Class, paid employment and family roles: Making sense of structural disadvantage, gender and health status

Vol. 32, No. 4, pp. 425-436, 1991 Printed in Great Britain

0277-9536/91 $3.00+ 0.00 PergamonPress plc

Soc. Sci. Med.

CLASS, PAID EMPLOYMENT A N D FAMILY ROLES: MAKING SENSE OF STRUCTURAL DISADVANTAGE, GENDER A N D HEALTH STATUS* SARA ARBER

Department of Sociology, University of Surrey, Guildford, Surrey GU2 5XH, England Abstract--The British tradition of analysing differences in health has been dominated by class, with women belatedly entering this debate. The American tradition has been dominated by role analysis, with women's health considered primarily in terms of their marital, parental and employment roles, with recent research coming to contradictory conclusions. Research in both traditions has reached an impasse. This paper uses a sample of over 25,000 men and women from the 1985 and 1986 British General Household Survey to show how both traditions need to be reformulated and integrated. The ways in which family roles are associated with women's health status is determined by material circumstances, but the material circumstances cannot be captured by occupational class alone. Participation in the labour market and consumption divisions, in the form of housing tenure, are crucial additional indicators of structural disadvantage. Standardised limiting long-standing illness ratios and multivariate logit analysis confirm that occupational class and paid employment are the most important attributes associated with health status for women and men. Family roles are important for women; women without children and previously married women have particularly poor health status especially those not in paid employment and living in local authority housing.

INTRODUCTION The major concern of the Black Report [1] was inequalities in men's health. Since its publication nearly 10 years ago, a growing number of studies have examined inequalities in women's health. However, these studies present largely contradictory findings [2], because of a neglect of how women's material circumstances intersect with their family (marital and parental) roles and their partipation in paid employment. The paper reviews how roles and how structural factors have been conceptualised in analyses of inequalities in health. The lack of attention given to health selection, other than in the class inequalities literature, is discussed. The paper uses data from the 1985 and 1986 General Household Survey to show the need for an integrated structural and role framework, and demonstrates the ways in which different factors are associated with poor health status for men and women. ROLES AND HEALTH

The legacy of Parsons' functionalist conception of gender roles underlies the majority of work on inequalities in health. Men are seen primarily in terms of their occupational role, as ~main breadwinner', women in terms of their family roles. Occupational class has dominated analyses of men's health. Differences in women's health have been primarily conceived and analysed using a very different framework--that of role analysis. In America, *Revised version of paper presented to the SwedishBritish Medical Sociology Workshop, Stockholm, 31 May-4 June 1989.

Gove's [3-5] analyses of women's health have been particularly influential. He analysed the higher levels of morbidity among women as compared with men mainly in terms of the demands of women's nurturant roles and demonstrated the poorer mental health of married women compared to married men. He argued that marital and parental roles were detrimental to women's mental health. This school of work extended its role conceptual framework to consider how the additional role of being in paid employment fitted into the balance sheet of roles and women's health [6]. Some argued the benefits of role accumulation [7-10]--that paid work for women provided an additional source of self esteem and social contacts with consequent health benefits. Others argued that role accumulation produced role strain, role conflict and role overload-full-time paid employment, in addition to parental and domestic responsibilities, having detrimental health consequences [11, 12]. In these analyses, paid employment for women is conceptualised as an additional role rather than as a structural variable. This is in marked contrast to the literature on men's employment status, where unemployment is treated as a structural variable which provides a major axis of disadvantage [13]. Studies of inequalities in men's health have paid less attention to their marital and parental roles, although there are some exceptions in Britain [14] and in America [4, 6, 8, 15]. In Britain, the Black Report's [1] much quoted four explanations of inequalities in health (a) methodological/artefact (b) health selection (c) cultural/behavioural, and (d) materialist/structural do not entertain the possibility of family roles influencing health. There is a need to integrate the insights from role analysis within a structural framework.

425

426

SARA ARBER

Women's paid employment should be examined additional stresses from the role demands of being a housewife, responsible for childcare and a paid worker in the labour market, and as a structural variable relating to women's own position in the labour market, her command over financial resources, and as influencing her and her family's life style and life chances [16, 17]. The role analyses quoted above largely neglect any consideration of the structural position of women. The paper argues that it is necessary to disentangle the complex associations of paid employment with structural variables, such as poor housing and occupational class, and analyse how these relate to a woman's nurturant (marital and parental) roles in order to fully understand women's health.

and the critics of this work [30, 31] have failed to consider how gender articulates with dependence on 'socialised' housing and that the meaning of consumption may differ for men and women. Some consumption indicators are gender-biased, and so cannot be interpreted in the same way for women and men, for example, more men than women are car owners and drivers [32]. The aim of this paper is to understand women's and men's health by considering their combination of roles within a structural context. The meaning of the same roles--being a parent, being married or not, and being in paid employment or not--is likely to vary (a) between men and women, and (b) along a structural dimension, relating to occupational class, consumption indicators and the position of the individual in the labour market.

CONCEPTUALISING MATERIAL/STRUCTU RAL INEQUALITIES

HEALTH SELECTION, ROLES AND STRUCTURAL DISADVANTAGE

Although materialist/structural explanations have been predominant in British debates on inequalities in health, they rely on a fairly narrow conception of occupational class. This is particularly problematic for woman, raising questions about whether a single measure of a woman's material circumstances can be adequate for women living in a diversity of family forms. Is a married woman's material circumstances adequately captured by her husband's occupation? This is the sole method of analysing health in the General Household Survey annual reports [18]. It is supported by Goldthorpe [19,20], who calls it the "conventional approach", but criticised by Stanworth and others [21,22]. An alternative is to use a woman's own current (or last) occupation to measure her class, irrespective of her marital status and position in the labour m a r k e t - - a n "individualistic approach" [17, 23]. Researchers using occupation-based measures of class must decide how to measure class for those who are not in paid work. Is it valid to simply use last occupation, thereby subsuming employment status within occupational class? This solution is adopted in virtually all analyses of men's health, but may be inappropriate for women who left the labour market many years ago, and less appropriate for men where there is a high level of unemployment or early retirement [24]. The paper demonstrates that it is important to separately analyse the two structural variables of occupational class and employment status. Some authors have argued that it is advantageous to use alternative measures of material circumstances which are universal, simple to collect and easy to apply to all households irrespective of their composition or age structure. These criteria are fulfilled by indicators of consumption, such as housing tenure and car ownership [25, 26]. Saunders [27-29] identifies a major division in Britain between owner occupiers who are able to provide for their own 'privatised' housing and local authority tenants who are dependent on ~socialised' housing provision. He argues that this housing cleavage may come to outweigh class location in determining many spheres of people's existence, including their life chances. However, advocates of consumption sector theory

