investigating socio-economic explanations for gender and ethnic inequalities in health

Social Science a Medicine 54 (2002) 693–706

Investigating socio-economic explanations for gender andethnic inequalities in health

Helen Cooper*

Methodology Group, Office for National Statistics, Titchfield, Fareham PO15 5RR, UK

Abstract

This paper examines inequalities in the self-reported health of men and women from white and minority ethnic

groups in the UK using representative data from the Health Survey for England, 1993–1996. The results show

substantially poorer health among all minority ethnic groups compared to whites of working-age. The absence of

gender inequality in health among white adults contrasts with higher morbidity for many minority ethnic women

compared to men in the same ethnic group. The analysis addresses whether socio-economic inequality is a potential

explanation for this pattern of health inequality using measures of educational level, employment status, occupational

social class and material deprivation. There are marked socio-economic differences according to gender and ethnic

group; high morbidity is concentrated among adults who are most socio-economically disadvantaged, notably

Pakistanis and Bangladeshis. Logistic regression analyses show that socio-economic inequality can account for a

sizeable proportion of the health disadvantage experienced by minority ethnic men and women, but gender inequality in

minority ethnic health remains after adjusting for socio-economic characteristics. Crown Copyright r 2002 Published

by Elsevier Science Ltd. All rights reserved.

Keywords: Gender; Ethnicity; Health inequality; Socio-economic

Introduction

The established finding of higher morbidity among

women than men (Nathanson, 1975, 1977; Verbrugge,

1979) was drawn into question in the 1990s by research

showing much smaller gender inequality in self-reported

health than hitherto reported. Macintyre, Hunt, a

Sweeting (1996) found no consistent disadvantage in

self-reported health for women relative to men and

argued that the typical characterisation of a female

‘excess’ in morbidity may over-simplify the relationship

between gender and health.

This paper argues that ethnicity is a neglected

dimension in comparative studies of gender and health

and that it is timely to assess the health of men and

women in terms of ethnic group, not least because

minority ethnic groups comprise a growing proportion

of the UK population. Census records show that

minority ethnic groups comprised 6.2 percent of the

population of England in 1991, of which 1 percent were

classified as Black Caribbean and 3 percent as ‘South

Asian’; a diverse ethnic category that includes Indian,

Pakistani and Bangladeshi adults (Owen, 1992).

British surveys have found considerable variation in

reported health among these ethnic groups (Nazroo,

1997; Fenton, Hughes, a Hine, 1995; Rudat, 1994).

Indians typically have a more ‘advantaged’ health

profile, not too dissimilar to white adults, but reported

morbidity is substantially higher for other minority

ethnic groupsFparticularly for Pakistanis and Bangla-

deshis. Nazroo (1997) found gender differences in health

among adults within the same ethnic group, reporting

that white, Black Caribbean, Indian, Pakistani and

Bangladeshi women interviewed in the Fourth National

Survey of Ethnic Minorities (FNS) were more likely

than men in these ethnic groups to rate their health as

‘fair’ or ‘poor’.*Tel.: +44-1392-733886; fax: +44-1329-841760.

E-mail address: [email protected] (H. Cooper).

0277-9536/02/$ - see front matter Crown Copyright r 2002 Published by Elsevier Science Ltd. All rights reserved.

PII: S 0 2 7 7 - 9 5 3 6 ( 0 1 ) 0 0 1 1 8 - 6

Whilst the socio-economic basis of health inequality

has been compared for men and women (Arber, 1997a;

Denton a Walters, 1999), there has been neglect of

potential ethnic differences in the nature of these

relationships. Although this neglect partly reflects the

lack of survey data with sufficiently large samples of

minority ethnic men and women, a general criticism

levelled at ethnic health research is an undue emphasis

on cultural or behavioural factors specific to minority

ethnic groups, rather than general explanations

grounded in the social and economic living conditions

of white and minority ethnic groups (Nazroo, 1998).

However, where British research has shown a strong

socio-economic basis to ethnic inequality in health (e.g.

Nazroo, 1997; Fenton et al., 1995), standardising for the

effects of gender in these analyses obscures differences

between men and women in the same ethnic group.

Connecting gender and ethnicity draws attention to

ethnic differences in the health and socio-economic

position of women and men, as well as gender inequality

within ethnic groups.

The analysis presented in this paper focuses on the

interplay between gender and ethnicity by first examin-

ing the extent and nature of health inequality among

men and women classified as; white, Black Caribbean,

Indian, Pakistani and Bangladeshi, and second, examin-

ing to what extent key measures of individual socio-

economic position can explain differences in the self-

reported health of these ‘gender and ethnic groups’.

Results are based on a pooled sample of working-age

adults (age 20–60) interviewed in 4 years of the Health

Survey for England (1993–1996). The next section

reviews research on gender, ethnicity and health and

considers the extent to which socio-economic inequality

may underlie these relationships.

Gender and ethnic inequality in health

Both gender and ethnicity are significant factors for

health. Higher reported morbidity among women than

men was a common finding in the 1970s (Nathanson,

1975; Verbrugge, 1979) and British minority ethnic

groups report substantially poorer health compared to

the UK average (Rudat, 1994). The Fourth National

Survey (FNS) represents the most comprehensive data

on British minority ethnic groups to date, showing

inequality in self-assessed health across and within

ethnic groups according to gender (Nazroo, 1997).

There is considerable debate about the interplay

between biological, genetic and social factors for the

health of men and women and members of minority

ethnic groups. Unequal social relations, characterised by

discrimination, exclusion and exploitation, are thought

to have profound consequences for the economic and

social well-being of gender and ethnic groups that may

ultimately be expressed as inequalities in health (Krie-

ger, 2000). Minority ethnic women in particular may be

exposed to adverse health consequences associated with

‘multiple discrimination’ on the grounds of gender and

ethnicity. Thus, it is important to consider the extent to

which the health of men and women from different

ethnic groups is mediated by inequalities in their socio-

economic position.

