Kalwant Bhopal: How Gender and Ethnicity Intersect

Kalwant Bhopal (1998) 'How Gender and Ethnicity Intersect: The Significance of Education, Employment and Marital Status'
Sociological Research Online, vol. 3, no. 3, <http://www.socresonline.org.uk/3/3/6.html>

To cite articles published in Sociological Research Online, please reference the above information and include paragraph numbers if necessary

Received: 8/1/98 Accepted: 29/9/98 Published: 30/6/98

Abstract

This paper examines Labour Force Survey (LFS) statistics on economic activity, highest educational qualification, marital status and ethnicity. The paper will specifically explore comparisons within South Asian groups and between other ethnic groups (Afro-Caribbean and white), to investigate whether marriage has a differential impact for different ethnic groups, and if there have been any changes over time (1984-1994). The LFS data indicates that marital status has a differential impact on economic activity and education for different ethnic groups. When controlling for age (25-30), martial status has more impact on Indian and Pakistani/Bangladeshi groups, than it does for Afro-Caribbean and white groups. There are differences between ethnic groups and there are also differences within the South Asian category. Rapid social change is taking place for the 25-30 age cohort, where some South Asian women are becoming highly educated and entering professional occupations. This finding supports recent research carried out on South Asian women in East London (Bhopal, 1997).

Keywords:

Education; Employment; Ethnicity; Gender; Marital Status; South Asian Women

Introduction

1.1: This paper examines Labour Force Survey (LFS) statistics on economic activity, highest educational qualification, marital status and ethnicity, by comparing all women and women aged 25-30. The paper will specifically explore comparisons within South Asian groups (Indian/Pakistani/Bangladeshi) and between other ethnic groups (Afro-Caribbean and white). The paper will argue that there has been an increase in economic activity for all women from all ethnic groups in the last 11 years (1984-1994). For the 25-30 age cohort, the increase is more apparent. There are different rates of change for different ethnic groups, the greatest being for Pakistani/Bangladeshi women. Indian women aged 25-30 are more likely to be economically active than Pakistani/Bangladeshi women. Indian women are also more likely to have degrees than white and other women. The paper will compare data for three years (1984, 1990, 1994) to examine changes in economic activity and education, and how marital status affects this. The paper shows that single women (from all ethnic groups) and cohabiting women (from all ethnic groups, with the exception of Afro-Caribbean in 1990) were more likely to be employed than married women. The paper supports recent research which indicates that South Asian women with high levels of education are in professional occupations. Some of these women reject 'arranged marriages', but instead choose to cohabit or remain single (Bhopal, 1997).

Gender, Ethnicity and Education

2.1

Whilst many of the writings on gender and education have underestimated the significance of difference and diversity, (Delamont, 1980; Weiller, 1988), other writings have attempted to examine the relationship between ethnicity and educational achievement (Tanna, 1990; Penn and Scattergood, 1992; Ghuman, 1994). The existence and nature of ethnic and gender differences in attainment in national qualifications are significant factors in determining future educational and employment prospects, and are of particular relevance to the consideration of equality in access to such opportunities. Modood and Shiner (1994) argue that academic performance is an important part of the explanation of ethnic differences in access to higher education. Recent research suggests that the performance of ethnic minority groups may now exceed that of majority groups; for example for three consecutive years in GCSE, evidence has shown that no ethnic group performs worse than the 'white' classification (Thomas, 1992; 1993; 1994; Sammons, 1994).

2.2

Recent research (Ballard, 1994) has demonstrated that compared with the white majority, a higher proportion of South Asians continue in full- time education. Furthermore, their rate of enrolment on degree courses is double that of the white norm. Yet they still continue to suffer the same forms of discrimination and exclusion as their parents. Gardner and Shukur (1994) have indicated that an increasing number of Bengali women are continuing into higher education and within the next few decades the numbers of Bengali women in the labour market will show a significant increase. One of the reasons for this is that higher education is seen as a route for greater earning potential and upward social mobility. Shaw's (1994) research on the Pakistani community in Oxford, demonstrates that only a small minority of girls have gained higher qualifications, but have stayed within the community, finding few problems in combining career with an arranged marriage and participation in family and community events. Shaw (1994) argues that even though the younger generation obtain educational and professional qualifications, they are often still committed to upholding their community values and identity.

