Ethnic Minority Women and Local Labour Markets

10 downloads 263 Views 5MB Size Report
and progression of ethnic minority women in the labour market. The overall aim of ... women are included because they have the lowest rates of employment of any other ethnic group ... into deep-seated issues of gender inequality or discrimination, and to make .... variable access to and success in their local labour markets.
Moving on up? Ethnic minority women and work

Ethnic Minority Women and Local Labour Markets

Lisa Buckner, Sue Yeandle and Sue Botcherby

Ethnic Minority Women and Local Labour Markets

Lisa Buckner and Sue Yeandle CIRCLE Centre for International Research on Care, Labour and Equalities University of Leeds

Sue Botcherby Equal Opportunities Commission

© Equal Opportunities Commission 2007 First published Winter 2007 ISBN: 978 1 84206 011 7 The views expressed in this report are those of the authors and do not necessarily represent the views of the Commission or other participating organisations. The Commission is publishing the report as a contribution to discussion and debate. Please contact the Research and Resources team for further information about other EOC research reports, or visit our website: Research and Resources Equal Opportunities Commission Arndale House Arndale Centre Manchester M4 3EQ Email: [email protected] Telephone: 0161 838 8340 Website: www.eoc.org.uk/research You can download a copy of this report as a PDF from our website, or call our Helpline to order a copy: Website: www.eoc.org.uk/research Email: [email protected] Helpline: 0845 601 5901 (calls charged at local rates) Interpreting service available for callers to the Helpline Typetalk service available: 18001 0845 601 5901

About the investigation In October 2005, the Equal Opportunities Commission launched 'Moving on up? Ethnic minority women at work', a GB wide investigation into the participation, pay and progression of ethnic minority women in the labour market. The overall aim of the investigation is to understand more about the diverse experiences and aspirations of ethnic minority women in relation to work, including barriers to progress, so that effective action can be taken to improve their labour market prospects. The focus is on women, as there is insufficient labour market evidence available that seeks to understand how gender, race and faith intersect in the labour market. The investigation focuses particularly, though not exclusively, on Bangladeshi, Pakistani and Black Caribbean women. Pakistani and Bangladeshi women are included because they have the lowest rates of employment of any other ethnic group, and Black Caribbean women because they are under-represented in senior level jobs, despite being more likely than white women to work full-time. A focus on these three groups has meant that resources can be channelled more effectively for depth research and analysis, and in order to avoid over generalisations about ethnic minority women. The EOC has commissioned new research and analysis to support the investigation, including the voices of women at every stage. Moving on up? is a statutory investigation under the Sex Discrimination Act 1975. The legislation gives the EOC the power to undertake general formal investigations into deep-seated issues of gender inequality or discrimination, and to make recommendations to those in a position to make changes, including Government. This report is one of a series of research reports commissioned for the Moving on up investigation, which is supported by the European Social Fund. We will publish all the research on our website at www.eoc.org.uk . Please email [email protected] or phone our helpline if you require a printed copy of the interim report. For more information on the investigation visit our website www.eoc.org.uk/bme .

CONTENTS FIGURES AND TABLES ACKNOWLEDGEMENTS EXECUTIVE SUMMARY

ii iii iv

1.

INTRODUCTION 1.1 Background 1.2 Methodology 1.3 Evidence from the GELLM research programme 1.4 The National Picture: economic activity and unemployment

1 1 2 3 6

2.

ECONOMIC INACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS 2.1 Mapping the situation of ethnic minority women 2.2 Districts where ethnic minority women are 5+ per cent of the population 2.3 The major cities of England and two London boroughs 2.4 Labour force participation in places with the same overall rates 2.5 The Neighbourhood Renewal areas

3.

CONCLUSION References Appendix 1: Data tables on Local Authority districts in England and Wales Appendix 2: Data tables on the 86 Neighbourhood Renewal areas

i

10 19 20 22 24 28 32 33 50

TABLES AND FIGURES TABLES 1. Numbers of women of working age, selected ethnic minority groups: England and Wales 2. Women’s economic activity rates, selected ethnic groups: England’s major cities and two London authorities 3. Women’s unemployment rates, selected ethnic groups: England’s major cities and two London authorities 4. Women’s highest/lowest rates of economic activity in selected ethnic groups in Neighbourhood Renewal districts 5. Women’s highest/lowest rates of unemployment in selected ethnic groups in Neighbourhood Renewal districts FIGURES 1. Economic activity rates of women aged 16-59, selected ethnic groups: England and Wales 2. Unemployment among economically active women aged 16-59, by selected ethnic groups: England and Wales 3. Variation in the economic activity rates of White British women aged 16-59, by local authority district: England and Wales 4. Variation in the economic activity rates of Pakistani women aged 16-59, by local authority district: England and Wales 5. Variation in the economic activity rates of Black Caribbean women aged 16-59, by local authority district: England and Wales 6. Variation in the economic activity rates of Bangladeshi women aged 16-59, by local authority district: England and Wales 7. Variation in the economic activity rates of Indian women aged 16-59, by local authority district: England and Wales

ii

6 20 21 26 26

7 8 14 15 16 17 18

ACKNOWLEDGEMENTS All Crown Copyright material is reproduced with the permission of the Controller of HMSO.

iii

EXECUTIVE SUMMARY

EXECUTIVE SUMMARY This paper examines the situation of selected groups of ethnic minority women in the labour market, using data for local authority districts in England and Wales to explore differences in the labour market experience and participation of ethnic minority women and to consider the reasons why it is sometimes so different from that of White British women. The paper uses data from the 2001 Census to explore the economic activity and unemployment rates of women of working age in four selected ethnic minority groups: Indian women; Black Caribbean women; Pakistani women; and Bangladeshi women. Data for all 376 local authority districts in England and Wales were examined, permitting comparison both between and within local authority districts. The paper has been prepared by researchers at the University of Leeds, building on their previous work on ethnic minority women’s experiences in selected local labour markets, and the EOC as part of its investigation into the participation, pay and progression of ethnic minority women in the Great Britain (GB) labour market. To provide the context for the new statistical analysis, key findings from a recent study of ethnic minority women in five English local authorities (Yeandle et al 2006), and recent national data from the EOC’s ongoing investigation into ethnic minority women and the labour market are presented. These highlight the relative disadvantage experienced by the selected groups of ethnic minority women; all the selected groups experience higher rates of unemployment, and (except for Black Caribbean women), lower rates of economic activity, than White British women. Geography is particularly important in our analysis because ethnic minority populations are clustered in particular districts. Furthermore, women more than men rely on local labour markets for employment; as a result ethnic minority women have variable access to and success in their local labour markets. Although relying on national level analysis can lead to inappropriate conclusions which reinforce stereotypes, there is limited awareness of these local patterns and variations. The paper presents detailed statistical evidence with the aim of addressing three questions: •

Are ethnic minority women disadvantaged in the labour market mainly because they are clustered in localities where labour market opportunities are generally poor?

iv

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS



Do different groups of ethnic minority women have consistently high/low levels of economic activity and unemployment, irrespective of where they live, related to cultural preferences or arising from discrimination?



Is it sensible to set national targets for labour force participation, either for all women or for women from different ethnic minority groups?

KEY FINDINGS Local authority data on economic activity and unemployment rates The report maps the economic activity rates of women in the selected groups in local authority districts in England and Wales, revealing large variations. Economic activity rates reached 94 per cent (the highest found, among Black Caribbean women, in one district) but also fell as low as 16 per cent (for Bangladeshi women in one district). Unemployment rates also varied markedly. The highest unemployment rates were found among Pakistani and Bangladeshi women, reaching well over 20 per cent in some areas, although the national rate for all women was only 4 per cent. The variation in economic activity and unemployment rates across districts is much greater for ethnic minority women than for White British women. For White British women, the gap between the highest and lowest economic activity rates found at district level is 29 percentage points. The gap for Indian women is very much higher, at 50 percentage points, and the variation for other groups of women is also large: 40 percentage points for Black Caribbean women, 38 percentage points for Pakistani women and 34 percentage points for Bangladeshi women. The gap between the lowest and highest unemployment rates found at district level for White British women is 6 percentage points. The gap for Bangladeshi women is four times higher, at 23 percentage points, with much larger gaps for Pakistani women (21 percentage points), Indian women (17 percentage points) and Black Caribbean women (11 percentage points) as well. Districts where ethnic minorities are at least 5 per cent of the population Large differences between local authority districts in the economic activity and unemployment rates of different groups of ethnic minority women are also found when analysis is confined to those districts where resident ethnic minority populations form at least 5 per cent of the population. In this part of the analysis, wide variations in the economic activity and unemployment rates of women from ethnic minority groups were also found, although the differences are not quite so large. This confirms that the variation cannot simply be attributed to anomalous features of very small populations. Examples cited in the report include v

EXECUTIVE SUMMARY

data on economic activity rates for Bangladeshi women (47 per cent in Slough compared with 22 per cent in Birmingham), and unemployment rates for this group (32 per cent in Kirklees, compared with 14 per cent in Manchester). Major cities Data for England’s major cities were also analysed, since these are places where high proportions of people from ethnic minority communities live and where a relatively wide variety of labour market opportunities are available. The analysis shows that women from different ethnic minority groups experience the opportunities in England’s major cities in different ways. Again, some wide variations in economic activity and uenmployment rates were found. This analysis showed that there is no major conurbation where all of the selected groups of women fare better (or worse) than the national average. Some cities simultaneously had very positive labour market outcomes for some groups of women, alongside negative outcomes for others. In London, for example, Bangladeshi and Black Caribbean women are economically active at the national average level, while Indian and Pakistani women’s economic activity rates are above average. In another example, the unemployment rates of Indian and Pakistani women in Manchester are at the national average level, whereas unemployment rates for Bangladeshi women are lower and for Black Caribbean women higher in this northern city. ‘Pairing’ localities to make comparisons with all/White British women Pairs of districts where economic activity and/or unemployment rates for all women, or for all White British women, are the same or very similar, were also examined. This test was chosen as, in a situation of equal access for all women, one would also expect ethnic minority women to have similar experiences in these districts. The analysis showed large variations affecting women from all of the ethnic minority groups. It was found that in Tameside (North West) and Croydon (South East), where economic activity rates for all women are very similar (72 per cent and 70 per cent respectively), for Bangladeshi women these rates are very different: extremely low in Tameside, at 20 per cent, but very much higher in Croydon, at 51 per cent. Similarly, while the two London boroughs of Tower Hamlets and Camden both have a 5 per cent unemployment rate for White British women, for Bangladeshi women the unemployment rates are 20 per cent and 13 per cent respectively. We conclude from this analysis that strengths or weaknesses in the structure of local labour market opportunities cannot fully explain the variations in disadvantage experienced by ethnic minority women.

vi

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Districts that are the most ‘deprived’ in England Bangladeshi, Pakistani, Indian and Black Caribbean women of working age are much more likely than White British women to live in the most deprived districts of England, referred to as the Neighbourhood Renewal (NR) areas, with Pakistani, Bangladeshi and Black Caribbean women about twice as likely as White British women to live in these most deprived districts. The analysis here suggests a complex relationship between the residential clustering of communities of working age women from different ethnic groups in deprived districts, and their access to labour market opportunities. On average, economic activity rates are slightly lower in these districts, and unemployment rates are slightly higher, compared with the national average for all groups; however detailed examination showed that some of the NR districts had better labour market outcomes, and others considerably worse outcomes, for particular groups of ethnic minority women. Thus clustering in localities where labour market opportunities are generally poor does not adequately explain the disadvantage experienced by ethnic minority women in the labour market. In these data, no locality stands out as ‘the best’ to live/work, as measured by a combination of high economic activity rates and low unemployment rates. However on this measure some districts are ‘the worst’ to live/work for certain groups of women: Liverpool (for White British women); Blackburn (for Indian women); Birmingham (for Pakistani women); and Hackney (for Black Caribbean women). Conclusions Even where outcomes for all women are similar and close to the national average, outcomes for ethnic minority women can be highly variable; in some places, some groups of ethnic minority women are seriously disadvantaged in their access to employment. While some of this variation may be related to the age distribution, qualification levels, migration histories, and households of the women themselves, the analysis concludes that local labour markets are simply working better in some districts than in others for ethnic minority women. The paper concludes that: (i) Although ethnic minority women are disadvantaged partly because they are clustered in places where labour market opportunities are poor, this is not an adequate explanation, since the structure of local labour market opportunities benefits some groups of women while simultaneously leading to labour market disadvantage for others living in the same place.

vii

EXECUTIVE SUMMARY

(ii) Different groups of ethnic minority women do not have levels of economic activity and unemployment which are consistently high/low, irrespective of where they live. At the local level, cultural factors and discriminatory practices may be relevant – but it cannot be assumed that these exist or operate in the same way everywhere. (iii) Marked locality variations make it very hard for some localities to reach national targets. It might be more appropriate to focus on addressing poor labour market indicators for particular ethnic minority groups, including a gender analysis, and supporting those doing badly in comparison with other local groups. District level analysis of the clustering of populations indicates that significant proportions of the overall populations of economically active ethnic minority women could be supported by targeting certain districts. If they are to serve their local populations of ethnic minority women well, agencies need good information. In addition to data on economic activity and unemployment, and on the structure of local job opportunities, information is needed, for women in relevant ethnic minority groups, about: levels of qualification; facility in the English language; age; and household composition.

viii

INTRODUCTION

1.