Health selection has been scrutinised by researchers as a possible explanation of social class differences in health [33-35], and has dominated the debate on unemployment and men's health [36-39], but has been surprisingly neglected in health research on women's roles and their employment status. A major research effort has shown that unemployment causes poor mental and physical health rather than simply less healthy men being selected into unemployment [38-41]. This contrasts with the majority of literature on women's health and their paid employment which has focussed on whether employment improves women's health through 'role accumulation' [8, 10,42,43]. Few studies have discussed whether and how poor health might affect women's re-entry into paid employment after childbearing, and the likelihood of women leaving employment because of ill-health [15, 16, 43, 44]. In the area of consumption, housing researchers have argued that council housing in the 1980s has become a 'residual' tenure, both in terms of housing quality and the socio-economic profile of tenants with a high representation of the poor, unemployed and unskilled [31, 45]. If the unskilled, the poor and state benefit recipients are concentrated in the council sector it is vital to establish the direction of causation between housing tenure and health status. Does council housing tenure reduce life chances, or has housing tenure increasingly become an index of achieved life chances, including poor health status? There has been little research on the direction of causation between health status and family roles [15]. There may be real health risks associated with being a marital status, and/or selectivity into a marital status due to prior health. Whether health status influences the formation and dissolution of marriage may be differentiated by gender. The association between parenthood and health status for women probably involves a substantial component of health selection, although this effect would be expected to weaken as children grow older. Overall, there has been a surprising lack of attention in longitudinal studies to documenting the magnitude of health selection according to housing tenure, family roles and, for women, participation in paid employment.

both as an additional role, which may result in

427

Class, paid employment and family roles OCCUPATIONALCLASS AND MATERIAL CIRCUMSTANCES

This paper uses data from the cross-sectional General Household Survey, and so cannot provide answers to the above questions about the direction of causation. However, the fact that very different levels of health are found for women and men occupying different combinations of roles in varying structural circumstances, urges the need to conduct more specific studies to analyse the predominant direction of causation between health status and various combinations of roles in disadvantaged material circumstances.

For men there is a much stronger class gradient with limiting long-standing illness (standardised to remove the effects of age differences between classes) than for women using their own current or last occupation (an 'individualistic approach'), see Table 2. The gradient for men is very similar to the social class mortality gradient in the U.K. Decennial Supplement [49]. Unskilled men have a very disadvantaged health status, reporting 60% more limiting long-standing illness than the national average. The class gradient for men is linear but for women the DATA SOURCE AND MEASURES gradient is curved. Women 'employers and managers' The research data are drawn from the 1985 and have a poorer health status than other women in non1986 British General Household Survey (GHS), manual occupations. The 'individualistic approach' providing a nationally representative sample of adults shows small differences between women employed in the three manual classes, unlike for men where living in private households. A response rate of 82% and 84% was obtained in these two years respectively occupational skill level differentiates health status. Occupational class is associated with health status, [46, 47]. Analyses are restricted to men and women both because of direct effects of the individual's aged 20-59. Limiting long-standing illness (LLI) is used as a occupation and because an individual's occupational measure of health status [48]. In the General House- class influences the material/structural circumstances hold Survey the respondent was asked "Have you any of the household. Wider class inequalities in health long-standing illness, disability or infirmity?" If the status are found for men than women primarily answer was "Yes", the respondent was asked whether because a man's occupational class provides a better it limited his or her activities in any way. This measure of the household's material circumstances measure is related to function and represents a self- than is the case for women. Women's health status assessment of the effect of any chronic ill health on measured by the 'conventional approach' (classifying married women by their husband's occupation and daily life. For both men and women health status is other women by their own current or last occupation) strongly associated with age, see Table I. Compar- shows a pattern which is linear, and is only slightly able levels of both 'limiting' and 'non-limiting' weaker than for men. This finding suggests that long-standing illness are reported by men and household circumstances, measured by husband's women in each age group. Since age is closely related occupational class, are more strongly associated with to health status, differences in age structure are a woman's health status than her own occupation. Although, the Black Report concentrated on social removed from Tables 2-5 by using the indirect method of standardisation to calculate Standard- class as a way of measuring the disadvantages of structural position, recent studies have used various ised Limiting Long-Standing Illness (SLLI) Ratios, using 10 year age bands: 20-29, 30-39, 40-49 and alternative measures of the structural position of 50-59. The analysis examines the relationship households. Housing tenure and car ownership have between health status and, firstly, occupational been used by Fox and his colleagues working on the class and material circumstances, secondly, pos- OPCS Longitudinal study [25, 50-53]. Townsend e t al. [26] constructed a deprivation index which inition in the labour market, and thirdly, family roles. Multivariate logit analysis extends the earlier tabu- cluded housing tenure and car ownership, together lar analyses, confirming the poor health status of with a measure of unemployment and overcrowding. men and women in disadvantaged structural pos- They argue that car ownership is the best available itions, and showing the ways in which women's surrogate measure of current income. Their depriroles intersect with various aspects of structural vation index explained more of the variance in an disadvantage to compound the poor health of index measuring the health of wards in the North of women. England, than was explained by occupational class. Table 1. Long-standingillnessby age and sex (age 20-59) 20-29 30-39 40~9 50-59

Total

Me?l

--limits activities --non-limiting --no long-standingillness N: Women

--limits activities --non-limiting --no long-standingillness

9 II 80 100% (3473)

11 13 76 100% (3677)

9 12 10 11 80 77 Ioo% lOO% (3707) (3864) N= Source: General Household Survey, 1985-86(own analyses).