An extensive literature testifies to the fact that men

and women differ markedly in social roles within the

family and that gender differences in type of occupation,

pay, work-hours and experience in the workplace often

place women at a disadvantage to men (Dakin a

Doyal, 1999; Annandale a Hunt, 2000). Occupational

gender segregation is a key characteristic of the labour

market; women are disproportionately concentrated in a

narrow range of low paid and low status jobs; women

who are employed in traditionally male-dominated jobs

tend to remain in the least senior positions whereas men

working in ‘female’ occupations are over-represented at

a senior level (Jacobs, 1993). However, structural

changes in the nature of employment over recent

decades have begun to change these traditional gender

patterns, with evidence that men suffered disproportio-

nately from the decline in manufacturing, for example.

Annandale a Hunt (2000) argue that because these

changes have not been uniform for different subgroups

of men and women, new forms of inequality between the

sexes have emerged, and ethnicity may be a key

parameter of difference among men and women.

Brah (1993) emphasises that labour market experi-

ences are mediated not only through relations of gender

but also of ‘race’ or ethnicity. Racial discrimination may

function to confine minority ethnic workers to certain

types of low paid and low status occupations on the

periphery of the labour market associated with poor pay

and working conditions. Studies confirm that minority

ethnic workers are under-represented at a managerial

level and are more likely than white adults to be

employed in temporary or shift-work in labour-intensive

occupations (Modood et al., 1997; Office for National

Statistics, 1996; Jones, 1993). Minority ethnic adults are

more likely to be outside the formal labour market than

whites; their unemployment rate is considerably higher

and of longer duration (Amin a Oppenheim, 1996) and

recruitment procedures may discriminate against min-

ority ethnic applicants.

There is, however, considerable diversity in the socio-

economic profiles of minority ethnic groups. Although

the socio-economic position of Black Caribbean and

Indian adults is less advantaged than for whites,

Pakistanis and Bangladeshis of both sexes emerge as

the poorest and most deprived groups across a range of

social indicators (Modood et al., 1997). However, the

economic activity and employment of ethnic groups is

inextricably linked with gender and leads to differences

H. Cooper / Social Science a Medicine (2002) 693–706694

in employment status, class position and material

resources for men and women. The following section

considers in more detail the inter-relationships between

gender, ethnicity and socio-economic position and their

significance for assessing health inequalities among these

groups.

Socio-economic inequality and health

The finding that health is influenced by socio-

economic position is well established; reported poor

health is greatest for individuals at the bottom of the

social hierarchy, but a step-wise gradient in morbidity

extends across the whole social spectrum. It is argued

that the social and economic standing of individuals’ in

society shapes exposure to health-damaging agents, as

well as determining individual resources to promote

health (Lynch a Kaplan, 2000).

Social class and employment

Of the number of different measures used to assess the

extent and magnitude of socio-economic inequality in

health, the most common in UK health research is

occupational social class, based on current or last main

occupation. Class gradients in self-assessed health have

been found for men and women (Matthews, Orly, a

Power, 1999; Arber, 1996) although some report that

individual class differences are less marked for women’s

health (Yuen, Machin, a Balarajan, 1990; Stronks, van

de Mheen, van den Bos, a Mackenbach, 1995).

However, these studies have not considered how class

position may be differentially related to the reported

health of men and women from different ethnic groups.

Nazroo (1997) reports a similar relationship between

class and self-assessed health for white and minority

ethnic groups, based on the social class of the household

rather than the individual. However, this analysis

standardised for sex and age, thus precluding compar-

ison of gender and ethnic differences in the relationship

between social class and health. Investigation of social

class and ethnic health inequality rarely use individual

occupational class for men and women, although many

studies of gender inequality and health advocate the use

of women’s own occupational class to assess socio-

economic inequalities in health (e.g. Arber, 1996),

particularly at a time of increased employment partici-

pation among white and minority ethnic women

(Bhopal, 1998).

In part, this approach reflects conceptual and

measurement problems associated with the use of

occupational class particularly for women and members

of minority ethnic groups. The internal heterogeneity of

occupational groups and evidence that, within any given

class, women and adults from minority ethnic groups

are disproportionately disadvantaged in terms of pay,

working conditions and job status are two main reasons

why occupational class may present a misleading picture

of socio-economic inequality in health (Macran, Clarke,

Sloggett, a Bethune, 1994; Emslie, Hunt, a Macintyre,

1999).

The reliance of occupational class on information

about the current or previous occupation of individuals

means that it is not inclusive of those who have never

had a paid job. Although the never employed constitute

a very small proportion of working-age adults (Arber,

1997b), this proportion varies markedly by gender and

ethnicity; a sizeable proportion of Pakistani and

Bangladeshi women report never having had a paid

job (West a Pilgrim, 1995; Modood et al., 1997)

although some of these women may be engaged in

home-working (Phizacklea a Wolkowitz, 1995).

Social class is also likely to be a less sensitive measure

of current social and economic conditions for the

growing number of ‘non-employed’ adults outside the

labour market. Reasons for non-employment are

gendered; approximately one-quarter of non-employed

women are housewives, whereas a greater percentage of

men are unemployed (Arber, 1996).

Studies show that the employment characteristics of

men and women are also related in different ways to

ethnic group. Minority ethnic men have a greater risk of

unemployment and non-employment than white men.

With the notable exception of Indian men, minority

ethnic men are more likely to work part-time and are

disproportionately concentrated in occupations asso-

ciated with low pay and job insecurity (ONS, 1996).

A low level of formal employment among Pakistani

and Bangladeshi women can largely be understood from

their domestic and child-care responsibilities (West a

Pilgrim, 1995). White, Black Caribbean and Indian

women of working-age are more likely to be employed

by comparison, but only white women have a high level

of part-time employment and a low risk of unemploy-

ment (ONS, 1996).

Within white and minority ethnic groups, women are

more likely than men to be non-employed, but this

gender difference is much greater for Pakistanis and

Bangladeshis than for other ethnic groups because of the

low economic activity of Pakistani and Bangladeshi

women (Modood et al., 1997; ONS, 1996). Unlike other

ethnic groups, where women tend to be disadvantaged

relative to men in terms of occupational level, pay and

working conditions, studies suggest greater non-manual

employment, lower unemployment and higher income

for Black Caribbean women than for Black Caribbean

men (Modood et al., 1997; ONS, 1996). Thus, patterns

of employment vary within and across ethnic groups

according to gender.