2.3

Although many studies have attempted to investigate the relationship between 'race', ethnicity and education, very few studies have examined the educational achievements of South Asian women. Examining the relationship between gender, ethnicity and education enables us to explore the issue of difference for South Asian women. How do South Asian women perform in education? How do performance levels affect their marital status? Such questions will enable us to explore the impact of 'race', gender and education upon women's lives.

Gender, Ethnicity and Employment

3.1

Throughout the last four decades, evidence of discrimination against South Asian groups has grown steadily (Owen and Green, 1992). More recently Brah and Shaw (1992) have argued that Muslim women's involvement in paid work, (whether inside or outside the home), was not seen by them as unequivocally advantageous. 'The double shift', of combining paid work with domestic responsibilities, mitigated against many of the advantages of having a job. Furthermore, Brah (1993) has stressed the role of multiple determinations in the relation of young Muslim women to the labour market. More recently West and Pilgrim (1995) have recognised the diversity of experience of different groups of South 'Asian' women in the labour market, the impact of migration and the local economy on women's familial responsibilities. They found that the participation of Bengali women in the labour force was virtually nil and experience for Pakistani women was minimal, but Indian women (Gujeratis) had extensive employment experiences (Owen and Green, 1992; Rafiq, 1992).

3.2

There are important differences in the economic, political, legal and ideological position of migrant black women when compared to other women. This is no more evident than in the labour market. As migrants, they experience racial subordination which acts to confine them in certain types of work and reinforces their exploitation as waged workers. These forms of exploitation are shaped and experienced in a particular way because they share with all women subordination as a gender.

3.3

Recent research has indicated the degree of racialised segregation that continues to exist in British labour markets (Bhavnani, 1994; Bruegel, 1994; Jones, 1994; Owen, 1994). Roberts (1994) has shown that even when black and ethnic minority women are skilled and experienced, they are twice as likely to be unemployed and work longer hours in poorer conditions for lower pay than white women. Furthermore, the figures are considerably lower for Pakistani and Bangladeshi women, who are five times more likely to be unemployed than white women, the gap being greatest in recessionary periods. The Equal Opportunities Commission suggest three reasons for this (Roberts, 1994):

Ethnic minority women may have less access to informal organisational networks which may help them in gaining access to a wider range of jobs.
Some ethnic minority groups are more likely to use 'word of mouth' recruitment methods which distance them from formal job search methods.
Employers are more likely to be operating discriminatory practices in a recession.

3.4

Women's experiences in the labour market vary considerably by 'race' and gender which may have a significant impact on women's pay and job levels (Elias and Gregory, 1992; Leffler, 1992; McGuire and Reskin, 1993; Bruegel, 1994; West and Pilgrim, 1995). An understanding of racist structures must be considered as an essential part of the explanation of gender relations in paid employment, as the interactive impacts of 'race' and gender on labour force experience remain unclear. Although the research has examined non-white women's experiences in the labour market it has not specifically examined in any detail, the labour market experiences of South Asian women and the diversity of South Asian women's experience itself. To what extent has the labour market experiences of South Asian women changed in the past decade? Are there significant differences between South Asian groups? Such questions will enable us to examine the diversity of experiences which exist for South Asian women and explore the interrelationship between 'race', gender and martial status.

Labour Force Survey Data

4.1

The relevant LFS data was sorted by gender (women), ethnic group (all ethnic groups), age (all ages and 25-30), education (highest qualification), employment (economic activity) and marital status (single, married, cohabiting and divorced). The LFS data was taken for eleven years (1984-1994). Correlations between marital status, economic activity, education and ethnicity were examined for three years (1984, 1990 and 1994).

4.2

The Labour Force Survey (LFS) is a quarterly sample survey of around 60,000 households and people living in NHS accommodation (ie. nurses). The questionnaire covers a wide range of demographic and employment-related information. Questions about economic activity (paid work, job search) are asked of all people aged sixteen or over, and relate to a specific reference period, (normally a period of one week or four weeks, depending on the topic), immediately prior to interview. If any household member is unavailable for interview, information for that person is provided by a related adult member of the same household. Students living away from home in halls of residence are also included.

