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:
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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.
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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