The likelihood of non-employment is greater for lower

social class groups and is associated with high levels of

H. Cooper / Social Science a Medicine (2002) 693–706 695

reported morbidity. An analysis of the General House-

hold Survey by Arber (1996) found stronger class

gradients in health for non-employed men compared

with those in paid employment, but less class variation

for non-employed women. Given the variation in

employment status between gender and ethnic groups

described above, it is important to include employment

status as a structural variable in analyses of health and

to distinguish between those in full-time and part-time

employment.

Education

Level of educational qualification may be important

in the creation and maintenance of social inequalities in

health, through shaping cognitive skills and learning

that are important for maintaining good health (Lynch

a Kaplan, 2000) or determining future labour market

success and material resources (Wadsworth, 1991).

A socio-economic measure based on educational

qualifications is more inclusive than occupational class

because it can represent adults who have never had a

paid job. Arber (1997a) found that educational qualifi-

cations were strongly associated with the general health

of working-age adults and unlike class, education could

differentiate the health of women who were non-

employed.

As well as being of particular value for assessing

socio-economic inequality in women’s health, education

may be better suited to the investigation of ethnic

inequality in health than social class because it can

overcome difficulties associated with the classification of

never employed and non-employed groups. Surveys

show marked differences in the educational profiles of

white and minority ethnic groups with lower educational

qualifications among Pakistanis and Bangladeshis than

for other ethnic groups (Modood et al., 1997). Within

ethnic groups, women tend to have a poorer educational

profile than men, with the notable exception of Black

Caribbean women (Blackburn, Dale, a Jarman, 1996).

To date, educational level has rarely been used in

British research to examine the socio-economic basis of

ethnic health inequality. It is, however, important to

examine the relationship between education and health

along with other socio-economic measures because for

any given level of qualification, the ‘economic return’ in

terms of future employment status and occupational

class may be less for minority ethnic men and women

because of inequality in job opportunities, pay and

working conditions (Lynch a Kaplan, 2000; Krieger,

Rowley, Herman, Avery a Phillips, 1993).

Material deprivation

Socio-economic measures grounded in everyday

material conditions or ‘standard of living’ are indepen-

dently associated with health inequalities among men

and women (Yuen et al., 1990; Arber, 1996). Indices of

deprivation, as well as single-item measures of car

ownership and housing tenure, often show a linear

relationship with health (Arber, 1997a). Material

resources may themselves have immediate benefits for

health in terms of improved living conditions, or may

primarily reflect labour market position or income.

Nazroo (1997) found that standard of living among

different ethnic groups was better able to explain the

poor self-assessed health of minority ethnic groups than

either household class or housing tenure. Thus, material

conditions may more directly reflect health-related

exposures and resources for members of minority

ethnic groups, although it is impossible to establish the

causal direction between health and material resources

from cross-sectional data (Berkman a Macintyre,

1997).

Methodology

The aim of this paper is to investigate patterns of

reported health among gender and ethnic groups and to

assess both the relative and overall contribution of

education, occupational class, employment status and

material circumstances to these gender and ethnic health

inequalities.

The paper analyses data from the Health Survey for

England (HSE) combined over 4 years from 1993 to

1996. The HSE provides representative data for white

and minority ethnic men and women living in private

households in England. Approximately 16,000 inter-

views were gained with adults aged 16 and above in 1993

and 1994 (Bennett et al., 1995; Colhoun a Prescott-

Clarke, 1996). The 1995 and 1996 surveys each include

interviews with more than 19,000 adults (Prescott-

Clarke a Primatesta, 1998, 1997).

The overall response rate to the HSE was approxi-

mately 77 percent in each year of the survey used here

(Colhoun a Prescott-Clarke, 1996). The paper analyses

approximately 43,500 adults in the combined HSE data-

set who were of working-age, defined here as between 20

and 60 years. Combining 4 years of survey data in this

way increased the number of minority ethnic men and

women in the sample. Ethnic group is based on the self-

identification of respondents with the following ethnic

groups; white (N=41,500), Black Caribbean (N=519),

Indian (N=900), Pakistani (N=430) and Bangladeshi

(N=116). Although the measurement of ethnicity in this

way is considered preferable to interviewer identification

of ethnic group, the meaning of fixed-choice categories

used in this type of survey question has been contested

(Senior a Bhopal, 1994).


Socio-economic measures

Four measures are used to represent individual socio-

economic position in this analysis. As each may have a

different meaning and significance for health, the use of

multiple socio-economic indicators provides a fuller and

more adequate adjustment for socio-economic differ-

ences between gender and ethnic groups than any single

measure (Smaje, 1995).

1. Educational level is based on the highest educational

qualification of each respondent. These are divided into

the following five categories; higher qualifications

(degree, professional or nursing qualifications), A’ Level

or equivalent; GCSE/ O’Level or equivalent, other

qualifications (e.g. vocational) and no qualifications.

2. Employment status is based on the individuals’

current labour market position. Distinctions are made

between adults who are currently employed, who are

unemployed (defined as actively seeking work) or have

never had a paid job. Due to the numbers in the HSE

sample, non-employed groups are grouped together in a

separate category that includes any of the following;

full-time students, retired adults, those looking after

home or family, the sick and/or disabled. It is, however,

recognised that this composite measure will conceal

diversity among different non-employed groups of men

and women. In the multivariate analysis, the

employment status variable further distinguishes

workers who are employed full-time (more than 30 h/

week) or part-time (30 h/week or less), as well as

adults who are ‘looking after the home’ because the

proportion of adults in these groups varies by gender

and ethnicity.

3. Occupational social class (socio-economic group,

SEG) is based on the individuals’ current or last main

job. Socio-economic groups include; professional and

managerial, routine non-manual, skilled manual and

semi-/unskilled manual workers. A separate category

includes the ‘never employed’ who constitute a large

proportion of Pakistani and Bangladeshi women.

3. An index of material deprivation was constructed

from five questions about individuals’ access to material

resources within their household. A score of +1 was

added for any of the following five items that applied; no

central heating in household; no telephone in household;

no car in household; home not owned; Income Support

received by anyone in the household. This gave a

maximum material deprivation score of 5 and a

minimum score of 0. Scores of 3 or more represent a

high level of material deprivation.