4.3

Each quarter's LFS sample of 60,000 households is made up of five 'waves' each of approximately 12,000 households. Each wave is interviewed in five successive quarters; in any one quarter one wave will be receiving their first interview, one wave their second and so on, with one wave receiving their fifth and final interview. Thus there is an 80 per cent overlap in the samples for successive quarters. The survey results are 'grossed up' to give the correct population total for Great Britain and reflect the distributions by sex, age and region shown by the population figures. Estimates relating to 10,000 people or fewer (after grossing up) are not shown, since they are based on small samples and are therefore unlikely to be reliable. This is in line with current practice for all LFS based analyses (Sly, 1994; 1995).

Ethnic Origin

4.4

People interviewed in the quarterly LFS are asked to classify their own ethnic origin and that of others in their household by answering the question: 'To which of these groups do you consider.......belongs: White, Black-Caribbean, Black-African, Black- Other, Black-Mixed, Indian, Pakistani, Bangladeshi, Chinese and Other'. However, LFS estimates relating to ethnic origin (or country of origin and nationality), are subject to high sampling errors as the populations in question are relatively small in number and tend to be highly clustered, both within particular geographical areas and within households. This limits the detail with which results can be presented. The use of computer assisted interviewing techniques in the quarterly LFS has improved the quality of data collection. As a result, there are fewer non-responses to the question relating to ethnic origin. The revised classification of ethnic origin conforms to that of the 1991 census. Previous classifications of ethnic origin used in the LFS were: West Indian/Guyanese, African, Indian, Pakistani/Bangladeshi, Chinese/other. Although the new classifications based on the 1991 census include a greater number of categories, there is still the danger of some people classifying themselves as 'other', without knowing how 'other' is really defined. This risks problems of categorisation and definition.

Economic Activity and Classification

4.5

People in employment in the survey are regarded as those aged sixteen and over who did some paid work in the reference week, (whether as an employee or self-employed), those who had a job that they were temporarily away from, (on holiday for example), those on government employment or training programmes and unpaid family workers. Unemployed people in the survey are regarded as those aged sixteen and over without a paid job, who said they were available to start work in the next two weeks and who either had looked for work at some time during the four weeks prior to the interview, or were waiting to start a job they had already obtained.

4.6

The 'economically active' population or labour force, comprises people in employment together with unemployed people. The 'economically inactive' population comprises people who are neither in employment nor unemployed. This group includes all people aged under sixteen, together with those who were looking after a home or retired, and discouraged workers who were not seeking work, because they believe there were no jobs available.

Highest Qualification

4.7

Educational qualifications in the LFS are asked of those aged sixteen and over. Individuals are asked to state their highest qualification, which ranges from higher degree to GCSE down to no qualifications, as well as other qualifications such as nursing certificates, YTS and BTEC.

LFS Data

Economic Activity

5.1

This section will compare data for the 25-30 age cohort in 1993, (1993 was taken as a year for analysis for the 25-30 age group), with data for all women. Recent qualitative research of 60 in-depth interviews carried out in the same year (Bhopal, 1997) has shown that access to education for young South Asian women (aged 25-30) is transforming their ability to gain employment, as a result these women are rejecting 'arranged marriages'. The aim was to examine national LFS data to explore whether such data supports this thesis. It is important to make a comparison between the 25-30 age cohort and all women as data from this age cohort will produce a different picture from data for all women. The rapidity of social change for South Asian women (for the 25-30 age cohort), may be greater than for all women, hence it is important to examine both groups.

Table 1: Economic activity and ethnicity, 1993 (all women)

ECONOMIC ACTIVITY (ALL WOMEN)	ETHNIC GROUP (per cent)
1993	All	WHITE	AFRO-CARIBBEAN	INDIAN	PAKISTANI/ BANGLADESHI
Economically Active	53	53	62	56	24
In Employment	49	50	49	50	17
ILO^[1] Unemployed	4	4	13	6	7
Inactive	47	47	38	44	76

Source: LFS 1993^[2].

^[1] This figure is laid down by the International Labour Organisation (ILO), and is also used by the OECD (Organisation of Economic Corporation), and includes those aged sixteen and over without a paid job, who said they were available to start work in the next two weeks.