INTRODUCTION

1.1 Background This paper explores the situation of selected groups of ethnic minority women in the labour market in England and Wales, building on previous work which produced Gender Profiles of 12 local labour markets in England (Buckner et al 2004-06) and a study of ethnic minority women in 5 English local authority districts (Yeandle et al 2006a). This showed important differences in the labour market behaviour, experiences and situation of ethnic minority women according to their own definition of their ethnic heritage and where they live 1. After summarising some of the GELLM programme’s key findings about ethnic minority women and the labour market, this paper presents an analysis of data relating to the whole of England and Wales, using 2001 Census data for all 376 local authority districts. It raises some important questions about why the labour market experience and participation of ethnic minority women is sometimes so different from that of White British 2 women, and contributes to the EOC’s General Formal Investigation into ethnic minority women and the labour market, Moving on Up 3. By participating in the GELLM research programme 2003-6, and by conducting its own investigation into the labour market participation and progression of ethnic minority women in Britain, the EOC has made it a priority to analyse statistical data at a level that goes beyond the standard national 'average'. These investigations have already shown that for ethnic minority women, more than for White British women, labour market patterns differ by age, generation, ethnic group, level of qualification, presence of children, and geography. The patterns and variations revealed are nevertheless still not well known, remain poorly understood, and deserve further attention. Indeed it is evident that relying on national level analysis, and using ‘standard’ statistical averages, can lead policy makers and others to extrapolate inappropriately from the 'broad' national picture which emerges, drawing conclusions about ethnic minority women which reinforce stereotypes, or which offer only simplistic explanations of the labour market situation of whole groups of women.

1

The Gender and Employment in Local Labour Markets research programme (2003-2006) was funded by a core grant from the European Social Fund to Prof. Sue Yeandle, then at Sheffield Hallam University. The award was conditional upon partner contributions provided by 12 English local authorities, the Equal Opportunities Commission and the Trades Union Congress (http://www.leeds.ac.uk/sociology/circle). 2 Throughout this paper, we use ethnicity categories employed in the 2001 Census of Population. 3 In October 2005 the Equal Opportunities Commission launched a new investigation into the participation, pay and progression of ethnic minority women in the GB labour market, 'Moving on up? Ethnic minority women at work’ (http://www.eoc.org.uk/Default.aspx?page=17696).

1

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

In the paper, we look in detail at geographical differences in the labour force participation of ethnic minority women. We believe that analysis which takes account of geography is particularly important because: • Ethnic minority communities tend to be clustered in certain districts, and are considerably less dispersed than the White British population. • Women, rather more than men, rely upon their local labour markets for employment; in some cases this narrows their effective labour market to a few square miles from their homes. • Access to and success in the local labour market is inextricably linked to several key government objectives: reducing poverty; increasing community cohesion; and diminishing social exclusion. 1.2 Methodology The paper presents a new analysis of 2001 Census data, focusing on two key indicators of labour force participation: economic activity rates (the percentage of all women of working age in the selected category who are either in employment or actively seeking work) and the unemployment rate (the percentage of economically active women who described themselves as actively looking for paid work and available to start a job within 2 weeks). After briefly outlining some of the evidence from Yeandle et al’s recent research, the paper presents some national data about ethnic minority women’s labour market situation. Section 2 of the paper then explores various analyses of the labour market situation of ethnic minority women in England and Wales, using the following methodology. First, we mapped the geographical distribution of economic activity among selected groups of ethnic minority women in England and Wales, and identified the highest, lowest and average economic activity and unemployment rates for women of different ethnicity by local authority district. This highlighted important geographical variations, confirming our earlier judgment that because national level data about women in the labour market (and especially ethnic minority women) ‘smooths out’ significant data variations, it is a poor guide to the issues facing local employers, policymakers and planners in addressing the need to achieve gender equality in the labour market. To explore these findings in further depth, the study then examined patterns of economic activity and unemployment for ethnic minority women in districts where women from ethnic minority groups formed at least 5 per cent of the working age female population. This is presented in Section 2.2. Our approach was designed to eliminate results associated with very small populations which could be atypical or unusual in some respect which was likely to affect labour force participation.

2

INTRODUCTION

Next, we investigated the data for some of the major conurbations - London, six other major cities, and two London boroughs - choosing these because they have large resident populations of ethnic minority women of working age. This approach was designed to explore labour force participation data for women living in large and complex local labour markets which include significant numbers of jobs in different sectors and offer employment opportunities (theoretically open to all) in a wide range of occupations (Section 2.3). We also conducted a comparison of ‘pairs’ of local authority districts where the economic activity and unemployment rates were the same or very similar for all women/White British women, to see if ethnic minority women in our selected groups also had similar labour market experiences in these districts (Section 2.4). Finally we explored the situation of the 86 Neighbourhood Renewal areas in England, where a very high proportion of the UK’s ethnic minority population lives (presented in Section 2.5). These districts, identified by central government as the ‘most deprived’ in the country (using the Indices of Deprivation 2004 4), have been the focus of considerable public investment and policy innovation in recent years. Addressing inequalities associated with ethnicity has been a key target for relevant agencies in developing interventions in these localities, but gender issues have been relatively neglected, potentially missing key opportunities to improve the labour market fortunes of large numbers of ethnic minority women (Oxfam 2005; Escott et al 2006). 1.3 Evidence from the GELLM research programme In 2001, just over 2.1 million women of working age in England were from ethnic minority groups (Yeandle et al 2006a). The ‘White Other’ group 5 and the Indian group were numerically the largest of these. The 5 localities studied in the GELLM research 6 had populations of ethnic minority women which were very different, both in their size relative to the total population, in their particular ethnic composition, and in their age structure. In these localities, about 70 per cent of Black African women, and around 50 per cent of Indian, Pakistani, Bangladeshi and Black Caribbean women had been born outside the UK, with some variations from place to place. We showed that Bangladeshi and Black African women were much more likely to be unemployed if they were born outside the UK. We also found that:

4

For further details of how the NR areas selected, see http://www.neighbourhood.gov.uk/page.asp?id=612 5 The White Other group includes Romany Gypsies, Turkish Cypriots, people of Eastern European origin and people from other European countries, South Africa, USA, Canada, Australia and New Zealand. 6 The five localities were: Camden, Leicester, Newcastle, Somerset and Southwark.

3

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

• Indian and Pakistani women aged 25-59 were much more likely to be permanently sick or disabled in Leicester than in our other study localities. • Among young women, the percentages of women looking after their home and family full-time were much higher for Pakistani and Bangladeshi women than for other groups. However in most groups, rates were well above the national average for almost all the groups studied. • Full-time employment rates among women aged 25-59 varied between the localities. In almost all cases, they were higher in our two London boroughs (Camden and Southwark), most notably among Pakistani women. • Part-time employment rates among women aged 25-59 also varied by both locality and ethnicity. In the two London boroughs, these were low by national standards for most groups of women, but this was not the case for Pakistani or Black African women (numerically a very large group) resident in Southwark. • Even in the same locality, female unemployment rates among the economically active population were much higher for some ethnic groups than for others - in the most extreme case, 6 times higher. This problem was very marked among young women aged 16-24. • Having demanding unpaid caring responsibilities (which involve looking after a frail, sick or disabled adult or a disabled child) was a more common experience for women in some ethnic groups than in others. The figures for Bangladeshi women were particularly striking for women of working age in Leicester and in the two London boroughs. When we considered only women in paid employment, we still found that Bangladeshi women were much more likely to have unpaid care responsibilities of this type, especially in Leicester. We also found evidence strongly suggesting that local labour markets were operating in ways which disadvantaged some groups of ethnic minority women. For example, data on the 24,000 Indian women of working age living in Leicester showed that: • 34 per cent of Indian women were working in the manufacturing sector, compared with 12 per cent of White British women. • Within manufacturing, Indian women were more strongly concentrated in lower level jobs than White British women or than Indian men.

4

INTRODUCTION

• Only 4 per cent of Indian women, compared with 18 per cent of White British women (and 12 per cent of Indian men), held better paid jobs in the sector, as managers, professionals, or technicians. Furthermore, examination of the occupational and industrial distribution of women in employment and of some other factors (not explored in this paper), showed that: • There is significant clustering of some groups of ethnic minority women in certain categories of employment, at a level significantly above the average for all women. Among women in employment, for example: 28 per cent of Indian women in Leicester worked in plant, process and machine operative jobs; 40 per cent of Bangladeshi women in Camden worked in sales and customer service jobs; 42 per cent of Black African women in Birmingham worked in health and social work occupations; 33 per cent of Pakistani women in Newcastle, and 49 per cent of Chinese women in Sandwell, worked in the wholesale, retail, restaurants and hotels sector; 31 per cent of Black Caribbean women in Southwark worked in administrative and secretarial jobs (Buckner et al 2004-06) • In the social care sector, in all the districts studied (as in England as a whole), Black women were disproportionately concentrated in jobs as care workers/care assistants, whereas Asian women were under-represented in this segment of the labour market (Yeandle et al 2006b). • While unemployment rates were variable between districts for all women, variations in the unemployment rate were in some places very much greater for Pakistani and Bangladeshi women (Grant and Buckner 2006:8). • There were very high concentrations of ethnic minority women in some of the most disadvantaged districts studied (Escott and Buckner 2006:12). • In England as a whole, even among very young women (aged 16-24), the proportion who were looking after home and family full-time, which stood at 7 per cent for young White British women, rose to 19 per cent for Pakistani and 22 per cent for Bangladeshi young women (Yeandle et al 2006a:13)

5

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

• A little over half of all Bangladeshi and Indian women and girls in England were born outside the UK. This compares with around 45 per cent of Pakistani and Black Caribbean women and girls, with about two-thirds of Black African women and girls, and with over 80 per cent of ‘White Other’ female residents (Yeandle et al 2006a:11). • Female ethnic minority populations in England are relatively young. This is particularly true of the Bangladeshi group (Yeandle et al 2006a:10). • The proportions of ethnic minority women aged 25-44 who were graduates rose rapidly between 1991 and 2001 for all the major ethnic minority groups. For example, in 2001, 20 per cent of Pakistani women of this age living in England were graduates, compared with just 4 per cent in 1991 (Yeandle et al 2006a:17). 1.4 The national picture: economic activity and unemployment The numbers of women from selected ethnic groups who are of working age and economically active, in employment or unemployed are included in Table 1. Table 1

Numbers Women of working age Women who are economically active Women who are in employment Women who are unemployed

Numbers of women of working age, selected ethnic minority groups: England and Wales White British

Indian

Pakistani

Bangladeshi

Black Caribbean

13,521,869

352,769

209,611

80,238

200,342

9,621,003

222,087

63,665

21,322

145,761

8,836,288

192,191

46,525

14,023

124,549

362,910

13,710

9,404

3,522

11,625

Source: 2001 Census Standard Tables, Crown Copyright 2003

As is relatively well known, in England and Wales the economic activity rate for Black Caribbean women (73 per cent) is very similar to that of White British women (72 per cent) 7, while Pakistani women (30 per cent) and Bangladeshi women (27 per cent) are very much less likely than White women to be economically active. Indian women have much higher average economic activity rates than Pakistani or Bangladeshi 7

Black Caribbean have patterns of working hours which are very different from those of White British women, however: the national data show that White British women are much more likely than Black Caribbean women to work part-time.

6

INTRODUCTION

women, although their rates are still well below those recorded for Black Caribbean and White British women (Figure 1). Figure 1 Economic activity rates of women aged 16-59 in selected ethnic groups: England and Wales

80

72

73

70

63

Percentages

60 50 40

30

30

27

20 10 0 White British

Black Caribbean

Indian

Pakistani

Bangladeshi

Ethnic groups Source: 2001 Census Standard Tables, Crown Copyright 2003, 2001 Census Commissioned Tables, Crown Copyright 2003.

It is also relatively well known that Black Caribbean women are more than twice as likely as White British women to be unemployed. As shown in Figure 2, however, the situation is in fact considerably worse for Pakistani and Bangladeshi women, who are about four times more likely to be in this position. Although Indian women have the lowest average unemployment rate of the ethnic minority groups studied here, theirs is nevertheless a much higher unemployment rate than is found among White British women. Although White women experience gendered inequality in the labour market in a wide range of ways (EOC, 2006a; EOC, 2007), at the national level their employment patterns are (by comparison with most other women, whether in England and Wales or in many other part of Western Europe) characterised by relatively high levels of economic activity and relatively low levels of unemployment. By contrast, Pakistani and Bangladeshi women, overall, have rather low rates of economic activity in England and Wales, and rather high levels of unemployment. The pattern for Black

7

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Caribbean women, again taken as a single group, is different, showing high levels of economic activity, but also relatively high rates of unemployment. Figure 2 Unemployment rates of women aged 16-59 in selected ethnic groups: England and Wales 17

18 15

Percentages

16 14 12 10

8

8 6

6 4

4 2 0 White British

Black Caribbean

Indian

Pakistani

Bangladeshi

Ethnic groups

Source: 2001 Census Standard Tables, Crown Copyright 2003, 2001 Census Commissioned Tables, Crown Copyright 2003.

