Journal of Population Research

, Volume 29, Issue 1, pp 23–44 | Cite as

A comparison of internal migration by ethnic group in Great Britain using a district classification

Article

Abstract

This paper considers the ethnic dimension of changing usual residence using 1-year data from the 2001 Census, the only available comprehensive source of origin-destination flow data on Whites and non-white minority groups. The analysis identifies the variation in migration propensities at national level by ethnic group and by age and investigates spatial patterns of ethnic migration at local authority district scale using an area classification based on migration variables extracted from the 2001 Census.

Keywords

Migration Ethnicity Census Classification Great Britain 

Introduction

Migration has been a big issue in Great Britain (GB) over the last few years. Immigration has been the focus of debate by politicians concerned to establish balanced international migration in the face of significant inflows of economic migrants in the years prior to the onset of recession in 2008. However, considerable attention has been paid by the Office for National Statistics (ONS) in England and Wales and the General Registrar Office for Scotland (GROS) to internal migration and the use of administrative data sets with which to estimate annual (mid-year to mid-year) migration flows between local authority districts (Migration Statistics Unit 2007). The volume of internal migration is significantly larger than that of international migration; the 2001 Census indicates that in the 12 months before the April census date, 6.2 million people changed their usual residence within the whole of the United Kingdom (UK) whereas 467,000 migrated into the UK from elsewhere in the world and a further 406,800 individual migrants were recorded by the 2001 Census whose destination locations were known but whose origins were unstated.

Ethnicity, together with race and religion, have also been at the heart of ‘big’ social issues relating to community relations, housing, health, crime and labour market dynamics in GB. Ethnicity and race are both concepts for which there is no universally accepted definition. Ethnicity refers to selected cultural and sometimes physical characteristics used to classify people into groups whereas race distinguishes human species based on anatomical features. Several research projects reflecting recent ethnicity-based research across the social sciences are reported in Stillwell and van Ham (2010) and much work has been undertaken on the demography and geography of ethnic populations, particularly associated with the concentration of minority ethnic groups in British cities as exemplified through the work of Peach (1996), Simpson (2004) and Johnston et al. (2010) on measuring ethnic segregation. Amongst the welter of research on ethnicity, we find an increasing volume of studies addressing research questions about ethnic migration or ‘ethnomigration’, many of which are geographical in focus. Recent studies in the UK include those by Champion (1996, 2005), Finney and Simpson (2009a, b) Simpson and Finney (2009), Finney (2010a), Raymer and Giulietti (2009), Simon (2010), Stillwell et al. (2008), Stillwell and Hussain (2010a, b) and Stillwell (2010a, b) and Catney and Simpson (2010), all of which use census data to examine variations between ethnic groups in the propensities, composition and spatial patterns of migrants.

The aim of the current paper is to examine ethnic internal migration using a new spatial system that has not been used previously so as to provide some new insights into the propensities and spatial patterns of migration of ethnic groups in Britain. Initially, we adopt a conventional comparison of ethnic migration propensities at national level, but then introduce a new migration-based area classification system as the framework for comparing spatial patterns of migration at district scale. All-age flows within and between eight clusters of districts are examined for each ethnic group while age-specific migration churn rates and net migration balances are presented for ethnic groups in each district type category. Some discussion of the findings and conclusions are provided in the final section. However, we begin with an introduction to the data sets and spatial units used in the analysis.

Data sources and spatial systems

The 2001 Census is particularly important for research on ethnic migration because it is the source of the most reliable data on ethnic populations and migrants by ethnic group in Britain. Sadly, relatively little is known about the detailed ethnic complexion of the GB population since the 2001 Census because the lack of data sources, although ONS have produced estimates of ethnic populations from 2002 to 2009 (ONS 2011) and Mateos (2007) has demonstrated the potential for tracking ethnic distributions using surname data that are available from sources such as GP registers. Other data sets, such as the annual School Census, also have potential to tell us a great deal more about the ethnic structure of the GB population (Harland and Stillwell 2007).