^[2] There are no base numbers available for any of the tables provided.

Table 2: Economic activity and ethnicity, 1993 (women aged 25-30)

ECONOMIC ACTIVITY (WOMEN AGED 25-30)	ETHNIC GROUP (per cent)
1993	ALL	WHITE	AFRO-CARIBBEAN	INDIAN	PAKISTANI/ BANGLADESHI
Economically Active	72	73	65	70	29
In Employment	66	67	50	60	2 2
ILO Unemployed	6	6	15	10	7
Inactive	28	27	35	30	71

Source: LFS 1993

5.2

As shown in table 2, 60% of Indian women in the 25-30 age cohort were in employment which is slightly lower than the numbers of white women. Pakistani/Bangladeshi women have the lowest number of women in employment. The LFS data indicates that women aged 25-30 were more likely to be working than all women.

Highest Qualification

5.3

What is the relationship between education and ethnic origin? Do different ethnic groups obtain different levels of education? This section will examine data for the 25-30 age cohort for highest educational qualification and ethnic origin (1993) and compare data for all women.

Table 3: Highest qualification and ethnicity 1993, (all women)

HIGHEST QUALIFICATION (ALL WOMEN)	ETHNIC GROUP (per cent)
1993	ALL	WHITE	AFRO-CARIBBEAN	INDIAN	PAKISTANI/BANGLADESHI
First Degree	3	3	4	5	1
'A' Levels	4	4	4	4	2
'O' Levels	12	12	10	9	5
CSE below grade 1	3	3	3	2	2
No Qualifications	19	18	19	26	32

Source: LFS 1993

Table 4: Highest qualification and ethnicity 1993, (women aged 25-30)

HIGHEST QUALIFICATION (WOMEN AGED 25-30)	ETHNIC GROUP (per cent)
1993	ALL	WHITE	AFRO-CARIBBEAN	INDIAN	PAKISTANI/BANGLADESHI
First Degree	9	9	8	14	4
'A' Levels	7	7	7	8	6
'O' Levels	26	27	17	13	10
CSE below grade 1	9	10	8	6	2
No Qualifications	17	16	20	27	50

Source: LFS 1993.

5.4

The LFS data demonstrates that Indian women were more likely to have degrees than white and other women in 1993. Pakistani/Bangladeshi women were less likely to have degrees than any other ethnic group.

5.5

When comparing the numbers for all women, Indian women were more likely to have degrees than Pakistani/Bangladeshi women. Pakistani/Bangladeshi women were more likely to have no qualifications than other women. There are significant differences between ethnic groups in terms of educational qualifications. Indian women were more educated than white women, followed by Afro- Caribbean women. Pakistani/Bangladeshi women were the least educated. Education has a similar impact for women of all ethnic groups, for example enabling them to enter employment, however women from different ethnic groups have differential access to education.

Economic Activity, Marital Status and Ethnicity

6.1

What effect does marital status have on women's economic activity? The following tables provide figures from 1984, 1990 and 1994 for economic activity, marital status and ethnicity, for all women and women aged 25-30. Abbreviations used are, SING= single, MARR= married, COHAB= cohabiting and DIVOR= divorced.

Table 5: Economic activity, marital status and ethnicity (all women)

ETHNIC GROUP	IN EMPLOYMENT MARITAL STATUS					ILO/OECD UNEMPLOYED MARITAL STATUS (per cent)
YEAR	ALL	SING	MARR	COHAB	DIVOR	ALL	SING	MARR	COHAB	DIVOR
ALL WOMEN
1984	35	24	47	-	44	4	4	4	-	9
1990	40	27	54	77	53	3	2	3	6	6
1994	39	24	55	71	50	3	3	3	6	7
WHITE WOMEN
1984	35	25	47	-	49	4	4	4	-	9
1990	41	28	54	77	53	3	2	3	6	6
1994	40	25	54	71	51	3	3	3	6	6
INDIAN WOMEN
1984	30	12	47	-	34	7	4	9	-	37
1990	35	13	54	79	33	5	3	6	21	11
1994	35	18	51	82	46	5	2	6	18	35
1994	11	7	16< /td>	36	25	4	3	4	31	26
AFRO-CARIBBEAN WOMEN
1984	43	28	64	-	60	8	7	9	-	16
1990	46	36	70	57	54	6	6	5	9	5
1994	36	25	57	70	42	8	8	7	4	15

Source: LFS 1984, 1990 and 1994.