At the level of national analysis, ethnic minority women are thus ‘positioned’ rather differently in the labour market than White women. The factors which explain this difference, including the impact of the decisions and choices which ethnic minority women feel are available to them in relation to the labour market, require further research, and are still not fully understood. Nevertheless the national figures alone suggest that, for whatever reasons, these groups of ethnic minority women meet with considerably less success than White British women in securing employment (Botcherby, 2006). In this paper, we examine detailed local labour market data to open up some of the questions we need to ask about the labour market inequalities revealed. As we will see, the national data tells only part of the story, and hides some very marked variations in levels of economic inactivity and of unemployment. In the next section, we present some of this local level data, highlighting a number of striking differences. Our approach is designed to inform debate about how salient a variety of possible explanations might be for the differences found. For example:

8

INTRODUCTION

• Are ethnic minority women disadvantaged in the labour market mainly because of where they live – i.e. mainly because they are clustered in localities where labour market opportunities are generally poor? • Do different groups of ethnic minority women have levels of economic activity and unemployment which are consistently high/low, irrespective of where they live, perhaps suggesting strong cultural preferences about participation in the labour market, or indicating widespread/universal experience of discrimination or of labour market integration? • Is it sensible to set national targets for labour force participation, either for all women or for women from different ethnic minority groups, when locality variations can be so large?

Section 2 of the paper examines relevant local labour market data from a variety of angles: mapping the situation of women in our four selected ethnic minority groups; examining districts where these groups have a notable numerical presence; considering the situation in some of the major cities; comparing the circumstances of ethnic minority women in pairs of districts where the patterns of White British women are very similar; and examining women’s labour market situation in deprived districts by looking at the 86 Neighbourhood Renewal areas. In the concluding section of the paper we highlight our key findings, before going on to consider the policy implications of our analysis, to identify some of the further research needed to underpin effective policy in this area, and to note some of the information needs of local agencies seeking to achieve fair access to labour market opportunities for ethnic minority women.

9

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

2.

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

2.1 Mapping the situation of ethnic minority women In Figures 3-7 we map the economic activity rates of women from four ethnic minority groups, as well as for White British women, in the local authority districts in England and Wales. Each map compares the situation of all working age women in the stated ethnic group, by district. Visual comparison of the different maps immediately shows that differential labour market opportunity in each district cannot adequately explain why, for a particular group, economic activity rates are much higher in some places than in others, as the patterns of darkest shading are not found in the same places. In Figure 3 we see (as expected) that the entire map is shaded (indicating that White British women of working age are present in significant numbers throughout England and Wales). The darker/lighter shading in the map reveals the economic activity rates of this group of women, showing variation by local authority district between the highest (87.1 per cent) and the lowest (58.3 per cent) recorded for this group. Figures 4-7 use the same approach to map the economic activity rates of our four selected ethnic minority groups, again relating only to women of working age. In these figures areas not shaded at all indicate, for the selected ethnic minority group, that the working age population is fewer than 100 women 8; in these unshaded areas we have not shown economic activity rates. The shaded areas in these figures reveal the geographical dispersal of women in the relevant groups in notable numbers (populations of 100 or more). Dark shading represents a relatively high economic activity rate at the district level, and light shading a relatively low economic activity rate. The upper and lower rates vary by ethnic group (as indicated in the key to each figure), as follows: Highest rate

White British women Pakistani women Black Caribbean women Bangladeshi women Indian women

(Fig. 3) (Fig. 4) (Fig. 5) (Fig. 6) (Fig. 7)

87.1 60.1 94.0 50.6 80.4

8

Lowest rate

58.3 21.7 54.5 16.4 30.7

Note that the districts shown as unshaded in these maps are those where the number of women of working age for that particular ethnic group was below 100. This level was chosen to minimise the effect of Small Cell Adjustment on the data, a Census procedure whereby all small cells in a table are rounded to either 0 or 3 as part of disclosure control.

10

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

More detailed analysis of the data used to compile each map reveals some particularly striking contrasts, as outlined below. White British women Figure 3 shows the much greater dispersal of White British women throughout England and Wales, with high economic activity rates found in large areas of the country, notably in parts of Central and Southern England and in the North. Lower level economic activity rates for this group are however also quite dispersed, including some districts in Wales, in the extreme South West, in the North East and in Eastern England. The very highest economic activity rates for this group of women are recorded in Blaby (East Midlands) (81 per cent) and the Scilly Isles (87 per cent); the lowest rates are found in Merthyr Tydfil (Wales) (58 per cent) and in Liverpool (North West) (59 per cent).

Unemployment rates for white British women are lowest in the Isles of Scilly (1.7 per cent), Surrey Heath (South East England) (1.8 per cent), South Cambridgeshire (Eastern England), Mid Sussex (South East England) and St Albans (South East England) (all 1.9 per cent), and reach their highest levels in Kingston upon Hull (Northern England) (7.7 per cent) and Knowsley (North West England) (7.6 per cent). Pakistani women Figure 4 shows the labour market position of Pakistani women at the national and local scales, and the geographic dispersion of this group of women. The areas highlighted in the 3 darkest shades are those local authority districts where Pakistani women are more economically active than the national average for this group, which stands at 30 per cent.

The detailed figures (presented in Appendix 1) reveal that Pakistani women’s economic activity rates are lowest in Telford and Wrekin (22 per cent) (West Midlands), and highest in Richmond upon Thames (60 per cent) (Greater London). Thus there is a remarkable 38 percentage point difference between the economic activity rates of Pakistani women living in Telford and Wrekin and those of Pakistani women living in Richmond upon Thames. We find similar variations when we explore the data on unemployment. The national (average) figure for the percentage of Pakistani women who were unemployed is 15 per cent. The highest unemployment rates for this group of women are found in Sandwell (West Midlands) (25 per cent), while the lowest rates are found in

11

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Cambridge (Eastern England) (4 per cent). Pakistani women in Sandwell were thus 6 times more likely to be unemployed than those in Cambridge 9. Black Caribbean women Figure 5 shows the economic activity rates of Black Caribbean women, again at the national and local labour market levels, and giving a snapshot of the geographic dispersion of Black Caribbean women of working age. The areas highlighted in the 3 darkest shades are those where Black Caribbean women are more economically active in local labour markets than the national average figure, which is high, at 73 per cent. Detailed data shows that in Liverpool (North West England), however, only 55 per cent of Black Caribbean women are economically active. By contrast, in Huntingdonshire (Eastern England) the economic activity rate of Black Caribbean women (93 per cent) is extremely high. There is again a very large difference – of 38 percentage points – between the economic activity rates of Black Caribbean women in Liverpool and in Huntingdonshire.

Analysis of unemployment among this group shows that, at the national level, quite a high percentage, 8 per cent of Black Caribbean women are unemployed. Very much higher unemployment rates are recorded for Black Caribbean women in Westminster (13 per cent), while unemployment rates are very much lower in St Alban's and in Welwyn Hatfield (both in South East England), at 2 per cent in both cases. Black Caribbean women in Westminster are thus 6 times more likely to be unemployed than those living in St Alban's. Bangladeshi women The economic activity rates and geographical dispersal of Bangladeshi women are shown in Figure 6. Again, the darkest shading shows the areas where Bangladeshi women have higher economic activity rates than the national average for this group of women, which is extremely low at just 27 per cent. The district with the lowest economic activity rate for this group of women is Blackburn with Darwen in the North West, where only 16 per cent of Bangladeshi women are economically active. At the other end of the scale, the highest economic activity rate is found in Croydon (Greater London), where a remarkable 51 per cent of Bangladeshi women are economically active.

The national average figure for unemployment among Bangladeshi women is very high, at 17 per cent. This figure rises to 29 per cent in Swindon (South East England) and Wirral (North West England). By contrast the unemployment rate for Bangladeshi 9

There are some instances where the unemployment rate is 0. These tend to be in local authorities with population just above the threshold level of 100. This may be due to the effects of the Small Cell Adjustment methodology used by the Census office, or could be a feature of that particular group.

12

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

women is comparatively very low in St. Alban's and in Richmond upon Thames, both in the South East (6 per cent in both cases). A Bangladeshi woman living in Swindon is thus five times more likely to be unemployed than one living in St Albans or Richmond on Thames. Indian women Figure 7 shows the economic activity rates of Indian women by local authority district, again indicating the geographic dispersion of this group. The three most darkly shaded areas are those where Indian women are more economically active in local labour markets than the national average, which stands at 63 per cent for this group of women. Very much lower economic activity rates are found for Indian women in Blackburn with Darwen (31 per cent) and in Kirklees (37 per cent), both in the North of England. By contrast, extremely high economic activity rates are found for Indian women in Daventry (West Midlands) (80 per cent).

As we saw earlier, the national average figures indicate that 6 per cent of all Indian women are unemployed. Much higher unemployment rates are found for this group of women in Kingston upon Hull in the North of England (19 per cent), while extremely low rates were found elsewhere in the North East (North Tyneside), in Chelmsford (South East) and in Harborough (in the East Midlands) (2 per cent in all three cases). This section has shown that for two key indicators (unemployment rates and economic activity rates) there are some very marked variations between districts for ethnic minority women. The variation in economic activity and unemployment rates across districts is much greater for ethnic minority women than for White British women. For White British women, the gap between the highest and lowest economic activity rates found at district level is 29 percentage points. The gap for Indian women is very much higher, at 50 percentage points, and the variation for other groups of women is also large: 40 percentage points for Black Caribbean women, 38 percentage points for Pakistani women and 34 percentage points for Bangladeshi women. The gap between the lowest and highest unemployment rates found at district level for White British women is 6 percentage points. The gap for Bangladeshi women is four times higher, at 23 percentage points, with large gaps for Pakistani women (21 percentage points), Indian women (17 percentage points) and Black Caribbean women (11 percentage points) as well.

13

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Figure 3 Variation in the economic activity rates of White British women aged 16-59, by local authority district: England and Wales 10

Economic activity rates - White British women 76.2 74.3 72.6 69.7 58.3

to 87.1 to 76.2 to 74.3 to 72.6 to 69.7

10

(74) (73) (77) (76) (77)

All maps presented use 2001 Census Standard Tables, 2001 Census Commissioned Tables, Crown Copyright 2003. 2001 Census, Super Output Area Boundaries, Crown Copyright 2003. This work is based on data provided through EDINA UKBORDERS with the support of the ESRC and JISC and uses boundary material which is Copyright of the Crown

14

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

Figure 4 Variation in the economic activity rates of Pakistani women aged 1659, by local authority district: England and Wales

Economic activity rates: Pakistani women 44 to 60.1 (29) 37.5 to 44 (31) 29 to 37.5 (31) 21.7 to 29 (32) all others (254)

15

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Figure 5 Variation in the economic activity rates of Black Caribbean women aged 16-59, by local authority district: England and Wales

Economic activity rates: Black Caribbean women 78 to 94 (30) 75 to 78 (31) 71 to 75 (32) 54.5 to 71 (29) all others (255)

16

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

Figure 6 Variation in the economic activity rates of Bangladeshi women aged 16-59, by local authority district: England and Wales

Economic activity rates: Bangladeshi women 34 to 50.6 (20) 26 to 34 (20) 23.2 to 26 (18) 16.4 to 23.2 (19) all others (300)

17

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Figure 7 Variation in the economic activity rates of Indian women aged 16-59, by local authority district: England and Wales

Economic activity rates - Indian women 72.7 to 80.4 (42) 69.3 to 72.7 (45) 65.5 to 69.3 (43) 59 to 65.5 (44) 30.7 to 59 (45) all others (158)

18

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

2.2. Districts where ethnic minorities are 5+ per cent of the population In the next part of the report we explore the data for those local authorities where at least 5 per cent of the population were in any one of the ethnic minority groups 11. This analysis enables us to explore whether the large differences between local authority districts in the economic activity rates of different groups of ethnic minority women still occur when we confine our analysis to those districts where ethnic minority populations are resident in relatively high proportions. Our results show that, even when we exclude data for local authority districts with only rather small ethnic minority populations, we still find wide variations in the economic activity and unemployment rates of women from ethnic minority groups. Below we cite some of these examples, indicating the scale and extent of the variation found. •

While only 23 per cent of Pakistani women living in Birmingham and in Walsall (both in the West Midlands) are economically active, the rate is much higher, at 40 per cent, in Slough (in the South East), a difference of 17 percentage points. Pakistani women in Birmingham and Walsall also have high unemployment rates, at 22 per cent in both districts, compared with a much lower figure of 10 per cent in Slough, a difference of 12 percentage points. •

Only 22 per cent of Bangladeshi women are economically active in each of the four large local authority districts of Birmingham and Walsall, and Bradford and Oldham (both northern England). This compares with a much higher economic activity rate for this group in Slough (South East) (47 per cent), a difference of 25 percentage points. Large differences are also seen in two different northern localities: in Kirklees (northern England) 32 per cent of Bangladeshi women are unemployed, compared with just 14 per cent in Manchester, a difference of 18 percentage points.



In Manchester, only 43 per cent of Indian women are economically active, compared with 73 per cent in Slough, a difference of 30 percentage points. Only 4 per cent of Indian women in Leeds are unemployed, compared with 8 per cent in Walsall, a difference of 4 percentage points. •

In Oldham, 82 per cent of Black Caribbean women are economically active, compared with 66 per cent in nearby Manchester, a difference of 16 percentage

11

This measure was chosen so that the Census SARs11 could be used to explore differences between different local authorities with respect to qualifications and other variables which are not readily available as standard census output, as well as to avoid the extreme results which can arise when small, atypical, populations are examined.