15 13 71 100% (2985)

26 17 57 100% (2652)

15 13 72 100% (12787)

18 14 68 IO0% (2919)

27 17 56 100% (2793)

16 13 71 I00% (13283)

428

SARAARBER Table 2. Standardised limiting long-standing illness ratios by socio-economicgroup and sex (a) men--own occupation, (b) women--conventionalapproach,~ (c) women--own occupation Socio-economicgroup Non-manual Manual (a) Men --Own occupation (b) Women --Conventional approacha (c) Women --Own occupation (current or last) Socio-economic

1

2

3a

3b

4

5

6

All

61

71

86

94

107

129

160

99

60

78

75

100

105

126

152

99

48b

88

73

90

119

114

123

99

group

I Higher professional 2 Employers and managers 3a Lower professional 3b Supervisory and junior non-manual 4 Skilled manual and own account 5 Semi-skilledmanual and personal service 6 Unskilled manual ~Women with husbands are classifiedby their husband's occupation, womenof other marital statuses are attributed their own (current or last) occupational class. bExpected frequency 10 < 20. Source: General Household Survey, 1985-86 (own analyses).

These measures o f c o n s u m p t i o n are easier to collect than occupational class and more likely to be collected reliably. All households can be characterised by c o n s u m p t i o n measures irrespective o f their household c o m p o s i t i o n or the labour market position o f m e m b e r s o f the household. F o r women, these advantages are particularly significant because o f problems o f measuring class for women. Saunders [27, 28] proposes that c o n s u m p t i o n is a m a j o r new axis o f social division, but has not analysed the relationship between health status and housing tenure. Housing tenure and car ownership reveal large differences in health status (Table 3). Local authority tenants are a b o u t 40% m o r e likely to have a limiting long-standing illness than the national average, and owner occupiers are 14% less likely to report p o o r health status. Housing tenure, unlike occupational class, indicates c o m p a r a b l e inequalities in health for w o m e n and men. C o m b i n i n g these two measures o f c o n s u m p t i o n , housing tenure and car ownership, yields two 'extreme' g r o u p s - - o w n e r occupiers with a car and local authority tenants without a car. The G H S data reveal very large health differentials, for men with SLLI ratio is 84 for owner occupiers with cars and double this figure--168 for local authority tenants without cars. The differential is almost as large for women, SLLI ratios o f 82 and 151 respectively. These Table 3. Standardisedlimitinglong-standingillnessratios by housing tenure, car ownership and sex. Age 20-59 Owner Privately Local occupiers r e n t e d authority All Men

One or more cars No car in household All

84 113 86

83 117 93

122 168 145

82 115 86 69%

90 116 98 8%

126 151 139 23%

89 144 100

Women

One or more cars No car in household All % Distribution (Men and Women)

89 136 100 100% (26060)

Source: General Household Survey, 1985-86 (own analyses).

data confirm the pattern o f mortality findings using tenure and car ownership found in the OPCS Longitudinal Study [50, 51]. Thus, c o n s u m p t i o n variables are strongly associated with health status. However, health status is associated with labour force participation and for w o m e n with family roles. The following two sections analyse u n e m p l o y m e n t and family roles, while taking into account structural factors. UNEMPLOYMENT AND LABOUR FORCE

PARTICIPATION Both unemployed men and w o m e n and housewives report p o o r e r health [39, 40, 43]. Table 4 suggests that part o f the reason for the association between housing tenure and health status is because o f differences in e m p l o y m e n t status. E m p l o y e d men and w o m e n have below average limiting long-standing illness, irrespective o f their housing tenure. U n e m p l o y e d men, whatever their housing tenure, have a disadvantaged health status, but unemployed local authority tenants are particularly likely to report p o o r health. A higher p r o p o r t i o n o f local authority residents are unemployed. Thus, the key divide appears to be between men with and without paid employment, and Table 4. Standardised limiting long-standingillnessratios by housing tenure, employmentstatus and sex. Age 20-59 Owner Privately Local occupiers r e n t e d authority All Men

Employed Unemployed Other 'non-employed" All

73 110 286 86

76 116 294b 93

94 148 290 145

77 132 291 100

71 93 101 239 86

78 96

86 149 177 342 139

75 110 128 269 100

Women

Employed Housewife Unemployed Other 'non-employed'~ All

218b 98

aThis category includes people not employed because of long-term sickness, disability,early retirement and full-timestudents, and for men--'housewives'. bExpeeted frequency 10 < 20. Source: General Household Survey, 1985-86 (own analyses).

429

Class, paid employment and family roles

each marital status, with only moderate class differences in evidence. Similarly, among the unemployed, marital status and class make little difference. In Britain in the 1980s, the unemployed receive low levels of state benefits, and prolonged unemployment causes financial problems for the household [40]. Thus, for men unemployment is a key structural disadvantage associated with poor health. For women a striking interaction is present between participation in paid employment, marital status and class. All non-manual women report relatively good health, e x c e p t previously married housewives/unemployed. The pattern is similar for women in manual classes, although their health is generally poorer than that of non-manual women. Thus, previously married women in paid employment report relatively good health. Previously married housewives/unemployed women are nearly twice as likely to report limiting long-standing illness as equivalent employed women. Divorced, separated and widowed women without paid jobs are likely to live in poor material circumstances, and have high reliance on state benefits [54]. They face particular structural and health disadvantages, whereas for men the major structural disadvantage is unemployment. In summary, the above discussion has shown that to understand health status it is important to simultaneously analyse occupational class, housing tenure, participation in paid employment and family roles. An analysis restricted to only one or two of these variables can lead to misleading conclusions about the extent and nature of inequalities in health. The usual practice of simply using occupational class as the basis for analysing inequalities in men's health is particularly inadequate for women, and it is therefore no surprise that contradictory findings have emerged from the research literature which has been cast in this unidimensional class framework. For men, the key factors seem to be unemployment and occupational class. For women it is essential to consider the interactions between family roles, employment status and material circumstances. Groups of women who are disadvantaged on all these factors experience particularly poor health status.

