Distributing Immigrants to Local AuthoritiesHelena Howarth and Ben WinkleyGSS Methodology Symposium: 6 July 2011

OverviewSection 1:BackgroundOverview of current methodOverview of proposed method

Section 2:Detailed description of the student stream

BackgroundThe Migration Statistics Improvement Programme is a cross government initiative.

MSIP has already led to changes in the migration estimates.

Currently developing a distributional approach to LA level migration estimates using administrative data.

Why are the estimates important?

Population estimates are used for calculating funding allocations for local government: schools, hospitals etc.

In 2009 natural change became the largest component of population change

Overview of the current methodThe International Passenger Survey samples 250k people at ports a small percentage of whom are migrants.Produces a robust national level migration estimate.Produces Intermediate Geography estimates (between Region and LA).LA level estimates modelled using information from admin sources, the Census and other indicators.ONSIPSWelcome to StanstedONS

Why change?Some of the issues with the current method are:The IPS sample is not robust enough for distribution to LA levelThe method is not transparent for usersIt includes centralising tendencyThe IPS is intentions based

The new method is designed to solve these issues.

Overview of the distribution methodIPS long-term in-migration is split into different streams by reason for visit Workers

Students

Returning migrants

Others

StudentsStudents can be HE or FEData for split provided by IPS 2004/2005HE can be at government or private institutions.There is no private/government split for FE

HE data from HESA and FE data from BIS and WAG.11%20%69%

Higher Education GovernmentSource: Students in Higher Education Institutions 2008/09 Table 9

Higher Education Government(1)Sub-setting HESA data(2)Linking to other administrative sources(3)Imputation of missing term-time addresses

Higher Education - PrivateHESA conducted a census of Private Providers of HE education in 2010.

This asked for aggregate data by: 1) mode of study2) level of study 3) domicile 4) Subject

Institution address used to allocate students: Outside London Term-time address distribution of HESA government institutions in the LA where available and LA of institution where not.London Term-time address distribution of all HESA government institutions in the London GOR

Further EducationDatasets:Individualised Learner Record (BIS)Lifelong Learning Wales Record (Welsh Government)

Key Points:Domicile knownTerm-time address not knownNo length of stay dataCoverage based on funding

Any Questions?

In this presentation I will talk through the current method for estimating LA level long-term (LT) international in-migration.Helena with then discuss in detail the methodology on one of the four streams we have split migrants into. That defined by the study reason for migration.

The Migration Statistics Improvement Programme was set up as a cross-government initiative to improve migration estimates as, at the time, estimates were not widely trusted amongst users.

The MSIP VisionMigration and Population Statistics meeting user needs:at the right time covering the relevant populations measuring change accurately (national and local) detecting turning pointsAnd trusted as authoritative by highly engaged users:based on range of developed best up-to-date sources using enhanced transparent sustainable statistical methods with quality measures (and low gap with 2011 Census)

The programme has already led to changes in how migration statistics are created that led to revisions to the estimates.

2007 Improvements:Addressed issues of centralising tendencyLFS to distribute immigration to UK countries/GORsA new intermediate geography (NMGi)2001 Census used to distribute to LAs

2010 Improvements:Census distributions replaced by a model-based approachIPS LA estimate is dependent variableModel uses a range of data sources including administrative data

We are currently working on a alternative to the modelling approach to distributing migrants as it is not transparent to users, is hard to explain and leads to some centralising tendency. (i.e. more migrants are distributed to cities than there should be as those going to areas surrounding big cities often name the city itself rather than the less well known area nearby)

This method aims to use the administrative sources that ONS has gained access to through the powers imbued by the Statistics and Registration Services Act 2007. These sources will be used to distribute IPS totals for England and Wales to local Authority Levels based on IPS totals split by reason for migration.

The migration estimates are important because they feed into the LA level population estimates. These estimates are used in the calculation of funding allocations for local government as well as provision of schools, hospitals, public transport etc.