19

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

points. And while just 4 per cent of Black Caribbean women are unemployed in Slough, in Birmingham the figure is 10 per cent, a difference of 6 percentage points. These examples demonstrate that the large variations seen between localities in the labour force participation patterns of these different groups of ethnic minority women are also found in localities where these are relatively large groups in the resident population. This confirms that they cannot simply be attributed to potentially anomalous differences related to very small population sizes, and that it is not appropriate to present these differences as reflecting a simple north-south divide. 2.3 The major cities of England and two London boroughs To test our analysis still further, we also examined the data on economic activity and unemployment rates for different groups of women living in several of England’s major cities, in London as a whole, and in two London boroughs which have particularly large ethnic minority populations 12. This is presented in Tables 1 and 2, which show ethnic minority women's economic activity and unemployment rates. The + and – signs used in the table indicate variation from the national average in each case, with ++ and -- indicating large variations. Table 2 %

Women’s economic activity rates in selected ethnic groups: England's major cities and two London authorities Indian Pakistani Bangladeshi Black White Caribbean British

London

+

66

Manchester

--

++

38

43 63 59

----

24 22 22

--

---

30 23 24

66 72 73

65

--

25

-

25

-

71

73

Leicester Newham

---

52 57 50

++

28 38 29

-

26 26 25

-

73 69 71

70 68 62

Tower Hamlets

--

52

++

42

--

23

-

68

70

73

72

Birmingham Bradford

-

Leeds Sheffield

ENGLAND

63

27

30

27

73

73 --

--

61 69 74

Source: 2001 Census Standard Tables, Crown Copyright 2003, 2001 Census Commissioned Tables, Crown Copyright 2003. Note: +/- represents 5-10 per cent variation from the England average; ++/-- represents 1020 per cent variation.

12

None of the Welsh unitary authorities had any ethnic minority groups that made up 5% of the population as a whole.

20

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

Table 1 shows that in England’s major cities, women from different ethnic minority groups experience the opportunities in their local labour market in very different ways. There is no major conurbation among those shown where all of the selected groups of women fare better (or worse) than the national average. For example, the cities of Manchester and Leicester both have economic activity rates for Indian, Bangladeshi and Black Caribbean women which are low compared with the English average for comparable women, yet these are the only major cities outside London where Pakistani women’s economic activity rates are at or above average. We can also observe that Pakistani women are much more likely to be economically active (by 15 percentage points) in London and in Leicester than in Birmingham, and that they are almost twice as likely to be unemployed in Sheffield as in London or Leicester (Table 2). In the London borough of Tower Hamlets, economic activity rates for Pakistani women are well above the national average, yet this London Borough is where economic activity rates are significantly below the national average for Indian, Bangladeshi and Black Caribbean women. Table 3

Women’s unemployment rates, selected ethnic groups: England's major cities and two London authorities Indian

London Manchester Birmingham Bradford Leeds Sheffield Leicester Newham Tower Hamlets ENGLAND

Pakistani

6 6 +8 7 -4 6 +8 ++9 7 6

--

++ -++

12 16 22 20 15 22 12 18

++

21

++ ++

Bangladeshi

-++ ++ ++

++

15

16 14 22 21 25 17 17 18

Black Caribbean 8 + 10 + 10 6 6 9 9 + 10

20

++

17

11 8

White British

++ ++

4 6 5 4 4 4 6 7

++

6

++ +

4

Source: 2001 Census Standard Tables, Crown Copyright 2003, 2001 Census Commissioned Tables, Crown Copyright 2003. Note: +/- represents 5-10 per cent variation from the England average; ++/-- represents 1020 per cent variation.

In Table 2 we compare unemployment rates in a similar way. Here Manchester and Leeds offer particularly interesting examples. In Manchester, Bangladeshi women have low unemployment rates compared with the national average for comparable 21

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

women, whereas unemployment rates for Black Caribbean women in this city are rather high. In Leeds, we find exceptionally high unemployment rates among Bangladeshi women, although the unemployment rates for Indian and Black Caribbean women in Leeds are rather low by national standards. A similar situation is found in Bradford, where both Pakistani and Bangladeshi women have very high rates of unemployment, while in Leicester it is Indian women whose unemployment rates are rather high, while rates for Pakistani women are comparatively low. Our data for women’s unemployment and economic activity rates in some of England’s major conurbations thus demonstrate that the likelihood of being either economically active or unemployed varies for women of different ethnicity, even when they theoretically have access to the same set of labour market opportunities. As noted above (and by others), some groups – notably women of Pakistani and Bangladeshi heritage – do have much higher rates of unemployment and much lower rates of economic activity than other women; however it is also clear that opportunity structures in the labour market, and possibly localised cultural factors, are mediating this picture, so that women are much worse/better off in some places than in others. Statistical analysis alone cannot tell us whether this is because of locality differences in the industries and occupations recruiting labour, or their employment practices, or of different support structures and services available in particular communities. However, these are important questions which should be further investigated using a mixed method approach to identify good practice, surmountable barriers, and entrenched problems which could potentially include some direct or indirect discrimination affecting particular groups. 2.4. Labour force participation in places with the same overall rates To further explore the extent of ethnic minority women’s disadvantage in the labour market, we also examined the situation of ethnic minority women in pairs of districts where the economic activity and/or unemployment rates for all women, or for all White British women, were the same or very similar. In a (hypothetical) situation of perfectly equal access for all women, one would expect that ethnic minority women would have similar experiences in these districts too, particularly insofar as they were actively seeking to enter employment (as measured by the unemployment rate). Here our analysis reveals that even in localities where the overall economic activity and/or unemployment rates for all women/all White British women are the same or very similar, women from different ethnic minority groups often have very different labour market experiences. For example:

22

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

Indian women In Blackburn (North West) and in Gravesham (South East), the economic activity rates for White British women are exactly the same (both 71 per cent), yet we find that the economic activity rates for Indian women are extremely low (31 per cent) in Blackburn, compared with a very much higher figure (68 per cent) in Gravesham.

Merton (Greater London) and Preston (North West) both have unemployment rates for all women of 4 per cent, yet while the unemployment rate for Indian women is also 4 per cent in Merton, it is double this (8 per cent) in Preston. Pakistani women The economic activity rates for all women in Kirklees (West Yorkshire) and Slough (South East) are high and very similar (70 per cent and 72 per cent respectively), yet for Pakistani women in these areas the rates are very different (25 per cent and 40 per cent respectively), although low in both cases.

In both Redbridge (South East) and Hyndburn (North West) the unemployment rate for all women is 4 per cent, yet the rates of unemployment for the Pakistani women in these areas are very much higher, although again quite different (10 per cent and 21 per cent respectively). Bangladeshi women Our analysis also showed that there are large variations affecting the Bangladeshi group. Here we compared Tameside (North West) and Croydon (South East), where the economic activity rates for all women are very similar (72 per cent and 70 per cent respectively). The rates for Bangladeshi women in these areas are very different, however - extremely low in Tameside, at 20 per cent, and relatively high for this group in Croydon, at 51 per cent.

We also looked at two London boroughs, Tower Hamlets and Camden, finding that while both had a 5 per cent unemployment rate for White British women, the unemployment rates for Bangladeshi women in these two districts were very much higher, although again quite different - 20 per cent and 13 per cent respectively. Black Caribbean women Nationally, Black Caribbean women have economic activity rates very close to those of the White British population, as already discussed. We examined data for Luton (South East) and Islington (London), which both recorded similar economic activity rates for all women (67 per cent and 65 per cent respectively), yet found that while 78 per cent of Black Caribbean women in Luton are economically active, this is true of only 66 per cent in Islington.

23

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Turning to unemployment among this group, we find that while the two greater London districts of Redbridge and Wandsworth both had female unemployment rates of 4 per cent (all women), for Black Caribbean women the unemployment rate was 5 per cent in Redbridge, but in Wandsworth 10 per cent. It is possible that some of the differences in economic activity and unemployment rates between women from the same ethnic group in localities where White British women have very similar economic activity and/or unemployment rates are attributable to differences in the age or qualifications profiles of the groups of women concerned. To explore this, we identified two labour markets where there are significant populations of ethnic minority women (in this case Pakistani women) and where the ethnic minority women selected have very similar age and qualifications profiles. These were the city of Birmingham and the town of Luton. Comparison of the economic activity and unemployment data for these two districts revealed that, even in this case, the economic activity rates for Pakistani women were different: 23 per cent in Birmingham and 27 per cent in Luton. The unemployment rates for this group were even more divergent, at 22 per cent for Pakistani women in Birmingham, compared with 15 per cent for the same group of women in Luton. Variations in age structure and level of qualifications are thus not sufficient explanations, able fully to account for the differences observed, although they are undoubtedly factors relevant to understanding local variability 13. 2.5 The Neighbourhood Renewal areas To test the hypothesis that ethnic minority women’s labour market disadvantage arises mainly because of their high concentration in areas of economic deprivation, we also examined the labour force participation of ethnic minority women in the 86 districts defined as the most deprived districts in England (the Neighbourhood Renewal [NR] areas) 14. Over a third of the total working age population live in the NR areas, but Bangladeshi, Pakistani, Indian and Black Caribbean women of working age are very much more likely than White British women to live in these districts, the detailed figures being: Bangladeshi women 76 per cent; Black Caribbean women 74 per cent; Pakistani women 73 per cent; Indian women 59 per cent; White British women 37 per cent. For our analysis, we selected only the 47 districts where the local working age population reached 1,000 or more in at least one of our selected groups of ethnic minority women. We judged that this ‘baseline’ was adequate to enable us to make 13

Evidence at the national level about age, qualifications and country of birth is available elsewhere (EOC 2006b) for groups of ethnic minority women, and shows that these factors do correlate with variations in economic activity and unemployment rates. We have been unable to explore country of birth as a factor here because of data limitations at the level of local geography. 14 See appendix 2 for list of the 86 Neighbourhood Renewal areas and accompanying data.

24

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

reasonable comparisons 15. This analysis shows the very strong concentration of working age ethnic minority women in these areas, as these 47 NR areas accounted for 58 per cent of all Indian women of working age in England, 71 per cent of all Pakistani women, 75 per cent of all Bangladeshi women, and 75 per cent of all Black Caribbean women. (By contrast, only 22 per cent of all White British women and 27 per cent of all women in England live in these areas.) Turning to those who are unemployed and seeking work, we find that 62 per cent of all unemployed Indian women, 74 per cent of all unemployed Pakistani women, 76 per cent of all unemployed Bangladeshi women and 82 per cent of all unemployed Black Caribbean women live in these 47 areas (compared with only 26 per cent of all unemployed White British women). Taking only communities of the selected size or larger, we found that Indian women are the most dispersed, being found in these numbers in 39 NR areas; Black Caribbean and Pakistani women were found in communities of this size in 30 of the NR areas and Bangladeshi women in 12. There were only 3 districts where all of the selected groups of ethnic minority women are found in working age communities of 1,000 or more women: Newham, Manchester and Birmingham. Tables 3 and 4 show a complex relationship between residence (the clustering of communities of working age women from different ethnic groups in particular NR areas) and access to employment. For all groups, average economic activity rates are slightly lower in the NR areas (compared with the national average for all women) (Table 3). However, in the best performing individual NR areas, economic activity rates are significantly higher than the national average for women from the relevant ethnic minority group. As shown in Table 3, for example, Black Caribbean and Indian women register high rates of economic activity in some of the London NR areas.

15

This approach avoids any discrepancies arising from the small cell adjustment methods applied by the Census Office, which could affect areas where populations are very small.

25

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Table 4

Women’s highest/lowest rates of economic activity in selected ethnic groups in Neighbourhood Renewal districts

Percentages

White British

Average economic activity rates (England)* Average economic activity rates in NR areas (England) NR areas with highest economic activity rates NR areas with lowest economic activity rates

Table 5

Indian

Pakistani

Bangladeshi Black Caribbean

72

64

30

27

73

69

59

28

25

72

78

72

45

36

79

Lambeth

Enfield

Croydon

Enfield

Croydon

23 59 Liverpool

31 Blackburn

Birmingham, Walsall, Stoke, Blackburn

22 Oldham, Birmingham, Bradford

63 Hackney

Women’s highest/lowest rates of unemployment in selected ethnic groups in Neighbourhood Renewal districts

Percentages

Average unemployment rates for England* Average unemployment rates in NR areas (England) NR areas with lowest unemployment rates

NR areas with highest unemployment rates

White British

Indian

Pakistani Bangladeshi

Black Caribbean

4

6

15

17

8

5

7

17

18

9

3

10

10

Tameside

Brent

Southwark

12

25

Blackburn, Hackney

Sandwell

3 Ealing, Bristol, Preston

8 Knowsley

22 Birmingham

5 Barking and Dagenham

13 Westminster

Notes for Tables 3 and 4: The best and worst are derived from figures for those districts where populations of women of working age are 1,000 and above in number. *These figures are derived from all of the districts of England, whereas the other figures in the table are derived from the 86 deprived districts of England.

26

ECONOMIC ACTIVITY AND UNEMPLOYMENT IN LOCAL LABOUR MARKETS

For all the selected groups, the average unemployment rates for the NR areas are slightly higher than the average for England as a whole. As Table 4 shows, the highest unemployment rates for women in the selected ethnic minority groups are found in a range of locations – some in London boroughs, but others in the west midlands and in the north-west. Equally, the lowest unemployment rates in the NR areas are found not only in the London boroughs but also, for some groups, in other parts of the country. In these data, no locality stands out as ‘the best’ to live/work as measured by a combination of high economic activity rates and low unemployment rates. However on this measure some districts are ‘the worst’ to live/work for certain groups of women: Liverpool (for White British women); Blackburn (for Indian women); Birmingham (for Pakistani women); and Hackney (for Black Caribbean women). This part of our analysis suggests that certain districts could usefully be targeted for particular attention to improve outcomes for significant proportions of Pakistani and Bangladeshi women. We discuss this and other implications of our findings below in Section 3.