the consumption of 'privatised' or 'socialised' housing is primarily relevant for the unemployed. For women, there is a stronger interaction between housing tenure and employment status. Housewives and unemployed women in owner occupied and privately rented housing have an average health status, but housewives, and particularly unemployed women, living in council housing report very poor health. Thus, for women, the health disadvantage of non-employment differs sharply according to housing tenure, in other words, 'socialised' consumption interacts with employment status and is associated with very poor health status. Men and women who are not employed for 'other' reasons report the highest level of LLI--they include the early retired, long-term sick and disabled, full-time students and men who are 'housewives'. Many of these people have been selected out of the labour market because of poor health. They are omitted from subsequent analysis. However, it is important to recognise that these groups have very considerable health disadvantages and are concentrated in local authority accommodation and in manual classes [24]. FAMILY ROLES There are larger health differentials by marital status for women than for men (see Table 5, bottom row). Previously married women are 47% more likely to have a limiting long-standing illness than all women. The adverse health status of previously married men is not as great (SLLI ratio--132). Never married women report poorer health (SLLI ratity-103), than married women (SLLI ratio--91), but there is no comparable disadvantage for single compared to married men. The direction of influence between marital status and poor health is not clear, poor health status may be a disadvantageous factor in the marriage and remarriage markets, especially for women. In addition, there may be particular financial and parenting stresses associated with being no longer married, which are greater for women than for men. Since marital status is associated with both being in paid employment and occupational class, it is helpful to examine the relationship between these three variables and health status. Table 5 shows that for men being unemployed is the key factor associated with poor health in both manual and non-manual classes. The health of employed men is similar for

MULTIVARIATEANALYSES Multivariate analysis is used to clarify the relative strengths of association between family roles (marital and parental status) and structural variables (occupational class, housing tenure, and participation in

Table 5. Standardisedlimitinglong-standingillnessratios for men and womenby marital status, employmentstatus and class Men Women No longer No longer Married Single married All Married Single married All Employed

--Non-manualclass --Manual class Unemployed/ Housewit,es

67 86

64 83 ~--v

68 94

67 86

67 78

74 109

85 98

70 83

J

--Non-manualclass 125b 127b 126 89 72b 167 96 --Manual class 143 121 144b 137 118 123b 201 131 All" 96 99 132 1DO 91 103 147 1DO "Includespeoplenot employedbecauseof long-termsickness,disability,early retirementand full-timestudents,and for men--'housewives'. bExpected frequency l0 < 20. Source: General Household Survey, 1985-86(own analyses).

430

SARA ARBER

paid employment). Since the dependent variable is a dichotomy, and the independent variables are categorical (except for age), the most appropriate technique is logit analysis using the statistical package G L I M [55, 56]. Using this technique the binary dependent variable is the log of the odds. The odds are the ratio of the probability of someone with specific characteristics having a limiting long-standing illness ( p ) compared to the probability that a person with the same characteristics does not have a limiting longstanding illness (1 - p). The initial model fitted was a straightforward additive one:

In this model, p is the probability of having a limiting long-standing illness (LLI), a, b, c, d . . . are the coefficients to be estimated, and X~, 1"2, X3 • • • are the independent variables. The analysis excludes men and women coded as 'other' non-employed, i.e. the early retired, disabled, long term sick and full-time students. Since these groups have high levels of limiting long-standing illness, the present analysis may underrepresent social divisions in health. For men the most parsimonious logistic model which fitted the data included the effects of age, social class, employment status and housing tenure, see model (d) in Table 6. Marital status and parental status were not significant additional variables at the 0.05 level, models (e) and (f). The availability of a car in the household was not statistically significant as an additional variable in model (d), but was significant at the 0.05 level when added to model (c). Thus, either housing tenure or car ownership is significantly associated with poor health but not both variables. Housing tenure represents a smaller effect that occupational class and employment status. The three-category tenure variable--owner occupier, local authority tenant and other renting--only barely reached the 5% significance level. Interactions were tested between each of the variables in model (d), but none were statistically significant at the 0.05 level. A comparable picture emerges for the LLI of women in relation to the structural variables, but there are marked differences between women and men concerning domestic role attributes. Both the 'individualistic' and the 'conventional approach' to measuring women's class were fitted in the logit

model. The 'conventional' class measure, based on husband's occupation for married women and for other women their own occupation, produced the larger reduction in G squared. A model was also examined for married women which included both the husband's occupational class and the woman's own occupational class, but the addition of the woman's own class failed to produce a statistically significant reduction in G squared. These findings suggest that the 'conventional approach' to measuring women's class provides a better predictor of women's health than an 'individualistic' class measure, and that little additional explanatory power is gained by using both measures. Various logit models for women are compared in Table 7. The three structural variables--'conventional' class, employment status and housing tenure--are all highly significant effects. Housing tenure reaches a higher level of statistical significance for women than for men. Housing tenure and car ownership can be interpreted as substitutable variables. When car ownership was added to model (d)(i), the change in G squared did not reach the 5% significance level. The major difference between women and men is that for women, but not for men, marital and parental status were highly significant additional effects, see model (f). Another key gender difference is that for women there are a number of statistically significant interactions. Before discussing these interactions, the parameter estimates for the best fitting additive models for men [model (d)] and for women [model (f)] will be discussed. The parameter estimates for the variables in model (d)(ii) are given in Table 8. In the following discussion, the term 'effects' will be used when discussing the parameter estimates and odds, but this terminology is not intended to imply that the variable necessarily causes poor health status. Estimates of the log of the odds of having a limiting long-standing illness are also expressed as the multiplicative effect of the odds (in parentheses in Table 8). This transformation allows a more convenient interpretation of the coefficients [57]. The coefficient estimates for each variable are related to a particular reference category, for example, the reference category for occupational class is 'higher professionals'. The constant parameter relates to the reference category for each variable. It gives the odds of