Migration is a major component of change, in fact the graph shows that in the years to mid-2008 and mid-2009 it accounted for approximately half total population growth and that in years prior to that it was a larger component of change than natural change (births and deaths).

In addition to the non-statistical purposes a accurate population count by LA is important in the production of statistics involving percentages and rates. For example its no use knowing that LA A has 25 teenage pregnancies in a year and that LA B had 50 without knowing their respective population sizes.

The International Passenger survey (the IPS) counts visitors and migrants into and out of the UK. It is a sample survey of 250,000 people and used for several statistical products including the National Accounts and Migration estimates.

Passengers are surveyed at various times in all major ports and airports in the UK. The surveys produces a robust count of Migration at National Level. This has been made more robust by increasing sample sizes at airports known to be routes of entry into the UK such as Stansted, Manchester and Luton.

The estimates are actually robust enough to use below national level, but not all the way down to LA level, so a intermediate geography was produced which groups small numbers of LAs.

The LA level estimates are then modelled using the Intermediate geography as the total for all LAs in the group.

The model is sometimes confusing for users as it is difficult to explain exactly why the numbers of migrants increased or decreased in a year. Each available variable went into a regression model and estimates were produced, but there was no explanation for why a change was observed.The fact that the model is hard to explain to users and the lack of transparency as to what causes the changes is only one of the reasons why ONS is proposing further changes.

Other issues include the fact that we cannot currently distribute directly to LA level and we have to use the Intermediate geographies. Not all users agree with the Intermediate geographies as in some cases they group LAs with very different migrant profiles.

Thirdly, there is some centralising tendency the IPS asks migrants where they are going in the UK many of them may say London because its the capital city and they know it when in actual fact they wont end up there. A similar thing may happen with the big cities around the UK if someone is headed to a small LA outside of a big city, for example Bury outside Manchester, they might say Manchester when they arent actually going there.

Lastly the IPS is intentions based, i.e. they may stay they are going to London but actually move very quickly onwards to another city such as Birmingham.

The first step in the proposed method is to split the IPS LT in-migration by different reasons for visit, which we will call streams. This is done using the responses to two IPS questions What is your reason for visit to the UK and Have you previously lived on the UK. This creates four streams.

The reason for splitting the national level in-migration total into streams is because groups of migrants have tendencies to go to different places in the UK. For example migrants who have lived in the UK before tend to behave differently to new migrants. They have more of an idea of where they are going and are less likely to go to London. Also students tend to migrate to places in the UK with a university, whereas workers will go to places they think they can find work.

The rest of this presentation will focus on the student stream which is the second largest stream and is becoming increasingly important with the provisional 2010 figures showing 46% of all migrants (including returning) coming for study compared to only 36% coming for work!Hi, Im Helena and I was working specifically on the student stream of the distributional approach.

In order to create a student distribution we had to consider the different types of students. We split students into three groups HE at government institutions, HE at Private institutions and FE. These groups have very different distributions of institutions, for example you get FE colleges in even quite small towns whereas its rare to get a University outside of a city and private education is predominately in London.

The proportions assigned to each group comes from a variety of sources. The 80/20 split between FE and HE comes from historical IPS data as in 2004 and 2005 migrant students were asked what type of education they were coming for. The split of HE into government and private comes from the results of some research on VISA applications done by the Home Office and was validated by looking at the total number of students in the datasets we obtained relating to these two groups.

The importance of the accuracy of this split was tested as we were applying it from mid2005 onwards and we found that if it remained within 2.5% of that calculated it did not significantly change the estimates. In this case significance was deemed as an LA level change of over 100 individuals in the migration estimate or the absolute change times the percentage change being greater than 15 (with absolute change being over 15).

In addition we have added an extra question into the 2011 IPS so that migrant students will once again be asked what type of education they are coming for. These splits will be fed into migration estimates in the future.