27

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

3.

CONCLUSIONS

The data presented in this paper suggests that our understanding of inequality in the labour market, particularly at the level of local labour markets, is significantly enhanced when we look in detail at district level data. For this, we have necessarily relied upon 2001 data, as the Census of Population is the only appropriate source. We have seen that while national level statistical data tells an important story about the disadvantage experienced by selected groups of ethnic minority women across the entire labour market in England and Wales, district level data reveal some even more marked differences. By mapping economic activity rates for different groups of women, we showed that it was not appropriate to see the variations revealed as reflecting a simple ‘north-south’ divide. We noted both the importance of recognising the differential geographical dispersal of ethnic minority women across the country, and that, for the different ethnic groups selected, highs and lows in economic activity rates do not appear in the same places. In the maps shown we excluded the smallest groups of ethnic minority women (in case they should distort the analysis), but we also tested our analysis further by examining only those areas where the selected ethnic minority groups were quite large - representing at least 5 per cent of the working age population. Again, we found very marked locality variations. To explore the situation of women living in places where access to jobs would include a relatively large and varied range of opportunities, we focused attention on the larger cities in England, where significant populations of ethnic minority women live. Here we found interesting and important variations, with access to the labour market, as measured by economic activity and unemployment rates, quite different for some groups, both within and between cities. We also noted that if we compared ‘pairs’ of districts where White British women had the same or very similar rates of economic activity or unemployment, we still found very large variations in the rates recorded for ethnic minority women. This reinforced our view that, although important, the structure and extent of available employment opportunities (key aspects of conditions in the local labour market) could not fully explain the marked differences between the experiences of women of different ethnicity; residential clustering in localities where employment opportunities are poor is by no means the whole story.

28

CONCLUSIONS

The 86 Neighbourhood Renewal areas – the most deprived districts in England – are home to a much higher proportion of women in the selected ethnic minority groups than of White British women. We looked in detail at the 47 districts where the population of working age women reached at least 1,000 in at least one of our selected ethnic minority groups. These 47 districts are important as they are where more than half of all Indian women, and around three-quarters of all Pakistani, Black Caribbean and Bangladeshi women in England and Wales live, with differing degrees of dispersal. We noted, for example, that Bangladeshi women of working age are found in communities of at least 1,000 women in only 12 of these districts. Again, the relationship between residential clustering of population groups and access to employment proved complex. Average economic activity and unemployment rates are worse in the NR areas (taken together) than in the country as a whole, but within this, some districts showed particularly good, and others particularly bad, outcomes for specific ethnic minority groups. We noted, for example, that Birmingham showed particularly poor labour market outcomes for Pakistani women, Hackney (London) for Black Caribbean women, and Blackburn for Indian women. It is not possible from our statistical analysis alone to explore exactly what the sources of inequality for these groups of women are; however our analysis shows very clearly that the explanation is both complex and potentially amenable to improvement through localised policy interventions. We know from other recent evidence (outlined in Section 1) that the clustering of ethnic minority women in particular segments within local labour markets may be partly to blame, especially when these are in sectors under pressure (as seen in the Leicester manufacturing sector example from the GELLM study). In Section 1, we highlighted three questions about the labour market disadvantage experienced by ethnic minority women on which we hoped our analysis would shed some light. To the first, ‘are ethnic minority women disadvantaged in the labour market mainly because they are clustered in localities where labour market opportunities are generally poor?’ we can answer that this may be part of the explanation, but it is certainly not an adequate or complete explanation. The structure of local labour market opportunities can work to the benefit of some groups of women whilst simultaneously leading to labour market disadvantage for others living in the same place. Further research is needed to explore why this is the case. The answer to our second question, ‘do different groups of ethnic minority women have levels of economic activity and unemployment which are consistently high/low, irrespective of where they live, because of their attitudes to paid work or the 29

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

discrimination they face?’ must be ‘No’. This is not to say that, at the local level, cultural factors affecting some groups of women’s attitudes and preferences, or discriminatory practices in recruiting labour on the part of some local employers, are irrelevant – but it cannot and should not be assumed that these factors exist or operate in the same way everywhere. This is important as it suggests that both attitudes and discriminatory practices can be changed. Again, more research is needed, using appropriate methods, into why in some localities particular groups of women have such very poor labour market outcomes. Finally, we asked, ‘is it sensible to set national targets for labour force participation, either for all women or for women from different ethnic minority groups, when locality variations can be so large?’ Clearly, given the very marked locality variations revealed in this paper, it is considerably more challenging for some localities to reach national targets than for others. It might be more appropriate for local authorities and their partner agencies to set detailed targets for different population groups, paying particular attention to addressing poor labour market indicators (especially when these are much more negative than those seen in other similar areas), and to supporting groups which are doing particularly badly in their locality in comparison with other local groups. This approach will require better local intelligence and information, and we therefore end with some suggestions about the information needs of local agencies. As we have seen, ethnic minority women fare considerably better in some local labour markets than in others. Local agencies need to know whether this is a result of different kinds of local labour market opportunity, different levels of support for those entering the labour market, differences in the infrastructure women need to successfully access employment (childcare, transport, etc.), differences in education, skills, guidance and language support, or differences in local traditions, practices and preferences (which are likely to change over time with length of residence in the UK, access to higher levels of education, and with changes in household structure and family composition). If they are to serve their local populations of ethnic minority women well, agencies need to have good information about them. In addition to the data on economic activity and unemployment examined here, and to good information about the structure of local industry and job opportunities, the data needed include information, for women in relevant ethnic minority groups, about: • Levels of qualification – how many are graduates; have intermediate qualifications; have no educational qualifications at all? What support do they need if their qualifications were obtained outside the UK?

30

CONCLUSIONS

• Facility in the English language – this will be particularly relevant in localities where significant numbers of ethnic minority women were not born in the UK, especially if they have come to the UK as mature women. Is access to effective language support capable of assisting women in securing employment difficult, and for which groups? How could this be overcome? • Age – some groups have an age structure which is very different from the White British population. There are significant local variations in this, and authorities need to consider which groups have the poorest labour market outcomes for women of their age, so that policy support and interventions can be targeted accordingly. • Household composition – it has not been possible in this paper to explore this dimension, but the extent and range of childcare and other caring responsibilities is known to vary significantly according to age, ethnicity and level of education. This information will be extremely valuable in targeting support, and in ascertaining why economic activity rates for some groups in some places are extremely low.

The evidence presented in this paper thus suggests a range of ways in which local agencies could be tackling low levels of economic activity among ethnic minority women and high levels of unemployment where they are evident for these groups. It is certainly not appropriate to assume that all ethnic minority women who are not in employment prefer to be at home and do not want paid work – although equally for some women this may be both practical and their own preference. Nor can it be assumed that the ‘clustering’ of some ethnic minority women in some industries and occupations (or their under-representation) necessarily reflects their own attributes or preferences. As we have seen, it is possible to achieve quite high economic activity rates, even among those groups of ethnic minority women who have low levels of employment at the national level, and to have relatively low unemployment rates, both desirable economic and social goals. Further examination of those local labour markets which have been most successful in doing this is also needed, so that their experience can inform policy-making and employment strategy at the local, regional and national levels. At the national level, policy-makers need also to consider whether support is sufficiently targeted on those localities where it is most needed: as we have seen, most ethnic minority women live in a small number of districts, which may need additional support specifically directed at the particular labour market needs of local ethnic minority women.

31

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

References Botcherby, S. (2006) Pakistani, Bangladeshi and Black Caribbean women and employment survey: aspirations, experiences and choices Manchester: EOC. Buckner, L., Tang, N. and Yeandle, S. (2004, 2005, 2006) Gender Profiles of Local Labour Markets Sheffield: Centre for Social Inclusion, Sheffield Hallam University. EOC (2006a) Facts About Women and Men in Great Britain Manchester: EOC. EOC (2006b) Moving on Up? Bangladeshi, Pakistani and Black Caribbean women and work. Manchester: EOC. EOC (2007) Sex and Power: Who Runs Britain? Manchester: EOC. Escott, K. and Buckner, L. (2006) Addressing women's poverty: local labour market initiatives: synthesis report Sheffield: Centre for Social Inclusion, Sheffield Hallam University. Grant, L. and Buckner, L. (2006) Connecting women with the labour market: synthesis report, Sheffield: Centre for Social Inclusion, Sheffield Hallam University. OXFAM (2005) Into the lion’s den: a practical guide to including women in regeneration Oxford: Oxfam UK Poverty Programme. Yeandle, S., Stiell, B. and Buckner, L. (2006a) Ethnic Minority Women and Access to the Labour Market : Synthesis Report Sheffield: Centre for Social Inclusion, Sheffield Hallam University. Yeandle, S., Shipton, L. and Buckner, L. (2006b) Local challenges in meeting demand for domiciliary care: synthesis report Sheffield: Centre for Social Inclusion, Sheffield Hallam University.

32

APPENDIX 1

Appendix 1 Data tables on local authority districts of England and Wales

Barnet Bexley Brent Bromley Camden Croydon Ealing Enfield Greenwich Hackney Hammersmith and Fulham Haringey Harrow Havering Hillingdon Hounslow Islington Kensington and Chelsea

79

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Bangladeshi

Pakistani

Indian

ALL City of London Barking and Dagenham

Economic activity rates

White British

Numbers

2,369

1,420

49,439 99,890 65,022 87,237 88,092 70,782 104,686 98,343 86,089 68,001 68,362

38,840 54,310 56,083 22,964 74,139 34,742 63,008 41,471 49,087 45,787 28,847

1,325 9,439 1,875 16,617 1,513 1,831 7,280 16,594 3,833 3,197 2,617

930 1,245 106 3,335 197 444 2,356 3,517 586 606 723

219 489 135 375 240 3,660 546 369 1,085 360 1,775

1,330 1,620 680 9,521 1,556 1,459 10,298 4,995 5,593 2,655 7,212

64 72 74 74 74 72 74 77 70 68 71

61 68 69 66 71 59 70 68 72 66 41

33 44 43 37 44 48 45 39 49 39 35

32 42 43 36 45 25 51 45 36 23 24

77 75 80 73 77 67 79 74 77 75 63

5.6 3.6 3.5 4.2 3.1 5.0 3.7 3.4 4.0 6.3 5.5

3.6 5.5 5.5 5.4 5.3 5.7 6.7 5.4 4.8 5.8 11.9

18.8 10.9 6.5 10.1 11.6 9.9 11.2 11.4 12.5 20.7 8.3

14.1 13.6 25.9 14.9 7.3 13.4 10.5 13.3 14.0 21.7 15.0

4.5 5.8 5.5 8.4 6.3 9.4 5.3 6.2 5.7 7.5 12.3

59,405 74,487 64,413 65,569 75,241 68,671 62,350

32,854 32,070 28,853 59,483 51,971 36,147 34,033

1,002 2,207 15,784 1,021 8,038 12,348 1,119

524 617 1,432 148 1,255 2,873 283

294 932 326

75 73 74 72 74 74 71

62 63 71 69 71 69 62

45 42 43 55 47 43 42

28 34 41 34 37 23

69 70 80 75 78 75 66

4.3 4.4 2.9 3.2 3.0 3.3 5.7

5.8 5.0 4.6 5.1 4.8 4.8 7.9

17.5 8.4 12.9 15.9 8.0 9.9 5.0

0.0 14.5 7.5

440 393 1,283

2,947 7,718 2,412 577 1,288 1,040 3,120

12.2 8.2 22.2

10.6 11.3 4.8 4.2 4.9 5.4 12.7

56,145

25,167

1,197

415

398

1,446

69

61

51

35

68

5.5

4.3

9.0

13.0

11.5

33

2.0

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Merton Newham Redbridge Richmond upon Thames Southwark Sutton Tower Hamlets Waltham Forest Wandsworth Westminster Bolton Bury Manchester Oldham Rochdale Salford Stockport Tameside Trafford Wigan

46 43 42 45 29 34

48 30 39 49 25 38

75 70 75 82 71 76

2.5 4.3 5.0 3.3 6.6 3.5

3.7 5.9 5.3 4.1 9.1 5.9

8.1 13.2 8.5 6.0 18.0 9.7

21.1 14.6 22.4 11.0 18.4 10.3

5.8 11.2 8.3 5.6 9.8 4.5

55,620 81,497 55,134 62,791 70,306 95,114 64,055 76,670 53,329 122,065 64,348 60,871 62,469 83,593 63,146 62,251 90,077

41,275 39,953 44,844 26,775 37,115 60,127 27,639 66,827 48,489 91,078 55,055 52,696 57,654 77,302 58,553 53,939 87,956

1,560 1,358 1,488 1,158 2,571 2,685 2,185 4,973 354 2,235 519 258 442 650 1,020 1,261 224

213 348 382 437 5,352 1,620 623 1,835 1,575 6,814 3,849 4,554 272 910 770 1,074 109

172 1,066 168 18,403 672 376 1,552

264 7,351 805 1,875 6,790 4,563 2,155 248 172 2,873 274

76 70 76 70 74 78 74 73 75 61 73 72 68 76 73 76 70

72 56 73 52 57 64 58 43 65 43 62 59 58 65 70 41 62

60 48 50 42 30 38 45 25 29 30 24 30 38 38 32 34 37

32 27 41 23 33 42 27

77 68 76 68 76 71 67 65 85 66 82

2.8 5.4 2.8 5.5 4.3 3.3 4.9 3.7 3.2 5.7 3.8 4.3 4.4 2.7 3.8 2.7 3.9

4.8 4.3 3.5 6.8 7.2 7.0 5.6 7.6 8.3 5.6 4.0 6.5 4.7 4.0 3.4 11.1 8.0

10.2 9.0 5.2 21.1 14.7 10.1 13.2 12.8 13.3 15.6 16.6 14.2 16.3 11.0 10.4 11.7 10.0

