migrants from east/central europe: a new settlement pattern? mike coombescurds newcastle university...
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Migrants from East/Central Europe: a new settlement pattern?
Mike Coombes CURDS Newcastle University
Acknowledgements
CURDS colleagues Tony Champion Simon RaybouldAlison Stenning Ranald Richardson Cheryl Conway Stuart Dawley Liz Dixon
Funding *preliminary results, not to be quoted* Office of the Deputy Prime Minister (New Horizons)
Data access Home Office
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Structure of the talk
• Which migrants are the focus of the study?
• Outline of the statistical analysis….by area
• Establish ‘before’ pattern from Census data
• What can we know about the A8 migrants?
• CURDS results: A8 migrant LQs
• Overview of conclusions
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Which migrants is the study about?
Migrants from the “A8”countries: 8 former eastern bloc
countries which joined the EU in 2003 (the 2 other
countries joining at the same time were Malta and
Cyprus and they had different immigration status)
Estimates of A8 migrant numbers (made before 2003)
proved to be far too low because the calculations
did not reckon on other western European countries
deciding not to admit A8 labour migrants
A8 migrants registered in the UK have all gained work
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Migrant numbers reflect country size
Czech Rep
Estonia
Hungary
Latvia
Lithuania
Poland
Slovakia
Slovenia
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The Observer Sunday April 23, 2006Heather Stewart, economics correspondent
Migrants boost UK's growth Eastern Europe migrants have brought powerful benefits to Britain's economy since 10 new countries joined the European Union in 2004, according to research by the Ernst & Young Item Club. Item, which uses the Treasury's forecasting model, says interest rates are half a percentage point lower than they would have been without the influx of low-cost workers From the new members of the EU club. It calculates that economic growth will be boosted by 0.2 per cent this year, and 0.4 per cent in 2007.
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What of local impacts/implications?
“…people make places (the influx of new residents is
part of the construction of a new local uniqueness) …
bound into wider sets of social relations”
Doreen Massey & Pat Jess (1995 p.221)
A place in the world? Places, culture and
globalisation Oxford University Press
CURDS study for ODPM included both the summary
statistical analysis (as below), plus a brief look at the
detailed outcomes in Newcastle and Peterborough
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Outline of statistical analysis by areaFor the total migrant flow, and sub-groups by
nationality / job type / with(out) dependents
~ place each migrant in a local authority (LA)
~ calculate location quotients (LQs): ratio produced by dividing LA share of group by LA share of jobs)
~ summarise these to indicate clustering (sum overall LAs, absolute differences between LQs and 1.0)
~ correlate them vs. LAs’ employment rates
and vs. LAs’ Urbanisation Index values
~ compare vs. earlier migrant groups’ patterns
~ summarise within a simple classification
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Attraction of tighter labour markets?
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Establish ‘before’ pattern (in 2001)
Where were there already people who were born in
east/central Europe (ECE)? (new ECE migrants
may be attracted to these areas through social
relations or due to unique local facilities)
What of more recent migrant groups who may be more
similar to the ECE migrants? (new ECE migrants
known to be mainly young and unattached, and may
be well qualified and staying relatively short periods)
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E.Europe-born in 2001: ‘county’ LQs [ red = high blue = low ]
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Selected comparator country-of-birth (CoB) group 2001 location indicators: clustering/urbanisation/employment%
-0.25
-0.15
-0.05
0.05
0.15
0.25
0.35
0.45
co
rre
lati
on
s (
ba
rs)
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
clu
ste
rin
g
Urban correlation Employment rate correlation clustering (LA)
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employment rate correlation
-0.10 0.15urbanisation correlation
negative neutral positive
urban-concentrated
? strong0.35
Bangladesh Pakistan
India Greece Turkey
scattered/dispersed
? slightPoland(pre-A8)
BalticStates(pre-A8)
Cyprus Spain Hong Kong
0.08
employ-ment linked neutral
or negativeAustralia
South Africa
BOLD = clustered @ > .8
Typology of selected CoB groups
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Summary classification: 3 key types
Type Urban-isation
Employ-ment rate
Clust-ering
CoB examples
urban con-centrated
high low high South Asian groups; Turkey; Greece
employ-mentlinked
low high low Australia; S. Africa
scattered/dispersed
medium medium mixed Hong Kong; Cyprus; Spain; Baltic States (pre-A8); Poland (pre-A8)
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What is known about A8 migrants?