Our definition of migration follows that used by the ONS and includes any person who was usually resident 1 year prior to the census at an address and recorded on census date as usually resident at another address. The tables of population counts that are output from the 2001 Census use an ethnicity classification comprising 16 groups (Table 1) but ONS adopted a reduced, broad categorisation of seven groups for the Special Migration Statistics (SMS), the data set that provides counts of flows of migrants between origin and destination areas (Stillwell et al.2010). Since the analysis in this paper utilises the SMS data, we are constrained to the broad classification but recognise the limitations that this puts on identifying the variations that will exist between sub-groups of migrants within each broad ethnic category (Table 1): the White group, for example, includes both British-born Whites, Irish-born Whites and those Whites born elsewhere in the world, whereas the non-white Other group contains an array of different ethnicities and nationalities including those non-whites from Japan, Philippines, Republic of Korea, Malaysia and the USA.
Table 1

The 2001 census classification of ethnic groups

Label used in paper

Ethnic group defined in special migration statistics (level 1)

Ethnic group defined in key statistics

White

White

White British; White Irish; Other White

Indian

Indian

Indian

POSA

Pakistani and Other South Asian

Pakistani; Bangladeshi; Other Asian

Chinese

Chinese

Chinese

Black

Caribbean, African, Black British and Black Other

Caribbean; African; Other Black

Mixed

Mixed

White and Black Caribbean; White and Black African; White and Asian; Other mixed

Other

Other

Other

At the district scale (called level 1 by ONS), the 2001 Census SMS provide one table containing ‘Migrants by ethnic group by sex’ (Table MG103) and another table with ‘Migrants by age by sex (Table MG101)’. Unfortunately, there is no cross-classification of ethnicity by age, the dimension that enables some insights into changing migration propensities at different life cycle stages. Following negotiation with ONS Customer Services over the age and ethnicity categories to be used, Table C0711 was commissioned that provides district-to-district flows for seven age groups (0–15, 16–19, 20–24, 25–29, 30–44, 45–59, 60+) and seven ethnic groups (Table 1).

The system of GB districts for which the commissioned ethnic migration data are available from the 2001 Census is illustrated in Fig. 1. Each of the three home countries has a different set of areas for local authority administration and governance that are referred to generically as local authority districts (LADs). There are 32 Council Areas in Scotland and 22 Unitary Authorities in Wales, whereas England has a mixture of 36 single tier UAs, 46 Metropolitan Districts (MDs) and 239 other Local Authorities (LAs) along with 32 London boroughs and the City of London. The average population size in each of these categories is shown in the legend in Fig. 1. These 408 districts form the building blocks that are grouped together to create the new spatial system used for spatial analysis later in the paper.
Fig. 1

Administrative districts of Great Britain in 2001

Ethnic composition of Britain’s usually resident and internal migrant populations

Before turning to the spatial analysis, it is important to understand the ethnic composition of the national usually resident (UR) population and of the sub-groups that move internally either within or between districts. In this section we concentrate briefly on providing this contextual information and on showing how national migration propensities for different ethnic groups vary by age. According to the 2001 Census, the usually resident population of GB numbered 57.1 million people on census date (29 April), of which 8% were recorded as being non-white. Table 2 indicates that those of Pakistani and Other South Asian (POSA) ethnicity formed the largest ethnic minority group, with the Black and Indian groups also having populations in excess of 1 million. Thereafter, the Mixed group accounted for 1.2% of the national population with the Chinese and Other groups each representing 0.4%. Ethnic minority migrants, on the other hand, comprised a slightly higher percentage of total internal migration (9%) within GB in the year prior to the 2001 Census. The largest group of migrants were those classified as Black and there were more migrants classified as Other than those recorded as Chinese. So approximately half the non-white usually resident and migrant populations of GB in 2001 were either of POSA or Black ethnicity.
Table 2

