against Women and Girls Strategy.

Children’s social care datasets detailing support provided under Section 17 of the Children Act also lack fields for immigration status or NRPF. Migrant mothers housed with their children because of insecurity, exploitation, or abuse remain indistinguishable in the data, and their housing journeys cannot be tracked across systems. Local population datasets similarly fail to capture women living in informal or unsafe accommodation, leading to under‑representation in the data used for commissioning.

Although, portals like NRPF Connect give a national picture of NRPF households and social care involvement, they only reflect those who reach social care. They do not capture the wider discretionary or preventative support provided by councils and third sector organisations, especially to single migrant women who fall outside statutory thresholds.

Conclusion

Across employment, health, and housing, migrant women remain invisible within public systems because data structures are not designed to capture their experiences. Addressing this requires more than additional data collection: it demands migration‑ and gender‑sensitive data frameworks that recognise how legal status, gender, and inequality intersect. Without this shift, migrant women will continue to be overlooked in policy and planning.

Written by Saqib Ali Khan, who worked as the Data Officer at the Refugee and Migrant Centre until December 2025. 

Edited by Jasmyn Dixon, who is the Impact and Evaluation Manager at the Refugee and Migrant Centre.

The Refugee and Migrant Centre (RMC) is an award‑winning charity founded in 1999, with centres in Birmingham, Wolverhampton, Walsall and Dudley. 

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The views and opinions expressed in this blogpost are those of the author’s and do not necessarily reflect the official policy position of the Women’s Budget Group.