Table 6. Alternative logit models predicting limiting long-standing illness for men aged 20-59 Model (a) (b) (c) (d)

(e) (f)

Age Age + Class Age + Class + Work status Age + Class + Work status +Tenure (i) ( + Tenure (ii)) ( + C a r (iii)) Age + Class + Work status +Tenure (i) + Marital status Age + Class + Work status +Tenure (i) + Children

G2

df

8413 8344.8 8310.4

11718 11712 11711

8304.6 8304.4 8306.0

AG 2

~df

P

68.2 34.4

6 1

P < 0.001 P < 0.001

11710 11709 11710

5.8 6.0 4.4

1 2 1

P < 0.05 P < 0.05 P <0.05

8301.3

11708

3.3

2

P(ns)

8299.8

11704

4.8

3

P(ns)

Parameter estimates for each variable in model (d) are given in Table 8. Three versions of model (d) are shown above. Tenure (i) provides a dichotomy--Owner occupier or other renting vs Local Authority housing tenant; Tenure (ii) has 3 categories--Owner occupier, Other renting and Local Authority Tenant. The third version of model (d) includes car ownership instead of housing tenure. Source: General Household Survey, 1985-86 (own analysis),

Class, paid employment and family roles

431

Table 7. Alternative logit models predicting limiting long-standing illness for women aged 20-59 Model (a) (b) (c) (d)

Age Age + Conventional Class Age + Class + Work status Age + Class + Work status +Tenure (i) (+Tenure (ii)) (+Car (iii)) Age + Class + Work status +Tenure (i) + Marital status Age + Class + Work status +Tenure (i) + Marital status + Children

(e) (f)

G2

df

AG 2

Adf

P

9270.1 9198.1 9130

11968 11962 11960

72 68.1

6 2

P < 0.001 P < 0.001

9108 9107.9 9119.4

11959 11958 11959

28 28.1 10.6

1 2 1

P < 0,001 P < 0.001 P < 0.01

9080.6

11957

27.4

2

P < 0.001

9052.2

! 1954

28.4

3

P < 0.001

Parameter estimates for each variable in model (f) are given in Table 9. Three versions of model (d) are shown above. Tenure (i) provides a dichotomy--Owner occupier or other renting vs Local Authority housing tenant; Tenure (ii) has 3 categories--Owner occupier, Other renting and Local Authority Tenant. The third version of model (d) includes car ownership instead of housing tenure. Source: General Household Survey, 1985-86 (own analysis).

h a v i n g a l i m i t i n g l o n g - s t a n d i n g illness f o r a married, employed man, with a higher professional o c c u p a t i o n w h o lives in a n o w n e r o c c u p i e d h o u s e . A g e is a c o n t i n u o u s v a r i a b l e , w h i c h c a n be c o n v e r t e d to o d d s b y m u l t i p l y i n g t h e p a r a m e t e r e s t i m a t e b y a g e in years. T a k i n g o c c u p a t i o n a l class first, t h e p a r a m e t e r estim a t e s a n d t s t a t i s t i c s in T a b l e 8 s h o w t h a t ' l o w e r professionals', 'junior non-manual workers' and the three manual classes have a significantly higher c h a n c e o f h a v i n g a L L I (the t v a l u e s a r e g r e a t e r t h a n 1.96) c o m p a r e d to h i g h e r p r o f e s s i o n a l s (the r e f e r e n c e c a t e g o r y ) , a f t e r c o n t r o l l i n g for t h e effects o f t h e o t h e r v a r i a b l e s in t h e m o d e l . T h e m a g n i t u d e o f t h e s e class d i f f e r e n c e s c a n be c o m p a r e d u s i n g t h e m u l t i p l i c a t i v e o d d s (in p a r e n t h e s e s ) . T h e o d d s o f ' u n s k i l l e d m a n u a l '

w o r k e r s h a v i n g a L L I a r e 2.03 c o m p a r e d to 1.00 for ' h i g h e r p r o f e s s i o n a l ' m e n , a n d 1.45 for 'skilled manual' men. The pattern and strength of the relationship between class and health status revealed b y this m u l t i v a r i a t e m o d e l is very s i m i l a r to t h e class differences found using standardised limiting l o n g - s t a n d i n g illness r a t i o s in T a b l e 2(a). A m a n living in local a u t h o r i t y h o u s i n g h a s a twenty percent higher odds of reporting a limiting l o n g - s t a n d i n g illness t h a n a n o w n e r o c c u p i e r . T h u s , a f t e r c o n t r o l l i n g f o r class, e m p l o y m e n t s t a t u s a n d age, t h e r e a r e s i g n i f i c a n t effects o n h e a l t h s t a t u s o f living in c o u n c i l h o u s i n g , b u t t h e effects a r e n o t as g r e a t as t h o s e o f o c c u p a t i o n a l c l a s s a n d e m p l o y m e n t s t a t u s . T h i s f i n d i n g calls i n t o q u e s t i o n , S a u n d e r s [27-29] p o s i t i o n t h a t h o u s i n g c o n s u m p t i o n is a m o r e

Table 8. (a) Parameter estimates of limiting long-standing illness for logit additive model (d), men aged 20-59 Parameter Constant Age Higher Professionals Employers & Managers Lower Professionals Intermediate & Junior Non-Manual Skilled Manual Semi-skilled Manual Unskilled Manual Employed Unemployed Owner Occupier Local Authority/Housing Association Other renting

Estimatea

T statistic

- 3.786 (0.023) 0,0345 (I.035) - - (1.00) 0.071 (I.07) 0.323 (1.38) 0.383 (1.47) 0.372 (I.45) 0.515 (1.68) 0.708 (2.03) - - (I.00) 0.466 (1.59) - - (1.00) 0.183 (I.20) 0.042 (I.04)