The ethnic composition of Britain’s population and internal migration

Ethnic group

UR population

Internal migrants

Number

% of total

% of non-white

Number

% of total

% of non-white

White

52,481,225

91.9

5,512,052

91.0

POSA

1,277,023

2.2

27.6

131,831

2.2

24.2

Black

1,147,394

2.0

24.8

139,942

2.3

25.7

Indian

1,051,862

1.8

22.8

103,991

1.7

19.1

Mixed

673,917

1.2

14.6

97,350

1.6

17.9

Chinese

243,192

0.4

5.3

35,853

0.6

6.6

Other

229,238

0.4

5.0

35,985

0.6

6.6

Total

57,103,851

100.0

100.0

6,057,004

100.0

100.0

Sources: 2001 Census Standard Table ST101; SMS Table MG103

Given that migration is highly dependent on age, the variations between ethnic groups in their national propensities to migrate may be partly explained by the different age structures of the ethnic groups. Consequently, it is important to compute age-specific migration intensities and Fig. 2 is a radar diagram that conveys the variation in the national internal migration propensities between ethnic groups by age group. This form of visualisation is an alternative to the formal age schedule of migration (Rogers and Castro 1981) and is preferred here because of the inconsistent number of years in each of the age ranges. We observe from Fig. 2 that the highest migration rates for all ethnic groups are for those in their twenties, particularly those aged 20–24 and it is the Chinese that have the highest rates in this age group whereas the POSA group has the lowest rates for those aged 20–24 and 25–29. In the late teen ages (16–19), the Chinese are also found to have the highest propensities whereas the rates of migration for the POSA group aged 16–19 are again much lower, relative to other groups; cultural differences associated with the age of leaving home and going to away to higher education are likely to be important here in explaining this variation (Stillwell 2010a). The 0–15 and 30–44 age groups have similar rates since these groups include children moving with their parents as families and there is much less variation between the ethnicities than there is for those in the intervening ages. The two remaining older age groups, those aged 45–59 and 60+, have the lowest migration propensities and variations by ethnic group are relatively small.
Fig. 2

Age-specific migration propensities by ethnic group. Sources: 2001 Census Standard Table ST01; Commissioned Table CO711

District-level analysis of ethnic migration in Britain

Let us now turn our attention to the sub-national spatial variations in ethnic migration across GB using net migration balances defined by subtracting the total gross out-migration from the total gross in-migration for each district. In Fig. 3, we illustrate the net migration balances across the districts for all migrants, for Whites and for non-whites. The extent to which the aggregate pattern is determined by the White population is clear in terms of absolute numbers, with the maps showing a familiar pattern of counterurbanisation involving losses from the major cities and metropolitan areas and gains in areas lower down the urban hierarchy and in rural areas (Champion 1989, 2005; Dennett and Stillwell 2010a, b). In the case of non-whites, the pattern of absolute net migration gains and losses is much more confined to London and some of the major metropolitan areas, including Bradford, Birmingham, Leicester and Nottingham.
Fig. 3

Net migration balances for all persons, Whites and non-whites by GB district. Source: 2001 Census SMS MG103

Net migration maps at the district scale have been produced for each of the ethnic groups but these are not included here because the patterns of net flows, often involving very small positive or negative balances form a mosaic in each case that is difficult to visualise effectively and to interpret meaningfully. Consequently, we have developed a LAD classification using cluster analysis with a range of migration variables from the 2001 Census in order to provide a framework for interpretation and summary.

The new district classification has been derived on the assumption that our understanding of complex migration processes can be enhanced through identification of areas with particular ‘migrant characteristics’. A single-tier classification has been created with k-means clustering (in MATLAB) using 44 internal migration variables derived from the 2001 Census (capturing different categories of age, ethnicity, employment status, family status, tenure and economic activity measured using either migration rates or migration efficiencies). Details of the clustering procedure are reported fully in Dennett (2010) and Dennett and Stillwell (2009) and the eight district categories emerging from the classification (Fig. 4) are as follows:
Fig. 4

Migration-based district classification system. Source: Dennett and Stillwell (2009)

  • Cluster 1 Coastal and Rural Retirement Migrants—featuring districts around the periphery of Britain which attract older, often retirement age, migrants seeking the physical and social characteristics associated with these coastal and rural areas.

  • Cluster 2 Low-Mobility Britain—characterised by lower levels of migration activity experienced by all sub-groups.

  • Cluster 3 Student Towns and Cities—with very high levels of young in-migrants and non-household migrants moving into privately rented accommodation.

  • Cluster 4 Moderate Mobility, Non-Household, Mixed Occupations—featuring low levels of migration, but where migration is occurring, it tends to involve single migrants and those in more intermediate occupations.

  • Cluster 5 Declining Industrial, Working-Class, Local Britain—a very distinctive cluster located in ex-industrial areas, where in-migration and out-migration is less common, but local, short-distance moves predominate.

  • Cluster 6 Footloose, Middle-Class, Commuter Britain—almost the antithesis of the previous cluster where in-migration and out-migration are very common and the migrants tend to be in the higher socio-economic groups.

