BSPS Annual Conference, University of St Andrews,
11-13 September 2007
Poles apart? Assessing whether labour
migration to England from the A8
countries has a distinctive geography
Mike Coombes and Tony Champion
mike.coombes@ncl.ac.uk tony.champion@ncl.ac.uk
Acknowledgements:
Simon Raybould for the maps
Poles apart? Assessing whether labour
migration to England from the A8
countries has a distinctive geography
• Background to A8 migration
• Data and approach
• Comparison of A8 migration with earlier total
international immigration
• Comparison of A8 and other labour
immigration using NINo data for 2005-06
• Analysis of the ‘drivers’ of A8 and other
labour migration
• Main findings and conclusions
Background to A8 migration
• EU enlargement in May 2004: 10 countries
comprising Malta, Cyprus and 8 Accession (A8)
countries from Central & Eastern Europe
• UK, Ireland and Sweden opened borders fully
from outset, but transitional arrangements
made by the other 12 EU countries
• Large numbers have registered for work in UK
(>0.6 million under WRS alone), though length
of stay not known (so no ‘stock’ counts)
• Aim of study: to assess how far this workrelated (and mainly short-term) migration has a
geography different from other inflows
Data and approach
• Analyse data on A8 labour migrants
using data from:
- Workers Registration Scheme (WRS) set up
in UK for employed A8 migrants (but not selfemployed), covering first 12 months of work
- National Insurance registrations (NINOs),
covering both employed and self-employed
• Focus on England
- as principal destination of A8 migrants
• Use TTWAs (170 best-fits from LA/UAs)
- consistent with work-related emphasis
- raises likelihood of residence and workplace
being in same zone (for multivariate analyses)
Comparison of A8 migration with earlier
total international immigration
Six migration inflows to be compared:
• WRSp1: WRS registrations in the first 14 months
(May 2004 to June 2005 inclusive)
• WRSp2: WRS registrations over the next 18 months
(July 2005 to Dec 2006 inclusive)
• NI0506A8: NINo registrations by A8 nationals
(year ending June 2006)
• NI0506all: NINo registrations by all foreign nationals
(year ending June 2006)
• CensusEA: Census-based counts of economically
active residents living outside the UK one year before
• IPS0102: IPS-based estimates of immigration from
outside the UK and Republic of Ireland for 2001-02
Distribution of England’s immigrant flows
between TTWA size groups
NB. Bold = overrepresentation cf 2001 pop (ie. LQ>1.0)
TTWA size
groups
Total
pop
2001
WRS
P1
WRS
P2
London
15.3
24.1
13.0
Other 1m+
10.1
8.8
9.8
0.5-0.7m
NI
0506
A8
NI
0506
all
Census
EA
IPS
0102
9.3
23.3
12.5
36.5
13.1
5.6
8.6
7.2
6.6
5.4
6.2
14.2
10.8
13.0
12.4
10.9
12.1
11.1
0.4-0.5m
9.2
5.5
7.2
5.8
5.9
7.4
7.1
0.25-0.4m
13.3
9.7
8.4
7.7
16.3
15.5
10.8
11.2
10.0
<125k
11.7
17.6
18.0
13.4
13.1
125-250k
14.9
18.0
12.2
10.2
6.6
8.2
5.3
100.0
100.0
100.0
100.0
100.0
0.7-1m
Total
35.9 36.6
11.6 16.0
100.0 100.0
Location Quotients, by TTWA size group,
for NINO registrations 2005-06
2.5
A8
All foreign
Location Quotient
2.0
1.5
1.0
0.5
0.0
London Other
1m+
0.71.0m
0.50.7m
0.40.5m
0.250.4m
125250k
<125k
Location Quotients for 2005-06 NINOs:
A8 compared with All foreign, 170 TTWAs
A8
All foreign
Location Quotients for 2005-06 NINOs:
top 10 TTWAs for All foreign, A8 & Other
All foreign
LQ
A8
LQ
Other
LQ
1
Boston
3.520
Boston
7.148
London
2.943
2
Peterborough
2.755
Peterborough
5.370
Slough&Woking
2.501
3
London
2.386
Spalding&Holbeach
3.900
Cambridge
1.582
4
Slough&Woking
2.383
Wisbech
3.394
Oxford
1.503
5
Spalding&Holbeach
2.126
Hereford/Leominster
2.913
MiltonKeynes
1.348
6
Cambridge
1.687
Luton
2.388
Brighton
1.256
7
Luton
1.661
Slough&Woking
2.200
Reading
1.255
8
Wisbech
1.602
Kettering&Corby
2.173
Mildenhall
1.245
9
Mildenhall
1.567
Northampton
2.125
Leicester
1.213
10
Oxford
1.401
Mildenhall
2.046
Luton
1.199
Location Quotients (logged) for 2005-06 NINOs:
A8 compared with Non-A8
0.5
(O is crossover of LQ=1.0, unlogged)
r = 0.557
0.0
Non-A8
O
-0.5
-1.0
-1.5
-1.0
-0.5
0.0
A8
0.5
1.0
A8/Poles apart? Correlation analysis
•
•
•
•
•
•
Just seen relationship between A8 and all non-A8,
r=0.557
How far does the A8’s LQ pattern across 170 TTWAs
compare with that for country groups and individual
countries?