Analyses

The analysis first examines the nature of gender

inequality in health among different ethnic groups. The

differential socio-economic position of gender and

ethnic groups is then focused upon, before using

multivariate logistic regression analysis to show the

relative and overall contribution of socio-economic

position to inequalities in self-assessed health. All

analyses control for age, important because minority

ethnic groups have a much younger age structure than

the white population (ONS, 1996) and age is likely to

influence both socio-economic position and reported

health. In tables, age standardised percentages are

calculated by direct age-standardisation in 10-year age

groups, using the combined HSE sample as the standard

population. All logistic regression tables include age as

an independent variable in each model.

Results

Gender and ethnic inequality in health

A commonly used global indicator of morbidity is

self-assessed health, which is measured by the HSE

question ‘How is your health in general? Would you say it

was; very good, good, fair, bad or very bad?’ Poor self-

assessed health has been shown to be a good predictor of

mortality in other studies (Idler a Benyamini, 1997)

and has good test re-test reliability (Lundberg a

Manderbacka, 1996).

Table 1 shows that the proportion of all adults who

rated their health as ‘less than good’ (responses of ‘fair’,

‘bad’ or ‘very bad’ combined) was very similar for men

and women aged between 20 and 60 years. There was

very little gender difference using this measure of

reported morbidity for white adults, although the

slightly higher morbidity of white women can be seen

from the sex ratio of 1.06 in Table 1.

In contrast to whites, there were larger gender

differences in health for each minority ethnic group.

Black Caribbean, Indian and Pakistani women were

more likely than men in these ethnic groups to report

poor health. It is notable that gender differences in

health became more pronounced for minority ethnic

groups after standardising for age (see sex ratios in

Table 1). For Bangladeshis, higher morbidity among

men than women of working-age was reversed after

standardising for age-related differences. However, the

generalisability of this finding is limited by the small

number of Bangladeshis in the HSE sample.

The higher morbidity of minority ethnic women than

men after age standardisation occurs because gender

differences in the timing of migration mean that

minority ethnic women tend, on average, to be of a

younger age than minority ethnic men and white adults

(Blakemore a Boneham, 1994) and might therefore be

expected to report better, not worse, health than these

groups.

Table 2 compares the health of white women and

minority ethnic groups with that of white men, because

white men have the lowest level of reported morbidity in


Table 1. Results are presented in this way to show the

extent and nature of health inequality among gender and

ethnic groups, and it is not intended that the health of

white men be interpreted as the ‘norm’ from which other

gender and ethnic groups deviate.

Ratios are presented in Table 2 to show the relative

health ‘advantage’ of white men compared to men and

women from other ethnic groups after standardising for

age. The health ratios show poorer health for minority

ethnic groups than for white men, especially for

Pakistanis and Bangladeshis. Compared to white men,

Black Caribbean, Indian, Pakistani and Bangladeshi

women were at a greater health disadvantage than men

in these minority ethnic groups.

Socio-economic inequality among gender and ethnic

groups

This part of the analysis examines the nature and

magnitude of socio-economic inequality associated with

gender and ethnicity as a precursor to exploring the

health implications of socio-economic disadvantage.

Pakistani and Bangladeshi groups, who had in common

a high level of reported morbidity, are combined where

age-standardised percentages are shown. Whilst this was

necessary due to the small numbers of Pakistani and

Bangladeshi men and women in the HSE sample, Fig. 1

also reports unstandardised percentages for both of

these ethnic groups to show any socio-economic

differences between them.

Fig. 1 shows that the likelihood of being in paid

employment varied according to gender and ethnic

group. For white, Indian and Pakistani/Bangladeshi

groups, a greater proportion of men than women were in

paid employment. The exception is for Black Caribbean

adults where there was little gender difference in overall

employment, but greater unemployment among men

than women.

For white and minority ethnic groups, a higher

percentage of women than men were non-employed; a

gender difference that will primarily reflect the number

of working-age women who report looking after family

and home. However, it is notable that nearly 20 percent

of Pakistani and Bangladeshi men aged 20–60 were non-

employed; this is much higher than for other men and

may reflect withdrawal from paid work on the grounds

Table 1

Gender differences in reported ‘less than good’ health by ethnic groupa

Men Women Sex ratio

Men/women

Age std

sex ratio

% Age std % N % Age std % N

All adults aged 20–60 18 18 20,793 19 19 23,923 1.06*** 1.06

White 17 17 19,330 18 18 22,233 1.06** 1.06

Black Caribbean 23 23 206 32 36 312 1.39* 1.56

Indian 22 24 431 28 30 469 1.27* 1.25

Pakistani 31 34 214 35 42 216 1.13 (ns) 1.23

Bangladeshi 36 36 66 28 48 50 0.77 (ns) 1.33

a (1)*Statistical significance of gender difference in health; *Po0:05; **Po0:01; ***Po0:001: (2) Age standardisation in 10-year age

groups. Source: Health Survey for England (1993–96).

Table 2

Gender and ethnic differences in reported ‘less than good health’: a comparison with white men

White Black Indian Pakistani Bangladeshi

Caribbean

Age standardised percentages

Men 17 23 24 34 36

Ratioa 1.00 1.35 1.41 2.00 2.12

Women 18 36 30 42 48

Ratio 1.06 2.12 1.76 2.47 2.82

Base numbers

Men 19,330 206 431 214 66

Women 22,233 312 469 216 50

aRatio=white women or minority ethnic groups/ white men. Source: Health Survey for England (1993–96).


of ill-health. Nearly 60 percent of Pakistani and

Bangladeshi women of working-age reported never

having had a paid job, with only 15 percent currently

in paid employment. This differs markedly from the

employment profile of other women, where the majority

were employed. Greater unemployment and economic

inactivity among working-age Pakistani and Banglade-

shi women suggests that they are likely to be most

disadvantaged in terms of income, material resources

and work-related benefits.

Table 3 compares the percentage of men and women

in each ethnic group who are located in low educational

and social class groups, and who score highly on an

index of material deprivation, because these positions of

‘socio-economic disadvantage’ are thought to be most

potentially damaging to health.

For all adults of working-age, women were more

likely than men to have no educational qualifications or

to be in semi or unskilled manual jobs and approxi-

mately 10 percent of men and women were in the most

materially deprived group with a score of 3 or more on

the material deprivation index. These results suggest

that working-age women are disproportionately located

in low socio-economic positions relative to men.