  • Cluster 7 Dynamic London—located almost entirely within the M25, London’s orbital motorway, where levels of in-migration and out-migration are very high across for all sub-groups.

  • Cluster 8 Successful Family In-migrants—a clear destination for family migrants who are owner occupiers and often the origin of student migrants.

The net migration balances for each area type for each ethnic group and for all migrants are presented in Table 3. The only area type that shows consistency in the sign of the net migration balances across each ethnic category is Student Towns and Cities, where net gains are recorded for each ethnic group. The net gain of almost 16,500 in this district type is predominantly due the net inflow of White migrants, but highlights the importance of the student element in the internal migration taking place in GB. In addition to Student Towns and Cities, the other major areas of net in-migration are those groups labelled Coastal and Rural Retirement Migrants and Successful Family In-migrants, although these areas lose Chinese and Other migrants in net terms. The net gain of nearly 48,000 White migrants in Coastal and Rural Retirement Migrants areas is the largest net balance in the matrix and indicates the significance of counterurbanisation moves for the White population.
Table 3

Net migration by type of district by ethnic group

Cluster

White

Indian

POSA

Chinese

Black

Mixed

Other

Total

Coastal and rural retirement migrants

47,993

48

458

351

186

360

197

48,497

Low mobility Britain

8,830

130

63

65

1,046

246

22

7,388

Student towns and cities

11,453

711

369

590

1,270

483

78

15,454

Moderate mobility, non-household, mixed occupations

4,306

921

1,101

−96

3,801

496

367

2,284

Declining industrial, working-class, local Britain

17,722

1,656

536

395

253

116

1

20,171

Footloose, middle-class, commuter Britain

17,269

1,625

1,065

240

1,515

549

8

12,267

Dynamic London

33,051

1,938

3,087

406

8,410

2,255

198

48,533

Successful family in-migrants

21,732

159

67

329

339

237

81

22,124

Source: 2001 Census SMS MG103

Net migration losses shown in italics

Another important feature evident in Table 3 is the extent and consistency of net migration loss from Dynamic London across the ethnic groups, where only the Chinese show a positive in-migration balance. Black net out-migration reaches 8,410 from these areas with all other clusters showing Black gains. Losses are also experienced by Asian, Chinese and Mixed groups, as well as Whites, in areas classified as Declining Industrial, Working Class, Local Britain, with the highest absolute losses of Asians from this district type. In contrast, the largest net migration gains for the two Asian groups are associated with Footloose, Middle Class Commuter Britain, whereas the highest net gains for Black migrants are into Moderate Mobility, Non-Household, Mixed Occupation areas.

We now examine the migration flows within and between the clusters in the migration classification in more detail. First, the ordering of migrants by ethnic group according to percentage moving within vis á vis between clusters highlights the difference between Chinese migrants who experience the smallest percentage of intra-cluster migration and POSA migrants who have the highest share of intra-cluster migrants (Fig. 5) as a proportional share of own group migration. The Chinese population is well-known to be dispersed widely across the country showing the least dissimilarity of all non-white groups when compared with Whites in 1981, 1991 and 2001 (Rees and Butt 2004). In contrast, we know from local evidence in Leeds (Stillwell and Phillips 2006), for example, that Bangladeshis, one of the groups comprising the POSA category, have a particular tendency to live in close proximity to one another and to be disinclined to migrate over long distances. Work by Platt (2005) has indicated lower social mobility within the POSA group as a whole, and this may be manifest here in the predominance of moves within clusters and an underrepresentation of moves between clusters. The other Asian group, Indians, appear to have a higher percentage of individuals moving between clusters and are more akin to the Chinese than the POSA population. It is likely that the higher propensity of Indians to move further distances is related to the occupational structure of this ethnic group, which contains a significant number of professionals, particularly in the medical sector.
Fig. 5

Inter-cluster and intra-cluster migration shares by ethnic group. Source: 2001 Census SMS MG103

Fig. 7

Inter-cluster in-migration and out-migration as % of total migration by cluster and ethnic group. Source: 2001 Census SMS MG103