Similarly, how does that for Polish nationals differ
from that for the other A8 countries?
Correlation analysis, using the publicly available
NINO dataset for 2005-06 (data rounded to 10s)
Log of recalculated LQs (nb. 10 added to all NINO
raw counts (to remove zeros in the rounded raw data)
For country groups (all non-A8, EU14, rest) and
selected countries with 7,000+ NINO registrations
A8 apart? Correlations (r) of logged LQs with
country groups, and selected countries (ranked)
Country group
r with A8
Country
r with A8
All non-A8
0.557
Portugal
0.529
EU14
0.624
South Africa
0.486
Rest of world
0.485
India
0.357
Italy
0.346
Spain
0.260
USA
0.236
Australia
0.200
New Zealand
0.182
Bangladesh
0.122
Pakistan
0.120
Poles apart? Correlations (r) with logged LQs
of other A8 nationals (ranked)
Country group /
Country
r with Poles
All A8
0.946
All A8 excl Poles
0.663
Lithuania
0.508
Latvia
0.504
Slovak Rep
0.484
Czech Rep
0.387
Hungary
0.279
Estonia, Slovenia
N <7k
A8/Poles apart? Cluster analysis
• Cluster analysis to identify communality of log-LQ pattern
across the 170 TTWAs
• All A8 countries with >7k NINO registering 2005-06, plus
ten countries representing southern EU, New World, S Asia
• K-mean cluster analysis, requesting 5 clusters, produces:
Cluster 1
Cluster 2
Hungary
India
Portugal
Bangladesh
Cluster 3
Pakistan
Cluster 4
Cluster 5
Czech Rep
Australia
Latvia
Italy
Lithuania
New
Zealand
Poland
S Africa
Slovak Rep
Spain
USA
A8/Poles apart? Explanatory analysis
What features of the geographical context are
correlated with the NINO-based patterns?
Selection of ‘drivers’ to test the effect of:
•
•
•
•
•
•
•
local economic structure, e.g. % agric, manufacturing,
construction/transport, retail/hospitality, other sectors
tightness of local labour market, e.g. employment rate
(for all, those with degrees, those without quals)
population size (& log pop), urbanization index
population composition (% non-white, % unqualified)
previous migration (net internal migration rate, net
international migration rate, % born in E Europe)
housing costs (unaffordable housing index)
regional location (South vs North)
Simple correlation (r) with logged LQs of A8
and Non-A8: significant at 5% or better, ranked
A8
Non-A8
% born in Eastern Europe
% born in Eastern Europe
% working in retail & hospitality
% non-white
Employment rate, for all
Log population 2001
Net international migration rate
Urbanization index
% non-white
Net internal migration rate
Employment rate, for no quals
Net international migration rate
% working in manufacturing
Population 2001
South (binary cf North)
% working in other sectors
% working in agriculture etc
% with no qualifications
nb.- bold italics denotes
negative correlation
% working in manufacturing
A8/Poles apart? Regression analysis
• Regression analysis to identify the separate key ‘drivers’
and their relative importance for the selected migrant groups
• Need to omit variables that are highly (r=>0.6) correlated