Table 6: Economic activity, marital status and ethnicity (Women aged 25-30)

ETHNIC GROUP	IN EMPLOYMENT MARITAL STATUS					ILO/OECD UNEMPLOYED MARITAL STATUS (per cent)
YEAR	ALL	SING	MARR	COHAB	DIVOR	ALL	SING	MARR	COHAB	DIVOR
ALL WOMEN
1984	53	77	48	-	47	8	8	8	-	11
1990	65	76	61	80	47	6	6	6	6	8
1994	65	68	64	76	41	6	8	5	5	10
WHITE WOMEN
1984	54	79	49	-	47	8	8	8	-	11
1990	66	77	62	81	47	6	5	6	5	8
1994	67	69	67	76	42	6	7	5	5	9
INDIAN WOMEN
1984	47	77	44	-	66	11	14	11	-	34
1990	64	93	60	10 0	100	8	7	7	-	-
1994	65	88	55	10 0	49	7	3	7	-	25
PAKISTANI/BANGLADESHI WOMEN
1984	13	-	13	-	-	5	-	5	-	-
1990	22	-	22	-	51	6	-	7	-	-
1994	25	72	19	-	21	4	19	1	-	20
AFRO-CARIBBEAN WOMEN
1984	52	52	51	-	63	14	13	13	-	37
1990	66	65	80	37	41	10	13	-	25	-
1994	56	58	53	66	27	12	17	10	-	-

Source: LFS 1984, 1990 and 1994.

6.2

As shown in table 5, single women from all ethnic groups, (with the exception of Afro-Caribbean), had significantly higher numbers of women who were in employment compared to married women, which is similar to those cohabiting. Single women (from all ethnic groups), and cohabiting women, (from all ethnic groups, with the exception of Afro-Caribbean in 1990), were more likely to be in employment than married women.

6.3

Marital status has a disproportionate impact on economic activity for different ethnic groups. When we control for age (table 6), the findings suggest that marital status has more impact on Indian and Pakistani/Bangladeshi women than for Afro- Caribbean and white women. There are differences between ethnic groups and there are also differences within the South Asian category.

Chances Over Time

7.1

The following sections will examine data from the LFS for eleven years (1984-1994) on economic activity, education and marital status, to investigate whether changes for different ethnic groups are occurring at different rates. Is rapid social change taking place amongst the 25-30 age group?

Economic Activity

7.2

What is the relationship between economic activity and ethnicity? Have the numbers of South Asian women in employment shown an increase? Data from eleven years of the LFS was analysed (1984-1994), as well as data for the 25-30 age cohort. The following tables provide figures for all women from 1984-1994 and women aged 25-30 for economic activity and ethnic origin.

Table 7: Economic activity and ethnicity (all women)

YEAR	ETHNIC ORIGIN (per cent)
	ALL (TOTAL)	WHITE	AFROCARIBBEAN	INDIAN	PAKISTANI/BANGLADESHI
1984	35	35	43	30	6
1985	36	36	40	28	5
1986	36	36	40	31	7
1987	37	37	45	30	8
1988	38	39	48	34	9
1989	40	40	49	37	10
1990	40	41	46	35	11
1991	40	40	49	34	11
1992	49	49	54	52 ^[1]	16
1993	49	50	49	50	17
1994	49	50	51	49	20

Source: LFS 1984-1994.^[2]

[1] There has been a dramatic increase for Indian women who are economically active from 1991 to 1992. This may be due to the changing of LFS data collection. Between 1984 and 1991, the survey was carried out annually, with results published relating to the March to May quarter. From 1992, the LFS questionnaire changed and has been carried out every quarter with a sample size of 60,000 households. There are methodological and quarterly differences between the annual and quarterly series which may affect comparability. Quarterly estimates for the population of Indian origin have been more variable than other ethnic groups, probably as a result of sampling variability. This change should not be taken as indicative of a trend. Furthermore, there has also been a change in the question on ethnic definition since 1991 (Sly, 1994; 1995). The quarterly LFS provides more frequent and timely results than the annual LFS. It uses improved sampling procedures which enhances reliability of data, especially results relating to ethnic minority groups which are no longer expressed as three year averages, as they were previously. It also uses computer-assisted interviewing techniques which has improved the quality of data collected. Lastly, it employs a revised classification of ethnic origins which conforms to that used in the 1991 census. As a result, change in data collection may affect comparability and the data should be read with this in mind.