5.5 9.5 8.7 20.4 11.7 12.0 13.8

7.4 10.0 2.6 11.3 6.8 10.0 12.9 9.3 5.5 9.7 6.7

Indian

Black Caribbean

68 66 69 69 50 62

Bangladeshi

Black Caribbean

76 78 72 76 62 73

Pakistani

Bangladeshi

320 11,406 11,740 2,541 6,591 3,469

White British

Pakistani

119 659 371 578 6,084 1,368

Bangladeshi

611 839 340 1,443 6,076 4,561

Pakistani

2,045 1,835 1,275 2,802 9,678 11,271

Indian

33,771 44,221 44,187 36,723 24,055 39,581

White British

Indian

Lewisham

of

White British

Lambeth

Unemployment rates (unemployment as a percentage economically active women)

47,037 91,776 82,092 60,667 76,450 73,322

ALL Kingston Thames

Economic activity rates

Black Caribbean

Numbers

upon

1,071 2,599 713 113 116 672

154 234 109 996

34

24 22 22 35 40 20

76 83 77 76

14.3 20.6 15.7 15.0 13.0 23.9

10.3 6.2 9.5 4.1

APPENDIX 1

Liverpool St. Helens Sefton Wirral Barnsley Doncaster Rotherham Sheffield Gateshead Newcastle upon Tyne North Tyneside South Tyneside Sunderland Birmingham Coventry Dudley Sandwell Solihull Walsall Wolverhampton Bradford Calderdale Kirklees Leeds

738 131 193 207 138 386 174 1,200 145 1,051 232 250 310 18,806 7,860 1,649 8,651 1,221 4,477 9,640 4,228 278 4,792 4,245

318

168

308

101 403 1,327 4,390 146 1,421

104 29,112 1,790 1,815 2,411 304 2,676 886 19,582 2,791 7,754 4,388

189 549 719 135 217 262 5,830 521

1,644

1,367

16,019 1,000 734 3,230 517 914 2,735 952

699

1,303 2,185

968 678

35

61 59 67 70 68 65 67 70 70 69 65 72 67 67 69 70 74 68 75 69 68 74 76 75 73

51 68 70 65 64 56 70 52 74 59 72 53 65 58 63 67 62 71 61 61 59 62 37 65

38

24

55

21 34 27 28 44 34

50 23 29 27 24 51 23 33 24 27 25 27

74 26 25 19 19 26 22 25

73

22

72 72 71 75 76 74 72 73

25

77 71

24 22

7.6 7.3 5.2 4.3 5.2 5.4 5.2 4.8 4.2 4.3 4.9 4.5 7.4 5.6 5.3 4.1 4.2 5.9 3.5 5.3 5.6 3.8 3.8 3.5 3.5

5.9 7.9 8.1 9.6 3.4 11.2 6.6 5.9 3.7 4.5 2.4 9.0 6.0 8.2 7.3 6.1 10.0 5.4 8.4 9.9 6.5 4.0 10.1 3.9

10.8

26.8

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Bangladeshi

44,152 124,971 50,982 77,925 87,712 62,727 80,154 70,064 135,779 54,113 71,766 54,560 42,212 81,595 187,759 67,578 81,326 62,314 52,550 61,183 50,203 104,838 51,772 97,481 193,714

Pakistani

45,341 136,696 52,166 80,679 91,089 63,948 82,836 72,891 152,497 55,984 79,751 56,448 43,670 84,125 287,522 87,177 88,131 81,533 57,870 72,345 68,000 137,490 56,838 115,988 218,472

Indian

White British

Knowsley

Economic activity rates

ALL

Numbers

11.9

28.6 20.1 23.4 21.6 7.8 11.5

11.5 22.2 17.1 20.9 25.2 5.1 22.3 19.4 19.8 18.0 16.8 14.5

8.6 16.7 18.5 0.0 22.0 23.9 21.5 12.4

8.7

21.4

9.9 6.6 7.7 7.5 6.4 8.6 10.2 6.1

24.6

7.2 6.2

21.8 21.1

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Middlesbrough UA Redcar and Cleveland UA Stockton-on-Tees UA Darlington UA Halton UA Warrington UA Blackburn with Darwen UA Blackpool UA Kingston upon Hull, City of UA East Riding of Yorkshire UA North East Lincolnshire UA North Lincolnshire UA York UA Derby UA Leicester UA Rutland UA Nottingham UA Herefordshire, County of UA

90,535 25,332 37,297

40,525 53,455 28,606 36,531 57,211

39,694 51,496 27,624 35,613 54,882

39,600 39,762

904

281

1,423

233 154

70 65 63

104 560

218

208

30,247 38,357

4,298

3,404

71,133

68,310

215

140

89,782

87,384

44,885 44,110 55,707 64,930 86,522 9,470 80,998

43,763 42,341 52,417 54,586 50,936 9,053 65,383

48,651

47,214

65 69 72 68 75

51 68

38 26

66

36

71 71

31

23

65

53

34

310

74

150 281 203 2,837 23,773

2,513 1,399

567

971 1,555

2,156

2,818

154

3,010

69 70 75 71 68 74 63

123

128

275

74

36

6.4 5.4 4.4 5.5 3.5

3.6

14.6

5.1

17.4

6.7 5.8

of

Black Caribbean

Bangladeshi

Pakistani

29

4.5 6.7 7.0

Indian

48

White British

24

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

68

Bangladeshi

White British

Black Caribbean

Bangladeshi

Pakistani

Indian

325

Pakistani

Hartlepool UA

93,666 25,842 40,311

Indian

Wakefield

Economic activity rates

White British

ALL

Numbers

7.7 20.5

5.6

12.0

4.4 4.8

11.9

16.7

7.7

19.1

16.7

67

3.8

7.2

65 64 64 66 57

29 38

26

73 69

7.1 1.7 10.0 5.5 8.3

20.0 11.7

16.6

5.7 9.2

52

27

27

68

7.0 4.9 2.9 4.4 5.5 3.4 6.1

7.0

14.5

12.2

11.2

28

16

27

3.9

11.8

14.3

19.2

APPENDIX 1

Torbay UA Bournemouth UA Poole UA Swindon UA Peterborough UA Luton UA Southend-on-Sea UA Thurrock UA Medway UA Bracknell Forest UA West Berkshire UA Reading UA Slough UA Windsor and Maidenhead UA Wokingham UA

891 420

434 1,785

50,047 118,265 53,135

46,221 102,641 51,104

200 1,653 109

1,294

130 1,864

74 71 75

48 65 66

34

73,392 71,560 35,295 47,083 39,413 53,922 47,211 55,306 45,614 43,879 75,591 33,957 44,000 44,467 37,072

69,970 68,762 34,014 42,581 37,404 49,415 40,654 35,377 41,931 40,583 69,163 30,357 41,016 34,748 21,018

358

195

66

224 362 2,740 109 217 340 156 142 1,144 1,124

79 69 71 72 74 79 74 75 71 73 72 79 78 75 79

184 107 693 938 2,558 413 650 1,747 433 240 885 5,565

63 64 70 63 67 74 73 67 66 72 66 73

39,975 46,379

32,766 40,857

1,110 1,014

123 250

76 78

70 73

201 1,970 4,913 203

2,179 131

285

164

1,136 4,350

112

647 359

121

37

4.2 5.1

8.7 6.1

20.2 20.2

24.0

4.1 8.0

37

74 70

2.6 3.4 2.7

0.0 4.7 4.2

7.0

11.0

6.3 7.5

77

4.7

77 76 78 77 75 69 72 84 79 79

2.2 4.2 5.2 3.7 2.9 2.8 3.9 3.7 4.2 4.1 4.3 2.4 2.1 2.7 3.1

6.0 4.4 5.3 6.0 5.3 4.9 10.7 6.1 8.7 4.6 5.5 5.1

66 83

2.4 1.9

4.6 5.4

50 26 27 38

31

41

26

39 40

32

31 51

25 24

Black Caribbean

387

28

73 60

Bangladeshi

22 23

Pakistani

69 58

Indian

72 67

White British

Black Caribbean

177

165 186

Bangladeshi

Pakistani

Indian

44,742 66,640

of

Black Caribbean

Plymouth UA

48,049 71,145

Unemployment rates (unemployment as a percentage economically active women)

Bangladeshi

North Somerset UA South Gloucestershire UA

Pakistani

Bristol, City of UA

Indian

Stoke-on-Trent UA Bath and North East Somerset UA

White British

Telford and Wrekin UA

Economic activity rates

White British

ALL

Numbers

4.0

8.0 16.5 14.7 6.5

28.9

9.3

9.5

9.9 10.2

8.3

13.8 3.8

18.5 19.4

4.6 7.6 6.3 7.1 4.9 9.8 7.1 5.0 5.2 4.2 3.7 0.0

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

756

77

71

37

25

82

3.3

5.3

10.4

18.6

4.7

77,906 56,041 66,323 35,741 18,799 33,178 29,124 25,738 44,111 38,130 34,434 22,230 31,732 48,898 65,164 38,571 37,273

66,276 50,703 58,213 34,514 18,259 31,828 28,078 24,967 43,031 37,062 33,460 21,062 30,717 47,364 62,085 37,604 36,145

873 451 1,558

183

203

61 54 54

68

3.9 10.2 6.9

7.2

13.3

12.2 13.7 0.0

100

292

55

45

24

4.2 3.6 3.3 4.8 6.0 5.2 4.6 4.2 3.5 4.0 3.6 4.1 5.6 4.3 4.1 5.5 4.6

10.1

35

27 27 21

192

72 73 68 71 64 66 70 69 72 70 73 61 65 64 65 61 66

54

500

279 749 249

Isle of Wight UA Isle of Anglesey Gwynedd Conwy Denbighshire Flintshire Wrexham Powys Ceredigion Pembrokeshire Carmarthenshire Swansea

10.4

6.7

13.0

Neath Port Talbot Bridgend The Vale Glamorgan

34,707 67,951 16,353 49,903 19,897

33,050 66,130 15,991 48,807 19,529

103 172

71 61 58 63 60

62 65

Caerphilly Blaenau Gwent

77

Pakistani

Indian

Pakistani

Indian

of

Rhondda, Cynon, Taff Merthyr Tydfil

317

38

3.9 4.9 6.0 4.6 6.3

9.4 2.7

Black Caribbean

Black Caribbean

282

Bangladeshi

Bangladeshi

493

White British

Pakistani

1,346

Bangladeshi

56,026

White British

Indian

Southampton UA

of

White British

Portsmouth UA

Unemployment rates (unemployment as a percentage economically active women)

65,155

ALL Milton Keynes UA Brighton and Hove UA

Economic activity rates

Black Caribbean

Numbers

4.9

APPENDIX 1

Torfaen Monmouthshire Newport Cardiff Mid Bedfordshire Bedford South Bedfordshire Aylesbury Vale Chiltern South Bucks Wycombe Cambridge East Cambridgeshire Fenland Huntingdonshire South Cambridgeshire Chester Congleton Crewe and Nantwich Ellesmere Port and Neston Macclesfield Vale Royal Caradon

26,151 24,021 39,725 96,169 36,334 44,022 33,695 50,789 26,050 17,985 49,482 36,299 21,440 23,387 47,358

25,618 23,235 37,020 84,433 34,120 35,479 31,270 45,734 22,933 15,031 40,543 27,061 19,888 22,607 44,058

39,005 35,501 27,026 32,264

35,777 33,584 26,163 31,000

23,693 43,686 35,751 22,817

22,949 41,311 34,688 22,182

130 1,403 220 2,073 290 365 238 599 634 746

1,010 402 111 2,794 178

219

176

594 1,268

238 732

126 415

688

576

899 151 380

274

1,125 230

102

221 113

67 72 68 68 77 75 76 77 73 74 76 64 76 71 78 79 72 74 73 73 76 73 72

176

39

58 53 72 70 75 69 73 73 67 51

31 26

19 23

65 70

35

21

77 78 74

79

44

78 65

75

24 29 59 31 48

25

75 55

93

4.7 3.1 4.8 3.2 2.5 3.1 3.3 2.4 2.1 2.5 2.2 3.2 3.0 3.7 2.5 1.9 2.8 2.7 3.6 3.6 2.3 3.4 3.9

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Bangladeshi

Pakistani

Indian

Black Caribbean

Economic activity rates

White British

ALL

Numbers

4.0 6.0 3.1 5.9 4.6 3.6 6.4 2.5 7.3 3.7

12.5 12.8

13.0 18.6

4.9 7.9

12.9

23.0

9.8 13.0 7.6 10.2 3.5

7.3 4.2 5.7

10.3

4.6

12.8

2.9 5.5

3.0

4.9 3.1

4.2

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Carrick Kerrier North Cornwall Penwith Restormel Isles of Scilly Allerdale Barrow-in-Furness Carlisle Copeland Eden South Lakeland Amber Valley Bolsover Chesterfield Derbyshire Dales Erewash High Peak North Derbyshire South Derbyshire East Devon Exeter Mid Devon North Devon

24,892 26,141 22,139 17,557 26,811 617 26,736 20,521 29,433 19,770 14,090 28,601 34,087 20,669 28,759 19,443 32,411 26,250