with each other, with labour market emphasised in selection:
Selected
AGRIC
MANUF
CONTRAN
RETHOSP
EMPRATQ0
EMPRATQ4
NOQUAL
BORN1EEU
NTIM0203
SOUTH
Excluded because r=>0.6 with selected variable
LOGPOP01 (-), URBINDEX (-), NTIN0203 (+)
UNAFFORD(-), OTHIND (-)
EMPRATOT (+)
EMPRATOT (-), OTHIND (-)
MYEPOP01 (+), NONWHITE (+)
Regression results for A8 versus NonA8
NB. Bold red = significant at 5% level, N = 170 TTWAs
Variable [other variables with r=>0.6]
Agriculture etc [-logpop, -urb, +mig]
Manufacturing [-unafford, -othind]
A8
0.252
0.122
NonA8
-0.216
-0.065
Construction & transport
Retail & hospitality
No-quals employment rate [+emprat]
With degree employment rate
0.117
0.432
0.181
-0.049
0.044
0.236
0.070
0.010
No qualifications [-emprat, -othind]
Born in East Europe [+pop, +nonwhite]
0.001
0.496
-0.083
0.510
Net international migration rate
0.220
0.280
0.118
(0.412)
0.120
(0.597)
South
(Adjusted R2)
Regression results for A8 versus NonA8
Standardised (beta) coefficient
-0.3
Agric etc
Manufacturing
Constr & transp
Retail & hosp'y
No-quals empl rate
With degree empl rate
No qualifications
Born in E Europe
Net immigration
South
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
A8
NonA8
0.6
Regression results for Poles vs Other A8
NB. Bold red = significant at 5% level, N = 170 TTWAs
Variable [other variables with r=>0.6]
Agriculture etc [-logpop, -urb, +mig]
Manufacturing [-unafford, -othind]
Construction & transport
Retail & hospitality
No-quals employment rate [+emprat]
With degree employment rate
No qualifications [-emprat, -othind]
Born in E Europe [+pop, +nonwhite]
Net international migration rate
South
(Adjusted R2)
Poles
Other A8
0.275
0.189
0.122
0.261
0.020
0.075
0.469
0.133
-0.076
-0.149
0.468
0.240
0.052
(0.378)
0.271
0.168
-0.050
0.200
0.448
0.155
0.209
(0.338)
Regression results for Poles vs Other A8
Standardised (beta) coefficient
-0.2
Agric etc
Manufacturing
Constr & transp
Retail & hosp'y
No-quals empl rate
With degree empl rate
No qualifications
Born in E Europe
Net immigration
South
-0.1
0.0
0.1
0.2
0.3
0.4
Poland
Other A8
0.5
Poles apart? Main findings and conclusions
• A8 inflow is less focused on London than total
immigration is, but still more than ‘expected’
• More A8s going to smaller TTWAs than for total inflow,
but NINO-based share smaller than WRS-based
• A8 patterning across 170 TTWAs is closer to EU14
than Rest of World, and most similar to Portugal
• 5 of the 6 largest A8 national inflows cluster in one
group by themselves – Hungary with just Portugal
• Poles parallel Rest-A8 for pull of areas with % born in
East Europe, % agric, net immig and retail/hospit’y, but
differ on no-quals (-/+) and manufacturing (++/+)
• A8 aggregate differs from non-A8 on % agric (+/-),
manufacturing (+/-); also pulled more by retail/hospit’y,
constr/transp & empl rate among no-quals. But similar
response to EEurope-born, South & net immig.
• Much ‘unexplained’; check for proxy variables.