[2] There is a discrepancy between the figures in this table and those in table 5. The figures in table 5 separate those who are employed and unemployed, whereas the figures in this table are for those who are economically active.

Table 8: Economic activity and ethnicity (women aged 25- 30)

YEAR	ETHNIC ORIGIN (per cent)
	ALL (TOTAL)	WHITE	AFRO CARIBBEAN	INDIAN	PAKISTANI/BANGLA DESHI
1984	53	54	52	4 7	11
1985	54	54	56	47	10
1986	54	55	52	50	13
1987	56	57	63	41	10
1988	59	59	66	58	12
1989	63	64	72	67	10
1990	65	66	66	64	22
1991	64	65	68	56	16
1992	64	66	59	61	15
1993	66	67	50	60	22
1994	66	67	57	66	25

Source: LFS 1984-1994.

7.3

As shown in table 7, there has been an increase in economic activity for women from all ethnic groups in the last ten years. The numbers of white and Indian women who are economically active are similar, for Afro-Caribbean women the numbers are higher. For Pakistani/Bangladeshi women, the numbers remain low, but there has been a dramatic increase. The numbers of Pakistani/Bangladeshi women who are economically active remain less than half of those from all other ethnic groups. However for the 25-30 age cohort (table 8), the increase is more apparent. There are different rates of change for different ethnic groups, the greatest being for Pakistani/Bangladeshi women.

Highest Qualification

8.1

What is the relationship between highest qualification and ethnicity? Data from eleven years of the LFS was analysed (1984 to 1994). The following tables provide figures from 1984-1994 for highest qualification and ethnic origin for all women and for women aged 25-30.

Table 9: Highest qualification (degrees) and ethnicity (all women)

YEAR	ETHNIC ORIGIN (per cent)
	ALL	WHITE	AFRO-CARIBBEAN	INDIAN	PAKISTANI/BANGLADESHI
1984	3	2	3	5	2
1985	3	3	3	4	2
1986	3	3	3	4	1
1987	3	3	4	5	2
1988	3	3	4	5	2
1989	3	3	4	5	2
1990	4	4	5	7	2
1991	4	4	6	6	3
1992	3	3	3	4	2
1993	3	3	4	5	1
1994	4	4	3	4	2

Source: LFS 1984-1994.

Table 10: Highest qualification (degrees) and ethnicity (women aged 25-30)

YEAR	ETHNIC ORIGIN (per cent)
	ALL	WHITE	AFRO-CARIBBEAN	INDIAN	PAKISTANI/BANGLADESHI
1984	9	9	3	13	2
1985	10	10	1	11	8
1986	9	9	4	7	3
1987	9	9	3	6	5
1988	8	9	3	8	3
1989	9	9	2	15	1
1990	9	10	5	13	3
1991	9	9	6	16	7
1992	9	9	9	15	3
1993	9	9	8	14	4
1994	10	10	8	21	5

Source: LFS 1984-1994.

8.2

As shown in table 9, the numbers of white women obtaining degrees has slightly increased. For Afro-Caribbean and Indian women the numbers have fluctuated. For the 25- 30 age cohort (table 10), the numbers for Afro-Caribbean and Pakistani/Bangladeshi women have fluctuated, but have shown an increase. However, the numbers for Indian women have shown a significant increase. In comparison to other ethnic groups, the numbers of Indian women obtaining degrees is relatively high. They are more likely to obtain a degree than any other ethnic group. Although, the numbers of Pakistani/Bangladeshi women are comparatively low, overall there has been an increase.