Second, we can examine the percentage shares of migration that are taking place within clusters (Fig. 6) and between clusters (Fig. 7) by ethnic group and by cluster. The largest shares of intra-cluster migration across all the ethnic groups are to be found in the Student Towns and Cities and in Dynamic London, with over 45% of Black migration and 35% of Other migration taking place within the latter cluster of districts. White, Asian and Mixed flows are relatively high in Declining Industrial, Working Class, Local Britain, whereas 10% of intra-cluster White migration takes place in the Coastal and Rural Retirement Migrants areas. The shares of total migration into clusters (Fig. 6 top graph) are much smaller in most cases, with Student Towns and Cities experiencing the greatest turnover across all ethnic groups, particularly Indians and Chinese.
Fig. 6

Intra-cluster migration as % of total migration by cluster and ethnic group. Source: 2001 Census SMS MG103

Out-migration shares are highest for Dynamic London and the percentage for Whites is lower than for all non-white ethnic groups for this cluster of districts, as it is for in-migration. As has been found in earlier research on migration rates in the UK (Cordey-Hayes and Gleave 1973) and in the USA (Meuser and White 1987), there is usually a strong positive relationship between out-migration and in-migration; this is also apparent when using percentage shares.

Third, we consider the major inter-cluster directional flows that are taking place in the GB migration system. Flows involving in excess of 5% of total migration (Fig. 8) occur between Student Towns and Cities and Declining Industrial, Working Class, Local Britain—a pattern which might seem counter-intuitive given the descriptions of the cluster characteristics earlier, but which is partly a function of the large share of the total population contained in these clusters—whereas flows involving 3–5% of total migration link Student Towns and Cities with Coastal Rural Retirement Migrants areas in both directions. There are also flows of this relative magnitude into Student Towns and Cities from Successful Family In-migrants and from Dynamic London into Footloose, Middle Class Commuter Britain.
Fig. 8

Major inter-cluster directional migration flows. Source: 2001 Census SMS MG103

Schematic flow maps of the same type can be used to compare the major flows between the eight district clusters for each ethnic group (Fig. 9). The flow maps have been arranged in order of the volume of inter-cluster movement for each ethnic group, starting with the Whites whose total inter-cluster migration involves 1.67 million individuals. There is an important distinction in cluster connectivity between the White migrants, where the Low Mobility Britain and Moderate Mobility Non-Household Mixed Occupations are not connected by any flows over 3% of the total flows in the White migration matrix, and the non-white groups where the less connected clusters are Coastal and Rural Retirement Migrants, Low Mobility Britain and Successful Family In-migrants. The Coastal and Rural Retirement Migrants areas have primary connections to the Student Towns and Cities cluster only in the case of White and Chinese migrants. However, the Student Towns and Cities cluster is important as either the origin or the destination or both for major flows to the Dynamic London, Declining Industrial, Working Class, Local Britain and Moderate Mobility, Non-Household Mixed Occupations clusters. The Successful Family In-migrants cluster only appears to provide relatively large flows of Whites and Chinese migrants to Student Towns and Cities. Dynamic London is at the hub of the largest flows of non-white groups, including a flow of over 3–5% of Black migrants to Low Mobility Britain. The link between Dynamic London and Footloose, Middle Class Commuter Britain is important in both directions for the Asian, Chinese and Other groups but away from Dynamic London for Whites. There are relatively strong connections between the Declining Industrial, Working Class, Local Britain and the Student Towns and Centres for both the Indian and POSA groups, though this connection is important for all ethnic groups. Districts that constitute the Moderate Mobility, Non-Household Mixed Occupations cluster either receive migrants from or send migrants to Dynamic London or Student Towns and Cities in the case of all the non-white groups.
Fig. 9

Major inter-cluster directional migration flows by ethnic group. Source: 2001 Census SMS MG103

Propensities and patterns of migration vary with stage in life cycle and our final comparison of ethnic migration using the migration classification considers two further questions: How do migration propensities by age vary between district types and how consistent are the district type patterns of ethnic group net migration by age group? Figure 10 contains a radar graph of age-specific ethnic migration propensities (akin to Fig. 2) for each district type, but the migration variable used in this case is a measure of ‘churn’ where the numerator is the total in-migration plus out-migration plus intra-cluster migration for each cluster and the denominator is the cluster population in 2001, following Dennett and Stillwell (2008). Migration propensities increase outwards from the centre of each graph where each age group is represented by an octagonal shape. More irregularity in an octagon is indicative of greater variation between ethnic groups. Across all the district types, there is some consistency in the ordering of migration propensities in general terms, i.e. 20–24 and 25–29 year olds have the highest propensities, followed by those aged 16–19. The 0–15 and 30–44 age groups are always in unison since they represent family movement to a large extent and the two older age groups have the lowest propensities.
Fig. 10