There is no data on the ‘stock’ of A8 migrants who are
in the UK at 1 point in time (ie. a Census equivalent)
Data available is from the Worker Registration Scheme
(WRS) and measures the ‘flow’ arriving to take jobs
There is no information on how long people stayed and
so many in the WRS data may have already left and,
in fact, may be in the data again having then returned
Some migrants from A8 countries are not in the WRS
(eg. they are self-employed or working illegally)
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A8 migrants in WRS (by end 2005)
345,000 applicants (up to 30% in UK before May 2004)
83% of registered workers aged between 18 and 34
97% have no dependants living with them in the UK
57% male
seasonal labour – probably in agriculture-related and
hospitality sectors – suggested by the summer peak
each year has seen a higher peak than the one before
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Concentration on London reducingWRS applications, by calendar quarter, May 2004 to September 2005, by region
(arranged by total applications for 17-month period)
0
1,000
2,000
3,000
4,000
5,000
6,000
7,000
8,000
9,000
10,000
Lond
on
Anglia
Midl
ands
Centra
l
South
Wes
t
South
Eas
t
Scotla
nd
North
Wes
t
North
Eas
t
North
ern
Irelan
d
Wale
s
Not st
ated
Q2 2004 Q3 2004 Q4 2004 Q1 2005 Q2 2005 Q3 2005
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WRS data: records of gate-keeping
CURDS analyses cover WRS data up to end June 2005
Postcode District (eg. PE4) of registration address
Gender Date of Birth Nationality
Outcome of Application
Number of Dependents (by whether under 16)
NOTHING on qualifications
Job Title & Job Description [not standard categories]
for example:other legal occupation – poultry catchermagician – production workerbarrister – waiter
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Job types identified by CURDS
A8 job typen
(England)%
totalmanagers and (semi-)professional workers 3374 1.8
other office-based workers 8182 4.4
hospitality/leisure/retail/wholesale workers 57178 30.4
personal service and domestic workers 18232 9.7
mechanics and transport or construction workers 15159 8.1
manufacturing/process/other low skilled workers 57027 30.3
agricultural and food processing workers 28806 15.3
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CURDS results: A8 migrant LQs
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Total n LQ Boston 2645 12.2 E. Cambridgeshire 1694 8.1 S. Holland 1959 7.0 King's Lynn & W. Norfolk 2756 5.8 Peterborough 3999 5.3 Fenland 1312 4.9 Luton 3275 4.6 Northampton 4123 4.4 Arun 1620 4.2 Herefordshire 2690 4.1
A8 inflow to English LAs: top 10 LQs(‘best fit’ of postcode districts to LAs)
(2645 migrants to Boston represent a share of the total A8 migrant flow to England which is 12.2 times higher than Boston’s share of all 2001 jobs in the country)
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Agriculture and food processing
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Hospitality retail and leisure workers
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Manufacturing/processing workers
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A8 country group location indicators: clustering/urbanisation/employment%
-0.25
-0.15
-0.05
0.05
0.15
0.25
0.35
0.45
TotalA
8
women
Poland
Lithuan
ia
Slova
kia
Latvi
a
Czech
Rep
Hungary
Estonia
Slove
nia
co
rre
lati
on
s (
ba
rs)
0.4
0.5
0.6
0.7
0.8
0.9
1.0
1.1
1.2
1.3
1.4
clu
ste
rin
g (
lin
e)
Urban correlation Employment rate correlation clustering (LA)
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Summary of results from analysisemployment rate correlation
-0.10 0.15 urbanisation correlationNegative neutral Positive
urban-concentrated
?
strong0.35
scattered/dispersed
?
slightCzech Republic Slovenia 0.08
all A8 in-migrants all A8
women Poland
Baltic States
Hungary Slovakia
employment-linked neutral
or negative
BOLD = clustered @ > .8
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Overview of conclusionsA new settlement pattern? Most similar to people from:
Baltic States / Poland (pre-2001 in-migrants)
Cyprus (‘A#9’) and Spain (previous Accession round)
Hong Kong (classic example of ‘scattered/dispersed’)
...BUT… less urban-focussed than most of these
more ‘white-collar’ workers more focussed on cities
Methodological issues and/or future research needs
Findings robust despite analysing flow and not stock?
Other data sources (NI records, LFS…?) allow analyses
of qualifications/pay/…? ABOVE ALL length of stay