23,789 25,454 21,525 16,737 25,977 593 26,275 20,070 28,735 19,375 13,853 27,677 33,278 20,306 27,837 18,879 31,254 25,335

28,052 24,553 32,282 34,487 19,572 24,166

27,454 23,475 31,254 32,197 19,008 23,477

228

121

70 67 69 66 70 87 72 66 75 69 77 77 73 66 71 75 74 76

68

76

3.9 4.8 4.4 5.4 4.4 1.7 4.7 5.0 4.3 6.4 2.5 2.5 4.0 5.9 5.1 2.6 4.0 3.2

6.5

East

73 74 72 72 75 73

391 110

40

73 52

3.8 3.0 2.9 2.9 2.9 4.3

3.5 0.0

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Bangladeshi

Indian

Pakistani

Economic activity rates

White British

ALL

Numbers

9.8

APPENDIX 1

South Hams Teignbridge Torridge West Devon Christchurch East Dorset North Dorset Purbeck West Dorset Weymouth Portland Chester-le-Street Derwentside Durham Easington Sedgefield Teesdale Wear Valley Eastbourne Hastings Lewes Rother Wealden Basildon Braintree

22,836 33,316 16,187 13,420 11,137 22,229 16,443 12,190 24,358

21,914 32,206 15,770 12,995 10,702 21,389 15,829 11,611 23,341

73 74 71 73 73 74 74 75 73

3.4 3.1 5.1 3.4 3.0 2.3 2.6 3.1 2.9

17,628 16,159 24,766 27,941 27,183 25,818 6,765 17,785 24,082 24,124 24,996 21,128 38,788 50,150 39,163

17,054 15,894 24,426 26,356 26,832 25,425 6,641 17,497 21,709 22,431 23,516 19,973 36,338 47,218 37,312

73 72 69 64 60 68 72 66 71 69 74 70 73 70 74

3.5 3.4 4.8 3.8 5.9 5.3 3.2 6.0 3.7 4.8 2.6 3.2 2.5 4.3 2.9

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Bangladeshi

Indian

Pakistani

Economic activity rates

White British

ALL

Numbers

and

129

122 128

359 117

183

41

50

58 63

73 65

74

12.5

9.9 10.0

8.0 3.9

8.1

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Brentwood Castle Point Chelmsford Colchester Epping Forest Harlow Maldon Rochford Tendring Uttlesford Cheltenham Cotswold Forest of Dean Gloucester Stroud Tewkesbury Basingstoke Deane East Hampshire Eastleigh Fareham Gosport Hart Havant New Forest

19,753 25,207 47,328 46,704 35,872 23,813 17,487 22,721 34,948 20,091 32,803 22,585 23,206 32,314 30,800 22,110

17,935 24,317 44,107 42,542 32,143 21,703 16,804 21,851 33,642 18,822 30,298 21,441 22,583 29,208 29,492 21,283

160

675

589

46,970 31,841 34,607 31,284 22,294 25,156 33,276 46,255

43,469 30,020 32,812 29,957 21,555 23,384 32,095 44,392

439

181

341 257 731 184

102 134 141

134 110 166 133

399

73 71 75 73 71 76 71 73 68 74 75 75 72 77 76 77

68 72 56 69 76

44 43 43

78 67 76 80

70

45

76

79

80

2.3 3.3 2.5 3.3 3.3 4.1 3.0 2.5 4.3 2.1 3.0 2.4 4.1 3.7 3.2 2.5

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Indian

Pakistani

Bangladeshi

Economic activity rates

White British

ALL

Numbers

7.3 2.0 4.1 5.0 4.3

20.0 6.9 13.3

7.6 5.4 4.8 2.8

2.8

5.9

6.0

2.9

4.9

and

332 112 150

42

78 74 79 78 76 77 73 74

75 73 69

2.4 2.3 1.9 2.2 3.3 2.0 3.5 2.6

4.8 7.3 0.0

APPENDIX 1

Rushmoor Test Valley Winchester Broxbourne Dacorum East Hertfordshire Hertsmere North Hertfordshire St Albans Stevenage Three Rivers Watford Welwyn Hatfield Ashford Canterbury Dartford Dover Gravesham Maidstone Sevenoaks Shepway Swale Thanet Tonbridge Malling

25,257 30,751 29,640 23,440 36,978 36,287 23,463 30,285 32,604 21,687 20,468 18,881 24,734 28,257 36,065 23,383 28,210 24,031 38,973 29,380 25,188 34,195 32,569

219 175 116 195 451 261 801 1,035 431 298 1,018 669 611 111 268 519

31,614

30,006

69 75 75 74 70 67 71 79 74 78 68 74 63 74 60 72

113 128

79 77 74 75 76 76 74 76 76 76 74 79 73 73 68 75 71 71 74 71 71 70 68

109

74

2,249 245 148

146

117

314

223 151

204 126 1,079

467

160 380 290 159 163 392 187

128

82

37

84 79

77

2.4

3.6

39 53 38

31

74 84 76 75 74 80 73

77

5.5 0.0 0.0 0.0 3.5

and

43

of

Black Caribbean

Bangladeshi

Pakistani

Indian

0.0 3.0 3.4 2.8 6.3 4.6 5.3 3.9 3.8 4.7 4.6 3.6 2.6 4.9 3.1 4.8

74 67

2.6 2.0 2.2 3.2 2.8 2.0 2.5 2.6 1.9 3.6 2.7 2.6 2.5 2.9 3.5 3.3 4.7 4.0 2.8 2.5 4.6 4.7 5.6

68 71 72

38

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Bangladeshi

Pakistani

Indian

27,616 32,527 31,738 26,373 40,809 39,553 28,186 34,402 38,511 23,873 24,308 24,538 28,711 29,988 40,023 25,737 29,533 27,979 41,462 31,717 26,755 35,637 34,462

Black Caribbean

Economic activity rates

White British

ALL

Numbers

0.0

6.3

4.3

4.3 0.0

3.8 9.0 9.9

6.2

2.5 5.0 1.8 3.4 3.3 3.8 2.2

6.1

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Tunbridge Wells Burnley Chorley Fylde Hyndburn Lancaster Pendle Preston Ribble Valley Rossendale South Ribble West Lancashire Wyre Blaby Charnwood Harborough Hinckley Bosworth

Oadby and Wigston East Lindsey Lincoln

28,742 13,641

264

78 77

72

3.3 3.3

2.1

25,203 16,270 15,538 34,955 25,946

24,437 13,068 15,046 34,084 24,655

113 2,062

74 78 72 68 68

73 71

3.4 3.0 3.7 4.7 4.9

0.0 4.7

227 3,612

3,447 873 253

259 224

189 113 1,010 2,470 205

102 361

21

24 44 45

24 31 23

65 21

70 68 76 71 75

81 28

0.0 5.3 0.0

12.5

of

Black Caribbean

Bangladeshi

Pakistani

29,907 14,091

1,507

25

2.4 3.4 3.2 2.7 3.9 4.4 4.0 3.4 2.1 3.3 2.4 3.8 3.0 2.4 3.3 2.3

Indian

White British

Black Caribbean

Bangladeshi

28,275 23,735 28,793 18,769 21,086 37,893 21,481 32,539 14,941 18,346 29,690 31,608 27,750 24,427 40,933 21,455

402

71 51 61

Pakistani

30,610 26,279 29,784 19,531 23,307 40,147 25,687 39,297 15,455 19,450 30,825 32,680 28,485 26,638 46,381 22,511

1,279

73 72 76 74 73 68 72 72 78 73 78 70 74 81 74 78

Indian

White British

Black Caribbean

Bangladeshi

Pakistani

Indian

110 111 123

Unemployment rates (unemployment as a percentage economically active women)

23.3

21.3 7.0 8.4

17.1 8.8 17.5

6.0 25.5

3.8 9.1 5.5 5.2 1.9

0.0 9.9

and

Melton North West Leicestershire Boston

Economic activity rates

White British

ALL

Numbers

152

118

116

44

70

43

78

9.9

10.6

3.3

APPENDIX 1

North Kesteven South Holland South Kesteven West Lindsey Breckland Broadland Great Yarmouth King's Lynn and West Norfolk North Norfolk Norwich South Norfolk Corby Daventry East Northamptonshire Kettering Northampton South Northamptonshire Wellingborough Alnwick Berwick-upon-Tweed Blyth Valley Castle Morpeth Tynedale

26,755 20,853 36,493 22,531 33,473 33,807 25,427

25,874 20,173 35,124 21,974 31,841 32,700 24,622

37,218 25,650 37,557 31,541 15,765 21,433

35,585 24,948 34,400 30,486 14,919 20,473

22,430 24,325 60,051

21,392 22,688 52,404

23,680 21,319 8,791 7,136 24,989 13,635 16,819

22,498 18,756 8,651 7,034 24,571 13,141 16,452

74 73 75 71 73 77 68

130

102

72 70 68 74 74 77

377 1,207

76 78 76

197

890

254

479

940

428

45

78 76 72 73 71 71 74

3.6 3.3 3.2 4.6 4.0 2.5 6.8

76

80

3.8 3.6 4.5 2.9 5.3 2.9

73 71

3.2 3.1 3.4

49

72

44

26

76

74

2.1 3.8 5.0 4.6 5.3 3.2 3.2

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Bangladeshi

Pakistani

Indian

Black Caribbean

Economic activity rates

White British

ALL

Numbers

0.0

12.5

3.7

3.2 4.9

4.8

15.3

13.6

4.1

6.6

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Wansbeck Craven Hambleton Harrogate Richmondshire Ryedale Scarborough Selby Ashfield Bassetlaw Broxtowe Gedling Mansfield Newark Sherwood Cherwell Oxford South Oxfordshire Vale of White Horse Bridgnorth North Shropshire Oswestry Shrewsbury and

17,945 14,815 23,943 44,066 13,074 13,787 29,710 22,876 33,035 31,218 32,060 33,328 28,812

17,668 14,352 23,382 41,387 12,618 13,483 28,884 22,447 32,389 30,458 29,540 31,232 27,983

30,624 31,367 39,899 45,208 38,061 33,896 27,959 14,693 15,832 10,693 27,498

29,700 29,068 36,779 33,199 35,113 31,100 26,422 14,304 15,405 10,372 26,475

129 133

524 280 922 157 147

168 306 779

147 312

67 71 64

33 50

67 79 53 69 78

39 48 32

73 82

5.6 2.4 2.7 2.5 4.8 2.9 4.5 3.1 5.8 5.4 3.3 3.0 5.9

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Bangladeshi

Pakistani

Indian

454 314 131

70 79 77 77 74 76 72 76 69 69 75 77 67

Unemployment rates (unemployment as a percentage economically active women)

3.6 5.4 3.6

7.0 13.6

5.6 4.7

6.6 5.9 4.7 2.8 6.1

0.0 7.5 8.5

0.0 3.1

and

Rushcliffe

West Oxfordshire

Economic activity rates

White British

ALL

Numbers

267

122 569

46

70 77 79 67 77 78 79 75 72 74 76

33

80 74

4.3 2.4 2.4 2.6 2.3 2.1 2.0 3.1 3.6 4.4 2.7

9.1

APPENDIX 1

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Bangladeshi

Indian

Pakistani

Economic activity rates

White British

ALL

Numbers

Atcham South Shropshire Mendip Sedgemoor South Somerset Taunton Deane West Somerset Cannock Chase East Staffordshire Lichfield Newcastle-underLyme South Staffordshire Stafford Staffordshire Moorlands Tamworth Babergh Forest Heath Ipswich Mid Suffolk St. Edmundsbury Suffolk Coastal Waveney Elmbridge

10,793 29,648 29,683 41,461 29,423 9,101 27,659 30,266 27,455

10,507 28,477 28,864 40,146 28,230 8,814 27,034 28,016 26,499

144 177

36,380 31,012 35,183

34,944 30,060 33,531

163 261 264

27,333 23,071 23,758 16,118 33,657 24,544 28,656 31,438 30,441 36,259

26,857 22,217 22,856 12,489 30,634 23,756 27,034 29,844 29,560 29,313

1,126

130

131

105

270

254

439

586

47

72 74 73 75 76 71 73 74 75

68 79

70 76 77

48 70 66

74 74 74 77 74 74 78 73 69 73

27

72

79

79

59

69

84

24

73

3.1 3.3 3.8 2.9 3.0 4.0 4.3 3.7 3.3

5.1 4.3

3.4 2.9 3.3

8.9 3.8 7.4

2.9 4.5 2.8 3.3 3.9 2.9 2.8 2.8 5.2 2.4

19.9

0.0

8.7

10.8

10.1

3.7

16.1

5.9

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Mole Valley Reigate Banstead Spelthorne Surrey Heath Tandridge Waverley Woking

North Warwickshire Nuneaton and Bedworth Stratford-on-Avon Warwick Adur Arun Chichester Crawley Horsham Mid Sussex Worthing Kennet

37,259 24,261 26,710 24,456 23,441 33,835 27,159 18,555

33,074 20,464 23,258 21,655 21,399 30,665 22,343 18,014

422 315 623 272 161 140 350

35,295 25,482 32,471 37,725 16,243 36,703 29,282 30,285 35,625 37,604 26,469 21,292

32,881 23,190 30,960 32,734 15,423 35,031 27,563 25,213 33,328 34,605 24,521 20,287