Age-specific migration churn propensities by ethnic group and district type. Sources: 2001 Census Standard Table ST01; Commissioned Table C0711

In Coastal Rural Retirement Migrants areas, it appears that the Chinese have the highest churn propensities for those in their early twenties, with the Black and Indian migrants also having high rates for those aged 25–29 and 30–44. Chinese rates are relatively low in the family age groups as they are in the Low Mobility cluster also. The octagonal pattern is relatively regular in this cluster of low rates for all age groups, but Black propensities aged 20–24 are relatively high and the propensity for the POSA group aged 25–29 is greater than that for those aged 20–24. As expected, the migration propensities for those aged 20–24 are significantly higher for all ethnic groups than the other age-specific propensities in the Student Towns and Cities cluster although the rate for the POSA group is noticeably lower for this age group as well as for those in their late teens and late twenties. Both Asian groups have relatively low propensities also in the Moderate Mobility, Non-Household, Mixed Occupations cluster, where the Chinese aged 20–24 exhibit the highest rates and the rates for family and older age migration are very low. Migration propensities by age and ethnic group are lowest in Declining Industrial, Working Class Local Britain whereas Footloose, Middle Class Commuter Britain has relatively high rates of Chinese migration aged 20–24 and relatively low rates of migration for Asians in their twenties. The highest migration propensities for those in their twenties in Dynamic London are experienced by White migrants with relatively low propensities for the major non-white minorities in this cluster. Thus, despite the greater representation of non-whites in this cluster, White migration is driving the dynamism of London. Finally, migration propensities are at their highest in the Successful Family In-migrants cluster, particularly for Chinese aged 20–24, migrants of Indian and Mixed ethnicity aged 25–29 and Black migrants aged 16–19.

Net migration balances are shown in Table 4 for each district cluster (see coding in Table 3) by ethnic group in blocks for each age group, with net migration losses italicised. The patterns for family migrants aged 0–15 and 30–44 are fairly consistent across the ethnic groups by cluster type with Dynamic London (7) and Student Towns and Cities (3) losing and other clusters gaining in the large majority of cases. The opposite pattern is apparent for 16–19 year olds since this is the age group involving migration for higher education or employment. However, Dynamic London only has net gains of White and Chinese migrants in this age group and there are Black migrant gains in all the other clusters apart from Declining Industrial, Working Class, Local Britain (5) in this age group. There is less consistency in the net migration balances across the ethnic groups for those in their twenties, although Student Towns and Cities have gains in all ethnic groups of those aged 20–24 and districts of Moderate Mobility, Non-Household, Mixed Occupations (4) have consistent gains of those aged 25–29. Other than Dynamic London, the balances are also rather inconsistent for those in that older working age group (45–59) and in the oldest age group (60+) although the balances for non-white groups in the latter age category are quite small.
Table 4

Net migration balances by cluster and ethnic group for each age group

Cluster

White

Indian

POSA

Chinese

Black

Mixed

Other

Age 0–15

 1

15,549

48

71

52

33

400

64

 2

3,339

190

104

53

339

193

6

 3

14,910

151

70

94

91

240

74

 4

1,463

327

342

45

966

394

130

 5

4,329

36

206

8

65

116

32

 6

1,066

431

341

151

418

391’

70

 7

19,697

870

1,011

203

1,985

1,550

144

 8

11,787

61

17

4

73

305

56

Age 16–19

 1

13,095

40

44

231

10

135

−51

 2

8,232

176

81

78

20

−38

28

 3

62,870

1,474

709

670

647

879

262

 4

6,482

365

104

−222

45

140

52

 5

16,545

538

273

127

−35

138

48

 6

10,596

116

51

114

150

197

14

 7

5,187

−160

154

310

865

94

29

 8

13,107

79

2

208

28

137

−40

Age 20–24

 1

7,614

39

28

159

42

147

−43

 2

6,940

154

72

67

11

40

4

 3

6,206

101

396

223

347

80

54

 4

4,129

12

66

78

179

38

17

 5

8,745

376

354

176

26

159

21

 6

3,959

176

16

75

106

10

21

 7

26,547

332

54

481

693

361

119

 8

9,624

52

6

149

18

−123

101

Age 25–29

 

