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 Championmike.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 work-related (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 self-employed), 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

WRSP1

WRSP2

NI0506A8

NI0506

all

Cen-susEA

IPS0102

London 15.3 24.1 13.0 23.3 36.5 35.9 36.6

Other 1m+ 10.1 8.8 9.3 12.5 13.1 11.6 16.00.7-1m 9.8 5.6 8.6 7.2 6.6 5.4 6.2

0.5-0.7m 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 14.9 17.6 13.1 9.7 8.4 7.7

125-250k 16.3 18.0 18.0 15.5 10.8 11.2 10.0

<125k 11.7 12.2 13.4 10.2 6.6 8.2 5.3

Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Location Quotients, by TTWA size group, for NINO registrations 2005-06

0.0

0.5

1.0

1.5

2.0

2.5

London Other1m+

0.7-1.0m

0.5-0.7m

0.4-0.5m

0.25-0.4m

125-250k

<125k

Lo

cati

on

Qu

oti

ent

A8 All foreign

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

-1.0

-0.5

0.0

0.5

-1.5 -1.0 -0.5 0.0 0.5 1.0

A8

No

n-A

8

r = 0.557

O

(O is crossover of LQ=1.0, unlogged)

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 1 Cluster 2 Cluster 3 Cluster 4 Cluster 5

Hungary India Pakistan Czech Rep Australia

Portugal Bangladesh Latvia Italy

Lithuania New Zealand

Poland S Africa

Slovak Rep Spain

USA

• 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:

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 Excluded because r=>0.6 with selected variable

AGRIC LOGPOP01 (-), URBINDEX (-), NTIN0203 (+)MANUF UNAFFORD(-), OTHIND (-)CONTRAN

RETHOSP

EMPRATQ0 EMPRATOT (+)EMPRATQ4

NOQUAL EMPRATOT (-), OTHIND (-)BORN1EEU MYEPOP01 (+), NONWHITE (+)NTIM0203

SOUTH

Regression results for A8 versus NonA8 NB. Bold red = significant at 5% level, N = 170 TTWAs

Variable [other variables with r=>0.6] A8 NonA8

Agriculture etc [-logpop, -urb, +mig] 0.252 -0.216

Manufacturing [-unafford, -othind] 0.122 -0.065

Construction & transport 0.117 0.044

Retail & hospitality 0.432 0.236

No-quals employment rate [+emprat] 0.181 0.070

With degree employment rate -0.049 0.010

No qualifications [-emprat, -othind] 0.001 -0.083

Born in East Europe [+pop, +nonwhite] 0.496 0.510

Net international migration rate 0.220 0.280

South 0.118 0.120

(Adjusted R2) (0.412) (0.597)

Regression results for A8 versus NonA8

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6

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

Standardised (beta) coefficient

A8

NonA8

Regression results for Poles vs Other A8 NB. Bold red = significant at 5% level, N = 170 TTWAs

Variable [other variables with r=>0.6] Poles Other A8

Agriculture etc [-logpop, -urb, +mig] 0.275 0.261

Manufacturing [-unafford, -othind] 0.189 0.020

Construction & transport 0.122 0.075

Retail & hospitality 0.469 0.271

No-quals employment rate [+emprat] 0.133 0.168

With degree employment rate -0.076 -0.050

No qualifications [-emprat, -othind] -0.149 0.200

Born in E Europe [+pop, +nonwhite] 0.468 0.448

Net international migration rate 0.240 0.155

South 0.052 0.209

(Adjusted R2) (0.378) (0.338)

Regression results for Poles vs Other A8

-0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5

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

Standardised (beta) coefficient

Poland

Other A8

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 Championmike.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

Descriptive Statistics

170 .105 3.520 .65701 .465795 2.918 .186

170 .090 7.195 .91250 .808327 4.442 .186

170 .107 5.874 .90613 .714070 3.317 .186

170 .000 9.572 .92395 1.111269 4.839 .186

170 .000 15.800 .95413 2.000129 5.172 .186

170 .000 6.760 .89037 .770363 3.436 .186

170 .000 22.610 1.12220 2.212179 6.659 .186

170 .000 4.229 .84597 .664567 1.602 .186

170 .108 2.943 .49420 .381688 2.946 .186

170 .000 3.147 .49300 .514167 2.508 .186

170 .070 2.881 .49457 .367227 3.117 .186

170 .000 4.003 .51871 .537051 3.521 .186

170 .000 9.604 .64004 .890535 6.517 .186

170 .000 4.248 .32716 .451872 5.426 .186

170 .000 5.967 .45146 .906435 3.402 .186

170 .000 3.478 .41753 .492320 3.048 .186

170 .000 3.446 .50842 .523568 2.565 .186

170 .000 4.745 .62193 .748955 2.359 .186

170 .000 4.014 .26090 .502624 4.020 .186

170 .000 16.493 .97688 2.125768 5.477 .186

170 .000 3.888 .37657 .508221 3.258 .186

170 .000 5.459 .53816 .716413 3.256 .186

170 .000 2.990 .45522 .498228 1.891 .186

170 .000 9.620 .56016 .919616 6.394 .186

170 .000 3.700 .43062 .489831 3.287 .186

170 .000 3.159 .87631 .618748 .787 .186

170 .000 4.757 .72088 .873818 2.470 .186

170 .000 4.083 .34558 .534190 3.767 .186

170

TOTAL

A8

POLAND

A8NPOL

LITH

SLOVAK

LATVIA

CZECH

NONA8

EU14

NONEUA

INDIA

SAFRIC

OZ

PAKI

FRANCE

GERMAN

CHINA

NIGERI

PORTUG

ITALY

SPAIN

IRELAN

USA

BANGLA

PHILIP

HUNGAR

NZ

Valid N (listwise)

Statistic Statistic Statistic Statistic Statistic Statistic Std. Error

N Minimum Maximum Mean Std.Deviation

Skewness

Annex 2: List of independent variables Label DescriptionUNAFFORD Unaffordable Housing Index 2003

URBINDEX Urbanization Index 2001

NONWHITE % non-white 2001

NOQUALIF % all 16-74 unqualified 2001

EMPRATOT % 16-PA employed 2003/4

EMPRATQ0 % unqualified in 16-74 employed 2003/4 (also EMPR0)

EMPRATQ4 % with degree in 16-74 employed 2003/4 (also EMPR4)

NTIM0203 Net total international migration 2002-03 (also NETIM)

NTIN0203 Net internal migration 2002-03

BORN1IRE % born in Republic of Ireland 2001

BORN1EEU % born in Eastern Europe 2001 (also BNEEU)

BORN1CZE % born in Czech Republic 2001

BORN1POL % born in Poland 2001

BORN1BAL % born in Baltic States 2001

AGRIC % employed in agriculture etc 2001

MANUF % employed in manufacturing 2001

CONTRAN % employed in construction or transport 2001 (also CONTR)

RETHOSP % employed in retail or hospitality 2001 (also RETHP)

OTHIND % employed in other sectors 2001

MYEPOP01 total population 2001

LOGPOP01 log of 2001 total population

NOSO South (cf North)

Regression results for 5 largest A8 NINOs NB. Bold red = significant at 5% level, N = 170 TTWAs

Variable Poland Lith-uania

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

-0.3 -0.2 -0.1 0.0 0.1 0.2 0.3 0.4 0.5 0.6

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

Standardised (beta) coefficient

Poland

Lithuania

Latvia

Slovak

Czech