White women were more likely than white men to be

without formal qualifications, but there was no greater

educational disadvantage for Black Caribbean and

Indian women than for men in these respective ethnic

groups after adjusting for age-related differences (Table

3a). The results suggest that a greater proportion of

Pakistani women were without educational qualifica-

tions than Pakistani men, but the opposite was found for

Bangladeshis. More than half of Bangladeshi men and

women of working-age were without formal qualifica-

tions, highlighting that educational disadvantage in this

ethnic group extends to both sexes.

Fig. 1. Employment status of men and women aged 20–60 by ethnic group.


Based on current or last main occupation, the

disadvantaged class position of white, Black Caribbean

and Indian women relative to men in their respective

ethnic groups is evident from Table 3b. Within each of

these ethnic groups, a greater proportion of women than

men were classified in semi-skilled or unskilled manual

occupations, and these gender differences remained after

adjusting for age. A small proportion of all Pakistani

and Bangladeshi women were in the semi-skilled or

unskilled manual class compared to men in this ethnic

group. This gender difference results from the high

percentage of never employed Pakistani and Banglade-

shi women (see Fig. 1), who are excluded from occupa-

tional class measures of socio-economic position.

In all ethnic groups, a greater proportion of women

than men were in the most materially deprived group

(Table 3c). Adjusting for age, approximately one-

quarter of Black Caribbean women had poor material

living conditions according to this measure, compared

with 21 percent of Black Caribbean men. The magnitude

of gender inequality in material deprivation was less

marked for other ethnic groups by comparison, and

Indian men and women were least likely to be classed as

materially deprived.

These results show that women and minority ethnic

groups were often over-represented in positions asso-

ciated with socio-economic disadvantage, whereas white

men had a high level of paid employment and were least

likely to be in low educational and class groups. What is

striking is that patterns of socio-economic disadvantage

among gender and ethnic groups appear to follow

patterns of health inequality reported in Table 1; the

poorest health and greatest socio-economic disadvan-

tage was concentrated among Pakistanis and Banglade-

shis, although there were marked gender differences in

employment participation within this ethnic group.

There was also considerable variation among gender

and ethnic groups according to the socio-economic

measure used; the finding of low material deprivation

among Indian men and women highlights that minority

groups cannot be characterised as uniformly disadvan-

taged. These socio-economic differences suggest, firstly,

Table 3

Socio-economic disadvantage among men and women aged 20–60 by ethnic group

Men Women

% Age std % N % Age std % N

(a) % with no educational

qualifications

White 23 23 19,323 29 29 22,233

Black Caribbean 35 35 207 23 35 311

Indian 24 25 431 35 25 468

PakistaniBangladeshi

3862

�47 214

664654

�54 216

50

All 24 23 20,782 29 31 23,915

(b) % in semi or unskilled manual

socio-economic groupsa

White 16 14 19,161 27 27 22,211


Indian 20 20 430 29 30 469


2951

�35 214

65186

�32 216

50

All 16 16 20,609 27 27 23,894

(c) % in most materially disadvantaged

group (score of 3+)

White 9 9 18,835 11 11 21,682


Indian 6 6 418 8 7 460


1327

�17 208

661732

�19 210

50

All 10 10 20,801 11 11 23,569

aClass based on current or last main job. Source: Health Survey for England (1993–96).


that socio-economic correlates of health may differ for

gender and ethnic groups, and secondly, that the sole use

of occupational class measures of socio-economic

inequality will be inadequate for some groups of

minority ethnic women because they exclude the never

employed.

Multivariate analyses

This section uses logistic regression analyses to

examine how gender and ethnic differences in self-

assessed health change when the socio-economic char-

acteristics of individuals are included in the same

analysis. The aim of this section is to assess the extent

to which inequality in health across gender and ethnic

groups can be accounted for by their differential socio-

economic characteristics. The interaction between gen-

der and ethnicity is included as a single independent

variable in the logistic models presented in Table 4.

Model 1 gives the odds ratios for reported ‘less than

good’ health for gender and ethnic groups with white

men defined as the reference category (1.00), and age in

5-year groups included in the model. Being white and

male was associated with the best health, with the odds

of ‘less than good’ health significantly higher for

minority ethnic men, white and minority ethnic women

in comparison. The odds ratios of poor health were

more than two times higher for Black Caribbean and

Indian women and over three times greater for Pakistani

women. The odds ratios of poor health for Black

Caribbean, Indian and Pakistani men were somewhat

lower than for women in each of these ethnic groups, but

minority ethnic men were clearly disadvantaged in their

health compared to white men. Bangladeshi men had a

higher odds ratio of poor health than Bangladeshi

women, but for both sexes the odds were markedly

higher than the reference category of white men. These

substantial gender and ethnic differences in health were

all highly statistically significant in the model.

Subsequent models in Table 4 show how these odds

ratios of poor health were modified by controlling for

different socio-economic characteristics. Socio-economic

measures are entered sequentially into the logistic

regression models in order to assess their relative

contribution to the self-assessed health of gender and

ethnic groups. Model 2 includes educational level and

this shows a consistent linear relationship with health.

Adults with the highest level of education were least

likely to report ‘less than good’ health and the odds of

poor health became consistently greater with a lower

level of educational qualification and were over 3.5 for

those with no qualifications compared to those with a

degree or above.

Black Caribbean men did not have a significantly

higher odds ratio of morbidity when education was

added to the model, but Black Caribbean women

continued to have poorer health than white men

(OR=2.46). Educational level made little difference to

the odds ratio of poor health for Indian men, and

although the odds were reduced for Indian women, their

health remained significantly poorer than that of white

men. For Pakistanis, the odds of poor health were

substantially reduced for both sexes once education was

added to the model. A similarly large reduction in the

odds ratio of poor health was found for Bangladeshi

men, but the greatest change was for Bangladeshi

women where the odds of poor health were no longer

significantly different from white men.

After controlling for education, there was no gender

difference in health for white adults, but women who

were Black Caribbean, Indian or Pakistani continued to

have higher odds ratios of poor health than men from

the same ethnic group. The exception was a higher odds

ratio for Bangladeshi men than for Bangladeshi women.