 1

1,802

53

100

40

29

−22

3

 2

504

27

46

12

104

37

10

 3

7,583

345

126

51

145

143

105

 4

3,641

335

203

22

573

82

29

 5

1,305

312

44

115

41

10

43

 6

218

236

174

38

156

142

49

 7

3,592

5

337

143

998

85

86

 8

139

1

18

35

8

1

11

Age 30–44

 1

18,619

10

233

58

69

193

−54

 2

4,314

159

35

3

495

82

51

 3

20,895

174

4

255

90

49

88

 4

553

509

411

133

1,705

106

207

 5

3,724

−364

52

29

97

39

9

 6

4,629

713

510

263

590

230

36

 7

26,890

1,018

1,243

270

3,252

703

−162

 8

15,946

185

102

39

206

102

1

Age 45–59

 1

17,696

25

31

18

52

36

13

 2

1,356

59

24

22

40

7

7

 3

6,156

125

2

9

−63

−8

28

 4

2,816

110

112

5

258

12

29

 5

584

49

27

0

27

32

9

 6

−5,596

123

64

32

87

9

−6

 7

10,408

170

−188

40

439

120

62

 8

8,052

27

18

18

38

56

14

Age 60+

 1

15,036

11

39

13

9

35

5

 2

549

25

5

10

37

5

6

 3

8,079

69

46

4

13

36

1

 4

1,868

7

71

9

75

28

7

 5

236

19

8

2

32

4

5

 6

3,031

62

43

21

8

16

−8

 7

11,382

57

100

15

178

55

−6

 8

8,539

16

20

2

4

35

0

All age

 1

47,993

48

458

351

186

360

197

 2

8,830

130

63

65

1,046

246

22

 3

11,453

711

869

590

1,270

483

78

 4

−4,306

921

1,101

−96

3,801

496

367

 5

17,722

1,656

536

395

253

116

1

 6

17,269

1,625

1,065

240

1,515

549

8

 7

33,051

1,938

3,087

406

8,410

−2,255

198

 8

21,732

159

67

329

339

237

81

Source: 2001 Census SMS MG103

Net migration losses shown in italics

Discussion and conclusions

Internal migration in Britain in the 12 months before the 2001 Census is complex both in terms of its ethnic composition and the spatial patterns of movement that occurred during this period. Migrants in general are influenced by different drivers according to their circumstances and the stage in their life course (Champion et al. 1998; Stillwell 2008) but the Census does not provide information on the reasons that make individuals or groups of individuals decide to migrate or influence their choice of destination. Consequently, it is necessary to use what data we do have from the Census on migration—in this case, origin, destination, ethnicity and age—to try to establish some of the features that characterise the propensities to move and the geography of redistribution. Our analysis is also hampered by the broad categories with which the ONS have chosen to classify ethnicity and by the methods that have been used to adjust cell counts in the 2001 Census tables in order to preserve confidentiality and remove any risk of disclosure. The latter is particularly problematic when dealing with very small counts of migrants at the output area scale and much less destructive at the district scale used in this paper.

Despite the limitations associated with the ethnic classification and the adjustments for confidentiality, the 2001 Census migration data for 2000–2001 reveal that national migration propensities do vary by ethnic group and by age: the Chinese experience the highest internal migration propensities whereas the POSA group appear to have the lowest propensities, particularly at ages 16–19 and 20–24. This spectrum of variation also applies to the proportions moving between districts, with more Chinese moving between districts in Britain than within them and the POSA group having the highest proportion of intra-district migrants. Chinese people have been living in GB since the nineteenth century with the 1881 Census counting 224 Chinese in total and with Chinatowns starting to appear in London and Liverpool with grocery stores, eating houses and meeting places. The Chinese population had increased to nearly 2,500 by 1921, including 547 laundrymen, 455 seamen and 26 restaurant workers. Whilst thousands of Chinese seamen served in the British merchant navy during the Second World War, it was during the 1950s and 1960s that the largest wave of Chinese immigrants arrived, consisting predominantly of male agricultural labourers from the rural villages of the New Territories in Hong Kong, and Guangdong province in mainland China. The 1971 Census recorded just over 96,000 Chinese and nearly every small town and major suburb in GB had a Chinese restaurant or takeaway. By 2001, there were 12,000 Chinese takeaways and 3,000 Chinese restaurants in the UK. This wide distribution of Chinese population and business ventures is likely to one reason for Chinese having the highest proportion of internal migrations that cross district boundaries. The explanation for the higher migration propensities may also be associated with the relatively high proportion of the Chinese population that are full-time students (28% in 2001), in contrast to the relatively low proportion of the POSA group that are classified as students (15%). It is also likely that cultural factors come into play, particularly for Pakistanis, Bangladeshis and Other Asians whose close family and community ties are a disincentive to migration. Finney (2010b), for example, has usefully explored the ways in which migration in young adulthood are associated with partnership formation (marriage and cohabitation), having children and study.