Conclusion

9.1

This paper has used LFS data to examine the effect of social change and whether there are differences between ethnic groups. Marital status has a differential impact on economic activity and education for different ethnic groups. When controlling for age (25-30), marital status has more impact on Indian and Pakistani/Bangladeshi groups, than it does for Afro-Caribbean and white groups. There are differences between ethnic groups and there are also differences within the South Asian category. The data has also indicated the extent of rapid social change for women from the 25-30 age cohort. The national LFS data supports recent research carried out on South Asian women aged 25-30 which has demonstrated that South Asian women with high levels of education were in professional occupations, and as a result were choosing to reject 'arranged marriages', but instead to cohabit or remain single (Bhopal, 1997). The causal links between the variables seem to indicate that education affects women's access to the labour market. Once women have gained secure access and participation in the labour market, this affects the choices they have available to them. In relation to marriage, some women are able to have the choice to reject 'arranged marriages'. For some South Asian women, education acts as a catalyst for women's access to the labour market and their route to independence (Bhopal, 1997).

9.2

Previous research (Ballard, 1994) has demonstrated that compared with the white majority, a higher proportion of South Asians continue in full- time education and their rate of degree courses is double that of the white norm. This was also found in the present research in which the numbers of South Asian women (especially Indian) who obtain degrees is significantly higher than the white majority. Gardner and Shukur (1994) have indicated that an increasing number of Bengali women are continuing into higher education and within the next few decades the numbers of Bengali women in the labour market will show a significant increase. This was also found in the present research in which the numbers of Bengali/Pakistani women with higher levels of education have increased, however these numbers remain low. Shaw (1994) has argued that a small number of Pakistani girls have gained higher qualifications, but have stayed within the community to have an arranged marriage. The present analysis of LFS indicates that for the younger age cohort (25-30), women who obtain high levels of education are less likely to marry. Previous research (West and Pilgrim, 1995) has recognised the diversity of experience of different groups of South Asian women in the labour market. They found the participation of Bengali women in the labour force was virtually nil and experience for Pakistani women was minimal, but Indian women had extensive employment experiences (Owen and Green, 1992; Rafiq, 1992). The present research also indicates the diversity of South Asian women's experience in the labour market with very small numbers of Bengali/Pakistani women, but significant numbers of Indian women in the labour market.

9.3

Education and Employment influence women's marital status. Women are less likely to marry before the age of 30 if they are highly educated and employed. Marital status has a significant impact on women's lives. However, marital status has a differential impact for different ethnic groups. High numbers of white and Indian women who are not married, (single/cohabiting/divorced), are more likely to be employed and highly educated than Pakistani/Bangladeshi women who are married. Indian women, if they are highly educated, are less likely to marry than Pakistani/Bangladeshi women.

9.4

In the last ten years, there has been a significant change in economic activity for South Asian women. The numbers of Indian women in employment has shown a gradual increase and is comparable to white women. There has been a rapid increase for Pakistani/Bangladeshi women, however these numbers remain low. South Asian women are more likely to be employed, although there are differences within the South Asian category. The numbers of Indian women in employment are similar to those of white and Afro- Caribbean women, yet the numbers for Pakistani/ Bangladeshi women are less than half that of women from other ethnic groups. This indicates that the 25-30 age cohort are an important group, since they demonstrate the extent of rapid social change.

9.5

There are more Indian women who have degrees than women in other ethnic groups. However, there are differences for the South Asian category. The numbers for Pakistani/Bangladeshi have increased, but remain low. For the 25-30 age cohort, the numbers have fluctuated for all ethnic groups, but shown an overall increase. Indian women are still the highest group obtaining degrees in the 25-30 age cohort. This data also indicates that the 25-30 age cohort are the most important group in which rapid social change is taking place.

9.6

The LFS indicates that marital status has a differential impact on economic activity and education for different ethnic groups. When controlling for age (25-30), marital status has more impact on Indian and Pakistani/Bangladeshi groups than it does for Afro-Caribbean and white groups. There are differences within the ethnic groups and there are also differences within the South Asian category. Furthermore, the national LFS data supports recent research carried out on South Asian women in East London (Bhopal, 1997).

Acknowledgements

: I would like to thank Marie Derby (of Quantime) for producing Labour Force Statistics for me and answering all my queries. I would also like to thank Sylvia Walby, Harriet Bradley, Rohit Barot and Chris Wale for reading earlier drafts of this paper and providing very useful comments.

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