The differential impact of education on the health of

gender and ethnic groups suggests that educational

disadvantage is a major factor in accounting for the

higher morbidity of Black Caribbean men, white and

Bangladeshi women relative to white men, and to some

extent contributes to the poor health of Pakistanis,

Indian women and Bangladeshi men. However, adjust-

ing for education does little to alter gender differences in

health found within minority ethnic groups.

One way in which educational qualifications may

influence health is through labour market position.

Model 3 shows that both education and employment

status have strong and independent relationships with

health. Adults of working-age who were employed full-

time had the best health, with the odds significantly

higher for part-time workers in comparison. Being

unemployed or looking after the home were both

associated with high reported morbidity, with odds

approximately twice as high odds ratio as for the full-

time employed. It is, however, impossible here to assess

the extent to which poor health precedes job loss or

economic inactivity. The highest odds ratio of poor

health was for other non-employed groups, which is

expected as this category includes the long-term sick and

disabled. Adults of working-age who have never been

employed approx 3 times higher odds of poor health

than the ref. cat.

As shown in Fig. 1, the never employed group

included a large proportion of Pakistani and Banglade-

shi women; controlling for employment status substan-

tially reduced their odds of poor health relative to white

men (to under 1.00 for Bangladeshi women). The odds

of poor health were lowered by over one-third for Black

Caribbean and Indian women along with Pakistani men,

all of whom had a lower level of employment than white

men in Fig. 1. With the exception of Indian men, whose

employment profile was comparable to white men in

Fig. 1, these results show that the poor position of


minority ethnic men and women in the labour market

may serve to disadvantage their health.

The odds of poor health for white women, a

disproportionate number of whom are employed

part-time, is significantly lower than for white men

once employment status is included in the model.

Thus, controlling for both education and employment

status reverses the gender difference in health for white

adults of working-age found in Model 1. Employment

status could account for a greater proportion of the

poor health reported by Pakistani and Indian women

than for men in these respective ethnic groups, thus the

gender gap is narrowed for Indians and reversed for

Pakistanis. By contrast, marked gender differences in

health remain for Black Caribbean and Bangladeshi

adults.

Model 4 considers how the occupational class of all

those currently or previously employed is related to

Table 4

Logistic regression models of ‘less than good’ healtha

Model 1 Model 2 Model 3 Model 4 Model 5

Age (in 5 year groups) +++ +++ +++ +++ +++

Gender and ethnic group +++ +++ +++ +++ +++

White men 1.00 1.00 1.00 1.00 1.00

White women 1.08** 0.99 0.89** 0.97 0.99

Black Caribbean men 1.61** 1.39 1.25 1.22 1.15

Black Caribbean women 2.55*** 2.46*** 2.09*** 2.26*** 1.98***

Indian men 1.46** 1.47** 1.42** 1.43** 1.50**

Indian women 2.07*** 1.81*** 1.47** 1.53*** 1.70***

Pakistani men 2.31*** 1.97*** 1.61** 1.57** 1.52*

Pakistani women 3.24*** 2.38*** 1.43* 1.53** 1.68**

Bangladeshi men 2.75*** 1.94* 1.62 1.66 1.56

Bangaldeshi women 2.31** 1.57 0.93 1.03 1.05

Educational qualifications +++ +++ +++ +++

Higher 1.00 1.00 1.00 1.00

A level or equivalent 1.45*** 1.35*** 1.23*** 1.23***

GCSE/O’Level or equivalent 1.55*** 1.53*** 1.34*** 1.31***

Other 2.01*** 1.90*** 1.59*** 1.50***

None 3.57*** 3.04*** 2.34*** 2.03***

Employment status +++ +++ +++

Employed full-time (30+ h/week) 1.00 1.00 1.00

Employed part-time (o30 h/week) 1.22*** 1.16** 1.13**

Unemployed 2.12*** 2.01*** 1.48***

Looking after home 1.92*** 1.83*** 1.51***

Other non-employed 7.24*** 7.00*** 5.77***

Never been employed 2.97*** 3.78*** 2.74***

Socio-economic Group (SEG) +++ +++

Professional /managerial 1.00 1.00

Routine non-manual 1.03 1.03

Skilled manual 1.49*** 1.42***

Semi or unskilled manual 1.60*** 1.42***

Material deprivation score +++

0 (none) 1.00

1 1.34***

2 1.77***

3+ 2.26***

DLLR (Ddf) 1023 (16) 1219 (4) 2404 (5) 192 (3) 363 (3)

N= 42202

a (1)+++ Statistical significance of variable in the model: +Po0:05; ++Po0:01; +++Po0:001. (2)**Statistical significance of

difference from the reference category; *Po0:05; ***Po0:001 Source: Health Survey for England (1993-96).


health (the never employed are not excluded from this

model). The odds of poor health were increased by 49

percent for the skilled manual class and 60 percent for

those classified in semi-skilled or unskilled manual

occupations compared to the professional/managerial

class, but the health of the routine non-manual class was

not significantly different to this group.

Controlling for occupational class made little differ-

ence to the pattern of health inequality across gender

and ethnic groups; the odds of poor reported health

remained significantly higher for Indians, Pakistanis and

Black Caribbean women compared to white men. Prior

to adding occupational class, white women reported

significantly better health than men, but the odds of

occupational class in model 4 removed this gender

difference for whites but did not alter the gender

difference for minority ethnic groups.

The index of material deprivation is the final socio-

economic measure added in Model 5. There was a highly

significant material deprivation gradient in health; the

best health was found for materially advantaged adults

on this measure (score 0) rising to an odds ratio of 2.26

for those with a score of 3+ on the material deprivation

index. It is notable that material living conditions

appear to reduce the reported health disadvantage for

Black Caribbean women to a greater extent than either

education or occupational class, although Black Car-

ibbean women continue to have a significantly high odds

ratio of poor health. This could suggest that education

and, particularly social class, poorly represent the socio-

economic position of these women, if as some authors

have suggested, there is a disparity between educational

qualifications, occupation and material living conditions

(Bruegel, 1994; Krieger et al., 1993).