1,322 716

197

1,745

116

167

73

73

70

Black Caribbean

61 76

Bangladeshi

74 77 77 76 75 74 73 80 76 77 76 75

Pakistani

73 59 78 77 65 64 72

Indian

77 74 79 77 75 73 78 74

63

White British

Black Caribbean

72 67 71

Bangladeshi

76 74 75

Pakistani

Black Caribbean

Bangladeshi

Pakistani

Indian

435 368 131

of

2.2 2.1 2.2

4.8 2.4 6.5

2.0 2.2 2.2 1.8 2.0 2.2 2.2 3.2

3.2 4.9 5.2 4.3 0.0 3.4 3.6

3.6 3.1 2.4 2.7 2.2 3.1 2.7 2.4 2.1 1.9 2.3 3.1

8.9 5.5

10.5

3.2

7.4

3.8

and

Runnymede

Rugby

16,567 34,718 20,845

Indian

Guildford

20,059 39,896 22,770

Unemployment rates (unemployment as a percentage economically active women)

White British

Epsom and Ewell

Economic activity rates

White British

ALL

Numbers

1,465 120 194 129

228 116

144

113

1,055

900

105

48

73 69 73 64

51 31

76

29

39

45

81

4.3 3.6 3.5 7.2

12.9 8.3

5.5

0.0

9.1

10.4

0.0

APPENDIX 1

36,798 35,096 102 76 32,402 30,911 77 Salisbury 33,783 32,219 76 West Wiltshire 25,352 24,201 168 78 Bromsgrove 19,894 18,998 73 Malvern Hills 24,684 22,997 121 470 148 77 Redditch 28,741 26,974 130 369 77 Worcester 32,566 31,390 76 Wychavon 28,494 27,502 75 Wyre Forest Source: 2001 Census Standard and Commissioned Tables, Crown Copyright 2003 North Wiltshire

49

69

77 65 68

29 30

78

2.7 2.6 3.1 2.4 2.6 4.1 3.3 3.0 3.7

of

Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Unemployment rates (unemployment as a percentage economically active women) Black Caribbean

Bangladeshi

Pakistani

Indian

White British

Black Caribbean

Bangladeshi

Pakistani

Indian

Economic activity rates

White British

ALL

Numbers

0.0

5.4 10.1 6.8

11.2 10.7

6.0

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

Appendix 2 Data tables on the Neigbourhood Renewal areas – the 86 ‘deprived’ districts of England

38,840 54,310 62,727 20,070 187,759

1,325 9,439 138 13 18,806

930 1,245 44 9 29,112

219 489 13 0 5,830

1,330 1,620 27 4 16,019

64 72 65 66 69

61 68 64

33 44

32 42

77 75

58

23

22

72

39,600 39,762 20,669 76,670 137,490 87,237 77,906 118,265 26,279 70,782 87,177 104,686 64,930 24,766 82,836

30,247 38,357 20,306 66,827 104,838 22,964 66,276 102,641 23,735 34,742 67,578 63,008 54,586 24,426 80,154

4,298 69 46 4,973 4,228 16,617 873 1,653 111 1,831 7,860 7,280 2,837 26 386

3,404 58 14 1,835 19,582 3,335 183 1,294 1,279 444 1,790 2,356 2,513 15 403

128 51 0 63 1,367 375 279 387 402 3,660 521 546 56 12 21

30 26 12 248 952 9,521 203 1,864 10 1,459 1,000 10,298 971 3 189

71 71 66 73 74 74 72 71 72 72 70 74 71 69 67

31

23

16

43 59 66 61 65 51 59 63 70 66

25 24 37 54 34 25 48 29 45 29

56

34

50

65 73 73 68 70 67 72 79 73 74

Black Caribbean

49,439 99,890 63,948 20,521 287,522

Bangladeshi

72 69

Pakistani

3 41

5.6 3.6 5.4 5.0 5.3

3.6 5.5 3.4

18.8 10.9

14.1 13.6

4.5 5.8

8.2

22.2

21.5

9.9

4.4 4.8 5.9 3.7 3.8 4.2 4.2 3.4 3.4 5.0 4.1 3.7 4.4 4.8 5.2

11.9 5.7

16.7 25.0

14.3

7.6 6.5 5.4 3.9 4.7 5.3 5.7 7.3 6.7 5.5

12.8 19.8 10.1 10.1 7.0 12.5 9.9 17.1 11.2 20.0

0.0 21.4 14.9 12.2 11.0 23.3 13.4 12.4 10.5 15.8

11.2

20.1

Indian

6 6

White British

White British

10 16

22 36 27 37 21 25 25 51

Black Caribbean

Black Caribbean

19 97

Bangladeshi

Bangladeshi

26,275 32,389

Pakistani

Pakistani

26,736 33,035

Indian

Indian

Unemployment rates (unemployment as a percentage of economically active women)

White British

Allerdale Ashfield Barking and Dagenham Barnet Barnsley Barrow-in-Furness Birmingham Blackburn with Darwen UA Blackpool UA Bolsover Bolton Bradford Brent Brighton and Hove UA Bristol, City of UA Burnley Camden Coventry Croydon Derby UA Derwentside Doncaster

Economic activity rates

ALL

Numbers

4.7 5.8

11.3

9.3 6.1 8.4 7.2 7.5 9.4 6.6 5.3 5.7 8.6

APPENDIX 2

32,854 32,070 25,332 22,431 21,086 34,033 25,454

1,002 2,207 64 128 79 1,119 21

524 617 59 19 1,507 283 3

294 932 19 30 36 1,283 9

2,947 7,718 3 62 9 3,120 23

75 73 65 69 73 71 67

71,133 115,988 45,341 91,776 218,472 86,522 82,092

68,310 97,481 44,152 44,221 193,714 50,936 44,187

215 4,792 68 1,835 4,245 23,773 1,275

140 7,754 17 839 4,388 1,399 340

91 99 3 659 699 567 371

50 1,303 33 11,406 2,185 1,555 11,740

65 75 61 78 73 68 72

45

71 74

49 44

36

77

66 41

39 35

23 24

75 63

62 63

45 42

28 34

69 70

72 74

4.2 3.4 5.9 4.0 4.3 6.8 6.3 5.5 5.5

6.1 5.4 5.5 4.8 3.7 7.5 5.8 11.9 0.0

4.3 4.4 6.7 4.8 3.9 5.7 4.8

5.8 5.0 7.5 10.0 15.6 7.9

7.7 3.5 7.6 4.3 3.5 5.5 5.0

19.1 10.1 11.9 5.9 3.9 8.3 5.3

Black Caribbean

59,405 74,487 25,842 24,124 23,307 62,350 26,141

27 39

67 68

Bangladeshi

74 77 60 70 69 68 68 71 68

Pakistani

734 4,995 0 5,593 19 17 2,655 7,212 7

Indian

White British

89 369 3 1,085 45 0 360 1,775 13

White British

Black Caribbean

1,815 3,517 23 586 146 28 606 723 8

Black Caribbean

Bangladeshi

1,649 16,594 74 3,833 145 60 3,197 2,617 55

Bangladeshi

Pakistani

81,326 41,471 26,832 49,087 54,113 24,622 45,787 28,847 35,613

Unemployment rates (unemployment as a percentage of economically active women)

Pakistani

Indian

88,131 98,343 27,183 86,089 55,984 25,427 68,001 68,362 36,531

Indian

White British

Dudley Ealing Easington Enfield Gateshead Great Yarmouth Greenwich Hackney Halton UA Hammersmith Fulham Haringey Hartlepool UA Hastings Hyndburn Islington Kerrier Kingston upon City of UA Kirklees Knowsley Lambeth Leeds Leicester UA Lewisham

Economic activity rates

ALL

Numbers

20.9 11.4

7.3 13.3

7.7 6.2

12.5 7.8

14.0

5.7

20.7 8.3

21.7 15.0

7.5 12.3

17.5 8.4 0.0

0.0 14.5

10.6 11.3

and

63 62

24 42

53 37

34 25

66 65 57 69

43 27 38 42

23

66

8.9 21.3 5.0

22.2

12.7

16.7 16.8

40.0 32.0

7.2

13.2 14.5 11.7 8.5

14.6 24.6 16.6 22.4

11.2 6.2 9.2 8.3

Hull,

51

77 30 25 26 39

70 71 69 75

ETHNIC MINORITY WOMEN AND LOCAL LABOUR MARKETS

70 51 43 64 48 59 50

44,885 56,448 37,557 80,998 64,348 25,687 17,557 71,560 39,297

43,763 54,560 34,400 65,383 55,055 21,481 16,737 68,762 32,539

150 232 197 2,156 519 80 21 98 3,612

29 45 35 2,818 3,849 3,447 4 24 873

33 135 63 154 2,599 13 15 43 91

23 19 56 3,010 274 14 12 50 259

69 72 68 63 73 72 66 69 72

65 72 49 52 62

40,525 60,871 72,891 62,469 81,533 25,818 80,679

39,694 52,696 70,064 57,654 62,314 25,425 77,925

55 258 174 442 8,651 37 193

104 4,554 1,327 272 2,411 6 53

32 713 10 113 968 3 80

3 90 54 154 3,230 6 40

65 72 70 68 68 68 70

52

38 30

24 24

55 66

29 34 29

71

19 27 24 24

45

31

59 70 58 62

38 30 27 38 24

70

25 25

27 22

68 82

65

22 35 24

76 75

Black Caribbean

68 59 61 67 63 65 62

Bangladeshi

Indian

38 308 2,873 40 35 49 6,591

Pakistani

White British

34 168 1,071 27 24 719 6,084

10.8 15.6

26.8 14.3

11.9 9.7

17.4 11.5 18.0

18.5 18.4

9.8

0.0 0.0 12.2 20.6

0.0 11.2 6.7

9.7

6.0

15.7

4.3 0.0 10.3 7.5

Indian

Black Caribbean

40 318 6,814 21 1,423 1,421 6,076

White British

Bangladeshi

116 738 2,235 131 281 1,051 9,678

Black Caribbean

Pakistani

24,655 124,971 91,078 27,983 37,297 71,766 24,055

Bangladeshi

Indian

25,946 136,696 122,065 28,812 40,311 79,751 76,450

Unemployment rates (unemployment as a percentage of economically active women)

Pakistani

White British

Lincoln Liverpool Manchester Mansfield Middlesbrough UA Newcastle upon Tyne Newham North East Lincolnshire UA North Tyneside Norwich Nottingham UA Oldham Pendle Penwith Plymouth UA Preston Redcar and Cleveland UA Rochdale Rotherham Salford Sandwell Sedgefield Sefton

Economic activity rates

ALL

Numbers

4.9 7.3 5.7 5.9 7.0 4.9 6.6

9.9 5.9 5.6 3.6 5.1 4.5 9.1

7.0 4.5 4.5 6.1 3.8 4.0 5.4 4.2 3.4

7.1 2.4 12.5 7.0 4.0 16.3

14.5 16.6 17.1

14.0 8.4

8.8

6.4 4.3 4.8 4.4 5.9 5.3 4.3

8.1 6.5 6.6 4.7 10.0

7.7 14.2 23.4 16.3 25.2

15.0 21.8

8.1

8.1

19.4

APPENDIX 2

53

52 53 56 68 51 58 65 70 52 68 61 57

58 62 65 61

28 48 26 23 50 32 42 24 23 30

45 37

26 19 27

73 68

28 26 20 23

60 77 68

22 33

74 76

27

67

21 33

72

4.2 7.4 5.4 5.2 5.4 5.1 5.6 3.8 5.5 4.5 5.3 4.3 5.6 6.0 4.9 3.9 5.2 5.6

5.9 9.0 4.3 7.9 6.7 6.1 6.0 3.4 6.8 3.6 8.4 7.2

5.6 8.0 9.6 9.9

Black Caribbean

Bangladeshi

70 67 70 67 69 67 67 73 70 70 69 74 70 66 74 70 68 68

Pakistani

1,644 6 7,351 19 17 186 29 109 1,875 68 914 6,790 3 9 2,155 49 64 2,735

Indian

White British

549 217 1,066 20 15 177 262 672 18,403 13 678 672 19 10 1,552 17 101 72

White British

Black Caribbean

4,390 84 348 30 560 1,785 104 770 437 904 2,676 5,352 17 9 623 109 33 886

Unemployment rates (unemployment as a percentage of economically active women) Black Caribbean

Bangladeshi

1,200 250 1,358 131 233 420 310 1,020 1,158 325 4,477 2,571 59 25 2,185 224 207 9,640

Bangladeshi

Pakistani

135,779 42,212 39,953 50,982 51,496 66,640 81,595 58,553 26,775 90,535 61,183 37,115 17,668 17,497 27,639 87,956 87,712 50,203

Pakistani

Indian

152,497 43,670 81,497 52,166 53,455 71,145 84,125 63,146 62,791 93,666 72,345 70,306 17,945 17,785 64,055 90,077 91,089 68,000

Indian

White British

Sheffield South Tyneside Southwark St. Helens Stockton-on-Tees UA Stoke-on-Trent UA Sunderland Tameside Tower Hamlets Wakefield Walsall Waltham Forest Wansbeck Wear Valley Westminster Wigan Wirral Wolverhampton

Economic activity rates

ALL

Numbers

21.6 0.0 9.0

16.7 22.0 9.5

8.7

24.0 23.9 23.9 20.4

8.0

20.5 20.2 11.5 10.4 21.1 14.6 22.3 14.7

13.2 10.0 19.4

10.0

21.1 11.7

9.5 11.3 8.7 8.6 6.8

13.8

12.9

28.6 13.6

10.2