23. I 1* 13.19" -0.51 1.98* 2.47* 2.83* 3.55* 3.94* -5.22* -2.46* 0.37

G squared = 8304.4. d r = 11709. *Statistically different from the reference category, P < 0.05, ~The figures in parentheses are parameter estimates in multiplicative form. Men who are 'non-employed' for reasons other than unemployment are excluded (i.e. the long-term sick, disabled, early retired, and full-time students). Source: General Household Survey, 1985-86 (own analyses). (b) Estimated odds and probabilities of limiting long-standing illness

Age 25, professional, employed, owner occupier (Paradigmatic man, age 25) (25 x 0.0345) + constant (-3.786) Age 25, unskilled, employed, owner occupier (25 x 0.0345) + 0.708 - 3.786 Age 25, unskilled, unemployed, local authority (25 x 0.0345) + 0.708 + 0.466 + 0.183 - 3.786 Age 55, unskilled, unemployed, local authority (55 x 0.0345) + 0.708 + 0.466 + 0.183 - 3.786

SSM 32 4 ~ F

Odds of LLI

Probability of LLI

= - 2.924

0.05

0.05

= -2.216

0.11

0.10

= -1.566

0.21

0.17

= -0.532

0.59

0.37

432

SARA ARBER

i m p o r t a n t d e t e r m i n a n t o f m a t e r i a l life c h a n c e s t h a n class a n d p a i d e m p l o y m e n t . T h o s e living in ' o t h e r r e n t e d ' a c c o m m o d a t i o n a n d o w n e r o c c u p i e r s have similar o d d s o f c h r o n i c illness. T h e o d d s o f a n u n e m p l o y e d m a n h a v i n g a L L I are n e a r l y 6 0 % h i g h e r t h a n for a n e m p l o y e d m a n , after c o n t r o l l i n g for age, o c c u p a t i o n a l class a n d h o u s i n g tenure. A s n o t e d earlier, t h e r e is n o a d d i t i o n a l signific a n t effect o f car o w n e r s h i p ( d i c h o t o m i s e d as n o c a r in the h o u s e h o l d a n d o n e o r m o r e cars) after including h o u s i n g t e n u r e a n d t h e o t h e r variables in the m o d e l in T a b l e 8. A n o t h e r w a y o f a n a l y s i n g these effects is p r o v i d e d in the l o w e r p a r t o f T a b l e 8, w h i c h gives b o t h t h e o d d s o f L L I a n d the p r o b a b i l i t y o f L L I for m e n w i t h specific c o m b i n a t i o n s o f characteristics. T h e p r o b ability o f L L I is c a l c u l a t e d f r o m odds (1 + odds). A n e x a m p l e will illustrate the difference b e t w e e n o d d s a n d p r o b a b i l i t i e s - - i f 40 m e n h a v e a L L I a n d 60 d o n o t (out o f a total o f 100 m e n ) , the p r o b a b i l i t y o f L L I is 4 0 % , b u t the o d d s o f h a v i n g L L I r a t h e r t h a n n o t h a v i n g L L I are 40:60, w h i c h is 0.67. T h e effect o f c h a n g i n g t h e 25 year old ' p a r a d i g matic man' from a 'higher professional' occupation

to a n 'unskilled m a n u a l ' w o r k e r is to increase the o d d s o f L L I f r o m 0.05 to 0.11. A structurally d i s a d v a n t a g e d 25 year o l d m a n w h o is u n e m p l o y e d , living in c o u n c i l a c c o m m o d a t i o n a n d w h o last w o r k e d in a n unskilled j o b w o u l d have a 17% p r o b a b i l i t y o f LLI. This is over t h r e e times h i g h e r t h a n a m a n w h o is w o r k i n g in a ' h i g h e r p r o f e s s i o n a l ' o c c u p a t i o n a n d o w n s his h o m e . A g e h a s a linear effect, thus, o l d e r m e n in c o m p a r a b l y d i s a d v a n t a g e d m a t e r i a l c i r c u m s t a n c e s - u n e m p l o y e d , council t e n a n t , a n d last w o r k ing in a n unskilled j o b , h a v e a 3 7 % p r o b a b i l i t y o f limiting l o n g - s t a n d i n g illness. In s u m m a r y , for m e n the logit analysis largely r e i n f o r c e s the earlier t a b u l a r analyses, T h e additive effects o f o c c u p a t i o n a l class, u n e m p l o y m e n t , a n d h o u s i n g t e n u r e o n limiting l o n g - s t a n d i n g illness are c o n f i r m e d , with h o u s i n g t e n u r e the smallest o f these t h r e e effects. T h e r e is n o effect o f m a r i t a l status o r p a r e n t a l status for m e n . T o facilitate c o m p a r i s o n b e t w e e n the n a t u r e o f inequalities in h e a l t h for w o m e n a n d m e n , a c o m p a r able additive logit m o d e l , b u t i n c l u d i n g m a r i t a l a n d p a r e n t a l status [model (f)], is e s t i m a t e d for w o m e n , see T a b l e 9. It was n o t the m o s t p a r s i m o n i o u s m o d e l ,

Table 9. (a) Parameter estimates of limiting long-standing illness for Iogit additive model (f), women aged 20-59 Parameter Constant Age Higher Professionals Employers & Managers Lower Professionals Intermediate and Junior Non-Manual Skilled Manual Semi-skilled Manual Unskilled Manual Employed Unemployed Housewife Owner Occupier Local Authority/Housing Association Other renting Married Single Previously Married No children Children under 5 years Children 5 < 16 years Children 16+

Estimate a

T statistic

-3.671 (0.025) 0.030 (1.03) - - (I.00) 0.338 (I.40) 0.312 (1.37) 0.372 (I.45) 0.580 (1.79) 0.531 (1.70) 0.807 (2.24) - - (I.00) 0.575 (1.78) 0.517 (I.68) - - (1.00) 0.264 (1.30) -0.017 (0.98) - - (I .00) 0.028 (1.03) 0.439 (1.55) - - (1.00) -0.552 (0.58) -0.124 (0.88) -0.031 (0.97)