The results did not suggest that the material living

conditions of Indian men and women contributed to

their high morbidity, and this is likely to reflect the

smaller proportion of Indians than white men and

women living in the most materially disadvantaged

conditions (see Table 3c). There was higher morbidity

among Indian and Pakistani women than men after

controlling for the measure of material deprivation, but

the odds of poor health were significantly greater for

both sexes relative to white men.

The overall contribution of socio-economic position

to gender and ethnic inequality in health is shown in

Fig. 2 where unadjusted odds ratios can be compared

with odds ratios adjusted for socio-economic position. A

key finding is that socio-economic characteristics sub-

stantially reduce the magnitude of ethnic inequality in

health, especially for Black Caribbean, Pakistani and

Bangladeshi adults, but taking into account socio-

Fig. 2. Odds ratios of ‘less than good’ health for gender and ethnic groups: figures adjusted for age and socio-economic position.


economic inequality does little to alter gender differences

in health within minority ethnic groups.

Socio-economic disadvantage made most contribu-

tion to the poor health reported by Bangladeshis, and

this was more marked for women than for men.

Morbidity reported by Pakistanis can also be largely

attributed to poor socio-economic circumstances,

particularly for women, although the odds of poor

health for Pakistanis remained significantly higher

relative to white men after adjusting for socio-economic

position.

Measures of socio-economic position made less over-

all contribution to the poor self-assessed health of

Indian adults, most notably men, whose socio-economic

position was most comparable to that of white men. A

sizeable gender difference in morbidity remains for

Black Caribbean adults; only women in this ethnic

group have a significantly higher odds ratio of poor

health.

Conclusions

The finding of little overall gender difference in self-

assessed health for white adults of working-age contrasts

with substantial inequality in health among men and

women from different ethnic groups. Consistent

with other studies, reported morbidity was greater

for minority ethnic groups than for whites of

both sexes, with the greatest health disadvantage

found for Pakistanis and Bangladeshis. An additional

finding of this study was of marked gender differences in

health within minority ethnic groups. These gender

inequalities were accentuated by standardising for age,

suggesting that minority ethnic women report particu-

larly poor health despite their younger average age

profile.

The key finding of this paper is that, despite the

problems associated with the use of socio-economic

measures for gender and ethnic groups, these

accounted for a substantial proportion of ethnic

inequality in health. Adjusting for educational qualifica-

tions substantially reduced the likelihood of poor health

for Black Caribbean men, Pakistanis and Bangladeshi

women. Being in paid employment was positively

associated with good health, and controlling for employ-

ment status reduced the odds of poor health for

working-age Pakistani and Bangladeshi womenFa

substantial proportion of whom were non-employed.

As expected, occupational class made less contribution

to patterns of gender and ethnic inequality in health

than education or employment status, and is a

particularly poor marker of socio-economic inequality

in health for minority ethnic women because of its

reliance on previous occupation. Material deprivation

was independently associated with health and the results

suggest that this measure better accounts for high

morbidity among Black Caribbean women than other

socio-economic measures.

Whilst socio-economic disadvantage can explain in

large part why many minority ethnic adults report

poorer health than white men, significant ethnic inequal-

ity in health remained after adjusting for socio-economic

position, particularly for minority ethnic women. This

suggests that socio-economic measures are important,

but cannot fully ‘explain’ gender and ethnic inequality in

health for the following reasons;

Firstly, it is recognised that ethnicity is not simply

‘reducible’ to socio-economic position (Nazroo, 1998).

The findings from this study show considerable diversity

among ethnic groups who cannot be characterised as

uniformly disadvantaged relative to whites. The poor

health reported by Indian men, for example, was not due

to their socio-economic disadvantage relative to white

men, since there were more similarities than differences

in socio-economic position for men in these ethnic

groups. However, for other minority ethnic groups, it is

important to show that poor socio-economic conditions

have a sizeable impact on the health because this

detracts from an undue emphasis on individual or

cultural explanations that risk stereotyping assumed

differences from the white population.

Secondly, although this analysis examines how key

socio-economic measures are related to the pattern of

health inequality across gender and ethnic groups, this

does not represent a ‘complete’ adjustment for social

and economic living conditions, or the economic and

emotional health consequences of discrimination. After

adjusting for socio-economic position, many minority

ethnic women had a higher odds ratio of morbidity than

men in the same ethnic group. This gender difference

was most marked for Black Caribbean women who,

unlike Black Caribbean men, continued to have

significantly higher odds of poor health relative to white

men. Part of the explanation may concern a disparity

between educational qualifications and class positionFwhere studies suggest Black Caribbean women are more

‘advantaged’ than Black Caribbean menFand actual

living conditions that are relevant to health (Blackburn

et al., 1996).

Socio-economic position is of course only one of

many possible determinants of health. Further research

should address how socio-economic circumstances

intersect with family responsibilities or the health-

related behaviours of gender and ethnic groups. It is

important to investigate, for example, whether the high

level of smoking reported for Bangladeshi men (Rudat,

1994) makes an independent contribution to their poor

health and how socio-economic disadvantage experi-

enced by gender and ethnic groups may be compounded

by large family sizes, caring for dependent children or

domestic work.


Acknowledgements

This doctoral research was funded by the ESRC and

data from the Health Survey for England was made

available from the Data Archive on-line at MIMAS.

The author would like to thank Sara Arber and Chris

Smaje at the University of Surrey for their helpful

comments and discussion relating to this paper.

References

Amin, K., a Oppenheim, C. (1996). Poverty in black and white:

Deprivation and ethnic minorities. London: CPAG Runny-

mede Trust.

Annandale, E., a Hunt, K. (2000). Gender inequalities in

health: research at the crossroads. In E. Annandale, a K.

Hunt (Eds.), Gender inequalities in health. Buckingham:

Open University Press.

Arber, S. (1996). Integrating non-employment into research

on health inequalities. Health and Social Policy, 26(3),

445–481.

Arber, S. (1997a). Comparing inequalities in women’s and

men’s health: Britain in the 1990s. Social Science a

Medicine, 44, 773–787.

Arber, S. (1997b). Insights about the non-employed, class and

health: Evidence from the General Household Survey. In D.

Rose, a K. O’Reilly (Eds.), Constructing classes (pp. 78–

92). Swindon: ESRC/ONS.

