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  3. Day 39, Year of #Mygration: Mind the gap: the migration data

Day 39, Year of #Mygration: Mind the gap: the migration data

This is a featured blog post by Dr Belinda Wu, Research Fellow in Development Policy and Planning.

The Office for National Statistics (ONS) has recently published its latest Migration Statistics Quarterly Report.

Migration, especially international migration, is a complicated process. As a result, the data collection often proves to be difficult and there are many misreadings and misuses concerning such data.  Such issues have been long recognised in research (Wu, 2013).

Despite the growing concerns over an 'invasion' of refugees and migrants helping to elect Donald Trump and sway Brexit voters, the data suggests that the situation is far from how it is often portrayed. In the last report in November 2017, the ONS confirmed in its first full year of data since the EU referendum vote a decrease in net migration of 106,000. Although we can see from the 10 year migration pattern that the peak of immigration during this period occurred in 2014-15, it is worth noting that this happened to follow a major historical decrease in 2011-2012. It also coincided with the Syrian War (Figure 1). Meanwhile, misleading reports about high rates of migration into Europe, which are much lower than those into Africa and Asia, are creating unjustified fears about refugees and undermining efforts of humanitarian assistance.

In addition to the issue of misperceptin of the migration data, international migration data are often acused to be inadequate. As a result, the Migration Statistics Improvement Programme (MSIP) has been set up by the ONS.

To begin with, the definitions diverge such that some approaches to reducing net-migration statistics might be poorly tailored to the actual sources of public concern. For instance, the government's definition differs from common language on the length of stay required to become a "Long-Term International Migrant" (LTIM). The Government's definition considers someone a LTIM when "A person who moves to a country other than that of his or her usual residence for a period of at least a year (12 months), so that the country of destination effectively becomes his or her new country of usual residence". While in reality the majority of international students only stay in the UK only temporarily, it may not be commonly included in the notion of LTIM by the public. However, they are LTIMs to facilitate policies of student immigration reduction and fraud/illegal overstaying of visas.  In practice, however, it is the data collection and quality assurance that cause most concerns for many researchers.

Even the ONS migration data is only an estimate, as the source of its emigration data is the International Passenger Survey (IPS), where about 0.2% of those who enter and leave the UK were interviewed.  The emigrant status data also depend on people's self-reported intentions and does not differentiate different types of migrants (eg. workers, students, family migrants).  Immigration data are based on ONS' estimates of LTIM, which yield very different estimates from other sources, including the Home Office administrative data, which include two more sources on immigration of non-EEA citizens: entry clearance visas issued and passengers entering the UK through border control.  Such issues have been long recognised and scholars have suggested the combined use of various administrative data including GP registration, National Insurance Number and others to produce international administrative data including GP registration, National Insurance Number and others to produce international migration data (Wu, 2013).  Since then, the ONS have attempted to strengthen the international migration data collection, for instance, by working with the Home Office to include the Syrian Vulnerable Persons Relocation Scheme figures within the LTIM estimates. The first LTIM data to include figures from the expanded resettlement scheme were published in May 2016 and cover the year ending December 2015. However, the data needs a much more robust overhaul to be able to make a real difference from the current IPS based approach.

The international migration statistics often, such as in the case of the United National High Commissioner for Refugees (UNHCR), which is also chronically underfunded, have to rely on voluntary contributions mostly from governments. Such a situation makes it even more difficult to make the international migration data accurate to facilitate in policy and practical terms - in instituting a more effective response to migration issues.

Migration alters the population in many ways: demographically, socio-economicaly, spatially and culturally and the integration of immigrants is a complex process. Such complexity requires new tools to guide policy makers on a wide range of immigration-related issues, including social cohesion, labour market needs and changes, poverty and inequalities, or education and language skill formation. However, if these tools are to make any real impact, the migration data need a great push to improve first.

Reference: Wu, M. Belinda (2012) A hybrid microsimulation model for a UK city population with dynamic, spatial and agent based features. PhD thesis, University of Leeds.

Dr Belinda Wu joined The Open University in January 2016 as a Research Fellow in Development Policy and Planning. Upon completion of a PhD in Human Geography with the University of Leeds in 2013, she became the named researcher for the ESRC project SYLLS to generate a synthetic microdata for UK Longitudinal Study in the Centre for Advanced Spatial Analysis, UCL, where she also used a Complexity approach to study the International Development Aid for the EPSRC project Enfolding 2014-15.

Previously she has worked on two ESRC projects: Moses and Genesis, at the University of Leeds. Both ambitiously attempt to provide a generic modelling and simulation basis for various decision making support and e-Social Science research. She is also a Fellow of Royal Geographical Society (FRGS) and a Member of the Institute of Logistics and Transportation (MILT).

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