18.25" 9.49" -2.29* 1.87 2.38* 4.09* 3.47* 4.30* -4.87* 8.15* -3.96* 0.14 -0.26 5.08* -5.12" 1.66 0.43

G squared = 9052.2, df= 11953. *Statistically different from the reference category, P < 0.05. aThe figures in parentheses are parameter estimates in multiplicative form. Women who are 'non-employed' for reasons other than unemployment are excluded (i.e. the long-term sick, disabled, early retired, and full-time students). (b) Estimated odds and probabilities of limiting long-standing illness

Age 25, professional, employed, owner occupier, married, no children (Paradigmatic woman, age 25) (25 x 0.03) + constant (-3.671) Age 25, unskilled, unemployed, owner occupier, married, no children (25 × 0.03) + 0.807 + 0.575 - 3.671 Age 25, unskilled, unemployed, local authority, previously married, no children (25 × 0.03) + 0.807 + 0.575 + 0.264 + 0.439 - 3.671 Age 55, unskilled, unemployed, local authority, previously married, no children (55 x 0.03) + 0.807 + 0.575 + 0.264 + 0.439 - 3.671 Source: General Household Survey, 1985-86 (own analysis).

Odds of LLI

Probability of LLI

= -2.921

0.05

0.05

= - 1.539

0.21

0.18

= -0.836

0.43

0.30

= 0.064

1.07

0.52

Class, paid e m p l o y m e n t a n d family roles

433

Table 10. (a) Parameter estimates of limiting long-standing illness for logit model with interaction terms, Women aged 20-59 Estimatea Constant Age Higher Professionals Employers & Managers Lower Professionals Intermediate & Junior Non-Manual Skilled Manual Semi-skilled Manual Unskilled Manual Employed Unemployed Housewife Owner Occupier/Other renting Local Authority/Housing Association Married Single Previously Married No children/Children 16+ Children under 5 years Children 5 < 16 years Unemployed, Local Authority Housewife, Local Authority Unemployed, Single Unemployed, Previously Married Housewife, Single Housewife, Prey. Married Unemployed, Child < 5 years Unemployed, Child 5 < 16 years Housewife, Child < 5 years Housewife, Child 5 < 16 years

T statistic

- 3.307 (0.037) 0.0294 (I .03) - - (1.00) 0.338 (1.40) 0.321 (1.38) 0.368 (I.44) 0.584 (1.79) 0.513 (I.67) 0.803 (2.23) - - (1.00) 0.506 (I.66) 0.574 (I.78) - - (1.00) 0.124(1.14) - - (1.00) 0.132 (I.14) 0.273 (I.31) - - (1.00) -0.064(0.94) -0.037 (0.96) 0.561 (1.75) 0.290(I.34) -0.626 (0.53) 0.273 (1.31) -0.244 (0.78) 0.402 (I.49) -0.624 (0.54) -0.124(0.88) -0.802 (0.45) -0.239 (0.79)

19.50" 9.10" -2.29* 1.87 2.38* 4.09* 3.47* 4.30* -2.28* 5.89* -1.40 -1.31 2.36* -0.44 0.44 2.25* 2.18" 1.82 0.92 0.74 2.31" 1.74 0.43 4.35* 1.73

G squared = 9012.8, d r = 11945. *Statistically different from the reference category, P < 0.05. ~The figures in parentheses are parameter estimates in multiplicative form. Women who are 'non-employed' for reasons other than unemployment are excluded (i.e. the long-term sick, disabled, early retired, and full-time students). (b) Parameter estimates for two variable interactions Employed estimate~ Owner occupier/Other renting Local Authority Married Single Previously Married No children/Children age 16+ Children < 5 years Children 5 < 16 years

Unemployed estimate"

- - (1.00) 0.124(I.13) - - (1.00) 0.132 (1.14) 0.273 (I.31) - - (1.00) -0.064(0.94) -0.037 (0.96)

Housewife estimatea

0.506 (1.66) 1.191 (3.29) 0.506(I.66) 0.012 (1.01 ) 1.052 (2.86) 0.506(I.66) -0.182 (0.84) 0.346 (I.41)

0.574 (1.78) 0.987(2.68) 0.574(I.78) 0.462 (1.59) 1.249 (3.49) 0.574(1.78) -0.292(0.75) 0.298 (1.35)

~The figures in parentheses are parameter estimates in multiplicative form. (c) Estimated odds and probabilities of limiting long-standing illness

Age 25, professional, employed, owner occupier, married, no children (Paradigmatic women, age 25) (25 x 0.0294)+ constant (-3.307) Age 25, professional, housewife, owner occupier, married, children < 5 years (25 x 0.294) + 0.574 - 0.064 - 0.802 - 3.307 Age 25, unskilled, housewife, local authority, previously married, children < 5 years (25 × 0.0294) + 0.803 + 0.574 + 0.124 + 0.273 -0.064 - 0.802 + 0.290 + 0.402 - 3.307 Age 25, unskilled, housewife, local authority, previously married, no children (25 × 0.0294) + 0.803 + 0.574 + 0.124 + 0.273 +0.290 + 0.402 - 3.307 Age 55, unskilled, housewife, local authority, previously married, no children (55 x 0.0294) + 0.803 + 0.574 + 0.124 + 0.273 +0.290 + 0.402 - 3.307 Source: General Household Survey, 1985-86 (own analysis).

Odds of LLI

Probability of LLI

= -2.573

0.07

0.07

= - 2.86

0.06

0.05

= -0.974

0.38

0.27

= - 0.108

0.90

0.47

= - 0.773

2.17

0.68

434

SARA ARaER

since there were significant interactions among some of the variables. These will be discussed later and are shown in Table 10. For women, the 'conventional approach' to measuring occupational class produced a higher G squared than the 'individualistic approach' and is therefore used in Table 9. Women in the three manual classes all have substantially greater odds of LLI than women classed as 'higher professional', with 'unskilled manual' women having over twice as high odds (after controlling for housing tenure, employment status, marital status, parental status and age). Women who are 'employers and managers' have a 40% higher odds of LLI than 'higher professional class' women, an effect which is statistically significant. It is interesting that the health status of 'employers and managers' is significantly poorer than that of 'higher professionals' for women but not for men.