Bennett, N., et al. (Eds.) (1995). Health survey for England:

1993. London: H.M.S.O.

Berkman, L.F., a Macintyre, S. (1997). The measurement of

social class in health studies: Old measures and new

formulations. In: Kogevinas, M., et al. (Eds.), Social

inequalities and cancer. IARC Scientific publications, No.

138.

Bhopal, K. (1998) How gender and ethnicity intersect: The

significance of education, employment and marital status.

Sociological Research Online, 3, 3. URL: http://www.socre-

sonline.org.uk/socresonline/3/3/6.html

Blackburn, R. M., Dale, A., a Jarman, J. (1996). Ethnic

differences in attainment in education, occupation and

lifestyle. In Kahn, V. (Ed.), Ethnicity in the 1991 Census. Vol

4. London: The Stationary Office.

Blakemore, K., a Boneham, M. (1994). Age, race and

ethnicity: A comparative approach. Buckingham: Open

University Press.

Brah, A. (1993). ‘Race’ and ‘culture’ in the gendering of labour

markets: South Asian young Muslim women and the labour

market. New Community, 45819(3), 441.

Bruegel, I. (1994). Labour market prospects for women from

ethnic minorities. In R. Lindley (Ed.), Labour market

structures and prospects for women. Manchester EOC.

Colhoun, H., Prescott-Clarke, P. (Eds.) (1996). Health Survey

for England, 1994. London: H.M.S.O.

Emslie, C., Hunt, K., a Macintyre, S. (1999). Problematising

gender, work and health: The relationship between gender,

occupational grade, working conditions and minor morbid-

ity in full-time bank employees. Social ScienceaMedicine,

48(1), 33–48.

Fenton, S., Hughes, A. O., a Hine, C. E. (1995). Self-assessed

health, economic status and ethnic origin. New Community,

21(1), 55–69.

Idler, E. L., a Benyamini, Y. (1997). Self-rated health

and mortality: A review of twenty-seven community

studies. Journal of Health and Social Behavior, 38(1),

21–37.

Jacobs, J. A. (1993). Men in female-dominated fields trends and

turnover. In C. L. Williams (Ed.), Doing womens work. Men

in non-traditional occupations (pp. 49–63). Newbury Park,

CA: Sage.

Jones, T. (1993). Britains ethnic minorities. London: Policy

Studies Institute.

Krieger, N. (2000). Discrimination and health. In L. F.

Berkman, a I. Kawachi (Eds.), Social epidemiology.

Oxford: Oxford University Press.

Krieger, N., Rowley, P. L., Herman A. A., Avery, B., a

Phillips, M. T. (1993). Racism, sexism and social class:

Implications for studies of health, disease and well-being.

American Journal of Preventative Medicine, 9(6 Suppl), 82–

122.

Lundberg, O., a Manderbacka, K. (1996). Assessing the

reliability of a measure of self-rated health. Scandinavian

Journal of Social Medicine, 24(3), 218–224.

Lynch, J., a Kaplan, G. (2000). Socioeconomic position. In L.

F. Berkman, a I. Kawachi (Eds.), Social epidemiology.

Oxford: Oxford University Press.

Macintyre, S., Hunt, K., a Sweeting, H. (1996). Gender

differences in health: Are things really as simple as they

seem? Social Science a Medicine, 42(4), 614–624.

Macran, S., Clarke, L., Sloggett, A., a Bethune, A. (1994).

Womens socio-economic status and self-assessed health:

Identifying some disadvantaged groups. Sociology of Health

and Illness, 16, 182–208.

Matthews, S., Orly, M., a Power, C. (1999). Social inequalities

in health: are there gender differences? Social Science a

Medicine, 48(1), 49–60.

Modood, T., Berthoud, R., Lakey, J., Nazroo, J., Smith, P.,

Virdee, S., a Beishon, S. (1997). Ethnic minorities in

Britain: Diversity and disadvantage. London: Policy Studies

Institute.

Nathanson, C. (1975). Illness and the feminine role: A

theoretical review. Social Science a Medicine, 9, 57–62.

Nathanson, C. (1977). Sex, illness and medical care: A review of

data, theory and method. Social Science a Medicine, 11,

13–25.

Nazroo, J. Y. (1997). The health of Britains ethnic minorities.

London: Policy Studies Institute.

Nazroo, J. Y. (1998). Genetic, cultural or socio-economic

vulnerability? Explaining ethnic inequalities in health. In M.

Bartley, D. Blane, a G. Davey Smith (Eds.), The sociology

of health inequalities. Oxford: Blackwell Publishers.

Office for National Statistics (1996). Social focus on ethnic

minorities. London: H.M.S.O.

Owen, D. (1992). Ethnic minorities in Britain: Settlement

patterns. Coventry: University of Warwick, Centre for

Research.

Phizacklea, A., a Wolkowitz, C. (1995). Homeworking women:

gender, racism and class at work. London: Sage.

Prescott-Clarke, P., a Primatesta, P. (1997). In Health Survey



Prescott-Clarke, P., a Primatesta, P. (1998). In Health Survey


Rudat, K. (1994). Black and minority ethnic groups in England.

London: Health Education Authority.

Senior, P. A., a Bhopal, R. (1994). Ethnicity as a variable in

epidemiological research. British Medical Journal, 309, 327–

330.

Smaje, C. (1995). Health, ‘race’ and ethnicity: Making sense of

the evidence. London: Kings Fund Institute.

Stronks, K., van de Mheen, H., van den Bos, J., a Mackenbach,

J. P. (1995). Smaller socio-economic inequalities in health

among women: The role of employment status. International

Journal of Epidemiology, 24(3), 559–568.

Verbrugge, L. (1979). Females and illness: Recent trends in sex

differences in the United States. Journal of Health and Social

Behavior, 17, 387–403.

Wadsworth, M. (1991). The imprint of time. Oxford: Clarendon

Press.

West, J., a Pilgrim, S. (1995). South Asian women in

employment: The impact of migration, ethnic origin and

the local economy. New Community, 21(3), 357–378.

Yuen, P., Machin, D., a Balarajan, R. (1990). Inequalities in

health: Socio-economic differences in self-reported morbid-

ity. Public Health, 104(1), 65–71.


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