Each group is then distributed differently as different administrative data for each group available at varying levels of detail. Data on HE students at government institutions has been provided by HESA at record level for all years. Aggregate data on HE students at Private institutions is available for 2009/10 only and aggregate level FE data is available from BIS and the Welsh Government for England and Wales respectively.The main group is the HE students at government institutions. These are distributed according to the proportions of non-UK domiciled students in the HESA student record. The graph comes from HESAs published data and shows how migrant students distribute differently to students in general even at the GOR level.

However further subtleties are found within the record level data and thus processing occurs in three stages:

Firstly we subset the HESA data to take out various groups including those:With a UK domicileNot in their first year of studyNot 17 59 years oldDistance LearningAt a campus abroad

We also use the length of the course to identify long and short-term migrants. This is not a perfect method as migrants may stay beyond their length of course however this is still a better split than possible for other streams.

Secondly we link the HESA data longitudinally to identify those students who are in the first year of their course but for whom this is a second course in the UK. These are removed as these individuals are not new migrants. We also link to the Migrant Workers Scan to identify those students that arrived over three months before the start of study. These are all removed. Those that still arrived in the year of interest are removed as they are thought to be part of another stream likely the workers since they have registered for a NINo, and those that arrived in a previous year are not part of the current inflow.

We also linked the data to the PRD but found that having already linked to other years of HESA and the MWS the effect of removing additional people through this was insignificant and so it has not been incorporated into the method.

Thirdly we have to impute missing term-time addresses. This is because the proportion of term-time addresses that are not available changes by campus and hence without imputation there is bias against certain cities. The imputation is done by using the known distribution of term-time LAs for the campus of those with no term-time LA. Additionally this variable was only introduced into the dataset from 2007/08 onwards. Hence all addresses for 2005/06 and 2006/07 have to be imputed. To do this we took the average distribution for 2007/08 through to 2009/10 by campus. Where the campus closed down before any term-time address information was collected the campus LA was input for all students however this affected less than 200 individuals per year.

The lack of data on Private Education in the UK has been previously noted and in 2010 the Higher Education Statistics Agency conducted a Census of private providers of HE. Although this may not be a full census HESA is confident of its results. This census asked for aggregate totals by mode of study, level of study, domicile and subject. This allowed us to subset the data to take out those with a UK domicile, distance learning or studying at FE level.

However year and total length of study of study was unknown. This is not a major issue as we are only using the data to provide a distribution not a count. The major issue is that we only have one years worth of data hence we have had to apply the same institution distribution to all years for which we are producing figures. We hope that this data collection will continue into the future.

Lastly the term-time address is not known. To get around this we used the distribution of Government HE institutions in the LA where possible outside of London. If there was no Government Institution in the LA we assigned all migrants students in that institution to the LA of the institution. Within London a slightly different method was used. We combined the term-time address distributions for all Government Institutions in the London GOR and used this to distribute all Private Institutions in the GOR. This is because in London you are far less likely to live in the LA of your institution (~25% versus ~85% outside of London) and hence assigning those with no HESA Student Records distribution to the LA of the institution would be incorrect.

Further education includes all adult education below degree level. This includes vocational courses and learning English as a second language. England and Wales deal with FE separately so two equivalent sources are combined.

There are some issues with using this data for example although the domicile country is given this is the country in which the majority of the last three years was spent so you could be part of the previous years migrant inflow and still have a domicile abroad.

In addition the current address is not known and hence students have to be distributed using the campus address. As FE colleges are more prevalent that HE colleges it is assumed that he majority of student attend a college in their home LA.

There is no length of stay data on the source not even the length of course which is what is used to estimate length of stay in the HESA Student Record. Hence we have to assume that LT and ST FE student migrants distribute similarly.

Lastly institutions only have to provide information on those students who are government funded. Although some institutions voluntarily give information on all students. So coverage is not full. As Non-EU students cannot receive government funding for a certain period after migration this biases the dataset against non-EU migrants and so we have to assume they are distributed similarly to EU migrants.