Women living in local authority housing have a 30% higher odds of LLI than owner occupiers. This tenure effect suggests there may be worse health consequences of local authority tenure for women than men. There are large effects of employment status on LLI for women, similar to those for men. Unemployed women have a 78% higher odds and housewives a 68% higher odds of LLI than employed women. Thus, after controlling for other structural and role variables, being in paid employment for women is strongly associated with better health status. A major gender difference is the association of women's health status with family roles. Previously married women have a 55% higher odds of LLI compared to married women, but there is no difference betwen single and married women. Women with children under age 16 have significantly lower odds of limiting long-standing illness than women without children. The latter are similar to women with older children living in the household. It is particularly noteworthy that women with pre-school children have a 42% lower odds of LLI than women without children, after controlling for age, marital status, and the structural variables. The lower part of Table 9 gives some illustrative estimated odds and probabilities for women with various combinations of characteristics. The most disadvantaged are previously married women, who are unemployed, live in council accommodation and in an unskilled manual class. Women aged 25 with these characteristics have a 30% probability of having a LLI, six times higher than women with the ~paradigmatic' characteristics. Over half (52%) of women in their mid-fifties with these disadvantaged characteristics report limiting long-standing illness. Thus, the chances of LLI are somewhat greater for women in structurally disadvantaged circumstances than for equivalent men. These findings are consistent with the earlier tabular analyses. The model in Table 9 is not the most parsimonious, because of the significant interactions, which will now be discussed. A full logit model to predict limiting long-standing illness for women includes significant interactions, see Table 10. In this analysis, women in 'other' rented accommodation have been combined with owner

occupiers, because Table 9 showed the odds of LLI were comparable for these two categories, and women with children over age 16 living at home have been combined with the reference category of 'no children'. There are three significant interactions: between employment and parental status, between employment and marital status, and between employment and housing tenure. Table 10, part (b) shows the parameter estimates and odds for each of these three pairs of interactions. Considering the interaction between employment and housing tenure, employed women have good health irrespective of housing tenure. Employed women living in local authority accommodation have an only slightly higher odds of LLI (i.13) than the reference category. Thus, paid employment is 'protective' of health, even if women have other characteristics which usually confer health disadvantage. The health status of housewives and unemployed women living in council housing is considerably poorer. The odds for such unemployed women are 3.29, compared to 1.66 for women living in other housing. Paid employment also seems to be protective of health for previously married women. There is only a slightly higher odds of LLI for previously married women who are employed (1.31) compared to married employed women (1.00), but previously married housewives have twice as high odds of LLI (3.49) as married housewives (1.78). Again unemployed women and housewives are similar in terms of their health disadvantage. Housewives with a child under five have a lower odds of LLI (0.75) compared with the reference category (employed women without dependent children). Thus, housewives and unemployed women with young children do not report poorer health. However, housewives and unemployed women with older children, and especially those with no dependent children, are more likely to report poor health. Among employed women, parental status is unrelated to health status. The estimates of odds and probabilities in part (c) of Table 10 illustrate the magnitude of health disadvantage faced by previously married women with no dependent children who are structurally disadvantaged, The probability of LLI is 68% for previously married housewives in their mid-fifties without dependent children who live in council accommodation, and are in an unskilled class. The probability of LLI for married housewives in a higher professional class in their twenties, living in owner occupation with young children is only 5%. Table 10, therefore, indicates the magnitude of health divisions between structurally 'advantaged' and 'disadvantaged' women. CONCLUSION

This paper has shown large differences between women and men in the extent and nature of inequalities in health status, using the 1985 and 1986 General Household Survey. Although it is limited by all the shortcomings of cross-sectional analysis, it has demonstrated massive health divisions according to structural characteristics, and for women the

Class, paid employment and family roles necessity of examining women's marital and parental roles within a structural framework. The multivariate logit analysis confirmed and extended the tabular analysis based on standardised limiting long-standing illness ratios. The conclusion is that health inequalities for men are primarily associated with unemployment, occupational class and to a lesser extent with living in local authority housing. This points to the crucial importance of financial and material disadvantages in explaining inequalities in men's health. The picture is more complex for women. Health status is even more likely to be poor for structurally disadvantaged women than for equivalent men. Health disadvantage for women is associated with non-employment (either being a housewife or unemployed), being in a manual class, being divorced, separated or widowed, living in local authority housing, and not having dependent children. When considering women's health, there are a number of interactions between employment status and other variables. Employed women report better health, irrespective of housing tenure, marital and parental status. W o m e n who are not in paid employment and have no dependent children report poor health, whereas women with young children report good health irrespective of their employment status. Previously married women without a paid job report particularly poor health. A m o n g housewives and unemployed women, local authority residents report markedly poorer health than owner occupiers. Thus, the disadvantages of 'socialised' housing are only evident for women who do not have the financial buffer of paid employment. Exclusion from the labour market for women is associated with poorer health apart from for women with young children. This study has shown that lack of paid employment and occupational class are important factors associated with poor health status for both women and men. Consumption indicators, such as housing tenure and car ownership, are easy to collect, but are less powerful correlates of poor health, especially for men. To fully understand women's health it is necessary to examine women's parental and marital roles within a structural context. Acknowledgements--I would like to thank the Office of

Population Censuses and Surveys for permission to use the General Household Survey and the ESRC Data Archive, University of Essex for supplying the data. I am grateful to Jay Ginn for her assistance in extracting the GHS data files, to Roger Burrows, Jane Fielding and Colin Mills for their advice on using GLIM, and to anonymous reviewers for their helpful comments on an earlier draft of this paper.

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