One problem arises because of the difference in the relative sizes of the populations and the migrant streams involved when considering ethnicity, particularly when one group (White) is so predominant and when the numbers comprising the minority ethnic populations or migrants can be very small. The small number problem actually provides further justification for the broad based ethnic classification used by ONS, despite the recognition that most groups will contain a variety of migrants with very different cultural and linguistic characteristics. There are also practical problems of how to handle migration interaction matrices containing lots of empty cells and how to display information in map form. In responding to these problems in this instance we have chosen to use a new district classification system based on migration variables from the 2001 Census as a summarizing framework for net and gross flows between clusters as well as flows within clusters of districts. The paper has demonstrated some of the features that distinguish ethnic migration both within and between eight groups of areas of similar type.

The origins of the spatial patterns of internal migration for some ethnic minority are defined by the locations of previous waves of immigrants, most of whom arrived in British cities in search of work and who settled where cheap housing was available and distance to work was minimised. We know that certain groups, such as the Indians, contain professionals who are more footloose in terms of residential location and that the migration streams of those aged 16–24 contain a significant number of those who are moving to or from areas where higher education establishments are located. In 2000–2001, student migration was a major component of Britain’s inter-district migration—as it has been in the past—with a pattern of flows towards towns and cities that was opposite to the main counterurbanisation movements that characterised aggregate migration in this period and that have been a key feature of Britain’s population redistribution over several decades. The district cluster of Student Towns and Cities is therefore prominent as both an origin and a destination for migrants in all ethnic groups but showing gains in net migration across all ethnic groups. This cluster is also important for generating intra-cluster flows which include moves between towns and cities that have major higher education institutions but also within these places as students move from halls of residence to flats or change flats during the period.

We also recognise that Dynamic London is the other district cluster that is of key importance in the GB migration system, not least because it lost almost 50,000 migrants in net terms in total during 2000–2001, despite gaining 35,000 net migrants aged 16–29. Amongst the remaining clusters, the Coastal and Rural Retirements cluster is particularly important as a destination for White migrants in the family and the older ages. The two clusters where there is most evidence of inconsistency between White and minority ethnic migrants in aggregate net terms are the Moderate Mobility, Non-Household Mixed Occupations and Footloose Middle Class Commuter Britain, both of which lose White migrants in net terms but who gain migrants in all non-white groups apart from the Chinese.

In conclusion, the district classification system that we have used in this paper has provided a more nuanced analysis than that based, for example, on different types of administrative area; it has provided a framework for summarizing what initially was a huge volume of data (408 × 408 × 7 = 1,165,248 cell values). Further spatial analyses of these data might include comparison of the mean or median distances over which the different ethnic groups have moved and the calibration of migration models to identify variations in the frictional effect of distance. However, we also look forward to applying the same analysis to the results of the 2011 Census to identify what changes have taken place both in the migration propensities and in the spatial patterns of redistribution of Britain’s ethnic populations between 2000–2001 and 2010–2011.

Notes

Acknowledgments

This research has been supported by grants from the Economic and Social Research Council (ESRC) under the ‘Understanding Population Trends and Patterns’ (UPTAP) and Census Programmes. We are also very grateful for the comments of two anonymous referees on the initial manuscript.

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Copyright information

© Springer Science & Business Media B.V. 2011

Authors and Affiliations

  1. 1.School of GeographyUniversity of LeedsLeedsUK
  2. 2.Centre for Advanced Spatial AnalysisUniversity College LondonLondonUK

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