BSPS Annual Conference, University of St Andrews,
11-13 September 2007
Poles apart? Assessing whether labour
migration to England from the A8
countries has a distinctive geography
Mike Coombes and Tony Champion
mike.coombes@ncl.ac.uk tony.champion@ncl.ac.uk
Acknowledgements:
Simon Raybould for the maps
Annex 1: NINO 2005-06, descriptive stats for LQs of
selected country groups and countries, 170 TTWAs
De scri ptive Statistics
TOTAL
A8
POLAND
A8NPOL
LITH
SLOVA K
LA TVIA
CZECH
NONA 8
EU14
NONE UA
INDIA
SA FRIC
OZ
PA KI
FRANCE
GE RMAN
CHINA
NIGERI
PORTUG
ITA LY
SP AIN
IRE LA N
US A
BA NGLA
PHILIP
HUNGAR
NZ
Valid N (lis twis e)
N
St atist ic
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
170
Minimum
St atist ic
.105
.090
.107
.000
.000
.000
.000
.000
.108
.000
.070
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
.000
Maximum
St atist ic
3.520
7.195
5.874
9.572
15.800
6.760
22.610
4.229
2.943
3.147
2.881
4.003
9.604
4.248
5.967
3.478
3.446
4.745
4.014
16.493
3.888
5.459
2.990
9.620
3.700
3.159
4.757
4.083
Mean
St atist ic
.65701
.91250
.90613
.92395
.95413
.89037
1.12220
.84597
.49420
.49300
.49457
.51871
.64004
.32716
.45146
.41753
.50842
.62193
.26090
.97688
.37657
.53816
.45522
.56016
.43062
.87631
.72088
.34558
St d.
Deviation
St atist ic
.465795
.808327
.714070
1.111269
2.000129
.770363
2.212179
.664567
.381688
.514167
.367227
.537051
.890535
.451872
.906435
.492320
.523568
.748955
.502624
2.125768
.508221
.716413
.498228
.919616
.489831
.618748
.873818
.534190
Sk ewness
St atist ic
St d.
2.918
4.442
3.317
4.839
5.172
3.436
6.659
1.602
2.946
2.508
3.117
3.521
6.517
5.426
3.402
3.048
2.565
2.359
4.020
5.477
3.258
3.256
1.891
6.394
3.287
.787
2.470
3.767
E rror
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
.186
Annex 2: List of independent variables
Label
Description
UNAFFORD
URBINDEX
NONWHITE
NOQUALIF
EMPRATOT
EMPRATQ0
EMPRATQ4
NTIM0203
NTIN0203
BORN1IRE
BORN1EEU
BORN1CZE
BORN1POL
BORN1BAL
AGRIC
MANUF
CONTRAN
RETHOSP
OTHIND
MYEPOP01
LOGPOP01
NOSO
Unaffordable Housing Index 2003
Urbanization Index 2001
% non-white 2001
% all 16-74 unqualified 2001
% 16-PA employed 2003/4
% unqualified in 16-74 employed 2003/4 (also EMPR0)
% with degree in 16-74 employed 2003/4 (also EMPR4)
Net total international migration 2002-03 (also NETIM)
Net internal migration 2002-03
% born in Republic of Ireland 2001
% born in Eastern Europe 2001 (also BNEEU)
% born in Czech Republic 2001
% born in Poland 2001
% born in Baltic States 2001
% employed in agriculture etc 2001
% employed in manufacturing 2001
% employed in construction or transport 2001 (also CONTR)
% employed in retail or hospitality 2001 (also RETHP)
% employed in other sectors 2001
total population 2001
log of 2001 total population
South (cf North)
Regression results for 5 largest A8 NINOs
NB. Bold red = significant at 5% level, N = 170 TTWAs
Variable
Poland
Lithuania
Latvia
Slovak
Czech
Agriculture etc
0.275
0.437
0.415
0.330
0.474
Manufacturing
0.189
-0.014
-0.010
0.182
0.030
Constr & transport
0.122
0.039
0.002
0.130
0.072
Retail & hospitality
0.469
0.137
0.174
0.212
0.052
No-quals empl rate
0.133
0.078
0.130
0.020
0.021
With degree empl rate
-0.076
-0.002
-0.037
-0.026
-0.048
No qualifications
-0.149
0.337
0.385
-0.216
-0.140
Born in East Europe
0.468
0.351
0.307
0.359
0.349
Net immigration rate
0.240
0.083
0.021
0.113
0.140
South
0.052
0.284
0.186
0.078
-0.027
Adjusted R2
0.378
0.341
0.311
0.183
0.235
Regression results for 5 largest A8 NINOs
Standardised (beta) coefficient
-0.3
-0.2
-0.1
0.0
0.1
0.2
0.3
0.4
0.5
Agric etc
Manufacturing
Constr & transp
Retail & hosp'y
Poland
No-quals empl rate
With degree empl rate
Lithuania
Latvia
Slovak
No qualifications
Born in E Europe
Net immigration
South
Czech
0.6