Migrants from East/Central
Europe: a new settlement pattern?
Mike Coombes
CURDS
Newcastle University
Acknowledgements
CURDS colleagues
Tony Champion Simon Raybould
Alison 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
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
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
Migrant numbers reflect country size
Czech Rep
Estonia
Hungary
Latvia
Lithuania
Poland
Slovakia
Slovenia
The Observer
Sunday April 23, 2006
Heather 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.
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
Outline of statistical analysis by area
For 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 over
all 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
Attraction of tighter labour markets?
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)
E.Europe-born in 2001: ‘county’ LQs
[ red = high  blue = low ]
Selected comparator country-of-birth
(CoB) group 2001 location indicators:
clustering/urbanisation/employment%
Urban correlation
Employment rate correlation
clustering (LA)
1.4
0.45
1.3
1.2
1.1
0.25
0.15
0.9
0.05
-0.25
0.6
B
al
tic
St
at
es
e
G
re
ec
ai
n
Sp
ey
rk
Tu
la
nd
Po
s
C
yp
ru
g
on
K
on
g
H
A
us
t
ra
ic
a
S.
A
fr
sh
B
an
gl
ad
e
st
an
Pa
ki
-0.15
lia
0.7
di
a
-0.05
0.8
0.5
0.4
clustering
1.0
In
correlations (bars)
0.35
Typology of selected CoB groups
employment rate correlation
-0.10
0.15
negative
neutral
positive
urbanisation
correlation
urban-concentrated
Bangladesh
Pakistan
India
Greece
Turkey
?
strong
0.35
scattered/dispersed
Poland(preA8)
BalticStates(pr
e-A8)
Cyprus Spain
Hong Kong
?
0.08
employment linked
Australia
South Africa
BOLD = clustered @ > .8
slight
neutral
or
negative
Summary classification: 3 key types
Type
Urbanisation
Employment rate
Clust- CoB examples
ering
urban con- high
centrated
low
high
South Asian
groups; Turkey;
Greece
employment
linked
low
high
low
Australia; S. Africa
scattered/
dispersed
medium medium
mixed Hong Kong; Cyprus;
Spain; Baltic States
(pre-A8); Poland
(pre-A8)
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)
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
Concentration on London reducing
WRS applications, by calendar quarter, May 2004 to September 2005, by region
(arranged by total applications for 17-month period)
10,000
9,000
Q2 2004
Q3 2004
Q4 2004
Q1 2005
Q2 2005
Q3 2005
8,000
7,000
6,000
5,000
4,000
3,000
2,000
1,000
te
d
es
st
a
ot
N
la
Ire
er
n
th
or
N
W
al
nd
st
Ea
th
N
or
N
or
th
W
es
t
nd
Sc
ot
la
st
Ea
h
So
ut
es
t
W
So
ut
h
en
tra
l
C
nd
s
M
id
la
ia
An
gl
Lo
nd
on
0
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 catcher
magician – production worker
barrister – waiter
Job types identified by CURDS
n
(England)
%
total
3374
1.8
8182
4.4
hospitality/leisure/retail/wholesale workers
57178
30.4
personal service and domestic workers
mechanics and transport or construction
workers
manufacturing/process/other low skilled
workers
18232
9.7
15159
8.1
57027
30.3
agricultural and food processing workers
28806
15.3
A8 job type
managers and
(semi-)professional workers
other office-based
workers
CURDS results: A8 migrant LQs
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)
Total
Boston
E. Cambridgeshire
S. Holland
King's Lynn & W. Norfolk
Peterborough
Fenland
Luton
Northampton
Arun
Herefordshire
n
2645
1694
1959
2756
3999
1312
3275
4123
1620
2690
LQ
12.2
8.1
7.0
5.8
5.3
4.9
4.6
4.4
4.2
4.1
Agriculture and food processing
Hospitality retail and leisure workers
Manufacturing/processing workers
A8 country group location indicators:
clustering/urbanisation/employment%
Urban correlation
Employment rate correlation
clustering (LA)
1.4
0.45
1.3
0.35
1.2
0.15
0.9
0.05
0.8
ov
en
ia
Sl
a
ni
Es
to
ry
un
ga
H
0.6
C
ze
ch
R
ep
La
tv
ia
ov
ak
ia
Sl
ia
ua
n
Li
th
Po
w
om
en
8
A
ta
l
-0.15
la
nd
0.7
-0.05
0.5
-0.25
0.4
clustering (line)
1.0
To
correlations (bars)
1.1
0.25
Summary of results from analysis
employment rate correlation
-0.10
0.15
Negative
neutral
Positive
urbanisation
correlation
urban-concentrated
strong
0.35
?
scattered/dispersed
Czech
Republic
Slovenia
all A8 inmigrants
all A8
women
Poland
Baltic States
slight
?
employmentlinked
Hungary
Slovakia
BOLD = clustered @ > .8
0.08
neutral
or
negative
Overview of conclusions
A 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